,code,docs,doc_len,words,lang,prompt
0,"def rolling(self, *args, **kwargs) -> RollingGroupby:
        
        from pandas.core.window import RollingGroupby

        return RollingGroupby(
            self._selected_obj,
            *args,
            _grouper=self.grouper,
            _as_index=self.as_index,
            **kwargs,
        )
","
        Return a rolling grouper, providing rolling functionality per group.
        ",9,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rolling(self, *args, **kwargs) -> RollingGroupby:
        
        from pandas.core.window import RollingGroupby

        return RollingGroupby(
            self._selected_obj,
            *args,
            _grouper=self.grouper,
            _as_index=self.as_index,
            **kwargs,
        )

        ```
    ###Assistant : 
        Return a rolling grouper, providing rolling functionality per group.
        
    "
1,"def expected_degree_graph(w, seed=None, selfloops=True):
    r
    n = len(w)
    G = nx.empty_graph(n)

    # If there are no nodes are no edges in the graph, return the empty graph.
    if n == 0 or max(w) == 0:
        return G

    rho = 1 / sum(w)
    # Sort the weights in decreasing order. The original order of the
    # weights dictates the order of the (integer) node labels, so we
    # need to remember the permutation applied in the sorting.
    order = sorted(enumerate(w), key=itemgetter(1), reverse=True)
    mapping = {c: u for c, (u, v) in enumerate(order)}
    seq = [v for u, v in order]
    last = n
    if not selfloops:
        last -= 1
    for u in range(last):
        v = u
        if not selfloops:
            v += 1
        factor = seq[u] * rho
        p = min(seq[v] * factor, 1)
        while v < n and p > 0:
            if p != 1:
                r = seed.random()
                v += math.floor(math.log(r, 1 - p))
            if v < n:
                q = min(seq[v] * factor, 1)
                if seed.random() < q / p:
                    G.add_edge(mapping[u], mapping[v])
                v += 1
                p = q
    return G

","Returns a random graph with given expected degrees.

    Given a sequence of expected degrees $W=(w_0,w_1,\ldots,w_{n-1})$
    of length $n$ this algorithm assigns an edge between node $u$ and
    node $v$ with probability

    .. math::

       p_{uv} = \frac{w_u w_v}{\sum_k w_k} .

    Parameters
    ----------
    w : list
        The list of expected degrees.
    selfloops: bool (default=True)
        Set to False to remove the possibility of self-loop edges.
    seed : integer, random_state, or None (default)
        Indicator of random number generation state.
        See :ref:`Randomness<randomness>`.

    Returns
    -------
    Graph

    Examples
    --------
    >>> z = [10 for i in range(100)]
    >>> G = nx.expected_degree_graph(z)

    Notes
    -----
    The nodes have integer labels corresponding to index of expected degrees
    input sequence.

    The complexity of this algorithm is $\mathcal{O}(n+m)$ where $n$ is the
    number of nodes and $m$ is the expected number of edges.

    The model in [1]_ includes the possibility of self-loop edges.
    Set selfloops=False to produce a graph without self loops.

    For finite graphs this model doesn't produce exactly the given
    expected degree sequence.  Instead the expected degrees are as
    follows.

    For the case without self loops (selfloops=False),

    .. math::

       E[deg(u)] = \sum_{v \ne u} p_{uv}
                = w_u \left( 1 - \frac{w_u}{\sum_k w_k} \right) .


    NetworkX uses the standard convention that a self-loop edge counts 2
    in the degree of a node, so with self loops (selfloops=True),

    .. math::

       E[deg(u)] =  \sum_{v \ne u} p_{uv}  + 2 p_{uu}
                = w_u \left( 1 + \frac{w_u}{\sum_k w_k} \right) .

    References
    ----------
    .. [1] Fan Chung and L. Lu, Connected components in random graphs with
       given expected degree sequences, Ann. Combinatorics, 6,
       pp. 125-145, 2002.
    .. [2] Joel Miller and Aric Hagberg,
       Efficient generation of networks with given expected degrees,
       in Algorithms and Models for the Web-Graph (WAW 2011),
       Alan Frieze, Paul Horn, and Paweł Prałat (Eds), LNCS 6732,
       pp. 115-126, 2011.
    ",298,179,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def expected_degree_graph(w, seed=None, selfloops=True):
    r
    n = len(w)
    G = nx.empty_graph(n)

    # If there are no nodes are no edges in the graph, return the empty graph.
    if n == 0 or max(w) == 0:
        return G

    rho = 1 / sum(w)
    # Sort the weights in decreasing order. The original order of the
    # weights dictates the order of the (integer) node labels, so we
    # need to remember the permutation applied in the sorting.
    order = sorted(enumerate(w), key=itemgetter(1), reverse=True)
    mapping = {c: u for c, (u, v) in enumerate(order)}
    seq = [v for u, v in order]
    last = n
    if not selfloops:
        last -= 1
    for u in range(last):
        v = u
        if not selfloops:
            v += 1
        factor = seq[u] * rho
        p = min(seq[v] * factor, 1)
        while v < n and p > 0:
            if p != 1:
                r = seed.random()
                v += math.floor(math.log(r, 1 - p))
            if v < n:
                q = min(seq[v] * factor, 1)
                if seed.random() < q / p:
                    G.add_edge(mapping[u], mapping[v])
                v += 1
                p = q
    return G


        ```
    ###Assistant : Returns a random graph with given expected degrees.

    Given a sequence of expected degrees $W=(w_0,w_1,\ldots,w_{n-1})$
    of length $n$ this algorithm assigns an edge between node $u$ and
    node $v$ with probability

    .. math::

       p_{uv} = \frac{w_u w_v}{\sum_k w_k} .

    Parameters
    ----------
    w : list
        The list of expected degrees.
    selfloops: bool (default=True)
        Set to False to remove the possibility of self-loop edges.
    seed : integer, random_state, or None (default)
        Indicator of random number generation state.
        See :ref:`Randomness<randomness>`.

    Returns
    -------
    Graph

    Examples
    --------
    >>> z = [10 for i in range(100)]
    >>> G = nx.expected_degree_graph(z)

    Notes
    -----
    The nodes have integer labels corresponding to index of expected degrees
    input sequence.

    The complexity of this algorithm is $\mathcal{O}(n+m)$ where $n$ is the
    number of nodes and $m$ is the expected number of edges.

    The model in [1]_ includes the possibility of self-loop edges.
    Set selfloops=False to produce a graph without self loops.

    For finite graphs this model doesn't produce exactly the given
    expected degree sequence.  Instead the expected degrees are as
    follows.

    For the case without self loops (selfloops=False),

    .. math::

       E[deg(u)] = \sum_{v \ne u} p_{uv}
                = w_u \left( 1 - \frac{w_u}{\sum_k w_k} \right) .


    NetworkX uses the standard convention that a self-loop edge counts 2
    in the degree of a node, so with self loops (selfloops=True),

    .. math::

       E[deg(u)] =  \sum_{v \ne u} p_{uv}  + 2 p_{uu}
                = w_u \left( 1 + \frac{w_u}{\sum_k w_k} \right) .

    References
    ----------
    .. [1] Fan Chung and L. Lu, Connected components in random graphs with
       given expected degree sequences, Ann. Combinatorics, 6,
       pp. 125-145, 2002.
    .. [2] Joel Miller and Aric Hagberg,
       Efficient generation of networks with given expected degrees,
       in Algorithms and Models for the Web-Graph (WAW 2011),
       Alan Frieze, Paul Horn, and Paweł Prałat (Eds), LNCS 6732,
       pp. 115-126, 2011.
    
    "
2,"def save(self, path):
        
        os.makedirs(path, exist_ok=True)
        with open(os.path.join(path, ""metrics.json""), ""w"") as fp:
            json.dump(self.metrics, fp)

        artifacts_metadata = {
            artifact_name: {
                ""uri"": artifact.uri,
                ""class_name"": _get_fully_qualified_class_name(artifact),
            }
            for artifact_name, artifact in self.artifacts.items()
        }
        with open(os.path.join(path, ""artifacts_metadata.json""), ""w"") as fp:
            json.dump(artifacts_metadata, fp)

        artifacts_dir = os.path.join(path, ""artifacts"")
        os.mkdir(artifacts_dir)

        for artifact_name, artifact in self.artifacts.items():
            artifact._save(os.path.join(artifacts_dir, artifact_name))
",Write the evaluation results to the specified local filesystem path,10,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save(self, path):
        
        os.makedirs(path, exist_ok=True)
        with open(os.path.join(path, ""metrics.json""), ""w"") as fp:
            json.dump(self.metrics, fp)

        artifacts_metadata = {
            artifact_name: {
                ""uri"": artifact.uri,
                ""class_name"": _get_fully_qualified_class_name(artifact),
            }
            for artifact_name, artifact in self.artifacts.items()
        }
        with open(os.path.join(path, ""artifacts_metadata.json""), ""w"") as fp:
            json.dump(artifacts_metadata, fp)

        artifacts_dir = os.path.join(path, ""artifacts"")
        os.mkdir(artifacts_dir)

        for artifact_name, artifact in self.artifacts.items():
            artifact._save(os.path.join(artifacts_dir, artifact_name))

        ```
    ###Assistant : Write the evaluation results to the specified local filesystem path
    "
3,"def test_build_group_generic_issue_attachment(self):
        
        event = self.store_event(
            data={""message"": ""Hello world"", ""level"": ""error""}, project_id=self.project.id
        )
        event = event.for_group(event.groups[0])
        occurrence = self.build_occurrence(level=""info"")
        occurrence.save(project_id=self.project.id)
        event.occurrence = occurrence

        event.group.type = GroupType.PROFILE_BLOCKED_THREAD

        attachments = SlackIssuesMessageBuilder(group=event.group, event=event).build()

        assert attachments[""title""] == occurrence.issue_title
        assert attachments[""text""] == occurrence.evidence_display[0].value
        assert attachments[""fallback""] == f""[{self.project.slug}] {occurrence.issue_title}""
        assert attachments[""color""] == ""#2788CE""  # blue for info level
",Test that a generic issue type's Slack alert contains the expected values,12,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_build_group_generic_issue_attachment(self):
        
        event = self.store_event(
            data={""message"": ""Hello world"", ""level"": ""error""}, project_id=self.project.id
        )
        event = event.for_group(event.groups[0])
        occurrence = self.build_occurrence(level=""info"")
        occurrence.save(project_id=self.project.id)
        event.occurrence = occurrence

        event.group.type = GroupType.PROFILE_BLOCKED_THREAD

        attachments = SlackIssuesMessageBuilder(group=event.group, event=event).build()

        assert attachments[""title""] == occurrence.issue_title
        assert attachments[""text""] == occurrence.evidence_display[0].value
        assert attachments[""fallback""] == f""[{self.project.slug}] {occurrence.issue_title}""
        assert attachments[""color""] == ""#2788CE""  # blue for info level

        ```
    ###Assistant : Test that a generic issue type's Slack alert contains the expected values
    "
4,"def apply(self, func, mask=None) -> 'ImageProcessor':
        
        img = orig_img = self._img
        img = func(img).astype(orig_img.dtype)
        if img.ndim != 4:
            raise Exception('func used in ImageProcessor.apply changed format of image')

        if mask is not None:
            mask = self._check_normalize_mask(mask)
            img = ne.evaluate('orig_img*(1-mask) + img*mask').astype(orig_img.dtype)

        self._img = img
        return self
","
        apply your own function on internal image

        image has NHWC format. Do not change format, but dims can be changed.

         func   callable  (img) -> img

        example:

         .apply( lambda img: img-[102,127,63] )
        ",31,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply(self, func, mask=None) -> 'ImageProcessor':
        
        img = orig_img = self._img
        img = func(img).astype(orig_img.dtype)
        if img.ndim != 4:
            raise Exception('func used in ImageProcessor.apply changed format of image')

        if mask is not None:
            mask = self._check_normalize_mask(mask)
            img = ne.evaluate('orig_img*(1-mask) + img*mask').astype(orig_img.dtype)

        self._img = img
        return self

        ```
    ###Assistant : 
        apply your own function on internal image

        image has NHWC format. Do not change format, but dims can be changed.

         func   callable  (img) -> img

        example:

         .apply( lambda img: img-[102,127,63] )
        
    "
5,"def predict(self, x):
        

        # start the timer
        self.timer.start()

        v_, _ = self.eval_out()  # evaluate the ratings and the associated probabilities
        vp = self.sess.run(v_, feed_dict={self.vu: x})
        
        # stop the timer
        self.timer.stop()

        log.info(""Done inference, time %f2"" % self.timer.interval)

        return vp
","Returns the inferred ratings. This method is similar to recommend_k_items() with the
        exceptions that it returns all the inferred ratings

        Basic mechanics:

        The method samples new ratings from the learned joint distribution, together with
        their probabilities. The input x must have the same number of columns as the one used
        for training the model, i.e. the same number of items, but it can have an arbitrary number
        of rows (users).

        Args:
            x (numpy.ndarray, int32): Input user/affinity matrix. Note that this can be a single vector, i.e.
            the ratings of a single user.

        Returns:
            numpy.ndarray, float:
            - A matrix with the inferred ratings.
            - The elapsed time for predediction.
        ",108,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def predict(self, x):
        

        # start the timer
        self.timer.start()

        v_, _ = self.eval_out()  # evaluate the ratings and the associated probabilities
        vp = self.sess.run(v_, feed_dict={self.vu: x})
        
        # stop the timer
        self.timer.stop()

        log.info(""Done inference, time %f2"" % self.timer.interval)

        return vp

        ```
    ###Assistant : Returns the inferred ratings. This method is similar to recommend_k_items() with the
        exceptions that it returns all the inferred ratings

        Basic mechanics:

        The method samples new ratings from the learned joint distribution, together with
        their probabilities. The input x must have the same number of columns as the one used
        for training the model, i.e. the same number of items, but it can have an arbitrary number
        of rows (users).

        Args:
            x (numpy.ndarray, int32): Input user/affinity matrix. Note that this can be a single vector, i.e.
            the ratings of a single user.

        Returns:
            numpy.ndarray, float:
            - A matrix with the inferred ratings.
            - The elapsed time for predediction.
        
    "
6,"def raw_decode(self, s, idx=0):
        
        try:
            obj, end = self.scan_once(s, idx)
        except StopIteration as err:
            raise JSONDecodeError(""Expecting value"", s, err.value) from None
        return obj, end
","Decode a JSON document from ``s`` (a ``str`` beginning with
        a JSON document) and return a 2-tuple of the Python
        representation and the index in ``s`` where the document ended.

        This can be used to decode a JSON document from a string that may
        have extraneous data at the end.

        ",50,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def raw_decode(self, s, idx=0):
        
        try:
            obj, end = self.scan_once(s, idx)
        except StopIteration as err:
            raise JSONDecodeError(""Expecting value"", s, err.value) from None
        return obj, end

        ```
    ###Assistant : Decode a JSON document from ``s`` (a ``str`` beginning with
        a JSON document) and return a 2-tuple of the Python
        representation and the index in ``s`` where the document ended.

        This can be used to decode a JSON document from a string that may
        have extraneous data at the end.

        
    "
7,"def has_bridges(G, root=None):
    
    try:
        next(bridges(G))
    except StopIteration:
        return False
    else:
        return True


@not_implemented_for(""multigraph"")
@not_implemented_for(""directed"")","Decide whether a graph has any bridges.

    A *bridge* in a graph is an edge whose removal causes the number of
    connected components of the graph to increase.

    Parameters
    ----------
    G : undirected graph

    root : node (optional)
       A node in the graph `G`. If specified, only the bridges in the
       connected component containing this node will be considered.

    Returns
    -------
    bool
       Whether the graph (or the connected component containing `root`)
       has any bridges.

    Raises
    ------
    NodeNotFound
       If `root` is not in the graph `G`.

    NetworkXNotImplemented
        If `G` is a directed graph.

    Examples
    --------
    The barbell graph with parameter zero has a single bridge::

        >>> G = nx.barbell_graph(10, 0)
        >>> nx.has_bridges(G)
        True

    On the other hand, the cycle graph has no bridges::

        >>> G = nx.cycle_graph(5)
        >>> nx.has_bridges(G)
        False

    Notes
    -----
    This implementation uses the :func:`networkx.bridges` function, so
    it shares its worst-case time complexity, $O(m + n)$, ignoring
    polylogarithmic factors, where $n$ is the number of nodes in the
    graph and $m$ is the number of edges.

    ",167,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def has_bridges(G, root=None):
    
    try:
        next(bridges(G))
    except StopIteration:
        return False
    else:
        return True


@not_implemented_for(""multigraph"")
@not_implemented_for(""directed"")
        ```
    ###Assistant : Decide whether a graph has any bridges.

    A *bridge* in a graph is an edge whose removal causes the number of
    connected components of the graph to increase.

    Parameters
    ----------
    G : undirected graph

    root : node (optional)
       A node in the graph `G`. If specified, only the bridges in the
       connected component containing this node will be considered.

    Returns
    -------
    bool
       Whether the graph (or the connected component containing `root`)
       has any bridges.

    Raises
    ------
    NodeNotFound
       If `root` is not in the graph `G`.

    NetworkXNotImplemented
        If `G` is a directed graph.

    Examples
    --------
    The barbell graph with parameter zero has a single bridge::

        >>> G = nx.barbell_graph(10, 0)
        >>> nx.has_bridges(G)
        True

    On the other hand, the cycle graph has no bridges::

        >>> G = nx.cycle_graph(5)
        >>> nx.has_bridges(G)
        False

    Notes
    -----
    This implementation uses the :func:`networkx.bridges` function, so
    it shares its worst-case time complexity, $O(m + n)$, ignoring
    polylogarithmic factors, where $n$ is the number of nodes in the
    graph and $m$ is the number of edges.

    
    "
8,"def wheel_metadata(source, dist_info_dir):
    # type: (ZipFile, str) -> Message
    
    path = f""{dist_info_dir}/WHEEL""
    # Zip file path separators must be /
    wheel_contents = read_wheel_metadata_file(source, path)

    try:
        wheel_text = wheel_contents.decode()
    except UnicodeDecodeError as e:
        raise UnsupportedWheel(f""error decoding {path!r}: {e!r}"")

    # FeedParser (used by Parser) does not raise any exceptions. The returned
    # message may have .defects populated, but for backwards-compatibility we
    # currently ignore them.
    return Parser().parsestr(wheel_text)

","Return the WHEEL metadata of an extracted wheel, if possible.
    Otherwise, raise UnsupportedWheel.
    ",13,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wheel_metadata(source, dist_info_dir):
    # type: (ZipFile, str) -> Message
    
    path = f""{dist_info_dir}/WHEEL""
    # Zip file path separators must be /
    wheel_contents = read_wheel_metadata_file(source, path)

    try:
        wheel_text = wheel_contents.decode()
    except UnicodeDecodeError as e:
        raise UnsupportedWheel(f""error decoding {path!r}: {e!r}"")

    # FeedParser (used by Parser) does not raise any exceptions. The returned
    # message may have .defects populated, but for backwards-compatibility we
    # currently ignore them.
    return Parser().parsestr(wheel_text)


        ```
    ###Assistant : Return the WHEEL metadata of an extracted wheel, if possible.
    Otherwise, raise UnsupportedWheel.
    
    "
9,"def remove_column(self, i, *args, **kwargs):
        
        table = self.table.remove_column(i, *args, **kwargs)
        name = self.table.column_names[i]
        blocks = []
        for tables in self.blocks:
            blocks.append(
                [
                    t.remove_column(t.column_names.index(name), *args, **kwargs) if name in t.column_names else t
                    for t in tables
                ]
            )
        return ConcatenationTable(table, blocks)
","
        Create new Table with the indicated column removed.

        Args:
            i (:obj:`int`):
                Index of column to remove.

        Returns:
            :class:`datasets.table.Table`:
                New table without the column.
        ",23,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def remove_column(self, i, *args, **kwargs):
        
        table = self.table.remove_column(i, *args, **kwargs)
        name = self.table.column_names[i]
        blocks = []
        for tables in self.blocks:
            blocks.append(
                [
                    t.remove_column(t.column_names.index(name), *args, **kwargs) if name in t.column_names else t
                    for t in tables
                ]
            )
        return ConcatenationTable(table, blocks)

        ```
    ###Assistant : 
        Create new Table with the indicated column removed.

        Args:
            i (:obj:`int`):
                Index of column to remove.

        Returns:
            :class:`datasets.table.Table`:
                New table without the column.
        
    "
10,"def test_cable_cannot_terminate_to_a_wireless_interface(self):
        
        wireless_interface = Interface(device=self.device1, name=""W1"", type=InterfaceTypeChoices.TYPE_80211A)
        cable = Cable(a_terminations=[self.interface2], b_terminations=[wireless_interface])
        with self.assertRaises(ValidationError):
            cable.clean()
","
        A cable cannot terminate to a wireless interface
        ",8,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_cable_cannot_terminate_to_a_wireless_interface(self):
        
        wireless_interface = Interface(device=self.device1, name=""W1"", type=InterfaceTypeChoices.TYPE_80211A)
        cable = Cable(a_terminations=[self.interface2], b_terminations=[wireless_interface])
        with self.assertRaises(ValidationError):
            cable.clean()

        ```
    ###Assistant : 
        A cable cannot terminate to a wireless interface
        
    "
11,"def get_test_db_clone_settings(self, suffix):
        
        # When this function is called, the test database has been created
        # already and its name has been copied to settings_dict['NAME'] so
        # we don't need to call _get_test_db_name.
        orig_settings_dict = self.connection.settings_dict
        return {
            **orig_settings_dict,
            ""NAME"": ""{}_{}"".format(orig_settings_dict[""NAME""], suffix),
        }
","
        Return a modified connection settings dict for the n-th clone of a DB.
        ",13,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_test_db_clone_settings(self, suffix):
        
        # When this function is called, the test database has been created
        # already and its name has been copied to settings_dict['NAME'] so
        # we don't need to call _get_test_db_name.
        orig_settings_dict = self.connection.settings_dict
        return {
            **orig_settings_dict,
            ""NAME"": ""{}_{}"".format(orig_settings_dict[""NAME""], suffix),
        }

        ```
    ###Assistant : 
        Return a modified connection settings dict for the n-th clone of a DB.
        
    "
12,"def open(self, host='', port=IMAP4_PORT, timeout=None):
        
        self.host = host
        self.port = port
        self.sock = self._create_socket(timeout)
        self.file = self.sock.makefile('rb')

","Setup connection to remote server on ""host:port""
            (default: localhost:standard IMAP4 port).
        This connection will be used by the routines:
            read, readline, send, shutdown.
        ",23,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def open(self, host='', port=IMAP4_PORT, timeout=None):
        
        self.host = host
        self.port = port
        self.sock = self._create_socket(timeout)
        self.file = self.sock.makefile('rb')


        ```
    ###Assistant : Setup connection to remote server on ""host:port""
            (default: localhost:standard IMAP4 port).
        This connection will be used by the routines:
            read, readline, send, shutdown.
        
    "
13,"def synchronized_output_end_sequence(self) -> str:
        
        if self.synchronised_output:
            return TERMINAL_MODES_ANSI_SEQUENCES[Mode.SynchronizedOutput][""end_sync""]
        return """"
","
        Returns the ANSI sequence that we should send to the terminal to tell it that
        it should stop buffering the content we're about to send.
        If the terminal doesn't seem to support synchronised updates the string will be empty.

        Returns:
            str: the ""synchronised output stop"" ANSI sequence. It will be ab empty string
                if the terminal emulator doesn't seem to support the ""synchronised updates"" mode.
        ",65,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def synchronized_output_end_sequence(self) -> str:
        
        if self.synchronised_output:
            return TERMINAL_MODES_ANSI_SEQUENCES[Mode.SynchronizedOutput][""end_sync""]
        return """"

        ```
    ###Assistant : 
        Returns the ANSI sequence that we should send to the terminal to tell it that
        it should stop buffering the content we're about to send.
        If the terminal doesn't seem to support synchronised updates the string will be empty.

        Returns:
            str: the ""synchronised output stop"" ANSI sequence. It will be ab empty string
                if the terminal emulator doesn't seem to support the ""synchronised updates"" mode.
        
    "
14,"def _band_penalty_coefficients(self, fc, q, gain, filter_frs):
        
        ref_frs = biquad.digital_coeffs(self.frequency, 192e3, *biquad.peaking(fc, q, gain, fs=192e3))
        est_sums = np.sum(filter_frs, axis=1)
        ref_sums = np.sum(ref_frs, axis=1)
        penalties = np.zeros((len(fc),))
        mask = np.squeeze(ref_sums) != 0.0
        penalties[mask] = est_sums[mask] / ref_sums[mask]
        return 10 * (1 - np.expand_dims(penalties, 1))
","Calculates penalty coefficients for filters if their transition bands extend beyond Nyquist frequency

        The calculation is based on ratio of frequency response integrals between 44.1 kHz and 192 kHz

        Args:
            fc: Filter center frequencies, 1-D array
            q: Filter qualities, 1-D array
            gain: Filter gains, 1-D array
            filter_frs: Filter frequency responses, 2-D array, one fr per row

        Returns:
            Column array of penalty coefficients, one per filter
        ",65,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _band_penalty_coefficients(self, fc, q, gain, filter_frs):
        
        ref_frs = biquad.digital_coeffs(self.frequency, 192e3, *biquad.peaking(fc, q, gain, fs=192e3))
        est_sums = np.sum(filter_frs, axis=1)
        ref_sums = np.sum(ref_frs, axis=1)
        penalties = np.zeros((len(fc),))
        mask = np.squeeze(ref_sums) != 0.0
        penalties[mask] = est_sums[mask] / ref_sums[mask]
        return 10 * (1 - np.expand_dims(penalties, 1))

        ```
    ###Assistant : Calculates penalty coefficients for filters if their transition bands extend beyond Nyquist frequency

        The calculation is based on ratio of frequency response integrals between 44.1 kHz and 192 kHz

        Args:
            fc: Filter center frequencies, 1-D array
            q: Filter qualities, 1-D array
            gain: Filter gains, 1-D array
            filter_frs: Filter frequency responses, 2-D array, one fr per row

        Returns:
            Column array of penalty coefficients, one per filter
        
    "
15,"def test_predict_on_toy_problem(global_random_seed):
    
    clf1 = LogisticRegression(random_state=global_random_seed)
    clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
    clf3 = GaussianNB()

    X = np.array(
        [[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]]
    )

    y = np.array([1, 1, 1, 2, 2, 2])

    assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
    assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
    assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])

    eclf = VotingClassifier(
        estimators=[(""lr"", clf1), (""rf"", clf2), (""gnb"", clf3)],
        voting=""hard"",
        weights=[1, 1, 1],
    )
    assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])

    eclf = VotingClassifier(
        estimators=[(""lr"", clf1), (""rf"", clf2), (""gnb"", clf3)],
        voting=""soft"",
        weights=[1, 1, 1],
    )
    assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])

",Manually check predicted class labels for toy dataset.,8,104,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_predict_on_toy_problem(global_random_seed):
    
    clf1 = LogisticRegression(random_state=global_random_seed)
    clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
    clf3 = GaussianNB()

    X = np.array(
        [[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]]
    )

    y = np.array([1, 1, 1, 2, 2, 2])

    assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
    assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
    assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])

    eclf = VotingClassifier(
        estimators=[(""lr"", clf1), (""rf"", clf2), (""gnb"", clf3)],
        voting=""hard"",
        weights=[1, 1, 1],
    )
    assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])

    eclf = VotingClassifier(
        estimators=[(""lr"", clf1), (""rf"", clf2), (""gnb"", clf3)],
        voting=""soft"",
        weights=[1, 1, 1],
    )
    assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])


        ```
    ###Assistant : Manually check predicted class labels for toy dataset.
    "
16,"def fit_transform(self, X, y=None):
        
        self._validate_params()
        return self._transform(X, fitting=True)
","Learn a list of feature name -> indices mappings and transform X.

        Like fit(X) followed by transform(X), but does not require
        materializing X in memory.

        Parameters
        ----------
        X : Mapping or iterable over Mappings
            Dict(s) or Mapping(s) from feature names (arbitrary Python
            objects) to feature values (strings or convertible to dtype).

            .. versionchanged:: 0.24
               Accepts multiple string values for one categorical feature.

        y : (ignored)
            Ignored parameter.

        Returns
        -------
        Xa : {array, sparse matrix}
            Feature vectors; always 2-d.
        ",78,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit_transform(self, X, y=None):
        
        self._validate_params()
        return self._transform(X, fitting=True)

        ```
    ###Assistant : Learn a list of feature name -> indices mappings and transform X.

        Like fit(X) followed by transform(X), but does not require
        materializing X in memory.

        Parameters
        ----------
        X : Mapping or iterable over Mappings
            Dict(s) or Mapping(s) from feature names (arbitrary Python
            objects) to feature values (strings or convertible to dtype).

            .. versionchanged:: 0.24
               Accepts multiple string values for one categorical feature.

        y : (ignored)
            Ignored parameter.

        Returns
        -------
        Xa : {array, sparse matrix}
            Feature vectors; always 2-d.
        
    "
17,"def _on_feature_permission_requested(self, url, feature):
        
        page = self._widget.page()
        grant_permission = functools.partial(
            page.setFeaturePermission, url, feature,
            QWebEnginePage.PermissionPolicy.PermissionGrantedByUser)
        deny_permission = functools.partial(
            page.setFeaturePermission, url, feature,
            QWebEnginePage.PermissionPolicy.PermissionDeniedByUser)

        permission_str = debug.qenum_key(QWebEnginePage, feature)

        if not url.isValid():
            # WORKAROUND for https://bugreports.qt.io/browse/QTBUG-85116
            is_qtbug = (qtutils.version_check('5.15.0',
                                              compiled=False,
                                              exact=True) and
                        self._tab.is_private and
                        feature == QWebEnginePage.Feature.Notifications)
            logger = log.webview.debug if is_qtbug else log.webview.warning
            logger(""Ignoring feature permission {} for invalid URL {}"".format(
                permission_str, url))
            deny_permission()
            return

        if feature not in self._options:
            log.webview.error(""Unhandled feature permission {}"".format(
                permission_str))
            deny_permission()
            return

        if (
                feature in [QWebEnginePage.Feature.DesktopVideoCapture,
                            QWebEnginePage.Feature.DesktopAudioVideoCapture] and
                qtutils.version_check('5.13', compiled=False) and
                not qtutils.version_check('5.13.2', compiled=False)
        ):
            # WORKAROUND for https://bugreports.qt.io/browse/QTBUG-78016
            log.webview.warning(""Ignoring desktop sharing request due to ""
                                ""crashes in Qt < 5.13.2"")
            deny_permission()
            return

        question = shared.feature_permission(
            url=url.adjusted(QUrl.UrlFormattingOption.RemovePath),
            option=self._options[feature], msg=self._messages[feature],
            yes_action=grant_permission, no_action=deny_permission,
            abort_on=[self._tab.abort_questions])

        if question is not None:
            page.featurePermissionRequestCanceled.connect(
                functools.partial(self._on_feature_permission_cancelled,
                                  question, url, feature))
",Ask the user for approval for geolocation/media/etc..,7,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _on_feature_permission_requested(self, url, feature):
        
        page = self._widget.page()
        grant_permission = functools.partial(
            page.setFeaturePermission, url, feature,
            QWebEnginePage.PermissionPolicy.PermissionGrantedByUser)
        deny_permission = functools.partial(
            page.setFeaturePermission, url, feature,
            QWebEnginePage.PermissionPolicy.PermissionDeniedByUser)

        permission_str = debug.qenum_key(QWebEnginePage, feature)

        if not url.isValid():
            # WORKAROUND for https://bugreports.qt.io/browse/QTBUG-85116
            is_qtbug = (qtutils.version_check('5.15.0',
                                              compiled=False,
                                              exact=True) and
                        self._tab.is_private and
                        feature == QWebEnginePage.Feature.Notifications)
            logger = log.webview.debug if is_qtbug else log.webview.warning
            logger(""Ignoring feature permission {} for invalid URL {}"".format(
                permission_str, url))
            deny_permission()
            return

        if feature not in self._options:
            log.webview.error(""Unhandled feature permission {}"".format(
                permission_str))
            deny_permission()
            return

        if (
                feature in [QWebEnginePage.Feature.DesktopVideoCapture,
                            QWebEnginePage.Feature.DesktopAudioVideoCapture] and
                qtutils.version_check('5.13', compiled=False) and
                not qtutils.version_check('5.13.2', compiled=False)
        ):
            # WORKAROUND for https://bugreports.qt.io/browse/QTBUG-78016
            log.webview.warning(""Ignoring desktop sharing request due to ""
                                ""crashes in Qt < 5.13.2"")
            deny_permission()
            return

        question = shared.feature_permission(
            url=url.adjusted(QUrl.UrlFormattingOption.RemovePath),
            option=self._options[feature], msg=self._messages[feature],
            yes_action=grant_permission, no_action=deny_permission,
            abort_on=[self._tab.abort_questions])

        if question is not None:
            page.featurePermissionRequestCanceled.connect(
                functools.partial(self._on_feature_permission_cancelled,
                                  question, url, feature))

        ```
    ###Assistant : Ask the user for approval for geolocation/media/etc..
    "
18,"def add_find_python(self):
        

        start = 402
        for ver in self.versions:
            install_path = r""SOFTWARE\Python\PythonCore\%s\InstallPath"" % ver
            machine_reg = ""python.machine."" + ver
            user_reg = ""python.user."" + ver
            machine_prop = ""PYTHON.MACHINE."" + ver
            user_prop = ""PYTHON.USER."" + ver
            machine_action = ""PythonFromMachine"" + ver
            user_action = ""PythonFromUser"" + ver
            exe_action = ""PythonExe"" + ver
            target_dir_prop = ""TARGETDIR"" + ver
            exe_prop = ""PYTHON"" + ver
            if msilib.Win64:
                # type: msidbLocatorTypeRawValue + msidbLocatorType64bit
                Type = 2+16
            else:
                Type = 2
            add_data(self.db, ""RegLocator"",
                    [(machine_reg, 2, install_path, None, Type),
                     (user_reg, 1, install_path, None, Type)])
            add_data(self.db, ""AppSearch"",
                    [(machine_prop, machine_reg),
                     (user_prop, user_reg)])
            add_data(self.db, ""CustomAction"",
                    [(machine_action, 51+256, target_dir_prop, ""["" + machine_prop + ""]""),
                     (user_action, 51+256, target_dir_prop, ""["" + user_prop + ""]""),
                     (exe_action, 51+256, exe_prop, ""["" + target_dir_prop + ""]\\python.exe""),
                    ])
            add_data(self.db, ""InstallExecuteSequence"",
                    [(machine_action, machine_prop, start),
                     (user_action, user_prop, start + 1),
                     (exe_action, None, start + 2),
                    ])
            add_data(self.db, ""InstallUISequence"",
                    [(machine_action, machine_prop, start),
                     (user_action, user_prop, start + 1),
                     (exe_action, None, start + 2),
                    ])
            add_data(self.db, ""Condition"",
                    [(""Python"" + ver, 0, ""NOT TARGETDIR"" + ver)])
            start += 4
            assert start < 500
","Adds code to the installer to compute the location of Python.

        Properties PYTHON.MACHINE.X.Y and PYTHON.USER.X.Y will be set from the
        registry for each version of Python.

        Properties TARGETDIRX.Y will be set from PYTHON.USER.X.Y if defined,
        else from PYTHON.MACHINE.X.Y.

        Properties PYTHONX.Y will be set to TARGETDIRX.Y\\python.exe",45,167,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_find_python(self):
        

        start = 402
        for ver in self.versions:
            install_path = r""SOFTWARE\Python\PythonCore\%s\InstallPath"" % ver
            machine_reg = ""python.machine."" + ver
            user_reg = ""python.user."" + ver
            machine_prop = ""PYTHON.MACHINE."" + ver
            user_prop = ""PYTHON.USER."" + ver
            machine_action = ""PythonFromMachine"" + ver
            user_action = ""PythonFromUser"" + ver
            exe_action = ""PythonExe"" + ver
            target_dir_prop = ""TARGETDIR"" + ver
            exe_prop = ""PYTHON"" + ver
            if msilib.Win64:
                # type: msidbLocatorTypeRawValue + msidbLocatorType64bit
                Type = 2+16
            else:
                Type = 2
            add_data(self.db, ""RegLocator"",
                    [(machine_reg, 2, install_path, None, Type),
                     (user_reg, 1, install_path, None, Type)])
            add_data(self.db, ""AppSearch"",
                    [(machine_prop, machine_reg),
                     (user_prop, user_reg)])
            add_data(self.db, ""CustomAction"",
                    [(machine_action, 51+256, target_dir_prop, ""["" + machine_prop + ""]""),
                     (user_action, 51+256, target_dir_prop, ""["" + user_prop + ""]""),
                     (exe_action, 51+256, exe_prop, ""["" + target_dir_prop + ""]\\python.exe""),
                    ])
            add_data(self.db, ""InstallExecuteSequence"",
                    [(machine_action, machine_prop, start),
                     (user_action, user_prop, start + 1),
                     (exe_action, None, start + 2),
                    ])
            add_data(self.db, ""InstallUISequence"",
                    [(machine_action, machine_prop, start),
                     (user_action, user_prop, start + 1),
                     (exe_action, None, start + 2),
                    ])
            add_data(self.db, ""Condition"",
                    [(""Python"" + ver, 0, ""NOT TARGETDIR"" + ver)])
            start += 4
            assert start < 500

        ```
    ###Assistant : Adds code to the installer to compute the location of Python.

        Properties PYTHON.MACHINE.X.Y and PYTHON.USER.X.Y will be set from the
        registry for each version of Python.

        Properties TARGETDIRX.Y will be set from PYTHON.USER.X.Y if defined,
        else from PYTHON.MACHINE.X.Y.

        Properties PYTHONX.Y will be set to TARGETDIRX.Y\\python.exe
    "
19,"def write_exports(self, exports):
        
        rf = self.get_distinfo_file(EXPORTS_FILENAME)
        with open(rf, 'w') as f:
            write_exports(exports, f)
","
        Write a dictionary of exports to a file in .ini format.
        :param exports: A dictionary of exports, mapping an export category to
                        a list of :class:`ExportEntry` instances describing the
                        individual export entries.
        ",32,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def write_exports(self, exports):
        
        rf = self.get_distinfo_file(EXPORTS_FILENAME)
        with open(rf, 'w') as f:
            write_exports(exports, f)

        ```
    ###Assistant : 
        Write a dictionary of exports to a file in .ini format.
        :param exports: A dictionary of exports, mapping an export category to
                        a list of :class:`ExportEntry` instances describing the
                        individual export entries.
        
    "
20,"def _get_action_handler_with_module_context(self, connection, templar):
        
        module_collection, separator, module_name = self._task.action.rpartition(""."")
        module_prefix = module_name.split('_')[0]
        if module_collection:
            # For network modules, which look for one action plugin per platform, look for the
            # action plugin in the same collection as the module by prefixing the action plugin
            # with the same collection.
            network_action = ""{0}.{1}"".format(module_collection, module_prefix)
        else:
            network_action = module_prefix

        collections = self._task.collections

        # Check if the module has specified an action handler
        module = self._shared_loader_obj.module_loader.find_plugin_with_context(
            self._task.action, collection_list=collections
        )
        if not module.resolved or not module.action_plugin:
            module = None
        if module is not None:
            handler_name = module.action_plugin
        # let action plugin override module, fallback to 'normal' action plugin otherwise
        elif self._shared_loader_obj.action_loader.has_plugin(self._task.action, collection_list=collections):
            handler_name = self._task.action
        elif all((module_prefix in C.NETWORK_GROUP_MODULES, self._shared_loader_obj.action_loader.has_plugin(network_action, collection_list=collections))):
            handler_name = network_action
            display.vvvv(""Using network group action {handler} for {action}"".format(handler=handler_name,
                                                                                    action=self._task.action),
                         host=self._play_context.remote_addr)
        else:
            # use ansible.legacy.normal to allow (historic) local action_plugins/ override without collections search
            handler_name = 'ansible.legacy.normal'
            collections = None  # until then, we don't want the task's collection list to be consulted; use the builtin

        handler = self._shared_loader_obj.action_loader.get(
            handler_name,
            task=self._task,
            connection=connection,
            play_context=self._play_context,
            loader=self._loader,
            templar=templar,
            shared_loader_obj=self._shared_loader_obj,
            collection_list=collections
        )

        if not handler:
            raise AnsibleError(""the handler '%s' was not found"" % handler_name)

        return handler, module

","
        Returns the correct action plugin to handle the requestion task action and the module context
        ",15,191,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_action_handler_with_module_context(self, connection, templar):
        
        module_collection, separator, module_name = self._task.action.rpartition(""."")
        module_prefix = module_name.split('_')[0]
        if module_collection:
            # For network modules, which look for one action plugin per platform, look for the
            # action plugin in the same collection as the module by prefixing the action plugin
            # with the same collection.
            network_action = ""{0}.{1}"".format(module_collection, module_prefix)
        else:
            network_action = module_prefix

        collections = self._task.collections

        # Check if the module has specified an action handler
        module = self._shared_loader_obj.module_loader.find_plugin_with_context(
            self._task.action, collection_list=collections
        )
        if not module.resolved or not module.action_plugin:
            module = None
        if module is not None:
            handler_name = module.action_plugin
        # let action plugin override module, fallback to 'normal' action plugin otherwise
        elif self._shared_loader_obj.action_loader.has_plugin(self._task.action, collection_list=collections):
            handler_name = self._task.action
        elif all((module_prefix in C.NETWORK_GROUP_MODULES, self._shared_loader_obj.action_loader.has_plugin(network_action, collection_list=collections))):
            handler_name = network_action
            display.vvvv(""Using network group action {handler} for {action}"".format(handler=handler_name,
                                                                                    action=self._task.action),
                         host=self._play_context.remote_addr)
        else:
            # use ansible.legacy.normal to allow (historic) local action_plugins/ override without collections search
            handler_name = 'ansible.legacy.normal'
            collections = None  # until then, we don't want the task's collection list to be consulted; use the builtin

        handler = self._shared_loader_obj.action_loader.get(
            handler_name,
            task=self._task,
            connection=connection,
            play_context=self._play_context,
            loader=self._loader,
            templar=templar,
            shared_loader_obj=self._shared_loader_obj,
            collection_list=collections
        )

        if not handler:
            raise AnsibleError(""the handler '%s' was not found"" % handler_name)

        return handler, module


        ```
    ###Assistant : 
        Returns the correct action plugin to handle the requestion task action and the module context
        
    "
21,"def forward(self, y_hat, y, length):
        
        mask = sequence_mask(sequence_length=length, max_len=y.size(1)).unsqueeze(2)
        y_norm = sample_wise_min_max(y, mask)
        y_hat_norm = sample_wise_min_max(y_hat, mask)
        ssim_loss = self.loss_func((y_norm * mask).unsqueeze(1), (y_hat_norm * mask).unsqueeze(1))

        if ssim_loss.item() > 1.0:
            print(f"" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0"")
            ssim_loss == 1.0

        if ssim_loss.item() < 0.0:
            print(f"" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0"")
            ssim_loss == 0.0

        return ssim_loss

","
        Args:
            y_hat (tensor): model prediction values.
            y (tensor): target values.
            length (tensor): length of each sample in a batch for masking.

        Shapes:
            y_hat: B x T X D
            y: B x T x D
            length: B

         Returns:
            loss: An average loss value in range [0, 1] masked by the length.
        ",50,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, y_hat, y, length):
        
        mask = sequence_mask(sequence_length=length, max_len=y.size(1)).unsqueeze(2)
        y_norm = sample_wise_min_max(y, mask)
        y_hat_norm = sample_wise_min_max(y_hat, mask)
        ssim_loss = self.loss_func((y_norm * mask).unsqueeze(1), (y_hat_norm * mask).unsqueeze(1))

        if ssim_loss.item() > 1.0:
            print(f"" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0"")
            ssim_loss == 1.0

        if ssim_loss.item() < 0.0:
            print(f"" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0"")
            ssim_loss == 0.0

        return ssim_loss


        ```
    ###Assistant : 
        Args:
            y_hat (tensor): model prediction values.
            y (tensor): target values.
            length (tensor): length of each sample in a batch for masking.

        Shapes:
            y_hat: B x T X D
            y: B x T x D
            length: B

         Returns:
            loss: An average loss value in range [0, 1] masked by the length.
        
    "
22,"def get_commands():
    
    commands = {name: 'django.core' for name in find_commands(__path__[0])}

    if not settings.configured:
        return commands

    for app_config in reversed(apps.get_app_configs()):
        path = os.path.join(app_config.path, 'management')
        commands.update({name: app_config.name for name in find_commands(path)})

    return commands

","
    Return a dictionary mapping command names to their callback applications.

    Look for a management.commands package in django.core, and in each
    installed application -- if a commands package exists, register all
    commands in that package.

    Core commands are always included. If a settings module has been
    specified, also include user-defined commands.

    The dictionary is in the format {command_name: app_name}. Key-value
    pairs from this dictionary can then be used in calls to
    load_command_class(app_name, command_name)

    If a specific version of a command must be loaded (e.g., with the
    startapp command), the instantiated module can be placed in the
    dictionary in place of the application name.

    The dictionary is cached on the first call and reused on subsequent
    calls.
    ",115,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_commands():
    
    commands = {name: 'django.core' for name in find_commands(__path__[0])}

    if not settings.configured:
        return commands

    for app_config in reversed(apps.get_app_configs()):
        path = os.path.join(app_config.path, 'management')
        commands.update({name: app_config.name for name in find_commands(path)})

    return commands


        ```
    ###Assistant : 
    Return a dictionary mapping command names to their callback applications.

    Look for a management.commands package in django.core, and in each
    installed application -- if a commands package exists, register all
    commands in that package.

    Core commands are always included. If a settings module has been
    specified, also include user-defined commands.

    The dictionary is in the format {command_name: app_name}. Key-value
    pairs from this dictionary can then be used in calls to
    load_command_class(app_name, command_name)

    If a specific version of a command must be loaded (e.g., with the
    startapp command), the instantiated module can be placed in the
    dictionary in place of the application name.

    The dictionary is cached on the first call and reused on subsequent
    calls.
    
    "
23,"def getphraselist(self):
        
        plist = []

        while self.pos < len(self.field):
            if self.field[self.pos] in self.FWS:
                self.pos += 1
            elif self.field[self.pos] == '""':
                plist.append(self.getquote())
            elif self.field[self.pos] == '(':
                self.commentlist.append(self.getcomment())
            elif self.field[self.pos] in self.phraseends:
                break
            else:
                plist.append(self.getatom(self.phraseends))

        return plist
","Parse a sequence of RFC 2822 phrases.

        A phrase is a sequence of words, which are in turn either RFC 2822
        atoms or quoted-strings.  Phrases are canonicalized by squeezing all
        runs of continuous whitespace into one space.
        ",37,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getphraselist(self):
        
        plist = []

        while self.pos < len(self.field):
            if self.field[self.pos] in self.FWS:
                self.pos += 1
            elif self.field[self.pos] == '""':
                plist.append(self.getquote())
            elif self.field[self.pos] == '(':
                self.commentlist.append(self.getcomment())
            elif self.field[self.pos] in self.phraseends:
                break
            else:
                plist.append(self.getatom(self.phraseends))

        return plist

        ```
    ###Assistant : Parse a sequence of RFC 2822 phrases.

        A phrase is a sequence of words, which are in turn either RFC 2822
        atoms or quoted-strings.  Phrases are canonicalized by squeezing all
        runs of continuous whitespace into one space.
        
    "
24,"def set_location(self, location):
        

        # This puts the rectangle into figure-relative coordinates.
        if isinstance(location, str):
            _api.check_in_list(self._locstrings, location=location)
            self._pos = 1. if location in ('top', 'right') else 0.
        elif isinstance(location, numbers.Real):
            self._pos = location
        else:
            raise ValueError(
                f""location must be {self._locstrings[0]!r}, ""
                f""{self._locstrings[1]!r}, or a float, not {location!r}"")

        self._loc = location

        if self._orientation == 'x':
            # An x-secondary axes is like an inset axes from x = 0 to x = 1 and
            # from y = pos to y = pos + eps, in the parent's transAxes coords.
            bounds = [0, self._pos, 1., 1e-10]
        else:  # 'y'
            bounds = [self._pos, 0, 1e-10, 1]

        # this locator lets the axes move in the parent axes coordinates.
        # so it never needs to know where the parent is explicitly in
        # figure coordinates.
        # it gets called in ax.apply_aspect() (of all places)
        self.set_axes_locator(
            _TransformedBoundsLocator(bounds, self._parent.transAxes))
","
        Set the vertical or horizontal location of the axes in
        parent-normalized coordinates.

        Parameters
        ----------
        location : {'top', 'bottom', 'left', 'right'} or float
            The position to put the secondary axis.  Strings can be 'top' or
            'bottom' for orientation='x' and 'right' or 'left' for
            orientation='y'. A float indicates the relative position on the
            parent axes to put the new axes, 0.0 being the bottom (or left)
            and 1.0 being the top (or right).
        ",71,142,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_location(self, location):
        

        # This puts the rectangle into figure-relative coordinates.
        if isinstance(location, str):
            _api.check_in_list(self._locstrings, location=location)
            self._pos = 1. if location in ('top', 'right') else 0.
        elif isinstance(location, numbers.Real):
            self._pos = location
        else:
            raise ValueError(
                f""location must be {self._locstrings[0]!r}, ""
                f""{self._locstrings[1]!r}, or a float, not {location!r}"")

        self._loc = location

        if self._orientation == 'x':
            # An x-secondary axes is like an inset axes from x = 0 to x = 1 and
            # from y = pos to y = pos + eps, in the parent's transAxes coords.
            bounds = [0, self._pos, 1., 1e-10]
        else:  # 'y'
            bounds = [self._pos, 0, 1e-10, 1]

        # this locator lets the axes move in the parent axes coordinates.
        # so it never needs to know where the parent is explicitly in
        # figure coordinates.
        # it gets called in ax.apply_aspect() (of all places)
        self.set_axes_locator(
            _TransformedBoundsLocator(bounds, self._parent.transAxes))

        ```
    ###Assistant : 
        Set the vertical or horizontal location of the axes in
        parent-normalized coordinates.

        Parameters
        ----------
        location : {'top', 'bottom', 'left', 'right'} or float
            The position to put the secondary axis.  Strings can be 'top' or
            'bottom' for orientation='x' and 'right' or 'left' for
            orientation='y'. A float indicates the relative position on the
            parent axes to put the new axes, 0.0 being the bottom (or left)
            and 1.0 being the top (or right).
        
    "
25,"def length(self):
        
        if self._length_cache is None:
            if len(self.call_queue):
                self.drain_call_queue()
            else:
                self._length_cache, self._width_cache = _get_index_and_columns.remote(
                    self.oid
                )
        if isinstance(self._length_cache, ObjectIDType):
            self._length_cache = ray.get(self._length_cache)
        return self._length_cache
","
        Get the length of the object wrapped by this partition.

        Returns
        -------
        int
            The length of the object.
        ",18,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def length(self):
        
        if self._length_cache is None:
            if len(self.call_queue):
                self.drain_call_queue()
            else:
                self._length_cache, self._width_cache = _get_index_and_columns.remote(
                    self.oid
                )
        if isinstance(self._length_cache, ObjectIDType):
            self._length_cache = ray.get(self._length_cache)
        return self._length_cache

        ```
    ###Assistant : 
        Get the length of the object wrapped by this partition.

        Returns
        -------
        int
            The length of the object.
        
    "
26,"def dmp_l2_norm_squared(f, u, K):
    
    if not u:
        return dup_l2_norm_squared(f, K)

    v = u - 1

    return sum([ dmp_l2_norm_squared(c, v, K) for c in f ])

","
    Returns squared l2 norm of a polynomial in ``K[X]``.

    Examples
    ========

    >>> from sympy.polys import ring, ZZ
    >>> R, x,y = ring(""x,y"", ZZ)

    >>> R.dmp_l2_norm_squared(2*x*y - x - 3)
    14

    ",30,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dmp_l2_norm_squared(f, u, K):
    
    if not u:
        return dup_l2_norm_squared(f, K)

    v = u - 1

    return sum([ dmp_l2_norm_squared(c, v, K) for c in f ])


        ```
    ###Assistant : 
    Returns squared l2 norm of a polynomial in ``K[X]``.

    Examples
    ========

    >>> from sympy.polys import ring, ZZ
    >>> R, x,y = ring(""x,y"", ZZ)

    >>> R.dmp_l2_norm_squared(2*x*y - x - 3)
    14

    
    "
27,"def cloud_filter(args, targets):  # type: (IntegrationConfig, t.Tuple[IntegrationTarget, ...]) -> t.List[str]
    
    if args.metadata.cloud_config is not None:
        return []  # cloud filter already performed prior to delegation

    exclude = []  # type: t.List[str]

    for provider in get_cloud_providers(args, targets):
        provider.filter(targets, exclude)

    return exclude

",Return a list of target names to exclude based on the given targets.,13,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cloud_filter(args, targets):  # type: (IntegrationConfig, t.Tuple[IntegrationTarget, ...]) -> t.List[str]
    
    if args.metadata.cloud_config is not None:
        return []  # cloud filter already performed prior to delegation

    exclude = []  # type: t.List[str]

    for provider in get_cloud_providers(args, targets):
        provider.filter(targets, exclude)

    return exclude


        ```
    ###Assistant : Return a list of target names to exclude based on the given targets.
    "
28,"def test_upgrade_available_none():
    

    chk_upgrade_out = (
        ""Last metadata expiration check: 22:5:48 ago on Mon Dec  6 19:26:36 EST 2021.""
    )

    dnf_call = MagicMock(return_value={""retcode"": 100, ""stdout"": chk_upgrade_out})
    version_mock = MagicMock(return_value=""6.6-2"")
    with patch(""pathlib.Path.is_file"", return_value=True):
        with patch.dict(
            aixpkg.__salt__,
            {""cmd.run_all"": dnf_call, ""config.get"": MagicMock(return_value=False)},
        ), patch.object(aixpkg, ""version"", version_mock):
            result = aixpkg.upgrade_available(""info"")
            assert dnf_call.call_count == 1
            libpath_env = {""LIBPATH"": ""/opt/freeware/lib:/usr/lib""}
            dnf_call.assert_any_call(
                ""/opt/freeware/bin/dnf check-update info"",
                env=libpath_env,
                ignore_retcode=True,
                python_shell=False,
            )
            assert not result

","
    test upgrade available where a valid upgrade is not available
    ",10,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_upgrade_available_none():
    

    chk_upgrade_out = (
        ""Last metadata expiration check: 22:5:48 ago on Mon Dec  6 19:26:36 EST 2021.""
    )

    dnf_call = MagicMock(return_value={""retcode"": 100, ""stdout"": chk_upgrade_out})
    version_mock = MagicMock(return_value=""6.6-2"")
    with patch(""pathlib.Path.is_file"", return_value=True):
        with patch.dict(
            aixpkg.__salt__,
            {""cmd.run_all"": dnf_call, ""config.get"": MagicMock(return_value=False)},
        ), patch.object(aixpkg, ""version"", version_mock):
            result = aixpkg.upgrade_available(""info"")
            assert dnf_call.call_count == 1
            libpath_env = {""LIBPATH"": ""/opt/freeware/lib:/usr/lib""}
            dnf_call.assert_any_call(
                ""/opt/freeware/bin/dnf check-update info"",
                env=libpath_env,
                ignore_retcode=True,
                python_shell=False,
            )
            assert not result


        ```
    ###Assistant : 
    test upgrade available where a valid upgrade is not available
    
    "
29,"def test_too_many_boosted_releases_do_not_boost_anymore(self):
        
        release_2 = Release.get_or_create(self.project, ""2.0"")
        release_3 = Release.get_or_create(self.project, ""3.0"")

        for release_id in (self.release.id, release_2.id):
            self.redis_client.set(f""ds::p:{self.project.id}:r:{release_id}"", 1, 60 * 60 * 24)
            self.redis_client.hset(
                f""ds::p:{self.project.id}:boosted_releases"",
                release_id,
                time(),
            )

        with self.options(
            {
                ""dynamic-sampling:boost-latest-release"": True,
            }
        ):
            self.make_release_transaction(
                release_version=release_3.version,
                environment_name=self.environment1.name,
                project_id=self.project.id,
                checksum=""b"" * 32,
                timestamp=self.timestamp,
            )
            assert self.redis_client.hgetall(f""ds::p:{self.project.id}:boosted_releases"") == {
                str(self.release.id): str(time()),
                str(release_2.id): str(time()),
            }
            assert self.redis_client.get(f""ds::p:{self.project.id}:r:{release_3.id}"") is None

","
        This test tests the case when we have already too many boosted releases, in this case we want to skip the
        boosting of anymore releases
        ",25,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_too_many_boosted_releases_do_not_boost_anymore(self):
        
        release_2 = Release.get_or_create(self.project, ""2.0"")
        release_3 = Release.get_or_create(self.project, ""3.0"")

        for release_id in (self.release.id, release_2.id):
            self.redis_client.set(f""ds::p:{self.project.id}:r:{release_id}"", 1, 60 * 60 * 24)
            self.redis_client.hset(
                f""ds::p:{self.project.id}:boosted_releases"",
                release_id,
                time(),
            )

        with self.options(
            {
                ""dynamic-sampling:boost-latest-release"": True,
            }
        ):
            self.make_release_transaction(
                release_version=release_3.version,
                environment_name=self.environment1.name,
                project_id=self.project.id,
                checksum=""b"" * 32,
                timestamp=self.timestamp,
            )
            assert self.redis_client.hgetall(f""ds::p:{self.project.id}:boosted_releases"") == {
                str(self.release.id): str(time()),
                str(release_2.id): str(time()),
            }
            assert self.redis_client.get(f""ds::p:{self.project.id}:r:{release_3.id}"") is None


        ```
    ###Assistant : 
        This test tests the case when we have already too many boosted releases, in this case we want to skip the
        boosting of anymore releases
        
    "
30,"def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True):
    
    import numpy as np
    import scipy as sp
    import scipy.sparse.linalg  # call as sp.sparse.linalg

    if len(G) == 0:
        return {}, {}
    A = nx.adjacency_matrix(G, nodelist=list(G), dtype=float)

    if nstart is None:
        u, s, vt = sp.sparse.linalg.svds(A, k=1, maxiter=max_iter, tol=tol)
    else:
        nstart = np.array(list(nstart.values()))
        u, s, vt = sp.sparse.linalg.svds(A, k=1, v0=nstart, maxiter=max_iter, tol=tol)

    a = vt.flatten().real
    h = A @ a
    if normalized:
        h = h / h.sum()
        a = a / a.sum()
    hubs = dict(zip(G, map(float, h)))
    authorities = dict(zip(G, map(float, a)))
    return hubs, authorities

","Returns HITS hubs and authorities values for nodes.

    The HITS algorithm computes two numbers for a node.
    Authorities estimates the node value based on the incoming links.
    Hubs estimates the node value based on outgoing links.

    Parameters
    ----------
    G : graph
      A NetworkX graph

    max_iter : integer, optional
      Maximum number of iterations in power method.

    tol : float, optional
      Error tolerance used to check convergence in power method iteration.

    nstart : dictionary, optional
      Starting value of each node for power method iteration.

    normalized : bool (default=True)
       Normalize results by the sum of all of the values.

    Returns
    -------
    (hubs,authorities) : two-tuple of dictionaries
       Two dictionaries keyed by node containing the hub and authority
       values.

    Raises
    ------
    PowerIterationFailedConvergence
        If the algorithm fails to converge to the specified tolerance
        within the specified number of iterations of the power iteration
        method.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> h, a = nx.hits(G)

    Notes
    -----
    The eigenvector calculation is done by the power iteration method
    and has no guarantee of convergence.  The iteration will stop
    after max_iter iterations or an error tolerance of
    number_of_nodes(G)*tol has been reached.

    The HITS algorithm was designed for directed graphs but this
    algorithm does not check if the input graph is directed and will
    execute on undirected graphs.

    References
    ----------
    .. [1] A. Langville and C. Meyer,
       ""A survey of eigenvector methods of web information retrieval.""
       http://citeseer.ist.psu.edu/713792.html
    .. [2] Jon Kleinberg,
       Authoritative sources in a hyperlinked environment
       Journal of the ACM 46 (5): 604-32, 1999.
       doi:10.1145/324133.324140.
       http://www.cs.cornell.edu/home/kleinber/auth.pdf.
    ",248,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True):
    
    import numpy as np
    import scipy as sp
    import scipy.sparse.linalg  # call as sp.sparse.linalg

    if len(G) == 0:
        return {}, {}
    A = nx.adjacency_matrix(G, nodelist=list(G), dtype=float)

    if nstart is None:
        u, s, vt = sp.sparse.linalg.svds(A, k=1, maxiter=max_iter, tol=tol)
    else:
        nstart = np.array(list(nstart.values()))
        u, s, vt = sp.sparse.linalg.svds(A, k=1, v0=nstart, maxiter=max_iter, tol=tol)

    a = vt.flatten().real
    h = A @ a
    if normalized:
        h = h / h.sum()
        a = a / a.sum()
    hubs = dict(zip(G, map(float, h)))
    authorities = dict(zip(G, map(float, a)))
    return hubs, authorities


        ```
    ###Assistant : Returns HITS hubs and authorities values for nodes.

    The HITS algorithm computes two numbers for a node.
    Authorities estimates the node value based on the incoming links.
    Hubs estimates the node value based on outgoing links.

    Parameters
    ----------
    G : graph
      A NetworkX graph

    max_iter : integer, optional
      Maximum number of iterations in power method.

    tol : float, optional
      Error tolerance used to check convergence in power method iteration.

    nstart : dictionary, optional
      Starting value of each node for power method iteration.

    normalized : bool (default=True)
       Normalize results by the sum of all of the values.

    Returns
    -------
    (hubs,authorities) : two-tuple of dictionaries
       Two dictionaries keyed by node containing the hub and authority
       values.

    Raises
    ------
    PowerIterationFailedConvergence
        If the algorithm fails to converge to the specified tolerance
        within the specified number of iterations of the power iteration
        method.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> h, a = nx.hits(G)

    Notes
    -----
    The eigenvector calculation is done by the power iteration method
    and has no guarantee of convergence.  The iteration will stop
    after max_iter iterations or an error tolerance of
    number_of_nodes(G)*tol has been reached.

    The HITS algorithm was designed for directed graphs but this
    algorithm does not check if the input graph is directed and will
    execute on undirected graphs.

    References
    ----------
    .. [1] A. Langville and C. Meyer,
       ""A survey of eigenvector methods of web information retrieval.""
       http://citeseer.ist.psu.edu/713792.html
    .. [2] Jon Kleinberg,
       Authoritative sources in a hyperlinked environment
       Journal of the ACM 46 (5): 604-32, 1999.
       doi:10.1145/324133.324140.
       http://www.cs.cornell.edu/home/kleinber/auth.pdf.
    
    "
31,"def test_connection(self) -> Tuple[bool, str]:
        
        try:
            conn = self.get_conn()
            conn.pwd
            return True, ""Connection successfully tested""
        except Exception as e:
            return False, str(e)

",Test the FTP connection by calling path with directory,9,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_connection(self) -> Tuple[bool, str]:
        
        try:
            conn = self.get_conn()
            conn.pwd
            return True, ""Connection successfully tested""
        except Exception as e:
            return False, str(e)


        ```
    ###Assistant : Test the FTP connection by calling path with directory
    "
32,"def call_price(self, other_args):
        
        parser = argparse.ArgumentParser(
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            prog=""price"",
            description=,
        )
        parser.add_argument(
            ""-s"",
            ""--symbol"",
            required=""-h"" not in other_args,
            type=str,
            dest=""symbol"",
            help=""Symbol of coin to load data for, ~100 symbols are available"",
        )
        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-s"")

        ns_parser = self.parse_known_args_and_warn(parser, other_args)

        if ns_parser:

            if ns_parser.symbol in pyth_model.ASSETS.keys():
                console.print(
                    ""[param]If it takes too long, you can use 'Ctrl + C' to cancel.\n[/param]""
                )
                pyth_view.display_price(ns_parser.symbol)
            else:
                console.print(""[red]The symbol selected does not exist.[/red]\n"")
",Process price commandDisplay price and interval of confidence in real-time. [Source: Pyth],12,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_price(self, other_args):
        
        parser = argparse.ArgumentParser(
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            prog=""price"",
            description=,
        )
        parser.add_argument(
            ""-s"",
            ""--symbol"",
            required=""-h"" not in other_args,
            type=str,
            dest=""symbol"",
            help=""Symbol of coin to load data for, ~100 symbols are available"",
        )
        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-s"")

        ns_parser = self.parse_known_args_and_warn(parser, other_args)

        if ns_parser:

            if ns_parser.symbol in pyth_model.ASSETS.keys():
                console.print(
                    ""[param]If it takes too long, you can use 'Ctrl + C' to cancel.\n[/param]""
                )
                pyth_view.display_price(ns_parser.symbol)
            else:
                console.print(""[red]The symbol selected does not exist.[/red]\n"")

        ```
    ###Assistant : Process price commandDisplay price and interval of confidence in real-time. [Source: Pyth]
    "
33,"def _single_map_nested(args):
    
    function, data_struct, types, rank, disable_tqdm, desc = args

    # Singleton first to spare some computation
    if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
        return function(data_struct)

    # Reduce logging to keep things readable in multiprocessing with tqdm
    if rank is not None and logging.get_verbosity() < logging.WARNING:
        logging.set_verbosity_warning()
    # Print at least one thing to fix tqdm in notebooks in multiprocessing
    # see https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308
    if rank is not None and not disable_tqdm and any(""notebook"" in tqdm_cls.__name__ for tqdm_cls in tqdm.__mro__):
        print("" "", end="""", flush=True)

    # Loop over single examples or batches and write to buffer/file if examples are to be updated
    pbar_iterable = data_struct.items() if isinstance(data_struct, dict) else data_struct
    pbar_desc = (desc + "" "" if desc is not None else """") + ""#"" + str(rank) if rank is not None else desc
    pbar = utils.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit=""obj"", desc=pbar_desc)

    if isinstance(data_struct, dict):
        return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
    else:
        mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
        if isinstance(data_struct, list):
            return mapped
        elif isinstance(data_struct, tuple):
            return tuple(mapped)
        else:
            return np.array(mapped)

",Apply a function recursively to each element of a nested data struct.,12,182,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _single_map_nested(args):
    
    function, data_struct, types, rank, disable_tqdm, desc = args

    # Singleton first to spare some computation
    if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
        return function(data_struct)

    # Reduce logging to keep things readable in multiprocessing with tqdm
    if rank is not None and logging.get_verbosity() < logging.WARNING:
        logging.set_verbosity_warning()
    # Print at least one thing to fix tqdm in notebooks in multiprocessing
    # see https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308
    if rank is not None and not disable_tqdm and any(""notebook"" in tqdm_cls.__name__ for tqdm_cls in tqdm.__mro__):
        print("" "", end="""", flush=True)

    # Loop over single examples or batches and write to buffer/file if examples are to be updated
    pbar_iterable = data_struct.items() if isinstance(data_struct, dict) else data_struct
    pbar_desc = (desc + "" "" if desc is not None else """") + ""#"" + str(rank) if rank is not None else desc
    pbar = utils.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit=""obj"", desc=pbar_desc)

    if isinstance(data_struct, dict):
        return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
    else:
        mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
        if isinstance(data_struct, list):
            return mapped
        elif isinstance(data_struct, tuple):
            return tuple(mapped)
        else:
            return np.array(mapped)


        ```
    ###Assistant : Apply a function recursively to each element of a nested data struct.
    "
34,"def test_unified(self):
        
        self.write_settings(""settings_to_diff.py"", sdict={""FOO"": '""bar""'})
        args = [""diffsettings"", ""--settings=settings_to_diff"", ""--output=unified""]
        out, err = self.run_manage(args)
        self.assertNoOutput(err)
        self.assertOutput(out, ""+ FOO = 'bar'"")
        self.assertOutput(out, ""- SECRET_KEY = ''"")
        self.assertOutput(out, ""+ SECRET_KEY = 'django_tests_secret_key'"")
        self.assertNotInOutput(out, ""  APPEND_SLASH = True"")
",--output=unified emits settings diff in unified mode.,7,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unified(self):
        
        self.write_settings(""settings_to_diff.py"", sdict={""FOO"": '""bar""'})
        args = [""diffsettings"", ""--settings=settings_to_diff"", ""--output=unified""]
        out, err = self.run_manage(args)
        self.assertNoOutput(err)
        self.assertOutput(out, ""+ FOO = 'bar'"")
        self.assertOutput(out, ""- SECRET_KEY = ''"")
        self.assertOutput(out, ""+ SECRET_KEY = 'django_tests_secret_key'"")
        self.assertNotInOutput(out, ""  APPEND_SLASH = True"")

        ```
    ###Assistant : --output=unified emits settings diff in unified mode.
    "
35,"def runtime_env(self):
        

        return RuntimeEnv.deserialize(self._get_runtime_env_string())
","Get the runtime env of the current job/worker.

        If this API is called in driver or ray client, returns the job level runtime
        env.
        If this API is called in workers/actors, returns the worker level runtime env.

        Returns:
            A new ray.runtime_env.RuntimeEnv instance.

        To merge from the current runtime env in some specific cases, you can get the
        current runtime env by this API and modify it by yourself.

        Example:
        >>> # Inherit current runtime env, except `env_vars`
        >>> Actor.options( # doctest: +SKIP
        ...     runtime_env=ray.get_runtime_context().runtime_env.update(
        ...     {""env_vars"": {""A"": ""a"", ""B"": ""b""}})
        ... ) # doctest: +SKIP
        ",95,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def runtime_env(self):
        

        return RuntimeEnv.deserialize(self._get_runtime_env_string())

        ```
    ###Assistant : Get the runtime env of the current job/worker.

        If this API is called in driver or ray client, returns the job level runtime
        env.
        If this API is called in workers/actors, returns the worker level runtime env.

        Returns:
            A new ray.runtime_env.RuntimeEnv instance.

        To merge from the current runtime env in some specific cases, you can get the
        current runtime env by this API and modify it by yourself.

        Example:
        >>> # Inherit current runtime env, except `env_vars`
        >>> Actor.options( # doctest: +SKIP
        ...     runtime_env=ray.get_runtime_context().runtime_env.update(
        ...     {""env_vars"": {""A"": ""a"", ""B"": ""b""}})
        ... ) # doctest: +SKIP
        
    "
36,"def sleeper(self, duration):
        
        s = time()
        yield
        time_to_sleep = duration - (time() - s)
        if time_to_sleep > 0:
            self.wait(time_to_sleep)
","
        Do something and then wait for a given duration minus the time it took doing something
        ",16,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sleeper(self, duration):
        
        s = time()
        yield
        time_to_sleep = duration - (time() - s)
        if time_to_sleep > 0:
            self.wait(time_to_sleep)

        ```
    ###Assistant : 
        Do something and then wait for a given duration minus the time it took doing something
        
    "
37,"def test_BoundaryNorm():
    

    boundaries = [0, 1.1, 2.2]
    vals = [-1, 0, 1, 2, 2.2, 4]

    # Without interpolation
    expected = [-1, 0, 0, 1, 2, 2]
    ncolors = len(boundaries) - 1
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # ncolors != len(boundaries) - 1 triggers interpolation
    expected = [-1, 0, 0, 2, 3, 3]
    ncolors = len(boundaries)
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # with a single region and interpolation
    expected = [-1, 1, 1, 1, 3, 3]
    bn = mcolors.BoundaryNorm([0, 2.2], ncolors)
    assert_array_equal(bn(vals), expected)

    # more boundaries for a third color
    boundaries = [0, 1, 2, 3]
    vals = [-1, 0.1, 1.1, 2.2, 4]
    ncolors = 5
    expected = [-1, 0, 2, 4, 5]
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # a scalar as input should not trigger an error and should return a scalar
    boundaries = [0, 1, 2]
    vals = [-1, 0.1, 1.1, 2.2]
    bn = mcolors.BoundaryNorm(boundaries, 2)
    expected = [-1, 0, 1, 2]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert isinstance(ret, int)
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # same with interp
    bn = mcolors.BoundaryNorm(boundaries, 3)
    expected = [-1, 0, 2, 3]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert isinstance(ret, int)
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # Clipping
    bn = mcolors.BoundaryNorm(boundaries, 3, clip=True)
    expected = [0, 0, 2, 2]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert isinstance(ret, int)
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # Masked arrays
    boundaries = [0, 1.1, 2.2]
    vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9])

    # Without interpolation
    ncolors = len(boundaries) - 1
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0])
    assert_array_equal(bn(vals), expected)

    # With interpolation
    bn = mcolors.BoundaryNorm(boundaries, len(boundaries))
    expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0])
    assert_array_equal(bn(vals), expected)

    # Non-trivial masked arrays
    vals = np.ma.masked_invalid([np.Inf, np.NaN])
    assert np.all(bn(vals).mask)
    vals = np.ma.masked_invalid([np.Inf])
    assert np.all(bn(vals).mask)

    # Incompatible extend and clip
    with pytest.raises(ValueError, match=""not compatible""):
        mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True)

    # Too small ncolors argument
    with pytest.raises(ValueError, match=""ncolors must equal or exceed""):
        mcolors.BoundaryNorm(np.arange(4), 2)

    with pytest.raises(ValueError, match=""ncolors must equal or exceed""):
        mcolors.BoundaryNorm(np.arange(4), 3, extend='min')

    with pytest.raises(ValueError, match=""ncolors must equal or exceed""):
        mcolors.BoundaryNorm(np.arange(4), 4, extend='both')

    # Testing extend keyword, with interpolation (large cmap)
    bounds = [1, 2, 3]
    cmap = mpl.colormaps['viridis']
    mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both')
    refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N)
    x = np.random.randn(100) * 10 + 2
    ref = refnorm(x)
    ref[ref == 0] = -1
    ref[ref == cmap.N - 1] = cmap.N
    assert_array_equal(mynorm(x), ref)

    # Without interpolation
    cmref = mcolors.ListedColormap(['blue', 'red'])
    cmref.set_over('black')
    cmref.set_under('white')
    cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black'])

    assert mcolors.same_color(cmref.get_over(), 'black')
    assert mcolors.same_color(cmref.get_under(), 'white')

    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both')
    assert mynorm.vmin == refnorm.vmin
    assert mynorm.vmax == refnorm.vmax

    assert mynorm(bounds[0] - 0.1) == -1  # under
    assert mynorm(bounds[0] + 0.1) == 1   # first bin -> second color
    assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2  # next-to-last color
    assert mynorm(bounds[-1] + 0.1) == cmshould.N  # over

    x = [-1, 1.2, 2.3, 9.6]
    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3]))
    x = np.random.randn(100) * 10 + 2
    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))

    # Just min
    cmref = mcolors.ListedColormap(['blue', 'red'])
    cmref.set_under('white')
    cmshould = mcolors.ListedColormap(['white', 'blue', 'red'])

    assert mcolors.same_color(cmref.get_under(), 'white')

    assert cmref.N == 2
    assert cmshould.N == 3
    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min')
    assert mynorm.vmin == refnorm.vmin
    assert mynorm.vmax == refnorm.vmax
    x = [-1, 1.2, 2.3]
    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
    x = np.random.randn(100) * 10 + 2
    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))

    # Just max
    cmref = mcolors.ListedColormap(['blue', 'red'])
    cmref.set_over('black')
    cmshould = mcolors.ListedColormap(['blue', 'red', 'black'])

    assert mcolors.same_color(cmref.get_over(), 'black')

    assert cmref.N == 2
    assert cmshould.N == 3
    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='max')
    assert mynorm.vmin == refnorm.vmin
    assert mynorm.vmax == refnorm.vmax
    x = [1.2, 2.3, 4]
    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
    x = np.random.randn(100) * 10 + 2
    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))

","
    GitHub issue #1258: interpolation was failing with numpy
    1.7 pre-release.
    ",10,623,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_BoundaryNorm():
    

    boundaries = [0, 1.1, 2.2]
    vals = [-1, 0, 1, 2, 2.2, 4]

    # Without interpolation
    expected = [-1, 0, 0, 1, 2, 2]
    ncolors = len(boundaries) - 1
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # ncolors != len(boundaries) - 1 triggers interpolation
    expected = [-1, 0, 0, 2, 3, 3]
    ncolors = len(boundaries)
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # with a single region and interpolation
    expected = [-1, 1, 1, 1, 3, 3]
    bn = mcolors.BoundaryNorm([0, 2.2], ncolors)
    assert_array_equal(bn(vals), expected)

    # more boundaries for a third color
    boundaries = [0, 1, 2, 3]
    vals = [-1, 0.1, 1.1, 2.2, 4]
    ncolors = 5
    expected = [-1, 0, 2, 4, 5]
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # a scalar as input should not trigger an error and should return a scalar
    boundaries = [0, 1, 2]
    vals = [-1, 0.1, 1.1, 2.2]
    bn = mcolors.BoundaryNorm(boundaries, 2)
    expected = [-1, 0, 1, 2]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert isinstance(ret, int)
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # same with interp
    bn = mcolors.BoundaryNorm(boundaries, 3)
    expected = [-1, 0, 2, 3]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert isinstance(ret, int)
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # Clipping
    bn = mcolors.BoundaryNorm(boundaries, 3, clip=True)
    expected = [0, 0, 2, 2]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert isinstance(ret, int)
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # Masked arrays
    boundaries = [0, 1.1, 2.2]
    vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9])

    # Without interpolation
    ncolors = len(boundaries) - 1
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0])
    assert_array_equal(bn(vals), expected)

    # With interpolation
    bn = mcolors.BoundaryNorm(boundaries, len(boundaries))
    expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0])
    assert_array_equal(bn(vals), expected)

    # Non-trivial masked arrays
    vals = np.ma.masked_invalid([np.Inf, np.NaN])
    assert np.all(bn(vals).mask)
    vals = np.ma.masked_invalid([np.Inf])
    assert np.all(bn(vals).mask)

    # Incompatible extend and clip
    with pytest.raises(ValueError, match=""not compatible""):
        mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True)

    # Too small ncolors argument
    with pytest.raises(ValueError, match=""ncolors must equal or exceed""):
        mcolors.BoundaryNorm(np.arange(4), 2)

    with pytest.raises(ValueError, match=""ncolors must equal or exceed""):
        mcolors.BoundaryNorm(np.arange(4), 3, extend='min')

    with pytest.raises(ValueError, match=""ncolors must equal or exceed""):
        mcolors.BoundaryNorm(np.arange(4), 4, extend='both')

    # Testing extend keyword, with interpolation (large cmap)
    bounds = [1, 2, 3]
    cmap = mpl.colormaps['viridis']
    mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both')
    refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N)
    x = np.random.randn(100) * 10 + 2
    ref = refnorm(x)
    ref[ref == 0] = -1
    ref[ref == cmap.N - 1] = cmap.N
    assert_array_equal(mynorm(x), ref)

    # Without interpolation
    cmref = mcolors.ListedColormap(['blue', 'red'])
    cmref.set_over('black')
    cmref.set_under('white')
    cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black'])

    assert mcolors.same_color(cmref.get_over(), 'black')
    assert mcolors.same_color(cmref.get_under(), 'white')

    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both')
    assert mynorm.vmin == refnorm.vmin
    assert mynorm.vmax == refnorm.vmax

    assert mynorm(bounds[0] - 0.1) == -1  # under
    assert mynorm(bounds[0] + 0.1) == 1   # first bin -> second color
    assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2  # next-to-last color
    assert mynorm(bounds[-1] + 0.1) == cmshould.N  # over

    x = [-1, 1.2, 2.3, 9.6]
    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3]))
    x = np.random.randn(100) * 10 + 2
    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))

    # Just min
    cmref = mcolors.ListedColormap(['blue', 'red'])
    cmref.set_under('white')
    cmshould = mcolors.ListedColormap(['white', 'blue', 'red'])

    assert mcolors.same_color(cmref.get_under(), 'white')

    assert cmref.N == 2
    assert cmshould.N == 3
    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min')
    assert mynorm.vmin == refnorm.vmin
    assert mynorm.vmax == refnorm.vmax
    x = [-1, 1.2, 2.3]
    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
    x = np.random.randn(100) * 10 + 2
    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))

    # Just max
    cmref = mcolors.ListedColormap(['blue', 'red'])
    cmref.set_over('black')
    cmshould = mcolors.ListedColormap(['blue', 'red', 'black'])

    assert mcolors.same_color(cmref.get_over(), 'black')

    assert cmref.N == 2
    assert cmshould.N == 3
    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='max')
    assert mynorm.vmin == refnorm.vmin
    assert mynorm.vmax == refnorm.vmax
    x = [1.2, 2.3, 4]
    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
    x = np.random.randn(100) * 10 + 2
    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))


        ```
    ###Assistant : 
    GitHub issue #1258: interpolation was failing with numpy
    1.7 pre-release.
    
    "
38,"def _join_by_index(self, other_modin_frames, how, sort, ignore_index):
        
        if how == ""outer"":
            raise NotImplementedError(""outer join is not supported in HDK engine"")

        lhs = self._maybe_materialize_rowid()
        reset_index_names = False
        for rhs in other_modin_frames:
            rhs = rhs._maybe_materialize_rowid()
            if len(lhs._index_cols) != len(rhs._index_cols):
                raise NotImplementedError(
                    ""join by indexes with different sizes is not supported""
                )

            reset_index_names = reset_index_names or lhs._index_cols != rhs._index_cols

            condition = lhs._build_equi_join_condition(
                rhs, lhs._index_cols, rhs._index_cols
            )

            exprs = lhs._index_exprs()
            new_columns = lhs.columns.to_list()
            for col in lhs.columns:
                exprs[col] = lhs.ref(col)
            for col in rhs.columns:
                # Handle duplicating column names here. When user specifies
                # suffixes to make a join, actual renaming is done in front-end.
                new_col_name = col
                rename_idx = 0
                while new_col_name in exprs:
                    new_col_name = f""{col}{rename_idx}""
                    rename_idx += 1
                exprs[new_col_name] = rhs.ref(col)
                new_columns.append(new_col_name)

            op = JoinNode(
                lhs,
                rhs,
                how=how,
                exprs=exprs,
                condition=condition,
            )

            new_columns = Index.__new__(
                Index, data=new_columns, dtype=self.columns.dtype
            )
            lhs = lhs.__constructor__(
                dtypes=lhs._dtypes_for_exprs(exprs),
                columns=new_columns,
                index_cols=lhs._index_cols,
                op=op,
                force_execution_mode=self._force_execution_mode,
            )

        if sort:
            lhs = lhs.sort_rows(
                lhs._index_cols,
                ascending=True,
                ignore_index=False,
                na_position=""last"",
            )

        if reset_index_names:
            lhs = lhs._reset_index_names()

        if ignore_index:
            new_columns = Index.__new__(RangeIndex, data=range(len(lhs.columns)))
            lhs = lhs._set_columns(new_columns)

        return lhs
","
        Perform equi-join operation for multiple frames by index columns.

        Parameters
        ----------
        other_modin_frames : list of HdkOnNativeDataframe
            Frames to join with.
        how : str
            A type of join.
        sort : bool
            Sort the result by join keys.
        ignore_index : bool
            If True then reset column index for the resulting frame.

        Returns
        -------
        HdkOnNativeDataframe
            The new frame.
        ",55,171,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _join_by_index(self, other_modin_frames, how, sort, ignore_index):
        
        if how == ""outer"":
            raise NotImplementedError(""outer join is not supported in HDK engine"")

        lhs = self._maybe_materialize_rowid()
        reset_index_names = False
        for rhs in other_modin_frames:
            rhs = rhs._maybe_materialize_rowid()
            if len(lhs._index_cols) != len(rhs._index_cols):
                raise NotImplementedError(
                    ""join by indexes with different sizes is not supported""
                )

            reset_index_names = reset_index_names or lhs._index_cols != rhs._index_cols

            condition = lhs._build_equi_join_condition(
                rhs, lhs._index_cols, rhs._index_cols
            )

            exprs = lhs._index_exprs()
            new_columns = lhs.columns.to_list()
            for col in lhs.columns:
                exprs[col] = lhs.ref(col)
            for col in rhs.columns:
                # Handle duplicating column names here. When user specifies
                # suffixes to make a join, actual renaming is done in front-end.
                new_col_name = col
                rename_idx = 0
                while new_col_name in exprs:
                    new_col_name = f""{col}{rename_idx}""
                    rename_idx += 1
                exprs[new_col_name] = rhs.ref(col)
                new_columns.append(new_col_name)

            op = JoinNode(
                lhs,
                rhs,
                how=how,
                exprs=exprs,
                condition=condition,
            )

            new_columns = Index.__new__(
                Index, data=new_columns, dtype=self.columns.dtype
            )
            lhs = lhs.__constructor__(
                dtypes=lhs._dtypes_for_exprs(exprs),
                columns=new_columns,
                index_cols=lhs._index_cols,
                op=op,
                force_execution_mode=self._force_execution_mode,
            )

        if sort:
            lhs = lhs.sort_rows(
                lhs._index_cols,
                ascending=True,
                ignore_index=False,
                na_position=""last"",
            )

        if reset_index_names:
            lhs = lhs._reset_index_names()

        if ignore_index:
            new_columns = Index.__new__(RangeIndex, data=range(len(lhs.columns)))
            lhs = lhs._set_columns(new_columns)

        return lhs

        ```
    ###Assistant : 
        Perform equi-join operation for multiple frames by index columns.

        Parameters
        ----------
        other_modin_frames : list of HdkOnNativeDataframe
            Frames to join with.
        how : str
            A type of join.
        sort : bool
            Sort the result by join keys.
        ignore_index : bool
            If True then reset column index for the resulting frame.

        Returns
        -------
        HdkOnNativeDataframe
            The new frame.
        
    "
39,"def _object2proto(self) -> RunFunctionOrConstructorAction_PB:
        
        return RunFunctionOrConstructorAction_PB(
            path=self.path,
            args=[serialize(x, to_bytes=True) for x in self.args],
            kwargs={k: serialize(v, to_bytes=True) for k, v in self.kwargs.items()},
            id_at_location=serialize(self.id_at_location),
            address=serialize(self.address),
            msg_id=serialize(self.id),
        )
","Returns a protobuf serialization of self.

        As a requirement of all objects which inherit from Serializable,
        this method transforms the current object into the corresponding
        Protobuf object so that it can be further serialized.

        :return: returns a protobuf object
        :rtype: RunFunctionOrConstructorAction_PB

        .. note::
            This method is purely an internal method. Please use serialize(object) or one of
            the other public serialization methods if you wish to serialize an
            object.
        ",68,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _object2proto(self) -> RunFunctionOrConstructorAction_PB:
        
        return RunFunctionOrConstructorAction_PB(
            path=self.path,
            args=[serialize(x, to_bytes=True) for x in self.args],
            kwargs={k: serialize(v, to_bytes=True) for k, v in self.kwargs.items()},
            id_at_location=serialize(self.id_at_location),
            address=serialize(self.address),
            msg_id=serialize(self.id),
        )

        ```
    ###Assistant : Returns a protobuf serialization of self.

        As a requirement of all objects which inherit from Serializable,
        this method transforms the current object into the corresponding
        Protobuf object so that it can be further serialized.

        :return: returns a protobuf object
        :rtype: RunFunctionOrConstructorAction_PB

        .. note::
            This method is purely an internal method. Please use serialize(object) or one of
            the other public serialization methods if you wish to serialize an
            object.
        
    "
40,"def truncated_cube_graph(create_using=None):
    
    description = [
        ""adjacencylist"",
        ""Truncated Cube Graph"",
        24,
        [
            [2, 3, 5],
            [12, 15],
            [4, 5],
            [7, 9],
            [6],
            [17, 19],
            [8, 9],
            [11, 13],
            [10],
            [18, 21],
            [12, 13],
            [15],
            [14],
            [22, 23],
            [16],
            [20, 24],
            [18, 19],
            [21],
            [20],
            [24],
            [22],
            [23],
            [24],
            [],
        ],
    ]
    G = make_small_undirected_graph(description, create_using)
    return G

","
    Returns the skeleton of the truncated cube.

    The truncated cube is an Archimedean solid with 14 regular
    faces (6 octagonal and 8 triangular), 36 edges and 24 nodes [1]_.
    The truncated cube is created by truncating (cutting off) the tips
    of the cube one third of the way into each edge [2]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Skeleton of the truncated cube

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Truncated_cube
    .. [2] https://www.coolmath.com/reference/polyhedra-truncated-cube

    ",91,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def truncated_cube_graph(create_using=None):
    
    description = [
        ""adjacencylist"",
        ""Truncated Cube Graph"",
        24,
        [
            [2, 3, 5],
            [12, 15],
            [4, 5],
            [7, 9],
            [6],
            [17, 19],
            [8, 9],
            [11, 13],
            [10],
            [18, 21],
            [12, 13],
            [15],
            [14],
            [22, 23],
            [16],
            [20, 24],
            [18, 19],
            [21],
            [20],
            [24],
            [22],
            [23],
            [24],
            [],
        ],
    ]
    G = make_small_undirected_graph(description, create_using)
    return G


        ```
    ###Assistant : 
    Returns the skeleton of the truncated cube.

    The truncated cube is an Archimedean solid with 14 regular
    faces (6 octagonal and 8 triangular), 36 edges and 24 nodes [1]_.
    The truncated cube is created by truncating (cutting off) the tips
    of the cube one third of the way into each edge [2]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Skeleton of the truncated cube

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Truncated_cube
    .. [2] https://www.coolmath.com/reference/polyhedra-truncated-cube

    
    "
41,"def get_admin_urls_for_registration(self):
        
        urls = ()
        for instance in self.modeladmin_instances:
            urls += instance.get_admin_urls_for_registration()
        return urls
","
        Utilised by Wagtail's 'register_admin_urls' hook to register urls for
        used by any associated ModelAdmin instances
        ",15,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_admin_urls_for_registration(self):
        
        urls = ()
        for instance in self.modeladmin_instances:
            urls += instance.get_admin_urls_for_registration()
        return urls

        ```
    ###Assistant : 
        Utilised by Wagtail's 'register_admin_urls' hook to register urls for
        used by any associated ModelAdmin instances
        
    "
42,"def setName(self, name):
        
        self.name = name
        self.errmsg = ""Expected "" + self.name
        if __diag__.enable_debug_on_named_expressions:
            self.setDebug()
        return self
","
        Define name for this expression, makes debugging and exception messages clearer.

        Example::

            Word(nums).parseString(""ABC"")  # -> Exception: Expected W:(0123...) (at char 0), (line:1, col:1)
            Word(nums).setName(""integer"").parseString(""ABC"")  # -> Exception: Expected integer (at char 0), (line:1, col:1)
        ",34,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setName(self, name):
        
        self.name = name
        self.errmsg = ""Expected "" + self.name
        if __diag__.enable_debug_on_named_expressions:
            self.setDebug()
        return self

        ```
    ###Assistant : 
        Define name for this expression, makes debugging and exception messages clearer.

        Example::

            Word(nums).parseString(""ABC"")  # -> Exception: Expected W:(0123...) (at char 0), (line:1, col:1)
            Word(nums).setName(""integer"").parseString(""ABC"")  # -> Exception: Expected integer (at char 0), (line:1, col:1)
        
    "
43,"def get_func(cls, key, **kwargs):
        
        if ""agg_func"" in kwargs:
            return cls.inplace_applyier_builder(key, kwargs[""agg_func""])
        elif ""func_dict"" in kwargs:
            return cls.inplace_applyier_builder(key, kwargs[""func_dict""])
        else:
            return cls.inplace_applyier_builder(key)
","
        Extract aggregation function from groupby arguments.

        Parameters
        ----------
        key : callable or str
            Default aggregation function. If aggregation function is not specified
            via groupby arguments, then `key` function is used.
        **kwargs : dict
            GroupBy arguments that may contain aggregation function.

        Returns
        -------
        callable
            Aggregation function.

        Notes
        -----
        There are two ways of how groupby aggregation can be invoked:
            1. Explicitly with query compiler method: `qc.groupby_sum()`.
            2. By passing aggregation function as an argument: `qc.groupby_agg(""sum"")`.
        Both are going to produce the same result, however in the first case actual aggregation
        function can be extracted from the method name, while for the second only from the method arguments.
        ",106,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_func(cls, key, **kwargs):
        
        if ""agg_func"" in kwargs:
            return cls.inplace_applyier_builder(key, kwargs[""agg_func""])
        elif ""func_dict"" in kwargs:
            return cls.inplace_applyier_builder(key, kwargs[""func_dict""])
        else:
            return cls.inplace_applyier_builder(key)

        ```
    ###Assistant : 
        Extract aggregation function from groupby arguments.

        Parameters
        ----------
        key : callable or str
            Default aggregation function. If aggregation function is not specified
            via groupby arguments, then `key` function is used.
        **kwargs : dict
            GroupBy arguments that may contain aggregation function.

        Returns
        -------
        callable
            Aggregation function.

        Notes
        -----
        There are two ways of how groupby aggregation can be invoked:
            1. Explicitly with query compiler method: `qc.groupby_sum()`.
            2. By passing aggregation function as an argument: `qc.groupby_agg(""sum"")`.
        Both are going to produce the same result, however in the first case actual aggregation
        function can be extracted from the method name, while for the second only from the method arguments.
        
    "
44,"def update_scheduler(self, metric):
        
        self.worker_group.apply_all_operators(
            lambda op: [sched.step(metric) for sched in op._schedulers]
        )
","Calls ``scheduler.step(metric)`` on all registered schedulers.

        This is useful for lr_schedulers such as ``ReduceLROnPlateau``.
        ",14,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_scheduler(self, metric):
        
        self.worker_group.apply_all_operators(
            lambda op: [sched.step(metric) for sched in op._schedulers]
        )

        ```
    ###Assistant : Calls ``scheduler.step(metric)`` on all registered schedulers.

        This is useful for lr_schedulers such as ``ReduceLROnPlateau``.
        
    "
45,"def paired_cosine_distances(X, Y):
    
    X, Y = check_paired_arrays(X, Y)
    return 0.5 * row_norms(normalize(X) - normalize(Y), squared=True)


PAIRED_DISTANCES = {
    ""cosine"": paired_cosine_distances,
    ""euclidean"": paired_euclidean_distances,
    ""l2"": paired_euclidean_distances,
    ""l1"": paired_manhattan_distances,
    ""manhattan"": paired_manhattan_distances,
    ""cityblock"": paired_manhattan_distances,
}

","
    Compute the paired cosine distances between X and Y.

    Read more in the :ref:`User Guide <metrics>`.

    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        An array where each row is a sample and each column is a feature.

    Y : array-like of shape (n_samples, n_features)
        An array where each row is a sample and each column is a feature.

    Returns
    -------
    distances : ndarray of shape (n_samples,)
        Returns the distances between the row vectors of `X`
        and the row vectors of `Y`, where `distances[i]` is the
        distance between `X[i]` and `Y[i]`.

    Notes
    -----
    The cosine distance is equivalent to the half the squared
    euclidean distance if each sample is normalized to unit norm.
    ",114,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def paired_cosine_distances(X, Y):
    
    X, Y = check_paired_arrays(X, Y)
    return 0.5 * row_norms(normalize(X) - normalize(Y), squared=True)


PAIRED_DISTANCES = {
    ""cosine"": paired_cosine_distances,
    ""euclidean"": paired_euclidean_distances,
    ""l2"": paired_euclidean_distances,
    ""l1"": paired_manhattan_distances,
    ""manhattan"": paired_manhattan_distances,
    ""cityblock"": paired_manhattan_distances,
}


        ```
    ###Assistant : 
    Compute the paired cosine distances between X and Y.

    Read more in the :ref:`User Guide <metrics>`.

    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        An array where each row is a sample and each column is a feature.

    Y : array-like of shape (n_samples, n_features)
        An array where each row is a sample and each column is a feature.

    Returns
    -------
    distances : ndarray of shape (n_samples,)
        Returns the distances between the row vectors of `X`
        and the row vectors of `Y`, where `distances[i]` is the
        distance between `X[i]` and `Y[i]`.

    Notes
    -----
    The cosine distance is equivalent to the half the squared
    euclidean distance if each sample is normalized to unit norm.
    
    "
46,"def torchdynamo_smart_context_manager(self):
        
        ctx_manager = contextlib.nullcontext()
        if is_torchdynamo_available():
            import torchdynamo
            from torchdynamo.optimizations.training import aot_autograd_speedup_strategy

            if self.args.torchdynamo == ""eager"":
                ctx_manager = torchdynamo.optimize(""eager"")
            elif self.args.torchdynamo == ""nvfuser"":
                ctx_manager = torchdynamo.optimize(aot_autograd_speedup_strategy)
        return ctx_manager
","
        A helper wrapper that creates an appropriate context manager for `torchdynamo`.
        ",11,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def torchdynamo_smart_context_manager(self):
        
        ctx_manager = contextlib.nullcontext()
        if is_torchdynamo_available():
            import torchdynamo
            from torchdynamo.optimizations.training import aot_autograd_speedup_strategy

            if self.args.torchdynamo == ""eager"":
                ctx_manager = torchdynamo.optimize(""eager"")
            elif self.args.torchdynamo == ""nvfuser"":
                ctx_manager = torchdynamo.optimize(aot_autograd_speedup_strategy)
        return ctx_manager

        ```
    ###Assistant : 
        A helper wrapper that creates an appropriate context manager for `torchdynamo`.
        
    "
47,"def check_aug_version(self) -> bool:
        

        self.aug.set(""/test/path/testing/arg"", ""aRgUMeNT"")
        try:
            matches = self.aug.match(
                ""/test//*[self::arg=~regexp('argument', 'i')]"")
        except RuntimeError:
            self.aug.remove(""/test/path"")
            return False
        self.aug.remove(""/test/path"")
        return matches
"," Checks that we have recent enough version of libaugeas.
        If augeas version is recent enough, it will support case insensitive
        regexp matching",22,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_aug_version(self) -> bool:
        

        self.aug.set(""/test/path/testing/arg"", ""aRgUMeNT"")
        try:
            matches = self.aug.match(
                ""/test//*[self::arg=~regexp('argument', 'i')]"")
        except RuntimeError:
            self.aug.remove(""/test/path"")
            return False
        self.aug.remove(""/test/path"")
        return matches

        ```
    ###Assistant :  Checks that we have recent enough version of libaugeas.
        If augeas version is recent enough, it will support case insensitive
        regexp matching
    "
48,"def resize_depth(depth, width, height):
    
    depth = torch.squeeze(depth[0, :, :, :]).to(""cpu"")

    depth_resized = cv2.resize(
        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
    )

    return depth_resized
","Resize depth map and bring to CPU (numpy).

    Args:
        depth (tensor): depth
        width (int): image width
        height (int): image height

    Returns:
        array: processed depth
    ",24,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resize_depth(depth, width, height):
    
    depth = torch.squeeze(depth[0, :, :, :]).to(""cpu"")

    depth_resized = cv2.resize(
        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
    )

    return depth_resized

        ```
    ###Assistant : Resize depth map and bring to CPU (numpy).

    Args:
        depth (tensor): depth
        width (int): image width
        height (int): image height

    Returns:
        array: processed depth
    
    "
49,"def comp(z1, z2, tol=None):
    r
    if type(z2) is str:
        if not pure_complex(z1, or_real=True):
            raise ValueError('when z2 is a str z1 must be a Number')
        return str(z1) == z2
    if not z1:
        z1, z2 = z2, z1
    if not z1:
        return True
    if not tol:
        a, b = z1, z2
        if tol == '':
            return str(a) == str(b)
        if tol is None:
            a, b = sympify(a), sympify(b)
            if not all(i.is_number for i in (a, b)):
                raise ValueError('expecting 2 numbers')
            fa = a.atoms(Float)
            fb = b.atoms(Float)
            if not fa and not fb:
                # no floats -- compare exactly
                return a == b
            # get a to be pure_complex
            for _ in range(2):
                ca = pure_complex(a, or_real=True)
                if not ca:
                    if fa:
                        a = a.n(prec_to_dps(min([i._prec for i in fa])))
                        ca = pure_complex(a, or_real=True)
                        break
                    else:
                        fa, fb = fb, fa
                        a, b = b, a
            cb = pure_complex(b)
            if not cb and fb:
                b = b.n(prec_to_dps(min([i._prec for i in fb])))
                cb = pure_complex(b, or_real=True)
            if ca and cb and (ca[1] or cb[1]):
                return all(comp(i, j) for i, j in zip(ca, cb))
            tol = 10**prec_to_dps(min(a._prec, getattr(b, '_prec', a._prec)))
            return int(abs(a - b)*tol) <= 5
    diff = abs(z1 - z2)
    az1 = abs(z1)
    if z2 and az1 > 1:
        return diff/az1 <= tol
    else:
        return diff <= tol

","Return a bool indicating whether the error between z1 and z2
    is $\le$ ``tol``.

    Examples
    ========

    If ``tol`` is ``None`` then ``True`` will be returned if
    :math:`|z1 - z2|\times 10^p \le 5` where $p$ is minimum value of the
    decimal precision of each value.

    >>> from sympy import comp, pi
    >>> pi4 = pi.n(4); pi4
    3.142
    >>> comp(_, 3.142)
    True
    >>> comp(pi4, 3.141)
    False
    >>> comp(pi4, 3.143)
    False

    A comparison of strings will be made
    if ``z1`` is a Number and ``z2`` is a string or ``tol`` is ''.

    >>> comp(pi4, 3.1415)
    True
    >>> comp(pi4, 3.1415, '')
    False

    When ``tol`` is provided and $z2$ is non-zero and
    :math:`|z1| > 1` the error is normalized by :math:`|z1|`:

    >>> abs(pi4 - 3.14)/pi4
    0.000509791731426756
    >>> comp(pi4, 3.14, .001)  # difference less than 0.1%
    True
    >>> comp(pi4, 3.14, .0005)  # difference less than 0.1%
    False

    When :math:`|z1| \le 1` the absolute error is used:

    >>> 1/pi4
    0.3183
    >>> abs(1/pi4 - 0.3183)/(1/pi4)
    3.07371499106316e-5
    >>> abs(1/pi4 - 0.3183)
    9.78393554684764e-6
    >>> comp(1/pi4, 0.3183, 1e-5)
    True

    To see if the absolute error between ``z1`` and ``z2`` is less
    than or equal to ``tol``, call this as ``comp(z1 - z2, 0, tol)``
    or ``comp(z1 - z2, tol=tol)``:

    >>> abs(pi4 - 3.14)
    0.00160156249999988
    >>> comp(pi4 - 3.14, 0, .002)
    True
    >>> comp(pi4 - 3.14, 0, .001)
    False
    ",217,213,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def comp(z1, z2, tol=None):
    r
    if type(z2) is str:
        if not pure_complex(z1, or_real=True):
            raise ValueError('when z2 is a str z1 must be a Number')
        return str(z1) == z2
    if not z1:
        z1, z2 = z2, z1
    if not z1:
        return True
    if not tol:
        a, b = z1, z2
        if tol == '':
            return str(a) == str(b)
        if tol is None:
            a, b = sympify(a), sympify(b)
            if not all(i.is_number for i in (a, b)):
                raise ValueError('expecting 2 numbers')
            fa = a.atoms(Float)
            fb = b.atoms(Float)
            if not fa and not fb:
                # no floats -- compare exactly
                return a == b
            # get a to be pure_complex
            for _ in range(2):
                ca = pure_complex(a, or_real=True)
                if not ca:
                    if fa:
                        a = a.n(prec_to_dps(min([i._prec for i in fa])))
                        ca = pure_complex(a, or_real=True)
                        break
                    else:
                        fa, fb = fb, fa
                        a, b = b, a
            cb = pure_complex(b)
            if not cb and fb:
                b = b.n(prec_to_dps(min([i._prec for i in fb])))
                cb = pure_complex(b, or_real=True)
            if ca and cb and (ca[1] or cb[1]):
                return all(comp(i, j) for i, j in zip(ca, cb))
            tol = 10**prec_to_dps(min(a._prec, getattr(b, '_prec', a._prec)))
            return int(abs(a - b)*tol) <= 5
    diff = abs(z1 - z2)
    az1 = abs(z1)
    if z2 and az1 > 1:
        return diff/az1 <= tol
    else:
        return diff <= tol


        ```
    ###Assistant : Return a bool indicating whether the error between z1 and z2
    is $\le$ ``tol``.

    Examples
    ========

    If ``tol`` is ``None`` then ``True`` will be returned if
    :math:`|z1 - z2|\times 10^p \le 5` where $p$ is minimum value of the
    decimal precision of each value.

    >>> from sympy import comp, pi
    >>> pi4 = pi.n(4); pi4
    3.142
    >>> comp(_, 3.142)
    True
    >>> comp(pi4, 3.141)
    False
    >>> comp(pi4, 3.143)
    False

    A comparison of strings will be made
    if ``z1`` is a Number and ``z2`` is a string or ``tol`` is ''.

    >>> comp(pi4, 3.1415)
    True
    >>> comp(pi4, 3.1415, '')
    False

    When ``tol`` is provided and $z2$ is non-zero and
    :math:`|z1| > 1` the error is normalized by :math:`|z1|`:

    >>> abs(pi4 - 3.14)/pi4
    0.000509791731426756
    >>> comp(pi4, 3.14, .001)  # difference less than 0.1%
    True
    >>> comp(pi4, 3.14, .0005)  # difference less than 0.1%
    False

    When :math:`|z1| \le 1` the absolute error is used:

    >>> 1/pi4
    0.3183
    >>> abs(1/pi4 - 0.3183)/(1/pi4)
    3.07371499106316e-5
    >>> abs(1/pi4 - 0.3183)
    9.78393554684764e-6
    >>> comp(1/pi4, 0.3183, 1e-5)
    True

    To see if the absolute error between ``z1`` and ``z2`` is less
    than or equal to ``tol``, call this as ``comp(z1 - z2, 0, tol)``
    or ``comp(z1 - z2, tol=tol)``:

    >>> abs(pi4 - 3.14)
    0.00160156249999988
    >>> comp(pi4 - 3.14, 0, .002)
    True
    >>> comp(pi4 - 3.14, 0, .001)
    False
    
    "
50,"def _parse_img_level_ann(self, image_level_ann_file):
        

        item_lists = defaultdict(list)
        with self.file_client.get_local_path(
                image_level_ann_file) as local_path:
            with open(local_path, 'r') as f:
                reader = csv.reader(f)
                i = -1
                for line in reader:
                    i += 1
                    if i == 0:
                        continue
                    else:
                        img_id = line[0]
                        label_id = line[1]
                        assert label_id in self.label_id_mapping
                        image_level_label = int(
                            self.label_id_mapping[label_id])
                        confidence = float(line[2])
                        item_lists[img_id].append(
                            dict(
                                image_level_label=image_level_label,
                                confidence=confidence))
        return item_lists
","Parse image level annotations from csv style ann_file.

        Args:
            image_level_ann_file (str): CSV style image level annotation
                file path.

        Returns:
            defaultdict[list[dict]]: Annotations where item of the defaultdict
            indicates an image, each of which has (n) dicts.
            Keys of dicts are:

                - `image_level_label` (int): of shape 1.
                - `confidence` (float): of shape 1.
        ",51,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_img_level_ann(self, image_level_ann_file):
        

        item_lists = defaultdict(list)
        with self.file_client.get_local_path(
                image_level_ann_file) as local_path:
            with open(local_path, 'r') as f:
                reader = csv.reader(f)
                i = -1
                for line in reader:
                    i += 1
                    if i == 0:
                        continue
                    else:
                        img_id = line[0]
                        label_id = line[1]
                        assert label_id in self.label_id_mapping
                        image_level_label = int(
                            self.label_id_mapping[label_id])
                        confidence = float(line[2])
                        item_lists[img_id].append(
                            dict(
                                image_level_label=image_level_label,
                                confidence=confidence))
        return item_lists

        ```
    ###Assistant : Parse image level annotations from csv style ann_file.

        Args:
            image_level_ann_file (str): CSV style image level annotation
                file path.

        Returns:
            defaultdict[list[dict]]: Annotations where item of the defaultdict
            indicates an image, each of which has (n) dicts.
            Keys of dicts are:

                - `image_level_label` (int): of shape 1.
                - `confidence` (float): of shape 1.
        
    "
51,"def logical_and(self, a, b):
        
        a = _convert_other(a, raiseit=True)
        return a.logical_and(b, context=self)
","Applies the logical operation 'and' between each operand's digits.

        The operands must be both logical numbers.

        >>> ExtendedContext.logical_and(Decimal('0'), Decimal('0'))
        Decimal('0')
        >>> ExtendedContext.logical_and(Decimal('0'), Decimal('1'))
        Decimal('0')
        >>> ExtendedContext.logical_and(Decimal('1'), Decimal('0'))
        Decimal('0')
        >>> ExtendedContext.logical_and(Decimal('1'), Decimal('1'))
        Decimal('1')
        >>> ExtendedContext.logical_and(Decimal('1100'), Decimal('1010'))
        Decimal('1000')
        >>> ExtendedContext.logical_and(Decimal('1111'), Decimal('10'))
        Decimal('10')
        >>> ExtendedContext.logical_and(110, 1101)
        Decimal('100')
        >>> ExtendedContext.logical_and(Decimal(110), 1101)
        Decimal('100')
        >>> ExtendedContext.logical_and(110, Decimal(1101))
        Decimal('100')
        ",52,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def logical_and(self, a, b):
        
        a = _convert_other(a, raiseit=True)
        return a.logical_and(b, context=self)

        ```
    ###Assistant : Applies the logical operation 'and' between each operand's digits.

        The operands must be both logical numbers.

        >>> ExtendedContext.logical_and(Decimal('0'), Decimal('0'))
        Decimal('0')
        >>> ExtendedContext.logical_and(Decimal('0'), Decimal('1'))
        Decimal('0')
        >>> ExtendedContext.logical_and(Decimal('1'), Decimal('0'))
        Decimal('0')
        >>> ExtendedContext.logical_and(Decimal('1'), Decimal('1'))
        Decimal('1')
        >>> ExtendedContext.logical_and(Decimal('1100'), Decimal('1010'))
        Decimal('1000')
        >>> ExtendedContext.logical_and(Decimal('1111'), Decimal('10'))
        Decimal('10')
        >>> ExtendedContext.logical_and(110, 1101)
        Decimal('100')
        >>> ExtendedContext.logical_and(Decimal(110), 1101)
        Decimal('100')
        >>> ExtendedContext.logical_and(110, Decimal(1101))
        Decimal('100')
        
    "
52,"def decrement_part_small(self, part, ub):
        
        if self.lpart >= ub - 1:
            self.p1 += 1  # increment to keep track of usefulness of tests
            return False
        plen = len(part)
        for j in range(plen - 1, -1, -1):
            # Knuth's mod, (answer to problem 7.2.1.5.69)
            if j == 0 and (part[0].v - 1)*(ub - self.lpart) < part[0].u:
                self.k1 += 1
                return False

            if j == 0 and part[j].v > 1 or j > 0 and part[j].v > 0:
                # found val to decrement
                part[j].v -= 1
                # Reset trailing parts back to maximum
                for k in range(j + 1, plen):
                    part[k].v = part[k].u

                # Have now decremented part, but are we doomed to
                # failure when it is expanded?  Check one oddball case
                # that turns out to be surprisingly common - exactly
                # enough room to expand the leading component, but no
                # room for the second component, which has v=0.
                if (plen > 1 and part[1].v == 0 and
                    (part[0].u - part[0].v) ==
                        ((ub - self.lpart - 1) * part[0].v)):
                    self.k2 += 1
                    self.db_trace(""Decrement fails test 3"")
                    return False
                return True
        return False
","Decrements part (a subrange of pstack), if possible, returning
        True iff the part was successfully decremented.

        Parameters
        ==========

        part
            part to be decremented (topmost part on the stack)

        ub
            the maximum number of parts allowed in a partition
            returned by the calling traversal.

        Notes
        =====

        The goal of this modification of the ordinary decrement method
        is to fail (meaning that the subtree rooted at this part is to
        be skipped) when it can be proved that this part can only have
        child partitions which are larger than allowed by ``ub``. If a
        decision is made to fail, it must be accurate, otherwise the
        enumeration will miss some partitions.  But, it is OK not to
        capture all the possible failures -- if a part is passed that
        should not be, the resulting too-large partitions are filtered
        by the enumeration one level up.  However, as is usual in
        constrained enumerations, failing early is advantageous.

        The tests used by this method catch the most common cases,
        although this implementation is by no means the last word on
        this problem.  The tests include:

        1) ``lpart`` must be less than ``ub`` by at least 2.  This is because
           once a part has been decremented, the partition
           will gain at least one child in the spread step.

        2) If the leading component of the part is about to be
           decremented, check for how many parts will be added in
           order to use up the unallocated multiplicity in that
           leading component, and fail if this number is greater than
           allowed by ``ub``.  (See code for the exact expression.)  This
           test is given in the answer to Knuth's problem 7.2.1.5.69.

        3) If there is *exactly* enough room to expand the leading
           component by the above test, check the next component (if
           it exists) once decrementing has finished.  If this has
           ``v == 0``, this next component will push the expansion over the
           limit by 1, so fail.
        ",319,182,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def decrement_part_small(self, part, ub):
        
        if self.lpart >= ub - 1:
            self.p1 += 1  # increment to keep track of usefulness of tests
            return False
        plen = len(part)
        for j in range(plen - 1, -1, -1):
            # Knuth's mod, (answer to problem 7.2.1.5.69)
            if j == 0 and (part[0].v - 1)*(ub - self.lpart) < part[0].u:
                self.k1 += 1
                return False

            if j == 0 and part[j].v > 1 or j > 0 and part[j].v > 0:
                # found val to decrement
                part[j].v -= 1
                # Reset trailing parts back to maximum
                for k in range(j + 1, plen):
                    part[k].v = part[k].u

                # Have now decremented part, but are we doomed to
                # failure when it is expanded?  Check one oddball case
                # that turns out to be surprisingly common - exactly
                # enough room to expand the leading component, but no
                # room for the second component, which has v=0.
                if (plen > 1 and part[1].v == 0 and
                    (part[0].u - part[0].v) ==
                        ((ub - self.lpart - 1) * part[0].v)):
                    self.k2 += 1
                    self.db_trace(""Decrement fails test 3"")
                    return False
                return True
        return False

        ```
    ###Assistant : Decrements part (a subrange of pstack), if possible, returning
        True iff the part was successfully decremented.

        Parameters
        ==========

        part
            part to be decremented (topmost part on the stack)

        ub
            the maximum number of parts allowed in a partition
            returned by the calling traversal.

        Notes
        =====

        The goal of this modification of the ordinary decrement method
        is to fail (meaning that the subtree rooted at this part is to
        be skipped) when it can be proved that this part can only have
        child partitions which are larger than allowed by ``ub``. If a
        decision is made to fail, it must be accurate, otherwise the
        enumeration will miss some partitions.  But, it is OK not to
        capture all the possible failures -- if a part is passed that
        should not be, the resulting too-large partitions are filtered
        by the enumeration one level up.  However, as is usual in
        constrained enumerations, failing early is advantageous.

        The tests used by this method catch the most common cases,
        although this implementation is by no means the last word on
        this problem.  The tests include:

        1) ``lpart`` must be less than ``ub`` by at least 2.  This is because
           once a part has been decremented, the partition
           will gain at least one child in the spread step.

        2) If the leading component of the part is about to be
           decremented, check for how many parts will be added in
           order to use up the unallocated multiplicity in that
           leading component, and fail if this number is greater than
           allowed by ``ub``.  (See code for the exact expression.)  This
           test is given in the answer to Knuth's problem 7.2.1.5.69.

        3) If there is *exactly* enough room to expand the leading
           component by the above test, check the next component (if
           it exists) once decrementing has finished.  If this has
           ``v == 0``, this next component will push the expansion over the
           limit by 1, so fail.
        
    "
53,"def get_node_id(self) -> str:
        
        node_id = self.worker.current_node_id
        assert not node_id.is_nil()
        return node_id.hex()
","Get current node ID for this worker or driver.

        Node ID is the id of a node that your driver, task, or actor runs.
        The ID will be in hex format.

        Returns:
            A node id in hex format for this worker or driver.
        ",43,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_node_id(self) -> str:
        
        node_id = self.worker.current_node_id
        assert not node_id.is_nil()
        return node_id.hex()

        ```
    ###Assistant : Get current node ID for this worker or driver.

        Node ID is the id of a node that your driver, task, or actor runs.
        The ID will be in hex format.

        Returns:
            A node id in hex format for this worker or driver.
        
    "
54,"def list_option(*, info):
    
    return _option(
        info,
        ""List options"",
        lambda opt: (isinstance(info.config.get_obj(opt.name), list) and
            not opt.no_autoconfig)
    )

",A CompletionModel filled with settings whose values are lists.,9,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list_option(*, info):
    
    return _option(
        info,
        ""List options"",
        lambda opt: (isinstance(info.config.get_obj(opt.name), list) and
            not opt.no_autoconfig)
    )


        ```
    ###Assistant : A CompletionModel filled with settings whose values are lists.
    "
55,"def get_instance_from_config_with_end_date(config, query):
    start_date = ""2021-03-04""
    end_date = ""2021-04-04""
    conversion_window_days = 14
    google_api = GoogleAds(credentials=config[""credentials""], customer_id=config[""customer_id""])

    instance = CustomQuery(
        api=google_api,
        conversion_window_days=conversion_window_days,
        start_date=start_date,
        end_date=end_date,
        time_zone=""local"",
        custom_query_config={""query"": query, ""table_name"": ""whatever_table""},
    )
    return instance


@pytest.mark.parametrize(
    ""query, fields"",
    [
        (
            ,
            [""campaign.id"", ""campaign.name"", ""campaign.status"", ""metrics.impressions""],
        ),
        (
            ,
            [""campaign.accessible_bidding_strategy"", ""segments.ad_destination_type"", ""campaign.start_date"", ""campaign.end_date""],
        ),
        (, []),
    ],
)","
    SELecT
  campaign.id,
  campaign.name,
  campaign.status,
  metrics.impressions FROM campaign
wheRe campaign.status = 'PAUSED'
AND metrics.impressions > 100
order by campaign.status
    
        SELECT
            campaign.accessible_bidding_strategy,
            segments.ad_destination_type,
            campaign.start_date,
            campaign.end_date
        FROM campaign
    selet aasdasd from aaa",29,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_instance_from_config_with_end_date(config, query):
    start_date = ""2021-03-04""
    end_date = ""2021-04-04""
    conversion_window_days = 14
    google_api = GoogleAds(credentials=config[""credentials""], customer_id=config[""customer_id""])

    instance = CustomQuery(
        api=google_api,
        conversion_window_days=conversion_window_days,
        start_date=start_date,
        end_date=end_date,
        time_zone=""local"",
        custom_query_config={""query"": query, ""table_name"": ""whatever_table""},
    )
    return instance


@pytest.mark.parametrize(
    ""query, fields"",
    [
        (
            ,
            [""campaign.id"", ""campaign.name"", ""campaign.status"", ""metrics.impressions""],
        ),
        (
            ,
            [""campaign.accessible_bidding_strategy"", ""segments.ad_destination_type"", ""campaign.start_date"", ""campaign.end_date""],
        ),
        (, []),
    ],
)
        ```
    ###Assistant : 
    SELecT
  campaign.id,
  campaign.name,
  campaign.status,
  metrics.impressions FROM campaign
wheRe campaign.status = 'PAUSED'
AND metrics.impressions > 100
order by campaign.status
    
        SELECT
            campaign.accessible_bidding_strategy,
            segments.ad_destination_type,
            campaign.start_date,
            campaign.end_date
        FROM campaign
    selet aasdasd from aaa
    "
56,"def node_degree_xy(G, x=""out"", y=""in"", weight=None, nodes=None):
    
    nodes = set(G) if nodes is None else set(nodes)
    if G.is_directed():
        direction = {""out"": G.out_degree, ""in"": G.in_degree}
        xdeg = direction[x]
        ydeg = direction[y]
    else:
        xdeg = ydeg = G.degree

    for u, degu in xdeg(nodes, weight=weight):
        # use G.edges to treat multigraphs correctly
        neighbors = (nbr for _, nbr in G.edges(u) if nbr in nodes)
        for _, degv in ydeg(neighbors, weight=weight):
            yield degu, degv
","Generate node degree-degree pairs for edges in G.

    Parameters
    ----------
    G: NetworkX graph

    x: string ('in','out')
       The degree type for source node (directed graphs only).

    y: string ('in','out')
       The degree type for target node (directed graphs only).

    weight: string or None, optional (default=None)
       The edge attribute that holds the numerical value used
       as a weight.  If None, then each edge has weight 1.
       The degree is the sum of the edge weights adjacent to the node.

    nodes: list or iterable (optional)
        Use only edges that are adjacency to specified nodes.
        The default is all nodes.

    Returns
    -------
    (x, y): 2-tuple
        Generates 2-tuple of (degree, degree) values.


    Examples
    --------
    >>> G = nx.DiGraph()
    >>> G.add_edge(1, 2)
    >>> list(nx.node_degree_xy(G, x=""out"", y=""in""))
    [(1, 1)]
    >>> list(nx.node_degree_xy(G, x=""in"", y=""out""))
    [(0, 0)]

    Notes
    -----
    For undirected graphs each edge is produced twice, once for each edge
    representation (u, v) and (v, u), with the exception of self-loop edges
    which only appear once.
    ",157,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def node_degree_xy(G, x=""out"", y=""in"", weight=None, nodes=None):
    
    nodes = set(G) if nodes is None else set(nodes)
    if G.is_directed():
        direction = {""out"": G.out_degree, ""in"": G.in_degree}
        xdeg = direction[x]
        ydeg = direction[y]
    else:
        xdeg = ydeg = G.degree

    for u, degu in xdeg(nodes, weight=weight):
        # use G.edges to treat multigraphs correctly
        neighbors = (nbr for _, nbr in G.edges(u) if nbr in nodes)
        for _, degv in ydeg(neighbors, weight=weight):
            yield degu, degv

        ```
    ###Assistant : Generate node degree-degree pairs for edges in G.

    Parameters
    ----------
    G: NetworkX graph

    x: string ('in','out')
       The degree type for source node (directed graphs only).

    y: string ('in','out')
       The degree type for target node (directed graphs only).

    weight: string or None, optional (default=None)
       The edge attribute that holds the numerical value used
       as a weight.  If None, then each edge has weight 1.
       The degree is the sum of the edge weights adjacent to the node.

    nodes: list or iterable (optional)
        Use only edges that are adjacency to specified nodes.
        The default is all nodes.

    Returns
    -------
    (x, y): 2-tuple
        Generates 2-tuple of (degree, degree) values.


    Examples
    --------
    >>> G = nx.DiGraph()
    >>> G.add_edge(1, 2)
    >>> list(nx.node_degree_xy(G, x=""out"", y=""in""))
    [(1, 1)]
    >>> list(nx.node_degree_xy(G, x=""in"", y=""out""))
    [(0, 0)]

    Notes
    -----
    For undirected graphs each edge is produced twice, once for each edge
    representation (u, v) and (v, u), with the exception of self-loop edges
    which only appear once.
    
    "
57,"def validate(self, num_steps=None, profile=False, reduce_results=True, info=None):
        
        worker_stats = self.worker_group.validate(
            num_steps=num_steps, profile=profile, info=info
        )

        if reduce_results:
            return self._process_stats(worker_stats)
        else:
            return worker_stats
","Evaluates the model on the validation data set.

        Args:
            num_steps (int): Number of batches to compute update steps on
                per worker. This corresponds also to the number of times
                ``TrainingOperator.validate_batch`` is called per worker.
            profile (bool): Returns time stats for the evaluation procedure.
            reduce_results (bool): Whether to average all metrics across
                all workers into one dict. If a metric is a non-numerical
                value (or nested dictionaries), one value will be randomly
                selected among the workers. If False, returns a list of dicts.
            info (dict): Optional dictionary passed to the training
                operator for `validate` and `validate_batch`.

        Returns:
            A dictionary of metrics for validation.
                You can provide custom metrics by passing in a custom
                ``training_operator_cls``.
        ",113,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate(self, num_steps=None, profile=False, reduce_results=True, info=None):
        
        worker_stats = self.worker_group.validate(
            num_steps=num_steps, profile=profile, info=info
        )

        if reduce_results:
            return self._process_stats(worker_stats)
        else:
            return worker_stats

        ```
    ###Assistant : Evaluates the model on the validation data set.

        Args:
            num_steps (int): Number of batches to compute update steps on
                per worker. This corresponds also to the number of times
                ``TrainingOperator.validate_batch`` is called per worker.
            profile (bool): Returns time stats for the evaluation procedure.
            reduce_results (bool): Whether to average all metrics across
                all workers into one dict. If a metric is a non-numerical
                value (or nested dictionaries), one value will be randomly
                selected among the workers. If False, returns a list of dicts.
            info (dict): Optional dictionary passed to the training
                operator for `validate` and `validate_batch`.

        Returns:
            A dictionary of metrics for validation.
                You can provide custom metrics by passing in a custom
                ``training_operator_cls``.
        
    "
58,"def set_raw_scale(self, in_, scale):
        
        self.__check_input(in_)
        self.raw_scale[in_] = scale
","
        Set the scale of raw features s.t. the input blob = input * scale.
        While Python represents images in [0, 1], certain Caffe models
        like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
        of these models must be 255.

        Parameters
        ----------
        in_ : which input to assign this scale factor
        scale : scale coefficient
        ",57,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_raw_scale(self, in_, scale):
        
        self.__check_input(in_)
        self.raw_scale[in_] = scale

        ```
    ###Assistant : 
        Set the scale of raw features s.t. the input blob = input * scale.
        While Python represents images in [0, 1], certain Caffe models
        like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
        of these models must be 255.

        Parameters
        ----------
        in_ : which input to assign this scale factor
        scale : scale coefficient
        
    "
59,"def test_add_rule_to_best_shard():
    

    # If we start with an empty list, then add to first shard
    shards: List[List[bazel_sharding.BazelRule]] = [list() for _ in range(4)]
    optimum = 600

    rule = bazel_sharding.BazelRule(""mock"", ""medium"")
    bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
    assert shards[0][0] == rule
    assert all(not shard for shard in shards[1:])

    # Add to first shard below optimum
    old_rule = bazel_sharding.BazelRule(""mock"", ""medium"")
    shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
    shards[3] = []
    optimum = old_rule.actual_timeout_s

    rule = bazel_sharding.BazelRule(""mock"", ""small"")
    bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
    assert shards[3][0] == rule
    assert all(shard[-1] == old_rule for shard in shards[0:3])

    # If all shards are above or equal optimum, add to the one with the smallest
    # difference
    old_rule = bazel_sharding.BazelRule(""mock"", ""large"")
    shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
    optimum = old_rule.actual_timeout_s
    old_rule_medium = bazel_sharding.BazelRule(""mock"", ""medium"")
    shards[3][0] = old_rule_medium

    rule = bazel_sharding.BazelRule(""mock"", ""small"")
    bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
    assert shards[3][0] == old_rule_medium
    assert shards[3][-1] == rule
    assert all(shard[-1] == old_rule for shard in shards[0:3])

",Test that the best shard in optimal strategy is chosen correctly.,11,151,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_add_rule_to_best_shard():
    

    # If we start with an empty list, then add to first shard
    shards: List[List[bazel_sharding.BazelRule]] = [list() for _ in range(4)]
    optimum = 600

    rule = bazel_sharding.BazelRule(""mock"", ""medium"")
    bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
    assert shards[0][0] == rule
    assert all(not shard for shard in shards[1:])

    # Add to first shard below optimum
    old_rule = bazel_sharding.BazelRule(""mock"", ""medium"")
    shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
    shards[3] = []
    optimum = old_rule.actual_timeout_s

    rule = bazel_sharding.BazelRule(""mock"", ""small"")
    bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
    assert shards[3][0] == rule
    assert all(shard[-1] == old_rule for shard in shards[0:3])

    # If all shards are above or equal optimum, add to the one with the smallest
    # difference
    old_rule = bazel_sharding.BazelRule(""mock"", ""large"")
    shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
    optimum = old_rule.actual_timeout_s
    old_rule_medium = bazel_sharding.BazelRule(""mock"", ""medium"")
    shards[3][0] = old_rule_medium

    rule = bazel_sharding.BazelRule(""mock"", ""small"")
    bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
    assert shards[3][0] == old_rule_medium
    assert shards[3][-1] == rule
    assert all(shard[-1] == old_rule for shard in shards[0:3])


        ```
    ###Assistant : Test that the best shard in optimal strategy is chosen correctly.
    "
60,"def async_heartbeat(self) -> None:
        
        self._computed_state = False
        self._restart_timer()
        self.async_write_ha_state()
","Mark the device as online, and restart the 25 hour timer.

        This gets called when the heartbeat node beats, but also when the
        parent sensor sends any events, as we can trust that to mean the device
        is online. This mitigates the risk of false positives due to a single
        missed heartbeat event.
        ",53,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def async_heartbeat(self) -> None:
        
        self._computed_state = False
        self._restart_timer()
        self.async_write_ha_state()

        ```
    ###Assistant : Mark the device as online, and restart the 25 hour timer.

        This gets called when the heartbeat node beats, but also when the
        parent sensor sends any events, as we can trust that to mean the device
        is online. This mitigates the risk of false positives due to a single
        missed heartbeat event.
        
    "
61,"def test_assert_series_equal_interval_dtype_mismatch():
    # https://github.com/pandas-dev/pandas/issues/32747
    left = Series([pd.Interval(0, 1, ""right"")], dtype=""interval"")
    right = left.astype(object)

    msg = 

    tm.assert_series_equal(left, right, check_dtype=False)

    with pytest.raises(AssertionError, match=msg):
        tm.assert_series_equal(left, right, check_dtype=True)

","Attributes of Series are different

Attribute ""dtype"" are different
\\[left\\]:  interval\\[int64, right\\]
\\[right\\]: object",14,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_assert_series_equal_interval_dtype_mismatch():
    # https://github.com/pandas-dev/pandas/issues/32747
    left = Series([pd.Interval(0, 1, ""right"")], dtype=""interval"")
    right = left.astype(object)

    msg = 

    tm.assert_series_equal(left, right, check_dtype=False)

    with pytest.raises(AssertionError, match=msg):
        tm.assert_series_equal(left, right, check_dtype=True)


        ```
    ###Assistant : Attributes of Series are different

Attribute ""dtype"" are different
\\[left\\]:  interval\\[int64, right\\]
\\[right\\]: object
    "
62,"def rc_file_defaults():
    
    # Deprecation warnings were already handled when creating rcParamsOrig, no
    # need to reemit them here.
    with _api.suppress_matplotlib_deprecation_warning():
        from .style.core import STYLE_BLACKLIST
        rcParams.update({k: rcParamsOrig[k] for k in rcParamsOrig
                         if k not in STYLE_BLACKLIST})

","
    Restore the `.rcParams` from the original rc file loaded by Matplotlib.

    Style-blacklisted `.rcParams` (defined in
    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.
    ",19,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rc_file_defaults():
    
    # Deprecation warnings were already handled when creating rcParamsOrig, no
    # need to reemit them here.
    with _api.suppress_matplotlib_deprecation_warning():
        from .style.core import STYLE_BLACKLIST
        rcParams.update({k: rcParamsOrig[k] for k in rcParamsOrig
                         if k not in STYLE_BLACKLIST})


        ```
    ###Assistant : 
    Restore the `.rcParams` from the original rc file loaded by Matplotlib.

    Style-blacklisted `.rcParams` (defined in
    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.
    
    "
63,"def lexer(self) -> Optional[Lexer]:
        

        if isinstance(self._lexer, Lexer):
            return self._lexer
        try:
            return get_lexer_by_name(
                self._lexer,
                stripnl=False,
                ensurenl=True,
                tabsize=self.tab_size,
            )
        except ClassNotFound:
            return None
","The lexer for this syntax, or None if no lexer was found.

        Tries to find the lexer by name if a string was passed to the constructor.
        ",27,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def lexer(self) -> Optional[Lexer]:
        

        if isinstance(self._lexer, Lexer):
            return self._lexer
        try:
            return get_lexer_by_name(
                self._lexer,
                stripnl=False,
                ensurenl=True,
                tabsize=self.tab_size,
            )
        except ClassNotFound:
            return None

        ```
    ###Assistant : The lexer for this syntax, or None if no lexer was found.

        Tries to find the lexer by name if a string was passed to the constructor.
        
    "
64,"def test_numeric_repl(file, multiline_file):
    
    file.replace(multiline_file, r""Etiam"", 123)
    assert ""123"" in multiline_file.read_text()

","
    This test covers cases where the replacement string is numeric. The CLI
    parser yaml-fies it into a numeric type. If not converted back to a string
    type in file.replace, a TypeError occurs when the replace is attempted. See
    https://github.com/saltstack/salt/issues/9097 for more information.
    ",42,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_numeric_repl(file, multiline_file):
    
    file.replace(multiline_file, r""Etiam"", 123)
    assert ""123"" in multiline_file.read_text()


        ```
    ###Assistant : 
    This test covers cases where the replacement string is numeric. The CLI
    parser yaml-fies it into a numeric type. If not converted back to a string
    type in file.replace, a TypeError occurs when the replace is attempted. See
    https://github.com/saltstack/salt/issues/9097 for more information.
    
    "
65,"def set_interpret_parameters(self, segments=16):
        
        self.interpretation_segments = segments
        return self
","
        Calculates interpretation score of image subsections by splitting the image into subsections, then using a ""leave one out"" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value.
        Parameters:
        segments (int): Number of interpretation segments to split image into.
        ",50,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_interpret_parameters(self, segments=16):
        
        self.interpretation_segments = segments
        return self

        ```
    ###Assistant : 
        Calculates interpretation score of image subsections by splitting the image into subsections, then using a ""leave one out"" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value.
        Parameters:
        segments (int): Number of interpretation segments to split image into.
        
    "
66,"def can_native_upsert(self) -> bool:
        
        return sqlite3.sqlite_version_info >= (3, 24, 0)
","
        Do we support native UPSERTs? This requires SQLite3 3.24+, plus some
        more work we haven't done yet to tell what was inserted vs updated.
        ",24,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def can_native_upsert(self) -> bool:
        
        return sqlite3.sqlite_version_info >= (3, 24, 0)

        ```
    ###Assistant : 
        Do we support native UPSERTs? This requires SQLite3 3.24+, plus some
        more work we haven't done yet to tell what was inserted vs updated.
        
    "
67,"async def get_actors(self) -> dict:
        
        reply = await self._client.get_all_actor_info(timeout=DEFAULT_RPC_TIMEOUT)
        result = {}
        for message in reply.actor_table_data:
            data = self._message_to_dict(message=message, fields_to_decode=[""actor_id""])
            data = filter_fields(data, ActorState)
            result[data[""actor_id""]] = data
        return result
","List all actor information from the cluster.

        Returns:
            {actor_id -> actor_data_in_dict}
            actor_data_in_dict's schema is in ActorState
        ",16,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def get_actors(self) -> dict:
        
        reply = await self._client.get_all_actor_info(timeout=DEFAULT_RPC_TIMEOUT)
        result = {}
        for message in reply.actor_table_data:
            data = self._message_to_dict(message=message, fields_to_decode=[""actor_id""])
            data = filter_fields(data, ActorState)
            result[data[""actor_id""]] = data
        return result

        ```
    ###Assistant : List all actor information from the cluster.

        Returns:
            {actor_id -> actor_data_in_dict}
            actor_data_in_dict's schema is in ActorState
        
    "
68,"def insert_predictor_answer(self, insert):
        
        model_interface = self.session.model_interface
        data_store = self.session.data_store

        select_data_query = insert.get('select_data_query')
        if isinstance(select_data_query, str) is False or len(select_data_query) == 0:
            self.packet(
                ErrPacket,
                err_code=ERR.ER_WRONG_ARGUMENTS,
                msg=""'select_data_query' should not be empty""
            ).send()
            return

        models = model_interface.get_models()
        if insert['name'] in [x['name'] for x in models]:
            self.packet(
                ErrPacket,
                err_code=ERR.ER_WRONG_ARGUMENTS,
                msg=f""predictor with name '{insert['name']}'' already exists""
            ).send()
            return

        kwargs = {}
        if isinstance(insert.get('training_options'), str) \
                and len(insert['training_options']) > 0:
            try:
                kwargs = json.loads(insert['training_options'])
            except Exception:
                self.packet(
                    ErrPacket,
                    err_code=ERR.ER_WRONG_ARGUMENTS,
                    msg='training_options should be in valid JSON string'
                ).send()
                return

        integration = self.session.integration
        if isinstance(integration, str) is False or len(integration) == 0:
            self.packet(
                ErrPacket,
                err_code=ERR.ER_WRONG_ARGUMENTS,
                msg='select_data_query can be used only in query from database'
            ).send()
            return
        insert['select_data_query'] = insert['select_data_query'].replace(r""\'"", ""'"")
        ds_name = data_store.get_vacant_name(insert['name'])
        ds = data_store.save_datasource(ds_name, integration, {'query': insert['select_data_query']})

        insert['predict'] = [x.strip() for x in insert['predict'].split(',')]

        ds_data = data_store.get_datasource(ds_name)
        if ds_data is None:
            raise Exception(f""DataSource '{ds_name}' does not exists"")
        ds_columns = [x['name'] for x in ds_data['columns']]
        for col in insert['predict']:
            if col not in ds_columns:
                data_store.delete_datasource(ds_name)
                raise Exception(f""Column '{col}' not exists"")

        try:
            insert['predict'] = self._check_predict_columns(insert['predict'], ds_columns)
        except Exception:
            data_store.delete_datasource(ds_name)
            raise

        model_interface.learn(
            insert['name'], ds, insert['predict'], ds_data['id'], kwargs=kwargs, delete_ds_on_fail=True
        )

        self.packet(OkPacket).send()
"," Start learn new predictor.
            Parameters:
             - insert - dict with keys as columns of mindsb.predictors table.
        ",16,181,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def insert_predictor_answer(self, insert):
        
        model_interface = self.session.model_interface
        data_store = self.session.data_store

        select_data_query = insert.get('select_data_query')
        if isinstance(select_data_query, str) is False or len(select_data_query) == 0:
            self.packet(
                ErrPacket,
                err_code=ERR.ER_WRONG_ARGUMENTS,
                msg=""'select_data_query' should not be empty""
            ).send()
            return

        models = model_interface.get_models()
        if insert['name'] in [x['name'] for x in models]:
            self.packet(
                ErrPacket,
                err_code=ERR.ER_WRONG_ARGUMENTS,
                msg=f""predictor with name '{insert['name']}'' already exists""
            ).send()
            return

        kwargs = {}
        if isinstance(insert.get('training_options'), str) \
                and len(insert['training_options']) > 0:
            try:
                kwargs = json.loads(insert['training_options'])
            except Exception:
                self.packet(
                    ErrPacket,
                    err_code=ERR.ER_WRONG_ARGUMENTS,
                    msg='training_options should be in valid JSON string'
                ).send()
                return

        integration = self.session.integration
        if isinstance(integration, str) is False or len(integration) == 0:
            self.packet(
                ErrPacket,
                err_code=ERR.ER_WRONG_ARGUMENTS,
                msg='select_data_query can be used only in query from database'
            ).send()
            return
        insert['select_data_query'] = insert['select_data_query'].replace(r""\'"", ""'"")
        ds_name = data_store.get_vacant_name(insert['name'])
        ds = data_store.save_datasource(ds_name, integration, {'query': insert['select_data_query']})

        insert['predict'] = [x.strip() for x in insert['predict'].split(',')]

        ds_data = data_store.get_datasource(ds_name)
        if ds_data is None:
            raise Exception(f""DataSource '{ds_name}' does not exists"")
        ds_columns = [x['name'] for x in ds_data['columns']]
        for col in insert['predict']:
            if col not in ds_columns:
                data_store.delete_datasource(ds_name)
                raise Exception(f""Column '{col}' not exists"")

        try:
            insert['predict'] = self._check_predict_columns(insert['predict'], ds_columns)
        except Exception:
            data_store.delete_datasource(ds_name)
            raise

        model_interface.learn(
            insert['name'], ds, insert['predict'], ds_data['id'], kwargs=kwargs, delete_ds_on_fail=True
        )

        self.packet(OkPacket).send()

        ```
    ###Assistant :  Start learn new predictor.
            Parameters:
             - insert - dict with keys as columns of mindsb.predictors table.
        
    "
69,"def populate_historical_trade_data(self):
        
        trade_data = self.__orderbook.pivot(
            index=""Date"",
            columns=""Ticker"",
            values=[
                ""Type"",
                ""Sector"",
                ""Industry"",
                ""Country"",
                ""Price"",
                ""Quantity"",
                ""Fees"",
                ""Premium"",
                ""Investment"",
                ""Side"",
                ""Currency"",
            ],
        )

        # Make historical prices columns a multi-index. This helps the merging.
        self.portfolio_historical_prices.columns = pd.MultiIndex.from_product(
            [[""Close""], self.portfolio_historical_prices.columns]
        )

        # Merge with historical close prices (and fillna)
        trade_data = pd.merge(
            trade_data,
            self.portfolio_historical_prices,
            how=""right"",
            left_index=True,
            right_index=True,
        ).fillna(0)

        # Accumulate quantity held by trade date
        trade_data[""Quantity""] = trade_data[""Quantity""].cumsum()

        trade_data[""Investment""] = trade_data[""Investment""].cumsum()

        trade_data.loc[:, (""Investment"", ""Total"")] = trade_data[""Investment""][
            self.tickers_list
        ].sum(axis=1)

        self.historical_trade_data = trade_data
",Create a new dataframe to store historical prices by ticker,10,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def populate_historical_trade_data(self):
        
        trade_data = self.__orderbook.pivot(
            index=""Date"",
            columns=""Ticker"",
            values=[
                ""Type"",
                ""Sector"",
                ""Industry"",
                ""Country"",
                ""Price"",
                ""Quantity"",
                ""Fees"",
                ""Premium"",
                ""Investment"",
                ""Side"",
                ""Currency"",
            ],
        )

        # Make historical prices columns a multi-index. This helps the merging.
        self.portfolio_historical_prices.columns = pd.MultiIndex.from_product(
            [[""Close""], self.portfolio_historical_prices.columns]
        )

        # Merge with historical close prices (and fillna)
        trade_data = pd.merge(
            trade_data,
            self.portfolio_historical_prices,
            how=""right"",
            left_index=True,
            right_index=True,
        ).fillna(0)

        # Accumulate quantity held by trade date
        trade_data[""Quantity""] = trade_data[""Quantity""].cumsum()

        trade_data[""Investment""] = trade_data[""Investment""].cumsum()

        trade_data.loc[:, (""Investment"", ""Total"")] = trade_data[""Investment""][
            self.tickers_list
        ].sum(axis=1)

        self.historical_trade_data = trade_data

        ```
    ###Assistant : Create a new dataframe to store historical prices by ticker
    "
70,"def test_fetch_openml_equivalence_array_dataframe(monkeypatch, parser):
    
    pytest.importorskip(""pandas"")

    data_id = 61
    _monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
    bunch_as_frame_true = fetch_openml(
        data_id=data_id,
        as_frame=True,
        cache=False,
        parser=parser,
    )

    bunch_as_frame_false = fetch_openml(
        data_id=data_id,
        as_frame=False,
        cache=False,
        parser=parser,
    )

    assert_allclose(bunch_as_frame_false.data, bunch_as_frame_true.data)
    assert_array_equal(bunch_as_frame_false.target, bunch_as_frame_true.target)


# Known failure of PyPy for OpenML. See the following issue:
# https://github.com/scikit-learn/scikit-learn/issues/18906
@fails_if_pypy
@pytest.mark.parametrize(""parser"", [""liac-arff"", ""pandas""])","Check the equivalence of the dataset when using `as_frame=False` and
    `as_frame=True`.
    ",11,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_fetch_openml_equivalence_array_dataframe(monkeypatch, parser):
    
    pytest.importorskip(""pandas"")

    data_id = 61
    _monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
    bunch_as_frame_true = fetch_openml(
        data_id=data_id,
        as_frame=True,
        cache=False,
        parser=parser,
    )

    bunch_as_frame_false = fetch_openml(
        data_id=data_id,
        as_frame=False,
        cache=False,
        parser=parser,
    )

    assert_allclose(bunch_as_frame_false.data, bunch_as_frame_true.data)
    assert_array_equal(bunch_as_frame_false.target, bunch_as_frame_true.target)


# Known failure of PyPy for OpenML. See the following issue:
# https://github.com/scikit-learn/scikit-learn/issues/18906
@fails_if_pypy
@pytest.mark.parametrize(""parser"", [""liac-arff"", ""pandas""])
        ```
    ###Assistant : Check the equivalence of the dataset when using `as_frame=False` and
    `as_frame=True`.
    
    "
71,"def wire_type(self):
            
            if hasattr(self, '_m_wire_type'):
                return self._m_wire_type

            self._m_wire_type = KaitaiStream.resolve_enum(GoogleProtobuf.Pair.WireTypes, (self.key.value & 7))
            return getattr(self, '_m_wire_type', None)
","""Wire type"" is a part of the ""key"" that carries enough
            information to parse value from the wire, i.e. read correct
            amount of bytes, but there's not enough informaton to
            interprete in unambiguously. For example, one can't clearly
            distinguish 64-bit fixed-sized integers from 64-bit floats,
            signed zigzag-encoded varints from regular unsigned varints,
            arbitrary bytes from UTF-8 encoded strings, etc.
            ",59,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wire_type(self):
            
            if hasattr(self, '_m_wire_type'):
                return self._m_wire_type

            self._m_wire_type = KaitaiStream.resolve_enum(GoogleProtobuf.Pair.WireTypes, (self.key.value & 7))
            return getattr(self, '_m_wire_type', None)

        ```
    ###Assistant : ""Wire type"" is a part of the ""key"" that carries enough
            information to parse value from the wire, i.e. read correct
            amount of bytes, but there's not enough informaton to
            interprete in unambiguously. For example, one can't clearly
            distinguish 64-bit fixed-sized integers from 64-bit floats,
            signed zigzag-encoded varints from regular unsigned varints,
            arbitrary bytes from UTF-8 encoded strings, etc.
            
    "
72,"def address(self):  # pragma: no cover
        
        warnings.warn(
            ""Client.address is deprecated, use Client.peername instead."",
            DeprecationWarning,
            stacklevel=2,
        )
        return self.peername
",*Deprecated:* An outdated alias for Client.peername.,6,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def address(self):  # pragma: no cover
        
        warnings.warn(
            ""Client.address is deprecated, use Client.peername instead."",
            DeprecationWarning,
            stacklevel=2,
        )
        return self.peername

        ```
    ###Assistant : *Deprecated:* An outdated alias for Client.peername.
    "
73,"def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs):
        
        if 'rows' in kwargs or 'cols' in kwargs:
            msg = 
            if 'rows' in kwargs and 'cols' in kwargs:
                msg += f","Returns a Jordan block

        Parameters
        ==========

        size : Integer, optional
            Specifies the shape of the Jordan block matrix.

        eigenvalue : Number or Symbol
            Specifies the value for the main diagonal of the matrix.

            .. note::
                The keyword ``eigenval`` is also specified as an alias
                of this keyword, but it is not recommended to use.

                We may deprecate the alias in later release.

        band : 'upper' or 'lower', optional
            Specifies the position of the off-diagonal to put `1` s on.

        cls : Matrix, optional
            Specifies the matrix class of the output form.

            If it is not specified, the class type where the method is
            being executed on will be returned.

        rows, cols : Integer, optional
            Specifies the shape of the Jordan block matrix. See Notes
            section for the details of how these key works.

            .. deprecated:: 1.4
                The rows and cols parameters are deprecated and will be
                removed in a future version.


        Returns
        =======

        Matrix
            A Jordan block matrix.

        Raises
        ======

        ValueError
            If insufficient arguments are given for matrix size
            specification, or no eigenvalue is given.

        Examples
        ========

        Creating a default Jordan block:

        >>> from sympy import Matrix
        >>> from sympy.abc import x
        >>> Matrix.jordan_block(4, x)
        Matrix([
        [x, 1, 0, 0],
        [0, x, 1, 0],
        [0, 0, x, 1],
        [0, 0, 0, x]])

        Creating an alternative Jordan block matrix where `1` is on
        lower off-diagonal:

        >>> Matrix.jordan_block(4, x, band='lower')
        Matrix([
        [x, 0, 0, 0],
        [1, x, 0, 0],
        [0, 1, x, 0],
        [0, 0, 1, x]])

        Creating a Jordan block with keyword arguments

        >>> Matrix.jordan_block(size=4, eigenvalue=x)
        Matrix([
        [x, 1, 0, 0],
        [0, x, 1, 0],
        [0, 0, x, 1],
        [0, 0, 0, x]])

        Notes
        =====

        .. deprecated:: 1.4
            This feature is deprecated and will be removed in a future
            version.

        The keyword arguments ``size``, ``rows``, ``cols`` relates to
        the Jordan block size specifications.

        If you want to create a square Jordan block, specify either
        one of the three arguments.

        If you want to create a rectangular Jordan block, specify
        ``rows`` and ``cols`` individually.

        +--------------------------------+---------------------+
        |        Arguments Given         |     Matrix Shape    |
        +----------+----------+----------+----------+----------+
        |   size   |   rows   |   cols   |   rows   |   cols   |
        +==========+==========+==========+==========+==========+
        |   size   |         Any         |   size   |   size   |
        +----------+----------+----------+----------+----------+
        |          |        None         |     ValueError      |
        |          +----------+----------+----------+----------+
        |   None   |   rows   |   None   |   rows   |   rows   |
        |          +----------+----------+----------+----------+
        |          |   None   |   cols   |   cols   |   cols   |
        +          +----------+----------+----------+----------+
        |          |   rows   |   cols   |   rows   |   cols   |
        +----------+----------+----------+----------+----------+

        References
        ==========

        .. [1] https://en.wikipedia.org/wiki/Jordan_matrix
        
                The 'rows' and 'cols' keywords to Matrix.jordan_block() are
                deprecated. Use the 'size' parameter instead.
                \
                To get a non-square Jordan block matrix use a more generic
                banded matrix constructor, like
",442,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs):
        
        if 'rows' in kwargs or 'cols' in kwargs:
            msg = 
            if 'rows' in kwargs and 'cols' in kwargs:
                msg += f
        ```
    ###Assistant : Returns a Jordan block

        Parameters
        ==========

        size : Integer, optional
            Specifies the shape of the Jordan block matrix.

        eigenvalue : Number or Symbol
            Specifies the value for the main diagonal of the matrix.

            .. note::
                The keyword ``eigenval`` is also specified as an alias
                of this keyword, but it is not recommended to use.

                We may deprecate the alias in later release.

        band : 'upper' or 'lower', optional
            Specifies the position of the off-diagonal to put `1` s on.

        cls : Matrix, optional
            Specifies the matrix class of the output form.

            If it is not specified, the class type where the method is
            being executed on will be returned.

        rows, cols : Integer, optional
            Specifies the shape of the Jordan block matrix. See Notes
            section for the details of how these key works.

            .. deprecated:: 1.4
                The rows and cols parameters are deprecated and will be
                removed in a future version.


        Returns
        =======

        Matrix
            A Jordan block matrix.

        Raises
        ======

        ValueError
            If insufficient arguments are given for matrix size
            specification, or no eigenvalue is given.

        Examples
        ========

        Creating a default Jordan block:

        >>> from sympy import Matrix
        >>> from sympy.abc import x
        >>> Matrix.jordan_block(4, x)
        Matrix([
        [x, 1, 0, 0],
        [0, x, 1, 0],
        [0, 0, x, 1],
        [0, 0, 0, x]])

        Creating an alternative Jordan block matrix where `1` is on
        lower off-diagonal:

        >>> Matrix.jordan_block(4, x, band='lower')
        Matrix([
        [x, 0, 0, 0],
        [1, x, 0, 0],
        [0, 1, x, 0],
        [0, 0, 1, x]])

        Creating a Jordan block with keyword arguments

        >>> Matrix.jordan_block(size=4, eigenvalue=x)
        Matrix([
        [x, 1, 0, 0],
        [0, x, 1, 0],
        [0, 0, x, 1],
        [0, 0, 0, x]])

        Notes
        =====

        .. deprecated:: 1.4
            This feature is deprecated and will be removed in a future
            version.

        The keyword arguments ``size``, ``rows``, ``cols`` relates to
        the Jordan block size specifications.

        If you want to create a square Jordan block, specify either
        one of the three arguments.

        If you want to create a rectangular Jordan block, specify
        ``rows`` and ``cols`` individually.

        +--------------------------------+---------------------+
        |        Arguments Given         |     Matrix Shape    |
        +----------+----------+----------+----------+----------+
        |   size   |   rows   |   cols   |   rows   |   cols   |
        +==========+==========+==========+==========+==========+
        |   size   |         Any         |   size   |   size   |
        +----------+----------+----------+----------+----------+
        |          |        None         |     ValueError      |
        |          +----------+----------+----------+----------+
        |   None   |   rows   |   None   |   rows   |   rows   |
        |          +----------+----------+----------+----------+
        |          |   None   |   cols   |   cols   |   cols   |
        +          +----------+----------+----------+----------+
        |          |   rows   |   cols   |   rows   |   cols   |
        +----------+----------+----------+----------+----------+

        References
        ==========

        .. [1] https://en.wikipedia.org/wiki/Jordan_matrix
        
                The 'rows' and 'cols' keywords to Matrix.jordan_block() are
                deprecated. Use the 'size' parameter instead.
                \
                To get a non-square Jordan block matrix use a more generic
                banded matrix constructor, like

    "
74,"def get_openapi_specs() -> dict:
    

    app = get_app()
    return get_openapi(
        title=app.title,
        version=app.version,
        openapi_version=app.openapi_version,
        description=app.description,
        routes=app.routes,
        servers=[{""url"": ""http://localhost:8000""}],
    )
","
    Used to autogenerate OpenAPI specs file to use in the documentation.

    Returns `servers` to specify base URL for OpenAPI Playground (see https://swagger.io/docs/specification/api-host-and-base-path/)

    See `.github/utils/generate_openapi_specs.py`
    ",24,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_openapi_specs() -> dict:
    

    app = get_app()
    return get_openapi(
        title=app.title,
        version=app.version,
        openapi_version=app.openapi_version,
        description=app.description,
        routes=app.routes,
        servers=[{""url"": ""http://localhost:8000""}],
    )

        ```
    ###Assistant : 
    Used to autogenerate OpenAPI specs file to use in the documentation.

    Returns `servers` to specify base URL for OpenAPI Playground (see https://swagger.io/docs/specification/api-host-and-base-path/)

    See `.github/utils/generate_openapi_specs.py`
    
    "
75,"def get_all_employee_emails(company):
	
	employee_list = frappe.get_all(
		""Employee"", fields=[""name"", ""employee_name""], filters={""status"": ""Active"", ""company"": company}
	)
	employee_emails = []
	for employee in employee_list:
		if not employee:
			continue
		user, company_email, personal_email = frappe.db.get_value(
			""Employee"", employee, [""user_id"", ""company_email"", ""personal_email""]
		)
		email = user or company_email or personal_email
		if email:
			employee_emails.append(email)
	return employee_emails

",Returns list of employee emails either based on user_id or company_email,11,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all_employee_emails(company):
	
	employee_list = frappe.get_all(
		""Employee"", fields=[""name"", ""employee_name""], filters={""status"": ""Active"", ""company"": company}
	)
	employee_emails = []
	for employee in employee_list:
		if not employee:
			continue
		user, company_email, personal_email = frappe.db.get_value(
			""Employee"", employee, [""user_id"", ""company_email"", ""personal_email""]
		)
		email = user or company_email or personal_email
		if email:
			employee_emails.append(email)
	return employee_emails


        ```
    ###Assistant : Returns list of employee emails either based on user_id or company_email
    "
76,"def test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release(self):
        
        release_1 = self.create_release(
            date_added=timezone.now() - timedelta(minutes=45), version=""foobar 1""
        )
        release_2 = self.create_release(version=""foobar 2"")
        self.create_release(version=""foobar 3"")

        group = self.store_event(
            data={
                ""timestamp"": iso_format(before_now(seconds=12)),
                ""fingerprint"": [""group-1""],
                ""release"": release_1.version,
            },
            project_id=self.project.id,
        ).group

        self.login_as(user=self.user)

        response = self.get_success_response(
            qs_params={""id"": group.id}, status=""resolvedInNextRelease""
        )
        assert response.data[""status""] == ""resolved""
        assert response.data[""statusDetails""][""inNextRelease""]

        grp_resolution = GroupResolution.objects.filter(group=group)

        assert len(grp_resolution) == 1
        grp_resolution = grp_resolution[0]

        assert grp_resolution.current_release_version == release_1.version
        assert grp_resolution.release.id == release_2.id
        assert grp_resolution.type == GroupResolution.Type.in_release
        assert grp_resolution.status == GroupResolution.Status.resolved

        activity = Activity.objects.filter(
            group=grp_resolution.group,
            type=Activity.SET_RESOLVED_IN_RELEASE,
            ident=grp_resolution.id,
        ).first()
        assert activity.data[""version""] == release_2.version
","
        Test that ensures that if we basically know the next release when clicking on Resolved
        In Next Release because that release exists, then we can short circuit setting
        GroupResolution to type ""inNextRelease"", and then having `clear_exrired_resolutions` run
        once a new release is created to convert GroupResolution to in_release and set Activity.
        Basically we treat ""ResolvedInNextRelease"" as ""ResolvedInRelease"" when there is a release
        that was created after the last release associated with the group being resolved
        ",75,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release(self):
        
        release_1 = self.create_release(
            date_added=timezone.now() - timedelta(minutes=45), version=""foobar 1""
        )
        release_2 = self.create_release(version=""foobar 2"")
        self.create_release(version=""foobar 3"")

        group = self.store_event(
            data={
                ""timestamp"": iso_format(before_now(seconds=12)),
                ""fingerprint"": [""group-1""],
                ""release"": release_1.version,
            },
            project_id=self.project.id,
        ).group

        self.login_as(user=self.user)

        response = self.get_success_response(
            qs_params={""id"": group.id}, status=""resolvedInNextRelease""
        )
        assert response.data[""status""] == ""resolved""
        assert response.data[""statusDetails""][""inNextRelease""]

        grp_resolution = GroupResolution.objects.filter(group=group)

        assert len(grp_resolution) == 1
        grp_resolution = grp_resolution[0]

        assert grp_resolution.current_release_version == release_1.version
        assert grp_resolution.release.id == release_2.id
        assert grp_resolution.type == GroupResolution.Type.in_release
        assert grp_resolution.status == GroupResolution.Status.resolved

        activity = Activity.objects.filter(
            group=grp_resolution.group,
            type=Activity.SET_RESOLVED_IN_RELEASE,
            ident=grp_resolution.id,
        ).first()
        assert activity.data[""version""] == release_2.version

        ```
    ###Assistant : 
        Test that ensures that if we basically know the next release when clicking on Resolved
        In Next Release because that release exists, then we can short circuit setting
        GroupResolution to type ""inNextRelease"", and then having `clear_exrired_resolutions` run
        once a new release is created to convert GroupResolution to in_release and set Activity.
        Basically we treat ""ResolvedInNextRelease"" as ""ResolvedInRelease"" when there is a release
        that was created after the last release associated with the group being resolved
        
    "
77,"def subscription_order_updated_webhook(subscription_webhook):
    return subscription_webhook(
        ORDER_UPDATED_SUBSCRIPTION_QUERY, WebhookEventAsyncType.ORDER_UPDATED
    )


ORDER_CONFIRMED_SUBSCRIPTION_QUERY = 


@pytest.fixture","
    subscription{
      event{
        ...on OrderConfirmed{
          order{
            id
          }
        }
      }
    }
",10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def subscription_order_updated_webhook(subscription_webhook):
    return subscription_webhook(
        ORDER_UPDATED_SUBSCRIPTION_QUERY, WebhookEventAsyncType.ORDER_UPDATED
    )


ORDER_CONFIRMED_SUBSCRIPTION_QUERY = 


@pytest.fixture
        ```
    ###Assistant : 
    subscription{
      event{
        ...on OrderConfirmed{
          order{
            id
          }
        }
      }
    }

    "
78,"def cast_to_compatible_types(table):
        
        schema = table.schema
        new_schema = schema
        need_cast = False
        uint_to_int_cast = False
        new_cols = {}
        uint_to_int_map = {
            pa.uint8(): pa.int16(),
            pa.uint16(): pa.int32(),
            pa.uint32(): pa.int64(),
            pa.uint64(): pa.int64(),  # May cause overflow
        }
        for i, field in enumerate(schema):
            # Currently OmniSci doesn't support Arrow table import with
            # dictionary columns. Here we cast dictionaries until support
            # is in place.
            # https://github.com/modin-project/modin/issues/1738
            if pa.types.is_dictionary(field.type):
                # Conversion for dictionary of null type to string is not supported
                # in Arrow. Build new column for this case for now.
                if pa.types.is_null(field.type.value_type):
                    mask = np.full(table.num_rows, True, dtype=bool)
                    new_col_data = np.empty(table.num_rows, dtype=str)
                    new_col = pa.array(new_col_data, pa.string(), mask)
                    new_cols[i] = new_col
                else:
                    need_cast = True
                new_field = pa.field(
                    field.name, pa.string(), field.nullable, field.metadata
                )
                new_schema = new_schema.set(i, new_field)
            # OmniSci doesn't support importing Arrow's date type:
            # https://github.com/omnisci/omniscidb/issues/678
            elif pa.types.is_date(field.type):
                # Arrow's date is the number of days since the UNIX-epoch, so we can convert it
                # to a timestamp[s] (number of seconds since the UNIX-epoch) without losing precision
                new_field = pa.field(
                    field.name, pa.timestamp(""s""), field.nullable, field.metadata
                )
                new_schema = new_schema.set(i, new_field)
                need_cast = True
            # OmniSci doesn't support unsigned types
            elif pa.types.is_unsigned_integer(field.type):
                new_field = pa.field(
                    field.name,
                    uint_to_int_map[field.type],
                    field.nullable,
                    field.metadata,
                )
                new_schema = new_schema.set(i, new_field)
                need_cast = True
                uint_to_int_cast = True

        # Such cast may affect the data, so we have to raise a warning about it
        if uint_to_int_cast:
            ErrorMessage.single_warning(
                ""OmniSci does not support unsigned integer types, such types will be rounded up to the signed equivalent.""
            )

        for i, col in new_cols.items():
            table = table.set_column(i, new_schema[i], col)

        if need_cast:
            try:
                table = table.cast(new_schema)
            except pa.lib.ArrowInvalid as e:
                raise (OverflowError if uint_to_int_cast else RuntimeError)(
                    ""An error occurred when trying to convert unsupported by OmniSci 'dtypes' ""
                    + f""to the supported ones, the schema to cast was: \n{new_schema}.""
                ) from e

        return table
","
        Cast PyArrow table to be fully compatible with OmniSci.

        Parameters
        ----------
        table : pyarrow.Table
            Source table.

        Returns
        -------
        pyarrow.Table
            Table with fully compatible types with OmniSci.
        ",26,295,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cast_to_compatible_types(table):
        
        schema = table.schema
        new_schema = schema
        need_cast = False
        uint_to_int_cast = False
        new_cols = {}
        uint_to_int_map = {
            pa.uint8(): pa.int16(),
            pa.uint16(): pa.int32(),
            pa.uint32(): pa.int64(),
            pa.uint64(): pa.int64(),  # May cause overflow
        }
        for i, field in enumerate(schema):
            # Currently OmniSci doesn't support Arrow table import with
            # dictionary columns. Here we cast dictionaries until support
            # is in place.
            # https://github.com/modin-project/modin/issues/1738
            if pa.types.is_dictionary(field.type):
                # Conversion for dictionary of null type to string is not supported
                # in Arrow. Build new column for this case for now.
                if pa.types.is_null(field.type.value_type):
                    mask = np.full(table.num_rows, True, dtype=bool)
                    new_col_data = np.empty(table.num_rows, dtype=str)
                    new_col = pa.array(new_col_data, pa.string(), mask)
                    new_cols[i] = new_col
                else:
                    need_cast = True
                new_field = pa.field(
                    field.name, pa.string(), field.nullable, field.metadata
                )
                new_schema = new_schema.set(i, new_field)
            # OmniSci doesn't support importing Arrow's date type:
            # https://github.com/omnisci/omniscidb/issues/678
            elif pa.types.is_date(field.type):
                # Arrow's date is the number of days since the UNIX-epoch, so we can convert it
                # to a timestamp[s] (number of seconds since the UNIX-epoch) without losing precision
                new_field = pa.field(
                    field.name, pa.timestamp(""s""), field.nullable, field.metadata
                )
                new_schema = new_schema.set(i, new_field)
                need_cast = True
            # OmniSci doesn't support unsigned types
            elif pa.types.is_unsigned_integer(field.type):
                new_field = pa.field(
                    field.name,
                    uint_to_int_map[field.type],
                    field.nullable,
                    field.metadata,
                )
                new_schema = new_schema.set(i, new_field)
                need_cast = True
                uint_to_int_cast = True

        # Such cast may affect the data, so we have to raise a warning about it
        if uint_to_int_cast:
            ErrorMessage.single_warning(
                ""OmniSci does not support unsigned integer types, such types will be rounded up to the signed equivalent.""
            )

        for i, col in new_cols.items():
            table = table.set_column(i, new_schema[i], col)

        if need_cast:
            try:
                table = table.cast(new_schema)
            except pa.lib.ArrowInvalid as e:
                raise (OverflowError if uint_to_int_cast else RuntimeError)(
                    ""An error occurred when trying to convert unsupported by OmniSci 'dtypes' ""
                    + f""to the supported ones, the schema to cast was: \n{new_schema}.""
                ) from e

        return table

        ```
    ###Assistant : 
        Cast PyArrow table to be fully compatible with OmniSci.

        Parameters
        ----------
        table : pyarrow.Table
            Source table.

        Returns
        -------
        pyarrow.Table
            Table with fully compatible types with OmniSci.
        
    "
79,"def data(self) -> 'DataRequest._DataContent':
        
        return DataRequest._DataContent(self.proto.data)
","Get the data contaned in this data request

        :return: the data content as an instance of _DataContent wrapping docs and groundtruths
        ",21,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def data(self) -> 'DataRequest._DataContent':
        
        return DataRequest._DataContent(self.proto.data)

        ```
    ###Assistant : Get the data contaned in this data request

        :return: the data content as an instance of _DataContent wrapping docs and groundtruths
        
    "
80,"def model_from_json(json_string, custom_objects=None):
    
    from keras.layers import (
        deserialize_from_json,
    )  # pylint: disable=g-import-not-at-top

    return deserialize_from_json(json_string, custom_objects=custom_objects)
","Parses a JSON model configuration string and returns a model instance.

    Usage:

    >>> model = tf.keras.Sequential([
    ...     tf.keras.layers.Dense(5, input_shape=(3,)),
    ...     tf.keras.layers.Softmax()])
    >>> config = model.to_json()
    >>> loaded_model = tf.keras.models.model_from_json(config)

    Args:
        json_string: JSON string encoding a model configuration.
        custom_objects: Optional dictionary mapping names
            (strings) to custom classes or functions to be
            considered during deserialization.

    Returns:
        A Keras model instance (uncompiled).
    ",59,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def model_from_json(json_string, custom_objects=None):
    
    from keras.layers import (
        deserialize_from_json,
    )  # pylint: disable=g-import-not-at-top

    return deserialize_from_json(json_string, custom_objects=custom_objects)

        ```
    ###Assistant : Parses a JSON model configuration string and returns a model instance.

    Usage:

    >>> model = tf.keras.Sequential([
    ...     tf.keras.layers.Dense(5, input_shape=(3,)),
    ...     tf.keras.layers.Softmax()])
    >>> config = model.to_json()
    >>> loaded_model = tf.keras.models.model_from_json(config)

    Args:
        json_string: JSON string encoding a model configuration.
        custom_objects: Optional dictionary mapping names
            (strings) to custom classes or functions to be
            considered during deserialization.

    Returns:
        A Keras model instance (uncompiled).
    
    "
81,"def test_do_nothing_if_converted(self, run_convert_mock):
        

        stdout, _ = self.call_command()
        run_convert_mock.assert_not_called()
        self.assertIn(""Converting all PNG thumbnails to WebP"", stdout)
","
        GIVEN:
            - Document exists with default WebP thumbnail path
        WHEN:
            - Thumbnail conversion is attempted
        THEN:
            - Nothing is converted
        ",20,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_do_nothing_if_converted(self, run_convert_mock):
        

        stdout, _ = self.call_command()
        run_convert_mock.assert_not_called()
        self.assertIn(""Converting all PNG thumbnails to WebP"", stdout)

        ```
    ###Assistant : 
        GIVEN:
            - Document exists with default WebP thumbnail path
        WHEN:
            - Thumbnail conversion is attempted
        THEN:
            - Nothing is converted
        
    "
82,"def __ror__(self, other):
        
        if isinstance(other, str_type):
            other = self._literalStringClass(other)
        if not isinstance(other, ParserElement):
            raise TypeError(
                ""Cannot combine element of type {} with ParserElement"".format(
                    type(other).__name__
                )
            )
        return other | self
","
        Implementation of ``|`` operator when left operand is not a :class:`ParserElement`
        ",11,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __ror__(self, other):
        
        if isinstance(other, str_type):
            other = self._literalStringClass(other)
        if not isinstance(other, ParserElement):
            raise TypeError(
                ""Cannot combine element of type {} with ParserElement"".format(
                    type(other).__name__
                )
            )
        return other | self

        ```
    ###Assistant : 
        Implementation of ``|`` operator when left operand is not a :class:`ParserElement`
        
    "
83,"def shash(value):
        

        length = len(value)

        if length == 0:
            return 0

        x = Hash.ordinal(value[0]) << 7
        for c in value:
            x = (1000003 * x) ^ Hash.ordinal(c)

        x ^= length
        x &= 0xFFFFFFFFFFFFFFFF
        if x == -1:
            x = -2

        # Convert to C long type
        return ctypes.c_long(x).value
","
        Returns a Python 2.7 hash for a string.

        Logic ported from the 2.7 Python branch: cpython/Objects/stringobject.c
        Method: static long string_hash(PyStringObject *a)

        Args:
            value: input string

        Returns:
            Python 2.7 hash
        ",29,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def shash(value):
        

        length = len(value)

        if length == 0:
            return 0

        x = Hash.ordinal(value[0]) << 7
        for c in value:
            x = (1000003 * x) ^ Hash.ordinal(c)

        x ^= length
        x &= 0xFFFFFFFFFFFFFFFF
        if x == -1:
            x = -2

        # Convert to C long type
        return ctypes.c_long(x).value

        ```
    ###Assistant : 
        Returns a Python 2.7 hash for a string.

        Logic ported from the 2.7 Python branch: cpython/Objects/stringobject.c
        Method: static long string_hash(PyStringObject *a)

        Args:
            value: input string

        Returns:
            Python 2.7 hash
        
    "
84,"def data_files_with_one_split_and_metadata(tmp_path, auto_text_file):
    data_dir = tmp_path / ""autofolder_data_dir_with_metadata_one_split""
    data_dir.mkdir(parents=True, exist_ok=True)
    subdir = data_dir / ""subdir""
    subdir.mkdir(parents=True, exist_ok=True)

    filename = data_dir / ""file.txt""
    shutil.copyfile(auto_text_file, filename)
    filename2 = data_dir / ""file2.txt""
    shutil.copyfile(auto_text_file, filename2)
    filename3 = subdir / ""file3.txt""  # in subdir
    shutil.copyfile(auto_text_file, filename3)

    metadata_filename = data_dir / ""metadata.jsonl""
    metadata = textwrap.dedent(
        
    )
    with open(metadata_filename, ""w"", encoding=""utf-8"") as f:
        f.write(metadata)
    data_files_with_one_split_and_metadata = DataFilesDict.from_local_or_remote(
        get_data_patterns_locally(data_dir), data_dir
    )
    assert len(data_files_with_one_split_and_metadata) == 1
    assert len(data_files_with_one_split_and_metadata[""train""]) == 4
    return data_files_with_one_split_and_metadata


@pytest.fixture","\
        {""file_name"": ""file.txt"", ""additional_feature"": ""Dummy file""}
        {""file_name"": ""file2.txt"", ""additional_feature"": ""Second dummy file""}
        {""file_name"": ""subdir/file3.txt"", ""additional_feature"": ""Third dummy file""}
        ",18,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def data_files_with_one_split_and_metadata(tmp_path, auto_text_file):
    data_dir = tmp_path / ""autofolder_data_dir_with_metadata_one_split""
    data_dir.mkdir(parents=True, exist_ok=True)
    subdir = data_dir / ""subdir""
    subdir.mkdir(parents=True, exist_ok=True)

    filename = data_dir / ""file.txt""
    shutil.copyfile(auto_text_file, filename)
    filename2 = data_dir / ""file2.txt""
    shutil.copyfile(auto_text_file, filename2)
    filename3 = subdir / ""file3.txt""  # in subdir
    shutil.copyfile(auto_text_file, filename3)

    metadata_filename = data_dir / ""metadata.jsonl""
    metadata = textwrap.dedent(
        
    )
    with open(metadata_filename, ""w"", encoding=""utf-8"") as f:
        f.write(metadata)
    data_files_with_one_split_and_metadata = DataFilesDict.from_local_or_remote(
        get_data_patterns_locally(data_dir), data_dir
    )
    assert len(data_files_with_one_split_and_metadata) == 1
    assert len(data_files_with_one_split_and_metadata[""train""]) == 4
    return data_files_with_one_split_and_metadata


@pytest.fixture
        ```
    ###Assistant : \
        {""file_name"": ""file.txt"", ""additional_feature"": ""Dummy file""}
        {""file_name"": ""file2.txt"", ""additional_feature"": ""Second dummy file""}
        {""file_name"": ""subdir/file3.txt"", ""additional_feature"": ""Third dummy file""}
        
    "
85,"def test_upload_room_keys_wrong_version(self) -> None:
        
        version = self.get_success(
            self.handler.create_version(
                self.local_user,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""first_version_auth_data"",
                },
            )
        )
        self.assertEqual(version, ""1"")

        version = self.get_success(
            self.handler.create_version(
                self.local_user,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""second_version_auth_data"",
                },
            )
        )
        self.assertEqual(version, ""2"")

        e = self.get_failure(
            self.handler.upload_room_keys(self.local_user, ""1"", room_keys), SynapseError
        )
        res = e.value.code
        self.assertEqual(res, 403)
",Check that we get a 403 on uploading keys for an old version,13,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_upload_room_keys_wrong_version(self) -> None:
        
        version = self.get_success(
            self.handler.create_version(
                self.local_user,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""first_version_auth_data"",
                },
            )
        )
        self.assertEqual(version, ""1"")

        version = self.get_success(
            self.handler.create_version(
                self.local_user,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""second_version_auth_data"",
                },
            )
        )
        self.assertEqual(version, ""2"")

        e = self.get_failure(
            self.handler.upload_room_keys(self.local_user, ""1"", room_keys), SynapseError
        )
        res = e.value.code
        self.assertEqual(res, 403)

        ```
    ###Assistant : Check that we get a 403 on uploading keys for an old version
    "
86,"def previewtrain(self) -> Dict[str, List[Union[Image.Image, ImageTk.PhotoImage, None, float]]]:
        
        return self._previewtrain
"," dict or ``None``: The training preview images. Dictionary key is the image name
        (`str`). Dictionary values are a `list` of the training image (:class:`PIL.Image`), the
        image formatted for tkinter display (:class:`PIL.ImageTK.PhotoImage`), the last
        modification time of the image (`float`).

        The value of this property is ``None`` if training is not running or there are no preview
        images available.
        ",58,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def previewtrain(self) -> Dict[str, List[Union[Image.Image, ImageTk.PhotoImage, None, float]]]:
        
        return self._previewtrain

        ```
    ###Assistant :  dict or ``None``: The training preview images. Dictionary key is the image name
        (`str`). Dictionary values are a `list` of the training image (:class:`PIL.Image`), the
        image formatted for tkinter display (:class:`PIL.ImageTK.PhotoImage`), the last
        modification time of the image (`float`).

        The value of this property is ``None`` if training is not running or there are no preview
        images available.
        
    "
87,"def test_padding(self):
        
        n = coord_net_spec()
        ax, a, b = coord_map_from_to(n.deconv, n.data)
        pad = random.randint(0, 10)
        # conv padding
        n = coord_net_spec(pad=pad)
        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
        self.assertEquals(a, a_pad)
        self.assertEquals(b - pad, b_pad)
        # deconv padding
        n = coord_net_spec(dpad=pad)
        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
        self.assertEquals(a, a_pad)
        self.assertEquals(b + pad, b_pad)
        # pad both to cancel out
        n = coord_net_spec(pad=pad, dpad=pad)
        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
        self.assertEquals(a, a_pad)
        self.assertEquals(b, b_pad)
","
        Padding conv adds offset while padding deconv subtracts offset.
        ",9,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_padding(self):
        
        n = coord_net_spec()
        ax, a, b = coord_map_from_to(n.deconv, n.data)
        pad = random.randint(0, 10)
        # conv padding
        n = coord_net_spec(pad=pad)
        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
        self.assertEquals(a, a_pad)
        self.assertEquals(b - pad, b_pad)
        # deconv padding
        n = coord_net_spec(dpad=pad)
        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
        self.assertEquals(a, a_pad)
        self.assertEquals(b + pad, b_pad)
        # pad both to cancel out
        n = coord_net_spec(pad=pad, dpad=pad)
        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
        self.assertEquals(a, a_pad)
        self.assertEquals(b, b_pad)

        ```
    ###Assistant : 
        Padding conv adds offset while padding deconv subtracts offset.
        
    "
88,"def require_cuda(test_case):
    
    return unittest.skipUnless(torch.cuda.is_available(), ""test requires a GPU"")(test_case)

","
    Decorator marking a test that requires CUDA. These tests are skipped when there are no GPU available.
    ",17,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def require_cuda(test_case):
    
    return unittest.skipUnless(torch.cuda.is_available(), ""test requires a GPU"")(test_case)


        ```
    ###Assistant : 
    Decorator marking a test that requires CUDA. These tests are skipped when there are no GPU available.
    
    "
89,"def _flush_periodically(self) -> None:
        

        while self._active:
            # flush is thread-safe; it acquires and releases the lock internally
            self.flush()
            time.sleep(self._flush_period)
","
        Whilst this handler is active, flush the handler periodically.
        ",9,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _flush_periodically(self) -> None:
        

        while self._active:
            # flush is thread-safe; it acquires and releases the lock internally
            self.flush()
            time.sleep(self._flush_period)

        ```
    ###Assistant : 
        Whilst this handler is active, flush the handler periodically.
        
    "
90,"def save_flagged(self, dir, label, data, encryption_key) -> str | Dict:
        
        if ""confidences"" in data:
            return json.dumps(
                {
                    example[""label""]: example[""confidence""]
                    for example in data[""confidences""]
                }
            )
        else:
            return data[""label""]
","
        Returns:
            Either a string representing the main category label, or a dictionary with category keys mapping to confidence levels.
        ",19,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_flagged(self, dir, label, data, encryption_key) -> str | Dict:
        
        if ""confidences"" in data:
            return json.dumps(
                {
                    example[""label""]: example[""confidence""]
                    for example in data[""confidences""]
                }
            )
        else:
            return data[""label""]

        ```
    ###Assistant : 
        Returns:
            Either a string representing the main category label, or a dictionary with category keys mapping to confidence levels.
        
    "
91,"def test_higher_rank_inputs_for_importance_weights(self):
        
        for fw in framework_iterator(frameworks=(""torch"", ""tf""), session=True):
            vtrace = vtrace_tf if fw != ""torch"" else vtrace_torch
            if fw == ""tf"":
                inputs_ = {
                    ""log_rhos"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, None, 1]
                    ),
                    ""discounts"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, None, 1]
                    ),
                    ""rewards"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, None, 42]
                    ),
                    ""values"": tf1.placeholder(dtype=tf.float32, shape=[None, None, 42]),
                    ""bootstrap_value"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, 42]
                    ),
                }
            else:
                inputs_ = {
                    ""log_rhos"": Box(-1.0, 1.0, (8, 10, 1)).sample(),
                    ""discounts"": Box(-1.0, 1.0, (8, 10, 1)).sample(),
                    ""rewards"": Box(-1.0, 1.0, (8, 10, 42)).sample(),
                    ""values"": Box(-1.0, 1.0, (8, 10, 42)).sample(),
                    ""bootstrap_value"": Box(-1.0, 1.0, (10, 42)).sample(),
                }
            output = vtrace.from_importance_weights(**inputs_)
            check(int(output.vs.shape[-1]), 42)
",Checks support for additional dimensions in inputs.,7,96,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_higher_rank_inputs_for_importance_weights(self):
        
        for fw in framework_iterator(frameworks=(""torch"", ""tf""), session=True):
            vtrace = vtrace_tf if fw != ""torch"" else vtrace_torch
            if fw == ""tf"":
                inputs_ = {
                    ""log_rhos"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, None, 1]
                    ),
                    ""discounts"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, None, 1]
                    ),
                    ""rewards"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, None, 42]
                    ),
                    ""values"": tf1.placeholder(dtype=tf.float32, shape=[None, None, 42]),
                    ""bootstrap_value"": tf1.placeholder(
                        dtype=tf.float32, shape=[None, 42]
                    ),
                }
            else:
                inputs_ = {
                    ""log_rhos"": Box(-1.0, 1.0, (8, 10, 1)).sample(),
                    ""discounts"": Box(-1.0, 1.0, (8, 10, 1)).sample(),
                    ""rewards"": Box(-1.0, 1.0, (8, 10, 42)).sample(),
                    ""values"": Box(-1.0, 1.0, (8, 10, 42)).sample(),
                    ""bootstrap_value"": Box(-1.0, 1.0, (10, 42)).sample(),
                }
            output = vtrace.from_importance_weights(**inputs_)
            check(int(output.vs.shape[-1]), 42)

        ```
    ###Assistant : Checks support for additional dimensions in inputs.
    "
92,"def test_task_fail_duration(app, admin_client, dag_maker, session):
    
    with dag_maker() as dag:
        op1 = BashOperator(task_id='fail', bash_command='exit 1')
        op2 = BashOperator(task_id='success', bash_command='exit 0')

    with pytest.raises(AirflowException):
        op1.run()
    op2.run()

    op1_fails = (
        session.query(TaskFail)
        .filter(
            TaskFail.task_id == 'fail',
            TaskFail.dag_id == dag.dag_id,
        )
        .all()
    )

    op2_fails = (
        session.query(TaskFail)
        .filter(
            TaskFail.task_id == 'success',
            TaskFail.dag_id == dag.dag_id,
        )
        .all()
    )

    assert len(op1_fails) == 1
    assert len(op2_fails) == 0

    with unittest.mock.patch.object(app, 'dag_bag') as mocked_dag_bag:
        mocked_dag_bag.get_dag.return_value = dag
        resp = admin_client.get(f""dags/{dag.dag_id}/duration"", follow_redirects=True)
        html = resp.get_data().decode()
        cumulative_chart = json.loads(re.search(""data_cumlinechart=(.*);"", html).group(1))
        line_chart = json.loads(re.search(""data_linechart=(.*);"", html).group(1))

        assert resp.status_code == 200
        assert sorted(item[""key""] for item in cumulative_chart) == [""fail"", ""success""]
        assert sorted(item[""key""] for item in line_chart) == [""fail"", ""success""]
",Task duration page with a TaskFail entry should render without error.,11,104,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_task_fail_duration(app, admin_client, dag_maker, session):
    
    with dag_maker() as dag:
        op1 = BashOperator(task_id='fail', bash_command='exit 1')
        op2 = BashOperator(task_id='success', bash_command='exit 0')

    with pytest.raises(AirflowException):
        op1.run()
    op2.run()

    op1_fails = (
        session.query(TaskFail)
        .filter(
            TaskFail.task_id == 'fail',
            TaskFail.dag_id == dag.dag_id,
        )
        .all()
    )

    op2_fails = (
        session.query(TaskFail)
        .filter(
            TaskFail.task_id == 'success',
            TaskFail.dag_id == dag.dag_id,
        )
        .all()
    )

    assert len(op1_fails) == 1
    assert len(op2_fails) == 0

    with unittest.mock.patch.object(app, 'dag_bag') as mocked_dag_bag:
        mocked_dag_bag.get_dag.return_value = dag
        resp = admin_client.get(f""dags/{dag.dag_id}/duration"", follow_redirects=True)
        html = resp.get_data().decode()
        cumulative_chart = json.loads(re.search(""data_cumlinechart=(.*);"", html).group(1))
        line_chart = json.loads(re.search(""data_linechart=(.*);"", html).group(1))

        assert resp.status_code == 200
        assert sorted(item[""key""] for item in cumulative_chart) == [""fail"", ""success""]
        assert sorted(item[""key""] for item in line_chart) == [""fail"", ""success""]

        ```
    ###Assistant : Task duration page with a TaskFail entry should render without error.
    "
93,"def test_all_users(self) -> None:
        
        self._create_users(2)

        channel = self.make_request(
            ""GET"",
            self.url + ""?deactivated=true"",
            {},
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(3, len(channel.json_body[""users""]))
        self.assertEqual(3, channel.json_body[""total""])

        # Check that all fields are available
        self._check_fields(channel.json_body[""users""])
","
        List all users, including deactivated users.
        ",6,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_all_users(self) -> None:
        
        self._create_users(2)

        channel = self.make_request(
            ""GET"",
            self.url + ""?deactivated=true"",
            {},
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(3, len(channel.json_body[""users""]))
        self.assertEqual(3, channel.json_body[""total""])

        # Check that all fields are available
        self._check_fields(channel.json_body[""users""])

        ```
    ###Assistant : 
        List all users, including deactivated users.
        
    "
94,"def real_quick_ratio(self):
        

        la, lb = len(self.a), len(self.b)
        # can't have more matches than the number of elements in the
        # shorter sequence
        return _calculate_ratio(min(la, lb), la + lb)

    __class_getitem__ = classmethod(GenericAlias)

","Return an upper bound on ratio() very quickly.

        This isn't defined beyond that it is an upper bound on .ratio(), and
        is faster to compute than either .ratio() or .quick_ratio().
        ",30,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def real_quick_ratio(self):
        

        la, lb = len(self.a), len(self.b)
        # can't have more matches than the number of elements in the
        # shorter sequence
        return _calculate_ratio(min(la, lb), la + lb)

    __class_getitem__ = classmethod(GenericAlias)


        ```
    ###Assistant : Return an upper bound on ratio() very quickly.

        This isn't defined beyond that it is an upper bound on .ratio(), and
        is faster to compute than either .ratio() or .quick_ratio().
        
    "
95,"def test_unpublish_view_invalid_page_id(self):
        
        # Request confirm unpublish page but with illegal page id
        response = self.client.get(
            reverse(
                ""wagtail_bulk_action"",
                args=(
                    ""wagtailcore"",
                    ""page"",
                    ""unpublish"",
                ),
            )
        )

        # Check that the user received a 404 response
        self.assertEqual(response.status_code, 404)
","
        This tests that the unpublish view returns an error if the page id is invalid
        ",15,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unpublish_view_invalid_page_id(self):
        
        # Request confirm unpublish page but with illegal page id
        response = self.client.get(
            reverse(
                ""wagtail_bulk_action"",
                args=(
                    ""wagtailcore"",
                    ""page"",
                    ""unpublish"",
                ),
            )
        )

        # Check that the user received a 404 response
        self.assertEqual(response.status_code, 404)

        ```
    ###Assistant : 
        This tests that the unpublish view returns an error if the page id is invalid
        
    "
96,"def test_warn_report():
    

    fn = report

    with warnings.catch_warnings(record=True) as record:
        # Ignore Deprecation warnings.
        warnings.filterwarnings(""ignore"", category=DeprecationWarning)
        assert not fn(dict())

    assert fn.__name__ in record[0].message.args[0]

    reset_log_once_with_str(fn.__name__)

",Checks if calling session.report function outside of session raises warning.,10,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_warn_report():
    

    fn = report

    with warnings.catch_warnings(record=True) as record:
        # Ignore Deprecation warnings.
        warnings.filterwarnings(""ignore"", category=DeprecationWarning)
        assert not fn(dict())

    assert fn.__name__ in record[0].message.args[0]

    reset_log_once_with_str(fn.__name__)


        ```
    ###Assistant : Checks if calling session.report function outside of session raises warning.
    "
97,"def forward(self, feats, img_metas):
        
        batch_size = len(img_metas)
        mask_features, multi_scale_memorys = self.pixel_decoder(feats)
        # multi_scale_memorys (from low resolution to high resolution)
        decoder_inputs = []
        decoder_positional_encodings = []
        for i in range(self.num_transformer_feat_level):
            decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i])
            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
            decoder_input = decoder_input.flatten(2).permute(2, 0, 1)
            level_embed = self.level_embed.weight[i].view(1, 1, -1)
            decoder_input = decoder_input + level_embed
            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
            mask = decoder_input.new_zeros(
                (batch_size, ) + multi_scale_memorys[i].shape[-2:],
                dtype=torch.bool)
            decoder_positional_encoding = self.decoder_positional_encoding(
                mask)
            decoder_positional_encoding = decoder_positional_encoding.flatten(
                2).permute(2, 0, 1)
            decoder_inputs.append(decoder_input)
            decoder_positional_encodings.append(decoder_positional_encoding)
        # shape (num_queries, c) -> (num_queries, batch_size, c)
        query_feat = self.query_feat.weight.unsqueeze(1).repeat(
            (1, batch_size, 1))
        query_embed = self.query_embed.weight.unsqueeze(1).repeat(
            (1, batch_size, 1))

        cls_pred_list = []
        mask_pred_list = []
        cls_pred, mask_pred, attn_mask = self.forward_head(
            query_feat, mask_features, multi_scale_memorys[0].shape[-2:])
        cls_pred_list.append(cls_pred)
        mask_pred_list.append(mask_pred)

        for i in range(self.num_transformer_decoder_layers):
            level_idx = i % self.num_transformer_feat_level
            # if a mask is all True(all background), then set it all False.
            attn_mask[torch.where(
                attn_mask.sum(-1) == attn_mask.shape[-1])] = False

            # cross_attn + self_attn
            layer = self.transformer_decoder.layers[i]
            attn_masks = [attn_mask, None]
            query_feat = layer(
                query=query_feat,
                key=decoder_inputs[level_idx],
                value=decoder_inputs[level_idx],
                query_pos=query_embed,
                key_pos=decoder_positional_encodings[level_idx],
                attn_masks=attn_masks,
                query_key_padding_mask=None,
                # here we do not apply masking on padded region
                key_padding_mask=None)
            cls_pred, mask_pred, attn_mask = self.forward_head(
                query_feat, mask_features, multi_scale_memorys[
                    (i + 1) % self.num_transformer_feat_level].shape[-2:])

            cls_pred_list.append(cls_pred)
            mask_pred_list.append(mask_pred)

        return cls_pred_list, mask_pred_list
","Forward function.

        Args:
            feats (list[Tensor]): Multi scale Features from the
                upstream network, each is a 4D-tensor.
            img_metas (list[dict]): List of image information.

        Returns:
            tuple: A tuple contains two elements.

            - cls_pred_list (list[Tensor)]: Classification logits \
                for each decoder layer. Each is a 3D-tensor with shape \
                (batch_size, num_queries, cls_out_channels). \
                Note `cls_out_channels` should includes background.
            - mask_pred_list (list[Tensor]): Mask logits for each \
                decoder layer. Each with shape (batch_size, num_queries, \
                 h, w).
        ",73,201,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, feats, img_metas):
        
        batch_size = len(img_metas)
        mask_features, multi_scale_memorys = self.pixel_decoder(feats)
        # multi_scale_memorys (from low resolution to high resolution)
        decoder_inputs = []
        decoder_positional_encodings = []
        for i in range(self.num_transformer_feat_level):
            decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i])
            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
            decoder_input = decoder_input.flatten(2).permute(2, 0, 1)
            level_embed = self.level_embed.weight[i].view(1, 1, -1)
            decoder_input = decoder_input + level_embed
            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
            mask = decoder_input.new_zeros(
                (batch_size, ) + multi_scale_memorys[i].shape[-2:],
                dtype=torch.bool)
            decoder_positional_encoding = self.decoder_positional_encoding(
                mask)
            decoder_positional_encoding = decoder_positional_encoding.flatten(
                2).permute(2, 0, 1)
            decoder_inputs.append(decoder_input)
            decoder_positional_encodings.append(decoder_positional_encoding)
        # shape (num_queries, c) -> (num_queries, batch_size, c)
        query_feat = self.query_feat.weight.unsqueeze(1).repeat(
            (1, batch_size, 1))
        query_embed = self.query_embed.weight.unsqueeze(1).repeat(
            (1, batch_size, 1))

        cls_pred_list = []
        mask_pred_list = []
        cls_pred, mask_pred, attn_mask = self.forward_head(
            query_feat, mask_features, multi_scale_memorys[0].shape[-2:])
        cls_pred_list.append(cls_pred)
        mask_pred_list.append(mask_pred)

        for i in range(self.num_transformer_decoder_layers):
            level_idx = i % self.num_transformer_feat_level
            # if a mask is all True(all background), then set it all False.
            attn_mask[torch.where(
                attn_mask.sum(-1) == attn_mask.shape[-1])] = False

            # cross_attn + self_attn
            layer = self.transformer_decoder.layers[i]
            attn_masks = [attn_mask, None]
            query_feat = layer(
                query=query_feat,
                key=decoder_inputs[level_idx],
                value=decoder_inputs[level_idx],
                query_pos=query_embed,
                key_pos=decoder_positional_encodings[level_idx],
                attn_masks=attn_masks,
                query_key_padding_mask=None,
                # here we do not apply masking on padded region
                key_padding_mask=None)
            cls_pred, mask_pred, attn_mask = self.forward_head(
                query_feat, mask_features, multi_scale_memorys[
                    (i + 1) % self.num_transformer_feat_level].shape[-2:])

            cls_pred_list.append(cls_pred)
            mask_pred_list.append(mask_pred)

        return cls_pred_list, mask_pred_list

        ```
    ###Assistant : Forward function.

        Args:
            feats (list[Tensor]): Multi scale Features from the
                upstream network, each is a 4D-tensor.
            img_metas (list[dict]): List of image information.

        Returns:
            tuple: A tuple contains two elements.

            - cls_pred_list (list[Tensor)]: Classification logits \
                for each decoder layer. Each is a 3D-tensor with shape \
                (batch_size, num_queries, cls_out_channels). \
                Note `cls_out_channels` should includes background.
            - mask_pred_list (list[Tensor]): Mask logits for each \
                decoder layer. Each with shape (batch_size, num_queries, \
                 h, w).
        
    "
98,"def formfield_for_manytomany(self, db_field, request, **kwargs):
        
        # If it uses an intermediary model that isn't auto created, don't show
        # a field in admin.
        if not db_field.remote_field.through._meta.auto_created:
            return None
        db = kwargs.get(""using"")

        if ""widget"" not in kwargs:
            autocomplete_fields = self.get_autocomplete_fields(request)
            if db_field.name in autocomplete_fields:
                kwargs[""widget""] = AutocompleteSelectMultiple(
                    db_field,
                    self.admin_site,
                    using=db,
                )
            elif db_field.name in self.raw_id_fields:
                kwargs[""widget""] = widgets.ManyToManyRawIdWidget(
                    db_field.remote_field,
                    self.admin_site,
                    using=db,
                )
            elif db_field.name in [*self.filter_vertical, *self.filter_horizontal]:
                kwargs[""widget""] = widgets.FilteredSelectMultiple(
                    db_field.verbose_name, db_field.name in self.filter_vertical
                )
        if ""queryset"" not in kwargs:
            queryset = self.get_field_queryset(db, db_field, request)
            if queryset is not None:
                kwargs[""queryset""] = queryset

        form_field = db_field.formfield(**kwargs)
        if isinstance(form_field.widget, SelectMultiple) and not isinstance(
            form_field.widget, (CheckboxSelectMultiple, AutocompleteSelectMultiple)
        ):
            msg = _(
                ""Hold down “Control”, or “Command” on a Mac, to select more than one.""
            )
            help_text = form_field.help_text
            form_field.help_text = (
                format_lazy(""{} {}"", help_text, msg) if help_text else msg
            )
        return form_field
","
        Get a form Field for a ManyToManyField.
        ",7,139,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def formfield_for_manytomany(self, db_field, request, **kwargs):
        
        # If it uses an intermediary model that isn't auto created, don't show
        # a field in admin.
        if not db_field.remote_field.through._meta.auto_created:
            return None
        db = kwargs.get(""using"")

        if ""widget"" not in kwargs:
            autocomplete_fields = self.get_autocomplete_fields(request)
            if db_field.name in autocomplete_fields:
                kwargs[""widget""] = AutocompleteSelectMultiple(
                    db_field,
                    self.admin_site,
                    using=db,
                )
            elif db_field.name in self.raw_id_fields:
                kwargs[""widget""] = widgets.ManyToManyRawIdWidget(
                    db_field.remote_field,
                    self.admin_site,
                    using=db,
                )
            elif db_field.name in [*self.filter_vertical, *self.filter_horizontal]:
                kwargs[""widget""] = widgets.FilteredSelectMultiple(
                    db_field.verbose_name, db_field.name in self.filter_vertical
                )
        if ""queryset"" not in kwargs:
            queryset = self.get_field_queryset(db, db_field, request)
            if queryset is not None:
                kwargs[""queryset""] = queryset

        form_field = db_field.formfield(**kwargs)
        if isinstance(form_field.widget, SelectMultiple) and not isinstance(
            form_field.widget, (CheckboxSelectMultiple, AutocompleteSelectMultiple)
        ):
            msg = _(
                ""Hold down “Control”, or “Command” on a Mac, to select more than one.""
            )
            help_text = form_field.help_text
            form_field.help_text = (
                format_lazy(""{} {}"", help_text, msg) if help_text else msg
            )
        return form_field

        ```
    ###Assistant : 
        Get a form Field for a ManyToManyField.
        
    "
99,"def test_expiry_logic(self) -> None:
        
        self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
            ""1""
        ] = 100000
        self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
            ""2""
        ] = 200000
        self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
            ""3""
        ] = 300000

        self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
        # All entries within time frame
        self.assertEqual(
            len(
                self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
            ),
            3,
        )
        # Oldest room to expire
        self.pump(1.01)
        self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
        self.assertEqual(
            len(
                self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
            ),
            2,
        )
        # All rooms to expire
        self.pump(2)
        self.assertEqual(
            len(
                self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
            ),
            0,
        )
","Simple test to ensure that _expire_rooms_to_exclude_from_dummy_event_insertion()
        expires old entries correctly.
        ",10,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_expiry_logic(self) -> None:
        
        self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
            ""1""
        ] = 100000
        self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
            ""2""
        ] = 200000
        self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
            ""3""
        ] = 300000

        self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
        # All entries within time frame
        self.assertEqual(
            len(
                self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
            ),
            3,
        )
        # Oldest room to expire
        self.pump(1.01)
        self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
        self.assertEqual(
            len(
                self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
            ),
            2,
        )
        # All rooms to expire
        self.pump(2)
        self.assertEqual(
            len(
                self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
            ),
            0,
        )

        ```
    ###Assistant : Simple test to ensure that _expire_rooms_to_exclude_from_dummy_event_insertion()
        expires old entries correctly.
        
    "
100,"def _tune_legacy_checkpoint_score_attr(self) -> Optional[str]:
        
        if self.checkpoint_score_attribute is None:
            return self.checkpoint_score_attribute
        prefix = """"
        if self.checkpoint_score_order == MIN:
            prefix = ""min-""
        return f""{prefix}{self.checkpoint_score_attribute}""


# Alias for backwards compatibility

deprecation_message = (
    ""`CheckpointStrategy` is deprecated and will be removed in ""
    ""the future. Please use `ray.air.config.CheckpointStrategy` ""
    ""instead.""
)


@Deprecated(message=deprecation_message)
@dataclass","Same as ``checkpoint_score_attr`` in ``tune.run``.

        Only used for Legacy API compatibility.
        ",11,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _tune_legacy_checkpoint_score_attr(self) -> Optional[str]:
        
        if self.checkpoint_score_attribute is None:
            return self.checkpoint_score_attribute
        prefix = """"
        if self.checkpoint_score_order == MIN:
            prefix = ""min-""
        return f""{prefix}{self.checkpoint_score_attribute}""


# Alias for backwards compatibility

deprecation_message = (
    ""`CheckpointStrategy` is deprecated and will be removed in ""
    ""the future. Please use `ray.air.config.CheckpointStrategy` ""
    ""instead.""
)


@Deprecated(message=deprecation_message)
@dataclass
        ```
    ###Assistant : Same as ``checkpoint_score_attr`` in ``tune.run``.

        Only used for Legacy API compatibility.
        
    "
101,"def test_dynamic_sampling_bias_activation(self):
        

        project = self.project  # force creation
        project.update_option(
            ""sentry:dynamic_sampling_biases"",
            [
                {""id"": ""boostEnvironments"", ""active"": False},
            ],
        )
        self.login_as(self.user)

        token = ApiToken.objects.create(user=self.user, scope_list=[""project:write""])
        authorization = f""Bearer {token.token}""

        url = reverse(
            ""sentry-api-0-project-details"",
            kwargs={
                ""organization_slug"": self.project.organization.slug,
                ""project_slug"": self.project.slug,
            },
        )

        with Feature({self.new_ds_flag: True}):
            self.client.put(
                url,
                format=""json"",
                HTTP_AUTHORIZATION=authorization,
                data={
                    ""dynamicSamplingBiases"": [
                        {""id"": ""boostEnvironments"", ""active"": True},
                    ]
                },
            )

            assert AuditLogEntry.objects.filter(
                organization=self.project.organization,
                event=audit_log.get_event_id(""SAMPLING_BIAS_ENABLED""),
            ).exists()
","
        Tests that when sending a request to enable a dynamic sampling bias,
        the bias will be successfully enabled and the audit log 'SAMPLING_BIAS_ENABLED' will be triggered
        ",26,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dynamic_sampling_bias_activation(self):
        

        project = self.project  # force creation
        project.update_option(
            ""sentry:dynamic_sampling_biases"",
            [
                {""id"": ""boostEnvironments"", ""active"": False},
            ],
        )
        self.login_as(self.user)

        token = ApiToken.objects.create(user=self.user, scope_list=[""project:write""])
        authorization = f""Bearer {token.token}""

        url = reverse(
            ""sentry-api-0-project-details"",
            kwargs={
                ""organization_slug"": self.project.organization.slug,
                ""project_slug"": self.project.slug,
            },
        )

        with Feature({self.new_ds_flag: True}):
            self.client.put(
                url,
                format=""json"",
                HTTP_AUTHORIZATION=authorization,
                data={
                    ""dynamicSamplingBiases"": [
                        {""id"": ""boostEnvironments"", ""active"": True},
                    ]
                },
            )

            assert AuditLogEntry.objects.filter(
                organization=self.project.organization,
                event=audit_log.get_event_id(""SAMPLING_BIAS_ENABLED""),
            ).exists()

        ```
    ###Assistant : 
        Tests that when sending a request to enable a dynamic sampling bias,
        the bias will be successfully enabled and the audit log 'SAMPLING_BIAS_ENABLED' will be triggered
        
    "
102,"def call(self, inputs, *args, **kwargs):
        
        input_shape = K.int_shape(inputs)
        if len(input_shape) != 4:
            raise ValueError('Inputs should have rank ' +
                             str(4) +
                             '; Received input shape:', str(input_shape))

        if self.data_format == 'channels_first':
            batch_size, channels, height, width = input_shape
            if batch_size is None:
                batch_size = -1
            r_height, r_width = self.size
            o_height, o_width = height * r_height, width * r_width
            o_channels = channels // (r_height * r_width)

            out = K.reshape(inputs, (batch_size, r_height, r_width, o_channels, height, width))
            out = K.permute_dimensions(out, (0, 3, 4, 1, 5, 2))
            out = K.reshape(out, (batch_size, o_channels, o_height, o_width))
        elif self.data_format == 'channels_last':
            batch_size, height, width, channels = input_shape
            if batch_size is None:
                batch_size = -1
            r_height, r_width = self.size
            o_height, o_width = height * r_height, width * r_width
            o_channels = channels // (r_height * r_width)

            out = K.reshape(inputs, (batch_size, height, width, r_height, r_width, o_channels))
            out = K.permute_dimensions(out, (0, 1, 3, 2, 4, 5))
            out = K.reshape(out, (batch_size, o_height, o_width, o_channels))
        return out
","This is where the layer's logic lives.

        Parameters
        ----------
        inputs: tensor
            Input tensor, or list/tuple of input tensors
        args: tuple
            Additional standard keras Layer arguments
        kwargs: dict
            Additional standard keras Layer keyword arguments

        Returns
        -------
        tensor
            A tensor or list/tuple of tensors
        ",42,152,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call(self, inputs, *args, **kwargs):
        
        input_shape = K.int_shape(inputs)
        if len(input_shape) != 4:
            raise ValueError('Inputs should have rank ' +
                             str(4) +
                             '; Received input shape:', str(input_shape))

        if self.data_format == 'channels_first':
            batch_size, channels, height, width = input_shape
            if batch_size is None:
                batch_size = -1
            r_height, r_width = self.size
            o_height, o_width = height * r_height, width * r_width
            o_channels = channels // (r_height * r_width)

            out = K.reshape(inputs, (batch_size, r_height, r_width, o_channels, height, width))
            out = K.permute_dimensions(out, (0, 3, 4, 1, 5, 2))
            out = K.reshape(out, (batch_size, o_channels, o_height, o_width))
        elif self.data_format == 'channels_last':
            batch_size, height, width, channels = input_shape
            if batch_size is None:
                batch_size = -1
            r_height, r_width = self.size
            o_height, o_width = height * r_height, width * r_width
            o_channels = channels // (r_height * r_width)

            out = K.reshape(inputs, (batch_size, height, width, r_height, r_width, o_channels))
            out = K.permute_dimensions(out, (0, 1, 3, 2, 4, 5))
            out = K.reshape(out, (batch_size, o_height, o_width, o_channels))
        return out

        ```
    ###Assistant : This is where the layer's logic lives.

        Parameters
        ----------
        inputs: tensor
            Input tensor, or list/tuple of input tensors
        args: tuple
            Additional standard keras Layer arguments
        kwargs: dict
            Additional standard keras Layer keyword arguments

        Returns
        -------
        tensor
            A tensor or list/tuple of tensors
        
    "
103,"def to_native_types(self, slicer=None, **kwargs) -> np.ndarray:
        
        warnings.warn(
            ""The 'to_native_types' method is deprecated and will be removed in ""
            ""a future version. Use 'astype(str)' instead."",
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        values = self
        if slicer is not None:
            values = values[slicer]
        return values._format_native_types(**kwargs)
","
        Format specified values of `self` and return them.

        .. deprecated:: 1.2.0

        Parameters
        ----------
        slicer : int, array-like
            An indexer into `self` that specifies which values
            are used in the formatting process.
        kwargs : dict
            Options for specifying how the values should be formatted.
            These options include the following:

            1) na_rep : str
                The value that serves as a placeholder for NULL values
            2) quoting : bool or None
                Whether or not there are quoted values in `self`
            3) date_format : str
                The format used to represent date-like values.

        Returns
        -------
        numpy.ndarray
            Formatted values.
        ",93,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_native_types(self, slicer=None, **kwargs) -> np.ndarray:
        
        warnings.warn(
            ""The 'to_native_types' method is deprecated and will be removed in ""
            ""a future version. Use 'astype(str)' instead."",
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        values = self
        if slicer is not None:
            values = values[slicer]
        return values._format_native_types(**kwargs)

        ```
    ###Assistant : 
        Format specified values of `self` and return them.

        .. deprecated:: 1.2.0

        Parameters
        ----------
        slicer : int, array-like
            An indexer into `self` that specifies which values
            are used in the formatting process.
        kwargs : dict
            Options for specifying how the values should be formatted.
            These options include the following:

            1) na_rep : str
                The value that serves as a placeholder for NULL values
            2) quoting : bool or None
                Whether or not there are quoted values in `self`
            3) date_format : str
                The format used to represent date-like values.

        Returns
        -------
        numpy.ndarray
            Formatted values.
        
    "
104,"def periphery(G, e=None, usebounds=False, weight=None):
    
    if usebounds is True and e is None and not G.is_directed():
        return _extrema_bounding(G, compute=""periphery"", weight=weight)
    if e is None:
        e = eccentricity(G, weight=weight)
    diameter = max(e.values())
    p = [v for v in e if e[v] == diameter]
    return p

","Returns the periphery of the graph G.

    The periphery is the set of nodes with eccentricity equal to the diameter.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    e : eccentricity dictionary, optional
      A precomputed dictionary of eccentricities.

    weight : string, function, or None
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number.

        If this is None, every edge has weight/distance/cost 1.

        Weights stored as floating point values can lead to small round-off
        errors in distances. Use integer weights to avoid this.

        Weights should be positive, since they are distances.

    Returns
    -------
    p : list
       List of nodes in periphery

    Examples
    --------
    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
    >>> nx.periphery(G)
    [2, 5]

    See Also
    --------
    barycenter
    center
    ",212,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def periphery(G, e=None, usebounds=False, weight=None):
    
    if usebounds is True and e is None and not G.is_directed():
        return _extrema_bounding(G, compute=""periphery"", weight=weight)
    if e is None:
        e = eccentricity(G, weight=weight)
    diameter = max(e.values())
    p = [v for v in e if e[v] == diameter]
    return p


        ```
    ###Assistant : Returns the periphery of the graph G.

    The periphery is the set of nodes with eccentricity equal to the diameter.

    Parameters
    ----------
    G : NetworkX graph
       A graph

    e : eccentricity dictionary, optional
      A precomputed dictionary of eccentricities.

    weight : string, function, or None
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number.

        If this is None, every edge has weight/distance/cost 1.

        Weights stored as floating point values can lead to small round-off
        errors in distances. Use integer weights to avoid this.

        Weights should be positive, since they are distances.

    Returns
    -------
    p : list
       List of nodes in periphery

    Examples
    --------
    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
    >>> nx.periphery(G)
    [2, 5]

    See Also
    --------
    barycenter
    center
    
    "
105,"def _current_mode_setpoint_enums(self) -> list[ThermostatSetpointType | None]:
        
        if self._current_mode is None:
            # Thermostat(valve) with no support for setting a mode is considered heating-only
            return [ThermostatSetpointType.HEATING]
        return THERMOSTAT_MODE_SETPOINT_MAP.get(int(self._current_mode.value), [])  # type: ignore[no-any-return]
",Return the list of enums that are relevant to the current thermostat mode.,13,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _current_mode_setpoint_enums(self) -> list[ThermostatSetpointType | None]:
        
        if self._current_mode is None:
            # Thermostat(valve) with no support for setting a mode is considered heating-only
            return [ThermostatSetpointType.HEATING]
        return THERMOSTAT_MODE_SETPOINT_MAP.get(int(self._current_mode.value), [])  # type: ignore[no-any-return]

        ```
    ###Assistant : Return the list of enums that are relevant to the current thermostat mode.
    "
106,"def line(loc, strg):
    
    lastCR = strg.rfind(""\n"", 0, loc)
    nextCR = strg.find(""\n"", loc)
    if nextCR >= 0:
        return strg[lastCR + 1:nextCR]
    else:
        return strg[lastCR + 1:]
","Returns the line of text containing loc within a string, counting newlines as line separators.
       ",15,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def line(loc, strg):
    
    lastCR = strg.rfind(""\n"", 0, loc)
    nextCR = strg.find(""\n"", loc)
    if nextCR >= 0:
        return strg[lastCR + 1:nextCR]
    else:
        return strg[lastCR + 1:]

        ```
    ###Assistant : Returns the line of text containing loc within a string, counting newlines as line separators.
       
    "
107,"def _parse_configs(self, config_files):
        
        formatted = """"
        for cfile in config_files:
            fname = os.path.basename(cfile)
            ext = os.path.splitext(cfile)[1]
            formatted += f""\n--------- {fname} ---------\n""
            if ext == "".ini"":
                formatted += self._parse_ini(cfile)
            elif fname == "".faceswap"":
                formatted += self._parse_json(cfile)
        return formatted
"," Parse the given list of config files into a human readable format.

        Parameters
        ----------
        config_files: list
            A list of paths to the faceswap config files

        Returns
        -------
        str
            The current configuration in the config files formatted in a human readable format
        ",41,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_configs(self, config_files):
        
        formatted = """"
        for cfile in config_files:
            fname = os.path.basename(cfile)
            ext = os.path.splitext(cfile)[1]
            formatted += f""\n--------- {fname} ---------\n""
            if ext == "".ini"":
                formatted += self._parse_ini(cfile)
            elif fname == "".faceswap"":
                formatted += self._parse_json(cfile)
        return formatted

        ```
    ###Assistant :  Parse the given list of config files into a human readable format.

        Parameters
        ----------
        config_files: list
            A list of paths to the faceswap config files

        Returns
        -------
        str
            The current configuration in the config files formatted in a human readable format
        
    "
108,"def parent(self) -> DOMNode:
        
        if self._parent is None:
            raise NoParent(f""{self} has no parent"")
        assert isinstance(self._parent, DOMNode)
        return self._parent
","Get the parent node.

        Raises:
            NoParent: If this is the root node.

        Returns:
            DOMNode: The node which is the direct parent of this node.
        ",24,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parent(self) -> DOMNode:
        
        if self._parent is None:
            raise NoParent(f""{self} has no parent"")
        assert isinstance(self._parent, DOMNode)
        return self._parent

        ```
    ###Assistant : Get the parent node.

        Raises:
            NoParent: If this is the root node.

        Returns:
            DOMNode: The node which is the direct parent of this node.
        
    "
109,"def user_documents_dir(self) -> str:
        
        documents_dir = _get_user_dirs_folder(""XDG_DOCUMENTS_DIR"")
        if documents_dir is None:
            documents_dir = os.environ.get(""XDG_DOCUMENTS_DIR"", """").strip()
            if not documents_dir:
                documents_dir = os.path.expanduser(""~/Documents"")

        return documents_dir
","
        :return: documents directory tied to the user, e.g. ``~/Documents``
        ",9,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def user_documents_dir(self) -> str:
        
        documents_dir = _get_user_dirs_folder(""XDG_DOCUMENTS_DIR"")
        if documents_dir is None:
            documents_dir = os.environ.get(""XDG_DOCUMENTS_DIR"", """").strip()
            if not documents_dir:
                documents_dir = os.path.expanduser(""~/Documents"")

        return documents_dir

        ```
    ###Assistant : 
        :return: documents directory tied to the user, e.g. ``~/Documents``
        
    "
110,"def test_iforest_sparse(global_random_seed):
    
    rng = check_random_state(global_random_seed)
    X_train, X_test = train_test_split(diabetes.data[:50], random_state=rng)
    grid = ParameterGrid({""max_samples"": [0.5, 1.0], ""bootstrap"": [True, False]})

    for sparse_format in [csc_matrix, csr_matrix]:
        X_train_sparse = sparse_format(X_train)
        X_test_sparse = sparse_format(X_test)

        for params in grid:
            # Trained on sparse format
            sparse_classifier = IsolationForest(
                n_estimators=10, random_state=global_random_seed, **params
            ).fit(X_train_sparse)
            sparse_results = sparse_classifier.predict(X_test_sparse)

            # Trained on dense format
            dense_classifier = IsolationForest(
                n_estimators=10, random_state=global_random_seed, **params
            ).fit(X_train)
            dense_results = dense_classifier.predict(X_test)

            assert_array_equal(sparse_results, dense_results)

",Check IForest for various parameter settings on sparse input.,9,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_iforest_sparse(global_random_seed):
    
    rng = check_random_state(global_random_seed)
    X_train, X_test = train_test_split(diabetes.data[:50], random_state=rng)
    grid = ParameterGrid({""max_samples"": [0.5, 1.0], ""bootstrap"": [True, False]})

    for sparse_format in [csc_matrix, csr_matrix]:
        X_train_sparse = sparse_format(X_train)
        X_test_sparse = sparse_format(X_test)

        for params in grid:
            # Trained on sparse format
            sparse_classifier = IsolationForest(
                n_estimators=10, random_state=global_random_seed, **params
            ).fit(X_train_sparse)
            sparse_results = sparse_classifier.predict(X_test_sparse)

            # Trained on dense format
            dense_classifier = IsolationForest(
                n_estimators=10, random_state=global_random_seed, **params
            ).fit(X_train)
            dense_results = dense_classifier.predict(X_test)

            assert_array_equal(sparse_results, dense_results)


        ```
    ###Assistant : Check IForest for various parameter settings on sparse input.
    "
111,"def get_scorer_names():
    
    return sorted(_SCORERS.keys())


for name, metric in [
    (""precision"", precision_score),
    (""recall"", recall_score),
    (""f1"", f1_score),
    (""jaccard"", jaccard_score),
]:
    _SCORERS[name] = make_scorer(metric, average=""binary"")
    for average in [""macro"", ""micro"", ""samples"", ""weighted""]:
        qualified_name = ""{0}_{1}"".format(name, average)
        _SCORERS[qualified_name] = make_scorer(metric, pos_label=None, average=average)

SCORERS = _DeprecatedScorers(_SCORERS)
","Get the names of all available scorers.

    These names can be passed to :func:`~sklearn.metrics.get_scorer` to
    retrieve the scorer object.

    Returns
    -------
    list of str
        Names of all available scorers.
    ",29,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_scorer_names():
    
    return sorted(_SCORERS.keys())


for name, metric in [
    (""precision"", precision_score),
    (""recall"", recall_score),
    (""f1"", f1_score),
    (""jaccard"", jaccard_score),
]:
    _SCORERS[name] = make_scorer(metric, average=""binary"")
    for average in [""macro"", ""micro"", ""samples"", ""weighted""]:
        qualified_name = ""{0}_{1}"".format(name, average)
        _SCORERS[qualified_name] = make_scorer(metric, pos_label=None, average=average)

SCORERS = _DeprecatedScorers(_SCORERS)

        ```
    ###Assistant : Get the names of all available scorers.

    These names can be passed to :func:`~sklearn.metrics.get_scorer` to
    retrieve the scorer object.

    Returns
    -------
    list of str
        Names of all available scorers.
    
    "
112,"def _split_generators(self, dl_manager):
        

        # Download extract and return path of data file.
        dl_dir = dl_manager.download_and_extract(_URL)
        # Use swda/ folder.
        data_dir = os.path.join(dl_dir, ""swda"")
        # Handle partitions files: download extract and return paths of split files.
        downloaded_files = dl_manager.download(self._URLS)

        return [
            # Return whole data path and train splits file downloaded path.
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={""data_dir"": data_dir, ""split_file"": downloaded_files[""train""]}
            ),
            # Return whole data path and dev splits file downloaded path.
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={""data_dir"": data_dir, ""split_file"": downloaded_files[""dev""]},
            ),
            # Return whole data path and train splits file downloaded path.
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={""data_dir"": data_dir, ""split_file"": downloaded_files[""test""]}
            ),
        ]
","
        Returns SplitGenerators.
        This method is tasked with downloading/extracting the data and defining the splits.

         Args:
            dl_manager (:obj:`datasets.utils.download_manager.DownloadManager`):
                Download manager to download and extract data files from urls.

        Returns:
            :obj:`list[str]`:
                List of paths to data.
        ",34,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _split_generators(self, dl_manager):
        

        # Download extract and return path of data file.
        dl_dir = dl_manager.download_and_extract(_URL)
        # Use swda/ folder.
        data_dir = os.path.join(dl_dir, ""swda"")
        # Handle partitions files: download extract and return paths of split files.
        downloaded_files = dl_manager.download(self._URLS)

        return [
            # Return whole data path and train splits file downloaded path.
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={""data_dir"": data_dir, ""split_file"": downloaded_files[""train""]}
            ),
            # Return whole data path and dev splits file downloaded path.
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={""data_dir"": data_dir, ""split_file"": downloaded_files[""dev""]},
            ),
            # Return whole data path and train splits file downloaded path.
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={""data_dir"": data_dir, ""split_file"": downloaded_files[""test""]}
            ),
        ]

        ```
    ###Assistant : 
        Returns SplitGenerators.
        This method is tasked with downloading/extracting the data and defining the splits.

         Args:
            dl_manager (:obj:`datasets.utils.download_manager.DownloadManager`):
                Download manager to download and extract data files from urls.

        Returns:
            :obj:`list[str]`:
                List of paths to data.
        
    "
113,"def print_help(self):
        
        help_text = 
        console.print(text=help_text, menu=""Stocks - Discovery"")
","Print help[cmds]
[src][Geek of Wall St][/src]
    rtearn         realtime earnings from and expected moves
[src][Finnhub][/src]
    pipo           past IPOs dates
    fipo           future IPOs dates
[src][Yahoo Finance][/src]
    gainers        show latest top gainers
    losers         show latest top losers
    ugs            undervalued stocks with revenue and earnings growth in excess of 25%
    gtech          tech stocks with revenue and earnings growth more than 25%
    active         most active stocks by intraday trade volume
    ulc            potentially undervalued large cap stocks
    asc            small cap stocks with earnings growth rates better than 25%
[src][Fidelity][/src]
    ford           orders by Fidelity Customers
[src][Cathiesark.com][/src]
    arkord         orders by ARK Investment Management LLC
[src][Seeking Alpha][/src]
    upcoming       upcoming earnings release dates
    trending       trending news
    cnews          customized news (buybacks, ipos, spacs, healthcare, politics)
[src][Shortinterest.com][/src]
    lowfloat       low float stocks under 10M shares float
[src][Pennystockflow.com][/src]
    hotpenny       today's hot penny stocks
[src][NASDAQ Data Link (Formerly Quandl)][/src]
    rtat           top 10 retail traded stocks per day[/cmds]
",142,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        
        help_text = 
        console.print(text=help_text, menu=""Stocks - Discovery"")

        ```
    ###Assistant : Print help[cmds]
[src][Geek of Wall St][/src]
    rtearn         realtime earnings from and expected moves
[src][Finnhub][/src]
    pipo           past IPOs dates
    fipo           future IPOs dates
[src][Yahoo Finance][/src]
    gainers        show latest top gainers
    losers         show latest top losers
    ugs            undervalued stocks with revenue and earnings growth in excess of 25%
    gtech          tech stocks with revenue and earnings growth more than 25%
    active         most active stocks by intraday trade volume
    ulc            potentially undervalued large cap stocks
    asc            small cap stocks with earnings growth rates better than 25%
[src][Fidelity][/src]
    ford           orders by Fidelity Customers
[src][Cathiesark.com][/src]
    arkord         orders by ARK Investment Management LLC
[src][Seeking Alpha][/src]
    upcoming       upcoming earnings release dates
    trending       trending news
    cnews          customized news (buybacks, ipos, spacs, healthcare, politics)
[src][Shortinterest.com][/src]
    lowfloat       low float stocks under 10M shares float
[src][Pennystockflow.com][/src]
    hotpenny       today's hot penny stocks
[src][NASDAQ Data Link (Formerly Quandl)][/src]
    rtat           top 10 retail traded stocks per day[/cmds]

    "
114,"def handle_fk_field(self, obj, field):
        
        self._start_relational_field(field)
        related_att = getattr(obj, field.get_attname())
        if related_att is not None:
            if self.use_natural_foreign_keys and hasattr(
                field.remote_field.model, ""natural_key""
            ):
                related = getattr(obj, field.name)
                # If related object has a natural key, use it
                related = related.natural_key()
                # Iterable natural keys are rolled out as subelements
                for key_value in related:
                    self.xml.startElement(""natural"", {})
                    self.xml.characters(str(key_value))
                    self.xml.endElement(""natural"")
            else:
                self.xml.characters(str(related_att))
        else:
            self.xml.addQuickElement(""None"")
        self.xml.endElement(""field"")
","
        Handle a ForeignKey (they need to be treated slightly
        differently from regular fields).
        ",13,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def handle_fk_field(self, obj, field):
        
        self._start_relational_field(field)
        related_att = getattr(obj, field.get_attname())
        if related_att is not None:
            if self.use_natural_foreign_keys and hasattr(
                field.remote_field.model, ""natural_key""
            ):
                related = getattr(obj, field.name)
                # If related object has a natural key, use it
                related = related.natural_key()
                # Iterable natural keys are rolled out as subelements
                for key_value in related:
                    self.xml.startElement(""natural"", {})
                    self.xml.characters(str(key_value))
                    self.xml.endElement(""natural"")
            else:
                self.xml.characters(str(related_att))
        else:
            self.xml.addQuickElement(""None"")
        self.xml.endElement(""field"")

        ```
    ###Assistant : 
        Handle a ForeignKey (they need to be treated slightly
        differently from regular fields).
        
    "
115,"def test_metrics_folder():
    
    with _ray_start(include_dashboard=True) as context:
        session_dir = context[""session_dir""]
        assert os.path.exists(
            f""{session_dir}/metrics/grafana/provisioning/dashboards/default.yml""
        )
        assert os.path.exists(
            f""{session_dir}/metrics/grafana/provisioning/dashboards""
            ""/default_grafana_dashboard.json""
        )
        assert os.path.exists(
            f""{session_dir}/metrics/grafana/provisioning/datasources/default.yml""
        )
        assert os.path.exists(f""{session_dir}/metrics/prometheus/prometheus.yml"")

","
    Tests that the default dashboard files get created.
    ",8,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_metrics_folder():
    
    with _ray_start(include_dashboard=True) as context:
        session_dir = context[""session_dir""]
        assert os.path.exists(
            f""{session_dir}/metrics/grafana/provisioning/dashboards/default.yml""
        )
        assert os.path.exists(
            f""{session_dir}/metrics/grafana/provisioning/dashboards""
            ""/default_grafana_dashboard.json""
        )
        assert os.path.exists(
            f""{session_dir}/metrics/grafana/provisioning/datasources/default.yml""
        )
        assert os.path.exists(f""{session_dir}/metrics/prometheus/prometheus.yml"")


        ```
    ###Assistant : 
    Tests that the default dashboard files get created.
    
    "
116,"def state_dict(self):
        
        state_dict = {}
        state_dict['dynamic_loss_scale'] = self.dynamic_loss_scale
        state_dict['cur_scale'] = self.cur_scale
        state_dict['cur_iter'] = self.cur_iter
        if state_dict['dynamic_loss_scale']:
            state_dict['last_overflow_iter'] = self.last_overflow_iter
            state_dict['scale_factor'] = self.scale_factor
            state_dict['scale_window'] = self.scale_window
        state_dict[OPTIMIZER_STATE_DICT] = self.optimizer.state_dict()
        state_dict['fp32_groups_flat'] = self.fp32_groups_flat
        state_dict['clip_grad'] = self.clip_grad
        return state_dict

    # Refresh fp32 master params from fp16 copies","
        Returns a dict containing the current state of this :class:`FP16_Optimizer` instance.
        This dict contains attributes of :class:`FP16_Optimizer`, as well as the state_dict
        of the contained Pytorch optimizer.
        Example::
            checkpoint = {}
            checkpoint['model'] = model.state_dict()
            checkpoint['optimizer'] = optimizer.state_dict()
            torch.save(checkpoint, ""saved.pth"")
        ",39,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def state_dict(self):
        
        state_dict = {}
        state_dict['dynamic_loss_scale'] = self.dynamic_loss_scale
        state_dict['cur_scale'] = self.cur_scale
        state_dict['cur_iter'] = self.cur_iter
        if state_dict['dynamic_loss_scale']:
            state_dict['last_overflow_iter'] = self.last_overflow_iter
            state_dict['scale_factor'] = self.scale_factor
            state_dict['scale_window'] = self.scale_window
        state_dict[OPTIMIZER_STATE_DICT] = self.optimizer.state_dict()
        state_dict['fp32_groups_flat'] = self.fp32_groups_flat
        state_dict['clip_grad'] = self.clip_grad
        return state_dict

    # Refresh fp32 master params from fp16 copies
        ```
    ###Assistant : 
        Returns a dict containing the current state of this :class:`FP16_Optimizer` instance.
        This dict contains attributes of :class:`FP16_Optimizer`, as well as the state_dict
        of the contained Pytorch optimizer.
        Example::
            checkpoint = {}
            checkpoint['model'] = model.state_dict()
            checkpoint['optimizer'] = optimizer.state_dict()
            torch.save(checkpoint, ""saved.pth"")
        
    "
117,"def _update_mean_variance(n_past, mu, var, X, sample_weight=None):
        
        if X.shape[0] == 0:
            return mu, var

        # Compute (potentially weighted) mean and variance of new datapoints
        if sample_weight is not None:
            n_new = float(sample_weight.sum())
            if np.isclose(n_new, 0.0):
                return mu, var
            new_mu = np.average(X, axis=0, weights=sample_weight)
            new_var = np.average((X - new_mu) ** 2, axis=0, weights=sample_weight)
        else:
            n_new = X.shape[0]
            new_var = np.var(X, axis=0)
            new_mu = np.mean(X, axis=0)

        if n_past == 0:
            return new_mu, new_var

        n_total = float(n_past + n_new)

        # Combine mean of old and new data, taking into consideration
        # (weighted) number of observations
        total_mu = (n_new * new_mu + n_past * mu) / n_total

        # Combine variance of old and new data, taking into consideration
        # (weighted) number of observations. This is achieved by combining
        # the sum-of-squared-differences (ssd)
        old_ssd = n_past * var
        new_ssd = n_new * new_var
        total_ssd = old_ssd + new_ssd + (n_new * n_past / n_total) * (mu - new_mu) ** 2
        total_var = total_ssd / n_total

        return total_mu, total_var
","Compute online update of Gaussian mean and variance.

        Given starting sample count, mean, and variance, a new set of
        points X, and optionally sample weights, return the updated mean and
        variance. (NB - each dimension (column) in X is treated as independent
        -- you get variance, not covariance).

        Can take scalar mean and variance, or vector mean and variance to
        simultaneously update a number of independent Gaussians.

        See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

        http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf

        Parameters
        ----------
        n_past : int
            Number of samples represented in old mean and variance. If sample
            weights were given, this should contain the sum of sample
            weights represented in old mean and variance.

        mu : array-like of shape (number of Gaussians,)
            Means for Gaussians in original set.

        var : array-like of shape (number of Gaussians,)
            Variances for Gaussians in original set.

        sample_weight : array-like of shape (n_samples,), default=None
            Weights applied to individual samples (1. for unweighted).

        Returns
        -------
        total_mu : array-like of shape (number of Gaussians,)
            Updated mean for each Gaussian over the combined set.

        total_var : array-like of shape (number of Gaussians,)
            Updated variance for each Gaussian over the combined set.
        ",191,162,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _update_mean_variance(n_past, mu, var, X, sample_weight=None):
        
        if X.shape[0] == 0:
            return mu, var

        # Compute (potentially weighted) mean and variance of new datapoints
        if sample_weight is not None:
            n_new = float(sample_weight.sum())
            if np.isclose(n_new, 0.0):
                return mu, var
            new_mu = np.average(X, axis=0, weights=sample_weight)
            new_var = np.average((X - new_mu) ** 2, axis=0, weights=sample_weight)
        else:
            n_new = X.shape[0]
            new_var = np.var(X, axis=0)
            new_mu = np.mean(X, axis=0)

        if n_past == 0:
            return new_mu, new_var

        n_total = float(n_past + n_new)

        # Combine mean of old and new data, taking into consideration
        # (weighted) number of observations
        total_mu = (n_new * new_mu + n_past * mu) / n_total

        # Combine variance of old and new data, taking into consideration
        # (weighted) number of observations. This is achieved by combining
        # the sum-of-squared-differences (ssd)
        old_ssd = n_past * var
        new_ssd = n_new * new_var
        total_ssd = old_ssd + new_ssd + (n_new * n_past / n_total) * (mu - new_mu) ** 2
        total_var = total_ssd / n_total

        return total_mu, total_var

        ```
    ###Assistant : Compute online update of Gaussian mean and variance.

        Given starting sample count, mean, and variance, a new set of
        points X, and optionally sample weights, return the updated mean and
        variance. (NB - each dimension (column) in X is treated as independent
        -- you get variance, not covariance).

        Can take scalar mean and variance, or vector mean and variance to
        simultaneously update a number of independent Gaussians.

        See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

        http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf

        Parameters
        ----------
        n_past : int
            Number of samples represented in old mean and variance. If sample
            weights were given, this should contain the sum of sample
            weights represented in old mean and variance.

        mu : array-like of shape (number of Gaussians,)
            Means for Gaussians in original set.

        var : array-like of shape (number of Gaussians,)
            Variances for Gaussians in original set.

        sample_weight : array-like of shape (n_samples,), default=None
            Weights applied to individual samples (1. for unweighted).

        Returns
        -------
        total_mu : array-like of shape (number of Gaussians,)
            Updated mean for each Gaussian over the combined set.

        total_var : array-like of shape (number of Gaussians,)
            Updated variance for each Gaussian over the combined set.
        
    "
118,"def is_prime(n):
        sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.11"",
        active_deprecations_target='deprecated-carmichael-static-methods',
        )
        return isprime(n)
","
is_prime is just a wrapper around sympy.ntheory.primetest.isprime so use that
directly instead.
        ",12,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_prime(n):
        sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.11"",
        active_deprecations_target='deprecated-carmichael-static-methods',
        )
        return isprime(n)

        ```
    ###Assistant : 
is_prime is just a wrapper around sympy.ntheory.primetest.isprime so use that
directly instead.
        
    "
119,"def standard_b64decode(s):
    
    return b64decode(s)


_urlsafe_encode_translation = bytes.maketrans(b'+/', b'-_')
_urlsafe_decode_translation = bytes.maketrans(b'-_', b'+/')
","Decode bytes encoded with the standard Base64 alphabet.

    Argument s is a bytes-like object or ASCII string to decode.  The result
    is returned as a bytes object.  A binascii.Error is raised if the input
    is incorrectly padded.  Characters that are not in the standard alphabet
    are discarded prior to the padding check.
    ",52,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def standard_b64decode(s):
    
    return b64decode(s)


_urlsafe_encode_translation = bytes.maketrans(b'+/', b'-_')
_urlsafe_decode_translation = bytes.maketrans(b'-_', b'+/')

        ```
    ###Assistant : Decode bytes encoded with the standard Base64 alphabet.

    Argument s is a bytes-like object or ASCII string to decode.  The result
    is returned as a bytes object.  A binascii.Error is raised if the input
    is incorrectly padded.  Characters that are not in the standard alphabet
    are discarded prior to the padding check.
    
    "
120,"def inception_score(self, imgs, batch_size=32, splits=1):
        
        N = len(imgs)
        dataloader = torch.utils.data.DataLoader(imgs, batch_size=batch_size)
        up = nn.Upsample(
            size=(28, 28),
            mode=""bilinear"",
            align_corners=False,  # This is to reduce user warnings from torch.
        ).type(torch.FloatTensor)
",Calculate the inception score of the generated images.,8,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def inception_score(self, imgs, batch_size=32, splits=1):
        
        N = len(imgs)
        dataloader = torch.utils.data.DataLoader(imgs, batch_size=batch_size)
        up = nn.Upsample(
            size=(28, 28),
            mode=""bilinear"",
            align_corners=False,  # This is to reduce user warnings from torch.
        ).type(torch.FloatTensor)

        ```
    ###Assistant : Calculate the inception score of the generated images.
    "
121,"def execute():
	frappe.reload_doc(""stock"", ""doctype"", ""shipment"")

	# update submitted status
	frappe.db.sql(
		
	)

	# update cancelled status
	frappe.db.sql(
		
	)
","UPDATE `tabShipment`
					SET status = ""Submitted""
					WHERE status = ""Draft"" AND docstatus = 1UPDATE `tabShipment`
					SET status = ""Cancelled""
					WHERE status = ""Draft"" AND docstatus = 2",27,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	frappe.reload_doc(""stock"", ""doctype"", ""shipment"")

	# update submitted status
	frappe.db.sql(
		
	)

	# update cancelled status
	frappe.db.sql(
		
	)

        ```
    ###Assistant : UPDATE `tabShipment`
					SET status = ""Submitted""
					WHERE status = ""Draft"" AND docstatus = 1UPDATE `tabShipment`
					SET status = ""Cancelled""
					WHERE status = ""Draft"" AND docstatus = 2
    "
122,"def url(self, name):
        
        raise NotImplementedError(""subclasses of Storage must provide a url() method"")
","
        Return an absolute URL where the file's contents can be accessed
        directly by a web browser.
        ",16,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def url(self, name):
        
        raise NotImplementedError(""subclasses of Storage must provide a url() method"")

        ```
    ###Assistant : 
        Return an absolute URL where the file's contents can be accessed
        directly by a web browser.
        
    "
123,"def load_attributes_from_hdf5_group(group, name):
    
    if name in group.attrs:
        data = [
            n.decode(""utf8"") if hasattr(n, ""decode"") else n
            for n in group.attrs[name]
        ]
    else:
        data = []
        chunk_id = 0
        while ""%s%d"" % (name, chunk_id) in group.attrs:
            data.extend(
                [
                    n.decode(""utf8"") if hasattr(n, ""decode"") else n
                    for n in group.attrs[""%s%d"" % (name, chunk_id)]
                ]
            )
            chunk_id += 1
    return data

","Loads attributes of the specified name from the HDF5 group.

    This method deals with an inherent problem
    of HDF5 file which is not able to store
    data larger than HDF5_OBJECT_HEADER_LIMIT bytes.

    Args:
        group: A pointer to a HDF5 group.
        name: A name of the attributes to load.

    Returns:
        data: Attributes data.
    ",51,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_attributes_from_hdf5_group(group, name):
    
    if name in group.attrs:
        data = [
            n.decode(""utf8"") if hasattr(n, ""decode"") else n
            for n in group.attrs[name]
        ]
    else:
        data = []
        chunk_id = 0
        while ""%s%d"" % (name, chunk_id) in group.attrs:
            data.extend(
                [
                    n.decode(""utf8"") if hasattr(n, ""decode"") else n
                    for n in group.attrs[""%s%d"" % (name, chunk_id)]
                ]
            )
            chunk_id += 1
    return data


        ```
    ###Assistant : Loads attributes of the specified name from the HDF5 group.

    This method deals with an inherent problem
    of HDF5 file which is not able to store
    data larger than HDF5_OBJECT_HEADER_LIMIT bytes.

    Args:
        group: A pointer to a HDF5 group.
        name: A name of the attributes to load.

    Returns:
        data: Attributes data.
    
    "
124,"def _find_root_block_schema(block_schemas_with_references):
    
    return next(
        (
            block_schema
            for (
                block_schema,
                _,
                parent_block_schema_id,
            ) in block_schemas_with_references
            if parent_block_schema_id is None
        ),
        None,
    )

","
    Attempts to find the root block schema from a list of block schemas
    with references. Returns None if a root block schema is not found.
    Returns only the first potential root block schema if multiple are found.
    ",37,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _find_root_block_schema(block_schemas_with_references):
    
    return next(
        (
            block_schema
            for (
                block_schema,
                _,
                parent_block_schema_id,
            ) in block_schemas_with_references
            if parent_block_schema_id is None
        ),
        None,
    )


        ```
    ###Assistant : 
    Attempts to find the root block schema from a list of block schemas
    with references. Returns None if a root block schema is not found.
    Returns only the first potential root block schema if multiple are found.
    
    "
125,"def feature_embedding(input_feats, out_feat_len):
    
    assert input_feats.ndim == 2
    assert isinstance(out_feat_len, int)
    assert out_feat_len >= input_feats.shape[1]

    num_nodes = input_feats.shape[0]
    feat_dim = input_feats.shape[1]
    feat_repeat_times = out_feat_len // feat_dim
    residue_dim = out_feat_len % feat_dim

    if residue_dim > 0:
        embed_wave = np.array([
            np.power(1000, 2.0 * (j // 2) / feat_repeat_times + 1)
            for j in range(feat_repeat_times + 1)
        ]).reshape((feat_repeat_times + 1, 1, 1))
        repeat_feats = np.repeat(
            np.expand_dims(
                input_feats, axis=0), feat_repeat_times, axis=0)
        residue_feats = np.hstack([
            input_feats[:, 0:residue_dim], np.zeros(
                (num_nodes, feat_dim - residue_dim))
        ])
        residue_feats = np.expand_dims(residue_feats, axis=0)
        repeat_feats = np.concatenate([repeat_feats, residue_feats], axis=0)
        embedded_feats = repeat_feats / embed_wave
        embedded_feats[:, 0::2] = np.sin(embedded_feats[:, 0::2])
        embedded_feats[:, 1::2] = np.cos(embedded_feats[:, 1::2])
        embedded_feats = np.transpose(embedded_feats, (1, 0, 2)).reshape(
            (num_nodes, -1))[:, 0:out_feat_len]
    else:
        embed_wave = np.array([
            np.power(1000, 2.0 * (j // 2) / feat_repeat_times)
            for j in range(feat_repeat_times)
        ]).reshape((feat_repeat_times, 1, 1))
        repeat_feats = np.repeat(
            np.expand_dims(
                input_feats, axis=0), feat_repeat_times, axis=0)
        embedded_feats = repeat_feats / embed_wave
        embedded_feats[:, 0::2] = np.sin(embedded_feats[:, 0::2])
        embedded_feats[:, 1::2] = np.cos(embedded_feats[:, 1::2])
        embedded_feats = np.transpose(embedded_feats, (1, 0, 2)).reshape(
            (num_nodes, -1)).astype(np.float32)

    return embedded_feats

","Embed features. This code was partially adapted from
    https://github.com/GXYM/DRRG licensed under the MIT license.

    Args:
        input_feats (ndarray): The input features of shape (N, d), where N is
            the number of nodes in graph, d is the input feature vector length.
        out_feat_len (int): The length of output feature vector.

    Returns:
        embedded_feats (ndarray): The embedded features.
    ",54,162,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def feature_embedding(input_feats, out_feat_len):
    
    assert input_feats.ndim == 2
    assert isinstance(out_feat_len, int)
    assert out_feat_len >= input_feats.shape[1]

    num_nodes = input_feats.shape[0]
    feat_dim = input_feats.shape[1]
    feat_repeat_times = out_feat_len // feat_dim
    residue_dim = out_feat_len % feat_dim

    if residue_dim > 0:
        embed_wave = np.array([
            np.power(1000, 2.0 * (j // 2) / feat_repeat_times + 1)
            for j in range(feat_repeat_times + 1)
        ]).reshape((feat_repeat_times + 1, 1, 1))
        repeat_feats = np.repeat(
            np.expand_dims(
                input_feats, axis=0), feat_repeat_times, axis=0)
        residue_feats = np.hstack([
            input_feats[:, 0:residue_dim], np.zeros(
                (num_nodes, feat_dim - residue_dim))
        ])
        residue_feats = np.expand_dims(residue_feats, axis=0)
        repeat_feats = np.concatenate([repeat_feats, residue_feats], axis=0)
        embedded_feats = repeat_feats / embed_wave
        embedded_feats[:, 0::2] = np.sin(embedded_feats[:, 0::2])
        embedded_feats[:, 1::2] = np.cos(embedded_feats[:, 1::2])
        embedded_feats = np.transpose(embedded_feats, (1, 0, 2)).reshape(
            (num_nodes, -1))[:, 0:out_feat_len]
    else:
        embed_wave = np.array([
            np.power(1000, 2.0 * (j // 2) / feat_repeat_times)
            for j in range(feat_repeat_times)
        ]).reshape((feat_repeat_times, 1, 1))
        repeat_feats = np.repeat(
            np.expand_dims(
                input_feats, axis=0), feat_repeat_times, axis=0)
        embedded_feats = repeat_feats / embed_wave
        embedded_feats[:, 0::2] = np.sin(embedded_feats[:, 0::2])
        embedded_feats[:, 1::2] = np.cos(embedded_feats[:, 1::2])
        embedded_feats = np.transpose(embedded_feats, (1, 0, 2)).reshape(
            (num_nodes, -1)).astype(np.float32)

    return embedded_feats


        ```
    ###Assistant : Embed features. This code was partially adapted from
    https://github.com/GXYM/DRRG licensed under the MIT license.

    Args:
        input_feats (ndarray): The input features of shape (N, d), where N is
            the number of nodes in graph, d is the input feature vector length.
        out_feat_len (int): The length of output feature vector.

    Returns:
        embedded_feats (ndarray): The embedded features.
    
    "
126,"def decision_function(self, X):
        
        check_is_fitted(self)
        xp, _ = get_namespace(X)

        X = self._validate_data(X, accept_sparse=""csr"", reset=False)
        scores = safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_
        return xp.reshape(scores, -1) if scores.shape[1] == 1 else scores
","
        Predict confidence scores for samples.

        The confidence score for a sample is proportional to the signed
        distance of that sample to the hyperplane.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The data matrix for which we want to get the confidence scores.

        Returns
        -------
        scores : ndarray of shape (n_samples,) or (n_samples, n_classes)
            Confidence scores per `(n_samples, n_classes)` combination. In the
            binary case, confidence score for `self.classes_[1]` where >0 means
            this class would be predicted.
        ",79,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def decision_function(self, X):
        
        check_is_fitted(self)
        xp, _ = get_namespace(X)

        X = self._validate_data(X, accept_sparse=""csr"", reset=False)
        scores = safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_
        return xp.reshape(scores, -1) if scores.shape[1] == 1 else scores

        ```
    ###Assistant : 
        Predict confidence scores for samples.

        The confidence score for a sample is proportional to the signed
        distance of that sample to the hyperplane.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The data matrix for which we want to get the confidence scores.

        Returns
        -------
        scores : ndarray of shape (n_samples,) or (n_samples, n_classes)
            Confidence scores per `(n_samples, n_classes)` combination. In the
            binary case, confidence score for `self.classes_[1]` where >0 means
            this class would be predicted.
        
    "
127,"def get_ranking(pairs):
    
    if len(pairs) == 1:
        return list(pairs[0])
    w = get_winner(pairs)
    # now remove the winner from the list of pairs
    p_new = np.array([(a, b) for a, b in pairs if a != w])
    return [w] + get_ranking(p_new)

","
    Abuses concordance property to get a (not necessarily unqiue) ranking.
    The lack of uniqueness is due to the potential existance of multiple
    equally ranked winners. We have to pick one, which is where
    the non-uniqueness comes from
    ",37,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ranking(pairs):
    
    if len(pairs) == 1:
        return list(pairs[0])
    w = get_winner(pairs)
    # now remove the winner from the list of pairs
    p_new = np.array([(a, b) for a, b in pairs if a != w])
    return [w] + get_ranking(p_new)


        ```
    ###Assistant : 
    Abuses concordance property to get a (not necessarily unqiue) ranking.
    The lack of uniqueness is due to the potential existance of multiple
    equally ranked winners. We have to pick one, which is where
    the non-uniqueness comes from
    
    "
128,"def backup_dir(dir, ext="".bak""):
    # type: (str, str) -> str
    
    n = 1
    extension = ext
    while os.path.exists(dir + extension):
        n += 1
        extension = ext + str(n)
    return dir + extension

","Figure out the name of a directory to back up the given dir to
    (adding .bak, .bak2, etc)",18,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def backup_dir(dir, ext="".bak""):
    # type: (str, str) -> str
    
    n = 1
    extension = ext
    while os.path.exists(dir + extension):
        n += 1
        extension = ext + str(n)
    return dir + extension


        ```
    ###Assistant : Figure out the name of a directory to back up the given dir to
    (adding .bak, .bak2, etc)
    "
129,"def synchronize(local_filters, remotes, update_remote=True):
        
        remote_filters = ray.get(
            [r.get_filters.remote(flush_after=True) for r in remotes]
        )
        for rf in remote_filters:
            for k in local_filters:
                local_filters[k].apply_changes(rf[k], with_buffer=False)
        if update_remote:
            copies = {k: v.as_serializable() for k, v in local_filters.items()}
            remote_copy = ray.put(copies)
            [r.sync_filters.remote(remote_copy) for r in remotes]
","Aggregates all filters from remote evaluators.

        Local copy is updated and then broadcasted to all remote evaluators.

        Args:
            local_filters: Filters to be synchronized.
            remotes: Remote evaluators with filters.
            update_remote: Whether to push updates to remote filters.
        ",36,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def synchronize(local_filters, remotes, update_remote=True):
        
        remote_filters = ray.get(
            [r.get_filters.remote(flush_after=True) for r in remotes]
        )
        for rf in remote_filters:
            for k in local_filters:
                local_filters[k].apply_changes(rf[k], with_buffer=False)
        if update_remote:
            copies = {k: v.as_serializable() for k, v in local_filters.items()}
            remote_copy = ray.put(copies)
            [r.sync_filters.remote(remote_copy) for r in remotes]

        ```
    ###Assistant : Aggregates all filters from remote evaluators.

        Local copy is updated and then broadcasted to all remote evaluators.

        Args:
            local_filters: Filters to be synchronized.
            remotes: Remote evaluators with filters.
            update_remote: Whether to push updates to remote filters.
        
    "
130,"def test_estimator_empty_instance_dict(estimator):
    
    state = estimator.__getstate__()
    expected = {""_sklearn_version"": sklearn.__version__}
    assert state == expected

    # this should not raise
    pickle.loads(pickle.dumps(BaseEstimator()))

","Check that ``__getstate__`` returns an empty ``dict`` with an empty
    instance.

    Python 3.11+ changed behaviour by returning ``None`` instead of raising an
    ``AttributeError``. Non-regression test for gh-25188.
    ",27,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_estimator_empty_instance_dict(estimator):
    
    state = estimator.__getstate__()
    expected = {""_sklearn_version"": sklearn.__version__}
    assert state == expected

    # this should not raise
    pickle.loads(pickle.dumps(BaseEstimator()))


        ```
    ###Assistant : Check that ``__getstate__`` returns an empty ``dict`` with an empty
    instance.

    Python 3.11+ changed behaviour by returning ``None`` instead of raising an
    ``AttributeError``. Non-regression test for gh-25188.
    
    "
131,"def test_with_variables(self):
        context = Context({""name"": ""jonathan wells""})

        template = 

        expected = 

        self.assertHTMLEqual(expected, Template(template).render(context))
","
            {% load wagtailadmin_tags %}
            {% fragment as my_fragment %}
                <p>Hello, {{ name|title }}</p>
            {% endfragment %}
            Text coming after:
            {{ my_fragment }}
        
            Text coming after:
            <p>Hello, Jonathan Wells</p>
        ",28,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_with_variables(self):
        context = Context({""name"": ""jonathan wells""})

        template = 

        expected = 

        self.assertHTMLEqual(expected, Template(template).render(context))

        ```
    ###Assistant : 
            {% load wagtailadmin_tags %}
            {% fragment as my_fragment %}
                <p>Hello, {{ name|title }}</p>
            {% endfragment %}
            Text coming after:
            {{ my_fragment }}
        
            Text coming after:
            <p>Hello, Jonathan Wells</p>
        
    "
132,"def _extract_interpreter(b_module_data):
    

    interpreter = None
    args = []
    b_lines = b_module_data.split(b""\n"", 1)
    if b_lines[0].startswith(b""#!""):
        b_shebang = b_lines[0].strip()

        # shlex.split on python-2.6 needs bytes.  On python-3.x it needs text
        cli_split = shlex.split(to_native(b_shebang[2:], errors='surrogate_or_strict'))

        # convert args to text
        cli_split = [to_text(a, errors='surrogate_or_strict') for a in cli_split]
        interpreter = cli_split[0]
        args = cli_split[1:]

    return interpreter, args

","
    Used to extract shebang expression from binary module data and return a text
    string with the shebang, or None if no shebang is detected.
    ",24,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _extract_interpreter(b_module_data):
    

    interpreter = None
    args = []
    b_lines = b_module_data.split(b""\n"", 1)
    if b_lines[0].startswith(b""#!""):
        b_shebang = b_lines[0].strip()

        # shlex.split on python-2.6 needs bytes.  On python-3.x it needs text
        cli_split = shlex.split(to_native(b_shebang[2:], errors='surrogate_or_strict'))

        # convert args to text
        cli_split = [to_text(a, errors='surrogate_or_strict') for a in cli_split]
        interpreter = cli_split[0]
        args = cli_split[1:]

    return interpreter, args


        ```
    ###Assistant : 
    Used to extract shebang expression from binary module data and return a text
    string with the shebang, or None if no shebang is detected.
    
    "
133,"def test_resource_exhausted_info(self):
        

        # generate some random data to be captured implicitly in training func.
        from sklearn.datasets import fetch_olivetti_faces

        a_large_array = []
        for i in range(50):
            a_large_array.append(fetch_olivetti_faces())
","This is to test if helpful information is displayed when
        the objects captured in trainable/training function are too
        large and RESOURCES_EXHAUSTED error of gRPC is triggered.",26,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_resource_exhausted_info(self):
        

        # generate some random data to be captured implicitly in training func.
        from sklearn.datasets import fetch_olivetti_faces

        a_large_array = []
        for i in range(50):
            a_large_array.append(fetch_olivetti_faces())

        ```
    ###Assistant : This is to test if helpful information is displayed when
        the objects captured in trainable/training function are too
        large and RESOURCES_EXHAUSTED error of gRPC is triggered.
    "
134,"def testing_session_settings():
    
    with tempfile.TemporaryDirectory() as tmpdir:
        profile = prefect.settings.Profile(
            name=""test-session"",
            settings={
                # Set PREFECT_HOME to a temporary directory to avoid clobbering
                # environments and settings
                PREFECT_HOME: tmpdir,
                PREFECT_PROFILES_PATH: ""$PREFECT_HOME/profiles.toml"",
                # Enable debug logging
                PREFECT_LOGGING_LEVEL: ""DEBUG"",
                # Disable shipping logs to the API;
                # can be enabled by the `enable_orion_handler` mark
                PREFECT_LOGGING_ORION_ENABLED: False,
                # Disable services for test runs
                PREFECT_ORION_ANALYTICS_ENABLED: False,
                PREFECT_ORION_SERVICES_LATE_RUNS_ENABLED: False,
                PREFECT_ORION_SERVICES_SCHEDULER_ENABLED: False,
            },
            source=__file__,
        )

        with prefect.settings.use_profile(
            profile,
            override_environment_variables=True,
            include_current_context=False,
        ) as ctx:
            yield ctx
","
    Creates a fixture for the scope of the test session that modifies setting defaults.

    This ensures that tests are isolated from existing settings, databases, etc.
    ",25,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def testing_session_settings():
    
    with tempfile.TemporaryDirectory() as tmpdir:
        profile = prefect.settings.Profile(
            name=""test-session"",
            settings={
                # Set PREFECT_HOME to a temporary directory to avoid clobbering
                # environments and settings
                PREFECT_HOME: tmpdir,
                PREFECT_PROFILES_PATH: ""$PREFECT_HOME/profiles.toml"",
                # Enable debug logging
                PREFECT_LOGGING_LEVEL: ""DEBUG"",
                # Disable shipping logs to the API;
                # can be enabled by the `enable_orion_handler` mark
                PREFECT_LOGGING_ORION_ENABLED: False,
                # Disable services for test runs
                PREFECT_ORION_ANALYTICS_ENABLED: False,
                PREFECT_ORION_SERVICES_LATE_RUNS_ENABLED: False,
                PREFECT_ORION_SERVICES_SCHEDULER_ENABLED: False,
            },
            source=__file__,
        )

        with prefect.settings.use_profile(
            profile,
            override_environment_variables=True,
            include_current_context=False,
        ) as ctx:
            yield ctx

        ```
    ###Assistant : 
    Creates a fixture for the scope of the test session that modifies setting defaults.

    This ensures that tests are isolated from existing settings, databases, etc.
    
    "
135,"def bot_start(self, **kwargs) -> None:
        
        self.buy_rsi = IntParameter([0, 50], default=30, space='buy')
","
        Parameters can also be defined here ...
        ",7,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bot_start(self, **kwargs) -> None:
        
        self.buy_rsi = IntParameter([0, 50], default=30, space='buy')

        ```
    ###Assistant : 
        Parameters can also be defined here ...
        
    "
136,"def getvalue(self):
        
        if callable(getattr(self.stream, ""getvalue"", None)):
            return self.stream.getvalue()

","
        Return the fully serialized queryset (or None if the output stream is
        not seekable).
        ",14,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getvalue(self):
        
        if callable(getattr(self.stream, ""getvalue"", None)):
            return self.stream.getvalue()


        ```
    ###Assistant : 
        Return the fully serialized queryset (or None if the output stream is
        not seekable).
        
    "
137,"def boxplot(self, X, win=None, env=None, opts=None):
        

        X = np.squeeze(X)
        assert X.ndim == 1 or X.ndim == 2, ""X should be one or two-dimensional""
        if X.ndim == 1:
            X = X[:, None]

        opts = {} if opts is None else opts
        _title2str(opts)
        _assert_opts(opts)

        if opts.get(""legend"") is not None:
            assert (
                len(opts[""legend""]) == X.shape[1]
            ), ""number of legened labels must match number of columns""

        data = []
        for k in range(X.shape[1]):
            _data = {
                ""y"": X.take(k, 1).tolist(),
                ""type"": ""box"",
            }
            if opts.get(""legend""):
                _data[""name""] = opts[""legend""][k]
            else:
                _data[""name""] = ""column "" + str(k)

            data.append(_data)

        return self._send(
            {
                ""data"": data,
                ""win"": win,
                ""eid"": env,
                ""layout"": _opts2layout(opts),
                ""opts"": opts,
            }
        )
","
        This function draws boxplots of the specified data. It takes as input
        an `N` or an `NxM` tensor `X` that specifies the `N` data values of
        which to construct the `M` boxplots.

        The following plot-specific `opts` are currently supported:
        - `opts.legend`: labels for each of the columns in `X`
        ",49,106,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def boxplot(self, X, win=None, env=None, opts=None):
        

        X = np.squeeze(X)
        assert X.ndim == 1 or X.ndim == 2, ""X should be one or two-dimensional""
        if X.ndim == 1:
            X = X[:, None]

        opts = {} if opts is None else opts
        _title2str(opts)
        _assert_opts(opts)

        if opts.get(""legend"") is not None:
            assert (
                len(opts[""legend""]) == X.shape[1]
            ), ""number of legened labels must match number of columns""

        data = []
        for k in range(X.shape[1]):
            _data = {
                ""y"": X.take(k, 1).tolist(),
                ""type"": ""box"",
            }
            if opts.get(""legend""):
                _data[""name""] = opts[""legend""][k]
            else:
                _data[""name""] = ""column "" + str(k)

            data.append(_data)

        return self._send(
            {
                ""data"": data,
                ""win"": win,
                ""eid"": env,
                ""layout"": _opts2layout(opts),
                ""opts"": opts,
            }
        )

        ```
    ###Assistant : 
        This function draws boxplots of the specified data. It takes as input
        an `N` or an `NxM` tensor `X` that specifies the `N` data values of
        which to construct the `M` boxplots.

        The following plot-specific `opts` are currently supported:
        - `opts.legend`: labels for each of the columns in `X`
        
    "
138,"def test_body_after_POST_multipart_related(self):
        
        # Ticket #9054
        # There are cases in which the multipart data is related instead of
        # being a binary upload, in which case it should still be accessible
        # via body.
        payload_data = b""\r\n"".join([
            b'--boundary',
            b'Content-ID: id; name=""name""',
            b'',
            b'value',
            b'--boundary--'
        ])
        payload = FakePayload(payload_data)
        request = WSGIRequest({
            'REQUEST_METHOD': 'POST',
            'CONTENT_TYPE': 'multipart/related; boundary=boundary',
            'CONTENT_LENGTH': len(payload),
            'wsgi.input': payload,
        })
        self.assertEqual(request.POST, {})
        self.assertEqual(request.body, payload_data)
","
        Reading body after parsing multipart that isn't form-data is allowed
        ",10,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_body_after_POST_multipart_related(self):
        
        # Ticket #9054
        # There are cases in which the multipart data is related instead of
        # being a binary upload, in which case it should still be accessible
        # via body.
        payload_data = b""\r\n"".join([
            b'--boundary',
            b'Content-ID: id; name=""name""',
            b'',
            b'value',
            b'--boundary--'
        ])
        payload = FakePayload(payload_data)
        request = WSGIRequest({
            'REQUEST_METHOD': 'POST',
            'CONTENT_TYPE': 'multipart/related; boundary=boundary',
            'CONTENT_LENGTH': len(payload),
            'wsgi.input': payload,
        })
        self.assertEqual(request.POST, {})
        self.assertEqual(request.body, payload_data)

        ```
    ###Assistant : 
        Reading body after parsing multipart that isn't form-data is allowed
        
    "
139,"def query(self, query, **kwargs) -> Result:
        
        try:
            if self.db_conn:
                result = self.db_conn.aql.execute(query, **kwargs)
                return result
            else:
                raise AirflowException(
                    f""Failed to execute AQLQuery, error connecting to database: {self.database}""
                )
        except AQLQueryExecuteError as error:
            raise AirflowException(f""Failed to execute AQLQuery, error: {str(error)}"")
","
        Function to create a arangodb session
        and execute the AQL query in the session.

        :param query: AQL query
        :return: Result
        ",20,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def query(self, query, **kwargs) -> Result:
        
        try:
            if self.db_conn:
                result = self.db_conn.aql.execute(query, **kwargs)
                return result
            else:
                raise AirflowException(
                    f""Failed to execute AQLQuery, error connecting to database: {self.database}""
                )
        except AQLQueryExecuteError as error:
            raise AirflowException(f""Failed to execute AQLQuery, error: {str(error)}"")

        ```
    ###Assistant : 
        Function to create a arangodb session
        and execute the AQL query in the session.

        :param query: AQL query
        :return: Result
        
    "
140,"def test_post_process_frame(feature_names, target_names):
    
    pd = pytest.importorskip(""pandas"")

    X_original = pd.DataFrame(
        {
            ""col_int_as_integer"": [1, 2, 3],
            ""col_int_as_numeric"": [1, 2, 3],
            ""col_float_as_real"": [1.0, 2.0, 3.0],
            ""col_float_as_numeric"": [1.0, 2.0, 3.0],
            ""col_categorical"": [""a"", ""b"", ""c""],
            ""col_string"": [""a"", ""b"", ""c""],
        }
    )

    X, y = _post_process_frame(X_original, feature_names, target_names)
    assert isinstance(X, pd.DataFrame)
    if len(target_names) >= 2:
        assert isinstance(y, pd.DataFrame)
    elif len(target_names) == 1:
        assert isinstance(y, pd.Series)
    else:
        assert y is None

",Check the behaviour of the post-processing function for splitting a dataframe.,11,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_post_process_frame(feature_names, target_names):
    
    pd = pytest.importorskip(""pandas"")

    X_original = pd.DataFrame(
        {
            ""col_int_as_integer"": [1, 2, 3],
            ""col_int_as_numeric"": [1, 2, 3],
            ""col_float_as_real"": [1.0, 2.0, 3.0],
            ""col_float_as_numeric"": [1.0, 2.0, 3.0],
            ""col_categorical"": [""a"", ""b"", ""c""],
            ""col_string"": [""a"", ""b"", ""c""],
        }
    )

    X, y = _post_process_frame(X_original, feature_names, target_names)
    assert isinstance(X, pd.DataFrame)
    if len(target_names) >= 2:
        assert isinstance(y, pd.DataFrame)
    elif len(target_names) == 1:
        assert isinstance(y, pd.Series)
    else:
        assert y is None


        ```
    ###Assistant : Check the behaviour of the post-processing function for splitting a dataframe.
    "
141,"def cleanse_setting(self, key, value):
        
        try:
            is_sensitive = self.hidden_settings.search(key)
        except TypeError:
            is_sensitive = False

        if is_sensitive:
            cleansed = self.cleansed_substitute
        elif isinstance(value, dict):
            cleansed = {k: self.cleanse_setting(k, v) for k, v in value.items()}
        elif isinstance(value, list):
            cleansed = [self.cleanse_setting("""", v) for v in value]
        elif isinstance(value, tuple):
            cleansed = tuple([self.cleanse_setting("""", v) for v in value])
        else:
            cleansed = value

        if callable(cleansed):
            cleansed = CallableSettingWrapper(cleansed)

        return cleansed
","
        Cleanse an individual setting key/value of sensitive content. If the
        value is a dictionary, recursively cleanse the keys in that dictionary.
        ",21,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cleanse_setting(self, key, value):
        
        try:
            is_sensitive = self.hidden_settings.search(key)
        except TypeError:
            is_sensitive = False

        if is_sensitive:
            cleansed = self.cleansed_substitute
        elif isinstance(value, dict):
            cleansed = {k: self.cleanse_setting(k, v) for k, v in value.items()}
        elif isinstance(value, list):
            cleansed = [self.cleanse_setting("""", v) for v in value]
        elif isinstance(value, tuple):
            cleansed = tuple([self.cleanse_setting("""", v) for v in value])
        else:
            cleansed = value

        if callable(cleansed):
            cleansed = CallableSettingWrapper(cleansed)

        return cleansed

        ```
    ###Assistant : 
        Cleanse an individual setting key/value of sensitive content. If the
        value is a dictionary, recursively cleanse the keys in that dictionary.
        
    "
142,"def func_dump(func):
    
    if os.name == ""nt"":
        raw_code = marshal.dumps(func.__code__).replace(b""\\"", b""/"")
        code = codecs.encode(raw_code, ""base64"").decode(""ascii"")
    else:
        raw_code = marshal.dumps(func.__code__)
        code = codecs.encode(raw_code, ""base64"").decode(""ascii"")
    defaults = func.__defaults__
    if func.__closure__:
        closure = tuple(c.cell_contents for c in func.__closure__)
    else:
        closure = None
    return code, defaults, closure

","Serializes a user defined function.

    Args:
        func: the function to serialize.

    Returns:
        A tuple `(code, defaults, closure)`.
    ",17,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def func_dump(func):
    
    if os.name == ""nt"":
        raw_code = marshal.dumps(func.__code__).replace(b""\\"", b""/"")
        code = codecs.encode(raw_code, ""base64"").decode(""ascii"")
    else:
        raw_code = marshal.dumps(func.__code__)
        code = codecs.encode(raw_code, ""base64"").decode(""ascii"")
    defaults = func.__defaults__
    if func.__closure__:
        closure = tuple(c.cell_contents for c in func.__closure__)
    else:
        closure = None
    return code, defaults, closure


        ```
    ###Assistant : Serializes a user defined function.

    Args:
        func: the function to serialize.

    Returns:
        A tuple `(code, defaults, closure)`.
    
    "
143,"def cauchy_upper_bound(f):
        
        if not f.lev:
            return dup_cauchy_upper_bound(f.rep, f.dom)
        else:
            raise ValueError('univariate polynomial expected')
",Computes the Cauchy upper bound on the roots of ``f``. ,10,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cauchy_upper_bound(f):
        
        if not f.lev:
            return dup_cauchy_upper_bound(f.rep, f.dom)
        else:
            raise ValueError('univariate polynomial expected')

        ```
    ###Assistant : Computes the Cauchy upper bound on the roots of ``f``. 
    "
144,"def kernS(s):
    
    hit = False
    quoted = '""' in s or ""'"" in s
    if '(' in s and not quoted:
        if s.count('(') != s.count("")""):
            raise SympifyError('unmatched left parenthesis')

        # strip all space from s
        s = ''.join(s.split())
        olds = s
        # now use space to represent a symbol that
        # will
        # step 1. turn potential 2-arg Muls into 3-arg versions
        # 1a. *( -> * *(
        s = s.replace('*(', '* *(')
        # 1b. close up exponentials
        s = s.replace('** *', '**')
        # 2. handle the implied multiplication of a negated
        # parenthesized expression in two steps
        # 2a:  -(...)  -->  -( *(...)
        target = '-( *('
        s = s.replace('-(', target)
        # 2b: double the matching closing parenthesis
        # -( *(...)  -->  -( *(...))
        i = nest = 0
        assert target.endswith('(')  # assumption below
        while True:
            j = s.find(target, i)
            if j == -1:
                break
            j += len(target) - 1
            for j in range(j, len(s)):
                if s[j] == ""("":
                    nest += 1
                elif s[j] == "")"":
                    nest -= 1
                if nest == 0:
                    break
            s = s[:j] + "")"" + s[j:]
            i = j + 2  # the first char after 2nd )
        if ' ' in s:
            # get a unique kern
            kern = '_'
            while kern in s:
                kern += choice(string.ascii_letters + string.digits)
            s = s.replace(' ', kern)
            hit = kern in s
        else:
            hit = False

    for i in range(2):
        try:
            expr = sympify(s)
            break
        except TypeError:  # the kern might cause unknown errors...
            if hit:
                s = olds  # maybe it didn't like the kern; use un-kerned s
                hit = False
                continue
            expr = sympify(s)  # let original error raise

    if not hit:
        return expr

    from .symbol import Symbol
    rep = {Symbol(kern): 1}","Use a hack to try keep autosimplification from distributing a
    a number into an Add; this modification does not
    prevent the 2-arg Mul from becoming an Add, however.

    Examples
    ========

    >>> from sympy.core.sympify import kernS
    >>> from sympy.abc import x, y

    The 2-arg Mul distributes a number (or minus sign) across the terms
    of an expression, but kernS will prevent that:

    >>> 2*(x + y), -(x + 1)
    (2*x + 2*y, -x - 1)
    >>> kernS('2*(x + y)')
    2*(x + y)
    >>> kernS('-(x + 1)')
    -(x + 1)

    If use of the hack fails, the un-hacked string will be passed to sympify...
    and you get what you get.

    XXX This hack should not be necessary once issue 4596 has been resolved.
    ",121,288,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def kernS(s):
    
    hit = False
    quoted = '""' in s or ""'"" in s
    if '(' in s and not quoted:
        if s.count('(') != s.count("")""):
            raise SympifyError('unmatched left parenthesis')

        # strip all space from s
        s = ''.join(s.split())
        olds = s
        # now use space to represent a symbol that
        # will
        # step 1. turn potential 2-arg Muls into 3-arg versions
        # 1a. *( -> * *(
        s = s.replace('*(', '* *(')
        # 1b. close up exponentials
        s = s.replace('** *', '**')
        # 2. handle the implied multiplication of a negated
        # parenthesized expression in two steps
        # 2a:  -(...)  -->  -( *(...)
        target = '-( *('
        s = s.replace('-(', target)
        # 2b: double the matching closing parenthesis
        # -( *(...)  -->  -( *(...))
        i = nest = 0
        assert target.endswith('(')  # assumption below
        while True:
            j = s.find(target, i)
            if j == -1:
                break
            j += len(target) - 1
            for j in range(j, len(s)):
                if s[j] == ""("":
                    nest += 1
                elif s[j] == "")"":
                    nest -= 1
                if nest == 0:
                    break
            s = s[:j] + "")"" + s[j:]
            i = j + 2  # the first char after 2nd )
        if ' ' in s:
            # get a unique kern
            kern = '_'
            while kern in s:
                kern += choice(string.ascii_letters + string.digits)
            s = s.replace(' ', kern)
            hit = kern in s
        else:
            hit = False

    for i in range(2):
        try:
            expr = sympify(s)
            break
        except TypeError:  # the kern might cause unknown errors...
            if hit:
                s = olds  # maybe it didn't like the kern; use un-kerned s
                hit = False
                continue
            expr = sympify(s)  # let original error raise

    if not hit:
        return expr

    from .symbol import Symbol
    rep = {Symbol(kern): 1}
        ```
    ###Assistant : Use a hack to try keep autosimplification from distributing a
    a number into an Add; this modification does not
    prevent the 2-arg Mul from becoming an Add, however.

    Examples
    ========

    >>> from sympy.core.sympify import kernS
    >>> from sympy.abc import x, y

    The 2-arg Mul distributes a number (or minus sign) across the terms
    of an expression, but kernS will prevent that:

    >>> 2*(x + y), -(x + 1)
    (2*x + 2*y, -x - 1)
    >>> kernS('2*(x + y)')
    2*(x + y)
    >>> kernS('-(x + 1)')
    -(x + 1)

    If use of the hack fails, the un-hacked string will be passed to sympify...
    and you get what you get.

    XXX This hack should not be necessary once issue 4596 has been resolved.
    
    "
145,"def test_load_corrupt_file(self, patched_pickle_load):
        
        # First load is the schema version
        patched_pickle_load.side_effect = [DocumentClassifier.FORMAT_VERSION, OSError()]

        with self.assertRaises(ClassifierModelCorruptError):
            self.classifier.load()
","
        GIVEN:
            - Corrupted classifier pickle file
        WHEN:
            - An attempt is made to load the classifier
        THEN:
            - The ClassifierModelCorruptError is raised
        ",22,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_load_corrupt_file(self, patched_pickle_load):
        
        # First load is the schema version
        patched_pickle_load.side_effect = [DocumentClassifier.FORMAT_VERSION, OSError()]

        with self.assertRaises(ClassifierModelCorruptError):
            self.classifier.load()

        ```
    ###Assistant : 
        GIVEN:
            - Corrupted classifier pickle file
        WHEN:
            - An attempt is made to load the classifier
        THEN:
            - The ClassifierModelCorruptError is raised
        
    "
146,"def current_option(self) -> str:
        
        return self.device[self.entity_description.current_option_key]
",Return the selected entity option to represent the entity state.,10,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def current_option(self) -> str:
        
        return self.device[self.entity_description.current_option_key]

        ```
    ###Assistant : Return the selected entity option to represent the entity state.
    "
147,"def gen_html(img):
    
    html_code = img['html']['structure']['tokens'].copy()
    to_insert = [i for i, tag in enumerate(html_code) if tag in ('<td>', '>')]
    for i, cell in zip(to_insert[::-1], img['html']['cells'][::-1]):
        if cell['tokens']:
            text = ''.join(cell['tokens'])
            # skip empty text
            sp_char_list = ['<b>', '</b>', '\u2028', ' ', '<i>', '</i>']
            text_remove_style = skip_char(text, sp_char_list)
            if len(text_remove_style) == 0:
                continue
            html_code.insert(i + 1, text)
    html_code = ''.join(html_code)
    html_code = '<html><body><table>{}</table></body></html>'.format(html_code)
    return html_code

"," 
    Formats HTML code from tokenized annotation of img
    ",8,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def gen_html(img):
    
    html_code = img['html']['structure']['tokens'].copy()
    to_insert = [i for i, tag in enumerate(html_code) if tag in ('<td>', '>')]
    for i, cell in zip(to_insert[::-1], img['html']['cells'][::-1]):
        if cell['tokens']:
            text = ''.join(cell['tokens'])
            # skip empty text
            sp_char_list = ['<b>', '</b>', '\u2028', ' ', '<i>', '</i>']
            text_remove_style = skip_char(text, sp_char_list)
            if len(text_remove_style) == 0:
                continue
            html_code.insert(i + 1, text)
    html_code = ''.join(html_code)
    html_code = '<html><body><table>{}</table></body></html>'.format(html_code)
    return html_code


        ```
    ###Assistant :  
    Formats HTML code from tokenized annotation of img
    
    "
148,"def key_aliases(self) -> Iterable[str]:
        
        for alias in _get_key_aliases(self.key):
            yield _normalize_key(alias)
","Get the aliases for the key, including the key itself",10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def key_aliases(self) -> Iterable[str]:
        
        for alias in _get_key_aliases(self.key):
            yield _normalize_key(alias)

        ```
    ###Assistant : Get the aliases for the key, including the key itself
    "
149,"def test_create_single_available_ip(self):
        
        vrf = VRF.objects.create(name='VRF 1')
        prefix = Prefix.objects.create(prefix=IPNetwork('192.0.2.0/30'), vrf=vrf, is_pool=True)
        url = reverse('ipam-api:prefix-available-ips', kwargs={'pk': prefix.pk})
        self.add_permissions('ipam.view_prefix', 'ipam.add_ipaddress')

        # Create all four available IPs with individual requests
        for i in range(1, 5):
            data = {
                'description': 'Test IP {}'.format(i)
            }
            response = self.client.post(url, data, format='json', **self.header)
            self.assertHttpStatus(response, status.HTTP_201_CREATED)
            self.assertEqual(response.data['vrf']['id'], vrf.pk)
            self.assertEqual(response.data['description'], data['description'])

        # Try to create one more IP
        response = self.client.post(url, {}, format='json', **self.header)
        self.assertHttpStatus(response, status.HTTP_409_CONFLICT)
        self.assertIn('detail', response.data)
","
        Test retrieval of the first available IP address within a parent prefix.
        ",12,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_create_single_available_ip(self):
        
        vrf = VRF.objects.create(name='VRF 1')
        prefix = Prefix.objects.create(prefix=IPNetwork('192.0.2.0/30'), vrf=vrf, is_pool=True)
        url = reverse('ipam-api:prefix-available-ips', kwargs={'pk': prefix.pk})
        self.add_permissions('ipam.view_prefix', 'ipam.add_ipaddress')

        # Create all four available IPs with individual requests
        for i in range(1, 5):
            data = {
                'description': 'Test IP {}'.format(i)
            }
            response = self.client.post(url, data, format='json', **self.header)
            self.assertHttpStatus(response, status.HTTP_201_CREATED)
            self.assertEqual(response.data['vrf']['id'], vrf.pk)
            self.assertEqual(response.data['description'], data['description'])

        # Try to create one more IP
        response = self.client.post(url, {}, format='json', **self.header)
        self.assertHttpStatus(response, status.HTTP_409_CONFLICT)
        self.assertIn('detail', response.data)

        ```
    ###Assistant : 
        Test retrieval of the first available IP address within a parent prefix.
        
    "
150,"def climate_adc_t3000_missing_setpoint_fixture(client, climate_adc_t3000_state):
    
    data = copy.deepcopy(climate_adc_t3000_state)
    data[""name""] = f""{data['name']} missing setpoint""
    for value in data[""values""][:]:
        if (
            value[""commandClassName""] == ""Humidity Control Setpoint""
            and value[""propertyKeyName""] == ""De-humidifier""
        ):
            data[""values""].remove(value)
    node = Node(client, data)
    client.driver.controller.nodes[node.node_id] = node
    return node


@pytest.fixture(name=""climate_adc_t3000_missing_mode"")",Mock a climate ADC-T3000 node with missing de-humidify setpoint.,9,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def climate_adc_t3000_missing_setpoint_fixture(client, climate_adc_t3000_state):
    
    data = copy.deepcopy(climate_adc_t3000_state)
    data[""name""] = f""{data['name']} missing setpoint""
    for value in data[""values""][:]:
        if (
            value[""commandClassName""] == ""Humidity Control Setpoint""
            and value[""propertyKeyName""] == ""De-humidifier""
        ):
            data[""values""].remove(value)
    node = Node(client, data)
    client.driver.controller.nodes[node.node_id] = node
    return node


@pytest.fixture(name=""climate_adc_t3000_missing_mode"")
        ```
    ###Assistant : Mock a climate ADC-T3000 node with missing de-humidify setpoint.
    "
151,"def classify_ode(eq, func=None, dict=False, ics=None, *, prep=True, xi=None, eta=None, n=None, **kwargs):
    r","
    Returns a tuple of possible :py:meth:`~sympy.solvers.ode.dsolve`
    classifications for an ODE.

    The tuple is ordered so that first item is the classification that
    :py:meth:`~sympy.solvers.ode.dsolve` uses to solve the ODE by default.  In
    general, classifications at the near the beginning of the list will
    produce better solutions faster than those near the end, thought there are
    always exceptions.  To make :py:meth:`~sympy.solvers.ode.dsolve` use a
    different classification, use ``dsolve(ODE, func,
    hint=<classification>)``.  See also the
    :py:meth:`~sympy.solvers.ode.dsolve` docstring for different meta-hints
    you can use.

    If ``dict`` is true, :py:meth:`~sympy.solvers.ode.classify_ode` will
    return a dictionary of ``hint:match`` expression terms. This is intended
    for internal use by :py:meth:`~sympy.solvers.ode.dsolve`.  Note that
    because dictionaries are ordered arbitrarily, this will most likely not be
    in the same order as the tuple.

    You can get help on different hints by executing
    ``help(ode.ode_hintname)``, where ``hintname`` is the name of the hint
    without ``_Integral``.

    See :py:data:`~sympy.solvers.ode.allhints` or the
    :py:mod:`~sympy.solvers.ode` docstring for a list of all supported hints
    that can be returned from :py:meth:`~sympy.solvers.ode.classify_ode`.

    Notes
    =====

    These are remarks on hint names.

    ``_Integral``

        If a classification has ``_Integral`` at the end, it will return the
        expression with an unevaluated :py:class:`~.Integral`",184,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def classify_ode(eq, func=None, dict=False, ics=None, *, prep=True, xi=None, eta=None, n=None, **kwargs):
    r
        ```
    ###Assistant : 
    Returns a tuple of possible :py:meth:`~sympy.solvers.ode.dsolve`
    classifications for an ODE.

    The tuple is ordered so that first item is the classification that
    :py:meth:`~sympy.solvers.ode.dsolve` uses to solve the ODE by default.  In
    general, classifications at the near the beginning of the list will
    produce better solutions faster than those near the end, thought there are
    always exceptions.  To make :py:meth:`~sympy.solvers.ode.dsolve` use a
    different classification, use ``dsolve(ODE, func,
    hint=<classification>)``.  See also the
    :py:meth:`~sympy.solvers.ode.dsolve` docstring for different meta-hints
    you can use.

    If ``dict`` is true, :py:meth:`~sympy.solvers.ode.classify_ode` will
    return a dictionary of ``hint:match`` expression terms. This is intended
    for internal use by :py:meth:`~sympy.solvers.ode.dsolve`.  Note that
    because dictionaries are ordered arbitrarily, this will most likely not be
    in the same order as the tuple.

    You can get help on different hints by executing
    ``help(ode.ode_hintname)``, where ``hintname`` is the name of the hint
    without ``_Integral``.

    See :py:data:`~sympy.solvers.ode.allhints` or the
    :py:mod:`~sympy.solvers.ode` docstring for a list of all supported hints
    that can be returned from :py:meth:`~sympy.solvers.ode.classify_ode`.

    Notes
    =====

    These are remarks on hint names.

    ``_Integral``

        If a classification has ``_Integral`` at the end, it will return the
        expression with an unevaluated :py:class:`~.Integral`
    "
152,"def mode(a, axis=0, nan_policy='propagate'):
    
    a, axis = _chk_asarray(a, axis)
    if a.size == 0:
        return ModeResult(np.array([]), np.array([]))

    contains_nan, nan_policy = _contains_nan(a, nan_policy)

    if contains_nan and nan_policy == 'omit':
        a = ma.masked_invalid(a)
        return mstats_basic.mode(a, axis)

    if a.dtype == object and np.nan in set(a.ravel()):
        # Fall back to a slower method since np.unique does not work with NaN
        scores = set(np.ravel(a))  # get ALL unique values
        testshape = list(a.shape)
        testshape[axis] = 1
        oldmostfreq = np.zeros(testshape, dtype=a.dtype)
        oldcounts = np.zeros(testshape, dtype=int)

        for score in scores:
            template = (a == score)
            counts = np.sum(template, axis, keepdims=True)
            mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
            oldcounts = np.maximum(counts, oldcounts)
            oldmostfreq = mostfrequent

        return ModeResult(mostfrequent, oldcounts)
","Return an array of the modal (most common) value in the passed array.

    If there is more than one such value, only the smallest is returned.
    The bin-count for the modal bins is also returned.

    Parameters
    ----------
    a : array_like
        n-dimensional array of which to find mode(s).
    axis : int or None, optional
        Axis along which to operate. Default is 0. If None, compute over
        the whole array `a`.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan.
        The following options are available (default is 'propagate'):

          * 'propagate': returns nan
          * 'raise': throws an error
          * 'omit': performs the calculations ignoring nan values

    Returns
    -------
    mode : ndarray
        Array of modal values.
    count : ndarray
        Array of counts for each mode.

    Examples
    --------
    >>> a = np.array([[6, 8, 3, 0],
    ...               [3, 2, 1, 7],
    ...               [8, 1, 8, 4],
    ...               [5, 3, 0, 5],
    ...               [4, 7, 5, 9]])
    >>> from scipy import stats
    >>> stats.mode(a)
    ModeResult(mode=array([[3, 1, 0, 0]]), count=array([[1, 1, 1, 1]]))

    To get mode of whole array, specify ``axis=None``:

    >>> stats.mode(a, axis=None)
    ModeResult(mode=array([3]), count=array([3]))

    ",183,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mode(a, axis=0, nan_policy='propagate'):
    
    a, axis = _chk_asarray(a, axis)
    if a.size == 0:
        return ModeResult(np.array([]), np.array([]))

    contains_nan, nan_policy = _contains_nan(a, nan_policy)

    if contains_nan and nan_policy == 'omit':
        a = ma.masked_invalid(a)
        return mstats_basic.mode(a, axis)

    if a.dtype == object and np.nan in set(a.ravel()):
        # Fall back to a slower method since np.unique does not work with NaN
        scores = set(np.ravel(a))  # get ALL unique values
        testshape = list(a.shape)
        testshape[axis] = 1
        oldmostfreq = np.zeros(testshape, dtype=a.dtype)
        oldcounts = np.zeros(testshape, dtype=int)

        for score in scores:
            template = (a == score)
            counts = np.sum(template, axis, keepdims=True)
            mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
            oldcounts = np.maximum(counts, oldcounts)
            oldmostfreq = mostfrequent

        return ModeResult(mostfrequent, oldcounts)

        ```
    ###Assistant : Return an array of the modal (most common) value in the passed array.

    If there is more than one such value, only the smallest is returned.
    The bin-count for the modal bins is also returned.

    Parameters
    ----------
    a : array_like
        n-dimensional array of which to find mode(s).
    axis : int or None, optional
        Axis along which to operate. Default is 0. If None, compute over
        the whole array `a`.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan.
        The following options are available (default is 'propagate'):

          * 'propagate': returns nan
          * 'raise': throws an error
          * 'omit': performs the calculations ignoring nan values

    Returns
    -------
    mode : ndarray
        Array of modal values.
    count : ndarray
        Array of counts for each mode.

    Examples
    --------
    >>> a = np.array([[6, 8, 3, 0],
    ...               [3, 2, 1, 7],
    ...               [8, 1, 8, 4],
    ...               [5, 3, 0, 5],
    ...               [4, 7, 5, 9]])
    >>> from scipy import stats
    >>> stats.mode(a)
    ModeResult(mode=array([[3, 1, 0, 0]]), count=array([[1, 1, 1, 1]]))

    To get mode of whole array, specify ``axis=None``:

    >>> stats.mode(a, axis=None)
    ModeResult(mode=array([3]), count=array([3]))

    
    "
153,"def wait_for_instance(self) -> AnsibleCoreCI:
        
        core_ci = self.get_instance()
        core_ci.wait()

        return core_ci
",Wait for an AnsibleCoreCI VM instance to become ready.,9,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wait_for_instance(self) -> AnsibleCoreCI:
        
        core_ci = self.get_instance()
        core_ci.wait()

        return core_ci

        ```
    ###Assistant : Wait for an AnsibleCoreCI VM instance to become ready.
    "
154,"def spherical_bessel_fn(n, x=None, polys=False):
    

    if n < 0:
        dup = dup_spherical_bessel_fn_minus(-int(n), ZZ)
    else:
        dup = dup_spherical_bessel_fn(int(n), ZZ)

    poly = DMP(dup, ZZ)

    if x is not None:
        poly = Poly.new(poly, 1/x)
    else:
        poly = PurePoly.new(poly, 1/Dummy('x'))

    return poly if polys else poly.as_expr()
","
    Coefficients for the spherical Bessel functions.

    Those are only needed in the jn() function.

    The coefficients are calculated from:

    fn(0, z) = 1/z
    fn(1, z) = 1/z**2
    fn(n-1, z) + fn(n+1, z) == (2*n+1)/z * fn(n, z)

    Parameters
    ==========

    n : int
        `n` decides the degree of polynomial
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.

    Examples
    ========

    >>> from sympy.polys.orthopolys import spherical_bessel_fn as fn
    >>> from sympy import Symbol
    >>> z = Symbol(""z"")
    >>> fn(1, z)
    z**(-2)
    >>> fn(2, z)
    -1/z + 3/z**3
    >>> fn(3, z)
    -6/z**2 + 15/z**4
    >>> fn(4, z)
    1/z - 45/z**3 + 105/z**5

    ",107,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def spherical_bessel_fn(n, x=None, polys=False):
    

    if n < 0:
        dup = dup_spherical_bessel_fn_minus(-int(n), ZZ)
    else:
        dup = dup_spherical_bessel_fn(int(n), ZZ)

    poly = DMP(dup, ZZ)

    if x is not None:
        poly = Poly.new(poly, 1/x)
    else:
        poly = PurePoly.new(poly, 1/Dummy('x'))

    return poly if polys else poly.as_expr()

        ```
    ###Assistant : 
    Coefficients for the spherical Bessel functions.

    Those are only needed in the jn() function.

    The coefficients are calculated from:

    fn(0, z) = 1/z
    fn(1, z) = 1/z**2
    fn(n-1, z) + fn(n+1, z) == (2*n+1)/z * fn(n, z)

    Parameters
    ==========

    n : int
        `n` decides the degree of polynomial
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.

    Examples
    ========

    >>> from sympy.polys.orthopolys import spherical_bessel_fn as fn
    >>> from sympy import Symbol
    >>> z = Symbol(""z"")
    >>> fn(1, z)
    z**(-2)
    >>> fn(2, z)
    -1/z + 3/z**3
    >>> fn(3, z)
    -6/z**2 + 15/z**4
    >>> fn(4, z)
    1/z - 45/z**3 + 105/z**5

    
    "
155,"def raft_large(*, pretrained=False, progress=True, **kwargs):
    

    return _raft(
        arch=""raft_large"",
        pretrained=pretrained,
        progress=progress,
        # Feature encoder
        feature_encoder_layers=(64, 64, 96, 128, 256),
        feature_encoder_block=ResidualBlock,
        feature_encoder_norm_layer=InstanceNorm2d,
        # Context encoder
        context_encoder_layers=(64, 64, 96, 128, 256),
        context_encoder_block=ResidualBlock,
        context_encoder_norm_layer=BatchNorm2d,
        # Correlation block
        corr_block_num_levels=4,
        corr_block_radius=4,
        # Motion encoder
        motion_encoder_corr_layers=(256, 192),
        motion_encoder_flow_layers=(128, 64),
        motion_encoder_out_channels=128,
        # Recurrent block
        recurrent_block_hidden_state_size=128,
        recurrent_block_kernel_size=((1, 5), (5, 1)),
        recurrent_block_padding=((0, 2), (2, 0)),
        # Flow head
        flow_head_hidden_size=256,
        # Mask predictor
        use_mask_predictor=True,
        **kwargs,
    )

","RAFT model from
    `RAFT: Recurrent All Pairs Field Transforms for Optical Flow <https://arxiv.org/abs/2003.12039>`_.

    Args:
        pretrained (bool): Whether to use weights that have been pre-trained on
            :class:`~torchvsion.datasets.FlyingChairs` + :class:`~torchvsion.datasets.FlyingThings3D`
            with two fine-tuning steps:

            - one on :class:`~torchvsion.datasets.Sintel` + :class:`~torchvsion.datasets.FlyingThings3D`
            - one on :class:`~torchvsion.datasets.KittiFlow`.

            This corresponds to the ``C+T+S/K`` strategy in the paper.

        progress (bool): If True, displays a progress bar of the download to stderr.

    Returns:
        nn.Module: The model.
    ",68,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def raft_large(*, pretrained=False, progress=True, **kwargs):
    

    return _raft(
        arch=""raft_large"",
        pretrained=pretrained,
        progress=progress,
        # Feature encoder
        feature_encoder_layers=(64, 64, 96, 128, 256),
        feature_encoder_block=ResidualBlock,
        feature_encoder_norm_layer=InstanceNorm2d,
        # Context encoder
        context_encoder_layers=(64, 64, 96, 128, 256),
        context_encoder_block=ResidualBlock,
        context_encoder_norm_layer=BatchNorm2d,
        # Correlation block
        corr_block_num_levels=4,
        corr_block_radius=4,
        # Motion encoder
        motion_encoder_corr_layers=(256, 192),
        motion_encoder_flow_layers=(128, 64),
        motion_encoder_out_channels=128,
        # Recurrent block
        recurrent_block_hidden_state_size=128,
        recurrent_block_kernel_size=((1, 5), (5, 1)),
        recurrent_block_padding=((0, 2), (2, 0)),
        # Flow head
        flow_head_hidden_size=256,
        # Mask predictor
        use_mask_predictor=True,
        **kwargs,
    )


        ```
    ###Assistant : RAFT model from
    `RAFT: Recurrent All Pairs Field Transforms for Optical Flow <https://arxiv.org/abs/2003.12039>`_.

    Args:
        pretrained (bool): Whether to use weights that have been pre-trained on
            :class:`~torchvsion.datasets.FlyingChairs` + :class:`~torchvsion.datasets.FlyingThings3D`
            with two fine-tuning steps:

            - one on :class:`~torchvsion.datasets.Sintel` + :class:`~torchvsion.datasets.FlyingThings3D`
            - one on :class:`~torchvsion.datasets.KittiFlow`.

            This corresponds to the ``C+T+S/K`` strategy in the paper.

        progress (bool): If True, displays a progress bar of the download to stderr.

    Returns:
        nn.Module: The model.
    
    "
156,"def _get_prettyprint_usage(self, console, executor_name, usage_kind=None):
        from rich.panel import Panel
        from rich.syntax import Syntax

        flow_plain = f

        flow_docker = f

        flow_sandbox = f
        panels = [
            Panel(
                Syntax(
                    p[0],
                    'python',
                    theme='monokai',
                    word_wrap=True,
                ),
                title=p[1],
                width=80,
                expand=False,
            )
            for p in [
                (flow_plain, 'Use via source'),
                (flow_docker, 'Use in Docker'),
                (flow_sandbox, 'Use in Sandbox'),
            ]
        ]

        if usage_kind == 'docker':
            console.print(panels[2])
        elif usage_kind == 'source':
            console.print(panels[1])
        else:
            console.print(*reversed(panels))
","from jina import Flow

f = Flow().add(uses='jinahub://{executor_name}')
from jina import Flow

f = Flow().add(uses='jinahub+docker://{executor_name}')
from jina import Flow

f = Flow().add(uses='jinahub+sandbox://{executor_name}')
",21,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_prettyprint_usage(self, console, executor_name, usage_kind=None):
        from rich.panel import Panel
        from rich.syntax import Syntax

        flow_plain = f

        flow_docker = f

        flow_sandbox = f
        panels = [
            Panel(
                Syntax(
                    p[0],
                    'python',
                    theme='monokai',
                    word_wrap=True,
                ),
                title=p[1],
                width=80,
                expand=False,
            )
            for p in [
                (flow_plain, 'Use via source'),
                (flow_docker, 'Use in Docker'),
                (flow_sandbox, 'Use in Sandbox'),
            ]
        ]

        if usage_kind == 'docker':
            console.print(panels[2])
        elif usage_kind == 'source':
            console.print(panels[1])
        else:
            console.print(*reversed(panels))

        ```
    ###Assistant : from jina import Flow

f = Flow().add(uses='jinahub://{executor_name}')
from jina import Flow

f = Flow().add(uses='jinahub+docker://{executor_name}')
from jina import Flow

f = Flow().add(uses='jinahub+sandbox://{executor_name}')

    "
157,"def encoding(self, val):
        
        self._encoding = val
        if hasattr(self, ""GET""):
            del self.GET
        if hasattr(self, ""_post""):
            del self._post
","
        Set the encoding used for GET/POST accesses. If the GET or POST
        dictionary has already been created, remove and recreate it on the
        next access (so that it is decoded correctly).
        ",31,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def encoding(self, val):
        
        self._encoding = val
        if hasattr(self, ""GET""):
            del self.GET
        if hasattr(self, ""_post""):
            del self._post

        ```
    ###Assistant : 
        Set the encoding used for GET/POST accesses. If the GET or POST
        dictionary has already been created, remove and recreate it on the
        next access (so that it is decoded correctly).
        
    "
158,"def scale(self, factor, scale_tips=False, **kwargs):
        r
        if self.get_length() == 0:
            return self

        if scale_tips:
            super().scale(factor, **kwargs)
            self._set_stroke_width_from_length()
            return self

        has_tip = self.has_tip()
        has_start_tip = self.has_start_tip()
        if has_tip or has_start_tip:
            old_tips = self.pop_tips()

        super().scale(factor, **kwargs)
        self._set_stroke_width_from_length()

        if has_tip:
            self.add_tip(tip=old_tips[0])
        if has_start_tip:
            self.add_tip(tip=old_tips[1], at_start=True)
        return self
","Scale an arrow, but keep stroke width and arrow tip size fixed.

        See Also
        --------
        :meth:`~.Mobject.scale`

        Examples
        --------
        ::

            >>> arrow = Arrow(np.array([-1, -1, 0]), np.array([1, 1, 0]), buff=0)
            >>> scaled_arrow = arrow.scale(2)
            >>> np.round(scaled_arrow.get_start_and_end(), 8) + 0
            array([[-2., -2.,  0.],
                   [ 2.,  2.,  0.]])
            >>> arrow.tip.length == scaled_arrow.tip.length
            True

        Manually scaling the object using the default method
        :meth:`~.Mobject.scale` does not have the same properties::

            >>> new_arrow = Arrow(np.array([-1, -1, 0]), np.array([1, 1, 0]), buff=0)
            >>> another_scaled_arrow = VMobject.scale(new_arrow, 2)
            >>> another_scaled_arrow.tip.length == arrow.tip.length
            False

        ",85,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def scale(self, factor, scale_tips=False, **kwargs):
        r
        if self.get_length() == 0:
            return self

        if scale_tips:
            super().scale(factor, **kwargs)
            self._set_stroke_width_from_length()
            return self

        has_tip = self.has_tip()
        has_start_tip = self.has_start_tip()
        if has_tip or has_start_tip:
            old_tips = self.pop_tips()

        super().scale(factor, **kwargs)
        self._set_stroke_width_from_length()

        if has_tip:
            self.add_tip(tip=old_tips[0])
        if has_start_tip:
            self.add_tip(tip=old_tips[1], at_start=True)
        return self

        ```
    ###Assistant : Scale an arrow, but keep stroke width and arrow tip size fixed.

        See Also
        --------
        :meth:`~.Mobject.scale`

        Examples
        --------
        ::

            >>> arrow = Arrow(np.array([-1, -1, 0]), np.array([1, 1, 0]), buff=0)
            >>> scaled_arrow = arrow.scale(2)
            >>> np.round(scaled_arrow.get_start_and_end(), 8) + 0
            array([[-2., -2.,  0.],
                   [ 2.,  2.,  0.]])
            >>> arrow.tip.length == scaled_arrow.tip.length
            True

        Manually scaling the object using the default method
        :meth:`~.Mobject.scale` does not have the same properties::

            >>> new_arrow = Arrow(np.array([-1, -1, 0]), np.array([1, 1, 0]), buff=0)
            >>> another_scaled_arrow = VMobject.scale(new_arrow, 2)
            >>> another_scaled_arrow.tip.length == arrow.tip.length
            False

        
    "
159,"def effective(file, line, frame):
    
    possibles = Breakpoint.bplist[file, line]
    for b in possibles:
        if not b.enabled:
            continue
        if not checkfuncname(b, frame):
            continue
        # Count every hit when bp is enabled
        b.hits += 1
        if not b.cond:
            # If unconditional, and ignoring go on to next, else break
            if b.ignore > 0:
                b.ignore -= 1
                continue
            else:
                # breakpoint and marker that it's ok to delete if temporary
                return (b, True)
        else:
            # Conditional bp.
            # Ignore count applies only to those bpt hits where the
            # condition evaluates to true.
            try:
                val = eval(b.cond, frame.f_globals, frame.f_locals)
                if val:
                    if b.ignore > 0:
                        b.ignore -= 1
                        # continue
                    else:
                        return (b, True)
                # else:
                #   continue
            except:
                # if eval fails, most conservative thing is to stop on
                # breakpoint regardless of ignore count.  Don't delete
                # temporary, as another hint to user.
                return (b, False)
    return (None, None)


# -------------------- testing --------------------
","Determine which breakpoint for this file:line is to be acted upon.

    Called only if we know there is a breakpoint at this location.  Return
    the breakpoint that was triggered and a boolean that indicates if it is
    ok to delete a temporary breakpoint.  Return (None, None) if there is no
    matching breakpoint.
    ",52,151,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def effective(file, line, frame):
    
    possibles = Breakpoint.bplist[file, line]
    for b in possibles:
        if not b.enabled:
            continue
        if not checkfuncname(b, frame):
            continue
        # Count every hit when bp is enabled
        b.hits += 1
        if not b.cond:
            # If unconditional, and ignoring go on to next, else break
            if b.ignore > 0:
                b.ignore -= 1
                continue
            else:
                # breakpoint and marker that it's ok to delete if temporary
                return (b, True)
        else:
            # Conditional bp.
            # Ignore count applies only to those bpt hits where the
            # condition evaluates to true.
            try:
                val = eval(b.cond, frame.f_globals, frame.f_locals)
                if val:
                    if b.ignore > 0:
                        b.ignore -= 1
                        # continue
                    else:
                        return (b, True)
                # else:
                #   continue
            except:
                # if eval fails, most conservative thing is to stop on
                # breakpoint regardless of ignore count.  Don't delete
                # temporary, as another hint to user.
                return (b, False)
    return (None, None)


# -------------------- testing --------------------

        ```
    ###Assistant : Determine which breakpoint for this file:line is to be acted upon.

    Called only if we know there is a breakpoint at this location.  Return
    the breakpoint that was triggered and a boolean that indicates if it is
    ok to delete a temporary breakpoint.  Return (None, None) if there is no
    matching breakpoint.
    
    "
160,"def _log_gauss_mass(a, b):
  
  a, b = jnp.array(a), jnp.array(b)
  a, b = jnp.broadcast_arrays(a, b)

  # Note: Docstring carried over from scipy
  # Calculations in right tail are inaccurate, so we'll exploit the
  # symmetry and work only in the left tail
  case_left = b <= 0
  case_right = a > 0
  case_central = ~(case_left | case_right)

  def mass_case_left(a, b):
    return _log_diff(log_ndtr(b), log_ndtr(a))

  def mass_case_right(a, b):
    return mass_case_left(-b, -a)

  def mass_case_central(a, b):
    # Note: Docstring carried over from scipy
    # Previously, this was implemented as:
    # left_mass = mass_case_left(a, 0)
    # right_mass = mass_case_right(0, b)
    # return _log_sum(left_mass, right_mass)
    # Catastrophic cancellation occurs as np.exp(log_mass) approaches 1.
    # Correct for this with an alternative formulation.
    # We're not concerned with underflow here: if only one term
    # underflows, it was insignificant; if both terms underflow,
    # the result can't accurately be represented in logspace anyway
    # because sc.log1p(x) ~ x for small x.
    return jnp.log1p(-ndtr(a) - ndtr(-b))

  out = jnp.select(
    [case_left, case_right, case_central],
    [mass_case_left(a, b), mass_case_right(a, b), mass_case_central(a, b)]
  )
  return out


@_wraps(osp_stats.truncnorm.logpdf, update_doc=False)",Log of Gaussian probability mass within an interval,8,172,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _log_gauss_mass(a, b):
  
  a, b = jnp.array(a), jnp.array(b)
  a, b = jnp.broadcast_arrays(a, b)

  # Note: Docstring carried over from scipy
  # Calculations in right tail are inaccurate, so we'll exploit the
  # symmetry and work only in the left tail
  case_left = b <= 0
  case_right = a > 0
  case_central = ~(case_left | case_right)

  def mass_case_left(a, b):
    return _log_diff(log_ndtr(b), log_ndtr(a))

  def mass_case_right(a, b):
    return mass_case_left(-b, -a)

  def mass_case_central(a, b):
    # Note: Docstring carried over from scipy
    # Previously, this was implemented as:
    # left_mass = mass_case_left(a, 0)
    # right_mass = mass_case_right(0, b)
    # return _log_sum(left_mass, right_mass)
    # Catastrophic cancellation occurs as np.exp(log_mass) approaches 1.
    # Correct for this with an alternative formulation.
    # We're not concerned with underflow here: if only one term
    # underflows, it was insignificant; if both terms underflow,
    # the result can't accurately be represented in logspace anyway
    # because sc.log1p(x) ~ x for small x.
    return jnp.log1p(-ndtr(a) - ndtr(-b))

  out = jnp.select(
    [case_left, case_right, case_central],
    [mass_case_left(a, b), mass_case_right(a, b), mass_case_central(a, b)]
  )
  return out


@_wraps(osp_stats.truncnorm.logpdf, update_doc=False)
        ```
    ###Assistant : Log of Gaussian probability mass within an interval
    "
161,"def test_fx_validator_integration(tmpdir):
    
    not_supported = {
        None: ""`self.trainer` reference is not registered"",
        ""on_before_accelerator_backend_setup"": ""You can't"",
        ""setup"": ""You can't"",
        ""configure_sharded_model"": ""You can't"",
        ""on_configure_sharded_model"": ""You can't"",
        ""configure_optimizers"": ""You can't"",
        ""on_fit_start"": ""You can't"",
        ""on_pretrain_routine_start"": ""You can't"",
        ""on_pretrain_routine_end"": ""You can't"",
        ""on_train_dataloader"": ""You can't"",
        ""train_dataloader"": ""You can't"",
        ""on_val_dataloader"": ""You can't"",
        ""val_dataloader"": ""You can't"",
        ""on_validation_end"": ""You can't"",
        ""on_train_end"": ""You can't"",
        ""on_fit_end"": ""You can't"",
        ""teardown"": ""You can't"",
        ""on_sanity_check_start"": ""You can't"",
        ""on_sanity_check_end"": ""You can't"",
        ""prepare_data"": ""You can't"",
        ""configure_callbacks"": ""You can't"",
        ""on_validation_model_eval"": ""You can't"",
        ""on_validation_model_train"": ""You can't"",
        ""lr_scheduler_step"": ""You can't"",
        ""summarize"": ""not managed by the `Trainer"",
    }
    model = HookedModel(not_supported)

    with pytest.warns(UserWarning, match=not_supported[None]):
        model.log(""foo"", 1)

    callback = HookedCallback(not_supported)
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=2,
        limit_train_batches=1,
        limit_val_batches=1,
        limit_test_batches=1,
        limit_predict_batches=1,
        callbacks=callback,
    )
    with pytest.deprecated_call(match=""on_train_dataloader` is deprecated in v1.5""):
        trainer.fit(model)

    not_supported.update(
        {
            # `lightning_module` ref is now present from the `fit` call
            ""on_before_accelerator_backend_setup"": ""You can't"",
            ""on_test_dataloader"": ""You can't"",
            ""test_dataloader"": ""You can't"",
            ""on_test_model_eval"": ""You can't"",
            ""on_test_model_train"": ""You can't"",
            ""on_test_end"": ""You can't"",
        }
    )
    with pytest.deprecated_call(match=""on_test_dataloader` is deprecated in v1.5""):
        trainer.test(model, verbose=False)

    not_supported.update({k: ""result collection is not registered yet"" for k in not_supported})
    not_supported.update(
        {
            ""on_predict_dataloader"": ""result collection is not registered yet"",
            ""predict_dataloader"": ""result collection is not registered yet"",
            ""on_predict_model_eval"": ""result collection is not registered yet"",
            ""on_predict_start"": ""result collection is not registered yet"",
            ""on_predict_epoch_start"": ""result collection is not registered yet"",
            ""on_predict_batch_start"": ""result collection is not registered yet"",
            ""predict_step"": ""result collection is not registered yet"",
            ""on_predict_batch_end"": ""result collection is not registered yet"",
            ""on_predict_epoch_end"": ""result collection is not registered yet"",
            ""on_predict_end"": ""result collection is not registered yet"",
        }
    )
    with pytest.deprecated_call(match=""on_predict_dataloader` is deprecated in v1.5""):
        trainer.predict(model)


@RunIf(min_gpus=2)",Tries to log inside all `LightningModule` and `Callback` hooks to check any expected errors.,14,249,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_fx_validator_integration(tmpdir):
    
    not_supported = {
        None: ""`self.trainer` reference is not registered"",
        ""on_before_accelerator_backend_setup"": ""You can't"",
        ""setup"": ""You can't"",
        ""configure_sharded_model"": ""You can't"",
        ""on_configure_sharded_model"": ""You can't"",
        ""configure_optimizers"": ""You can't"",
        ""on_fit_start"": ""You can't"",
        ""on_pretrain_routine_start"": ""You can't"",
        ""on_pretrain_routine_end"": ""You can't"",
        ""on_train_dataloader"": ""You can't"",
        ""train_dataloader"": ""You can't"",
        ""on_val_dataloader"": ""You can't"",
        ""val_dataloader"": ""You can't"",
        ""on_validation_end"": ""You can't"",
        ""on_train_end"": ""You can't"",
        ""on_fit_end"": ""You can't"",
        ""teardown"": ""You can't"",
        ""on_sanity_check_start"": ""You can't"",
        ""on_sanity_check_end"": ""You can't"",
        ""prepare_data"": ""You can't"",
        ""configure_callbacks"": ""You can't"",
        ""on_validation_model_eval"": ""You can't"",
        ""on_validation_model_train"": ""You can't"",
        ""lr_scheduler_step"": ""You can't"",
        ""summarize"": ""not managed by the `Trainer"",
    }
    model = HookedModel(not_supported)

    with pytest.warns(UserWarning, match=not_supported[None]):
        model.log(""foo"", 1)

    callback = HookedCallback(not_supported)
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=2,
        limit_train_batches=1,
        limit_val_batches=1,
        limit_test_batches=1,
        limit_predict_batches=1,
        callbacks=callback,
    )
    with pytest.deprecated_call(match=""on_train_dataloader` is deprecated in v1.5""):
        trainer.fit(model)

    not_supported.update(
        {
            # `lightning_module` ref is now present from the `fit` call
            ""on_before_accelerator_backend_setup"": ""You can't"",
            ""on_test_dataloader"": ""You can't"",
            ""test_dataloader"": ""You can't"",
            ""on_test_model_eval"": ""You can't"",
            ""on_test_model_train"": ""You can't"",
            ""on_test_end"": ""You can't"",
        }
    )
    with pytest.deprecated_call(match=""on_test_dataloader` is deprecated in v1.5""):
        trainer.test(model, verbose=False)

    not_supported.update({k: ""result collection is not registered yet"" for k in not_supported})
    not_supported.update(
        {
            ""on_predict_dataloader"": ""result collection is not registered yet"",
            ""predict_dataloader"": ""result collection is not registered yet"",
            ""on_predict_model_eval"": ""result collection is not registered yet"",
            ""on_predict_start"": ""result collection is not registered yet"",
            ""on_predict_epoch_start"": ""result collection is not registered yet"",
            ""on_predict_batch_start"": ""result collection is not registered yet"",
            ""predict_step"": ""result collection is not registered yet"",
            ""on_predict_batch_end"": ""result collection is not registered yet"",
            ""on_predict_epoch_end"": ""result collection is not registered yet"",
            ""on_predict_end"": ""result collection is not registered yet"",
        }
    )
    with pytest.deprecated_call(match=""on_predict_dataloader` is deprecated in v1.5""):
        trainer.predict(model)


@RunIf(min_gpus=2)
        ```
    ###Assistant : Tries to log inside all `LightningModule` and `Callback` hooks to check any expected errors.
    "
162,"def enable_application_mode() -> Callable[[], None]:
    

    terminal_in = sys.stdin
    terminal_out = sys.stdout

    current_console_mode_in = _get_console_mode(terminal_in)
    current_console_mode_out = _get_console_mode(terminal_out)
","Enable application mode.

    Returns:
        Callable[[], None]: A callable that will restore terminal to previous state.
    ",15,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def enable_application_mode() -> Callable[[], None]:
    

    terminal_in = sys.stdin
    terminal_out = sys.stdout

    current_console_mode_in = _get_console_mode(terminal_in)
    current_console_mode_out = _get_console_mode(terminal_out)

        ```
    ###Assistant : Enable application mode.

    Returns:
        Callable[[], None]: A callable that will restore terminal to previous state.
    
    "
163,"def get_attributes(self) -> dict[str, str]:
        
        return _attributes(
            message=self.message,
            type=self.type,
        )
",Return a dictionary of attributes for this instance.,8,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_attributes(self) -> dict[str, str]:
        
        return _attributes(
            message=self.message,
            type=self.type,
        )

        ```
    ###Assistant : Return a dictionary of attributes for this instance.
    "
164,"def check_handle_timedout(self) -> None:
        

        for trade in Trade.get_open_order_trades():
            try:
                if not trade.open_order_id:
                    continue
                order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
            except (ExchangeError):
                logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
                continue

            fully_cancelled = self.update_trade_state(trade, trade.open_order_id, order)

            if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
                    fully_cancelled
                    or self.strategy.ft_check_timed_out(
                        'buy', trade, order, datetime.now(timezone.utc))
                        )):
                self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])

            elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
                  fully_cancelled
                  or self.strategy.ft_check_timed_out(
                      'sell', trade, order, datetime.now(timezone.utc)))
                  ):
                self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
                canceled_count = trade.get_exit_order_count()
                max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
                if max_timeouts > 0 and canceled_count >= max_timeouts:
                    logger.warning(f'Emergencyselling trade {trade}, as the sell order '
                                   f'timed out {max_timeouts} times.')
                    try:
                        self.execute_trade_exit(
                            trade, order.get('price'),
                            sell_reason=SellCheckTuple(sell_type=SellType.EMERGENCY_SELL))
                    except DependencyException as exception:
                        logger.warning(f'Unable to emergency sell trade {trade.pair}: {exception}')
","
        Check if any orders are timed out and cancel if necessary
        :param timeoutvalue: Number of minutes until order is considered timed out
        :return: None
        ",24,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_handle_timedout(self) -> None:
        

        for trade in Trade.get_open_order_trades():
            try:
                if not trade.open_order_id:
                    continue
                order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
            except (ExchangeError):
                logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
                continue

            fully_cancelled = self.update_trade_state(trade, trade.open_order_id, order)

            if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
                    fully_cancelled
                    or self.strategy.ft_check_timed_out(
                        'buy', trade, order, datetime.now(timezone.utc))
                        )):
                self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])

            elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
                  fully_cancelled
                  or self.strategy.ft_check_timed_out(
                      'sell', trade, order, datetime.now(timezone.utc)))
                  ):
                self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
                canceled_count = trade.get_exit_order_count()
                max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
                if max_timeouts > 0 and canceled_count >= max_timeouts:
                    logger.warning(f'Emergencyselling trade {trade}, as the sell order '
                                   f'timed out {max_timeouts} times.')
                    try:
                        self.execute_trade_exit(
                            trade, order.get('price'),
                            sell_reason=SellCheckTuple(sell_type=SellType.EMERGENCY_SELL))
                    except DependencyException as exception:
                        logger.warning(f'Unable to emergency sell trade {trade.pair}: {exception}')

        ```
    ###Assistant : 
        Check if any orders are timed out and cancel if necessary
        :param timeoutvalue: Number of minutes until order is considered timed out
        :return: None
        
    "
165,"def test_user_misery_denominator(self):
        
        ProjectTransactionThreshold.objects.create(
            project=self.project,
            organization=self.project.organization,
            threshold=600,
            metric=TransactionMetric.LCP.value,
        )
        lcps = [
            400,
            400,
            300,
            3000,
            3000,
            3000,
        ]
        for idx, lcp in enumerate(lcps):
            data = self.load_data(
                timestamp=before_now(minutes=(10 + idx)),
            )
            data[""event_id""] = f""{idx}"" * 32
            data[""transaction""] = ""/misery/new/""
            data[""user""] = {""email"": f""{idx}@example.com""}
            data[""measurements""] = {
                ""lcp"": {""value"": lcp},
            }
            self.store_event(data, project_id=self.project.id)

        # Shouldn't count towards misery
        data = self.load_data(timestamp=self.ten_mins_ago, duration=timedelta(milliseconds=0))
        data[""transaction""] = ""/misery/new/""
        data[""user""] = {""email"": ""7@example.com""}
        data[""measurements""] = {}
        self.store_event(data, project_id=self.project.id)

        query = {
            ""field"": [
                ""transaction"",
                ""user_misery()"",
            ],
            ""query"": ""event.type:transaction"",
            ""project"": [self.project.id],
            ""sort"": ""-user_misery"",
        }

        response = self.do_request(
            query,
        )

        assert response.status_code == 200, response.content
        assert len(response.data[""data""]) == 1
        data = response.data[""data""]
        # (3 frustrated + 5.8875) / (6 + 117.75)
        assert abs(data[0][""user_misery()""] - 0.071818) < 0.0001
","This is to test against a bug where the denominator of misery(total unique users) was wrong
        This is because the total unique users for a LCP misery should only count users that have had a txn with lcp,
        and not count all transactions (ie. uniq_if(transaction has lcp) not just uniq())
        ",50,119,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_user_misery_denominator(self):
        
        ProjectTransactionThreshold.objects.create(
            project=self.project,
            organization=self.project.organization,
            threshold=600,
            metric=TransactionMetric.LCP.value,
        )
        lcps = [
            400,
            400,
            300,
            3000,
            3000,
            3000,
        ]
        for idx, lcp in enumerate(lcps):
            data = self.load_data(
                timestamp=before_now(minutes=(10 + idx)),
            )
            data[""event_id""] = f""{idx}"" * 32
            data[""transaction""] = ""/misery/new/""
            data[""user""] = {""email"": f""{idx}@example.com""}
            data[""measurements""] = {
                ""lcp"": {""value"": lcp},
            }
            self.store_event(data, project_id=self.project.id)

        # Shouldn't count towards misery
        data = self.load_data(timestamp=self.ten_mins_ago, duration=timedelta(milliseconds=0))
        data[""transaction""] = ""/misery/new/""
        data[""user""] = {""email"": ""7@example.com""}
        data[""measurements""] = {}
        self.store_event(data, project_id=self.project.id)

        query = {
            ""field"": [
                ""transaction"",
                ""user_misery()"",
            ],
            ""query"": ""event.type:transaction"",
            ""project"": [self.project.id],
            ""sort"": ""-user_misery"",
        }

        response = self.do_request(
            query,
        )

        assert response.status_code == 200, response.content
        assert len(response.data[""data""]) == 1
        data = response.data[""data""]
        # (3 frustrated + 5.8875) / (6 + 117.75)
        assert abs(data[0][""user_misery()""] - 0.071818) < 0.0001

        ```
    ###Assistant : This is to test against a bug where the denominator of misery(total unique users) was wrong
        This is because the total unique users for a LCP misery should only count users that have had a txn with lcp,
        and not count all transactions (ie. uniq_if(transaction has lcp) not just uniq())
        
    "
166,"def _determine_base_url(document, page_url):
    # type: (HTMLElement, str) -> str
    
    for base in document.findall("".//base""):
        href = base.get(""href"")
        if href is not None:
            return href
    return page_url

","Determine the HTML document's base URL.

    This looks for a ``<base>`` tag in the HTML document. If present, its href
    attribute denotes the base URL of anchor tags in the document. If there is
    no such tag (or if it does not have a valid href attribute), the HTML
    file's URL is used as the base URL.

    :param document: An HTML document representation. The current
        implementation expects the result of ``html5lib.parse()``.
    :param page_url: The URL of the HTML document.
    ",79,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _determine_base_url(document, page_url):
    # type: (HTMLElement, str) -> str
    
    for base in document.findall("".//base""):
        href = base.get(""href"")
        if href is not None:
            return href
    return page_url


        ```
    ###Assistant : Determine the HTML document's base URL.

    This looks for a ``<base>`` tag in the HTML document. If present, its href
    attribute denotes the base URL of anchor tags in the document. If there is
    no such tag (or if it does not have a valid href attribute), the HTML
    file's URL is used as the base URL.

    :param document: An HTML document representation. The current
        implementation expects the result of ``html5lib.parse()``.
    :param page_url: The URL of the HTML document.
    
    "
167,"def pairwise_distances(self, U, V):
        
        return self._distance_block.eval(feed_dict={self._features_batch1: U, self._features_batch2: V})

#----------------------------------------------------------------------------
",Evaluate pairwise distances between two batches of feature vectors.,9,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pairwise_distances(self, U, V):
        
        return self._distance_block.eval(feed_dict={self._features_batch1: U, self._features_batch2: V})

#----------------------------------------------------------------------------

        ```
    ###Assistant : Evaluate pairwise distances between two batches of feature vectors.
    "
168,"def upgrade():
    op.create_table(
        'project',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('created_at', sa.DateTime(), nullable=True),
        sa.Column('updated_at', sa.DateTime(), nullable=True),
        sa.Column('deleted_at', sa.DateTime(), nullable=True),
        sa.Column('name', sa.String(), nullable=False),
        sa.Column('company_id', sa.Integer(), nullable=True),
        sa.PrimaryKeyConstraint('id'),
        sa.UniqueConstraint('name', 'company_id', name='unique_integration_name_company_id')
    )

    conn = op.get_bind()
    session = sa.orm.Session(bind=conn)

    project_record = db.Project(name='mindsdb')
    session.add(project_record)
    session.commit()

    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.add_column(sa.Column('project_id', sa.Integer()))
        batch_op.create_foreign_key('fk_project_id', 'project', ['project_id'], ['id'])

    conn.execute(sa.sql.text(), project_id=project_record.id)

    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.alter_column(
            'project_id',
            existing_type=sa.INTEGER(),
            nullable=False
        )

    with op.batch_alter_table('view', schema=None) as batch_op:
        batch_op.add_column(sa.Column('project_id', sa.Integer()))
        batch_op.create_foreign_key('fk_project_id', 'project', ['project_id'], ['id'])

    conn.execute(sa.sql.text(), project_id=project_record.id)

    with op.batch_alter_table('view', schema=None) as batch_op:
        batch_op.alter_column(
            'project_id',
            existing_type=sa.INTEGER(),
            nullable=False
        )

    views = conn.execute().fetchall()

    for row in views:
        conn.execute(
            text(), {
                'name': f""{row['name']}_view"",
                'view_id': row['id']
            }
        )

    view_integration = session.query(db.Integration).filter_by(name='views').first()
    if view_integration is not None:
        session.delete(view_integration)

    session.commit()

","
        update predictor set project_id = :project_id
    
        update view set project_id = :project_id
    
        select id, name from view
        where exists (select 1 from predictor where view.name = predictor.name)
    
                update view
                set name = :name
                where id = :view_id
            ",37,110,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def upgrade():
    op.create_table(
        'project',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('created_at', sa.DateTime(), nullable=True),
        sa.Column('updated_at', sa.DateTime(), nullable=True),
        sa.Column('deleted_at', sa.DateTime(), nullable=True),
        sa.Column('name', sa.String(), nullable=False),
        sa.Column('company_id', sa.Integer(), nullable=True),
        sa.PrimaryKeyConstraint('id'),
        sa.UniqueConstraint('name', 'company_id', name='unique_integration_name_company_id')
    )

    conn = op.get_bind()
    session = sa.orm.Session(bind=conn)

    project_record = db.Project(name='mindsdb')
    session.add(project_record)
    session.commit()

    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.add_column(sa.Column('project_id', sa.Integer()))
        batch_op.create_foreign_key('fk_project_id', 'project', ['project_id'], ['id'])

    conn.execute(sa.sql.text(), project_id=project_record.id)

    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.alter_column(
            'project_id',
            existing_type=sa.INTEGER(),
            nullable=False
        )

    with op.batch_alter_table('view', schema=None) as batch_op:
        batch_op.add_column(sa.Column('project_id', sa.Integer()))
        batch_op.create_foreign_key('fk_project_id', 'project', ['project_id'], ['id'])

    conn.execute(sa.sql.text(), project_id=project_record.id)

    with op.batch_alter_table('view', schema=None) as batch_op:
        batch_op.alter_column(
            'project_id',
            existing_type=sa.INTEGER(),
            nullable=False
        )

    views = conn.execute().fetchall()

    for row in views:
        conn.execute(
            text(), {
                'name': f""{row['name']}_view"",
                'view_id': row['id']
            }
        )

    view_integration = session.query(db.Integration).filter_by(name='views').first()
    if view_integration is not None:
        session.delete(view_integration)

    session.commit()


        ```
    ###Assistant : 
        update predictor set project_id = :project_id
    
        update view set project_id = :project_id
    
        select id, name from view
        where exists (select 1 from predictor where view.name = predictor.name)
    
                update view
                set name = :name
                where id = :view_id
            
    "
169,"def clear(self) -> None:
        
        self._patterns: List[PatternType] = []
        self.matcher: Matcher = Matcher(self.nlp.vocab, validate=self.validate)
        self.phrase_matcher: PhraseMatcher = PhraseMatcher(
            self.nlp.vocab,
            attr=self.phrase_matcher_attr,
            validate=self.validate,
        )
","Reset all patterns.

        RETURNS: None
        DOCS: https://spacy.io/api/spanruler#clear
        ",7,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clear(self) -> None:
        
        self._patterns: List[PatternType] = []
        self.matcher: Matcher = Matcher(self.nlp.vocab, validate=self.validate)
        self.phrase_matcher: PhraseMatcher = PhraseMatcher(
            self.nlp.vocab,
            attr=self.phrase_matcher_attr,
            validate=self.validate,
        )

        ```
    ###Assistant : Reset all patterns.

        RETURNS: None
        DOCS: https://spacy.io/api/spanruler#clear
        
    "
170,"def _from_module(self, module, object):
        
        if module is None:
            return True
        elif inspect.getmodule(object) is not None:
            return module is inspect.getmodule(object)
        elif inspect.isfunction(object):
            return module.__dict__ is object.__globals__
        elif inspect.ismethoddescriptor(object):
            if hasattr(object, '__objclass__'):
                obj_mod = object.__objclass__.__module__
            elif hasattr(object, '__module__'):
                obj_mod = object.__module__
            else:
                return True # [XX] no easy way to tell otherwise
            return module.__name__ == obj_mod
        elif inspect.isclass(object):
            return module.__name__ == object.__module__
        elif hasattr(object, '__module__'):
            return module.__name__ == object.__module__
        elif isinstance(object, property):
            return True # [XX] no way not be sure.
        else:
            raise ValueError(""object must be a class or function"")
","
        Return true if the given object is defined in the given
        module.
        ",12,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _from_module(self, module, object):
        
        if module is None:
            return True
        elif inspect.getmodule(object) is not None:
            return module is inspect.getmodule(object)
        elif inspect.isfunction(object):
            return module.__dict__ is object.__globals__
        elif inspect.ismethoddescriptor(object):
            if hasattr(object, '__objclass__'):
                obj_mod = object.__objclass__.__module__
            elif hasattr(object, '__module__'):
                obj_mod = object.__module__
            else:
                return True # [XX] no easy way to tell otherwise
            return module.__name__ == obj_mod
        elif inspect.isclass(object):
            return module.__name__ == object.__module__
        elif hasattr(object, '__module__'):
            return module.__name__ == object.__module__
        elif isinstance(object, property):
            return True # [XX] no way not be sure.
        else:
            raise ValueError(""object must be a class or function"")

        ```
    ###Assistant : 
        Return true if the given object is defined in the given
        module.
        
    "
171,"def cut_ansi_string_into_parts(string_with_ansi_codes):
    
    color_codes_english = ['Black', 'Red', 'Green', 'Yellow', 'Blue', 'Magenta', 'Cyan', 'White', 'Reset']
    color_codes = [""30m"", ""31m"", ""32m"", ""33m"", ""34m"", ""35m"", ""36m"", ""37m"", ""0m""]
    effect_codes_english = ['Italic', 'Underline', 'Slow Blink', 'Rapid Blink', 'Crossed Out']
    effect_codes = [""3m"", ""4m"", ""5m"", ""6m"", ""9m""]
    background_codes = [""40m"", ""41m"", ""42m"", ""43m"", ""44m"", ""45m"", ""46m"", ""47m""]
    background_codes_english = [""Black"", ""Red"", ""Green"", ""Yellow"", ""Blue"", ""Magenta"", ""Cyan"", ""White""]

    ansi_codes = color_codes + effect_codes

    tuple_list = []

    string_list = string_with_ansi_codes.split(""\u001b["")

    if (len(string_list)) == 1:
        string_list = string_with_ansi_codes.split(""\033["")

    for teststring in string_list:
        if teststring == string_with_ansi_codes:
            tuple_list += [(teststring, None, None, None)]
            break
        if any(code in teststring for code in ansi_codes):
            static_string = None
            color_used = None
            effect_used = None
            background_used = None
            for color in range(0, len(color_codes)):
                if teststring.startswith(color_codes[color]):
                    working_thread = teststring.split(color_codes[color])
                    ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
                    static_string = ansi_strip.sub('', working_thread[1])
                    color_used = color_codes_english[color]
            for effect in range(0, len(effect_codes)):
                if teststring.startswith(effect_codes[effect]):
                    working_thread = teststring.split(effect_codes[effect])
                    ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
                    static_string = ansi_strip.sub('', working_thread[1])
                    effect_used = effect_codes_english[effect]
            for background in range(0, len(background_codes)):
                if teststring.startswith(background_codes[background]):
                    working_thread = teststring.split(background_codes[background])
                    ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
                    static_string = ansi_strip.sub('', working_thread[1])
                    background_used = background_codes_english[background]
            try:
                if not tuple_list[len(tuple_list) - 1][0]:
                    if not tuple_list[len(tuple_list) - 1][1] == None:
                        color_used = tuple_list[len(tuple_list) - 1][1]
                    if not tuple_list[len(tuple_list) - 1][2] == None:
                        background_used = tuple_list[len(tuple_list) - 1][2]
                    if not tuple_list[len(tuple_list) - 1][3] == None:
                        effect_used = tuple_list[len(tuple_list) - 1][3]
                    tuple_list += [(static_string, color_used, background_used, effect_used)]
                else:
                    tuple_list += [(static_string, color_used, background_used, effect_used)]
            except Exception:
                tuple_list += [(static_string, color_used, background_used, effect_used)]

    new_tuple_list = []

    for x in range(0, len(tuple_list)):
        if tuple_list[x][0]:
            new_tuple_list += [[tuple_list[x][0], tuple_list[x][1], tuple_list[x][2], tuple_list[x][3]]]

    return new_tuple_list

","
    Converts a string with ambedded ANSI Color Codes and parses it to create
    a list of tuples describing pieces of the input string.
    :param string_with_ansi_codes:
    :return: [(sty, str, str, str), ...] A list of tuples. Each tuple has format: (text, text color, background color, effects)
    ",45,258,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cut_ansi_string_into_parts(string_with_ansi_codes):
    
    color_codes_english = ['Black', 'Red', 'Green', 'Yellow', 'Blue', 'Magenta', 'Cyan', 'White', 'Reset']
    color_codes = [""30m"", ""31m"", ""32m"", ""33m"", ""34m"", ""35m"", ""36m"", ""37m"", ""0m""]
    effect_codes_english = ['Italic', 'Underline', 'Slow Blink', 'Rapid Blink', 'Crossed Out']
    effect_codes = [""3m"", ""4m"", ""5m"", ""6m"", ""9m""]
    background_codes = [""40m"", ""41m"", ""42m"", ""43m"", ""44m"", ""45m"", ""46m"", ""47m""]
    background_codes_english = [""Black"", ""Red"", ""Green"", ""Yellow"", ""Blue"", ""Magenta"", ""Cyan"", ""White""]

    ansi_codes = color_codes + effect_codes

    tuple_list = []

    string_list = string_with_ansi_codes.split(""\u001b["")

    if (len(string_list)) == 1:
        string_list = string_with_ansi_codes.split(""\033["")

    for teststring in string_list:
        if teststring == string_with_ansi_codes:
            tuple_list += [(teststring, None, None, None)]
            break
        if any(code in teststring for code in ansi_codes):
            static_string = None
            color_used = None
            effect_used = None
            background_used = None
            for color in range(0, len(color_codes)):
                if teststring.startswith(color_codes[color]):
                    working_thread = teststring.split(color_codes[color])
                    ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
                    static_string = ansi_strip.sub('', working_thread[1])
                    color_used = color_codes_english[color]
            for effect in range(0, len(effect_codes)):
                if teststring.startswith(effect_codes[effect]):
                    working_thread = teststring.split(effect_codes[effect])
                    ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
                    static_string = ansi_strip.sub('', working_thread[1])
                    effect_used = effect_codes_english[effect]
            for background in range(0, len(background_codes)):
                if teststring.startswith(background_codes[background]):
                    working_thread = teststring.split(background_codes[background])
                    ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
                    static_string = ansi_strip.sub('', working_thread[1])
                    background_used = background_codes_english[background]
            try:
                if not tuple_list[len(tuple_list) - 1][0]:
                    if not tuple_list[len(tuple_list) - 1][1] == None:
                        color_used = tuple_list[len(tuple_list) - 1][1]
                    if not tuple_list[len(tuple_list) - 1][2] == None:
                        background_used = tuple_list[len(tuple_list) - 1][2]
                    if not tuple_list[len(tuple_list) - 1][3] == None:
                        effect_used = tuple_list[len(tuple_list) - 1][3]
                    tuple_list += [(static_string, color_used, background_used, effect_used)]
                else:
                    tuple_list += [(static_string, color_used, background_used, effect_used)]
            except Exception:
                tuple_list += [(static_string, color_used, background_used, effect_used)]

    new_tuple_list = []

    for x in range(0, len(tuple_list)):
        if tuple_list[x][0]:
            new_tuple_list += [[tuple_list[x][0], tuple_list[x][1], tuple_list[x][2], tuple_list[x][3]]]

    return new_tuple_list


        ```
    ###Assistant : 
    Converts a string with ambedded ANSI Color Codes and parses it to create
    a list of tuples describing pieces of the input string.
    :param string_with_ansi_codes:
    :return: [(sty, str, str, str), ...] A list of tuples. Each tuple has format: (text, text color, background color, effects)
    
    "
172,"def interpolate(self, f=None, f_step=DEFAULT_STEP, pol_order=1, f_min=DEFAULT_F_MIN, f_max=DEFAULT_F_MAX):
        
        # Remove None values
        i = 0
        while i < len(self.raw):
            if self.raw[i] is None:
                self.raw = np.delete(self.raw, i)
                self.frequency = np.delete(self.frequency, i)
            else:
                i += 1

        # Interpolation functions
        keys = 'raw error error_smoothed equalization equalized_raw equalized_smoothed target'.split()
        interpolators = dict()
        log_f = np.log10(self.frequency)
        for key in keys:
            if len(self.__dict__[key]):
                interpolators[key] = InterpolatedUnivariateSpline(log_f, self.__dict__[key], k=pol_order)

        if f is None:
            self.frequency = self.generate_frequencies(f_min=f_min, f_max=f_max, f_step=f_step)
        else:
            self.frequency = np.array(f)

        # Prevent log10 from exploding by replacing zero frequency with small value
        zero_freq_fix = False
        if self.frequency[0] == 0:
            self.frequency[0] = 0.001
            zero_freq_fix = True

        # Run interpolators
        log_f = np.log10(self.frequency)
        for key in keys:
            if len(self.__dict__[key]) and key in interpolators:
                self.__dict__[key] = interpolators[key](log_f)

        if zero_freq_fix:
            # Restore zero frequency
            self.frequency[0] = 0

        # Everything but the interpolated data is affected by interpolating, reset them
        self.reset(**{key: False for key in keys})
",Interpolates missing values from previous and next value. Resets all but raw data.,13,147,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def interpolate(self, f=None, f_step=DEFAULT_STEP, pol_order=1, f_min=DEFAULT_F_MIN, f_max=DEFAULT_F_MAX):
        
        # Remove None values
        i = 0
        while i < len(self.raw):
            if self.raw[i] is None:
                self.raw = np.delete(self.raw, i)
                self.frequency = np.delete(self.frequency, i)
            else:
                i += 1

        # Interpolation functions
        keys = 'raw error error_smoothed equalization equalized_raw equalized_smoothed target'.split()
        interpolators = dict()
        log_f = np.log10(self.frequency)
        for key in keys:
            if len(self.__dict__[key]):
                interpolators[key] = InterpolatedUnivariateSpline(log_f, self.__dict__[key], k=pol_order)

        if f is None:
            self.frequency = self.generate_frequencies(f_min=f_min, f_max=f_max, f_step=f_step)
        else:
            self.frequency = np.array(f)

        # Prevent log10 from exploding by replacing zero frequency with small value
        zero_freq_fix = False
        if self.frequency[0] == 0:
            self.frequency[0] = 0.001
            zero_freq_fix = True

        # Run interpolators
        log_f = np.log10(self.frequency)
        for key in keys:
            if len(self.__dict__[key]) and key in interpolators:
                self.__dict__[key] = interpolators[key](log_f)

        if zero_freq_fix:
            # Restore zero frequency
            self.frequency[0] = 0

        # Everything but the interpolated data is affected by interpolating, reset them
        self.reset(**{key: False for key in keys})

        ```
    ###Assistant : Interpolates missing values from previous and next value. Resets all but raw data.
    "
173,"def step(self, closure=None):
        
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        device = self.param_groups[0]['params'][0].device
        one_tensor = torch.tensor(1.0, device=device)  # because torch.where doesn't handle scalars correctly

        for group in self.param_groups:
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']
            trust_coeff = group['trust_coeff']
            eps = group['eps']

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad

                # apply LARS LR adaptation, LARC clipping, weight decay
                # ref: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
                if weight_decay != 0 or group['always_adapt']:
                    w_norm = p.norm(2.0)
                    g_norm = grad.norm(2.0)
                    trust_ratio = trust_coeff * w_norm / (g_norm + w_norm * weight_decay + eps)
                    # FIXME nested where required since logical and/or not working in PT XLA
                    trust_ratio = torch.where(
                        w_norm > 0,
                        torch.where(g_norm > 0, trust_ratio, one_tensor),
                        one_tensor,
                    )
                    if group['trust_clip']:
                        trust_ratio = torch.minimum(trust_ratio / group['lr'], one_tensor)
                    grad.add_(p, alpha=weight_decay)
                    grad.mul_(trust_ratio)

                # apply SGD update https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
                if momentum != 0:
                    param_state = self.state[p]
                    if 'momentum_buffer' not in param_state:
                        buf = param_state['momentum_buffer'] = torch.clone(grad).detach()
                    else:
                        buf = param_state['momentum_buffer']
                        buf.mul_(momentum).add_(grad, alpha=1. - dampening)
                    if nesterov:
                        grad = grad.add(buf, alpha=momentum)
                    else:
                        grad = buf

                p.add_(grad, alpha=-group['lr'])

        return loss","Performs a single optimization step.

        Args:
            closure (callable, optional): A closure that reevaluates the model and returns the loss.
        ",19,182,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def step(self, closure=None):
        
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        device = self.param_groups[0]['params'][0].device
        one_tensor = torch.tensor(1.0, device=device)  # because torch.where doesn't handle scalars correctly

        for group in self.param_groups:
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']
            trust_coeff = group['trust_coeff']
            eps = group['eps']

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad

                # apply LARS LR adaptation, LARC clipping, weight decay
                # ref: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
                if weight_decay != 0 or group['always_adapt']:
                    w_norm = p.norm(2.0)
                    g_norm = grad.norm(2.0)
                    trust_ratio = trust_coeff * w_norm / (g_norm + w_norm * weight_decay + eps)
                    # FIXME nested where required since logical and/or not working in PT XLA
                    trust_ratio = torch.where(
                        w_norm > 0,
                        torch.where(g_norm > 0, trust_ratio, one_tensor),
                        one_tensor,
                    )
                    if group['trust_clip']:
                        trust_ratio = torch.minimum(trust_ratio / group['lr'], one_tensor)
                    grad.add_(p, alpha=weight_decay)
                    grad.mul_(trust_ratio)

                # apply SGD update https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
                if momentum != 0:
                    param_state = self.state[p]
                    if 'momentum_buffer' not in param_state:
                        buf = param_state['momentum_buffer'] = torch.clone(grad).detach()
                    else:
                        buf = param_state['momentum_buffer']
                        buf.mul_(momentum).add_(grad, alpha=1. - dampening)
                    if nesterov:
                        grad = grad.add(buf, alpha=momentum)
                    else:
                        grad = buf

                p.add_(grad, alpha=-group['lr'])

        return loss
        ```
    ###Assistant : Performs a single optimization step.

        Args:
            closure (callable, optional): A closure that reevaluates the model and returns the loss.
        
    "
174,"async def _async_force_resync(self, *_):
        
        self._forced_resync = None
        await self._async_force_refresh_state()
",Force a resync after an update since the hub may have stale state.,13,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _async_force_resync(self, *_):
        
        self._forced_resync = None
        await self._async_force_refresh_state()

        ```
    ###Assistant : Force a resync after an update since the hub may have stale state.
    "
175,"def set_color_codes(palette=""deep""):
    
    if palette == ""reset"":
        colors = [
            (0., 0., 1.),
            (0., .5, 0.),
            (1., 0., 0.),
            (.75, 0., .75),
            (.75, .75, 0.),
            (0., .75, .75),
            (0., 0., 0.)
        ]
    elif not isinstance(palette, str):
        err = ""set_color_codes requires a named seaborn palette""
        raise TypeError(err)
    elif palette in SEABORN_PALETTES:
        if not palette.endswith(""6""):
            palette = palette + ""6""
        colors = SEABORN_PALETTES[palette] + [(.1, .1, .1)]
    else:
        err = f""Cannot set colors with palette '{palette}'""
        raise ValueError(err)

    for code, color in zip(""bgrmyck"", colors):
        rgb = mpl.colors.colorConverter.to_rgb(color)
        mpl.colors.colorConverter.colors[code] = rgb
        mpl.colors.colorConverter.cache[code] = rgb
","Change how matplotlib color shorthands are interpreted.

    Calling this will change how shorthand codes like ""b"" or ""g""
    are interpreted by matplotlib in subsequent plots.

    Parameters
    ----------
    palette : {deep, muted, pastel, dark, bright, colorblind}
        Named seaborn palette to use as the source of colors.

    See Also
    --------
    set : Color codes can be set through the high-level seaborn style
          manager.
    set_palette : Color codes can also be set through the function that
                  sets the matplotlib color cycle.

    ",78,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_color_codes(palette=""deep""):
    
    if palette == ""reset"":
        colors = [
            (0., 0., 1.),
            (0., .5, 0.),
            (1., 0., 0.),
            (.75, 0., .75),
            (.75, .75, 0.),
            (0., .75, .75),
            (0., 0., 0.)
        ]
    elif not isinstance(palette, str):
        err = ""set_color_codes requires a named seaborn palette""
        raise TypeError(err)
    elif palette in SEABORN_PALETTES:
        if not palette.endswith(""6""):
            palette = palette + ""6""
        colors = SEABORN_PALETTES[palette] + [(.1, .1, .1)]
    else:
        err = f""Cannot set colors with palette '{palette}'""
        raise ValueError(err)

    for code, color in zip(""bgrmyck"", colors):
        rgb = mpl.colors.colorConverter.to_rgb(color)
        mpl.colors.colorConverter.colors[code] = rgb
        mpl.colors.colorConverter.cache[code] = rgb

        ```
    ###Assistant : Change how matplotlib color shorthands are interpreted.

    Calling this will change how shorthand codes like ""b"" or ""g""
    are interpreted by matplotlib in subsequent plots.

    Parameters
    ----------
    palette : {deep, muted, pastel, dark, bright, colorblind}
        Named seaborn palette to use as the source of colors.

    See Also
    --------
    set : Color codes can be set through the high-level seaborn style
          manager.
    set_palette : Color codes can also be set through the function that
                  sets the matplotlib color cycle.

    
    "
176,"def test_mapped_literal_length_increase_at_runtime_adds_additional_tis(dag_maker, session):
    
    from airflow.models import Variable

    Variable.set(key='arg1', value=[1, 2, 3])
","Test that when the length of mapped literal increases at runtime, additional ti is added",15,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_mapped_literal_length_increase_at_runtime_adds_additional_tis(dag_maker, session):
    
    from airflow.models import Variable

    Variable.set(key='arg1', value=[1, 2, 3])

        ```
    ###Assistant : Test that when the length of mapped literal increases at runtime, additional ti is added
    "
177,"def create_png_thumbnail_file(self, thumb_dir):
        
        thumb_file = Path(thumb_dir) / Path(f""{self.doc.pk:07}.png"")
        thumb_file.write_text(""this is a dummy png file"")
        return thumb_file
","
        Creates a dummy PNG thumbnail file in the given directory, based on
        the database Document
        ",15,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_png_thumbnail_file(self, thumb_dir):
        
        thumb_file = Path(thumb_dir) / Path(f""{self.doc.pk:07}.png"")
        thumb_file.write_text(""this is a dummy png file"")
        return thumb_file

        ```
    ###Assistant : 
        Creates a dummy PNG thumbnail file in the given directory, based on
        the database Document
        
    "
178,"def test_parse_html(self):
        assert validate(parse_html(), '<!DOCTYPE html><body>&quot;perfectly&quot;<a>valid<div>HTML').tag == ""html""
        with self.assertRaises(ValueError) as cm:
            validate(parse_html(), None)
        assert_validationerror(cm.exception, )
","
            ValidationError:
              Unable to parse HTML: can only parse strings (None)
        ",10,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_parse_html(self):
        assert validate(parse_html(), '<!DOCTYPE html><body>&quot;perfectly&quot;<a>valid<div>HTML').tag == ""html""
        with self.assertRaises(ValueError) as cm:
            validate(parse_html(), None)
        assert_validationerror(cm.exception, )

        ```
    ###Assistant : 
            ValidationError:
              Unable to parse HTML: can only parse strings (None)
        
    "
179,"def _check_feature_names_in(estimator, input_features=None, *, generate_names=True):
    

    feature_names_in_ = getattr(estimator, ""feature_names_in_"", None)
    n_features_in_ = getattr(estimator, ""n_features_in_"", None)

    if input_features is not None:
        input_features = np.asarray(input_features, dtype=object)
        if feature_names_in_ is not None and not np.array_equal(
            feature_names_in_, input_features
        ):
            raise ValueError(""input_features is not equal to feature_names_in_"")

        if n_features_in_ is not None and len(input_features) != n_features_in_:
            raise ValueError(
                ""input_features should have length equal to number of ""
                f""features ({n_features_in_}), got {len(input_features)}""
            )
        return input_features

    if feature_names_in_ is not None:
        return feature_names_in_

    if not generate_names:
        return

    # Generates feature names if `n_features_in_` is defined
    if n_features_in_ is None:
        raise ValueError(""Unable to generate feature names without n_features_in_"")

    return np.asarray([f""x{i}"" for i in range(n_features_in_)], dtype=object)

","Check `input_features` and generate names if needed.

    Commonly used in :term:`get_feature_names_out`.

    Parameters
    ----------
    input_features : array-like of str or None, default=None
        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is
            used as feature names in. If `feature_names_in_` is not defined,
            then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
        - If `input_features` is an array-like, then `input_features` must
            match `feature_names_in_` if `feature_names_in_` is defined.

    generate_names : bool, default=True
        Whether to generate names when `input_features` is `None` and
        `estimator.feature_names_in_` is not defined. This is useful for transformers
        that validates `input_features` but do not require them in
        :term:`get_feature_names_out` e.g. `PCA`.

    Returns
    -------
    feature_names_in : ndarray of str or `None`
        Feature names in.
    ",110,107,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_feature_names_in(estimator, input_features=None, *, generate_names=True):
    

    feature_names_in_ = getattr(estimator, ""feature_names_in_"", None)
    n_features_in_ = getattr(estimator, ""n_features_in_"", None)

    if input_features is not None:
        input_features = np.asarray(input_features, dtype=object)
        if feature_names_in_ is not None and not np.array_equal(
            feature_names_in_, input_features
        ):
            raise ValueError(""input_features is not equal to feature_names_in_"")

        if n_features_in_ is not None and len(input_features) != n_features_in_:
            raise ValueError(
                ""input_features should have length equal to number of ""
                f""features ({n_features_in_}), got {len(input_features)}""
            )
        return input_features

    if feature_names_in_ is not None:
        return feature_names_in_

    if not generate_names:
        return

    # Generates feature names if `n_features_in_` is defined
    if n_features_in_ is None:
        raise ValueError(""Unable to generate feature names without n_features_in_"")

    return np.asarray([f""x{i}"" for i in range(n_features_in_)], dtype=object)


        ```
    ###Assistant : Check `input_features` and generate names if needed.

    Commonly used in :term:`get_feature_names_out`.

    Parameters
    ----------
    input_features : array-like of str or None, default=None
        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is
            used as feature names in. If `feature_names_in_` is not defined,
            then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
        - If `input_features` is an array-like, then `input_features` must
            match `feature_names_in_` if `feature_names_in_` is defined.

    generate_names : bool, default=True
        Whether to generate names when `input_features` is `None` and
        `estimator.feature_names_in_` is not defined. This is useful for transformers
        that validates `input_features` but do not require them in
        :term:`get_feature_names_out` e.g. `PCA`.

    Returns
    -------
    feature_names_in : ndarray of str or `None`
        Feature names in.
    
    "
180,"def all_pairs_bellman_ford_path(G, weight=""weight""):
    
    path = single_source_bellman_ford_path
    # TODO This can be trivially parallelized.
    for n in G:
        yield (n, path(G, n, weight=weight))

","Compute shortest paths between all nodes in a weighted graph.

    Parameters
    ----------
    G : NetworkX graph

    weight : string or function (default=""weight"")
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number.

    Returns
    -------
    distance : dictionary
        Dictionary, keyed by source and target, of shortest paths.

    Examples
    --------
    >>> G = nx.path_graph(5)
    >>> path = dict(nx.all_pairs_bellman_ford_path(G))
    >>> path[0][4]
    [0, 1, 2, 3, 4]

    Notes
    -----
    Edge weight attributes must be numerical.
    Distances are calculated as sums of weighted edges traversed.

    See Also
    --------
    floyd_warshall, all_pairs_dijkstra_path

    ",170,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def all_pairs_bellman_ford_path(G, weight=""weight""):
    
    path = single_source_bellman_ford_path
    # TODO This can be trivially parallelized.
    for n in G:
        yield (n, path(G, n, weight=weight))


        ```
    ###Assistant : Compute shortest paths between all nodes in a weighted graph.

    Parameters
    ----------
    G : NetworkX graph

    weight : string or function (default=""weight"")
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number.

    Returns
    -------
    distance : dictionary
        Dictionary, keyed by source and target, of shortest paths.

    Examples
    --------
    >>> G = nx.path_graph(5)
    >>> path = dict(nx.all_pairs_bellman_ford_path(G))
    >>> path[0][4]
    [0, 1, 2, 3, 4]

    Notes
    -----
    Edge weight attributes must be numerical.
    Distances are calculated as sums of weighted edges traversed.

    See Also
    --------
    floyd_warshall, all_pairs_dijkstra_path

    
    "
181,"def update_cost_in_all_boms_in_test():
	
	log = enqueue_update_cost()  # create BOM Update Log

	while log.status != ""Completed"":
		resume_bom_cost_update_jobs()  # run cron job until complete
		log.reload()

	return log
","
	Utility to run 'Update Cost' job in tests without Cron job until fully complete.
	",14,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_cost_in_all_boms_in_test():
	
	log = enqueue_update_cost()  # create BOM Update Log

	while log.status != ""Completed"":
		resume_bom_cost_update_jobs()  # run cron job until complete
		log.reload()

	return log

        ```
    ###Assistant : 
	Utility to run 'Update Cost' job in tests without Cron job until fully complete.
	
    "
182,"def clear(self) -> None:
        
        self.row_count = 0
        self._clear_caches()
        self._y_offsets.clear()
        self.data.clear()
        self.rows.clear()
        self._line_no = 0
        self._require_update_dimensions = True
        self.refresh()
","Clear the table.

        Args:
            columns (bool, optional): Also clear the columns. Defaults to False.
        ",14,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clear(self) -> None:
        
        self.row_count = 0
        self._clear_caches()
        self._y_offsets.clear()
        self.data.clear()
        self.rows.clear()
        self._line_no = 0
        self._require_update_dimensions = True
        self.refresh()

        ```
    ###Assistant : Clear the table.

        Args:
            columns (bool, optional): Also clear the columns. Defaults to False.
        
    "
183,"def copyFile(source_path, dest_path):
    

    while 1:
        try:
            shutil.copyfile(source_path, dest_path)
        except PermissionError as e:
            if e.errno != errno.EACCES:
                raise

            general.warning(""Problem copying file %s:"" % e)

            try:
                reply = raw_input(""Retry? (YES/no) "") or ""yes""
            except EOFError:
                reply = ""no""

            if reply.upper() == ""YES"":
                continue

            raise

        break

","Improved version of shutil.copy

    This handles errors with a chance to correct them, e.g. on Windows, files might be
    locked by running program or virus checkers.
    ",26,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copyFile(source_path, dest_path):
    

    while 1:
        try:
            shutil.copyfile(source_path, dest_path)
        except PermissionError as e:
            if e.errno != errno.EACCES:
                raise

            general.warning(""Problem copying file %s:"" % e)

            try:
                reply = raw_input(""Retry? (YES/no) "") or ""yes""
            except EOFError:
                reply = ""no""

            if reply.upper() == ""YES"":
                continue

            raise

        break


        ```
    ###Assistant : Improved version of shutil.copy

    This handles errors with a chance to correct them, e.g. on Windows, files might be
    locked by running program or virus checkers.
    
    "
184,"def test_iforest(global_random_seed):
    
    X_train = np.array([[0, 1], [1, 2]])
    X_test = np.array([[2, 1], [1, 1]])

    grid = ParameterGrid(
        {""n_estimators"": [3], ""max_samples"": [0.5, 1.0, 3], ""bootstrap"": [True, False]}
    )

    with ignore_warnings():
        for params in grid:
            IsolationForest(random_state=global_random_seed, **params).fit(
                X_train
            ).predict(X_test)

",Check Isolation Forest for various parameter settings.,7,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_iforest(global_random_seed):
    
    X_train = np.array([[0, 1], [1, 2]])
    X_test = np.array([[2, 1], [1, 1]])

    grid = ParameterGrid(
        {""n_estimators"": [3], ""max_samples"": [0.5, 1.0, 3], ""bootstrap"": [True, False]}
    )

    with ignore_warnings():
        for params in grid:
            IsolationForest(random_state=global_random_seed, **params).fit(
                X_train
            ).predict(X_test)


        ```
    ###Assistant : Check Isolation Forest for various parameter settings.
    "
185,"def invalidate_caches(self):
        

_register(MetaPathFinder, machinery.BuiltinImporter, machinery.FrozenImporter,
          machinery.PathFinder, machinery.WindowsRegistryFinder)

","An optional method for clearing the finder's cache, if any.
        This method is used by importlib.invalidate_caches().
        ",16,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def invalidate_caches(self):
        

_register(MetaPathFinder, machinery.BuiltinImporter, machinery.FrozenImporter,
          machinery.PathFinder, machinery.WindowsRegistryFinder)


        ```
    ###Assistant : An optional method for clearing the finder's cache, if any.
        This method is used by importlib.invalidate_caches().
        
    "
186,"def reap(instance=None, status='failed', excluded_uuids=[]):
    
    me = instance
    if me is None:
        try:
            me = Instance.objects.me()
        except RuntimeError as e:
            logger.warning(f'Local instance is not registered, not running reaper: {e}')
            return
    workflow_ctype_id = ContentType.objects.get_for_model(WorkflowJob).id
    jobs = UnifiedJob.objects.filter(
        Q(status='running') & (Q(execution_node=me.hostname) | Q(controller_node=me.hostname)) & ~Q(polymorphic_ctype_id=workflow_ctype_id)
    ).exclude(celery_task_id__in=excluded_uuids)
    for j in jobs:
        reap_job(j, status)
","
    Reap all jobs in running for this instance.
    ",8,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reap(instance=None, status='failed', excluded_uuids=[]):
    
    me = instance
    if me is None:
        try:
            me = Instance.objects.me()
        except RuntimeError as e:
            logger.warning(f'Local instance is not registered, not running reaper: {e}')
            return
    workflow_ctype_id = ContentType.objects.get_for_model(WorkflowJob).id
    jobs = UnifiedJob.objects.filter(
        Q(status='running') & (Q(execution_node=me.hostname) | Q(controller_node=me.hostname)) & ~Q(polymorphic_ctype_id=workflow_ctype_id)
    ).exclude(celery_task_id__in=excluded_uuids)
    for j in jobs:
        reap_job(j, status)

        ```
    ###Assistant : 
    Reap all jobs in running for this instance.
    
    "
187,"def closure(self, rel, depth=-1):
        

        from nltk.util import acyclic_breadth_first

        for synset in acyclic_breadth_first(self, rel, depth):
            if synset != self:
                yield synset

    from nltk.util import acyclic_depth_first as acyclic_tree
    from nltk.util import unweighted_minimum_spanning_tree as mst

    # Also add this shortcut?
    #    from nltk.util import unweighted_minimum_spanning_digraph as umsd
","
        Return the transitive closure of source under the rel
        relationship, breadth-first, discarding cycles:

        >>> from nltk.corpus import wordnet as wn
        >>> computer = wn.synset('computer.n.01')
        >>> topic = lambda s:s.topic_domains()
        >>> print(list(computer.closure(topic)))
        [Synset('computer_science.n.01')]

        UserWarning: Discarded redundant search for Synset('computer.n.01') at depth 2


        Include redundant paths (but only once), avoiding duplicate searches
        (from 'animal.n.01' to 'entity.n.01'):

        >>> dog = wn.synset('dog.n.01')
        >>> hyp = lambda s:s.hypernyms()
        >>> print(list(dog.closure(hyp)))
        [Synset('canine.n.02'), Synset('domestic_animal.n.01'), Synset('carnivore.n.01'),\
 Synset('animal.n.01'), Synset('placental.n.01'), Synset('organism.n.01'),\
 Synset('mammal.n.01'), Synset('living_thing.n.01'), Synset('vertebrate.n.01'),\
 Synset('whole.n.02'), Synset('chordate.n.01'), Synset('object.n.01'),\
 Synset('physical_entity.n.01'), Synset('entity.n.01')]

        UserWarning: Discarded redundant search for Synset('animal.n.01') at depth 7
        ",88,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def closure(self, rel, depth=-1):
        

        from nltk.util import acyclic_breadth_first

        for synset in acyclic_breadth_first(self, rel, depth):
            if synset != self:
                yield synset

    from nltk.util import acyclic_depth_first as acyclic_tree
    from nltk.util import unweighted_minimum_spanning_tree as mst

    # Also add this shortcut?
    #    from nltk.util import unweighted_minimum_spanning_digraph as umsd

        ```
    ###Assistant : 
        Return the transitive closure of source under the rel
        relationship, breadth-first, discarding cycles:

        >>> from nltk.corpus import wordnet as wn
        >>> computer = wn.synset('computer.n.01')
        >>> topic = lambda s:s.topic_domains()
        >>> print(list(computer.closure(topic)))
        [Synset('computer_science.n.01')]

        UserWarning: Discarded redundant search for Synset('computer.n.01') at depth 2


        Include redundant paths (but only once), avoiding duplicate searches
        (from 'animal.n.01' to 'entity.n.01'):

        >>> dog = wn.synset('dog.n.01')
        >>> hyp = lambda s:s.hypernyms()
        >>> print(list(dog.closure(hyp)))
        [Synset('canine.n.02'), Synset('domestic_animal.n.01'), Synset('carnivore.n.01'),\
 Synset('animal.n.01'), Synset('placental.n.01'), Synset('organism.n.01'),\
 Synset('mammal.n.01'), Synset('living_thing.n.01'), Synset('vertebrate.n.01'),\
 Synset('whole.n.02'), Synset('chordate.n.01'), Synset('object.n.01'),\
 Synset('physical_entity.n.01'), Synset('entity.n.01')]

        UserWarning: Discarded redundant search for Synset('animal.n.01') at depth 7
        
    "
188,"def _get_save_args(self) -> Tuple[int, ...]:
        
        filetype = self.config[""format""]
        args: Tuple[int, ...] = tuple()
        if filetype == ""jpg"" and self.config[""jpg_quality""] > 0:
            args = (cv2.IMWRITE_JPEG_QUALITY,  # pylint: disable=no-member
                    self.config[""jpg_quality""])
        if filetype == ""png"" and self.config[""png_compress_level""] > -1:
            args = (cv2.IMWRITE_PNG_COMPRESSION,  # pylint: disable=no-member
                    self.config[""png_compress_level""])
        logger.debug(args)
        return args
"," Obtain the save parameters for the file format.

        Returns
        -------
        tuple
            The OpenCV specific arguments for the selected file format
         ",20,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_save_args(self) -> Tuple[int, ...]:
        
        filetype = self.config[""format""]
        args: Tuple[int, ...] = tuple()
        if filetype == ""jpg"" and self.config[""jpg_quality""] > 0:
            args = (cv2.IMWRITE_JPEG_QUALITY,  # pylint: disable=no-member
                    self.config[""jpg_quality""])
        if filetype == ""png"" and self.config[""png_compress_level""] > -1:
            args = (cv2.IMWRITE_PNG_COMPRESSION,  # pylint: disable=no-member
                    self.config[""png_compress_level""])
        logger.debug(args)
        return args

        ```
    ###Assistant :  Obtain the save parameters for the file format.

        Returns
        -------
        tuple
            The OpenCV specific arguments for the selected file format
         
    "
189,"def set_constrained_layout_pads(self, **kwargs):
        
        if isinstance(self.get_layout_engine(), ConstrainedLayoutEngine):
            self.get_layout_engine().set(**kwargs)
","
        Set padding for ``constrained_layout``.

        Tip: The parameters can be passed from a dictionary by using
        ``fig.set_constrained_layout(**pad_dict)``.

        See :doc:`/tutorials/intermediate/constrainedlayout_guide`.

        Parameters
        ----------
        w_pad : float, default: :rc:`figure.constrained_layout.w_pad`
            Width padding in inches.  This is the pad around Axes
            and is meant to make sure there is enough room for fonts to
            look good.  Defaults to 3 pts = 0.04167 inches

        h_pad : float, default: :rc:`figure.constrained_layout.h_pad`
            Height padding in inches. Defaults to 3 pts.

        wspace : float, default: :rc:`figure.constrained_layout.wspace`
            Width padding between subplots, expressed as a fraction of the
            subplot width.  The total padding ends up being w_pad + wspace.

        hspace : float, default: :rc:`figure.constrained_layout.hspace`
            Height padding between subplots, expressed as a fraction of the
            subplot width. The total padding ends up being h_pad + hspace.

        ",122,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_constrained_layout_pads(self, **kwargs):
        
        if isinstance(self.get_layout_engine(), ConstrainedLayoutEngine):
            self.get_layout_engine().set(**kwargs)

        ```
    ###Assistant : 
        Set padding for ``constrained_layout``.

        Tip: The parameters can be passed from a dictionary by using
        ``fig.set_constrained_layout(**pad_dict)``.

        See :doc:`/tutorials/intermediate/constrainedlayout_guide`.

        Parameters
        ----------
        w_pad : float, default: :rc:`figure.constrained_layout.w_pad`
            Width padding in inches.  This is the pad around Axes
            and is meant to make sure there is enough room for fonts to
            look good.  Defaults to 3 pts = 0.04167 inches

        h_pad : float, default: :rc:`figure.constrained_layout.h_pad`
            Height padding in inches. Defaults to 3 pts.

        wspace : float, default: :rc:`figure.constrained_layout.wspace`
            Width padding between subplots, expressed as a fraction of the
            subplot width.  The total padding ends up being w_pad + wspace.

        hspace : float, default: :rc:`figure.constrained_layout.hspace`
            Height padding between subplots, expressed as a fraction of the
            subplot width. The total padding ends up being h_pad + hspace.

        
    "
190,"def track_current_candle(self):
        
        if self.dd.current_candle > self.current_candle:
            self.get_corr_dataframes = True
            self.pair_it = 0
            self.current_candle = self.dd.current_candle

    # Following methods which are overridden by user made prediction models.
    # See freqai/prediction_models/CatboostPredictionModel.py for an example.
","
        Checks if the latest candle appended by the datadrawer is
        equivalent to the latest candle seen by FreqAI. If not, it
        asks to refresh the cached corr_dfs, and resets the pair
        counter.
        ",32,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def track_current_candle(self):
        
        if self.dd.current_candle > self.current_candle:
            self.get_corr_dataframes = True
            self.pair_it = 0
            self.current_candle = self.dd.current_candle

    # Following methods which are overridden by user made prediction models.
    # See freqai/prediction_models/CatboostPredictionModel.py for an example.

        ```
    ###Assistant : 
        Checks if the latest candle appended by the datadrawer is
        equivalent to the latest candle seen by FreqAI. If not, it
        asks to refresh the cached corr_dfs, and resets the pair
        counter.
        
    "
191,"def find(self, req):
        
        dist = self.by_key.get(req.key)
        if dist is not None and dist not in req:
            # XXX add more info
            raise VersionConflict(dist, req)
        return dist
","Find a distribution matching requirement `req`

        If there is an active distribution for the requested project, this
        returns it as long as it meets the version requirement specified by
        `req`.  But, if there is an active distribution for the project and it
        does *not* meet the `req` requirement, ``VersionConflict`` is raised.
        If there is no active distribution for the requested project, ``None``
        is returned.
        ",64,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find(self, req):
        
        dist = self.by_key.get(req.key)
        if dist is not None and dist not in req:
            # XXX add more info
            raise VersionConflict(dist, req)
        return dist

        ```
    ###Assistant : Find a distribution matching requirement `req`

        If there is an active distribution for the requested project, this
        returns it as long as it meets the version requirement specified by
        `req`.  But, if there is an active distribution for the project and it
        does *not* meet the `req` requirement, ``VersionConflict`` is raised.
        If there is no active distribution for the requested project, ``None``
        is returned.
        
    "
192,"def current() -> dict | None:
        
        try:
            ContextStack.top(_FROZEN_CONTEXT_KEY)
            sample: Sample = {}
            for ctx in ContextStack.stack(_FROZEN_CONTEXT_KEY):
                if not isinstance(ctx, dict):
                    raise TypeError(f'Expect architecture to be a dict, found: {ctx}')
                sample.update(ctx)
            return sample
        except NoContextError:
            return None
","Retrieve the current frozen context.
        If multiple layers have been found, they would be merged from bottom to top.

        Returns
        -------
        The sample in frozen context.
        If no sample is found, return none.
        ",33,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def current() -> dict | None:
        
        try:
            ContextStack.top(_FROZEN_CONTEXT_KEY)
            sample: Sample = {}
            for ctx in ContextStack.stack(_FROZEN_CONTEXT_KEY):
                if not isinstance(ctx, dict):
                    raise TypeError(f'Expect architecture to be a dict, found: {ctx}')
                sample.update(ctx)
            return sample
        except NoContextError:
            return None

        ```
    ###Assistant : Retrieve the current frozen context.
        If multiple layers have been found, they would be merged from bottom to top.

        Returns
        -------
        The sample in frozen context.
        If no sample is found, return none.
        
    "
193,"def test_rept_child() -> None:
    
    rows = 10_000
    cols = 7
    rept_row_count = 5

    # these times and sizes are based on the above constants
    # and Madhavas MacBook Pro 2019
    expected_rept_mem_size = 4.010650634765625
    expected_rept_ser_size = 7.4926300048828125
    macbook_pro_2019_ser_time = 0.18791760900000032
    macbook_pro_2019_de_time = 0.1726598199999998

    sept = make_sept(rows=rows, cols=cols)
    rept_rows = [sept.copy() for i in range(rept_row_count)]

    rept = REPT(rows=rept_rows)

    start = timeit.default_timer()
    ser = sy.serialize(rept, to_bytes=True)
    end = timeit.default_timer()
    time_ser = end - start

    start = timeit.default_timer()
    de = sy.deserialize(ser, from_bytes=True)
    end = timeit.default_timer()
    time_de = end - start

    assert rept == de

    current_rept_mem_size = size(rept)
    mem_diff = (current_rept_mem_size / expected_rept_mem_size * 100) - 100

    current_rept_bytes_size = size(ser)
    bytes_diff = (current_rept_bytes_size / expected_rept_ser_size * 100) - 100

    ser_time_diff = (time_ser / macbook_pro_2019_ser_time * 100) - 100
    de_time_diff = (time_de / macbook_pro_2019_de_time * 100) - 100

    print(""REPT Stats"")
    print(""=========="")
    print(""In-memory size of REPT"", size(rept))
    print(""Serialized size of REPT"", size(ser))
    print(f""Serializing {rept_row_count}x{rows}x{cols} took {time_ser} secs"")
    print(f""Deserializing {rept_row_count}x{rows}x{cols} took {time_de} secs"")

    print(""Current Results"")
    print(""==============="")
    print(f""In-memory size delta: {mem_diff}%"")
    print(f""Serialized size delta: {bytes_diff}%"")
    print(f""Serializing time delta: {ser_time_diff}%"")
    print(f""Deserializing time delta: {de_time_diff}%"")

    # we want to assert that our calculated values are smaller than the old values with
    # some tolerance
    assert (current_rept_mem_size - expected_rept_mem_size) < 1e-3
    assert (current_rept_bytes_size - expected_rept_ser_size) < 2e-2
    # TODO: make time benchmarks stable (probably can't run in parallel)
    # assert (time_ser - macbook_pro_2019_ser_time) < 2e-1
    # assert (time_de - macbook_pro_2019_de_time) < 2e-1

",We need to benchmark both the size and time to serialize and deserialize REPTs,14,230,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_rept_child() -> None:
    
    rows = 10_000
    cols = 7
    rept_row_count = 5

    # these times and sizes are based on the above constants
    # and Madhavas MacBook Pro 2019
    expected_rept_mem_size = 4.010650634765625
    expected_rept_ser_size = 7.4926300048828125
    macbook_pro_2019_ser_time = 0.18791760900000032
    macbook_pro_2019_de_time = 0.1726598199999998

    sept = make_sept(rows=rows, cols=cols)
    rept_rows = [sept.copy() for i in range(rept_row_count)]

    rept = REPT(rows=rept_rows)

    start = timeit.default_timer()
    ser = sy.serialize(rept, to_bytes=True)
    end = timeit.default_timer()
    time_ser = end - start

    start = timeit.default_timer()
    de = sy.deserialize(ser, from_bytes=True)
    end = timeit.default_timer()
    time_de = end - start

    assert rept == de

    current_rept_mem_size = size(rept)
    mem_diff = (current_rept_mem_size / expected_rept_mem_size * 100) - 100

    current_rept_bytes_size = size(ser)
    bytes_diff = (current_rept_bytes_size / expected_rept_ser_size * 100) - 100

    ser_time_diff = (time_ser / macbook_pro_2019_ser_time * 100) - 100
    de_time_diff = (time_de / macbook_pro_2019_de_time * 100) - 100

    print(""REPT Stats"")
    print(""=========="")
    print(""In-memory size of REPT"", size(rept))
    print(""Serialized size of REPT"", size(ser))
    print(f""Serializing {rept_row_count}x{rows}x{cols} took {time_ser} secs"")
    print(f""Deserializing {rept_row_count}x{rows}x{cols} took {time_de} secs"")

    print(""Current Results"")
    print(""==============="")
    print(f""In-memory size delta: {mem_diff}%"")
    print(f""Serialized size delta: {bytes_diff}%"")
    print(f""Serializing time delta: {ser_time_diff}%"")
    print(f""Deserializing time delta: {de_time_diff}%"")

    # we want to assert that our calculated values are smaller than the old values with
    # some tolerance
    assert (current_rept_mem_size - expected_rept_mem_size) < 1e-3
    assert (current_rept_bytes_size - expected_rept_ser_size) < 2e-2
    # TODO: make time benchmarks stable (probably can't run in parallel)
    # assert (time_ser - macbook_pro_2019_ser_time) < 2e-1
    # assert (time_de - macbook_pro_2019_de_time) < 2e-1


        ```
    ###Assistant : We need to benchmark both the size and time to serialize and deserialize REPTs
    "
194,"def _ecm_one_factor(n, B1=10000, B2=100000, max_curve=200):
    
    n = as_int(n)
    if B1 % 2 != 0 or B2 % 2 != 0:
        raise ValueError(""The Bounds should be an even integer"")
    sieve.extend(B2)

    if isprime(n):
        return n

    from sympy.functions.elementary.miscellaneous import sqrt
    from sympy.polys.polytools import gcd
    curve = 0
    D = int(sqrt(B2))
    beta = [0]*(D + 1)
    S = [0]*(D + 1)
    k = 1
    for p in sieve.primerange(1, B1 + 1):
        k *= pow(p, integer_log(B1, p)[0])
    while(curve <= max_curve):
        curve += 1

        #Suyama's Paramatrization
        sigma = rgen.randint(6, n - 1)
        u = (sigma*sigma - 5) % n
        v = (4*sigma) % n
        diff = v - u
        u_3 = pow(u, 3, n)

        try:
            C = (pow(diff, 3, n)*(3*u + v)*mod_inverse(4*u_3*v, n) - 2) % n
        except ValueError:
            #If the mod_inverse(4*u_3*v, n) doesn't exist
            return gcd(4*u_3*v, n)

        a24 = (C + 2)*mod_inverse(4, n) % n
        Q = Point(u_3, pow(v, 3, n), a24, n)
        Q = Q.mont_ladder(k)
        g = gcd(Q.z_cord, n)

        #Stage 1 factor
        if g != 1 and g != n:
            return g
        #Stage 1 failure. Q.z = 0, Try another curve
        elif g == n:
            continue

        #Stage 2 - Improved Standard Continuation
        S[1] = Q.double()
        S[2] = S[1].double()
        beta[1] = (S[1].x_cord*S[1].z_cord) % n
        beta[2] = (S[2].x_cord*S[2].z_cord) % n

        for d in range(3, D + 1):
            S[d] = S[d - 1].add(S[1], S[d - 2])
            beta[d] = (S[d].x_cord*S[d].z_cord) % n

        g = 1
        B = B1 - 1
        T = Q.mont_ladder(B - 2*D)
        R = Q.mont_ladder(B)

        for r in  range(B, B2, 2*D):
            alpha = (R.x_cord*R.z_cord) % n
            for q in sieve.primerange(r + 2, r + 2*D + 1):
                delta = (q - r) // 2
                f = (R.x_cord - S[d].x_cord)*(R.z_cord + S[d].z_cord) -\
                alpha + beta[delta]
                g = (g*f) % n
            #Swap
            T, R = R, R.add(S[D], T)
        g = gcd(n, g)

        #Stage 2 Factor found
        if g != 1 and g != n:
            return g

    #ECM failed, Increase the bounds
    raise ValueError(""Increase the bounds"")

","Returns one factor of n using
    Lenstra's 2 Stage Elliptic curve Factorization
    with Suyama's Parameterization. Here Montgomery
    arithmetic is used for fast computation of addition
    and doubling of points in elliptic curve.

    This ECM method considers elliptic curves in Montgomery
    form (E : b*y**2*z = x**3 + a*x**2*z + x*z**2) and involves
    elliptic curve operations (mod N), where the elements in
    Z are reduced (mod N). Since N is not a prime, E over FF(N)
    is not really an elliptic curve but we can still do point additions
    and doubling as if FF(N) was a field.

    Stage 1 : The basic algorithm involves taking a random point (P) on an
    elliptic curve in FF(N). The compute k*P using Montgomery ladder algorithm.
    Let q be an unknown factor of N. Then the order of the curve E, |E(FF(q))|,
    might be a smooth number that divides k. Then we have k = l * |E(FF(q))|
    for some l. For any point belonging to the curve E, |E(FF(q))|*P = O,
    hence k*P = l*|E(FF(q))|*P. Thus kP.z_cord = 0 (mod q), and the unknownn
    factor of N (q) can be recovered by taking gcd(kP.z_cord, N).

    Stage 2 : This is a continuation of Stage 1 if k*P != O. The idea utilize
    the fact that even if kP != 0, the value of k might miss just one large
    prime divisor of |E(FF(q))|. In this case we only need to compute the
    scalar multiplication by p to get p*k*P = O. Here a second bound B2
    restrict the size of possible values of p.

    Parameters
    ==========

    n : Number to be Factored
    B1 : Stage 1 Bound
    B2 : Stage 2 Bound
    max_curve : Maximum number of curves generated

    References
    ==========

    .. [1]  Carl Pomerance and Richard Crandall ""Prime Numbers:
        A Computational Perspective"" (2nd Ed.), page 344
    ",303,319,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _ecm_one_factor(n, B1=10000, B2=100000, max_curve=200):
    
    n = as_int(n)
    if B1 % 2 != 0 or B2 % 2 != 0:
        raise ValueError(""The Bounds should be an even integer"")
    sieve.extend(B2)

    if isprime(n):
        return n

    from sympy.functions.elementary.miscellaneous import sqrt
    from sympy.polys.polytools import gcd
    curve = 0
    D = int(sqrt(B2))
    beta = [0]*(D + 1)
    S = [0]*(D + 1)
    k = 1
    for p in sieve.primerange(1, B1 + 1):
        k *= pow(p, integer_log(B1, p)[0])
    while(curve <= max_curve):
        curve += 1

        #Suyama's Paramatrization
        sigma = rgen.randint(6, n - 1)
        u = (sigma*sigma - 5) % n
        v = (4*sigma) % n
        diff = v - u
        u_3 = pow(u, 3, n)

        try:
            C = (pow(diff, 3, n)*(3*u + v)*mod_inverse(4*u_3*v, n) - 2) % n
        except ValueError:
            #If the mod_inverse(4*u_3*v, n) doesn't exist
            return gcd(4*u_3*v, n)

        a24 = (C + 2)*mod_inverse(4, n) % n
        Q = Point(u_3, pow(v, 3, n), a24, n)
        Q = Q.mont_ladder(k)
        g = gcd(Q.z_cord, n)

        #Stage 1 factor
        if g != 1 and g != n:
            return g
        #Stage 1 failure. Q.z = 0, Try another curve
        elif g == n:
            continue

        #Stage 2 - Improved Standard Continuation
        S[1] = Q.double()
        S[2] = S[1].double()
        beta[1] = (S[1].x_cord*S[1].z_cord) % n
        beta[2] = (S[2].x_cord*S[2].z_cord) % n

        for d in range(3, D + 1):
            S[d] = S[d - 1].add(S[1], S[d - 2])
            beta[d] = (S[d].x_cord*S[d].z_cord) % n

        g = 1
        B = B1 - 1
        T = Q.mont_ladder(B - 2*D)
        R = Q.mont_ladder(B)

        for r in  range(B, B2, 2*D):
            alpha = (R.x_cord*R.z_cord) % n
            for q in sieve.primerange(r + 2, r + 2*D + 1):
                delta = (q - r) // 2
                f = (R.x_cord - S[d].x_cord)*(R.z_cord + S[d].z_cord) -\
                alpha + beta[delta]
                g = (g*f) % n
            #Swap
            T, R = R, R.add(S[D], T)
        g = gcd(n, g)

        #Stage 2 Factor found
        if g != 1 and g != n:
            return g

    #ECM failed, Increase the bounds
    raise ValueError(""Increase the bounds"")


        ```
    ###Assistant : Returns one factor of n using
    Lenstra's 2 Stage Elliptic curve Factorization
    with Suyama's Parameterization. Here Montgomery
    arithmetic is used for fast computation of addition
    and doubling of points in elliptic curve.

    This ECM method considers elliptic curves in Montgomery
    form (E : b*y**2*z = x**3 + a*x**2*z + x*z**2) and involves
    elliptic curve operations (mod N), where the elements in
    Z are reduced (mod N). Since N is not a prime, E over FF(N)
    is not really an elliptic curve but we can still do point additions
    and doubling as if FF(N) was a field.

    Stage 1 : The basic algorithm involves taking a random point (P) on an
    elliptic curve in FF(N). The compute k*P using Montgomery ladder algorithm.
    Let q be an unknown factor of N. Then the order of the curve E, |E(FF(q))|,
    might be a smooth number that divides k. Then we have k = l * |E(FF(q))|
    for some l. For any point belonging to the curve E, |E(FF(q))|*P = O,
    hence k*P = l*|E(FF(q))|*P. Thus kP.z_cord = 0 (mod q), and the unknownn
    factor of N (q) can be recovered by taking gcd(kP.z_cord, N).

    Stage 2 : This is a continuation of Stage 1 if k*P != O. The idea utilize
    the fact that even if kP != 0, the value of k might miss just one large
    prime divisor of |E(FF(q))|. In this case we only need to compute the
    scalar multiplication by p to get p*k*P = O. Here a second bound B2
    restrict the size of possible values of p.

    Parameters
    ==========

    n : Number to be Factored
    B1 : Stage 1 Bound
    B2 : Stage 2 Bound
    max_curve : Maximum number of curves generated

    References
    ==========

    .. [1]  Carl Pomerance and Richard Crandall ""Prime Numbers:
        A Computational Perspective"" (2nd Ed.), page 344
    
    "
195,"async def test_unique_id(hass):
    
    await setup_test_entity(
        hass,
        {
            ""unique"": {
                ""command_open"": ""echo open"",
                ""command_close"": ""echo close"",
                ""command_stop"": ""echo stop"",
                ""unique_id"": ""unique"",
            },
            ""not_unique_1"": {
                ""command_open"": ""echo open"",
                ""command_close"": ""echo close"",
                ""command_stop"": ""echo stop"",
                ""unique_id"": ""not-so-unique-anymore"",
            },
            ""not_unique_2"": {
                ""command_open"": ""echo open"",
                ""command_close"": ""echo close"",
                ""command_stop"": ""echo stop"",
                ""unique_id"": ""not-so-unique-anymore"",
            },
        },
    )

    assert len(hass.states.async_all()) == 2

    ent_reg = entity_registry.async_get(hass)

    assert len(ent_reg.entities) == 2
    assert ent_reg.async_get_entity_id(""cover"", ""command_line"", ""unique"") is not None
    assert (
        ent_reg.async_get_entity_id(""cover"", ""command_line"", ""not-so-unique-anymore"")
        is not None
    )
",Test unique_id option and if it only creates one cover per id.,12,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_unique_id(hass):
    
    await setup_test_entity(
        hass,
        {
            ""unique"": {
                ""command_open"": ""echo open"",
                ""command_close"": ""echo close"",
                ""command_stop"": ""echo stop"",
                ""unique_id"": ""unique"",
            },
            ""not_unique_1"": {
                ""command_open"": ""echo open"",
                ""command_close"": ""echo close"",
                ""command_stop"": ""echo stop"",
                ""unique_id"": ""not-so-unique-anymore"",
            },
            ""not_unique_2"": {
                ""command_open"": ""echo open"",
                ""command_close"": ""echo close"",
                ""command_stop"": ""echo stop"",
                ""unique_id"": ""not-so-unique-anymore"",
            },
        },
    )

    assert len(hass.states.async_all()) == 2

    ent_reg = entity_registry.async_get(hass)

    assert len(ent_reg.entities) == 2
    assert ent_reg.async_get_entity_id(""cover"", ""command_line"", ""unique"") is not None
    assert (
        ent_reg.async_get_entity_id(""cover"", ""command_line"", ""not-so-unique-anymore"")
        is not None
    )

        ```
    ###Assistant : Test unique_id option and if it only creates one cover per id.
    "
196,"def mock_ssl_context():
    
    with patch(
        ""homeassistant.components.mqtt.config_flow.SSLContext""
    ) as mock_context, patch(
        ""homeassistant.components.mqtt.config_flow.load_pem_private_key""
    ) as mock_key_check, patch(
        ""homeassistant.components.mqtt.config_flow.load_pem_x509_certificate""
    ) as mock_cert_check:
        yield {
            ""context"": mock_context,
            ""load_pem_x509_certificate"": mock_cert_check,
            ""load_pem_private_key"": mock_key_check,
        }


@pytest.fixture",Mock the SSL context used to load the cert chain and to load verify locations.,15,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mock_ssl_context():
    
    with patch(
        ""homeassistant.components.mqtt.config_flow.SSLContext""
    ) as mock_context, patch(
        ""homeassistant.components.mqtt.config_flow.load_pem_private_key""
    ) as mock_key_check, patch(
        ""homeassistant.components.mqtt.config_flow.load_pem_x509_certificate""
    ) as mock_cert_check:
        yield {
            ""context"": mock_context,
            ""load_pem_x509_certificate"": mock_cert_check,
            ""load_pem_private_key"": mock_key_check,
        }


@pytest.fixture
        ```
    ###Assistant : Mock the SSL context used to load the cert chain and to load verify locations.
    "
197,"def test_task_states_for_dag_run_when_dag_run_not_exists(self):
        
        with pytest.raises(DagRunNotFound):
            default_date2 = timezone.datetime(2016, 1, 9)
            task_command.task_states_for_dag_run(
                self.parser.parse_args(
                    [
                        'tasks',
                        'states-for-dag-run',
                        'not_exists_dag',
                        default_date2.isoformat(),
                        '--output',
                        ""json"",
                    ]
                )
            )
","
        task_states_for_dag_run should return an AirflowException when invalid dag id is passed
        ",11,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_task_states_for_dag_run_when_dag_run_not_exists(self):
        
        with pytest.raises(DagRunNotFound):
            default_date2 = timezone.datetime(2016, 1, 9)
            task_command.task_states_for_dag_run(
                self.parser.parse_args(
                    [
                        'tasks',
                        'states-for-dag-run',
                        'not_exists_dag',
                        default_date2.isoformat(),
                        '--output',
                        ""json"",
                    ]
                )
            )

        ```
    ###Assistant : 
        task_states_for_dag_run should return an AirflowException when invalid dag id is passed
        
    "
198,"def test_title_present(self):
        
        response = self.get(4)
        self.assertContains(response, ""Christmas"", 3)
","
        The page title should appear three times. Once in the header, and two times
        in the field listing (as the actual title and as the draft title)
        ",27,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_title_present(self):
        
        response = self.get(4)
        self.assertContains(response, ""Christmas"", 3)

        ```
    ###Assistant : 
        The page title should appear three times. Once in the header, and two times
        in the field listing (as the actual title and as the draft title)
        
    "
199,"def _set_position(self, pos, which='both'):
        
        if not isinstance(pos, mtransforms.BboxBase):
            pos = mtransforms.Bbox.from_bounds(*pos)
        for ax in self._twinned_axes.get_siblings(self):
            if which in ('both', 'active'):
                ax._position.set(pos)
            if which in ('both', 'original'):
                ax._originalPosition.set(pos)
        self.stale = True
","
        Private version of set_position.

        Call this internally to get the same functionality of `set_position`,
        but not to take the axis out of the constrained_layout hierarchy.
        ",25,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_position(self, pos, which='both'):
        
        if not isinstance(pos, mtransforms.BboxBase):
            pos = mtransforms.Bbox.from_bounds(*pos)
        for ax in self._twinned_axes.get_siblings(self):
            if which in ('both', 'active'):
                ax._position.set(pos)
            if which in ('both', 'original'):
                ax._originalPosition.set(pos)
        self.stale = True

        ```
    ###Assistant : 
        Private version of set_position.

        Call this internally to get the same functionality of `set_position`,
        but not to take the axis out of the constrained_layout hierarchy.
        
    "
200,"def hvac_modes(self) -> list[HVACMode]:
        
        hvac_state = HVAC_MODES_MAPPING[self._climate.get_hvac_state()]
        return [HVACMode.AUTO, hvac_state]
","Return the list of available hvac operation modes.

        HEAT and COOL mode are exclusive. End user has to enable a mode manually within the Somfy application.
        So only one mode can be displayed. Auto mode is a scheduler.
        ",38,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def hvac_modes(self) -> list[HVACMode]:
        
        hvac_state = HVAC_MODES_MAPPING[self._climate.get_hvac_state()]
        return [HVACMode.AUTO, hvac_state]

        ```
    ###Assistant : Return the list of available hvac operation modes.

        HEAT and COOL mode are exclusive. End user has to enable a mode manually within the Somfy application.
        So only one mode can be displayed. Auto mode is a scheduler.
        
    "
201,"def _assert_expected_task_states(self, dagrun, expected_states):
        

        tis = dagrun.get_task_instances()
        for ti in tis:
            try:
                expected_state = expected_states[ti.task_id]
            except KeyError:
                raise ValueError(f""Invalid task id {ti.task_id} found!"")
            else:
                assert ti.state == expected_state

    all_downstream_skipped_states = {
        ""short_circuit"": State.SUCCESS,
        ""op1"": State.SKIPPED,
        ""op2"": State.SKIPPED,
    }
    all_success_states = {""short_circuit"": State.SUCCESS, ""op1"": State.SUCCESS, ""op2"": State.SUCCESS}
",Helper function that asserts `TaskInstances` of a given `task_id` are in a given state.,14,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _assert_expected_task_states(self, dagrun, expected_states):
        

        tis = dagrun.get_task_instances()
        for ti in tis:
            try:
                expected_state = expected_states[ti.task_id]
            except KeyError:
                raise ValueError(f""Invalid task id {ti.task_id} found!"")
            else:
                assert ti.state == expected_state

    all_downstream_skipped_states = {
        ""short_circuit"": State.SUCCESS,
        ""op1"": State.SKIPPED,
        ""op2"": State.SKIPPED,
    }
    all_success_states = {""short_circuit"": State.SUCCESS, ""op1"": State.SUCCESS, ""op2"": State.SUCCESS}

        ```
    ###Assistant : Helper function that asserts `TaskInstances` of a given `task_id` are in a given state.
    "
202,"def set_customer_info(fieldname, customer, value=""""):
	if fieldname == ""loyalty_program"":
		frappe.db.set_value(""Customer"", customer, ""loyalty_program"", value)

	contact = frappe.get_cached_value(""Customer"", customer, ""customer_primary_contact"")
	if not contact:
		contact = frappe.db.sql(
			,
			(customer),
			as_dict=1,
		)
		contact = contact[0].get(""parent"") if contact else None

	if not contact:
		new_contact = frappe.new_doc(""Contact"")
		new_contact.is_primary_contact = 1
		new_contact.first_name = customer
		new_contact.set(""links"", [{""link_doctype"": ""Customer"", ""link_name"": customer}])
		new_contact.save()
		contact = new_contact.name
		frappe.db.set_value(""Customer"", customer, ""customer_primary_contact"", contact)

	contact_doc = frappe.get_doc(""Contact"", contact)
	if fieldname == ""email_id"":
		contact_doc.set(""email_ids"", [{""email_id"": value, ""is_primary"": 1}])
		frappe.db.set_value(""Customer"", customer, ""email_id"", value)
	elif fieldname == ""mobile_no"":
		contact_doc.set(""phone_nos"", [{""phone"": value, ""is_primary_mobile_no"": 1}])
		frappe.db.set_value(""Customer"", customer, ""mobile_no"", value)
	contact_doc.save()


@frappe.whitelist()","
			SELECT parent FROM `tabDynamic Link`
			WHERE
				parenttype = 'Contact' AND
				parentfield = 'links' AND
				link_doctype = 'Customer' AND
				link_name = %s
			",21,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_customer_info(fieldname, customer, value=""""):
	if fieldname == ""loyalty_program"":
		frappe.db.set_value(""Customer"", customer, ""loyalty_program"", value)

	contact = frappe.get_cached_value(""Customer"", customer, ""customer_primary_contact"")
	if not contact:
		contact = frappe.db.sql(
			,
			(customer),
			as_dict=1,
		)
		contact = contact[0].get(""parent"") if contact else None

	if not contact:
		new_contact = frappe.new_doc(""Contact"")
		new_contact.is_primary_contact = 1
		new_contact.first_name = customer
		new_contact.set(""links"", [{""link_doctype"": ""Customer"", ""link_name"": customer}])
		new_contact.save()
		contact = new_contact.name
		frappe.db.set_value(""Customer"", customer, ""customer_primary_contact"", contact)

	contact_doc = frappe.get_doc(""Contact"", contact)
	if fieldname == ""email_id"":
		contact_doc.set(""email_ids"", [{""email_id"": value, ""is_primary"": 1}])
		frappe.db.set_value(""Customer"", customer, ""email_id"", value)
	elif fieldname == ""mobile_no"":
		contact_doc.set(""phone_nos"", [{""phone"": value, ""is_primary_mobile_no"": 1}])
		frappe.db.set_value(""Customer"", customer, ""mobile_no"", value)
	contact_doc.save()


@frappe.whitelist()
        ```
    ###Assistant : 
			SELECT parent FROM `tabDynamic Link`
			WHERE
				parenttype = 'Contact' AND
				parentfield = 'links' AND
				link_doctype = 'Customer' AND
				link_name = %s
			
    "
203,"def test_unpublish_not_include_children_view_post(self):
        
        # Post to the unpublish page
        response = self.client.post(
            reverse(""wagtailadmin_pages:unpublish"", args=(self.test_page.id,)), {}
        )

        # Should be redirected to explorer page
        self.assertRedirects(
            response, reverse(""wagtailadmin_explore"", args=(self.root_page.id,))
        )

        # Check that the page was unpublished
        self.assertFalse(SimplePage.objects.get(id=self.test_page.id).live)

        # Check that the descendant pages were not unpublished
        self.assertTrue(SimplePage.objects.get(id=self.test_child_page.id).live)
        self.assertTrue(SimplePage.objects.get(id=self.test_another_child_page.id).live)
","
        This posts to the unpublish view and checks that the page was unpublished but its descendants were not
        ",18,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unpublish_not_include_children_view_post(self):
        
        # Post to the unpublish page
        response = self.client.post(
            reverse(""wagtailadmin_pages:unpublish"", args=(self.test_page.id,)), {}
        )

        # Should be redirected to explorer page
        self.assertRedirects(
            response, reverse(""wagtailadmin_explore"", args=(self.root_page.id,))
        )

        # Check that the page was unpublished
        self.assertFalse(SimplePage.objects.get(id=self.test_page.id).live)

        # Check that the descendant pages were not unpublished
        self.assertTrue(SimplePage.objects.get(id=self.test_child_page.id).live)
        self.assertTrue(SimplePage.objects.get(id=self.test_another_child_page.id).live)

        ```
    ###Assistant : 
        This posts to the unpublish view and checks that the page was unpublished but its descendants were not
        
    "
204,"def _get_data_from_filepath(self, filepath_or_buffer):
        
        # if it is a string but the file does not exist, it might be a JSON string
        filepath_or_buffer = stringify_path(filepath_or_buffer)
        if (
            not isinstance(filepath_or_buffer, str)
            or is_url(filepath_or_buffer)
            or is_fsspec_url(filepath_or_buffer)
            or file_exists(filepath_or_buffer)
        ):
            self.handles = get_handle(
                filepath_or_buffer,
                ""r"",
                encoding=self.encoding,
                compression=self.compression,
                storage_options=self.storage_options,
                errors=self.encoding_errors,
            )
            filepath_or_buffer = self.handles.handle
        elif (
            isinstance(filepath_or_buffer, str)
            and filepath_or_buffer.lower().endswith(
                ("".json"",) + tuple(f"".json{c}"" for c in _extension_to_compression)
            )
            and not file_exists(filepath_or_buffer)
        ):
            raise FileNotFoundError(f""File {filepath_or_buffer} does not exist"")

        return filepath_or_buffer
","
        The function read_json accepts three input types:
            1. filepath (string-like)
            2. file-like object (e.g. open file object, StringIO)
            3. JSON string

        This method turns (1) into (2) to simplify the rest of the processing.
        It returns input types (2) and (3) unchanged.

        It raises FileNotFoundError if the input is a string ending in
        one of .json, .json.gz, .json.bz2, etc. but no such file exists.
        ",64,75,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_data_from_filepath(self, filepath_or_buffer):
        
        # if it is a string but the file does not exist, it might be a JSON string
        filepath_or_buffer = stringify_path(filepath_or_buffer)
        if (
            not isinstance(filepath_or_buffer, str)
            or is_url(filepath_or_buffer)
            or is_fsspec_url(filepath_or_buffer)
            or file_exists(filepath_or_buffer)
        ):
            self.handles = get_handle(
                filepath_or_buffer,
                ""r"",
                encoding=self.encoding,
                compression=self.compression,
                storage_options=self.storage_options,
                errors=self.encoding_errors,
            )
            filepath_or_buffer = self.handles.handle
        elif (
            isinstance(filepath_or_buffer, str)
            and filepath_or_buffer.lower().endswith(
                ("".json"",) + tuple(f"".json{c}"" for c in _extension_to_compression)
            )
            and not file_exists(filepath_or_buffer)
        ):
            raise FileNotFoundError(f""File {filepath_or_buffer} does not exist"")

        return filepath_or_buffer

        ```
    ###Assistant : 
        The function read_json accepts three input types:
            1. filepath (string-like)
            2. file-like object (e.g. open file object, StringIO)
            3. JSON string

        This method turns (1) into (2) to simplify the rest of the processing.
        It returns input types (2) and (3) unchanged.

        It raises FileNotFoundError if the input is a string ending in
        one of .json, .json.gz, .json.bz2, etc. but no such file exists.
        
    "
205,"def _run_pip(args, additional_paths=None):
    # Run the bootstraping in a subprocess to avoid leaking any state that happens
    # after pip has executed. Particulary, this avoids the case when pip holds onto
    # the files in *additional_paths*, preventing us to remove them at the end of the
    # invocation.
    code = f
    return subprocess.run([sys.executable, '-W', 'ignore::DeprecationWarning',
                           ""-c"", code], check=True).returncode

","
import runpy
import sys
sys.path = {additional_paths or []} + sys.path
sys.argv[1:] = {args}
runpy.run_module(""pip"", run_name=""__main__"", alter_sys=True)
",17,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _run_pip(args, additional_paths=None):
    # Run the bootstraping in a subprocess to avoid leaking any state that happens
    # after pip has executed. Particulary, this avoids the case when pip holds onto
    # the files in *additional_paths*, preventing us to remove them at the end of the
    # invocation.
    code = f
    return subprocess.run([sys.executable, '-W', 'ignore::DeprecationWarning',
                           ""-c"", code], check=True).returncode


        ```
    ###Assistant : 
import runpy
import sys
sys.path = {additional_paths or []} + sys.path
sys.argv[1:] = {args}
runpy.run_module(""pip"", run_name=""__main__"", alter_sys=True)

    "
206,"def test_pprint_heap_allocated_type():
    
    module_name = ""xxlimited"" if sys.version_info < (3, 10) else ""xxlimited_35""
    expected_output = (
        ""xxlimited.Null"" if sys.version_info < (3, 11) else ""xxlimited_35.Null""
    )
    xxlimited = pytest.importorskip(module_name)
    output = pretty.pretty(xxlimited.Null)
    assert output == expected_output

","
    Test that pprint works for heap allocated types.
    ",8,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pprint_heap_allocated_type():
    
    module_name = ""xxlimited"" if sys.version_info < (3, 10) else ""xxlimited_35""
    expected_output = (
        ""xxlimited.Null"" if sys.version_info < (3, 11) else ""xxlimited_35.Null""
    )
    xxlimited = pytest.importorskip(module_name)
    output = pretty.pretty(xxlimited.Null)
    assert output == expected_output


        ```
    ###Assistant : 
    Test that pprint works for heap allocated types.
    
    "
207,"def test_orderby_percentile_with_many_fields_transactions_unsupported_fields(self):
        
        response = self.get_response(
            self.organization.slug,
            field=[
                ""p50(sentry.transactions.measurements.lcp)"",
                ""sum(user_misery)"",
            ],
            statsPeriod=""1h"",
            interval=""1h"",
            datasource=""snuba"",
            groupBy=[""project_id"", ""transaction""],
            orderBy=""p50(sentry.transactions.measurements.lcp)"",
        )
        assert response.status_code == 400
        assert (
            response.json()[""detail""]
            == ""Multi-field select order by queries is not supported for metric user_misery""
        )
","
        Test that contains a field in the `select` that is performance related but currently
        not supported should return a 400
        ",20,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_orderby_percentile_with_many_fields_transactions_unsupported_fields(self):
        
        response = self.get_response(
            self.organization.slug,
            field=[
                ""p50(sentry.transactions.measurements.lcp)"",
                ""sum(user_misery)"",
            ],
            statsPeriod=""1h"",
            interval=""1h"",
            datasource=""snuba"",
            groupBy=[""project_id"", ""transaction""],
            orderBy=""p50(sentry.transactions.measurements.lcp)"",
        )
        assert response.status_code == 400
        assert (
            response.json()[""detail""]
            == ""Multi-field select order by queries is not supported for metric user_misery""
        )

        ```
    ###Assistant : 
        Test that contains a field in the `select` that is performance related but currently
        not supported should return a 400
        
    "
208,"def generate_dependency_map(integration_targets):  # type: (t.List[IntegrationTarget]) -> t.Dict[str, t.Set[IntegrationTarget]]
    
    targets_dict = dict((target.name, target) for target in integration_targets)
    target_dependencies = analyze_integration_target_dependencies(integration_targets)
    dependency_map = {}  # type: t.Dict[str, t.Set[IntegrationTarget]]

    invalid_targets = set()

    for dependency, dependents in target_dependencies.items():
        dependency_target = targets_dict.get(dependency)

        if not dependency_target:
            invalid_targets.add(dependency)
            continue

        for dependent in dependents:
            if dependent not in dependency_map:
                dependency_map[dependent] = set()

            dependency_map[dependent].add(dependency_target)

    if invalid_targets:
        raise ApplicationError('Non-existent target dependencies: %s' % ', '.join(sorted(invalid_targets)))

    return dependency_map

",Analyze the given list of integration test targets and return a dictionary expressing target names and the targets on which they depend.,22,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_dependency_map(integration_targets):  # type: (t.List[IntegrationTarget]) -> t.Dict[str, t.Set[IntegrationTarget]]
    
    targets_dict = dict((target.name, target) for target in integration_targets)
    target_dependencies = analyze_integration_target_dependencies(integration_targets)
    dependency_map = {}  # type: t.Dict[str, t.Set[IntegrationTarget]]

    invalid_targets = set()

    for dependency, dependents in target_dependencies.items():
        dependency_target = targets_dict.get(dependency)

        if not dependency_target:
            invalid_targets.add(dependency)
            continue

        for dependent in dependents:
            if dependent not in dependency_map:
                dependency_map[dependent] = set()

            dependency_map[dependent].add(dependency_target)

    if invalid_targets:
        raise ApplicationError('Non-existent target dependencies: %s' % ', '.join(sorted(invalid_targets)))

    return dependency_map


        ```
    ###Assistant : Analyze the given list of integration test targets and return a dictionary expressing target names and the targets on which they depend.
    "
209,"def text(self) -> str:
        
        if self.error is None:
            return str(self)
        return self.error.databaseText()

","Get a short text description of the error.

        This is a string suitable to show to the user as error message.
        ",21,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def text(self) -> str:
        
        if self.error is None:
            return str(self)
        return self.error.databaseText()


        ```
    ###Assistant : Get a short text description of the error.

        This is a string suitable to show to the user as error message.
        
    "
210,"def test_recover_start_from_replica_actor_names(serve_instance):
    
    # Test failed to deploy with total of 2 replicas,
    # but first constructor call fails.","Test controller is able to recover starting -> running replicas from
    actor names.
    ",13,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_recover_start_from_replica_actor_names(serve_instance):
    
    # Test failed to deploy with total of 2 replicas,
    # but first constructor call fails.
        ```
    ###Assistant : Test controller is able to recover starting -> running replicas from
    actor names.
    
    "
211,"def associated_entities(self) -> list[dict[str, Any]]:
        
        ha_entity_registry = self.device.gateway.ha_entity_registry
        zha_device_registry = self.device.gateway.device_registry
        return [
            GroupEntityReference(
                ha_entity_registry.async_get(entity_ref.reference_id).name,
                ha_entity_registry.async_get(entity_ref.reference_id).original_name,
                entity_ref.reference_id,
            )._asdict()
            for entity_ref in zha_device_registry.get(self.device.ieee)
            if list(entity_ref.cluster_channels.values())[
                0
            ].cluster.endpoint.endpoint_id
            == self.endpoint_id
        ]
",Return the list of entities that were derived from this endpoint.,11,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def associated_entities(self) -> list[dict[str, Any]]:
        
        ha_entity_registry = self.device.gateway.ha_entity_registry
        zha_device_registry = self.device.gateway.device_registry
        return [
            GroupEntityReference(
                ha_entity_registry.async_get(entity_ref.reference_id).name,
                ha_entity_registry.async_get(entity_ref.reference_id).original_name,
                entity_ref.reference_id,
            )._asdict()
            for entity_ref in zha_device_registry.get(self.device.ieee)
            if list(entity_ref.cluster_channels.values())[
                0
            ].cluster.endpoint.endpoint_id
            == self.endpoint_id
        ]

        ```
    ###Assistant : Return the list of entities that were derived from this endpoint.
    "
212,"def getheader(self, name, default=None):
        
        if self.headers is None:
            raise ResponseNotReady()
        headers = self.headers.get_all(name) or default
        if isinstance(headers, str) or not hasattr(headers, '__iter__'):
            return headers
        else:
            return ', '.join(headers)
","Returns the value of the header matching *name*.

        If there are multiple matching headers, the values are
        combined into a single string separated by commas and spaces.

        If no matching header is found, returns *default* or None if
        the *default* is not specified.

        If the headers are unknown, raises http.client.ResponseNotReady.

        ",50,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getheader(self, name, default=None):
        
        if self.headers is None:
            raise ResponseNotReady()
        headers = self.headers.get_all(name) or default
        if isinstance(headers, str) or not hasattr(headers, '__iter__'):
            return headers
        else:
            return ', '.join(headers)

        ```
    ###Assistant : Returns the value of the header matching *name*.

        If there are multiple matching headers, the values are
        combined into a single string separated by commas and spaces.

        If no matching header is found, returns *default* or None if
        the *default* is not specified.

        If the headers are unknown, raises http.client.ResponseNotReady.

        
    "
213,"def test_generate_pipeline_code_2():
    

    pipeline = [
        'KNeighborsClassifier',
        [
            'CombineDFs',
            [
                'GradientBoostingClassifier',
                'input_matrix',
                38.0,
                5,
                5,
                5,
                0.05,
                0.5],
            [
                'CombineDFs',
                [
                    'MinMaxScaler',
                    'input_matrix'
                ],
                ['ZeroCount',
                    [
                        'MaxAbsScaler',
                        'input_matrix'
                    ]
                ]
            ]
        ],
        18,
        'uniform',
        2
    ]

    expected_code = 

    assert expected_code == generate_pipeline_code(pipeline, tpot_obj.operators)

","Assert that generate_pipeline_code() returns the correct code given a specific pipeline with two CombineDFs.make_pipeline(
    make_union(
        StackingEstimator(estimator=GradientBoostingClassifier(learning_rate=38.0, max_depth=5, max_features=5, min_samples_leaf=5, min_samples_split=0.05, n_estimators=0.5)),
        make_union(
            MinMaxScaler(),
            make_pipeline(
                MaxAbsScaler(),
                ZeroCount()
            )
        )
    ),
    KNeighborsClassifier(n_neighbors=18, p=""uniform"", weights=2)
)",33,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_generate_pipeline_code_2():
    

    pipeline = [
        'KNeighborsClassifier',
        [
            'CombineDFs',
            [
                'GradientBoostingClassifier',
                'input_matrix',
                38.0,
                5,
                5,
                5,
                0.05,
                0.5],
            [
                'CombineDFs',
                [
                    'MinMaxScaler',
                    'input_matrix'
                ],
                ['ZeroCount',
                    [
                        'MaxAbsScaler',
                        'input_matrix'
                    ]
                ]
            ]
        ],
        18,
        'uniform',
        2
    ]

    expected_code = 

    assert expected_code == generate_pipeline_code(pipeline, tpot_obj.operators)


        ```
    ###Assistant : Assert that generate_pipeline_code() returns the correct code given a specific pipeline with two CombineDFs.make_pipeline(
    make_union(
        StackingEstimator(estimator=GradientBoostingClassifier(learning_rate=38.0, max_depth=5, max_features=5, min_samples_leaf=5, min_samples_split=0.05, n_estimators=0.5)),
        make_union(
            MinMaxScaler(),
            make_pipeline(
                MaxAbsScaler(),
                ZeroCount()
            )
        )
    ),
    KNeighborsClassifier(n_neighbors=18, p=""uniform"", weights=2)
)
    "
214,"def _script_names(dist, script_name, is_gui):
    # type: (Distribution, str, bool) -> List[str]
    
    if dist_in_usersite(dist):
        bin_dir = get_bin_user()
    else:
        bin_dir = get_bin_prefix()
    exe_name = os.path.join(bin_dir, script_name)
    paths_to_remove = [exe_name]
    if WINDOWS:
        paths_to_remove.append(exe_name + '.exe')
        paths_to_remove.append(exe_name + '.exe.manifest')
        if is_gui:
            paths_to_remove.append(exe_name + '-script.pyw')
        else:
            paths_to_remove.append(exe_name + '-script.py')
    return paths_to_remove

","Create the fully qualified name of the files created by
    {console,gui}_scripts for the given ``dist``.
    Returns the list of file names
    ",21,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _script_names(dist, script_name, is_gui):
    # type: (Distribution, str, bool) -> List[str]
    
    if dist_in_usersite(dist):
        bin_dir = get_bin_user()
    else:
        bin_dir = get_bin_prefix()
    exe_name = os.path.join(bin_dir, script_name)
    paths_to_remove = [exe_name]
    if WINDOWS:
        paths_to_remove.append(exe_name + '.exe')
        paths_to_remove.append(exe_name + '.exe.manifest')
        if is_gui:
            paths_to_remove.append(exe_name + '-script.pyw')
        else:
            paths_to_remove.append(exe_name + '-script.py')
    return paths_to_remove


        ```
    ###Assistant : Create the fully qualified name of the files created by
    {console,gui}_scripts for the given ``dist``.
    Returns the list of file names
    
    "
215,"def bbox_center(boxes):
    
    boxes_cx = (boxes[..., 0] + boxes[..., 2]) / 2
    boxes_cy = (boxes[..., 1] + boxes[..., 3]) / 2
    return paddle.stack([boxes_cx, boxes_cy], axis=-1)

","Get bbox centers from boxes.
    Args:
        boxes (Tensor): boxes with shape (..., 4), ""xmin, ymin, xmax, ymax"" format.
    Returns:
        Tensor: boxes centers with shape (..., 2), ""cx, cy"" format.
    ",29,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bbox_center(boxes):
    
    boxes_cx = (boxes[..., 0] + boxes[..., 2]) / 2
    boxes_cy = (boxes[..., 1] + boxes[..., 3]) / 2
    return paddle.stack([boxes_cx, boxes_cy], axis=-1)


        ```
    ###Assistant : Get bbox centers from boxes.
    Args:
        boxes (Tensor): boxes with shape (..., 4), ""xmin, ymin, xmax, ymax"" format.
    Returns:
        Tensor: boxes centers with shape (..., 2), ""cx, cy"" format.
    
    "
216,"def predict(self, input):
        

        input_names = self.predictor.get_input_names()
        input_tensor = self.predictor.get_input_handle(input_names[0])

        output_names = self.predictor.get_output_names()
        output_tensor = self.predictor.get_output_handle(output_names[0])

        # preprocess
        self.recognize_times.preprocess_time_s.start()
        if type(input) == str:
            inputs = self.preprocess_video(input)
        else:
            inputs = self.preprocess_frames(input)
        self.recognize_times.preprocess_time_s.end()

        inputs = np.expand_dims(
            inputs, axis=0).repeat(
                self.batch_size, axis=0).copy()

        input_tensor.copy_from_cpu(inputs)

        # model prediction
        self.recognize_times.inference_time_s.start()
        self.predictor.run()
        self.recognize_times.inference_time_s.end()

        output = output_tensor.copy_to_cpu()

        # postprocess
        self.recognize_times.postprocess_time_s.start()
        classes, scores = self.postprocess(output)
        self.recognize_times.postprocess_time_s.end()

        return classes, scores
","
        Args:
            input (str) or (list): video file path or image data list
        Returns:
            results (dict): 
        ",15,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def predict(self, input):
        

        input_names = self.predictor.get_input_names()
        input_tensor = self.predictor.get_input_handle(input_names[0])

        output_names = self.predictor.get_output_names()
        output_tensor = self.predictor.get_output_handle(output_names[0])

        # preprocess
        self.recognize_times.preprocess_time_s.start()
        if type(input) == str:
            inputs = self.preprocess_video(input)
        else:
            inputs = self.preprocess_frames(input)
        self.recognize_times.preprocess_time_s.end()

        inputs = np.expand_dims(
            inputs, axis=0).repeat(
                self.batch_size, axis=0).copy()

        input_tensor.copy_from_cpu(inputs)

        # model prediction
        self.recognize_times.inference_time_s.start()
        self.predictor.run()
        self.recognize_times.inference_time_s.end()

        output = output_tensor.copy_to_cpu()

        # postprocess
        self.recognize_times.postprocess_time_s.start()
        classes, scores = self.postprocess(output)
        self.recognize_times.postprocess_time_s.end()

        return classes, scores

        ```
    ###Assistant : 
        Args:
            input (str) or (list): video file path or image data list
        Returns:
            results (dict): 
        
    "
217,"def deep_deconstruct(self, obj):
        
        if isinstance(obj, list):
            return [self.deep_deconstruct(value) for value in obj]
        elif isinstance(obj, tuple):
            return tuple(self.deep_deconstruct(value) for value in obj)
        elif isinstance(obj, dict):
            return {key: self.deep_deconstruct(value) for key, value in obj.items()}
        elif isinstance(obj, functools.partial):
            return (
                obj.func,
                self.deep_deconstruct(obj.args),
                self.deep_deconstruct(obj.keywords),
            )
        elif isinstance(obj, COMPILED_REGEX_TYPE):
            return RegexObject(obj)
        elif isinstance(obj, type):
            # If this is a type that implements 'deconstruct' as an instance method,
            # avoid treating this as being deconstructible itself - see #22951
            return obj
        elif hasattr(obj, ""deconstruct""):
            deconstructed = obj.deconstruct()
            if isinstance(obj, models.Field):
                # we have a field which also returns a name
                deconstructed = deconstructed[1:]
            path, args, kwargs = deconstructed
            return (
                path,
                [self.deep_deconstruct(value) for value in args],
                {key: self.deep_deconstruct(value) for key, value in kwargs.items()},
            )
        else:
            return obj
","
        Recursive deconstruction for a field and its arguments.
        Used for full comparison for rename/alter; sometimes a single-level
        deconstruction will not compare correctly.
        ",22,121,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deep_deconstruct(self, obj):
        
        if isinstance(obj, list):
            return [self.deep_deconstruct(value) for value in obj]
        elif isinstance(obj, tuple):
            return tuple(self.deep_deconstruct(value) for value in obj)
        elif isinstance(obj, dict):
            return {key: self.deep_deconstruct(value) for key, value in obj.items()}
        elif isinstance(obj, functools.partial):
            return (
                obj.func,
                self.deep_deconstruct(obj.args),
                self.deep_deconstruct(obj.keywords),
            )
        elif isinstance(obj, COMPILED_REGEX_TYPE):
            return RegexObject(obj)
        elif isinstance(obj, type):
            # If this is a type that implements 'deconstruct' as an instance method,
            # avoid treating this as being deconstructible itself - see #22951
            return obj
        elif hasattr(obj, ""deconstruct""):
            deconstructed = obj.deconstruct()
            if isinstance(obj, models.Field):
                # we have a field which also returns a name
                deconstructed = deconstructed[1:]
            path, args, kwargs = deconstructed
            return (
                path,
                [self.deep_deconstruct(value) for value in args],
                {key: self.deep_deconstruct(value) for key, value in kwargs.items()},
            )
        else:
            return obj

        ```
    ###Assistant : 
        Recursive deconstruction for a field and its arguments.
        Used for full comparison for rename/alter; sometimes a single-level
        deconstruction will not compare correctly.
        
    "
218,"def test_cancellation(self):
        
        deferred: ""Deferred[str]"" = Deferred()
        wrapper_deferred = stop_cancellation(deferred)

        # Cancel the new `Deferred`.
        wrapper_deferred.cancel()
        self.assertTrue(wrapper_deferred.called)
        self.failureResultOf(wrapper_deferred, CancelledError)
        self.assertFalse(
            deferred.called, ""Original `Deferred` was unexpectedly cancelled.""
        )

        # Now make the inner `Deferred` fail.
        # The `Failure` must be consumed, otherwise unwanted tracebacks will be printed
        # in logs.
        deferred.errback(ValueError(""abc""))
        self.assertIsNone(deferred.result, ""`Failure` was not consumed"")
",Test that cancellation of the new `Deferred` leaves the original running.,11,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_cancellation(self):
        
        deferred: ""Deferred[str]"" = Deferred()
        wrapper_deferred = stop_cancellation(deferred)

        # Cancel the new `Deferred`.
        wrapper_deferred.cancel()
        self.assertTrue(wrapper_deferred.called)
        self.failureResultOf(wrapper_deferred, CancelledError)
        self.assertFalse(
            deferred.called, ""Original `Deferred` was unexpectedly cancelled.""
        )

        # Now make the inner `Deferred` fail.
        # The `Failure` must be consumed, otherwise unwanted tracebacks will be printed
        # in logs.
        deferred.errback(ValueError(""abc""))
        self.assertIsNone(deferred.result, ""`Failure` was not consumed"")

        ```
    ###Assistant : Test that cancellation of the new `Deferred` leaves the original running.
    "
219,"def getgeneratorlocals(generator):
    

    if not isgenerator(generator):
        raise TypeError(""{!r} is not a Python generator"".format(generator))

    frame = getattr(generator, ""gi_frame"", None)
    if frame is not None:
        return generator.gi_frame.f_locals
    else:
        return {}


# ------------------------------------------------ coroutine introspection

CORO_CREATED = 'CORO_CREATED'
CORO_RUNNING = 'CORO_RUNNING'
CORO_SUSPENDED = 'CORO_SUSPENDED'
CORO_CLOSED = 'CORO_CLOSED'
","
    Get the mapping of generator local variables to their current values.

    A dict is returned, with the keys the local variable names and values the
    bound values.",27,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getgeneratorlocals(generator):
    

    if not isgenerator(generator):
        raise TypeError(""{!r} is not a Python generator"".format(generator))

    frame = getattr(generator, ""gi_frame"", None)
    if frame is not None:
        return generator.gi_frame.f_locals
    else:
        return {}


# ------------------------------------------------ coroutine introspection

CORO_CREATED = 'CORO_CREATED'
CORO_RUNNING = 'CORO_RUNNING'
CORO_SUSPENDED = 'CORO_SUSPENDED'
CORO_CLOSED = 'CORO_CLOSED'

        ```
    ###Assistant : 
    Get the mapping of generator local variables to their current values.

    A dict is returned, with the keys the local variable names and values the
    bound values.
    "
220,"async def test_last_bin_contains_end_date(client, route):
    
    response = await client.post(
        f""/{route}/history"",
        json=dict(
            history_start=str(dt),
            history_end=str(dt.add(days=1, minutes=30)),
            history_interval_seconds=timedelta(days=1).total_seconds(),
        ),
    )

    assert response.status_code == status.HTTP_200_OK
    parsed = pydantic.parse_obj_as(List[responses.HistoryResponse], response.json())
    assert len(parsed) == 2
    assert parsed[0].interval_start == dt
    assert parsed[0].interval_end == dt.add(days=1)
    assert parsed[1].interval_start == dt.add(days=1)
    assert parsed[1].interval_end == dt.add(days=2)

","The last bin contains the end date, so its own end could be after the history end",17,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_last_bin_contains_end_date(client, route):
    
    response = await client.post(
        f""/{route}/history"",
        json=dict(
            history_start=str(dt),
            history_end=str(dt.add(days=1, minutes=30)),
            history_interval_seconds=timedelta(days=1).total_seconds(),
        ),
    )

    assert response.status_code == status.HTTP_200_OK
    parsed = pydantic.parse_obj_as(List[responses.HistoryResponse], response.json())
    assert len(parsed) == 2
    assert parsed[0].interval_start == dt
    assert parsed[0].interval_end == dt.add(days=1)
    assert parsed[1].interval_start == dt.add(days=1)
    assert parsed[1].interval_end == dt.add(days=2)


        ```
    ###Assistant : The last bin contains the end date, so its own end could be after the history end
    "
221,"def _validate_attributes(self):
        
        # Run config
        if not isinstance(self.run_config, RunConfig):
            raise ValueError(
                f""`run_config` should be an instance of `ray.air.RunConfig`, ""
                f""found {type(self.run_config)} with value `{self.run_config}`.""
            )
        # Scaling config
        # Todo: move to ray.air.ScalingConfig
        if not isinstance(self.scaling_config, dict):
            raise ValueError(
                f""`scaling_config` should be an instance of `dict`, ""
                f""found {type(self.scaling_config)} with value `{self.scaling_config}`.""
            )
        # Datasets
        if not isinstance(self.datasets, dict):
            raise ValueError(
                f""`datasets` should be a dict mapping from a string to ""
                f""`ray.data.Dataset` objects, ""
                f""found {type(self.datasets)} with value `{self.datasets}`.""
            )
        elif any(
            not isinstance(ds, ray.data.Dataset) and not callable(ds)
            for ds in self.datasets.values()
        ):
            raise ValueError(
                f""At least one value in the `datasets` dict is not a ""
                f""`ray.data.Dataset`: {self.datasets}""
            )
        # Preprocessor
        if self.preprocessor is not None and not isinstance(
            self.preprocessor, ray.data.Preprocessor
        ):
            raise ValueError(
                f""`preprocessor` should be an instance of `ray.data.Preprocessor`, ""
                f""found {type(self.preprocessor)} with value `{self.preprocessor}`.""
            )

        if self.resume_from_checkpoint is not None and not isinstance(
            self.resume_from_checkpoint, ray.air.Checkpoint
        ):
            raise ValueError(
                f""`resume_from_checkpoint` should be an instance of ""
                f""`ray.air.Checkpoint`, found {type(self.resume_from_checkpoint)} ""
                f""with value `{self.resume_from_checkpoint}`.""
            )
",Called on __init()__ to validate trainer attributes.,7,168,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate_attributes(self):
        
        # Run config
        if not isinstance(self.run_config, RunConfig):
            raise ValueError(
                f""`run_config` should be an instance of `ray.air.RunConfig`, ""
                f""found {type(self.run_config)} with value `{self.run_config}`.""
            )
        # Scaling config
        # Todo: move to ray.air.ScalingConfig
        if not isinstance(self.scaling_config, dict):
            raise ValueError(
                f""`scaling_config` should be an instance of `dict`, ""
                f""found {type(self.scaling_config)} with value `{self.scaling_config}`.""
            )
        # Datasets
        if not isinstance(self.datasets, dict):
            raise ValueError(
                f""`datasets` should be a dict mapping from a string to ""
                f""`ray.data.Dataset` objects, ""
                f""found {type(self.datasets)} with value `{self.datasets}`.""
            )
        elif any(
            not isinstance(ds, ray.data.Dataset) and not callable(ds)
            for ds in self.datasets.values()
        ):
            raise ValueError(
                f""At least one value in the `datasets` dict is not a ""
                f""`ray.data.Dataset`: {self.datasets}""
            )
        # Preprocessor
        if self.preprocessor is not None and not isinstance(
            self.preprocessor, ray.data.Preprocessor
        ):
            raise ValueError(
                f""`preprocessor` should be an instance of `ray.data.Preprocessor`, ""
                f""found {type(self.preprocessor)} with value `{self.preprocessor}`.""
            )

        if self.resume_from_checkpoint is not None and not isinstance(
            self.resume_from_checkpoint, ray.air.Checkpoint
        ):
            raise ValueError(
                f""`resume_from_checkpoint` should be an instance of ""
                f""`ray.air.Checkpoint`, found {type(self.resume_from_checkpoint)} ""
                f""with value `{self.resume_from_checkpoint}`.""
            )

        ```
    ###Assistant : Called on __init()__ to validate trainer attributes.
    "
222,"def longitude(self) -> float | None:
        
        if (
            self.extra_state_attributes is not None
            and ATTR_LONGITUDE in self.extra_state_attributes
        ):
            longitude: float = self.extra_state_attributes[ATTR_LONGITUDE]
            return longitude
        return None
",Return longitude if provided in extra_state_attributes or None.,8,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def longitude(self) -> float | None:
        
        if (
            self.extra_state_attributes is not None
            and ATTR_LONGITUDE in self.extra_state_attributes
        ):
            longitude: float = self.extra_state_attributes[ATTR_LONGITUDE]
            return longitude
        return None

        ```
    ###Assistant : Return longitude if provided in extra_state_attributes or None.
    "
223,"def user_cache_dir(self) -> str:
        
        path = os.path.normpath(get_win_folder(""CSIDL_LOCAL_APPDATA""))
        return self._append_parts(path, opinion_value=""Cache"")
","
        :return: cache directory tied to the user (if opinionated with ``Cache`` folder within ``$appname``) e.g.
         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname\\Cache\\$version``
        ",16,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def user_cache_dir(self) -> str:
        
        path = os.path.normpath(get_win_folder(""CSIDL_LOCAL_APPDATA""))
        return self._append_parts(path, opinion_value=""Cache"")

        ```
    ###Assistant : 
        :return: cache directory tied to the user (if opinionated with ``Cache`` folder within ``$appname``) e.g.
         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname\\Cache\\$version``
        
    "
224,"def get_project_config(project, full_config=True, project_keys=None):
    
    with sentry_sdk.push_scope() as scope:
        scope.set_tag(""project"", project.id)
        with metrics.timer(""relay.config.get_project_config.duration""):
            return _get_project_config(project, full_config=full_config, project_keys=project_keys)

","Constructs the ProjectConfig information.
    :param project: The project to load configuration for. Ensure that
        organization is bound on this object; otherwise it will be loaded from
        the database.
    :param full_config: True if only the full config is required, False
        if only the restricted (for external relays) is required
        (default True, i.e. full configuration)
    :param project_keys: Pre-fetched project keys for performance. However, if
        no project keys are provided it is assumed that the config does not
        need to contain auth information (this is the case when used in
        python's StoreView)
    :return: a ProjectConfig object for the given project
    ",97,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_project_config(project, full_config=True, project_keys=None):
    
    with sentry_sdk.push_scope() as scope:
        scope.set_tag(""project"", project.id)
        with metrics.timer(""relay.config.get_project_config.duration""):
            return _get_project_config(project, full_config=full_config, project_keys=project_keys)


        ```
    ###Assistant : Constructs the ProjectConfig information.
    :param project: The project to load configuration for. Ensure that
        organization is bound on this object; otherwise it will be loaded from
        the database.
    :param full_config: True if only the full config is required, False
        if only the restricted (for external relays) is required
        (default True, i.e. full configuration)
    :param project_keys: Pre-fetched project keys for performance. However, if
        no project keys are provided it is assumed that the config does not
        need to contain auth information (this is the case when used in
        python's StoreView)
    :return: a ProjectConfig object for the given project
    
    "
225,"def _query_kg(self, sparql_query):
        
        try:
            response = self.knowledge_graph.query(sparql_query=sparql_query)

            # unpack different answer styles
            if isinstance(response, list):
                if len(response) == 0:
                    result = """"
                else:
                    result = []
                    for x in response:
                        for k, v in x.items():
                            result.append(v[""value""])
            elif isinstance(response, bool):
                result = str(response)
            elif ""count"" in response[0]:
                result = str(int(response[0][""count""][""value""]))
            else:
                result = """"

        except Exception:
            result = """"

        return result, sparql_query
","
        Execute a single SPARQL query on the knowledge graph to retrieve an answer and unpack
        different answer styles for boolean queries, count queries, and list queries.

        :param sparql_query: SPARQL query that shall be executed on the knowledge graph
        ",38,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _query_kg(self, sparql_query):
        
        try:
            response = self.knowledge_graph.query(sparql_query=sparql_query)

            # unpack different answer styles
            if isinstance(response, list):
                if len(response) == 0:
                    result = """"
                else:
                    result = []
                    for x in response:
                        for k, v in x.items():
                            result.append(v[""value""])
            elif isinstance(response, bool):
                result = str(response)
            elif ""count"" in response[0]:
                result = str(int(response[0][""count""][""value""]))
            else:
                result = """"

        except Exception:
            result = """"

        return result, sparql_query

        ```
    ###Assistant : 
        Execute a single SPARQL query on the knowledge graph to retrieve an answer and unpack
        different answer styles for boolean queries, count queries, and list queries.

        :param sparql_query: SPARQL query that shall be executed on the knowledge graph
        
    "
226,"def _path_importer_cache(cls, path):
        
        if path == '':
            try:
                path = _os.getcwd()
            except FileNotFoundError:
                # Don't cache the failure as the cwd can easily change to
                # a valid directory later on.
                return None
        try:
            finder = sys.path_importer_cache[path]
        except KeyError:
            finder = cls._path_hooks(path)
            sys.path_importer_cache[path] = finder
        return finder
","Get the finder for the path entry from sys.path_importer_cache.

        If the path entry is not in the cache, find the appropriate finder
        and cache it. If no finder is available, store None.

        ",32,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _path_importer_cache(cls, path):
        
        if path == '':
            try:
                path = _os.getcwd()
            except FileNotFoundError:
                # Don't cache the failure as the cwd can easily change to
                # a valid directory later on.
                return None
        try:
            finder = sys.path_importer_cache[path]
        except KeyError:
            finder = cls._path_hooks(path)
            sys.path_importer_cache[path] = finder
        return finder

        ```
    ###Assistant : Get the finder for the path entry from sys.path_importer_cache.

        If the path entry is not in the cache, find the appropriate finder
        and cache it. If no finder is available, store None.

        
    "
227,"def test_publish_parts(self):
        
        import docutils

        self.assertNotEqual(
            docutils.parsers.rst.roles.DEFAULT_INTERPRETED_ROLE, ""cmsreference""
        )
        source = ""reST, `interpreted text`, default role.""
        markup = ""<p>reST, <cite>interpreted text</cite>, default role.</p>\n""
        parts = docutils.core.publish_parts(source=source, writer_name=""html4css1"")
        self.assertEqual(parts[""fragment""], markup)
","
        Django shouldn't break the default role for interpreted text
        when ``publish_parts`` is used directly, by setting it to
        ``cmsreference`` (#6681).
        ",20,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_publish_parts(self):
        
        import docutils

        self.assertNotEqual(
            docutils.parsers.rst.roles.DEFAULT_INTERPRETED_ROLE, ""cmsreference""
        )
        source = ""reST, `interpreted text`, default role.""
        markup = ""<p>reST, <cite>interpreted text</cite>, default role.</p>\n""
        parts = docutils.core.publish_parts(source=source, writer_name=""html4css1"")
        self.assertEqual(parts[""fragment""], markup)

        ```
    ###Assistant : 
        Django shouldn't break the default role for interpreted text
        when ``publish_parts`` is used directly, by setting it to
        ``cmsreference`` (#6681).
        
    "
228,"def test_get_backfill_points_in_room(self):
        
        setup_info = self._setup_room_for_backfill_tests()
        room_id = setup_info.room_id
        depth_map = setup_info.depth_map

        # Try at ""B""
        backfill_points = self.get_success(
            self.store.get_backfill_points_in_room(room_id, depth_map[""B""], limit=100)
        )
        backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
        self.assertListEqual(
            backfill_event_ids, [""b6"", ""b5"", ""b4"", ""2"", ""b3"", ""b2"", ""b1""]
        )

        # Try at ""A""
        backfill_points = self.get_success(
            self.store.get_backfill_points_in_room(room_id, depth_map[""A""], limit=100)
        )
        backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
        # Event ""2"" has a depth of 2 but is not included here because we only
        # know the approximate depth of 5 from our event ""3"".
        self.assertListEqual(backfill_event_ids, [""b3"", ""b2"", ""b1""])
","
        Test to make sure only backfill points that are older and come before
        the `current_depth` are returned.
        ",17,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_backfill_points_in_room(self):
        
        setup_info = self._setup_room_for_backfill_tests()
        room_id = setup_info.room_id
        depth_map = setup_info.depth_map

        # Try at ""B""
        backfill_points = self.get_success(
            self.store.get_backfill_points_in_room(room_id, depth_map[""B""], limit=100)
        )
        backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
        self.assertListEqual(
            backfill_event_ids, [""b6"", ""b5"", ""b4"", ""2"", ""b3"", ""b2"", ""b1""]
        )

        # Try at ""A""
        backfill_points = self.get_success(
            self.store.get_backfill_points_in_room(room_id, depth_map[""A""], limit=100)
        )
        backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
        # Event ""2"" has a depth of 2 but is not included here because we only
        # know the approximate depth of 5 from our event ""3"".
        self.assertListEqual(backfill_event_ids, [""b3"", ""b2"", ""b1""])

        ```
    ###Assistant : 
        Test to make sure only backfill points that are older and come before
        the `current_depth` are returned.
        
    "
229,"def theano_code(expr, cache=None, **kwargs):
    
    sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.8"",
    active_deprecations_target='theanocode-deprecated')

    if not theano:
        raise ImportError(""theano is required for theano_code"")

    if cache is None:
        cache = global_cache

    return TheanoPrinter(cache=cache, settings={}).doprint(expr, **kwargs)

","
    Convert a SymPy expression into a Theano graph variable.

    .. deprecated:: 1.8

      ``sympy.printing.theanocode`` is deprecated. Theano has been renamed to
      Aesara. Use ``sympy.printing.aesaracode`` instead. See
      :ref:`theanocode-deprecated` for more information.

    Parameters
    ==========

    expr : sympy.core.expr.Expr
        SymPy expression object to convert.

    cache : dict
        Cached Theano variables (see :class:`TheanoPrinter.cache
        <TheanoPrinter>`). Defaults to the module-level global cache.

    dtypes : dict
        Passed to :meth:`.TheanoPrinter.doprint`.

    broadcastables : dict
        Passed to :meth:`.TheanoPrinter.doprint`.

    Returns
    =======

    theano.gof.graph.Variable
        A variable corresponding to the expression's value in a Theano symbolic
        expression graph.

    
        sympy.printing.theanocode is deprecated. Theano has been renamed to
        Aesara. Use sympy.printing.aesaracode instead.",94,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def theano_code(expr, cache=None, **kwargs):
    
    sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.8"",
    active_deprecations_target='theanocode-deprecated')

    if not theano:
        raise ImportError(""theano is required for theano_code"")

    if cache is None:
        cache = global_cache

    return TheanoPrinter(cache=cache, settings={}).doprint(expr, **kwargs)


        ```
    ###Assistant : 
    Convert a SymPy expression into a Theano graph variable.

    .. deprecated:: 1.8

      ``sympy.printing.theanocode`` is deprecated. Theano has been renamed to
      Aesara. Use ``sympy.printing.aesaracode`` instead. See
      :ref:`theanocode-deprecated` for more information.

    Parameters
    ==========

    expr : sympy.core.expr.Expr
        SymPy expression object to convert.

    cache : dict
        Cached Theano variables (see :class:`TheanoPrinter.cache
        <TheanoPrinter>`). Defaults to the module-level global cache.

    dtypes : dict
        Passed to :meth:`.TheanoPrinter.doprint`.

    broadcastables : dict
        Passed to :meth:`.TheanoPrinter.doprint`.

    Returns
    =======

    theano.gof.graph.Variable
        A variable corresponding to the expression's value in a Theano symbolic
        expression graph.

    
        sympy.printing.theanocode is deprecated. Theano has been renamed to
        Aesara. Use sympy.printing.aesaracode instead.
    "
230,"def _validate_axes_lengths(self):
        
        if self._row_lengths_cache is not None and len(self.index) > 0:
            # An empty frame can have 0 rows but a nonempty index. If the frame
            # does have rows, the number of rows must equal the size of the
            # index.
            num_rows = sum(self._row_lengths_cache)
            if num_rows > 0:
                ErrorMessage.catch_bugs_and_request_email(
                    num_rows != len(self._index_cache),
                    f""Row lengths: {num_rows} != {len(self._index_cache)}"",
                )
            ErrorMessage.catch_bugs_and_request_email(
                any(val < 0 for val in self._row_lengths_cache),
                f""Row lengths cannot be negative: {self._row_lengths_cache}"",
            )
        if self._column_widths_cache is not None and len(self.columns) > 0:
            # An empty frame can have 0 column but a nonempty column index. If
            # the frame does have columns, the number of columns must equal the
            # size of the columns.
            num_columns = sum(self._column_widths_cache)
            if num_columns > 0:
                ErrorMessage.catch_bugs_and_request_email(
                    num_columns != len(self._columns_cache),
                    f""Column widths: {num_columns} != {len(self._columns_cache)}"",
                )
            ErrorMessage.catch_bugs_and_request_email(
                any(val < 0 for val in self._column_widths_cache),
                f""Column widths cannot be negative: {self._column_widths_cache}"",
            )
",Validate that labels are split correctly if split is known.,10,147,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate_axes_lengths(self):
        
        if self._row_lengths_cache is not None and len(self.index) > 0:
            # An empty frame can have 0 rows but a nonempty index. If the frame
            # does have rows, the number of rows must equal the size of the
            # index.
            num_rows = sum(self._row_lengths_cache)
            if num_rows > 0:
                ErrorMessage.catch_bugs_and_request_email(
                    num_rows != len(self._index_cache),
                    f""Row lengths: {num_rows} != {len(self._index_cache)}"",
                )
            ErrorMessage.catch_bugs_and_request_email(
                any(val < 0 for val in self._row_lengths_cache),
                f""Row lengths cannot be negative: {self._row_lengths_cache}"",
            )
        if self._column_widths_cache is not None and len(self.columns) > 0:
            # An empty frame can have 0 column but a nonempty column index. If
            # the frame does have columns, the number of columns must equal the
            # size of the columns.
            num_columns = sum(self._column_widths_cache)
            if num_columns > 0:
                ErrorMessage.catch_bugs_and_request_email(
                    num_columns != len(self._columns_cache),
                    f""Column widths: {num_columns} != {len(self._columns_cache)}"",
                )
            ErrorMessage.catch_bugs_and_request_email(
                any(val < 0 for val in self._column_widths_cache),
                f""Column widths cannot be negative: {self._column_widths_cache}"",
            )

        ```
    ###Assistant : Validate that labels are split correctly if split is known.
    "
231,"def test_get_name_capability_sid():
    
    cap_sid = ""S-1-15-3-1024-1065365936-1281604716-3511738428-1654721687-432734479-3232135806-4053264122-3456934681""
    sid_obj = win32security.ConvertStringSidToSid(cap_sid)
    assert salt.utils.win_dacl.get_name(sid_obj) is None

","
    Test get_name with a compatibility SID. Should return `None` as we want to
    ignore these SIDs
    ",16,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_name_capability_sid():
    
    cap_sid = ""S-1-15-3-1024-1065365936-1281604716-3511738428-1654721687-432734479-3232135806-4053264122-3456934681""
    sid_obj = win32security.ConvertStringSidToSid(cap_sid)
    assert salt.utils.win_dacl.get_name(sid_obj) is None


        ```
    ###Assistant : 
    Test get_name with a compatibility SID. Should return `None` as we want to
    ignore these SIDs
    
    "
232,"def entry_points(group=None):
    
    eps = importlib.metadata.entry_points()
    if group:
        try:
            return eps.select(group=group)
        except AttributeError:
            return eps.get(group, [])
    return eps
","Returns an iterable of entrypoints.

    For compatibility with Python 3.8/3.9.
    In 3.10 the return type changed from a dict to an ``importlib.metadata.EntryPoints``.
    This compatibility utility can be removed once Python 3.10 is the minimum.
    ",34,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def entry_points(group=None):
    
    eps = importlib.metadata.entry_points()
    if group:
        try:
            return eps.select(group=group)
        except AttributeError:
            return eps.get(group, [])
    return eps

        ```
    ###Assistant : Returns an iterable of entrypoints.

    For compatibility with Python 3.8/3.9.
    In 3.10 the return type changed from a dict to an ``importlib.metadata.EntryPoints``.
    This compatibility utility can be removed once Python 3.10 is the minimum.
    
    "
233,"def elliptic_curve(self) -> Optional[str]:
        
        key = self._private_key()
        if isinstance(key, EllipticCurvePrivateKey):
            return key.curve.name
        return None
","
        :returns: If the private key is an elliptic key, the name of its curve.
        :rtype: str
        ",16,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def elliptic_curve(self) -> Optional[str]:
        
        key = self._private_key()
        if isinstance(key, EllipticCurvePrivateKey):
            return key.curve.name
        return None

        ```
    ###Assistant : 
        :returns: If the private key is an elliptic key, the name of its curve.
        :rtype: str
        
    "
234,"def page_type_display_name(self):
        
        if not self.specific_class or self.is_root():
            return """"
        else:
            return self.specific_class.get_verbose_name()
","
        A human-readable version of this page's type
        ",7,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def page_type_display_name(self):
        
        if not self.specific_class or self.is_root():
            return """"
        else:
            return self.specific_class.get_verbose_name()

        ```
    ###Assistant : 
        A human-readable version of this page's type
        
    "
235,"def save(self, fname, **kwargs) -> Plot:
        
        # TODO expose important keyword arugments in our signature?
        self.plot().save(fname, **kwargs)
        return self
","
        Render the plot and write it to a buffer or file on disk.

        Parameters
        ----------
        fname : str, path, or buffer
            Location on disk to save the figure, or a buffer to write into.
        Other keyword arguments are passed to :meth:`matplotlib.figure.Figure.savefig`.

        ",41,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save(self, fname, **kwargs) -> Plot:
        
        # TODO expose important keyword arugments in our signature?
        self.plot().save(fname, **kwargs)
        return self

        ```
    ###Assistant : 
        Render the plot and write it to a buffer or file on disk.

        Parameters
        ----------
        fname : str, path, or buffer
            Location on disk to save the figure, or a buffer to write into.
        Other keyword arguments are passed to :meth:`matplotlib.figure.Figure.savefig`.

        
    "
236,"def get_install_candidate(self, link_evaluator, link):
        # type: (LinkEvaluator, Link) -> Optional[InstallationCandidate]
        
        is_candidate, result = link_evaluator.evaluate_link(link)
        if not is_candidate:
            if result:
                self._log_skipped_link(link, reason=result)
            return None

        return InstallationCandidate(
            name=link_evaluator.project_name,
            link=link,
            version=result,
        )
","
        If the link is a candidate for install, convert it to an
        InstallationCandidate and return it. Otherwise, return None.
        ",19,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_install_candidate(self, link_evaluator, link):
        # type: (LinkEvaluator, Link) -> Optional[InstallationCandidate]
        
        is_candidate, result = link_evaluator.evaluate_link(link)
        if not is_candidate:
            if result:
                self._log_skipped_link(link, reason=result)
            return None

        return InstallationCandidate(
            name=link_evaluator.project_name,
            link=link,
            version=result,
        )

        ```
    ###Assistant : 
        If the link is a candidate for install, convert it to an
        InstallationCandidate and return it. Otherwise, return None.
        
    "
237,"def bar(self, x=None, y=None, **kwargs) -> PlotAccessor:
        
        return self(kind=""bar"", x=x, y=y, **kwargs)
","
        Vertical bar plot.

        A bar plot is a plot that presents categorical data with
        rectangular bars with lengths proportional to the values that they
        represent. A bar plot shows comparisons among discrete categories. One
        axis of the plot shows the specific categories being compared, and the
        other axis represents a measured value.
        ",52,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bar(self, x=None, y=None, **kwargs) -> PlotAccessor:
        
        return self(kind=""bar"", x=x, y=y, **kwargs)

        ```
    ###Assistant : 
        Vertical bar plot.

        A bar plot is a plot that presents categorical data with
        rectangular bars with lengths proportional to the values that they
        represent. A bar plot shows comparisons among discrete categories. One
        axis of the plot shows the specific categories being compared, and the
        other axis represents a measured value.
        
    "
238,"def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
    
    input_shape = shape(y_pred)
    num_samples, num_steps = input_shape[0], input_shape[1]
    y_pred = tf.math.log(
        tf.compat.v1.transpose(y_pred, perm=[1, 0, 2]) + epsilon()
    )
    input_length = tf.cast(input_length, tf.int32)

    if greedy:
        (decoded, log_prob) = tf.nn.ctc_greedy_decoder(
            inputs=y_pred, sequence_length=input_length
        )
    else:
        (decoded, log_prob) = tf.compat.v1.nn.ctc_beam_search_decoder(
            inputs=y_pred,
            sequence_length=input_length,
            beam_width=beam_width,
            top_paths=top_paths,
        )
    decoded_dense = []
    for st in decoded:
        st = tf.SparseTensor(st.indices, st.values, (num_samples, num_steps))
        decoded_dense.append(tf.sparse.to_dense(sp_input=st, default_value=-1))
    return (decoded_dense, log_prob)


# HIGH ORDER FUNCTIONS


@keras_export(""keras.backend.map_fn"")
@doc_controls.do_not_generate_docs","Decodes the output of a softmax.

    Can use either greedy search (also known as best path)
    or a constrained dictionary search.

    Args:
        y_pred: tensor `(samples, time_steps, num_categories)`
            containing the prediction, or output of the softmax.
        input_length: tensor `(samples, )` containing the sequence length for
            each batch item in `y_pred`.
        greedy: perform much faster best-path search if `true`.
            This does not use a dictionary.
        beam_width: if `greedy` is `false`: a beam search decoder will be used
            with a beam of this width.
        top_paths: if `greedy` is `false`,
            how many of the most probable paths will be returned.

    Returns:
        Tuple:
            List: if `greedy` is `true`, returns a list of one element that
                contains the decoded sequence.
                If `false`, returns the `top_paths` most probable
                decoded sequences.
                Each decoded sequence has shape (samples, time_steps).
                Important: blank labels are returned as `-1`.
            Tensor `(top_paths, )` that contains
                the log probability of each decoded sequence.
    ",149,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
    
    input_shape = shape(y_pred)
    num_samples, num_steps = input_shape[0], input_shape[1]
    y_pred = tf.math.log(
        tf.compat.v1.transpose(y_pred, perm=[1, 0, 2]) + epsilon()
    )
    input_length = tf.cast(input_length, tf.int32)

    if greedy:
        (decoded, log_prob) = tf.nn.ctc_greedy_decoder(
            inputs=y_pred, sequence_length=input_length
        )
    else:
        (decoded, log_prob) = tf.compat.v1.nn.ctc_beam_search_decoder(
            inputs=y_pred,
            sequence_length=input_length,
            beam_width=beam_width,
            top_paths=top_paths,
        )
    decoded_dense = []
    for st in decoded:
        st = tf.SparseTensor(st.indices, st.values, (num_samples, num_steps))
        decoded_dense.append(tf.sparse.to_dense(sp_input=st, default_value=-1))
    return (decoded_dense, log_prob)


# HIGH ORDER FUNCTIONS


@keras_export(""keras.backend.map_fn"")
@doc_controls.do_not_generate_docs
        ```
    ###Assistant : Decodes the output of a softmax.

    Can use either greedy search (also known as best path)
    or a constrained dictionary search.

    Args:
        y_pred: tensor `(samples, time_steps, num_categories)`
            containing the prediction, or output of the softmax.
        input_length: tensor `(samples, )` containing the sequence length for
            each batch item in `y_pred`.
        greedy: perform much faster best-path search if `true`.
            This does not use a dictionary.
        beam_width: if `greedy` is `false`: a beam search decoder will be used
            with a beam of this width.
        top_paths: if `greedy` is `false`,
            how many of the most probable paths will be returned.

    Returns:
        Tuple:
            List: if `greedy` is `true`, returns a list of one element that
                contains the decoded sequence.
                If `false`, returns the `top_paths` most probable
                decoded sequences.
                Each decoded sequence has shape (samples, time_steps).
                Important: blank labels are returned as `-1`.
            Tensor `(top_paths, )` that contains
                the log probability of each decoded sequence.
    
    "
239,"def display_time_updates(bar):
    
    threading.Thread(target=_show_time_updates, args=(bar,)).start()

","
    Start displaying the progress `bar` in a notebook.

    Parameters
    ----------
    bar : tqdm.tqdm
        The progress bar wrapper to display in a notebook cell.
    ",23,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def display_time_updates(bar):
    
    threading.Thread(target=_show_time_updates, args=(bar,)).start()


        ```
    ###Assistant : 
    Start displaying the progress `bar` in a notebook.

    Parameters
    ----------
    bar : tqdm.tqdm
        The progress bar wrapper to display in a notebook cell.
    
    "
240,"def remove(name=None, pkgs=None, **kwargs):
    
    targets = salt.utils.args.split_input(pkgs) if pkgs else [name]
    if not targets:
        return {}

    if pkgs:
        log.debug(""Removing these fileset(s)/rpm package(s) %s: %s"", name, targets)

    errors = []

    # Get a list of the currently installed pkgs.
    old = list_pkgs()

    # Remove the fileset or rpm package(s)
    for target in targets:
        try:
            named, versionpkg, rpmpkg = _check_pkg(target)
        except CommandExecutionError as exc:
            if exc.info:
                errors.append(exc.info[""errors""])
            continue

        if rpmpkg:

            # assume use dnf or yum
            cmdflags = "" -y remove ""
            if pathlib.Path(""/opt/freeware/bin/dnf"").is_file():
                cmdexe = ""/opt/freeware/bin/dnf""
            elif pathlib.Path(""/opt/freeware/bin/yum"").is_file():
                cmdexe = ""/opt/freeware/bin/yum""
            elif pathlib.Path(""/usr/bin/yum"").is_file():
                cmdexe = ""/usr/bin/yum""
            else:
                cmdexe = ""/usr/bin/rpm""
                cmdflags = "" -e ""

            cmd = [cmdexe, cmdflags, named]
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)
        else:
            cmd = [""/usr/sbin/installp"", ""-u"", named]
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)

    # Get a list of the packages after the uninstall
    __context__.pop(""pkg.list_pkgs"", None)
    new = list_pkgs()
    ret = salt.utils.data.compare_dicts(old, new)

    if errors:
        raise CommandExecutionError(
            ""Problems encountered removing filesets(s)/package(s)"",
            info={""changes"": ret, ""errors"": errors},
        )

    return ret

","
    Remove specified fileset(s)/rpm package(s).

    name
        The name of the fileset or rpm package to be deleted.

    .. versionadded:: 3005

        preference to install rpm packages are to use in the following order:
            /opt/freeware/bin/dnf
            /opt/freeware/bin/yum
            /usr/bin/yum
            /usr/bin/rpm

    Multiple Package Options:

    pkgs
        A list of filesets and/or rpm packages to delete.
        Must be passed as a python list. The ``name`` parameter will be
        ignored if this option is passed.


    Returns a list containing the removed packages.

    CLI Example:

    .. code-block:: bash

        salt '*' pkg.remove <fileset/rpm package name>
        salt '*' pkg.remove tcsh
        salt '*' pkg.remove xlC.rte
        salt '*' pkg.remove Firefox.base.adt
        salt '*' pkg.remove pkgs='[""foo"", ""bar""]'
    ",101,157,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def remove(name=None, pkgs=None, **kwargs):
    
    targets = salt.utils.args.split_input(pkgs) if pkgs else [name]
    if not targets:
        return {}

    if pkgs:
        log.debug(""Removing these fileset(s)/rpm package(s) %s: %s"", name, targets)

    errors = []

    # Get a list of the currently installed pkgs.
    old = list_pkgs()

    # Remove the fileset or rpm package(s)
    for target in targets:
        try:
            named, versionpkg, rpmpkg = _check_pkg(target)
        except CommandExecutionError as exc:
            if exc.info:
                errors.append(exc.info[""errors""])
            continue

        if rpmpkg:

            # assume use dnf or yum
            cmdflags = "" -y remove ""
            if pathlib.Path(""/opt/freeware/bin/dnf"").is_file():
                cmdexe = ""/opt/freeware/bin/dnf""
            elif pathlib.Path(""/opt/freeware/bin/yum"").is_file():
                cmdexe = ""/opt/freeware/bin/yum""
            elif pathlib.Path(""/usr/bin/yum"").is_file():
                cmdexe = ""/usr/bin/yum""
            else:
                cmdexe = ""/usr/bin/rpm""
                cmdflags = "" -e ""

            cmd = [cmdexe, cmdflags, named]
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)
        else:
            cmd = [""/usr/sbin/installp"", ""-u"", named]
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)

    # Get a list of the packages after the uninstall
    __context__.pop(""pkg.list_pkgs"", None)
    new = list_pkgs()
    ret = salt.utils.data.compare_dicts(old, new)

    if errors:
        raise CommandExecutionError(
            ""Problems encountered removing filesets(s)/package(s)"",
            info={""changes"": ret, ""errors"": errors},
        )

    return ret


        ```
    ###Assistant : 
    Remove specified fileset(s)/rpm package(s).

    name
        The name of the fileset or rpm package to be deleted.

    .. versionadded:: 3005

        preference to install rpm packages are to use in the following order:
            /opt/freeware/bin/dnf
            /opt/freeware/bin/yum
            /usr/bin/yum
            /usr/bin/rpm

    Multiple Package Options:

    pkgs
        A list of filesets and/or rpm packages to delete.
        Must be passed as a python list. The ``name`` parameter will be
        ignored if this option is passed.


    Returns a list containing the removed packages.

    CLI Example:

    .. code-block:: bash

        salt '*' pkg.remove <fileset/rpm package name>
        salt '*' pkg.remove tcsh
        salt '*' pkg.remove xlC.rte
        salt '*' pkg.remove Firefox.base.adt
        salt '*' pkg.remove pkgs='[""foo"", ""bar""]'
    
    "
241,"def _from_ordinalf(x, tz=None):
    

    tz = _get_tzinfo(tz)

    dt = (np.datetime64(get_epoch()) +
          np.timedelta64(int(np.round(x * MUSECONDS_PER_DAY)), 'us'))
    if dt < np.datetime64('0001-01-01') or dt >= np.datetime64('10000-01-01'):
        raise ValueError(f'Date ordinal {x} converts to {dt} (using '
                         f'epoch {get_epoch()}), but Matplotlib dates must be '
                          'between year 0001 and 9999.')
    # convert from datetime64 to datetime:
    dt = dt.tolist()

    # datetime64 is always UTC:
    dt = dt.replace(tzinfo=dateutil.tz.gettz('UTC'))
    # but maybe we are working in a different timezone so move.
    dt = dt.astimezone(tz)
    # fix round off errors
    if np.abs(x) > 70 * 365:
        # if x is big, round off to nearest twenty microseconds.
        # This avoids floating point roundoff error
        ms = round(dt.microsecond / 20) * 20
        if ms == 1000000:
            dt = dt.replace(microsecond=0) + datetime.timedelta(seconds=1)
        else:
            dt = dt.replace(microsecond=ms)

    return dt


# a version of _from_ordinalf that can operate on numpy arrays
_from_ordinalf_np_vectorized = np.vectorize(_from_ordinalf, otypes=""O"")


# a version of dateutil.parser.parse that can operate on numpy arrays
_dateutil_parser_parse_np_vectorized = np.vectorize(dateutil.parser.parse)

","
    Convert Gregorian float of the date, preserving hours, minutes,
    seconds and microseconds.  Return value is a `.datetime`.

    The input date *x* is a float in ordinal days at UTC, and the output will
    be the specified `.datetime` object corresponding to that time in
    timezone *tz*, or if *tz* is ``None``, in the timezone specified in
    :rc:`timezone`.
    ",56,156,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _from_ordinalf(x, tz=None):
    

    tz = _get_tzinfo(tz)

    dt = (np.datetime64(get_epoch()) +
          np.timedelta64(int(np.round(x * MUSECONDS_PER_DAY)), 'us'))
    if dt < np.datetime64('0001-01-01') or dt >= np.datetime64('10000-01-01'):
        raise ValueError(f'Date ordinal {x} converts to {dt} (using '
                         f'epoch {get_epoch()}), but Matplotlib dates must be '
                          'between year 0001 and 9999.')
    # convert from datetime64 to datetime:
    dt = dt.tolist()

    # datetime64 is always UTC:
    dt = dt.replace(tzinfo=dateutil.tz.gettz('UTC'))
    # but maybe we are working in a different timezone so move.
    dt = dt.astimezone(tz)
    # fix round off errors
    if np.abs(x) > 70 * 365:
        # if x is big, round off to nearest twenty microseconds.
        # This avoids floating point roundoff error
        ms = round(dt.microsecond / 20) * 20
        if ms == 1000000:
            dt = dt.replace(microsecond=0) + datetime.timedelta(seconds=1)
        else:
            dt = dt.replace(microsecond=ms)

    return dt


# a version of _from_ordinalf that can operate on numpy arrays
_from_ordinalf_np_vectorized = np.vectorize(_from_ordinalf, otypes=""O"")


# a version of dateutil.parser.parse that can operate on numpy arrays
_dateutil_parser_parse_np_vectorized = np.vectorize(dateutil.parser.parse)


        ```
    ###Assistant : 
    Convert Gregorian float of the date, preserving hours, minutes,
    seconds and microseconds.  Return value is a `.datetime`.

    The input date *x* is a float in ordinal days at UTC, and the output will
    be the specified `.datetime` object corresponding to that time in
    timezone *tz*, or if *tz* is ``None``, in the timezone specified in
    :rc:`timezone`.
    
    "
242,"def getdoc(object):
    
    try:
        doc = object.__doc__
    except AttributeError:
        return None
    if doc is None:
        try:
            doc = _finddoc(object)
        except (AttributeError, TypeError):
            return None
    if not isinstance(doc, str):
        return None
    return cleandoc(doc)
","Get the documentation string for an object.

    All tabs are expanded to spaces.  To clean up docstrings that are
    indented to line up with blocks of code, any whitespace than can be
    uniformly removed from the second line onwards is removed.",41,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getdoc(object):
    
    try:
        doc = object.__doc__
    except AttributeError:
        return None
    if doc is None:
        try:
            doc = _finddoc(object)
        except (AttributeError, TypeError):
            return None
    if not isinstance(doc, str):
        return None
    return cleandoc(doc)

        ```
    ###Assistant : Get the documentation string for an object.

    All tabs are expanded to spaces.  To clean up docstrings that are
    indented to line up with blocks of code, any whitespace than can be
    uniformly removed from the second line onwards is removed.
    "
243,"def load_config_with_kwargs(cls, kwargs):
    
    assert_is_a_marshmallow_class(cls)
    schema = cls.Schema()
    fields = schema.fields.keys()
    return load_config(cls, **{k: v for k, v in kwargs.items() if k in fields}), {
        k: v for k, v in kwargs.items() if k not in fields
    }

",Takes a marshmallow class and dict of parameter values and appropriately instantiantes the schema.,14,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_config_with_kwargs(cls, kwargs):
    
    assert_is_a_marshmallow_class(cls)
    schema = cls.Schema()
    fields = schema.fields.keys()
    return load_config(cls, **{k: v for k, v in kwargs.items() if k in fields}), {
        k: v for k, v in kwargs.items() if k not in fields
    }


        ```
    ###Assistant : Takes a marshmallow class and dict of parameter values and appropriately instantiantes the schema.
    "
244,"def test_payment_refund_or_void_refund_called_txn_exist(refund_mock, payment):
    
    # given
    payment.charge_status = ChargeStatus.FULLY_CHARGED
    payment.save(update_fields=[""charge_status""])
    assert payment.can_refund() is True
    payment.captured_amount = payment.total
    payment.save(update_fields=[""captured_amount""])
    txn = payment.transactions.create(
        is_success=True,
        action_required=False,
        kind=TransactionKind.REFUND_ONGOING,
        amount=payment.captured_amount / 2,
        currency=payment.currency,
        token=""test"",
        gateway_response={},
    )

    # when
    gateway.payment_refund_or_void(
        payment, get_plugins_manager(), None, transaction_id=txn.token
    )

    # then
    assert refund_mock.called_once()


@patch(""saleor.payment.gateway.refund"")","Ensure that the refund method is called when the refund process
    is already ongoing but not covered full payment captured amount.",21,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_payment_refund_or_void_refund_called_txn_exist(refund_mock, payment):
    
    # given
    payment.charge_status = ChargeStatus.FULLY_CHARGED
    payment.save(update_fields=[""charge_status""])
    assert payment.can_refund() is True
    payment.captured_amount = payment.total
    payment.save(update_fields=[""captured_amount""])
    txn = payment.transactions.create(
        is_success=True,
        action_required=False,
        kind=TransactionKind.REFUND_ONGOING,
        amount=payment.captured_amount / 2,
        currency=payment.currency,
        token=""test"",
        gateway_response={},
    )

    # when
    gateway.payment_refund_or_void(
        payment, get_plugins_manager(), None, transaction_id=txn.token
    )

    # then
    assert refund_mock.called_once()


@patch(""saleor.payment.gateway.refund"")
        ```
    ###Assistant : Ensure that the refund method is called when the refund process
    is already ongoing but not covered full payment captured amount.
    "
245,"def _all(self):
        
        groups = super(Deprecated, self).values()
        return EntryPoints(itertools.chain.from_iterable(groups))
","
        Reconstruct a list of all entrypoints from the groups.
        ",9,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _all(self):
        
        groups = super(Deprecated, self).values()
        return EntryPoints(itertools.chain.from_iterable(groups))

        ```
    ###Assistant : 
        Reconstruct a list of all entrypoints from the groups.
        
    "
246,"def readlines(self, sizehint=None, keepends=True):

        
        data = self.read()
        return data.splitlines(keepends)
"," Read all lines available on the input stream
            and return them as a list.

            Line breaks are implemented using the codec's decoder
            method and are included in the list entries.

            sizehint, if given, is ignored since there is no efficient
            way to finding the true end-of-line.

        ",46,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def readlines(self, sizehint=None, keepends=True):

        
        data = self.read()
        return data.splitlines(keepends)

        ```
    ###Assistant :  Read all lines available on the input stream
            and return them as a list.

            Line breaks are implemented using the codec's decoder
            method and are included in the list entries.

            sizehint, if given, is ignored since there is no efficient
            way to finding the true end-of-line.

        
    "
247,"async def notify_clients(cls) -> None:
        
        while not cls.STOP:
            await asyncio.sleep(cls.UPDATE_INTERVALS)
            if cls.EVENT_QUEUE:
                await cls.broadcast_estimations()
","
        Notify clients about events statuses in the queue periodically.
        ",9,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def notify_clients(cls) -> None:
        
        while not cls.STOP:
            await asyncio.sleep(cls.UPDATE_INTERVALS)
            if cls.EVENT_QUEUE:
                await cls.broadcast_estimations()

        ```
    ###Assistant : 
        Notify clients about events statuses in the queue periodically.
        
    "
248,"def _readPyPIFile(self):
        

        # Complex stuff, pylint: disable=too-many-branches,too-many-statements
        if self.used_modules is None:
            pyi_filename = self.getPyIFilename()

            if os.path.exists(pyi_filename):
                pyi_deps = OrderedSet()

                # Flag signalling multiline import handling
                in_import = False
                in_import_part = """"

                for line in getFileContentByLine(pyi_filename):
                    line = line.strip()

                    if not in_import:
                        if line.startswith(""import ""):
                            imported = line[7:]

                            pyi_deps.add(imported)
                        elif line.startswith(""from ""):
                            parts = line.split(None, 3)
                            assert parts[0] == ""from""
                            assert parts[2] == ""import""

                            origin_name = parts[1]

                            if origin_name == ""typing"":
                                continue

                            if origin_name == ""."":
                                origin_name = self.getFullName()
                            else:
                                dot_count = 0
                                while origin_name.startswith("".""):
                                    origin_name = origin_name[1:]
                                    dot_count += 1

                                if dot_count > 0:
                                    if origin_name:
                                        origin_name = (
                                            self.getFullName()
                                            .getRelativePackageName(level=dot_count + 1)
                                            .getChildNamed(origin_name)
                                        )
                                    else:
                                        origin_name = (
                                            self.getFullName().getRelativePackageName(
                                                level=dot_count + 1
                                            )
                                        )

                            if origin_name != self.getFullName():
                                pyi_deps.add(origin_name)

                            imported = parts[3]
                            if imported.startswith(""(""):
                                # Handle multiline imports
                                if not imported.endswith("")""):
                                    in_import = True
                                    imported = imported[1:]
                                    in_import_part = origin_name
                                    assert in_import_part, (
                                        ""Multiline part in file %s cannot be empty""
                                        % pyi_filename
                                    )
                                else:
                                    in_import = False
                                    imported = imported[1:-1]
                                    assert imported

                            if imported == ""*"":
                                continue

                            for name in imported.split("",""):
                                if name:
                                    name = name.strip()
                                    pyi_deps.add(origin_name + ""."" + name)

                    else:  # In import
                        imported = line
                        if imported.endswith("")""):
                            imported = imported[0:-1]
                            in_import = False

                        for name in imported.split("",""):
                            name = name.strip()
                            if name:
                                pyi_deps.add(in_import_part + ""."" + name)

                if ""typing"" in pyi_deps:
                    pyi_deps.discard(""typing"")
                if ""__future__"" in pyi_deps:
                    pyi_deps.discard(""__future__"")

                if self.getFullName() in pyi_deps:
                    pyi_deps.discard(self.getFullName())
                if self.getFullName().getPackageName() in pyi_deps:
                    pyi_deps.discard(self.getFullName().getPackageName())

                self.used_modules = tuple((pyi_dep, None) for pyi_dep in pyi_deps)
            else:
                self.used_modules = ()
",Read the .pyi file if present and scan for dependencies.,10,244,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _readPyPIFile(self):
        

        # Complex stuff, pylint: disable=too-many-branches,too-many-statements
        if self.used_modules is None:
            pyi_filename = self.getPyIFilename()

            if os.path.exists(pyi_filename):
                pyi_deps = OrderedSet()

                # Flag signalling multiline import handling
                in_import = False
                in_import_part = """"

                for line in getFileContentByLine(pyi_filename):
                    line = line.strip()

                    if not in_import:
                        if line.startswith(""import ""):
                            imported = line[7:]

                            pyi_deps.add(imported)
                        elif line.startswith(""from ""):
                            parts = line.split(None, 3)
                            assert parts[0] == ""from""
                            assert parts[2] == ""import""

                            origin_name = parts[1]

                            if origin_name == ""typing"":
                                continue

                            if origin_name == ""."":
                                origin_name = self.getFullName()
                            else:
                                dot_count = 0
                                while origin_name.startswith("".""):
                                    origin_name = origin_name[1:]
                                    dot_count += 1

                                if dot_count > 0:
                                    if origin_name:
                                        origin_name = (
                                            self.getFullName()
                                            .getRelativePackageName(level=dot_count + 1)
                                            .getChildNamed(origin_name)
                                        )
                                    else:
                                        origin_name = (
                                            self.getFullName().getRelativePackageName(
                                                level=dot_count + 1
                                            )
                                        )

                            if origin_name != self.getFullName():
                                pyi_deps.add(origin_name)

                            imported = parts[3]
                            if imported.startswith(""(""):
                                # Handle multiline imports
                                if not imported.endswith("")""):
                                    in_import = True
                                    imported = imported[1:]
                                    in_import_part = origin_name
                                    assert in_import_part, (
                                        ""Multiline part in file %s cannot be empty""
                                        % pyi_filename
                                    )
                                else:
                                    in_import = False
                                    imported = imported[1:-1]
                                    assert imported

                            if imported == ""*"":
                                continue

                            for name in imported.split("",""):
                                if name:
                                    name = name.strip()
                                    pyi_deps.add(origin_name + ""."" + name)

                    else:  # In import
                        imported = line
                        if imported.endswith("")""):
                            imported = imported[0:-1]
                            in_import = False

                        for name in imported.split("",""):
                            name = name.strip()
                            if name:
                                pyi_deps.add(in_import_part + ""."" + name)

                if ""typing"" in pyi_deps:
                    pyi_deps.discard(""typing"")
                if ""__future__"" in pyi_deps:
                    pyi_deps.discard(""__future__"")

                if self.getFullName() in pyi_deps:
                    pyi_deps.discard(self.getFullName())
                if self.getFullName().getPackageName() in pyi_deps:
                    pyi_deps.discard(self.getFullName().getPackageName())

                self.used_modules = tuple((pyi_dep, None) for pyi_dep in pyi_deps)
            else:
                self.used_modules = ()

        ```
    ###Assistant : Read the .pyi file if present and scan for dependencies.
    "
249,"def topological_sort(self):
        
        result = []
        # Make a shallow copy of the adjacency list
        alist = {}
        for k, v in self.adjacency_list.items():
            alist[k] = v[:]
        while True:
            # See what we can remove in this run
            to_remove = []
            for k, v in list(alist.items())[:]:
                if not v:
                    to_remove.append(k)
                    del alist[k]
            if not to_remove:
                # What's left in alist (if anything) is a cycle.
                break
            # Remove from the adjacency list of others
            for k, v in alist.items():
                alist[k] = [(d, r) for d, r in v if d not in to_remove]
            logger.debug('Moving to result: %s',
                         ['%s (%s)' % (d.name, d.version) for d in to_remove])
            result.extend(to_remove)
        return result, list(alist.keys())
","
        Perform a topological sort of the graph.
        :return: A tuple, the first element of which is a topologically sorted
                 list of distributions, and the second element of which is a
                 list of distributions that cannot be sorted because they have
                 circular dependencies and so form a cycle.
        ",47,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def topological_sort(self):
        
        result = []
        # Make a shallow copy of the adjacency list
        alist = {}
        for k, v in self.adjacency_list.items():
            alist[k] = v[:]
        while True:
            # See what we can remove in this run
            to_remove = []
            for k, v in list(alist.items())[:]:
                if not v:
                    to_remove.append(k)
                    del alist[k]
            if not to_remove:
                # What's left in alist (if anything) is a cycle.
                break
            # Remove from the adjacency list of others
            for k, v in alist.items():
                alist[k] = [(d, r) for d, r in v if d not in to_remove]
            logger.debug('Moving to result: %s',
                         ['%s (%s)' % (d.name, d.version) for d in to_remove])
            result.extend(to_remove)
        return result, list(alist.keys())

        ```
    ###Assistant : 
        Perform a topological sort of the graph.
        :return: A tuple, the first element of which is a topologically sorted
                 list of distributions, and the second element of which is a
                 list of distributions that cannot be sorted because they have
                 circular dependencies and so form a cycle.
        
    "
250,"def soft_reset(self) -> None:
        
        self.length = 0
        self.episode_id = random.randrange(int(2e9))
        self.total_reward = 0.0
        self.agent_rewards = defaultdict(float)
        self._agent_reward_history = defaultdict(list)
","Clears rewards and metrics, but retains RNN and other state.

        This is used to carry state across multiple logical episodes in the
        same env (i.e., if `soft_horizon` is set).
        ",29,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def soft_reset(self) -> None:
        
        self.length = 0
        self.episode_id = random.randrange(int(2e9))
        self.total_reward = 0.0
        self.agent_rewards = defaultdict(float)
        self._agent_reward_history = defaultdict(list)

        ```
    ###Assistant : Clears rewards and metrics, but retains RNN and other state.

        This is used to carry state across multiple logical episodes in the
        same env (i.e., if `soft_horizon` is set).
        
    "
251,"def get_ordering_field(self, field_name):
        
        try:
            field = self.opts.get_field(field_name)
            return field.name
        except FieldDoesNotExist:
            # See whether field_name is a name of a non-field
            # that allows sorting.
            if callable(field_name):
                attr = field_name
            elif hasattr(self.model_admin, field_name):
                attr = getattr(self.model_admin, field_name)
            else:
                attr = getattr(self.model, field_name)
            return getattr(attr, ""admin_order_field"", None)
","
        Returns the proper model field name corresponding to the given
        field_name to use for ordering. field_name may either be the name of a
        proper model field or the name of a method (on the admin or model) or a
        callable with the 'admin_order_field' attribute. Returns None if no
        proper model field name can be matched.
        ",55,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ordering_field(self, field_name):
        
        try:
            field = self.opts.get_field(field_name)
            return field.name
        except FieldDoesNotExist:
            # See whether field_name is a name of a non-field
            # that allows sorting.
            if callable(field_name):
                attr = field_name
            elif hasattr(self.model_admin, field_name):
                attr = getattr(self.model_admin, field_name)
            else:
                attr = getattr(self.model, field_name)
            return getattr(attr, ""admin_order_field"", None)

        ```
    ###Assistant : 
        Returns the proper model field name corresponding to the given
        field_name to use for ordering. field_name may either be the name of a
        proper model field or the name of a method (on the admin or model) or a
        callable with the 'admin_order_field' attribute. Returns None if no
        proper model field name can be matched.
        
    "
252,"def _check_ordering(self, obj):
        

        # ordering = None
        if obj.ordering is None:  # The default value is None
            return []
        elif not isinstance(obj.ordering, (list, tuple)):
            return must_be(
                ""a list or tuple"", option=""ordering"", obj=obj, id=""admin.E031""
            )
        else:
            return list(
                chain.from_iterable(
                    self._check_ordering_item(obj, field_name, ""ordering[%d]"" % index)
                    for index, field_name in enumerate(obj.ordering)
                )
            )
",Check that ordering refers to existing fields or is random.,10,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_ordering(self, obj):
        

        # ordering = None
        if obj.ordering is None:  # The default value is None
            return []
        elif not isinstance(obj.ordering, (list, tuple)):
            return must_be(
                ""a list or tuple"", option=""ordering"", obj=obj, id=""admin.E031""
            )
        else:
            return list(
                chain.from_iterable(
                    self._check_ordering_item(obj, field_name, ""ordering[%d]"" % index)
                    for index, field_name in enumerate(obj.ordering)
                )
            )

        ```
    ###Assistant : Check that ordering refers to existing fields or is random.
    "
253,"def parsing_hooks(cls) -> Tuple[Type[""Block""], Type[""Sentence""], Type[""Statements""]]:
        
        return Block, Sentence, Statements
","Returns object types that this class should be able to `parse` recusrively.
        The order of the objects indicates the order in which the parser should
        try to parse each subitem.
        :returns: A list of Parsable classes.
        :rtype list:
        ",38,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parsing_hooks(cls) -> Tuple[Type[""Block""], Type[""Sentence""], Type[""Statements""]]:
        
        return Block, Sentence, Statements

        ```
    ###Assistant : Returns object types that this class should be able to `parse` recusrively.
        The order of the objects indicates the order in which the parser should
        try to parse each subitem.
        :returns: A list of Parsable classes.
        :rtype list:
        
    "
254,"def _cast_inplace(terms, acceptable_dtypes, dtype) -> None:
    
    dt = np.dtype(dtype)
    for term in terms:
        if term.type in acceptable_dtypes:
            continue

        try:
            new_value = term.value.astype(dt)
        except AttributeError:
            new_value = dt.type(term.value)
        term.update(new_value)

","
    Cast an expression inplace.

    Parameters
    ----------
    terms : Op
        The expression that should cast.
    acceptable_dtypes : list of acceptable numpy.dtype
        Will not cast if term's dtype in this list.
    dtype : str or numpy.dtype
        The dtype to cast to.
    ",39,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _cast_inplace(terms, acceptable_dtypes, dtype) -> None:
    
    dt = np.dtype(dtype)
    for term in terms:
        if term.type in acceptable_dtypes:
            continue

        try:
            new_value = term.value.astype(dt)
        except AttributeError:
            new_value = dt.type(term.value)
        term.update(new_value)


        ```
    ###Assistant : 
    Cast an expression inplace.

    Parameters
    ----------
    terms : Op
        The expression that should cast.
    acceptable_dtypes : list of acceptable numpy.dtype
        Will not cast if term's dtype in this list.
    dtype : str or numpy.dtype
        The dtype to cast to.
    
    "
255,"def _deployment_created(self, external_id, request):
        payload = request.data[""payload""]
        vercel_project_id = (
            payload[""projectId""] if payload.get(""projectId"") else payload[""project""][""id""]
        )
        # Only create releases for production deploys for now
        if payload[""target""] != ""production"":
            logger.info(
                f""Ignoring deployment for environment: {payload['target']}"",
                extra={""external_id"": external_id, ""vercel_project_id"": vercel_project_id},
            )
            return self.respond(status=204)
        

        logging_params = {""external_id"": external_id, ""vercel_project_id"": vercel_project_id}

        org_integrations = OrganizationIntegration.objects.select_related(""organization"").filter(
            integration__external_id=external_id, integration__provider=self.provider
        )
        if not org_integrations:
            logger.info(""Integration not found"", extra=logging_params)
            return self.respond({""detail"": ""Integration not found""}, status=404)

        # for each org integration, search the configs to find one that matches the vercel project of the webhook
        for org_integration in org_integrations:
            project_mappings = org_integration.config.get(""project_mappings"") or []
            matched_mappings = list(filter(lambda x: x[1] == vercel_project_id, project_mappings))
            if matched_mappings:
                organization = org_integration.organization
                sentry_project_id = matched_mappings[0][0]

                logging_params[""organization_id""] = organization.id
                logging_params[""project_id""] = sentry_project_id

                try:
                    release_payload, token = get_payload_and_token(
                        payload, organization.id, sentry_project_id
                    )
                except Project.DoesNotExist:
                    logger.info(""Project not found"", extra=logging_params)
                    return self.respond({""detail"": ""Project not found""}, status=404)
                except SentryAppInstallationForProvider.DoesNotExist:
                    logger.info(""Installation not found"", extra=logging_params)
                    return self.respond({""detail"": ""Installation not found""}, status=404)
                except SentryAppInstallationToken.DoesNotExist:
                    logger.info(""Token not found"", extra=logging_params)
                    return self.respond({""detail"": ""Token not found""}, status=404)
                except NoCommitFoundError:
                    logger.info(""No commit found"", extra=logging_params)
                    return self.respond({""detail"": ""No commit found""}, status=404)
                except MissingRepositoryError:
                    logger.info(""Could not determine repository"", extra=logging_params)
                    return self.respond({""detail"": ""Could not determine repository""}, status=400)

                url = absolute_uri(f""/api/0/organizations/{organization.slug}/releases/"")
                headers = {
                    ""Accept"": ""application/json"",
                    ""Authorization"": f""Bearer {token}"",
                    ""User-Agent"": f""sentry_vercel/{VERSION}"",
                }
                json_error = None

                # create the basic release payload without refs
                no_ref_payload = release_payload.copy()
                del no_ref_payload[""refs""]

                with http.build_session() as session:
                    try:
                        resp = session.post(url, json=no_ref_payload, headers=headers)
                        json_error = safe_json_parse(resp)
                        resp.raise_for_status()
                    except RequestException as e:
                        # errors here should be uncommon but we should be aware of them
                        logger.error(
                            f""Error creating release: {e} - {json_error}"",
                            extra=logging_params,
                            exc_info=True,
                        )
                        # 400 probably isn't the right status code but oh well
                        return self.respond({""detail"": f""Error creating release: {e}""}, status=400)

                    # set the refs
                    try:
                        resp = session.post(
                            url,
                            json=release_payload,
                            headers=headers,
                        )
                        json_error = safe_json_parse(resp)
                        resp.raise_for_status()
                    except RequestException as e:
                        # errors will probably be common if the user doesn't have repos set up
                        logger.info(
                            f""Error setting refs: {e} - {json_error}"",
                            extra=logging_params,
                            exc_info=True,
                        )
                        # 400 probably isn't the right status code but oh well
                        return self.respond({""detail"": f""Error setting refs: {e}""}, status=400)

                # we are going to quit after the first project match as there shouldn't be multiple matches
                return self.respond(status=201)

        return self.respond(status=204)
","
        Steps:
            1. Find all org integrations that match the external id
            2. Search the configs to find one that matches the vercel project of the webhook
            3. Look up the Sentry project that matches
            4. Look up the connected internal integration
            5. Find the token associated with that installation
            6. Determine the commit sha and repo based on what provider is used
            7. Create the release using the token WITHOUT refs
            8. Update the release with refs
        ",77,360,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _deployment_created(self, external_id, request):
        payload = request.data[""payload""]
        vercel_project_id = (
            payload[""projectId""] if payload.get(""projectId"") else payload[""project""][""id""]
        )
        # Only create releases for production deploys for now
        if payload[""target""] != ""production"":
            logger.info(
                f""Ignoring deployment for environment: {payload['target']}"",
                extra={""external_id"": external_id, ""vercel_project_id"": vercel_project_id},
            )
            return self.respond(status=204)
        

        logging_params = {""external_id"": external_id, ""vercel_project_id"": vercel_project_id}

        org_integrations = OrganizationIntegration.objects.select_related(""organization"").filter(
            integration__external_id=external_id, integration__provider=self.provider
        )
        if not org_integrations:
            logger.info(""Integration not found"", extra=logging_params)
            return self.respond({""detail"": ""Integration not found""}, status=404)

        # for each org integration, search the configs to find one that matches the vercel project of the webhook
        for org_integration in org_integrations:
            project_mappings = org_integration.config.get(""project_mappings"") or []
            matched_mappings = list(filter(lambda x: x[1] == vercel_project_id, project_mappings))
            if matched_mappings:
                organization = org_integration.organization
                sentry_project_id = matched_mappings[0][0]

                logging_params[""organization_id""] = organization.id
                logging_params[""project_id""] = sentry_project_id

                try:
                    release_payload, token = get_payload_and_token(
                        payload, organization.id, sentry_project_id
                    )
                except Project.DoesNotExist:
                    logger.info(""Project not found"", extra=logging_params)
                    return self.respond({""detail"": ""Project not found""}, status=404)
                except SentryAppInstallationForProvider.DoesNotExist:
                    logger.info(""Installation not found"", extra=logging_params)
                    return self.respond({""detail"": ""Installation not found""}, status=404)
                except SentryAppInstallationToken.DoesNotExist:
                    logger.info(""Token not found"", extra=logging_params)
                    return self.respond({""detail"": ""Token not found""}, status=404)
                except NoCommitFoundError:
                    logger.info(""No commit found"", extra=logging_params)
                    return self.respond({""detail"": ""No commit found""}, status=404)
                except MissingRepositoryError:
                    logger.info(""Could not determine repository"", extra=logging_params)
                    return self.respond({""detail"": ""Could not determine repository""}, status=400)

                url = absolute_uri(f""/api/0/organizations/{organization.slug}/releases/"")
                headers = {
                    ""Accept"": ""application/json"",
                    ""Authorization"": f""Bearer {token}"",
                    ""User-Agent"": f""sentry_vercel/{VERSION}"",
                }
                json_error = None

                # create the basic release payload without refs
                no_ref_payload = release_payload.copy()
                del no_ref_payload[""refs""]

                with http.build_session() as session:
                    try:
                        resp = session.post(url, json=no_ref_payload, headers=headers)
                        json_error = safe_json_parse(resp)
                        resp.raise_for_status()
                    except RequestException as e:
                        # errors here should be uncommon but we should be aware of them
                        logger.error(
                            f""Error creating release: {e} - {json_error}"",
                            extra=logging_params,
                            exc_info=True,
                        )
                        # 400 probably isn't the right status code but oh well
                        return self.respond({""detail"": f""Error creating release: {e}""}, status=400)

                    # set the refs
                    try:
                        resp = session.post(
                            url,
                            json=release_payload,
                            headers=headers,
                        )
                        json_error = safe_json_parse(resp)
                        resp.raise_for_status()
                    except RequestException as e:
                        # errors will probably be common if the user doesn't have repos set up
                        logger.info(
                            f""Error setting refs: {e} - {json_error}"",
                            extra=logging_params,
                            exc_info=True,
                        )
                        # 400 probably isn't the right status code but oh well
                        return self.respond({""detail"": f""Error setting refs: {e}""}, status=400)

                # we are going to quit after the first project match as there shouldn't be multiple matches
                return self.respond(status=201)

        return self.respond(status=204)

        ```
    ###Assistant : 
        Steps:
            1. Find all org integrations that match the external id
            2. Search the configs to find one that matches the vercel project of the webhook
            3. Look up the Sentry project that matches
            4. Look up the connected internal integration
            5. Find the token associated with that installation
            6. Determine the commit sha and repo based on what provider is used
            7. Create the release using the token WITHOUT refs
            8. Update the release with refs
        
    "
256,"def _app_user(self) -> User | None:
        
        return self.user if isinstance(self.user, User) else None
","The user, if they are represented persistently in our app.",10,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _app_user(self) -> User | None:
        
        return self.user if isinstance(self.user, User) else None

        ```
    ###Assistant : The user, if they are represented persistently in our app.
    "
257,"def new_locator(self, nx, nx1=None):
        
        return AxesLocator(self, nx, 0, nx1 if nx1 is not None else nx + 1, 1)
","
        Create a new `.AxesLocator` for the specified cell.

        Parameters
        ----------
        nx, nx1 : int
            Integers specifying the column-position of the
            cell. When *nx1* is None, a single *nx*-th column is
            specified. Otherwise, location of columns spanning between *nx*
            to *nx1* (but excluding *nx1*-th column) is specified.
        ",46,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def new_locator(self, nx, nx1=None):
        
        return AxesLocator(self, nx, 0, nx1 if nx1 is not None else nx + 1, 1)

        ```
    ###Assistant : 
        Create a new `.AxesLocator` for the specified cell.

        Parameters
        ----------
        nx, nx1 : int
            Integers specifying the column-position of the
            cell. When *nx1* is None, a single *nx*-th column is
            specified. Otherwise, location of columns spanning between *nx*
            to *nx1* (but excluding *nx1*-th column) is specified.
        
    "
258,"def test_edit_cases(self) -> None:
        
        self.login(""hamlet"")
        hamlet = self.example_user(""hamlet"")
        msg_id = self.send_stream_message(
            self.example_user(""hamlet""), ""Denmark"", topic_name=""topic 1"", content=""content 1""
        )
        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""content"": ""content 2"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][""prev_content""], ""content 1"")
        self.assertEqual(history[0][""user_id""], hamlet.id)
        self.assertEqual(
            set(history[0].keys()),
            {
                ""timestamp"",
                ""prev_content"",
                ""user_id"",
                ""prev_rendered_content"",
                ""prev_rendered_content_version"",
            },
        )

        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""topic"": ""topic 2"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 1"")
        self.assertEqual(history[0][""user_id""], hamlet.id)
        self.assertEqual(set(history[0].keys()), {""timestamp"", LEGACY_PREV_TOPIC, ""user_id""})

        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""content"": ""content 3"",
                ""topic"": ""topic 3"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][""prev_content""], ""content 2"")
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 2"")
        self.assertEqual(history[0][""user_id""], hamlet.id)
        self.assertEqual(
            set(history[0].keys()),
            {
                ""timestamp"",
                LEGACY_PREV_TOPIC,
                ""prev_content"",
                ""user_id"",
                ""prev_rendered_content"",
                ""prev_rendered_content_version"",
            },
        )

        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""content"": ""content 4"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][""prev_content""], ""content 3"")
        self.assertEqual(history[0][""user_id""], hamlet.id)

        self.login(""iago"")
        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""topic"": ""topic 4"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 3"")
        self.assertEqual(history[0][""user_id""], self.example_user(""iago"").id)

        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 3"")
        self.assertEqual(history[2][LEGACY_PREV_TOPIC], ""topic 2"")
        self.assertEqual(history[3][LEGACY_PREV_TOPIC], ""topic 1"")
        self.assertEqual(history[1][""prev_content""], ""content 3"")
        self.assertEqual(history[2][""prev_content""], ""content 2"")
        self.assertEqual(history[4][""prev_content""], ""content 1"")

        # Now, we verify that the edit history data sent back has the
        # correct filled-out fields
        message_edit_history = self.client_get(f""/json/messages/{msg_id}/history"")

        json_response = orjson.loads(message_edit_history.content)

        # We reverse the message history view output so that the IDs line up with the above.
        message_history = list(reversed(json_response[""message_history""]))
        i = 0
        for entry in message_history:
            expected_entries = {""content"", ""rendered_content"", ""topic"", ""timestamp"", ""user_id""}
            if i in {0, 2, 3}:
                expected_entries.add(""prev_topic"")
            if i in {1, 2, 4}:
                expected_entries.add(""prev_content"")
                expected_entries.add(""prev_rendered_content"")
                expected_entries.add(""content_html_diff"")
            i += 1
            self.assertEqual(expected_entries, set(entry.keys()))
        self.assert_length(message_history, 6)
        self.assertEqual(message_history[0][""prev_topic""], ""topic 3"")
        self.assertEqual(message_history[0][""topic""], ""topic 4"")
        self.assertEqual(message_history[1][""topic""], ""topic 3"")
        self.assertEqual(message_history[2][""topic""], ""topic 3"")
        self.assertEqual(message_history[2][""prev_topic""], ""topic 2"")
        self.assertEqual(message_history[3][""topic""], ""topic 2"")
        self.assertEqual(message_history[3][""prev_topic""], ""topic 1"")
        self.assertEqual(message_history[4][""topic""], ""topic 1"")

        self.assertEqual(message_history[0][""content""], ""content 4"")
        self.assertEqual(message_history[1][""content""], ""content 4"")
        self.assertEqual(message_history[1][""prev_content""], ""content 3"")
        self.assertEqual(message_history[2][""content""], ""content 3"")
        self.assertEqual(message_history[2][""prev_content""], ""content 2"")
        self.assertEqual(message_history[3][""content""], ""content 2"")
        self.assertEqual(message_history[4][""content""], ""content 2"")
        self.assertEqual(message_history[4][""prev_content""], ""content 1"")

        self.assertEqual(message_history[5][""content""], ""content 1"")
        self.assertEqual(message_history[5][""topic""], ""topic 1"")
","This test verifies the accuracy of construction of Zulip's edit
        history data structures.",13,310,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_edit_cases(self) -> None:
        
        self.login(""hamlet"")
        hamlet = self.example_user(""hamlet"")
        msg_id = self.send_stream_message(
            self.example_user(""hamlet""), ""Denmark"", topic_name=""topic 1"", content=""content 1""
        )
        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""content"": ""content 2"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][""prev_content""], ""content 1"")
        self.assertEqual(history[0][""user_id""], hamlet.id)
        self.assertEqual(
            set(history[0].keys()),
            {
                ""timestamp"",
                ""prev_content"",
                ""user_id"",
                ""prev_rendered_content"",
                ""prev_rendered_content_version"",
            },
        )

        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""topic"": ""topic 2"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 1"")
        self.assertEqual(history[0][""user_id""], hamlet.id)
        self.assertEqual(set(history[0].keys()), {""timestamp"", LEGACY_PREV_TOPIC, ""user_id""})

        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""content"": ""content 3"",
                ""topic"": ""topic 3"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][""prev_content""], ""content 2"")
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 2"")
        self.assertEqual(history[0][""user_id""], hamlet.id)
        self.assertEqual(
            set(history[0].keys()),
            {
                ""timestamp"",
                LEGACY_PREV_TOPIC,
                ""prev_content"",
                ""user_id"",
                ""prev_rendered_content"",
                ""prev_rendered_content_version"",
            },
        )

        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""content"": ""content 4"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][""prev_content""], ""content 3"")
        self.assertEqual(history[0][""user_id""], hamlet.id)

        self.login(""iago"")
        result = self.client_patch(
            f""/json/messages/{msg_id}"",
            {
                ""message_id"": msg_id,
                ""topic"": ""topic 4"",
            },
        )
        self.assert_json_success(result)
        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 3"")
        self.assertEqual(history[0][""user_id""], self.example_user(""iago"").id)

        history = orjson.loads(Message.objects.get(id=msg_id).edit_history)
        self.assertEqual(history[0][LEGACY_PREV_TOPIC], ""topic 3"")
        self.assertEqual(history[2][LEGACY_PREV_TOPIC], ""topic 2"")
        self.assertEqual(history[3][LEGACY_PREV_TOPIC], ""topic 1"")
        self.assertEqual(history[1][""prev_content""], ""content 3"")
        self.assertEqual(history[2][""prev_content""], ""content 2"")
        self.assertEqual(history[4][""prev_content""], ""content 1"")

        # Now, we verify that the edit history data sent back has the
        # correct filled-out fields
        message_edit_history = self.client_get(f""/json/messages/{msg_id}/history"")

        json_response = orjson.loads(message_edit_history.content)

        # We reverse the message history view output so that the IDs line up with the above.
        message_history = list(reversed(json_response[""message_history""]))
        i = 0
        for entry in message_history:
            expected_entries = {""content"", ""rendered_content"", ""topic"", ""timestamp"", ""user_id""}
            if i in {0, 2, 3}:
                expected_entries.add(""prev_topic"")
            if i in {1, 2, 4}:
                expected_entries.add(""prev_content"")
                expected_entries.add(""prev_rendered_content"")
                expected_entries.add(""content_html_diff"")
            i += 1
            self.assertEqual(expected_entries, set(entry.keys()))
        self.assert_length(message_history, 6)
        self.assertEqual(message_history[0][""prev_topic""], ""topic 3"")
        self.assertEqual(message_history[0][""topic""], ""topic 4"")
        self.assertEqual(message_history[1][""topic""], ""topic 3"")
        self.assertEqual(message_history[2][""topic""], ""topic 3"")
        self.assertEqual(message_history[2][""prev_topic""], ""topic 2"")
        self.assertEqual(message_history[3][""topic""], ""topic 2"")
        self.assertEqual(message_history[3][""prev_topic""], ""topic 1"")
        self.assertEqual(message_history[4][""topic""], ""topic 1"")

        self.assertEqual(message_history[0][""content""], ""content 4"")
        self.assertEqual(message_history[1][""content""], ""content 4"")
        self.assertEqual(message_history[1][""prev_content""], ""content 3"")
        self.assertEqual(message_history[2][""content""], ""content 3"")
        self.assertEqual(message_history[2][""prev_content""], ""content 2"")
        self.assertEqual(message_history[3][""content""], ""content 2"")
        self.assertEqual(message_history[4][""content""], ""content 2"")
        self.assertEqual(message_history[4][""prev_content""], ""content 1"")

        self.assertEqual(message_history[5][""content""], ""content 1"")
        self.assertEqual(message_history[5][""topic""], ""topic 1"")

        ```
    ###Assistant : This test verifies the accuracy of construction of Zulip's edit
        history data structures.
    "
259,"def get_project_name(doctype, txt, searchfield, start, page_len, filters):
	doctype = ""Project""
	cond = """"
	if filters and filters.get(""customer""):
		cond =  % (
			frappe.db.escape(filters.get(""customer""))
		)

	fields = get_fields(doctype, [""name"", ""project_name""])
	searchfields = frappe.get_meta(doctype).get_search_fields()
	searchfields = "" or "".join([""`tabProject`."" + field + "" like %(txt)s"" for field in searchfields])

	return frappe.db.sql(
		.format(
			fields="", "".join([""`tabProject`.{0}"".format(f) for f in fields]),
			cond=cond,
			scond=searchfields,
			match_cond=get_match_cond(doctype),
			start=start,
			page_len=page_len,
		),
		{""txt"": ""%{0}%"".format(txt), ""_txt"": txt.replace(""%"", """")},
	)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs","(`tabProject`.customer = %s or
			ifnull(`tabProject`.customer,"""")="""") andselect {fields} from `tabProject`
		where
			`tabProject`.status not in ('Completed', 'Cancelled')
			and {cond} {scond} {match_cond}
		order by
			(case when locate(%(_txt)s, `tabProject`.name) > 0 then locate(%(_txt)s, `tabProject`.name) else 99999 end),
			`tabProject`.idx desc,
			`tabProject`.name asc
		limit {page_len} offset {start}",41,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_project_name(doctype, txt, searchfield, start, page_len, filters):
	doctype = ""Project""
	cond = """"
	if filters and filters.get(""customer""):
		cond =  % (
			frappe.db.escape(filters.get(""customer""))
		)

	fields = get_fields(doctype, [""name"", ""project_name""])
	searchfields = frappe.get_meta(doctype).get_search_fields()
	searchfields = "" or "".join([""`tabProject`."" + field + "" like %(txt)s"" for field in searchfields])

	return frappe.db.sql(
		.format(
			fields="", "".join([""`tabProject`.{0}"".format(f) for f in fields]),
			cond=cond,
			scond=searchfields,
			match_cond=get_match_cond(doctype),
			start=start,
			page_len=page_len,
		),
		{""txt"": ""%{0}%"".format(txt), ""_txt"": txt.replace(""%"", """")},
	)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs
        ```
    ###Assistant : (`tabProject`.customer = %s or
			ifnull(`tabProject`.customer,"""")="""") andselect {fields} from `tabProject`
		where
			`tabProject`.status not in ('Completed', 'Cancelled')
			and {cond} {scond} {match_cond}
		order by
			(case when locate(%(_txt)s, `tabProject`.name) > 0 then locate(%(_txt)s, `tabProject`.name) else 99999 end),
			`tabProject`.idx desc,
			`tabProject`.name asc
		limit {page_len} offset {start}
    "
260,"def test_autosuggest_at_EOL(text, cursor, suggestion, called):
    

    event = make_event(text, cursor, suggestion)
    event.current_buffer.insert_text = Mock()
    _apply_autosuggest(event)
    if called:
        event.current_buffer.insert_text.assert_called()
    else:
        event.current_buffer.insert_text.assert_not_called()
        # event.current_buffer.document.get_end_of_line_position.assert_called()
","
    test that autosuggest is only applied at end of line.
    ",10,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_autosuggest_at_EOL(text, cursor, suggestion, called):
    

    event = make_event(text, cursor, suggestion)
    event.current_buffer.insert_text = Mock()
    _apply_autosuggest(event)
    if called:
        event.current_buffer.insert_text.assert_called()
    else:
        event.current_buffer.insert_text.assert_not_called()
        # event.current_buffer.document.get_end_of_line_position.assert_called()

        ```
    ###Assistant : 
    test that autosuggest is only applied at end of line.
    
    "
261,"def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
    
    x = block3(x, filters, stride=stride1, groups=groups, name=name + ""_block1"")
    for i in range(2, blocks + 1):
        x = block3(
            x,
            filters,
            groups=groups,
            conv_shortcut=False,
            name=name + ""_block"" + str(i),
        )
    return x


@keras_export(
    ""keras.applications.resnet50.ResNet50"",
    ""keras.applications.resnet.ResNet50"",
    ""keras.applications.ResNet50"",
)","A set of stacked residual blocks.

    Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer in a block.
      blocks: integer, blocks in the stacked blocks.
      stride1: default 2, stride of the first layer in the first block.
      groups: default 32, group size for grouped convolution.
      name: string, stack label.

    Returns:
      Output tensor for the stacked blocks.
    ",58,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
    
    x = block3(x, filters, stride=stride1, groups=groups, name=name + ""_block1"")
    for i in range(2, blocks + 1):
        x = block3(
            x,
            filters,
            groups=groups,
            conv_shortcut=False,
            name=name + ""_block"" + str(i),
        )
    return x


@keras_export(
    ""keras.applications.resnet50.ResNet50"",
    ""keras.applications.resnet.ResNet50"",
    ""keras.applications.ResNet50"",
)
        ```
    ###Assistant : A set of stacked residual blocks.

    Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer in a block.
      blocks: integer, blocks in the stacked blocks.
      stride1: default 2, stride of the first layer in the first block.
      groups: default 32, group size for grouped convolution.
      name: string, stack label.

    Returns:
      Output tensor for the stacked blocks.
    
    "
262,"def is_mixed(self) -> bool:
        
        warnings.warn(
            ""Index.is_mixed is deprecated and will be removed in a future version. ""
            ""Check index.inferred_type directly instead."",
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        return self.inferred_type in [""mixed""]
","
        Check if the Index holds data with mixed data types.

        Returns
        -------
        bool
            Whether or not the Index holds data with mixed data types.

        See Also
        --------
        is_boolean : Check if the Index only consists of booleans.
        is_integer : Check if the Index only consists of integers.
        is_floating : Check if the Index is a floating type.
        is_numeric : Check if the Index only consists of numeric data.
        is_object : Check if the Index is of the object dtype.
        is_categorical : Check if the Index holds categorical data.
        is_interval : Check if the Index holds Interval objects.

        Examples
        --------
        >>> idx = pd.Index(['a', np.nan, 'b'])
        >>> idx.is_mixed()
        True

        >>> idx = pd.Index([1.0, 2.0, 3.0, 5.0])
        >>> idx.is_mixed()
        False
        ",118,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_mixed(self) -> bool:
        
        warnings.warn(
            ""Index.is_mixed is deprecated and will be removed in a future version. ""
            ""Check index.inferred_type directly instead."",
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        return self.inferred_type in [""mixed""]

        ```
    ###Assistant : 
        Check if the Index holds data with mixed data types.

        Returns
        -------
        bool
            Whether or not the Index holds data with mixed data types.

        See Also
        --------
        is_boolean : Check if the Index only consists of booleans.
        is_integer : Check if the Index only consists of integers.
        is_floating : Check if the Index is a floating type.
        is_numeric : Check if the Index only consists of numeric data.
        is_object : Check if the Index is of the object dtype.
        is_categorical : Check if the Index holds categorical data.
        is_interval : Check if the Index holds Interval objects.

        Examples
        --------
        >>> idx = pd.Index(['a', np.nan, 'b'])
        >>> idx.is_mixed()
        True

        >>> idx = pd.Index([1.0, 2.0, 3.0, 5.0])
        >>> idx.is_mixed()
        False
        
    "
263,"def responder(request):
    
    # Find an available port
    with socket.socket() as sock:
        sock.bind((""localhost"", 0))
        port = sock.getsockname()[1]

    server_process = multiprocessing.Process(
        target=process_server, args=(request.param, port)
    )
    server_process.start()
    yield port
    server_process.join(10)
    server_process.terminate()
    kill_time = 5
    wait_time = 0
    while server_process.is_alive():
        if wait_time > kill_time:
            server_process.kill()
            break
        else:
            wait_time += 0.1
            time.sleep(0.1)
    server_process.close()


@pytest.mark.parametrize(
    ""responder, read_method, parquet_engine"",
    [
        (CSVUserAgentResponder, pd.read_csv, None),
        (JSONUserAgentResponder, pd.read_json, None),
        (ParquetPyArrowUserAgentResponder, pd.read_parquet, ""pyarrow""),
        pytest.param(
            ParquetFastParquetUserAgentResponder,
            pd.read_parquet,
            ""fastparquet"",
            # TODO(ArrayManager) fastparquet
            marks=[
                td.skip_array_manager_not_yet_implemented,
                pytest.mark.xfail(PY310, reason=""fastparquet failing on 3.10""),
            ],
        ),
        (PickleUserAgentResponder, pd.read_pickle, None),
        (StataUserAgentResponder, pd.read_stata, None),
        (GzippedCSVUserAgentResponder, pd.read_csv, None),
        (GzippedJSONUserAgentResponder, pd.read_json, None),
    ],
    indirect=[""responder""],
)","
    Fixture that starts a local http server in a separate process on localhost
    and returns the port.

    Running in a separate process instead of a thread to allow termination/killing
    of http server upon cleanup.
    ",34,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def responder(request):
    
    # Find an available port
    with socket.socket() as sock:
        sock.bind((""localhost"", 0))
        port = sock.getsockname()[1]

    server_process = multiprocessing.Process(
        target=process_server, args=(request.param, port)
    )
    server_process.start()
    yield port
    server_process.join(10)
    server_process.terminate()
    kill_time = 5
    wait_time = 0
    while server_process.is_alive():
        if wait_time > kill_time:
            server_process.kill()
            break
        else:
            wait_time += 0.1
            time.sleep(0.1)
    server_process.close()


@pytest.mark.parametrize(
    ""responder, read_method, parquet_engine"",
    [
        (CSVUserAgentResponder, pd.read_csv, None),
        (JSONUserAgentResponder, pd.read_json, None),
        (ParquetPyArrowUserAgentResponder, pd.read_parquet, ""pyarrow""),
        pytest.param(
            ParquetFastParquetUserAgentResponder,
            pd.read_parquet,
            ""fastparquet"",
            # TODO(ArrayManager) fastparquet
            marks=[
                td.skip_array_manager_not_yet_implemented,
                pytest.mark.xfail(PY310, reason=""fastparquet failing on 3.10""),
            ],
        ),
        (PickleUserAgentResponder, pd.read_pickle, None),
        (StataUserAgentResponder, pd.read_stata, None),
        (GzippedCSVUserAgentResponder, pd.read_csv, None),
        (GzippedJSONUserAgentResponder, pd.read_json, None),
    ],
    indirect=[""responder""],
)
        ```
    ###Assistant : 
    Fixture that starts a local http server in a separate process on localhost
    and returns the port.

    Running in a separate process instead of a thread to allow termination/killing
    of http server upon cleanup.
    
    "
264,"def _pre_setup(self):
        
        super()._pre_setup()
        if self.available_apps is not None:
            apps.set_available_apps(self.available_apps)
            setting_changed.send(
                sender=settings._wrapped.__class__,
                setting=""INSTALLED_APPS"",
                value=self.available_apps,
                enter=True,
            )
            for db_name in self._databases_names(include_mirrors=False):
                emit_post_migrate_signal(verbosity=0, interactive=False, db=db_name)
        try:
            self._fixture_setup()
        except Exception:
            if self.available_apps is not None:
                apps.unset_available_apps()
                setting_changed.send(
                    sender=settings._wrapped.__class__,
                    setting=""INSTALLED_APPS"",
                    value=settings.INSTALLED_APPS,
                    enter=False,
                )
            raise
        # Clear the queries_log so that it's less likely to overflow (a single
        # test probably won't execute 9K queries). If queries_log overflows,
        # then assertNumQueries() doesn't work.
        for db_name in self._databases_names(include_mirrors=False):
            connections[db_name].queries_log.clear()
","
        Perform pre-test setup:
        * If the class has an 'available_apps' attribute, restrict the app
          registry to these applications, then fire the post_migrate signal --
          it must run with the correct set of applications for the test case.
        * If the class has a 'fixtures' attribute, install those fixtures.
        ",48,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _pre_setup(self):
        
        super()._pre_setup()
        if self.available_apps is not None:
            apps.set_available_apps(self.available_apps)
            setting_changed.send(
                sender=settings._wrapped.__class__,
                setting=""INSTALLED_APPS"",
                value=self.available_apps,
                enter=True,
            )
            for db_name in self._databases_names(include_mirrors=False):
                emit_post_migrate_signal(verbosity=0, interactive=False, db=db_name)
        try:
            self._fixture_setup()
        except Exception:
            if self.available_apps is not None:
                apps.unset_available_apps()
                setting_changed.send(
                    sender=settings._wrapped.__class__,
                    setting=""INSTALLED_APPS"",
                    value=settings.INSTALLED_APPS,
                    enter=False,
                )
            raise
        # Clear the queries_log so that it's less likely to overflow (a single
        # test probably won't execute 9K queries). If queries_log overflows,
        # then assertNumQueries() doesn't work.
        for db_name in self._databases_names(include_mirrors=False):
            connections[db_name].queries_log.clear()

        ```
    ###Assistant : 
        Perform pre-test setup:
        * If the class has an 'available_apps' attribute, restrict the app
          registry to these applications, then fire the post_migrate signal --
          it must run with the correct set of applications for the test case.
        * If the class has a 'fixtures' attribute, install those fixtures.
        
    "
265,"def test_multiple_actions_form(self):
        
        action_data = {
            ACTION_CHECKBOX_NAME: [self.s1.pk],
            # Two different actions selected on the two forms...
            ""action"": [""external_mail"", ""delete_selected""],
            # ...but ""go"" was clicked on the top form.
            ""index"": 0,
        }
        self.client.post(
            reverse(""admin:admin_views_externalsubscriber_changelist""), action_data
        )
        # The action sends mail rather than deletes.
        self.assertEqual(len(mail.outbox), 1)
        self.assertEqual(mail.outbox[0].subject, ""Greetings from a function action"")
","
        Actions come from the form whose submit button was pressed (#10618).
        ",11,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_multiple_actions_form(self):
        
        action_data = {
            ACTION_CHECKBOX_NAME: [self.s1.pk],
            # Two different actions selected on the two forms...
            ""action"": [""external_mail"", ""delete_selected""],
            # ...but ""go"" was clicked on the top form.
            ""index"": 0,
        }
        self.client.post(
            reverse(""admin:admin_views_externalsubscriber_changelist""), action_data
        )
        # The action sends mail rather than deletes.
        self.assertEqual(len(mail.outbox), 1)
        self.assertEqual(mail.outbox[0].subject, ""Greetings from a function action"")

        ```
    ###Assistant : 
        Actions come from the form whose submit button was pressed (#10618).
        
    "
266,"def test_empty_message(self) -> None:
        
        self.login(""hamlet"")
        othello = self.example_user(""othello"")
        result = self.client_post(
            ""/json/messages"",
            {""type"": ""private"", ""content"": "" "", ""to"": othello.email},
        )
        self.assert_json_error(result, ""Message must not be empty"")
","
        Sending a message that is empty or only whitespace should fail
        ",11,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_empty_message(self) -> None:
        
        self.login(""hamlet"")
        othello = self.example_user(""othello"")
        result = self.client_post(
            ""/json/messages"",
            {""type"": ""private"", ""content"": "" "", ""to"": othello.email},
        )
        self.assert_json_error(result, ""Message must not be empty"")

        ```
    ###Assistant : 
        Sending a message that is empty or only whitespace should fail
        
    "
267,"def freqai_feature_engineering_generic(self, dataframe, **kwargs):
        
        dataframe[""%-pct-change""] = dataframe[""close""].pct_change()
        dataframe[""%-raw_volume""] = dataframe[""volume""]
        dataframe[""%-raw_price""] = dataframe[""close""]
        return dataframe
","
        This optional function will be called for all include_timeframes (including corr_pairs).
        After that, the features will be shifted by the number of candles in the
        include_shifted_candles.
        :param df: strategy dataframe which will receive the features
        dataframe[""%-pct-change""] = dataframe[""close""].pct_change()
        ",38,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def freqai_feature_engineering_generic(self, dataframe, **kwargs):
        
        dataframe[""%-pct-change""] = dataframe[""close""].pct_change()
        dataframe[""%-raw_volume""] = dataframe[""volume""]
        dataframe[""%-raw_price""] = dataframe[""close""]
        return dataframe

        ```
    ###Assistant : 
        This optional function will be called for all include_timeframes (including corr_pairs).
        After that, the features will be shifted by the number of candles in the
        include_shifted_candles.
        :param df: strategy dataframe which will receive the features
        dataframe[""%-pct-change""] = dataframe[""close""].pct_change()
        
    "
268,"def next_sample(uid):
    
    return next(_SHARED_SEQUENCES[uid])


@keras_export(""keras.utils.GeneratorEnqueuer"")","Gets the next value from the generator `uid`.

    To allow multiple generators to be used at the same time, we use `uid` to
    get a specific one. A single generator would cause the validation to
    overwrite the training generator.

    Args:
        uid: int, generator identifier

    Returns:
        The next value of generator `uid`.
    ",51,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def next_sample(uid):
    
    return next(_SHARED_SEQUENCES[uid])


@keras_export(""keras.utils.GeneratorEnqueuer"")
        ```
    ###Assistant : Gets the next value from the generator `uid`.

    To allow multiple generators to be used at the same time, we use `uid` to
    get a specific one. A single generator would cause the validation to
    overwrite the training generator.

    Args:
        uid: int, generator identifier

    Returns:
        The next value of generator `uid`.
    
    "
269,"def check_settings(base_url=None):
    
    if base_url is None:
        base_url = settings.STATIC_URL
    if not base_url:
        raise ImproperlyConfigured(
            ""You're using the staticfiles app ""
            ""without having set the required STATIC_URL setting."")
    if settings.MEDIA_URL == base_url:
        raise ImproperlyConfigured(
            ""The MEDIA_URL and STATIC_URL settings must have different values""
        )
    if (settings.DEBUG and settings.MEDIA_URL and settings.STATIC_URL and
            settings.MEDIA_URL.startswith(settings.STATIC_URL)):
        raise ImproperlyConfigured(
            ""runserver can't serve media if MEDIA_URL is within STATIC_URL.""
        )
    if ((settings.MEDIA_ROOT and settings.STATIC_ROOT) and
            (settings.MEDIA_ROOT == settings.STATIC_ROOT)):
        raise ImproperlyConfigured(
            ""The MEDIA_ROOT and STATIC_ROOT settings must have different values""
        )
","
    Check if the staticfiles settings have sane values.
    ",8,83,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_settings(base_url=None):
    
    if base_url is None:
        base_url = settings.STATIC_URL
    if not base_url:
        raise ImproperlyConfigured(
            ""You're using the staticfiles app ""
            ""without having set the required STATIC_URL setting."")
    if settings.MEDIA_URL == base_url:
        raise ImproperlyConfigured(
            ""The MEDIA_URL and STATIC_URL settings must have different values""
        )
    if (settings.DEBUG and settings.MEDIA_URL and settings.STATIC_URL and
            settings.MEDIA_URL.startswith(settings.STATIC_URL)):
        raise ImproperlyConfigured(
            ""runserver can't serve media if MEDIA_URL is within STATIC_URL.""
        )
    if ((settings.MEDIA_ROOT and settings.STATIC_ROOT) and
            (settings.MEDIA_ROOT == settings.STATIC_ROOT)):
        raise ImproperlyConfigured(
            ""The MEDIA_ROOT and STATIC_ROOT settings must have different values""
        )

        ```
    ###Assistant : 
    Check if the staticfiles settings have sane values.
    
    "
270,"def kubernetes_dict(self, **kwargs) -> Dict:
        

        config = self.dict(**kwargs)
        for idx, deployment in enumerate(config[""deployments""]):

            if isinstance(deployment.get(""ray_actor_options""), dict):

                # JSON-serialize ray_actor_options' resources dictionary
                if isinstance(deployment[""ray_actor_options""].get(""resources""), dict):
                    deployment[""ray_actor_options""][""resources""] = json.dumps(
                        deployment[""ray_actor_options""][""resources""]
                    )

                # JSON-serialize ray_actor_options' runtime_env dictionary
                if isinstance(deployment[""ray_actor_options""].get(""runtime_env""), dict):
                    deployment[""ray_actor_options""][""runtime_env""] = json.dumps(
                        deployment[""ray_actor_options""][""runtime_env""]
                    )

                # Convert ray_actor_options' keys
                deployment[""ray_actor_options""] = dict_keys_snake_to_camel_case(
                    deployment[""ray_actor_options""]
                )

            # JSON-serialize user_config dictionary
            if isinstance(deployment.get(""user_config""), dict):
                deployment[""user_config""] = json.dumps(deployment[""user_config""])

            # Convert deployment's keys
            config[""deployments""][idx] = dict_keys_snake_to_camel_case(deployment)

        # Convert top-level runtime_env
        if isinstance(config.get(""runtime_env""), dict):
            config[""runtime_env""] = json.dumps(config[""runtime_env""])

        # Convert top-level option's keys
        config = dict_keys_snake_to_camel_case(config)

        return config


@PublicAPI(stability=""beta"")","Returns dictionary in Kubernetes format.

        Dictionary can be yaml-dumped to a Serve config file directly and then
        copy-pasted into a RayService Kubernetes config.

        Args: all kwargs are passed directly into schema's dict() function.
        ",33,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def kubernetes_dict(self, **kwargs) -> Dict:
        

        config = self.dict(**kwargs)
        for idx, deployment in enumerate(config[""deployments""]):

            if isinstance(deployment.get(""ray_actor_options""), dict):

                # JSON-serialize ray_actor_options' resources dictionary
                if isinstance(deployment[""ray_actor_options""].get(""resources""), dict):
                    deployment[""ray_actor_options""][""resources""] = json.dumps(
                        deployment[""ray_actor_options""][""resources""]
                    )

                # JSON-serialize ray_actor_options' runtime_env dictionary
                if isinstance(deployment[""ray_actor_options""].get(""runtime_env""), dict):
                    deployment[""ray_actor_options""][""runtime_env""] = json.dumps(
                        deployment[""ray_actor_options""][""runtime_env""]
                    )

                # Convert ray_actor_options' keys
                deployment[""ray_actor_options""] = dict_keys_snake_to_camel_case(
                    deployment[""ray_actor_options""]
                )

            # JSON-serialize user_config dictionary
            if isinstance(deployment.get(""user_config""), dict):
                deployment[""user_config""] = json.dumps(deployment[""user_config""])

            # Convert deployment's keys
            config[""deployments""][idx] = dict_keys_snake_to_camel_case(deployment)

        # Convert top-level runtime_env
        if isinstance(config.get(""runtime_env""), dict):
            config[""runtime_env""] = json.dumps(config[""runtime_env""])

        # Convert top-level option's keys
        config = dict_keys_snake_to_camel_case(config)

        return config


@PublicAPI(stability=""beta"")
        ```
    ###Assistant : Returns dictionary in Kubernetes format.

        Dictionary can be yaml-dumped to a Serve config file directly and then
        copy-pasted into a RayService Kubernetes config.

        Args: all kwargs are passed directly into schema's dict() function.
        
    "
271,"def reload_images(self, group_method, img_list):
        
        logger.info(""Preparing to group..."")
        if group_method == 'group_blur':
            filename_list, image_list = self._get_images()
            blurs = [self.estimate_blur(img) for img in image_list]
            temp_list = list(zip(filename_list, blurs))
        elif group_method == 'group_blur_fft':
            filename_list, image_list = self._get_images()
            fft_blurs = [self.estimate_blur_fft(img) for img in image_list]
            temp_list = list(zip(filename_list, fft_blurs))
        elif group_method == 'group_face_cnn':
            filename_list, image_list, landmarks = self._get_landmarks()
            temp_list = list(zip(filename_list, landmarks))
        elif group_method == 'group_face_yaw':
            filename_list, image_list, landmarks = self._get_landmarks()
            yaws = [self.calc_landmarks_face_yaw(mark) for mark in landmarks]
            temp_list = list(zip(filename_list, yaws))
        elif group_method == 'group_hist':
            filename_list, image_list = self._get_images()
            histograms = [cv2.calcHist([img], [0], None, [256], [0, 256]) for img in image_list]
            temp_list = list(zip(filename_list, histograms))
        elif group_method == 'group_black_pixels':
            filename_list, image_list = self._get_images()
            black_pixels = [np.ndarray.all(img == [0, 0, 0], axis=2).sum()/img.size*100*3
                            for img in image_list]
            temp_list = list(zip(filename_list, black_pixels))
        else:
            raise ValueError(f""{group_method} group_method not found."")

        return self.splice_lists(img_list, temp_list)
","
        Reloads the image list by replacing the comparative values with those
        that the chosen grouping method expects.
        :param group_method: str name of the grouping method that will be used.
        :param img_list: image list that has been sorted by one of the sort
        methods.
        :return: img_list but with the comparative values that the chosen
        grouping method expects.
        ",56,135,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reload_images(self, group_method, img_list):
        
        logger.info(""Preparing to group..."")
        if group_method == 'group_blur':
            filename_list, image_list = self._get_images()
            blurs = [self.estimate_blur(img) for img in image_list]
            temp_list = list(zip(filename_list, blurs))
        elif group_method == 'group_blur_fft':
            filename_list, image_list = self._get_images()
            fft_blurs = [self.estimate_blur_fft(img) for img in image_list]
            temp_list = list(zip(filename_list, fft_blurs))
        elif group_method == 'group_face_cnn':
            filename_list, image_list, landmarks = self._get_landmarks()
            temp_list = list(zip(filename_list, landmarks))
        elif group_method == 'group_face_yaw':
            filename_list, image_list, landmarks = self._get_landmarks()
            yaws = [self.calc_landmarks_face_yaw(mark) for mark in landmarks]
            temp_list = list(zip(filename_list, yaws))
        elif group_method == 'group_hist':
            filename_list, image_list = self._get_images()
            histograms = [cv2.calcHist([img], [0], None, [256], [0, 256]) for img in image_list]
            temp_list = list(zip(filename_list, histograms))
        elif group_method == 'group_black_pixels':
            filename_list, image_list = self._get_images()
            black_pixels = [np.ndarray.all(img == [0, 0, 0], axis=2).sum()/img.size*100*3
                            for img in image_list]
            temp_list = list(zip(filename_list, black_pixels))
        else:
            raise ValueError(f""{group_method} group_method not found."")

        return self.splice_lists(img_list, temp_list)

        ```
    ###Assistant : 
        Reloads the image list by replacing the comparative values with those
        that the chosen grouping method expects.
        :param group_method: str name of the grouping method that will be used.
        :param img_list: image list that has been sorted by one of the sort
        methods.
        :return: img_list but with the comparative values that the chosen
        grouping method expects.
        
    "
272,"def get_sales_orders(quotations):
	if not quotations:
		return []

	quotation_names = [q.name for q in quotations]

	return frappe.db.sql(
		.format(
			"", "".join([""%s""] * len(quotation_names))
		),
		tuple(quotation_names),
		as_dict=1,
	)  # nosec

","
	SELECT so.`name`, so.`base_grand_total`, soi.prevdoc_docname as quotation
	FROM `tabSales Order` so, `tabSales Order Item` soi
	WHERE so.docstatus=1 AND so.name = soi.parent AND soi.prevdoc_docname in ({0})
	",24,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_sales_orders(quotations):
	if not quotations:
		return []

	quotation_names = [q.name for q in quotations]

	return frappe.db.sql(
		.format(
			"", "".join([""%s""] * len(quotation_names))
		),
		tuple(quotation_names),
		as_dict=1,
	)  # nosec


        ```
    ###Assistant : 
	SELECT so.`name`, so.`base_grand_total`, soi.prevdoc_docname as quotation
	FROM `tabSales Order` so, `tabSales Order Item` soi
	WHERE so.docstatus=1 AND so.name = soi.parent AND soi.prevdoc_docname in ({0})
	
    "
273,"def download_extract(name, folder=None):
    
    fname = download(name)
    base_dir = os.path.dirname(fname)
    data_dir, ext = os.path.splitext(fname)
    if ext == '.zip':
        fp = zipfile.ZipFile(fname, 'r')
    elif ext in ('.tar', '.gz'):
        fp = tarfile.open(fname, 'r')
    else:
        assert False, 'Only zip/tar files can be extracted.'
    fp.extractall(base_dir)
    return os.path.join(base_dir, folder) if folder else data_dir

","Download and extract a zip/tar file.

    Defined in :numref:`sec_utils`",9,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def download_extract(name, folder=None):
    
    fname = download(name)
    base_dir = os.path.dirname(fname)
    data_dir, ext = os.path.splitext(fname)
    if ext == '.zip':
        fp = zipfile.ZipFile(fname, 'r')
    elif ext in ('.tar', '.gz'):
        fp = tarfile.open(fname, 'r')
    else:
        assert False, 'Only zip/tar files can be extracted.'
    fp.extractall(base_dir)
    return os.path.join(base_dir, folder) if folder else data_dir


        ```
    ###Assistant : Download and extract a zip/tar file.

    Defined in :numref:`sec_utils`
    "
274,"def sample_weights_mismatch(self):
        
        # If there is a mismatch between sample weight mode and the placeholders
        # created, then recompile the sub-graphs that depend on sample weights.
        return (
            self.sample_weight_mode is not None and self.sample_weight is None
        ) or (
            self.sample_weight_mode is None and self.sample_weight is not None
        )
",Check if the sample weight and the mode match or not.,11,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sample_weights_mismatch(self):
        
        # If there is a mismatch between sample weight mode and the placeholders
        # created, then recompile the sub-graphs that depend on sample weights.
        return (
            self.sample_weight_mode is not None and self.sample_weight is None
        ) or (
            self.sample_weight_mode is None and self.sample_weight is not None
        )

        ```
    ###Assistant : Check if the sample weight and the mode match or not.
    "
275,"def shebang(self, line, cell):
        

        # Create the event loop in which to run script magics
        # this operates on a background thread
        if self.event_loop is None:
            if sys.platform == ""win32"":
                # don't override the current policy,
                # just create an event loop
                event_loop = asyncio.WindowsProactorEventLoopPolicy().new_event_loop()
            else:
                event_loop = asyncio.new_event_loop()
            self.event_loop = event_loop

            # start the loop in a background thread
            asyncio_thread = Thread(target=event_loop.run_forever, daemon=True)
            asyncio_thread.start()
        else:
            event_loop = self.event_loop
","Run a cell via a shell command

        The `%%script` line is like the #! line of script,
        specifying a program (bash, perl, ruby, etc.) with which to run.

        The rest of the cell is run by that program.

        Examples
        --------
        ::

            In [1]: %%script bash
               ...: for i in 1 2 3; do
               ...:   echo $i
               ...: done
            1
            2
            3
        ",61,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def shebang(self, line, cell):
        

        # Create the event loop in which to run script magics
        # this operates on a background thread
        if self.event_loop is None:
            if sys.platform == ""win32"":
                # don't override the current policy,
                # just create an event loop
                event_loop = asyncio.WindowsProactorEventLoopPolicy().new_event_loop()
            else:
                event_loop = asyncio.new_event_loop()
            self.event_loop = event_loop

            # start the loop in a background thread
            asyncio_thread = Thread(target=event_loop.run_forever, daemon=True)
            asyncio_thread.start()
        else:
            event_loop = self.event_loop

        ```
    ###Assistant : Run a cell via a shell command

        The `%%script` line is like the #! line of script,
        specifying a program (bash, perl, ruby, etc.) with which to run.

        The rest of the cell is run by that program.

        Examples
        --------
        ::

            In [1]: %%script bash
               ...: for i in 1 2 3; do
               ...:   echo $i
               ...: done
            1
            2
            3
        
    "
276,"def statistics(self):
        
        try:
            return self._local.statistics
        except AttributeError:
            self._local.statistics = {}
            return self._local.statistics
","Return a dictionary of runtime statistics.

        This dictionary will be empty when the controller has never been
        ran. When it is running or has ran previously it should have (but
        may not) have useful and/or informational keys and values when
        running is underway and/or completed.

        .. warning:: The keys in this dictionary **should** be some what
                     stable (not changing), but there existence **may**
                     change between major releases as new statistics are
                     gathered or removed so before accessing keys ensure that
                     they actually exist and handle when they do not.

        .. note:: The values in this dictionary are local to the thread
                  running call (so if multiple threads share the same retrying
                  object - either directly or indirectly) they will each have
                  there own view of statistics they have collected (in the
                  future we may provide a way to aggregate the various
                  statistics from each thread).
        ",145,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def statistics(self):
        
        try:
            return self._local.statistics
        except AttributeError:
            self._local.statistics = {}
            return self._local.statistics

        ```
    ###Assistant : Return a dictionary of runtime statistics.

        This dictionary will be empty when the controller has never been
        ran. When it is running or has ran previously it should have (but
        may not) have useful and/or informational keys and values when
        running is underway and/or completed.

        .. warning:: The keys in this dictionary **should** be some what
                     stable (not changing), but there existence **may**
                     change between major releases as new statistics are
                     gathered or removed so before accessing keys ensure that
                     they actually exist and handle when they do not.

        .. note:: The values in this dictionary are local to the thread
                  running call (so if multiple threads share the same retrying
                  object - either directly or indirectly) they will each have
                  there own view of statistics they have collected (in the
                  future we may provide a way to aggregate the various
                  statistics from each thread).
        
    "
277,"def send(self, sender, **named):
        
        if (
            not self.receivers
            or self.sender_receivers_cache.get(sender) is NO_RECEIVERS
        ):
            return []

        return [
            (receiver, receiver(signal=self, sender=sender, **named))
            for receiver in self._live_receivers(sender)
        ]
","
        Send signal from sender to all connected receivers.

        If any receiver raises an error, the error propagates back through send,
        terminating the dispatch loop. So it's possible that all receivers
        won't be called if an error is raised.

        Arguments:

            sender
                The sender of the signal. Either a specific object or None.

            named
                Named arguments which will be passed to receivers.

        Return a list of tuple pairs [(receiver, response), ... ].
        ",70,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def send(self, sender, **named):
        
        if (
            not self.receivers
            or self.sender_receivers_cache.get(sender) is NO_RECEIVERS
        ):
            return []

        return [
            (receiver, receiver(signal=self, sender=sender, **named))
            for receiver in self._live_receivers(sender)
        ]

        ```
    ###Assistant : 
        Send signal from sender to all connected receivers.

        If any receiver raises an error, the error propagates back through send,
        terminating the dispatch loop. So it's possible that all receivers
        won't be called if an error is raised.

        Arguments:

            sender
                The sender of the signal. Either a specific object or None.

            named
                Named arguments which will be passed to receivers.

        Return a list of tuple pairs [(receiver, response), ... ].
        
    "
278,"def deploy_ray_func(func, *args):  # pragma: no cover
    
    result = func(*args)
    ip = get_node_ip_address()
    if isinstance(result, pandas.DataFrame):
        return result, len(result), len(result.columns), ip
    elif all(isinstance(r, pandas.DataFrame) for r in result):
        return [i for r in result for i in [r, len(r), len(r.columns), ip]]
    else:
        return [i for r in result for i in [r, None, None, ip]]
","
    Execute a function on an axis partition in a worker process.

    Parameters
    ----------
    func : callable
        Function to be executed on an axis partition.
    *args : iterable
        Additional arguments that need to passed in ``func``.

    Returns
    -------
    list
        The result of the function ``func`` and metadata for it.

    Notes
    -----
    Ray functions are not detected by codecov (thus pragma: no cover).
    ",61,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deploy_ray_func(func, *args):  # pragma: no cover
    
    result = func(*args)
    ip = get_node_ip_address()
    if isinstance(result, pandas.DataFrame):
        return result, len(result), len(result.columns), ip
    elif all(isinstance(r, pandas.DataFrame) for r in result):
        return [i for r in result for i in [r, len(r), len(r.columns), ip]]
    else:
        return [i for r in result for i in [r, None, None, ip]]

        ```
    ###Assistant : 
    Execute a function on an axis partition in a worker process.

    Parameters
    ----------
    func : callable
        Function to be executed on an axis partition.
    *args : iterable
        Additional arguments that need to passed in ``func``.

    Returns
    -------
    list
        The result of the function ``func`` and metadata for it.

    Notes
    -----
    Ray functions are not detected by codecov (thus pragma: no cover).
    
    "
279,"def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, ViltEncoder):
            module.gradient_checkpointing = value


VILT_START_DOCSTRING = r

VILT_INPUTS_DOCSTRING = r

VILT_IMAGES_AND_TEXT_CLASSIFICATION_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare ViLT Model transformer outputting raw hidden-states without any specific head on top."",
    VILT_START_DOCSTRING,
)","
    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ViltConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
            IDs?](../glossary#input-ids)

        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)

        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)

        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See
            [`ViltFeatureExtractor.__call__`] for details.

        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:

            - 1 for pixels that are real (i.e. **not masked**),
            - 0 for pixels that are padding (i.e. **masked**).
            `What are attention masks? <../glossary.html#attention-mask>`__

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.

        image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
            Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `pixel_values` into patch embeddings.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.

    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
            IDs?](../glossary#input-ids)

        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)

        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)

        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_images, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See
            [`ViltFeatureExtractor.__call__`] for details.

        pixel_mask (`torch.LongTensor` of shape `(batch_size, num_images, height, width)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:

            - 1 for pixels that are real (i.e. **not masked**),
            - 0 for pixels that are padding (i.e. **masked**).
            `What are attention masks? <../glossary.html#attention-mask>`__

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.

        image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
            Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `pixel_values` into patch embeddings.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
",802,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, ViltEncoder):
            module.gradient_checkpointing = value


VILT_START_DOCSTRING = r

VILT_INPUTS_DOCSTRING = r

VILT_IMAGES_AND_TEXT_CLASSIFICATION_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare ViLT Model transformer outputting raw hidden-states without any specific head on top."",
    VILT_START_DOCSTRING,
)
        ```
    ###Assistant : 
    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ViltConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
            IDs?](../glossary#input-ids)

        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)

        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)

        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See
            [`ViltFeatureExtractor.__call__`] for details.

        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:

            - 1 for pixels that are real (i.e. **not masked**),
            - 0 for pixels that are padding (i.e. **masked**).
            `What are attention masks? <../glossary.html#attention-mask>`__

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.

        image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
            Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `pixel_values` into patch embeddings.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.

    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
            IDs?](../glossary#input-ids)

        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)

        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)

        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_images, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See
            [`ViltFeatureExtractor.__call__`] for details.

        pixel_mask (`torch.LongTensor` of shape `(batch_size, num_images, height, width)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:

            - 1 for pixels that are real (i.e. **not masked**),
            - 0 for pixels that are padding (i.e. **masked**).
            `What are attention masks? <../glossary.html#attention-mask>`__

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.

        image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
            Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `pixel_values` into patch embeddings.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.

    "
280,"def needs_document_end_workaround(self):
        
        if objects.backend == usertypes.Backend.QtWebKit:
            return False

        assert objects.backend == usertypes.Backend.QtWebEngine, objects.backend

        broken_scripts = [
            ('http://userstyles.org', None),
            ('https://github.com/ParticleCore', 'Iridium'),
        ]
        return any(self._matches_id(namespace=namespace, name=name)
                   for namespace, name in broken_scripts)
","Check whether to force @run-at document-end.

        This needs to be done on QtWebEngine for known-broken scripts.

        On Qt 5.12, accessing the DOM isn't possible with ""@run-at
        document-start"". It was documented to be impossible before, but seems
        to work fine.

        However, some scripts do DOM access with ""@run-at document-start"". Fix
        those by forcing them to use document-end instead.
        ",57,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def needs_document_end_workaround(self):
        
        if objects.backend == usertypes.Backend.QtWebKit:
            return False

        assert objects.backend == usertypes.Backend.QtWebEngine, objects.backend

        broken_scripts = [
            ('http://userstyles.org', None),
            ('https://github.com/ParticleCore', 'Iridium'),
        ]
        return any(self._matches_id(namespace=namespace, name=name)
                   for namespace, name in broken_scripts)

        ```
    ###Assistant : Check whether to force @run-at document-end.

        This needs to be done on QtWebEngine for known-broken scripts.

        On Qt 5.12, accessing the DOM isn't possible with ""@run-at
        document-start"". It was documented to be impossible before, but seems
        to work fine.

        However, some scripts do DOM access with ""@run-at document-start"". Fix
        those by forcing them to use document-end instead.
        
    "
281,"def _temperature_unit(self) -> str:
        
        if (
            weather_option_temperature_unit := self._weather_option_temperature_unit
        ) is not None:
            return weather_option_temperature_unit

        return self._default_temperature_unit
","Return the converted unit of measurement for temperature.

        Should not be set by integrations.
        ",14,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _temperature_unit(self) -> str:
        
        if (
            weather_option_temperature_unit := self._weather_option_temperature_unit
        ) is not None:
            return weather_option_temperature_unit

        return self._default_temperature_unit

        ```
    ###Assistant : Return the converted unit of measurement for temperature.

        Should not be set by integrations.
        
    "
282,"def fontName(self, fontprop):
        

        if isinstance(fontprop, str):
            filenames = [fontprop]
        elif mpl.rcParams['pdf.use14corefonts']:
            filenames = _fontManager._find_fonts_by_props(
                fontprop, fontext='afm', directory=RendererPdf._afm_font_dir
            )
        else:
            filenames = _fontManager._find_fonts_by_props(fontprop)
        first_Fx = None
        for fname in filenames:
            Fx = self.fontNames.get(fname)
            if not first_Fx:
                first_Fx = Fx
            if Fx is None:
                Fx = next(self._internal_font_seq)
                self.fontNames[fname] = Fx
                _log.debug('Assigning font %s = %r', Fx, fname)
                if not first_Fx:
                    first_Fx = Fx

        # find_fontsprop's first value always adheres to
        # findfont's value, so technically no behaviour change
        return first_Fx
","
        Select a font based on fontprop and return a name suitable for
        Op.selectfont. If fontprop is a string, it will be interpreted
        as the filename of the font.
        ",28,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fontName(self, fontprop):
        

        if isinstance(fontprop, str):
            filenames = [fontprop]
        elif mpl.rcParams['pdf.use14corefonts']:
            filenames = _fontManager._find_fonts_by_props(
                fontprop, fontext='afm', directory=RendererPdf._afm_font_dir
            )
        else:
            filenames = _fontManager._find_fonts_by_props(fontprop)
        first_Fx = None
        for fname in filenames:
            Fx = self.fontNames.get(fname)
            if not first_Fx:
                first_Fx = Fx
            if Fx is None:
                Fx = next(self._internal_font_seq)
                self.fontNames[fname] = Fx
                _log.debug('Assigning font %s = %r', Fx, fname)
                if not first_Fx:
                    first_Fx = Fx

        # find_fontsprop's first value always adheres to
        # findfont's value, so technically no behaviour change
        return first_Fx

        ```
    ###Assistant : 
        Select a font based on fontprop and return a name suitable for
        Op.selectfont. If fontprop is a string, it will be interpreted
        as the filename of the font.
        
    "
283,"def center_to_corners_format(x):
    
    x_center, y_center, width, height = x.unbind(-1)
    boxes = [(x_center - 0.5 * width), (y_center - 0.5 * height), (x_center + 0.5 * width), (y_center + 0.5 * height)]
    return torch.stack(boxes, dim=-1)

","
    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
    (left, top, right, bottom).
    ",21,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def center_to_corners_format(x):
    
    x_center, y_center, width, height = x.unbind(-1)
    boxes = [(x_center - 0.5 * width), (y_center - 0.5 * height), (x_center + 0.5 * width), (y_center + 0.5 * height)]
    return torch.stack(boxes, dim=-1)


        ```
    ###Assistant : 
    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
    (left, top, right, bottom).
    
    "
284,"def add(self, key, value, timeout=DEFAULT_TIMEOUT, version=None):
        
        raise NotImplementedError(
            ""subclasses of BaseCache must provide an add() method""
        )
","
        Set a value in the cache if the key does not already exist. If
        timeout is given, use that timeout for the key; otherwise use the
        default cache timeout.

        Return True if the value was stored, False otherwise.
        ",38,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add(self, key, value, timeout=DEFAULT_TIMEOUT, version=None):
        
        raise NotImplementedError(
            ""subclasses of BaseCache must provide an add() method""
        )

        ```
    ###Assistant : 
        Set a value in the cache if the key does not already exist. If
        timeout is given, use that timeout for the key; otherwise use the
        default cache timeout.

        Return True if the value was stored, False otherwise.
        
    "
285,"def normalized_laplacian_matrix(G, nodelist=None, weight=""weight""):
    r
    import numpy as np
    import scipy as sp
    import scipy.sparse  # call as sp.sparse

    if nodelist is None:
        nodelist = list(G)
    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format=""csr"")
    n, m = A.shape
    diags = A.sum(axis=1)
    # TODO: rm csr_array wrapper when spdiags can produce arrays
    D = sp.sparse.csr_array(sp.sparse.spdiags(diags, 0, m, n, format=""csr""))
    L = D - A
    with sp.errstate(divide=""ignore""):
        diags_sqrt = 1.0 / np.sqrt(diags)
    diags_sqrt[np.isinf(diags_sqrt)] = 0
    # TODO: rm csr_array wrapper when spdiags can produce arrays
    DH = sp.sparse.csr_array(sp.sparse.spdiags(diags_sqrt, 0, m, n, format=""csr""))
    return DH @ (L @ DH)

","Returns the normalized Laplacian matrix of G.

    The normalized graph Laplacian is the matrix

    .. math::

        N = D^{-1/2} L D^{-1/2}

    where `L` is the graph Laplacian and `D` is the diagonal matrix of
    node degrees [1]_.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list, optional
       The rows and columns are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    weight : string or None, optional (default='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    Returns
    -------
    N : SciPy sparse array
      The normalized Laplacian matrix of G.

    Notes
    -----
    For MultiGraph, the edges weights are summed.
    See :func:`to_numpy_array` for other options.

    If the Graph contains selfloops, D is defined as ``diag(sum(A, 1))``, where A is
    the adjacency matrix [2]_.

    See Also
    --------
    laplacian_matrix
    normalized_laplacian_spectrum

    References
    ----------
    .. [1] Fan Chung-Graham, Spectral Graph Theory,
       CBMS Regional Conference Series in Mathematics, Number 92, 1997.
    .. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized
       Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
       March 2007.
    ",190,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def normalized_laplacian_matrix(G, nodelist=None, weight=""weight""):
    r
    import numpy as np
    import scipy as sp
    import scipy.sparse  # call as sp.sparse

    if nodelist is None:
        nodelist = list(G)
    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format=""csr"")
    n, m = A.shape
    diags = A.sum(axis=1)
    # TODO: rm csr_array wrapper when spdiags can produce arrays
    D = sp.sparse.csr_array(sp.sparse.spdiags(diags, 0, m, n, format=""csr""))
    L = D - A
    with sp.errstate(divide=""ignore""):
        diags_sqrt = 1.0 / np.sqrt(diags)
    diags_sqrt[np.isinf(diags_sqrt)] = 0
    # TODO: rm csr_array wrapper when spdiags can produce arrays
    DH = sp.sparse.csr_array(sp.sparse.spdiags(diags_sqrt, 0, m, n, format=""csr""))
    return DH @ (L @ DH)


        ```
    ###Assistant : Returns the normalized Laplacian matrix of G.

    The normalized graph Laplacian is the matrix

    .. math::

        N = D^{-1/2} L D^{-1/2}

    where `L` is the graph Laplacian and `D` is the diagonal matrix of
    node degrees [1]_.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list, optional
       The rows and columns are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    weight : string or None, optional (default='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    Returns
    -------
    N : SciPy sparse array
      The normalized Laplacian matrix of G.

    Notes
    -----
    For MultiGraph, the edges weights are summed.
    See :func:`to_numpy_array` for other options.

    If the Graph contains selfloops, D is defined as ``diag(sum(A, 1))``, where A is
    the adjacency matrix [2]_.

    See Also
    --------
    laplacian_matrix
    normalized_laplacian_spectrum

    References
    ----------
    .. [1] Fan Chung-Graham, Spectral Graph Theory,
       CBMS Regional Conference Series in Mathematics, Number 92, 1997.
    .. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized
       Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
       March 2007.
    
    "
286,"def extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None):
    
    i_h, i_w = image.shape[:2]
    p_h, p_w = patch_size

    if p_h > i_h:
        raise ValueError(
            ""Height of the patch should be less than the height of the image.""
        )

    if p_w > i_w:
        raise ValueError(
            ""Width of the patch should be less than the width of the image.""
        )

    image = check_array(image, allow_nd=True)
    image = image.reshape((i_h, i_w, -1))
    n_colors = image.shape[-1]

    extracted_patches = _extract_patches(
        image, patch_shape=(p_h, p_w, n_colors), extraction_step=1
    )

    n_patches = _compute_n_patches(i_h, i_w, p_h, p_w, max_patches)
    if max_patches:
        rng = check_random_state(random_state)
        i_s = rng.randint(i_h - p_h + 1, size=n_patches)
        j_s = rng.randint(i_w - p_w + 1, size=n_patches)
        patches = extracted_patches[i_s, j_s, 0]
    else:
        patches = extracted_patches

    patches = patches.reshape(-1, p_h, p_w, n_colors)
    # remove the color dimension if useless
    if patches.shape[-1] == 1:
        return patches.reshape((n_patches, p_h, p_w))
    else:
        return patches

","Reshape a 2D image into a collection of patches.

    The resulting patches are allocated in a dedicated array.

    Read more in the :ref:`User Guide <image_feature_extraction>`.

    Parameters
    ----------
    image : ndarray of shape (image_height, image_width) or \
        (image_height, image_width, n_channels)
        The original image data. For color images, the last dimension specifies
        the channel: a RGB image would have `n_channels=3`.

    patch_size : tuple of int (patch_height, patch_width)
        The dimensions of one patch.

    max_patches : int or float, default=None
        The maximum number of patches to extract. If `max_patches` is a float
        between 0 and 1, it is taken to be a proportion of the total number
        of patches.

    random_state : int, RandomState instance, default=None
        Determines the random number generator used for random sampling when
        `max_patches` is not None. Use an int to make the randomness
        deterministic.
        See :term:`Glossary <random_state>`.

    Returns
    -------
    patches : array of shape (n_patches, patch_height, patch_width) or \
        (n_patches, patch_height, patch_width, n_channels)
        The collection of patches extracted from the image, where `n_patches`
        is either `max_patches` or the total number of patches that can be
        extracted.

    Examples
    --------
    >>> from sklearn.datasets import load_sample_image
    >>> from sklearn.feature_extraction import image
    >>> # Use the array data from the first image in this dataset:
    >>> one_image = load_sample_image(""china.jpg"")
    >>> print('Image shape: {}'.format(one_image.shape))
    Image shape: (427, 640, 3)
    >>> patches = image.extract_patches_2d(one_image, (2, 2))
    >>> print('Patches shape: {}'.format(patches.shape))
    Patches shape: (272214, 2, 2, 3)
    >>> # Here are just two of these patches:
    >>> print(patches[1])
    [[[174 201 231]
      [174 201 231]]
     [[173 200 230]
      [173 200 230]]]
    >>> print(patches[800])
    [[[187 214 243]
      [188 215 244]]
     [[187 214 243]
      [188 215 244]]]
    ",266,136,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None):
    
    i_h, i_w = image.shape[:2]
    p_h, p_w = patch_size

    if p_h > i_h:
        raise ValueError(
            ""Height of the patch should be less than the height of the image.""
        )

    if p_w > i_w:
        raise ValueError(
            ""Width of the patch should be less than the width of the image.""
        )

    image = check_array(image, allow_nd=True)
    image = image.reshape((i_h, i_w, -1))
    n_colors = image.shape[-1]

    extracted_patches = _extract_patches(
        image, patch_shape=(p_h, p_w, n_colors), extraction_step=1
    )

    n_patches = _compute_n_patches(i_h, i_w, p_h, p_w, max_patches)
    if max_patches:
        rng = check_random_state(random_state)
        i_s = rng.randint(i_h - p_h + 1, size=n_patches)
        j_s = rng.randint(i_w - p_w + 1, size=n_patches)
        patches = extracted_patches[i_s, j_s, 0]
    else:
        patches = extracted_patches

    patches = patches.reshape(-1, p_h, p_w, n_colors)
    # remove the color dimension if useless
    if patches.shape[-1] == 1:
        return patches.reshape((n_patches, p_h, p_w))
    else:
        return patches


        ```
    ###Assistant : Reshape a 2D image into a collection of patches.

    The resulting patches are allocated in a dedicated array.

    Read more in the :ref:`User Guide <image_feature_extraction>`.

    Parameters
    ----------
    image : ndarray of shape (image_height, image_width) or \
        (image_height, image_width, n_channels)
        The original image data. For color images, the last dimension specifies
        the channel: a RGB image would have `n_channels=3`.

    patch_size : tuple of int (patch_height, patch_width)
        The dimensions of one patch.

    max_patches : int or float, default=None
        The maximum number of patches to extract. If `max_patches` is a float
        between 0 and 1, it is taken to be a proportion of the total number
        of patches.

    random_state : int, RandomState instance, default=None
        Determines the random number generator used for random sampling when
        `max_patches` is not None. Use an int to make the randomness
        deterministic.
        See :term:`Glossary <random_state>`.

    Returns
    -------
    patches : array of shape (n_patches, patch_height, patch_width) or \
        (n_patches, patch_height, patch_width, n_channels)
        The collection of patches extracted from the image, where `n_patches`
        is either `max_patches` or the total number of patches that can be
        extracted.

    Examples
    --------
    >>> from sklearn.datasets import load_sample_image
    >>> from sklearn.feature_extraction import image
    >>> # Use the array data from the first image in this dataset:
    >>> one_image = load_sample_image(""china.jpg"")
    >>> print('Image shape: {}'.format(one_image.shape))
    Image shape: (427, 640, 3)
    >>> patches = image.extract_patches_2d(one_image, (2, 2))
    >>> print('Patches shape: {}'.format(patches.shape))
    Patches shape: (272214, 2, 2, 3)
    >>> # Here are just two of these patches:
    >>> print(patches[1])
    [[[174 201 231]
      [174 201 231]]
     [[173 200 230]
      [173 200 230]]]
    >>> print(patches[800])
    [[[187 214 243]
      [188 215 244]]
     [[187 214 243]
      [188 215 244]]]
    
    "
287,"def refresh_from_db(self, using=None, fields=None):
        
        if fields is None:
            self._prefetched_objects_cache = {}
        else:
            prefetched_objects_cache = getattr(self, ""_prefetched_objects_cache"", ())
            for field in fields:
                if field in prefetched_objects_cache:
                    del prefetched_objects_cache[field]
                    fields.remove(field)
            if not fields:
                return
            if any(LOOKUP_SEP in f for f in fields):
                raise ValueError(
                    'Found ""%s"" in fields argument. Relations and transforms '
                    ""are not allowed in fields."" % LOOKUP_SEP
                )

        hints = {""instance"": self}
        db_instance_qs = self.__class__._base_manager.db_manager(
            using, hints=hints
        ).filter(pk=self.pk)

        # Use provided fields, if not set then reload all non-deferred fields.
        deferred_fields = self.get_deferred_fields()
        if fields is not None:
            fields = list(fields)
            db_instance_qs = db_instance_qs.only(*fields)
        elif deferred_fields:
            fields = [
                f.attname
                for f in self._meta.concrete_fields
                if f.attname not in deferred_fields
            ]
            db_instance_qs = db_instance_qs.only(*fields)

        db_instance = db_instance_qs.get()
        non_loaded_fields = db_instance.get_deferred_fields()
        for field in self._meta.concrete_fields:
            if field.attname in non_loaded_fields:
                # This field wasn't refreshed - skip ahead.
                continue
            setattr(self, field.attname, getattr(db_instance, field.attname))
            # Clear cached foreign keys.
            if field.is_relation and field.is_cached(self):
                field.delete_cached_value(self)

        # Clear cached relations.
        for field in self._meta.related_objects:
            if field.is_cached(self):
                field.delete_cached_value(self)

        self._state.db = db_instance._state.db
","
        Reload field values from the database.

        By default, the reloading happens from the database this instance was
        loaded from, or by the read router if this instance wasn't loaded from
        any database. The using parameter will override the default.

        Fields can be used to specify which fields to reload. The fields
        should be an iterable of field attnames. If fields is None, then
        all non-deferred fields are reloaded.

        When accessing deferred fields of an instance, the deferred loading
        of the field will call this method.
        ",85,165,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def refresh_from_db(self, using=None, fields=None):
        
        if fields is None:
            self._prefetched_objects_cache = {}
        else:
            prefetched_objects_cache = getattr(self, ""_prefetched_objects_cache"", ())
            for field in fields:
                if field in prefetched_objects_cache:
                    del prefetched_objects_cache[field]
                    fields.remove(field)
            if not fields:
                return
            if any(LOOKUP_SEP in f for f in fields):
                raise ValueError(
                    'Found ""%s"" in fields argument. Relations and transforms '
                    ""are not allowed in fields."" % LOOKUP_SEP
                )

        hints = {""instance"": self}
        db_instance_qs = self.__class__._base_manager.db_manager(
            using, hints=hints
        ).filter(pk=self.pk)

        # Use provided fields, if not set then reload all non-deferred fields.
        deferred_fields = self.get_deferred_fields()
        if fields is not None:
            fields = list(fields)
            db_instance_qs = db_instance_qs.only(*fields)
        elif deferred_fields:
            fields = [
                f.attname
                for f in self._meta.concrete_fields
                if f.attname not in deferred_fields
            ]
            db_instance_qs = db_instance_qs.only(*fields)

        db_instance = db_instance_qs.get()
        non_loaded_fields = db_instance.get_deferred_fields()
        for field in self._meta.concrete_fields:
            if field.attname in non_loaded_fields:
                # This field wasn't refreshed - skip ahead.
                continue
            setattr(self, field.attname, getattr(db_instance, field.attname))
            # Clear cached foreign keys.
            if field.is_relation and field.is_cached(self):
                field.delete_cached_value(self)

        # Clear cached relations.
        for field in self._meta.related_objects:
            if field.is_cached(self):
                field.delete_cached_value(self)

        self._state.db = db_instance._state.db

        ```
    ###Assistant : 
        Reload field values from the database.

        By default, the reloading happens from the database this instance was
        loaded from, or by the read router if this instance wasn't loaded from
        any database. The using parameter will override the default.

        Fields can be used to specify which fields to reload. The fields
        should be an iterable of field attnames. If fields is None, then
        all non-deferred fields are reloaded.

        When accessing deferred fields of an instance, the deferred loading
        of the field will call this method.
        
    "
288,"def set_tunnel(self, host, port=None, headers=None):
        

        if self.sock:
            raise RuntimeError(""Can't set up tunnel for established connection"")

        self._tunnel_host, self._tunnel_port = self._get_hostport(host, port)
        if headers:
            self._tunnel_headers = headers
        else:
            self._tunnel_headers.clear()
","Set up host and port for HTTP CONNECT tunnelling.

        In a connection that uses HTTP CONNECT tunneling, the host passed to the
        constructor is used as a proxy server that relays all communication to
        the endpoint passed to `set_tunnel`. This done by sending an HTTP
        CONNECT request to the proxy server when the connection is established.

        This method must be called before the HTTP connection has been
        established.

        The headers argument should be a mapping of extra HTTP headers to send
        with the CONNECT request.
        ",85,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_tunnel(self, host, port=None, headers=None):
        

        if self.sock:
            raise RuntimeError(""Can't set up tunnel for established connection"")

        self._tunnel_host, self._tunnel_port = self._get_hostport(host, port)
        if headers:
            self._tunnel_headers = headers
        else:
            self._tunnel_headers.clear()

        ```
    ###Assistant : Set up host and port for HTTP CONNECT tunnelling.

        In a connection that uses HTTP CONNECT tunneling, the host passed to the
        constructor is used as a proxy server that relays all communication to
        the endpoint passed to `set_tunnel`. This done by sending an HTTP
        CONNECT request to the proxy server when the connection is established.

        This method must be called before the HTTP connection has been
        established.

        The headers argument should be a mapping of extra HTTP headers to send
        with the CONNECT request.
        
    "
289,"def explicit_device_get_scope() -> Iterator[None]:
  
  state = transfer_guard_lib.thread_local_state()
  prev = state.explicit_device_get
  state.explicit_device_get = True
  try:
    yield
  finally:
    state.explicit_device_get = prev
",Indicates that the current context is an explicit device_get() call.,10,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def explicit_device_get_scope() -> Iterator[None]:
  
  state = transfer_guard_lib.thread_local_state()
  prev = state.explicit_device_get
  state.explicit_device_get = True
  try:
    yield
  finally:
    state.explicit_device_get = prev

        ```
    ###Assistant : Indicates that the current context is an explicit device_get() call.
    "
290,"def accessory_info(self) -> Service:
        
        return self.accessory.services.first(
            service_type=ServicesTypes.ACCESSORY_INFORMATION
        )
",Information about the make and model of an accessory.,9,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def accessory_info(self) -> Service:
        
        return self.accessory.services.first(
            service_type=ServicesTypes.ACCESSORY_INFORMATION
        )

        ```
    ###Assistant : Information about the make and model of an accessory.
    "
291,"def _always_object(classes):
    
    if object not in classes:
        return classes + (object,)
    return classes

","
    Ensure object appears in the mro even
    for old-style classes.
    ",10,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _always_object(classes):
    
    if object not in classes:
        return classes + (object,)
    return classes


        ```
    ###Assistant : 
    Ensure object appears in the mro even
    for old-style classes.
    
    "
292,"def addIncludedDataFilesFromFileOptions():
    

    for included_datafile in _addIncludedDataFilesFromFileOptions():
        addIncludedDataFile(included_datafile)

","Early data files, from user options that work with file system.",11,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def addIncludedDataFilesFromFileOptions():
    

    for included_datafile in _addIncludedDataFilesFromFileOptions():
        addIncludedDataFile(included_datafile)


        ```
    ###Assistant : Early data files, from user options that work with file system.
    "
293,"def _attributes(**kwargs) -> dict[str, str]:
    
    return {key: str(value) for key, value in kwargs.items() if value is not None}

",Return the given kwargs as a dictionary with values converted to strings. Items with a value of None will be omitted.,21,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _attributes(**kwargs) -> dict[str, str]:
    
    return {key: str(value) for key, value in kwargs.items() if value is not None}


        ```
    ###Assistant : Return the given kwargs as a dictionary with values converted to strings. Items with a value of None will be omitted.
    "
294,"def test_charpp(self):
        
        dll = CDLL(_ctypes_test.__file__)
        func = dll._testfunc_c_p_p
        func.restype = c_char_p
        argv = (c_char_p * 2)()
        argc = c_int( 2 )
        argv[0] = b'hello'
        argv[1] = b'world'
        result = func( byref(argc), argv )
        self.assertEqual(result, b'world')
",Test that a character pointer-to-pointer is correctly passed,8,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_charpp(self):
        
        dll = CDLL(_ctypes_test.__file__)
        func = dll._testfunc_c_p_p
        func.restype = c_char_p
        argv = (c_char_p * 2)()
        argc = c_int( 2 )
        argv[0] = b'hello'
        argv[1] = b'world'
        result = func( byref(argc), argv )
        self.assertEqual(result, b'world')

        ```
    ###Assistant : Test that a character pointer-to-pointer is correctly passed
    "
295,"def test_error_message_unsigned(self):
        
        # Ensure to test for potential overflow in the case of:
        #        x - y
        # and
        #        y - x
        x = np.asarray([0, 1, 8], dtype='uint8')
        y = np.asarray([4, 4, 4], dtype='uint8')
        with pytest.raises(AssertionError) as exc_info:
            assert_allclose(x, y, atol=3)
        msgs = str(exc_info.value).split('\n')
        assert_equal(msgs[4], 'Max absolute difference: 4')

","Check the the message is formatted correctly when overflow can occur
           (gh21768)",12,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_error_message_unsigned(self):
        
        # Ensure to test for potential overflow in the case of:
        #        x - y
        # and
        #        y - x
        x = np.asarray([0, 1, 8], dtype='uint8')
        y = np.asarray([4, 4, 4], dtype='uint8')
        with pytest.raises(AssertionError) as exc_info:
            assert_allclose(x, y, atol=3)
        msgs = str(exc_info.value).split('\n')
        assert_equal(msgs[4], 'Max absolute difference: 4')


        ```
    ###Assistant : Check the the message is formatted correctly when overflow can occur
           (gh21768)
    "
296,"def _join_or_get_room(self, room_id_or_alias):
        
        rooms = self._client.get_rooms()
        if room_id_or_alias in rooms:
            _LOGGER.debug(""Already in room %s"", room_id_or_alias)
            return rooms[room_id_or_alias]

        for room in rooms.values():
            if room.room_id not in self._aliases_fetched_for:
                room.update_aliases()
                self._aliases_fetched_for.add(room.room_id)

            if (
                room_id_or_alias in room.aliases
                or room_id_or_alias == room.canonical_alias
            ):
                _LOGGER.debug(
                    ""Already in room %s (known as %s)"", room.room_id, room_id_or_alias
                )
                return room

        room = self._client.join_room(room_id_or_alias)
        _LOGGER.info(""Joined room %s (known as %s)"", room.room_id, room_id_or_alias)
        return room
","Join a room or get it, if we are already in the room.

        We can't just always call join_room(), since that seems to crash
        the client if we're already in the room.
        ",32,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _join_or_get_room(self, room_id_or_alias):
        
        rooms = self._client.get_rooms()
        if room_id_or_alias in rooms:
            _LOGGER.debug(""Already in room %s"", room_id_or_alias)
            return rooms[room_id_or_alias]

        for room in rooms.values():
            if room.room_id not in self._aliases_fetched_for:
                room.update_aliases()
                self._aliases_fetched_for.add(room.room_id)

            if (
                room_id_or_alias in room.aliases
                or room_id_or_alias == room.canonical_alias
            ):
                _LOGGER.debug(
                    ""Already in room %s (known as %s)"", room.room_id, room_id_or_alias
                )
                return room

        room = self._client.join_room(room_id_or_alias)
        _LOGGER.info(""Joined room %s (known as %s)"", room.room_id, room_id_or_alias)
        return room

        ```
    ###Assistant : Join a room or get it, if we are already in the room.

        We can't just always call join_room(), since that seems to crash
        the client if we're already in the room.
        
    "
297,"def _is_function_class_equation(func_class, f, symbol):
    
    if f.is_Mul or f.is_Add:
        return all(_is_function_class_equation(func_class, arg, symbol)
                   for arg in f.args)

    if f.is_Pow:
        if not f.exp.has(symbol):
            return _is_function_class_equation(func_class, f.base, symbol)
        else:
            return False

    if not f.has(symbol):
        return True

    if isinstance(f, func_class):
        try:
            g = Poly(f.args[0], symbol)
            return g.degree() <= 1
        except PolynomialError:
            return False
    else:
        return False

"," Tests whether the equation is an equation of the given function class.

    The given equation belongs to the given function class if it is
    comprised of functions of the function class which are multiplied by
    or added to expressions independent of the symbol. In addition, the
    arguments of all such functions must be linear in the symbol as well.

    Examples
    ========

    >>> from sympy.solvers.solveset import _is_function_class_equation
    >>> from sympy import tan, sin, tanh, sinh, exp
    >>> from sympy.abc import x
    >>> from sympy.functions.elementary.trigonometric import TrigonometricFunction
    >>> from sympy.functions.elementary.hyperbolic import HyperbolicFunction
    >>> _is_function_class_equation(TrigonometricFunction, exp(x) + tan(x), x)
    False
    >>> _is_function_class_equation(TrigonometricFunction, tan(x) + sin(x), x)
    True
    >>> _is_function_class_equation(TrigonometricFunction, tan(x**2), x)
    False
    >>> _is_function_class_equation(TrigonometricFunction, tan(x + 2), x)
    True
    >>> _is_function_class_equation(HyperbolicFunction, tanh(x) + sinh(x), x)
    True
    ",123,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _is_function_class_equation(func_class, f, symbol):
    
    if f.is_Mul or f.is_Add:
        return all(_is_function_class_equation(func_class, arg, symbol)
                   for arg in f.args)

    if f.is_Pow:
        if not f.exp.has(symbol):
            return _is_function_class_equation(func_class, f.base, symbol)
        else:
            return False

    if not f.has(symbol):
        return True

    if isinstance(f, func_class):
        try:
            g = Poly(f.args[0], symbol)
            return g.degree() <= 1
        except PolynomialError:
            return False
    else:
        return False


        ```
    ###Assistant :  Tests whether the equation is an equation of the given function class.

    The given equation belongs to the given function class if it is
    comprised of functions of the function class which are multiplied by
    or added to expressions independent of the symbol. In addition, the
    arguments of all such functions must be linear in the symbol as well.

    Examples
    ========

    >>> from sympy.solvers.solveset import _is_function_class_equation
    >>> from sympy import tan, sin, tanh, sinh, exp
    >>> from sympy.abc import x
    >>> from sympy.functions.elementary.trigonometric import TrigonometricFunction
    >>> from sympy.functions.elementary.hyperbolic import HyperbolicFunction
    >>> _is_function_class_equation(TrigonometricFunction, exp(x) + tan(x), x)
    False
    >>> _is_function_class_equation(TrigonometricFunction, tan(x) + sin(x), x)
    True
    >>> _is_function_class_equation(TrigonometricFunction, tan(x**2), x)
    False
    >>> _is_function_class_equation(TrigonometricFunction, tan(x + 2), x)
    True
    >>> _is_function_class_equation(HyperbolicFunction, tanh(x) + sinh(x), x)
    True
    
    "
298,"def get_feature_names_out(self, input_features=None):
        
        class_name = self.__class__.__name__.lower()
        return np.asarray([f""{class_name}0""], dtype=object)
","Get output feature names for transformation.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Ignored.

        Returns
        -------
        feature_names_out : ndarray of str objects
            An ndarray with one string i.e. [""isotonicregression0""].
        ",32,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_feature_names_out(self, input_features=None):
        
        class_name = self.__class__.__name__.lower()
        return np.asarray([f""{class_name}0""], dtype=object)

        ```
    ###Assistant : Get output feature names for transformation.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Ignored.

        Returns
        -------
        feature_names_out : ndarray of str objects
            An ndarray with one string i.e. [""isotonicregression0""].
        
    "
299,"def get_distance(self, f, dist_val, lookup_type):
        
        # Getting the distance parameter
        value = dist_val[0]

        # Shorthand boolean flags.
        geodetic = f.geodetic(self.connection)
        geography = f.geography

        if isinstance(value, Distance):
            if geography:
                dist_param = value.m
            elif geodetic:
                if lookup_type == ""dwithin"":
                    raise ValueError(
                        ""Only numeric values of degree units are ""
                        ""allowed on geographic DWithin queries.""
                    )
                dist_param = value.m
            else:
                dist_param = getattr(
                    value, Distance.unit_attname(f.units_name(self.connection))
                )
        else:
            # Assuming the distance is in the units of the field.
            dist_param = value

        return [dist_param]
","
        Retrieve the distance parameters for the given geometry field,
        distance lookup value, and the distance lookup type.

        This is the most complex implementation of the spatial backends due to
        what is supported on geodetic geometry columns vs. what's available on
        projected geometry columns.  In addition, it has to take into account
        the geography column type.
        ",55,80,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_distance(self, f, dist_val, lookup_type):
        
        # Getting the distance parameter
        value = dist_val[0]

        # Shorthand boolean flags.
        geodetic = f.geodetic(self.connection)
        geography = f.geography

        if isinstance(value, Distance):
            if geography:
                dist_param = value.m
            elif geodetic:
                if lookup_type == ""dwithin"":
                    raise ValueError(
                        ""Only numeric values of degree units are ""
                        ""allowed on geographic DWithin queries.""
                    )
                dist_param = value.m
            else:
                dist_param = getattr(
                    value, Distance.unit_attname(f.units_name(self.connection))
                )
        else:
            # Assuming the distance is in the units of the field.
            dist_param = value

        return [dist_param]

        ```
    ###Assistant : 
        Retrieve the distance parameters for the given geometry field,
        distance lookup value, and the distance lookup type.

        This is the most complex implementation of the spatial backends due to
        what is supported on geodetic geometry columns vs. what's available on
        projected geometry columns.  In addition, it has to take into account
        the geography column type.
        
    "
300,"def test_type_eventpage_two_indexes(self):
        
        self.make_event_section(""Other events"")
        self.assertEqual(
            self.get_best_root({""page_type"": ""tests.EventPage""}), self.home_page
        )
","
        The chooser should start at the home page, as there are two
        EventIndexes with EventPages.
        ",15,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_type_eventpage_two_indexes(self):
        
        self.make_event_section(""Other events"")
        self.assertEqual(
            self.get_best_root({""page_type"": ""tests.EventPage""}), self.home_page
        )

        ```
    ###Assistant : 
        The chooser should start at the home page, as there are two
        EventIndexes with EventPages.
        
    "
301,"def parse(self, filename, constraint):
        # type: (str, bool) -> Iterator[ParsedLine]
        
        yield from self._parse_and_recurse(filename, constraint)
","Parse a given file, yielding parsed lines.
        ",7,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse(self, filename, constraint):
        # type: (str, bool) -> Iterator[ParsedLine]
        
        yield from self._parse_and_recurse(filename, constraint)

        ```
    ###Assistant : Parse a given file, yielding parsed lines.
        
    "
302,"def sparse_bincount(inputs, depth, binary_output, dtype, count_weights=None):
    
    result = tf.sparse.bincount(
        inputs,
        weights=count_weights,
        minlength=depth,
        maxlength=depth,
        axis=-1,
        binary_output=binary_output,
    )
    result = tf.cast(result, dtype)
    if inputs.shape.rank == 1:
        output_shape = (depth,)
    else:
        batch_size = tf.shape(result)[0]
        output_shape = (batch_size, depth)
    result = tf.SparseTensor(
        indices=result.indices, values=result.values, dense_shape=output_shape
    )
    return result

",Apply binary or count encoding to an input and return a sparse tensor.,13,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sparse_bincount(inputs, depth, binary_output, dtype, count_weights=None):
    
    result = tf.sparse.bincount(
        inputs,
        weights=count_weights,
        minlength=depth,
        maxlength=depth,
        axis=-1,
        binary_output=binary_output,
    )
    result = tf.cast(result, dtype)
    if inputs.shape.rank == 1:
        output_shape = (depth,)
    else:
        batch_size = tf.shape(result)[0]
        output_shape = (batch_size, depth)
    result = tf.SparseTensor(
        indices=result.indices, values=result.values, dense_shape=output_shape
    )
    return result


        ```
    ###Assistant : Apply binary or count encoding to an input and return a sparse tensor.
    "
303,"def unmarshal(self, serialized_data):
        
        logger.debug(""data type: %s"", type(serialized_data))
        try:
            retval = self._unmarshal(serialized_data)
        except Exception as err:
            msg = f""Error unserializing data for type {type(serialized_data)}: {str(err)}""
            raise FaceswapError(msg) from err
        logger.debug(""returned data type: %s"", type(retval))
        return retval
"," Unserialize data to its original object type

        Parameters
        ----------
        serialized_data: varies
            Data in serializer format that is to be unmarshalled to its original object

        Returns
        -------
        data: varies
            The data in a python object format

        Example
        ------
        >>> serializer = get_serializer('json')
        >>> json_data = <json object>
        >>> data = serializer.unmarshal(json_data)
        ",50,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unmarshal(self, serialized_data):
        
        logger.debug(""data type: %s"", type(serialized_data))
        try:
            retval = self._unmarshal(serialized_data)
        except Exception as err:
            msg = f""Error unserializing data for type {type(serialized_data)}: {str(err)}""
            raise FaceswapError(msg) from err
        logger.debug(""returned data type: %s"", type(retval))
        return retval

        ```
    ###Assistant :  Unserialize data to its original object type

        Parameters
        ----------
        serialized_data: varies
            Data in serializer format that is to be unmarshalled to its original object

        Returns
        -------
        data: varies
            The data in a python object format

        Example
        ------
        >>> serializer = get_serializer('json')
        >>> json_data = <json object>
        >>> data = serializer.unmarshal(json_data)
        
    "
304,"def copyDataFiles():
    

    for included_datafile in getIncludedDataFiles():
        # TODO: directories should be resolved to files.
        if (
            not isinstance(included_datafile, (IncludedDataFile))
            or included_datafile.needsCopy()
        ):
            _handleDataFile(
                included_datafile,
            )
","Copy the data files needed for standalone distribution.

    Notes:
        This is for data files only, not DLLs or even extension modules,
        those must be registered as entry points, and would not go through
        necessary handling if provided like this.
    ",39,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copyDataFiles():
    

    for included_datafile in getIncludedDataFiles():
        # TODO: directories should be resolved to files.
        if (
            not isinstance(included_datafile, (IncludedDataFile))
            or included_datafile.needsCopy()
        ):
            _handleDataFile(
                included_datafile,
            )

        ```
    ###Assistant : Copy the data files needed for standalone distribution.

    Notes:
        This is for data files only, not DLLs or even extension modules,
        those must be registered as entry points, and would not go through
        necessary handling if provided like this.
    
    "
305,"def toggler(self, attr):
        
        if attr not in self._options:
            raise KeyError(""No such option: %s"" % attr)
        o = self._options[attr]
        if o.typespec != bool:
            raise ValueError(""Toggler can only be used with boolean options"")
","
        Generate a toggler for a boolean attribute. This returns a callable
        that takes no arguments.
        ",15,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def toggler(self, attr):
        
        if attr not in self._options:
            raise KeyError(""No such option: %s"" % attr)
        o = self._options[attr]
        if o.typespec != bool:
            raise ValueError(""Toggler can only be used with boolean options"")

        ```
    ###Assistant : 
        Generate a toggler for a boolean attribute. This returns a callable
        that takes no arguments.
        
    "
306,"def disable_terminal_wrapping(monkeypatch):
    
    monkeypatch.setattr(
        ""prefect.cli.profile.console"", rich.console.Console(soft_wrap=True)
    )
","
    Sometimes, line wrapping makes it hard to make deterministic assertions about the
    output of a CLI command. Wrapping can be disabled by using this fixture.
    ",25,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def disable_terminal_wrapping(monkeypatch):
    
    monkeypatch.setattr(
        ""prefect.cli.profile.console"", rich.console.Console(soft_wrap=True)
    )

        ```
    ###Assistant : 
    Sometimes, line wrapping makes it hard to make deterministic assertions about the
    output of a CLI command. Wrapping can be disabled by using this fixture.
    
    "
307,"def __add__(self, other):
        
        rank = (self.rank() + other) % self.cardinality
        rv = self.unrank_lex(self.size, rank)
        rv._rank = rank
        return rv
","Return permutation that is other higher in rank than self.

        The rank is the lexicographical rank, with the identity permutation
        having rank of 0.

        Examples
        ========

        >>> from sympy.combinatorics import Permutation
        >>> I = Permutation([0, 1, 2, 3])
        >>> a = Permutation([2, 1, 3, 0])
        >>> I + a.rank() == a
        True

        See Also
        ========

        __sub__, inversion_vector

        ",57,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __add__(self, other):
        
        rank = (self.rank() + other) % self.cardinality
        rv = self.unrank_lex(self.size, rank)
        rv._rank = rank
        return rv

        ```
    ###Assistant : Return permutation that is other higher in rank than self.

        The rank is the lexicographical rank, with the identity permutation
        having rank of 0.

        Examples
        ========

        >>> from sympy.combinatorics import Permutation
        >>> I = Permutation([0, 1, 2, 3])
        >>> a = Permutation([2, 1, 3, 0])
        >>> I + a.rank() == a
        True

        See Also
        ========

        __sub__, inversion_vector

        
    "
308,"def test_set_presence_from_syncing_not_set(self):
        
        user_id = ""@test:server""
        status_msg = ""I'm here!""

        self._set_presencestate_with_status_msg(
            user_id, PresenceState.UNAVAILABLE, status_msg
        )

        self.get_success(
            self.presence_handler.user_syncing(user_id, False, PresenceState.ONLINE)
        )

        state = self.get_success(
            self.presence_handler.get_state(UserID.from_string(user_id))
        )
        # we should still be unavailable
        self.assertEqual(state.state, PresenceState.UNAVAILABLE)
        # and status message should still be the same
        self.assertEqual(state.status_msg, status_msg)
",Test that presence is not set by syncing if affect_presence is false,12,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_set_presence_from_syncing_not_set(self):
        
        user_id = ""@test:server""
        status_msg = ""I'm here!""

        self._set_presencestate_with_status_msg(
            user_id, PresenceState.UNAVAILABLE, status_msg
        )

        self.get_success(
            self.presence_handler.user_syncing(user_id, False, PresenceState.ONLINE)
        )

        state = self.get_success(
            self.presence_handler.get_state(UserID.from_string(user_id))
        )
        # we should still be unavailable
        self.assertEqual(state.state, PresenceState.UNAVAILABLE)
        # and status message should still be the same
        self.assertEqual(state.status_msg, status_msg)

        ```
    ###Assistant : Test that presence is not set by syncing if affect_presence is false
    "
309,"def test_stringy_integers(self):
        
        input = {
            ""a"": ""100"",
            ""b"": {
                ""foo"": 99,
                ""bar"": ""-98"",
            },
            ""d"": ""0999"",
        }
        output = copy_and_fixup_power_levels_contents(input)
        expected_output = {
            ""a"": 100,
            ""b"": {
                ""foo"": 99,
                ""bar"": -98,
            },
            ""d"": 999,
        }

        self.assertEqual(output, expected_output)
",String representations of decimal integers are converted to integers.,9,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stringy_integers(self):
        
        input = {
            ""a"": ""100"",
            ""b"": {
                ""foo"": 99,
                ""bar"": ""-98"",
            },
            ""d"": ""0999"",
        }
        output = copy_and_fixup_power_levels_contents(input)
        expected_output = {
            ""a"": 100,
            ""b"": {
                ""foo"": 99,
                ""bar"": -98,
            },
            ""d"": 999,
        }

        self.assertEqual(output, expected_output)

        ```
    ###Assistant : String representations of decimal integers are converted to integers.
    "
310,"def get_ec_matching_query(bank_account, company, amount_condition):
	# get matching Expense Claim query
	mode_of_payments = [
		x[""parent""]
		for x in frappe.db.get_all(
			""Mode of Payment Account"", filters={""default_account"": bank_account}, fields=[""parent""]
		)
	]
	mode_of_payments = ""('"" + ""', '"".join(mode_of_payments) + ""' )""
	company_currency = get_company_currency(company)
	return f
","
		SELECT
			( CASE WHEN employee = %(party)s THEN 1 ELSE 0 END
			+ 1 ) AS rank,
			'Expense Claim' as doctype,
			name,
			total_sanctioned_amount as paid_amount,
			'' as reference_no,
			'' as reference_date,
			employee as party,
			'Employee' as party_type,
			posting_date,
			'{company_currency}' as currency
		FROM
			`tabExpense Claim`
		WHERE
			total_sanctioned_amount {amount_condition} %(amount)s
			AND docstatus = 1
			AND is_paid = 1
			AND ifnull(clearance_date, '') = """"
			AND mode_of_payment in {mode_of_payments}
	",65,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ec_matching_query(bank_account, company, amount_condition):
	# get matching Expense Claim query
	mode_of_payments = [
		x[""parent""]
		for x in frappe.db.get_all(
			""Mode of Payment Account"", filters={""default_account"": bank_account}, fields=[""parent""]
		)
	]
	mode_of_payments = ""('"" + ""', '"".join(mode_of_payments) + ""' )""
	company_currency = get_company_currency(company)
	return f

        ```
    ###Assistant : 
		SELECT
			( CASE WHEN employee = %(party)s THEN 1 ELSE 0 END
			+ 1 ) AS rank,
			'Expense Claim' as doctype,
			name,
			total_sanctioned_amount as paid_amount,
			'' as reference_no,
			'' as reference_date,
			employee as party,
			'Employee' as party_type,
			posting_date,
			'{company_currency}' as currency
		FROM
			`tabExpense Claim`
		WHERE
			total_sanctioned_amount {amount_condition} %(amount)s
			AND docstatus = 1
			AND is_paid = 1
			AND ifnull(clearance_date, '') = """"
			AND mode_of_payment in {mode_of_payments}
	
    "
311,"def run_exec_plan(cls, plan, index_cols, dtypes, columns):
        
        omniSession = DbWorker()

        # First step is to make sure all partitions are in HDK.
        frames = plan.collect_frames()
        for frame in frames:
            if frame._partitions.size != 1:
                raise NotImplementedError(
                    ""HdkOnNative engine doesn't suport partitioned frames""
                )
            for p in frame._partitions.flatten():
                if p.frame_id is None:
                    obj = p.get()
                    if isinstance(obj, (pandas.DataFrame, pandas.Series)):
                        p.frame_id = omniSession.import_pandas_dataframe(obj)
                    else:
                        assert isinstance(obj, pyarrow.Table)
                        p.frame_id = omniSession.import_arrow_table(obj)

        calcite_plan = CalciteBuilder().build(plan)
        calcite_json = CalciteSerializer().serialize(calcite_plan)

        cmd_prefix = ""execute relalg ""

        if DoUseCalcite.get():
            cmd_prefix = ""execute calcite ""

        at = omniSession.executeRA(cmd_prefix + calcite_json)

        res = np.empty((1, 1), dtype=np.dtype(object))
        # workaround for https://github.com/modin-project/modin/issues/1851
        if DoUseCalcite.get():
            at = at.rename_columns([""F_"" + str(c) for c in columns])
        res[0][0] = cls._partition_class.put_arrow(at)

        return res
","
        Run execution plan in HDK storage format to materialize frame.

        Parameters
        ----------
        plan : DFAlgNode
            A root of an execution plan tree.
        index_cols : list of str
            A list of index columns.
        dtypes : pandas.Index
            Column data types.
        columns : list of str
            A frame column names.

        Returns
        -------
        np.array
            Created frame's partitions.
        ",53,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def run_exec_plan(cls, plan, index_cols, dtypes, columns):
        
        omniSession = DbWorker()

        # First step is to make sure all partitions are in HDK.
        frames = plan.collect_frames()
        for frame in frames:
            if frame._partitions.size != 1:
                raise NotImplementedError(
                    ""HdkOnNative engine doesn't suport partitioned frames""
                )
            for p in frame._partitions.flatten():
                if p.frame_id is None:
                    obj = p.get()
                    if isinstance(obj, (pandas.DataFrame, pandas.Series)):
                        p.frame_id = omniSession.import_pandas_dataframe(obj)
                    else:
                        assert isinstance(obj, pyarrow.Table)
                        p.frame_id = omniSession.import_arrow_table(obj)

        calcite_plan = CalciteBuilder().build(plan)
        calcite_json = CalciteSerializer().serialize(calcite_plan)

        cmd_prefix = ""execute relalg ""

        if DoUseCalcite.get():
            cmd_prefix = ""execute calcite ""

        at = omniSession.executeRA(cmd_prefix + calcite_json)

        res = np.empty((1, 1), dtype=np.dtype(object))
        # workaround for https://github.com/modin-project/modin/issues/1851
        if DoUseCalcite.get():
            at = at.rename_columns([""F_"" + str(c) for c in columns])
        res[0][0] = cls._partition_class.put_arrow(at)

        return res

        ```
    ###Assistant : 
        Run execution plan in HDK storage format to materialize frame.

        Parameters
        ----------
        plan : DFAlgNode
            A root of an execution plan tree.
        index_cols : list of str
            A list of index columns.
        dtypes : pandas.Index
            Column data types.
        columns : list of str
            A frame column names.

        Returns
        -------
        np.array
            Created frame's partitions.
        
    "
312,"def to_numeric_dtype(dtype):
  
  dtype = np.dtype(dtype)
  return np.dtype('int32') if dtype == np.dtype('bool') else dtype

","Promotes a dtype into an numeric dtype, if it is not already one.",13,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_numeric_dtype(dtype):
  
  dtype = np.dtype(dtype)
  return np.dtype('int32') if dtype == np.dtype('bool') else dtype


        ```
    ###Assistant : Promotes a dtype into an numeric dtype, if it is not already one.
    "
313,"def test_do_schedule_max_active_runs_task_removed(self, session, dag_maker):
        
        with dag_maker(
            dag_id='test_do_schedule_max_active_runs_task_removed',
            start_date=DEFAULT_DATE,
            schedule_interval='@once',
            max_active_runs=1,
            session=session,
        ):
            # Can't use EmptyOperator as that goes straight to success
            BashOperator(task_id='dummy1', bash_command='true')

        run1 = dag_maker.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE + timedelta(hours=1),
            state=State.RUNNING,
        )

        self.scheduler_job = SchedulerJob(subdir=os.devnull)
        self.scheduler_job.executor = MockExecutor(do_update=False)
        self.scheduler_job.processor_agent = mock.MagicMock(spec=DagFileProcessorAgent)

        num_queued = self.scheduler_job._do_scheduling(session)
        assert num_queued == 1

        session.flush()
        ti = run1.task_instances[0]
        ti.refresh_from_db(session=session)
        assert ti.state == State.QUEUED
",Test that tasks in removed state don't count as actively running.,11,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_do_schedule_max_active_runs_task_removed(self, session, dag_maker):
        
        with dag_maker(
            dag_id='test_do_schedule_max_active_runs_task_removed',
            start_date=DEFAULT_DATE,
            schedule_interval='@once',
            max_active_runs=1,
            session=session,
        ):
            # Can't use EmptyOperator as that goes straight to success
            BashOperator(task_id='dummy1', bash_command='true')

        run1 = dag_maker.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE + timedelta(hours=1),
            state=State.RUNNING,
        )

        self.scheduler_job = SchedulerJob(subdir=os.devnull)
        self.scheduler_job.executor = MockExecutor(do_update=False)
        self.scheduler_job.processor_agent = mock.MagicMock(spec=DagFileProcessorAgent)

        num_queued = self.scheduler_job._do_scheduling(session)
        assert num_queued == 1

        session.flush()
        ti = run1.task_instances[0]
        ti.refresh_from_db(session=session)
        assert ti.state == State.QUEUED

        ```
    ###Assistant : Test that tasks in removed state don't count as actively running.
    "
314,"def forward(self, xs, masks):
        
        if isinstance(self.embed, (Conv2dSubsampling, VGG2L)):
            xs, masks = self.embed(xs, masks)
        else:
            xs = self.embed(xs)

        xs, masks = self.encoders(xs, masks)
        if isinstance(xs, tuple):
            xs = xs[0]

        if self.normalize_before:
            xs = self.after_norm(xs)
        return xs, masks
","Encode input sequence.

        :param torch.Tensor xs: input tensor
        :param torch.Tensor masks: input mask
        :return: position embedded tensor and mask
        :rtype Tuple[torch.Tensor, torch.Tensor]:
        ",22,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, xs, masks):
        
        if isinstance(self.embed, (Conv2dSubsampling, VGG2L)):
            xs, masks = self.embed(xs, masks)
        else:
            xs = self.embed(xs)

        xs, masks = self.encoders(xs, masks)
        if isinstance(xs, tuple):
            xs = xs[0]

        if self.normalize_before:
            xs = self.after_norm(xs)
        return xs, masks

        ```
    ###Assistant : Encode input sequence.

        :param torch.Tensor xs: input tensor
        :param torch.Tensor masks: input mask
        :return: position embedded tensor and mask
        :rtype Tuple[torch.Tensor, torch.Tensor]:
        
    "
315,"def save_class(self):
        
        if gtff.REMEMBER_CONTEXTS:
            controllers[self.PATH] = self
",Saves the current instance of the class to be loaded later,11,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_class(self):
        
        if gtff.REMEMBER_CONTEXTS:
            controllers[self.PATH] = self

        ```
    ###Assistant : Saves the current instance of the class to be loaded later
    "
316,"def _decode_messages(self, messages):
        
        messages_len = len(messages)
        # if it was one message, then its old style
        if messages_len == 1:
            payload = salt.payload.loads(messages[0])
        # 2 includes a header which says who should do it
        elif messages_len == 2:
            message_target = salt.utils.stringutils.to_str(messages[0])
            if (
                self.opts.get(""__role"") != ""syndic""
                and message_target not in (""broadcast"", self.hexid)
            ) or (
                self.opts.get(""__role"") == ""syndic""
                and message_target not in (""broadcast"", ""syndic"")
            ):
                log.debug(""Publish received for not this minion: %s"", message_target)
                raise salt.ext.tornado.gen.Return(None)
            payload = salt.payload.loads(messages[1])
        else:
            raise Exception(
                ""Invalid number of messages ({}) in zeromq pubmessage from master"".format(
                    len(messages_len)
                )
            )
        # Yield control back to the caller. When the payload has been decoded, assign
        # the decoded payload to 'ret' and resume operation
        raise salt.ext.tornado.gen.Return(payload)
","
        Take the zmq messages, decrypt/decode them into a payload

        :param list messages: A list of messages to be decoded
        ",19,119,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _decode_messages(self, messages):
        
        messages_len = len(messages)
        # if it was one message, then its old style
        if messages_len == 1:
            payload = salt.payload.loads(messages[0])
        # 2 includes a header which says who should do it
        elif messages_len == 2:
            message_target = salt.utils.stringutils.to_str(messages[0])
            if (
                self.opts.get(""__role"") != ""syndic""
                and message_target not in (""broadcast"", self.hexid)
            ) or (
                self.opts.get(""__role"") == ""syndic""
                and message_target not in (""broadcast"", ""syndic"")
            ):
                log.debug(""Publish received for not this minion: %s"", message_target)
                raise salt.ext.tornado.gen.Return(None)
            payload = salt.payload.loads(messages[1])
        else:
            raise Exception(
                ""Invalid number of messages ({}) in zeromq pubmessage from master"".format(
                    len(messages_len)
                )
            )
        # Yield control back to the caller. When the payload has been decoded, assign
        # the decoded payload to 'ret' and resume operation
        raise salt.ext.tornado.gen.Return(payload)

        ```
    ###Assistant : 
        Take the zmq messages, decrypt/decode them into a payload

        :param list messages: A list of messages to be decoded
        
    "
317,"def test_get_release_wheel_url():
    
    # This should be a commit for which wheels have already been built for
    # all platforms and python versions at
    # `s3://ray-wheels/releases/2.2.0/<commit>/`.
    test_commits = {""2.2.0"": ""b6af0887ee5f2e460202133791ad941a41f15beb""}
    for sys_platform in [""darwin"", ""linux"", ""win32""]:
        for py_version in ray_constants.RUNTIME_ENV_CONDA_PY_VERSIONS:
            for version, commit in test_commits.items():
                if sys_platform == ""win32"" and py_version == (3, 6):
                    # Windows wheels are not built for py3.6 anymore
                    continue
                url = get_release_wheel_url(commit, sys_platform, version, py_version)
                assert requests.head(url).status_code == 200, url

",Test the code that generates the filenames of the `release` branch wheels.,12,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_release_wheel_url():
    
    # This should be a commit for which wheels have already been built for
    # all platforms and python versions at
    # `s3://ray-wheels/releases/2.2.0/<commit>/`.
    test_commits = {""2.2.0"": ""b6af0887ee5f2e460202133791ad941a41f15beb""}
    for sys_platform in [""darwin"", ""linux"", ""win32""]:
        for py_version in ray_constants.RUNTIME_ENV_CONDA_PY_VERSIONS:
            for version, commit in test_commits.items():
                if sys_platform == ""win32"" and py_version == (3, 6):
                    # Windows wheels are not built for py3.6 anymore
                    continue
                url = get_release_wheel_url(commit, sys_platform, version, py_version)
                assert requests.head(url).status_code == 200, url


        ```
    ###Assistant : Test the code that generates the filenames of the `release` branch wheels.
    "
318,"def test_keep_media_by_date(self) -> None:
        

        # timestamp before upload
        now_ms = self.clock.time_msec()
        server_and_media_id = self._create_media()

        self._access_media(server_and_media_id)

        channel = self.make_request(
            ""POST"",
            self.url + ""?before_ts="" + str(now_ms),
            access_token=self.admin_user_tok,
        )
        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(0, channel.json_body[""total""])

        self._access_media(server_and_media_id)

        # timestamp after upload
        now_ms = self.clock.time_msec()
        channel = self.make_request(
            ""POST"",
            self.url + ""?before_ts="" + str(now_ms),
            access_token=self.admin_user_tok,
        )
        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(1, channel.json_body[""total""])
        self.assertEqual(
            server_and_media_id.split(""/"")[1],
            channel.json_body[""deleted_media""][0],
        )

        self._access_media(server_and_media_id, False)
","
        Tests that media is not deleted if it is newer than `before_ts`
        ",12,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_keep_media_by_date(self) -> None:
        

        # timestamp before upload
        now_ms = self.clock.time_msec()
        server_and_media_id = self._create_media()

        self._access_media(server_and_media_id)

        channel = self.make_request(
            ""POST"",
            self.url + ""?before_ts="" + str(now_ms),
            access_token=self.admin_user_tok,
        )
        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(0, channel.json_body[""total""])

        self._access_media(server_and_media_id)

        # timestamp after upload
        now_ms = self.clock.time_msec()
        channel = self.make_request(
            ""POST"",
            self.url + ""?before_ts="" + str(now_ms),
            access_token=self.admin_user_tok,
        )
        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(1, channel.json_body[""total""])
        self.assertEqual(
            server_and_media_id.split(""/"")[1],
            channel.json_body[""deleted_media""][0],
        )

        self._access_media(server_and_media_id, False)

        ```
    ###Assistant : 
        Tests that media is not deleted if it is newer than `before_ts`
        
    "
319,"def test_fetch_openml_requires_pandas_in_future(monkeypatch):
    
    params = {""as_frame"": False, ""parser"": ""auto""}
    data_id = 1119
    try:
        check_pandas_support(""test_fetch_openml_requires_pandas"")
    except ImportError:
        _monkey_patch_webbased_functions(monkeypatch, data_id, True)
        warn_msg = (
            ""From version 1.4, `parser='auto'` with `as_frame=False` will use pandas""
        )
        with pytest.warns(FutureWarning, match=warn_msg):
            fetch_openml(data_id=data_id, **params)
    else:
        raise SkipTest(""This test requires pandas to not be installed."")


@pytest.mark.filterwarnings(""ignore:Version 1 of dataset Australian is inactive"")
# TODO(1.4): remove this filterwarning decorator for `parser`
@pytest.mark.filterwarnings(""ignore:The default value of `parser` will change"")
@pytest.mark.parametrize(
    ""params, err_msg"",
    [
        (
            {""parser"": ""pandas""},
            ""Sparse ARFF datasets cannot be loaded with parser='pandas'"",
        ),
        (
            {""as_frame"": True},
            ""Sparse ARFF datasets cannot be loaded with as_frame=True."",
        ),
        (
            {""parser"": ""pandas"", ""as_frame"": True},
            ""Sparse ARFF datasets cannot be loaded with as_frame=True."",
        ),
    ],
)",Check that we raise a warning that pandas will be required in the future.,14,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_fetch_openml_requires_pandas_in_future(monkeypatch):
    
    params = {""as_frame"": False, ""parser"": ""auto""}
    data_id = 1119
    try:
        check_pandas_support(""test_fetch_openml_requires_pandas"")
    except ImportError:
        _monkey_patch_webbased_functions(monkeypatch, data_id, True)
        warn_msg = (
            ""From version 1.4, `parser='auto'` with `as_frame=False` will use pandas""
        )
        with pytest.warns(FutureWarning, match=warn_msg):
            fetch_openml(data_id=data_id, **params)
    else:
        raise SkipTest(""This test requires pandas to not be installed."")


@pytest.mark.filterwarnings(""ignore:Version 1 of dataset Australian is inactive"")
# TODO(1.4): remove this filterwarning decorator for `parser`
@pytest.mark.filterwarnings(""ignore:The default value of `parser` will change"")
@pytest.mark.parametrize(
    ""params, err_msg"",
    [
        (
            {""parser"": ""pandas""},
            ""Sparse ARFF datasets cannot be loaded with parser='pandas'"",
        ),
        (
            {""as_frame"": True},
            ""Sparse ARFF datasets cannot be loaded with as_frame=True."",
        ),
        (
            {""parser"": ""pandas"", ""as_frame"": True},
            ""Sparse ARFF datasets cannot be loaded with as_frame=True."",
        ),
    ],
)
        ```
    ###Assistant : Check that we raise a warning that pandas will be required in the future.
    "
320,"def get_connection(self, url, proxies=None):
        
        proxy = select_proxy(url, proxies)

        if proxy:
            proxy = prepend_scheme_if_needed(proxy, ""http"")
            proxy_url = parse_url(proxy)
            if not proxy_url.host:
                raise InvalidProxyURL(
                    ""Please check proxy URL. It is malformed ""
                    ""and could be missing the host.""
                )
            proxy_manager = self.proxy_manager_for(proxy)
            conn = proxy_manager.connection_from_url(url)
        else:
            # Only scheme should be lower case
            parsed = urlparse(url)
            url = parsed.geturl()
            conn = self.poolmanager.connection_from_url(url)

        return conn
","Returns a urllib3 connection for the given URL. This should not be
        called from user code, and is only exposed for use when subclassing the
        :class:`HTTPAdapter <requests.adapters.HTTPAdapter>`.

        :param url: The URL to connect to.
        :param proxies: (optional) A Requests-style dictionary of proxies used on this request.
        :rtype: urllib3.ConnectionPool
        ",48,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_connection(self, url, proxies=None):
        
        proxy = select_proxy(url, proxies)

        if proxy:
            proxy = prepend_scheme_if_needed(proxy, ""http"")
            proxy_url = parse_url(proxy)
            if not proxy_url.host:
                raise InvalidProxyURL(
                    ""Please check proxy URL. It is malformed ""
                    ""and could be missing the host.""
                )
            proxy_manager = self.proxy_manager_for(proxy)
            conn = proxy_manager.connection_from_url(url)
        else:
            # Only scheme should be lower case
            parsed = urlparse(url)
            url = parsed.geturl()
            conn = self.poolmanager.connection_from_url(url)

        return conn

        ```
    ###Assistant : Returns a urllib3 connection for the given URL. This should not be
        called from user code, and is only exposed for use when subclassing the
        :class:`HTTPAdapter <requests.adapters.HTTPAdapter>`.

        :param url: The URL to connect to.
        :param proxies: (optional) A Requests-style dictionary of proxies used on this request.
        :rtype: urllib3.ConnectionPool
        
    "
321,"def feature_engineering_expand_all(self, dataframe, period, **kwargs):
        

        dataframe[""%-rsi-period""] = ta.RSI(dataframe, timeperiod=period)
        dataframe[""%-mfi-period""] = ta.MFI(dataframe, timeperiod=period)
        dataframe[""%-adx-period""] = ta.ADX(dataframe, timeperiod=period)
        dataframe[""%-sma-period""] = ta.SMA(dataframe, timeperiod=period)
        dataframe[""%-ema-period""] = ta.EMA(dataframe, timeperiod=period)

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=period, stds=2.2
        )
        dataframe[""bb_lowerband-period""] = bollinger[""lower""]
        dataframe[""bb_middleband-period""] = bollinger[""mid""]
        dataframe[""bb_upperband-period""] = bollinger[""upper""]

        dataframe[""%-bb_width-period""] = (
            dataframe[""bb_upperband-period""]
            - dataframe[""bb_lowerband-period""]
        ) / dataframe[""bb_middleband-period""]
        dataframe[""%-close-bb_lower-period""] = (
            dataframe[""close""] / dataframe[""bb_lowerband-period""]
        )

        dataframe[""%-roc-period""] = ta.ROC(dataframe, timeperiod=period)

        dataframe[""%-relative_volume-period""] = (
            dataframe[""volume""] / dataframe[""volume""].rolling(period).mean()
        )

        return dataframe
","
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
        `include_corr_pairs`. In other words, a single feature defined in this function
        will automatically expand to a total of
        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
        `include_corr_pairs` numbers of features added to the model.

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features

        :param df: strategy dataframe which will receive the features
        :param period: period of the indicator - usage example:
        dataframe[""%-ema-period""] = ta.EMA(dataframe, timeperiod=period)
        ",106,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def feature_engineering_expand_all(self, dataframe, period, **kwargs):
        

        dataframe[""%-rsi-period""] = ta.RSI(dataframe, timeperiod=period)
        dataframe[""%-mfi-period""] = ta.MFI(dataframe, timeperiod=period)
        dataframe[""%-adx-period""] = ta.ADX(dataframe, timeperiod=period)
        dataframe[""%-sma-period""] = ta.SMA(dataframe, timeperiod=period)
        dataframe[""%-ema-period""] = ta.EMA(dataframe, timeperiod=period)

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=period, stds=2.2
        )
        dataframe[""bb_lowerband-period""] = bollinger[""lower""]
        dataframe[""bb_middleband-period""] = bollinger[""mid""]
        dataframe[""bb_upperband-period""] = bollinger[""upper""]

        dataframe[""%-bb_width-period""] = (
            dataframe[""bb_upperband-period""]
            - dataframe[""bb_lowerband-period""]
        ) / dataframe[""bb_middleband-period""]
        dataframe[""%-close-bb_lower-period""] = (
            dataframe[""close""] / dataframe[""bb_lowerband-period""]
        )

        dataframe[""%-roc-period""] = ta.ROC(dataframe, timeperiod=period)

        dataframe[""%-relative_volume-period""] = (
            dataframe[""volume""] / dataframe[""volume""].rolling(period).mean()
        )

        return dataframe

        ```
    ###Assistant : 
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
        `include_corr_pairs`. In other words, a single feature defined in this function
        will automatically expand to a total of
        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
        `include_corr_pairs` numbers of features added to the model.

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features

        :param df: strategy dataframe which will receive the features
        :param period: period of the indicator - usage example:
        dataframe[""%-ema-period""] = ta.EMA(dataframe, timeperiod=period)
        
    "
322,"def get_script_prefix(self, scope):
        
        if settings.FORCE_SCRIPT_NAME:
            return settings.FORCE_SCRIPT_NAME
        return scope.get(""root_path"", """") or """"
","
        Return the script prefix to use from either the scope or a setting.
        ",13,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_script_prefix(self, scope):
        
        if settings.FORCE_SCRIPT_NAME:
            return settings.FORCE_SCRIPT_NAME
        return scope.get(""root_path"", """") or """"

        ```
    ###Assistant : 
        Return the script prefix to use from either the scope or a setting.
        
    "
323,"def test_get_first_menu_and_fail():
    
    part_one = f

    part_two = f
    command = [""storage"", ""create""]
    invoke_and_assert_in(
        command=command,
        desired_contents=(part_one, part_two),
        expected_code=1,
        user_input=f""{INVALID_OPTION}\n"",
    )

","
    Make sure that our utility function is returning as expected
    
    Found the following storage types:
    0) Azure Blob Storage
        Store data in an Azure blob storage container.
    1) File Storage
        Store data as a file on local or remote file systems.
    2) Google Cloud Storage
        Store data in a GCS bucket.
    3) Local Storage
        Store data in a run's local file system.
    
    Select a storage type to create: 99999999
    Invalid selection {INVALID_OPTION}
    ",72,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_first_menu_and_fail():
    
    part_one = f

    part_two = f
    command = [""storage"", ""create""]
    invoke_and_assert_in(
        command=command,
        desired_contents=(part_one, part_two),
        expected_code=1,
        user_input=f""{INVALID_OPTION}\n"",
    )


        ```
    ###Assistant : 
    Make sure that our utility function is returning as expected
    
    Found the following storage types:
    0) Azure Blob Storage
        Store data in an Azure blob storage container.
    1) File Storage
        Store data as a file on local or remote file systems.
    2) Google Cloud Storage
        Store data in a GCS bucket.
    3) Local Storage
        Store data in a run's local file system.
    
    Select a storage type to create: 99999999
    Invalid selection {INVALID_OPTION}
    
    "
324,"def check_planarity(G, counterexample=False):
    

    planarity_state = LRPlanarity(G)
    embedding = planarity_state.lr_planarity()
    if embedding is None:
        # graph is not planar
        if counterexample:
            return False, get_counterexample(G)
        else:
            return False, None
    else:
        # graph is planar
        return True, embedding

","Check if a graph is planar and return a counterexample or an embedding.

    A graph is planar iff it can be drawn in a plane without
    any edge intersections.

    Parameters
    ----------
    G : NetworkX graph
    counterexample : bool
        A Kuratowski subgraph (to proof non planarity) is only returned if set
        to true.

    Returns
    -------
    (is_planar, certificate) : (bool, NetworkX graph) tuple
        is_planar is true if the graph is planar.
        If the graph is planar `certificate` is a PlanarEmbedding
        otherwise it is a Kuratowski subgraph.

    Examples
    --------
    >>> G = nx.Graph([(0, 1), (0, 2)])
    >>> is_planar, P = nx.check_planarity(G)
    >>> print(is_planar)
    True

    When `G` is planar, a `PlanarEmbedding` instance is returned:

    >>> P.get_data()
    {0: [1, 2], 1: [0], 2: [0]}

    Notes
    -----
    A (combinatorial) embedding consists of cyclic orderings of the incident
    edges at each vertex. Given such an embedding there are multiple approaches
    discussed in literature to drawing the graph (subject to various
    constraints, e.g. integer coordinates), see e.g. [2].

    The planarity check algorithm and extraction of the combinatorial embedding
    is based on the Left-Right Planarity Test [1].

    A counterexample is only generated if the corresponding parameter is set,
    because the complexity of the counterexample generation is higher.

    References
    ----------
    .. [1] Ulrik Brandes:
        The Left-Right Planarity Test
        2009
        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.9208
    .. [2] Takao Nishizeki, Md Saidur Rahman:
        Planar graph drawing
        Lecture Notes Series on Computing: Volume 12
        2004
    ",228,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_planarity(G, counterexample=False):
    

    planarity_state = LRPlanarity(G)
    embedding = planarity_state.lr_planarity()
    if embedding is None:
        # graph is not planar
        if counterexample:
            return False, get_counterexample(G)
        else:
            return False, None
    else:
        # graph is planar
        return True, embedding


        ```
    ###Assistant : Check if a graph is planar and return a counterexample or an embedding.

    A graph is planar iff it can be drawn in a plane without
    any edge intersections.

    Parameters
    ----------
    G : NetworkX graph
    counterexample : bool
        A Kuratowski subgraph (to proof non planarity) is only returned if set
        to true.

    Returns
    -------
    (is_planar, certificate) : (bool, NetworkX graph) tuple
        is_planar is true if the graph is planar.
        If the graph is planar `certificate` is a PlanarEmbedding
        otherwise it is a Kuratowski subgraph.

    Examples
    --------
    >>> G = nx.Graph([(0, 1), (0, 2)])
    >>> is_planar, P = nx.check_planarity(G)
    >>> print(is_planar)
    True

    When `G` is planar, a `PlanarEmbedding` instance is returned:

    >>> P.get_data()
    {0: [1, 2], 1: [0], 2: [0]}

    Notes
    -----
    A (combinatorial) embedding consists of cyclic orderings of the incident
    edges at each vertex. Given such an embedding there are multiple approaches
    discussed in literature to drawing the graph (subject to various
    constraints, e.g. integer coordinates), see e.g. [2].

    The planarity check algorithm and extraction of the combinatorial embedding
    is based on the Left-Right Planarity Test [1].

    A counterexample is only generated if the corresponding parameter is set,
    because the complexity of the counterexample generation is higher.

    References
    ----------
    .. [1] Ulrik Brandes:
        The Left-Right Planarity Test
        2009
        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.9208
    .. [2] Takao Nishizeki, Md Saidur Rahman:
        Planar graph drawing
        Lecture Notes Series on Computing: Volume 12
        2004
    
    "
325,"def _get_bool(val) -> Optional[bool]:
    
    if isinstance(val, bool):
        return val
    elif isinstance(val, str):
        if val.strip().lower() == 'true':
            return True
        elif val.strip().lower() == 'false':
            return False
    return None
","
    Converts val to bool if can be done with certainty.
    If we cannot infer intention we return None.
    ",18,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_bool(val) -> Optional[bool]:
    
    if isinstance(val, bool):
        return val
    elif isinstance(val, str):
        if val.strip().lower() == 'true':
            return True
        elif val.strip().lower() == 'false':
            return False
    return None

        ```
    ###Assistant : 
    Converts val to bool if can be done with certainty.
    If we cannot infer intention we return None.
    
    "
326,"def load_data_ptb(batch_size, max_window_size, num_noise_words):
    
    sentences = read_ptb()
    vocab = d2l.Vocab(sentences, min_freq=10)
    subsampled, counter = subsample(sentences, vocab)
    corpus = [vocab[line] for line in subsampled]
    all_centers, all_contexts = get_centers_and_contexts(
        corpus, max_window_size)
    all_negatives = get_negatives(
        all_contexts, vocab, counter, num_noise_words)
    dataset = gluon.data.ArrayDataset(
        all_centers, all_contexts, all_negatives)
    data_iter = gluon.data.DataLoader(
        dataset, batch_size, shuffle=True,batchify_fn=batchify,
        num_workers=d2l.get_dataloader_workers())
    return data_iter, vocab

d2l.DATA_HUB['glove.6b.50d'] = (d2l.DATA_URL + 'glove.6B.50d.zip',
                                '0b8703943ccdb6eb788e6f091b8946e82231bc4d')

d2l.DATA_HUB['glove.6b.100d'] = (d2l.DATA_URL + 'glove.6B.100d.zip',
                                 'cd43bfb07e44e6f27cbcc7bc9ae3d80284fdaf5a')

d2l.DATA_HUB['glove.42b.300d'] = (d2l.DATA_URL + 'glove.42B.300d.zip',
                                  'b5116e234e9eb9076672cfeabf5469f3eec904fa')

d2l.DATA_HUB['wiki.en'] = (d2l.DATA_URL + 'wiki.en.zip',
                           'c1816da3821ae9f43899be655002f6c723e91b88')
","Download the PTB dataset and then load it into memory.

    Defined in :numref:`subsec_word2vec-minibatch-loading`",13,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_data_ptb(batch_size, max_window_size, num_noise_words):
    
    sentences = read_ptb()
    vocab = d2l.Vocab(sentences, min_freq=10)
    subsampled, counter = subsample(sentences, vocab)
    corpus = [vocab[line] for line in subsampled]
    all_centers, all_contexts = get_centers_and_contexts(
        corpus, max_window_size)
    all_negatives = get_negatives(
        all_contexts, vocab, counter, num_noise_words)
    dataset = gluon.data.ArrayDataset(
        all_centers, all_contexts, all_negatives)
    data_iter = gluon.data.DataLoader(
        dataset, batch_size, shuffle=True,batchify_fn=batchify,
        num_workers=d2l.get_dataloader_workers())
    return data_iter, vocab

d2l.DATA_HUB['glove.6b.50d'] = (d2l.DATA_URL + 'glove.6B.50d.zip',
                                '0b8703943ccdb6eb788e6f091b8946e82231bc4d')

d2l.DATA_HUB['glove.6b.100d'] = (d2l.DATA_URL + 'glove.6B.100d.zip',
                                 'cd43bfb07e44e6f27cbcc7bc9ae3d80284fdaf5a')

d2l.DATA_HUB['glove.42b.300d'] = (d2l.DATA_URL + 'glove.42B.300d.zip',
                                  'b5116e234e9eb9076672cfeabf5469f3eec904fa')

d2l.DATA_HUB['wiki.en'] = (d2l.DATA_URL + 'wiki.en.zip',
                           'c1816da3821ae9f43899be655002f6c723e91b88')

        ```
    ###Assistant : Download the PTB dataset and then load it into memory.

    Defined in :numref:`subsec_word2vec-minibatch-loading`
    "
327,"async def async_config_changed(self) -> None:
        
        assert self.driver is not None
        await self.hass.async_add_executor_job(self.driver.config_changed)
",Call config changed which writes out the new config to disk.,11,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_config_changed(self) -> None:
        
        assert self.driver is not None
        await self.hass.async_add_executor_job(self.driver.config_changed)

        ```
    ###Assistant : Call config changed which writes out the new config to disk.
    "
328,"def setDebugActions(self, startAction, successAction, exceptionAction):
        
        self.debugActions = (startAction or _defaultStartDebugAction,
                             successAction or _defaultSuccessDebugAction,
                             exceptionAction or _defaultExceptionDebugAction)
        self.debug = True
        return self
","
        Enable display of debugging messages while doing pattern matching.
        ",9,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setDebugActions(self, startAction, successAction, exceptionAction):
        
        self.debugActions = (startAction or _defaultStartDebugAction,
                             successAction or _defaultSuccessDebugAction,
                             exceptionAction or _defaultExceptionDebugAction)
        self.debug = True
        return self

        ```
    ###Assistant : 
        Enable display of debugging messages while doing pattern matching.
        
    "
329,"def get_qty_amount_data_for_cumulative(pr_doc, doc, items=None):
	if items is None:
		items = []
	sum_qty, sum_amt = [0, 0]
	doctype = doc.get(""parenttype"") or doc.doctype

	date_field = (
		""transaction_date"" if frappe.get_meta(doctype).has_field(""transaction_date"") else ""posting_date""
	)

	child_doctype = ""{0} Item"".format(doctype)
	apply_on = frappe.scrub(pr_doc.get(""apply_on""))

	values = [pr_doc.valid_from, pr_doc.valid_upto]
	condition = """"

	if pr_doc.warehouse:
		warehouses = get_child_warehouses(pr_doc.warehouse)

		condition += .format(
			child_doc=child_doctype, warehouses="","".join([""%s""] * len(warehouses))
		)

		values.extend(warehouses)

	if items:
		condition = "" and `tab{child_doc}`.{apply_on} in ({items})"".format(
			child_doc=child_doctype, apply_on=apply_on, items="","".join([""%s""] * len(items))
		)

		values.extend(items)

	data_set = frappe.db.sql(
		.format(
			parent_doc=doctype, child_doc=child_doctype, condition=condition, date_field=date_field
		),
		tuple(values),
		as_dict=1,
	)

	for data in data_set:
		sum_qty += data.get(""stock_qty"")
		sum_amt += data.get(""amount"")

	return [sum_qty, sum_amt]

"," and `tab{child_doc}`.warehouse in ({warehouses})
			 SELECT `tab{child_doc}`.stock_qty,
			`tab{child_doc}`.amount
		FROM `tab{child_doc}`, `tab{parent_doc}`
		WHERE
			`tab{child_doc}`.parent = `tab{parent_doc}`.name and `tab{parent_doc}`.{date_field}
			between %s and %s and `tab{parent_doc}`.docstatus = 1
			{condition} group by `tab{child_doc}`.name
	",28,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_qty_amount_data_for_cumulative(pr_doc, doc, items=None):
	if items is None:
		items = []
	sum_qty, sum_amt = [0, 0]
	doctype = doc.get(""parenttype"") or doc.doctype

	date_field = (
		""transaction_date"" if frappe.get_meta(doctype).has_field(""transaction_date"") else ""posting_date""
	)

	child_doctype = ""{0} Item"".format(doctype)
	apply_on = frappe.scrub(pr_doc.get(""apply_on""))

	values = [pr_doc.valid_from, pr_doc.valid_upto]
	condition = """"

	if pr_doc.warehouse:
		warehouses = get_child_warehouses(pr_doc.warehouse)

		condition += .format(
			child_doc=child_doctype, warehouses="","".join([""%s""] * len(warehouses))
		)

		values.extend(warehouses)

	if items:
		condition = "" and `tab{child_doc}`.{apply_on} in ({items})"".format(
			child_doc=child_doctype, apply_on=apply_on, items="","".join([""%s""] * len(items))
		)

		values.extend(items)

	data_set = frappe.db.sql(
		.format(
			parent_doc=doctype, child_doc=child_doctype, condition=condition, date_field=date_field
		),
		tuple(values),
		as_dict=1,
	)

	for data in data_set:
		sum_qty += data.get(""stock_qty"")
		sum_amt += data.get(""amount"")

	return [sum_qty, sum_amt]


        ```
    ###Assistant :  and `tab{child_doc}`.warehouse in ({warehouses})
			 SELECT `tab{child_doc}`.stock_qty,
			`tab{child_doc}`.amount
		FROM `tab{child_doc}`, `tab{parent_doc}`
		WHERE
			`tab{child_doc}`.parent = `tab{parent_doc}`.name and `tab{parent_doc}`.{date_field}
			between %s and %s and `tab{parent_doc}`.docstatus = 1
			{condition} group by `tab{child_doc}`.name
	
    "
330,"def make_system(A, M, x0, b):
    
    A_ = A
    A = aslinearoperator(A)

    if A.shape[0] != A.shape[1]:
        raise ValueError(f'expected square matrix, but got shape={(A.shape,)}')

    N = A.shape[0]

    b = asanyarray(b)

    if not (b.shape == (N,1) or b.shape == (N,)):
        raise ValueError(f'shapes of A {A.shape} and b {b.shape} are '
                         'incompatible')

    if b.dtype.char not in 'fdFD':
        b = b.astype('d')  # upcast non-FP types to double
","Make a linear system Ax=b

    Parameters
    ----------
    A : LinearOperator
        sparse or dense matrix (or any valid input to aslinearoperator)
    M : {LinearOperator, Nones}
        preconditioner
        sparse or dense matrix (or any valid input to aslinearoperator)
    x0 : {array_like, str, None}
        initial guess to iterative method.
        ``x0 = 'Mb'`` means using the nonzero initial guess ``M @ b``.
        Default is `None`, which means using the zero initial guess.
    b : array_like
        right hand side

    Returns
    -------
    (A, M, x, b, postprocess)
        A : LinearOperator
            matrix of the linear system
        M : LinearOperator
            preconditioner
        x : rank 1 ndarray
            initial guess
        b : rank 1 ndarray
            right hand side
        postprocess : function
            converts the solution vector to the appropriate
            type and dimensions (e.g. (N,1) matrix)

    ",123,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_system(A, M, x0, b):
    
    A_ = A
    A = aslinearoperator(A)

    if A.shape[0] != A.shape[1]:
        raise ValueError(f'expected square matrix, but got shape={(A.shape,)}')

    N = A.shape[0]

    b = asanyarray(b)

    if not (b.shape == (N,1) or b.shape == (N,)):
        raise ValueError(f'shapes of A {A.shape} and b {b.shape} are '
                         'incompatible')

    if b.dtype.char not in 'fdFD':
        b = b.astype('d')  # upcast non-FP types to double

        ```
    ###Assistant : Make a linear system Ax=b

    Parameters
    ----------
    A : LinearOperator
        sparse or dense matrix (or any valid input to aslinearoperator)
    M : {LinearOperator, Nones}
        preconditioner
        sparse or dense matrix (or any valid input to aslinearoperator)
    x0 : {array_like, str, None}
        initial guess to iterative method.
        ``x0 = 'Mb'`` means using the nonzero initial guess ``M @ b``.
        Default is `None`, which means using the zero initial guess.
    b : array_like
        right hand side

    Returns
    -------
    (A, M, x, b, postprocess)
        A : LinearOperator
            matrix of the linear system
        M : LinearOperator
            preconditioner
        x : rank 1 ndarray
            initial guess
        b : rank 1 ndarray
            right hand side
        postprocess : function
            converts the solution vector to the appropriate
            type and dimensions (e.g. (N,1) matrix)

    
    "
331,"def dodecahedral_graph(create_using=None):
    
    G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using)
    G.name = ""Dodecahedral Graph""
    return G

","
    Returns the Platonic Dodecahedral graph.

    The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the
    dodecahedron forms a graph. It is one of 5 Platonic graphs [1]_.
    It can be described in LCF notation as:
    ``[10, 7, 4, -4, -7, 10, -4, 7, -7, 4]^2`` [2]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Dodecahedral Graph with 20 nodes and 30 edges

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Regular_dodecahedron#Dodecahedral_graph
    .. [2] https://mathworld.wolfram.com/DodecahedralGraph.html

    ",91,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dodecahedral_graph(create_using=None):
    
    G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using)
    G.name = ""Dodecahedral Graph""
    return G


        ```
    ###Assistant : 
    Returns the Platonic Dodecahedral graph.

    The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the
    dodecahedron forms a graph. It is one of 5 Platonic graphs [1]_.
    It can be described in LCF notation as:
    ``[10, 7, 4, -4, -7, 10, -4, 7, -7, 4]^2`` [2]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Dodecahedral Graph with 20 nodes and 30 edges

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Regular_dodecahedron#Dodecahedral_graph
    .. [2] https://mathworld.wolfram.com/DodecahedralGraph.html

    
    "
332,"def arc_tangent(value, default=_SENTINEL):
    
    try:
        return math.atan(float(value))
    except (ValueError, TypeError):
        if default is _SENTINEL:
            raise_no_default(""atan"", value)
        return default

",Filter and function to get arc tangent of the value.,10,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def arc_tangent(value, default=_SENTINEL):
    
    try:
        return math.atan(float(value))
    except (ValueError, TypeError):
        if default is _SENTINEL:
            raise_no_default(""atan"", value)
        return default


        ```
    ###Assistant : Filter and function to get arc tangent of the value.
    "
333,"def sparse_top_k_categorical_matches(y_true, y_pred, k=5):
    
    reshape_matches = False
    y_true = tf.convert_to_tensor(y_true)
    y_pred = tf.convert_to_tensor(y_pred)
    y_true_rank = y_true.shape.ndims
    y_pred_rank = y_pred.shape.ndims
    y_true_org_shape = tf.shape(y_true)

    # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
    if (y_true_rank is not None) and (y_pred_rank is not None):
        if y_pred_rank > 2:
            y_pred = tf.reshape(y_pred, [-1, y_pred.shape[-1]])
        if y_true_rank > 1:
            reshape_matches = True
            y_true = tf.reshape(y_true, [-1])

    matches = tf.cast(
        tf.math.in_top_k(
            predictions=y_pred, targets=tf.cast(y_true, ""int32""), k=k
        ),
        dtype=backend.floatx(),
    )

    # returned matches is expected to have same shape as y_true input
    if reshape_matches:
        return tf.reshape(matches, shape=y_true_org_shape)

    return matches
","Creates float Tensor, 1.0 for label-TopK_prediction match, 0.0 for mismatch.

    Args:
      y_true: tensor of true targets.
      y_pred: tensor of predicted targets.
      k: (Optional) Number of top elements to look at for computing accuracy.
        Defaults to 5.

    Returns:
      Match tensor: 1.0 for label-prediction match, 0.0 for mismatch.
    ",46,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sparse_top_k_categorical_matches(y_true, y_pred, k=5):
    
    reshape_matches = False
    y_true = tf.convert_to_tensor(y_true)
    y_pred = tf.convert_to_tensor(y_pred)
    y_true_rank = y_true.shape.ndims
    y_pred_rank = y_pred.shape.ndims
    y_true_org_shape = tf.shape(y_true)

    # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
    if (y_true_rank is not None) and (y_pred_rank is not None):
        if y_pred_rank > 2:
            y_pred = tf.reshape(y_pred, [-1, y_pred.shape[-1]])
        if y_true_rank > 1:
            reshape_matches = True
            y_true = tf.reshape(y_true, [-1])

    matches = tf.cast(
        tf.math.in_top_k(
            predictions=y_pred, targets=tf.cast(y_true, ""int32""), k=k
        ),
        dtype=backend.floatx(),
    )

    # returned matches is expected to have same shape as y_true input
    if reshape_matches:
        return tf.reshape(matches, shape=y_true_org_shape)

    return matches

        ```
    ###Assistant : Creates float Tensor, 1.0 for label-TopK_prediction match, 0.0 for mismatch.

    Args:
      y_true: tensor of true targets.
      y_pred: tensor of predicted targets.
      k: (Optional) Number of top elements to look at for computing accuracy.
        Defaults to 5.

    Returns:
      Match tensor: 1.0 for label-prediction match, 0.0 for mismatch.
    
    "
334,"def tax_account_query(doctype, txt, searchfield, start, page_len, filters):
	company_currency = erpnext.get_company_currency(filters.get(""company""))

	def get_accounts(with_account_type_filter):
		account_type_condition = """"
		if with_account_type_filter:
			account_type_condition = ""AND account_type in %(account_types)s""

		accounts = frappe.db.sql(
			.format(
				account_type_condition=account_type_condition,
				searchfield=searchfield,
				mcond=get_match_cond(doctype),
			),
			dict(
				account_types=filters.get(""account_type""),
				company=filters.get(""company""),
				disabled=filters.get(""disabled"", 0),
				currency=company_currency,
				txt=""%{}%"".format(txt),
				offset=start,
				limit=page_len,
			),
		)

		return accounts

	tax_accounts = get_accounts(True)

	if not tax_accounts:
		tax_accounts = get_accounts(False)

	return tax_accounts


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs","
			SELECT name, parent_account
			FROM `tabAccount`
			WHERE `tabAccount`.docstatus!=2
				{account_type_condition}
				AND is_group = 0
				AND company = %(company)s
				AND disabled = %(disabled)s
				AND (account_currency = %(currency)s or ifnull(account_currency, '') = '')
				AND `{searchfield}` LIKE %(txt)s
				{mcond}
			ORDER BY idx DESC, name
			LIMIT %(offset)s, %(limit)s
		",42,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tax_account_query(doctype, txt, searchfield, start, page_len, filters):
	company_currency = erpnext.get_company_currency(filters.get(""company""))

	def get_accounts(with_account_type_filter):
		account_type_condition = """"
		if with_account_type_filter:
			account_type_condition = ""AND account_type in %(account_types)s""

		accounts = frappe.db.sql(
			.format(
				account_type_condition=account_type_condition,
				searchfield=searchfield,
				mcond=get_match_cond(doctype),
			),
			dict(
				account_types=filters.get(""account_type""),
				company=filters.get(""company""),
				disabled=filters.get(""disabled"", 0),
				currency=company_currency,
				txt=""%{}%"".format(txt),
				offset=start,
				limit=page_len,
			),
		)

		return accounts

	tax_accounts = get_accounts(True)

	if not tax_accounts:
		tax_accounts = get_accounts(False)

	return tax_accounts


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs
        ```
    ###Assistant : 
			SELECT name, parent_account
			FROM `tabAccount`
			WHERE `tabAccount`.docstatus!=2
				{account_type_condition}
				AND is_group = 0
				AND company = %(company)s
				AND disabled = %(disabled)s
				AND (account_currency = %(currency)s or ifnull(account_currency, '') = '')
				AND `{searchfield}` LIKE %(txt)s
				{mcond}
			ORDER BY idx DESC, name
			LIMIT %(offset)s, %(limit)s
		
    "
335,"def test_add_post_duplicate_choose_permission(self):
        

        # Create group with access to admin and add permission.
        bakers_group = Group.objects.create(name=""Bakers"")
        access_admin_perm = Permission.objects.get(
            content_type__app_label=""wagtailadmin"", codename=""access_admin""
        )
        bakers_group.permissions.add(access_admin_perm)

        # Create the ""Bakery"" Collection and grant ""add"" permission to the Bakers group.
        root = Collection.objects.get(id=get_root_collection_id())
        bakery_collection = root.add_child(instance=Collection(name=""Bakery""))
        GroupCollectionPermission.objects.create(
            group=bakers_group,
            collection=bakery_collection,
            permission=Permission.objects.get(
                content_type__app_label=""wagtailimages"", codename=""add_image""
            ),
        )
","
        When a duplicate image is added but the user doesn't have permission to choose the original image,
        the add views lets the user upload it as if it weren't a duplicate.
        ",31,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_add_post_duplicate_choose_permission(self):
        

        # Create group with access to admin and add permission.
        bakers_group = Group.objects.create(name=""Bakers"")
        access_admin_perm = Permission.objects.get(
            content_type__app_label=""wagtailadmin"", codename=""access_admin""
        )
        bakers_group.permissions.add(access_admin_perm)

        # Create the ""Bakery"" Collection and grant ""add"" permission to the Bakers group.
        root = Collection.objects.get(id=get_root_collection_id())
        bakery_collection = root.add_child(instance=Collection(name=""Bakery""))
        GroupCollectionPermission.objects.create(
            group=bakers_group,
            collection=bakery_collection,
            permission=Permission.objects.get(
                content_type__app_label=""wagtailimages"", codename=""add_image""
            ),
        )

        ```
    ###Assistant : 
        When a duplicate image is added but the user doesn't have permission to choose the original image,
        the add views lets the user upload it as if it weren't a duplicate.
        
    "
336,"def netmiko_commands(*commands, **kwargs):
    
    conn = _netmiko_conn(**kwargs)
    ret = []
    for cmd in commands:
        ret.append(conn.send_command(cmd))
    return ret


@proxy_napalm_wrap","
    .. versionadded:: 2019.2.0

    Invoke one or more commands to be executed on the remote device, via Netmiko.
    Returns a list of strings, with the output from each command.

    commands
        A list of commands to be executed.

    expect_string
        Regular expression pattern to use for determining end of output.
        If left blank will default to being based on router prompt.

    delay_factor: ``1``
        Multiplying factor used to adjust delays (default: ``1``).

    max_loops: ``500``
        Controls wait time in conjunction with delay_factor. Will default to be
        based upon self.timeout.

    auto_find_prompt: ``True``
        Whether it should try to auto-detect the prompt (default: ``True``).

    strip_prompt: ``True``
        Remove the trailing router prompt from the output (default: ``True``).

    strip_command: ``True``
        Remove the echo of the command from the output (default: ``True``).

    normalize: ``True``
        Ensure the proper enter is sent at end of command (default: ``True``).

    use_textfsm: ``False``
        Process command output through TextFSM template (default: ``False``).

    CLI Example:

    .. code-block:: bash

        salt '*' napalm.netmiko_commands 'show version' 'show interfaces'
    ",157,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def netmiko_commands(*commands, **kwargs):
    
    conn = _netmiko_conn(**kwargs)
    ret = []
    for cmd in commands:
        ret.append(conn.send_command(cmd))
    return ret


@proxy_napalm_wrap
        ```
    ###Assistant : 
    .. versionadded:: 2019.2.0

    Invoke one or more commands to be executed on the remote device, via Netmiko.
    Returns a list of strings, with the output from each command.

    commands
        A list of commands to be executed.

    expect_string
        Regular expression pattern to use for determining end of output.
        If left blank will default to being based on router prompt.

    delay_factor: ``1``
        Multiplying factor used to adjust delays (default: ``1``).

    max_loops: ``500``
        Controls wait time in conjunction with delay_factor. Will default to be
        based upon self.timeout.

    auto_find_prompt: ``True``
        Whether it should try to auto-detect the prompt (default: ``True``).

    strip_prompt: ``True``
        Remove the trailing router prompt from the output (default: ``True``).

    strip_command: ``True``
        Remove the echo of the command from the output (default: ``True``).

    normalize: ``True``
        Ensure the proper enter is sent at end of command (default: ``True``).

    use_textfsm: ``False``
        Process command output through TextFSM template (default: ``False``).

    CLI Example:

    .. code-block:: bash

        salt '*' napalm.netmiko_commands 'show version' 'show interfaces'
    
    "
337,"def decoder(self, side):
        
        input_ = Input(shape=(8, 8, 512))
        var_x = input_
        var_x = UpscaleBlock(256, activation=""leakyrelu"")(var_x)
        var_x = UpscaleBlock(128, activation=""leakyrelu"")(var_x)
        var_x = UpscaleBlock(64, activation=""leakyrelu"")(var_x)
        var_x = Conv2DOutput(3, 5, name=f""face_out_{side}"")(var_x)
        outputs = [var_x]

        if self.learn_mask:
            var_y = input_
            var_y = UpscaleBlock(256, activation=""leakyrelu"")(var_y)
            var_y = UpscaleBlock(128, activation=""leakyrelu"")(var_y)
            var_y = UpscaleBlock(64, activation=""leakyrelu"")(var_y)
            var_y = Conv2DOutput(1, 5, name=f""mask_out_{side}"")(var_y)
            outputs.append(var_y)
        return KerasModel(input_, outputs=outputs, name=f""decoder_{side}"")

"," The original Faceswap Decoder Network.

        The decoders for the original model have separate weights for each side ""A"" and ""B"", so two
        instances are created in :func:`build_model`, one for each side.

        Parameters
        ----------
        side: str
            Either `""a` or `""b""`. This is used for naming the decoder model.

        Returns
        -------
        :class:`keras.models.Model`
            The Keras decoder model. This will be called twice, once for each side.
        ",63,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def decoder(self, side):
        
        input_ = Input(shape=(8, 8, 512))
        var_x = input_
        var_x = UpscaleBlock(256, activation=""leakyrelu"")(var_x)
        var_x = UpscaleBlock(128, activation=""leakyrelu"")(var_x)
        var_x = UpscaleBlock(64, activation=""leakyrelu"")(var_x)
        var_x = Conv2DOutput(3, 5, name=f""face_out_{side}"")(var_x)
        outputs = [var_x]

        if self.learn_mask:
            var_y = input_
            var_y = UpscaleBlock(256, activation=""leakyrelu"")(var_y)
            var_y = UpscaleBlock(128, activation=""leakyrelu"")(var_y)
            var_y = UpscaleBlock(64, activation=""leakyrelu"")(var_y)
            var_y = Conv2DOutput(1, 5, name=f""mask_out_{side}"")(var_y)
            outputs.append(var_y)
        return KerasModel(input_, outputs=outputs, name=f""decoder_{side}"")

        ```
    ###Assistant :  The original Faceswap Decoder Network.

        The decoders for the original model have separate weights for each side ""A"" and ""B"", so two
        instances are created in :func:`build_model`, one for each side.

        Parameters
        ----------
        side: str
            Either `""a` or `""b""`. This is used for naming the decoder model.

        Returns
        -------
        :class:`keras.models.Model`
            The Keras decoder model. This will be called twice, once for each side.
        
    "
338,"def test_users_getting_add_peer_event(self) -> None:
        
        streams_to_sub = [""multi_user_stream""]
        othello = self.example_user(""othello"")
        cordelia = self.example_user(""cordelia"")
        iago = self.example_user(""iago"")
        orig_user_ids_to_subscribe = [self.test_user.id, othello.id]
        self.common_subscribe_to_streams(
            self.test_user,
            streams_to_sub,
            dict(principals=orjson.dumps(orig_user_ids_to_subscribe).decode()),
        )

        new_user_ids_to_subscribe = [iago.id, cordelia.id]
        events: List[Mapping[str, Any]] = []
        with self.tornado_redirected_to_list(events, expected_num_events=5):
            self.common_subscribe_to_streams(
                self.test_user,
                streams_to_sub,
                dict(principals=orjson.dumps(new_user_ids_to_subscribe).decode()),
            )

        add_peer_events = [event for event in events if event[""event""].get(""op"") == ""peer_add""]
        (add_peer_event,) = add_peer_events

        self.assertEqual(add_peer_event[""event""][""type""], ""subscription"")
        self.assertEqual(add_peer_event[""event""][""op""], ""peer_add"")
        event_sent_to_ids = add_peer_event[""users""]
        for user_id in new_user_ids_to_subscribe:
            # Make sure new users subscribed to stream is not in
            # peer_add event recipient list
            self.assertNotIn(user_id, event_sent_to_ids)
        for old_user in orig_user_ids_to_subscribe:
            # Check non-new users are in peer_add event recipient list.
            self.assertIn(old_user, event_sent_to_ids)
","
        Check users getting add_peer_event is correct
        ",6,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_users_getting_add_peer_event(self) -> None:
        
        streams_to_sub = [""multi_user_stream""]
        othello = self.example_user(""othello"")
        cordelia = self.example_user(""cordelia"")
        iago = self.example_user(""iago"")
        orig_user_ids_to_subscribe = [self.test_user.id, othello.id]
        self.common_subscribe_to_streams(
            self.test_user,
            streams_to_sub,
            dict(principals=orjson.dumps(orig_user_ids_to_subscribe).decode()),
        )

        new_user_ids_to_subscribe = [iago.id, cordelia.id]
        events: List[Mapping[str, Any]] = []
        with self.tornado_redirected_to_list(events, expected_num_events=5):
            self.common_subscribe_to_streams(
                self.test_user,
                streams_to_sub,
                dict(principals=orjson.dumps(new_user_ids_to_subscribe).decode()),
            )

        add_peer_events = [event for event in events if event[""event""].get(""op"") == ""peer_add""]
        (add_peer_event,) = add_peer_events

        self.assertEqual(add_peer_event[""event""][""type""], ""subscription"")
        self.assertEqual(add_peer_event[""event""][""op""], ""peer_add"")
        event_sent_to_ids = add_peer_event[""users""]
        for user_id in new_user_ids_to_subscribe:
            # Make sure new users subscribed to stream is not in
            # peer_add event recipient list
            self.assertNotIn(user_id, event_sent_to_ids)
        for old_user in orig_user_ids_to_subscribe:
            # Check non-new users are in peer_add event recipient list.
            self.assertIn(old_user, event_sent_to_ids)

        ```
    ###Assistant : 
        Check users getting add_peer_event is correct
        
    "
339,"def show_trace_2d(f, results):
    
    d2l.set_figsize()
    d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e')
    x1, x2 = d2l.meshgrid(d2l.arange(-5.5, 1.0, 0.1),
                          d2l.arange(-3.0, 1.0, 0.1))
    d2l.plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')
    d2l.plt.xlabel('x1')
    d2l.plt.ylabel('x2')

d2l.DATA_HUB['airfoil'] = (d2l.DATA_URL + 'airfoil_self_noise.dat',
                           '76e5be1548fd8222e5074cf0faae75edff8cf93f')
","Show the trace of 2D variables during optimization.

    Defined in :numref:`subsec_gd-learningrate`",11,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def show_trace_2d(f, results):
    
    d2l.set_figsize()
    d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e')
    x1, x2 = d2l.meshgrid(d2l.arange(-5.5, 1.0, 0.1),
                          d2l.arange(-3.0, 1.0, 0.1))
    d2l.plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')
    d2l.plt.xlabel('x1')
    d2l.plt.ylabel('x2')

d2l.DATA_HUB['airfoil'] = (d2l.DATA_URL + 'airfoil_self_noise.dat',
                           '76e5be1548fd8222e5074cf0faae75edff8cf93f')

        ```
    ###Assistant : Show the trace of 2D variables during optimization.

    Defined in :numref:`subsec_gd-learningrate`
    "
340,"def test_dataset(ray_start_4_cpus, use_local):
    

    model_creator = mlp_identity.model_creator
    optimizer_creator = mlp_identity.optimizer_creator
    dataset_creator = mlp_identity.dataset_creator

    DatasetOperator = TrainingOperator.from_creators(
        model_creator=model_creator,
        optimizer_creator=optimizer_creator,
        loss_creator=nn.MSELoss,
    )

    trainer = TorchTrainer(
        training_operator_cls=DatasetOperator,
        use_local=use_local,
        num_workers=2,
    )

    dataset = dataset_creator()
    for i in range(5):
        trainer.train(dataset=dataset, num_steps=100)

    x = mlp_identity.to_mat(0.5)
    prediction = float(trainer.get_model()(x)[0][0])
    assert 0.4 <= prediction <= 0.6
    trainer.shutdown()


@pytest.mark.parametrize(""use_local"", [True, False])","
    This test tries training the mlp_identity example. We check the accuracy of
    the model as an all inclusive way of ensuring that we are properly sharding
    and iterating over the entire dataset (instead of repeating the first set
    of points for example).
    ",42,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dataset(ray_start_4_cpus, use_local):
    

    model_creator = mlp_identity.model_creator
    optimizer_creator = mlp_identity.optimizer_creator
    dataset_creator = mlp_identity.dataset_creator

    DatasetOperator = TrainingOperator.from_creators(
        model_creator=model_creator,
        optimizer_creator=optimizer_creator,
        loss_creator=nn.MSELoss,
    )

    trainer = TorchTrainer(
        training_operator_cls=DatasetOperator,
        use_local=use_local,
        num_workers=2,
    )

    dataset = dataset_creator()
    for i in range(5):
        trainer.train(dataset=dataset, num_steps=100)

    x = mlp_identity.to_mat(0.5)
    prediction = float(trainer.get_model()(x)[0][0])
    assert 0.4 <= prediction <= 0.6
    trainer.shutdown()


@pytest.mark.parametrize(""use_local"", [True, False])
        ```
    ###Assistant : 
    This test tries training the mlp_identity example. We check the accuracy of
    the model as an all inclusive way of ensuring that we are properly sharding
    and iterating over the entire dataset (instead of repeating the first set
    of points for example).
    
    "
341,"def lovasz_softmax_flat(probas, labels, classes='present', weighted=None):
    
    if probas.numel() == 0:
        # only void pixels, the gradients should be 0
        return probas * 0.
    C = probas.size(1)
    losses = []
    class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
    for c in class_to_sum:
        fg = (labels == c).float()  # foreground for class c
        if (classes is 'present' and fg.sum() == 0):
            continue
        if C == 1:
            if len(classes) > 1:
                raise ValueError('Sigmoid output possible only with 1 class')
            class_pred = probas[:, 0]
        else:
            class_pred = probas[:, c]
        errors = (Variable(fg) - class_pred).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        if weighted is not None:
            losses.append(weighted[c]*torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
        else:
            losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
    return mean(losses)

","
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
    ",45,115,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def lovasz_softmax_flat(probas, labels, classes='present', weighted=None):
    
    if probas.numel() == 0:
        # only void pixels, the gradients should be 0
        return probas * 0.
    C = probas.size(1)
    losses = []
    class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
    for c in class_to_sum:
        fg = (labels == c).float()  # foreground for class c
        if (classes is 'present' and fg.sum() == 0):
            continue
        if C == 1:
            if len(classes) > 1:
                raise ValueError('Sigmoid output possible only with 1 class')
            class_pred = probas[:, 0]
        else:
            class_pred = probas[:, c]
        errors = (Variable(fg) - class_pred).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        if weighted is not None:
            losses.append(weighted[c]*torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
        else:
            losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
    return mean(losses)


        ```
    ###Assistant : 
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
    
    "
342,"def mixin_distributed_feature_parser(parser):
    

    gp = add_arg_group(parser, title='Distributed')

    gp.add_argument(
        '--quiet-remote-logs',
        action='store_true',
        default=False,
        help='Do not display the streaming of remote logs on local console',
    )

    gp.add_argument(
        '--upload-files',
        type=str,
        nargs='*',
        metavar='FILE',
        help=,
    )

    gp.add_argument(
        '--disable-remote',
        action='store_true',
        default=False,
        help='If set, remote pod invocation is avoided. This is used by pods created by JinaD'
        if _SHOW_ALL_ARGS
        else argparse.SUPPRESS,
    )
","Mixing in arguments required by :class:`BaseDeployment` into the given parser.
    :param parser: the parser instance to which we add arguments
    
The files on the host to be uploaded to the remote
workspace. This can be useful when your Deployment has more
file dependencies beyond a single YAML file, e.g.
Python files, data files.

Note,
- currently only flatten structure is supported, which means if you upload `[./foo/a.py, ./foo/b.pp, ./bar/c.yml]`, then they will be put under the _same_ workspace on the remote, losing all hierarchies.
- by default, `--uses` YAML file is always uploaded.
- uploaded files are by default isolated across the runs. To ensure files are submitted to the same workspace across different runs, use `--workspace-id` to specify the workspace.
",121,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mixin_distributed_feature_parser(parser):
    

    gp = add_arg_group(parser, title='Distributed')

    gp.add_argument(
        '--quiet-remote-logs',
        action='store_true',
        default=False,
        help='Do not display the streaming of remote logs on local console',
    )

    gp.add_argument(
        '--upload-files',
        type=str,
        nargs='*',
        metavar='FILE',
        help=,
    )

    gp.add_argument(
        '--disable-remote',
        action='store_true',
        default=False,
        help='If set, remote pod invocation is avoided. This is used by pods created by JinaD'
        if _SHOW_ALL_ARGS
        else argparse.SUPPRESS,
    )

        ```
    ###Assistant : Mixing in arguments required by :class:`BaseDeployment` into the given parser.
    :param parser: the parser instance to which we add arguments
    
The files on the host to be uploaded to the remote
workspace. This can be useful when your Deployment has more
file dependencies beyond a single YAML file, e.g.
Python files, data files.

Note,
- currently only flatten structure is supported, which means if you upload `[./foo/a.py, ./foo/b.pp, ./bar/c.yml]`, then they will be put under the _same_ workspace on the remote, losing all hierarchies.
- by default, `--uses` YAML file is always uploaded.
- uploaded files are by default isolated across the runs. To ensure files are submitted to the same workspace across different runs, use `--workspace-id` to specify the workspace.

    "
343,"def get_bootstrap_modules():
    
    # Import 'struct' modules to get real paths to module file names.
    mod_struct = __import__('struct')
    # Basic modules necessary for the bootstrap process.
    loader_mods = TOC()
    loaderpath = os.path.join(HOMEPATH, 'PyInstaller', 'loader')
    # On some platforms (Windows, Debian/Ubuntu) '_struct' and zlib modules are built-in modules (linked statically)
    # and thus does not have attribute __file__. 'struct' module is required for reading Python bytecode from
    # executable. 'zlib' is required to decompress this bytecode.
    for mod_name in ['_struct', 'zlib']:
        mod = __import__(mod_name)  # C extension.
        if hasattr(mod, '__file__'):
            mod_file = os.path.abspath(mod.__file__)
            if os.path.basename(os.path.dirname(mod_file)) == 'lib-dynload':
                # Divert extensions originating from python's lib-dynload directory, to match behavior of #5604.
                mod_name = os.path.join('lib-dynload', mod_name)
            loader_mods.append((mod_name, mod_file, 'EXTENSION'))
    # NOTE:These modules should be kept simple without any complicated dependencies.
    loader_mods += [
        ('struct', os.path.abspath(mod_struct.__file__), 'PYMODULE'),
        ('pyimod01_os_path', os.path.join(loaderpath, 'pyimod01_os_path.py'), 'PYMODULE'),
        ('pyimod02_archive', os.path.join(loaderpath, 'pyimod02_archive.py'), 'PYMODULE'),
        ('pyimod03_importers', os.path.join(loaderpath, 'pyimod03_importers.py'), 'PYMODULE'),
        ('pyimod04_ctypes', os.path.join(loaderpath, 'pyimod04_ctypes.py'), 'PYMODULE'),
        ('pyiboot01_bootstrap', os.path.join(loaderpath, 'pyiboot01_bootstrap.py'), 'PYSOURCE'),
    ]
    return loader_mods
","
    Get TOC with the bootstrapping modules and their dependencies.
    :return: TOC with modules
    ",13,155,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_bootstrap_modules():
    
    # Import 'struct' modules to get real paths to module file names.
    mod_struct = __import__('struct')
    # Basic modules necessary for the bootstrap process.
    loader_mods = TOC()
    loaderpath = os.path.join(HOMEPATH, 'PyInstaller', 'loader')
    # On some platforms (Windows, Debian/Ubuntu) '_struct' and zlib modules are built-in modules (linked statically)
    # and thus does not have attribute __file__. 'struct' module is required for reading Python bytecode from
    # executable. 'zlib' is required to decompress this bytecode.
    for mod_name in ['_struct', 'zlib']:
        mod = __import__(mod_name)  # C extension.
        if hasattr(mod, '__file__'):
            mod_file = os.path.abspath(mod.__file__)
            if os.path.basename(os.path.dirname(mod_file)) == 'lib-dynload':
                # Divert extensions originating from python's lib-dynload directory, to match behavior of #5604.
                mod_name = os.path.join('lib-dynload', mod_name)
            loader_mods.append((mod_name, mod_file, 'EXTENSION'))
    # NOTE:These modules should be kept simple without any complicated dependencies.
    loader_mods += [
        ('struct', os.path.abspath(mod_struct.__file__), 'PYMODULE'),
        ('pyimod01_os_path', os.path.join(loaderpath, 'pyimod01_os_path.py'), 'PYMODULE'),
        ('pyimod02_archive', os.path.join(loaderpath, 'pyimod02_archive.py'), 'PYMODULE'),
        ('pyimod03_importers', os.path.join(loaderpath, 'pyimod03_importers.py'), 'PYMODULE'),
        ('pyimod04_ctypes', os.path.join(loaderpath, 'pyimod04_ctypes.py'), 'PYMODULE'),
        ('pyiboot01_bootstrap', os.path.join(loaderpath, 'pyiboot01_bootstrap.py'), 'PYSOURCE'),
    ]
    return loader_mods

        ```
    ###Assistant : 
    Get TOC with the bootstrapping modules and their dependencies.
    :return: TOC with modules
    
    "
344,"def getquoted(self):
        
        if self.is_geometry:
            # Psycopg will figure out whether to use E'\\000' or '\000'.
            return b""%s(%s)"" % (
                b""ST_GeogFromWKB"" if self.geography else b""ST_GeomFromEWKB"",
                self._adapter.getquoted(),
            )
        else:
            # For rasters, add explicit type cast to WKB string.
            return b""'%s'::raster"" % self.ewkb.encode()
","
        Return a properly quoted string for use in PostgreSQL/PostGIS.
        ",9,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getquoted(self):
        
        if self.is_geometry:
            # Psycopg will figure out whether to use E'\\000' or '\000'.
            return b""%s(%s)"" % (
                b""ST_GeogFromWKB"" if self.geography else b""ST_GeomFromEWKB"",
                self._adapter.getquoted(),
            )
        else:
            # For rasters, add explicit type cast to WKB string.
            return b""'%s'::raster"" % self.ewkb.encode()

        ```
    ###Assistant : 
        Return a properly quoted string for use in PostgreSQL/PostGIS.
        
    "
345,"def _set_skip_list(self) -> None:
        
        if self._skip_num == 1 and not self._alignments.data:
            logger.debug(""No frames to be skipped"")
            return
        skip_list = []
        for idx, filename in enumerate(self._images.file_list):
            if idx % self._skip_num != 0:
                logger.trace(""Adding image '%s' to skip list due to extract_every_n = %s"",
                             filename, self._skip_num)
                skip_list.append(idx)
            # Items may be in the alignments file if skip-existing[-faces] is selected
            elif os.path.basename(filename) in self._alignments.data:
                self._existing_count += 1
                logger.trace(""Removing image: '%s' due to previously existing"", filename)
                skip_list.append(idx)
        if self._existing_count != 0:
            logger.info(""Skipping %s frames due to skip_existing/skip_existing_faces."",
                        self._existing_count)
        logger.debug(""Adding skip list: %s"", skip_list)
        self._images.add_skip_list(skip_list)
"," Add the skip list to the image loader

        Checks against `extract_every_n` and the existence of alignments data (can exist if
        `skip_existing` or `skip_existing_faces` has been provided) and compiles a list of frame
        indices that should not be processed, providing these to :class:`lib.image.ImagesLoader`.
        ",42,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_skip_list(self) -> None:
        
        if self._skip_num == 1 and not self._alignments.data:
            logger.debug(""No frames to be skipped"")
            return
        skip_list = []
        for idx, filename in enumerate(self._images.file_list):
            if idx % self._skip_num != 0:
                logger.trace(""Adding image '%s' to skip list due to extract_every_n = %s"",
                             filename, self._skip_num)
                skip_list.append(idx)
            # Items may be in the alignments file if skip-existing[-faces] is selected
            elif os.path.basename(filename) in self._alignments.data:
                self._existing_count += 1
                logger.trace(""Removing image: '%s' due to previously existing"", filename)
                skip_list.append(idx)
        if self._existing_count != 0:
            logger.info(""Skipping %s frames due to skip_existing/skip_existing_faces."",
                        self._existing_count)
        logger.debug(""Adding skip list: %s"", skip_list)
        self._images.add_skip_list(skip_list)

        ```
    ###Assistant :  Add the skip list to the image loader

        Checks against `extract_every_n` and the existence of alignments data (can exist if
        `skip_existing` or `skip_existing_faces` has been provided) and compiles a list of frame
        indices that should not be processed, providing these to :class:`lib.image.ImagesLoader`.
        
    "
346,"def caplog(caplog):
    

    config = setup_logging()

    for name, logger_config in config[""loggers""].items():
        if not logger_config.get(""propagate"", True):
            logger = get_logger(name)
            logger.handlers.append(caplog.handler)

    yield caplog
","
    Overrides caplog to apply to all of our loggers that do not propagate and
    consequently would not be captured by caplog.
    ",21,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def caplog(caplog):
    

    config = setup_logging()

    for name, logger_config in config[""loggers""].items():
        if not logger_config.get(""propagate"", True):
            logger = get_logger(name)
            logger.handlers.append(caplog.handler)

    yield caplog

        ```
    ###Assistant : 
    Overrides caplog to apply to all of our loggers that do not propagate and
    consequently would not be captured by caplog.
    
    "
347,"def _show_mesh(self, mesh_ids, face_index, detected_face, top_left):
        
        state = ""normal"" if (self._tk_vars[""selected_editor""].get() != ""Mask"" or
                             self._optional_annotations[""mesh""]) else ""hidden""
        kwargs = dict(polygon=dict(fill="""", width=2, outline=self._canvas.control_colors[""Mesh""]),
                      line=dict(fill=self._canvas.control_colors[""Mesh""], width=2))

        edited = (self._tk_vars[""edited""].get() and
                  self._tk_vars[""selected_editor""].get() not in (""Mask"", ""View""))
        landmarks = self._viewport.get_landmarks(self.frame_index,
                                                 face_index,
                                                 detected_face,
                                                 top_left,
                                                 edited)
        for key, kwarg in kwargs.items():
            for idx, mesh_id in enumerate(mesh_ids[key]):
                self._canvas.coords(mesh_id, *landmarks[key][idx].flatten())
                self._canvas.itemconfig(mesh_id, state=state, **kwarg)
                self._canvas.addtag_withtag(f""active_mesh_{key}"", mesh_id)

"," Display the mesh annotation for the given face, at the given location.

        Parameters
        ----------
        mesh_ids: dict
            Dictionary containing the `polygon` and `line` tkinter canvas identifiers that make up
            the mesh for the given face
        face_index: int
            The face index within the frame for the given face
        detected_face: :class:`~lib.align.DetectedFace`
            The detected face object that contains the landmarks for generating the mesh
        top_left: tuple
            The (x, y) top left co-ordinates of the mesh's bounding box
        ",73,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _show_mesh(self, mesh_ids, face_index, detected_face, top_left):
        
        state = ""normal"" if (self._tk_vars[""selected_editor""].get() != ""Mask"" or
                             self._optional_annotations[""mesh""]) else ""hidden""
        kwargs = dict(polygon=dict(fill="""", width=2, outline=self._canvas.control_colors[""Mesh""]),
                      line=dict(fill=self._canvas.control_colors[""Mesh""], width=2))

        edited = (self._tk_vars[""edited""].get() and
                  self._tk_vars[""selected_editor""].get() not in (""Mask"", ""View""))
        landmarks = self._viewport.get_landmarks(self.frame_index,
                                                 face_index,
                                                 detected_face,
                                                 top_left,
                                                 edited)
        for key, kwarg in kwargs.items():
            for idx, mesh_id in enumerate(mesh_ids[key]):
                self._canvas.coords(mesh_id, *landmarks[key][idx].flatten())
                self._canvas.itemconfig(mesh_id, state=state, **kwarg)
                self._canvas.addtag_withtag(f""active_mesh_{key}"", mesh_id)


        ```
    ###Assistant :  Display the mesh annotation for the given face, at the given location.

        Parameters
        ----------
        mesh_ids: dict
            Dictionary containing the `polygon` and `line` tkinter canvas identifiers that make up
            the mesh for the given face
        face_index: int
            The face index within the frame for the given face
        detected_face: :class:`~lib.align.DetectedFace`
            The detected face object that contains the landmarks for generating the mesh
        top_left: tuple
            The (x, y) top left co-ordinates of the mesh's bounding box
        
    "
348,"def _get_curr_status(self) -> Tuple[DeploymentStatusInfo, bool]:
        
        # TODO(edoakes): we could make this more efficient in steady-state by
        # having a ""healthy"" flag that gets flipped if an update or replica
        # failure happens.

        target_version = self._target_version
        target_replica_count = self._target_replicas

        all_running_replica_cnt = self._replicas.count(states=[ReplicaState.RUNNING])
        running_at_target_version_replica_cnt = self._replicas.count(
            states=[ReplicaState.RUNNING], version=target_version
        )

        failed_to_start_count = self._replica_constructor_retry_counter
        failed_to_start_threshold = min(
            MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, target_replica_count * 3
        )

        # Got to make a call to complete current deploy() goal after
        # start failure threshold reached, while we might still have
        # pending replicas in current goal.
        if (
            failed_to_start_count >= failed_to_start_threshold
            and failed_to_start_threshold != 0
        ):
            if running_at_target_version_replica_cnt > 0:
                # At least one RUNNING replica at target state, partial
                # success; We can stop tracking constructor failures and
                # leave it to the controller to fully scale to target
                # number of replicas and only return as completed once
                # reached target replica count
                self._replica_constructor_retry_counter = -1
            else:
                return (
                    DeploymentStatusInfo(
                        status=DeploymentStatus.FAILED,
                        message=(
                            ""The Deployment constructor failed ""
                            f""{failed_to_start_count} times in a row. See ""
                            ""logs for details.""
                        ),
                    ),
                    False,
                )

        # If we have pending ops, the current goal is *not* ready.
        if (
            self._replicas.count(
                states=[
                    ReplicaState.STARTING,
                    ReplicaState.UPDATING,
                    ReplicaState.RECOVERING,
                    ReplicaState.STOPPING,
                ]
            )
            == 0
        ):
            # Check for deleting.
            if target_replica_count == 0 and all_running_replica_cnt == 0:
                return DeploymentStatusInfo(status=DeploymentStatus.UPDATING), True

            # Check for a non-zero number of deployments.
            elif target_replica_count == running_at_target_version_replica_cnt:
                return DeploymentStatusInfo(status=DeploymentStatus.RUNNING), False

        return (
            DeploymentStatusInfo(
                status=DeploymentStatus.UPDATING,
                message=(
                    f""Running replicas of target version: ""
                    f""{running_at_target_version_replica_cnt}, target ""
                    ""replicas: {target_replica_count}""
                ),
            ),
            False,
        )
","Get the current deployment status.

        Checks the difference between the target vs. running replica count for
        the target version.

        TODO(edoakes): we should report the status as FAILED if replicas are
        repeatedly failing health checks. Need a reasonable heuristic here.

        Returns:
            (DeploymentStatusInfo, was_deleted)
        ",42,248,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_curr_status(self) -> Tuple[DeploymentStatusInfo, bool]:
        
        # TODO(edoakes): we could make this more efficient in steady-state by
        # having a ""healthy"" flag that gets flipped if an update or replica
        # failure happens.

        target_version = self._target_version
        target_replica_count = self._target_replicas

        all_running_replica_cnt = self._replicas.count(states=[ReplicaState.RUNNING])
        running_at_target_version_replica_cnt = self._replicas.count(
            states=[ReplicaState.RUNNING], version=target_version
        )

        failed_to_start_count = self._replica_constructor_retry_counter
        failed_to_start_threshold = min(
            MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, target_replica_count * 3
        )

        # Got to make a call to complete current deploy() goal after
        # start failure threshold reached, while we might still have
        # pending replicas in current goal.
        if (
            failed_to_start_count >= failed_to_start_threshold
            and failed_to_start_threshold != 0
        ):
            if running_at_target_version_replica_cnt > 0:
                # At least one RUNNING replica at target state, partial
                # success; We can stop tracking constructor failures and
                # leave it to the controller to fully scale to target
                # number of replicas and only return as completed once
                # reached target replica count
                self._replica_constructor_retry_counter = -1
            else:
                return (
                    DeploymentStatusInfo(
                        status=DeploymentStatus.FAILED,
                        message=(
                            ""The Deployment constructor failed ""
                            f""{failed_to_start_count} times in a row. See ""
                            ""logs for details.""
                        ),
                    ),
                    False,
                )

        # If we have pending ops, the current goal is *not* ready.
        if (
            self._replicas.count(
                states=[
                    ReplicaState.STARTING,
                    ReplicaState.UPDATING,
                    ReplicaState.RECOVERING,
                    ReplicaState.STOPPING,
                ]
            )
            == 0
        ):
            # Check for deleting.
            if target_replica_count == 0 and all_running_replica_cnt == 0:
                return DeploymentStatusInfo(status=DeploymentStatus.UPDATING), True

            # Check for a non-zero number of deployments.
            elif target_replica_count == running_at_target_version_replica_cnt:
                return DeploymentStatusInfo(status=DeploymentStatus.RUNNING), False

        return (
            DeploymentStatusInfo(
                status=DeploymentStatus.UPDATING,
                message=(
                    f""Running replicas of target version: ""
                    f""{running_at_target_version_replica_cnt}, target ""
                    ""replicas: {target_replica_count}""
                ),
            ),
            False,
        )

        ```
    ###Assistant : Get the current deployment status.

        Checks the difference between the target vs. running replica count for
        the target version.

        TODO(edoakes): we should report the status as FAILED if replicas are
        repeatedly failing health checks. Need a reasonable heuristic here.

        Returns:
            (DeploymentStatusInfo, was_deleted)
        
    "
349,"def generate_square_subsequent_mask(self, length):
        
        return paddle.tensor.triu(
            (paddle.ones(
                (length, length), dtype=paddle.get_default_dtype()) * -np.inf),
            1)
","
        Generate a square mask for the sequence. The mask ensures that the
        predictions for position i can depend only on the known outputs at
        positions less than i.

        Parameters:
            length (int|Tensor): The length of sequence.

        Returns:
            Tensor: Generated square mask according to the given length.

        Examples:
            .. code-block:: python

                import paddle
                from paddle.nn.layer.transformer import Transformer
                length = 5
                d_model, n_head, dim_feedforward = 8, 4, 64
                transformer_paddle = Transformer(
                    d_model, n_head, dim_feedforward=dim_feedforward)
                mask = transformer_paddle.generate_square_subsequent_mask(length)
                print(mask)

                # [[  0. -inf -inf -inf -inf]
                # [  0.   0. -inf -inf -inf]
                # [  0.   0.   0. -inf -inf]
                # [  0.   0.   0.   0. -inf]
                # [  0.   0.   0.   0.   0.]]

        ",110,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_square_subsequent_mask(self, length):
        
        return paddle.tensor.triu(
            (paddle.ones(
                (length, length), dtype=paddle.get_default_dtype()) * -np.inf),
            1)

        ```
    ###Assistant : 
        Generate a square mask for the sequence. The mask ensures that the
        predictions for position i can depend only on the known outputs at
        positions less than i.

        Parameters:
            length (int|Tensor): The length of sequence.

        Returns:
            Tensor: Generated square mask according to the given length.

        Examples:
            .. code-block:: python

                import paddle
                from paddle.nn.layer.transformer import Transformer
                length = 5
                d_model, n_head, dim_feedforward = 8, 4, 64
                transformer_paddle = Transformer(
                    d_model, n_head, dim_feedforward=dim_feedforward)
                mask = transformer_paddle.generate_square_subsequent_mask(length)
                print(mask)

                # [[  0. -inf -inf -inf -inf]
                # [  0.   0. -inf -inf -inf]
                # [  0.   0.   0. -inf -inf]
                # [  0.   0.   0.   0. -inf]
                # [  0.   0.   0.   0.   0.]]

        
    "
350,"def find_requirement(self, req, upgrade):
        # type: (InstallRequirement, bool) -> Optional[InstallationCandidate]
        
        hashes = req.hashes(trust_internet=False)
        best_candidate_result = self.find_best_candidate(
            req.name, specifier=req.specifier, hashes=hashes,
        )
        best_candidate = best_candidate_result.best_candidate

        installed_version = None    # type: Optional[_BaseVersion]
        if req.satisfied_by is not None:
            installed_version = parse_version(req.satisfied_by.version)
","Try to find a Link matching req

        Expects req, an InstallRequirement and upgrade, a boolean
        Returns a InstallationCandidate if found,
        Raises DistributionNotFound or BestVersionAlreadyInstalled otherwise
        ",25,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_requirement(self, req, upgrade):
        # type: (InstallRequirement, bool) -> Optional[InstallationCandidate]
        
        hashes = req.hashes(trust_internet=False)
        best_candidate_result = self.find_best_candidate(
            req.name, specifier=req.specifier, hashes=hashes,
        )
        best_candidate = best_candidate_result.best_candidate

        installed_version = None    # type: Optional[_BaseVersion]
        if req.satisfied_by is not None:
            installed_version = parse_version(req.satisfied_by.version)

        ```
    ###Assistant : Try to find a Link matching req

        Expects req, an InstallRequirement and upgrade, a boolean
        Returns a InstallationCandidate if found,
        Raises DistributionNotFound or BestVersionAlreadyInstalled otherwise
        
    "
351,"def wrap(self, source, outfile):
        
        if self.wrapcode:
            return self._wrap_div(self._wrap_pre(self._wrap_code(source)))
        else:
            return self._wrap_div(self._wrap_pre(source))
","
        Wrap the ``source``, which is a generator yielding
        individual lines, in custom generators. See docstring
        for `format`. Can be overridden.
        ",20,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wrap(self, source, outfile):
        
        if self.wrapcode:
            return self._wrap_div(self._wrap_pre(self._wrap_code(source)))
        else:
            return self._wrap_div(self._wrap_pre(source))

        ```
    ###Assistant : 
        Wrap the ``source``, which is a generator yielding
        individual lines, in custom generators. See docstring
        for `format`. Can be overridden.
        
    "
352,"def get_assessment_criteria(course):
	
	return frappe.get_all(
		""Course Assessment Criteria"",
		fields=[""assessment_criteria"", ""weightage""],
		filters={""parent"": course},
		order_by=""idx"",
	)


@frappe.whitelist()","Returns Assessmemt Criteria and their Weightage from Course Master.

	:param Course: Course
	",12,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_assessment_criteria(course):
	
	return frappe.get_all(
		""Course Assessment Criteria"",
		fields=[""assessment_criteria"", ""weightage""],
		filters={""parent"": course},
		order_by=""idx"",
	)


@frappe.whitelist()
        ```
    ###Assistant : Returns Assessmemt Criteria and their Weightage from Course Master.

	:param Course: Course
	
    "
353,"def rm_filesystems(name, device, config=""/etc/filesystems""):
    
    modified = False
    view_lines = []

    if ""AIX"" not in __grains__[""kernel""]:
        return modified

    criteria = _FileSystemsEntry(name=name, dev=device)
    try:
        fsys_filedict = _filesystems(config, False)
        for fsys_view in fsys_filedict.items():
            try:
                if criteria.match(fsys_view):
                    modified = True
                else:
                    view_lines.append(fsys_view)

            except _FileSystemsEntry.ParseError:
                view_lines.append(fsys_view)

    except OSError as exc:
        raise CommandExecutionError(""Couldn't read from {}: {}"".format(config, exc))

    if modified:
        try:
            with salt.utils.files.fopen(config, ""wb"") as ofile:
                for fsys_view in view_lines:
                    entry = fsys_view[1]
                    list_strgs = _FileSystemsEntry.dict_to_list_lines(entry)
                    ofile.writelines(salt.utils.data.encode(list_strgs))
        except OSError as exc:
            raise CommandExecutionError(""Couldn't write to {}: {}"".format(config, exc))
        except Exception as exc:
            raise CommandExecutionError(""rm_filesystems error exception {exc}"")

    return modified
","
    .. versionadded:: 2018.3.3

    Remove the mount point from the filesystems

    CLI Example:

    .. code-block:: bash

        salt '*' mount.rm_filesystems /mnt/foo /dev/sdg
    ",20,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rm_filesystems(name, device, config=""/etc/filesystems""):
    
    modified = False
    view_lines = []

    if ""AIX"" not in __grains__[""kernel""]:
        return modified

    criteria = _FileSystemsEntry(name=name, dev=device)
    try:
        fsys_filedict = _filesystems(config, False)
        for fsys_view in fsys_filedict.items():
            try:
                if criteria.match(fsys_view):
                    modified = True
                else:
                    view_lines.append(fsys_view)

            except _FileSystemsEntry.ParseError:
                view_lines.append(fsys_view)

    except OSError as exc:
        raise CommandExecutionError(""Couldn't read from {}: {}"".format(config, exc))

    if modified:
        try:
            with salt.utils.files.fopen(config, ""wb"") as ofile:
                for fsys_view in view_lines:
                    entry = fsys_view[1]
                    list_strgs = _FileSystemsEntry.dict_to_list_lines(entry)
                    ofile.writelines(salt.utils.data.encode(list_strgs))
        except OSError as exc:
            raise CommandExecutionError(""Couldn't write to {}: {}"".format(config, exc))
        except Exception as exc:
            raise CommandExecutionError(""rm_filesystems error exception {exc}"")

    return modified

        ```
    ###Assistant : 
    .. versionadded:: 2018.3.3

    Remove the mount point from the filesystems

    CLI Example:

    .. code-block:: bash

        salt '*' mount.rm_filesystems /mnt/foo /dev/sdg
    
    "
354,"def complete_graph(n, create_using=None):
    
    _, nodes = n
    G = empty_graph(nodes, create_using)
    if len(nodes) > 1:
        if G.is_directed():
            edges = itertools.permutations(nodes, 2)
        else:
            edges = itertools.combinations(nodes, 2)
        G.add_edges_from(edges)
    return G

","Return the complete graph `K_n` with n nodes.

    A complete graph on `n` nodes means that all pairs
    of distinct nodes have an edge connecting them.

    Parameters
    ----------
    n : int or iterable container of nodes
        If n is an integer, nodes are from range(n).
        If n is a container of nodes, those nodes appear in the graph.
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Examples
    --------
    >>> G = nx.complete_graph(9)
    >>> len(G)
    9
    >>> G.size()
    36
    >>> G = nx.complete_graph(range(11, 14))
    >>> list(G.nodes())
    [11, 12, 13]
    >>> G = nx.complete_graph(4, nx.DiGraph())
    >>> G.is_directed()
    True

    ",106,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def complete_graph(n, create_using=None):
    
    _, nodes = n
    G = empty_graph(nodes, create_using)
    if len(nodes) > 1:
        if G.is_directed():
            edges = itertools.permutations(nodes, 2)
        else:
            edges = itertools.combinations(nodes, 2)
        G.add_edges_from(edges)
    return G


        ```
    ###Assistant : Return the complete graph `K_n` with n nodes.

    A complete graph on `n` nodes means that all pairs
    of distinct nodes have an edge connecting them.

    Parameters
    ----------
    n : int or iterable container of nodes
        If n is an integer, nodes are from range(n).
        If n is a container of nodes, those nodes appear in the graph.
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Examples
    --------
    >>> G = nx.complete_graph(9)
    >>> len(G)
    9
    >>> G.size()
    36
    >>> G = nx.complete_graph(range(11, 14))
    >>> list(G.nodes())
    [11, 12, 13]
    >>> G = nx.complete_graph(4, nx.DiGraph())
    >>> G.is_directed()
    True

    
    "
355,"def fit_predict(self, X, y=None, **fit_params):
        
        self._validate_params()
        fit_params_steps = self._check_fit_params(**fit_params)
        Xt = self._fit(X, y, **fit_params_steps)

        fit_params_last_step = fit_params_steps[self.steps[-1][0]]
        with _print_elapsed_time(""Pipeline"", self._log_message(len(self.steps) - 1)):
            y_pred = self.steps[-1][1].fit_predict(Xt, y, **fit_params_last_step)
        return y_pred
","Transform the data, and apply `fit_predict` with the final estimator.

        Call `fit_transform` of each transformer in the pipeline. The
        transformed data are finally passed to the final estimator that calls
        `fit_predict` method. Only valid if the final estimator implements
        `fit_predict`.

        Parameters
        ----------
        X : iterable
            Training data. Must fulfill input requirements of first step of
            the pipeline.

        y : iterable, default=None
            Training targets. Must fulfill label requirements for all steps
            of the pipeline.

        **fit_params : dict of string -> object
            Parameters passed to the ``fit`` method of each step, where
            each parameter name is prefixed such that parameter ``p`` for step
            ``s`` has key ``s__p``.

        Returns
        -------
        y_pred : ndarray
            Result of calling `fit_predict` on the final estimator.
        ",118,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit_predict(self, X, y=None, **fit_params):
        
        self._validate_params()
        fit_params_steps = self._check_fit_params(**fit_params)
        Xt = self._fit(X, y, **fit_params_steps)

        fit_params_last_step = fit_params_steps[self.steps[-1][0]]
        with _print_elapsed_time(""Pipeline"", self._log_message(len(self.steps) - 1)):
            y_pred = self.steps[-1][1].fit_predict(Xt, y, **fit_params_last_step)
        return y_pred

        ```
    ###Assistant : Transform the data, and apply `fit_predict` with the final estimator.

        Call `fit_transform` of each transformer in the pipeline. The
        transformed data are finally passed to the final estimator that calls
        `fit_predict` method. Only valid if the final estimator implements
        `fit_predict`.

        Parameters
        ----------
        X : iterable
            Training data. Must fulfill input requirements of first step of
            the pipeline.

        y : iterable, default=None
            Training targets. Must fulfill label requirements for all steps
            of the pipeline.

        **fit_params : dict of string -> object
            Parameters passed to the ``fit`` method of each step, where
            each parameter name is prefixed such that parameter ``p`` for step
            ``s`` has key ``s__p``.

        Returns
        -------
        y_pred : ndarray
            Result of calling `fit_predict` on the final estimator.
        
    "
356,"def test_issue4849(entity_ruler_factory):
    nlp = English()
    patterns = [
        {""label"": ""PERSON"", ""pattern"": ""joe biden"", ""id"": ""joe-biden""},
        {""label"": ""PERSON"", ""pattern"": ""bernie sanders"", ""id"": ""bernie-sanders""},
    ]
    ruler = nlp.add_pipe(
        entity_ruler_factory,
        name=""entity_ruler"",
        config={""phrase_matcher_attr"": ""LOWER""},
    )
    ruler.add_patterns(patterns)
    text = 
    # USING 1 PROCESS
    count_ents = 0
    for doc in nlp.pipe([text], n_process=1):
        count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
    assert count_ents == 2
    # USING 2 PROCESSES
    if isinstance(get_current_ops, NumpyOps):
        count_ents = 0
        for doc in nlp.pipe([text], n_process=2):
            count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
        assert count_ents == 2


@pytest.mark.issue(5918)
@pytest.mark.parametrize(""entity_ruler_factory"", ENTITY_RULERS)","
    The left is starting to take aim at Democratic front-runner Joe Biden.
    Sen. Bernie Sanders joined in her criticism: ""There is no 'middle ground' when it comes to climate policy.""
    ",30,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_issue4849(entity_ruler_factory):
    nlp = English()
    patterns = [
        {""label"": ""PERSON"", ""pattern"": ""joe biden"", ""id"": ""joe-biden""},
        {""label"": ""PERSON"", ""pattern"": ""bernie sanders"", ""id"": ""bernie-sanders""},
    ]
    ruler = nlp.add_pipe(
        entity_ruler_factory,
        name=""entity_ruler"",
        config={""phrase_matcher_attr"": ""LOWER""},
    )
    ruler.add_patterns(patterns)
    text = 
    # USING 1 PROCESS
    count_ents = 0
    for doc in nlp.pipe([text], n_process=1):
        count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
    assert count_ents == 2
    # USING 2 PROCESSES
    if isinstance(get_current_ops, NumpyOps):
        count_ents = 0
        for doc in nlp.pipe([text], n_process=2):
            count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
        assert count_ents == 2


@pytest.mark.issue(5918)
@pytest.mark.parametrize(""entity_ruler_factory"", ENTITY_RULERS)
        ```
    ###Assistant : 
    The left is starting to take aim at Democratic front-runner Joe Biden.
    Sen. Bernie Sanders joined in her criticism: ""There is no 'middle ground' when it comes to climate policy.""
    
    "
357,"def get_serializer_context(self):
        
        context = super().get_serializer_context()

        if hasattr(self.queryset.model, 'custom_fields'):
            content_type = ContentType.objects.get_for_model(self.queryset.model)
            context.update({
                'custom_fields': content_type.custom_fields.all(),
            })

        return context
","
        For models which support custom fields, populate the `custom_fields` context.
        ",10,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_serializer_context(self):
        
        context = super().get_serializer_context()

        if hasattr(self.queryset.model, 'custom_fields'):
            content_type = ContentType.objects.get_for_model(self.queryset.model)
            context.update({
                'custom_fields': content_type.custom_fields.all(),
            })

        return context

        ```
    ###Assistant : 
        For models which support custom fields, populate the `custom_fields` context.
        
    "
358,"def execute (func, args, msg=None, verbose=0, dry_run=0):
    
    if msg is None:
        msg = ""%s%r"" % (func.__name__, args)
        if msg[-2:] == ',)':        # correct for singleton tuple
            msg = msg[0:-2] + ')'

    log.info(msg)
    if not dry_run:
        func(*args)

","Perform some action that affects the outside world (eg.  by
    writing to the filesystem).  Such actions are special because they
    are disabled by the 'dry_run' flag.  This method takes care of all
    that bureaucracy for you; all you have to do is supply the
    function to call and an argument tuple for it (to embody the
    ""external action"" being performed), and an optional message to
    print.
    ",66,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute (func, args, msg=None, verbose=0, dry_run=0):
    
    if msg is None:
        msg = ""%s%r"" % (func.__name__, args)
        if msg[-2:] == ',)':        # correct for singleton tuple
            msg = msg[0:-2] + ')'

    log.info(msg)
    if not dry_run:
        func(*args)


        ```
    ###Assistant : Perform some action that affects the outside world (eg.  by
    writing to the filesystem).  Such actions are special because they
    are disabled by the 'dry_run' flag.  This method takes care of all
    that bureaucracy for you; all you have to do is supply the
    function to call and an argument tuple for it (to embody the
    ""external action"" being performed), and an optional message to
    print.
    
    "
359,"def call(self, features, cols_to_output_tensors=None, training=None):
        
        if training is None:
            training = backend.learning_phase()
        if not isinstance(features, dict):
            raise ValueError(
                ""We expected a dictionary here. Instead we got: "", features
            )
        transformation_cache = (
            tf.__internal__.feature_column.FeatureTransformationCache(features)
        )
        output_tensors = []
        for column in self._feature_columns:
            with backend.name_scope(column.name):
                try:
                    tensor = column.get_dense_tensor(
                        transformation_cache,
                        self._state_manager,
                        training=training,
                    )
                except TypeError:
                    tensor = column.get_dense_tensor(
                        transformation_cache, self._state_manager
                    )
                processed_tensors = self._process_dense_tensor(column, tensor)
                if cols_to_output_tensors is not None:
                    cols_to_output_tensors[column] = processed_tensors
                output_tensors.append(processed_tensors)
        return self._verify_and_concat_tensors(output_tensors)
","Returns a dense tensor corresponding to the `feature_columns`.

        Example usage:

        >>> t1 = tf.feature_column.embedding_column(
        ...    tf.feature_column.categorical_column_with_hash_bucket(""t1"", 2),
        ...    dimension=8)
        >>> t2 = tf.feature_column.numeric_column('t2')
        >>> feature_layer = tf.compat.v1.keras.layers.DenseFeatures([t1, t2])
        >>> features = {""t1"": tf.constant([""a"", ""b""]), ""t2"": tf.constant([1, 2])}
        >>> dense_tensor = feature_layer(features, training=True)

        Args:
          features: A mapping from key to tensors. `FeatureColumn`s look up via
            these keys. For example `numeric_column('price')` will look at 'price'
            key in this dict. Values can be a `SparseTensor` or a `Tensor` depends
            on corresponding `FeatureColumn`.
          cols_to_output_tensors: If not `None`, this will be filled with a dict
            mapping feature columns to output tensors created.
          training: Python boolean or None, indicating whether to the layer is being
            run in training mode. This argument is passed to the call method of any
            `FeatureColumn` that takes a `training` argument. For example, if a
            `FeatureColumn` performed dropout, the column could expose a `training`
            argument to control whether the dropout should be applied. If `None`,
            defaults to `tf.keras.backend.learning_phase()`.


        Returns:
          A `Tensor` which represents input layer of a model. Its shape
          is (batch_size, first_layer_dimension) and its dtype is `float32`.
          first_layer_dimension is determined based on given `feature_columns`.

        Raises:
          ValueError: If features are not a dictionary.
        ",191,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call(self, features, cols_to_output_tensors=None, training=None):
        
        if training is None:
            training = backend.learning_phase()
        if not isinstance(features, dict):
            raise ValueError(
                ""We expected a dictionary here. Instead we got: "", features
            )
        transformation_cache = (
            tf.__internal__.feature_column.FeatureTransformationCache(features)
        )
        output_tensors = []
        for column in self._feature_columns:
            with backend.name_scope(column.name):
                try:
                    tensor = column.get_dense_tensor(
                        transformation_cache,
                        self._state_manager,
                        training=training,
                    )
                except TypeError:
                    tensor = column.get_dense_tensor(
                        transformation_cache, self._state_manager
                    )
                processed_tensors = self._process_dense_tensor(column, tensor)
                if cols_to_output_tensors is not None:
                    cols_to_output_tensors[column] = processed_tensors
                output_tensors.append(processed_tensors)
        return self._verify_and_concat_tensors(output_tensors)

        ```
    ###Assistant : Returns a dense tensor corresponding to the `feature_columns`.

        Example usage:

        >>> t1 = tf.feature_column.embedding_column(
        ...    tf.feature_column.categorical_column_with_hash_bucket(""t1"", 2),
        ...    dimension=8)
        >>> t2 = tf.feature_column.numeric_column('t2')
        >>> feature_layer = tf.compat.v1.keras.layers.DenseFeatures([t1, t2])
        >>> features = {""t1"": tf.constant([""a"", ""b""]), ""t2"": tf.constant([1, 2])}
        >>> dense_tensor = feature_layer(features, training=True)

        Args:
          features: A mapping from key to tensors. `FeatureColumn`s look up via
            these keys. For example `numeric_column('price')` will look at 'price'
            key in this dict. Values can be a `SparseTensor` or a `Tensor` depends
            on corresponding `FeatureColumn`.
          cols_to_output_tensors: If not `None`, this will be filled with a dict
            mapping feature columns to output tensors created.
          training: Python boolean or None, indicating whether to the layer is being
            run in training mode. This argument is passed to the call method of any
            `FeatureColumn` that takes a `training` argument. For example, if a
            `FeatureColumn` performed dropout, the column could expose a `training`
            argument to control whether the dropout should be applied. If `None`,
            defaults to `tf.keras.backend.learning_phase()`.


        Returns:
          A `Tensor` which represents input layer of a model. Its shape
          is (batch_size, first_layer_dimension) and its dtype is `float32`.
          first_layer_dimension is determined based on given `feature_columns`.

        Raises:
          ValueError: If features are not a dictionary.
        
    "
360,"def patch_pickle() -> Iterator[None]:
    
    orig_loads = pkl.loads
    try:
        setattr(pkl, ""loads"", loads)
        yield
    finally:
        setattr(pkl, ""loads"", orig_loads)
","
    Temporarily patch pickle to use our unpickler.
    ",7,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def patch_pickle() -> Iterator[None]:
    
    orig_loads = pkl.loads
    try:
        setattr(pkl, ""loads"", loads)
        yield
    finally:
        setattr(pkl, ""loads"", orig_loads)

        ```
    ###Assistant : 
    Temporarily patch pickle to use our unpickler.
    
    "
361,"def get_filesystem_type(filepath):
    
    # We import it locally so that click autocomplete works
    import psutil

    root_type = ""unknown""
    for part in psutil.disk_partitions():
        if part.mountpoint == '/':
            root_type = part.fstype
            continue
        if filepath.startswith(part.mountpoint):
            return part.fstype

    return root_type

","
    Determine the type of filesystem used - we might want to use different parameters if tmpfs is used.
    :param filepath: path to check
    :return: type of filesystem
    ",27,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_filesystem_type(filepath):
    
    # We import it locally so that click autocomplete works
    import psutil

    root_type = ""unknown""
    for part in psutil.disk_partitions():
        if part.mountpoint == '/':
            root_type = part.fstype
            continue
        if filepath.startswith(part.mountpoint):
            return part.fstype

    return root_type


        ```
    ###Assistant : 
    Determine the type of filesystem used - we might want to use different parameters if tmpfs is used.
    :param filepath: path to check
    :return: type of filesystem
    
    "
362,"def panther_similarity(G, source, k=5, path_length=5, c=0.5, delta=0.1, eps=None):
    r
    import numpy as np

    num_nodes = G.number_of_nodes()
    if num_nodes < k:
        warnings.warn(
            f""Number of nodes is {num_nodes}, but requested k is {k}. ""
            ""Setting k to number of nodes.""
        )
        k = num_nodes
    # According to [1], they empirically determined
    # a good value for ``eps`` to be sqrt( 1 / |E| )
    if eps is None:
        eps = np.sqrt(1.0 / G.number_of_edges())

    inv_node_map = {name: index for index, name in enumerate(G.nodes)}
    node_map = np.array(G)

    # Calculate the sample size ``R`` for how many paths
    # to randomly generate
    t_choose_2 = math.comb(path_length, 2)
    sample_size = int((c / eps**2) * (np.log2(t_choose_2) + 1 + np.log(1 / delta)))
    index_map = {}
    _ = list(
        generate_random_paths(
            G, sample_size, path_length=path_length, index_map=index_map
        )
    )
    S = np.zeros(num_nodes)

    inv_sample_size = 1 / sample_size

    source_paths = set(index_map[source])

    # Calculate the path similarities
    # between ``source`` (v) and ``node`` (v_j)
    # using our inverted index mapping of
    # vertices to paths
    for node, paths in index_map.items():
        # Only consider paths where both
        # ``node`` and ``source`` are present
        common_paths = source_paths.intersection(paths)
        S[inv_node_map[node]] = len(common_paths) * inv_sample_size

    # Retrieve top ``k`` similar
    # Note: the below performed anywhere from 4-10x faster
    # (depending on input sizes) vs the equivalent ``np.argsort(S)[::-1]``
    top_k_unsorted = np.argpartition(S, -k)[-k:]
    top_k_sorted = top_k_unsorted[np.argsort(S[top_k_unsorted])][::-1]

    # Add back the similarity scores
    top_k_sorted_names = map(lambda n: node_map[n], top_k_sorted)
    top_k_with_val = dict(zip(top_k_sorted_names, S[top_k_sorted]))

    # Remove the self-similarity
    top_k_with_val.pop(source, None)
    return top_k_with_val

","Returns the Panther similarity of nodes in the graph `G` to node ``v``.

    Panther is a similarity metric that says ""two objects are considered
    to be similar if they frequently appear on the same paths."" [1]_.

    Parameters
    ----------
    G : NetworkX graph
        A NetworkX graph
    source : node
        Source node for which to find the top `k` similar other nodes
    k : int (default = 5)
        The number of most similar nodes to return
    path_length : int (default = 5)
        How long the randomly generated paths should be (``T`` in [1]_)
    c : float (default = 0.5)
        A universal positive constant used to scale the number
        of sample random paths to generate.
    delta : float (default = 0.1)
        The probability that the similarity $S$ is not an epsilon-approximation to (R, phi),
        where $R$ is the number of random paths and $\phi$ is the probability
        that an element sampled from a set $A \subseteq D$, where $D$ is the domain.
    eps : float or None (default = None)
        The error bound. Per [1]_, a good value is ``sqrt(1/|E|)``. Therefore,
        if no value is provided, the recommended computed value will be used.

    Returns
    -------
    similarity : dictionary
        Dictionary of nodes to similarity scores (as floats). Note:
        the self-similarity (i.e., ``v``) will not be included in
        the returned dictionary.

    Examples
    --------
    >>> G = nx.star_graph(10)
    >>> sim = nx.panther_similarity(G, 0)

    References
    ----------
    .. [1] Zhang, J., Tang, J., Ma, C., Tong, H., Jing, Y., & Li, J.
           Panther: Fast top-k similarity search on large networks.
           In Proceedings of the ACM SIGKDD International Conference
           on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1445–1454).
           Association for Computing Machinery. https://doi.org/10.1145/2783258.2783267.
    ",275,240,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def panther_similarity(G, source, k=5, path_length=5, c=0.5, delta=0.1, eps=None):
    r
    import numpy as np

    num_nodes = G.number_of_nodes()
    if num_nodes < k:
        warnings.warn(
            f""Number of nodes is {num_nodes}, but requested k is {k}. ""
            ""Setting k to number of nodes.""
        )
        k = num_nodes
    # According to [1], they empirically determined
    # a good value for ``eps`` to be sqrt( 1 / |E| )
    if eps is None:
        eps = np.sqrt(1.0 / G.number_of_edges())

    inv_node_map = {name: index for index, name in enumerate(G.nodes)}
    node_map = np.array(G)

    # Calculate the sample size ``R`` for how many paths
    # to randomly generate
    t_choose_2 = math.comb(path_length, 2)
    sample_size = int((c / eps**2) * (np.log2(t_choose_2) + 1 + np.log(1 / delta)))
    index_map = {}
    _ = list(
        generate_random_paths(
            G, sample_size, path_length=path_length, index_map=index_map
        )
    )
    S = np.zeros(num_nodes)

    inv_sample_size = 1 / sample_size

    source_paths = set(index_map[source])

    # Calculate the path similarities
    # between ``source`` (v) and ``node`` (v_j)
    # using our inverted index mapping of
    # vertices to paths
    for node, paths in index_map.items():
        # Only consider paths where both
        # ``node`` and ``source`` are present
        common_paths = source_paths.intersection(paths)
        S[inv_node_map[node]] = len(common_paths) * inv_sample_size

    # Retrieve top ``k`` similar
    # Note: the below performed anywhere from 4-10x faster
    # (depending on input sizes) vs the equivalent ``np.argsort(S)[::-1]``
    top_k_unsorted = np.argpartition(S, -k)[-k:]
    top_k_sorted = top_k_unsorted[np.argsort(S[top_k_unsorted])][::-1]

    # Add back the similarity scores
    top_k_sorted_names = map(lambda n: node_map[n], top_k_sorted)
    top_k_with_val = dict(zip(top_k_sorted_names, S[top_k_sorted]))

    # Remove the self-similarity
    top_k_with_val.pop(source, None)
    return top_k_with_val


        ```
    ###Assistant : Returns the Panther similarity of nodes in the graph `G` to node ``v``.

    Panther is a similarity metric that says ""two objects are considered
    to be similar if they frequently appear on the same paths."" [1]_.

    Parameters
    ----------
    G : NetworkX graph
        A NetworkX graph
    source : node
        Source node for which to find the top `k` similar other nodes
    k : int (default = 5)
        The number of most similar nodes to return
    path_length : int (default = 5)
        How long the randomly generated paths should be (``T`` in [1]_)
    c : float (default = 0.5)
        A universal positive constant used to scale the number
        of sample random paths to generate.
    delta : float (default = 0.1)
        The probability that the similarity $S$ is not an epsilon-approximation to (R, phi),
        where $R$ is the number of random paths and $\phi$ is the probability
        that an element sampled from a set $A \subseteq D$, where $D$ is the domain.
    eps : float or None (default = None)
        The error bound. Per [1]_, a good value is ``sqrt(1/|E|)``. Therefore,
        if no value is provided, the recommended computed value will be used.

    Returns
    -------
    similarity : dictionary
        Dictionary of nodes to similarity scores (as floats). Note:
        the self-similarity (i.e., ``v``) will not be included in
        the returned dictionary.

    Examples
    --------
    >>> G = nx.star_graph(10)
    >>> sim = nx.panther_similarity(G, 0)

    References
    ----------
    .. [1] Zhang, J., Tang, J., Ma, C., Tong, H., Jing, Y., & Li, J.
           Panther: Fast top-k similarity search on large networks.
           In Proceedings of the ACM SIGKDD International Conference
           on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1445–1454).
           Association for Computing Machinery. https://doi.org/10.1145/2783258.2783267.
    
    "
363,"def _phonemize_preprocess(self, text) -> Tuple[List[str], List]:
        
        text = text.strip()
        if self._keep_puncs:
            # a tuple (text, punctuation marks)
            return self._punctuator.strip_to_restore(text)
        return [self._punctuator.strip(text)], []
","Preprocess the text before phonemization

        1. remove spaces
        2. remove punctuation

        Override this if you need a different behaviour
        ",19,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _phonemize_preprocess(self, text) -> Tuple[List[str], List]:
        
        text = text.strip()
        if self._keep_puncs:
            # a tuple (text, punctuation marks)
            return self._punctuator.strip_to_restore(text)
        return [self._punctuator.strip(text)], []

        ```
    ###Assistant : Preprocess the text before phonemization

        1. remove spaces
        2. remove punctuation

        Override this if you need a different behaviour
        
    "
364,"def ensure_pathy(path):
    
    from pathy import Pathy  # noqa: F811

    return Pathy.fluid(path)

","Temporary helper to prevent importing Pathy globally (which can cause
    slow and annoying Google Cloud warning).",16,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ensure_pathy(path):
    
    from pathy import Pathy  # noqa: F811

    return Pathy.fluid(path)


        ```
    ###Assistant : Temporary helper to prevent importing Pathy globally (which can cause
    slow and annoying Google Cloud warning).
    "
365,"def index(self, request, extra_context=None):
        
        app_list = self.get_app_list(request)

        context = {
            **self.each_context(request),
            ""title"": self.index_title,
            ""subtitle"": None,
            ""app_list"": app_list,
            **(extra_context or {}),
        }

        request.current_app = self.name

        return TemplateResponse(
            request, self.index_template or ""admin/index.html"", context
        )
","
        Display the main admin index page, which lists all of the installed
        apps that have been registered in this site.
        ",20,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def index(self, request, extra_context=None):
        
        app_list = self.get_app_list(request)

        context = {
            **self.each_context(request),
            ""title"": self.index_title,
            ""subtitle"": None,
            ""app_list"": app_list,
            **(extra_context or {}),
        }

        request.current_app = self.name

        return TemplateResponse(
            request, self.index_template or ""admin/index.html"", context
        )

        ```
    ###Assistant : 
        Display the main admin index page, which lists all of the installed
        apps that have been registered in this site.
        
    "
366,"def generate_config_from_template(config_dir, config_path, environ, ownership):
    
    for v in (""SYNAPSE_SERVER_NAME"", ""SYNAPSE_REPORT_STATS""):
        if v not in environ:
            error(
                ""Environment variable '%s' is mandatory when generating a config file.""
                % (v,)
            )

    # populate some params from data files (if they exist, else create new ones)
    environ = environ.copy()
    secrets = {
        ""registration"": ""SYNAPSE_REGISTRATION_SHARED_SECRET"",
        ""macaroon"": ""SYNAPSE_MACAROON_SECRET_KEY"",
    }

    for name, secret in secrets.items():
        if secret not in environ:
            filename = ""/data/%s.%s.key"" % (environ[""SYNAPSE_SERVER_NAME""], name)

            # if the file already exists, load in the existing value; otherwise,
            # generate a new secret and write it to a file

            if os.path.exists(filename):
                log(""Reading %s from %s"" % (secret, filename))
                with open(filename) as handle:
                    value = handle.read()
            else:
                log(""Generating a random secret for {}"".format(secret))
                value = codecs.encode(os.urandom(32), ""hex"").decode()
                with open(filename, ""w"") as handle:
                    handle.write(value)
            environ[secret] = value

    environ[""SYNAPSE_APPSERVICES""] = glob.glob(""/data/appservices/*.yaml"")
    if not os.path.exists(config_dir):
        os.mkdir(config_dir)

    # Convert SYNAPSE_NO_TLS to boolean if exists
    if ""SYNAPSE_NO_TLS"" in environ:
        tlsanswerstring = str.lower(environ[""SYNAPSE_NO_TLS""])
        if tlsanswerstring in (""true"", ""on"", ""1"", ""yes""):
            environ[""SYNAPSE_NO_TLS""] = True
        else:
            if tlsanswerstring in (""false"", ""off"", ""0"", ""no""):
                environ[""SYNAPSE_NO_TLS""] = False
            else:
                error(
                    'Environment variable ""SYNAPSE_NO_TLS"" found but value ""'
                    + tlsanswerstring
                    + '"" unrecognized; exiting.'
                )

    if ""SYNAPSE_LOG_CONFIG"" not in environ:
        environ[""SYNAPSE_LOG_CONFIG""] = config_dir + ""/log.config""

    log(""Generating synapse config file "" + config_path)
    convert(""/conf/homeserver.yaml"", config_path, environ)

    log_config_file = environ[""SYNAPSE_LOG_CONFIG""]
    log(""Generating log config file "" + log_config_file)
    convert(""/conf/log.config"", log_config_file, environ)

    # Hopefully we already have a signing key, but generate one if not.
    args = [
        sys.executable,
        ""-m"",
        ""synapse.app.homeserver"",
        ""--config-path"",
        config_path,
        # tell synapse to put generated keys in /data rather than /compiled
        ""--keys-directory"",
        config_dir,
        ""--generate-keys"",
    ]

    if ownership is not None:
        log(f""Setting ownership on /data to {ownership}"")
        subprocess.check_output([""chown"", ""-R"", ownership, ""/data""])
        args = [""gosu"", ownership] + args

    subprocess.check_output(args)

","Generate a homeserver.yaml from environment variables

    Args:
        config_dir (str): where to put generated config files
        config_path (str): where to put the main config file
        environ (dict): environment dictionary
        ownership (str|None): ""<user>:<group>"" string which will be used to set
            ownership of the generated configs. If None, ownership will not change.
    ",49,279,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_config_from_template(config_dir, config_path, environ, ownership):
    
    for v in (""SYNAPSE_SERVER_NAME"", ""SYNAPSE_REPORT_STATS""):
        if v not in environ:
            error(
                ""Environment variable '%s' is mandatory when generating a config file.""
                % (v,)
            )

    # populate some params from data files (if they exist, else create new ones)
    environ = environ.copy()
    secrets = {
        ""registration"": ""SYNAPSE_REGISTRATION_SHARED_SECRET"",
        ""macaroon"": ""SYNAPSE_MACAROON_SECRET_KEY"",
    }

    for name, secret in secrets.items():
        if secret not in environ:
            filename = ""/data/%s.%s.key"" % (environ[""SYNAPSE_SERVER_NAME""], name)

            # if the file already exists, load in the existing value; otherwise,
            # generate a new secret and write it to a file

            if os.path.exists(filename):
                log(""Reading %s from %s"" % (secret, filename))
                with open(filename) as handle:
                    value = handle.read()
            else:
                log(""Generating a random secret for {}"".format(secret))
                value = codecs.encode(os.urandom(32), ""hex"").decode()
                with open(filename, ""w"") as handle:
                    handle.write(value)
            environ[secret] = value

    environ[""SYNAPSE_APPSERVICES""] = glob.glob(""/data/appservices/*.yaml"")
    if not os.path.exists(config_dir):
        os.mkdir(config_dir)

    # Convert SYNAPSE_NO_TLS to boolean if exists
    if ""SYNAPSE_NO_TLS"" in environ:
        tlsanswerstring = str.lower(environ[""SYNAPSE_NO_TLS""])
        if tlsanswerstring in (""true"", ""on"", ""1"", ""yes""):
            environ[""SYNAPSE_NO_TLS""] = True
        else:
            if tlsanswerstring in (""false"", ""off"", ""0"", ""no""):
                environ[""SYNAPSE_NO_TLS""] = False
            else:
                error(
                    'Environment variable ""SYNAPSE_NO_TLS"" found but value ""'
                    + tlsanswerstring
                    + '"" unrecognized; exiting.'
                )

    if ""SYNAPSE_LOG_CONFIG"" not in environ:
        environ[""SYNAPSE_LOG_CONFIG""] = config_dir + ""/log.config""

    log(""Generating synapse config file "" + config_path)
    convert(""/conf/homeserver.yaml"", config_path, environ)

    log_config_file = environ[""SYNAPSE_LOG_CONFIG""]
    log(""Generating log config file "" + log_config_file)
    convert(""/conf/log.config"", log_config_file, environ)

    # Hopefully we already have a signing key, but generate one if not.
    args = [
        sys.executable,
        ""-m"",
        ""synapse.app.homeserver"",
        ""--config-path"",
        config_path,
        # tell synapse to put generated keys in /data rather than /compiled
        ""--keys-directory"",
        config_dir,
        ""--generate-keys"",
    ]

    if ownership is not None:
        log(f""Setting ownership on /data to {ownership}"")
        subprocess.check_output([""chown"", ""-R"", ownership, ""/data""])
        args = [""gosu"", ownership] + args

    subprocess.check_output(args)


        ```
    ###Assistant : Generate a homeserver.yaml from environment variables

    Args:
        config_dir (str): where to put generated config files
        config_path (str): where to put the main config file
        environ (dict): environment dictionary
        ownership (str|None): ""<user>:<group>"" string which will be used to set
            ownership of the generated configs. If None, ownership will not change.
    
    "
367,"def donation_vector(donate_argnums, args, kwargs) -> Tuple[bool, ...]:
  
  res: List[bool] = []
  for i, arg in enumerate(args):
    donate = bool(i in donate_argnums)
    res.extend((donate,) * tree_structure(arg).num_leaves)
  res.extend((False,) * tree_structure(kwargs).num_leaves)
  return tuple(res)
",Returns a tuple with a boolean value for each leaf in args.,12,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def donation_vector(donate_argnums, args, kwargs) -> Tuple[bool, ...]:
  
  res: List[bool] = []
  for i, arg in enumerate(args):
    donate = bool(i in donate_argnums)
    res.extend((donate,) * tree_structure(arg).num_leaves)
  res.extend((False,) * tree_structure(kwargs).num_leaves)
  return tuple(res)

        ```
    ###Assistant : Returns a tuple with a boolean value for each leaf in args.
    "
368,"def set_exception(self, exception):
        
        if self._state != _PENDING:
            raise exceptions.InvalidStateError(f'{self._state}: {self!r}')
        if isinstance(exception, type):
            exception = exception()
        if type(exception) is StopIteration:
            raise TypeError(""StopIteration interacts badly with generators ""
                            ""and cannot be raised into a Future"")
        self._exception = exception
        self._state = _FINISHED
        self.__schedule_callbacks()
        self.__log_traceback = True
","Mark the future done and set an exception.

        If the future is already done when this method is called, raises
        InvalidStateError.
        ",21,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_exception(self, exception):
        
        if self._state != _PENDING:
            raise exceptions.InvalidStateError(f'{self._state}: {self!r}')
        if isinstance(exception, type):
            exception = exception()
        if type(exception) is StopIteration:
            raise TypeError(""StopIteration interacts badly with generators ""
                            ""and cannot be raised into a Future"")
        self._exception = exception
        self._state = _FINISHED
        self.__schedule_callbacks()
        self.__log_traceback = True

        ```
    ###Assistant : Mark the future done and set an exception.

        If the future is already done when this method is called, raises
        InvalidStateError.
        
    "
369,"def statistics(self, refresh=False, approximate=False):
        
        # Prepare array with arguments for capi function
        smin, smax, smean, sstd = c_double(), c_double(), c_double(), c_double()
        stats_args = [
            self._ptr,
            c_int(approximate),
            byref(smin),
            byref(smax),
            byref(smean),
            byref(sstd),
            c_void_p(),
            c_void_p(),
        ]

        if refresh or self._stats_refresh:
            func = capi.compute_band_statistics
        else:
            # Add additional argument to force computation if there is no
            # existing PAM file to take the values from.
            force = True
            stats_args.insert(2, c_int(force))
            func = capi.get_band_statistics

        # Computation of statistics fails for empty bands.
        try:
            func(*stats_args)
            result = smin.value, smax.value, smean.value, sstd.value
        except GDALException:
            result = (None, None, None, None)

        self._stats_refresh = False

        return result
","
        Compute statistics on the pixel values of this band.

        The return value is a tuple with the following structure:
        (minimum, maximum, mean, standard deviation).

        If approximate=True, the statistics may be computed based on overviews
        or a subset of image tiles.

        If refresh=True, the statistics will be computed from the data directly,
        and the cache will be updated where applicable.

        For empty bands (where all pixel values are nodata), all statistics
        values are returned as None.

        For raster formats using Persistent Auxiliary Metadata (PAM) services,
        the statistics might be cached in an auxiliary file.
        ",93,98,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def statistics(self, refresh=False, approximate=False):
        
        # Prepare array with arguments for capi function
        smin, smax, smean, sstd = c_double(), c_double(), c_double(), c_double()
        stats_args = [
            self._ptr,
            c_int(approximate),
            byref(smin),
            byref(smax),
            byref(smean),
            byref(sstd),
            c_void_p(),
            c_void_p(),
        ]

        if refresh or self._stats_refresh:
            func = capi.compute_band_statistics
        else:
            # Add additional argument to force computation if there is no
            # existing PAM file to take the values from.
            force = True
            stats_args.insert(2, c_int(force))
            func = capi.get_band_statistics

        # Computation of statistics fails for empty bands.
        try:
            func(*stats_args)
            result = smin.value, smax.value, smean.value, sstd.value
        except GDALException:
            result = (None, None, None, None)

        self._stats_refresh = False

        return result

        ```
    ###Assistant : 
        Compute statistics on the pixel values of this band.

        The return value is a tuple with the following structure:
        (minimum, maximum, mean, standard deviation).

        If approximate=True, the statistics may be computed based on overviews
        or a subset of image tiles.

        If refresh=True, the statistics will be computed from the data directly,
        and the cache will be updated where applicable.

        For empty bands (where all pixel values are nodata), all statistics
        values are returned as None.

        For raster formats using Persistent Auxiliary Metadata (PAM) services,
        the statistics might be cached in an auxiliary file.
        
    "
370,"def test_smaller_request_deduplicated(self) -> None:
        
        req1 = ensureDeferred(
            self.state_datastore._get_state_for_group_using_inflight_cache(
                42, StateFilter.from_types(((""test.type"", None),))
            )
        )
        self.pump(by=0.1)

        # This should have gone to the database
        self.assertEqual(len(self.get_state_group_calls), 1)
        self.assertFalse(req1.called)

        req2 = ensureDeferred(
            self.state_datastore._get_state_for_group_using_inflight_cache(
                42, StateFilter.from_types(((""test.type"", ""b""),))
            )
        )
        self.pump(by=0.1)

        # No more calls should have gone to the database, because the second
        # request was already in the in-flight cache!
        self.assertEqual(len(self.get_state_group_calls), 1)
        self.assertFalse(req1.called)
        self.assertFalse(req2.called)

        groups, sf, d = self.get_state_group_calls[0]
        self.assertEqual(groups, (42,))
        # The state filter is expanded internally for increased cache hit rate,
        # so we the database sees a wider state filter than requested.
        self.assertEqual(sf, ALL_NON_MEMBERS_STATE_FILTER)

        # Now we can complete the request
        self._complete_request_fake(groups, sf, d)

        self.assertEqual(
            self.get_success(req1),
            {(""test.type"", ""a""): ""AAA"", (""test.type"", ""b""): ""BBB""},
        )
        self.assertEqual(self.get_success(req2), {(""test.type"", ""b""): ""BBB""})
","
        Tests that duplicate requests for state are deduplicated.

        This test:
        - requests some state (state group 42, 'all' state filter)
        - requests a subset of that state, before the first request finishes
        - checks to see that only one database query was made
        - completes the database query
        - checks that both requests see the correct retrieved state
        ",58,116,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_smaller_request_deduplicated(self) -> None:
        
        req1 = ensureDeferred(
            self.state_datastore._get_state_for_group_using_inflight_cache(
                42, StateFilter.from_types(((""test.type"", None),))
            )
        )
        self.pump(by=0.1)

        # This should have gone to the database
        self.assertEqual(len(self.get_state_group_calls), 1)
        self.assertFalse(req1.called)

        req2 = ensureDeferred(
            self.state_datastore._get_state_for_group_using_inflight_cache(
                42, StateFilter.from_types(((""test.type"", ""b""),))
            )
        )
        self.pump(by=0.1)

        # No more calls should have gone to the database, because the second
        # request was already in the in-flight cache!
        self.assertEqual(len(self.get_state_group_calls), 1)
        self.assertFalse(req1.called)
        self.assertFalse(req2.called)

        groups, sf, d = self.get_state_group_calls[0]
        self.assertEqual(groups, (42,))
        # The state filter is expanded internally for increased cache hit rate,
        # so we the database sees a wider state filter than requested.
        self.assertEqual(sf, ALL_NON_MEMBERS_STATE_FILTER)

        # Now we can complete the request
        self._complete_request_fake(groups, sf, d)

        self.assertEqual(
            self.get_success(req1),
            {(""test.type"", ""a""): ""AAA"", (""test.type"", ""b""): ""BBB""},
        )
        self.assertEqual(self.get_success(req2), {(""test.type"", ""b""): ""BBB""})

        ```
    ###Assistant : 
        Tests that duplicate requests for state are deduplicated.

        This test:
        - requests some state (state group 42, 'all' state filter)
        - requests a subset of that state, before the first request finishes
        - checks to see that only one database query was made
        - completes the database query
        - checks that both requests see the correct retrieved state
        
    "
371,"def get_observation(self, agent):
        
        speed = 0
        distance = self._config[""scenario_config""][""misc""][""max_distance""]
        if agent in self.simulation.veh_subscriptions:
            speed = round(
                self.simulation.veh_subscriptions[agent][tc.VAR_SPEED] * MS_TO_KMH
            )
            leader = self.simulation.veh_subscriptions[agent][tc.VAR_LEADER]
            if leader:  # compatible with traci
                veh, dist = leader
                if veh:
                    # compatible with libsumo
                    distance = round(dist)
        ret = [speed, distance]
        logger.debug(""Agent %s --> Obs: %s"", agent, pformat(ret))
        return ret
","
        Returns the observation of a given agent.
        See http://sumo.sourceforge.net/pydoc/traci._simulation.html
        ",9,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_observation(self, agent):
        
        speed = 0
        distance = self._config[""scenario_config""][""misc""][""max_distance""]
        if agent in self.simulation.veh_subscriptions:
            speed = round(
                self.simulation.veh_subscriptions[agent][tc.VAR_SPEED] * MS_TO_KMH
            )
            leader = self.simulation.veh_subscriptions[agent][tc.VAR_LEADER]
            if leader:  # compatible with traci
                veh, dist = leader
                if veh:
                    # compatible with libsumo
                    distance = round(dist)
        ret = [speed, distance]
        logger.debug(""Agent %s --> Obs: %s"", agent, pformat(ret))
        return ret

        ```
    ###Assistant : 
        Returns the observation of a given agent.
        See http://sumo.sourceforge.net/pydoc/traci._simulation.html
        
    "
372,"def register(self, name, color_list):
        
        if name in self._BUILTIN_COLOR_SEQUENCES:
            raise ValueError(f""{name!r} is a reserved name for a builtin ""
                             ""color sequence"")

        color_list = list(color_list)  # force copy and coerce type to list
        for color in color_list:
            try:
                to_rgba(color)
            except ValueError:
                raise ValueError(
                    f""{color!r} is not a valid color specification"")

        self._color_sequences[name] = color_list
","
        Register a new color sequence.

        The color sequence registry stores a copy of the given *color_list*, so
        that future changes to the original list do not affect the registered
        color sequence. Think of this as the registry taking a snapshot
        of *color_list* at registration.

        Parameters
        ----------
        name : str
            The name for the color sequence.

        color_list : list of colors
            An iterable returning valid Matplotlib colors when iterating over.
            Note however that the returned color sequence will always be a
            list regardless of the input type.

        ",86,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def register(self, name, color_list):
        
        if name in self._BUILTIN_COLOR_SEQUENCES:
            raise ValueError(f""{name!r} is a reserved name for a builtin ""
                             ""color sequence"")

        color_list = list(color_list)  # force copy and coerce type to list
        for color in color_list:
            try:
                to_rgba(color)
            except ValueError:
                raise ValueError(
                    f""{color!r} is not a valid color specification"")

        self._color_sequences[name] = color_list

        ```
    ###Assistant : 
        Register a new color sequence.

        The color sequence registry stores a copy of the given *color_list*, so
        that future changes to the original list do not affect the registered
        color sequence. Think of this as the registry taking a snapshot
        of *color_list* at registration.

        Parameters
        ----------
        name : str
            The name for the color sequence.

        color_list : list of colors
            An iterable returning valid Matplotlib colors when iterating over.
            Note however that the returned color sequence will always be a
            list regardless of the input type.

        
    "
373,"def test_get_states_no_attributes(hass_recorder):
    
    hass = hass_recorder()
    now, future, states = _setup_get_states(hass)
    for state in states:
        state.attributes = {}

    # Get states returns everything before POINT for all entities
    for state1, state2 in zip(
        states,
        sorted(
            history.get_states(hass, future, no_attributes=True),
            key=lambda state: state.entity_id,
        ),
    ):
        assert state1 == state2

    # Get states returns everything before POINT for tested entities
    entities = [f""test.point_in_time_{i % 5}"" for i in range(5)]
    for state1, state2 in zip(
        states,
        sorted(
            history.get_states(hass, future, entities, no_attributes=True),
            key=lambda state: state.entity_id,
        ),
    ):
        assert state1 == state2

    # Test get_state here because we have a DB setup
    assert states[0] == history.get_state(
        hass, future, states[0].entity_id, no_attributes=True
    )

    time_before_recorder_ran = now - timedelta(days=1000)
    assert history.get_states(hass, time_before_recorder_ran, no_attributes=True) == []

    assert (
        history.get_state(hass, time_before_recorder_ran, ""demo.id"", no_attributes=True)
        is None
    )


@pytest.mark.parametrize(
    ""attributes, no_attributes, limit"",
    [
        ({""attr"": True}, False, 5000),
        ({}, True, 5000),
        ({""attr"": True}, False, 3),
        ({}, True, 3),
    ],
)",Test getting states without attributes at a specific point in time.,11,145,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_states_no_attributes(hass_recorder):
    
    hass = hass_recorder()
    now, future, states = _setup_get_states(hass)
    for state in states:
        state.attributes = {}

    # Get states returns everything before POINT for all entities
    for state1, state2 in zip(
        states,
        sorted(
            history.get_states(hass, future, no_attributes=True),
            key=lambda state: state.entity_id,
        ),
    ):
        assert state1 == state2

    # Get states returns everything before POINT for tested entities
    entities = [f""test.point_in_time_{i % 5}"" for i in range(5)]
    for state1, state2 in zip(
        states,
        sorted(
            history.get_states(hass, future, entities, no_attributes=True),
            key=lambda state: state.entity_id,
        ),
    ):
        assert state1 == state2

    # Test get_state here because we have a DB setup
    assert states[0] == history.get_state(
        hass, future, states[0].entity_id, no_attributes=True
    )

    time_before_recorder_ran = now - timedelta(days=1000)
    assert history.get_states(hass, time_before_recorder_ran, no_attributes=True) == []

    assert (
        history.get_state(hass, time_before_recorder_ran, ""demo.id"", no_attributes=True)
        is None
    )


@pytest.mark.parametrize(
    ""attributes, no_attributes, limit"",
    [
        ({""attr"": True}, False, 5000),
        ({}, True, 5000),
        ({""attr"": True}, False, 3),
        ({}, True, 3),
    ],
)
        ```
    ###Assistant : Test getting states without attributes at a specific point in time.
    "
374,"def set_until(self, frame, lineno=None):
        
        # the name ""until"" is borrowed from gdb
        if lineno is None:
            lineno = frame.f_lineno + 1
        self._set_stopinfo(frame, frame, lineno)
","Stop when the line with the lineno greater than the current one is
        reached or when returning from current frame.",20,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_until(self, frame, lineno=None):
        
        # the name ""until"" is borrowed from gdb
        if lineno is None:
            lineno = frame.f_lineno + 1
        self._set_stopinfo(frame, frame, lineno)

        ```
    ###Assistant : Stop when the line with the lineno greater than the current one is
        reached or when returning from current frame.
    "
375,"async def get_device_state(self, hass):
        
        websession = async_get_clientsession(hass, self._verify_ssl)

        rendered_headers = template.render_complex(self._headers, parse_result=False)
        rendered_params = template.render_complex(self._params)
",Get the latest data from REST API and update the state.,11,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def get_device_state(self, hass):
        
        websession = async_get_clientsession(hass, self._verify_ssl)

        rendered_headers = template.render_complex(self._headers, parse_result=False)
        rendered_params = template.render_complex(self._params)

        ```
    ###Assistant : Get the latest data from REST API and update the state.
    "
376,"def reset_channel(self) -> None:
        
        if self.channel:
            self.channel.close()
        self.channel = grpc.insecure_channel(self.real_addr, options=GRPC_OPTIONS)
        grpc.channel_ready_future(self.channel)
        self.task_servicer.set_channel(self.channel)
        self.data_servicer.set_channel(self.channel)
        self.logs_servicer.set_channel(self.channel)
","
        Manually close and reopen the channel to the real ray server. This
        simulates a disconnection between the client and the server.
        ",21,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reset_channel(self) -> None:
        
        if self.channel:
            self.channel.close()
        self.channel = grpc.insecure_channel(self.real_addr, options=GRPC_OPTIONS)
        grpc.channel_ready_future(self.channel)
        self.task_servicer.set_channel(self.channel)
        self.data_servicer.set_channel(self.channel)
        self.logs_servicer.set_channel(self.channel)

        ```
    ###Assistant : 
        Manually close and reopen the channel to the real ray server. This
        simulates a disconnection between the client and the server.
        
    "
377,"def wait_scroll_pos_changed(self, x=None, y=None):
        
        __tracebackhide__ = (lambda e:
                             e.errisinstance(testprocess.WaitForTimeout))
        if (x is None and y is not None) or (y is None and x is not None):
            raise ValueError(""Either both x/y or neither must be given!"")

        if x is None and y is None:
            point = 'Py*.QtCore.QPoint(*, *)'  # not counting 0/0 here
        elif x == '0' and y == '0':
            point = 'Py*.QtCore.QPoint()'
        else:
            point = 'Py*.QtCore.QPoint({}, {})'.format(x, y)
        self.wait_for(category='webview',
                      message='Scroll position changed to ' + point)
","Wait until a ""Scroll position changed"" message was found.

        With QtWebEngine, on older Qt versions which lack
        QWebEnginePage.scrollPositionChanged, this also skips the test.
        ",23,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wait_scroll_pos_changed(self, x=None, y=None):
        
        __tracebackhide__ = (lambda e:
                             e.errisinstance(testprocess.WaitForTimeout))
        if (x is None and y is not None) or (y is None and x is not None):
            raise ValueError(""Either both x/y or neither must be given!"")

        if x is None and y is None:
            point = 'Py*.QtCore.QPoint(*, *)'  # not counting 0/0 here
        elif x == '0' and y == '0':
            point = 'Py*.QtCore.QPoint()'
        else:
            point = 'Py*.QtCore.QPoint({}, {})'.format(x, y)
        self.wait_for(category='webview',
                      message='Scroll position changed to ' + point)

        ```
    ###Assistant : Wait until a ""Scroll position changed"" message was found.

        With QtWebEngine, on older Qt versions which lack
        QWebEnginePage.scrollPositionChanged, this also skips the test.
        
    "
378,"def test_write_tfrecords(ray_start_regular_shared, tmp_path):
    

    import tensorflow as tf

    # The dataset we will write to a .tfrecords file.
    ds = ray.data.from_items(
        [
            # Row one.
            {
                ""int_item"": 1,
                ""int_list"": [2, 2, 3],
                ""float_item"": 1.0,
                ""float_list"": [2.0, 3.0, 4.0],
                ""bytes_item"": b""abc"",
                ""bytes_list"": [b""abc"", b""1234""],
            },
            # Row two.
            {
                ""int_item"": 2,
                ""int_list"": [3, 3, 4],
                ""float_item"": 2.0,
                ""float_list"": [2.0, 2.0, 3.0],
                ""bytes_item"": b""def"",
                ""bytes_list"": [b""def"", b""1234""],
            },
        ]
    )

    # The corresponding tf.train.Example that we would expect to read
    # from this dataset.

    expected_records = [
        # Record one (corresponding to row one).
        tf.train.Example(
            features=tf.train.Features(
                feature={
                    ""int_item"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[1])
                    ),
                    ""int_list"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[2, 2, 3])
                    ),
                    ""float_item"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[1.0])
                    ),
                    ""float_list"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[2.0, 3.0, 4.0])
                    ),
                    ""bytes_item"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""abc""])
                    ),
                    ""bytes_list"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""abc"", b""1234""])
                    ),
                }
            )
        ),
        # Record two (corresponding to row two).
        tf.train.Example(
            features=tf.train.Features(
                feature={
                    ""int_item"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[2])
                    ),
                    ""int_list"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[3, 3, 4])
                    ),
                    ""float_item"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[2.0])
                    ),
                    ""float_list"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[2.0, 2.0, 3.0])
                    ),
                    ""bytes_item"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""def""])
                    ),
                    ""bytes_list"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""def"", b""1234""])
                    ),
                }
            )
        ),
    ]

    # Perform the test.
    # Write the dataset to a .tfrecords file.
    ds.write_tfrecords(tmp_path)

    # Read the Examples back out from the .tfrecords file.
    # This follows the offical TFRecords tutorial:
    # https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file_2

    filenames = sorted(os.listdir(tmp_path))
    filepaths = [os.path.join(tmp_path, filename) for filename in filenames]
    raw_dataset = tf.data.TFRecordDataset(filepaths)

    tfrecords = []
    for raw_record in raw_dataset:
        example = tf.train.Example()
        example.ParseFromString(raw_record.numpy())
        tfrecords.append(example)

    assert tfrecords == expected_records

","Test that write_tfrecords writes TFRecords correctly.

    Test this by writing a Dataset to a TFRecord (function under test),
    reading it back out into a tf.train.Example,
    and checking that the result is analogous to the original Dataset.
    ",36,231,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_write_tfrecords(ray_start_regular_shared, tmp_path):
    

    import tensorflow as tf

    # The dataset we will write to a .tfrecords file.
    ds = ray.data.from_items(
        [
            # Row one.
            {
                ""int_item"": 1,
                ""int_list"": [2, 2, 3],
                ""float_item"": 1.0,
                ""float_list"": [2.0, 3.0, 4.0],
                ""bytes_item"": b""abc"",
                ""bytes_list"": [b""abc"", b""1234""],
            },
            # Row two.
            {
                ""int_item"": 2,
                ""int_list"": [3, 3, 4],
                ""float_item"": 2.0,
                ""float_list"": [2.0, 2.0, 3.0],
                ""bytes_item"": b""def"",
                ""bytes_list"": [b""def"", b""1234""],
            },
        ]
    )

    # The corresponding tf.train.Example that we would expect to read
    # from this dataset.

    expected_records = [
        # Record one (corresponding to row one).
        tf.train.Example(
            features=tf.train.Features(
                feature={
                    ""int_item"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[1])
                    ),
                    ""int_list"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[2, 2, 3])
                    ),
                    ""float_item"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[1.0])
                    ),
                    ""float_list"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[2.0, 3.0, 4.0])
                    ),
                    ""bytes_item"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""abc""])
                    ),
                    ""bytes_list"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""abc"", b""1234""])
                    ),
                }
            )
        ),
        # Record two (corresponding to row two).
        tf.train.Example(
            features=tf.train.Features(
                feature={
                    ""int_item"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[2])
                    ),
                    ""int_list"": tf.train.Feature(
                        int64_list=tf.train.Int64List(value=[3, 3, 4])
                    ),
                    ""float_item"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[2.0])
                    ),
                    ""float_list"": tf.train.Feature(
                        float_list=tf.train.FloatList(value=[2.0, 2.0, 3.0])
                    ),
                    ""bytes_item"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""def""])
                    ),
                    ""bytes_list"": tf.train.Feature(
                        bytes_list=tf.train.BytesList(value=[b""def"", b""1234""])
                    ),
                }
            )
        ),
    ]

    # Perform the test.
    # Write the dataset to a .tfrecords file.
    ds.write_tfrecords(tmp_path)

    # Read the Examples back out from the .tfrecords file.
    # This follows the offical TFRecords tutorial:
    # https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file_2

    filenames = sorted(os.listdir(tmp_path))
    filepaths = [os.path.join(tmp_path, filename) for filename in filenames]
    raw_dataset = tf.data.TFRecordDataset(filepaths)

    tfrecords = []
    for raw_record in raw_dataset:
        example = tf.train.Example()
        example.ParseFromString(raw_record.numpy())
        tfrecords.append(example)

    assert tfrecords == expected_records


        ```
    ###Assistant : Test that write_tfrecords writes TFRecords correctly.

    Test this by writing a Dataset to a TFRecord (function under test),
    reading it back out into a tf.train.Example,
    and checking that the result is analogous to the original Dataset.
    
    "
379,"def test_float_conversion_dtype(self):
        

        x = np.array([-1, 1])
        # Test all combinations of dtypes conversions
        dtype_combin = np.array(
            np.meshgrid(
                OutputPreprocessing.float_dtype_list,
                OutputPreprocessing.float_dtype_list,
            )
        ).T.reshape(-1, 2)

        for dtype_in, dtype_out in dtype_combin:
            x = x.astype(dtype_in)
            y = gr.processing_utils._convert(x, dtype_out)
            assert y.dtype == np.dtype(dtype_out)
",Test any convertion from a float dtype to an other.,10,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_float_conversion_dtype(self):
        

        x = np.array([-1, 1])
        # Test all combinations of dtypes conversions
        dtype_combin = np.array(
            np.meshgrid(
                OutputPreprocessing.float_dtype_list,
                OutputPreprocessing.float_dtype_list,
            )
        ).T.reshape(-1, 2)

        for dtype_in, dtype_out in dtype_combin:
            x = x.astype(dtype_in)
            y = gr.processing_utils._convert(x, dtype_out)
            assert y.dtype == np.dtype(dtype_out)

        ```
    ###Assistant : Test any convertion from a float dtype to an other.
    "
380,"def fix_mime_types():
    
    # Known to be problematic when Visual Studio is installed:
    # <https://github.com/tensorflow/tensorboard/issues/3120>
    # https://github.com/spotDL/spotify-downloader/issues/1540
    mimetypes.add_type(""application/javascript"", "".js"")
    # Not known to be problematic, but used by spotDL:
    mimetypes.add_type(""text/css"", "".css"")
    mimetypes.add_type(""image/svg+xml"", "".svg"")
    mimetypes.add_type(""text/html"", "".html"")


@app.server.websocket(""/api/ws"")","Fix incorrect entries in the `mimetypes` registry.
    On Windows, the Python standard library's `mimetypes` reads in
    mappings from file extension to MIME type from the Windows
    registry. Other applications can and do write incorrect values
    to this registry, which causes `mimetypes.guess_type` to return
    incorrect values, which causes spotDL to fail to render on
    the frontend.
    This method hard-codes the correct mappings for certain MIME
    types that are known to be either used by TensorBoard or
    problematic in general.
    ",78,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fix_mime_types():
    
    # Known to be problematic when Visual Studio is installed:
    # <https://github.com/tensorflow/tensorboard/issues/3120>
    # https://github.com/spotDL/spotify-downloader/issues/1540
    mimetypes.add_type(""application/javascript"", "".js"")
    # Not known to be problematic, but used by spotDL:
    mimetypes.add_type(""text/css"", "".css"")
    mimetypes.add_type(""image/svg+xml"", "".svg"")
    mimetypes.add_type(""text/html"", "".html"")


@app.server.websocket(""/api/ws"")
        ```
    ###Assistant : Fix incorrect entries in the `mimetypes` registry.
    On Windows, the Python standard library's `mimetypes` reads in
    mappings from file extension to MIME type from the Windows
    registry. Other applications can and do write incorrect values
    to this registry, which causes `mimetypes.guess_type` to return
    incorrect values, which causes spotDL to fail to render on
    the frontend.
    This method hard-codes the correct mappings for certain MIME
    types that are known to be either used by TensorBoard or
    problematic in general.
    
    "
381,"def _get_threads(self) -> MultiThread:
        
        # TODO Check if multiple threads actually speeds anything up
        save_queue = queue_manager.get_queue(""convert_out"")
        patch_queue = queue_manager.get_queue(""patch"")
        return MultiThread(self._converter.process, patch_queue, save_queue,
                           thread_count=self._pool_processes, name=""patch"")
"," Get the threads for patching the converted faces onto the frames.

        Returns
        :class:`lib.multithreading.MultiThread`
            The threads that perform the patching of swapped faces onto the output frames
        ",26,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_threads(self) -> MultiThread:
        
        # TODO Check if multiple threads actually speeds anything up
        save_queue = queue_manager.get_queue(""convert_out"")
        patch_queue = queue_manager.get_queue(""patch"")
        return MultiThread(self._converter.process, patch_queue, save_queue,
                           thread_count=self._pool_processes, name=""patch"")

        ```
    ###Assistant :  Get the threads for patching the converted faces onto the frames.

        Returns
        :class:`lib.multithreading.MultiThread`
            The threads that perform the patching of swapped faces onto the output frames
        
    "
382,"def verify_metadata(self):
        
        # send the info to the server and report the result
        (code, result) = self.post_to_server(self.build_post_data('verify'))
        log.info('Server response (%s): %s', code, result)
"," Send the metadata to the package index server to be checked.
        ",11,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def verify_metadata(self):
        
        # send the info to the server and report the result
        (code, result) = self.post_to_server(self.build_post_data('verify'))
        log.info('Server response (%s): %s', code, result)

        ```
    ###Assistant :  Send the metadata to the package index server to be checked.
        
    "
383,"def test_subscriptions_add_for_principal_invite_only(self) -> None:
        
        invitee = self.example_user(""iago"")
        current_streams = self.get_streams(invitee)
        invite_streams = self.make_random_stream_names(current_streams)
        self.assert_adding_subscriptions_for_principal(
            invitee.id,
            invitee.realm,
            invite_streams,
            invite_only=True,
            policy_name=""Private, protected history"",
        )
","
        You can subscribe other people to invite only streams.
        ",9,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_subscriptions_add_for_principal_invite_only(self) -> None:
        
        invitee = self.example_user(""iago"")
        current_streams = self.get_streams(invitee)
        invite_streams = self.make_random_stream_names(current_streams)
        self.assert_adding_subscriptions_for_principal(
            invitee.id,
            invitee.realm,
            invite_streams,
            invite_only=True,
            policy_name=""Private, protected history"",
        )

        ```
    ###Assistant : 
        You can subscribe other people to invite only streams.
        
    "
384,"def get_module_dict_key_from_name(name):
    
    key = name.replace(""."", ""__ludwig_punct_period__"")
    return key + FEATURE_NAME_SUFFIX

",Returns a key that's guaranteed to be compatible with torch.,10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_module_dict_key_from_name(name):
    
    key = name.replace(""."", ""__ludwig_punct_period__"")
    return key + FEATURE_NAME_SUFFIX


        ```
    ###Assistant : Returns a key that's guaranteed to be compatible with torch.
    "
385,"def test_hdk_import(import_strategy, has_other_engines):
    

    remove_other_engines = 

    if not has_other_engines:
        import_strategy = f""{remove_other_engines}\n{import_strategy}""

    res = subprocess.run(
        [sys.executable, ""-c"", import_strategy],
        stderr=subprocess.PIPE,
        stdout=subprocess.PIPE,
    )

    if res.returncode != 0:
        pytest.fail(str(res.stderr))


@pytest.mark.parametrize(
    ""import_strategy, expected_to_fail"",
    [
        pytest.param(
            ,
            True,
            id=""import_pydbe_first-pyarrow_gandiva_second"",
        ),
        pytest.param(
            ,
            False,
            id=""import_pyarrow_gandiva_first-pydbe_second"",
        ),
    ],
)","
    Test import of HDK engine.

    The import of DbWorker requires to set special dlopen flags which make it then
    incompatible to import some other libraries further (like ``pyarrow.gandiva``).
    This test verifies that it's not the case when a user naturally imports Modin
    with HDK engine.

    Parameters
    ----------
    import_strategy : str
        There are several scenarios of how a user can import Modin with HDK engine:
        configure Modin first to use HDK engine and then import ``modin.pandas`` or vice versa.
        This parameters holds a python code, implementing one of these scenarios.
    has_other_engines : bool
        The problem with import may appear depending on whether other engines are
        installed. This parameter indicates whether to remove modules for
        non-hdk engines before the test.

    Notes
    -----
    The failed import flow may cause segfault, which causes to crash the pytest itself.
    This makes us to run the test in a separate process and check its exit-code to
    decide the success of the test.
    
import sys
sys.modules['ray'] = None
sys.modules['dask'] = None

from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker
import pyarrow.gandiva

import pyarrow.gandiva
from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker
",176,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_hdk_import(import_strategy, has_other_engines):
    

    remove_other_engines = 

    if not has_other_engines:
        import_strategy = f""{remove_other_engines}\n{import_strategy}""

    res = subprocess.run(
        [sys.executable, ""-c"", import_strategy],
        stderr=subprocess.PIPE,
        stdout=subprocess.PIPE,
    )

    if res.returncode != 0:
        pytest.fail(str(res.stderr))


@pytest.mark.parametrize(
    ""import_strategy, expected_to_fail"",
    [
        pytest.param(
            ,
            True,
            id=""import_pydbe_first-pyarrow_gandiva_second"",
        ),
        pytest.param(
            ,
            False,
            id=""import_pyarrow_gandiva_first-pydbe_second"",
        ),
    ],
)
        ```
    ###Assistant : 
    Test import of HDK engine.

    The import of DbWorker requires to set special dlopen flags which make it then
    incompatible to import some other libraries further (like ``pyarrow.gandiva``).
    This test verifies that it's not the case when a user naturally imports Modin
    with HDK engine.

    Parameters
    ----------
    import_strategy : str
        There are several scenarios of how a user can import Modin with HDK engine:
        configure Modin first to use HDK engine and then import ``modin.pandas`` or vice versa.
        This parameters holds a python code, implementing one of these scenarios.
    has_other_engines : bool
        The problem with import may appear depending on whether other engines are
        installed. This parameter indicates whether to remove modules for
        non-hdk engines before the test.

    Notes
    -----
    The failed import flow may cause segfault, which causes to crash the pytest itself.
    This makes us to run the test in a separate process and check its exit-code to
    decide the success of the test.
    
import sys
sys.modules['ray'] = None
sys.modules['dask'] = None

from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker
import pyarrow.gandiva

import pyarrow.gandiva
from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker

    "
386,"def _iter_tree_entries_next(root_full, dir_rel, memo, on_error, follow_links):
    
    dir_full = os.path.join(root_full, dir_rel)
    dir_real = os.path.realpath(dir_full)

    # Remember each encountered ancestor directory and its canonical
    # (real) path. If a canonical path is encountered more than once,
    # recursion has occurred.
    if dir_real not in memo:
        memo[dir_real] = dir_rel
    else:
        raise RecursionError(
            real_path=dir_real, first_path=memo[dir_real], second_path=dir_rel
        )

    for node_name in os.listdir(dir_full):
        node_rel = os.path.join(dir_rel, node_name)
        node_full = os.path.join(root_full, node_rel)

        # Inspect child node.
        try:
            node_lstat = os.lstat(node_full)
        except OSError as e:
            if on_error is not None:
                on_error(e)
            continue

        if stat.S_ISLNK(node_lstat.st_mode):
            # Child node is a link, inspect the target node.
            is_link = True
            try:
                node_stat = os.stat(node_full)
            except OSError as e:
                if on_error is not None:
                    on_error(e)
                continue
        else:
            is_link = False
            node_stat = node_lstat

        if stat.S_ISDIR(node_stat.st_mode) and (follow_links or not is_link):
            # Child node is a directory, recurse into it and yield its
            # descendant files.
            yield TreeEntry(node_name, node_rel, node_lstat, node_stat)

            for entry in _iter_tree_entries_next(
                root_full, node_rel, memo, on_error, follow_links
            ):
                yield entry

        elif stat.S_ISREG(node_stat.st_mode) or is_link:
            # Child node is either a file or an unfollowed link, yield it.
            yield TreeEntry(node_name, node_rel, node_lstat, node_stat)

    # NOTE: Make sure to remove the canonical (real) path of the directory
    # from the ancestors memo once we are done with it. This allows the
    # same directory to appear multiple times. If this is not done, the
    # second occurrence of the directory will be incorrectly interpreted
    # as a recursion. See <https://github.com/cpburnz/python-path-specification/pull/7>.
    del memo[dir_real]

","
    Scan the directory for all descendant files.

    *root_full* (:class:`str`) the absolute path to the root directory.

    *dir_rel* (:class:`str`) the path to the directory to scan relative to
    *root_full*.

    *memo* (:class:`dict`) keeps track of ancestor directories
    encountered. Maps each ancestor real path (:class:`str`) to relative
    path (:class:`str`).

    *on_error* (:class:`~collections.abc.Callable` or :data:`None`)
    optionally is the error handler for file-system exceptions.

    *follow_links* (:class:`bool`) is whether to walk symbolic links that
    resolve to directories.

    Yields each entry (:class:`.TreeEntry`).
    ",74,240,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _iter_tree_entries_next(root_full, dir_rel, memo, on_error, follow_links):
    
    dir_full = os.path.join(root_full, dir_rel)
    dir_real = os.path.realpath(dir_full)

    # Remember each encountered ancestor directory and its canonical
    # (real) path. If a canonical path is encountered more than once,
    # recursion has occurred.
    if dir_real not in memo:
        memo[dir_real] = dir_rel
    else:
        raise RecursionError(
            real_path=dir_real, first_path=memo[dir_real], second_path=dir_rel
        )

    for node_name in os.listdir(dir_full):
        node_rel = os.path.join(dir_rel, node_name)
        node_full = os.path.join(root_full, node_rel)

        # Inspect child node.
        try:
            node_lstat = os.lstat(node_full)
        except OSError as e:
            if on_error is not None:
                on_error(e)
            continue

        if stat.S_ISLNK(node_lstat.st_mode):
            # Child node is a link, inspect the target node.
            is_link = True
            try:
                node_stat = os.stat(node_full)
            except OSError as e:
                if on_error is not None:
                    on_error(e)
                continue
        else:
            is_link = False
            node_stat = node_lstat

        if stat.S_ISDIR(node_stat.st_mode) and (follow_links or not is_link):
            # Child node is a directory, recurse into it and yield its
            # descendant files.
            yield TreeEntry(node_name, node_rel, node_lstat, node_stat)

            for entry in _iter_tree_entries_next(
                root_full, node_rel, memo, on_error, follow_links
            ):
                yield entry

        elif stat.S_ISREG(node_stat.st_mode) or is_link:
            # Child node is either a file or an unfollowed link, yield it.
            yield TreeEntry(node_name, node_rel, node_lstat, node_stat)

    # NOTE: Make sure to remove the canonical (real) path of the directory
    # from the ancestors memo once we are done with it. This allows the
    # same directory to appear multiple times. If this is not done, the
    # second occurrence of the directory will be incorrectly interpreted
    # as a recursion. See <https://github.com/cpburnz/python-path-specification/pull/7>.
    del memo[dir_real]


        ```
    ###Assistant : 
    Scan the directory for all descendant files.

    *root_full* (:class:`str`) the absolute path to the root directory.

    *dir_rel* (:class:`str`) the path to the directory to scan relative to
    *root_full*.

    *memo* (:class:`dict`) keeps track of ancestor directories
    encountered. Maps each ancestor real path (:class:`str`) to relative
    path (:class:`str`).

    *on_error* (:class:`~collections.abc.Callable` or :data:`None`)
    optionally is the error handler for file-system exceptions.

    *follow_links* (:class:`bool`) is whether to walk symbolic links that
    resolve to directories.

    Yields each entry (:class:`.TreeEntry`).
    
    "
387,"def _remove_gens(base, strong_gens, basic_orbits=None, strong_gens_distr=None):
    
    from sympy.combinatorics.perm_groups import _orbit
    base_len = len(base)
    degree = strong_gens[0].size
    if strong_gens_distr is None:
        strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
    if basic_orbits is None:
        basic_orbits = []
        for i in range(base_len):
            basic_orbit = _orbit(degree, strong_gens_distr[i], base[i])
            basic_orbits.append(basic_orbit)
    strong_gens_distr.append([])
    res = strong_gens[:]
    for i in range(base_len - 1, -1, -1):
        gens_copy = strong_gens_distr[i][:]
        for gen in strong_gens_distr[i]:
            if gen not in strong_gens_distr[i + 1]:
                temp_gens = gens_copy[:]
                temp_gens.remove(gen)
                if temp_gens == []:
                    continue
                temp_orbit = _orbit(degree, temp_gens, base[i])
                if temp_orbit == basic_orbits[i]:
                    gens_copy.remove(gen)
                    res.remove(gen)
    return res

","
    Remove redundant generators from a strong generating set.

    Parameters
    ==========

    ``base`` - a base
    ``strong_gens`` - a strong generating set relative to ``base``
    ``basic_orbits`` - basic orbits
    ``strong_gens_distr`` - strong generators distributed by membership in basic
    stabilizers

    Returns
    =======

    A strong generating set with respect to ``base`` which is a subset of
    ``strong_gens``.

    Examples
    ========

    >>> from sympy.combinatorics import SymmetricGroup
    >>> from sympy.combinatorics.util import _remove_gens
    >>> from sympy.combinatorics.testutil import _verify_bsgs
    >>> S = SymmetricGroup(15)
    >>> base, strong_gens = S.schreier_sims_incremental()
    >>> new_gens = _remove_gens(base, strong_gens)
    >>> len(new_gens)
    14
    >>> _verify_bsgs(S, base, new_gens)
    True

    Notes
    =====

    This procedure is outlined in [1],p.95.

    References
    ==========

    .. [1] Holt, D., Eick, B., O'Brien, E.
           ""Handbook of computational group theory""

    ",115,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _remove_gens(base, strong_gens, basic_orbits=None, strong_gens_distr=None):
    
    from sympy.combinatorics.perm_groups import _orbit
    base_len = len(base)
    degree = strong_gens[0].size
    if strong_gens_distr is None:
        strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
    if basic_orbits is None:
        basic_orbits = []
        for i in range(base_len):
            basic_orbit = _orbit(degree, strong_gens_distr[i], base[i])
            basic_orbits.append(basic_orbit)
    strong_gens_distr.append([])
    res = strong_gens[:]
    for i in range(base_len - 1, -1, -1):
        gens_copy = strong_gens_distr[i][:]
        for gen in strong_gens_distr[i]:
            if gen not in strong_gens_distr[i + 1]:
                temp_gens = gens_copy[:]
                temp_gens.remove(gen)
                if temp_gens == []:
                    continue
                temp_orbit = _orbit(degree, temp_gens, base[i])
                if temp_orbit == basic_orbits[i]:
                    gens_copy.remove(gen)
                    res.remove(gen)
    return res


        ```
    ###Assistant : 
    Remove redundant generators from a strong generating set.

    Parameters
    ==========

    ``base`` - a base
    ``strong_gens`` - a strong generating set relative to ``base``
    ``basic_orbits`` - basic orbits
    ``strong_gens_distr`` - strong generators distributed by membership in basic
    stabilizers

    Returns
    =======

    A strong generating set with respect to ``base`` which is a subset of
    ``strong_gens``.

    Examples
    ========

    >>> from sympy.combinatorics import SymmetricGroup
    >>> from sympy.combinatorics.util import _remove_gens
    >>> from sympy.combinatorics.testutil import _verify_bsgs
    >>> S = SymmetricGroup(15)
    >>> base, strong_gens = S.schreier_sims_incremental()
    >>> new_gens = _remove_gens(base, strong_gens)
    >>> len(new_gens)
    14
    >>> _verify_bsgs(S, base, new_gens)
    True

    Notes
    =====

    This procedure is outlined in [1],p.95.

    References
    ==========

    .. [1] Holt, D., Eick, B., O'Brien, E.
           ""Handbook of computational group theory""

    
    "
388,"def from_dict(cls, file_dict):
        
        return cls(
            file_dict[""filename""],
            file_dict[""content""],
            file_dict.get(""content-type"", ""text/plain""),
        )
","
        Create a SimpleUploadedFile object from a dictionary with keys:
           - filename
           - content-type
           - content
        ",15,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_dict(cls, file_dict):
        
        return cls(
            file_dict[""filename""],
            file_dict[""content""],
            file_dict.get(""content-type"", ""text/plain""),
        )

        ```
    ###Assistant : 
        Create a SimpleUploadedFile object from a dictionary with keys:
           - filename
           - content-type
           - content
        
    "
389,"def test_change_list_sorting_callable(self):
        
        response = self.client.get(
            reverse(""admin:admin_views_article_changelist""), {""o"": 2}
        )
        self.assertContentBefore(
            response,
            ""Oldest content"",
            ""Middle content"",
            ""Results of sorting on callable are out of order."",
        )
        self.assertContentBefore(
            response,
            ""Middle content"",
            ""Newest content"",
            ""Results of sorting on callable are out of order."",
        )
","
        Ensure we can sort on a list_display field that is a callable
        (column 2 is callable_year in ArticleAdmin)
        ",18,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_change_list_sorting_callable(self):
        
        response = self.client.get(
            reverse(""admin:admin_views_article_changelist""), {""o"": 2}
        )
        self.assertContentBefore(
            response,
            ""Oldest content"",
            ""Middle content"",
            ""Results of sorting on callable are out of order."",
        )
        self.assertContentBefore(
            response,
            ""Middle content"",
            ""Newest content"",
            ""Results of sorting on callable are out of order."",
        )

        ```
    ###Assistant : 
        Ensure we can sort on a list_display field that is a callable
        (column 2 is callable_year in ArticleAdmin)
        
    "
390,"def pre_encode(self) -> Optional[Callable[[np.ndarray], List[bytes]]]:
        
        dummy = np.zeros((20, 20, 3), dtype=""uint8"")
        test = self._writer.pre_encode(dummy)
        retval: Optional[Callable[[np.ndarray],
                                  List[bytes]]] = None if test is None else self._writer.pre_encode
        logger.debug(""Writer pre_encode function: %s"", retval)
        return retval
"," python function: Selected writer's pre-encode function, if it has one,
        otherwise ``None`` ",12,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pre_encode(self) -> Optional[Callable[[np.ndarray], List[bytes]]]:
        
        dummy = np.zeros((20, 20, 3), dtype=""uint8"")
        test = self._writer.pre_encode(dummy)
        retval: Optional[Callable[[np.ndarray],
                                  List[bytes]]] = None if test is None else self._writer.pre_encode
        logger.debug(""Writer pre_encode function: %s"", retval)
        return retval

        ```
    ###Assistant :  python function: Selected writer's pre-encode function, if it has one,
        otherwise ``None`` 
    "
391,"def disconnect(self):
        

        if self.is_connected is False:
            return

        self.connection.close()
        self.is_connected = False
        return self.is_connected

","
        Close any existing connections.
        ",4,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def disconnect(self):
        

        if self.is_connected is False:
            return

        self.connection.close()
        self.is_connected = False
        return self.is_connected

        ```
    ###Assistant : 
        Close any existing connections.
        
    "
392,"def resolve(self) -> Tuple[List, Dict]:
        
        objects_mapping = []
        for obj_ref in self.workflow_outputs:
            obj, ref = _resolve_object_ref(obj_ref.ref)
            objects_mapping.append(obj)

        workflow_ref_mapping = _resolve_dynamic_workflow_refs(self.workflow_refs)

        with serialization_context.workflow_args_resolving_context(
            objects_mapping, workflow_ref_mapping
        ):
            # reconstruct input arguments under correct serialization context
            flattened_args: List[Any] = ray.get(self.args)

        # dereference arguments like Ray remote functions
        flattened_args = [
            ray.get(a) if isinstance(a, ObjectRef) else a for a in flattened_args
        ]
        return signature.recover_args(flattened_args)
","
        This function resolves the inputs for the code inside
        a workflow step (works on the callee side). For outputs from other
        workflows, we resolve them into object instances inplace.

        For each ObjectRef argument, the function returns both the ObjectRef
        and the object instance. If the ObjectRef is a chain of nested
        ObjectRefs, then we resolve it recursively until we get the
        object instance, and we return the *direct* ObjectRef of the
        instance. This function does not resolve ObjectRef
        inside another object (e.g. list of ObjectRefs) to give users some
        flexibility.

        Returns:
            Instances of arguments.
        ",94,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resolve(self) -> Tuple[List, Dict]:
        
        objects_mapping = []
        for obj_ref in self.workflow_outputs:
            obj, ref = _resolve_object_ref(obj_ref.ref)
            objects_mapping.append(obj)

        workflow_ref_mapping = _resolve_dynamic_workflow_refs(self.workflow_refs)

        with serialization_context.workflow_args_resolving_context(
            objects_mapping, workflow_ref_mapping
        ):
            # reconstruct input arguments under correct serialization context
            flattened_args: List[Any] = ray.get(self.args)

        # dereference arguments like Ray remote functions
        flattened_args = [
            ray.get(a) if isinstance(a, ObjectRef) else a for a in flattened_args
        ]
        return signature.recover_args(flattened_args)

        ```
    ###Assistant : 
        This function resolves the inputs for the code inside
        a workflow step (works on the callee side). For outputs from other
        workflows, we resolve them into object instances inplace.

        For each ObjectRef argument, the function returns both the ObjectRef
        and the object instance. If the ObjectRef is a chain of nested
        ObjectRefs, then we resolve it recursively until we get the
        object instance, and we return the *direct* ObjectRef of the
        instance. This function does not resolve ObjectRef
        inside another object (e.g. list of ObjectRefs) to give users some
        flexibility.

        Returns:
            Instances of arguments.
        
    "
393,"def _maybe_create_attribute(self, name, default_value):
        
        if not hasattr(self, name):
            self.__setattr__(name, default_value)
","Create the attribute with the default value if it hasn't been created.

        This is useful for fields that is used for tracking purpose,
        _trainable_weights, or _layers. Note that user could create a layer subclass
        and assign an internal field before invoking the Layer.__init__(), the
        __setattr__() need to create the tracking fields and __init__() need to not
        override them.

        Args:
          name: String, the name of the attribute.
          default_value: Object, the default value of the attribute.
        ",74,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _maybe_create_attribute(self, name, default_value):
        
        if not hasattr(self, name):
            self.__setattr__(name, default_value)

        ```
    ###Assistant : Create the attribute with the default value if it hasn't been created.

        This is useful for fields that is used for tracking purpose,
        _trainable_weights, or _layers. Note that user could create a layer subclass
        and assign an internal field before invoking the Layer.__init__(), the
        __setattr__() need to create the tracking fields and __init__() need to not
        override them.

        Args:
          name: String, the name of the attribute.
          default_value: Object, the default value of the attribute.
        
    "
394,"def add_nodes_from(self, nodes_for_adding, **attr):
        
        for n in nodes_for_adding:
            try:
                newnode = n not in self._node
                newdict = attr
            except TypeError:
                n, ndict = n
                newnode = n not in self._node
                newdict = attr.copy()
                newdict.update(ndict)
            if newnode:
                if n is None:
                    raise ValueError(""None cannot be a node"")
                self._succ[n] = self.adjlist_inner_dict_factory()
                self._pred[n] = self.adjlist_inner_dict_factory()
                self._node[n] = self.node_attr_dict_factory()
            self._node[n].update(newdict)
","Add multiple nodes.

        Parameters
        ----------
        nodes_for_adding : iterable container
            A container of nodes (list, dict, set, etc.).
            OR
            A container of (node, attribute dict) tuples.
            Node attributes are updated using the attribute dict.
        attr : keyword arguments, optional (default= no attributes)
            Update attributes for all nodes in nodes.
            Node attributes specified in nodes as a tuple take
            precedence over attributes specified via keyword arguments.

        See Also
        --------
        add_node

        Notes
        -------
        When adding nodes from an iterator over the graph you are changing,
        a `RuntimeError` can be raised with message:
        `RuntimeError: dictionary changed size during iteration`. This
        happens when the graph's underlying dictionary is modified during
        iteration. To avoid this error, evaluate the iterator into a separate
        object, e.g. by using `list(iterator_of_nodes)`, and pass this
        object to `G.add_nodes_from`.

        Examples
        --------
        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> G.add_nodes_from(""Hello"")
        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
        >>> G.add_nodes_from(K3)
        >>> sorted(G.nodes(), key=str)
        [0, 1, 2, 'H', 'e', 'l', 'o']

        Use keywords to update specific node attributes for every node.

        >>> G.add_nodes_from([1, 2], size=10)
        >>> G.add_nodes_from([3, 4], weight=0.4)

        Use (node, attrdict) tuples to update attributes for specific nodes.

        >>> G.add_nodes_from([(1, dict(size=11)), (2, {""color"": ""blue""})])
        >>> G.nodes[1][""size""]
        11
        >>> H = nx.Graph()
        >>> H.add_nodes_from(G.nodes(data=True))
        >>> H.nodes[1][""size""]
        11

        Evaluate an iterator over a graph if using it to modify the same graph

        >>> G = nx.DiGraph([(0, 1), (1, 2), (3, 4)])
        >>> # wrong way - will raise RuntimeError
        >>> # G.add_nodes_from(n + 1 for n in G.nodes)
        >>> # correct way
        >>> G.add_nodes_from(list(n + 1 for n in G.nodes))
        ",260,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_nodes_from(self, nodes_for_adding, **attr):
        
        for n in nodes_for_adding:
            try:
                newnode = n not in self._node
                newdict = attr
            except TypeError:
                n, ndict = n
                newnode = n not in self._node
                newdict = attr.copy()
                newdict.update(ndict)
            if newnode:
                if n is None:
                    raise ValueError(""None cannot be a node"")
                self._succ[n] = self.adjlist_inner_dict_factory()
                self._pred[n] = self.adjlist_inner_dict_factory()
                self._node[n] = self.node_attr_dict_factory()
            self._node[n].update(newdict)

        ```
    ###Assistant : Add multiple nodes.

        Parameters
        ----------
        nodes_for_adding : iterable container
            A container of nodes (list, dict, set, etc.).
            OR
            A container of (node, attribute dict) tuples.
            Node attributes are updated using the attribute dict.
        attr : keyword arguments, optional (default= no attributes)
            Update attributes for all nodes in nodes.
            Node attributes specified in nodes as a tuple take
            precedence over attributes specified via keyword arguments.

        See Also
        --------
        add_node

        Notes
        -------
        When adding nodes from an iterator over the graph you are changing,
        a `RuntimeError` can be raised with message:
        `RuntimeError: dictionary changed size during iteration`. This
        happens when the graph's underlying dictionary is modified during
        iteration. To avoid this error, evaluate the iterator into a separate
        object, e.g. by using `list(iterator_of_nodes)`, and pass this
        object to `G.add_nodes_from`.

        Examples
        --------
        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> G.add_nodes_from(""Hello"")
        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
        >>> G.add_nodes_from(K3)
        >>> sorted(G.nodes(), key=str)
        [0, 1, 2, 'H', 'e', 'l', 'o']

        Use keywords to update specific node attributes for every node.

        >>> G.add_nodes_from([1, 2], size=10)
        >>> G.add_nodes_from([3, 4], weight=0.4)

        Use (node, attrdict) tuples to update attributes for specific nodes.

        >>> G.add_nodes_from([(1, dict(size=11)), (2, {""color"": ""blue""})])
        >>> G.nodes[1][""size""]
        11
        >>> H = nx.Graph()
        >>> H.add_nodes_from(G.nodes(data=True))
        >>> H.nodes[1][""size""]
        11

        Evaluate an iterator over a graph if using it to modify the same graph

        >>> G = nx.DiGraph([(0, 1), (1, 2), (3, 4)])
        >>> # wrong way - will raise RuntimeError
        >>> # G.add_nodes_from(n + 1 for n in G.nodes)
        >>> # correct way
        >>> G.add_nodes_from(list(n + 1 for n in G.nodes))
        
    "
395,"def test_submitted_email_notifications_sent(self):
        
        self.login(self.submitter)
        self.submit()

        self.assertEqual(len(mail.outbox), 4)

        task_submission_emails = [
            email for email in mail.outbox if ""task"" in email.subject
        ]
        task_submission_emailed_addresses = [
            address for email in task_submission_emails for address in email.to
        ]
        workflow_submission_emails = [
            email for email in mail.outbox if ""workflow"" in email.subject
        ]
        workflow_submission_emailed_addresses = [
            address for email in workflow_submission_emails for address in email.to
        ]

        self.assertEqual(len(task_submission_emails), 3)
        # the moderator is in the Group assigned to the GroupApproval task, so should get an email
        self.assertIn(self.moderator.email, task_submission_emailed_addresses)
        self.assertIn(self.moderator2.email, task_submission_emailed_addresses)
        # with `WAGTAILADMIN_NOTIFICATION_INCLUDE_SUPERUSERS`, the superuser should get a task email
        self.assertIn(self.superuser.email, task_submission_emailed_addresses)
        # the submitter triggered this workflow update, so should not get an email
        self.assertNotIn(self.submitter.email, task_submission_emailed_addresses)

        self.assertEqual(len(workflow_submission_emails), 1)
        # the moderator should not get a workflow email
        self.assertNotIn(self.moderator.email, workflow_submission_emailed_addresses)
        self.assertNotIn(self.moderator2.email, workflow_submission_emailed_addresses)
        # with `WAGTAILADMIN_NOTIFICATION_INCLUDE_SUPERUSERS`, the superuser should get a workflow email
        self.assertIn(self.superuser.email, workflow_submission_emailed_addresses)
        # as the submitter was the triggering user, the submitter should not get an email notification
        self.assertNotIn(self.submitter.email, workflow_submission_emailed_addresses)
",Test that 'submitted' notifications for WorkflowState and TaskState are both sent correctly,12,153,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_submitted_email_notifications_sent(self):
        
        self.login(self.submitter)
        self.submit()

        self.assertEqual(len(mail.outbox), 4)

        task_submission_emails = [
            email for email in mail.outbox if ""task"" in email.subject
        ]
        task_submission_emailed_addresses = [
            address for email in task_submission_emails for address in email.to
        ]
        workflow_submission_emails = [
            email for email in mail.outbox if ""workflow"" in email.subject
        ]
        workflow_submission_emailed_addresses = [
            address for email in workflow_submission_emails for address in email.to
        ]

        self.assertEqual(len(task_submission_emails), 3)
        # the moderator is in the Group assigned to the GroupApproval task, so should get an email
        self.assertIn(self.moderator.email, task_submission_emailed_addresses)
        self.assertIn(self.moderator2.email, task_submission_emailed_addresses)
        # with `WAGTAILADMIN_NOTIFICATION_INCLUDE_SUPERUSERS`, the superuser should get a task email
        self.assertIn(self.superuser.email, task_submission_emailed_addresses)
        # the submitter triggered this workflow update, so should not get an email
        self.assertNotIn(self.submitter.email, task_submission_emailed_addresses)

        self.assertEqual(len(workflow_submission_emails), 1)
        # the moderator should not get a workflow email
        self.assertNotIn(self.moderator.email, workflow_submission_emailed_addresses)
        self.assertNotIn(self.moderator2.email, workflow_submission_emailed_addresses)
        # with `WAGTAILADMIN_NOTIFICATION_INCLUDE_SUPERUSERS`, the superuser should get a workflow email
        self.assertIn(self.superuser.email, workflow_submission_emailed_addresses)
        # as the submitter was the triggering user, the submitter should not get an email notification
        self.assertNotIn(self.submitter.email, workflow_submission_emailed_addresses)

        ```
    ###Assistant : Test that 'submitted' notifications for WorkflowState and TaskState are both sent correctly
    "
396,"def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]:
        
        indices: List[Tuple[np.array]] = []

        preds_masks = masks_queries_logits
        preds_probs = class_queries_logits.softmax(dim=-1)
        # downsample all masks in one go -> save memory
        mask_labels = nn.functional.interpolate(mask_labels, size=preds_masks.shape[-2:], mode=""nearest"")
        # iterate through batch size
        for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels):
            # Compute the classification cost. Contrary to the loss, we don't use the NLL,
            # but approximate it in 1 - proba[target class].
            # The 1 is a constant that doesn't change the matching, it can be ommitted.
            cost_class = -pred_probs[:, labels]
            # flatten spatial dimension ""q h w -> q (h w)""
            num_queries, height, width = pred_mask.shape
            pred_mask_flat = pred_mask.view(num_queries, height * width)  # [num_queries, H*W]
            # same for target_mask ""c h w -> c (h w)""
            num_channels, height, width = target_mask.shape
            target_mask_flat = target_mask.view(num_channels, height * width)  # [num_total_labels, H*W]
            # compute the focal loss between each mask pairs -> shape [NUM_QUERIES, CLASSES]
            cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat)
            # Compute the dice loss betwen each mask pairs -> shape [NUM_QUERIES, CLASSES]
            cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat)
            # final cost matrix
            cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
            # do the assigmented using the hungarian algorithm in scipy
            assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.cpu())
            indices.append(assigned_indices)

        # It could be stacked in one tensor
        matched_indices = [
            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
        ]
        return matched_indices
","Performs the matching

        Params:
            masks_queries_logits (`torch.Tensor`):
                A tensor` of dim `batch_size, num_queries, num_classes` with the
                  classification logits.
            class_queries_logits (`torch.Tensor`):
                A tensor` of dim `batch_size, num_queries, height, width` with the
                  predicted masks.

            class_labels (`torch.Tensor`):
                A tensor` of dim `num_target_boxes` (where num_target_boxes is the number
                  of ground-truth objects in the target) containing the class labels.
            mask_labels (`torch.Tensor`):
                A tensor` of dim `num_target_boxes, height, width` containing the target
                  masks.

        Returns:
            `List[Tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected labels (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
        ",114,229,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]:
        
        indices: List[Tuple[np.array]] = []

        preds_masks = masks_queries_logits
        preds_probs = class_queries_logits.softmax(dim=-1)
        # downsample all masks in one go -> save memory
        mask_labels = nn.functional.interpolate(mask_labels, size=preds_masks.shape[-2:], mode=""nearest"")
        # iterate through batch size
        for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels):
            # Compute the classification cost. Contrary to the loss, we don't use the NLL,
            # but approximate it in 1 - proba[target class].
            # The 1 is a constant that doesn't change the matching, it can be ommitted.
            cost_class = -pred_probs[:, labels]
            # flatten spatial dimension ""q h w -> q (h w)""
            num_queries, height, width = pred_mask.shape
            pred_mask_flat = pred_mask.view(num_queries, height * width)  # [num_queries, H*W]
            # same for target_mask ""c h w -> c (h w)""
            num_channels, height, width = target_mask.shape
            target_mask_flat = target_mask.view(num_channels, height * width)  # [num_total_labels, H*W]
            # compute the focal loss between each mask pairs -> shape [NUM_QUERIES, CLASSES]
            cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat)
            # Compute the dice loss betwen each mask pairs -> shape [NUM_QUERIES, CLASSES]
            cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat)
            # final cost matrix
            cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
            # do the assigmented using the hungarian algorithm in scipy
            assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.cpu())
            indices.append(assigned_indices)

        # It could be stacked in one tensor
        matched_indices = [
            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
        ]
        return matched_indices

        ```
    ###Assistant : Performs the matching

        Params:
            masks_queries_logits (`torch.Tensor`):
                A tensor` of dim `batch_size, num_queries, num_classes` with the
                  classification logits.
            class_queries_logits (`torch.Tensor`):
                A tensor` of dim `batch_size, num_queries, height, width` with the
                  predicted masks.

            class_labels (`torch.Tensor`):
                A tensor` of dim `num_target_boxes` (where num_target_boxes is the number
                  of ground-truth objects in the target) containing the class labels.
            mask_labels (`torch.Tensor`):
                A tensor` of dim `num_target_boxes, height, width` containing the target
                  masks.

        Returns:
            `List[Tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected labels (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
        
    "
397,"def cancel_dispatcher_process(self):
        
        if not self.celery_task_id:
            return
        canceled = []
        try:
            # Use control and reply mechanism to cancel and obtain confirmation
            timeout = 5
            canceled = ControlDispatcher('dispatcher', self.controller_node).cancel([self.celery_task_id])
        except socket.timeout:
            logger.error(f'could not reach dispatcher on {self.controller_node} within {timeout}s')
        except Exception:
            logger.exception(""error encountered when checking task status"")
        return bool(self.celery_task_id in canceled)  # True or False, whether confirmation was obtained
",Returns True if dispatcher running this job acknowledged request and sent SIGTERM,12,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cancel_dispatcher_process(self):
        
        if not self.celery_task_id:
            return
        canceled = []
        try:
            # Use control and reply mechanism to cancel and obtain confirmation
            timeout = 5
            canceled = ControlDispatcher('dispatcher', self.controller_node).cancel([self.celery_task_id])
        except socket.timeout:
            logger.error(f'could not reach dispatcher on {self.controller_node} within {timeout}s')
        except Exception:
            logger.exception(""error encountered when checking task status"")
        return bool(self.celery_task_id in canceled)  # True or False, whether confirmation was obtained

        ```
    ###Assistant : Returns True if dispatcher running this job acknowledged request and sent SIGTERM
    "
398,"def _mangle_index_names(cls, names):
        
        return [
            f""__index__{i}_{'__None__' if n is None else n}""
            for i, n in enumerate(names)
        ]
","
        Return mangled index names for index labels.

        Mangled names are used for index columns because index
        labels cannot always be used as HDK table column
        names. E.e. label can be a non-string value or an
        unallowed string (empty strings, etc.) for a table column
        name.

        Parameters
        ----------
        names : list of str
            Index labels.

        Returns
        -------
        list of str
            Mangled names.
        ",61,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _mangle_index_names(cls, names):
        
        return [
            f""__index__{i}_{'__None__' if n is None else n}""
            for i, n in enumerate(names)
        ]

        ```
    ###Assistant : 
        Return mangled index names for index labels.

        Mangled names are used for index columns because index
        labels cannot always be used as HDK table column
        names. E.e. label can be a non-string value or an
        unallowed string (empty strings, etc.) for a table column
        name.

        Parameters
        ----------
        names : list of str
            Index labels.

        Returns
        -------
        list of str
            Mangled names.
        
    "
399,"def get_b2cs_json(data, gstin):
	company_state_number = gstin[0:2]

	out = []
	for d in data:
		if not d.get(""place_of_supply""):
			frappe.throw(
				_(
					
				).format(frappe.bold(""Place Of Supply""))
			)

		pos = d.get(""place_of_supply"").split(""-"")[0]
		tax_details = {}

		rate = d.get(""rate"", 0)
		tax = flt((d[""taxable_value""] * rate) / 100.0, 2)

		if company_state_number == pos:
			tax_details.update({""camt"": flt(tax / 2.0, 2), ""samt"": flt(tax / 2.0, 2)})
		else:
			tax_details.update({""iamt"": tax})

		inv = {
			""sply_ty"": ""INTRA"" if company_state_number == pos else ""INTER"",
			""pos"": pos,
			""typ"": d.get(""type""),
			""txval"": flt(d.get(""taxable_value""), 2),
			""rt"": rate,
			""iamt"": flt(tax_details.get(""iamt""), 2),
			""camt"": flt(tax_details.get(""camt""), 2),
			""samt"": flt(tax_details.get(""samt""), 2),
			""csamt"": flt(d.get(""cess_amount""), 2),
		}

		if d.get(""type"") == ""E"" and d.get(""ecommerce_gstin""):
			inv.update({""etin"": d.get(""ecommerce_gstin"")})

		out.append(inv)

	return out

","{0} not entered in some invoices.
				Please update and try again",11,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_b2cs_json(data, gstin):
	company_state_number = gstin[0:2]

	out = []
	for d in data:
		if not d.get(""place_of_supply""):
			frappe.throw(
				_(
					
				).format(frappe.bold(""Place Of Supply""))
			)

		pos = d.get(""place_of_supply"").split(""-"")[0]
		tax_details = {}

		rate = d.get(""rate"", 0)
		tax = flt((d[""taxable_value""] * rate) / 100.0, 2)

		if company_state_number == pos:
			tax_details.update({""camt"": flt(tax / 2.0, 2), ""samt"": flt(tax / 2.0, 2)})
		else:
			tax_details.update({""iamt"": tax})

		inv = {
			""sply_ty"": ""INTRA"" if company_state_number == pos else ""INTER"",
			""pos"": pos,
			""typ"": d.get(""type""),
			""txval"": flt(d.get(""taxable_value""), 2),
			""rt"": rate,
			""iamt"": flt(tax_details.get(""iamt""), 2),
			""camt"": flt(tax_details.get(""camt""), 2),
			""samt"": flt(tax_details.get(""samt""), 2),
			""csamt"": flt(d.get(""cess_amount""), 2),
		}

		if d.get(""type"") == ""E"" and d.get(""ecommerce_gstin""):
			inv.update({""etin"": d.get(""ecommerce_gstin"")})

		out.append(inv)

	return out


        ```
    ###Assistant : {0} not entered in some invoices.
				Please update and try again
    "
400,"def _roll_vectorized(M, roll_indices, axis):
    
    assert axis in [0, 1]
    ndim = M.ndim
    assert ndim == 3
    ndim_roll = roll_indices.ndim
    assert ndim_roll == 1
    sh = M.shape
    r, c = sh[-2:]
    assert sh[0] == roll_indices.shape[0]
    vec_indices = np.arange(sh[0], dtype=np.int32)

    # Builds the rolled matrix
    M_roll = np.empty_like(M)
    if axis == 0:
        for ir in range(r):
            for ic in range(c):
                M_roll[:, ir, ic] = M[vec_indices, (-roll_indices+ir) % r, ic]
    else:  # 1
        for ir in range(r):
            for ic in range(c):
                M_roll[:, ir, ic] = M[vec_indices, ir, (-roll_indices+ic) % c]
    return M_roll

","
    Roll an array of matrices along *axis* (0: rows, 1: columns) according to
    an array of indices *roll_indices*.
    ",18,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _roll_vectorized(M, roll_indices, axis):
    
    assert axis in [0, 1]
    ndim = M.ndim
    assert ndim == 3
    ndim_roll = roll_indices.ndim
    assert ndim_roll == 1
    sh = M.shape
    r, c = sh[-2:]
    assert sh[0] == roll_indices.shape[0]
    vec_indices = np.arange(sh[0], dtype=np.int32)

    # Builds the rolled matrix
    M_roll = np.empty_like(M)
    if axis == 0:
        for ir in range(r):
            for ic in range(c):
                M_roll[:, ir, ic] = M[vec_indices, (-roll_indices+ir) % r, ic]
    else:  # 1
        for ir in range(r):
            for ic in range(c):
                M_roll[:, ir, ic] = M[vec_indices, ir, (-roll_indices+ic) % c]
    return M_roll


        ```
    ###Assistant : 
    Roll an array of matrices along *axis* (0: rows, 1: columns) according to
    an array of indices *roll_indices*.
    
    "
401,"def test_calibration_1_vs_all_vis_api(experiment_to_use):
    
    experiment = experiment_to_use
    probabilities = experiment.probabilities
    viz_outputs = (""pdf"", ""png"")
    with TemporaryDirectory() as tmpvizdir:
        for viz_output in viz_outputs:
            vis_output_pattern_pdf = os.path.join(tmpvizdir, f""*.{viz_output}"")
            visualize.calibration_1_vs_all(
                [probabilities, probabilities],
                experiment.ground_truth,
                experiment.ground_truth_metadata,
                experiment.output_feature_name,
                top_n_classes=[6],
                labels_limit=0,
                model_namess=[""Model1"", ""Model2""],
                output_directory=tmpvizdir,
                file_format=viz_output,
            )
            figure_cnt = glob.glob(vis_output_pattern_pdf)
            assert 7 == len(figure_cnt)

","Ensure pdf and png figures can be saved via visualization API call.

    :param experiment_to_use: Object containing trained model and results to
        test visualization
    :return: None
    ",25,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_calibration_1_vs_all_vis_api(experiment_to_use):
    
    experiment = experiment_to_use
    probabilities = experiment.probabilities
    viz_outputs = (""pdf"", ""png"")
    with TemporaryDirectory() as tmpvizdir:
        for viz_output in viz_outputs:
            vis_output_pattern_pdf = os.path.join(tmpvizdir, f""*.{viz_output}"")
            visualize.calibration_1_vs_all(
                [probabilities, probabilities],
                experiment.ground_truth,
                experiment.ground_truth_metadata,
                experiment.output_feature_name,
                top_n_classes=[6],
                labels_limit=0,
                model_namess=[""Model1"", ""Model2""],
                output_directory=tmpvizdir,
                file_format=viz_output,
            )
            figure_cnt = glob.glob(vis_output_pattern_pdf)
            assert 7 == len(figure_cnt)


        ```
    ###Assistant : Ensure pdf and png figures can be saved via visualization API call.

    :param experiment_to_use: Object containing trained model and results to
        test visualization
    :return: None
    
    "
402,"def test_send_server_notice_delete_room(self) -> None:
        
        # user has no room memberships
        self._check_invite_and_join_status(self.other_user, 0, 0)

        # send first message
        channel = self.make_request(
            ""POST"",
            self.url,
            access_token=self.admin_user_tok,
            content={
                ""user_id"": self.other_user,
                ""content"": {""msgtype"": ""m.text"", ""body"": ""test msg one""},
            },
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)

        # user has one invite
        invited_rooms = self._check_invite_and_join_status(self.other_user, 1, 0)
        first_room_id = invited_rooms[0].room_id

        # user joins the room and is member now
        self.helper.join(
            room=first_room_id, user=self.other_user, tok=self.other_user_token
        )
        self._check_invite_and_join_status(self.other_user, 0, 1)

        # get messages
        messages = self._sync_and_get_messages(first_room_id, self.other_user_token)
        self.assertEqual(len(messages), 1)
        self.assertEqual(messages[0][""content""][""body""], ""test msg one"")
        self.assertEqual(messages[0][""sender""], ""@notices:test"")

        # shut down and purge room
        self.get_success(
            self.room_shutdown_handler.shutdown_room(first_room_id, self.admin_user)
        )
        self.get_success(self.pagination_handler.purge_room(first_room_id))

        # user is not member anymore
        self._check_invite_and_join_status(self.other_user, 0, 0)

        # It doesn't really matter what API we use here, we just want to assert
        # that the room doesn't exist.
        summary = self.get_success(self.store.get_room_summary(first_room_id))
        # The summary should be empty since the room doesn't exist.
        self.assertEqual(summary, {})

        # invalidate cache of server notices room_ids
        # if server tries to send to a cached room_id it gives an error
        self.server_notices_manager.get_or_create_notice_room_for_user.invalidate_all()

        # send second message
        channel = self.make_request(
            ""POST"",
            self.url,
            access_token=self.admin_user_tok,
            content={
                ""user_id"": self.other_user,
                ""content"": {""msgtype"": ""m.text"", ""body"": ""test msg two""},
            },
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)

        # user has one invite
        invited_rooms = self._check_invite_and_join_status(self.other_user, 1, 0)
        second_room_id = invited_rooms[0].room_id

        # user joins the room and is member now
        self.helper.join(
            room=second_room_id, user=self.other_user, tok=self.other_user_token
        )
        self._check_invite_and_join_status(self.other_user, 0, 1)

        # get message
        messages = self._sync_and_get_messages(second_room_id, self.other_user_token)

        self.assertEqual(len(messages), 1)
        self.assertEqual(messages[0][""content""][""body""], ""test msg two"")
        self.assertEqual(messages[0][""sender""], ""@notices:test"")
        # second room has new ID
        self.assertNotEqual(first_room_id, second_room_id)
","
        Tests that the user get server notice in a new room
        after the first server notice room was deleted.
        ",19,240,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_send_server_notice_delete_room(self) -> None:
        
        # user has no room memberships
        self._check_invite_and_join_status(self.other_user, 0, 0)

        # send first message
        channel = self.make_request(
            ""POST"",
            self.url,
            access_token=self.admin_user_tok,
            content={
                ""user_id"": self.other_user,
                ""content"": {""msgtype"": ""m.text"", ""body"": ""test msg one""},
            },
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)

        # user has one invite
        invited_rooms = self._check_invite_and_join_status(self.other_user, 1, 0)
        first_room_id = invited_rooms[0].room_id

        # user joins the room and is member now
        self.helper.join(
            room=first_room_id, user=self.other_user, tok=self.other_user_token
        )
        self._check_invite_and_join_status(self.other_user, 0, 1)

        # get messages
        messages = self._sync_and_get_messages(first_room_id, self.other_user_token)
        self.assertEqual(len(messages), 1)
        self.assertEqual(messages[0][""content""][""body""], ""test msg one"")
        self.assertEqual(messages[0][""sender""], ""@notices:test"")

        # shut down and purge room
        self.get_success(
            self.room_shutdown_handler.shutdown_room(first_room_id, self.admin_user)
        )
        self.get_success(self.pagination_handler.purge_room(first_room_id))

        # user is not member anymore
        self._check_invite_and_join_status(self.other_user, 0, 0)

        # It doesn't really matter what API we use here, we just want to assert
        # that the room doesn't exist.
        summary = self.get_success(self.store.get_room_summary(first_room_id))
        # The summary should be empty since the room doesn't exist.
        self.assertEqual(summary, {})

        # invalidate cache of server notices room_ids
        # if server tries to send to a cached room_id it gives an error
        self.server_notices_manager.get_or_create_notice_room_for_user.invalidate_all()

        # send second message
        channel = self.make_request(
            ""POST"",
            self.url,
            access_token=self.admin_user_tok,
            content={
                ""user_id"": self.other_user,
                ""content"": {""msgtype"": ""m.text"", ""body"": ""test msg two""},
            },
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)

        # user has one invite
        invited_rooms = self._check_invite_and_join_status(self.other_user, 1, 0)
        second_room_id = invited_rooms[0].room_id

        # user joins the room and is member now
        self.helper.join(
            room=second_room_id, user=self.other_user, tok=self.other_user_token
        )
        self._check_invite_and_join_status(self.other_user, 0, 1)

        # get message
        messages = self._sync_and_get_messages(second_room_id, self.other_user_token)

        self.assertEqual(len(messages), 1)
        self.assertEqual(messages[0][""content""][""body""], ""test msg two"")
        self.assertEqual(messages[0][""sender""], ""@notices:test"")
        # second room has new ID
        self.assertNotEqual(first_room_id, second_room_id)

        ```
    ###Assistant : 
        Tests that the user get server notice in a new room
        after the first server notice room was deleted.
        
    "
403,"def is_request_failed_response(resp):
        
        return len(
            resp.get('failures', [])
        ) > 0 or APIUtils.METASEQ_FAIL_MESSAGE_TEXT in resp.get('text', '')
","
        Whether the requests to Metaseq worker have failed.

        It checks this based on the existences of the failure reasons as they get
        accumulated in `_make_request` functionn calls.
        ",27,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_request_failed_response(resp):
        
        return len(
            resp.get('failures', [])
        ) > 0 or APIUtils.METASEQ_FAIL_MESSAGE_TEXT in resp.get('text', '')

        ```
    ###Assistant : 
        Whether the requests to Metaseq worker have failed.

        It checks this based on the existences of the failure reasons as they get
        accumulated in `_make_request` functionn calls.
        
    "
404,"def draw(self, renderer):
        
        if not self.get_visible():
            return

        self._recompute_transform()

        width = self.convert_xunits(self.width)
        height = self.convert_yunits(self.height)

        # If the width and height of ellipse are not equal, take into account
        # stretching when calculating angles to draw between","
        Draw the arc to the given *renderer*.

        Notes
        -----
        Ellipses are normally drawn using an approximation that uses
        eight cubic Bezier splines.  The error of this approximation
        is 1.89818e-6, according to this unverified source:

          Lancaster, Don.  *Approximating a Circle or an Ellipse Using
          Four Bezier Cubic Splines.*

          https://www.tinaja.com/glib/ellipse4.pdf

        There is a use case where very large ellipses must be drawn
        with very high accuracy, and it is too expensive to render the
        entire ellipse with enough segments (either splines or line
        segments).  Therefore, in the case where either radius of the
        ellipse is large enough that the error of the spline
        approximation will be visible (greater than one pixel offset
        from the ideal), a different technique is used.

        In that case, only the visible parts of the ellipse are drawn,
        with each visible arc using a fixed number of spline segments
        (8).  The algorithm proceeds as follows:

        1. The points where the ellipse intersects the axes (or figure)
           bounding box are located.  (This is done by performing an inverse
           transformation on the bbox such that it is relative to the unit
           circle -- this makes the intersection calculation much easier than
           doing rotated ellipse intersection directly.)

           This uses the ""line intersecting a circle"" algorithm from:

               Vince, John.  *Geometry for Computer Graphics: Formulae,
               Examples & Proofs.*  London: Springer-Verlag, 2005.

        2. The angles of each of the intersection points are calculated.

        3. Proceeding counterclockwise starting in the positive
           x-direction, each of the visible arc-segments between the
           pairs of vertices are drawn using the Bezier arc
           approximation technique implemented in `.Path.arc`.
        ",258,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def draw(self, renderer):
        
        if not self.get_visible():
            return

        self._recompute_transform()

        width = self.convert_xunits(self.width)
        height = self.convert_yunits(self.height)

        # If the width and height of ellipse are not equal, take into account
        # stretching when calculating angles to draw between
        ```
    ###Assistant : 
        Draw the arc to the given *renderer*.

        Notes
        -----
        Ellipses are normally drawn using an approximation that uses
        eight cubic Bezier splines.  The error of this approximation
        is 1.89818e-6, according to this unverified source:

          Lancaster, Don.  *Approximating a Circle or an Ellipse Using
          Four Bezier Cubic Splines.*

          https://www.tinaja.com/glib/ellipse4.pdf

        There is a use case where very large ellipses must be drawn
        with very high accuracy, and it is too expensive to render the
        entire ellipse with enough segments (either splines or line
        segments).  Therefore, in the case where either radius of the
        ellipse is large enough that the error of the spline
        approximation will be visible (greater than one pixel offset
        from the ideal), a different technique is used.

        In that case, only the visible parts of the ellipse are drawn,
        with each visible arc using a fixed number of spline segments
        (8).  The algorithm proceeds as follows:

        1. The points where the ellipse intersects the axes (or figure)
           bounding box are located.  (This is done by performing an inverse
           transformation on the bbox such that it is relative to the unit
           circle -- this makes the intersection calculation much easier than
           doing rotated ellipse intersection directly.)

           This uses the ""line intersecting a circle"" algorithm from:

               Vince, John.  *Geometry for Computer Graphics: Formulae,
               Examples & Proofs.*  London: Springer-Verlag, 2005.

        2. The angles of each of the intersection points are calculated.

        3. Proceeding counterclockwise starting in the positive
           x-direction, each of the visible arc-segments between the
           pairs of vertices are drawn using the Bezier arc
           approximation technique implemented in `.Path.arc`.
        
    "
405,"def validate_parameter_constraints(parameter_constraints, params, caller_name):
    
    for param_name, param_val in params.items():
        # We allow parameters to not have a constraint so that third party estimators
        # can inherit from sklearn estimators without having to necessarily use the
        # validation tools.
        if param_name not in parameter_constraints:
            continue

        constraints = parameter_constraints[param_name]

        if constraints == ""no_validation"":
            continue

        constraints = [make_constraint(constraint) for constraint in constraints]

        for constraint in constraints:
            if constraint.is_satisfied_by(param_val):
                # this constraint is satisfied, no need to check further.
                break
        else:
            # No constraint is satisfied, raise with an informative message.

            # Ignore constraints that we don't want to expose in the error message,
            # i.e. options that are for internal purpose or not officially supported.
            constraints = [
                constraint for constraint in constraints if not constraint.hidden
            ]

            if len(constraints) == 1:
                constraints_str = f""{constraints[0]}""
            else:
                constraints_str = (
                    f""{', '.join([str(c) for c in constraints[:-1]])} or""
                    f"" {constraints[-1]}""
                )

            raise ValueError(
                f""The {param_name!r} parameter of {caller_name} must be""
                f"" {constraints_str}. Got {param_val!r} instead.""
            )

","Validate types and values of given parameters.

    Parameters
    ----------
    parameter_constraints : dict or {""no_validation""}
        If ""no_validation"", validation is skipped for this parameter.

        If a dict, it must be a dictionary `param_name: list of constraints`.
        A parameter is valid if it satisfies one of the constraints from the list.
        Constraints can be:
        - an Interval object, representing a continuous or discrete range of numbers
        - the string ""array-like""
        - the string ""sparse matrix""
        - the string ""random_state""
        - callable
        - None, meaning that None is a valid value for the parameter
        - any type, meaning that any instance of this type is valid
        - an Options object, representing a set of elements of a given type
        - a StrOptions object, representing a set of strings
        - the string ""boolean""
        - the string ""verbose""
        - the string ""cv_object""
        - the string ""missing_values""
        - a HasMethods object, representing method(s) an object must have
        - a Hidden object, representing a constraint not meant to be exposed to the user

    params : dict
        A dictionary `param_name: param_value`. The parameters to validate against the
        constraints.

    caller_name : str
        The name of the estimator or function or method that called this function.
    ",195,160,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_parameter_constraints(parameter_constraints, params, caller_name):
    
    for param_name, param_val in params.items():
        # We allow parameters to not have a constraint so that third party estimators
        # can inherit from sklearn estimators without having to necessarily use the
        # validation tools.
        if param_name not in parameter_constraints:
            continue

        constraints = parameter_constraints[param_name]

        if constraints == ""no_validation"":
            continue

        constraints = [make_constraint(constraint) for constraint in constraints]

        for constraint in constraints:
            if constraint.is_satisfied_by(param_val):
                # this constraint is satisfied, no need to check further.
                break
        else:
            # No constraint is satisfied, raise with an informative message.

            # Ignore constraints that we don't want to expose in the error message,
            # i.e. options that are for internal purpose or not officially supported.
            constraints = [
                constraint for constraint in constraints if not constraint.hidden
            ]

            if len(constraints) == 1:
                constraints_str = f""{constraints[0]}""
            else:
                constraints_str = (
                    f""{', '.join([str(c) for c in constraints[:-1]])} or""
                    f"" {constraints[-1]}""
                )

            raise ValueError(
                f""The {param_name!r} parameter of {caller_name} must be""
                f"" {constraints_str}. Got {param_val!r} instead.""
            )


        ```
    ###Assistant : Validate types and values of given parameters.

    Parameters
    ----------
    parameter_constraints : dict or {""no_validation""}
        If ""no_validation"", validation is skipped for this parameter.

        If a dict, it must be a dictionary `param_name: list of constraints`.
        A parameter is valid if it satisfies one of the constraints from the list.
        Constraints can be:
        - an Interval object, representing a continuous or discrete range of numbers
        - the string ""array-like""
        - the string ""sparse matrix""
        - the string ""random_state""
        - callable
        - None, meaning that None is a valid value for the parameter
        - any type, meaning that any instance of this type is valid
        - an Options object, representing a set of elements of a given type
        - a StrOptions object, representing a set of strings
        - the string ""boolean""
        - the string ""verbose""
        - the string ""cv_object""
        - the string ""missing_values""
        - a HasMethods object, representing method(s) an object must have
        - a Hidden object, representing a constraint not meant to be exposed to the user

    params : dict
        A dictionary `param_name: param_value`. The parameters to validate against the
        constraints.

    caller_name : str
        The name of the estimator or function or method that called this function.
    
    "
406,"def _get_module_collection_mode(mode_dict, name):
    
    mode = 'pyc'  # Default mode

    # No settings available - return default.
    if not mode_dict:
        return mode

    # Search the parent modules/packages in top-down fashion, and take the last given setting. This ensures that
    # a setting given for the top-level package is recursively propagated to all its subpackages and submodules,
    # but also allows individual sub-modules to override the setting again.
    name_parts = name.split('.')
    for i in range(len(name_parts)):
        modlevel = ""."".join(name_parts[:i + 1])
        modlevel_mode = mode_dict.get(modlevel, None)
        if modlevel_mode is not None:
            mode = modlevel_mode

    return mode

","
    Determine the module/package collection mode for the given module name , based on the provided collection
    mode settings dictionary.
    ",19,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_module_collection_mode(mode_dict, name):
    
    mode = 'pyc'  # Default mode

    # No settings available - return default.
    if not mode_dict:
        return mode

    # Search the parent modules/packages in top-down fashion, and take the last given setting. This ensures that
    # a setting given for the top-level package is recursively propagated to all its subpackages and submodules,
    # but also allows individual sub-modules to override the setting again.
    name_parts = name.split('.')
    for i in range(len(name_parts)):
        modlevel = ""."".join(name_parts[:i + 1])
        modlevel_mode = mode_dict.get(modlevel, None)
        if modlevel_mode is not None:
            mode = modlevel_mode

    return mode


        ```
    ###Assistant : 
    Determine the module/package collection mode for the given module name , based on the provided collection
    mode settings dictionary.
    
    "
407,"def exact_gaussian_kernel(x, y, stddev):
    r
    x_aligned, y_aligned = _align_matrices(x, y)
    diff_squared_l2_norm = tf.reduce_sum(
        tf.math.squared_difference(x_aligned, y_aligned), 2
    )
    return tf.exp(-diff_squared_l2_norm / (2 * stddev * stddev))

","Computes exact Gaussian kernel value(s) for tensors x and y and stddev.

    The Gaussian kernel for vectors u, v is defined as follows:
         K(u, v) = exp(-||u-v||^2 / (2* stddev^2))
    where the norm is the l2-norm. x, y can be either vectors or matrices. If they
    are vectors, they must have the same dimension. If they are matrices, they
    must have the same number of columns. In the latter case, the method returns
    (as a matrix) K(u, v) values for all pairs (u, v) where u is a row from x and
    v is a row from y.

    Args:
      x: a tensor of rank 1 or 2. It's shape should be either [dim] or [m, dim].
      y: a tensor of rank 1 or 2. It's shape should be either [dim] or [n, dim].
      stddev: The width of the Gaussian kernel.

    Returns:
      A single value (scalar) with shape (1, 1) (if x, y are vectors) or a matrix
        of shape (m, n) with entries K(u, v) (where K is the Gaussian kernel) for
        all (u,v) pairs where u, v are rows from x and y respectively.

    Raises:
      ValueError: if the shapes of x, y are not compatible.
    ",196,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def exact_gaussian_kernel(x, y, stddev):
    r
    x_aligned, y_aligned = _align_matrices(x, y)
    diff_squared_l2_norm = tf.reduce_sum(
        tf.math.squared_difference(x_aligned, y_aligned), 2
    )
    return tf.exp(-diff_squared_l2_norm / (2 * stddev * stddev))


        ```
    ###Assistant : Computes exact Gaussian kernel value(s) for tensors x and y and stddev.

    The Gaussian kernel for vectors u, v is defined as follows:
         K(u, v) = exp(-||u-v||^2 / (2* stddev^2))
    where the norm is the l2-norm. x, y can be either vectors or matrices. If they
    are vectors, they must have the same dimension. If they are matrices, they
    must have the same number of columns. In the latter case, the method returns
    (as a matrix) K(u, v) values for all pairs (u, v) where u is a row from x and
    v is a row from y.

    Args:
      x: a tensor of rank 1 or 2. It's shape should be either [dim] or [m, dim].
      y: a tensor of rank 1 or 2. It's shape should be either [dim] or [n, dim].
      stddev: The width of the Gaussian kernel.

    Returns:
      A single value (scalar) with shape (1, 1) (if x, y are vectors) or a matrix
        of shape (m, n) with entries K(u, v) (where K is the Gaussian kernel) for
        all (u,v) pairs where u, v are rows from x and y respectively.

    Raises:
      ValueError: if the shapes of x, y are not compatible.
    
    "
408,"def enqueue_job(cls, func, name, obj_type, user, schedule_at=None, *args, **kwargs):
        
        job_result: JobResult = cls.objects.create(
            name=name,
            obj_type=obj_type,
            user=user,
            job_id=uuid.uuid4()
        )

        queue = django_rq.get_queue(""default"")

        if schedule_at:
            job_result.status = JobResultStatusChoices.STATUS_SCHEDULED
            job_result.scheduled_time = schedule_at
            job_result.save()

            queue.enqueue_at(schedule_at, func, job_id=str(job_result.job_id), job_result=job_result, **kwargs)
        else:
            queue.enqueue(func, job_id=str(job_result.job_id), job_result=job_result, **kwargs)

        return job_result

","
        Create a JobResult instance and enqueue a job using the given callable

        func: The callable object to be enqueued for execution
        name: Name for the JobResult instance
        obj_type: ContentType to link to the JobResult instance obj_type
        user: User object to link to the JobResult instance
        schedule_at: Schedule the job to be executed at the passed date and time
        args: additional args passed to the callable
        kwargs: additional kargs passed to the callable
        ",72,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def enqueue_job(cls, func, name, obj_type, user, schedule_at=None, *args, **kwargs):
        
        job_result: JobResult = cls.objects.create(
            name=name,
            obj_type=obj_type,
            user=user,
            job_id=uuid.uuid4()
        )

        queue = django_rq.get_queue(""default"")

        if schedule_at:
            job_result.status = JobResultStatusChoices.STATUS_SCHEDULED
            job_result.scheduled_time = schedule_at
            job_result.save()

            queue.enqueue_at(schedule_at, func, job_id=str(job_result.job_id), job_result=job_result, **kwargs)
        else:
            queue.enqueue(func, job_id=str(job_result.job_id), job_result=job_result, **kwargs)

        return job_result


        ```
    ###Assistant : 
        Create a JobResult instance and enqueue a job using the given callable

        func: The callable object to be enqueued for execution
        name: Name for the JobResult instance
        obj_type: ContentType to link to the JobResult instance obj_type
        user: User object to link to the JobResult instance
        schedule_at: Schedule the job to be executed at the passed date and time
        args: additional args passed to the callable
        kwargs: additional kargs passed to the callable
        
    "
409,"def text_style(self) -> Style:
        

        # TODO: Feels like there may be opportunity for caching here.

        style = Style()
        for node in reversed(self.ancestors):
            style += node.styles.text_style

        return style
","Get the text style object.

        A widget's style is influenced by its parent. For instance if a widgets background has an alpha,
        then its parent's background color will show through. Additionally, widgets will inherit their
        parent's text style (i.e. bold, italic etc).

        Returns:
            Style: Rich Style object.
        ",47,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def text_style(self) -> Style:
        

        # TODO: Feels like there may be opportunity for caching here.

        style = Style()
        for node in reversed(self.ancestors):
            style += node.styles.text_style

        return style

        ```
    ###Assistant : Get the text style object.

        A widget's style is influenced by its parent. For instance if a widgets background has an alpha,
        then its parent's background color will show through. Additionally, widgets will inherit their
        parent's text style (i.e. bold, italic etc).

        Returns:
            Style: Rich Style object.
        
    "
410,"def get_app_list(self, request):
        
        app_dict = self._build_app_dict(request)

        # Sort the apps alphabetically.
        app_list = sorted(app_dict.values(), key=lambda x: x[""name""].lower())

        # Sort the models alphabetically within each app.
        for app in app_list:
            app[""models""].sort(key=lambda x: x[""name""])

        return app_list
","
        Return a sorted list of all the installed apps that have been
        registered in this site.
        ",16,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_app_list(self, request):
        
        app_dict = self._build_app_dict(request)

        # Sort the apps alphabetically.
        app_list = sorted(app_dict.values(), key=lambda x: x[""name""].lower())

        # Sort the models alphabetically within each app.
        for app in app_list:
            app[""models""].sort(key=lambda x: x[""name""])

        return app_list

        ```
    ###Assistant : 
        Return a sorted list of all the installed apps that have been
        registered in this site.
        
    "
411,"def find_library_file (self, dirs, lib, debug=0):
        
        raise NotImplementedError

    # -- Filename generation methods -----------------------------------

    # The default implementation of the filename generating methods are
    # prejudiced towards the Unix/DOS/Windows view of the world:
    #   * object files are named by replacing the source file extension
    #     (eg. .c/.cpp -> .o/.obj)
    #   * library files (shared or static) are named by plugging the
    #     library name and extension into a format string, eg.
    #     ""lib%s.%s"" % (lib_name, "".a"") for Unix static libraries
    #   * executables are named by appending an extension (possibly
    #     empty) to the program name: eg. progname + "".exe"" for
    #     Windows
    #
    # To reduce redundant code, these methods expect to find
    # several attributes in the current object (presumably defined
    # as class attributes):
    #   * src_extensions -
    #     list of C/C++ source file extensions, eg. ['.c', '.cpp']
    #   * obj_extension -
    #     object file extension, eg. '.o' or '.obj'
    #   * static_lib_extension -
    #     extension for static library files, eg. '.a' or '.lib'
    #   * shared_lib_extension -
    #     extension for shared library/object files, eg. '.so', '.dll'
    #   * static_lib_format -
    #     format string for generating static library filenames,
    #     eg. 'lib%s.%s' or '%s.%s'
    #   * shared_lib_format
    #     format string for generating shared library filenames
    #     (probably same as static_lib_format, since the extension
    #     is one of the intended parameters to the format string)
    #   * exe_extension -
    #     extension for executable files, eg. '' or '.exe'
","Search the specified list of directories for a static or shared
        library file 'lib' and return the full path to that file.  If
        'debug' true, look for a debugging version (if that makes sense on
        the current platform).  Return None if 'lib' wasn't found in any of
        the specified directories.
        ",50,241,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_library_file (self, dirs, lib, debug=0):
        
        raise NotImplementedError

    # -- Filename generation methods -----------------------------------

    # The default implementation of the filename generating methods are
    # prejudiced towards the Unix/DOS/Windows view of the world:
    #   * object files are named by replacing the source file extension
    #     (eg. .c/.cpp -> .o/.obj)
    #   * library files (shared or static) are named by plugging the
    #     library name and extension into a format string, eg.
    #     ""lib%s.%s"" % (lib_name, "".a"") for Unix static libraries
    #   * executables are named by appending an extension (possibly
    #     empty) to the program name: eg. progname + "".exe"" for
    #     Windows
    #
    # To reduce redundant code, these methods expect to find
    # several attributes in the current object (presumably defined
    # as class attributes):
    #   * src_extensions -
    #     list of C/C++ source file extensions, eg. ['.c', '.cpp']
    #   * obj_extension -
    #     object file extension, eg. '.o' or '.obj'
    #   * static_lib_extension -
    #     extension for static library files, eg. '.a' or '.lib'
    #   * shared_lib_extension -
    #     extension for shared library/object files, eg. '.so', '.dll'
    #   * static_lib_format -
    #     format string for generating static library filenames,
    #     eg. 'lib%s.%s' or '%s.%s'
    #   * shared_lib_format
    #     format string for generating shared library filenames
    #     (probably same as static_lib_format, since the extension
    #     is one of the intended parameters to the format string)
    #   * exe_extension -
    #     extension for executable files, eg. '' or '.exe'

        ```
    ###Assistant : Search the specified list of directories for a static or shared
        library file 'lib' and return the full path to that file.  If
        'debug' true, look for a debugging version (if that makes sense on
        the current platform).  Return None if 'lib' wasn't found in any of
        the specified directories.
        
    "
412,"def test_simple_lock(self):
        
        # First to acquire this lock, so it should complete
        lock = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        assert lock is not None

        # Enter the context manager
        self.get_success(lock.__aenter__())

        # Attempting to acquire the lock again fails.
        lock2 = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        self.assertIsNone(lock2)

        # Calling `is_still_valid` reports true.
        self.assertTrue(self.get_success(lock.is_still_valid()))

        # Drop the lock
        self.get_success(lock.__aexit__(None, None, None))

        # We can now acquire the lock again.
        lock3 = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        assert lock3 is not None
        self.get_success(lock3.__aenter__())
        self.get_success(lock3.__aexit__(None, None, None))
","Test that we can take out a lock and that while we hold it nobody
        else can take it out.
        ",20,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_simple_lock(self):
        
        # First to acquire this lock, so it should complete
        lock = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        assert lock is not None

        # Enter the context manager
        self.get_success(lock.__aenter__())

        # Attempting to acquire the lock again fails.
        lock2 = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        self.assertIsNone(lock2)

        # Calling `is_still_valid` reports true.
        self.assertTrue(self.get_success(lock.is_still_valid()))

        # Drop the lock
        self.get_success(lock.__aexit__(None, None, None))

        # We can now acquire the lock again.
        lock3 = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        assert lock3 is not None
        self.get_success(lock3.__aenter__())
        self.get_success(lock3.__aexit__(None, None, None))

        ```
    ###Assistant : Test that we can take out a lock and that while we hold it nobody
        else can take it out.
        
    "
413,"def set_family(self, family):
        
        if family is None:
            family = mpl.rcParams['font.family']
        if isinstance(family, str):
            family = [family]
        self._family = family
","
        Change the font family.  May be either an alias (generic name
        is CSS parlance), such as: 'serif', 'sans-serif', 'cursive',
        'fantasy', or 'monospace', a real font name or a list of real
        font names.  Real font names are not supported when
        :rc:`text.usetex` is `True`. Default: :rc:`font.family`
        ",45,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_family(self, family):
        
        if family is None:
            family = mpl.rcParams['font.family']
        if isinstance(family, str):
            family = [family]
        self._family = family

        ```
    ###Assistant : 
        Change the font family.  May be either an alias (generic name
        is CSS parlance), such as: 'serif', 'sans-serif', 'cursive',
        'fantasy', or 'monospace', a real font name or a list of real
        font names.  Real font names are not supported when
        :rc:`text.usetex` is `True`. Default: :rc:`font.family`
        
    "
414,"def update_ema(biased_ema, value, decay):
    
    biased_ema = biased_ema * decay + (1 - decay) * value
    return biased_ema

","
    calculate biased stat and unbiased stat in each step using exponential moving average method

    Parameters
    ----------
    biased_ema : float
        previous stat value
    value : float
        current stat value
    decay : float
        the weight of previous stat value, larger means smoother curve

    Returns
    -------
    float, float
    ",45,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_ema(biased_ema, value, decay):
    
    biased_ema = biased_ema * decay + (1 - decay) * value
    return biased_ema


        ```
    ###Assistant : 
    calculate biased stat and unbiased stat in each step using exponential moving average method

    Parameters
    ----------
    biased_ema : float
        previous stat value
    value : float
        current stat value
    decay : float
        the weight of previous stat value, larger means smoother curve

    Returns
    -------
    float, float
    
    "
415,"def npartitions(n, verbose=False):
    
    n = int(n)
    if n < 0:
        return 0
    if n <= 5:
        return [1, 1, 2, 3, 5, 7][n]
    if '_factor' not in globals():
        _pre()
    # Estimate number of bits in p(n). This formula could be tidied
    pbits = int((
        math.pi*(2*n/3.)**0.5 -
        math.log(4*n))/math.log(10) + 1) * \
        math.log(10, 2)
    prec = p = int(pbits*1.1 + 100)
    s = fzero
    M = max(6, int(0.24*n**0.5 + 4))
    if M > 10**5:
        raise ValueError(""Input too big"") # Corresponds to n > 1.7e11
    sq23pi = mpf_mul(mpf_sqrt(from_rational(2, 3, p), p), mpf_pi(p), p)
    sqrt8 = mpf_sqrt(from_int(8), p)
    for q in range(1, M):
        a = _a(n, q, p)
        d = _d(n, q, p, sq23pi, sqrt8)
        s = mpf_add(s, mpf_mul(a, d), prec)
        if verbose:
            print(""step"", q, ""of"", M, to_str(a, 10), to_str(d, 10))
        # On average, the terms decrease rapidly in magnitude.
        # Dynamically reducing the precision greatly improves
        # performance.
        p = bitcount(abs(to_int(d))) + 50
    return int(to_int(mpf_add(s, fhalf, prec)))

__all__ = ['npartitions']
","
    Calculate the partition function P(n), i.e. the number of ways that
    n can be written as a sum of positive integers.

    P(n) is computed using the Hardy-Ramanujan-Rademacher formula [1]_.


    The correctness of this implementation has been tested through $10^10$.

    Examples
    ========

    >>> from sympy.ntheory import npartitions
    >>> npartitions(25)
    1958

    References
    ==========

    .. [1] http://mathworld.wolfram.com/PartitionFunctionP.html

    ",54,158,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def npartitions(n, verbose=False):
    
    n = int(n)
    if n < 0:
        return 0
    if n <= 5:
        return [1, 1, 2, 3, 5, 7][n]
    if '_factor' not in globals():
        _pre()
    # Estimate number of bits in p(n). This formula could be tidied
    pbits = int((
        math.pi*(2*n/3.)**0.5 -
        math.log(4*n))/math.log(10) + 1) * \
        math.log(10, 2)
    prec = p = int(pbits*1.1 + 100)
    s = fzero
    M = max(6, int(0.24*n**0.5 + 4))
    if M > 10**5:
        raise ValueError(""Input too big"") # Corresponds to n > 1.7e11
    sq23pi = mpf_mul(mpf_sqrt(from_rational(2, 3, p), p), mpf_pi(p), p)
    sqrt8 = mpf_sqrt(from_int(8), p)
    for q in range(1, M):
        a = _a(n, q, p)
        d = _d(n, q, p, sq23pi, sqrt8)
        s = mpf_add(s, mpf_mul(a, d), prec)
        if verbose:
            print(""step"", q, ""of"", M, to_str(a, 10), to_str(d, 10))
        # On average, the terms decrease rapidly in magnitude.
        # Dynamically reducing the precision greatly improves
        # performance.
        p = bitcount(abs(to_int(d))) + 50
    return int(to_int(mpf_add(s, fhalf, prec)))

__all__ = ['npartitions']

        ```
    ###Assistant : 
    Calculate the partition function P(n), i.e. the number of ways that
    n can be written as a sum of positive integers.

    P(n) is computed using the Hardy-Ramanujan-Rademacher formula [1]_.


    The correctness of this implementation has been tested through $10^10$.

    Examples
    ========

    >>> from sympy.ntheory import npartitions
    >>> npartitions(25)
    1958

    References
    ==========

    .. [1] http://mathworld.wolfram.com/PartitionFunctionP.html

    
    "
416,"def build_sleep_command(self) -> list[str]:
        
        docker_pull(self.args, self.config.image)
        inspect = docker_image_inspect(self.args, self.config.image)

        return ['sh', '-c', f'sleep 60; exec {shlex.join(inspect.cmd)}']
","
        Build and return the command to put the container to sleep.

        The sleep duration below was selected to:

          - Allow enough time to perform necessary operations in the container before waking it.
          - Make the delay obvious if the wake command doesn't run or succeed.
          - Avoid hanging indefinitely or for an unreasonably long time.

        NOTE: The container must have a POSIX-compliant default shell ""sh"" with a non-builtin ""sleep"" command.
        ",70,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def build_sleep_command(self) -> list[str]:
        
        docker_pull(self.args, self.config.image)
        inspect = docker_image_inspect(self.args, self.config.image)

        return ['sh', '-c', f'sleep 60; exec {shlex.join(inspect.cmd)}']

        ```
    ###Assistant : 
        Build and return the command to put the container to sleep.

        The sleep duration below was selected to:

          - Allow enough time to perform necessary operations in the container before waking it.
          - Make the delay obvious if the wake command doesn't run or succeed.
          - Avoid hanging indefinitely or for an unreasonably long time.

        NOTE: The container must have a POSIX-compliant default shell ""sh"" with a non-builtin ""sleep"" command.
        
    "
417,"def test_crash_only_one_worker_during_sampling_but_ignore(self):
        
        config = (
            pg.PGConfig()
            .rollouts(
                num_rollout_workers=2,
                num_envs_per_worker=3,
                # Ignore worker failures (continue with worker #2).
                ignore_worker_failures=True,
            )
            .environment(
                env=CartPoleCrashing,
                env_config={
                    # Crash prob=80%.
                    ""p_crash"": 0.8,
                    # Only crash on worker with index 1.
                    ""crash_on_worker_indices"": [1],
                    # Make sure nothing happens during pre-checks.
                    ""skip_env_checking"": True,
                },
            )
            .debugging(worker_cls=ForwardHealthCheckToEnvWorker)
        )
        # Pre-checking disables, so building the Algorithm is save.
        algo = config.build()
        # Expect some errors being logged here, but in general, should continue
        # as we ignore worker failures.
        algo.train()
        # One worker has been removed -> Only one left.
        self.assertEqual(algo.workers.num_healthy_remote_workers(), 1)
        algo.stop()
","Expect some sub-envs to fail (and not recover), but ignore.",10,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_crash_only_one_worker_during_sampling_but_ignore(self):
        
        config = (
            pg.PGConfig()
            .rollouts(
                num_rollout_workers=2,
                num_envs_per_worker=3,
                # Ignore worker failures (continue with worker #2).
                ignore_worker_failures=True,
            )
            .environment(
                env=CartPoleCrashing,
                env_config={
                    # Crash prob=80%.
                    ""p_crash"": 0.8,
                    # Only crash on worker with index 1.
                    ""crash_on_worker_indices"": [1],
                    # Make sure nothing happens during pre-checks.
                    ""skip_env_checking"": True,
                },
            )
            .debugging(worker_cls=ForwardHealthCheckToEnvWorker)
        )
        # Pre-checking disables, so building the Algorithm is save.
        algo = config.build()
        # Expect some errors being logged here, but in general, should continue
        # as we ignore worker failures.
        algo.train()
        # One worker has been removed -> Only one left.
        self.assertEqual(algo.workers.num_healthy_remote_workers(), 1)
        algo.stop()

        ```
    ###Assistant : Expect some sub-envs to fail (and not recover), but ignore.
    "
418,"def test_ohe_infrequent_three_levels_drop_infrequent_errors(drop):
    
    X_train = np.array([[""a""] * 5 + [""b""] * 20 + [""c""] * 10 + [""d""] * 3]).T
    ohe = OneHotEncoder(
        handle_unknown=""infrequent_if_exist"", sparse=False, max_categories=3, drop=drop
    )

    msg = f""Unable to drop category {drop[0]!r} from feature 0 because it is infrequent""
    with pytest.raises(ValueError, match=msg):
        ohe.fit(X_train)

",Test three levels and dropping the infrequent category.,8,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ohe_infrequent_three_levels_drop_infrequent_errors(drop):
    
    X_train = np.array([[""a""] * 5 + [""b""] * 20 + [""c""] * 10 + [""d""] * 3]).T
    ohe = OneHotEncoder(
        handle_unknown=""infrequent_if_exist"", sparse=False, max_categories=3, drop=drop
    )

    msg = f""Unable to drop category {drop[0]!r} from feature 0 because it is infrequent""
    with pytest.raises(ValueError, match=msg):
        ohe.fit(X_train)


        ```
    ###Assistant : Test three levels and dropping the infrequent category.
    "
419,"def is_a_private_model(model):
    
    if model in PRIVATE_MODELS:
        return True

    # Wrapper, Encoder and Decoder are all privates
    if model.endswith(""Wrapper""):
        return True
    if model.endswith(""Encoder""):
        return True
    if model.endswith(""Decoder""):
        return True
    return False

",Returns True if the model should not be in the main init.,12,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_a_private_model(model):
    
    if model in PRIVATE_MODELS:
        return True

    # Wrapper, Encoder and Decoder are all privates
    if model.endswith(""Wrapper""):
        return True
    if model.endswith(""Encoder""):
        return True
    if model.endswith(""Decoder""):
        return True
    return False


        ```
    ###Assistant : Returns True if the model should not be in the main init.
    "
420,"async def test_midnight_turnover_before_midnight_outside_period(hass):
    
    config = {
        ""binary_sensor"": [
            {""platform"": ""tod"", ""name"": ""Night"", ""after"": ""22:00"", ""before"": ""5:00""}
        ]
    }
    await async_setup_component(hass, ""binary_sensor"", config)
    await hass.async_block_till_done()

    state = hass.states.get(""binary_sensor.night"")
    assert state.state == STATE_OFF


@freeze_time(""2019-01-10 10:00:00-08:00"")",Test midnight turnover setting before midnight outside period.,8,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_midnight_turnover_before_midnight_outside_period(hass):
    
    config = {
        ""binary_sensor"": [
            {""platform"": ""tod"", ""name"": ""Night"", ""after"": ""22:00"", ""before"": ""5:00""}
        ]
    }
    await async_setup_component(hass, ""binary_sensor"", config)
    await hass.async_block_till_done()

    state = hass.states.get(""binary_sensor.night"")
    assert state.state == STATE_OFF


@freeze_time(""2019-01-10 10:00:00-08:00"")
        ```
    ###Assistant : Test midnight turnover setting before midnight outside period.
    "
421,"def _check_if_cleared(self) -> None:
        
        if self._blocks is None:
            raise ValueError(
                ""This Dataset's blocks have been moved, which means that you ""
                ""can no longer use this Dataset.""
            )
",Raise an error if this BlockList has been previously cleared.,10,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_if_cleared(self) -> None:
        
        if self._blocks is None:
            raise ValueError(
                ""This Dataset's blocks have been moved, which means that you ""
                ""can no longer use this Dataset.""
            )

        ```
    ###Assistant : Raise an error if this BlockList has been previously cleared.
    "
422,"def test_join_leave(self) -> None:
        
        channel = self.make_request(""GET"", ""/sync"", access_token=self.tok)
        self.assertEqual(channel.code, 200, channel.result)

        self.assertNotIn(self.excluded_room_id, channel.json_body[""rooms""][""join""])
        self.assertIn(self.included_room_id, channel.json_body[""rooms""][""join""])

        self.helper.leave(self.excluded_room_id, self.user_id, tok=self.tok)
        self.helper.leave(self.included_room_id, self.user_id, tok=self.tok)

        channel = self.make_request(
            ""GET"",
            ""/sync?since="" + channel.json_body[""next_batch""],
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 200, channel.result)

        self.assertNotIn(self.excluded_room_id, channel.json_body[""rooms""][""leave""])
        self.assertIn(self.included_room_id, channel.json_body[""rooms""][""leave""])
","Tests that rooms are correctly excluded from the 'join' and 'leave' sections of
        sync responses.
        ",15,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_join_leave(self) -> None:
        
        channel = self.make_request(""GET"", ""/sync"", access_token=self.tok)
        self.assertEqual(channel.code, 200, channel.result)

        self.assertNotIn(self.excluded_room_id, channel.json_body[""rooms""][""join""])
        self.assertIn(self.included_room_id, channel.json_body[""rooms""][""join""])

        self.helper.leave(self.excluded_room_id, self.user_id, tok=self.tok)
        self.helper.leave(self.included_room_id, self.user_id, tok=self.tok)

        channel = self.make_request(
            ""GET"",
            ""/sync?since="" + channel.json_body[""next_batch""],
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 200, channel.result)

        self.assertNotIn(self.excluded_room_id, channel.json_body[""rooms""][""leave""])
        self.assertIn(self.included_room_id, channel.json_body[""rooms""][""leave""])

        ```
    ###Assistant : Tests that rooms are correctly excluded from the 'join' and 'leave' sections of
        sync responses.
        
    "
423,"def test_short_description_is_used_as_field_label(self):
        
        response = self.client.get(""/admin/modeladmintest/author/inspect/1/"")
        self.assertContains(response, ""Birth information"")
        self.assertNotContains(response, ""author_birth_string"")

","
        A custom field has been added to the inspect view's `inspect_view_fields` and since
        this field has a `short_description` we expect it to be used as the field's label,
        and not use the name of the function.
        ",36,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_short_description_is_used_as_field_label(self):
        
        response = self.client.get(""/admin/modeladmintest/author/inspect/1/"")
        self.assertContains(response, ""Birth information"")
        self.assertNotContains(response, ""author_birth_string"")


        ```
    ###Assistant : 
        A custom field has been added to the inspect view's `inspect_view_fields` and since
        this field has a `short_description` we expect it to be used as the field's label,
        and not use the name of the function.
        
    "
424,"def check_stock_uom_with_bin(item, stock_uom):
	if stock_uom == frappe.db.get_value(""Item"", item, ""stock_uom""):
		return

	ref_uom = frappe.db.get_value(""Stock Ledger Entry"", {""item_code"": item}, ""stock_uom"")

	if ref_uom:
		if cstr(ref_uom) != cstr(stock_uom):
			frappe.throw(
				_(
					""Default Unit of Measure for Item {0} cannot be changed directly because you have already made some transaction(s) with another UOM. You will need to create a new Item to use a different Default UOM.""
				).format(item)
			)

	bin_list = frappe.db.sql(
		,
		(item, stock_uom),
		as_dict=1,
	)

	if bin_list:
		frappe.throw(
			_(
				""Default Unit of Measure for Item {0} cannot be changed directly because you have already made some transaction(s) with another UOM. You need to either cancel the linked documents or create a new Item.""
			).format(item)
		)

	# No SLE or documents against item. Bin UOM can be changed safely.
	frappe.db.sql(, (stock_uom, item))

","
			select * from tabBin where item_code = %s
				and (reserved_qty > 0 or ordered_qty > 0 or indented_qty > 0 or planned_qty > 0)
				and stock_uom != %s
			update tabBin set stock_uom=%s where item_code=%s",34,127,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_stock_uom_with_bin(item, stock_uom):
	if stock_uom == frappe.db.get_value(""Item"", item, ""stock_uom""):
		return

	ref_uom = frappe.db.get_value(""Stock Ledger Entry"", {""item_code"": item}, ""stock_uom"")

	if ref_uom:
		if cstr(ref_uom) != cstr(stock_uom):
			frappe.throw(
				_(
					""Default Unit of Measure for Item {0} cannot be changed directly because you have already made some transaction(s) with another UOM. You will need to create a new Item to use a different Default UOM.""
				).format(item)
			)

	bin_list = frappe.db.sql(
		,
		(item, stock_uom),
		as_dict=1,
	)

	if bin_list:
		frappe.throw(
			_(
				""Default Unit of Measure for Item {0} cannot be changed directly because you have already made some transaction(s) with another UOM. You need to either cancel the linked documents or create a new Item.""
			).format(item)
		)

	# No SLE or documents against item. Bin UOM can be changed safely.
	frappe.db.sql(, (stock_uom, item))


        ```
    ###Assistant : 
			select * from tabBin where item_code = %s
				and (reserved_qty > 0 or ordered_qty > 0 or indented_qty > 0 or planned_qty > 0)
				and stock_uom != %s
			update tabBin set stock_uom=%s where item_code=%s
    "
425,"def fix_group_counters():
    from django.db import connection

    click.echo(""Correcting Group.num_comments counter"")
    cursor = connection.cursor()
    cursor.execute(
        ,
        [ActivityType.NOTE.value],
    )


@click.command()
@click.option(
    ""--with-docs/--without-docs"",
    default=False,
    help=""Synchronize and repair embedded documentation. This "" ""is disabled by default."",
)
@configuration","
        UPDATE sentry_groupedmessage SET num_comments = (
            SELECT COUNT(*) from sentry_activity
            WHERE type = %s and group_id = sentry_groupedmessage.id
        )
    ",19,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fix_group_counters():
    from django.db import connection

    click.echo(""Correcting Group.num_comments counter"")
    cursor = connection.cursor()
    cursor.execute(
        ,
        [ActivityType.NOTE.value],
    )


@click.command()
@click.option(
    ""--with-docs/--without-docs"",
    default=False,
    help=""Synchronize and repair embedded documentation. This "" ""is disabled by default."",
)
@configuration
        ```
    ###Assistant : 
        UPDATE sentry_groupedmessage SET num_comments = (
            SELECT COUNT(*) from sentry_activity
            WHERE type = %s and group_id = sentry_groupedmessage.id
        )
    
    "
426,"def get_filter(query=None, params=None, parser_config_overrides=None):
    
    # NOTE: this function assumes project permissions check already happened
    parsed_terms = []
    if query is not None:
        try:
            parsed_terms = parse_search_query(
                query, params=params, config_overrides=parser_config_overrides
            )
        except ParseError as e:
            raise InvalidSearchQuery(f""Parse error: {e.expr.name} (column {e.column():d})"")

    kwargs = {
        ""start"": None,
        ""end"": None,
        ""conditions"": [],
        ""having"": [],
        ""user_id"": None,
        ""organization_id"": None,
        ""team_id"": [],
        ""project_ids"": [],
        ""group_ids"": [],
        ""condition_aggregates"": [],
        ""aliases"": params.get(""aliases"", {}) if params is not None else {},
    }

    projects_to_filter = []
    if any(
        isinstance(term, ParenExpression) or SearchBoolean.is_operator(term)
        for term in parsed_terms
    ):
        (
            condition,
            having,
            found_projects_to_filter,
            group_ids,
        ) = convert_search_boolean_to_snuba_query(parsed_terms, params)

        if condition:
            and_conditions = flatten_condition_tree(condition, SNUBA_AND)
            for func in and_conditions:
                kwargs[""conditions""].append(convert_function_to_condition(func))
        if having:
            kwargs[""condition_aggregates""] = [
                term.key.name for term in parsed_terms if isinstance(term, AggregateFilter)
            ]
            and_having = flatten_condition_tree(having, SNUBA_AND)
            for func in and_having:
                kwargs[""having""].append(convert_function_to_condition(func))
        if found_projects_to_filter:
            projects_to_filter = list(set(found_projects_to_filter))
        if group_ids is not None:
            kwargs[""group_ids""].extend(list(set(group_ids)))
    else:
        projects_to_filter = set()
        for term in parsed_terms:
            if isinstance(term, SearchFilter):
                conditions, found_projects_to_filter, group_ids = format_search_filter(term, params)
                if len(conditions) > 0:
                    kwargs[""conditions""].extend(conditions)
                if found_projects_to_filter:
                    projects_to_filter.update(found_projects_to_filter)
                if group_ids is not None:
                    kwargs[""group_ids""].extend(group_ids)
            elif isinstance(term, AggregateFilter):
                converted_filter = convert_aggregate_filter_to_snuba_query(term, params)
                kwargs[""condition_aggregates""].append(term.key.name)
                if converted_filter:
                    kwargs[""having""].append(converted_filter)
        projects_to_filter = list(projects_to_filter)

    # Keys included as url params take precedent if same key is included in search
    # They are also considered safe and to have had access rules applied unlike conditions
    # from the query string.
    if params:
        for key in (""start"", ""end""):
            kwargs[key] = params.get(key, None)
        if ""user_id"" in params:
            kwargs[""user_id""] = params[""user_id""]
        if ""organization_id"" in params:
            kwargs[""organization_id""] = params[""organization_id""]
        if ""team_id"" in params:
            kwargs[""team_id""] = params[""team_id""]
        # OrganizationEndpoint.get_filter() uses project_id, but eventstore.Filter uses project_ids
        if ""project_id"" in params:
            if projects_to_filter:
                kwargs[""project_ids""] = projects_to_filter
            else:
                kwargs[""project_ids""] = params[""project_id""]
        if ""environment"" in params:
            term = SearchFilter(SearchKey(""environment""), ""="", SearchValue(params[""environment""]))
            kwargs[""conditions""].append(convert_search_filter_to_snuba_query(term))
        if ""group_ids"" in params:
            kwargs[""group_ids""] = to_list(params[""group_ids""])
        # Deprecated alias, use `group_ids` instead
        if ISSUE_ID_ALIAS in params:
            kwargs[""group_ids""] = to_list(params[""issue.id""])

    return eventstore.Filter(**kwargs)

","
    Returns an eventstore filter given the search text provided by the user and
    URL params
    ",15,307,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_filter(query=None, params=None, parser_config_overrides=None):
    
    # NOTE: this function assumes project permissions check already happened
    parsed_terms = []
    if query is not None:
        try:
            parsed_terms = parse_search_query(
                query, params=params, config_overrides=parser_config_overrides
            )
        except ParseError as e:
            raise InvalidSearchQuery(f""Parse error: {e.expr.name} (column {e.column():d})"")

    kwargs = {
        ""start"": None,
        ""end"": None,
        ""conditions"": [],
        ""having"": [],
        ""user_id"": None,
        ""organization_id"": None,
        ""team_id"": [],
        ""project_ids"": [],
        ""group_ids"": [],
        ""condition_aggregates"": [],
        ""aliases"": params.get(""aliases"", {}) if params is not None else {},
    }

    projects_to_filter = []
    if any(
        isinstance(term, ParenExpression) or SearchBoolean.is_operator(term)
        for term in parsed_terms
    ):
        (
            condition,
            having,
            found_projects_to_filter,
            group_ids,
        ) = convert_search_boolean_to_snuba_query(parsed_terms, params)

        if condition:
            and_conditions = flatten_condition_tree(condition, SNUBA_AND)
            for func in and_conditions:
                kwargs[""conditions""].append(convert_function_to_condition(func))
        if having:
            kwargs[""condition_aggregates""] = [
                term.key.name for term in parsed_terms if isinstance(term, AggregateFilter)
            ]
            and_having = flatten_condition_tree(having, SNUBA_AND)
            for func in and_having:
                kwargs[""having""].append(convert_function_to_condition(func))
        if found_projects_to_filter:
            projects_to_filter = list(set(found_projects_to_filter))
        if group_ids is not None:
            kwargs[""group_ids""].extend(list(set(group_ids)))
    else:
        projects_to_filter = set()
        for term in parsed_terms:
            if isinstance(term, SearchFilter):
                conditions, found_projects_to_filter, group_ids = format_search_filter(term, params)
                if len(conditions) > 0:
                    kwargs[""conditions""].extend(conditions)
                if found_projects_to_filter:
                    projects_to_filter.update(found_projects_to_filter)
                if group_ids is not None:
                    kwargs[""group_ids""].extend(group_ids)
            elif isinstance(term, AggregateFilter):
                converted_filter = convert_aggregate_filter_to_snuba_query(term, params)
                kwargs[""condition_aggregates""].append(term.key.name)
                if converted_filter:
                    kwargs[""having""].append(converted_filter)
        projects_to_filter = list(projects_to_filter)

    # Keys included as url params take precedent if same key is included in search
    # They are also considered safe and to have had access rules applied unlike conditions
    # from the query string.
    if params:
        for key in (""start"", ""end""):
            kwargs[key] = params.get(key, None)
        if ""user_id"" in params:
            kwargs[""user_id""] = params[""user_id""]
        if ""organization_id"" in params:
            kwargs[""organization_id""] = params[""organization_id""]
        if ""team_id"" in params:
            kwargs[""team_id""] = params[""team_id""]
        # OrganizationEndpoint.get_filter() uses project_id, but eventstore.Filter uses project_ids
        if ""project_id"" in params:
            if projects_to_filter:
                kwargs[""project_ids""] = projects_to_filter
            else:
                kwargs[""project_ids""] = params[""project_id""]
        if ""environment"" in params:
            term = SearchFilter(SearchKey(""environment""), ""="", SearchValue(params[""environment""]))
            kwargs[""conditions""].append(convert_search_filter_to_snuba_query(term))
        if ""group_ids"" in params:
            kwargs[""group_ids""] = to_list(params[""group_ids""])
        # Deprecated alias, use `group_ids` instead
        if ISSUE_ID_ALIAS in params:
            kwargs[""group_ids""] = to_list(params[""issue.id""])

    return eventstore.Filter(**kwargs)


        ```
    ###Assistant : 
    Returns an eventstore filter given the search text provided by the user and
    URL params
    
    "
427,"def reset(self):
        
        self.result = {""encoding"": None, ""confidence"": 0.0, ""language"": None}
        self.done = False
        self._got_data = False
        self._has_win_bytes = False
        self._input_state = InputState.PURE_ASCII
        self._last_char = b""""
        if self._esc_charset_prober:
            self._esc_charset_prober.reset()
        if self._utf1632_prober:
            self._utf1632_prober.reset()
        for prober in self._charset_probers:
            prober.reset()
","
        Reset the UniversalDetector and all of its probers back to their
        initial states.  This is called by ``__init__``, so you only need to
        call this directly in between analyses of different documents.
        ",32,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reset(self):
        
        self.result = {""encoding"": None, ""confidence"": 0.0, ""language"": None}
        self.done = False
        self._got_data = False
        self._has_win_bytes = False
        self._input_state = InputState.PURE_ASCII
        self._last_char = b""""
        if self._esc_charset_prober:
            self._esc_charset_prober.reset()
        if self._utf1632_prober:
            self._utf1632_prober.reset()
        for prober in self._charset_probers:
            prober.reset()

        ```
    ###Assistant : 
        Reset the UniversalDetector and all of its probers back to their
        initial states.  This is called by ``__init__``, so you only need to
        call this directly in between analyses of different documents.
        
    "
428,"def test_redis_socket_parsing(self):
        

        for input, expected in [
            (None, (""redis://localhost:6379"", ""redis://localhost:6379"")),
            (
                ""redis+socket:///run/redis/redis.sock"",
                (
                    ""redis+socket:///run/redis/redis.sock"",
                    ""unix:///run/redis/redis.sock"",
                ),
            ),
            (
                ""unix:///run/redis/redis.sock"",
                (
                    ""redis+socket:///run/redis/redis.sock"",
                    ""unix:///run/redis/redis.sock"",
                ),
            ),
        ]:
            result = _parse_redis_url(input)
            self.assertTupleEqual(expected, result)
","
        GIVEN:
            - Various Redis connection URI formats
        WHEN:
            - The URI is parsed
        THEN:
            - Socket based URIs are translated
            - Non-socket URIs are unchanged
            - None provided uses default
        ",30,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_redis_socket_parsing(self):
        

        for input, expected in [
            (None, (""redis://localhost:6379"", ""redis://localhost:6379"")),
            (
                ""redis+socket:///run/redis/redis.sock"",
                (
                    ""redis+socket:///run/redis/redis.sock"",
                    ""unix:///run/redis/redis.sock"",
                ),
            ),
            (
                ""unix:///run/redis/redis.sock"",
                (
                    ""redis+socket:///run/redis/redis.sock"",
                    ""unix:///run/redis/redis.sock"",
                ),
            ),
        ]:
            result = _parse_redis_url(input)
            self.assertTupleEqual(expected, result)

        ```
    ###Assistant : 
        GIVEN:
            - Various Redis connection URI formats
        WHEN:
            - The URI is parsed
        THEN:
            - Socket based URIs are translated
            - Non-socket URIs are unchanged
            - None provided uses default
        
    "
429,"def path_components(self) -> tuple[str, ...]:
        
        path = urllib.parse.urlparse(self.url).path
        # This needs to be a tuple so that it's immutable.
        # Otherwise, this would fail silently:
        #   request.path_components.append(""foo"")
        return tuple(url.unquote(i) for i in path.split(""/"") if i)
","
        The URL's path components as a tuple of strings.
        Components are unquoted.
        ",12,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def path_components(self) -> tuple[str, ...]:
        
        path = urllib.parse.urlparse(self.url).path
        # This needs to be a tuple so that it's immutable.
        # Otherwise, this would fail silently:
        #   request.path_components.append(""foo"")
        return tuple(url.unquote(i) for i in path.split(""/"") if i)

        ```
    ###Assistant : 
        The URL's path components as a tuple of strings.
        Components are unquoted.
        
    "
430,"def gen_batches(n, batch_size, *, min_batch_size=0):
    
    if not isinstance(batch_size, numbers.Integral):
        raise TypeError(
            ""gen_batches got batch_size=%s, must be an integer"" % batch_size
        )
    if batch_size <= 0:
        raise ValueError(""gen_batches got batch_size=%s, must be positive"" % batch_size)
    start = 0
    for _ in range(int(n // batch_size)):
        end = start + batch_size
        if end + min_batch_size > n:
            continue
        yield slice(start, end)
        start = end
    if start < n:
        yield slice(start, n)

","Generator to create slices containing `batch_size` elements from 0 to `n`.

    The last slice may contain less than `batch_size` elements, when
    `batch_size` does not divide `n`.

    Parameters
    ----------
    n : int
        Size of the sequence.
    batch_size : int
        Number of elements in each batch.
    min_batch_size : int, default=0
        Minimum number of elements in each batch.

    Yields
    ------
    slice of `batch_size` elements

    See Also
    --------
    gen_even_slices: Generator to create n_packs slices going up to n.

    Examples
    --------
    >>> from sklearn.utils import gen_batches
    >>> list(gen_batches(7, 3))
    [slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
    >>> list(gen_batches(6, 3))
    [slice(0, 3, None), slice(3, 6, None)]
    >>> list(gen_batches(2, 3))
    [slice(0, 2, None)]
    >>> list(gen_batches(7, 3, min_batch_size=0))
    [slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
    >>> list(gen_batches(7, 3, min_batch_size=2))
    [slice(0, 3, None), slice(3, 7, None)]
    ",131,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def gen_batches(n, batch_size, *, min_batch_size=0):
    
    if not isinstance(batch_size, numbers.Integral):
        raise TypeError(
            ""gen_batches got batch_size=%s, must be an integer"" % batch_size
        )
    if batch_size <= 0:
        raise ValueError(""gen_batches got batch_size=%s, must be positive"" % batch_size)
    start = 0
    for _ in range(int(n // batch_size)):
        end = start + batch_size
        if end + min_batch_size > n:
            continue
        yield slice(start, end)
        start = end
    if start < n:
        yield slice(start, n)


        ```
    ###Assistant : Generator to create slices containing `batch_size` elements from 0 to `n`.

    The last slice may contain less than `batch_size` elements, when
    `batch_size` does not divide `n`.

    Parameters
    ----------
    n : int
        Size of the sequence.
    batch_size : int
        Number of elements in each batch.
    min_batch_size : int, default=0
        Minimum number of elements in each batch.

    Yields
    ------
    slice of `batch_size` elements

    See Also
    --------
    gen_even_slices: Generator to create n_packs slices going up to n.

    Examples
    --------
    >>> from sklearn.utils import gen_batches
    >>> list(gen_batches(7, 3))
    [slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
    >>> list(gen_batches(6, 3))
    [slice(0, 3, None), slice(3, 6, None)]
    >>> list(gen_batches(2, 3))
    [slice(0, 2, None)]
    >>> list(gen_batches(7, 3, min_batch_size=0))
    [slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
    >>> list(gen_batches(7, 3, min_batch_size=2))
    [slice(0, 3, None), slice(3, 7, None)]
    
    "
431,"def test_condinst_maskhead_loss(self):
        
        s = 256
        img_metas = [{
            'img_shape': (s, s, 3),
            'pad_shape': (s, s, 3),
            'scale_factor': 1,
        }]
        condinst_bboxhead = CondInstBboxHead(
            num_classes=4,
            in_channels=1,
            feat_channels=1,
            stacked_convs=1,
            norm_cfg=None)

        mask_feature_head = _fake_mask_feature_head()
        condinst_maskhead = CondInstMaskHead(
            mask_feature_head=mask_feature_head,
            loss_mask=dict(
                type='DiceLoss',
                use_sigmoid=True,
                activate=True,
                eps=5e-6,
                loss_weight=1.0))

        # Fcos head expects a multiple levels of features per image
        feats = []
        for i in range(len(condinst_bboxhead.strides)):
            feats.append(
                torch.rand(1, 1, s // (2**(i + 3)), s // (2**(i + 3))))
        feats = tuple(feats)
        cls_scores, bbox_preds, centernesses, param_preds =\
            condinst_bboxhead.forward(feats)

        # Test that empty ground truth encourages the network to
        # predict background
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.empty((0, 4))
        gt_instances.labels = torch.LongTensor([])
        gt_instances.masks = _rand_masks(0, gt_instances.bboxes.numpy(), s, s)

        _ = condinst_bboxhead.loss_by_feat(cls_scores, bbox_preds,
                                           centernesses, param_preds,
                                           [gt_instances], img_metas)
        # When truth is empty then all mask loss
        # should be zero for random inputs
        positive_infos = condinst_bboxhead.get_positive_infos()
        mask_outs = condinst_maskhead.forward(feats, positive_infos)
        empty_gt_mask_losses = condinst_maskhead.loss_by_feat(
            *mask_outs, [gt_instances], img_metas, positive_infos)
        loss_mask = empty_gt_mask_losses['loss_mask']
        self.assertEqual(loss_mask, 0, 'mask loss should be zero')

        # When truth is non-empty then all cls, box loss and centerness loss
        # should be nonzero for random inputs
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.Tensor(
            [[23.6667, 23.8757, 238.6326, 151.8874]])
        gt_instances.labels = torch.LongTensor([2])
        gt_instances.masks = _rand_masks(1, gt_instances.bboxes.numpy(), s, s)

        _ = condinst_bboxhead.loss_by_feat(cls_scores, bbox_preds,
                                           centernesses, param_preds,
                                           [gt_instances], img_metas)
        positive_infos = condinst_bboxhead.get_positive_infos()
        mask_outs = condinst_maskhead.forward(feats, positive_infos)
        one_gt_mask_losses = condinst_maskhead.loss_by_feat(
            *mask_outs, [gt_instances], img_metas, positive_infos)
        loss_mask = one_gt_mask_losses['loss_mask']
        self.assertGreater(loss_mask, 0, 'mask loss should be nonzero')
",Tests condinst maskhead loss when truth is empty and non-empty.,10,228,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_condinst_maskhead_loss(self):
        
        s = 256
        img_metas = [{
            'img_shape': (s, s, 3),
            'pad_shape': (s, s, 3),
            'scale_factor': 1,
        }]
        condinst_bboxhead = CondInstBboxHead(
            num_classes=4,
            in_channels=1,
            feat_channels=1,
            stacked_convs=1,
            norm_cfg=None)

        mask_feature_head = _fake_mask_feature_head()
        condinst_maskhead = CondInstMaskHead(
            mask_feature_head=mask_feature_head,
            loss_mask=dict(
                type='DiceLoss',
                use_sigmoid=True,
                activate=True,
                eps=5e-6,
                loss_weight=1.0))

        # Fcos head expects a multiple levels of features per image
        feats = []
        for i in range(len(condinst_bboxhead.strides)):
            feats.append(
                torch.rand(1, 1, s // (2**(i + 3)), s // (2**(i + 3))))
        feats = tuple(feats)
        cls_scores, bbox_preds, centernesses, param_preds =\
            condinst_bboxhead.forward(feats)

        # Test that empty ground truth encourages the network to
        # predict background
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.empty((0, 4))
        gt_instances.labels = torch.LongTensor([])
        gt_instances.masks = _rand_masks(0, gt_instances.bboxes.numpy(), s, s)

        _ = condinst_bboxhead.loss_by_feat(cls_scores, bbox_preds,
                                           centernesses, param_preds,
                                           [gt_instances], img_metas)
        # When truth is empty then all mask loss
        # should be zero for random inputs
        positive_infos = condinst_bboxhead.get_positive_infos()
        mask_outs = condinst_maskhead.forward(feats, positive_infos)
        empty_gt_mask_losses = condinst_maskhead.loss_by_feat(
            *mask_outs, [gt_instances], img_metas, positive_infos)
        loss_mask = empty_gt_mask_losses['loss_mask']
        self.assertEqual(loss_mask, 0, 'mask loss should be zero')

        # When truth is non-empty then all cls, box loss and centerness loss
        # should be nonzero for random inputs
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.Tensor(
            [[23.6667, 23.8757, 238.6326, 151.8874]])
        gt_instances.labels = torch.LongTensor([2])
        gt_instances.masks = _rand_masks(1, gt_instances.bboxes.numpy(), s, s)

        _ = condinst_bboxhead.loss_by_feat(cls_scores, bbox_preds,
                                           centernesses, param_preds,
                                           [gt_instances], img_metas)
        positive_infos = condinst_bboxhead.get_positive_infos()
        mask_outs = condinst_maskhead.forward(feats, positive_infos)
        one_gt_mask_losses = condinst_maskhead.loss_by_feat(
            *mask_outs, [gt_instances], img_metas, positive_infos)
        loss_mask = one_gt_mask_losses['loss_mask']
        self.assertGreater(loss_mask, 0, 'mask loss should be nonzero')

        ```
    ###Assistant : Tests condinst maskhead loss when truth is empty and non-empty.
    "
432,"def test_ContinuousSelector_2():
    
    cs = ContinuousSelector(threshold=5, svd_solver='randomized')
    X_transformed = cs.transform(iris_data[0:16, :])
    assert_equal(X_transformed.shape[1],3)

",Assert that ContinuousSelector works as expected with threshold=5.,8,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ContinuousSelector_2():
    
    cs = ContinuousSelector(threshold=5, svd_solver='randomized')
    X_transformed = cs.transform(iris_data[0:16, :])
    assert_equal(X_transformed.shape[1],3)


        ```
    ###Assistant : Assert that ContinuousSelector works as expected with threshold=5.
    "
433,"def test_add_view(self):
        
        add_dict = {
            ""title"": ""Døm ikke"",
            ""content"": ""<p>great article</p>"",
            ""date_0"": ""2008-03-18"",
            ""date_1"": ""10:54:39"",
            ""section"": self.s1.pk,
        }
        # Change User should not have access to add articles
        self.client.force_login(self.changeuser)
        # make sure the view removes test cookie
        self.assertIs(self.client.session.test_cookie_worked(), False)
        response = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertEqual(response.status_code, 403)
        # Try POST just to make sure
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertEqual(post.status_code, 403)
        self.assertEqual(Article.objects.count(), 3)
        self.client.get(reverse(""admin:logout""))

        # View User should not have access to add articles
        self.client.force_login(self.viewuser)
        response = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertEqual(response.status_code, 403)
        # Try POST just to make sure
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertEqual(post.status_code, 403)
        self.assertEqual(Article.objects.count(), 3)
        # Now give the user permission to add but not change.
        self.viewuser.user_permissions.add(
            get_perm(Article, get_permission_codename(""add"", Article._meta))
        )
        response = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertEqual(response.context[""title""], ""Add article"")
        self.assertContains(response, ""<title>Add article | Django site admin</title>"")
        self.assertContains(
            response, '<input type=""submit"" value=""Save and view"" name=""_continue"">'
        )
        post = self.client.post(
            reverse(""admin:admin_views_article_add""), add_dict, follow=False
        )
        self.assertEqual(post.status_code, 302)
        self.assertEqual(Article.objects.count(), 4)
        article = Article.objects.latest(""pk"")
        response = self.client.get(
            reverse(""admin:admin_views_article_change"", args=(article.pk,))
        )
        self.assertContains(
            response,
            '<li class=""success"">The article “Døm ikke” was added successfully.</li>',
        )
        article.delete()
        self.client.get(reverse(""admin:logout""))

        # Add user may login and POST to add view, then redirect to admin root
        self.client.force_login(self.adduser)
        addpage = self.client.get(reverse(""admin:admin_views_article_add""))
        change_list_link = '&rsaquo; <a href=""%s"">Articles</a>' % reverse(
            ""admin:admin_views_article_changelist""
        )
        self.assertNotContains(
            addpage,
            change_list_link,
            msg_prefix=""User restricted to add permission is given link to change list view in breadcrumbs."",
        )
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertRedirects(post, self.index_url)
        self.assertEqual(Article.objects.count(), 4)
        self.assertEqual(len(mail.outbox), 2)
        self.assertEqual(mail.outbox[0].subject, ""Greetings from a created object"")
        self.client.get(reverse(""admin:logout""))

        # The addition was logged correctly
        addition_log = LogEntry.objects.all()[0]
        new_article = Article.objects.last()
        article_ct = ContentType.objects.get_for_model(Article)
        self.assertEqual(addition_log.user_id, self.adduser.pk)
        self.assertEqual(addition_log.content_type_id, article_ct.pk)
        self.assertEqual(addition_log.object_id, str(new_article.pk))
        self.assertEqual(addition_log.object_repr, ""Døm ikke"")
        self.assertEqual(addition_log.action_flag, ADDITION)
        self.assertEqual(addition_log.get_change_message(), ""Added."")

        # Super can add too, but is redirected to the change list view
        self.client.force_login(self.superuser)
        addpage = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertContains(
            addpage,
            change_list_link,
            msg_prefix=""Unrestricted user is not given link to change list view in breadcrumbs."",
        )
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertRedirects(post, reverse(""admin:admin_views_article_changelist""))
        self.assertEqual(Article.objects.count(), 5)
        self.client.get(reverse(""admin:logout""))

        # 8509 - if a normal user is already logged in, it is possible
        # to change user into the superuser without error
        self.client.force_login(self.joepublicuser)
        # Check and make sure that if user expires, data still persists
        self.client.force_login(self.superuser)
        # make sure the view removes test cookie
        self.assertIs(self.client.session.test_cookie_worked(), False)
",Test add view restricts access and actually adds items.,9,342,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_add_view(self):
        
        add_dict = {
            ""title"": ""Døm ikke"",
            ""content"": ""<p>great article</p>"",
            ""date_0"": ""2008-03-18"",
            ""date_1"": ""10:54:39"",
            ""section"": self.s1.pk,
        }
        # Change User should not have access to add articles
        self.client.force_login(self.changeuser)
        # make sure the view removes test cookie
        self.assertIs(self.client.session.test_cookie_worked(), False)
        response = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertEqual(response.status_code, 403)
        # Try POST just to make sure
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertEqual(post.status_code, 403)
        self.assertEqual(Article.objects.count(), 3)
        self.client.get(reverse(""admin:logout""))

        # View User should not have access to add articles
        self.client.force_login(self.viewuser)
        response = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertEqual(response.status_code, 403)
        # Try POST just to make sure
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertEqual(post.status_code, 403)
        self.assertEqual(Article.objects.count(), 3)
        # Now give the user permission to add but not change.
        self.viewuser.user_permissions.add(
            get_perm(Article, get_permission_codename(""add"", Article._meta))
        )
        response = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertEqual(response.context[""title""], ""Add article"")
        self.assertContains(response, ""<title>Add article | Django site admin</title>"")
        self.assertContains(
            response, '<input type=""submit"" value=""Save and view"" name=""_continue"">'
        )
        post = self.client.post(
            reverse(""admin:admin_views_article_add""), add_dict, follow=False
        )
        self.assertEqual(post.status_code, 302)
        self.assertEqual(Article.objects.count(), 4)
        article = Article.objects.latest(""pk"")
        response = self.client.get(
            reverse(""admin:admin_views_article_change"", args=(article.pk,))
        )
        self.assertContains(
            response,
            '<li class=""success"">The article “Døm ikke” was added successfully.</li>',
        )
        article.delete()
        self.client.get(reverse(""admin:logout""))

        # Add user may login and POST to add view, then redirect to admin root
        self.client.force_login(self.adduser)
        addpage = self.client.get(reverse(""admin:admin_views_article_add""))
        change_list_link = '&rsaquo; <a href=""%s"">Articles</a>' % reverse(
            ""admin:admin_views_article_changelist""
        )
        self.assertNotContains(
            addpage,
            change_list_link,
            msg_prefix=""User restricted to add permission is given link to change list view in breadcrumbs."",
        )
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertRedirects(post, self.index_url)
        self.assertEqual(Article.objects.count(), 4)
        self.assertEqual(len(mail.outbox), 2)
        self.assertEqual(mail.outbox[0].subject, ""Greetings from a created object"")
        self.client.get(reverse(""admin:logout""))

        # The addition was logged correctly
        addition_log = LogEntry.objects.all()[0]
        new_article = Article.objects.last()
        article_ct = ContentType.objects.get_for_model(Article)
        self.assertEqual(addition_log.user_id, self.adduser.pk)
        self.assertEqual(addition_log.content_type_id, article_ct.pk)
        self.assertEqual(addition_log.object_id, str(new_article.pk))
        self.assertEqual(addition_log.object_repr, ""Døm ikke"")
        self.assertEqual(addition_log.action_flag, ADDITION)
        self.assertEqual(addition_log.get_change_message(), ""Added."")

        # Super can add too, but is redirected to the change list view
        self.client.force_login(self.superuser)
        addpage = self.client.get(reverse(""admin:admin_views_article_add""))
        self.assertContains(
            addpage,
            change_list_link,
            msg_prefix=""Unrestricted user is not given link to change list view in breadcrumbs."",
        )
        post = self.client.post(reverse(""admin:admin_views_article_add""), add_dict)
        self.assertRedirects(post, reverse(""admin:admin_views_article_changelist""))
        self.assertEqual(Article.objects.count(), 5)
        self.client.get(reverse(""admin:logout""))

        # 8509 - if a normal user is already logged in, it is possible
        # to change user into the superuser without error
        self.client.force_login(self.joepublicuser)
        # Check and make sure that if user expires, data still persists
        self.client.force_login(self.superuser)
        # make sure the view removes test cookie
        self.assertIs(self.client.session.test_cookie_worked(), False)

        ```
    ###Assistant : Test add view restricts access and actually adds items.
    "
434,"def install(collection, path, artifacts_manager):  # FIXME: mv to dataclasses?
    # type: (Candidate, str, ConcreteArtifactsManager) -> None
    
    b_artifact_path = (
        artifacts_manager.get_artifact_path if collection.is_concrete_artifact
        else artifacts_manager.get_galaxy_artifact_path
    )(collection)

    collection_path = os.path.join(path, collection.namespace, collection.name)
    b_collection_path = to_bytes(collection_path, errors='surrogate_or_strict')
    display.display(
        u""Installing '{coll!s}' to '{path!s}'"".
        format(coll=to_text(collection), path=collection_path),
    )

    if os.path.exists(b_collection_path):
        shutil.rmtree(b_collection_path)

    if collection.is_dir:
        install_src(collection, b_artifact_path, b_collection_path, artifacts_manager)
    else:
        install_artifact(
            b_artifact_path,
            b_collection_path,
            artifacts_manager._b_working_directory,
            collection.signatures,
            artifacts_manager.keyring
        )
        if (collection.is_online_index_pointer and isinstance(collection.src, GalaxyAPI)):
            write_source_metadata(
                collection,
                b_collection_path,
                artifacts_manager
            )

    display.display(
        '{coll!s} was installed successfully'.
        format(coll=to_text(collection)),
    )

","Install a collection under a given path.

    :param collection: Collection to be installed.
    :param path: Collection dirs layout path.
    :param artifacts_manager: Artifacts manager.
    ",23,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def install(collection, path, artifacts_manager):  # FIXME: mv to dataclasses?
    # type: (Candidate, str, ConcreteArtifactsManager) -> None
    
    b_artifact_path = (
        artifacts_manager.get_artifact_path if collection.is_concrete_artifact
        else artifacts_manager.get_galaxy_artifact_path
    )(collection)

    collection_path = os.path.join(path, collection.namespace, collection.name)
    b_collection_path = to_bytes(collection_path, errors='surrogate_or_strict')
    display.display(
        u""Installing '{coll!s}' to '{path!s}'"".
        format(coll=to_text(collection), path=collection_path),
    )

    if os.path.exists(b_collection_path):
        shutil.rmtree(b_collection_path)

    if collection.is_dir:
        install_src(collection, b_artifact_path, b_collection_path, artifacts_manager)
    else:
        install_artifact(
            b_artifact_path,
            b_collection_path,
            artifacts_manager._b_working_directory,
            collection.signatures,
            artifacts_manager.keyring
        )
        if (collection.is_online_index_pointer and isinstance(collection.src, GalaxyAPI)):
            write_source_metadata(
                collection,
                b_collection_path,
                artifacts_manager
            )

    display.display(
        '{coll!s} was installed successfully'.
        format(coll=to_text(collection)),
    )


        ```
    ###Assistant : Install a collection under a given path.

    :param collection: Collection to be installed.
    :param path: Collection dirs layout path.
    :param artifacts_manager: Artifacts manager.
    
    "
435,"def edit(self, parameter_s='',last_call=['','']):
        
        opts,args = self.parse_options(parameter_s,'prxn:')

        try:
            filename, lineno, is_temp = self._find_edit_target(self.shell, 
                                                       args, opts, last_call)
        except MacroToEdit as e:
            self._edit_macro(args, e.args[0])
            return
        except InteractivelyDefined as e:
            print(""Editing In[%i]"" % e.index)
            args = str(e.index)
            filename, lineno, is_temp = self._find_edit_target(self.shell, 
                                                       args, opts, last_call)
        if filename is None:
            # nothing was found, warnings have already been issued,
            # just give up.
            return

        if is_temp:
            self._knowntemps.add(filename)
        elif (filename in self._knowntemps):
            is_temp = True


        # do actual editing here
        print('Editing...', end=' ')
        sys.stdout.flush()
        filepath = Path(filename)
        try:
            # Quote filenames that may have spaces in them when opening
            # the editor
            quoted = filename = str(filepath.absolute())
            if "" "" in quoted:
                quoted = ""'%s'"" % quoted
            self.shell.hooks.editor(quoted, lineno)
        except TryNext:
            warn('Could not open editor')
            return

        # XXX TODO: should this be generalized for all string vars?
        # For now, this is special-cased to blocks created by cpaste
        if args.strip() == ""pasted_block"":
            self.shell.user_ns[""pasted_block""] = filepath.read_text(encoding='utf-8')

        if 'x' in opts:  # -x prevents actual execution
            print()
        else:
            print('done. Executing edited code...')
            with preserve_keys(self.shell.user_ns, '__file__'):
                if not is_temp:
                    self.shell.user_ns['__file__'] = filename
                if 'r' in opts:    # Untranslated IPython code
                    source = filepath.read_text(encoding='utf-8')
                    self.shell.run_cell(source, store_history=False)
                else:
                    self.shell.safe_execfile(filename, self.shell.user_ns,
                                             self.shell.user_ns)

        if is_temp:
            try:
                return filepath.read_text(encoding='utf-8')
            except IOError as msg:
                if Path(msg.filename) == filepath:
                    warn('File not found. Did you forget to save?')
                    return
                else:
                    self.shell.showtraceback()
","Bring up an editor and execute the resulting code.

        Usage:
          %edit [options] [args]

        %edit runs IPython's editor hook. The default version of this hook is
        set to call the editor specified by your $EDITOR environment variable.
        If this isn't found, it will default to vi under Linux/Unix and to
        notepad under Windows. See the end of this docstring for how to change
        the editor hook.

        You can also set the value of this editor via the
        ``TerminalInteractiveShell.editor`` option in your configuration file.
        This is useful if you wish to use a different editor from your typical
        default with IPython (and for Windows users who typically don't set
        environment variables).

        This command allows you to conveniently edit multi-line code right in
        your IPython session.

        If called without arguments, %edit opens up an empty editor with a
        temporary file and will execute the contents of this file when you
        close it (don't forget to save it!).


        Options:

        -n <number>: open the editor at a specified line number.  By default,
        the IPython editor hook uses the unix syntax 'editor +N filename', but
        you can configure this by providing your own modified hook if your
        favorite editor supports line-number specifications with a different
        syntax.

        -p: this will call the editor with the same data as the previous time
        it was used, regardless of how long ago (in your current session) it
        was.

        -r: use 'raw' input.  This option only applies to input taken from the
        user's history.  By default, the 'processed' history is used, so that
        magics are loaded in their transformed version to valid Python.  If
        this option is given, the raw input as typed as the command line is
        used instead.  When you exit the editor, it will be executed by
        IPython's own processor.

        -x: do not execute the edited code immediately upon exit. This is
        mainly useful if you are editing programs which need to be called with
        command line arguments, which you can then do using %run.


        Arguments:

        If arguments are given, the following possibilities exist:

        - If the argument is a filename, IPython will load that into the
          editor. It will execute its contents with execfile() when you exit,
          loading any code in the file into your interactive namespace.

        - The arguments are ranges of input history,  e.g. ""7 ~1/4-6"".
          The syntax is the same as in the %history magic.

        - If the argument is a string variable, its contents are loaded
          into the editor. You can thus edit any string which contains
          python code (including the result of previous edits).

        - If the argument is the name of an object (other than a string),
          IPython will try to locate the file where it was defined and open the
          editor at the point where it is defined. You can use `%edit function`
          to load an editor exactly at the point where 'function' is defined,
          edit it and have the file be executed automatically.

        - If the object is a macro (see %macro for details), this opens up your
          specified editor with a temporary file containing the macro's data.
          Upon exit, the macro is reloaded with the contents of the file.

        Note: opening at an exact line is only supported under Unix, and some
        editors (like kedit and gedit up to Gnome 2.8) do not understand the
        '+NUMBER' parameter necessary for this feature. Good editors like
        (X)Emacs, vi, jed, pico and joe all do.

        After executing your code, %edit will return as output the code you
        typed in the editor (except when it was an existing file). This way
        you can reload the code in further invocations of %edit as a variable,
        via _<NUMBER> or Out[<NUMBER>], where <NUMBER> is the prompt number of
        the output.

        Note that %edit is also available through the alias %ed.

        This is an example of creating a simple function inside the editor and
        then modifying it. First, start up the editor::

          In [1]: edit
          Editing... done. Executing edited code...
          Out[1]: 'def foo():\\n    print ""foo() was defined in an editing
          session""\\n'

        We can then call the function foo()::

          In [2]: foo()
          foo() was defined in an editing session

        Now we edit foo.  IPython automatically loads the editor with the
        (temporary) file where foo() was previously defined::

          In [3]: edit foo
          Editing... done. Executing edited code...

        And if we call foo() again we get the modified version::

          In [4]: foo()
          foo() has now been changed!

        Here is an example of how to edit a code snippet successive
        times. First we call the editor::

          In [5]: edit
          Editing... done. Executing edited code...
          hello
          Out[5]: ""print 'hello'\\n""

        Now we call it again with the previous output (stored in _)::

          In [6]: edit _
          Editing... done. Executing edited code...
          hello world
          Out[6]: ""print 'hello world'\\n""

        Now we call it with the output #8 (stored in _8, also as Out[8])::

          In [7]: edit _8
          Editing... done. Executing edited code...
          hello again
          Out[7]: ""print 'hello again'\\n""


        Changing the default editor hook:

        If you wish to write your own editor hook, you can put it in a
        configuration file which you load at startup time.  The default hook
        is defined in the IPython.core.hooks module, and you can use that as a
        starting example for further modifications.  That file also has
        general instructions on how to set a new hook for use once you've
        defined it.",882,214,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def edit(self, parameter_s='',last_call=['','']):
        
        opts,args = self.parse_options(parameter_s,'prxn:')

        try:
            filename, lineno, is_temp = self._find_edit_target(self.shell, 
                                                       args, opts, last_call)
        except MacroToEdit as e:
            self._edit_macro(args, e.args[0])
            return
        except InteractivelyDefined as e:
            print(""Editing In[%i]"" % e.index)
            args = str(e.index)
            filename, lineno, is_temp = self._find_edit_target(self.shell, 
                                                       args, opts, last_call)
        if filename is None:
            # nothing was found, warnings have already been issued,
            # just give up.
            return

        if is_temp:
            self._knowntemps.add(filename)
        elif (filename in self._knowntemps):
            is_temp = True


        # do actual editing here
        print('Editing...', end=' ')
        sys.stdout.flush()
        filepath = Path(filename)
        try:
            # Quote filenames that may have spaces in them when opening
            # the editor
            quoted = filename = str(filepath.absolute())
            if "" "" in quoted:
                quoted = ""'%s'"" % quoted
            self.shell.hooks.editor(quoted, lineno)
        except TryNext:
            warn('Could not open editor')
            return

        # XXX TODO: should this be generalized for all string vars?
        # For now, this is special-cased to blocks created by cpaste
        if args.strip() == ""pasted_block"":
            self.shell.user_ns[""pasted_block""] = filepath.read_text(encoding='utf-8')

        if 'x' in opts:  # -x prevents actual execution
            print()
        else:
            print('done. Executing edited code...')
            with preserve_keys(self.shell.user_ns, '__file__'):
                if not is_temp:
                    self.shell.user_ns['__file__'] = filename
                if 'r' in opts:    # Untranslated IPython code
                    source = filepath.read_text(encoding='utf-8')
                    self.shell.run_cell(source, store_history=False)
                else:
                    self.shell.safe_execfile(filename, self.shell.user_ns,
                                             self.shell.user_ns)

        if is_temp:
            try:
                return filepath.read_text(encoding='utf-8')
            except IOError as msg:
                if Path(msg.filename) == filepath:
                    warn('File not found. Did you forget to save?')
                    return
                else:
                    self.shell.showtraceback()

        ```
    ###Assistant : Bring up an editor and execute the resulting code.

        Usage:
          %edit [options] [args]

        %edit runs IPython's editor hook. The default version of this hook is
        set to call the editor specified by your $EDITOR environment variable.
        If this isn't found, it will default to vi under Linux/Unix and to
        notepad under Windows. See the end of this docstring for how to change
        the editor hook.

        You can also set the value of this editor via the
        ``TerminalInteractiveShell.editor`` option in your configuration file.
        This is useful if you wish to use a different editor from your typical
        default with IPython (and for Windows users who typically don't set
        environment variables).

        This command allows you to conveniently edit multi-line code right in
        your IPython session.

        If called without arguments, %edit opens up an empty editor with a
        temporary file and will execute the contents of this file when you
        close it (don't forget to save it!).


        Options:

        -n <number>: open the editor at a specified line number.  By default,
        the IPython editor hook uses the unix syntax 'editor +N filename', but
        you can configure this by providing your own modified hook if your
        favorite editor supports line-number specifications with a different
        syntax.

        -p: this will call the editor with the same data as the previous time
        it was used, regardless of how long ago (in your current session) it
        was.

        -r: use 'raw' input.  This option only applies to input taken from the
        user's history.  By default, the 'processed' history is used, so that
        magics are loaded in their transformed version to valid Python.  If
        this option is given, the raw input as typed as the command line is
        used instead.  When you exit the editor, it will be executed by
        IPython's own processor.

        -x: do not execute the edited code immediately upon exit. This is
        mainly useful if you are editing programs which need to be called with
        command line arguments, which you can then do using %run.


        Arguments:

        If arguments are given, the following possibilities exist:

        - If the argument is a filename, IPython will load that into the
          editor. It will execute its contents with execfile() when you exit,
          loading any code in the file into your interactive namespace.

        - The arguments are ranges of input history,  e.g. ""7 ~1/4-6"".
          The syntax is the same as in the %history magic.

        - If the argument is a string variable, its contents are loaded
          into the editor. You can thus edit any string which contains
          python code (including the result of previous edits).

        - If the argument is the name of an object (other than a string),
          IPython will try to locate the file where it was defined and open the
          editor at the point where it is defined. You can use `%edit function`
          to load an editor exactly at the point where 'function' is defined,
          edit it and have the file be executed automatically.

        - If the object is a macro (see %macro for details), this opens up your
          specified editor with a temporary file containing the macro's data.
          Upon exit, the macro is reloaded with the contents of the file.

        Note: opening at an exact line is only supported under Unix, and some
        editors (like kedit and gedit up to Gnome 2.8) do not understand the
        '+NUMBER' parameter necessary for this feature. Good editors like
        (X)Emacs, vi, jed, pico and joe all do.

        After executing your code, %edit will return as output the code you
        typed in the editor (except when it was an existing file). This way
        you can reload the code in further invocations of %edit as a variable,
        via _<NUMBER> or Out[<NUMBER>], where <NUMBER> is the prompt number of
        the output.

        Note that %edit is also available through the alias %ed.

        This is an example of creating a simple function inside the editor and
        then modifying it. First, start up the editor::

          In [1]: edit
          Editing... done. Executing edited code...
          Out[1]: 'def foo():\\n    print ""foo() was defined in an editing
          session""\\n'

        We can then call the function foo()::

          In [2]: foo()
          foo() was defined in an editing session

        Now we edit foo.  IPython automatically loads the editor with the
        (temporary) file where foo() was previously defined::

          In [3]: edit foo
          Editing... done. Executing edited code...

        And if we call foo() again we get the modified version::

          In [4]: foo()
          foo() has now been changed!

        Here is an example of how to edit a code snippet successive
        times. First we call the editor::

          In [5]: edit
          Editing... done. Executing edited code...
          hello
          Out[5]: ""print 'hello'\\n""

        Now we call it again with the previous output (stored in _)::

          In [6]: edit _
          Editing... done. Executing edited code...
          hello world
          Out[6]: ""print 'hello world'\\n""

        Now we call it with the output #8 (stored in _8, also as Out[8])::

          In [7]: edit _8
          Editing... done. Executing edited code...
          hello again
          Out[7]: ""print 'hello again'\\n""


        Changing the default editor hook:

        If you wish to write your own editor hook, you can put it in a
        configuration file which you load at startup time.  The default hook
        is defined in the IPython.core.hooks module, and you can use that as a
        starting example for further modifications.  That file also has
        general instructions on how to set a new hook for use once you've
        defined it.
    "
436,"def cell_length(self) -> int:
        
        # Done on demand and cached, as this is an O(n) operation
        if self._cell_length is None:
            self._cell_length = Segment.get_line_length(self._segments)
        return self._cell_length
",Get the number of cells required to render this object.,10,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cell_length(self) -> int:
        
        # Done on demand and cached, as this is an O(n) operation
        if self._cell_length is None:
            self._cell_length = Segment.get_line_length(self._segments)
        return self._cell_length

        ```
    ###Assistant : Get the number of cells required to render this object.
    "
437,"def test_show_message_twice(view):
    
    view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    assert len(view._messages) == 1

",Show the same message twice -> only one should be shown.,11,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_show_message_twice(view):
    
    view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    assert len(view._messages) == 1


        ```
    ###Assistant : Show the same message twice -> only one should be shown.
    "
438,"def create_default_plugins(request, placeholders, template, lang):
    
    from cms.api import add_plugin
","
    Create all default plugins for the given ``placeholders`` if they have
    a ""default_plugins"" configuration value in settings.
    return all plugins, children, grandchildren (etc.) created
    ",24,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_default_plugins(request, placeholders, template, lang):
    
    from cms.api import add_plugin

        ```
    ###Assistant : 
    Create all default plugins for the given ``placeholders`` if they have
    a ""default_plugins"" configuration value in settings.
    return all plugins, children, grandchildren (etc.) created
    
    "
439,"def _parse_jp2_header(fp):
    

    # Find the JP2 header box
    reader = BoxReader(fp)
    header = None
    mimetype = None
    while reader.has_next_box():
        tbox = reader.next_box_type()

        if tbox == b""jp2h"":
            header = reader.read_boxes()
            break
        elif tbox == b""ftyp"":
            if reader.read_fields("">4s"")[0] == b""jpx "":
                mimetype = ""image/jpx""

    size = None
    mode = None
    bpc = None
    nc = None
    dpi = None  # 2-tuple of DPI info, or None

    while header.has_next_box():
        tbox = header.next_box_type()

        if tbox == b""ihdr"":
            height, width, nc, bpc = header.read_fields("">IIHB"")
            size = (width, height)
            if nc == 1 and (bpc & 0x7F) > 8:
                mode = ""I;16""
            elif nc == 1:
                mode = ""L""
            elif nc == 2:
                mode = ""LA""
            elif nc == 3:
                mode = ""RGB""
            elif nc == 4:
                mode = ""RGBA""
        elif tbox == b""res "":
            res = header.read_boxes()
            while res.has_next_box():
                tres = res.next_box_type()
                if tres == b""resc"":
                    vrcn, vrcd, hrcn, hrcd, vrce, hrce = res.read_fields("">HHHHBB"")
                    hres = _res_to_dpi(hrcn, hrcd, hrce)
                    vres = _res_to_dpi(vrcn, vrcd, vrce)
                    if hres is not None and vres is not None:
                        dpi = (hres, vres)
                    break

    if size is None or mode is None:
        raise SyntaxError(""Malformed JP2 header"")

    return size, mode, mimetype, dpi


##
# Image plugin for JPEG2000 images.

","Parse the JP2 header box to extract size, component count,
    color space information, and optionally DPI information,
    returning a (size, mode, mimetype, dpi) tuple.",24,198,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_jp2_header(fp):
    

    # Find the JP2 header box
    reader = BoxReader(fp)
    header = None
    mimetype = None
    while reader.has_next_box():
        tbox = reader.next_box_type()

        if tbox == b""jp2h"":
            header = reader.read_boxes()
            break
        elif tbox == b""ftyp"":
            if reader.read_fields("">4s"")[0] == b""jpx "":
                mimetype = ""image/jpx""

    size = None
    mode = None
    bpc = None
    nc = None
    dpi = None  # 2-tuple of DPI info, or None

    while header.has_next_box():
        tbox = header.next_box_type()

        if tbox == b""ihdr"":
            height, width, nc, bpc = header.read_fields("">IIHB"")
            size = (width, height)
            if nc == 1 and (bpc & 0x7F) > 8:
                mode = ""I;16""
            elif nc == 1:
                mode = ""L""
            elif nc == 2:
                mode = ""LA""
            elif nc == 3:
                mode = ""RGB""
            elif nc == 4:
                mode = ""RGBA""
        elif tbox == b""res "":
            res = header.read_boxes()
            while res.has_next_box():
                tres = res.next_box_type()
                if tres == b""resc"":
                    vrcn, vrcd, hrcn, hrcd, vrce, hrce = res.read_fields("">HHHHBB"")
                    hres = _res_to_dpi(hrcn, hrcd, hrce)
                    vres = _res_to_dpi(vrcn, vrcd, vrce)
                    if hres is not None and vres is not None:
                        dpi = (hres, vres)
                    break

    if size is None or mode is None:
        raise SyntaxError(""Malformed JP2 header"")

    return size, mode, mimetype, dpi


##
# Image plugin for JPEG2000 images.


        ```
    ###Assistant : Parse the JP2 header box to extract size, component count,
    color space information, and optionally DPI information,
    returning a (size, mode, mimetype, dpi) tuple.
    "
440,"def _populate_events(self) -> None:
        
        self.get_success(
            self.store.db_pool.simple_upsert(
                ""rooms"",
                {""room_id"": self.room_id},
                {""room_version"": RoomVersions.V4.identifier},
            )
        )

        self.event_ids: List[str] = []
        for idx in range(20):
            event_json = {
                ""type"": f""test {idx}"",
                ""room_id"": self.room_id,
            }
            event = make_event_from_dict(event_json, room_version=RoomVersions.V4)
            event_id = event.event_id
            self.get_success(
                self.store.db_pool.simple_upsert(
                    ""events"",
                    {""event_id"": event_id},
                    {
                        ""event_id"": event_id,
                        ""room_id"": self.room_id,
                        ""topological_ordering"": idx,
                        ""stream_ordering"": idx,
                        ""type"": event.type,
                        ""processed"": True,
                        ""outlier"": False,
                    },
                )
            )
            self.get_success(
                self.store.db_pool.simple_upsert(
                    ""event_json"",
                    {""event_id"": event_id},
                    {
                        ""room_id"": self.room_id,
                        ""json"": json.dumps(event_json),
                        ""internal_metadata"": ""{}"",
                        ""format_version"": EventFormatVersions.V3,
                    },
                )
            )
            self.event_ids.append(event_id)
","Ensure that there are test events in the database.

        When testing with the in-memory SQLite database, all the events are lost during
        the simulated outage.

        To ensure consistency between `room_id`s and `event_id`s before and after the
        outage, rows are built and inserted manually.

        Upserts are used to handle the non-SQLite case where events are not lost.
        ",56,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _populate_events(self) -> None:
        
        self.get_success(
            self.store.db_pool.simple_upsert(
                ""rooms"",
                {""room_id"": self.room_id},
                {""room_version"": RoomVersions.V4.identifier},
            )
        )

        self.event_ids: List[str] = []
        for idx in range(20):
            event_json = {
                ""type"": f""test {idx}"",
                ""room_id"": self.room_id,
            }
            event = make_event_from_dict(event_json, room_version=RoomVersions.V4)
            event_id = event.event_id
            self.get_success(
                self.store.db_pool.simple_upsert(
                    ""events"",
                    {""event_id"": event_id},
                    {
                        ""event_id"": event_id,
                        ""room_id"": self.room_id,
                        ""topological_ordering"": idx,
                        ""stream_ordering"": idx,
                        ""type"": event.type,
                        ""processed"": True,
                        ""outlier"": False,
                    },
                )
            )
            self.get_success(
                self.store.db_pool.simple_upsert(
                    ""event_json"",
                    {""event_id"": event_id},
                    {
                        ""room_id"": self.room_id,
                        ""json"": json.dumps(event_json),
                        ""internal_metadata"": ""{}"",
                        ""format_version"": EventFormatVersions.V3,
                    },
                )
            )
            self.event_ids.append(event_id)

        ```
    ###Assistant : Ensure that there are test events in the database.

        When testing with the in-memory SQLite database, all the events are lost during
        the simulated outage.

        To ensure consistency between `room_id`s and `event_id`s before and after the
        outage, rows are built and inserted manually.

        Upserts are used to handle the non-SQLite case where events are not lost.
        
    "
441,"def check_if_ctx_is_empty(ctx):
    
    return all(check_if_project_is_empty(project_ctx) for project_ctx in ctx.projects.values())


# The entry point. This task is scheduled to run every week.
@instrumented_task(
    name=""sentry.tasks.weekly_reports.schedule_organizations"",
    queue=""reports.prepare"",
    max_retries=5,
    acks_late=True,
)","
    Check if the context is empty. If it is, we don't want to send an email.
    ",16,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_if_ctx_is_empty(ctx):
    
    return all(check_if_project_is_empty(project_ctx) for project_ctx in ctx.projects.values())


# The entry point. This task is scheduled to run every week.
@instrumented_task(
    name=""sentry.tasks.weekly_reports.schedule_organizations"",
    queue=""reports.prepare"",
    max_retries=5,
    acks_late=True,
)
        ```
    ###Assistant : 
    Check if the context is empty. If it is, we don't want to send an email.
    
    "
442,"def __delitem__(self, name):
        
        name = name.lower()
        newheaders = []
        for k, v in self._headers:
            if k.lower() != name:
                newheaders.append((k, v))
        self._headers = newheaders
","Delete all occurrences of a header, if present.

        Does not raise an exception if the header is missing.
        ",18,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __delitem__(self, name):
        
        name = name.lower()
        newheaders = []
        for k, v in self._headers:
            if k.lower() != name:
                newheaders.append((k, v))
        self._headers = newheaders

        ```
    ###Assistant : Delete all occurrences of a header, if present.

        Does not raise an exception if the header is missing.
        
    "
443,"def update_status(self):
        
        self.cleanup()
        ready = True
        while ready:
            # Use a loop as `ready` might return futures one by one
            ready, _ = ray.wait(list(self._staging_futures.keys()), timeout=0)

            for ready_fut in ready:
                self.handle_ready_future(ready_fut)
","Update placement group status.

        Moves ready placement groups from `self._staging` to
        `self._ready`.
        ",12,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_status(self):
        
        self.cleanup()
        ready = True
        while ready:
            # Use a loop as `ready` might return futures one by one
            ready, _ = ray.wait(list(self._staging_futures.keys()), timeout=0)

            for ready_fut in ready:
                self.handle_ready_future(ready_fut)

        ```
    ###Assistant : Update placement group status.

        Moves ready placement groups from `self._staging` to
        `self._ready`.
        
    "
444,"def execute():
	broken_sles = frappe.db.sql(,
			(
				"" %"",    # leading whitespace
				""% "",    # trailing whitespace
				""%\n %"", # leading whitespace on newline
				""% \n%"", # trailing whitespace on newline
			),
			as_dict=True,
		)

	frappe.db.MAX_WRITES_PER_TRANSACTION += len(broken_sles)

	if not broken_sles:
		return

	broken_serial_nos = set()

	for sle in broken_sles:
		serial_no_list = get_serial_nos(sle.serial_no)
		correct_sr_no = ""\n"".join(serial_no_list)

		if correct_sr_no == sle.serial_no:
			continue

		frappe.db.set_value(""Stock Ledger Entry"", sle.name, ""serial_no"", correct_sr_no, update_modified=False)
		broken_serial_nos.update(serial_no_list)

	if not broken_serial_nos:
		return

	broken_sr_no_records = [sr[0] for sr in frappe.db.sql(, (list(broken_serial_nos),)
							)]

	frappe.db.MAX_WRITES_PER_TRANSACTION += len(broken_sr_no_records)

	patch_savepoint = ""serial_no_patch""
	for serial_no in broken_sr_no_records:
		try:
			frappe.db.savepoint(patch_savepoint)
			sn = frappe.get_doc(""Serial No"", serial_no)
			sn.update_serial_no_reference()
			sn.db_update()
		except Exception:
			frappe.db.rollback(save_point=patch_savepoint)
","
			select name, serial_no
			from `tabStock Ledger Entry`
			where
				is_cancelled = 0
				and (serial_no like %s or serial_no like %s or serial_no like %s or serial_no like %s)
			
							select name
							from `tabSerial No`
							where status='Active'
								and coalesce(purchase_document_type, '') = ''
								and name in %s ",43,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	broken_sles = frappe.db.sql(,
			(
				"" %"",    # leading whitespace
				""% "",    # trailing whitespace
				""%\n %"", # leading whitespace on newline
				""% \n%"", # trailing whitespace on newline
			),
			as_dict=True,
		)

	frappe.db.MAX_WRITES_PER_TRANSACTION += len(broken_sles)

	if not broken_sles:
		return

	broken_serial_nos = set()

	for sle in broken_sles:
		serial_no_list = get_serial_nos(sle.serial_no)
		correct_sr_no = ""\n"".join(serial_no_list)

		if correct_sr_no == sle.serial_no:
			continue

		frappe.db.set_value(""Stock Ledger Entry"", sle.name, ""serial_no"", correct_sr_no, update_modified=False)
		broken_serial_nos.update(serial_no_list)

	if not broken_serial_nos:
		return

	broken_sr_no_records = [sr[0] for sr in frappe.db.sql(, (list(broken_serial_nos),)
							)]

	frappe.db.MAX_WRITES_PER_TRANSACTION += len(broken_sr_no_records)

	patch_savepoint = ""serial_no_patch""
	for serial_no in broken_sr_no_records:
		try:
			frappe.db.savepoint(patch_savepoint)
			sn = frappe.get_doc(""Serial No"", serial_no)
			sn.update_serial_no_reference()
			sn.db_update()
		except Exception:
			frappe.db.rollback(save_point=patch_savepoint)

        ```
    ###Assistant : 
			select name, serial_no
			from `tabStock Ledger Entry`
			where
				is_cancelled = 0
				and (serial_no like %s or serial_no like %s or serial_no like %s or serial_no like %s)
			
							select name
							from `tabSerial No`
							where status='Active'
								and coalesce(purchase_document_type, '') = ''
								and name in %s 
    "
445,"def test_individual_caches_from_environ(self):
        
        config = {}
        self.config._environ = {
            ""SYNAPSE_CACHE_FACTOR_SOMETHING_OR_OTHER"": ""2"",
            ""SYNAPSE_NOT_CACHE"": ""BLAH"",
        }
        self.config.read_config(config, config_dir_path="""", data_dir_path="""")
        self.config.resize_all_caches()

        self.assertEqual(dict(self.config.cache_factors), {""something_or_other"": 2.0})
","
        Individual cache factors will be loaded from the environment.
        ",9,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_individual_caches_from_environ(self):
        
        config = {}
        self.config._environ = {
            ""SYNAPSE_CACHE_FACTOR_SOMETHING_OR_OTHER"": ""2"",
            ""SYNAPSE_NOT_CACHE"": ""BLAH"",
        }
        self.config.read_config(config, config_dir_path="""", data_dir_path="""")
        self.config.resize_all_caches()

        self.assertEqual(dict(self.config.cache_factors), {""something_or_other"": 2.0})

        ```
    ###Assistant : 
        Individual cache factors will be loaded from the environment.
        
    "
446,"def test_already_checked_on_success(self):
        
        pod_name = ""test-"" + str(random.randint(0, 1000000))
        k = KubernetesPodOperator(
            namespace='default',
            image=""ubuntu:16.04"",
            cmds=[""bash"", ""-cx""],
            arguments=[""echo 10""],
            labels={""foo"": ""bar""},
            name=pod_name,
            task_id=""task"" + self.get_current_task_name(),
            in_cluster=False,
            do_xcom_push=False,
            is_delete_operator_pod=False,
        )
        context = create_context(k)
        k.execute(context)
        actual_pod = k.find_pod('default', context, exclude_checked=False)
        actual_pod = self.api_client.sanitize_for_serialization(actual_pod)
        assert actual_pod['metadata']['labels']['already_checked'] == 'True'
","
        When ``is_delete_operator_pod=False``, pod should have 'already_checked'
        label, whether pod is successful or not.
        ",13,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_already_checked_on_success(self):
        
        pod_name = ""test-"" + str(random.randint(0, 1000000))
        k = KubernetesPodOperator(
            namespace='default',
            image=""ubuntu:16.04"",
            cmds=[""bash"", ""-cx""],
            arguments=[""echo 10""],
            labels={""foo"": ""bar""},
            name=pod_name,
            task_id=""task"" + self.get_current_task_name(),
            in_cluster=False,
            do_xcom_push=False,
            is_delete_operator_pod=False,
        )
        context = create_context(k)
        k.execute(context)
        actual_pod = k.find_pod('default', context, exclude_checked=False)
        actual_pod = self.api_client.sanitize_for_serialization(actual_pod)
        assert actual_pod['metadata']['labels']['already_checked'] == 'True'

        ```
    ###Assistant : 
        When ``is_delete_operator_pod=False``, pod should have 'already_checked'
        label, whether pod is successful or not.
        
    "
447,"def get_invoice_cc_wh_map(invoice_list):
	si_items = frappe.db.sql(
		
		% "", "".join([""%s""] * len(invoice_list)),
		tuple(inv.name for inv in invoice_list),
		as_dict=1,
	)

	invoice_cc_wh_map = {}
	for d in si_items:
		if d.cost_center:
			invoice_cc_wh_map.setdefault(d.parent, frappe._dict()).setdefault(""cost_center"", []).append(
				d.cost_center
			)

		if d.warehouse:
			invoice_cc_wh_map.setdefault(d.parent, frappe._dict()).setdefault(""warehouse"", []).append(
				d.warehouse
			)

	return invoice_cc_wh_map

","select parent, cost_center, warehouse
		from `tabSales Invoice Item` where parent in (%s)
		and (ifnull(cost_center, '') != '' or ifnull(warehouse, '') != '')",22,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_invoice_cc_wh_map(invoice_list):
	si_items = frappe.db.sql(
		
		% "", "".join([""%s""] * len(invoice_list)),
		tuple(inv.name for inv in invoice_list),
		as_dict=1,
	)

	invoice_cc_wh_map = {}
	for d in si_items:
		if d.cost_center:
			invoice_cc_wh_map.setdefault(d.parent, frappe._dict()).setdefault(""cost_center"", []).append(
				d.cost_center
			)

		if d.warehouse:
			invoice_cc_wh_map.setdefault(d.parent, frappe._dict()).setdefault(""warehouse"", []).append(
				d.warehouse
			)

	return invoice_cc_wh_map


        ```
    ###Assistant : select parent, cost_center, warehouse
		from `tabSales Invoice Item` where parent in (%s)
		and (ifnull(cost_center, '') != '' or ifnull(warehouse, '') != '')
    "
448,"def parseSqliteTableSchema(value):
    

    retVal = False

    value = extractRegexResult(r""(?s)\((?P<result>.+)\)"", value)

    if value:
        table = {}
        columns = OrderedDict()

        value = re.sub(r""\(.+?\)"", """", value).strip()

        for match in re.finditer(r""(?:\A|,)\s*(([\""'`]).+?\2|\w+)(?:\s+(INT|INTEGER|TINYINT|SMALLINT|MEDIUMINT|BIGINT|UNSIGNED BIG INT|INT2|INT8|INTEGER|CHARACTER|VARCHAR|VARYING CHARACTER|NCHAR|NATIVE CHARACTER|NVARCHAR|TEXT|CLOB|LONGTEXT|BLOB|NONE|REAL|DOUBLE|DOUBLE PRECISION|FLOAT|REAL|NUMERIC|DECIMAL|BOOLEAN|DATE|DATETIME|NUMERIC)\b)?"", decodeStringEscape(value), re.I):
            column = match.group(1).strip(match.group(2) or """")
            if re.search(r""(?i)\A(CONSTRAINT|PRIMARY|UNIQUE|CHECK|FOREIGN)\b"", column.strip()):
                continue
            retVal = True

            columns[column] = match.group(3) or ""TEXT""

        table[safeSQLIdentificatorNaming(conf.tbl, True)] = columns
        kb.data.cachedColumns[conf.db] = table

    return retVal
","
    Parses table column names and types from specified SQLite table schema

    >>> kb.data.cachedColumns = {}
    >>> parseSqliteTableSchema(""CREATE TABLE users(\\n\\t\\tid INTEGER,\\n\\t\\tname TEXT\\n);"")
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('id', 'INTEGER'), ('name', 'TEXT'))
    True
    >>> parseSqliteTableSchema(""CREATE TABLE dummy(`foo bar` BIGINT, \\""foo\\"" VARCHAR, 'bar' TEXT)"");
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('foo bar', 'BIGINT'), ('foo', 'VARCHAR'), ('bar', 'TEXT'))
    True
    >>> parseSqliteTableSchema(""CREATE TABLE suppliers(\\n\\tsupplier_id INTEGER PRIMARY KEY DESC,\\n\\tname TEXT NOT NULL\\n);"");
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('supplier_id', 'INTEGER'), ('name', 'TEXT'))
    True
    >>> parseSqliteTableSchema(""CREATE TABLE country_languages (\\n\\tcountry_id INTEGER NOT NULL,\\n\\tlanguage_id INTEGER NOT NULL,\\n\\tPRIMARY KEY (country_id, language_id),\\n\\tFOREIGN KEY (country_id) REFERENCES countries (country_id) ON DELETE CASCADE ON UPDATE NO ACTION,\\tFOREIGN KEY (language_id) REFERENCES languages (language_id) ON DELETE CASCADE ON UPDATE NO ACTION);"");
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('country_id', 'INTEGER'), ('language_id', 'INTEGER'))
    True
    ",119,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parseSqliteTableSchema(value):
    

    retVal = False

    value = extractRegexResult(r""(?s)\((?P<result>.+)\)"", value)

    if value:
        table = {}
        columns = OrderedDict()

        value = re.sub(r""\(.+?\)"", """", value).strip()

        for match in re.finditer(r""(?:\A|,)\s*(([\""'`]).+?\2|\w+)(?:\s+(INT|INTEGER|TINYINT|SMALLINT|MEDIUMINT|BIGINT|UNSIGNED BIG INT|INT2|INT8|INTEGER|CHARACTER|VARCHAR|VARYING CHARACTER|NCHAR|NATIVE CHARACTER|NVARCHAR|TEXT|CLOB|LONGTEXT|BLOB|NONE|REAL|DOUBLE|DOUBLE PRECISION|FLOAT|REAL|NUMERIC|DECIMAL|BOOLEAN|DATE|DATETIME|NUMERIC)\b)?"", decodeStringEscape(value), re.I):
            column = match.group(1).strip(match.group(2) or """")
            if re.search(r""(?i)\A(CONSTRAINT|PRIMARY|UNIQUE|CHECK|FOREIGN)\b"", column.strip()):
                continue
            retVal = True

            columns[column] = match.group(3) or ""TEXT""

        table[safeSQLIdentificatorNaming(conf.tbl, True)] = columns
        kb.data.cachedColumns[conf.db] = table

    return retVal

        ```
    ###Assistant : 
    Parses table column names and types from specified SQLite table schema

    >>> kb.data.cachedColumns = {}
    >>> parseSqliteTableSchema(""CREATE TABLE users(\\n\\t\\tid INTEGER,\\n\\t\\tname TEXT\\n);"")
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('id', 'INTEGER'), ('name', 'TEXT'))
    True
    >>> parseSqliteTableSchema(""CREATE TABLE dummy(`foo bar` BIGINT, \\""foo\\"" VARCHAR, 'bar' TEXT)"");
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('foo bar', 'BIGINT'), ('foo', 'VARCHAR'), ('bar', 'TEXT'))
    True
    >>> parseSqliteTableSchema(""CREATE TABLE suppliers(\\n\\tsupplier_id INTEGER PRIMARY KEY DESC,\\n\\tname TEXT NOT NULL\\n);"");
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('supplier_id', 'INTEGER'), ('name', 'TEXT'))
    True
    >>> parseSqliteTableSchema(""CREATE TABLE country_languages (\\n\\tcountry_id INTEGER NOT NULL,\\n\\tlanguage_id INTEGER NOT NULL,\\n\\tPRIMARY KEY (country_id, language_id),\\n\\tFOREIGN KEY (country_id) REFERENCES countries (country_id) ON DELETE CASCADE ON UPDATE NO ACTION,\\tFOREIGN KEY (language_id) REFERENCES languages (language_id) ON DELETE CASCADE ON UPDATE NO ACTION);"");
    True
    >>> tuple(kb.data.cachedColumns[conf.db][conf.tbl].items()) == (('country_id', 'INTEGER'), ('language_id', 'INTEGER'))
    True
    
    "
449,"def get_phrase(value):
    
    phrase = Phrase()
    try:
        token, value = get_word(value)
        phrase.append(token)
    except errors.HeaderParseError:
        phrase.defects.append(errors.InvalidHeaderDefect(
            ""phrase does not start with word""))
    while value and value[0] not in PHRASE_ENDS:
        if value[0]=='.':
            phrase.append(DOT)
            phrase.defects.append(errors.ObsoleteHeaderDefect(
                ""period in 'phrase'""))
            value = value[1:]
        else:
            try:
                token, value = get_word(value)
            except errors.HeaderParseError:
                if value[0] in CFWS_LEADER:
                    token, value = get_cfws(value)
                    phrase.defects.append(errors.ObsoleteHeaderDefect(
                        ""comment found without atom""))
                else:
                    raise
            phrase.append(token)
    return phrase, value
"," phrase = 1*word / obs-phrase
        obs-phrase = word *(word / ""."" / CFWS)

    This means a phrase can be a sequence of words, periods, and CFWS in any
    order as long as it starts with at least one word.  If anything other than
    words is detected, an ObsoleteHeaderDefect is added to the token's defect
    list.  We also accept a phrase that starts with CFWS followed by a dot;
    this is registered as an InvalidHeaderDefect, since it is not supported by
    even the obsolete grammar.

    ",84,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_phrase(value):
    
    phrase = Phrase()
    try:
        token, value = get_word(value)
        phrase.append(token)
    except errors.HeaderParseError:
        phrase.defects.append(errors.InvalidHeaderDefect(
            ""phrase does not start with word""))
    while value and value[0] not in PHRASE_ENDS:
        if value[0]=='.':
            phrase.append(DOT)
            phrase.defects.append(errors.ObsoleteHeaderDefect(
                ""period in 'phrase'""))
            value = value[1:]
        else:
            try:
                token, value = get_word(value)
            except errors.HeaderParseError:
                if value[0] in CFWS_LEADER:
                    token, value = get_cfws(value)
                    phrase.defects.append(errors.ObsoleteHeaderDefect(
                        ""comment found without atom""))
                else:
                    raise
            phrase.append(token)
    return phrase, value

        ```
    ###Assistant :  phrase = 1*word / obs-phrase
        obs-phrase = word *(word / ""."" / CFWS)

    This means a phrase can be a sequence of words, periods, and CFWS in any
    order as long as it starts with at least one word.  If anything other than
    words is detected, an ObsoleteHeaderDefect is added to the token's defect
    list.  We also accept a phrase that starts with CFWS followed by a dot;
    this is registered as an InvalidHeaderDefect, since it is not supported by
    even the obsolete grammar.

    
    "
450,"async def test_group_media_states(hass, mz_mock):
    
    entity_id = ""media_player.speaker""
    reg = er.async_get(hass)

    info = get_fake_chromecast_info()

    chromecast, _ = await async_setup_media_player_cast(hass, info)
    _, conn_status_cb, media_status_cb, group_media_status_cb = get_status_callbacks(
        chromecast, mz_mock
    )

    connection_status = MagicMock()
    connection_status.status = ""CONNECTED""
    conn_status_cb(connection_status)
    await hass.async_block_till_done()

    state = hass.states.get(entity_id)
    assert state is not None
    assert state.name == ""Speaker""
    assert state.state == ""off""
    assert entity_id == reg.async_get_entity_id(""media_player"", ""cast"", str(info.uuid))

    group_media_status = MagicMock(images=None)
    player_media_status = MagicMock(images=None)

    # Player has no state, group is buffering -> Should report 'buffering'
    group_media_status.player_state = ""BUFFERING""
    group_media_status_cb(str(FakeGroupUUID), group_media_status)
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""buffering""

    # Player has no state, group is playing -> Should report 'playing'
    group_media_status.player_state = ""PLAYING""
    group_media_status_cb(str(FakeGroupUUID), group_media_status)
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""playing""

    # Player is paused, group is playing -> Should report 'paused'
    player_media_status.player_state = None
    player_media_status.player_is_paused = True
    media_status_cb(player_media_status)
    await hass.async_block_till_done()
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""paused""

    # Player is in unknown state, group is playing -> Should report 'playing'
    player_media_status.player_state = ""UNKNOWN""
    media_status_cb(player_media_status)
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""playing""

",Test media states are read from group if entity has no state.,12,172,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_group_media_states(hass, mz_mock):
    
    entity_id = ""media_player.speaker""
    reg = er.async_get(hass)

    info = get_fake_chromecast_info()

    chromecast, _ = await async_setup_media_player_cast(hass, info)
    _, conn_status_cb, media_status_cb, group_media_status_cb = get_status_callbacks(
        chromecast, mz_mock
    )

    connection_status = MagicMock()
    connection_status.status = ""CONNECTED""
    conn_status_cb(connection_status)
    await hass.async_block_till_done()

    state = hass.states.get(entity_id)
    assert state is not None
    assert state.name == ""Speaker""
    assert state.state == ""off""
    assert entity_id == reg.async_get_entity_id(""media_player"", ""cast"", str(info.uuid))

    group_media_status = MagicMock(images=None)
    player_media_status = MagicMock(images=None)

    # Player has no state, group is buffering -> Should report 'buffering'
    group_media_status.player_state = ""BUFFERING""
    group_media_status_cb(str(FakeGroupUUID), group_media_status)
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""buffering""

    # Player has no state, group is playing -> Should report 'playing'
    group_media_status.player_state = ""PLAYING""
    group_media_status_cb(str(FakeGroupUUID), group_media_status)
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""playing""

    # Player is paused, group is playing -> Should report 'paused'
    player_media_status.player_state = None
    player_media_status.player_is_paused = True
    media_status_cb(player_media_status)
    await hass.async_block_till_done()
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""paused""

    # Player is in unknown state, group is playing -> Should report 'playing'
    player_media_status.player_state = ""UNKNOWN""
    media_status_cb(player_media_status)
    await hass.async_block_till_done()
    state = hass.states.get(entity_id)
    assert state.state == ""playing""


        ```
    ###Assistant : Test media states are read from group if entity has no state.
    "
451,"def test_deserialize(self):
        
        block = blocks.PageChooserBlock()
        christmas_page = Page.objects.get(slug='christmas')

        self.assertEqual(block.to_python(christmas_page.id), christmas_page)

        # None should deserialize to None
        self.assertIsNone(block.to_python(None))
",The serialized value of a PageChooserBlock (an ID) should deserialize to a Page object,14,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_deserialize(self):
        
        block = blocks.PageChooserBlock()
        christmas_page = Page.objects.get(slug='christmas')

        self.assertEqual(block.to_python(christmas_page.id), christmas_page)

        # None should deserialize to None
        self.assertIsNone(block.to_python(None))

        ```
    ###Assistant : The serialized value of a PageChooserBlock (an ID) should deserialize to a Page object
    "
452,"def _is_current_explicit_device(device_type):
    
    device_type = device_type.upper()
    if device_type not in [""CPU"", ""GPU""]:
        raise ValueError('`device_type` should be either ""CPU"" or ""GPU"".')
    device = _get_current_tf_device()
    return device is not None and device.device_type == device_type.upper()

","Check if the current device is explicitly set on the device type specified.

    Args:
        device_type: A string containing `GPU` or `CPU` (case-insensitive).

    Returns:
        A boolean indicating if the current device scope is explicitly set on the
        device type.

    Raises:
        ValueError: If the `device_type` string indicates an unsupported device.
    ",48,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _is_current_explicit_device(device_type):
    
    device_type = device_type.upper()
    if device_type not in [""CPU"", ""GPU""]:
        raise ValueError('`device_type` should be either ""CPU"" or ""GPU"".')
    device = _get_current_tf_device()
    return device is not None and device.device_type == device_type.upper()


        ```
    ###Assistant : Check if the current device is explicitly set on the device type specified.

    Args:
        device_type: A string containing `GPU` or `CPU` (case-insensitive).

    Returns:
        A boolean indicating if the current device scope is explicitly set on the
        device type.

    Raises:
        ValueError: If the `device_type` string indicates an unsupported device.
    
    "
453,"def _return_datastructure_name(obj):
    
    if isinstance(obj, (text_type, binary_type)):
        if obj:
            yield to_native(obj, errors='surrogate_or_strict')
        return
    elif isinstance(obj, Mapping):
        for element in obj.items():
            for subelement in _return_datastructure_name(element[1]):
                yield subelement
    elif is_iterable(obj):
        for element in obj:
            for subelement in _return_datastructure_name(element):
                yield subelement
    elif obj is None or isinstance(obj, bool):
        # This must come before int because bools are also ints
        return
    elif isinstance(obj, tuple(list(integer_types) + [float])):
        yield to_native(obj, nonstring='simplerepr')
    else:
        raise TypeError('Unknown parameter type: %s' % (type(obj)))

"," Return native stringified values from datastructures.

    For use with removing sensitive values pre-jsonification.",13,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _return_datastructure_name(obj):
    
    if isinstance(obj, (text_type, binary_type)):
        if obj:
            yield to_native(obj, errors='surrogate_or_strict')
        return
    elif isinstance(obj, Mapping):
        for element in obj.items():
            for subelement in _return_datastructure_name(element[1]):
                yield subelement
    elif is_iterable(obj):
        for element in obj:
            for subelement in _return_datastructure_name(element):
                yield subelement
    elif obj is None or isinstance(obj, bool):
        # This must come before int because bools are also ints
        return
    elif isinstance(obj, tuple(list(integer_types) + [float])):
        yield to_native(obj, nonstring='simplerepr')
    else:
        raise TypeError('Unknown parameter type: %s' % (type(obj)))


        ```
    ###Assistant :  Return native stringified values from datastructures.

    For use with removing sensitive values pre-jsonification.
    "
454,"async def __aiter__(self):
        
        waiter = self._waiter
        while True:
            # Shield the future from being cancelled by a task waiting on it
            message, ts, waiter = await asyncio.shield(waiter)
            yield message, ts
","
        Iterate over the messages in the message stream
        ",8,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def __aiter__(self):
        
        waiter = self._waiter
        while True:
            # Shield the future from being cancelled by a task waiting on it
            message, ts, waiter = await asyncio.shield(waiter)
            yield message, ts

        ```
    ###Assistant : 
        Iterate over the messages in the message stream
        
    "
455,"def get_gridspec(self):
        
        return self._subplotspec.get_gridspec() if self._subplotspec else None
","Return the `.GridSpec` associated with the subplot, or None.",9,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_gridspec(self):
        
        return self._subplotspec.get_gridspec() if self._subplotspec else None

        ```
    ###Assistant : Return the `.GridSpec` associated with the subplot, or None.
    "
456,"def ESNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
    
    model = ESNet(scale=0.5, stages_pattern=MODEL_STAGES_PATTERN[""ESNet""], **kwargs)
    return model
","
    ESNet_x0_5
    Args:
        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                    If str, means the path of the pretrained model.
        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
    Returns:
        model: nn.Layer. Specific `ESNet_x0_5` model depends on args.
    ",40,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ESNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
    
    model = ESNet(scale=0.5, stages_pattern=MODEL_STAGES_PATTERN[""ESNet""], **kwargs)
    return model

        ```
    ###Assistant : 
    ESNet_x0_5
    Args:
        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                    If str, means the path of the pretrained model.
        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
    Returns:
        model: nn.Layer. Specific `ESNet_x0_5` model depends on args.
    
    "
457,"def require_rjieba(test_case):
    
    return unittest.skipUnless(is_rjieba_available(), ""test requires rjieba"")(test_case)

","
    Decorator marking a test that requires rjieba. These tests are skipped when rjieba isn't installed.
    ",15,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def require_rjieba(test_case):
    
    return unittest.skipUnless(is_rjieba_available(), ""test requires rjieba"")(test_case)


        ```
    ###Assistant : 
    Decorator marking a test that requires rjieba. These tests are skipped when rjieba isn't installed.
    
    "
458,"def get_yaxis(self):
        
        return self.yaxis

    get_xgridlines = _axis_method_wrapper(""xaxis"", ""get_gridlines"")
    get_xticklines = _axis_method_wrapper(""xaxis"", ""get_ticklines"")
    get_ygridlines = _axis_method_wrapper(""yaxis"", ""get_gridlines"")
    get_yticklines = _axis_method_wrapper(""yaxis"", ""get_ticklines"")

    # Adding and tracking artists
","
        [*Discouraged*] Return the YAxis instance.

        .. admonition:: Discouraged

            The use of this function is discouraged. You should instead
            directly access the attribute ``ax.yaxis``.
        ",23,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_yaxis(self):
        
        return self.yaxis

    get_xgridlines = _axis_method_wrapper(""xaxis"", ""get_gridlines"")
    get_xticklines = _axis_method_wrapper(""xaxis"", ""get_ticklines"")
    get_ygridlines = _axis_method_wrapper(""yaxis"", ""get_gridlines"")
    get_yticklines = _axis_method_wrapper(""yaxis"", ""get_ticklines"")

    # Adding and tracking artists

        ```
    ###Assistant : 
        [*Discouraged*] Return the YAxis instance.

        .. admonition:: Discouraged

            The use of this function is discouraged. You should instead
            directly access the attribute ``ax.yaxis``.
        
    "
459,"def disable_tf_random_generator():
    
    global _USE_GENERATOR_FOR_RNG
    _USE_GENERATOR_FOR_RNG = False

","Disable the `tf.random.Generator` as the RNG for Keras.

    See `tf.keras.backend.experimental.is_tf_random_generator_enabled` for more
    details.
    ",13,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def disable_tf_random_generator():
    
    global _USE_GENERATOR_FOR_RNG
    _USE_GENERATOR_FOR_RNG = False


        ```
    ###Assistant : Disable the `tf.random.Generator` as the RNG for Keras.

    See `tf.keras.backend.experimental.is_tf_random_generator_enabled` for more
    details.
    
    "
460,"def test_parse_timezone(all_parsers):
    # see gh-22256
    parser = all_parsers
    data = 
    result = parser.read_csv(StringIO(data), parse_dates=[""dt""])

    dti = DatetimeIndex(
        list(
            date_range(
                start=""2018-01-04 09:01:00"",
                end=""2018-01-04 09:05:00"",
                freq=""1min"",
                tz=timezone(timedelta(minutes=540)),
            )
        ),
        freq=None,
    )
    expected_data = {""dt"": dti, ""val"": [23350, 23400, 23400, 23400, 23400]}

    expected = DataFrame(expected_data)
    tm.assert_frame_equal(result, expected)


@skip_pyarrow
@pytest.mark.parametrize(
    ""date_string"",
    [""32/32/2019"", ""02/30/2019"", ""13/13/2019"", ""13/2019"", ""a3/11/2018"", ""10/11/2o17""],
)","dt,val
              2018-01-04 09:01:00+09:00,23350
              2018-01-04 09:02:00+09:00,23400
              2018-01-04 09:03:00+09:00,23400
              2018-01-04 09:04:00+09:00,23400
              2018-01-04 09:05:00+09:00,23400",11,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_parse_timezone(all_parsers):
    # see gh-22256
    parser = all_parsers
    data = 
    result = parser.read_csv(StringIO(data), parse_dates=[""dt""])

    dti = DatetimeIndex(
        list(
            date_range(
                start=""2018-01-04 09:01:00"",
                end=""2018-01-04 09:05:00"",
                freq=""1min"",
                tz=timezone(timedelta(minutes=540)),
            )
        ),
        freq=None,
    )
    expected_data = {""dt"": dti, ""val"": [23350, 23400, 23400, 23400, 23400]}

    expected = DataFrame(expected_data)
    tm.assert_frame_equal(result, expected)


@skip_pyarrow
@pytest.mark.parametrize(
    ""date_string"",
    [""32/32/2019"", ""02/30/2019"", ""13/13/2019"", ""13/2019"", ""a3/11/2018"", ""10/11/2o17""],
)
        ```
    ###Assistant : dt,val
              2018-01-04 09:01:00+09:00,23350
              2018-01-04 09:02:00+09:00,23400
              2018-01-04 09:03:00+09:00,23400
              2018-01-04 09:04:00+09:00,23400
              2018-01-04 09:05:00+09:00,23400
    "
461,"def alpn_proto_negotiated(self) -> Optional[bytes]:  # pragma: no cover
        
        warnings.warn(
            ""Connection.alpn_proto_negotiated is deprecated, use Connection.alpn instead."",
            DeprecationWarning,
        )
        return self.alpn

",*Deprecated:* An outdated alias for Connection.alpn.,6,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def alpn_proto_negotiated(self) -> Optional[bytes]:  # pragma: no cover
        
        warnings.warn(
            ""Connection.alpn_proto_negotiated is deprecated, use Connection.alpn instead."",
            DeprecationWarning,
        )
        return self.alpn


        ```
    ###Assistant : *Deprecated:* An outdated alias for Connection.alpn.
    "
462,"def device_class(self) -> CoverDeviceClass:
        
        if isinstance(self.node, Awning):
            return CoverDeviceClass.AWNING
        if isinstance(self.node, Blind):
            return CoverDeviceClass.BLIND
        if isinstance(self.node, GarageDoor):
            return CoverDeviceClass.GARAGE
        if isinstance(self.node, Gate):
            return CoverDeviceClass.GATE
        if isinstance(self.node, RollerShutter):
            return CoverDeviceClass.SHUTTER
        if isinstance(self.node, Window):
            return CoverDeviceClass.WINDOW
        return CoverDeviceClass.WINDOW
","Define this cover as either awning, blind, garage, gate, shutter or window.",12,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def device_class(self) -> CoverDeviceClass:
        
        if isinstance(self.node, Awning):
            return CoverDeviceClass.AWNING
        if isinstance(self.node, Blind):
            return CoverDeviceClass.BLIND
        if isinstance(self.node, GarageDoor):
            return CoverDeviceClass.GARAGE
        if isinstance(self.node, Gate):
            return CoverDeviceClass.GATE
        if isinstance(self.node, RollerShutter):
            return CoverDeviceClass.SHUTTER
        if isinstance(self.node, Window):
            return CoverDeviceClass.WINDOW
        return CoverDeviceClass.WINDOW

        ```
    ###Assistant : Define this cover as either awning, blind, garage, gate, shutter or window.
    "
463,"def get_yieldcurve(country) -> pd.DataFrame:
    

    data = investpy.bonds.get_bonds_overview(country)
    data.drop(columns=data.columns[0], axis=1, inplace=True)
    data.rename(
        columns={
            ""name"": ""Tenor"",
            ""last"": ""Current"",
            ""last_close"": ""Previous"",
            ""high"": ""High"",
            ""low"": ""Low"",
            ""change"": ""Change"",
            ""change_percentage"": ""% Change"",
        },
        inplace=True,
    )
    return data
","Get country yield curve [Source: Investing.com]

    Returns
    -------
    pd.DataFrame
        Country yield curve
    ",12,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_yieldcurve(country) -> pd.DataFrame:
    

    data = investpy.bonds.get_bonds_overview(country)
    data.drop(columns=data.columns[0], axis=1, inplace=True)
    data.rename(
        columns={
            ""name"": ""Tenor"",
            ""last"": ""Current"",
            ""last_close"": ""Previous"",
            ""high"": ""High"",
            ""low"": ""Low"",
            ""change"": ""Change"",
            ""change_percentage"": ""% Change"",
        },
        inplace=True,
    )
    return data

        ```
    ###Assistant : Get country yield curve [Source: Investing.com]

    Returns
    -------
    pd.DataFrame
        Country yield curve
    
    "
464,"async def test_report_humidifier_humidity_state(hass):
    
    hass.states.async_set(
        ""humidifier.dry"",
        ""on"",
        {
            ""friendly_name"": ""Humidifier dry"",
            ""supported_features"": 0,
            ""humidity"": 25,
            ""min_humidity"": 20,
            ""max_humidity"": 90,
        },
    )
    hass.states.async_set(
        ""humidifier.wet"",
        ""on"",
        {
            ""friendly_name"": ""Humidifier wet"",
            ""supported_features"": 0,
            ""humidity"": 80,
            ""min_humidity"": 20,
            ""max_humidity"": 90,
        },
    )
    properties = await reported_properties(hass, ""humidifier.dry"")
    properties.assert_equal(""Alexa.RangeController"", ""rangeValue"", 25)

    properties = await reported_properties(hass, ""humidifier.wet"")
    properties.assert_equal(""Alexa.RangeController"", ""rangeValue"", 80)

","Test PercentageController, PowerLevelController reports humidifier humidity correctly.",7,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_report_humidifier_humidity_state(hass):
    
    hass.states.async_set(
        ""humidifier.dry"",
        ""on"",
        {
            ""friendly_name"": ""Humidifier dry"",
            ""supported_features"": 0,
            ""humidity"": 25,
            ""min_humidity"": 20,
            ""max_humidity"": 90,
        },
    )
    hass.states.async_set(
        ""humidifier.wet"",
        ""on"",
        {
            ""friendly_name"": ""Humidifier wet"",
            ""supported_features"": 0,
            ""humidity"": 80,
            ""min_humidity"": 20,
            ""max_humidity"": 90,
        },
    )
    properties = await reported_properties(hass, ""humidifier.dry"")
    properties.assert_equal(""Alexa.RangeController"", ""rangeValue"", 25)

    properties = await reported_properties(hass, ""humidifier.wet"")
    properties.assert_equal(""Alexa.RangeController"", ""rangeValue"", 80)


        ```
    ###Assistant : Test PercentageController, PowerLevelController reports humidifier humidity correctly.
    "
465,"def serialize(self, value):
        
        if self.type == CustomFieldTypeChoices.TYPE_OBJECT and value is not None:
            return value.pk
        return value
","
        Prepare a value for storage as JSON data.
        ",8,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serialize(self, value):
        
        if self.type == CustomFieldTypeChoices.TYPE_OBJECT and value is not None:
            return value.pk
        return value

        ```
    ###Assistant : 
        Prepare a value for storage as JSON data.
        
    "
466,"def revert_to_saved(self, frame_index):
        
        if frame_index not in self._updated_frame_indices:
            logger.debug(""Alignments not amended. Returning"")
            return
        logger.verbose(""Reverting alignments for frame_index %s"", frame_index)
        print(frame_index)
        print(len(self._sorted_frame_names))
        alignments = self._alignments.data[self._sorted_frame_names[frame_index]][""faces""]
        faces = self._frame_faces[frame_index]

        reset_grid = self._add_remove_faces(alignments, faces)

        for detected_face, face in zip(faces, alignments):
            detected_face.from_alignment(face, with_thumb=True)
            detected_face.load_aligned(None, force=True)
            _ = detected_face.aligned.average_distance  # cache the distances

        self._updated_frame_indices.remove(frame_index)
        if not self._updated_frame_indices:
            self._tk_unsaved.set(False)

        if reset_grid:
            self._tk_face_count_changed.set(True)
        else:
            self._tk_edited.set(True)
        self._globals.tk_update.set(True)
"," Revert the frame's alignments to their saved version for the given frame index.

        Parameters
        ----------
        frame_index: int
            The frame that should have their faces reverted to their saved version
        ",29,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def revert_to_saved(self, frame_index):
        
        if frame_index not in self._updated_frame_indices:
            logger.debug(""Alignments not amended. Returning"")
            return
        logger.verbose(""Reverting alignments for frame_index %s"", frame_index)
        print(frame_index)
        print(len(self._sorted_frame_names))
        alignments = self._alignments.data[self._sorted_frame_names[frame_index]][""faces""]
        faces = self._frame_faces[frame_index]

        reset_grid = self._add_remove_faces(alignments, faces)

        for detected_face, face in zip(faces, alignments):
            detected_face.from_alignment(face, with_thumb=True)
            detected_face.load_aligned(None, force=True)
            _ = detected_face.aligned.average_distance  # cache the distances

        self._updated_frame_indices.remove(frame_index)
        if not self._updated_frame_indices:
            self._tk_unsaved.set(False)

        if reset_grid:
            self._tk_face_count_changed.set(True)
        else:
            self._tk_edited.set(True)
        self._globals.tk_update.set(True)

        ```
    ###Assistant :  Revert the frame's alignments to their saved version for the given frame index.

        Parameters
        ----------
        frame_index: int
            The frame that should have their faces reverted to their saved version
        
    "
467,"def parse_args(args):
    
    # Use the file's docstring for the help text and don't let argparse reformat it.
    parser = argparse.ArgumentParser(description=__doc__,
                                     formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument('--css', type=str, required=True, help='hostname of code signing server')
    group = parser.add_mutually_exclusive_group()
    # We use 'store_false' and a destination related to the other type of
    # artifact to cause the flag being set to disable publishing of the other
    # artifact. This makes using the parsed arguments later on a little simpler
    # and cleaner.
    group.add_argument('--snaps-only', action='store_false', dest='publish_windows',
                        help='Skip publishing other artifacts and only publish the snaps')
    group.add_argument('--windows-only', action='store_false', dest='publish_snaps',
                        help='Skip publishing other artifacts and only publish the Windows installer')
    return parser.parse_args(args)

    ","Parse command line arguments.

    :param args: command line arguments with the program name removed. This is
        usually taken from sys.argv[1:].
    :type args: `list` of `str`

    :returns: parsed arguments
    :rtype: argparse.Namespace

    ",30,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_args(args):
    
    # Use the file's docstring for the help text and don't let argparse reformat it.
    parser = argparse.ArgumentParser(description=__doc__,
                                     formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument('--css', type=str, required=True, help='hostname of code signing server')
    group = parser.add_mutually_exclusive_group()
    # We use 'store_false' and a destination related to the other type of
    # artifact to cause the flag being set to disable publishing of the other
    # artifact. This makes using the parsed arguments later on a little simpler
    # and cleaner.
    group.add_argument('--snaps-only', action='store_false', dest='publish_windows',
                        help='Skip publishing other artifacts and only publish the snaps')
    group.add_argument('--windows-only', action='store_false', dest='publish_snaps',
                        help='Skip publishing other artifacts and only publish the Windows installer')
    return parser.parse_args(args)

    
        ```
    ###Assistant : Parse command line arguments.

    :param args: command line arguments with the program name removed. This is
        usually taken from sys.argv[1:].
    :type args: `list` of `str`

    :returns: parsed arguments
    :rtype: argparse.Namespace

    
    "
468,"def is_conservative(field):
    

    # Field is conservative irrespective of frame
    # Take the first frame in the result of the separate method of Vector
    if field == Vector(0):
        return True
    frame = list(field.separate())[0]
    return curl(field, frame).simplify() == Vector(0)

","
    Checks if a field is conservative.

    Parameters
    ==========

    field : Vector
        The field to check for conservative property

    Examples
    ========

    >>> from sympy.physics.vector import ReferenceFrame
    >>> from sympy.physics.vector import is_conservative
    >>> R = ReferenceFrame('R')
    >>> is_conservative(R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z)
    True
    >>> is_conservative(R[2] * R.y)
    False

    ",46,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_conservative(field):
    

    # Field is conservative irrespective of frame
    # Take the first frame in the result of the separate method of Vector
    if field == Vector(0):
        return True
    frame = list(field.separate())[0]
    return curl(field, frame).simplify() == Vector(0)


        ```
    ###Assistant : 
    Checks if a field is conservative.

    Parameters
    ==========

    field : Vector
        The field to check for conservative property

    Examples
    ========

    >>> from sympy.physics.vector import ReferenceFrame
    >>> from sympy.physics.vector import is_conservative
    >>> R = ReferenceFrame('R')
    >>> is_conservative(R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z)
    True
    >>> is_conservative(R[2] * R.y)
    False

    
    "
469,"def fixup_build_ext(cmd):
    
    if os.name == 'nt':
        cmd.debug = sys.executable.endswith('_d.exe')
    elif sysconfig.get_config_var('Py_ENABLE_SHARED'):
        # To further add to the shared builds fun on Unix, we can't just add
        # library_dirs to the Extension() instance because that doesn't get
        # plumbed through to the final compiler command.
        runshared = sysconfig.get_config_var('RUNSHARED')
        if runshared is None:
            cmd.library_dirs = ['.']
        else:
            if sys.platform == 'darwin':
                cmd.library_dirs = []
            else:
                name, equals, value = runshared.partition('=')
                cmd.library_dirs = [d for d in value.split(os.pathsep) if d]
","Function needed to make build_ext tests pass.

    When Python was built with --enable-shared on Unix, -L. is not enough to
    find libpython<blah>.so, because regrtest runs in a tempdir, not in the
    source directory where the .so lives.

    When Python was built with in debug mode on Windows, build_ext commands
    need their debug attribute set, and it is not done automatically for
    some reason.

    This function handles both of these things.  Example use:

        cmd = build_ext(dist)
        support.fixup_build_ext(cmd)
        cmd.ensure_finalized()

    Unlike most other Unix platforms, Mac OS X embeds absolute paths
    to shared libraries into executables, so the fixup is not needed there.
    ",100,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fixup_build_ext(cmd):
    
    if os.name == 'nt':
        cmd.debug = sys.executable.endswith('_d.exe')
    elif sysconfig.get_config_var('Py_ENABLE_SHARED'):
        # To further add to the shared builds fun on Unix, we can't just add
        # library_dirs to the Extension() instance because that doesn't get
        # plumbed through to the final compiler command.
        runshared = sysconfig.get_config_var('RUNSHARED')
        if runshared is None:
            cmd.library_dirs = ['.']
        else:
            if sys.platform == 'darwin':
                cmd.library_dirs = []
            else:
                name, equals, value = runshared.partition('=')
                cmd.library_dirs = [d for d in value.split(os.pathsep) if d]

        ```
    ###Assistant : Function needed to make build_ext tests pass.

    When Python was built with --enable-shared on Unix, -L. is not enough to
    find libpython<blah>.so, because regrtest runs in a tempdir, not in the
    source directory where the .so lives.

    When Python was built with in debug mode on Windows, build_ext commands
    need their debug attribute set, and it is not done automatically for
    some reason.

    This function handles both of these things.  Example use:

        cmd = build_ext(dist)
        support.fixup_build_ext(cmd)
        cmd.ensure_finalized()

    Unlike most other Unix platforms, Mac OS X embeds absolute paths
    to shared libraries into executables, so the fixup is not needed there.
    
    "
470,"def batch_uses_naming_series():
	
	use_naming_series = cint(frappe.db.get_single_value(""Stock Settings"", ""use_naming_series""))
	return bool(use_naming_series)

","
	Verify if the Batch is to be named using a naming series
	:return: bool
	",14,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def batch_uses_naming_series():
	
	use_naming_series = cint(frappe.db.get_single_value(""Stock Settings"", ""use_naming_series""))
	return bool(use_naming_series)


        ```
    ###Assistant : 
	Verify if the Batch is to be named using a naming series
	:return: bool
	
    "
471,"def mixin_http_gateway_parser(parser=None):
    
    gp = add_arg_group(parser, title='HTTP Gateway')

    gp.add_argument(
        '--title',
        type=str,
        help='The title of this HTTP server. It will be used in automatics docs such as Swagger UI.',
    )

    gp.add_argument(
        '--description',
        type=str,
        help='The description of this HTTP server. It will be used in automatics docs such as Swagger UI.',
    )

    gp.add_argument(
        '--cors',
        action='store_true',
        default=False,
        help=,
    )

    gp.add_argument(
        '--no-debug-endpoints',
        action='store_true',
        default=False,
        help='If set, `/status` `/post` endpoints are removed from HTTP interface. ',
    )

    gp.add_argument(
        '--no-crud-endpoints',
        action='store_true',
        default=False,
        help=,
    )

    gp.add_argument(
        '--expose-endpoints',
        type=str,
        help=,
    )

    gp.add_argument(
        '--uvicorn-kwargs',
        action=KVAppendAction,
        metavar='KEY: VALUE',
        nargs='*',
        help=,
    )

    gp.add_argument(
        '--grpc-server-kwargs',
        action=KVAppendAction,
        metavar='KEY: VALUE',
        nargs='*',
        help=,
    )

    gp.add_argument(
        '--ssl-certfile',
        type=str,
        help=,
        dest='ssl_certfile',
    )

    gp.add_argument(
        '--ssl-keyfile',
        type=str,
        help=,
        dest='ssl_keyfile',
    )

","Add the options to rest server

    :param parser: the parser
    
        If set, a CORS middleware is added to FastAPI frontend to allow cross-origin access.
        
        If set, `/index`, `/search`, `/update`, `/delete` endpoints are removed from HTTP interface.

        Any executor that has `@requests(on=...)` bind with those values will receive data requests.
        
        A JSON string that represents a map from executor endpoints (`@requests(on=...)`) to HTTP endpoints.
        
Dictionary of kwargs arguments that will be passed to Uvicorn server when starting the server

More details can be found in Uvicorn docs: https://www.uvicorn.org/settings/


    Dictionary of kwargs arguments that will be passed to the grpc server when starting the server # todo update
    
        the path to the certificate file
        
        the path to the key file
        ",118,110,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mixin_http_gateway_parser(parser=None):
    
    gp = add_arg_group(parser, title='HTTP Gateway')

    gp.add_argument(
        '--title',
        type=str,
        help='The title of this HTTP server. It will be used in automatics docs such as Swagger UI.',
    )

    gp.add_argument(
        '--description',
        type=str,
        help='The description of this HTTP server. It will be used in automatics docs such as Swagger UI.',
    )

    gp.add_argument(
        '--cors',
        action='store_true',
        default=False,
        help=,
    )

    gp.add_argument(
        '--no-debug-endpoints',
        action='store_true',
        default=False,
        help='If set, `/status` `/post` endpoints are removed from HTTP interface. ',
    )

    gp.add_argument(
        '--no-crud-endpoints',
        action='store_true',
        default=False,
        help=,
    )

    gp.add_argument(
        '--expose-endpoints',
        type=str,
        help=,
    )

    gp.add_argument(
        '--uvicorn-kwargs',
        action=KVAppendAction,
        metavar='KEY: VALUE',
        nargs='*',
        help=,
    )

    gp.add_argument(
        '--grpc-server-kwargs',
        action=KVAppendAction,
        metavar='KEY: VALUE',
        nargs='*',
        help=,
    )

    gp.add_argument(
        '--ssl-certfile',
        type=str,
        help=,
        dest='ssl_certfile',
    )

    gp.add_argument(
        '--ssl-keyfile',
        type=str,
        help=,
        dest='ssl_keyfile',
    )


        ```
    ###Assistant : Add the options to rest server

    :param parser: the parser
    
        If set, a CORS middleware is added to FastAPI frontend to allow cross-origin access.
        
        If set, `/index`, `/search`, `/update`, `/delete` endpoints are removed from HTTP interface.

        Any executor that has `@requests(on=...)` bind with those values will receive data requests.
        
        A JSON string that represents a map from executor endpoints (`@requests(on=...)`) to HTTP endpoints.
        
Dictionary of kwargs arguments that will be passed to Uvicorn server when starting the server

More details can be found in Uvicorn docs: https://www.uvicorn.org/settings/


    Dictionary of kwargs arguments that will be passed to the grpc server when starting the server # todo update
    
        the path to the certificate file
        
        the path to the key file
        
    "
472,"def test_image_crafter_index(encoder_doc_array, tmpdir):
    
    create_test_img(path=str(tmpdir), file_name='1.jpg')
    with Flow().add(uses=ImageCrafter) as f:
        res = f.index(inputs=encoder_doc_array)
    assert len(res) == 1
    doc = res[0]
    assert doc.mime_type == 'image/jpeg'
    assert doc.tensor is not None

","In this test, we input one ``DocumentArray`` with one ``Document``,
    and the `craft` method in the ``ImageCrafter`` returns chunks.
    In the ``ImageCrafter``, we filtered out all the modalities and only kept `image/jpeg`.
    So the 2 chunks should left only 1 chunk.
    And the tensor value of the ``Document`` is not empty once we finished crafting since
    we converted image uri/datauri to tensor.
    ",62,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_image_crafter_index(encoder_doc_array, tmpdir):
    
    create_test_img(path=str(tmpdir), file_name='1.jpg')
    with Flow().add(uses=ImageCrafter) as f:
        res = f.index(inputs=encoder_doc_array)
    assert len(res) == 1
    doc = res[0]
    assert doc.mime_type == 'image/jpeg'
    assert doc.tensor is not None


        ```
    ###Assistant : In this test, we input one ``DocumentArray`` with one ``Document``,
    and the `craft` method in the ``ImageCrafter`` returns chunks.
    In the ``ImageCrafter``, we filtered out all the modalities and only kept `image/jpeg`.
    So the 2 chunks should left only 1 chunk.
    And the tensor value of the ``Document`` is not empty once we finished crafting since
    we converted image uri/datauri to tensor.
    
    "
473,"def find_file(path, saltenv=""base"", **kwargs):
    
    actual_saltenv = saltenv
    if ""env"" in kwargs:
        # ""env"" is not supported; Use ""saltenv"".
        kwargs.pop(""env"")

    path = os.path.normpath(path)
    fnd = {""path"": """", ""rel"": """"}
    if os.path.isabs(path):
        return fnd
    if saltenv not in __opts__[""file_roots""]:
        if ""__env__"" in __opts__[""file_roots""]:
            log.debug(
                ""salt environment '%s' maps to __env__ file_roots directory"", saltenv
            )
            saltenv = ""__env__""
        else:
            return fnd
","
    Search the environment for the relative path.
    ",7,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_file(path, saltenv=""base"", **kwargs):
    
    actual_saltenv = saltenv
    if ""env"" in kwargs:
        # ""env"" is not supported; Use ""saltenv"".
        kwargs.pop(""env"")

    path = os.path.normpath(path)
    fnd = {""path"": """", ""rel"": """"}
    if os.path.isabs(path):
        return fnd
    if saltenv not in __opts__[""file_roots""]:
        if ""__env__"" in __opts__[""file_roots""]:
            log.debug(
                ""salt environment '%s' maps to __env__ file_roots directory"", saltenv
            )
            saltenv = ""__env__""
        else:
            return fnd

        ```
    ###Assistant : 
    Search the environment for the relative path.
    
    "
474,"def get_prompt_templates(cls) -> List[PromptTemplate]:
        
        return list(cls.prompt_templates.values())
","
        Returns the list of supported prompt templates.
        :return: List of supported prompt templates.
        ",13,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_prompt_templates(cls) -> List[PromptTemplate]:
        
        return list(cls.prompt_templates.values())

        ```
    ###Assistant : 
        Returns the list of supported prompt templates.
        :return: List of supported prompt templates.
        
    "
475,"def test_views(self, postgres_db):

        query = 

        for cid, char in [(CID_A, 'a'), (CID_B, 'b')]:
            self.sql_via_http(
                query.format(f'test_view_{char}', char),
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.OK
            )

            tables = self.get_tables_in('mindsdb', cid)
            self.assert_list(
                tables, {
                    'models',
                    'models_versions',
                    f'test_view_{char}'
                }
            )

        for cid, char in [(CID_A, 'a'), (CID_B, 'b')]:
            response = self.sql_via_http(
                f""select * from mindsdb.test_view_{char}"",
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.TABLE
            )
            assert len(response['data']) == 50

            response = self.sql_via_http(
                f""DROP VIEW mindsdb.test_view_{char}"",
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.OK
            )

            tables = self.get_tables_in('mindsdb', cid)
            self.assert_list(
                tables, {
                    'models',
                    'models_versions'
                }
            )

            self.sql_via_http(
                f""select * from mindsdb.test_view_{char}"",
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.ERROR
            )
","
            CREATE VIEW mindsdb.{}
            FROM test_integration_{} (
                select * from rentals limit 50
            )
        ",13,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_views(self, postgres_db):

        query = 

        for cid, char in [(CID_A, 'a'), (CID_B, 'b')]:
            self.sql_via_http(
                query.format(f'test_view_{char}', char),
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.OK
            )

            tables = self.get_tables_in('mindsdb', cid)
            self.assert_list(
                tables, {
                    'models',
                    'models_versions',
                    f'test_view_{char}'
                }
            )

        for cid, char in [(CID_A, 'a'), (CID_B, 'b')]:
            response = self.sql_via_http(
                f""select * from mindsdb.test_view_{char}"",
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.TABLE
            )
            assert len(response['data']) == 50

            response = self.sql_via_http(
                f""DROP VIEW mindsdb.test_view_{char}"",
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.OK
            )

            tables = self.get_tables_in('mindsdb', cid)
            self.assert_list(
                tables, {
                    'models',
                    'models_versions'
                }
            )

            self.sql_via_http(
                f""select * from mindsdb.test_view_{char}"",
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.ERROR
            )

        ```
    ###Assistant : 
            CREATE VIEW mindsdb.{}
            FROM test_integration_{} (
                select * from rentals limit 50
            )
        
    "
476,"def convert(filename, cache):
    
    path = Path(filename)
    if not path.exists():
        raise IOError(f""{path} does not exist"")
    if path.suffix[1:] not in converter:
        import pytest
        pytest.skip(f""Don't know how to convert {path.suffix} files to png"")
    newpath = path.parent / f""{path.stem}_{path.suffix[1:]}.png""

    # Only convert the file if the destination doesn't already exist or
    # is out of date.
    if not newpath.exists() or newpath.stat().st_mtime < path.stat().st_mtime:
        cache_dir = _get_cache_path() if cache else None

        if cache_dir is not None:
            _register_conversion_cache_cleaner_once()
            hash_value = get_file_hash(path)
            cached_path = cache_dir / (hash_value + newpath.suffix)
            if cached_path.exists():
                _log.debug(""For %s: reusing cached conversion."", filename)
                shutil.copyfile(cached_path, newpath)
                return str(newpath)

        _log.debug(""For %s: converting to png."", filename)
        convert = converter[path.suffix[1:]]
        if path.suffix == "".svg"":
            contents = path.read_text()
            if 'style=""font:' in contents:
                # for svg.fonttype = none, we explicitly patch the font search
                # path so that fonts shipped by Matplotlib are found.
                convert = _svg_with_matplotlib_fonts_converter
        convert(path, newpath)

        if cache_dir is not None:
            _log.debug(""For %s: caching conversion result."", filename)
            shutil.copyfile(newpath, cached_path)

    return str(newpath)

","
    Convert the named file to png; return the name of the created file.

    If *cache* is True, the result of the conversion is cached in
    `matplotlib.get_cachedir() + '/test_cache/'`.  The caching is based on a
    hash of the exact contents of the input file.  Old cache entries are
    automatically deleted as needed to keep the size of the cache capped to
    twice the size of all baseline images.
    ",67,155,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert(filename, cache):
    
    path = Path(filename)
    if not path.exists():
        raise IOError(f""{path} does not exist"")
    if path.suffix[1:] not in converter:
        import pytest
        pytest.skip(f""Don't know how to convert {path.suffix} files to png"")
    newpath = path.parent / f""{path.stem}_{path.suffix[1:]}.png""

    # Only convert the file if the destination doesn't already exist or
    # is out of date.
    if not newpath.exists() or newpath.stat().st_mtime < path.stat().st_mtime:
        cache_dir = _get_cache_path() if cache else None

        if cache_dir is not None:
            _register_conversion_cache_cleaner_once()
            hash_value = get_file_hash(path)
            cached_path = cache_dir / (hash_value + newpath.suffix)
            if cached_path.exists():
                _log.debug(""For %s: reusing cached conversion."", filename)
                shutil.copyfile(cached_path, newpath)
                return str(newpath)

        _log.debug(""For %s: converting to png."", filename)
        convert = converter[path.suffix[1:]]
        if path.suffix == "".svg"":
            contents = path.read_text()
            if 'style=""font:' in contents:
                # for svg.fonttype = none, we explicitly patch the font search
                # path so that fonts shipped by Matplotlib are found.
                convert = _svg_with_matplotlib_fonts_converter
        convert(path, newpath)

        if cache_dir is not None:
            _log.debug(""For %s: caching conversion result."", filename)
            shutil.copyfile(newpath, cached_path)

    return str(newpath)


        ```
    ###Assistant : 
    Convert the named file to png; return the name of the created file.

    If *cache* is True, the result of the conversion is cached in
    `matplotlib.get_cachedir() + '/test_cache/'`.  The caching is based on a
    hash of the exact contents of the input file.  Old cache entries are
    automatically deleted as needed to keep the size of the cache capped to
    twice the size of all baseline images.
    
    "
477,"def resize(self, size, resample=None, box=None, reducing_gap=None):
        

        if resample is None:
            type_special = "";"" in self.mode
            resample = Resampling.NEAREST if type_special else Resampling.BICUBIC
        elif resample not in (
            Resampling.NEAREST,
            Resampling.BILINEAR,
            Resampling.BICUBIC,
            Resampling.LANCZOS,
            Resampling.BOX,
            Resampling.HAMMING,
        ):
            message = f""Unknown resampling filter ({resample}).""

            filters = [
                f""{filter[1]} ({filter[0]})""
                for filter in (
                    (Resampling.NEAREST, ""Image.Resampling.NEAREST""),
                    (Resampling.LANCZOS, ""Image.Resampling.LANCZOS""),
                    (Resampling.BILINEAR, ""Image.Resampling.BILINEAR""),
                    (Resampling.BICUBIC, ""Image.Resampling.BICUBIC""),
                    (Resampling.BOX, ""Image.Resampling.BOX""),
                    (Resampling.HAMMING, ""Image.Resampling.HAMMING""),
                )
            ]
            raise ValueError(
                message + "" Use "" + "", "".join(filters[:-1]) + "" or "" + filters[-1]
            )

        if reducing_gap is not None and reducing_gap < 1.0:
            raise ValueError(""reducing_gap must be 1.0 or greater"")

        size = tuple(size)

        if box is None:
            box = (0, 0) + self.size
        else:
            box = tuple(box)

        if self.size == size and box == (0, 0) + self.size:
            return self.copy()

        if self.mode in (""1"", ""P""):
            resample = Resampling.NEAREST

        if self.mode in [""LA"", ""RGBA""] and resample != Resampling.NEAREST:
            im = self.convert({""LA"": ""La"", ""RGBA"": ""RGBa""}[self.mode])
            im = im.resize(size, resample, box)
            return im.convert(self.mode)

        self.load()

        if reducing_gap is not None and resample != Resampling.NEAREST:
            factor_x = int((box[2] - box[0]) / size[0] / reducing_gap) or 1
            factor_y = int((box[3] - box[1]) / size[1] / reducing_gap) or 1
            if factor_x > 1 or factor_y > 1:
                reduce_box = self._get_safe_box(size, resample, box)
                factor = (factor_x, factor_y)
                if callable(self.reduce):
                    self = self.reduce(factor, box=reduce_box)
                else:
                    self = Image.reduce(self, factor, box=reduce_box)
                box = (
                    (box[0] - reduce_box[0]) / factor_x,
                    (box[1] - reduce_box[1]) / factor_y,
                    (box[2] - reduce_box[0]) / factor_x,
                    (box[3] - reduce_box[1]) / factor_y,
                )

        return self._new(self.im.resize(size, resample, box))
","
        Returns a resized copy of this image.

        :param size: The requested size in pixels, as a 2-tuple:
           (width, height).
        :param resample: An optional resampling filter.  This can be
           one of :py:data:`PIL.Image.Resampling.NEAREST`,
           :py:data:`PIL.Image.Resampling.BOX`,
           :py:data:`PIL.Image.Resampling.BILINEAR`,
           :py:data:`PIL.Image.Resampling.HAMMING`,
           :py:data:`PIL.Image.Resampling.BICUBIC` or
           :py:data:`PIL.Image.Resampling.LANCZOS`.
           If the image has mode ""1"" or ""P"", it is always set to
           :py:data:`PIL.Image.Resampling.NEAREST`.
           If the image mode specifies a number of bits, such as ""I;16"", then the
           default filter is :py:data:`PIL.Image.Resampling.NEAREST`.
           Otherwise, the default filter is
           :py:data:`PIL.Image.Resampling.BICUBIC`. See: :ref:`concept-filters`.
        :param box: An optional 4-tuple of floats providing
           the source image region to be scaled.
           The values must be within (0, 0, width, height) rectangle.
           If omitted or None, the entire source is used.
        :param reducing_gap: Apply optimization by resizing the image
           in two steps. First, reducing the image by integer times
           using :py:meth:`~PIL.Image.Image.reduce`.
           Second, resizing using regular resampling. The last step
           changes size no less than by ``reducing_gap`` times.
           ``reducing_gap`` may be None (no first step is performed)
           or should be greater than 1.0. The bigger ``reducing_gap``,
           the closer the result to the fair resampling.
           The smaller ``reducing_gap``, the faster resizing.
           With ``reducing_gap`` greater or equal to 3.0, the result is
           indistinguishable from fair resampling in most cases.
           The default value is None (no optimization).
        :returns: An :py:class:`~PIL.Image.Image` object.
        ",207,245,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resize(self, size, resample=None, box=None, reducing_gap=None):
        

        if resample is None:
            type_special = "";"" in self.mode
            resample = Resampling.NEAREST if type_special else Resampling.BICUBIC
        elif resample not in (
            Resampling.NEAREST,
            Resampling.BILINEAR,
            Resampling.BICUBIC,
            Resampling.LANCZOS,
            Resampling.BOX,
            Resampling.HAMMING,
        ):
            message = f""Unknown resampling filter ({resample}).""

            filters = [
                f""{filter[1]} ({filter[0]})""
                for filter in (
                    (Resampling.NEAREST, ""Image.Resampling.NEAREST""),
                    (Resampling.LANCZOS, ""Image.Resampling.LANCZOS""),
                    (Resampling.BILINEAR, ""Image.Resampling.BILINEAR""),
                    (Resampling.BICUBIC, ""Image.Resampling.BICUBIC""),
                    (Resampling.BOX, ""Image.Resampling.BOX""),
                    (Resampling.HAMMING, ""Image.Resampling.HAMMING""),
                )
            ]
            raise ValueError(
                message + "" Use "" + "", "".join(filters[:-1]) + "" or "" + filters[-1]
            )

        if reducing_gap is not None and reducing_gap < 1.0:
            raise ValueError(""reducing_gap must be 1.0 or greater"")

        size = tuple(size)

        if box is None:
            box = (0, 0) + self.size
        else:
            box = tuple(box)

        if self.size == size and box == (0, 0) + self.size:
            return self.copy()

        if self.mode in (""1"", ""P""):
            resample = Resampling.NEAREST

        if self.mode in [""LA"", ""RGBA""] and resample != Resampling.NEAREST:
            im = self.convert({""LA"": ""La"", ""RGBA"": ""RGBa""}[self.mode])
            im = im.resize(size, resample, box)
            return im.convert(self.mode)

        self.load()

        if reducing_gap is not None and resample != Resampling.NEAREST:
            factor_x = int((box[2] - box[0]) / size[0] / reducing_gap) or 1
            factor_y = int((box[3] - box[1]) / size[1] / reducing_gap) or 1
            if factor_x > 1 or factor_y > 1:
                reduce_box = self._get_safe_box(size, resample, box)
                factor = (factor_x, factor_y)
                if callable(self.reduce):
                    self = self.reduce(factor, box=reduce_box)
                else:
                    self = Image.reduce(self, factor, box=reduce_box)
                box = (
                    (box[0] - reduce_box[0]) / factor_x,
                    (box[1] - reduce_box[1]) / factor_y,
                    (box[2] - reduce_box[0]) / factor_x,
                    (box[3] - reduce_box[1]) / factor_y,
                )

        return self._new(self.im.resize(size, resample, box))

        ```
    ###Assistant : 
        Returns a resized copy of this image.

        :param size: The requested size in pixels, as a 2-tuple:
           (width, height).
        :param resample: An optional resampling filter.  This can be
           one of :py:data:`PIL.Image.Resampling.NEAREST`,
           :py:data:`PIL.Image.Resampling.BOX`,
           :py:data:`PIL.Image.Resampling.BILINEAR`,
           :py:data:`PIL.Image.Resampling.HAMMING`,
           :py:data:`PIL.Image.Resampling.BICUBIC` or
           :py:data:`PIL.Image.Resampling.LANCZOS`.
           If the image has mode ""1"" or ""P"", it is always set to
           :py:data:`PIL.Image.Resampling.NEAREST`.
           If the image mode specifies a number of bits, such as ""I;16"", then the
           default filter is :py:data:`PIL.Image.Resampling.NEAREST`.
           Otherwise, the default filter is
           :py:data:`PIL.Image.Resampling.BICUBIC`. See: :ref:`concept-filters`.
        :param box: An optional 4-tuple of floats providing
           the source image region to be scaled.
           The values must be within (0, 0, width, height) rectangle.
           If omitted or None, the entire source is used.
        :param reducing_gap: Apply optimization by resizing the image
           in two steps. First, reducing the image by integer times
           using :py:meth:`~PIL.Image.Image.reduce`.
           Second, resizing using regular resampling. The last step
           changes size no less than by ``reducing_gap`` times.
           ``reducing_gap`` may be None (no first step is performed)
           or should be greater than 1.0. The bigger ``reducing_gap``,
           the closer the result to the fair resampling.
           The smaller ``reducing_gap``, the faster resizing.
           With ``reducing_gap`` greater or equal to 3.0, the result is
           indistinguishable from fair resampling in most cases.
           The default value is None (no optimization).
        :returns: An :py:class:`~PIL.Image.Image` object.
        
    "
478,"def test_unrecognized_key(self) -> None:
        yaml_str = 
        output_error = self.get_errors_from_gen_backend_stubs(yaml_str)
        self.assertExpectedInline(output_error, )  # noqa: B950

    # if use_out_as_primary is provided, it must be a bool","\
backend: XLA
cpp_namespace: torch_xla
supported:
- abs
invalid_key: invalid_val contains unexpected keys: invalid_key. Only the following keys are supported: backend, cpp_namespace, extra_headers, supported, autograd, full_codegen",26,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unrecognized_key(self) -> None:
        yaml_str = 
        output_error = self.get_errors_from_gen_backend_stubs(yaml_str)
        self.assertExpectedInline(output_error, )  # noqa: B950

    # if use_out_as_primary is provided, it must be a bool
        ```
    ###Assistant : \
backend: XLA
cpp_namespace: torch_xla
supported:
- abs
invalid_key: invalid_val contains unexpected keys: invalid_key. Only the following keys are supported: backend, cpp_namespace, extra_headers, supported, autograd, full_codegen
    "
479,"def f2cexpr(expr):
    
    # TODO: support Fortran `len` function with optional kind parameter
    expr = re.sub(r'\blen\b', 'f2py_slen', expr)
    return expr

","Rewrite Fortran expression as f2py supported C expression.

    Due to the lack of a proper expression parser in f2py, this
    function uses a heuristic approach that assumes that Fortran
    arithmetic expressions are valid C arithmetic expressions when
    mapping Fortran function calls to the corresponding C function/CPP
    macros calls.

    ",48,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def f2cexpr(expr):
    
    # TODO: support Fortran `len` function with optional kind parameter
    expr = re.sub(r'\blen\b', 'f2py_slen', expr)
    return expr


        ```
    ###Assistant : Rewrite Fortran expression as f2py supported C expression.

    Due to the lack of a proper expression parser in f2py, this
    function uses a heuristic approach that assumes that Fortran
    arithmetic expressions are valid C arithmetic expressions when
    mapping Fortran function calls to the corresponding C function/CPP
    macros calls.

    
    "
480,"def build_args(self, category, command=None, generate=False):
        
        logger.debug(""Build cli arguments: (category: %s, command: %s, generate: %s)"",
                     category, command, generate)
        command = self.command if not command else command
        script = f""{category}.py""
        pathexecscript = os.path.join(self.pathscript, script)

        args = [sys.executable] if generate else [sys.executable, ""-u""]
        args.extend([pathexecscript, command])

        cli_opts = get_config().cli_opts
        for cliopt in cli_opts.gen_cli_arguments(command):
            args.extend(cliopt)
            if command == ""train"" and not generate:
                self._get_training_session_info(cliopt)

        if not generate:
            args.append(""-gui"")  # Indicate to Faceswap that we are running the GUI
        if generate:
            # Delimit args with spaces
            args = [f'""{arg}""' if "" "" in arg and not arg.startswith((""["", ""(""))
                    and not arg.endswith((""]"", "")"")) else arg
                    for arg in args]
        logger.debug(""Built cli arguments: (%s)"", args)
        return args
"," Build the faceswap command and arguments list.

        If training, pass the model folder and name to the training
        :class:`lib.gui.analysis.Session` for the GUI.
        ",22,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def build_args(self, category, command=None, generate=False):
        
        logger.debug(""Build cli arguments: (category: %s, command: %s, generate: %s)"",
                     category, command, generate)
        command = self.command if not command else command
        script = f""{category}.py""
        pathexecscript = os.path.join(self.pathscript, script)

        args = [sys.executable] if generate else [sys.executable, ""-u""]
        args.extend([pathexecscript, command])

        cli_opts = get_config().cli_opts
        for cliopt in cli_opts.gen_cli_arguments(command):
            args.extend(cliopt)
            if command == ""train"" and not generate:
                self._get_training_session_info(cliopt)

        if not generate:
            args.append(""-gui"")  # Indicate to Faceswap that we are running the GUI
        if generate:
            # Delimit args with spaces
            args = [f'""{arg}""' if "" "" in arg and not arg.startswith((""["", ""(""))
                    and not arg.endswith((""]"", "")"")) else arg
                    for arg in args]
        logger.debug(""Built cli arguments: (%s)"", args)
        return args

        ```
    ###Assistant :  Build the faceswap command and arguments list.

        If training, pass the model folder and name to the training
        :class:`lib.gui.analysis.Session` for the GUI.
        
    "
481,"def describe_v_switches(self, vpc_id=None):
        
        request = DescribeVSwitchesRequest()
        if vpc_id is not None:
            request.set_VpcId(vpc_id)
        response = self._send_request(request)
        if response is not None:
            return response.get(""VSwitches"").get(""VSwitch"")
        else:
            logging.error(""Describe VSwitches Failed."")
            return None
","Queries one or more VSwitches.

        :param vpc_id: The ID of the VPC to which the VSwitch belongs.
        :return: VSwitch list.
        ",20,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def describe_v_switches(self, vpc_id=None):
        
        request = DescribeVSwitchesRequest()
        if vpc_id is not None:
            request.set_VpcId(vpc_id)
        response = self._send_request(request)
        if response is not None:
            return response.get(""VSwitches"").get(""VSwitch"")
        else:
            logging.error(""Describe VSwitches Failed."")
            return None

        ```
    ###Assistant : Queries one or more VSwitches.

        :param vpc_id: The ID of the VPC to which the VSwitch belongs.
        :return: VSwitch list.
        
    "
482,"def line(self, Y, X=None, win=None, env=None, opts=None, update=None, name=None):
        
        if update is not None:
            if update == ""remove"":
                return self.scatter(
                    X=None,
                    Y=None,
                    opts=opts,
                    win=win,
                    env=env,
                    update=update,
                    name=name,
                )
            else:
                assert X is not None, ""must specify x-values for line update""
        assert Y.ndim == 1 or Y.ndim == 2, ""Y should have 1 or 2 dim""
        assert Y.shape[-1] > 0, ""must plot one line at least""

        if X is not None:
            assert X.ndim == 1 or X.ndim == 2, ""X should have 1 or 2 dim""
        else:
            X = np.linspace(0, 1, Y.shape[0])

        if Y.ndim == 2 and X.ndim == 1:
            X = np.tile(X, (Y.shape[1], 1)).transpose()

        assert X.shape == Y.shape, ""X and Y should be the same shape""

        opts = {} if opts is None else opts
        opts[""markers""] = opts.get(""markers"", False)
        opts[""fillarea""] = opts.get(""fillarea"", False)
        opts[""mode""] = ""lines+markers"" if opts.get(""markers"") else ""lines""

        _title2str(opts)
        _assert_opts(opts)

        if Y.ndim == 1:
            linedata = np.column_stack((X, Y))
        else:
            linedata = np.column_stack((X.ravel(order=""F""), Y.ravel(order=""F"")))

        labels = None
        if Y.ndim == 2:
            labels = np.arange(1, Y.shape[1] + 1)
            labels = np.tile(labels, (Y.shape[0], 1)).ravel(order=""F"")

        return self.scatter(
            X=linedata, Y=labels, opts=opts, win=win, env=env, update=update, name=name
        )
","
        This function draws a line plot. It takes in an `N` or `NxM` tensor
        `Y` that specifies the values of the `M` lines (that connect `N` points)
        to plot. It also takes an optional `X` tensor that specifies the
        corresponding x-axis values; `X` can be an `N` tensor (in which case all
        lines will share the same x-axis values) or have the same size as `Y`.

        `update` can be used to efficiently update the data of an existing line.
        Use 'append' to append data, 'replace' to use new data, and 'remove' to
        delete the trace that is specified in `name`. If updating a
        single trace, use `name` to specify the name of the trace to be updated.
        Update data that is all NaN is ignored (can be used for masking update).
        Using `update='append'` will create a plot if it doesn't exist
        and append to the existing plot otherwise.

        The following `opts` are supported:

        - `opts.fillarea`    : fill area below line (`boolean`)
        - `opts.markers`     : show markers (`boolean`; default = `false`)
        - `opts.markersymbol`: marker symbol (`string`; default = `'dot'`)
        - `opts.markersize`  : marker size (`number`; default = `'10'`)
        - `opts.linecolor`   : line colors (`np.array`; default = None)
        - `opts.dash`        : line dash type (`np.array`; default = None)
        - `opts.legend`      : `list` or `tuple` containing legend names

        If `update` is specified, the figure will be updated without
        creating a new plot -- this can be used for efficient updating.
        ",237,183,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def line(self, Y, X=None, win=None, env=None, opts=None, update=None, name=None):
        
        if update is not None:
            if update == ""remove"":
                return self.scatter(
                    X=None,
                    Y=None,
                    opts=opts,
                    win=win,
                    env=env,
                    update=update,
                    name=name,
                )
            else:
                assert X is not None, ""must specify x-values for line update""
        assert Y.ndim == 1 or Y.ndim == 2, ""Y should have 1 or 2 dim""
        assert Y.shape[-1] > 0, ""must plot one line at least""

        if X is not None:
            assert X.ndim == 1 or X.ndim == 2, ""X should have 1 or 2 dim""
        else:
            X = np.linspace(0, 1, Y.shape[0])

        if Y.ndim == 2 and X.ndim == 1:
            X = np.tile(X, (Y.shape[1], 1)).transpose()

        assert X.shape == Y.shape, ""X and Y should be the same shape""

        opts = {} if opts is None else opts
        opts[""markers""] = opts.get(""markers"", False)
        opts[""fillarea""] = opts.get(""fillarea"", False)
        opts[""mode""] = ""lines+markers"" if opts.get(""markers"") else ""lines""

        _title2str(opts)
        _assert_opts(opts)

        if Y.ndim == 1:
            linedata = np.column_stack((X, Y))
        else:
            linedata = np.column_stack((X.ravel(order=""F""), Y.ravel(order=""F"")))

        labels = None
        if Y.ndim == 2:
            labels = np.arange(1, Y.shape[1] + 1)
            labels = np.tile(labels, (Y.shape[0], 1)).ravel(order=""F"")

        return self.scatter(
            X=linedata, Y=labels, opts=opts, win=win, env=env, update=update, name=name
        )

        ```
    ###Assistant : 
        This function draws a line plot. It takes in an `N` or `NxM` tensor
        `Y` that specifies the values of the `M` lines (that connect `N` points)
        to plot. It also takes an optional `X` tensor that specifies the
        corresponding x-axis values; `X` can be an `N` tensor (in which case all
        lines will share the same x-axis values) or have the same size as `Y`.

        `update` can be used to efficiently update the data of an existing line.
        Use 'append' to append data, 'replace' to use new data, and 'remove' to
        delete the trace that is specified in `name`. If updating a
        single trace, use `name` to specify the name of the trace to be updated.
        Update data that is all NaN is ignored (can be used for masking update).
        Using `update='append'` will create a plot if it doesn't exist
        and append to the existing plot otherwise.

        The following `opts` are supported:

        - `opts.fillarea`    : fill area below line (`boolean`)
        - `opts.markers`     : show markers (`boolean`; default = `false`)
        - `opts.markersymbol`: marker symbol (`string`; default = `'dot'`)
        - `opts.markersize`  : marker size (`number`; default = `'10'`)
        - `opts.linecolor`   : line colors (`np.array`; default = None)
        - `opts.dash`        : line dash type (`np.array`; default = None)
        - `opts.legend`      : `list` or `tuple` containing legend names

        If `update` is specified, the figure will be updated without
        creating a new plot -- this can be used for efficient updating.
        
    "
483,"def test_delete_with_id(self, db_mock_class):
        
        op = DatabricksReposDeleteOperator(task_id=TASK_ID, repo_id=""123"")
        db_mock = db_mock_class.return_value
        db_mock.delete_repo.return_value = None

        op.execute(None)

        db_mock_class.assert_called_once_with(
            DEFAULT_CONN_ID, retry_limit=op.databricks_retry_limit, retry_delay=op.databricks_retry_delay
        )

        db_mock.delete_repo.assert_called_once_with('123')
","
        Test the execute function using Repo ID.
        ",7,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_with_id(self, db_mock_class):
        
        op = DatabricksReposDeleteOperator(task_id=TASK_ID, repo_id=""123"")
        db_mock = db_mock_class.return_value
        db_mock.delete_repo.return_value = None

        op.execute(None)

        db_mock_class.assert_called_once_with(
            DEFAULT_CONN_ID, retry_limit=op.databricks_retry_limit, retry_delay=op.databricks_retry_delay
        )

        db_mock.delete_repo.assert_called_once_with('123')

        ```
    ###Assistant : 
        Test the execute function using Repo ID.
        
    "
484,"def test_user_rate_reached(self):
        
        for i in range(5):
            group = self.store_event(
                data={
                    ""fingerprint"": [""group1""],
                    ""timestamp"": iso_format(before_now(minutes=5 + i)),
                    ""tags"": {""sentry:user"": i},
                },
                project_id=self.project.id,
            ).group

        snooze = GroupSnooze.objects.create(group=group, user_count=5, user_window=60)
        assert not snooze.is_valid(test_rates=True)
",Test that ignoring an error issue until it's hit by 10 users in an hour works.,16,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_user_rate_reached(self):
        
        for i in range(5):
            group = self.store_event(
                data={
                    ""fingerprint"": [""group1""],
                    ""timestamp"": iso_format(before_now(minutes=5 + i)),
                    ""tags"": {""sentry:user"": i},
                },
                project_id=self.project.id,
            ).group

        snooze = GroupSnooze.objects.create(group=group, user_count=5, user_window=60)
        assert not snooze.is_valid(test_rates=True)

        ```
    ###Assistant : Test that ignoring an error issue until it's hit by 10 users in an hour works.
    "
485,"def make_zipfile(base_name, base_dir, verbose=0, dry_run=0):
    
    zip_filename = base_name + "".zip""
    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)

    # If zipfile module is not available, try spawning an external
    # 'zip' command.
    if zipfile is None:
        if verbose:
            zipoptions = ""-r""
        else:
            zipoptions = ""-rq""

        try:
            spawn([""zip"", zipoptions, zip_filename, base_dir],
                  dry_run=dry_run)
        except DistutilsExecError:
            # XXX really should distinguish between ""couldn't find
            # external 'zip' command"" and ""zip failed"".
            raise DistutilsExecError((""unable to create zip file '%s': ""
                   ""could neither import the 'zipfile' module nor ""
                   ""find a standalone zip utility"") % zip_filename)

    else:
        log.info(""creating '%s' and adding '%s' to it"",
                 zip_filename, base_dir)

        if not dry_run:
            try:
                zip = zipfile.ZipFile(zip_filename, ""w"",
                                      compression=zipfile.ZIP_DEFLATED)
            except RuntimeError:
                zip = zipfile.ZipFile(zip_filename, ""w"",
                                      compression=zipfile.ZIP_STORED)

            with zip:
                if base_dir != os.curdir:
                    path = os.path.normpath(os.path.join(base_dir, ''))
                    zip.write(path, path)
                    log.info(""adding '%s'"", path)
                for dirpath, dirnames, filenames in os.walk(base_dir):
                    for name in dirnames:
                        path = os.path.normpath(os.path.join(dirpath, name, ''))
                        zip.write(path, path)
                        log.info(""adding '%s'"", path)
                    for name in filenames:
                        path = os.path.normpath(os.path.join(dirpath, name))
                        if os.path.isfile(path):
                            zip.write(path, path)
                            log.info(""adding '%s'"", path)

    return zip_filename

ARCHIVE_FORMATS = {
    'gztar': (make_tarball, [('compress', 'gzip')], ""gzip'ed tar-file""),
    'bztar': (make_tarball, [('compress', 'bzip2')], ""bzip2'ed tar-file""),
    'xztar': (make_tarball, [('compress', 'xz')], ""xz'ed tar-file""),
    'ztar':  (make_tarball, [('compress', 'compress')], ""compressed tar file""),
    'tar':   (make_tarball, [('compress', None)], ""uncompressed tar file""),
    'zip':   (make_zipfile, [],""ZIP file"")
    }
","Create a zip file from all the files under 'base_dir'.

    The output zip file will be named 'base_name' + "".zip"".  Uses either the
    ""zipfile"" Python module (if available) or the InfoZIP ""zip"" utility
    (if installed and found on the default search path).  If neither tool is
    available, raises DistutilsExecError.  Returns the name of the output zip
    file.
    ",57,203,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_zipfile(base_name, base_dir, verbose=0, dry_run=0):
    
    zip_filename = base_name + "".zip""
    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)

    # If zipfile module is not available, try spawning an external
    # 'zip' command.
    if zipfile is None:
        if verbose:
            zipoptions = ""-r""
        else:
            zipoptions = ""-rq""

        try:
            spawn([""zip"", zipoptions, zip_filename, base_dir],
                  dry_run=dry_run)
        except DistutilsExecError:
            # XXX really should distinguish between ""couldn't find
            # external 'zip' command"" and ""zip failed"".
            raise DistutilsExecError((""unable to create zip file '%s': ""
                   ""could neither import the 'zipfile' module nor ""
                   ""find a standalone zip utility"") % zip_filename)

    else:
        log.info(""creating '%s' and adding '%s' to it"",
                 zip_filename, base_dir)

        if not dry_run:
            try:
                zip = zipfile.ZipFile(zip_filename, ""w"",
                                      compression=zipfile.ZIP_DEFLATED)
            except RuntimeError:
                zip = zipfile.ZipFile(zip_filename, ""w"",
                                      compression=zipfile.ZIP_STORED)

            with zip:
                if base_dir != os.curdir:
                    path = os.path.normpath(os.path.join(base_dir, ''))
                    zip.write(path, path)
                    log.info(""adding '%s'"", path)
                for dirpath, dirnames, filenames in os.walk(base_dir):
                    for name in dirnames:
                        path = os.path.normpath(os.path.join(dirpath, name, ''))
                        zip.write(path, path)
                        log.info(""adding '%s'"", path)
                    for name in filenames:
                        path = os.path.normpath(os.path.join(dirpath, name))
                        if os.path.isfile(path):
                            zip.write(path, path)
                            log.info(""adding '%s'"", path)

    return zip_filename

ARCHIVE_FORMATS = {
    'gztar': (make_tarball, [('compress', 'gzip')], ""gzip'ed tar-file""),
    'bztar': (make_tarball, [('compress', 'bzip2')], ""bzip2'ed tar-file""),
    'xztar': (make_tarball, [('compress', 'xz')], ""xz'ed tar-file""),
    'ztar':  (make_tarball, [('compress', 'compress')], ""compressed tar file""),
    'tar':   (make_tarball, [('compress', None)], ""uncompressed tar file""),
    'zip':   (make_zipfile, [],""ZIP file"")
    }

        ```
    ###Assistant : Create a zip file from all the files under 'base_dir'.

    The output zip file will be named 'base_name' + "".zip"".  Uses either the
    ""zipfile"" Python module (if available) or the InfoZIP ""zip"" utility
    (if installed and found on the default search path).  If neither tool is
    available, raises DistutilsExecError.  Returns the name of the output zip
    file.
    
    "
486,"def test_power_levels_user_default(self):
        
        # The other user doesn't have the proper power level.
        channel = self._upgrade_room(self.other_token)
        self.assertEqual(403, channel.code, channel.result)

        # Increase the power levels so that this user can upgrade.
        power_levels = self.helper.get_state(
            self.room_id,
            ""m.room.power_levels"",
            tok=self.creator_token,
        )
        power_levels[""users_default""] = 100
        self.helper.send_state(
            self.room_id,
            ""m.room.power_levels"",
            body=power_levels,
            tok=self.creator_token,
        )

        # The upgrade should succeed!
        channel = self._upgrade_room(self.other_token)
        self.assertEqual(200, channel.code, channel.result)
","
        Another user can upgrade the room if the default power level for users is increased.
        ",15,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_power_levels_user_default(self):
        
        # The other user doesn't have the proper power level.
        channel = self._upgrade_room(self.other_token)
        self.assertEqual(403, channel.code, channel.result)

        # Increase the power levels so that this user can upgrade.
        power_levels = self.helper.get_state(
            self.room_id,
            ""m.room.power_levels"",
            tok=self.creator_token,
        )
        power_levels[""users_default""] = 100
        self.helper.send_state(
            self.room_id,
            ""m.room.power_levels"",
            body=power_levels,
            tok=self.creator_token,
        )

        # The upgrade should succeed!
        channel = self._upgrade_room(self.other_token)
        self.assertEqual(200, channel.code, channel.result)

        ```
    ###Assistant : 
        Another user can upgrade the room if the default power level for users is increased.
        
    "
487,"def get_exempt_total(filters):
	
	conditions = get_conditions(filters)
	try:
		return (
			frappe.db.sql(
				.format(
					where_conditions=conditions
				),
				filters,
			)[0][0]
			or 0
		)
	except (IndexError, TypeError):
		return 0

","Returns the sum of each Sales Invoice Item Amount which is Vat Exempt.
			select
				sum(i.base_amount) as total
			from
				`tabSales Invoice Item` i inner join `tabSales Invoice` s
			on
				i.parent = s.name
			where
				s.docstatus = 1 and  i.is_exempt = 1
				{where_conditions} ;
			",41,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_exempt_total(filters):
	
	conditions = get_conditions(filters)
	try:
		return (
			frappe.db.sql(
				.format(
					where_conditions=conditions
				),
				filters,
			)[0][0]
			or 0
		)
	except (IndexError, TypeError):
		return 0


        ```
    ###Assistant : Returns the sum of each Sales Invoice Item Amount which is Vat Exempt.
			select
				sum(i.base_amount) as total
			from
				`tabSales Invoice Item` i inner join `tabSales Invoice` s
			on
				i.parent = s.name
			where
				s.docstatus = 1 and  i.is_exempt = 1
				{where_conditions} ;
			
    "
488,"def encrypt(self, key, text, confounder=None, key_usage_number=None):
        
        if key_usage_number is None:
            key_usage_number = self.get_usage()[0]
        self.cipher = key.encrypt(key_usage_number, text, confounder=confounder)


EncryptionKey = lambda **kwargs: ASN1F_SEQUENCE(
    Int32(""keytype"", 0, explicit_tag=0x0),
    ASN1F_STRING(""keyvalue"", """", explicit_tag=0x1),
    **kwargs
)
KerberosFlags = ASN1F_FLAGS


_PADATA_TYPES = {
    1: ""PA-TGS-REQ"",
    2: ""PA-ENC-TIMESTAMP"",
    3: ""PA-PW-SALT"",
    11: ""PA-ETYPE-INFO"",
    14: ""PA-PK-AS-REQ-OLD"",
    15: ""PA-PK-AS-REP-OLD"",
    16: ""PA-PK-AS-REQ"",
    17: ""PA-PK-AS-REP"",
    19: ""PA-ETYPE-INFO2"",
    20: ""PA-SVR-REFERRAL-INFO"",
    128: ""PA-PAC-REQUEST"",
    133: ""PA-FX-COOKIE"",
    134: ""PA-AUTHENTICATION-SET"",
    135: ""PA-AUTH-SET-SELECTED"",
    136: ""PA-FX-FAST"",
    137: ""PA-FX-ERROR"",
    165: ""PA-SUPPORTED-ENCTYPES"",
    167: ""PA-PAC-OPTIONS"",
}

_PADATA_CLASSES = {
    # Filled elsewhere in this file
}


# RFC4120

","
        Encrypt text and set it into cipher.

        :param key: the key to use for encryption
        :param text: the bytes value to encode
        :param confounder: (optional) specify the confounder bytes. Random otherwise
        :param key_usage_number: (optional) specify the key usage number.
                                 Guessed otherwise
        ",41,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def encrypt(self, key, text, confounder=None, key_usage_number=None):
        
        if key_usage_number is None:
            key_usage_number = self.get_usage()[0]
        self.cipher = key.encrypt(key_usage_number, text, confounder=confounder)


EncryptionKey = lambda **kwargs: ASN1F_SEQUENCE(
    Int32(""keytype"", 0, explicit_tag=0x0),
    ASN1F_STRING(""keyvalue"", """", explicit_tag=0x1),
    **kwargs
)
KerberosFlags = ASN1F_FLAGS


_PADATA_TYPES = {
    1: ""PA-TGS-REQ"",
    2: ""PA-ENC-TIMESTAMP"",
    3: ""PA-PW-SALT"",
    11: ""PA-ETYPE-INFO"",
    14: ""PA-PK-AS-REQ-OLD"",
    15: ""PA-PK-AS-REP-OLD"",
    16: ""PA-PK-AS-REQ"",
    17: ""PA-PK-AS-REP"",
    19: ""PA-ETYPE-INFO2"",
    20: ""PA-SVR-REFERRAL-INFO"",
    128: ""PA-PAC-REQUEST"",
    133: ""PA-FX-COOKIE"",
    134: ""PA-AUTHENTICATION-SET"",
    135: ""PA-AUTH-SET-SELECTED"",
    136: ""PA-FX-FAST"",
    137: ""PA-FX-ERROR"",
    165: ""PA-SUPPORTED-ENCTYPES"",
    167: ""PA-PAC-OPTIONS"",
}

_PADATA_CLASSES = {
    # Filled elsewhere in this file
}


# RFC4120


        ```
    ###Assistant : 
        Encrypt text and set it into cipher.

        :param key: the key to use for encryption
        :param text: the bytes value to encode
        :param confounder: (optional) specify the confounder bytes. Random otherwise
        :param key_usage_number: (optional) specify the key usage number.
                                 Guessed otherwise
        
    "
489,"def create_v_switch(self, vpc_id, zone_id, cidr_block):
        
        request = CreateVSwitchRequest()
        request.set_ZoneId(zone_id)
        request.set_VpcId(vpc_id)
        request.set_CidrBlock(cidr_block)
        response = self._send_request(request)
        if response is not None:
            return response.get(""VSwitchId"")
        else:
            logging.error(""create_v_switch vpc_id %s failed."", vpc_id)
        return None
","Create vSwitches to divide the VPC into one or more subnets

        :param vpc_id: The ID of the VPC to which the VSwitch belongs.
        :param zone_id: The ID of the zone to which
                        the target VSwitch belongs.
        :param cidr_block: The CIDR block of the VSwitch.
        :return:
        ",45,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_v_switch(self, vpc_id, zone_id, cidr_block):
        
        request = CreateVSwitchRequest()
        request.set_ZoneId(zone_id)
        request.set_VpcId(vpc_id)
        request.set_CidrBlock(cidr_block)
        response = self._send_request(request)
        if response is not None:
            return response.get(""VSwitchId"")
        else:
            logging.error(""create_v_switch vpc_id %s failed."", vpc_id)
        return None

        ```
    ###Assistant : Create vSwitches to divide the VPC into one or more subnets

        :param vpc_id: The ID of the VPC to which the VSwitch belongs.
        :param zone_id: The ID of the zone to which
                        the target VSwitch belongs.
        :param cidr_block: The CIDR block of the VSwitch.
        :return:
        
    "
490,"def transparent_background(self) -> bool:
        
        return self.bgcolor is None or self.bgcolor.is_default
",Check if the style specified a transparent background.,8,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transparent_background(self) -> bool:
        
        return self.bgcolor is None or self.bgcolor.is_default

        ```
    ###Assistant : Check if the style specified a transparent background.
    "
491,"def isPerfectNumber(number):
    

    # precondition
    assert isinstance(number, int) and (
        number > 1
    ), ""'number' must been an int and >= 1""

    divisors = getDivisors(number)

    # precondition
    assert (
        isinstance(divisors, list)
        and (divisors[0] == 1)
        and (divisors[len(divisors) - 1] == number)
    ), ""Error in help-function getDivisiors(...)""

    # summed all divisors up to 'number' (exclusive), hence [:-1]
    return sum(divisors[:-1]) == number


# ------------------------------------------------------------

","
    input: positive integer 'number' > 1
    returns true if 'number' is a perfect number otherwise false.
    ",16,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def isPerfectNumber(number):
    

    # precondition
    assert isinstance(number, int) and (
        number > 1
    ), ""'number' must been an int and >= 1""

    divisors = getDivisors(number)

    # precondition
    assert (
        isinstance(divisors, list)
        and (divisors[0] == 1)
        and (divisors[len(divisors) - 1] == number)
    ), ""Error in help-function getDivisiors(...)""

    # summed all divisors up to 'number' (exclusive), hence [:-1]
    return sum(divisors[:-1]) == number


# ------------------------------------------------------------


        ```
    ###Assistant : 
    input: positive integer 'number' > 1
    returns true if 'number' is a perfect number otherwise false.
    
    "
492,"def _get_cmap(name=None, lut=None):
    
    if name is None:
        name = mpl.rcParams['image.cmap']
    if isinstance(name, colors.Colormap):
        return name
    _api.check_in_list(sorted(_colormaps), name=name)
    if lut is None:
        return _colormaps[name]
    else:
        return _colormaps[name].resampled(lut)

# do it in two steps like this so we can have an un-deprecated version in
# pyplot.
get_cmap = _api.deprecated(
    '3.6', pending=True, alternative=""``matplotlib.colormaps[name]``""
)(_get_cmap)


@_api.deprecated(
    '3.6',
    pending=True,
    alternative=""``matplotlib.colormaps.unregister_cmap(name)``""
)","
    Get a colormap instance, defaulting to rc values if *name* is None.

    Colormaps added with :func:`register_cmap` take precedence over
    built-in colormaps.

    Parameters
    ----------
    name : `matplotlib.colors.Colormap` or str or None, default: None
        If a `.Colormap` instance, it will be returned. Otherwise, the name of
        a colormap known to Matplotlib, which will be resampled by *lut*. The
        default, None, means :rc:`image.cmap`.
    lut : int or None, default: None
        If *name* is not already a Colormap instance and *lut* is not None, the
        colormap will be resampled to have *lut* entries in the lookup table.

    Returns
    -------
    Colormap
    ",96,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_cmap(name=None, lut=None):
    
    if name is None:
        name = mpl.rcParams['image.cmap']
    if isinstance(name, colors.Colormap):
        return name
    _api.check_in_list(sorted(_colormaps), name=name)
    if lut is None:
        return _colormaps[name]
    else:
        return _colormaps[name].resampled(lut)

# do it in two steps like this so we can have an un-deprecated version in
# pyplot.
get_cmap = _api.deprecated(
    '3.6', pending=True, alternative=""``matplotlib.colormaps[name]``""
)(_get_cmap)


@_api.deprecated(
    '3.6',
    pending=True,
    alternative=""``matplotlib.colormaps.unregister_cmap(name)``""
)
        ```
    ###Assistant : 
    Get a colormap instance, defaulting to rc values if *name* is None.

    Colormaps added with :func:`register_cmap` take precedence over
    built-in colormaps.

    Parameters
    ----------
    name : `matplotlib.colors.Colormap` or str or None, default: None
        If a `.Colormap` instance, it will be returned. Otherwise, the name of
        a colormap known to Matplotlib, which will be resampled by *lut*. The
        default, None, means :rc:`image.cmap`.
    lut : int or None, default: None
        If *name* is not already a Colormap instance and *lut* is not None, the
        colormap will be resampled to have *lut* entries in the lookup table.

    Returns
    -------
    Colormap
    
    "
493,"def cancel(self):
        
        with self._condition:
            if self._state in [RUNNING, FINISHED]:
                return False

            if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
                return True

            self._state = CANCELLED
            self._condition.notify_all()

        self._invoke_callbacks()
        return True
","Cancel the future if possible.

        Returns True if the future was cancelled, False otherwise. A future
        cannot be cancelled if it is running or has already completed.
        ",27,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cancel(self):
        
        with self._condition:
            if self._state in [RUNNING, FINISHED]:
                return False

            if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
                return True

            self._state = CANCELLED
            self._condition.notify_all()

        self._invoke_callbacks()
        return True

        ```
    ###Assistant : Cancel the future if possible.

        Returns True if the future was cancelled, False otherwise. A future
        cannot be cancelled if it is running or has already completed.
        
    "
494,"def get_hub_metadata(url, token=None):
    
    if token is None:
        token = HfFolder.get_token()
    headers = {""user-agent"": http_user_agent()}
    headers[""authorization""] = f""Bearer {token}""

    r = huggingface_hub.file_download._request_with_retry(
        method=""HEAD"", url=url, headers=headers, allow_redirects=False
    )
    huggingface_hub.file_download._raise_for_status(r)
    commit_hash = r.headers.get(HUGGINGFACE_HEADER_X_REPO_COMMIT)
    etag = r.headers.get(HUGGINGFACE_HEADER_X_LINKED_ETAG) or r.headers.get(""ETag"")
    if etag is not None:
        etag = huggingface_hub.file_download._normalize_etag(etag)
    return etag, commit_hash

","
    Returns the commit hash and associated etag for a given url.
    ",11,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_hub_metadata(url, token=None):
    
    if token is None:
        token = HfFolder.get_token()
    headers = {""user-agent"": http_user_agent()}
    headers[""authorization""] = f""Bearer {token}""

    r = huggingface_hub.file_download._request_with_retry(
        method=""HEAD"", url=url, headers=headers, allow_redirects=False
    )
    huggingface_hub.file_download._raise_for_status(r)
    commit_hash = r.headers.get(HUGGINGFACE_HEADER_X_REPO_COMMIT)
    etag = r.headers.get(HUGGINGFACE_HEADER_X_LINKED_ETAG) or r.headers.get(""ETag"")
    if etag is not None:
        etag = huggingface_hub.file_download._normalize_etag(etag)
    return etag, commit_hash


        ```
    ###Assistant : 
    Returns the commit hash and associated etag for a given url.
    
    "
495,"def aug_test(self, aug_batch_feats, aug_batch_img_metas, rescale=False):
        
        return self.aug_test_bboxes(
            aug_batch_feats, aug_batch_img_metas, rescale=rescale)
","Test function with test time augmentation.

        Args:
            aug_batch_feats (list[Tensor]): the outer list indicates test-time
                augmentations and inner Tensor should have a shape NxCxHxW,
                which contains features for all images in the batch.
            aug_batch_img_metas (list[list[dict]]): the outer list indicates
                test-time augs (multiscale, flip, etc.) and the inner list
                indicates images in a batch. each dict has image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[ndarray]: bbox results of each class
        ",75,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def aug_test(self, aug_batch_feats, aug_batch_img_metas, rescale=False):
        
        return self.aug_test_bboxes(
            aug_batch_feats, aug_batch_img_metas, rescale=rescale)

        ```
    ###Assistant : Test function with test time augmentation.

        Args:
            aug_batch_feats (list[Tensor]): the outer list indicates test-time
                augmentations and inner Tensor should have a shape NxCxHxW,
                which contains features for all images in the batch.
            aug_batch_img_metas (list[list[dict]]): the outer list indicates
                test-time augs (multiscale, flip, etc.) and the inner list
                indicates images in a batch. each dict has image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[ndarray]: bbox results of each class
        
    "
496,"def ensure_schema_for_first_block(self) -> Optional[Union[""pyarrow.Schema"", type]]:
        
        get_schema = cached_remote_fn(_get_schema)
        try:
            block = next(self.iter_blocks())
        except (StopIteration, ValueError):
            # Dataset is empty (no blocks) or was manually cleared.
            return None
        schema = ray.get(get_schema.remote(block))
        # Set the schema.
        self._metadata[0].schema = schema
        return schema

","Ensure that the schema is set for the first block.

        Returns None if the block list is empty.
        ",18,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ensure_schema_for_first_block(self) -> Optional[Union[""pyarrow.Schema"", type]]:
        
        get_schema = cached_remote_fn(_get_schema)
        try:
            block = next(self.iter_blocks())
        except (StopIteration, ValueError):
            # Dataset is empty (no blocks) or was manually cleared.
            return None
        schema = ray.get(get_schema.remote(block))
        # Set the schema.
        self._metadata[0].schema = schema
        return schema


        ```
    ###Assistant : Ensure that the schema is set for the first block.

        Returns None if the block list is empty.
        
    "
497,"def get_local_am_pm():
    
    am_local = time(1).strftime(""%p"")
    pm_local = time(13).strftime(""%p"")
    return am_local, pm_local


@pytest.fixture(params=[""string"", ""pathlike"", ""buffer""])",Return the AM and PM strings returned by strftime in current locale.,12,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_local_am_pm():
    
    am_local = time(1).strftime(""%p"")
    pm_local = time(13).strftime(""%p"")
    return am_local, pm_local


@pytest.fixture(params=[""string"", ""pathlike"", ""buffer""])
        ```
    ###Assistant : Return the AM and PM strings returned by strftime in current locale.
    "
498,"def validate_pages_layout(module, page):
    try:
        getattr(page, ""layout"")
    except AttributeError:
        raise exceptions.NoLayoutException(
            f
        )
","
            No layout found in {module + "".py""}
            A variable or a function named ""layout"" is required.
            ",16,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_pages_layout(module, page):
    try:
        getattr(page, ""layout"")
    except AttributeError:
        raise exceptions.NoLayoutException(
            f
        )

        ```
    ###Assistant : 
            No layout found in {module + "".py""}
            A variable or a function named ""layout"" is required.
            
    "
499,"def test_upscale_downscale_delay():
    

    upscale_delay_s = 30.0
    downscale_delay_s = 600.0

    config = AutoscalingConfig(
        min_replicas=1,
        max_replicas=2,
        target_num_ongoing_requests_per_replica=1,
        upscale_delay_s=30.0,
        downscale_delay_s=600.0,
    )

    policy = BasicAutoscalingPolicy(config)

    upscale_wait_periods = int(upscale_delay_s / CONTROL_LOOP_PERIOD_S)
    downscale_wait_periods = int(downscale_delay_s / CONTROL_LOOP_PERIOD_S)

    overload_requests = [100]

    # We should scale up only after enough consecutive scale-up decisions.
    for i in range(upscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
        )
        assert new_num_replicas == 1, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
    )
    assert new_num_replicas == 2

    no_requests = [0, 0]

    # We should scale down only after enough consecutive scale-down decisions.
    for i in range(downscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
        )
        assert new_num_replicas == 2, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
    )
    assert new_num_replicas == 1

    # Get some scale-up decisions, but not enough to trigger a scale up.
    for i in range(int(upscale_wait_periods / 2)):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
        )
        assert new_num_replicas == 1, i

    # Interrupt with a scale-down decision.
    policy.get_decision_num_replicas(
        current_num_ongoing_requests=[0], curr_target_num_replicas=1
    )

    # The counter should be reset, so it should require `upscale_wait_periods`
    # more periods before we actually scale up.
    for i in range(upscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
        )
        assert new_num_replicas == 1, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
    )
    assert new_num_replicas == 2

    # Get some scale-down decisions, but not enough to trigger a scale down.
    for i in range(int(downscale_wait_periods / 2)):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
        )
        assert new_num_replicas == 2, i

    # Interrupt with a scale-up decision.
    policy.get_decision_num_replicas(
        current_num_ongoing_requests=[100, 100], curr_target_num_replicas=2
    )

    # The counter should be reset so it should require `downscale_wait_periods`
    # more periods before we actually scale down.
    for i in range(downscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
        )
        assert new_num_replicas == 2, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
    )
    assert new_num_replicas == 1

",Unit test for upscale_delay_s and downscale_delay_s.,6,278,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_upscale_downscale_delay():
    

    upscale_delay_s = 30.0
    downscale_delay_s = 600.0

    config = AutoscalingConfig(
        min_replicas=1,
        max_replicas=2,
        target_num_ongoing_requests_per_replica=1,
        upscale_delay_s=30.0,
        downscale_delay_s=600.0,
    )

    policy = BasicAutoscalingPolicy(config)

    upscale_wait_periods = int(upscale_delay_s / CONTROL_LOOP_PERIOD_S)
    downscale_wait_periods = int(downscale_delay_s / CONTROL_LOOP_PERIOD_S)

    overload_requests = [100]

    # We should scale up only after enough consecutive scale-up decisions.
    for i in range(upscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
        )
        assert new_num_replicas == 1, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
    )
    assert new_num_replicas == 2

    no_requests = [0, 0]

    # We should scale down only after enough consecutive scale-down decisions.
    for i in range(downscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
        )
        assert new_num_replicas == 2, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
    )
    assert new_num_replicas == 1

    # Get some scale-up decisions, but not enough to trigger a scale up.
    for i in range(int(upscale_wait_periods / 2)):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
        )
        assert new_num_replicas == 1, i

    # Interrupt with a scale-down decision.
    policy.get_decision_num_replicas(
        current_num_ongoing_requests=[0], curr_target_num_replicas=1
    )

    # The counter should be reset, so it should require `upscale_wait_periods`
    # more periods before we actually scale up.
    for i in range(upscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
        )
        assert new_num_replicas == 1, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1
    )
    assert new_num_replicas == 2

    # Get some scale-down decisions, but not enough to trigger a scale down.
    for i in range(int(downscale_wait_periods / 2)):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
        )
        assert new_num_replicas == 2, i

    # Interrupt with a scale-up decision.
    policy.get_decision_num_replicas(
        current_num_ongoing_requests=[100, 100], curr_target_num_replicas=2
    )

    # The counter should be reset so it should require `downscale_wait_periods`
    # more periods before we actually scale down.
    for i in range(downscale_wait_periods):
        new_num_replicas = policy.get_decision_num_replicas(
            current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
        )
        assert new_num_replicas == 2, i

    new_num_replicas = policy.get_decision_num_replicas(
        current_num_ongoing_requests=no_requests, curr_target_num_replicas=2
    )
    assert new_num_replicas == 1


        ```
    ###Assistant : Unit test for upscale_delay_s and downscale_delay_s.
    "
500,"def set_aspect(self, aspect, adjustable=None, anchor=None, share=False):
        
        if cbook._str_equal(aspect, 'equal'):
            aspect = 1
        if not cbook._str_equal(aspect, 'auto'):
            aspect = float(aspect)  # raise ValueError if necessary
            if aspect<0:
                raise ValueError(""aspect must be positive"")

        if share:
            axes = {sibling for name in self._axis_names
                    for sibling in self._shared_axes[name].get_siblings(self)}
        else:
            axes = [self]

        for ax in axes:
            ax._aspect = aspect

        if adjustable is None:
            adjustable = self._adjustable
        self.set_adjustable(adjustable, share=share)  # Handle sharing.

        if anchor is not None:
            self.set_anchor(anchor, share=share)
        self.stale = True
","
        Set the aspect ratio of the axes scaling, i.e. y/x-scale.

        Parameters
        ----------
        aspect : {'auto', 'equal'} or float
            Possible values:

            - 'auto': fill the position rectangle with data.
            - 'equal': same as ``aspect=1``, i.e. same scaling for x and y.
            - *float*: The displayed size of 1 unit in y-data coordinates will
              be *aspect* times the displayed size of 1 unit in x-data
              coordinates; e.g. for ``aspect=2`` a square in data coordinates
              will be rendered with a height of twice its width.

        adjustable : None or {'box', 'datalim'}, optional
            If not ``None``, this defines which parameter will be adjusted to
            meet the required aspect. See `.set_adjustable` for further
            details.

        anchor : None or str or (float, float), optional
            If not ``None``, this defines where the Axes will be drawn if there
            is extra space due to aspect constraints. The most common way to
            to specify the anchor are abbreviations of cardinal directions:

            =====   =====================
            value   description
            =====   =====================
            'C'     centered
            'SW'    lower left corner
            'S'     middle of bottom edge
            'SE'    lower right corner
            etc.
            =====   =====================

            See `~.Axes.set_anchor` for further details.

        share : bool, default: False
            If ``True``, apply the settings to all shared Axes.

        See Also
        --------
        matplotlib.axes.Axes.set_adjustable
            Set how the Axes adjusts to achieve the required aspect ratio.
        matplotlib.axes.Axes.set_anchor
            Set the position in case of extra space.
        ",219,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_aspect(self, aspect, adjustable=None, anchor=None, share=False):
        
        if cbook._str_equal(aspect, 'equal'):
            aspect = 1
        if not cbook._str_equal(aspect, 'auto'):
            aspect = float(aspect)  # raise ValueError if necessary
            if aspect<0:
                raise ValueError(""aspect must be positive"")

        if share:
            axes = {sibling for name in self._axis_names
                    for sibling in self._shared_axes[name].get_siblings(self)}
        else:
            axes = [self]

        for ax in axes:
            ax._aspect = aspect

        if adjustable is None:
            adjustable = self._adjustable
        self.set_adjustable(adjustable, share=share)  # Handle sharing.

        if anchor is not None:
            self.set_anchor(anchor, share=share)
        self.stale = True

        ```
    ###Assistant : 
        Set the aspect ratio of the axes scaling, i.e. y/x-scale.

        Parameters
        ----------
        aspect : {'auto', 'equal'} or float
            Possible values:

            - 'auto': fill the position rectangle with data.
            - 'equal': same as ``aspect=1``, i.e. same scaling for x and y.
            - *float*: The displayed size of 1 unit in y-data coordinates will
              be *aspect* times the displayed size of 1 unit in x-data
              coordinates; e.g. for ``aspect=2`` a square in data coordinates
              will be rendered with a height of twice its width.

        adjustable : None or {'box', 'datalim'}, optional
            If not ``None``, this defines which parameter will be adjusted to
            meet the required aspect. See `.set_adjustable` for further
            details.

        anchor : None or str or (float, float), optional
            If not ``None``, this defines where the Axes will be drawn if there
            is extra space due to aspect constraints. The most common way to
            to specify the anchor are abbreviations of cardinal directions:

            =====   =====================
            value   description
            =====   =====================
            'C'     centered
            'SW'    lower left corner
            'S'     middle of bottom edge
            'SE'    lower right corner
            etc.
            =====   =====================

            See `~.Axes.set_anchor` for further details.

        share : bool, default: False
            If ``True``, apply the settings to all shared Axes.

        See Also
        --------
        matplotlib.axes.Axes.set_adjustable
            Set how the Axes adjusts to achieve the required aspect ratio.
        matplotlib.axes.Axes.set_anchor
            Set the position in case of extra space.
        
    "
501,"def test_pk_attributes(self):
        
        # pk can be used as a substitute for the primary key.
        # The primary key can be accessed via the pk property on the model.
        e = Employee.objects.get(pk=123)
        self.assertEqual(e.pk, 123)
        # Or we can use the real attribute name for the primary key:
        self.assertEqual(e.employee_code, 123)

        with self.assertRaisesMessage(
            AttributeError, ""'Employee' object has no attribute 'id'""
        ):
            e.id
","
        pk and attribute name are available on the model
        No default id attribute is added
        ",15,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pk_attributes(self):
        
        # pk can be used as a substitute for the primary key.
        # The primary key can be accessed via the pk property on the model.
        e = Employee.objects.get(pk=123)
        self.assertEqual(e.pk, 123)
        # Or we can use the real attribute name for the primary key:
        self.assertEqual(e.employee_code, 123)

        with self.assertRaisesMessage(
            AttributeError, ""'Employee' object has no attribute 'id'""
        ):
            e.id

        ```
    ###Assistant : 
        pk and attribute name are available on the model
        No default id attribute is added
        
    "
502,"async def __aenter__(self):
        
        if self._closed:
            # httpx.AsyncClient does not allow reuse so we will not either.
            raise RuntimeError(
                ""The client cannot be started again after closing. ""
                ""Retrieve a new client with `get_client()` instead.""
            )

        if self._started:
            # httpx.AsyncClient does not allow reentrancy so we will not either.
            raise RuntimeError(""The client cannot be started more than once."")

        await self._exit_stack.__aenter__()

        # Enter a lifespan context if using an ephemeral application.
        # See https://github.com/encode/httpx/issues/350
        if self._ephemeral_app and self.manage_lifespan:
            self._ephemeral_lifespan = await self._exit_stack.enter_async_context(
                app_lifespan_context(self._ephemeral_app)
            )

        # Enter the httpx client's context
        await self._exit_stack.enter_async_context(self._client)

        self._started = True

        return self
","
        Start the client.

        If the client is already started, this will raise an exception.

        If the client is already closed, this will raise an exception. Use a new client
        instance instead.
        ",31,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def __aenter__(self):
        
        if self._closed:
            # httpx.AsyncClient does not allow reuse so we will not either.
            raise RuntimeError(
                ""The client cannot be started again after closing. ""
                ""Retrieve a new client with `get_client()` instead.""
            )

        if self._started:
            # httpx.AsyncClient does not allow reentrancy so we will not either.
            raise RuntimeError(""The client cannot be started more than once."")

        await self._exit_stack.__aenter__()

        # Enter a lifespan context if using an ephemeral application.
        # See https://github.com/encode/httpx/issues/350
        if self._ephemeral_app and self.manage_lifespan:
            self._ephemeral_lifespan = await self._exit_stack.enter_async_context(
                app_lifespan_context(self._ephemeral_app)
            )

        # Enter the httpx client's context
        await self._exit_stack.enter_async_context(self._client)

        self._started = True

        return self

        ```
    ###Assistant : 
        Start the client.

        If the client is already started, this will raise an exception.

        If the client is already closed, this will raise an exception. Use a new client
        instance instead.
        
    "
503,"def training_iteration(self) -> ResultDict:
        
        # Sample n batches from n workers.
        new_sample_batches = synchronous_parallel_sample(
            worker_set=self.workers, concat=False
        )

        for batch in new_sample_batches:
            # Update counters.
            self._counters[NUM_ENV_STEPS_SAMPLED] += batch.env_steps()
            self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps()
            # Store new samples in the replay buffer.
            self.local_replay_buffer.add(batch)

        # Sample n batches from replay buffer until the total number of timesteps
        # reaches `train_batch_size`.
        train_batch = sample_min_n_steps_from_buffer(
            replay_buffer=self.local_replay_buffer,
            min_steps=self.config[""train_batch_size""],
            count_by_agent_steps=self._by_agent_steps,
        )
        if train_batch is None:
            return {}

        # Learn on the training batch.
        # Use simple optimizer (only for multi-agent or tf-eager; all other
        # cases should use the multi-GPU optimizer, even if only using 1 GPU)
        if self.config.get(""simple_optimizer"") is True:
            train_results = train_one_step(self, train_batch)
        else:
            train_results = multi_gpu_train_one_step(self, train_batch)

        # TODO: Move training steps counter update outside of `train_one_step()` method.
        # # Update train step counters.
        # self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps()
        # self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps()

        # Update target network every `target_network_update_freq` steps.
        cur_ts = self._counters[NUM_ENV_STEPS_SAMPLED]
        last_update = self._counters[LAST_TARGET_UPDATE_TS]
        if cur_ts - last_update >= self.config[""target_network_update_freq""]:
            to_update = self.workers.local_worker().get_policies_to_train()
            self.workers.local_worker().foreach_policy_to_train(
                lambda p, pid: pid in to_update and p.update_target()
            )
            self._counters[NUM_TARGET_UPDATES] += 1
            self._counters[LAST_TARGET_UPDATE_TS] = cur_ts

        # Update weights and global_vars - after learning on the local worker - on all
        # remote workers.
        global_vars = {
            ""timestep"": self._counters[NUM_ENV_STEPS_SAMPLED],
        }
        # Update remote workers' weights and global vars after learning on local worker.
        with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
            self.workers.sync_weights(global_vars=global_vars)

        # Return all collected metrics for the iteration.
        return train_results
","QMIX training iteration function.

        - Sample n MultiAgentBatches from n workers synchronously.
        - Store new samples in the replay buffer.
        - Sample one training MultiAgentBatch from the replay buffer.
        - Learn on the training batch.
        - Update the target network every `target_network_update_freq` steps.
        - Return all collected training metrics for the iteration.

        Returns:
            The results dict from executing the training iteration.
        ",61,224,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def training_iteration(self) -> ResultDict:
        
        # Sample n batches from n workers.
        new_sample_batches = synchronous_parallel_sample(
            worker_set=self.workers, concat=False
        )

        for batch in new_sample_batches:
            # Update counters.
            self._counters[NUM_ENV_STEPS_SAMPLED] += batch.env_steps()
            self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps()
            # Store new samples in the replay buffer.
            self.local_replay_buffer.add(batch)

        # Sample n batches from replay buffer until the total number of timesteps
        # reaches `train_batch_size`.
        train_batch = sample_min_n_steps_from_buffer(
            replay_buffer=self.local_replay_buffer,
            min_steps=self.config[""train_batch_size""],
            count_by_agent_steps=self._by_agent_steps,
        )
        if train_batch is None:
            return {}

        # Learn on the training batch.
        # Use simple optimizer (only for multi-agent or tf-eager; all other
        # cases should use the multi-GPU optimizer, even if only using 1 GPU)
        if self.config.get(""simple_optimizer"") is True:
            train_results = train_one_step(self, train_batch)
        else:
            train_results = multi_gpu_train_one_step(self, train_batch)

        # TODO: Move training steps counter update outside of `train_one_step()` method.
        # # Update train step counters.
        # self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps()
        # self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps()

        # Update target network every `target_network_update_freq` steps.
        cur_ts = self._counters[NUM_ENV_STEPS_SAMPLED]
        last_update = self._counters[LAST_TARGET_UPDATE_TS]
        if cur_ts - last_update >= self.config[""target_network_update_freq""]:
            to_update = self.workers.local_worker().get_policies_to_train()
            self.workers.local_worker().foreach_policy_to_train(
                lambda p, pid: pid in to_update and p.update_target()
            )
            self._counters[NUM_TARGET_UPDATES] += 1
            self._counters[LAST_TARGET_UPDATE_TS] = cur_ts

        # Update weights and global_vars - after learning on the local worker - on all
        # remote workers.
        global_vars = {
            ""timestep"": self._counters[NUM_ENV_STEPS_SAMPLED],
        }
        # Update remote workers' weights and global vars after learning on local worker.
        with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
            self.workers.sync_weights(global_vars=global_vars)

        # Return all collected metrics for the iteration.
        return train_results

        ```
    ###Assistant : QMIX training iteration function.

        - Sample n MultiAgentBatches from n workers synchronously.
        - Store new samples in the replay buffer.
        - Sample one training MultiAgentBatch from the replay buffer.
        - Learn on the training batch.
        - Update the target network every `target_network_update_freq` steps.
        - Return all collected training metrics for the iteration.

        Returns:
            The results dict from executing the training iteration.
        
    "
504,"def _announce() -> None:
    

    current_version = get_package_version()
    tag_name = f""v{current_version}""

    click.echo(
        f
    )

    if ""rc"" in tag_name:
        click.echo(
            
        )
    else:
        click.echo(
            
        )


@cli.command()
@click.option(""--gh-token"", envvar=[""GH_TOKEN"", ""GITHUB_TOKEN""], required=True)","Generate markdown to announce the release.
Hi everyone. Synapse {current_version} has just been released.

[notes](https://github.com/matrix-org/synapse/releases/tag/{tag_name}) | \
[docker](https://hub.docker.com/r/matrixdotorg/synapse/tags?name={tag_name}) | \
[debs](https://packages.matrix.org/debian/) | \
[pypi](https://pypi.org/project/matrix-synapse/{current_version}/)
Announce the RC in
- #homeowners:matrix.org (Synapse Announcements)
- #synapse-dev:matrix.org
Announce the release in
- #homeowners:matrix.org (Synapse Announcements), bumping the version in the topic
- #synapse:matrix.org (Synapse Admins), bumping the version in the topic
- #synapse-dev:matrix.org
- #synapse-package-maintainers:matrix.org

Ask the designated people to do the blog and tweets.",72,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _announce() -> None:
    

    current_version = get_package_version()
    tag_name = f""v{current_version}""

    click.echo(
        f
    )

    if ""rc"" in tag_name:
        click.echo(
            
        )
    else:
        click.echo(
            
        )


@cli.command()
@click.option(""--gh-token"", envvar=[""GH_TOKEN"", ""GITHUB_TOKEN""], required=True)
        ```
    ###Assistant : Generate markdown to announce the release.
Hi everyone. Synapse {current_version} has just been released.

[notes](https://github.com/matrix-org/synapse/releases/tag/{tag_name}) | \
[docker](https://hub.docker.com/r/matrixdotorg/synapse/tags?name={tag_name}) | \
[debs](https://packages.matrix.org/debian/) | \
[pypi](https://pypi.org/project/matrix-synapse/{current_version}/)
Announce the RC in
- #homeowners:matrix.org (Synapse Announcements)
- #synapse-dev:matrix.org
Announce the release in
- #homeowners:matrix.org (Synapse Announcements), bumping the version in the topic
- #synapse:matrix.org (Synapse Admins), bumping the version in the topic
- #synapse-dev:matrix.org
- #synapse-package-maintainers:matrix.org

Ask the designated people to do the blog and tweets.
    "
505,"def update(self, value=None, visible=None):
        
        if not self._widget_was_created():  # if widget hasn't been created yet, then don't allow
            return

        if value is not None:
            self._TKOut.output.delete('1.0', tk.END)
            self._TKOut.output.insert(tk.END, value)
        if visible is False:
            self._pack_forget_save_settings(self._TKOut.frame)
        elif visible is True:
            self._pack_restore_settings(self._TKOut.frame)

        if visible is not None:
            self._visible = visible
","
        Changes some of the settings for the Output Element. Must call `Window.Read` or `Window.Finalize` prior

        Changes will not be visible in your window until you call window.read or window.refresh.

        If you change visibility, your element may MOVE. If you want it to remain stationary, use the ""layout helper""
        function ""pin"" to ensure your element is ""pinned"" to that location in your layout so that it returns there
        when made visible.

        :param value:   string that will replace current contents of the output area
        :type value:    (str)
        :param visible: control visibility of element
        :type visible:  (bool)
        ",94,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self, value=None, visible=None):
        
        if not self._widget_was_created():  # if widget hasn't been created yet, then don't allow
            return

        if value is not None:
            self._TKOut.output.delete('1.0', tk.END)
            self._TKOut.output.insert(tk.END, value)
        if visible is False:
            self._pack_forget_save_settings(self._TKOut.frame)
        elif visible is True:
            self._pack_restore_settings(self._TKOut.frame)

        if visible is not None:
            self._visible = visible

        ```
    ###Assistant : 
        Changes some of the settings for the Output Element. Must call `Window.Read` or `Window.Finalize` prior

        Changes will not be visible in your window until you call window.read or window.refresh.

        If you change visibility, your element may MOVE. If you want it to remain stationary, use the ""layout helper""
        function ""pin"" to ensure your element is ""pinned"" to that location in your layout so that it returns there
        when made visible.

        :param value:   string that will replace current contents of the output area
        :type value:    (str)
        :param visible: control visibility of element
        :type visible:  (bool)
        
    "
506,"def example(self):
        
        result = getattr(self, ""_example"", None)
        if result is None:
            # No example batch was found, so get one from the `.train` dataset
            result = next(iter(self.train))
            # And cache it for next time
            self._example = result
        return result","Get and cache an example batch of `inputs, labels` for plotting.",11,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def example(self):
        
        result = getattr(self, ""_example"", None)
        if result is None:
            # No example batch was found, so get one from the `.train` dataset
            result = next(iter(self.train))
            # And cache it for next time
            self._example = result
        return result
        ```
    ###Assistant : Get and cache an example batch of `inputs, labels` for plotting.
    "
507,"def unit_attname(cls, unit_str):
        
        lower = unit_str.lower()
        if unit_str in cls.UNITS:
            return unit_str
        elif lower in cls.UNITS:
            return lower
        elif lower in cls.LALIAS:
            return cls.LALIAS[lower]
        else:
            raise Exception(
                'Could not find a unit keyword associated with ""%s""' % unit_str
            )

","
        Retrieve the unit attribute name for the given unit string.
        For example, if the given unit string is 'metre', return 'm'.
        Raise an exception if an attribute cannot be found.
        ",30,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unit_attname(cls, unit_str):
        
        lower = unit_str.lower()
        if unit_str in cls.UNITS:
            return unit_str
        elif lower in cls.UNITS:
            return lower
        elif lower in cls.LALIAS:
            return cls.LALIAS[lower]
        else:
            raise Exception(
                'Could not find a unit keyword associated with ""%s""' % unit_str
            )


        ```
    ###Assistant : 
        Retrieve the unit attribute name for the given unit string.
        For example, if the given unit string is 'metre', return 'm'.
        Raise an exception if an attribute cannot be found.
        
    "
508,"def test_dont_import_tf_error():
    
    # Do not import tf for testing purposes.
    os.environ[""RLLIB_TEST_NO_TF_IMPORT""] = ""1""

    config = ppo.PPOConfig().environment(""CartPole-v1"")
    for _ in framework_iterator(config, frameworks=(""tf"", ""tf2"")):
        with pytest.raises(ImportError, match=""However, no installation was found""):
            config.build()

","Check error being thrown, if tf not installed but configured.",10,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dont_import_tf_error():
    
    # Do not import tf for testing purposes.
    os.environ[""RLLIB_TEST_NO_TF_IMPORT""] = ""1""

    config = ppo.PPOConfig().environment(""CartPole-v1"")
    for _ in framework_iterator(config, frameworks=(""tf"", ""tf2"")):
        with pytest.raises(ImportError, match=""However, no installation was found""):
            config.build()


        ```
    ###Assistant : Check error being thrown, if tf not installed but configured.
    "
509,"def _current(self):
        
        if self._hmac:
            return self._hmac
        else:
            h = self._outer.copy()
            h.update(self._inner.digest())
            return h
","Return a hash object for the current state.

        To be used only internally with digest() and hexdigest().
        ",17,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _current(self):
        
        if self._hmac:
            return self._hmac
        else:
            h = self._outer.copy()
            h.update(self._inner.digest())
            return h

        ```
    ###Assistant : Return a hash object for the current state.

        To be used only internally with digest() and hexdigest().
        
    "
510,"def delete_links_from_desktop_icons(report):
	
	desktop_icons = frappe.db.get_values(""Desktop Icon"", {""_report"": report}, [""name""])
	for desktop_icon in desktop_icons:
		frappe.delete_doc(""Desktop Icon"", desktop_icon[0])", Check for one or multiple Desktop Icons and delete ,9,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_links_from_desktop_icons(report):
	
	desktop_icons = frappe.db.get_values(""Desktop Icon"", {""_report"": report}, [""name""])
	for desktop_icon in desktop_icons:
		frappe.delete_doc(""Desktop Icon"", desktop_icon[0])
        ```
    ###Assistant :  Check for one or multiple Desktop Icons and delete 
    "
511,"def get_bboxes_list(end2end_result, structure_master_result):
    
    # end2end
    end2end_xyxy_list = []
    end2end_xywh_list = []
    for end2end_item in end2end_result:
        src_bbox = end2end_item['bbox']
        end2end_xyxy_list.append(src_bbox)
        xywh_bbox = xyxy2xywh(src_bbox)
        end2end_xywh_list.append(xywh_bbox)
    end2end_xyxy_bboxes = np.array(end2end_xyxy_list)
    end2end_xywh_bboxes = np.array(end2end_xywh_list)

    # structure master
    src_bboxes = structure_master_result['bbox']
    src_bboxes = remove_empty_bboxes(src_bboxes)
    # structure_master_xywh_bboxes = src_bboxes
    # xyxy_bboxes = xywh2xyxy(src_bboxes)
    # structure_master_xyxy_bboxes = xyxy_bboxes
    structure_master_xyxy_bboxes = src_bboxes
    xywh_bbox = xyxy2xywh(src_bboxes)
    structure_master_xywh_bboxes = xywh_bbox

    return end2end_xyxy_bboxes, end2end_xywh_bboxes, structure_master_xywh_bboxes, structure_master_xyxy_bboxes

","
    This function is use to convert end2end results and structure master results to
    List of xyxy bbox format and List of xywh bbox format
    :param end2end_result: bbox's format is xyxy
    :param structure_master_result: bbox's format is xywh
    :return: 4 kind list of bbox ()
    ",43,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_bboxes_list(end2end_result, structure_master_result):
    
    # end2end
    end2end_xyxy_list = []
    end2end_xywh_list = []
    for end2end_item in end2end_result:
        src_bbox = end2end_item['bbox']
        end2end_xyxy_list.append(src_bbox)
        xywh_bbox = xyxy2xywh(src_bbox)
        end2end_xywh_list.append(xywh_bbox)
    end2end_xyxy_bboxes = np.array(end2end_xyxy_list)
    end2end_xywh_bboxes = np.array(end2end_xywh_list)

    # structure master
    src_bboxes = structure_master_result['bbox']
    src_bboxes = remove_empty_bboxes(src_bboxes)
    # structure_master_xywh_bboxes = src_bboxes
    # xyxy_bboxes = xywh2xyxy(src_bboxes)
    # structure_master_xyxy_bboxes = xyxy_bboxes
    structure_master_xyxy_bboxes = src_bboxes
    xywh_bbox = xyxy2xywh(src_bboxes)
    structure_master_xywh_bboxes = xywh_bbox

    return end2end_xyxy_bboxes, end2end_xywh_bboxes, structure_master_xywh_bboxes, structure_master_xyxy_bboxes


        ```
    ###Assistant : 
    This function is use to convert end2end results and structure master results to
    List of xyxy bbox format and List of xywh bbox format
    :param end2end_result: bbox's format is xyxy
    :param structure_master_result: bbox's format is xywh
    :return: 4 kind list of bbox ()
    
    "
512,"def autoname_elements() -> None:
    
    for name, var in sys._getframe().f_back.f_locals.items():
        if isinstance(var, ParserElement) and not var.customName:
            var.set_name(name)


dbl_quoted_string = Combine(
    Regex(r'""(?:[^""\n\r\\]|(?:"""")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '""'
).set_name(""string enclosed in double quotes"")

sgl_quoted_string = Combine(
    Regex(r""'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*"") + ""'""
).set_name(""string enclosed in single quotes"")

quoted_string = Combine(
    Regex(r'""(?:[^""\n\r\\]|(?:"""")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '""'
    | Regex(r""'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*"") + ""'""
).set_name(""quotedString using single or double quotes"")

unicode_string = Combine(""u"" + quoted_string.copy()).set_name(""unicode string literal"")


alphas8bit = srange(r""[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]"")
punc8bit = srange(r""[\0xa1-\0xbf\0xd7\0xf7]"")

# build list of built-in expressions, for future reference if a global default value
# gets updated
_builtin_exprs = [v for v in vars().values() if isinstance(v, ParserElement)]

# backward compatibility names
tokenMap = token_map
conditionAsParseAction = condition_as_parse_action
nullDebugAction = null_debug_action
sglQuotedString = sgl_quoted_string
dblQuotedString = dbl_quoted_string
quotedString = quoted_string
unicodeString = unicode_string
lineStart = line_start
lineEnd = line_end
stringStart = string_start
stringEnd = string_end
traceParseAction = trace_parse_action
","
    Utility to simplify mass-naming of parser elements, for
    generating railroad diagram with named subdiagrams.
    ",14,134,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def autoname_elements() -> None:
    
    for name, var in sys._getframe().f_back.f_locals.items():
        if isinstance(var, ParserElement) and not var.customName:
            var.set_name(name)


dbl_quoted_string = Combine(
    Regex(r'""(?:[^""\n\r\\]|(?:"""")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '""'
).set_name(""string enclosed in double quotes"")

sgl_quoted_string = Combine(
    Regex(r""'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*"") + ""'""
).set_name(""string enclosed in single quotes"")

quoted_string = Combine(
    Regex(r'""(?:[^""\n\r\\]|(?:"""")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '""'
    | Regex(r""'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*"") + ""'""
).set_name(""quotedString using single or double quotes"")

unicode_string = Combine(""u"" + quoted_string.copy()).set_name(""unicode string literal"")


alphas8bit = srange(r""[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]"")
punc8bit = srange(r""[\0xa1-\0xbf\0xd7\0xf7]"")

# build list of built-in expressions, for future reference if a global default value
# gets updated
_builtin_exprs = [v for v in vars().values() if isinstance(v, ParserElement)]

# backward compatibility names
tokenMap = token_map
conditionAsParseAction = condition_as_parse_action
nullDebugAction = null_debug_action
sglQuotedString = sgl_quoted_string
dblQuotedString = dbl_quoted_string
quotedString = quoted_string
unicodeString = unicode_string
lineStart = line_start
lineEnd = line_end
stringStart = string_start
stringEnd = string_end
traceParseAction = trace_parse_action

        ```
    ###Assistant : 
    Utility to simplify mass-naming of parser elements, for
    generating railroad diagram with named subdiagrams.
    
    "
513,"def test_valid_zero_ops_doesnt_require_backend_dispatch_key(self) -> None:
        yaml_str = 
        # External codegen on a yaml file with no operators is effectively a no-op,
        # so there's no reason to parse the backend
        self.assert_success_from_gen_backend_stubs(yaml_str)
","\
backend: BAD_XLA
cpp_namespace: torch_xla
supported:",6,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_valid_zero_ops_doesnt_require_backend_dispatch_key(self) -> None:
        yaml_str = 
        # External codegen on a yaml file with no operators is effectively a no-op,
        # so there's no reason to parse the backend
        self.assert_success_from_gen_backend_stubs(yaml_str)

        ```
    ###Assistant : \
backend: BAD_XLA
cpp_namespace: torch_xla
supported:
    "
514,"async def test_pseudo_remote_peas_topologies(gateway, head, worker):
    
    worker_port = random_port()
    head_port = random_port()
    port_expose = random_port()
    graph_description = '{""start-gateway"": [""pod0""], ""pod0"": [""end-gateway""]}'
    if head == 'remote':
        pods_addresses = f'{{""pod0"": [""{HOST}:{head_port}""]}}'
    else:
        pods_addresses = f'{{""pod0"": [""0.0.0.0:{head_port}""]}}'

    # create a single head pea
    head_pea = _create_head_pea(head, head_port)

    # create a single worker pea
    worker_pea = _create_worker_pea(worker, worker_port)

    # create a single gateway pea
    gateway_pea = _create_gateway_pea(
        gateway, graph_description, pods_addresses, port_expose
    )

    with gateway_pea, worker_pea, head_pea:
        await asyncio.sleep(1.0)
        # this would be done by the Pod, its adding the worker to the head
        activate_msg = ControlRequest(command='ACTIVATE')
        worker_host, worker_port = worker_pea.runtime_ctrl_address.split(':')
        if head == 'remote':
            worker_host = __docker_host__

        activate_msg.add_related_entity('worker', worker_host, int(worker_port))
        assert GrpcConnectionPool.send_request_sync(
            activate_msg, head_pea.runtime_ctrl_address
        )

        # send requests to the gateway
        c = Client(host='127.0.0.1', port=port_expose, asyncio=True)
        responses = c.post(
            '/', inputs=async_inputs, request_size=1, return_results=True
        )
        response_list = []","
    g(l)-h(l)-w(l) - works
    g(l)-h(l)-w(r) - works - head connects to worker via localhost
    g(l)-h(r)-w(r) - works - head (inside docker) connects to worker via dockerhost
    g(l)-h(r)-w(l) - doesn't work remote head need remote worker
    g(r)-... - doesn't work, as distributed parser not enabled for gateway
    After any 1 failure, segfault
    ",50,132,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_pseudo_remote_peas_topologies(gateway, head, worker):
    
    worker_port = random_port()
    head_port = random_port()
    port_expose = random_port()
    graph_description = '{""start-gateway"": [""pod0""], ""pod0"": [""end-gateway""]}'
    if head == 'remote':
        pods_addresses = f'{{""pod0"": [""{HOST}:{head_port}""]}}'
    else:
        pods_addresses = f'{{""pod0"": [""0.0.0.0:{head_port}""]}}'

    # create a single head pea
    head_pea = _create_head_pea(head, head_port)

    # create a single worker pea
    worker_pea = _create_worker_pea(worker, worker_port)

    # create a single gateway pea
    gateway_pea = _create_gateway_pea(
        gateway, graph_description, pods_addresses, port_expose
    )

    with gateway_pea, worker_pea, head_pea:
        await asyncio.sleep(1.0)
        # this would be done by the Pod, its adding the worker to the head
        activate_msg = ControlRequest(command='ACTIVATE')
        worker_host, worker_port = worker_pea.runtime_ctrl_address.split(':')
        if head == 'remote':
            worker_host = __docker_host__

        activate_msg.add_related_entity('worker', worker_host, int(worker_port))
        assert GrpcConnectionPool.send_request_sync(
            activate_msg, head_pea.runtime_ctrl_address
        )

        # send requests to the gateway
        c = Client(host='127.0.0.1', port=port_expose, asyncio=True)
        responses = c.post(
            '/', inputs=async_inputs, request_size=1, return_results=True
        )
        response_list = []
        ```
    ###Assistant : 
    g(l)-h(l)-w(l) - works
    g(l)-h(l)-w(r) - works - head connects to worker via localhost
    g(l)-h(r)-w(r) - works - head (inside docker) connects to worker via dockerhost
    g(l)-h(r)-w(l) - doesn't work remote head need remote worker
    g(r)-... - doesn't work, as distributed parser not enabled for gateway
    After any 1 failure, segfault
    
    "
515,"async def async_wait_start_success(self):
        
        import asyncio

        _timeout = self.args.timeout_ready
        if _timeout <= 0:
            _timeout = None
        else:
            _timeout /= 1e3

        timeout_ns = 1e9 * _timeout if _timeout else None
        now = time.time_ns()
        while timeout_ns is None or time.time_ns() - now < timeout_ns:

            if self.ready_or_shutdown.event.is_set():
                self._check_failed_to_start()
                self.logger.debug(__ready_msg__)
                return
            else:
                await asyncio.sleep(0.1)

        self._fail_start_timeout(_timeout)
","
        Wait for the `Pea` to start successfully in a non-blocking manner
        ",11,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_wait_start_success(self):
        
        import asyncio

        _timeout = self.args.timeout_ready
        if _timeout <= 0:
            _timeout = None
        else:
            _timeout /= 1e3

        timeout_ns = 1e9 * _timeout if _timeout else None
        now = time.time_ns()
        while timeout_ns is None or time.time_ns() - now < timeout_ns:

            if self.ready_or_shutdown.event.is_set():
                self._check_failed_to_start()
                self.logger.debug(__ready_msg__)
                return
            else:
                await asyncio.sleep(0.1)

        self._fail_start_timeout(_timeout)

        ```
    ###Assistant : 
        Wait for the `Pea` to start successfully in a non-blocking manner
        
    "
516,"def set_fontsize(self, s=None):
        
        if s is None:
            s = mpl.rcParams[""legend.fontsize""]

        self.prop = FontProperties(size=s)
        self.stale = True
","
        Set the fontsize in points.

        If *s* is not given, reset to :rc:`legend.fontsize`.
        ",13,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_fontsize(self, s=None):
        
        if s is None:
            s = mpl.rcParams[""legend.fontsize""]

        self.prop = FontProperties(size=s)
        self.stale = True

        ```
    ###Assistant : 
        Set the fontsize in points.

        If *s* is not given, reset to :rc:`legend.fontsize`.
        
    "
517,"def get_next(self, timeout=None):
        
        if not self.has_next():
            raise StopIteration(""No more results to get"")
        if self._next_return_index >= self._next_task_index:
            raise ValueError(
                ""It is not allowed to call get_next() after "" ""get_next_unordered().""
            )
        future = self._index_to_future[self._next_return_index]
        if timeout is not None:
            res, _ = ray.wait([future], timeout=timeout)
            if not res:
                raise TimeoutError(""Timed out waiting for result"")
        del self._index_to_future[self._next_return_index]
        self._next_return_index += 1

        future_key = tuple(future) if isinstance(future, list) else future
        i, a = self._future_to_actor.pop(future_key)

        self._return_actor(a)
        return ray.get(future)
","Returns the next pending result in order.

        This returns the next result produced by submit(), blocking for up to
        the specified timeout until it is available.

        Returns:
            The next result.

        Raises:
            TimeoutError if the timeout is reached.

        Examples:
            >>> pool = ActorPool(...)
            >>> pool.submit(lambda a, v: a.double.remote(v), 1)
            >>> print(pool.get_next())
            2
        ",51,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_next(self, timeout=None):
        
        if not self.has_next():
            raise StopIteration(""No more results to get"")
        if self._next_return_index >= self._next_task_index:
            raise ValueError(
                ""It is not allowed to call get_next() after "" ""get_next_unordered().""
            )
        future = self._index_to_future[self._next_return_index]
        if timeout is not None:
            res, _ = ray.wait([future], timeout=timeout)
            if not res:
                raise TimeoutError(""Timed out waiting for result"")
        del self._index_to_future[self._next_return_index]
        self._next_return_index += 1

        future_key = tuple(future) if isinstance(future, list) else future
        i, a = self._future_to_actor.pop(future_key)

        self._return_actor(a)
        return ray.get(future)

        ```
    ###Assistant : Returns the next pending result in order.

        This returns the next result produced by submit(), blocking for up to
        the specified timeout until it is available.

        Returns:
            The next result.

        Raises:
            TimeoutError if the timeout is reached.

        Examples:
            >>> pool = ActorPool(...)
            >>> pool.submit(lambda a, v: a.double.remote(v), 1)
            >>> print(pool.get_next())
            2
        
    "
518,"def expand_egg_links(self) -> None:
        
        prefixes = [
            Path(prefix)
            for prefix in self.base_paths[""libdirs""].split(os.pathsep)
            if vistir.path.is_in_path(prefix, self.prefix.as_posix())
        ]
        for loc in prefixes:
            if not loc.exists():
                continue
            for pth in loc.iterdir():
                if not pth.suffix == "".egg-link"":
                    continue
                contents = [
                    vistir.path.normalize_path(line.strip())
                    for line in pth.read_text().splitlines()
                ]
                pth.write_text(""\n"".join(contents))
","
        Expand paths specified in egg-link files to prevent pip errors during
        reinstall
        ",12,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def expand_egg_links(self) -> None:
        
        prefixes = [
            Path(prefix)
            for prefix in self.base_paths[""libdirs""].split(os.pathsep)
            if vistir.path.is_in_path(prefix, self.prefix.as_posix())
        ]
        for loc in prefixes:
            if not loc.exists():
                continue
            for pth in loc.iterdir():
                if not pth.suffix == "".egg-link"":
                    continue
                contents = [
                    vistir.path.normalize_path(line.strip())
                    for line in pth.read_text().splitlines()
                ]
                pth.write_text(""\n"".join(contents))

        ```
    ###Assistant : 
        Expand paths specified in egg-link files to prevent pip errors during
        reinstall
        
    "
519,"def test_page_with_og(self) -> None:
        html = b

        parser = OpenGraphParser(html, ""text/html; charset=UTF-8"")
        result = parser.extract_data()
        self.assertEqual(result.title, ""The Rock"")
        self.assertEqual(result.description, ""The Rock film"")
","<html>
          <head>
          <meta property=""og:title"" content=""The Rock"" />
          <meta property=""og:type"" content=""video.movie"" />
          <meta property=""og:url"" content=""http://www.imdb.com/title/tt0117500/"" />
          <meta property=""og:image"" content=""http://ia.media-imdb.com/images/rock.jpg"" />
          <meta property=""og:description"" content=""The Rock film"" />
          </head>
        </html>",27,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_page_with_og(self) -> None:
        html = b

        parser = OpenGraphParser(html, ""text/html; charset=UTF-8"")
        result = parser.extract_data()
        self.assertEqual(result.title, ""The Rock"")
        self.assertEqual(result.description, ""The Rock film"")

        ```
    ###Assistant : <html>
          <head>
          <meta property=""og:title"" content=""The Rock"" />
          <meta property=""og:type"" content=""video.movie"" />
          <meta property=""og:url"" content=""http://www.imdb.com/title/tt0117500/"" />
          <meta property=""og:image"" content=""http://ia.media-imdb.com/images/rock.jpg"" />
          <meta property=""og:description"" content=""The Rock film"" />
          </head>
        </html>
    "
520,"def is_rational_function(self, *syms):
        
        if self in _illegal:
            return False

        if syms:
            syms = set(map(sympify, syms))
        else:
            syms = self.free_symbols
            if not syms:
                return True

        return self._eval_is_rational_function(syms)
","
        Test whether function is a ratio of two polynomials in the given
        symbols, syms. When syms is not given, all free symbols will be used.
        The rational function does not have to be in expanded or in any kind of
        canonical form.

        This function returns False for expressions that are ""rational
        functions"" with symbolic exponents.  Thus, you should be able to call
        .as_numer_denom() and apply polynomial algorithms to the result for
        expressions for which this returns True.

        This is not part of the assumptions system.  You cannot do
        Symbol('z', rational_function=True).

        Examples
        ========

        >>> from sympy import Symbol, sin
        >>> from sympy.abc import x, y

        >>> (x/y).is_rational_function()
        True

        >>> (x**2).is_rational_function()
        True

        >>> (x/sin(y)).is_rational_function(y)
        False

        >>> n = Symbol('n', integer=True)
        >>> (x**n + 1).is_rational_function(x)
        False

        This function does not attempt any nontrivial simplifications that may
        result in an expression that does not appear to be a rational function
        to become one.

        >>> from sympy import sqrt, factor
        >>> y = Symbol('y', positive=True)
        >>> a = sqrt(y**2 + 2*y + 1)/y
        >>> a.is_rational_function(y)
        False
        >>> factor(a)
        (y + 1)/y
        >>> factor(a).is_rational_function(y)
        True

        See also is_algebraic_expr().

        ",182,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_rational_function(self, *syms):
        
        if self in _illegal:
            return False

        if syms:
            syms = set(map(sympify, syms))
        else:
            syms = self.free_symbols
            if not syms:
                return True

        return self._eval_is_rational_function(syms)

        ```
    ###Assistant : 
        Test whether function is a ratio of two polynomials in the given
        symbols, syms. When syms is not given, all free symbols will be used.
        The rational function does not have to be in expanded or in any kind of
        canonical form.

        This function returns False for expressions that are ""rational
        functions"" with symbolic exponents.  Thus, you should be able to call
        .as_numer_denom() and apply polynomial algorithms to the result for
        expressions for which this returns True.

        This is not part of the assumptions system.  You cannot do
        Symbol('z', rational_function=True).

        Examples
        ========

        >>> from sympy import Symbol, sin
        >>> from sympy.abc import x, y

        >>> (x/y).is_rational_function()
        True

        >>> (x**2).is_rational_function()
        True

        >>> (x/sin(y)).is_rational_function(y)
        False

        >>> n = Symbol('n', integer=True)
        >>> (x**n + 1).is_rational_function(x)
        False

        This function does not attempt any nontrivial simplifications that may
        result in an expression that does not appear to be a rational function
        to become one.

        >>> from sympy import sqrt, factor
        >>> y = Symbol('y', positive=True)
        >>> a = sqrt(y**2 + 2*y + 1)/y
        >>> a.is_rational_function(y)
        False
        >>> factor(a)
        (y + 1)/y
        >>> factor(a).is_rational_function(y)
        True

        See also is_algebraic_expr().

        
    "
521,"def reduce_alg_num(self, a):
        
        elt = self.ZK.parent.element_from_alg_num(a)
        red = self.reduce_element(elt)
        return a.field_element(list(reversed(red.QQ_col.flat())))

","
        Reduce an :py:class:`~.AlgebraicNumber` to a ""small representative""
        modulo this prime ideal.

        Parameters
        ==========

        elt : :py:class:`~.AlgebraicNumber`
            The element to be reduced.

        Returns
        =======

        :py:class:`~.AlgebraicNumber`
            The reduced element.

        See Also
        ========

        reduce_element
        reduce_ANP
        .Submodule.reduce_element

        ",33,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reduce_alg_num(self, a):
        
        elt = self.ZK.parent.element_from_alg_num(a)
        red = self.reduce_element(elt)
        return a.field_element(list(reversed(red.QQ_col.flat())))


        ```
    ###Assistant : 
        Reduce an :py:class:`~.AlgebraicNumber` to a ""small representative""
        modulo this prime ideal.

        Parameters
        ==========

        elt : :py:class:`~.AlgebraicNumber`
            The element to be reduced.

        Returns
        =======

        :py:class:`~.AlgebraicNumber`
            The reduced element.

        See Also
        ========

        reduce_element
        reduce_ANP
        .Submodule.reduce_element

        
    "
522,"def _auto_joiner(self, short_results, input_mapping, is_dict=False):
        
        concat_results = []
        elem_type = {} if is_dict else []
        for k, vs in input_mapping.items():
            single_results = elem_type
            for v in vs:
                if len(single_results) == 0:
                    single_results = short_results[v]
                elif isinstance(elem_type, list):
                    single_results.extend(short_results[v])
                elif isinstance(elem_type, dict):
                    for sk in single_results.keys():
                        if isinstance(single_results[sk], str):
                            single_results[sk] += short_results[v][sk]
                        else:
                            single_results[sk].extend(short_results[v][sk])
                else:
                    raise ValueError(
                        ""Invalid element type, the type of results ""
                        ""for each element should be list of dict, ""
                        ""but {} received."".format(type(single_results)))
            concat_results.append(single_results)
        return concat_results
","
        Join the short results automatically and generate the final results to match with the user inputs.
        Args:
            short_results (List[dict] / List[List[str]] / List[str]): input raw texts.
            input_mapping (dict): cutting length.
            is_dict (bool): whether the element type is dict, default to False.
        return:
            short_input_texts (List[str]): the short input texts for model inference.
        ",51,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _auto_joiner(self, short_results, input_mapping, is_dict=False):
        
        concat_results = []
        elem_type = {} if is_dict else []
        for k, vs in input_mapping.items():
            single_results = elem_type
            for v in vs:
                if len(single_results) == 0:
                    single_results = short_results[v]
                elif isinstance(elem_type, list):
                    single_results.extend(short_results[v])
                elif isinstance(elem_type, dict):
                    for sk in single_results.keys():
                        if isinstance(single_results[sk], str):
                            single_results[sk] += short_results[v][sk]
                        else:
                            single_results[sk].extend(short_results[v][sk])
                else:
                    raise ValueError(
                        ""Invalid element type, the type of results ""
                        ""for each element should be list of dict, ""
                        ""but {} received."".format(type(single_results)))
            concat_results.append(single_results)
        return concat_results

        ```
    ###Assistant : 
        Join the short results automatically and generate the final results to match with the user inputs.
        Args:
            short_results (List[dict] / List[List[str]] / List[str]): input raw texts.
            input_mapping (dict): cutting length.
            is_dict (bool): whether the element type is dict, default to False.
        return:
            short_input_texts (List[str]): the short input texts for model inference.
        
    "
523,"def get_variable_names_from_ckpt(path_ckpt, use_ema=True):
    
    v_all = tf.train.list_variables(path_ckpt)

    # keep name only
    v_name_all = [x[0] for x in v_all]

    if use_ema:
        v_name_all = [x for x in v_name_all if ""ExponentialMovingAverage"" in x]
    else:
        v_name_all = [
            x for x in v_name_all if ""ExponentialMovingAverage"" not in x
        ]

    # remove util variables used for RMSprop
    v_name_all = [x for x in v_name_all if ""RMS"" not in x]
    return v_name_all

","Get list of tensor names from checkpoint.

    Args:
      path_ckpt: str, path to the ckpt files
      use_ema: Bool, whether to use ExponentialMovingAverage result or not.
    Returns:
      List of variable names from checkpoint.
    ",31,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_variable_names_from_ckpt(path_ckpt, use_ema=True):
    
    v_all = tf.train.list_variables(path_ckpt)

    # keep name only
    v_name_all = [x[0] for x in v_all]

    if use_ema:
        v_name_all = [x for x in v_name_all if ""ExponentialMovingAverage"" in x]
    else:
        v_name_all = [
            x for x in v_name_all if ""ExponentialMovingAverage"" not in x
        ]

    # remove util variables used for RMSprop
    v_name_all = [x for x in v_name_all if ""RMS"" not in x]
    return v_name_all


        ```
    ###Assistant : Get list of tensor names from checkpoint.

    Args:
      path_ckpt: str, path to the ckpt files
      use_ema: Bool, whether to use ExponentialMovingAverage result or not.
    Returns:
      List of variable names from checkpoint.
    
    "
524,"def ode_order(expr, func):
    
    a = Wild('a', exclude=[func])
    if expr.match(a):
        return 0

    if isinstance(expr, Derivative):
        if expr.args[0] == func:
            return len(expr.variables)
        else:
            return max(ode_order(arg, func) for arg in expr.args[0].args) + len(expr.variables)
    else:
        return max(ode_order(arg, func) for arg in expr.args)

","
    Returns the order of a given differential
    equation with respect to func.

    This function is implemented recursively.

    Examples
    ========

    >>> from sympy import Function
    >>> from sympy.solvers.deutils import ode_order
    >>> from sympy.abc import x
    >>> f, g = map(Function, ['f', 'g'])
    >>> ode_order(f(x).diff(x, 2) + f(x).diff(x)**2 +
    ... f(x).diff(x), f(x))
    2
    >>> ode_order(f(x).diff(x, 2) + g(x).diff(x, 3), f(x))
    2
    >>> ode_order(f(x).diff(x, 2) + g(x).diff(x, 3), g(x))
    3

    ",67,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ode_order(expr, func):
    
    a = Wild('a', exclude=[func])
    if expr.match(a):
        return 0

    if isinstance(expr, Derivative):
        if expr.args[0] == func:
            return len(expr.variables)
        else:
            return max(ode_order(arg, func) for arg in expr.args[0].args) + len(expr.variables)
    else:
        return max(ode_order(arg, func) for arg in expr.args)


        ```
    ###Assistant : 
    Returns the order of a given differential
    equation with respect to func.

    This function is implemented recursively.

    Examples
    ========

    >>> from sympy import Function
    >>> from sympy.solvers.deutils import ode_order
    >>> from sympy.abc import x
    >>> f, g = map(Function, ['f', 'g'])
    >>> ode_order(f(x).diff(x, 2) + f(x).diff(x)**2 +
    ... f(x).diff(x), f(x))
    2
    >>> ode_order(f(x).diff(x, 2) + g(x).diff(x, 3), f(x))
    2
    >>> ode_order(f(x).diff(x, 2) + g(x).diff(x, 3), g(x))
    3

    
    "
525,"async def async_send_message(self, message, **kwargs):
        
        nextcord.VoiceClient.warn_nacl = False
        discord_bot = nextcord.Client()
        images = None
        embedding = None

        if ATTR_TARGET not in kwargs:
            _LOGGER.error(""No target specified"")
            return None

        data = kwargs.get(ATTR_DATA) or {}

        embeds: list[nextcord.Embed] = []
        if ATTR_EMBED in data:
            embedding = data[ATTR_EMBED]
            fields = embedding.get(ATTR_EMBED_FIELDS) or []

            if embedding:
                embed = nextcord.Embed(**embedding)
                for field in fields:
                    embed.add_field(**field)
                if ATTR_EMBED_FOOTER in embedding:
                    embed.set_footer(**embedding[ATTR_EMBED_FOOTER])
                if ATTR_EMBED_AUTHOR in embedding:
                    embed.set_author(**embedding[ATTR_EMBED_AUTHOR])
                if ATTR_EMBED_THUMBNAIL in embedding:
                    embed.set_thumbnail(**embedding[ATTR_EMBED_THUMBNAIL])
                embeds.append(embed)

        if ATTR_IMAGES in data:
            images = []

            for image in data.get(ATTR_IMAGES, []):
                image_exists = await self.hass.async_add_executor_job(
                    self.file_exists, image
                )

                if image_exists:
                    images.append(image)
                else:
                    _LOGGER.warning(""Image not found: %s"", image)

        await discord_bot.login(self.token)

        try:
            for channelid in kwargs[ATTR_TARGET]:
                channelid = int(channelid)
                try:
                    channel = await discord_bot.fetch_channel(channelid)
                except nextcord.NotFound:
                    try:
                        channel = await discord_bot.fetch_user(channelid)
                    except nextcord.NotFound:
                        _LOGGER.warning(""Channel not found for ID: %s"", channelid)
                        continue
                # Must create new instances of File for each channel.
                files = [nextcord.File(image) for image in images] if images else []
                await channel.send(message, files=files, embeds=embeds)
        except (nextcord.HTTPException, nextcord.NotFound) as error:
            _LOGGER.warning(""Communication error: %s"", error)
        await discord_bot.close()
","Login to Discord, send message to channel(s) and log out.",10,170,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_send_message(self, message, **kwargs):
        
        nextcord.VoiceClient.warn_nacl = False
        discord_bot = nextcord.Client()
        images = None
        embedding = None

        if ATTR_TARGET not in kwargs:
            _LOGGER.error(""No target specified"")
            return None

        data = kwargs.get(ATTR_DATA) or {}

        embeds: list[nextcord.Embed] = []
        if ATTR_EMBED in data:
            embedding = data[ATTR_EMBED]
            fields = embedding.get(ATTR_EMBED_FIELDS) or []

            if embedding:
                embed = nextcord.Embed(**embedding)
                for field in fields:
                    embed.add_field(**field)
                if ATTR_EMBED_FOOTER in embedding:
                    embed.set_footer(**embedding[ATTR_EMBED_FOOTER])
                if ATTR_EMBED_AUTHOR in embedding:
                    embed.set_author(**embedding[ATTR_EMBED_AUTHOR])
                if ATTR_EMBED_THUMBNAIL in embedding:
                    embed.set_thumbnail(**embedding[ATTR_EMBED_THUMBNAIL])
                embeds.append(embed)

        if ATTR_IMAGES in data:
            images = []

            for image in data.get(ATTR_IMAGES, []):
                image_exists = await self.hass.async_add_executor_job(
                    self.file_exists, image
                )

                if image_exists:
                    images.append(image)
                else:
                    _LOGGER.warning(""Image not found: %s"", image)

        await discord_bot.login(self.token)

        try:
            for channelid in kwargs[ATTR_TARGET]:
                channelid = int(channelid)
                try:
                    channel = await discord_bot.fetch_channel(channelid)
                except nextcord.NotFound:
                    try:
                        channel = await discord_bot.fetch_user(channelid)
                    except nextcord.NotFound:
                        _LOGGER.warning(""Channel not found for ID: %s"", channelid)
                        continue
                # Must create new instances of File for each channel.
                files = [nextcord.File(image) for image in images] if images else []
                await channel.send(message, files=files, embeds=embeds)
        except (nextcord.HTTPException, nextcord.NotFound) as error:
            _LOGGER.warning(""Communication error: %s"", error)
        await discord_bot.close()

        ```
    ###Assistant : Login to Discord, send message to channel(s) and log out.
    "
526,"def rand_series_with_duplicate_datetimeindex() -> Series:
    
    dates = [
        datetime(2000, 1, 2),
        datetime(2000, 1, 2),
        datetime(2000, 1, 2),
        datetime(2000, 1, 3),
        datetime(2000, 1, 3),
        datetime(2000, 1, 3),
        datetime(2000, 1, 4),
        datetime(2000, 1, 4),
        datetime(2000, 1, 4),
        datetime(2000, 1, 5),
    ]

    return Series(np.random.randn(len(dates)), index=dates)


# ----------------------------------------------------------------
# Scalars
# ----------------------------------------------------------------
@pytest.fixture(
    params=[
        (
            Interval(left=0, right=5, inclusive=""right""),
            IntervalDtype(""int64"", inclusive=""right""),
        ),
        (
            Interval(left=0.1, right=0.5, inclusive=""right""),
            IntervalDtype(""float64"", inclusive=""right""),
        ),
        (Period(""2012-01"", freq=""M""), ""period[M]""),
        (Period(""2012-02-01"", freq=""D""), ""period[D]""),
        (
            Timestamp(""2011-01-01"", tz=""US/Eastern""),
            DatetimeTZDtype(tz=""US/Eastern""),
        ),
        (Timedelta(seconds=500), ""timedelta64[ns]""),
    ]
)","
    Fixture for Series with a DatetimeIndex that has duplicates.
    ",9,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rand_series_with_duplicate_datetimeindex() -> Series:
    
    dates = [
        datetime(2000, 1, 2),
        datetime(2000, 1, 2),
        datetime(2000, 1, 2),
        datetime(2000, 1, 3),
        datetime(2000, 1, 3),
        datetime(2000, 1, 3),
        datetime(2000, 1, 4),
        datetime(2000, 1, 4),
        datetime(2000, 1, 4),
        datetime(2000, 1, 5),
    ]

    return Series(np.random.randn(len(dates)), index=dates)


# ----------------------------------------------------------------
# Scalars
# ----------------------------------------------------------------
@pytest.fixture(
    params=[
        (
            Interval(left=0, right=5, inclusive=""right""),
            IntervalDtype(""int64"", inclusive=""right""),
        ),
        (
            Interval(left=0.1, right=0.5, inclusive=""right""),
            IntervalDtype(""float64"", inclusive=""right""),
        ),
        (Period(""2012-01"", freq=""M""), ""period[M]""),
        (Period(""2012-02-01"", freq=""D""), ""period[D]""),
        (
            Timestamp(""2011-01-01"", tz=""US/Eastern""),
            DatetimeTZDtype(tz=""US/Eastern""),
        ),
        (Timedelta(seconds=500), ""timedelta64[ns]""),
    ]
)
        ```
    ###Assistant : 
    Fixture for Series with a DatetimeIndex that has duplicates.
    
    "
527,"def get_major_minor_version():
    # type: () -> str
    
    return ""{}.{}"".format(*sys.version_info)

","
    Return the major-minor version of the current Python as a string, e.g.
    ""3.7"" or ""3.10"".
    ",15,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_major_minor_version():
    # type: () -> str
    
    return ""{}.{}"".format(*sys.version_info)


        ```
    ###Assistant : 
    Return the major-minor version of the current Python as a string, e.g.
    ""3.7"" or ""3.10"".
    
    "
528,"def test_ssd_head_loss(self):
        
        s = 300
        img_metas = [{
            'img_shape': (s, s, 3),
            'scale_factor': 1,
        }]
        cfg = Config(
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.,
                    ignore_iof_thr=-1,
                    gt_max_assign_all=False),
                sampler=dict(type='PseudoSampler'),
                smoothl1_beta=1.,
                allowed_border=-1,
                pos_weight=-1,
                neg_pos_ratio=3,
                debug=False))
        ssd_head = SSDHead(
            num_classes=4,
            in_channels=(1, 1, 1, 1, 1, 1),
            stacked_convs=1,
            feat_channels=1,
            use_depthwise=True,
            anchor_generator=dict(
                type='SSDAnchorGenerator',
                scale_major=False,
                input_size=s,
                basesize_ratio_range=(0.15, 0.9),
                strides=[8, 16, 32, 64, 100, 300],
                ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
            train_cfg=cfg)

        # SSD head expects a multiple levels of features per image
        feats = (
            torch.rand(1, 1, ceil(s / stride[0]), ceil(s / stride[0]))
            for stride in ssd_head.prior_generator.strides)
        cls_scores, bbox_preds = ssd_head.forward(feats)

        # Test that empty ground truth encourages the network to
        # predict background
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.empty((0, 4))
        gt_instances.labels = torch.LongTensor([])

        empty_gt_losses = ssd_head.loss(cls_scores, bbox_preds, [gt_instances],
                                        img_metas)
        # When there is no truth, cls_loss and box_loss should all be zero.
        empty_cls_loss = sum(empty_gt_losses['loss_cls'])
        empty_box_loss = sum(empty_gt_losses['loss_bbox'])
        self.assertEqual(
            empty_cls_loss.item(), 0,
            'there should be no cls loss when there are no true boxes')
        self.assertEqual(
            empty_box_loss.item(), 0,
            'there should be no box loss when there are no true boxes')

        # When truth is non-empty then both cls and box loss
        # should be nonzero for random inputs
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.Tensor(
            [[23.6667, 23.8757, 238.6326, 151.8874]])
        gt_instances.labels = torch.LongTensor([2])

        one_gt_losses = ssd_head.loss(cls_scores, bbox_preds, [gt_instances],
                                      img_metas)
        onegt_cls_loss = sum(one_gt_losses['loss_cls'])
        onegt_box_loss = sum(one_gt_losses['loss_bbox'])
        self.assertGreater(onegt_cls_loss.item(), 0,
                           'cls loss should be non-zero')
        self.assertGreater(onegt_box_loss.item(), 0,
                           'box loss should be non-zero')
",Tests ssd head loss when truth is empty and non-empty.,10,232,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ssd_head_loss(self):
        
        s = 300
        img_metas = [{
            'img_shape': (s, s, 3),
            'scale_factor': 1,
        }]
        cfg = Config(
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.,
                    ignore_iof_thr=-1,
                    gt_max_assign_all=False),
                sampler=dict(type='PseudoSampler'),
                smoothl1_beta=1.,
                allowed_border=-1,
                pos_weight=-1,
                neg_pos_ratio=3,
                debug=False))
        ssd_head = SSDHead(
            num_classes=4,
            in_channels=(1, 1, 1, 1, 1, 1),
            stacked_convs=1,
            feat_channels=1,
            use_depthwise=True,
            anchor_generator=dict(
                type='SSDAnchorGenerator',
                scale_major=False,
                input_size=s,
                basesize_ratio_range=(0.15, 0.9),
                strides=[8, 16, 32, 64, 100, 300],
                ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
            train_cfg=cfg)

        # SSD head expects a multiple levels of features per image
        feats = (
            torch.rand(1, 1, ceil(s / stride[0]), ceil(s / stride[0]))
            for stride in ssd_head.prior_generator.strides)
        cls_scores, bbox_preds = ssd_head.forward(feats)

        # Test that empty ground truth encourages the network to
        # predict background
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.empty((0, 4))
        gt_instances.labels = torch.LongTensor([])

        empty_gt_losses = ssd_head.loss(cls_scores, bbox_preds, [gt_instances],
                                        img_metas)
        # When there is no truth, cls_loss and box_loss should all be zero.
        empty_cls_loss = sum(empty_gt_losses['loss_cls'])
        empty_box_loss = sum(empty_gt_losses['loss_bbox'])
        self.assertEqual(
            empty_cls_loss.item(), 0,
            'there should be no cls loss when there are no true boxes')
        self.assertEqual(
            empty_box_loss.item(), 0,
            'there should be no box loss when there are no true boxes')

        # When truth is non-empty then both cls and box loss
        # should be nonzero for random inputs
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.Tensor(
            [[23.6667, 23.8757, 238.6326, 151.8874]])
        gt_instances.labels = torch.LongTensor([2])

        one_gt_losses = ssd_head.loss(cls_scores, bbox_preds, [gt_instances],
                                      img_metas)
        onegt_cls_loss = sum(one_gt_losses['loss_cls'])
        onegt_box_loss = sum(one_gt_losses['loss_bbox'])
        self.assertGreater(onegt_cls_loss.item(), 0,
                           'cls loss should be non-zero')
        self.assertGreater(onegt_box_loss.item(), 0,
                           'box loss should be non-zero')

        ```
    ###Assistant : Tests ssd head loss when truth is empty and non-empty.
    "
529,"def _legacy_check(self) -> None:
        
        if self._min_size > 0 or self._arguments.extract_every_n != 1:
            logger.warning(""This alignments file was generated with the legacy extraction method."")
            logger.warning(""You should run this extraction job, but with 'min_size' set to 0 and ""
                           ""'extract-every-n' set to 1 to update the alignments file."")
            logger.warning(""You can then re-run this extraction job with your chosen options."")
            sys.exit(0)

        maskers = [""components"", ""extended""]
        nn_masks = [mask for mask in list(self._alignments.mask_summary) if mask not in maskers]
        logtype = logger.warning if nn_masks else logger.info
        logtype(""This alignments file was created with the legacy extraction method and will be ""
                ""updated."")
        logtype(""Faces will be extracted using the new method and landmarks based masks will be ""
                ""regenerated."")
        if nn_masks:
            logtype(""However, the NN based masks '%s' will be cropped to the legacy extraction ""
                    ""method, so you may want to run the mask tool to regenerate these ""
                    ""masks."", ""', '"".join(nn_masks))
        self._mask_pipeline = Extractor(None, None, maskers, multiprocess=True)
        self._mask_pipeline.launch()
        # Update alignments versioning
        self._alignments._version = _VERSION  # pylint:disable=protected-access
"," Check whether the alignments file was created with the legacy extraction method.

        If so, force user to re-extract all faces if any options have been specified, otherwise
        raise the appropriate warnings and set the legacy options.
        ",36,160,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _legacy_check(self) -> None:
        
        if self._min_size > 0 or self._arguments.extract_every_n != 1:
            logger.warning(""This alignments file was generated with the legacy extraction method."")
            logger.warning(""You should run this extraction job, but with 'min_size' set to 0 and ""
                           ""'extract-every-n' set to 1 to update the alignments file."")
            logger.warning(""You can then re-run this extraction job with your chosen options."")
            sys.exit(0)

        maskers = [""components"", ""extended""]
        nn_masks = [mask for mask in list(self._alignments.mask_summary) if mask not in maskers]
        logtype = logger.warning if nn_masks else logger.info
        logtype(""This alignments file was created with the legacy extraction method and will be ""
                ""updated."")
        logtype(""Faces will be extracted using the new method and landmarks based masks will be ""
                ""regenerated."")
        if nn_masks:
            logtype(""However, the NN based masks '%s' will be cropped to the legacy extraction ""
                    ""method, so you may want to run the mask tool to regenerate these ""
                    ""masks."", ""', '"".join(nn_masks))
        self._mask_pipeline = Extractor(None, None, maskers, multiprocess=True)
        self._mask_pipeline.launch()
        # Update alignments versioning
        self._alignments._version = _VERSION  # pylint:disable=protected-access

        ```
    ###Assistant :  Check whether the alignments file was created with the legacy extraction method.

        If so, force user to re-extract all faces if any options have been specified, otherwise
        raise the appropriate warnings and set the legacy options.
        
    "
530,"def FisherZ(name, d1, d2):
    r

    return rv(name, FisherZDistribution, (d1, d2))

#-------------------------------------------------------------------------------
# Frechet distribution ---------------------------------------------------------
","
    Create a Continuous Random Variable with an Fisher's Z distribution.

    Explanation
    ===========

    The density of the Fisher's Z distribution is given by

    .. math::
        f(x) := \frac{2d_1^{d_1/2} d_2^{d_2/2}} {\mathrm{B}(d_1/2, d_2/2)}
                \frac{e^{d_1z}}{\left(d_1e^{2z}+d_2\right)^{\left(d_1+d_2\right)/2}}


    .. TODO - What is the difference between these degrees of freedom?

    Parameters
    ==========

    d1 : `d_1 > 0`
        Degree of freedom.
    d2 : `d_2 > 0`
        Degree of freedom.

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import FisherZ, density
    >>> from sympy import Symbol, pprint

    >>> d1 = Symbol(""d1"", positive=True)
    >>> d2 = Symbol(""d2"", positive=True)
    >>> z = Symbol(""z"")

    >>> X = FisherZ(""x"", d1, d2)

    >>> D = density(X)(z)
    >>> pprint(D, use_unicode=False)
                                d1   d2
        d1   d2               - -- - --
        --   --                 2    2
        2    2  /    2*z     \           d1*z
    2*d1  *d2  *\d1*e    + d2/         *e
    -----------------------------------------
                     /d1  d2\
                    B|--, --|
                     \2   2 /

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Fisher%27s_z-distribution
    .. [2] http://mathworld.wolfram.com/Fishersz-Distribution.html

    ",145,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def FisherZ(name, d1, d2):
    r

    return rv(name, FisherZDistribution, (d1, d2))

#-------------------------------------------------------------------------------
# Frechet distribution ---------------------------------------------------------

        ```
    ###Assistant : 
    Create a Continuous Random Variable with an Fisher's Z distribution.

    Explanation
    ===========

    The density of the Fisher's Z distribution is given by

    .. math::
        f(x) := \frac{2d_1^{d_1/2} d_2^{d_2/2}} {\mathrm{B}(d_1/2, d_2/2)}
                \frac{e^{d_1z}}{\left(d_1e^{2z}+d_2\right)^{\left(d_1+d_2\right)/2}}


    .. TODO - What is the difference between these degrees of freedom?

    Parameters
    ==========

    d1 : `d_1 > 0`
        Degree of freedom.
    d2 : `d_2 > 0`
        Degree of freedom.

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import FisherZ, density
    >>> from sympy import Symbol, pprint

    >>> d1 = Symbol(""d1"", positive=True)
    >>> d2 = Symbol(""d2"", positive=True)
    >>> z = Symbol(""z"")

    >>> X = FisherZ(""x"", d1, d2)

    >>> D = density(X)(z)
    >>> pprint(D, use_unicode=False)
                                d1   d2
        d1   d2               - -- - --
        --   --                 2    2
        2    2  /    2*z     \           d1*z
    2*d1  *d2  *\d1*e    + d2/         *e
    -----------------------------------------
                     /d1  d2\
                    B|--, --|
                     \2   2 /

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Fisher%27s_z-distribution
    .. [2] http://mathworld.wolfram.com/Fishersz-Distribution.html

    
    "
531,"def find_batch_size(tensors):
    
    if isinstance(tensors, (list, tuple)):
        for t in tensors:
            result = find_batch_size(t)
            if result is not None:
                return result
    elif isinstance(tensors, Mapping):
        for key, value in tensors.items():
            result = find_batch_size(value)
            if result is not None:
                return result
    elif isinstance(tensors, torch.Tensor):
        return tensors.shape[0] if len(tensors.shape) >= 1 else None
    elif isinstance(tensors, np.ndarray):
        return tensors.shape[0] if len(tensors.shape) >= 1 else None

","
    Find the first dimension of a tensor in a nested list/tuple/dict of tensors.
    ",13,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_batch_size(tensors):
    
    if isinstance(tensors, (list, tuple)):
        for t in tensors:
            result = find_batch_size(t)
            if result is not None:
                return result
    elif isinstance(tensors, Mapping):
        for key, value in tensors.items():
            result = find_batch_size(value)
            if result is not None:
                return result
    elif isinstance(tensors, torch.Tensor):
        return tensors.shape[0] if len(tensors.shape) >= 1 else None
    elif isinstance(tensors, np.ndarray):
        return tensors.shape[0] if len(tensors.shape) >= 1 else None


        ```
    ###Assistant : 
    Find the first dimension of a tensor in a nested list/tuple/dict of tensors.
    
    "
532,"def post_process_segmentation(self, outputs, target_sizes, threshold=0.9, mask_threshold=0.5):
        
        out_logits, raw_masks = outputs.logits, outputs.pred_masks
        preds = []
","
        Converts the output of [`DetrForSegmentation`] into image segmentation predictions. Only supports PyTorch.

        Parameters:
            outputs ([`DetrSegmentationOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
                Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction.
            threshold (`float`, *optional*, defaults to 0.9):
                Threshold to use to filter out queries.
            mask_threshold (`float`, *optional*, defaults to 0.5):
                Threshold to use when turning the predicted masks into binary values.

        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, and masks for an image
            in the batch as predicted by the model.
        ",101,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def post_process_segmentation(self, outputs, target_sizes, threshold=0.9, mask_threshold=0.5):
        
        out_logits, raw_masks = outputs.logits, outputs.pred_masks
        preds = []

        ```
    ###Assistant : 
        Converts the output of [`DetrForSegmentation`] into image segmentation predictions. Only supports PyTorch.

        Parameters:
            outputs ([`DetrSegmentationOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
                Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction.
            threshold (`float`, *optional*, defaults to 0.9):
                Threshold to use to filter out queries.
            mask_threshold (`float`, *optional*, defaults to 0.5):
                Threshold to use when turning the predicted masks into binary values.

        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, and masks for an image
            in the batch as predicted by the model.
        
    "
533,"def iterate_graycode(self, k):
        
        unranked_code = GrayCode.unrank(self.superset_size,
                                       (self.rank_gray + k) % self.cardinality)
        return Subset.subset_from_bitlist(self.superset,
                                          unranked_code)
","
        Helper function used for prev_gray and next_gray.
        It performs ``k`` step overs to get the respective Gray codes.

        Examples
        ========

        >>> from sympy.combinatorics import Subset
        >>> a = Subset([1, 2, 3], [1, 2, 3, 4])
        >>> a.iterate_graycode(3).subset
        [1, 4]
        >>> a.iterate_graycode(-2).subset
        [1, 2, 4]

        See Also
        ========

        next_gray, prev_gray
        ",49,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def iterate_graycode(self, k):
        
        unranked_code = GrayCode.unrank(self.superset_size,
                                       (self.rank_gray + k) % self.cardinality)
        return Subset.subset_from_bitlist(self.superset,
                                          unranked_code)

        ```
    ###Assistant : 
        Helper function used for prev_gray and next_gray.
        It performs ``k`` step overs to get the respective Gray codes.

        Examples
        ========

        >>> from sympy.combinatorics import Subset
        >>> a = Subset([1, 2, 3], [1, 2, 3, 4])
        >>> a.iterate_graycode(3).subset
        [1, 4]
        >>> a.iterate_graycode(-2).subset
        [1, 2, 4]

        See Also
        ========

        next_gray, prev_gray
        
    "
534,"def is_sequence_right_padded(mask):
    
    max_seq_length = tf.shape(mask)[1]
    count_of_true = tf.reduce_sum(tf.cast(mask, tf.int32), axis=1)
    right_padded_mask = tf.sequence_mask(count_of_true, maxlen=max_seq_length)
    return tf.reduce_all(tf.equal(mask, right_padded_mask))

","Check the mask tensor and see if it right padded.

    For cuDNN kernel, it uses the sequence length param to skip the tailing
    timestep. If the data is left padded, or not a strict right padding (has
    masked value in the middle of the sequence), then cuDNN kernel won't be work
    properly in those cases.

    Left padded data: [[False, False, True, True, True]].
    Right padded data: [[True, True, True, False, False]].
    Mixture of mask/unmasked data: [[True, False, True, False, False]].

    Note that for the mixed data example above, the actually data RNN should see
    are those 2 Trues (index 0 and 2), the index 1 False should be ignored and not
    pollute the internal states.

    Args:
      mask: the Boolean tensor with shape [batch, timestep]

    Returns:
      boolean scalar tensor, whether the mask is strictly right padded.
    ",135,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_sequence_right_padded(mask):
    
    max_seq_length = tf.shape(mask)[1]
    count_of_true = tf.reduce_sum(tf.cast(mask, tf.int32), axis=1)
    right_padded_mask = tf.sequence_mask(count_of_true, maxlen=max_seq_length)
    return tf.reduce_all(tf.equal(mask, right_padded_mask))


        ```
    ###Assistant : Check the mask tensor and see if it right padded.

    For cuDNN kernel, it uses the sequence length param to skip the tailing
    timestep. If the data is left padded, or not a strict right padding (has
    masked value in the middle of the sequence), then cuDNN kernel won't be work
    properly in those cases.

    Left padded data: [[False, False, True, True, True]].
    Right padded data: [[True, True, True, False, False]].
    Mixture of mask/unmasked data: [[True, False, True, False, False]].

    Note that for the mixed data example above, the actually data RNN should see
    are those 2 Trues (index 0 and 2), the index 1 False should be ignored and not
    pollute the internal states.

    Args:
      mask: the Boolean tensor with shape [batch, timestep]

    Returns:
      boolean scalar tensor, whether the mask is strictly right padded.
    
    "
535,"def _send_event_over_federation(self) -> None:
        
        body = {
            ""pdus"": [
                {
                    ""sender"": self.user_id,
                    ""type"": EventTypes.Message,
                    ""state_key"": """",
                    ""content"": {""body"": ""hello world"", ""msgtype"": ""m.text""},
                    ""room_id"": self.room_id,
                    ""depth"": 0,
                    ""origin_server_ts"": self.clock.time_msec(),
                    ""prev_events"": [],
                    ""auth_events"": [],
                    ""signatures"": {},
                    ""unsigned"": {},
                }
            ],
        }

        channel = self.make_signed_federation_request(
            method=""PUT"",
            path=""/_matrix/federation/v1/send/1"",
            content=body,
        )

        self.assertEqual(channel.code, 200, channel.result)
",Send a dummy event over federation and check that the request succeeds.,12,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _send_event_over_federation(self) -> None:
        
        body = {
            ""pdus"": [
                {
                    ""sender"": self.user_id,
                    ""type"": EventTypes.Message,
                    ""state_key"": """",
                    ""content"": {""body"": ""hello world"", ""msgtype"": ""m.text""},
                    ""room_id"": self.room_id,
                    ""depth"": 0,
                    ""origin_server_ts"": self.clock.time_msec(),
                    ""prev_events"": [],
                    ""auth_events"": [],
                    ""signatures"": {},
                    ""unsigned"": {},
                }
            ],
        }

        channel = self.make_signed_federation_request(
            method=""PUT"",
            path=""/_matrix/federation/v1/send/1"",
            content=body,
        )

        self.assertEqual(channel.code, 200, channel.result)

        ```
    ###Assistant : Send a dummy event over federation and check that the request succeeds.
    "
536,"def _print_loss(self, loss):
        
        output = "", "".join([f""Loss {side}: {side_loss:.5f}""
                            for side, side_loss in zip((""A"", ""B""), loss)])
        timestamp = time.strftime(""%H:%M:%S"")
        output = f""[{timestamp}] [#{self._model.iterations:05d}] {output}""
        print(f""\r{output}"", end="""")
"," Outputs the loss for the current iteration to the console.

        Parameters
        ----------
        loss: list
            The loss for each side. List should contain 2 ``floats`` side ""a"" in position 0 and
            side ""b"" in position `.
         ",35,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _print_loss(self, loss):
        
        output = "", "".join([f""Loss {side}: {side_loss:.5f}""
                            for side, side_loss in zip((""A"", ""B""), loss)])
        timestamp = time.strftime(""%H:%M:%S"")
        output = f""[{timestamp}] [#{self._model.iterations:05d}] {output}""
        print(f""\r{output}"", end="""")

        ```
    ###Assistant :  Outputs the loss for the current iteration to the console.

        Parameters
        ----------
        loss: list
            The loss for each side. List should contain 2 ``floats`` side ""a"" in position 0 and
            side ""b"" in position `.
         
    "
537,"def itermonthdays2(self, year, month):
        
        for i, d in enumerate(self.itermonthdays(year, month), self.firstweekday):
            yield d, i % 7
","
        Like itermonthdates(), but will yield (day number, weekday number)
        tuples. For days outside the specified month the day number is 0.
        ",21,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def itermonthdays2(self, year, month):
        
        for i, d in enumerate(self.itermonthdays(year, month), self.firstweekday):
            yield d, i % 7

        ```
    ###Assistant : 
        Like itermonthdates(), but will yield (day number, weekday number)
        tuples. For days outside the specified month the day number is 0.
        
    "
538,"def test_distance_function_return_none_if_invalid_state(hass):
    
    hass.states.async_set(""test.object_2"", ""happy"", {""latitude"": 10})
    tpl = template.Template(""{{ distance(states.test.object_2) | round }}"", hass)
    with pytest.raises(TemplateError):
        tpl.async_render()

",Test distance function return None if invalid state.,8,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_distance_function_return_none_if_invalid_state(hass):
    
    hass.states.async_set(""test.object_2"", ""happy"", {""latitude"": 10})
    tpl = template.Template(""{{ distance(states.test.object_2) | round }}"", hass)
    with pytest.raises(TemplateError):
        tpl.async_render()


        ```
    ###Assistant : Test distance function return None if invalid state.
    "
539,"def Concatenate(self, parameters):
        
        return _concatenate_getitem(self, parameters)
# 3.7-8
elif sys.version_info[:2] >= (3, 7):","Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
        higher order function which adds, removes or transforms parameters of a
        callable.

        For example::

           Callable[Concatenate[int, P], int]

        See PEP 612 for detailed information.
        ",33,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def Concatenate(self, parameters):
        
        return _concatenate_getitem(self, parameters)
# 3.7-8
elif sys.version_info[:2] >= (3, 7):
        ```
    ###Assistant : Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
        higher order function which adds, removes or transforms parameters of a
        callable.

        For example::

           Callable[Concatenate[int, P], int]

        See PEP 612 for detailed information.
        
    "
540,"def compute_recall(result_neighbors, ground_truth_neighbors) -> float:
  
  assert len(
      result_neighbors.shape) == 2, ""shape = [num_queries, neighbors_per_query]""
  assert len(ground_truth_neighbors.shape
            ) == 2, ""shape = [num_queries, ground_truth_neighbors_per_query]""
  assert result_neighbors.shape[0] == ground_truth_neighbors.shape[0]
  gt_sets = [set(np.asarray(x)) for x in ground_truth_neighbors]
  hits = sum(
      len(list(x
               for x in nn_per_q
               if x.item() in gt_sets[q]))
      for q, nn_per_q in enumerate(result_neighbors))
  return hits / ground_truth_neighbors.size

","Computes the recall of an approximate nearest neighbor search.

  Args:
    result_neighbors: int32 numpy array of the shape [num_queries,
      neighbors_per_query] where the values are the indices of the dataset.
    ground_truth_neighbors: int32 numpy array of with shape [num_queries,
      ground_truth_neighbors_per_query] where the values are the indices of the
      dataset.

  Returns:
    The recall.
  ",49,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compute_recall(result_neighbors, ground_truth_neighbors) -> float:
  
  assert len(
      result_neighbors.shape) == 2, ""shape = [num_queries, neighbors_per_query]""
  assert len(ground_truth_neighbors.shape
            ) == 2, ""shape = [num_queries, ground_truth_neighbors_per_query]""
  assert result_neighbors.shape[0] == ground_truth_neighbors.shape[0]
  gt_sets = [set(np.asarray(x)) for x in ground_truth_neighbors]
  hits = sum(
      len(list(x
               for x in nn_per_q
               if x.item() in gt_sets[q]))
      for q, nn_per_q in enumerate(result_neighbors))
  return hits / ground_truth_neighbors.size


        ```
    ###Assistant : Computes the recall of an approximate nearest neighbor search.

  Args:
    result_neighbors: int32 numpy array of the shape [num_queries,
      neighbors_per_query] where the values are the indices of the dataset.
    ground_truth_neighbors: int32 numpy array of with shape [num_queries,
      ground_truth_neighbors_per_query] where the values are the indices of the
      dataset.

  Returns:
    The recall.
  
    "
541,"def isocalendar(self) -> DataFrame:
        
        from pandas import DataFrame

        values = self._local_timestamps()
        sarray = fields.build_isocalendar_sarray(values)
        iso_calendar_df = DataFrame(
            sarray, columns=[""year"", ""week"", ""day""], dtype=""UInt32""
        )
        if self._hasna:
            iso_calendar_df.iloc[self._isnan] = None
        return iso_calendar_df
","
        Returns a DataFrame with the year, week, and day calculated according to
        the ISO 8601 standard.

        .. versionadded:: 1.1.0

        Returns
        -------
        DataFrame
            with columns year, week and day

        See Also
        --------
        Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
            week number, and weekday for the given Timestamp object.
        datetime.date.isocalendar : Return a named tuple object with
            three components: year, week and weekday.

        Examples
        --------
        >>> idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
        >>> idx.isocalendar()
                    year  week  day
        2019-12-29  2019    52    7
        2019-12-30  2020     1    1
        2019-12-31  2020     1    2
        2020-01-01  2020     1    3
        >>> idx.isocalendar().week
        2019-12-29    52
        2019-12-30     1
        2019-12-31     1
        2020-01-01     1
        Freq: D, Name: week, dtype: UInt32
        ",108,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def isocalendar(self) -> DataFrame:
        
        from pandas import DataFrame

        values = self._local_timestamps()
        sarray = fields.build_isocalendar_sarray(values)
        iso_calendar_df = DataFrame(
            sarray, columns=[""year"", ""week"", ""day""], dtype=""UInt32""
        )
        if self._hasna:
            iso_calendar_df.iloc[self._isnan] = None
        return iso_calendar_df

        ```
    ###Assistant : 
        Returns a DataFrame with the year, week, and day calculated according to
        the ISO 8601 standard.

        .. versionadded:: 1.1.0

        Returns
        -------
        DataFrame
            with columns year, week and day

        See Also
        --------
        Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
            week number, and weekday for the given Timestamp object.
        datetime.date.isocalendar : Return a named tuple object with
            three components: year, week and weekday.

        Examples
        --------
        >>> idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
        >>> idx.isocalendar()
                    year  week  day
        2019-12-29  2019    52    7
        2019-12-30  2020     1    1
        2019-12-31  2020     1    2
        2020-01-01  2020     1    3
        >>> idx.isocalendar().week
        2019-12-29    52
        2019-12-30     1
        2019-12-31     1
        2020-01-01     1
        Freq: D, Name: week, dtype: UInt32
        
    "
542,"def putpixel(self, xy, value):
        

        if self.readonly:
            self._copy()
        self.load()

        if self.pyaccess:
            return self.pyaccess.putpixel(xy, value)

        if (
            self.mode in (""P"", ""PA"")
            and isinstance(value, (list, tuple))
            and len(value) in [3, 4]
        ):
            # RGB or RGBA value for a P or PA image
            if self.mode == ""PA"":
                alpha = value[3] if len(value) == 4 else 255
                value = value[:3]
            value = self.palette.getcolor(value, self)
            if self.mode == ""PA"":
                value = (value, alpha)
        return self.im.putpixel(xy, value)
","
        Modifies the pixel at the given position. The color is given as
        a single numerical value for single-band images, and a tuple for
        multi-band images. In addition to this, RGB and RGBA tuples are
        accepted for P and PA images.

        Note that this method is relatively slow.  For more extensive changes,
        use :py:meth:`~PIL.Image.Image.paste` or the :py:mod:`~PIL.ImageDraw`
        module instead.

        See:

        * :py:meth:`~PIL.Image.Image.paste`
        * :py:meth:`~PIL.Image.Image.putdata`
        * :py:mod:`~PIL.ImageDraw`

        :param xy: The pixel coordinate, given as (x, y). See
           :ref:`coordinate-system`.
        :param value: The pixel value.
        ",81,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def putpixel(self, xy, value):
        

        if self.readonly:
            self._copy()
        self.load()

        if self.pyaccess:
            return self.pyaccess.putpixel(xy, value)

        if (
            self.mode in (""P"", ""PA"")
            and isinstance(value, (list, tuple))
            and len(value) in [3, 4]
        ):
            # RGB or RGBA value for a P or PA image
            if self.mode == ""PA"":
                alpha = value[3] if len(value) == 4 else 255
                value = value[:3]
            value = self.palette.getcolor(value, self)
            if self.mode == ""PA"":
                value = (value, alpha)
        return self.im.putpixel(xy, value)

        ```
    ###Assistant : 
        Modifies the pixel at the given position. The color is given as
        a single numerical value for single-band images, and a tuple for
        multi-band images. In addition to this, RGB and RGBA tuples are
        accepted for P and PA images.

        Note that this method is relatively slow.  For more extensive changes,
        use :py:meth:`~PIL.Image.Image.paste` or the :py:mod:`~PIL.ImageDraw`
        module instead.

        See:

        * :py:meth:`~PIL.Image.Image.paste`
        * :py:meth:`~PIL.Image.Image.putdata`
        * :py:mod:`~PIL.ImageDraw`

        :param xy: The pixel coordinate, given as (x, y). See
           :ref:`coordinate-system`.
        :param value: The pixel value.
        
    "
543,"async def test_available_template_with_entities(hass):
    
    await setup.async_setup_component(
        hass,
        ""switch"",
        {
            ""switch"": {
                ""platform"": ""template"",
                ""switches"": {
                    ""test_template_switch"": {
                        **OPTIMISTIC_SWITCH_CONFIG,
                        ""value_template"": ""{{ 1 == 1 }}"",
                        ""availability_template"": ""{{ is_state('availability_state.state', 'on') }}"",
                    }
                },
            }
        },
    )

    await hass.async_block_till_done()
    await hass.async_start()
    await hass.async_block_till_done()

    hass.states.async_set(""availability_state.state"", STATE_ON)
    await hass.async_block_till_done()

    assert hass.states.get(""switch.test_template_switch"").state != STATE_UNAVAILABLE

    hass.states.async_set(""availability_state.state"", STATE_OFF)
    await hass.async_block_till_done()

    assert hass.states.get(""switch.test_template_switch"").state == STATE_UNAVAILABLE

",Test availability templates with values from other entities.,8,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_available_template_with_entities(hass):
    
    await setup.async_setup_component(
        hass,
        ""switch"",
        {
            ""switch"": {
                ""platform"": ""template"",
                ""switches"": {
                    ""test_template_switch"": {
                        **OPTIMISTIC_SWITCH_CONFIG,
                        ""value_template"": ""{{ 1 == 1 }}"",
                        ""availability_template"": ""{{ is_state('availability_state.state', 'on') }}"",
                    }
                },
            }
        },
    )

    await hass.async_block_till_done()
    await hass.async_start()
    await hass.async_block_till_done()

    hass.states.async_set(""availability_state.state"", STATE_ON)
    await hass.async_block_till_done()

    assert hass.states.get(""switch.test_template_switch"").state != STATE_UNAVAILABLE

    hass.states.async_set(""availability_state.state"", STATE_OFF)
    await hass.async_block_till_done()

    assert hass.states.get(""switch.test_template_switch"").state == STATE_UNAVAILABLE


        ```
    ###Assistant : Test availability templates with values from other entities.
    "
544,"def etfs_disc_command(sort=""""):
    

    # Debug
    if cfg.DEBUG:
        logger.debug(""etfs"")

    df_etfs = wsj_model.etf_movers(sort, export=True)

    if df_etfs.empty:
        raise Exception(""No available data found"")

    df_etfs.set_index("" "", inplace=True)
    prfx = ""Top""
    if sort == ""active"":
        prfx = ""Most""
    title = f""ETF Movers ({prfx} {sort.capitalize()})""

    dindex = len(df_etfs.index)
    if dindex > 15:
        embeds: list = []
        # Output
        i, i2, end = 0, 0, 15
        df_pg, embeds_img, images_list = [], [], []
        while i < dindex:
            df_pg = df_etfs.iloc[i:end]
            df_pg.append(df_pg)
            fig = df2img.plot_dataframe(
                df_pg,
                fig_size=(1200, (40 + (40 * dindex))),
                col_width=[1, 9, 1.5, 1.5, 1.5, 1.5],
                tbl_header=cfg.PLT_TBL_HEADER,
                tbl_cells=cfg.PLT_TBL_CELLS,
                font=cfg.PLT_TBL_FONT,
                row_fill_color=cfg.PLT_TBL_ROW_COLORS,
                paper_bgcolor=""rgba(0, 0, 0, 0)"",
            )
            fig.update_traces(cells=(dict(align=[""left""])))
            imagefile = ""disc-etfs.png""
            imagefile = helpers.save_image(imagefile, fig)

            if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != ""REPLACE_ME"":
                image_link = cfg.IMAGES_URL + imagefile
                images_list.append(imagefile)
            else:
                imagefile_save = cfg.IMG_DIR / imagefile
                uploaded_image = gst_imgur.upload_image(
                    imagefile_save, title=""something""
                )
                image_link = uploaded_image.link
                os.remove(imagefile_save)

            embeds_img.append(
                f""{image_link}"",
            )
            embeds.append(
                disnake.Embed(
                    title=title,
                    colour=cfg.COLOR,
                ),
            )
            i2 += 1
            i += 15
            end += 15

        # Author/Footer
        for i in range(0, i2):
            embeds[i].set_author(
                name=cfg.AUTHOR_NAME,
                url=cfg.AUTHOR_URL,
                icon_url=cfg.AUTHOR_ICON_URL,
            )
            embeds[i].set_footer(
                text=cfg.AUTHOR_NAME,
                icon_url=cfg.AUTHOR_ICON_URL,
            )

        i = 0
        for i in range(0, i2):
            embeds[i].set_image(url=embeds_img[i])

            i += 1
        embeds[0].set_footer(text=f""Page 1 of {len(embeds)}"")
        choices = [
            disnake.SelectOption(label=""Home"", value=""0"", emoji=""🟢""),
        ]

        output = {
            ""view"": Menu,
            ""title"": title,
            ""embed"": embeds,
            ""choices"": choices,
            ""embeds_img"": embeds_img,
            ""images_list"": images_list,
        }
    else:
        fig = df2img.plot_dataframe(
            df_etfs,
            fig_size=(1200, (40 + (40 * dindex))),
            col_width=[1, 9, 1.5, 1.5, 1.5, 1.5],
            tbl_header=cfg.PLT_TBL_HEADER,
            tbl_cells=cfg.PLT_TBL_CELLS,
            font=cfg.PLT_TBL_FONT,
            row_fill_color=cfg.PLT_TBL_ROW_COLORS,
            paper_bgcolor=""rgba(0, 0, 0, 0)"",
        )
        fig.update_traces(cells=(dict(align=[""left""])))
        imagefile = helpers.save_image(""disc-etfs.png"", fig)

        output = {
            ""title"": title,
            ""imagefile"": imagefile,
        }

    return output
","Displays ETF's Top Gainers/Decliners, Most Active  [Wall Street Journal]",9,247,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def etfs_disc_command(sort=""""):
    

    # Debug
    if cfg.DEBUG:
        logger.debug(""etfs"")

    df_etfs = wsj_model.etf_movers(sort, export=True)

    if df_etfs.empty:
        raise Exception(""No available data found"")

    df_etfs.set_index("" "", inplace=True)
    prfx = ""Top""
    if sort == ""active"":
        prfx = ""Most""
    title = f""ETF Movers ({prfx} {sort.capitalize()})""

    dindex = len(df_etfs.index)
    if dindex > 15:
        embeds: list = []
        # Output
        i, i2, end = 0, 0, 15
        df_pg, embeds_img, images_list = [], [], []
        while i < dindex:
            df_pg = df_etfs.iloc[i:end]
            df_pg.append(df_pg)
            fig = df2img.plot_dataframe(
                df_pg,
                fig_size=(1200, (40 + (40 * dindex))),
                col_width=[1, 9, 1.5, 1.5, 1.5, 1.5],
                tbl_header=cfg.PLT_TBL_HEADER,
                tbl_cells=cfg.PLT_TBL_CELLS,
                font=cfg.PLT_TBL_FONT,
                row_fill_color=cfg.PLT_TBL_ROW_COLORS,
                paper_bgcolor=""rgba(0, 0, 0, 0)"",
            )
            fig.update_traces(cells=(dict(align=[""left""])))
            imagefile = ""disc-etfs.png""
            imagefile = helpers.save_image(imagefile, fig)

            if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != ""REPLACE_ME"":
                image_link = cfg.IMAGES_URL + imagefile
                images_list.append(imagefile)
            else:
                imagefile_save = cfg.IMG_DIR / imagefile
                uploaded_image = gst_imgur.upload_image(
                    imagefile_save, title=""something""
                )
                image_link = uploaded_image.link
                os.remove(imagefile_save)

            embeds_img.append(
                f""{image_link}"",
            )
            embeds.append(
                disnake.Embed(
                    title=title,
                    colour=cfg.COLOR,
                ),
            )
            i2 += 1
            i += 15
            end += 15

        # Author/Footer
        for i in range(0, i2):
            embeds[i].set_author(
                name=cfg.AUTHOR_NAME,
                url=cfg.AUTHOR_URL,
                icon_url=cfg.AUTHOR_ICON_URL,
            )
            embeds[i].set_footer(
                text=cfg.AUTHOR_NAME,
                icon_url=cfg.AUTHOR_ICON_URL,
            )

        i = 0
        for i in range(0, i2):
            embeds[i].set_image(url=embeds_img[i])

            i += 1
        embeds[0].set_footer(text=f""Page 1 of {len(embeds)}"")
        choices = [
            disnake.SelectOption(label=""Home"", value=""0"", emoji=""🟢""),
        ]

        output = {
            ""view"": Menu,
            ""title"": title,
            ""embed"": embeds,
            ""choices"": choices,
            ""embeds_img"": embeds_img,
            ""images_list"": images_list,
        }
    else:
        fig = df2img.plot_dataframe(
            df_etfs,
            fig_size=(1200, (40 + (40 * dindex))),
            col_width=[1, 9, 1.5, 1.5, 1.5, 1.5],
            tbl_header=cfg.PLT_TBL_HEADER,
            tbl_cells=cfg.PLT_TBL_CELLS,
            font=cfg.PLT_TBL_FONT,
            row_fill_color=cfg.PLT_TBL_ROW_COLORS,
            paper_bgcolor=""rgba(0, 0, 0, 0)"",
        )
        fig.update_traces(cells=(dict(align=[""left""])))
        imagefile = helpers.save_image(""disc-etfs.png"", fig)

        output = {
            ""title"": title,
            ""imagefile"": imagefile,
        }

    return output

        ```
    ###Assistant : Displays ETF's Top Gainers/Decliners, Most Active  [Wall Street Journal]
    "
545,"def get(self):
        
        logger = get_logger()
        logger.debug(f""ENTER::Partition.get::{self._identity}"")
        if len(self.call_queue):
            self.drain_call_queue()
        result = ray.get(self.oid)
        logger.debug(f""EXIT::Partition.get::{self._identity}"")
        return result
","
        Get the object wrapped by this partition out of the Plasma store.

        Returns
        -------
        pandas.DataFrame
            The object from the Plasma store.
        ",21,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get(self):
        
        logger = get_logger()
        logger.debug(f""ENTER::Partition.get::{self._identity}"")
        if len(self.call_queue):
            self.drain_call_queue()
        result = ray.get(self.oid)
        logger.debug(f""EXIT::Partition.get::{self._identity}"")
        return result

        ```
    ###Assistant : 
        Get the object wrapped by this partition out of the Plasma store.

        Returns
        -------
        pandas.DataFrame
            The object from the Plasma store.
        
    "
546,"def dag_bag_ext():
    
    clear_db_runs()

    dag_bag = DagBag(dag_folder=DEV_NULL, include_examples=False)

    dag_0 = DAG(""dag_0"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_0 = EmptyOperator(task_id=""task_a_0"", dag=dag_0)
    task_b_0 = ExternalTaskMarker(
        task_id=""task_b_0"", external_dag_id=""dag_1"", external_task_id=""task_a_1"", recursion_depth=3, dag=dag_0
    )
    task_a_0 >> task_b_0

    dag_1 = DAG(""dag_1"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_1 = ExternalTaskSensor(
        task_id=""task_a_1"", external_dag_id=dag_0.dag_id, external_task_id=task_b_0.task_id, dag=dag_1
    )
    task_b_1 = ExternalTaskMarker(
        task_id=""task_b_1"", external_dag_id=""dag_2"", external_task_id=""task_a_2"", recursion_depth=2, dag=dag_1
    )
    task_a_1 >> task_b_1

    dag_2 = DAG(""dag_2"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_2 = ExternalTaskSensor(
        task_id=""task_a_2"", external_dag_id=dag_1.dag_id, external_task_id=task_b_1.task_id, dag=dag_2
    )
    task_b_2 = ExternalTaskMarker(
        task_id=""task_b_2"", external_dag_id=""dag_3"", external_task_id=""task_a_3"", recursion_depth=1, dag=dag_2
    )
    task_a_2 >> task_b_2

    dag_3 = DAG(""dag_3"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_3 = ExternalTaskSensor(
        task_id=""task_a_3"", external_dag_id=dag_2.dag_id, external_task_id=task_b_2.task_id, dag=dag_3
    )
    task_b_3 = EmptyOperator(task_id=""task_b_3"", dag=dag_3)
    task_a_3 >> task_b_3

    for dag in [dag_0, dag_1, dag_2, dag_3]:
        dag_bag.bag_dag(dag=dag, root_dag=dag)

    yield dag_bag

    clear_db_runs()


@pytest.fixture","
    Create a DagBag with DAGs looking like this. The dotted lines represent external dependencies
    set up using ExternalTaskMarker and ExternalTaskSensor.

    dag_0:   task_a_0 >> task_b_0
                             |
                             |
    dag_1:                   ---> task_a_1 >> task_b_1
                                                  |
                                                  |
    dag_2:                                        ---> task_a_2 >> task_b_2
                                                                       |
                                                                       |
    dag_3:                                                             ---> task_a_3 >> task_b_3
    ",45,111,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dag_bag_ext():
    
    clear_db_runs()

    dag_bag = DagBag(dag_folder=DEV_NULL, include_examples=False)

    dag_0 = DAG(""dag_0"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_0 = EmptyOperator(task_id=""task_a_0"", dag=dag_0)
    task_b_0 = ExternalTaskMarker(
        task_id=""task_b_0"", external_dag_id=""dag_1"", external_task_id=""task_a_1"", recursion_depth=3, dag=dag_0
    )
    task_a_0 >> task_b_0

    dag_1 = DAG(""dag_1"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_1 = ExternalTaskSensor(
        task_id=""task_a_1"", external_dag_id=dag_0.dag_id, external_task_id=task_b_0.task_id, dag=dag_1
    )
    task_b_1 = ExternalTaskMarker(
        task_id=""task_b_1"", external_dag_id=""dag_2"", external_task_id=""task_a_2"", recursion_depth=2, dag=dag_1
    )
    task_a_1 >> task_b_1

    dag_2 = DAG(""dag_2"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_2 = ExternalTaskSensor(
        task_id=""task_a_2"", external_dag_id=dag_1.dag_id, external_task_id=task_b_1.task_id, dag=dag_2
    )
    task_b_2 = ExternalTaskMarker(
        task_id=""task_b_2"", external_dag_id=""dag_3"", external_task_id=""task_a_3"", recursion_depth=1, dag=dag_2
    )
    task_a_2 >> task_b_2

    dag_3 = DAG(""dag_3"", start_date=DEFAULT_DATE, schedule_interval=None)
    task_a_3 = ExternalTaskSensor(
        task_id=""task_a_3"", external_dag_id=dag_2.dag_id, external_task_id=task_b_2.task_id, dag=dag_3
    )
    task_b_3 = EmptyOperator(task_id=""task_b_3"", dag=dag_3)
    task_a_3 >> task_b_3

    for dag in [dag_0, dag_1, dag_2, dag_3]:
        dag_bag.bag_dag(dag=dag, root_dag=dag)

    yield dag_bag

    clear_db_runs()


@pytest.fixture
        ```
    ###Assistant : 
    Create a DagBag with DAGs looking like this. The dotted lines represent external dependencies
    set up using ExternalTaskMarker and ExternalTaskSensor.

    dag_0:   task_a_0 >> task_b_0
                             |
                             |
    dag_1:                   ---> task_a_1 >> task_b_1
                                                  |
                                                  |
    dag_2:                                        ---> task_a_2 >> task_b_2
                                                                       |
                                                                       |
    dag_3:                                                             ---> task_a_3 >> task_b_3
    
    "
547,"def test_constrained_layout3():
    

    fig, axs = plt.subplots(2, 2, layout=""constrained"")
    for nn, ax in enumerate(axs.flat):
        pcm = example_pcolor(ax, fontsize=24)
        if nn == 3:
            pad = 0.08
        else:
            pad = 0.02  # default
        fig.colorbar(pcm, ax=ax, pad=pad)


@image_comparison(['constrained_layout4.png'])",Test constrained_layout for colorbars with subplots,6,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_constrained_layout3():
    

    fig, axs = plt.subplots(2, 2, layout=""constrained"")
    for nn, ax in enumerate(axs.flat):
        pcm = example_pcolor(ax, fontsize=24)
        if nn == 3:
            pad = 0.08
        else:
            pad = 0.02  # default
        fig.colorbar(pcm, ax=ax, pad=pad)


@image_comparison(['constrained_layout4.png'])
        ```
    ###Assistant : Test constrained_layout for colorbars with subplots
    "
548,"def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
        
        return self.grouper.indices
","
        Dict {group name -> group indices}.
        ",6,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
        
        return self.grouper.indices

        ```
    ###Assistant : 
        Dict {group name -> group indices}.
        
    "
549,"def get_hash(self, data, hasher=None):
        
        if hasher is None:
            hasher = self.hasher
        if hasher is None:
            hasher = hashlib.md5
            prefix = ''
        else:
            hasher = getattr(hashlib, hasher)
            prefix = '%s=' % self.hasher
        digest = hasher(data).digest()
        digest = base64.urlsafe_b64encode(digest).rstrip(b'=').decode('ascii')
        return '%s%s' % (prefix, digest)

","
        Get the hash of some data, using a particular hash algorithm, if
        specified.

        :param data: The data to be hashed.
        :type data: bytes
        :param hasher: The name of a hash implementation, supported by hashlib,
                       or ``None``. Examples of valid values are ``'sha1'``,
                       ``'sha224'``, ``'sha384'``, '``sha256'``, ``'md5'`` and
                       ``'sha512'``. If no hasher is specified, the ``hasher``
                       attribute of the :class:`InstalledDistribution` instance
                       is used. If the hasher is determined to be ``None``, MD5
                       is used as the hashing algorithm.
        :returns: The hash of the data. If a hasher was explicitly specified,
                  the returned hash will be prefixed with the specified hasher
                  followed by '='.
        :rtype: str
        ",104,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_hash(self, data, hasher=None):
        
        if hasher is None:
            hasher = self.hasher
        if hasher is None:
            hasher = hashlib.md5
            prefix = ''
        else:
            hasher = getattr(hashlib, hasher)
            prefix = '%s=' % self.hasher
        digest = hasher(data).digest()
        digest = base64.urlsafe_b64encode(digest).rstrip(b'=').decode('ascii')
        return '%s%s' % (prefix, digest)


        ```
    ###Assistant : 
        Get the hash of some data, using a particular hash algorithm, if
        specified.

        :param data: The data to be hashed.
        :type data: bytes
        :param hasher: The name of a hash implementation, supported by hashlib,
                       or ``None``. Examples of valid values are ``'sha1'``,
                       ``'sha224'``, ``'sha384'``, '``sha256'``, ``'md5'`` and
                       ``'sha512'``. If no hasher is specified, the ``hasher``
                       attribute of the :class:`InstalledDistribution` instance
                       is used. If the hasher is determined to be ``None``, MD5
                       is used as the hashing algorithm.
        :returns: The hash of the data. If a hasher was explicitly specified,
                  the returned hash will be prefixed with the specified hasher
                  followed by '='.
        :rtype: str
        
    "
550,"def test_async_call_same_actor_multiple_times(self):
        
        actors = [Actor.remote(i, maybe_crash=False) for i in range(4)]
        manager = FaultTolerantActorManager(actors=actors)

        # 2 asynchronous call to actor 0.
        num_of_calls = manager.foreach_actor_async(
            lambda w: w.call(),
            healthy_only=False,
            remote_actor_indices=[0, 0],
        )
        self.assertEqual(num_of_calls, 2)

        # Now, let's actually fetch the results.
        results = manager.fetch_ready_async_reqs(timeout_seconds=None)
        # Returns 1 and 2, representing the first and second calls to actor 0.
        self.assertEqual([r.get() for r in results.ignore_errors()], [1, 2])
",Test multiple asynchronous remote calls to the same actor.,9,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_async_call_same_actor_multiple_times(self):
        
        actors = [Actor.remote(i, maybe_crash=False) for i in range(4)]
        manager = FaultTolerantActorManager(actors=actors)

        # 2 asynchronous call to actor 0.
        num_of_calls = manager.foreach_actor_async(
            lambda w: w.call(),
            healthy_only=False,
            remote_actor_indices=[0, 0],
        )
        self.assertEqual(num_of_calls, 2)

        # Now, let's actually fetch the results.
        results = manager.fetch_ready_async_reqs(timeout_seconds=None)
        # Returns 1 and 2, representing the first and second calls to actor 0.
        self.assertEqual([r.get() for r in results.ignore_errors()], [1, 2])

        ```
    ###Assistant : Test multiple asynchronous remote calls to the same actor.
    "
551,"def _load(self):
        
        data = {}
        if not self._is_extract:
            if not self.have_alignments_file:
                return data
            data = super()._load()
            return data

        skip_existing = hasattr(self._args, 'skip_existing') and self._args.skip_existing
        skip_faces = hasattr(self._args, 'skip_faces') and self._args.skip_faces

        if not skip_existing and not skip_faces:
            logger.debug(""No skipping selected. Returning empty dictionary"")
            return data

        if not self.have_alignments_file and (skip_existing or skip_faces):
            logger.warning(""Skip Existing/Skip Faces selected, but no alignments file found!"")
            return data

        data = super()._load()

        if skip_faces:
            # Remove items from alignments that have no faces so they will
            # be re-detected
            del_keys = [key for key, val in data.items() if not val[""faces""]]
            logger.debug(""Frames with no faces selected for redetection: %s"", len(del_keys))
            for key in del_keys:
                if key in data:
                    logger.trace(""Selected for redetection: '%s'"", key)
                    del data[key]
        return data

"," Override the parent :func:`~lib.align.Alignments._load` to handle skip existing
        frames and faces on extract.

        If skip existing has been selected, existing alignments are loaded and returned to the
        calling script.

        Returns
        -------
        dict
            Any alignments that have already been extracted if skip existing has been selected
            otherwise an empty dictionary
        ",49,119,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _load(self):
        
        data = {}
        if not self._is_extract:
            if not self.have_alignments_file:
                return data
            data = super()._load()
            return data

        skip_existing = hasattr(self._args, 'skip_existing') and self._args.skip_existing
        skip_faces = hasattr(self._args, 'skip_faces') and self._args.skip_faces

        if not skip_existing and not skip_faces:
            logger.debug(""No skipping selected. Returning empty dictionary"")
            return data

        if not self.have_alignments_file and (skip_existing or skip_faces):
            logger.warning(""Skip Existing/Skip Faces selected, but no alignments file found!"")
            return data

        data = super()._load()

        if skip_faces:
            # Remove items from alignments that have no faces so they will
            # be re-detected
            del_keys = [key for key, val in data.items() if not val[""faces""]]
            logger.debug(""Frames with no faces selected for redetection: %s"", len(del_keys))
            for key in del_keys:
                if key in data:
                    logger.trace(""Selected for redetection: '%s'"", key)
                    del data[key]
        return data


        ```
    ###Assistant :  Override the parent :func:`~lib.align.Alignments._load` to handle skip existing
        frames and faces on extract.

        If skip existing has been selected, existing alignments are loaded and returned to the
        calling script.

        Returns
        -------
        dict
            Any alignments that have already been extracted if skip existing has been selected
            otherwise an empty dictionary
        
    "
552,"def get_gtech() -> pd.DataFrame:
    
    return get_df(
        ""https://finance.yahoo.com/screener/predefined/growth_technology_stocks""
    )


@log_start_end(log=logger)","Get technology stocks with revenue and earnings growth in excess of 25%. [Source: Yahoo Finance]

    Returns
    -------
    pd.DataFrame
        Growth technology stocks
    ",21,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_gtech() -> pd.DataFrame:
    
    return get_df(
        ""https://finance.yahoo.com/screener/predefined/growth_technology_stocks""
    )


@log_start_end(log=logger)
        ```
    ###Assistant : Get technology stocks with revenue and earnings growth in excess of 25%. [Source: Yahoo Finance]

    Returns
    -------
    pd.DataFrame
        Growth technology stocks
    
    "
553,"def test_bert2gpt2_summarization(self):
        model = EncoderDecoderModel.from_pretrained(""patrickvonplaten/bert2gpt2-cnn_dailymail-fp16"")

        model.to(torch_device)
        tokenizer_in = AutoTokenizer.from_pretrained(""bert-base-cased"")
        tokenizer_out = AutoTokenizer.from_pretrained(""../gpt2"")

        ARTICLE_STUDENTS = 

        EXPECTED_SUMMARY_STUDENTS = 

        input_dict = tokenizer_in(ARTICLE_STUDENTS, return_tensors=""pt"")
        output_ids = model.generate(input_dict[""input_ids""].to(torch_device))
        summary = tokenizer_out.batch_decode(output_ids, skip_special_tokens=True)

        self.assertEqual(summary, [EXPECTED_SUMMARY_STUDENTS])


@require_torch","(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David Boren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 1856, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which ""369 went to war for the Confederate States and seven for the Union Army,"" the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 ""colonies"" seeking full membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on the fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for ""health and safety incidents"" since 2010. At least 30 of the incidents involved hazing, and dozens more involved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members allegedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a fraternity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. ""The media has labeled us as the 'nation's deadliest fraternity,' "" Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. ""As a result, we are paying Lloyd's of London the highest insurance rates in the Greek-letter world,"" Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing incidents.SAS Alpha Epsilon suspended the students, but university president says it's permanent.\nThe fraternity has had to deal with a string of student deaths since 2010.\nSAS has more than 200,000 members, many of whom are students.\nA student died while being forced into excessive alcohol consumption.",403,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_bert2gpt2_summarization(self):
        model = EncoderDecoderModel.from_pretrained(""patrickvonplaten/bert2gpt2-cnn_dailymail-fp16"")

        model.to(torch_device)
        tokenizer_in = AutoTokenizer.from_pretrained(""bert-base-cased"")
        tokenizer_out = AutoTokenizer.from_pretrained(""../gpt2"")

        ARTICLE_STUDENTS = 

        EXPECTED_SUMMARY_STUDENTS = 

        input_dict = tokenizer_in(ARTICLE_STUDENTS, return_tensors=""pt"")
        output_ids = model.generate(input_dict[""input_ids""].to(torch_device))
        summary = tokenizer_out.batch_decode(output_ids, skip_special_tokens=True)

        self.assertEqual(summary, [EXPECTED_SUMMARY_STUDENTS])


@require_torch
        ```
    ###Assistant : (CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David Boren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 1856, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which ""369 went to war for the Confederate States and seven for the Union Army,"" the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 ""colonies"" seeking full membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on the fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for ""health and safety incidents"" since 2010. At least 30 of the incidents involved hazing, and dozens more involved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members allegedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a fraternity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. ""The media has labeled us as the 'nation's deadliest fraternity,' "" Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. ""As a result, we are paying Lloyd's of London the highest insurance rates in the Greek-letter world,"" Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing incidents.SAS Alpha Epsilon suspended the students, but university president says it's permanent.\nThe fraternity has had to deal with a string of student deaths since 2010.\nSAS has more than 200,000 members, many of whom are students.\nA student died while being forced into excessive alcohol consumption.
    "
554,"def _get_builtin_permissions(opts):  # noqa: D205, D212
    
    perms = []
    for action in opts.default_permissions:
        perms.append(
            (
                get_permission_codename(action, opts),
                ""Can %s %s"" % (action, opts.verbose_name_raw),
            )
        )
    return perms

","
    Return (codename, name) for all autogenerated permissions.
    By default, this is ('add', 'change', 'delete', 'view')
    ",15,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_builtin_permissions(opts):  # noqa: D205, D212
    
    perms = []
    for action in opts.default_permissions:
        perms.append(
            (
                get_permission_codename(action, opts),
                ""Can %s %s"" % (action, opts.verbose_name_raw),
            )
        )
    return perms


        ```
    ###Assistant : 
    Return (codename, name) for all autogenerated permissions.
    By default, this is ('add', 'change', 'delete', 'view')
    
    "
555,"def accumulate(self, model):
        
        self._do_sync()
        if self.sync_gradients:
            context = contextlib.nullcontext
        else:
            context = self.no_sync

        with context(model):
            yield
","
        A context manager that will lightly wrap around and perform gradient accumulation automatically

        Args:
            model (`torch.nn.Module`):
                PyTorch Module that was prepared with `Accelerator.prepare`
        ",23,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def accumulate(self, model):
        
        self._do_sync()
        if self.sync_gradients:
            context = contextlib.nullcontext
        else:
            context = self.no_sync

        with context(model):
            yield

        ```
    ###Assistant : 
        A context manager that will lightly wrap around and perform gradient accumulation automatically

        Args:
            model (`torch.nn.Module`):
                PyTorch Module that was prepared with `Accelerator.prepare`
        
    "
556,"def get_attribute(value):
    
    attribute = Attribute()
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        attribute.append(token)
    if value and value[0] in ATTRIBUTE_ENDS:
        raise errors.HeaderParseError(
            ""expected token but found '{}'"".format(value))
    token, value = get_attrtext(value)
    attribute.append(token)
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        attribute.append(token)
    return attribute, value
"," [CFWS] 1*attrtext [CFWS]

    This version of the BNF makes the CFWS explicit, and as usual we use a
    value terminal for the actual run of characters.  The RFC equivalent of
    attrtext is the token characters, with the subtraction of '*', ""'"", and '%'.
    We include tab in the excluded set just as we do for token.

    ",56,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_attribute(value):
    
    attribute = Attribute()
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        attribute.append(token)
    if value and value[0] in ATTRIBUTE_ENDS:
        raise errors.HeaderParseError(
            ""expected token but found '{}'"".format(value))
    token, value = get_attrtext(value)
    attribute.append(token)
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        attribute.append(token)
    return attribute, value

        ```
    ###Assistant :  [CFWS] 1*attrtext [CFWS]

    This version of the BNF makes the CFWS explicit, and as usual we use a
    value terminal for the actual run of characters.  The RFC equivalent of
    attrtext is the token characters, with the subtraction of '*', ""'"", and '%'.
    We include tab in the excluded set just as we do for token.

    
    "
557,"def eval_to_file(modin_obj, pandas_obj, fn, extension, **fn_kwargs):
    
    with ensure_clean_dir() as dirname:
        unique_filename_modin = get_unique_filename(
            extension=extension, data_dir=dirname
        )
        unique_filename_pandas = get_unique_filename(
            extension=extension, data_dir=dirname
        )

        # parameter `max_retries=0` is set for `to_csv` function on Ray engine,
        # in order to increase the stability of tests, we repeat the call of
        # the entire function manually
        last_exception = None
        for _ in range(3):
            try:
                getattr(modin_obj, fn)(unique_filename_modin, **fn_kwargs)
            except EXCEPTIONS as exc:
                last_exception = exc
                continue
            break
        else:
            raise last_exception

        getattr(pandas_obj, fn)(unique_filename_pandas, **fn_kwargs)

        assert assert_files_eq(unique_filename_modin, unique_filename_pandas)


@pytest.fixture","Helper function to test `to_<extension>` methods.

    Args:
        modin_obj: Modin DataFrame or Series to test `to_<extension>` method.
        pandas_obj: Pandas DataFrame or Series to test `to_<extension>` method.
        fn: name of the method, that should be tested.
        extension: Extension of the test file.
    ",40,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def eval_to_file(modin_obj, pandas_obj, fn, extension, **fn_kwargs):
    
    with ensure_clean_dir() as dirname:
        unique_filename_modin = get_unique_filename(
            extension=extension, data_dir=dirname
        )
        unique_filename_pandas = get_unique_filename(
            extension=extension, data_dir=dirname
        )

        # parameter `max_retries=0` is set for `to_csv` function on Ray engine,
        # in order to increase the stability of tests, we repeat the call of
        # the entire function manually
        last_exception = None
        for _ in range(3):
            try:
                getattr(modin_obj, fn)(unique_filename_modin, **fn_kwargs)
            except EXCEPTIONS as exc:
                last_exception = exc
                continue
            break
        else:
            raise last_exception

        getattr(pandas_obj, fn)(unique_filename_pandas, **fn_kwargs)

        assert assert_files_eq(unique_filename_modin, unique_filename_pandas)


@pytest.fixture
        ```
    ###Assistant : Helper function to test `to_<extension>` methods.

    Args:
        modin_obj: Modin DataFrame or Series to test `to_<extension>` method.
        pandas_obj: Pandas DataFrame or Series to test `to_<extension>` method.
        fn: name of the method, that should be tested.
        extension: Extension of the test file.
    
    "
558,"def _is_matching_generic_foreign_key(self, field):
        
        return (
            isinstance(field, GenericForeignKey)
            and field.ct_field == self.content_type_field_name
            and field.fk_field == self.object_id_field_name
        )
","
        Return True if field is a GenericForeignKey whose content type and
        object id fields correspond to the equivalent attributes on this
        GenericRelation.
        ",22,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _is_matching_generic_foreign_key(self, field):
        
        return (
            isinstance(field, GenericForeignKey)
            and field.ct_field == self.content_type_field_name
            and field.fk_field == self.object_id_field_name
        )

        ```
    ###Assistant : 
        Return True if field is a GenericForeignKey whose content type and
        object id fields correspond to the equivalent attributes on this
        GenericRelation.
        
    "
559,"def hashes_to_frame(self):
        
        if not self._hashes_to_frame:
            logger.debug(""Generating hashes to frame"")
            for frame_name, val in self._data.items():
                for idx, face in enumerate(val[""faces""]):
                    self._hashes_to_frame.setdefault(face[""hash""], {})[frame_name] = idx
        return self._hashes_to_frame
"," dict: The SHA1 hash of the face mapped to the frame(s) and face index within the frame
        that the hash corresponds to. The structure of the dictionary is:

        {**SHA1_hash** (`str`): {**filename** (`str`): **face_index** (`int`)}}.

        Notes
        -----
        This method is depractated and exists purely for updating legacy hash based alignments
        to new png header storage in :class:`lib.align.update_legacy_png_header`.

        The first time this property is referenced, the dictionary will be created and cached.
        Subsequent references will be made to this cached dictionary.
        ",79,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def hashes_to_frame(self):
        
        if not self._hashes_to_frame:
            logger.debug(""Generating hashes to frame"")
            for frame_name, val in self._data.items():
                for idx, face in enumerate(val[""faces""]):
                    self._hashes_to_frame.setdefault(face[""hash""], {})[frame_name] = idx
        return self._hashes_to_frame

        ```
    ###Assistant :  dict: The SHA1 hash of the face mapped to the frame(s) and face index within the frame
        that the hash corresponds to. The structure of the dictionary is:

        {**SHA1_hash** (`str`): {**filename** (`str`): **face_index** (`int`)}}.

        Notes
        -----
        This method is depractated and exists purely for updating legacy hash based alignments
        to new png header storage in :class:`lib.align.update_legacy_png_header`.

        The first time this property is referenced, the dictionary will be created and cached.
        Subsequent references will be made to this cached dictionary.
        
    "
560,"def _get_textdoc(self, index):
        
        assert self._opt is not None
        # FIXME we probably should do eliding here. See
        # qcommonstyle.cpp:viewItemDrawText
        # https://github.com/qutebrowser/qutebrowser/issues/118
        text_option = QTextOption()
        if self._opt.features & QStyleOptionViewItem.WrapText:
            text_option.setWrapMode(QTextOption.WordWrap)
        else:
            text_option.setWrapMode(QTextOption.ManualWrap)
        text_option.setTextDirection(self._opt.direction)
        text_option.setAlignment(QStyle.visualAlignment(
            self._opt.direction, self._opt.displayAlignment))

        if self._doc is not None:
            self._doc.deleteLater()
        self._doc = QTextDocument(self)
        self._doc.setDefaultFont(self._opt.font)
        self._doc.setDefaultTextOption(text_option)
        self._doc.setDocumentMargin(2)

        if index.parent().isValid():
            view = self.parent()
            assert isinstance(view, completionwidget.CompletionView), view
            pattern = view.pattern
            columns_to_filter = index.model().columns_to_filter(index)
            if index.column() in columns_to_filter and pattern:
                if self._opt.state & QStyle.State_Selected:
                    color = config.val.colors.completion.item.selected.match.fg
                else:
                    color = config.val.colors.completion.match.fg
                _Highlighter(self._doc, pattern, color)
            self._doc.setPlainText(self._opt.text)
        else:
            self._doc.setHtml(
                '<span style=""font: {};"">{}</span>'.format(
                    html.escape(config.val.fonts.completion.category),
                    html.escape(self._opt.text)))
","Create the QTextDocument of an item.

        Args:
            index: The QModelIndex of the item to draw.
        ",15,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_textdoc(self, index):
        
        assert self._opt is not None
        # FIXME we probably should do eliding here. See
        # qcommonstyle.cpp:viewItemDrawText
        # https://github.com/qutebrowser/qutebrowser/issues/118
        text_option = QTextOption()
        if self._opt.features & QStyleOptionViewItem.WrapText:
            text_option.setWrapMode(QTextOption.WordWrap)
        else:
            text_option.setWrapMode(QTextOption.ManualWrap)
        text_option.setTextDirection(self._opt.direction)
        text_option.setAlignment(QStyle.visualAlignment(
            self._opt.direction, self._opt.displayAlignment))

        if self._doc is not None:
            self._doc.deleteLater()
        self._doc = QTextDocument(self)
        self._doc.setDefaultFont(self._opt.font)
        self._doc.setDefaultTextOption(text_option)
        self._doc.setDocumentMargin(2)

        if index.parent().isValid():
            view = self.parent()
            assert isinstance(view, completionwidget.CompletionView), view
            pattern = view.pattern
            columns_to_filter = index.model().columns_to_filter(index)
            if index.column() in columns_to_filter and pattern:
                if self._opt.state & QStyle.State_Selected:
                    color = config.val.colors.completion.item.selected.match.fg
                else:
                    color = config.val.colors.completion.match.fg
                _Highlighter(self._doc, pattern, color)
            self._doc.setPlainText(self._opt.text)
        else:
            self._doc.setHtml(
                '<span style=""font: {};"">{}</span>'.format(
                    html.escape(config.val.fonts.completion.category),
                    html.escape(self._opt.text)))

        ```
    ###Assistant : Create the QTextDocument of an item.

        Args:
            index: The QModelIndex of the item to draw.
        
    "
561,"def feed(self, *args):
        
        assert len(args) != 0
        self.terminals = []
        for fed_layer in args:
            if isinstance(fed_layer, str):
                try:
                    fed_layer = self.layers[fed_layer]
                except KeyError:
                    raise KeyError('Unknown layer name fed: %s' % fed_layer)
            self.terminals.append(fed_layer)
        return self
","Set the input(s) for the next operation by replacing the terminal nodes.
        The arguments can be either layer names or the actual layers.
        ",23,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def feed(self, *args):
        
        assert len(args) != 0
        self.terminals = []
        for fed_layer in args:
            if isinstance(fed_layer, str):
                try:
                    fed_layer = self.layers[fed_layer]
                except KeyError:
                    raise KeyError('Unknown layer name fed: %s' % fed_layer)
            self.terminals.append(fed_layer)
        return self

        ```
    ###Assistant : Set the input(s) for the next operation by replacing the terminal nodes.
        The arguments can be either layer names or the actual layers.
        
    "
562,"def unpack_iterator_input(iterator):
    
    try:
        next_element = iterator.get_next()
    except tf.errors.OutOfRangeError:
        raise RuntimeError(
            ""Your dataset iterator ran out of data; ""
            ""Make sure that your dataset can generate ""
            ""required number of samples.""
        )

    if isinstance(next_element, (list, tuple)):
        if len(next_element) not in [2, 3]:
            raise ValueError(
                ""Please provide model inputs as a list or tuple of 2 or 3 ""
                ""elements: (input, target) or (input, target, sample_weights) ""
                ""Received %s"" % next_element
            )
        if len(next_element) == 2:
            x, y = next_element
            weights = None
        else:
            x, y, weights = next_element
    else:
        x = next_element
        y = None
        weights = None
    return x, y, weights

","Convert a dataset iterator to a tuple of tensors `x, y, sample_weights`.

    Args:
      iterator: Instance of a dataset iterator.

    Returns:
      Tuple of tensors `x, y, weights`. `y` and `weights` entry may be None.
    ",33,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unpack_iterator_input(iterator):
    
    try:
        next_element = iterator.get_next()
    except tf.errors.OutOfRangeError:
        raise RuntimeError(
            ""Your dataset iterator ran out of data; ""
            ""Make sure that your dataset can generate ""
            ""required number of samples.""
        )

    if isinstance(next_element, (list, tuple)):
        if len(next_element) not in [2, 3]:
            raise ValueError(
                ""Please provide model inputs as a list or tuple of 2 or 3 ""
                ""elements: (input, target) or (input, target, sample_weights) ""
                ""Received %s"" % next_element
            )
        if len(next_element) == 2:
            x, y = next_element
            weights = None
        else:
            x, y, weights = next_element
    else:
        x = next_element
        y = None
        weights = None
    return x, y, weights


        ```
    ###Assistant : Convert a dataset iterator to a tuple of tensors `x, y, sample_weights`.

    Args:
      iterator: Instance of a dataset iterator.

    Returns:
      Tuple of tensors `x, y, weights`. `y` and `weights` entry may be None.
    
    "
563,"def mock_2x2x4_devices(one_device_per_chip):
  
  return mock_devices(2, 2, 4, 'TPU v4', one_device_per_chip)

",Hard-coded reproduction of jax.devices() output on 2x2x4.,7,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mock_2x2x4_devices(one_device_per_chip):
  
  return mock_devices(2, 2, 4, 'TPU v4', one_device_per_chip)


        ```
    ###Assistant : Hard-coded reproduction of jax.devices() output on 2x2x4.
    "
564,"def export_triton(model_path, output_path=""model_repository"", model_name=""ludwig_model"", model_version=1, **kwargs):
    
    logger.info(f""Model path: {model_path}"")
    logger.info(f""Output path: {output_path}"")
    logger.info(f""Model name: {model_name}"")
    logger.info(f""Model version: {model_version}"")
    logger.info(""\n"")

    model = LudwigModel.load(model_path)
    os.makedirs(output_path, exist_ok=True)
    utils_export_triton(model, output_path, model_name, model_version)

    logger.info(f""Saved to: {output_path}"")

","Exports a model in torchscript format with config for Triton serving.

    # Inputs

    :param model_path: (str) filepath to pre-trained model.
    :param output_path: (str, default: `'model_repository'`)  directory to store the
        triton models.
    :param model_name: (str, default: `'ludwig_model'`) save triton under this name.
    :param model_name: (int, default: `1`) save neuropod under this verison.

    # Return

    :returns: (`None`)
    ",55,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def export_triton(model_path, output_path=""model_repository"", model_name=""ludwig_model"", model_version=1, **kwargs):
    
    logger.info(f""Model path: {model_path}"")
    logger.info(f""Output path: {output_path}"")
    logger.info(f""Model name: {model_name}"")
    logger.info(f""Model version: {model_version}"")
    logger.info(""\n"")

    model = LudwigModel.load(model_path)
    os.makedirs(output_path, exist_ok=True)
    utils_export_triton(model, output_path, model_name, model_version)

    logger.info(f""Saved to: {output_path}"")


        ```
    ###Assistant : Exports a model in torchscript format with config for Triton serving.

    # Inputs

    :param model_path: (str) filepath to pre-trained model.
    :param output_path: (str, default: `'model_repository'`)  directory to store the
        triton models.
    :param model_name: (str, default: `'ludwig_model'`) save triton under this name.
    :param model_name: (int, default: `1`) save neuropod under this verison.

    # Return

    :returns: (`None`)
    
    "
565,"def tags(self) -> Sequence[Tuple[str, str]]:
        
        tags_key_column = self._get_column_name(Columns.TAGS_KEY)
        tags_value_column = self._get_column_name(Columns.TAGS_VALUE)

        if tags_key_column in self._snuba_data and tags_value_column in self._snuba_data:
            keys = self._snuba_data[tags_key_column]
            values = self._snuba_data[tags_value_column]
            if keys and values and len(keys) == len(values):
                return sorted(zip(keys, values))
            else:
                return []
        # Nodestore implementation
        try:
            rv = sorted(
                (t, v)
                for t, v in get_path(self.data, ""tags"", filter=True) or ()
                if t is not None and v is not None
            )
            return rv
        except ValueError:
            # at one point Sentry allowed invalid tag sets such as (foo, bar)
            # vs ((tag, foo), (tag, bar))
            return []
","
        Tags property uses tags from snuba if loaded otherwise falls back to
        nodestore.
        ",13,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tags(self) -> Sequence[Tuple[str, str]]:
        
        tags_key_column = self._get_column_name(Columns.TAGS_KEY)
        tags_value_column = self._get_column_name(Columns.TAGS_VALUE)

        if tags_key_column in self._snuba_data and tags_value_column in self._snuba_data:
            keys = self._snuba_data[tags_key_column]
            values = self._snuba_data[tags_value_column]
            if keys and values and len(keys) == len(values):
                return sorted(zip(keys, values))
            else:
                return []
        # Nodestore implementation
        try:
            rv = sorted(
                (t, v)
                for t, v in get_path(self.data, ""tags"", filter=True) or ()
                if t is not None and v is not None
            )
            return rv
        except ValueError:
            # at one point Sentry allowed invalid tag sets such as (foo, bar)
            # vs ((tag, foo), (tag, bar))
            return []

        ```
    ###Assistant : 
        Tags property uses tags from snuba if loaded otherwise falls back to
        nodestore.
        
    "
566,"def test_cable_validates_compatible_types(self):
        
        # An interface cannot be connected to a power port
        cable = Cable(a_terminations=[self.interface1, self.interface2], b_terminations=[self.interface3])
        with self.assertRaises(ValidationError):
            cable.clean()

    # TODO: Remove this?
    # def test_cable_front_port_cannot_connect_to_corresponding_rear_port(self):
    #     
    #     cable = Cable(a_terminations=[self.front_port1], b_terminations=[self.rear_port1])
    #     with self.assertRaises(ValidationError):
    #         cable.clean()
","
        The clean method should have a check to ensure only compatible port types can be connected by a cable
        
    #     A cable cannot connect a front port to its corresponding rear port
    #     ",33,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_cable_validates_compatible_types(self):
        
        # An interface cannot be connected to a power port
        cable = Cable(a_terminations=[self.interface1, self.interface2], b_terminations=[self.interface3])
        with self.assertRaises(ValidationError):
            cable.clean()

    # TODO: Remove this?
    # def test_cable_front_port_cannot_connect_to_corresponding_rear_port(self):
    #     
    #     cable = Cable(a_terminations=[self.front_port1], b_terminations=[self.rear_port1])
    #     with self.assertRaises(ValidationError):
    #         cable.clean()

        ```
    ###Assistant : 
        The clean method should have a check to ensure only compatible port types can be connected by a cable
        
    #     A cable cannot connect a front port to its corresponding rear port
    #     
    "
567,"def save_model_to_hdf5(model, filepath, overwrite=True, include_optimizer=True):
    

    if h5py is None:
        raise ImportError(
            ""`save_model()` using h5 format requires h5py. Could not ""
            ""import h5py.""
        )

    # TODO(psv) Add warning when we save models that contain non-serializable
    # entities like metrics added using `add_metric` and losses added using
    # `add_loss.`
    if len(model.weights) != len(model._undeduplicated_weights):
        logging.warning(
            ""Found duplicated `Variable`s in Model's `weights`. ""
            ""This is usually caused by `Variable`s being shared by ""
            ""Layers in the Model. These `Variable`s will be treated ""
            ""as separate `Variable`s when the Model is restored. To ""
            'avoid this, please save with `save_format=""tf""`.'
        )

    if not isinstance(filepath, h5py.File):
        # If file exists and should not be overwritten.
        if not overwrite and os.path.isfile(filepath):
            proceed = ask_to_proceed_with_overwrite(filepath)
            if not proceed:
                return

        # Try creating dir if not exist
        dirpath = os.path.dirname(filepath)
        if not os.path.exists(dirpath):
            tf.io.gfile.makedirs(dirpath)

        f = h5py.File(filepath, mode=""w"")
        opened_new_file = True
    else:
        f = filepath
        opened_new_file = False

    try:
        model_metadata = saving_utils.model_metadata(model, include_optimizer)
        for k, v in model_metadata.items():
            if isinstance(v, (dict, list, tuple)):
                f.attrs[k] = json.dumps(
                    v, default=json_utils.get_json_type
                ).encode(""utf8"")
            else:
                f.attrs[k] = v

        model_weights_group = f.create_group(""model_weights"")
        save_weights_to_hdf5_group(model_weights_group, model)

        # TODO(b/128683857): Add integration tests between tf.keras and external
        # Keras, to avoid breaking TF.js users.
        if isinstance(model.optimizer, optimizer_experimental.Optimizer):
            logging.warning(
                ""HDF5 format does not save weights of""
                "" `optimizer_experimental.Optimizer`, your optimizer will""
                "" be recompiled at loading time.""
            )
        elif (
            include_optimizer
            and model.optimizer
            and not isinstance(model.optimizer, optimizer_v1.TFOptimizer)
        ):
            save_optimizer_weights_to_hdf5_group(f, model.optimizer)

        f.flush()
    finally:
        if opened_new_file:
            f.close()

","Saves a model to a HDF5 file.

    The saved model contains:
        - the model's configuration (topology)
        - the model's weights
        - the model's optimizer's state (if any)

    Thus the saved model can be reinstantiated in
    the exact same state, without any of the code
    used for model definition or training.

    Args:
        model: Keras model instance to be saved.
        filepath: One of the following:
            - String, path where to save the model
            - `h5py.File` object where to save the model
        overwrite: Whether we should overwrite any existing
            model at the target location, or instead
            ask the user with a manual prompt.
        include_optimizer: If True, save optimizer's state together.

    Raises:
        ImportError: if h5py is not available.
    ",114,235,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_model_to_hdf5(model, filepath, overwrite=True, include_optimizer=True):
    

    if h5py is None:
        raise ImportError(
            ""`save_model()` using h5 format requires h5py. Could not ""
            ""import h5py.""
        )

    # TODO(psv) Add warning when we save models that contain non-serializable
    # entities like metrics added using `add_metric` and losses added using
    # `add_loss.`
    if len(model.weights) != len(model._undeduplicated_weights):
        logging.warning(
            ""Found duplicated `Variable`s in Model's `weights`. ""
            ""This is usually caused by `Variable`s being shared by ""
            ""Layers in the Model. These `Variable`s will be treated ""
            ""as separate `Variable`s when the Model is restored. To ""
            'avoid this, please save with `save_format=""tf""`.'
        )

    if not isinstance(filepath, h5py.File):
        # If file exists and should not be overwritten.
        if not overwrite and os.path.isfile(filepath):
            proceed = ask_to_proceed_with_overwrite(filepath)
            if not proceed:
                return

        # Try creating dir if not exist
        dirpath = os.path.dirname(filepath)
        if not os.path.exists(dirpath):
            tf.io.gfile.makedirs(dirpath)

        f = h5py.File(filepath, mode=""w"")
        opened_new_file = True
    else:
        f = filepath
        opened_new_file = False

    try:
        model_metadata = saving_utils.model_metadata(model, include_optimizer)
        for k, v in model_metadata.items():
            if isinstance(v, (dict, list, tuple)):
                f.attrs[k] = json.dumps(
                    v, default=json_utils.get_json_type
                ).encode(""utf8"")
            else:
                f.attrs[k] = v

        model_weights_group = f.create_group(""model_weights"")
        save_weights_to_hdf5_group(model_weights_group, model)

        # TODO(b/128683857): Add integration tests between tf.keras and external
        # Keras, to avoid breaking TF.js users.
        if isinstance(model.optimizer, optimizer_experimental.Optimizer):
            logging.warning(
                ""HDF5 format does not save weights of""
                "" `optimizer_experimental.Optimizer`, your optimizer will""
                "" be recompiled at loading time.""
            )
        elif (
            include_optimizer
            and model.optimizer
            and not isinstance(model.optimizer, optimizer_v1.TFOptimizer)
        ):
            save_optimizer_weights_to_hdf5_group(f, model.optimizer)

        f.flush()
    finally:
        if opened_new_file:
            f.close()


        ```
    ###Assistant : Saves a model to a HDF5 file.

    The saved model contains:
        - the model's configuration (topology)
        - the model's weights
        - the model's optimizer's state (if any)

    Thus the saved model can be reinstantiated in
    the exact same state, without any of the code
    used for model definition or training.

    Args:
        model: Keras model instance to be saved.
        filepath: One of the following:
            - String, path where to save the model
            - `h5py.File` object where to save the model
        overwrite: Whether we should overwrite any existing
            model at the target location, or instead
            ask the user with a manual prompt.
        include_optimizer: If True, save optimizer's state together.

    Raises:
        ImportError: if h5py is not available.
    
    "
568,"def set(self, **kwargs) -> None:  # nosec
        
        attributes = {}
        user_id = kwargs[""user_id""]
        user = self.first(id_int=int(user_id))
        if not user:
            raise UserNotFoundError

        for k, v in kwargs.items():
            if k in user.__attr_searchable__:
                attributes[k] = v

        if kwargs.get(""email"", None):
            user.email = kwargs[""email""]
        elif kwargs.get(""role"", None):
            user.role = kwargs[""role""]
        elif kwargs.get(""name"", None):
            user.name = kwargs[""name""]
        elif kwargs.get(""budget"", None):
            user.budget = kwargs[""budget""]
        elif kwargs.get(""website"", None):
            user.website = kwargs[""website""]
        elif kwargs.get(""institution"", None):
            user.institution = kwargs[""institution""]
        else:
            raise Exception

        attributes[""__blob__""] = user.to_bytes()

        self.update_one({""id_int"": int(user_id)}, {""$set"": attributes})
","Updates the information for the given user id.

        Args:
            user_id (str): unique id of the user in the database.
            email (str, optional): email of the user. Defaults to """".
            password (str, optional): password of the user. Defaults to """".
            role (int, optional): role of the user. Defaults to 0.
            name (str, optional): name of the user. Defaults to """".
            website (str, optional): website of the institution of the user. Defaults to """".
            institution (str, optional): name of the institution of the user. Defaults to """".
            budget (float, optional): privacy budget allocated to the user. Defaults to 0.0.

        Raises:
            UserNotFoundError: Raised when a user does not exits for the given user id.
            Exception: Raised when an invalid argument/property is passed.
        ",119,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set(self, **kwargs) -> None:  # nosec
        
        attributes = {}
        user_id = kwargs[""user_id""]
        user = self.first(id_int=int(user_id))
        if not user:
            raise UserNotFoundError

        for k, v in kwargs.items():
            if k in user.__attr_searchable__:
                attributes[k] = v

        if kwargs.get(""email"", None):
            user.email = kwargs[""email""]
        elif kwargs.get(""role"", None):
            user.role = kwargs[""role""]
        elif kwargs.get(""name"", None):
            user.name = kwargs[""name""]
        elif kwargs.get(""budget"", None):
            user.budget = kwargs[""budget""]
        elif kwargs.get(""website"", None):
            user.website = kwargs[""website""]
        elif kwargs.get(""institution"", None):
            user.institution = kwargs[""institution""]
        else:
            raise Exception

        attributes[""__blob__""] = user.to_bytes()

        self.update_one({""id_int"": int(user_id)}, {""$set"": attributes})

        ```
    ###Assistant : Updates the information for the given user id.

        Args:
            user_id (str): unique id of the user in the database.
            email (str, optional): email of the user. Defaults to """".
            password (str, optional): password of the user. Defaults to """".
            role (int, optional): role of the user. Defaults to 0.
            name (str, optional): name of the user. Defaults to """".
            website (str, optional): website of the institution of the user. Defaults to """".
            institution (str, optional): name of the institution of the user. Defaults to """".
            budget (float, optional): privacy budget allocated to the user. Defaults to 0.0.

        Raises:
            UserNotFoundError: Raised when a user does not exits for the given user id.
            Exception: Raised when an invalid argument/property is passed.
        
    "
569,"def test_get_entity_and_validate_dependency_tree_of_a_single_entity_derived_metric(self):
        
        use_case_id = UseCaseKey.RELEASE_HEALTH
        expected_derived_metrics_entities = {
            SessionMRI.ALL.value: ""metrics_counters"",
            SessionMRI.ALL_USER.value: ""metrics_sets"",
            SessionMRI.CRASHED.value: ""metrics_counters"",
            SessionMRI.CRASHED_USER.value: ""metrics_sets"",
            SessionMRI.ABNORMAL.value: ""metrics_counters"",
            SessionMRI.ABNORMAL_USER.value: ""metrics_sets"",
            SessionMRI.CRASH_FREE_RATE.value: ""metrics_counters"",
            SessionMRI.CRASH_FREE_USER_RATE.value: ""metrics_sets"",
            SessionMRI.ERRORED_PREAGGREGATED.value: ""metrics_counters"",
            SessionMRI.ERRORED_SET.value: ""metrics_sets"",
            SessionMRI.ERRORED_USER_ALL.value: ""metrics_sets"",
            SessionMRI.CRASHED_AND_ABNORMAL_USER.value: ""metrics_sets"",
            SessionMRI.ERRORED_USER.value: ""metrics_sets"",
        }
        for key, value in expected_derived_metrics_entities.items():
            assert (
                MOCKED_DERIVED_METRICS[key].get_entity(
                    projects=[self.project], use_case_id=use_case_id
                )
            ) == value

        # Incorrectly setup SingularEntityDerivedMetric with metrics spanning multiple entities
        with pytest.raises(DerivedMetricParseException):
            self.crash_free_fake.get_entity(projects=[self.project], use_case_id=use_case_id)
","
        Tests that ensures that get_entity method works expected in the sense that:
        - Since it is the first function that is called by the query_builder, validation is
        applied there to ensure that if it is an instance of a SingleEntityDerivedMetric,
        then it is composed of only other SingleEntityDerivedMetric or
        RawMetric that belong to the same entity
        - Return the entity of that derived metric
        ",64,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_entity_and_validate_dependency_tree_of_a_single_entity_derived_metric(self):
        
        use_case_id = UseCaseKey.RELEASE_HEALTH
        expected_derived_metrics_entities = {
            SessionMRI.ALL.value: ""metrics_counters"",
            SessionMRI.ALL_USER.value: ""metrics_sets"",
            SessionMRI.CRASHED.value: ""metrics_counters"",
            SessionMRI.CRASHED_USER.value: ""metrics_sets"",
            SessionMRI.ABNORMAL.value: ""metrics_counters"",
            SessionMRI.ABNORMAL_USER.value: ""metrics_sets"",
            SessionMRI.CRASH_FREE_RATE.value: ""metrics_counters"",
            SessionMRI.CRASH_FREE_USER_RATE.value: ""metrics_sets"",
            SessionMRI.ERRORED_PREAGGREGATED.value: ""metrics_counters"",
            SessionMRI.ERRORED_SET.value: ""metrics_sets"",
            SessionMRI.ERRORED_USER_ALL.value: ""metrics_sets"",
            SessionMRI.CRASHED_AND_ABNORMAL_USER.value: ""metrics_sets"",
            SessionMRI.ERRORED_USER.value: ""metrics_sets"",
        }
        for key, value in expected_derived_metrics_entities.items():
            assert (
                MOCKED_DERIVED_METRICS[key].get_entity(
                    projects=[self.project], use_case_id=use_case_id
                )
            ) == value

        # Incorrectly setup SingularEntityDerivedMetric with metrics spanning multiple entities
        with pytest.raises(DerivedMetricParseException):
            self.crash_free_fake.get_entity(projects=[self.project], use_case_id=use_case_id)

        ```
    ###Assistant : 
        Tests that ensures that get_entity method works expected in the sense that:
        - Since it is the first function that is called by the query_builder, validation is
        applied there to ensure that if it is an instance of a SingleEntityDerivedMetric,
        then it is composed of only other SingleEntityDerivedMetric or
        RawMetric that belong to the same entity
        - Return the entity of that derived metric
        
    "
570,"def transform(self, X):
        
        check_is_fitted(self)

        X = self._validate_data(X, reset=False)
        X = X - self.mean_

        U = ridge_regression(
            self.components_.T, X.T, self.ridge_alpha, solver=""cholesky""
        )

        return U
","Least Squares projection of the data onto the sparse components.

        To avoid instability issues in case the system is under-determined,
        regularization can be applied (Ridge regression) via the
        `ridge_alpha` parameter.

        Note that Sparse PCA components orthogonality is not enforced as in PCA
        hence one cannot use a simple linear projection.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_features)
            Test data to be transformed, must have the same number of
            features as the data used to train the model.

        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components)
            Transformed data.
        ",90,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transform(self, X):
        
        check_is_fitted(self)

        X = self._validate_data(X, reset=False)
        X = X - self.mean_

        U = ridge_regression(
            self.components_.T, X.T, self.ridge_alpha, solver=""cholesky""
        )

        return U

        ```
    ###Assistant : Least Squares projection of the data onto the sparse components.

        To avoid instability issues in case the system is under-determined,
        regularization can be applied (Ridge regression) via the
        `ridge_alpha` parameter.

        Note that Sparse PCA components orthogonality is not enforced as in PCA
        hence one cannot use a simple linear projection.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_features)
            Test data to be transformed, must have the same number of
            features as the data used to train the model.

        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components)
            Transformed data.
        
    "
571,"def serialize_object(obj, extra=None):
    
    json_str = serialize('json', [obj])
    print(json_str)
    data = json.loads(json_str)[0]['fields']

    # Exclude any MPTTModel fields
    if issubclass(obj.__class__, MPTTModel):
        for field in ['level', 'lft', 'rght', 'tree_id']:
            data.pop(field)

    # Include custom_field_data as ""custom_fields""
    if hasattr(obj, 'custom_field_data'):
        data['custom_fields'] = data.pop('custom_field_data')

    # Include any tags. Check for tags cached on the instance; fall back to using the manager.
    if is_taggable(obj):
        tags = getattr(obj, '_tags', None) or obj.tags.all()
        data['tags'] = [tag.name for tag in tags]

    # Append any extra data
    if extra is not None:
        data.update(extra)

    # Copy keys to list to avoid 'dictionary changed size during iteration' exception
    for key in list(data):
        # Private fields shouldn't be logged in the object change
        if isinstance(key, str) and key.startswith('_'):
            data.pop(key)

    return data

","
    Return a generic JSON representation of an object using Django's built-in serializer. (This is used for things like
    change logging, not the REST API.) Optionally include a dictionary to supplement the object data. A list of keys
    can be provided to exclude them from the returned dictionary. Private fields (prefaced with an underscore) are
    implicitly excluded.
    ",56,117,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serialize_object(obj, extra=None):
    
    json_str = serialize('json', [obj])
    print(json_str)
    data = json.loads(json_str)[0]['fields']

    # Exclude any MPTTModel fields
    if issubclass(obj.__class__, MPTTModel):
        for field in ['level', 'lft', 'rght', 'tree_id']:
            data.pop(field)

    # Include custom_field_data as ""custom_fields""
    if hasattr(obj, 'custom_field_data'):
        data['custom_fields'] = data.pop('custom_field_data')

    # Include any tags. Check for tags cached on the instance; fall back to using the manager.
    if is_taggable(obj):
        tags = getattr(obj, '_tags', None) or obj.tags.all()
        data['tags'] = [tag.name for tag in tags]

    # Append any extra data
    if extra is not None:
        data.update(extra)

    # Copy keys to list to avoid 'dictionary changed size during iteration' exception
    for key in list(data):
        # Private fields shouldn't be logged in the object change
        if isinstance(key, str) and key.startswith('_'):
            data.pop(key)

    return data


        ```
    ###Assistant : 
    Return a generic JSON representation of an object using Django's built-in serializer. (This is used for things like
    change logging, not the REST API.) Optionally include a dictionary to supplement the object data. A list of keys
    can be provided to exclude them from the returned dictionary. Private fields (prefaced with an underscore) are
    implicitly excluded.
    
    "
572,"async def test_statistics_during_period(recorder_mock, hass, hass_ws_client, caplog):
    
    now = dt_util.utcnow()
    await async_setup_component(hass, ""history"", {})
    client = await hass_ws_client()

    # Test the WS API works and issues a warning
    await client.send_json(
        {
            ""id"": 1,
            ""type"": ""history/statistics_during_period"",
            ""start_time"": now.isoformat(),
            ""end_time"": now.isoformat(),
            ""statistic_ids"": [""sensor.test""],
            ""period"": ""hour"",
        }
    )
    response = await client.receive_json()
    assert response[""success""]
    assert response[""result""] == {}

    assert (
        ""WS API 'history/statistics_during_period' is deprecated and will be removed in ""
        ""Home Assistant Core 2022.12. Use 'recorder/statistics_during_period' instead""
    ) in caplog.text

    # Test the WS API forwards to recorder
    with patch(
        ""homeassistant.components.history.recorder_ws.ws_handle_get_statistics_during_period"",
        wraps=ws_handle_get_statistics_during_period,
    ) as ws_mock:
        await client.send_json(
            {
                ""id"": 2,
                ""type"": ""history/statistics_during_period"",
                ""start_time"": now.isoformat(),
                ""end_time"": now.isoformat(),
                ""statistic_ids"": [""sensor.test""],
                ""period"": ""hour"",
            }
        )
        await client.receive_json()
        ws_mock.assert_awaited_once()

",Test history/statistics_during_period forwards to recorder.,5,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_statistics_during_period(recorder_mock, hass, hass_ws_client, caplog):
    
    now = dt_util.utcnow()
    await async_setup_component(hass, ""history"", {})
    client = await hass_ws_client()

    # Test the WS API works and issues a warning
    await client.send_json(
        {
            ""id"": 1,
            ""type"": ""history/statistics_during_period"",
            ""start_time"": now.isoformat(),
            ""end_time"": now.isoformat(),
            ""statistic_ids"": [""sensor.test""],
            ""period"": ""hour"",
        }
    )
    response = await client.receive_json()
    assert response[""success""]
    assert response[""result""] == {}

    assert (
        ""WS API 'history/statistics_during_period' is deprecated and will be removed in ""
        ""Home Assistant Core 2022.12. Use 'recorder/statistics_during_period' instead""
    ) in caplog.text

    # Test the WS API forwards to recorder
    with patch(
        ""homeassistant.components.history.recorder_ws.ws_handle_get_statistics_during_period"",
        wraps=ws_handle_get_statistics_during_period,
    ) as ws_mock:
        await client.send_json(
            {
                ""id"": 2,
                ""type"": ""history/statistics_during_period"",
                ""start_time"": now.isoformat(),
                ""end_time"": now.isoformat(),
                ""statistic_ids"": [""sensor.test""],
                ""period"": ""hour"",
            }
        )
        await client.receive_json()
        ws_mock.assert_awaited_once()


        ```
    ###Assistant : Test history/statistics_during_period forwards to recorder.
    "
573,"def queryset_chunks(self, qs, chunk_size=DEFAULT_CHUNK_SIZE):
        
        i = 0
        while True:
            items = list(qs[i * chunk_size :][:chunk_size])
            if not items:
                break
            yield items
            i += 1
","
        Yield a queryset in chunks of at most ``chunk_size``. The chunk yielded
        will be a list, not a queryset. Iterating over the chunks is done in a
        transaction so that the order and count of items in the queryset
        remains stable.
        ",41,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def queryset_chunks(self, qs, chunk_size=DEFAULT_CHUNK_SIZE):
        
        i = 0
        while True:
            items = list(qs[i * chunk_size :][:chunk_size])
            if not items:
                break
            yield items
            i += 1

        ```
    ###Assistant : 
        Yield a queryset in chunks of at most ``chunk_size``. The chunk yielded
        will be a list, not a queryset. Iterating over the chunks is done in a
        transaction so that the order and count of items in the queryset
        remains stable.
        
    "
574,"def flattened_having(self) -> List[Condition]:
        
        flattened: List[Condition] = []
        boolean_conditions: List[BooleanCondition] = []

        for condition in self.having:
            if isinstance(condition, Condition):
                flattened.append(condition)
            elif isinstance(condition, BooleanCondition):
                boolean_conditions.append(condition)

        while len(boolean_conditions) > 0:
            boolean_condition = boolean_conditions.pop()
            for condition in boolean_condition.conditions:
                if isinstance(condition, Condition):
                    flattened.append(condition)
                elif isinstance(condition, BooleanCondition):
                    boolean_conditions.append(condition)

        return flattened
","Return self.having as a flattened list ignoring boolean operators
        This is because self.having can have a mix of BooleanConditions and Conditions. And each BooleanCondition can in
        turn be a mix of either type.
        ",33,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def flattened_having(self) -> List[Condition]:
        
        flattened: List[Condition] = []
        boolean_conditions: List[BooleanCondition] = []

        for condition in self.having:
            if isinstance(condition, Condition):
                flattened.append(condition)
            elif isinstance(condition, BooleanCondition):
                boolean_conditions.append(condition)

        while len(boolean_conditions) > 0:
            boolean_condition = boolean_conditions.pop()
            for condition in boolean_condition.conditions:
                if isinstance(condition, Condition):
                    flattened.append(condition)
                elif isinstance(condition, BooleanCondition):
                    boolean_conditions.append(condition)

        return flattened

        ```
    ###Assistant : Return self.having as a flattened list ignoring boolean operators
        This is because self.having can have a mix of BooleanConditions and Conditions. And each BooleanCondition can in
        turn be a mix of either type.
        
    "
575,"def _add_timedelta_arraylike(self, other):
        
        # overridden by PeriodArray

        if len(self) != len(other):
            raise ValueError(""cannot add indices of unequal length"")

        if isinstance(other, np.ndarray):
            # ndarray[timedelta64]; wrap in TimedeltaIndex for op
            from pandas.core.arrays import TimedeltaArray

            other = TimedeltaArray._from_sequence(other)

        self_i8 = self.asi8
        other_i8 = other.asi8
        new_values = checked_add_with_arr(
            self_i8, other_i8, arr_mask=self._isnan, b_mask=other._isnan
        )
        if self._hasna or other._hasna:
            mask = self._isnan | other._isnan
            np.putmask(new_values, mask, iNaT)

        return type(self)(new_values, dtype=self.dtype)
","
        Add a delta of a TimedeltaIndex

        Returns
        -------
        Same type as self
        ",12,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _add_timedelta_arraylike(self, other):
        
        # overridden by PeriodArray

        if len(self) != len(other):
            raise ValueError(""cannot add indices of unequal length"")

        if isinstance(other, np.ndarray):
            # ndarray[timedelta64]; wrap in TimedeltaIndex for op
            from pandas.core.arrays import TimedeltaArray

            other = TimedeltaArray._from_sequence(other)

        self_i8 = self.asi8
        other_i8 = other.asi8
        new_values = checked_add_with_arr(
            self_i8, other_i8, arr_mask=self._isnan, b_mask=other._isnan
        )
        if self._hasna or other._hasna:
            mask = self._isnan | other._isnan
            np.putmask(new_values, mask, iNaT)

        return type(self)(new_values, dtype=self.dtype)

        ```
    ###Assistant : 
        Add a delta of a TimedeltaIndex

        Returns
        -------
        Same type as self
        
    "
576,"def _map_graph_network(inputs, outputs):
    
    # ""depth"" is number of layers between output Node and the Node.
    # Nodes are ordered from inputs -> outputs.
    nodes_in_decreasing_depth, layer_indices = _build_map(outputs)
    network_nodes = {
        _make_node_key(node.layer.name, node.layer._inbound_nodes.index(node))
        for node in nodes_in_decreasing_depth
    }

    nodes_depths = {}  # dict {node: depth value}
    layers_depths = {}  # dict {layer: depth value}

    for node in reversed(nodes_in_decreasing_depth):
        # If the depth is not set, the node has no outbound nodes (depth 0).
        depth = nodes_depths.setdefault(node, 0)

        # Update the depth of the corresponding layer
        previous_depth = layers_depths.get(node.layer, 0)
        # If we've seen this layer before at a higher depth,
        # we should use that depth instead of the node depth.
        # This is necessary for shared layers that have inputs at different
        # depth levels in the graph.
        depth = max(depth, previous_depth)
        layers_depths[node.layer] = depth
        nodes_depths[node] = depth

        # Update the depth of inbound nodes.
        # The ""depth"" of a node is the max of the depths
        # of all nodes it is connected to + 1.
        for node_dep in node.parent_nodes:
            previous_depth = nodes_depths.get(node_dep, 0)
            nodes_depths[node_dep] = max(depth + 1, previous_depth)

    # Handle inputs that are not connected to outputs.
    # We do not error out here because the inputs may be used to compute losses
    # and metrics.
    for input_t in inputs:
        input_layer = input_t._keras_history[0]
        if input_layer not in layers_depths:
            layers_depths[input_layer] = 0
            layer_indices[input_layer] = -1
            nodes_depths[input_layer._inbound_nodes[0]] = 0
            network_nodes.add(_make_node_key(input_layer.name, 0))

    # Build a dict {depth: list of nodes with this depth}
    nodes_by_depth = collections.defaultdict(list)
    for node, depth in nodes_depths.items():
        nodes_by_depth[depth].append(node)

    # Build a dict {depth: list of layers with this depth}
    layers_by_depth = collections.defaultdict(list)
    for layer, depth in layers_depths.items():
        layers_by_depth[depth].append(layer)

    # Get sorted list of layer depths.
    depth_keys = list(layers_by_depth.keys())
    depth_keys.sort(reverse=True)

    # Set self.layers ordered by depth.
    layers = []
    for depth in depth_keys:
        layers_for_depth = layers_by_depth[depth]
        # Network.layers needs to have a deterministic order:
        # here we order them by traversal order.
        layers_for_depth.sort(key=lambda x: layer_indices[x])
        layers.extend(layers_for_depth)

    # Get sorted list of node depths.
    depth_keys = list(nodes_by_depth.keys())
    depth_keys.sort(reverse=True)

    # Check that all tensors required are computable.
    # computable_tensors: all tensors in the graph
    # that can be computed from the inputs provided.
    computable_tensors = set()
    for x in inputs:
        computable_tensors.add(id(x))

    layers_with_complete_input = []  # To provide a better error msg.
    for depth in depth_keys:
        for node in nodes_by_depth[depth]:
            layer = node.layer
            if layer and not node.is_input:
                for x in tf.nest.flatten(node.keras_inputs):
                    if id(x) not in computable_tensors:
                        raise ValueError(
                            f""Graph disconnected: cannot obtain value for tensor {x} ""
                            f'at layer ""{layer.name}"". The following previous layers '
                            f""were accessed without issue: {layers_with_complete_input}""
                        )
                for x in tf.nest.flatten(node.outputs):
                    computable_tensors.add(id(x))
                layers_with_complete_input.append(layer.name)

    # Ensure name unicity, which will be crucial for serialization
    # (since serialized nodes refer to layers by their name).
    all_names = [layer.name for layer in layers]
    for name in all_names:
        if all_names.count(name) != 1:
            raise ValueError(
                f'The name ""{name}"" is used {all_names.count(name)} '
                ""times in the model. All layer names should be unique.""
            )
    return network_nodes, nodes_by_depth, layers, layers_by_depth

","Validates a network's topology and gather its layers and nodes.

    Args:
      inputs: List of input tensors.
      outputs: List of outputs tensors.

    Returns:
      A tuple `(nodes, nodes_by_depth, layers, layers_by_depth)`.
      - nodes: list of Node instances.
      - nodes_by_depth: dict mapping ints (depth) to lists of node instances.
      - layers: list of Layer instances.
      - layers_by_depth: dict mapping ints (depth) to lists of layer instances.

    Raises:
      ValueError: In case the network is not valid (e.g. disconnected graph).
    ",74,488,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _map_graph_network(inputs, outputs):
    
    # ""depth"" is number of layers between output Node and the Node.
    # Nodes are ordered from inputs -> outputs.
    nodes_in_decreasing_depth, layer_indices = _build_map(outputs)
    network_nodes = {
        _make_node_key(node.layer.name, node.layer._inbound_nodes.index(node))
        for node in nodes_in_decreasing_depth
    }

    nodes_depths = {}  # dict {node: depth value}
    layers_depths = {}  # dict {layer: depth value}

    for node in reversed(nodes_in_decreasing_depth):
        # If the depth is not set, the node has no outbound nodes (depth 0).
        depth = nodes_depths.setdefault(node, 0)

        # Update the depth of the corresponding layer
        previous_depth = layers_depths.get(node.layer, 0)
        # If we've seen this layer before at a higher depth,
        # we should use that depth instead of the node depth.
        # This is necessary for shared layers that have inputs at different
        # depth levels in the graph.
        depth = max(depth, previous_depth)
        layers_depths[node.layer] = depth
        nodes_depths[node] = depth

        # Update the depth of inbound nodes.
        # The ""depth"" of a node is the max of the depths
        # of all nodes it is connected to + 1.
        for node_dep in node.parent_nodes:
            previous_depth = nodes_depths.get(node_dep, 0)
            nodes_depths[node_dep] = max(depth + 1, previous_depth)

    # Handle inputs that are not connected to outputs.
    # We do not error out here because the inputs may be used to compute losses
    # and metrics.
    for input_t in inputs:
        input_layer = input_t._keras_history[0]
        if input_layer not in layers_depths:
            layers_depths[input_layer] = 0
            layer_indices[input_layer] = -1
            nodes_depths[input_layer._inbound_nodes[0]] = 0
            network_nodes.add(_make_node_key(input_layer.name, 0))

    # Build a dict {depth: list of nodes with this depth}
    nodes_by_depth = collections.defaultdict(list)
    for node, depth in nodes_depths.items():
        nodes_by_depth[depth].append(node)

    # Build a dict {depth: list of layers with this depth}
    layers_by_depth = collections.defaultdict(list)
    for layer, depth in layers_depths.items():
        layers_by_depth[depth].append(layer)

    # Get sorted list of layer depths.
    depth_keys = list(layers_by_depth.keys())
    depth_keys.sort(reverse=True)

    # Set self.layers ordered by depth.
    layers = []
    for depth in depth_keys:
        layers_for_depth = layers_by_depth[depth]
        # Network.layers needs to have a deterministic order:
        # here we order them by traversal order.
        layers_for_depth.sort(key=lambda x: layer_indices[x])
        layers.extend(layers_for_depth)

    # Get sorted list of node depths.
    depth_keys = list(nodes_by_depth.keys())
    depth_keys.sort(reverse=True)

    # Check that all tensors required are computable.
    # computable_tensors: all tensors in the graph
    # that can be computed from the inputs provided.
    computable_tensors = set()
    for x in inputs:
        computable_tensors.add(id(x))

    layers_with_complete_input = []  # To provide a better error msg.
    for depth in depth_keys:
        for node in nodes_by_depth[depth]:
            layer = node.layer
            if layer and not node.is_input:
                for x in tf.nest.flatten(node.keras_inputs):
                    if id(x) not in computable_tensors:
                        raise ValueError(
                            f""Graph disconnected: cannot obtain value for tensor {x} ""
                            f'at layer ""{layer.name}"". The following previous layers '
                            f""were accessed without issue: {layers_with_complete_input}""
                        )
                for x in tf.nest.flatten(node.outputs):
                    computable_tensors.add(id(x))
                layers_with_complete_input.append(layer.name)

    # Ensure name unicity, which will be crucial for serialization
    # (since serialized nodes refer to layers by their name).
    all_names = [layer.name for layer in layers]
    for name in all_names:
        if all_names.count(name) != 1:
            raise ValueError(
                f'The name ""{name}"" is used {all_names.count(name)} '
                ""times in the model. All layer names should be unique.""
            )
    return network_nodes, nodes_by_depth, layers, layers_by_depth


        ```
    ###Assistant : Validates a network's topology and gather its layers and nodes.

    Args:
      inputs: List of input tensors.
      outputs: List of outputs tensors.

    Returns:
      A tuple `(nodes, nodes_by_depth, layers, layers_by_depth)`.
      - nodes: list of Node instances.
      - nodes_by_depth: dict mapping ints (depth) to lists of node instances.
      - layers: list of Layer instances.
      - layers_by_depth: dict mapping ints (depth) to lists of layer instances.

    Raises:
      ValueError: In case the network is not valid (e.g. disconnected graph).
    
    "
577,"def default_batch_format(self) -> Type:
          # noqa: E501
        import pandas as pd
        import pyarrow as pa

        schema = self.schema()
        assert isinstance(schema, (type, PandasBlockSchema, pa.Schema))

        if isinstance(schema, type):
            return list

        if isinstance(schema, (PandasBlockSchema, pa.Schema)):
            if schema.names == [VALUE_COL_NAME]:
                return np.ndarray
            return pd.DataFrame
","Return this dataset's default batch format.

        The default batch format describes what batches of data look like. To learn more
        about batch formats, read
        :ref:`writing user-defined functions <transform_datasets_writing_udfs>`.

        Example:

            If your dataset represents a list of Python objects, then the default batch
            format is ``list``.

            >>> ds = ray.data.range(100)
            >>> ds  # doctest: +SKIP
            Dataset(num_blocks=20, num_rows=100, schema=<class 'int'>)
            >>> ds.default_batch_format()
            <class 'list'>
            >>> next(ds.iter_batches(batch_size=4))
            [0, 1, 2, 3]

            If your dataset contains a single ``TensorDtype`` or ``ArrowTensorType``
            column named ``__value__`` (as created by :func:`ray.data.from_numpy`), then
            the default batch format is ``np.ndarray``. For more information on tensor
            datasets, read the :ref:`tensor support guide <datasets_tensor_support>`.

            >>> ds = ray.data.range_tensor(100)
            >>> ds  # doctest: +SKIP
            Dataset(num_blocks=20, num_rows=100, schema={__value__: ArrowTensorType(shape=(1,), dtype=int64)})
            >>> ds.default_batch_format()
            <class 'numpy.ndarray'>
            >>> next(ds.iter_batches(batch_size=4))
            array([[0],
                   [1],
                   [2],
                   [3]])

            If your dataset represents tabular data and doesn't only consist of a
            ``__value__`` tensor column (such as is created by
            :meth:`ray.data.from_numpy`), then the default batch format is
            ``pd.DataFrame``.

            >>> import pandas as pd
            >>> df = pd.DataFrame({""foo"": [""a"", ""b""], ""bar"": [0, 1]})
            >>> ds = ray.data.from_pandas(df)
            >>> ds  # doctest: +SKIP
            Dataset(num_blocks=1, num_rows=2, schema={foo: object, bar: int64})
            >>> ds.default_batch_format()
            <class 'pandas.core.frame.DataFrame'>
            >>> next(ds.iter_batches(batch_size=4))
              foo  bar
            0   a    0
            1   b    1

        .. seealso::

            :meth:`~Dataset.map_batches`
                Call this function to transform batches of data.

            :meth:`~Dataset.iter_batches`
                Call this function to iterate over batches of data.

        ",219,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def default_batch_format(self) -> Type:
          # noqa: E501
        import pandas as pd
        import pyarrow as pa

        schema = self.schema()
        assert isinstance(schema, (type, PandasBlockSchema, pa.Schema))

        if isinstance(schema, type):
            return list

        if isinstance(schema, (PandasBlockSchema, pa.Schema)):
            if schema.names == [VALUE_COL_NAME]:
                return np.ndarray
            return pd.DataFrame

        ```
    ###Assistant : Return this dataset's default batch format.

        The default batch format describes what batches of data look like. To learn more
        about batch formats, read
        :ref:`writing user-defined functions <transform_datasets_writing_udfs>`.

        Example:

            If your dataset represents a list of Python objects, then the default batch
            format is ``list``.

            >>> ds = ray.data.range(100)
            >>> ds  # doctest: +SKIP
            Dataset(num_blocks=20, num_rows=100, schema=<class 'int'>)
            >>> ds.default_batch_format()
            <class 'list'>
            >>> next(ds.iter_batches(batch_size=4))
            [0, 1, 2, 3]

            If your dataset contains a single ``TensorDtype`` or ``ArrowTensorType``
            column named ``__value__`` (as created by :func:`ray.data.from_numpy`), then
            the default batch format is ``np.ndarray``. For more information on tensor
            datasets, read the :ref:`tensor support guide <datasets_tensor_support>`.

            >>> ds = ray.data.range_tensor(100)
            >>> ds  # doctest: +SKIP
            Dataset(num_blocks=20, num_rows=100, schema={__value__: ArrowTensorType(shape=(1,), dtype=int64)})
            >>> ds.default_batch_format()
            <class 'numpy.ndarray'>
            >>> next(ds.iter_batches(batch_size=4))
            array([[0],
                   [1],
                   [2],
                   [3]])

            If your dataset represents tabular data and doesn't only consist of a
            ``__value__`` tensor column (such as is created by
            :meth:`ray.data.from_numpy`), then the default batch format is
            ``pd.DataFrame``.

            >>> import pandas as pd
            >>> df = pd.DataFrame({""foo"": [""a"", ""b""], ""bar"": [0, 1]})
            >>> ds = ray.data.from_pandas(df)
            >>> ds  # doctest: +SKIP
            Dataset(num_blocks=1, num_rows=2, schema={foo: object, bar: int64})
            >>> ds.default_batch_format()
            <class 'pandas.core.frame.DataFrame'>
            >>> next(ds.iter_batches(batch_size=4))
              foo  bar
            0   a    0
            1   b    1

        .. seealso::

            :meth:`~Dataset.map_batches`
                Call this function to transform batches of data.

            :meth:`~Dataset.iter_batches`
                Call this function to iterate over batches of data.

        
    "
578,"def test_decision_tree_regressor_sample_weight_consistency(criterion):
    
    tree_params = dict(criterion=criterion)
    tree = DecisionTreeRegressor(**tree_params, random_state=42)
    for kind in [""zeros"", ""ones""]:
        check_sample_weights_invariance(
            ""DecisionTreeRegressor_"" + criterion, tree, kind=""zeros""
        )

    rng = np.random.RandomState(0)
    n_samples, n_features = 10, 5

    X = rng.rand(n_samples, n_features)
    y = np.mean(X, axis=1) + rng.rand(n_samples)
    # make it positive in order to work also for poisson criterion
    y += np.min(y) + 0.1

    # check that multiplying sample_weight by 2 is equivalent
    # to repeating corresponding samples twice
    X2 = np.concatenate([X, X[: n_samples // 2]], axis=0)
    y2 = np.concatenate([y, y[: n_samples // 2]])
    sample_weight_1 = np.ones(len(y))
    sample_weight_1[: n_samples // 2] = 2

    tree1 = DecisionTreeRegressor(**tree_params).fit(
        X, y, sample_weight=sample_weight_1
    )

    tree2 = DecisionTreeRegressor(**tree_params).fit(X2, y2, sample_weight=None)

    assert tree1.tree_.node_count == tree2.tree_.node_count
    # Thresholds, tree.tree_.threshold, and values, tree.tree_.value, are not
    # exactly the same, but on the training set, those differences do not
    # matter and thus predictions are the same.
    assert_allclose(tree1.predict(X), tree2.predict(X))


# TODO: Remove in v1.2
@pytest.mark.parametrize(""Tree"", REG_TREES.values())
@pytest.mark.parametrize(
    ""old_criterion, new_criterion"",
    [
        (""mse"", ""squared_error""),
        (""mae"", ""absolute_error""),
    ],
)",Test that the impact of sample_weight is consistent.,8,159,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_decision_tree_regressor_sample_weight_consistency(criterion):
    
    tree_params = dict(criterion=criterion)
    tree = DecisionTreeRegressor(**tree_params, random_state=42)
    for kind in [""zeros"", ""ones""]:
        check_sample_weights_invariance(
            ""DecisionTreeRegressor_"" + criterion, tree, kind=""zeros""
        )

    rng = np.random.RandomState(0)
    n_samples, n_features = 10, 5

    X = rng.rand(n_samples, n_features)
    y = np.mean(X, axis=1) + rng.rand(n_samples)
    # make it positive in order to work also for poisson criterion
    y += np.min(y) + 0.1

    # check that multiplying sample_weight by 2 is equivalent
    # to repeating corresponding samples twice
    X2 = np.concatenate([X, X[: n_samples // 2]], axis=0)
    y2 = np.concatenate([y, y[: n_samples // 2]])
    sample_weight_1 = np.ones(len(y))
    sample_weight_1[: n_samples // 2] = 2

    tree1 = DecisionTreeRegressor(**tree_params).fit(
        X, y, sample_weight=sample_weight_1
    )

    tree2 = DecisionTreeRegressor(**tree_params).fit(X2, y2, sample_weight=None)

    assert tree1.tree_.node_count == tree2.tree_.node_count
    # Thresholds, tree.tree_.threshold, and values, tree.tree_.value, are not
    # exactly the same, but on the training set, those differences do not
    # matter and thus predictions are the same.
    assert_allclose(tree1.predict(X), tree2.predict(X))


# TODO: Remove in v1.2
@pytest.mark.parametrize(""Tree"", REG_TREES.values())
@pytest.mark.parametrize(
    ""old_criterion, new_criterion"",
    [
        (""mse"", ""squared_error""),
        (""mae"", ""absolute_error""),
    ],
)
        ```
    ###Assistant : Test that the impact of sample_weight is consistent.
    "
579,"def test_with_fk_to_field(self):
        
        response = self.client.get(
            reverse(""admin:auth_user_changelist"") + ""?q=joe&%s=id"" % TO_FIELD_VAR
        )
        self.assertContains(response, ""\n1 user\n"")
        self.assertContains(
            response,
            '<input type=""hidden"" name=""%s"" value=""id"">' % TO_FIELD_VAR,
            html=True,
        )
","
        The to_field GET parameter is preserved when a search is performed.
        Refs #10918.
        ",13,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_with_fk_to_field(self):
        
        response = self.client.get(
            reverse(""admin:auth_user_changelist"") + ""?q=joe&%s=id"" % TO_FIELD_VAR
        )
        self.assertContains(response, ""\n1 user\n"")
        self.assertContains(
            response,
            '<input type=""hidden"" name=""%s"" value=""id"">' % TO_FIELD_VAR,
            html=True,
        )

        ```
    ###Assistant : 
        The to_field GET parameter is preserved when a search is performed.
        Refs #10918.
        
    "
580,"def remove_module_load(state_dict):
    
    new_state_dict = OrderedDict()
    for k, v in state_dict.items(): new_state_dict[k[7:]] = v
    return new_state_dict
",create new OrderedDict that does not contain `module.`,8,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def remove_module_load(state_dict):
    
    new_state_dict = OrderedDict()
    for k, v in state_dict.items(): new_state_dict[k[7:]] = v
    return new_state_dict

        ```
    ###Assistant : create new OrderedDict that does not contain `module.`
    "
581,"def serving(self, inputs):
        
        output = self.call(inputs)

        return self.serving_output(output)


LAYOUTLMV3_START_DOCSTRING = r

LAYOUTLMV3_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top."",
    LAYOUTLMV3_START_DOCSTRING,
)","
        Method used for serving the model.

        Args:
            inputs (`Dict[str, tf.Tensor]`):
                The input of the saved model as a dictionary of tensors.
        
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.

    <Tip>

    TF 2.0 models accepts two formats as inputs:

    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional arguments.

    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.

    </Tip>

    Parameters:
        config ([`LayoutLMv3Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            Indices can be obtained using [`LayoutLMv3Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)

        bbox (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
            Bounding boxes of each input sequence tokens. Selected in the range `[0,
            config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
            format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
            y1) represents the position of the lower right corner.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
            config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
            config.patch_size) * (width / config.patch_size))`.

        attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
",689,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serving(self, inputs):
        
        output = self.call(inputs)

        return self.serving_output(output)


LAYOUTLMV3_START_DOCSTRING = r

LAYOUTLMV3_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top."",
    LAYOUTLMV3_START_DOCSTRING,
)
        ```
    ###Assistant : 
        Method used for serving the model.

        Args:
            inputs (`Dict[str, tf.Tensor]`):
                The input of the saved model as a dictionary of tensors.
        
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.

    <Tip>

    TF 2.0 models accepts two formats as inputs:

    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional arguments.

    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.

    </Tip>

    Parameters:
        config ([`LayoutLMv3Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            Indices can be obtained using [`LayoutLMv3Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)

        bbox (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
            Bounding boxes of each input sequence tokens. Selected in the range `[0,
            config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
            format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
            y1) represents the position of the lower right corner.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
            config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
            config.patch_size) * (width / config.patch_size))`.

        attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
            token. See `pixel_values` for `patch_sequence_length`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

    "
582,"def media_image_url(self):
        

        if self._table.active_track:
            return self._table.active_track.get_thumbnail_url(Track.ThumbnailSize.LARGE)

        return super().media_image_url
",Return the URL for a thumbnail image of the current track.,11,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def media_image_url(self):
        

        if self._table.active_track:
            return self._table.active_track.get_thumbnail_url(Track.ThumbnailSize.LARGE)

        return super().media_image_url

        ```
    ###Assistant : Return the URL for a thumbnail image of the current track.
    "
583,"def test_fluctuating_ongoing_requests(delay_s):
    

    config = AutoscalingConfig(
        min_replicas=1,
        max_replicas=10,
        target_num_ongoing_requests_per_replica=50,
        upscale_delay_s=delay_s,
        downscale_delay_s=delay_s)

    policy = BasicAutoscalingPolicy(config)

    if delay_s > 0:
        wait_periods = int(delay_s / CONTROL_LOOP_PERIOD_S)
        assert wait_periods > 1

    underload_requests, overload_requests = [20, 20], [100]
    trials = 1000

    new_num_replicas = None
    for trial in range(trials):
        if trial % 2 == 0:
            new_num_replicas = policy.get_decision_num_replicas(
                current_num_ongoing_requests=overload_requests,
                curr_target_num_replicas=1)
            if delay_s > 0:
                assert new_num_replicas == 1, trial
            else:
                assert new_num_replicas == 2, trial
        else:
            new_num_replicas = policy.get_decision_num_replicas(
                current_num_ongoing_requests=underload_requests,
                curr_target_num_replicas=2)
            if delay_s > 0:
                assert new_num_replicas == 2, trial
            else:
                assert new_num_replicas == 1, trial


@pytest.mark.parametrize(
    ""ongoing_requests"",
    [[7, 1, 8, 4], [8, 1, 8, 4], [6, 1, 8, 4], [0, 1, 8, 4]])","
    Simulates a workload that switches between too many and too few
    ongoing requests.
    ",13,107,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_fluctuating_ongoing_requests(delay_s):
    

    config = AutoscalingConfig(
        min_replicas=1,
        max_replicas=10,
        target_num_ongoing_requests_per_replica=50,
        upscale_delay_s=delay_s,
        downscale_delay_s=delay_s)

    policy = BasicAutoscalingPolicy(config)

    if delay_s > 0:
        wait_periods = int(delay_s / CONTROL_LOOP_PERIOD_S)
        assert wait_periods > 1

    underload_requests, overload_requests = [20, 20], [100]
    trials = 1000

    new_num_replicas = None
    for trial in range(trials):
        if trial % 2 == 0:
            new_num_replicas = policy.get_decision_num_replicas(
                current_num_ongoing_requests=overload_requests,
                curr_target_num_replicas=1)
            if delay_s > 0:
                assert new_num_replicas == 1, trial
            else:
                assert new_num_replicas == 2, trial
        else:
            new_num_replicas = policy.get_decision_num_replicas(
                current_num_ongoing_requests=underload_requests,
                curr_target_num_replicas=2)
            if delay_s > 0:
                assert new_num_replicas == 2, trial
            else:
                assert new_num_replicas == 1, trial


@pytest.mark.parametrize(
    ""ongoing_requests"",
    [[7, 1, 8, 4], [8, 1, 8, 4], [6, 1, 8, 4], [0, 1, 8, 4]])
        ```
    ###Assistant : 
    Simulates a workload that switches between too many and too few
    ongoing requests.
    
    "
584,"def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
    
    assert pad_sides in (1, 2)
    # return int(fsize // 2)
    pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
    if pad_sides == 1:
        return 0, pad
    else:
        return pad // 2, pad // 2 + pad % 2


# Conversions","compute right padding (final frame) or both sides padding (first and final frames)
    ",13,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
    
    assert pad_sides in (1, 2)
    # return int(fsize // 2)
    pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
    if pad_sides == 1:
        return 0, pad
    else:
        return pad // 2, pad // 2 + pad % 2


# Conversions
        ```
    ###Assistant : compute right padding (final frame) or both sides padding (first and final frames)
    
    "
585,"def setup_tpu(tpu_driver_version='tpu_driver-0.2'):
  
  global TPU_DRIVER_MODE

  if not TPU_DRIVER_MODE:
    colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0]
    url = f'http://{colab_tpu_addr}:8475/requestversion/{tpu_driver_version}'
    requests.post(url)
    TPU_DRIVER_MODE = 1

  # The following is required to use TPU Driver as JAX's backend.
  config.FLAGS.jax_xla_backend = ""tpu_driver""
  config.FLAGS.jax_backend_target = ""grpc://"" + os.environ['COLAB_TPU_ADDR']
","Sets up Colab to run on TPU.

  Note: make sure the Colab Runtime is set to Accelerator: TPU.

  Args
  ----
  tpu_driver_version : (str) specify the version identifier for the tpu driver.
    Defaults to ""tpu_driver-0.2"", which can be used with jaxlib 0.3.20. Set to
    ""tpu_driver_nightly"" to use the nightly tpu driver build.
  ",51,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setup_tpu(tpu_driver_version='tpu_driver-0.2'):
  
  global TPU_DRIVER_MODE

  if not TPU_DRIVER_MODE:
    colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0]
    url = f'http://{colab_tpu_addr}:8475/requestversion/{tpu_driver_version}'
    requests.post(url)
    TPU_DRIVER_MODE = 1

  # The following is required to use TPU Driver as JAX's backend.
  config.FLAGS.jax_xla_backend = ""tpu_driver""
  config.FLAGS.jax_backend_target = ""grpc://"" + os.environ['COLAB_TPU_ADDR']

        ```
    ###Assistant : Sets up Colab to run on TPU.

  Note: make sure the Colab Runtime is set to Accelerator: TPU.

  Args
  ----
  tpu_driver_version : (str) specify the version identifier for the tpu driver.
    Defaults to ""tpu_driver-0.2"", which can be used with jaxlib 0.3.20. Set to
    ""tpu_driver_nightly"" to use the nightly tpu driver build.
  
    "
586,"def test_timeout_lock(self):
        

        lock = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        assert lock is not None

        self.get_success(lock.__aenter__())

        # We simulate the process getting stuck by cancelling the looping call
        # that keeps the lock active.
        lock._looping_call.stop()

        # Wait for the lock to timeout.
        self.reactor.advance(2 * _LOCK_TIMEOUT_MS / 1000)

        lock2 = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        self.assertIsNotNone(lock2)

        self.assertFalse(self.get_success(lock.is_still_valid()))
",Test that we time out locks if they're not updated for ages,12,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_timeout_lock(self):
        

        lock = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        assert lock is not None

        self.get_success(lock.__aenter__())

        # We simulate the process getting stuck by cancelling the looping call
        # that keeps the lock active.
        lock._looping_call.stop()

        # Wait for the lock to timeout.
        self.reactor.advance(2 * _LOCK_TIMEOUT_MS / 1000)

        lock2 = self.get_success(self.store.try_acquire_lock(""name"", ""key""))
        self.assertIsNotNone(lock2)

        self.assertFalse(self.get_success(lock.is_still_valid()))

        ```
    ###Assistant : Test that we time out locks if they're not updated for ages
    "
587,"def random_brightness(x, brightness_range, scale=True):
    
    if len(brightness_range) != 2:
        raise ValueError(
            ""`brightness_range should be tuple or list of two floats. ""
            ""Received: %s"" % (brightness_range,)
        )

    u = np.random.uniform(brightness_range[0], brightness_range[1])
    return apply_brightness_shift(x, u, scale)

","Performs a random brightness shift.

    Deprecated: `tf.keras.preprocessing.image.random_brightness` does not operate
    on tensors and is not recommended for new code. Prefer
    `tf.keras.layers.RandomBrightness` which provides equivalent functionality as
    a preprocessing layer. For more information, see the tutorial for
    [augmenting images](
    https://www.tensorflow.org/tutorials/images/data_augmentation), as well as
    the [preprocessing layer guide](
    https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
        x: Input tensor. Must be 3D.
        brightness_range: Tuple of floats; brightness range.
        scale: Whether to rescale the image such that minimum and maximum values
            are 0 and 255 respectively. Default: True.

    Returns:
        Numpy image tensor.

    Raises:
        ValueError if `brightness_range` isn't a tuple.
    ",90,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def random_brightness(x, brightness_range, scale=True):
    
    if len(brightness_range) != 2:
        raise ValueError(
            ""`brightness_range should be tuple or list of two floats. ""
            ""Received: %s"" % (brightness_range,)
        )

    u = np.random.uniform(brightness_range[0], brightness_range[1])
    return apply_brightness_shift(x, u, scale)


        ```
    ###Assistant : Performs a random brightness shift.

    Deprecated: `tf.keras.preprocessing.image.random_brightness` does not operate
    on tensors and is not recommended for new code. Prefer
    `tf.keras.layers.RandomBrightness` which provides equivalent functionality as
    a preprocessing layer. For more information, see the tutorial for
    [augmenting images](
    https://www.tensorflow.org/tutorials/images/data_augmentation), as well as
    the [preprocessing layer guide](
    https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
        x: Input tensor. Must be 3D.
        brightness_range: Tuple of floats; brightness range.
        scale: Whether to rescale the image such that minimum and maximum values
            are 0 and 255 respectively. Default: True.

    Returns:
        Numpy image tensor.

    Raises:
        ValueError if `brightness_range` isn't a tuple.
    
    "
588,"def test_check_loop_sync(caplog):
    
    hasync.check_loop()
    assert ""Detected blocking call inside the event loop"" not in caplog.text

",Test check_loop does nothing when called from thread.,8,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_check_loop_sync(caplog):
    
    hasync.check_loop()
    assert ""Detected blocking call inside the event loop"" not in caplog.text


        ```
    ###Assistant : Test check_loop does nothing when called from thread.
    "
589,"def update(self) -> bool:
        
        try:
            # Add or remove DeploymentReplica instances in self._replicas.
            # This should be the only place we adjust total number of replicas
            # we manage.

            running_replicas_changed = self._scale_deployment_replicas()

            # Check the state of existing replicas and transition if necessary.
            running_replicas_changed |= self._check_and_update_replicas()

            if running_replicas_changed:
                self._notify_running_replicas_changed()

            deleted = self._check_curr_status()
        except Exception:
            self._curr_status_info = DeploymentStatusInfo(
                name=self._name,
                status=DeploymentStatus.UNHEALTHY,
                message=""Failed to update deployment:"" f""\n{traceback.format_exc()}"",
            )
            deleted = False

        return deleted
","Attempts to reconcile this deployment to match its goal state.

        This is an asynchronous call; it's expected to be called repeatedly.

        Also updates the internal DeploymentStatusInfo based on the current
        state of the system.

        Returns true if this deployment was successfully deleted.
        ",42,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self) -> bool:
        
        try:
            # Add or remove DeploymentReplica instances in self._replicas.
            # This should be the only place we adjust total number of replicas
            # we manage.

            running_replicas_changed = self._scale_deployment_replicas()

            # Check the state of existing replicas and transition if necessary.
            running_replicas_changed |= self._check_and_update_replicas()

            if running_replicas_changed:
                self._notify_running_replicas_changed()

            deleted = self._check_curr_status()
        except Exception:
            self._curr_status_info = DeploymentStatusInfo(
                name=self._name,
                status=DeploymentStatus.UNHEALTHY,
                message=""Failed to update deployment:"" f""\n{traceback.format_exc()}"",
            )
            deleted = False

        return deleted

        ```
    ###Assistant : Attempts to reconcile this deployment to match its goal state.

        This is an asynchronous call; it's expected to be called repeatedly.

        Also updates the internal DeploymentStatusInfo based on the current
        state of the system.

        Returns true if this deployment was successfully deleted.
        
    "
590,"def test_https_malformed_host(self):
        
        req = self._get_request(method=""POST"")
        req._is_secure_override = True
        req.META[""HTTP_HOST""] = ""@malformed""
        req.META[""HTTP_REFERER""] = ""https://www.evil.org/somepage""
        req.META[""SERVER_PORT""] = ""443""
        mw = CsrfViewMiddleware(token_view)
        expected = (
            ""Referer checking failed - https://www.evil.org/somepage does not ""
            ""match any trusted origins.""
        )
        with self.assertRaisesMessage(RejectRequest, expected):
            mw._check_referer(req)
        response = mw.process_view(req, token_view, (), {})
        self.assertEqual(response.status_code, 403)
","
        CsrfViewMiddleware generates a 403 response if it receives an HTTPS
        request with a bad host.
        ",15,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_https_malformed_host(self):
        
        req = self._get_request(method=""POST"")
        req._is_secure_override = True
        req.META[""HTTP_HOST""] = ""@malformed""
        req.META[""HTTP_REFERER""] = ""https://www.evil.org/somepage""
        req.META[""SERVER_PORT""] = ""443""
        mw = CsrfViewMiddleware(token_view)
        expected = (
            ""Referer checking failed - https://www.evil.org/somepage does not ""
            ""match any trusted origins.""
        )
        with self.assertRaisesMessage(RejectRequest, expected):
            mw._check_referer(req)
        response = mw.process_view(req, token_view, (), {})
        self.assertEqual(response.status_code, 403)

        ```
    ###Assistant : 
        CsrfViewMiddleware generates a 403 response if it receives an HTTPS
        request with a bad host.
        
    "
591,"def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, Any]]]:
        
        # TODO: self.authenticator should optionally pull from self._session.auth
        customers_stream = Customers(authenticator=self._session.auth)
        for customer in customers_stream.read_records(sync_mode=SyncMode.full_refresh):
            yield {""customer_id"": customer[""id""]}
","
        This stream is sliced per `customer_id`. This has two implications:
        (1) State can be checkpointed after processing each slice
        (2) The other parameters (e.g. request_params, path) can be dependent on this slice.

        This allows us to pull data on a per customer_id basis, since that's what Orb exposes.
        ",48,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, Any]]]:
        
        # TODO: self.authenticator should optionally pull from self._session.auth
        customers_stream = Customers(authenticator=self._session.auth)
        for customer in customers_stream.read_records(sync_mode=SyncMode.full_refresh):
            yield {""customer_id"": customer[""id""]}

        ```
    ###Assistant : 
        This stream is sliced per `customer_id`. This has two implications:
        (1) State can be checkpointed after processing each slice
        (2) The other parameters (e.g. request_params, path) can be dependent on this slice.

        This allows us to pull data on a per customer_id basis, since that's what Orb exposes.
        
    "
592,"def ragged_assert_compatible_and_get_flat_values(values, mask=None):
    
    if isinstance(values, list):
        is_all_ragged = all(isinstance(rt, tf.RaggedTensor) for rt in values)
        is_any_ragged = any(isinstance(rt, tf.RaggedTensor) for rt in values)
    else:
        is_all_ragged = isinstance(values, tf.RaggedTensor)
        is_any_ragged = is_all_ragged
    if is_all_ragged and ((mask is None) or isinstance(mask, tf.RaggedTensor)):
        to_be_stripped = False
        if not isinstance(values, list):
            values = [values]
            to_be_stripped = True

        # NOTE: we leave the flat_values compatibility to
        # tf.TensorShape `assert_is_compatible_with` check if both dynamic
        # dimensions are equal and then use the flat_values.
        nested_row_split_list = [rt.nested_row_splits for rt in values]
        assertion_list = _assert_splits_match(nested_row_split_list)

        # if both are ragged sample_weights also should be ragged with same
        # dims.
        if isinstance(mask, tf.RaggedTensor):
            assertion_list_for_mask = _assert_splits_match(
                [nested_row_split_list[0], mask.nested_row_splits]
            )
            with tf.control_dependencies(assertion_list_for_mask):
                mask = tf.expand_dims(mask.flat_values, -1)

        # values has at least 1 element.
        flat_values = []
        for value in values:
            with tf.control_dependencies(assertion_list):
                flat_values.append(tf.expand_dims(value.flat_values, -1))

        values = flat_values[0] if to_be_stripped else flat_values

    elif is_any_ragged:
        raise TypeError(
            ""Some of the inputs are not tf.RaggedTensor. ""
            f""Input received: {values}""
        )
    # values are empty or value are not ragged and mask is ragged.
    elif isinstance(mask, tf.RaggedTensor):
        raise TypeError(
            ""Ragged mask is not allowed with non-ragged inputs. ""
            f""Input received: {values}, mask received: {mask}""
        )

    return values, mask

","If ragged, it checks the compatibility and then returns the flat_values.

       Note: If two tensors are dense, it does not check their compatibility.
       Note: Although two ragged tensors with different ragged ranks could have
             identical overall rank and dimension sizes and hence be compatible,
             we do not support those cases.
    Args:
       values: A list of potentially ragged tensor of the same ragged_rank.
       mask: A potentially ragged tensor of the same ragged_rank as elements in
         Values.

    Returns:
       A tuple in which the first element is the list of tensors and the second
       is the mask tensor. ([Values], mask). Mask and the element in Values
       are equal to the flat_values of the input arguments (if they were
       ragged).
    ",116,193,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ragged_assert_compatible_and_get_flat_values(values, mask=None):
    
    if isinstance(values, list):
        is_all_ragged = all(isinstance(rt, tf.RaggedTensor) for rt in values)
        is_any_ragged = any(isinstance(rt, tf.RaggedTensor) for rt in values)
    else:
        is_all_ragged = isinstance(values, tf.RaggedTensor)
        is_any_ragged = is_all_ragged
    if is_all_ragged and ((mask is None) or isinstance(mask, tf.RaggedTensor)):
        to_be_stripped = False
        if not isinstance(values, list):
            values = [values]
            to_be_stripped = True

        # NOTE: we leave the flat_values compatibility to
        # tf.TensorShape `assert_is_compatible_with` check if both dynamic
        # dimensions are equal and then use the flat_values.
        nested_row_split_list = [rt.nested_row_splits for rt in values]
        assertion_list = _assert_splits_match(nested_row_split_list)

        # if both are ragged sample_weights also should be ragged with same
        # dims.
        if isinstance(mask, tf.RaggedTensor):
            assertion_list_for_mask = _assert_splits_match(
                [nested_row_split_list[0], mask.nested_row_splits]
            )
            with tf.control_dependencies(assertion_list_for_mask):
                mask = tf.expand_dims(mask.flat_values, -1)

        # values has at least 1 element.
        flat_values = []
        for value in values:
            with tf.control_dependencies(assertion_list):
                flat_values.append(tf.expand_dims(value.flat_values, -1))

        values = flat_values[0] if to_be_stripped else flat_values

    elif is_any_ragged:
        raise TypeError(
            ""Some of the inputs are not tf.RaggedTensor. ""
            f""Input received: {values}""
        )
    # values are empty or value are not ragged and mask is ragged.
    elif isinstance(mask, tf.RaggedTensor):
        raise TypeError(
            ""Ragged mask is not allowed with non-ragged inputs. ""
            f""Input received: {values}, mask received: {mask}""
        )

    return values, mask


        ```
    ###Assistant : If ragged, it checks the compatibility and then returns the flat_values.

       Note: If two tensors are dense, it does not check their compatibility.
       Note: Although two ragged tensors with different ragged ranks could have
             identical overall rank and dimension sizes and hence be compatible,
             we do not support those cases.
    Args:
       values: A list of potentially ragged tensor of the same ragged_rank.
       mask: A potentially ragged tensor of the same ragged_rank as elements in
         Values.

    Returns:
       A tuple in which the first element is the list of tensors and the second
       is the mask tensor. ([Values], mask). Mask and the element in Values
       are equal to the flat_values of the input arguments (if they were
       ragged).
    
    "
593,"def update_sandbox_args(self):
        
        if self.is_sandbox:
            host, port = HubIO.deploy_public_sandbox(self.args)
            self._sandbox_deployed = True
            self.first_pod_args.host = host
            self.first_pod_args.port = port
            if self.head_args:
                self.pod_args['head'].host = host
                self.pod_args['head'].port = port
",Update args of all its pods based on the host and port returned by Hubble,15,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_sandbox_args(self):
        
        if self.is_sandbox:
            host, port = HubIO.deploy_public_sandbox(self.args)
            self._sandbox_deployed = True
            self.first_pod_args.host = host
            self.first_pod_args.port = port
            if self.head_args:
                self.pod_args['head'].host = host
                self.pod_args['head'].port = port

        ```
    ###Assistant : Update args of all its pods based on the host and port returned by Hubble
    "
594,"def download_all():
    
    for name in DATA_HUB:
        download(name)

DATA_HUB['kaggle_house_train'] = (
    DATA_URL + 'kaggle_house_pred_train.csv',
    '585e9cc93e70b39160e7921475f9bcd7d31219ce')

DATA_HUB['kaggle_house_test'] = (
    DATA_URL + 'kaggle_house_pred_test.csv',
    'fa19780a7b011d9b009e8bff8e99922a8ee2eb90')
","Download all files in the DATA_HUB.

    Defined in :numref:`sec_kaggle_house`",9,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def download_all():
    
    for name in DATA_HUB:
        download(name)

DATA_HUB['kaggle_house_train'] = (
    DATA_URL + 'kaggle_house_pred_train.csv',
    '585e9cc93e70b39160e7921475f9bcd7d31219ce')

DATA_HUB['kaggle_house_test'] = (
    DATA_URL + 'kaggle_house_pred_test.csv',
    'fa19780a7b011d9b009e8bff8e99922a8ee2eb90')

        ```
    ###Assistant : Download all files in the DATA_HUB.

    Defined in :numref:`sec_kaggle_house`
    "
595,"def generate_matches(self, nodes):
        
        r = {}
        if nodes and self.match(nodes[0], r):
            yield 1, r

","
        Generator yielding all matches for this pattern.

        Default implementation for non-wildcard patterns.
        ",12,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_matches(self, nodes):
        
        r = {}
        if nodes and self.match(nodes[0], r):
            yield 1, r


        ```
    ###Assistant : 
        Generator yielding all matches for this pattern.

        Default implementation for non-wildcard patterns.
        
    "
596,"def user_can_delete_obj(self, user, obj):
        
        perm_codename = self.get_perm_codename(""delete"")
        return self.user_has_specific_permission(user, perm_codename)
","
        Return a boolean to indicate whether `user` is permitted to 'delete'
        a specific `self.model` instance.
        ",15,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def user_can_delete_obj(self, user, obj):
        
        perm_codename = self.get_perm_codename(""delete"")
        return self.user_has_specific_permission(user, perm_codename)

        ```
    ###Assistant : 
        Return a boolean to indicate whether `user` is permitted to 'delete'
        a specific `self.model` instance.
        
    "
597,"def _busy_indicator_trace(self, *args) -> None:
        
        logger.trace(""Busy indicator trace: %s"", args)  # type: ignore
        if self._busy_tkvar.get():
            self._start_busy_indicator()
        else:
            self._stop_busy_indicator()
"," Show or hide busy indicator based on whether the preview is updating.

        Parameters
        ----------
        args: unused
            Required for tkinter event, but unused
        ",22,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _busy_indicator_trace(self, *args) -> None:
        
        logger.trace(""Busy indicator trace: %s"", args)  # type: ignore
        if self._busy_tkvar.get():
            self._start_busy_indicator()
        else:
            self._stop_busy_indicator()

        ```
    ###Assistant :  Show or hide busy indicator based on whether the preview is updating.

        Parameters
        ----------
        args: unused
            Required for tkinter event, but unused
        
    "
598,"def _gen_html_string(self):
        
        self.html_string = _hilite_me(
            self.code_string,
            self.language,
            self.style,
            self.insert_line_no,
            ""border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"",
            self.file_path,
            self.line_no_from,
        )

        if self.generate_html_file:
            os.makedirs(
                os.path.join(""assets"", ""codes"", ""generated_html_files""),
                exist_ok=True,
            )
            with open(
                os.path.join(
                    ""assets"",
                    ""codes"",
                    ""generated_html_files"",
                    self.file_name + "".html"",
                ),
                ""w"",
            ) as file:
                file.write(self.html_string)
",Function to generate html string with code highlighted and stores in variable html_string.,13,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _gen_html_string(self):
        
        self.html_string = _hilite_me(
            self.code_string,
            self.language,
            self.style,
            self.insert_line_no,
            ""border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"",
            self.file_path,
            self.line_no_from,
        )

        if self.generate_html_file:
            os.makedirs(
                os.path.join(""assets"", ""codes"", ""generated_html_files""),
                exist_ok=True,
            )
            with open(
                os.path.join(
                    ""assets"",
                    ""codes"",
                    ""generated_html_files"",
                    self.file_name + "".html"",
                ),
                ""w"",
            ) as file:
                file.write(self.html_string)

        ```
    ###Assistant : Function to generate html string with code highlighted and stores in variable html_string.
    "
599,"def __getstate__(self):
        
        state = self.__dict__.copy()
        # remove the unpickleable RLock object
        state.pop(""_cookies_lock"")
        return state
","Unlike a normal CookieJar, this class is pickleable.",8,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __getstate__(self):
        
        state = self.__dict__.copy()
        # remove the unpickleable RLock object
        state.pop(""_cookies_lock"")
        return state

        ```
    ###Assistant : Unlike a normal CookieJar, this class is pickleable.
    "
600,"def get_model_dir(cfg):
    
    for key in cfg.keys():
        if type(cfg[key]) ==  dict and \
            (""enable"" in cfg[key].keys() and cfg[key]['enable']
                or ""enable"" not in cfg[key].keys()):

            if ""model_dir"" in cfg[key].keys():
                model_dir = cfg[key][""model_dir""]
                downloaded_model_dir = auto_download_model(model_dir)
                if downloaded_model_dir:
                    model_dir = downloaded_model_dir
                    cfg[key][""model_dir""] = model_dir
                print(key, "" model dir: "", model_dir)
            elif key == ""VEHICLE_PLATE"":
                det_model_dir = cfg[key][""det_model_dir""]
                downloaded_det_model_dir = auto_download_model(det_model_dir)
                if downloaded_det_model_dir:
                    det_model_dir = downloaded_det_model_dir
                    cfg[key][""det_model_dir""] = det_model_dir
                print(""det_model_dir model dir: "", det_model_dir)

                rec_model_dir = cfg[key][""rec_model_dir""]
                downloaded_rec_model_dir = auto_download_model(rec_model_dir)
                if downloaded_rec_model_dir:
                    rec_model_dir = downloaded_rec_model_dir
                    cfg[key][""rec_model_dir""] = rec_model_dir
                print(""rec_model_dir model dir: "", rec_model_dir)

        elif key == ""MOT"":  # for idbased and skeletonbased actions
            model_dir = cfg[key][""model_dir""]
            downloaded_model_dir = auto_download_model(model_dir)
            if downloaded_model_dir:
                model_dir = downloaded_model_dir
                cfg[key][""model_dir""] = model_dir
            print(""mot_model_dir model_dir: "", model_dir)

"," 
        Auto download inference model if the model_path is a url link. 
        Otherwise it will use the model_path directly.
    ",18,116,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_model_dir(cfg):
    
    for key in cfg.keys():
        if type(cfg[key]) ==  dict and \
            (""enable"" in cfg[key].keys() and cfg[key]['enable']
                or ""enable"" not in cfg[key].keys()):

            if ""model_dir"" in cfg[key].keys():
                model_dir = cfg[key][""model_dir""]
                downloaded_model_dir = auto_download_model(model_dir)
                if downloaded_model_dir:
                    model_dir = downloaded_model_dir
                    cfg[key][""model_dir""] = model_dir
                print(key, "" model dir: "", model_dir)
            elif key == ""VEHICLE_PLATE"":
                det_model_dir = cfg[key][""det_model_dir""]
                downloaded_det_model_dir = auto_download_model(det_model_dir)
                if downloaded_det_model_dir:
                    det_model_dir = downloaded_det_model_dir
                    cfg[key][""det_model_dir""] = det_model_dir
                print(""det_model_dir model dir: "", det_model_dir)

                rec_model_dir = cfg[key][""rec_model_dir""]
                downloaded_rec_model_dir = auto_download_model(rec_model_dir)
                if downloaded_rec_model_dir:
                    rec_model_dir = downloaded_rec_model_dir
                    cfg[key][""rec_model_dir""] = rec_model_dir
                print(""rec_model_dir model dir: "", rec_model_dir)

        elif key == ""MOT"":  # for idbased and skeletonbased actions
            model_dir = cfg[key][""model_dir""]
            downloaded_model_dir = auto_download_model(model_dir)
            if downloaded_model_dir:
                model_dir = downloaded_model_dir
                cfg[key][""model_dir""] = model_dir
            print(""mot_model_dir model_dir: "", model_dir)


        ```
    ###Assistant :  
        Auto download inference model if the model_path is a url link. 
        Otherwise it will use the model_path directly.
    
    "
601,"def component(self, x, y):
        
        if x >= 0 and x < self.__height and y >= 0 and y < self.__width:
            return self.__matrix[x][y]
        else:
            raise Exception(""changeComponent: indices out of bounds"")
","
        returns the specified (x,y) component
        ",5,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def component(self, x, y):
        
        if x >= 0 and x < self.__height and y >= 0 and y < self.__width:
            return self.__matrix[x][y]
        else:
            raise Exception(""changeComponent: indices out of bounds"")

        ```
    ###Assistant : 
        returns the specified (x,y) component
        
    "
602,"def toggle(self, all=None, ticks=None, ticklabels=None, label=None):
        
        if all:
            _ticks, _ticklabels, _label = True, True, True
        elif all is not None:
            _ticks, _ticklabels, _label = False, False, False
        else:
            _ticks, _ticklabels, _label = None, None, None

        if ticks is not None:
            _ticks = ticks
        if ticklabels is not None:
            _ticklabels = ticklabels
        if label is not None:
            _label = label

        if _ticks is not None:
            self.major_ticks.set_visible(_ticks)
            self.minor_ticks.set_visible(_ticks)
        if _ticklabels is not None:
            self.major_ticklabels.set_visible(_ticklabels)
            self.minor_ticklabels.set_visible(_ticklabels)
        if _label is not None:
            self.label.set_visible(_label)
","
        Toggle visibility of ticks, ticklabels, and (axis) label.
        To turn all off, ::

          axis.toggle(all=False)

        To turn all off but ticks on ::

          axis.toggle(all=False, ticks=True)

        To turn all on but (axis) label off ::

          axis.toggle(all=True, label=False)

        ",35,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def toggle(self, all=None, ticks=None, ticklabels=None, label=None):
        
        if all:
            _ticks, _ticklabels, _label = True, True, True
        elif all is not None:
            _ticks, _ticklabels, _label = False, False, False
        else:
            _ticks, _ticklabels, _label = None, None, None

        if ticks is not None:
            _ticks = ticks
        if ticklabels is not None:
            _ticklabels = ticklabels
        if label is not None:
            _label = label

        if _ticks is not None:
            self.major_ticks.set_visible(_ticks)
            self.minor_ticks.set_visible(_ticks)
        if _ticklabels is not None:
            self.major_ticklabels.set_visible(_ticklabels)
            self.minor_ticklabels.set_visible(_ticklabels)
        if _label is not None:
            self.label.set_visible(_label)

        ```
    ###Assistant : 
        Toggle visibility of ticks, ticklabels, and (axis) label.
        To turn all off, ::

          axis.toggle(all=False)

        To turn all off but ticks on ::

          axis.toggle(all=False, ticks=True)

        To turn all on but (axis) label off ::

          axis.toggle(all=True, label=False)

        
    "
603,"def mock_json_schema(request, monkeypatch, tmp_path):
    
    # Do not patch integration tests
    if ""integration"" in request.keywords:
        return

    # Mock the subclasses list to make it very small, containing only mock nodes
    monkeypatch.setattr(
        haystack.nodes._json_schema,
        ""find_subclasses_in_modules"",
        lambda *a, **k: [(conftest, MockDocumentStore), (conftest, MockReader), (conftest, MockRetriever)],
    )
    # Point the JSON schema path to tmp_path
    monkeypatch.setattr(haystack.pipelines.config, ""JSON_SCHEMAS_PATH"", tmp_path)

    # Generate mock schema in tmp_path
    filename = f""haystack-pipeline-master.schema.json""
    test_schema = _json_schema.get_json_schema(filename=filename, version=""ignore"")

    with open(tmp_path / filename, ""w"") as schema_file:
        json.dump(test_schema, schema_file, indent=4)


#
# Integration
#


@pytest.mark.integration
@pytest.mark.elasticsearch","
    JSON schema with the master version and only mocked nodes.
    ",10,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mock_json_schema(request, monkeypatch, tmp_path):
    
    # Do not patch integration tests
    if ""integration"" in request.keywords:
        return

    # Mock the subclasses list to make it very small, containing only mock nodes
    monkeypatch.setattr(
        haystack.nodes._json_schema,
        ""find_subclasses_in_modules"",
        lambda *a, **k: [(conftest, MockDocumentStore), (conftest, MockReader), (conftest, MockRetriever)],
    )
    # Point the JSON schema path to tmp_path
    monkeypatch.setattr(haystack.pipelines.config, ""JSON_SCHEMAS_PATH"", tmp_path)

    # Generate mock schema in tmp_path
    filename = f""haystack-pipeline-master.schema.json""
    test_schema = _json_schema.get_json_schema(filename=filename, version=""ignore"")

    with open(tmp_path / filename, ""w"") as schema_file:
        json.dump(test_schema, schema_file, indent=4)


#
# Integration
#


@pytest.mark.integration
@pytest.mark.elasticsearch
        ```
    ###Assistant : 
    JSON schema with the master version and only mocked nodes.
    
    "
604,"def _has_nchw_support():
    
    explicitly_on_cpu = _is_current_explicit_device(""CPU"")
    gpus_available = bool(_get_available_gpus())
    return not explicitly_on_cpu and gpus_available


# VARIABLE MANIPULATION

","Check whether the current scope supports NCHW ops.

    TensorFlow does not support NCHW on CPU. Therefore we check if we are not
    explicitly put on
    CPU, and have GPUs available. In this case there will be soft-placing on the
    GPU device.

    Returns:
        bool: if the current scope device placement would support nchw
    ",52,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _has_nchw_support():
    
    explicitly_on_cpu = _is_current_explicit_device(""CPU"")
    gpus_available = bool(_get_available_gpus())
    return not explicitly_on_cpu and gpus_available


# VARIABLE MANIPULATION


        ```
    ###Assistant : Check whether the current scope supports NCHW ops.

    TensorFlow does not support NCHW on CPU. Therefore we check if we are not
    explicitly put on
    CPU, and have GPUs available. In this case there will be soft-placing on the
    GPU device.

    Returns:
        bool: if the current scope device placement would support nchw
    
    "
605,"def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
    
    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
    n_dim = tensor.dim()
    if n_dim == 4:
        n_img = len(tensor)
        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 3:
        img_np = tensor.numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 2:
        img_np = tensor.numpy()
    else:
        raise TypeError(
            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
    if out_type == np.uint8:
        img_np = (img_np * 255.0).round()
        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
    return img_np.astype(out_type)




","
    Converts a torch Tensor into an image Numpy array of BGR channel order
    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
    
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
# The following two are enough.
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------
",62,117,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
    
    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
    n_dim = tensor.dim()
    if n_dim == 4:
        n_img = len(tensor)
        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 3:
        img_np = tensor.numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 2:
        img_np = tensor.numpy()
    else:
        raise TypeError(
            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
    if out_type == np.uint8:
        img_np = (img_np * 255.0).round()
        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
    return img_np.astype(out_type)





        ```
    ###Assistant : 
    Converts a torch Tensor into an image Numpy array of BGR channel order
    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
    
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
# The following two are enough.
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------

    "
606,"def get_local_ip_address() -> str:
    
    try:
        ip_address = requests.get(
            ""https://checkip.amazonaws.com/"", timeout=3
        ).text.strip()
    except (requests.ConnectionError, requests.exceptions.ReadTimeout):
        ip_address = ""No internet connection""
    return ip_address

","Gets the public IP address or returns the string ""No internet connection"" if unable to obtain it.",17,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_local_ip_address() -> str:
    
    try:
        ip_address = requests.get(
            ""https://checkip.amazonaws.com/"", timeout=3
        ).text.strip()
    except (requests.ConnectionError, requests.exceptions.ReadTimeout):
        ip_address = ""No internet connection""
    return ip_address


        ```
    ###Assistant : Gets the public IP address or returns the string ""No internet connection"" if unable to obtain it.
    "
607,"def get_tip(self):
        
        tips = self.get_tips()
        if len(tips) == 0:
            raise Exception(""tip not found"")
        else:
            return tips[0]
","Returns the TipableVMobject instance's (first) tip,
        otherwise throws an exception.",10,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_tip(self):
        
        tips = self.get_tips()
        if len(tips) == 0:
            raise Exception(""tip not found"")
        else:
            return tips[0]

        ```
    ###Assistant : Returns the TipableVMobject instance's (first) tip,
        otherwise throws an exception.
    "
608,"def shuffle(self, func, lengths, **kwargs):
        
        num_splits = len(lengths)
        # We add these to kwargs and will pop them off before performing the operation.
        kwargs[""manual_partition""] = True
        kwargs[""_lengths""] = lengths
        args = [self.axis, func, num_splits, False]
        args.extend(self.list_of_blocks)
        return self._wrap_partitions(self.deploy_axis_func(*args, **kwargs))
","
        Shuffle the order of the data in this axis partition based on the `lengths`.

        Parameters
        ----------
        func : callable
            The function to apply before splitting.
        lengths : list
            The list of partition lengths to split the result into.
        **kwargs : dict
            Additional keywords arguments to be passed in `func`.

        Returns
        -------
        list
            A list of `PandasDataframePartition` objects split by `lengths`.
        ",60,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def shuffle(self, func, lengths, **kwargs):
        
        num_splits = len(lengths)
        # We add these to kwargs and will pop them off before performing the operation.
        kwargs[""manual_partition""] = True
        kwargs[""_lengths""] = lengths
        args = [self.axis, func, num_splits, False]
        args.extend(self.list_of_blocks)
        return self._wrap_partitions(self.deploy_axis_func(*args, **kwargs))

        ```
    ###Assistant : 
        Shuffle the order of the data in this axis partition based on the `lengths`.

        Parameters
        ----------
        func : callable
            The function to apply before splitting.
        lengths : list
            The list of partition lengths to split the result into.
        **kwargs : dict
            Additional keywords arguments to be passed in `func`.

        Returns
        -------
        list
            A list of `PandasDataframePartition` objects split by `lengths`.
        
    "
609,"def test_background_add_room_type_column(self):
        

        # Create a room without a type
        room_id = self._generate_room()

        # Get event_id of the m.room.create event
        event_id = self.get_success(
            self.store.db_pool.simple_select_one_onecol(
                table=""current_state_events"",
                keyvalues={
                    ""room_id"": room_id,
                    ""type"": ""m.room.create"",
                },
                retcol=""event_id"",
            )
        )

        # Fake a room creation event with a room type
        event = {
            ""content"": {
                ""creator"": ""@user:server.org"",
                ""room_version"": ""9"",
                ""type"": RoomTypes.SPACE,
            },
            ""type"": ""m.room.create"",
        }
        self.get_success(
            self.store.db_pool.simple_update(
                table=""event_json"",
                keyvalues={""event_id"": event_id},
                updatevalues={""json"": json.dumps(event)},
                desc=""test"",
            )
        )

        # Insert and run the background update
        self.get_success(
            self.store.db_pool.simple_insert(
                ""background_updates"",
                {
                    ""update_name"": _BackgroundUpdates.ADD_ROOM_TYPE_COLUMN,
                    ""progress_json"": ""{}"",
                },
            )
        )

        # ... and tell the DataStore that it hasn't finished all updates yet
        self.store.db_pool.updates._all_done = False

        # Now let's actually drive the updates to completion
        self.wait_for_background_updates()

        # Make sure the background update filled in the room type
        room_type_after = self.get_success(
            self.store.db_pool.simple_select_one_onecol(
                table=""room_stats_state"",
                keyvalues={""room_id"": room_id},
                retcol=""room_type"",
                allow_none=True,
            )
        )
        self.assertEqual(room_type_after, RoomTypes.SPACE)
","Test that the background update to populate the `room_type` column in
        `room_stats_state` works properly.
        ",14,136,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_background_add_room_type_column(self):
        

        # Create a room without a type
        room_id = self._generate_room()

        # Get event_id of the m.room.create event
        event_id = self.get_success(
            self.store.db_pool.simple_select_one_onecol(
                table=""current_state_events"",
                keyvalues={
                    ""room_id"": room_id,
                    ""type"": ""m.room.create"",
                },
                retcol=""event_id"",
            )
        )

        # Fake a room creation event with a room type
        event = {
            ""content"": {
                ""creator"": ""@user:server.org"",
                ""room_version"": ""9"",
                ""type"": RoomTypes.SPACE,
            },
            ""type"": ""m.room.create"",
        }
        self.get_success(
            self.store.db_pool.simple_update(
                table=""event_json"",
                keyvalues={""event_id"": event_id},
                updatevalues={""json"": json.dumps(event)},
                desc=""test"",
            )
        )

        # Insert and run the background update
        self.get_success(
            self.store.db_pool.simple_insert(
                ""background_updates"",
                {
                    ""update_name"": _BackgroundUpdates.ADD_ROOM_TYPE_COLUMN,
                    ""progress_json"": ""{}"",
                },
            )
        )

        # ... and tell the DataStore that it hasn't finished all updates yet
        self.store.db_pool.updates._all_done = False

        # Now let's actually drive the updates to completion
        self.wait_for_background_updates()

        # Make sure the background update filled in the room type
        room_type_after = self.get_success(
            self.store.db_pool.simple_select_one_onecol(
                table=""room_stats_state"",
                keyvalues={""room_id"": room_id},
                retcol=""room_type"",
                allow_none=True,
            )
        )
        self.assertEqual(room_type_after, RoomTypes.SPACE)

        ```
    ###Assistant : Test that the background update to populate the `room_type` column in
        `room_stats_state` works properly.
        
    "
610,"def get_feedback():
    
    labels = DOCUMENT_STORE.get_all_labels()
    return labels


@router.delete(""/feedback"")","
    This endpoint allows the API user to retrieve all the feedback that has been submitted
    through the `POST /feedback` endpoint.
    ",20,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_feedback():
    
    labels = DOCUMENT_STORE.get_all_labels()
    return labels


@router.delete(""/feedback"")
        ```
    ###Assistant : 
    This endpoint allows the API user to retrieve all the feedback that has been submitted
    through the `POST /feedback` endpoint.
    
    "
611,"def get_formatter_for_filename(fn, **options):
    
    fn = basename(fn)
    for modname, name, _, filenames, _ in FORMATTERS.values():
        for filename in filenames:
            if _fn_matches(fn, filename):
                if name not in _formatter_cache:
                    _load_formatters(modname)
                return _formatter_cache[name](**options)
    for cls in find_plugin_formatters():
        for filename in cls.filenames:
            if _fn_matches(fn, filename):
                return cls(**options)
    raise ClassNotFound(""no formatter found for file name %r"" % fn)

","Lookup and instantiate a formatter by filename pattern.

    Raises ClassNotFound if not found.
    ",13,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_formatter_for_filename(fn, **options):
    
    fn = basename(fn)
    for modname, name, _, filenames, _ in FORMATTERS.values():
        for filename in filenames:
            if _fn_matches(fn, filename):
                if name not in _formatter_cache:
                    _load_formatters(modname)
                return _formatter_cache[name](**options)
    for cls in find_plugin_formatters():
        for filename in cls.filenames:
            if _fn_matches(fn, filename):
                return cls(**options)
    raise ClassNotFound(""no formatter found for file name %r"" % fn)


        ```
    ###Assistant : Lookup and instantiate a formatter by filename pattern.

    Raises ClassNotFound if not found.
    
    "
612,"def setup_sigterm_on_parent_death():
    
    try:
        import ctypes
        import signal

        libc = ctypes.CDLL(""libc.so.6"")
        # Set the parent process death signal of the command process to SIGTERM.
        libc.prctl(1, signal.SIGTERM)  # PR_SET_PDEATHSIG, see prctl.h
    except OSError as e:
        _logger.warning(f""Setup libc.prctl PR_SET_PDEATHSIG failed, error {repr(e)}."")
","
    Uses prctl to automatically send SIGTERM to the child process when its parent is
    dead. The child process itself should handle SIGTERM properly.
    ",23,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setup_sigterm_on_parent_death():
    
    try:
        import ctypes
        import signal

        libc = ctypes.CDLL(""libc.so.6"")
        # Set the parent process death signal of the command process to SIGTERM.
        libc.prctl(1, signal.SIGTERM)  # PR_SET_PDEATHSIG, see prctl.h
    except OSError as e:
        _logger.warning(f""Setup libc.prctl PR_SET_PDEATHSIG failed, error {repr(e)}."")

        ```
    ###Assistant : 
    Uses prctl to automatically send SIGTERM to the child process when its parent is
    dead. The child process itself should handle SIGTERM properly.
    
    "
613,"def force_reads(self) -> ""Dataset[T]"":
        
        blocks = self.get_internal_block_refs()
        bar = ProgressBar(""Force reads"", len(blocks))
        bar.block_until_complete(blocks)
        return self
","Force full evaluation of the blocks of this dataset.

        This can be used to read all blocks into memory. By default, Datasets
        doesn't read blocks from the datasource until the first transform.
        ",32,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def force_reads(self) -> ""Dataset[T]"":
        
        blocks = self.get_internal_block_refs()
        bar = ProgressBar(""Force reads"", len(blocks))
        bar.block_until_complete(blocks)
        return self

        ```
    ###Assistant : Force full evaluation of the blocks of this dataset.

        This can be used to read all blocks into memory. By default, Datasets
        doesn't read blocks from the datasource until the first transform.
        
    "
614,"def __iter__(self) -> Iterator[tuple[Widget, Region, Region, Size, Size]]:
        
        layers = sorted(self.map.items(), key=lambda item: item[1].order, reverse=True)
        intersection = Region.intersection
        for widget, (region, _order, clip, virtual_size, container_size) in layers:
            yield (
                widget,
                intersection(region, clip),
                region,
                virtual_size,
                container_size,
            )
","Iterate map with information regarding each widget and is position

        Yields:
            Iterator[tuple[Widget, Region, Region, Size, Size]]: Iterates a tuple of
                Widget, clip region, region, virtual size, and container size.
        ",29,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __iter__(self) -> Iterator[tuple[Widget, Region, Region, Size, Size]]:
        
        layers = sorted(self.map.items(), key=lambda item: item[1].order, reverse=True)
        intersection = Region.intersection
        for widget, (region, _order, clip, virtual_size, container_size) in layers:
            yield (
                widget,
                intersection(region, clip),
                region,
                virtual_size,
                container_size,
            )

        ```
    ###Assistant : Iterate map with information regarding each widget and is position

        Yields:
            Iterator[tuple[Widget, Region, Region, Size, Size]]: Iterates a tuple of
                Widget, clip region, region, virtual size, and container size.
        
    "
615,"def get_names_flat(adtype):
    
    listnames = []
    names = adtype.names
    for name in names:
        listnames.append(name)
        current = adtype[name]
        if current.names is not None:
            listnames.extend(get_names_flat(current))
    return tuple(listnames)

","
    Returns the field names of the input datatype as a tuple. Input datatype
    has to have fields otherwise error is raised.
    Nested structure are flattened beforehand.

    Parameters
    ----------
    adtype : dtype
        Input datatype

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> rfn.get_names_flat(np.empty((1,), dtype=[('A', int)]).dtype) is None
    False
    >>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', str)]).dtype)
    ('A', 'B')
    >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
    >>> rfn.get_names_flat(adtype)
    ('a', 'b', 'ba', 'bb')
    ",72,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_names_flat(adtype):
    
    listnames = []
    names = adtype.names
    for name in names:
        listnames.append(name)
        current = adtype[name]
        if current.names is not None:
            listnames.extend(get_names_flat(current))
    return tuple(listnames)


        ```
    ###Assistant : 
    Returns the field names of the input datatype as a tuple. Input datatype
    has to have fields otherwise error is raised.
    Nested structure are flattened beforehand.

    Parameters
    ----------
    adtype : dtype
        Input datatype

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> rfn.get_names_flat(np.empty((1,), dtype=[('A', int)]).dtype) is None
    False
    >>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', str)]).dtype)
    ('A', 'B')
    >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
    >>> rfn.get_names_flat(adtype)
    ('a', 'b', 'ba', 'bb')
    
    "
616,"def test_memory_leak(self):
        
        import gc
        import weakref

        results = {}
        for kind in plotting.PlotAccessor._all_kinds:

            args = {}
            if kind in [""hexbin"", ""scatter"", ""pie""]:
                df = DataFrame(
                    {
                        ""A"": np.random.uniform(size=20),
                        ""B"": np.random.uniform(size=20),
                        ""C"": np.arange(20) + np.random.uniform(size=20),
                    }
                )
                args = {""x"": ""A"", ""y"": ""B""}
            elif kind == ""area"":
                df = tm.makeTimeDataFrame().abs()
            else:
                df = tm.makeTimeDataFrame()

            # Use a weakref so we can see if the object gets collected without
            # also preventing it from being collected
            results[kind] = weakref.proxy(df.plot(kind=kind, **args))

        # have matplotlib delete all the figures
        tm.close()
        # force a garbage collection
        gc.collect()
        msg = ""weakly-referenced object no longer exists""
        for key in results:
            # check that every plot was collected
            with pytest.raises(ReferenceError, match=msg):
                # need to actually access something to get an error
                results[key].lines
",Check that every plot type gets properly collected.,8,124,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_memory_leak(self):
        
        import gc
        import weakref

        results = {}
        for kind in plotting.PlotAccessor._all_kinds:

            args = {}
            if kind in [""hexbin"", ""scatter"", ""pie""]:
                df = DataFrame(
                    {
                        ""A"": np.random.uniform(size=20),
                        ""B"": np.random.uniform(size=20),
                        ""C"": np.arange(20) + np.random.uniform(size=20),
                    }
                )
                args = {""x"": ""A"", ""y"": ""B""}
            elif kind == ""area"":
                df = tm.makeTimeDataFrame().abs()
            else:
                df = tm.makeTimeDataFrame()

            # Use a weakref so we can see if the object gets collected without
            # also preventing it from being collected
            results[kind] = weakref.proxy(df.plot(kind=kind, **args))

        # have matplotlib delete all the figures
        tm.close()
        # force a garbage collection
        gc.collect()
        msg = ""weakly-referenced object no longer exists""
        for key in results:
            # check that every plot was collected
            with pytest.raises(ReferenceError, match=msg):
                # need to actually access something to get an error
                results[key].lines

        ```
    ###Assistant : Check that every plot type gets properly collected.
    "
617,"def disabled_excepthook() -> Iterator[None]:
    
    old_excepthook = sys.excepthook
    sys.excepthook = sys.__excepthook__
    try:
        yield
    finally:
        # If the code we did run did change sys.excepthook, we leave it
        # unchanged. Otherwise, we reset it.
        if sys.excepthook is sys.__excepthook__:
            sys.excepthook = old_excepthook

",Run code with the exception hook temporarily disabled.,8,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def disabled_excepthook() -> Iterator[None]:
    
    old_excepthook = sys.excepthook
    sys.excepthook = sys.__excepthook__
    try:
        yield
    finally:
        # If the code we did run did change sys.excepthook, we leave it
        # unchanged. Otherwise, we reset it.
        if sys.excepthook is sys.__excepthook__:
            sys.excepthook = old_excepthook


        ```
    ###Assistant : Run code with the exception hook temporarily disabled.
    "
618,"def trigger_import(*dfs):
    
    if ASV_USE_STORAGE_FORMAT != ""hdk"" or ASV_USE_IMPL == ""pandas"":
        return

    from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import (
        DbWorker,
    )

    for df in dfs:
        df.shape  # to trigger real execution
        df._query_compiler._modin_frame._partitions[0][
            0
        ].frame_id = DbWorker().import_arrow_table(
            df._query_compiler._modin_frame._partitions[0][0].get()
        )  # to trigger real execution

","
    Trigger import execution for DataFrames obtained by HDK engine.

    Parameters
    ----------
    *dfs : iterable
        DataFrames to trigger import.
    ",18,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def trigger_import(*dfs):
    
    if ASV_USE_STORAGE_FORMAT != ""hdk"" or ASV_USE_IMPL == ""pandas"":
        return

    from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import (
        DbWorker,
    )

    for df in dfs:
        df.shape  # to trigger real execution
        df._query_compiler._modin_frame._partitions[0][
            0
        ].frame_id = DbWorker().import_arrow_table(
            df._query_compiler._modin_frame._partitions[0][0].get()
        )  # to trigger real execution


        ```
    ###Assistant : 
    Trigger import execution for DataFrames obtained by HDK engine.

    Parameters
    ----------
    *dfs : iterable
        DataFrames to trigger import.
    
    "
619,"def _jacfwd(f, primals):
    
    jac_flat = []
    flat_primals = tf.nest.flatten(primals)
    tangent_mask = [tf.zeros_like(primal) for primal in flat_primals]
    for primal_index, primal in enumerate(flat_primals):
        primal_vector = tf.reshape(primal, [-1])
        primal_vector_length = tf.size(primal_vector)
        jac_columns = []
        for element_index in tf.range(primal_vector_length):
            mask = tf.one_hot(element_index, primal_vector_length)
            tangent_mask[primal_index] = tf.reshape(mask, tf.shape(primal))
            jac_columns.append(
                tf.nest.map_structure(
                    functools.partial(tf.reshape, shape=[-1]),
                    _jvp(
                        f,
                        primals,
                        tf.nest.pack_sequence_as(primals, tangent_mask),
                    )[1],
                )
            )
        jac_flat.append(tf.stack(jac_columns, axis=1))
        tangent_mask[primal_index] = tf.zeros_like(primal)
    return tf.nest.pack_sequence_as(primals, jac_flat)

",Compute the jacobian of `f` at `primals` using forward-mode autodiff.,10,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _jacfwd(f, primals):
    
    jac_flat = []
    flat_primals = tf.nest.flatten(primals)
    tangent_mask = [tf.zeros_like(primal) for primal in flat_primals]
    for primal_index, primal in enumerate(flat_primals):
        primal_vector = tf.reshape(primal, [-1])
        primal_vector_length = tf.size(primal_vector)
        jac_columns = []
        for element_index in tf.range(primal_vector_length):
            mask = tf.one_hot(element_index, primal_vector_length)
            tangent_mask[primal_index] = tf.reshape(mask, tf.shape(primal))
            jac_columns.append(
                tf.nest.map_structure(
                    functools.partial(tf.reshape, shape=[-1]),
                    _jvp(
                        f,
                        primals,
                        tf.nest.pack_sequence_as(primals, tangent_mask),
                    )[1],
                )
            )
        jac_flat.append(tf.stack(jac_columns, axis=1))
        tangent_mask[primal_index] = tf.zeros_like(primal)
    return tf.nest.pack_sequence_as(primals, jac_flat)


        ```
    ###Assistant : Compute the jacobian of `f` at `primals` using forward-mode autodiff.
    "
620,"def record_timing(name, duration=None, description=None):
        
        timing_information = getattr(flask.g, ""timing_information"", {})

        if name in timing_information:
            raise KeyError(f'Duplicate resource name ""{name}"" found.')

        timing_information[name] = {""dur"": round(duration * 1000), ""desc"": description}

        setattr(flask.g, ""timing_information"", timing_information)
","Records timing information for a server resource.

        :param name: The name of the resource.
        :type name: string

        :param duration: The time in seconds to report. Internally, this
            is rounded to the nearest millisecond.
        :type duration: float or None

        :param description: A description of the resource.
        :type description: string or None
        ",50,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def record_timing(name, duration=None, description=None):
        
        timing_information = getattr(flask.g, ""timing_information"", {})

        if name in timing_information:
            raise KeyError(f'Duplicate resource name ""{name}"" found.')

        timing_information[name] = {""dur"": round(duration * 1000), ""desc"": description}

        setattr(flask.g, ""timing_information"", timing_information)

        ```
    ###Assistant : Records timing information for a server resource.

        :param name: The name of the resource.
        :type name: string

        :param duration: The time in seconds to report. Internally, this
            is rounded to the nearest millisecond.
        :type duration: float or None

        :param description: A description of the resource.
        :type description: string or None
        
    "
621,"def admin_actions(context):
    
    context[""action_index""] = context.get(""action_index"", -1) + 1
    return context


@register.tag(name=""admin_actions"")","
    Track the number of times the action field has been rendered on the page,
    so we know which value to use.
    ",21,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def admin_actions(context):
    
    context[""action_index""] = context.get(""action_index"", -1) + 1
    return context


@register.tag(name=""admin_actions"")
        ```
    ###Assistant : 
    Track the number of times the action field has been rendered on the page,
    so we know which value to use.
    
    "
622,"async def test_lights(hass, mock_bridge_v2, v2_resources_test_data):
    
    await mock_bridge_v2.api.load_test_data(v2_resources_test_data)

    await setup_platform(hass, mock_bridge_v2, ""light"")
    # there shouldn't have been any requests at this point
    assert len(mock_bridge_v2.mock_requests) == 0
    # 6 entities should be created from test data (grouped_lights are disabled by default)
    assert len(hass.states.async_all()) == 6

    # test light which supports color and color temperature
    light_1 = hass.states.get(""light.hue_light_with_color_and_color_temperature_1"")
    assert light_1 is not None
    assert (
        light_1.attributes[""friendly_name""]
        == ""Hue light with color and color temperature 1""
    )
    assert light_1.state == ""on""
    assert light_1.attributes[""brightness""] == int(46.85 / 100 * 255)
    assert light_1.attributes[""mode""] == ""normal""
    assert light_1.attributes[""color_mode""] == COLOR_MODE_XY
    assert set(light_1.attributes[""supported_color_modes""]) == {
        COLOR_MODE_COLOR_TEMP,
        COLOR_MODE_XY,
    }
    assert light_1.attributes[""xy_color""] == (0.5614, 0.4058)
    assert light_1.attributes[""min_mireds""] == 153
    assert light_1.attributes[""max_mireds""] == 500
    assert light_1.attributes[""dynamics""] == ""dynamic_palette""
    assert light_1.attributes[""effect_list""] == [""None"", ""candle"", ""fire""]
    assert light_1.attributes[""effect""] == ""None""

    # test light which supports color temperature only
    light_2 = hass.states.get(""light.hue_light_with_color_temperature_only"")
    assert light_2 is not None
    assert (
        light_2.attributes[""friendly_name""] == ""Hue light with color temperature only""
    )
    assert light_2.state == ""off""
    assert light_2.attributes[""mode""] == ""normal""
    assert light_2.attributes[""supported_color_modes""] == [COLOR_MODE_COLOR_TEMP]
    assert light_2.attributes[""min_mireds""] == 153
    assert light_2.attributes[""max_mireds""] == 454
    assert light_2.attributes[""dynamics""] == ""none""
    assert light_2.attributes[""effect_list""] == [""None"", ""candle"", ""sunrise""]

    # test light which supports color only
    light_3 = hass.states.get(""light.hue_light_with_color_only"")
    assert light_3 is not None
    assert light_3.attributes[""friendly_name""] == ""Hue light with color only""
    assert light_3.state == ""on""
    assert light_3.attributes[""brightness""] == 128
    assert light_3.attributes[""mode""] == ""normal""
    assert light_3.attributes[""supported_color_modes""] == [COLOR_MODE_XY]
    assert light_3.attributes[""color_mode""] == COLOR_MODE_XY
    assert light_3.attributes[""dynamics""] == ""dynamic_palette""

    # test light which supports on/off only
    light_4 = hass.states.get(""light.hue_on_off_light"")
    assert light_4 is not None
    assert light_4.attributes[""friendly_name""] == ""Hue on/off light""
    assert light_4.state == ""off""
    assert light_4.attributes[""mode""] == ""normal""
    assert light_4.attributes[""supported_color_modes""] == []

",Test if all v2 lights get created with correct features.,10,264,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_lights(hass, mock_bridge_v2, v2_resources_test_data):
    
    await mock_bridge_v2.api.load_test_data(v2_resources_test_data)

    await setup_platform(hass, mock_bridge_v2, ""light"")
    # there shouldn't have been any requests at this point
    assert len(mock_bridge_v2.mock_requests) == 0
    # 6 entities should be created from test data (grouped_lights are disabled by default)
    assert len(hass.states.async_all()) == 6

    # test light which supports color and color temperature
    light_1 = hass.states.get(""light.hue_light_with_color_and_color_temperature_1"")
    assert light_1 is not None
    assert (
        light_1.attributes[""friendly_name""]
        == ""Hue light with color and color temperature 1""
    )
    assert light_1.state == ""on""
    assert light_1.attributes[""brightness""] == int(46.85 / 100 * 255)
    assert light_1.attributes[""mode""] == ""normal""
    assert light_1.attributes[""color_mode""] == COLOR_MODE_XY
    assert set(light_1.attributes[""supported_color_modes""]) == {
        COLOR_MODE_COLOR_TEMP,
        COLOR_MODE_XY,
    }
    assert light_1.attributes[""xy_color""] == (0.5614, 0.4058)
    assert light_1.attributes[""min_mireds""] == 153
    assert light_1.attributes[""max_mireds""] == 500
    assert light_1.attributes[""dynamics""] == ""dynamic_palette""
    assert light_1.attributes[""effect_list""] == [""None"", ""candle"", ""fire""]
    assert light_1.attributes[""effect""] == ""None""

    # test light which supports color temperature only
    light_2 = hass.states.get(""light.hue_light_with_color_temperature_only"")
    assert light_2 is not None
    assert (
        light_2.attributes[""friendly_name""] == ""Hue light with color temperature only""
    )
    assert light_2.state == ""off""
    assert light_2.attributes[""mode""] == ""normal""
    assert light_2.attributes[""supported_color_modes""] == [COLOR_MODE_COLOR_TEMP]
    assert light_2.attributes[""min_mireds""] == 153
    assert light_2.attributes[""max_mireds""] == 454
    assert light_2.attributes[""dynamics""] == ""none""
    assert light_2.attributes[""effect_list""] == [""None"", ""candle"", ""sunrise""]

    # test light which supports color only
    light_3 = hass.states.get(""light.hue_light_with_color_only"")
    assert light_3 is not None
    assert light_3.attributes[""friendly_name""] == ""Hue light with color only""
    assert light_3.state == ""on""
    assert light_3.attributes[""brightness""] == 128
    assert light_3.attributes[""mode""] == ""normal""
    assert light_3.attributes[""supported_color_modes""] == [COLOR_MODE_XY]
    assert light_3.attributes[""color_mode""] == COLOR_MODE_XY
    assert light_3.attributes[""dynamics""] == ""dynamic_palette""

    # test light which supports on/off only
    light_4 = hass.states.get(""light.hue_on_off_light"")
    assert light_4 is not None
    assert light_4.attributes[""friendly_name""] == ""Hue on/off light""
    assert light_4.state == ""off""
    assert light_4.attributes[""mode""] == ""normal""
    assert light_4.attributes[""supported_color_modes""] == []


        ```
    ###Assistant : Test if all v2 lights get created with correct features.
    "
623,"def setup_data(self, path):
        
        for message, new_episode in super().setup_data(path):
            assert (
                message['text'] == '__SILENCE__'
            ), 'The expected original context string is not found!'
            message['text'] = 'Person 1:'
            yield message, new_episode

","
        Modify each output message to add in an OPT-compatible context string.
        ",11,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setup_data(self, path):
        
        for message, new_episode in super().setup_data(path):
            assert (
                message['text'] == '__SILENCE__'
            ), 'The expected original context string is not found!'
            message['text'] = 'Person 1:'
            yield message, new_episode


        ```
    ###Assistant : 
        Modify each output message to add in an OPT-compatible context string.
        
    "
624,"def _get_input_locations(self) -> List[str]:
        
        if not self._args.batch_mode or os.path.isfile(self._args.input_dir):
            return [self._args.input_dir]  # Not batch mode or a single file

        retval = [os.path.join(self._args.input_dir, fname)
                  for fname in os.listdir(self._args.input_dir)
                  if os.path.isdir(os.path.join(self._args.input_dir, fname))
                  or os.path.splitext(fname)[-1].lower() in _video_extensions]
        logger.debug(""Input locations: %s"", retval)
        return retval
"," Obtain the full path to input locations. Will be a list of locations if batch mode is
        selected, or a containing a single location if batch mode is not selected.

        Returns
        -------
        list:
            The list of input location paths
        ",39,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_input_locations(self) -> List[str]:
        
        if not self._args.batch_mode or os.path.isfile(self._args.input_dir):
            return [self._args.input_dir]  # Not batch mode or a single file

        retval = [os.path.join(self._args.input_dir, fname)
                  for fname in os.listdir(self._args.input_dir)
                  if os.path.isdir(os.path.join(self._args.input_dir, fname))
                  or os.path.splitext(fname)[-1].lower() in _video_extensions]
        logger.debug(""Input locations: %s"", retval)
        return retval

        ```
    ###Assistant :  Obtain the full path to input locations. Will be a list of locations if batch mode is
        selected, or a containing a single location if batch mode is not selected.

        Returns
        -------
        list:
            The list of input location paths
        
    "
625,"def get_party_gle_currency(party_type, party, company):
	def generator():
		existing_gle_currency = frappe.db.sql(
			,
			{""company"": company, ""party_type"": party_type, ""party"": party},
		)

		return existing_gle_currency[0][0] if existing_gle_currency else None

	return frappe.local_cache(
		""party_gle_currency"", (party_type, party, company), generator, regenerate_if_none=True
	)

","select account_currency from `tabGL Entry`
			where docstatus=1 and company=%(company)s and party_type=%(party_type)s and party=%(party)s
			limit 1",15,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_party_gle_currency(party_type, party, company):
	def generator():
		existing_gle_currency = frappe.db.sql(
			,
			{""company"": company, ""party_type"": party_type, ""party"": party},
		)

		return existing_gle_currency[0][0] if existing_gle_currency else None

	return frappe.local_cache(
		""party_gle_currency"", (party_type, party, company), generator, regenerate_if_none=True
	)


        ```
    ###Assistant : select account_currency from `tabGL Entry`
			where docstatus=1 and company=%(company)s and party_type=%(party_type)s and party=%(party)s
			limit 1
    "
626,"def load_breast_cancer(*, return_X_y=False, as_frame=False):
    
    data_file_name = ""breast_cancer.csv""
    data, target, target_names, fdescr = load_csv_data(
        data_file_name=data_file_name, descr_file_name=""breast_cancer.rst""
    )

    feature_names = np.array(
        [
            ""mean radius"",
            ""mean texture"",
            ""mean perimeter"",
            ""mean area"",
            ""mean smoothness"",
            ""mean compactness"",
            ""mean concavity"",
            ""mean concave points"",
            ""mean symmetry"",
            ""mean fractal dimension"",
            ""radius error"",
            ""texture error"",
            ""perimeter error"",
            ""area error"",
            ""smoothness error"",
            ""compactness error"",
            ""concavity error"",
            ""concave points error"",
            ""symmetry error"",
            ""fractal dimension error"",
            ""worst radius"",
            ""worst texture"",
            ""worst perimeter"",
            ""worst area"",
            ""worst smoothness"",
            ""worst compactness"",
            ""worst concavity"",
            ""worst concave points"",
            ""worst symmetry"",
            ""worst fractal dimension"",
        ]
    )

    frame = None
    target_columns = [
        ""target"",
    ]
    if as_frame:
        frame, data, target = _convert_data_dataframe(
            ""load_breast_cancer"", data, target, feature_names, target_columns
        )

    if return_X_y:
        return data, target

    return Bunch(
        data=data,
        target=target,
        frame=frame,
        target_names=target_names,
        DESCR=fdescr,
        feature_names=feature_names,
        filename=data_file_name,
        data_module=DATA_MODULE,
    )

","Load and return the breast cancer wisconsin dataset (classification).

    The breast cancer dataset is a classic and very easy binary classification
    dataset.

    =================   ==============
    Classes                          2
    Samples per class    212(M),357(B)
    Samples total                  569
    Dimensionality                  30
    Features            real, positive
    =================   ==============

    The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
    downloaded from:
    https://goo.gl/U2Uwz2

    Read more in the :ref:`User Guide <breast_cancer_dataset>`.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object.
        See below for more information about the `data` and `target` object.

        .. versionadded:: 0.18

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (569, 30)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target : {ndarray, Series} of shape (569,)
            The classification target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names : list
            The names of the dataset columns.
        target_names : list
            The names of target classes.
        frame : DataFrame of shape (569, 31)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR : str
            The full description of the dataset.
        filename : str
            The path to the location of the data.

            .. versionadded:: 0.20

    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarrays by default. The first contains a 2D ndarray of
        shape (569, 30) with each row representing one sample and each column
        representing the features. The second ndarray of shape (569,) contains
        the target samples.  If `as_frame=True`, both arrays are pandas objects,
        i.e. `X` a dataframe and `y` a series.

        .. versionadded:: 0.18

    Examples
    --------
    Let's say you are interested in the samples 10, 50, and 85, and want to
    know their class name.

    >>> from sklearn.datasets import load_breast_cancer
    >>> data = load_breast_cancer()
    >>> data.target[[10, 50, 85]]
    array([0, 1, 0])
    >>> list(data.target_names)
    ['malignant', 'benign']
    ",356,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_breast_cancer(*, return_X_y=False, as_frame=False):
    
    data_file_name = ""breast_cancer.csv""
    data, target, target_names, fdescr = load_csv_data(
        data_file_name=data_file_name, descr_file_name=""breast_cancer.rst""
    )

    feature_names = np.array(
        [
            ""mean radius"",
            ""mean texture"",
            ""mean perimeter"",
            ""mean area"",
            ""mean smoothness"",
            ""mean compactness"",
            ""mean concavity"",
            ""mean concave points"",
            ""mean symmetry"",
            ""mean fractal dimension"",
            ""radius error"",
            ""texture error"",
            ""perimeter error"",
            ""area error"",
            ""smoothness error"",
            ""compactness error"",
            ""concavity error"",
            ""concave points error"",
            ""symmetry error"",
            ""fractal dimension error"",
            ""worst radius"",
            ""worst texture"",
            ""worst perimeter"",
            ""worst area"",
            ""worst smoothness"",
            ""worst compactness"",
            ""worst concavity"",
            ""worst concave points"",
            ""worst symmetry"",
            ""worst fractal dimension"",
        ]
    )

    frame = None
    target_columns = [
        ""target"",
    ]
    if as_frame:
        frame, data, target = _convert_data_dataframe(
            ""load_breast_cancer"", data, target, feature_names, target_columns
        )

    if return_X_y:
        return data, target

    return Bunch(
        data=data,
        target=target,
        frame=frame,
        target_names=target_names,
        DESCR=fdescr,
        feature_names=feature_names,
        filename=data_file_name,
        data_module=DATA_MODULE,
    )


        ```
    ###Assistant : Load and return the breast cancer wisconsin dataset (classification).

    The breast cancer dataset is a classic and very easy binary classification
    dataset.

    =================   ==============
    Classes                          2
    Samples per class    212(M),357(B)
    Samples total                  569
    Dimensionality                  30
    Features            real, positive
    =================   ==============

    The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
    downloaded from:
    https://goo.gl/U2Uwz2

    Read more in the :ref:`User Guide <breast_cancer_dataset>`.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object.
        See below for more information about the `data` and `target` object.

        .. versionadded:: 0.18

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (569, 30)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target : {ndarray, Series} of shape (569,)
            The classification target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names : list
            The names of the dataset columns.
        target_names : list
            The names of target classes.
        frame : DataFrame of shape (569, 31)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR : str
            The full description of the dataset.
        filename : str
            The path to the location of the data.

            .. versionadded:: 0.20

    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarrays by default. The first contains a 2D ndarray of
        shape (569, 30) with each row representing one sample and each column
        representing the features. The second ndarray of shape (569,) contains
        the target samples.  If `as_frame=True`, both arrays are pandas objects,
        i.e. `X` a dataframe and `y` a series.

        .. versionadded:: 0.18

    Examples
    --------
    Let's say you are interested in the samples 10, 50, and 85, and want to
    know their class name.

    >>> from sklearn.datasets import load_breast_cancer
    >>> data = load_breast_cancer()
    >>> data.target[[10, 50, 85]]
    array([0, 1, 0])
    >>> list(data.target_names)
    ['malignant', 'benign']
    
    "
627,"def get_filter_by_name(filtername, **options):
    
    cls = find_filter_class(filtername)
    if cls:
        return cls(**options)
    else:
        raise ClassNotFound('filter %r not found' % filtername)

","Return an instantiated filter.

    Options are passed to the filter initializer if wanted.
    Raise a ClassNotFound if not found.
    ",19,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_filter_by_name(filtername, **options):
    
    cls = find_filter_class(filtername)
    if cls:
        return cls(**options)
    else:
        raise ClassNotFound('filter %r not found' % filtername)


        ```
    ###Assistant : Return an instantiated filter.

    Options are passed to the filter initializer if wanted.
    Raise a ClassNotFound if not found.
    
    "
628,"def wide_resnet50_2(pretrained=False, **kwargs):
    
    kwargs['width'] = 64 * 2
    return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)

","Wide ResNet-50-2 model from
    `""Wide Residual Networks"" <https://arxiv.org/pdf/1605.07146.pdf>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet

    Examples:
        .. code-block:: python

            import paddle
            from paddle.vision.models import wide_resnet50_2

            # build model
            model = wide_resnet50_2()

            # build model and load imagenet pretrained weight
            # model = wide_resnet50_2(pretrained=True)

            x = paddle.rand([1, 3, 224, 224])
            out = model(x)

            print(out.shape)
    ",57,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wide_resnet50_2(pretrained=False, **kwargs):
    
    kwargs['width'] = 64 * 2
    return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)


        ```
    ###Assistant : Wide ResNet-50-2 model from
    `""Wide Residual Networks"" <https://arxiv.org/pdf/1605.07146.pdf>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet

    Examples:
        .. code-block:: python

            import paddle
            from paddle.vision.models import wide_resnet50_2

            # build model
            model = wide_resnet50_2()

            # build model and load imagenet pretrained weight
            # model = wide_resnet50_2(pretrained=True)

            x = paddle.rand([1, 3, 224, 224])
            out = model(x)

            print(out.shape)
    
    "
629,"def test_username_available(self) -> None:
        

        url = ""%s?username=%s"" % (self.url, ""allowed"")
        channel = self.make_request(""GET"", url, access_token=self.admin_user_tok)

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertTrue(channel.json_body[""available""])
","
        The endpoint should return a HTTPStatus.OK response if the username does not exist
        ",13,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_username_available(self) -> None:
        

        url = ""%s?username=%s"" % (self.url, ""allowed"")
        channel = self.make_request(""GET"", url, access_token=self.admin_user_tok)

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertTrue(channel.json_body[""available""])

        ```
    ###Assistant : 
        The endpoint should return a HTTPStatus.OK response if the username does not exist
        
    "
630,"def test_with_include_glob_filtering_case4a_include_strong():
    
    incl_dom = {}
    incl_glob = {""*working""}
    incl_ent = {""binary_sensor.specificly_included""}
    excl_dom = {}
    excl_glob = {""*broken"", ""*notworking"", ""binary_sensor.*""}
    excl_ent = {""light.ignoreme""}
    testfilter = generate_filter(
        incl_dom, incl_ent, excl_dom, excl_ent, incl_glob, excl_glob
    )

    assert testfilter(""sensor.working"") is True
    assert testfilter(""sensor.notworking"") is True  # include is stronger
    assert testfilter(""sensor.broken"") is False
    assert testfilter(""light.test"") is False
    assert testfilter(""light.notworking"") is True  # include is stronger
    assert testfilter(""light.ignoreme"") is False
    assert testfilter(""binary_sensor.not_working"") is True  # include is stronger
    assert testfilter(""binary_sensor.another"") is False
    assert testfilter(""binary_sensor.specificly_included"") is True
    assert testfilter(""sun.sun"") is False

","Test case 4 - include and exclude specified, both have globs, and a specifically included entity.",16,84,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_with_include_glob_filtering_case4a_include_strong():
    
    incl_dom = {}
    incl_glob = {""*working""}
    incl_ent = {""binary_sensor.specificly_included""}
    excl_dom = {}
    excl_glob = {""*broken"", ""*notworking"", ""binary_sensor.*""}
    excl_ent = {""light.ignoreme""}
    testfilter = generate_filter(
        incl_dom, incl_ent, excl_dom, excl_ent, incl_glob, excl_glob
    )

    assert testfilter(""sensor.working"") is True
    assert testfilter(""sensor.notworking"") is True  # include is stronger
    assert testfilter(""sensor.broken"") is False
    assert testfilter(""light.test"") is False
    assert testfilter(""light.notworking"") is True  # include is stronger
    assert testfilter(""light.ignoreme"") is False
    assert testfilter(""binary_sensor.not_working"") is True  # include is stronger
    assert testfilter(""binary_sensor.another"") is False
    assert testfilter(""binary_sensor.specificly_included"") is True
    assert testfilter(""sun.sun"") is False


        ```
    ###Assistant : Test case 4 - include and exclude specified, both have globs, and a specifically included entity.
    "
631,"def get_sales_orders(self):
	so_filter = item_filter = """"
	bom_item = ""bom.item = so_item.item_code""

	date_field_mapper = {
		""from_date"": ("">="", ""so.transaction_date""),
		""to_date"": (""<="", ""so.transaction_date""),
		""from_delivery_date"": ("">="", ""so_item.delivery_date""),
		""to_delivery_date"": (""<="", ""so_item.delivery_date""),
	}

	for field, value in date_field_mapper.items():
		if self.get(field):
			so_filter += f"" and {value[1]} {value[0]} %({field})s""

	for field in [""customer"", ""project"", ""sales_order_status""]:
		if self.get(field):
			so_field = ""status"" if field == ""sales_order_status"" else field
			so_filter += f"" and so.{so_field} = %({field})s""

	if self.item_code and frappe.db.exists(""Item"", self.item_code):
		bom_item = self.get_bom_item() or bom_item
		item_filter += "" and so_item.item_code = %(item_code)s""

	open_so = frappe.db.sql(
		f,
		self.as_dict(),
		as_dict=1,
	)

	return open_so


@frappe.whitelist()","
		select distinct so.name, so.transaction_date, so.customer, so.base_grand_total
		from `tabSales Order` so, `tabSales Order Item` so_item
		where so_item.parent = so.name
			and so.docstatus = 1 and so.status not in (""Stopped"", ""Closed"")
			and so.company = %(company)s
			and so_item.qty > so_item.work_order_qty {so_filter} {item_filter}
			and (exists (select name from `tabBOM` bom where {bom_item}
					and bom.is_active = 1)
				or exists (select name from `tabPacked Item` pi
					where pi.parent = so.name and pi.parent_item = so_item.item_code
						and exists (select name from `tabBOM` bom where bom.item=pi.item_code
							and bom.is_active = 1)))
		",80,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_sales_orders(self):
	so_filter = item_filter = """"
	bom_item = ""bom.item = so_item.item_code""

	date_field_mapper = {
		""from_date"": ("">="", ""so.transaction_date""),
		""to_date"": (""<="", ""so.transaction_date""),
		""from_delivery_date"": ("">="", ""so_item.delivery_date""),
		""to_delivery_date"": (""<="", ""so_item.delivery_date""),
	}

	for field, value in date_field_mapper.items():
		if self.get(field):
			so_filter += f"" and {value[1]} {value[0]} %({field})s""

	for field in [""customer"", ""project"", ""sales_order_status""]:
		if self.get(field):
			so_field = ""status"" if field == ""sales_order_status"" else field
			so_filter += f"" and so.{so_field} = %({field})s""

	if self.item_code and frappe.db.exists(""Item"", self.item_code):
		bom_item = self.get_bom_item() or bom_item
		item_filter += "" and so_item.item_code = %(item_code)s""

	open_so = frappe.db.sql(
		f,
		self.as_dict(),
		as_dict=1,
	)

	return open_so


@frappe.whitelist()
        ```
    ###Assistant : 
		select distinct so.name, so.transaction_date, so.customer, so.base_grand_total
		from `tabSales Order` so, `tabSales Order Item` so_item
		where so_item.parent = so.name
			and so.docstatus = 1 and so.status not in (""Stopped"", ""Closed"")
			and so.company = %(company)s
			and so_item.qty > so_item.work_order_qty {so_filter} {item_filter}
			and (exists (select name from `tabBOM` bom where {bom_item}
					and bom.is_active = 1)
				or exists (select name from `tabPacked Item` pi
					where pi.parent = so.name and pi.parent_item = so_item.item_code
						and exists (select name from `tabBOM` bom where bom.item=pi.item_code
							and bom.is_active = 1)))
		
    "
632,"def test_copy_published_emits_signal(self):
        
        christmas_page = EventPage.objects.get(url_path=""/home/events/christmas/"")

        signal_fired = False
        signal_page = None
",Test that copying of a published page emits a page_published signal.,11,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_copy_published_emits_signal(self):
        
        christmas_page = EventPage.objects.get(url_path=""/home/events/christmas/"")

        signal_fired = False
        signal_page = None

        ```
    ###Assistant : Test that copying of a published page emits a page_published signal.
    "
633,"def get_all_tests():
    
    test_root_dir = os.path.join(PATH_TO_TRANFORMERS, ""tests"")

    # test folders/files directly under `tests` folder
    tests = os.listdir(test_root_dir)
    tests = sorted(
        list(filter(lambda x: os.path.isdir(x) or x.startswith(""tests/test_""), [f""tests/{x}"" for x in tests]))
    )

    # model specific test folders
    model_tests_folders = os.listdir(os.path.join(test_root_dir, ""models""))
    model_test_folders = sorted(list(filter(os.path.isdir, [f""tests/models/{x}"" for x in model_tests_folders])))

    tests.remove(""tests/models"")
    tests = model_test_folders + tests

    return tests

","
    Return a list of paths to all test folders and files under `tests`. All paths are rooted at `tests`.

    - folders under `tests`: `tokenization`, `pipelines`, etc. The folder `models` is excluded.
    - folders under `tests/models`: `bert`, `gpt2`, etc.
    - test files under `tests`: `test_modeling_common.py`, `test_tokenization_common.py`, etc.
    ",46,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all_tests():
    
    test_root_dir = os.path.join(PATH_TO_TRANFORMERS, ""tests"")

    # test folders/files directly under `tests` folder
    tests = os.listdir(test_root_dir)
    tests = sorted(
        list(filter(lambda x: os.path.isdir(x) or x.startswith(""tests/test_""), [f""tests/{x}"" for x in tests]))
    )

    # model specific test folders
    model_tests_folders = os.listdir(os.path.join(test_root_dir, ""models""))
    model_test_folders = sorted(list(filter(os.path.isdir, [f""tests/models/{x}"" for x in model_tests_folders])))

    tests.remove(""tests/models"")
    tests = model_test_folders + tests

    return tests


        ```
    ###Assistant : 
    Return a list of paths to all test folders and files under `tests`. All paths are rooted at `tests`.

    - folders under `tests`: `tokenization`, `pipelines`, etc. The folder `models` is excluded.
    - folders under `tests/models`: `bert`, `gpt2`, etc.
    - test files under `tests`: `test_modeling_common.py`, `test_tokenization_common.py`, etc.
    
    "
634,"def test_vtrace(self):
        
        seq_len = 5
        batch_size = 10

        # Create log_rhos such that rho will span from near-zero to above the
        # clipping thresholds. In particular, calculate log_rhos in
        # [-2.5, 2.5),
        # so that rho is in approx [0.08, 12.2).
        space_w_time = Box(-1.0, 1.0, (seq_len, batch_size), np.float32)
        space_only_batch = Box(-1.0, 1.0, (batch_size,), np.float32)
        log_rhos = space_w_time.sample() / (batch_size * seq_len)
        log_rhos = 5 * (log_rhos - 0.5)  # [0.0, 1.0) -> [-2.5, 2.5).
        values = {
            ""log_rhos"": log_rhos,
            # T, B where B_i: [0.9 / (i+1)] * T
            ""discounts"": np.array(
                [[0.9 / (b + 1) for b in range(batch_size)] for _ in range(seq_len)]
            ),
            ""rewards"": space_w_time.sample(),
            ""values"": space_w_time.sample() / batch_size,
            ""bootstrap_value"": space_only_batch.sample() + 1.0,
            ""clip_rho_threshold"": 3.7,
            ""clip_pg_rho_threshold"": 2.2,
        }

        for fw, sess in framework_iterator(frameworks=(""torch"", ""tf""), session=True):
            vtrace = vtrace_tf if fw != ""torch"" else vtrace_torch
            output = vtrace.from_importance_weights(**values)
            if sess:
                output = sess.run(output)

            ground_truth_v = _ground_truth_calculation(vtrace, **values)
            check(output, ground_truth_v)
",Tests V-trace against ground truth data calculated in python.,9,150,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_vtrace(self):
        
        seq_len = 5
        batch_size = 10

        # Create log_rhos such that rho will span from near-zero to above the
        # clipping thresholds. In particular, calculate log_rhos in
        # [-2.5, 2.5),
        # so that rho is in approx [0.08, 12.2).
        space_w_time = Box(-1.0, 1.0, (seq_len, batch_size), np.float32)
        space_only_batch = Box(-1.0, 1.0, (batch_size,), np.float32)
        log_rhos = space_w_time.sample() / (batch_size * seq_len)
        log_rhos = 5 * (log_rhos - 0.5)  # [0.0, 1.0) -> [-2.5, 2.5).
        values = {
            ""log_rhos"": log_rhos,
            # T, B where B_i: [0.9 / (i+1)] * T
            ""discounts"": np.array(
                [[0.9 / (b + 1) for b in range(batch_size)] for _ in range(seq_len)]
            ),
            ""rewards"": space_w_time.sample(),
            ""values"": space_w_time.sample() / batch_size,
            ""bootstrap_value"": space_only_batch.sample() + 1.0,
            ""clip_rho_threshold"": 3.7,
            ""clip_pg_rho_threshold"": 2.2,
        }

        for fw, sess in framework_iterator(frameworks=(""torch"", ""tf""), session=True):
            vtrace = vtrace_tf if fw != ""torch"" else vtrace_torch
            output = vtrace.from_importance_weights(**values)
            if sess:
                output = sess.run(output)

            ground_truth_v = _ground_truth_calculation(vtrace, **values)
            check(output, ground_truth_v)

        ```
    ###Assistant : Tests V-trace against ground truth data calculated in python.
    "
635,"def pdfdump(self, filename=None, **kargs):
        # type: (Optional[str], **Any) -> None
        
        from scapy.config import conf
        from scapy.utils import get_temp_file, ContextManagerSubprocess
        canvas = self.canvas_dump(**kargs)
        if filename is None:
            fname = get_temp_file(autoext=kargs.get(""suffix"", "".pdf""))
            canvas.writePDFfile(fname)
            if WINDOWS and not conf.prog.pdfreader:
                os.startfile(fname)
            else:
                with ContextManagerSubprocess(conf.prog.pdfreader):
                    subprocess.Popen([conf.prog.pdfreader, fname])
        else:
            canvas.writePDFfile(filename)
        print()
","
        pdfdump(filename=None, layer_shift=0, rebuild=1)

        Creates a PDF file describing a packet. If filename is not provided a
        temporary file is created and xpdf is called.

        :param filename: the file's filename
        ",29,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pdfdump(self, filename=None, **kargs):
        # type: (Optional[str], **Any) -> None
        
        from scapy.config import conf
        from scapy.utils import get_temp_file, ContextManagerSubprocess
        canvas = self.canvas_dump(**kargs)
        if filename is None:
            fname = get_temp_file(autoext=kargs.get(""suffix"", "".pdf""))
            canvas.writePDFfile(fname)
            if WINDOWS and not conf.prog.pdfreader:
                os.startfile(fname)
            else:
                with ContextManagerSubprocess(conf.prog.pdfreader):
                    subprocess.Popen([conf.prog.pdfreader, fname])
        else:
            canvas.writePDFfile(filename)
        print()

        ```
    ###Assistant : 
        pdfdump(filename=None, layer_shift=0, rebuild=1)

        Creates a PDF file describing a packet. If filename is not provided a
        temporary file is created and xpdf is called.

        :param filename: the file's filename
        
    "
636,"def index_sample(x, index):
    
    x_s = x.shape
    dim = len(index.shape) - 1
    assert x_s[:dim] == index.shape[:dim]

    if len(x_s) == 3 and dim == 1:
        r_x = paddle.reshape(x, shape=[-1, x_s[1], x_s[-1]])
    else:
        r_x = paddle.reshape(x, shape=[-1, x_s[-1]])

    index = paddle.reshape(index, shape=[len(r_x), -1, 1])
    # Generate arange index, shape like index
    arr_index = paddle.arange(start=0, end=len(index), dtype=index.dtype)
    arr_index = paddle.unsqueeze(arr_index, axis=[1, 2])
    arr_index = paddle.expand(arr_index, index.shape)
    # Genrate new index
    new_index = paddle.concat((arr_index, index), -1)
    new_index = paddle.reshape(new_index, (-1, 2))
    # Get output
    out = paddle.gather_nd(r_x, new_index)
    if len(x_s) == 3 and dim == 2:
        out = paddle.reshape(out, shape=[x_s[0], x_s[1], -1])
    else:
        out = paddle.reshape(out, shape=[x_s[0], -1])
    return out

","
    Select input value according to index
    
    Arags：
        input: input matrix
        index: index matrix
    Returns:
        output
    >>> input
    [
        [1, 2, 3],
        [4, 5, 6]
    ]
    >>> index
    [
        [1, 2],
        [0, 1]
    ]
    >>> index_sample(input, index)
    [
        [2, 3],
        [4, 5]
    ]
    ",42,105,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def index_sample(x, index):
    
    x_s = x.shape
    dim = len(index.shape) - 1
    assert x_s[:dim] == index.shape[:dim]

    if len(x_s) == 3 and dim == 1:
        r_x = paddle.reshape(x, shape=[-1, x_s[1], x_s[-1]])
    else:
        r_x = paddle.reshape(x, shape=[-1, x_s[-1]])

    index = paddle.reshape(index, shape=[len(r_x), -1, 1])
    # Generate arange index, shape like index
    arr_index = paddle.arange(start=0, end=len(index), dtype=index.dtype)
    arr_index = paddle.unsqueeze(arr_index, axis=[1, 2])
    arr_index = paddle.expand(arr_index, index.shape)
    # Genrate new index
    new_index = paddle.concat((arr_index, index), -1)
    new_index = paddle.reshape(new_index, (-1, 2))
    # Get output
    out = paddle.gather_nd(r_x, new_index)
    if len(x_s) == 3 and dim == 2:
        out = paddle.reshape(out, shape=[x_s[0], x_s[1], -1])
    else:
        out = paddle.reshape(out, shape=[x_s[0], -1])
    return out


        ```
    ###Assistant : 
    Select input value according to index
    
    Arags：
        input: input matrix
        index: index matrix
    Returns:
        output
    >>> input
    [
        [1, 2, 3],
        [4, 5, 6]
    ]
    >>> index
    [
        [1, 2],
        [0, 1]
    ]
    >>> index_sample(input, index)
    [
        [2, 3],
        [4, 5]
    ]
    
    "
637,"def __new__(cls, name, patch, symbols=None, relations={}, **kwargs):
        if not isinstance(name, Str):
            name = Str(name)

        # canonicallize the symbols
        if symbols is None:
            names = kwargs.get('names', None)
            if names is None:
                symbols = Tuple(
                    *[Symbol('%s_%s' % (name.name, i), real=True)
                      for i in range(patch.dim)]
                )
            else:
                sympy_deprecation_warning(
                    f,
                    deprecated_since_version=""1.7"",
                    active_deprecations_target=""deprecated-diffgeom-mutable"",
                )
                symbols = Tuple(
                    *[Symbol(n, real=True) for n in names]
                )
        else:
            syms = []
            for s in symbols:
                if isinstance(s, Symbol):
                    syms.append(Symbol(s.name, **s._assumptions.generator))
                elif isinstance(s, str):
                    sympy_deprecation_warning(
                        f,

                        deprecated_since_version=""1.7"",
                        active_deprecations_target=""deprecated-diffgeom-mutable"",
                    )
                    syms.append(Symbol(s, real=True))
            symbols = Tuple(*syms)

        # canonicallize the relations
        rel_temp = {}
        for k,v in relations.items():
            s1, s2 = k
            if not isinstance(s1, Str):
                s1 = Str(s1)
            if not isinstance(s2, Str):
                s2 = Str(s2)
            key = Tuple(s1, s2)

            # Old version used Lambda as a value.
            if isinstance(v, Lambda):
                v = (tuple(v.signature), tuple(v.expr))
            else:
                v = (tuple(v[0]), tuple(v[1]))
            rel_temp[key] = v
        relations = Dict(rel_temp)

        # construct the object
        obj = super().__new__(cls, name, patch, symbols, relations)

        # Add deprecated attributes
        obj.transforms = _deprecated_dict(
            , {})
        obj._names = [str(n) for n in symbols]
        obj.patch.coord_systems.append(obj) # deprecated
        obj._dummies = [Dummy(str(n)) for n in symbols] # deprecated
        obj._dummy = Dummy()

        return obj
","
The 'names' argument to CoordSystem is deprecated. Use 'symbols' instead. That
is, replace

    CoordSystem(..., names={names})

with

    CoordSystem(..., symbols=[{', '.join([""Symbol("" + repr(n) + "", real=True)"" for n in names])}])
                    

Passing a string as the coordinate symbol name to CoordSystem is deprecated.
Pass a Symbol with the appropriate name and assumptions instead.

That is, replace {s} with Symbol({s!r}, real=True).
                        
            CoordSystem.transforms is deprecated. The CoordSystem class is now
            immutable. Use the 'relations' keyword argument to the
            CoordSystems() constructor to specify relations.
            ",78,188,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __new__(cls, name, patch, symbols=None, relations={}, **kwargs):
        if not isinstance(name, Str):
            name = Str(name)

        # canonicallize the symbols
        if symbols is None:
            names = kwargs.get('names', None)
            if names is None:
                symbols = Tuple(
                    *[Symbol('%s_%s' % (name.name, i), real=True)
                      for i in range(patch.dim)]
                )
            else:
                sympy_deprecation_warning(
                    f,
                    deprecated_since_version=""1.7"",
                    active_deprecations_target=""deprecated-diffgeom-mutable"",
                )
                symbols = Tuple(
                    *[Symbol(n, real=True) for n in names]
                )
        else:
            syms = []
            for s in symbols:
                if isinstance(s, Symbol):
                    syms.append(Symbol(s.name, **s._assumptions.generator))
                elif isinstance(s, str):
                    sympy_deprecation_warning(
                        f,

                        deprecated_since_version=""1.7"",
                        active_deprecations_target=""deprecated-diffgeom-mutable"",
                    )
                    syms.append(Symbol(s, real=True))
            symbols = Tuple(*syms)

        # canonicallize the relations
        rel_temp = {}
        for k,v in relations.items():
            s1, s2 = k
            if not isinstance(s1, Str):
                s1 = Str(s1)
            if not isinstance(s2, Str):
                s2 = Str(s2)
            key = Tuple(s1, s2)

            # Old version used Lambda as a value.
            if isinstance(v, Lambda):
                v = (tuple(v.signature), tuple(v.expr))
            else:
                v = (tuple(v[0]), tuple(v[1]))
            rel_temp[key] = v
        relations = Dict(rel_temp)

        # construct the object
        obj = super().__new__(cls, name, patch, symbols, relations)

        # Add deprecated attributes
        obj.transforms = _deprecated_dict(
            , {})
        obj._names = [str(n) for n in symbols]
        obj.patch.coord_systems.append(obj) # deprecated
        obj._dummies = [Dummy(str(n)) for n in symbols] # deprecated
        obj._dummy = Dummy()

        return obj

        ```
    ###Assistant : 
The 'names' argument to CoordSystem is deprecated. Use 'symbols' instead. That
is, replace

    CoordSystem(..., names={names})

with

    CoordSystem(..., symbols=[{', '.join([""Symbol("" + repr(n) + "", real=True)"" for n in names])}])
                    

Passing a string as the coordinate symbol name to CoordSystem is deprecated.
Pass a Symbol with the appropriate name and assumptions instead.

That is, replace {s} with Symbol({s!r}, real=True).
                        
            CoordSystem.transforms is deprecated. The CoordSystem class is now
            immutable. Use the 'relations' keyword argument to the
            CoordSystems() constructor to specify relations.
            
    "
638,"def tab_focus(*, info):
    
    model = _tabs(win_id_filter=lambda win_id: win_id == info.win_id,
                  add_win_id=False, current_win_id=info.win_id)

    special = [
        (""last"", ""Focus the last-focused tab""),
        (""stack-next"", ""Go forward through a stack of focused tabs""),
        (""stack-prev"", ""Go backward through a stack of focused tabs""),
    ]
    model.add_category(listcategory.ListCategory(""Special"", special))

    return model

",A model to complete on open tabs in the current window.,11,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tab_focus(*, info):
    
    model = _tabs(win_id_filter=lambda win_id: win_id == info.win_id,
                  add_win_id=False, current_win_id=info.win_id)

    special = [
        (""last"", ""Focus the last-focused tab""),
        (""stack-next"", ""Go forward through a stack of focused tabs""),
        (""stack-prev"", ""Go backward through a stack of focused tabs""),
    ]
    model.add_category(listcategory.ListCategory(""Special"", special))

    return model


        ```
    ###Assistant : A model to complete on open tabs in the current window.
    "
639,"def get_mode_of_payment_details(filters):
	mode_of_payment_details = {}
	invoice_list = get_invoices(filters)
	invoice_list_names = "","".join(""'"" + invoice[""name""] + ""'"" for invoice in invoice_list)
	if invoice_list:
		inv_mop_detail = frappe.db.sql(
			.format(
				invoice_list_names=invoice_list_names
			),
			as_dict=1,
		)

		inv_change_amount = frappe.db.sql(
			.format(
				invoice_list_names=invoice_list_names
			),
			as_dict=1,
		)

		for d in inv_change_amount:
			for det in inv_mop_detail:
				if (
					det[""owner""] == d[""owner""]
					and det[""posting_date""] == d[""posting_date""]
					and det[""mode_of_payment""] == d[""mode_of_payment""]
				):
					paid_amount = det[""paid_amount""] - d[""change_amount""]
					det[""paid_amount""] = paid_amount

		for d in inv_mop_detail:
			mode_of_payment_details.setdefault(d[""owner""] + cstr(d[""posting_date""]), []).append(
				(d.mode_of_payment, d.paid_amount)
			)

	return mode_of_payment_details
","
			select t.owner,
			       t.posting_date,
				   t.mode_of_payment,
				   sum(t.paid_amount) as paid_amount
			from (
				select a.owner, a.posting_date,
				ifnull(b.mode_of_payment, '') as mode_of_payment, sum(b.base_amount) as paid_amount
				from `tabSales Invoice` a, `tabSales Invoice Payment` b
				where a.name = b.parent
				and a.docstatus = 1
				and a.name in ({invoice_list_names})
				group by a.owner, a.posting_date, mode_of_payment
				union
				select a.owner,a.posting_date,
				ifnull(b.mode_of_payment, '') as mode_of_payment, sum(c.allocated_amount) as paid_amount
				from `tabSales Invoice` a, `tabPayment Entry` b,`tabPayment Entry Reference` c
				where a.name = c.reference_name
				and b.name = c.parent
				and b.docstatus = 1
				and a.name in ({invoice_list_names})
				group by a.owner, a.posting_date, mode_of_payment
				union
				select a.owner, a.posting_date,
				ifnull(a.voucher_type,'') as mode_of_payment, sum(b.credit)
				from `tabJournal Entry` a, `tabJournal Entry Account` b
				where a.name = b.parent
				and a.docstatus = 1
				and b.reference_type = 'Sales Invoice'
				and b.reference_name in ({invoice_list_names})
				group by a.owner, a.posting_date, mode_of_payment
			) t
			group by t.owner, t.posting_date, t.mode_of_payment
			select a.owner, a.posting_date,
			ifnull(b.mode_of_payment, '') as mode_of_payment, sum(a.base_change_amount) as change_amount
			from `tabSales Invoice` a, `tabSales Invoice Payment` b
			where a.name = b.parent
			and a.name in ({invoice_list_names})
			and b.type = 'Cash'
			and a.base_change_amount > 0
			group by a.owner, a.posting_date, mode_of_payment",169,80,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_mode_of_payment_details(filters):
	mode_of_payment_details = {}
	invoice_list = get_invoices(filters)
	invoice_list_names = "","".join(""'"" + invoice[""name""] + ""'"" for invoice in invoice_list)
	if invoice_list:
		inv_mop_detail = frappe.db.sql(
			.format(
				invoice_list_names=invoice_list_names
			),
			as_dict=1,
		)

		inv_change_amount = frappe.db.sql(
			.format(
				invoice_list_names=invoice_list_names
			),
			as_dict=1,
		)

		for d in inv_change_amount:
			for det in inv_mop_detail:
				if (
					det[""owner""] == d[""owner""]
					and det[""posting_date""] == d[""posting_date""]
					and det[""mode_of_payment""] == d[""mode_of_payment""]
				):
					paid_amount = det[""paid_amount""] - d[""change_amount""]
					det[""paid_amount""] = paid_amount

		for d in inv_mop_detail:
			mode_of_payment_details.setdefault(d[""owner""] + cstr(d[""posting_date""]), []).append(
				(d.mode_of_payment, d.paid_amount)
			)

	return mode_of_payment_details

        ```
    ###Assistant : 
			select t.owner,
			       t.posting_date,
				   t.mode_of_payment,
				   sum(t.paid_amount) as paid_amount
			from (
				select a.owner, a.posting_date,
				ifnull(b.mode_of_payment, '') as mode_of_payment, sum(b.base_amount) as paid_amount
				from `tabSales Invoice` a, `tabSales Invoice Payment` b
				where a.name = b.parent
				and a.docstatus = 1
				and a.name in ({invoice_list_names})
				group by a.owner, a.posting_date, mode_of_payment
				union
				select a.owner,a.posting_date,
				ifnull(b.mode_of_payment, '') as mode_of_payment, sum(c.allocated_amount) as paid_amount
				from `tabSales Invoice` a, `tabPayment Entry` b,`tabPayment Entry Reference` c
				where a.name = c.reference_name
				and b.name = c.parent
				and b.docstatus = 1
				and a.name in ({invoice_list_names})
				group by a.owner, a.posting_date, mode_of_payment
				union
				select a.owner, a.posting_date,
				ifnull(a.voucher_type,'') as mode_of_payment, sum(b.credit)
				from `tabJournal Entry` a, `tabJournal Entry Account` b
				where a.name = b.parent
				and a.docstatus = 1
				and b.reference_type = 'Sales Invoice'
				and b.reference_name in ({invoice_list_names})
				group by a.owner, a.posting_date, mode_of_payment
			) t
			group by t.owner, t.posting_date, t.mode_of_payment
			select a.owner, a.posting_date,
			ifnull(b.mode_of_payment, '') as mode_of_payment, sum(a.base_change_amount) as change_amount
			from `tabSales Invoice` a, `tabSales Invoice Payment` b
			where a.name = b.parent
			and a.name in ({invoice_list_names})
			and b.type = 'Cash'
			and a.base_change_amount > 0
			group by a.owner, a.posting_date, mode_of_payment
    "
640,"def get_config_context(self):
        
        data = {}

        if not hasattr(self, 'config_context_data'):
            # The annotation is not available, so we fall back to manually querying for the config context objects
            config_context_data = ConfigContext.objects.get_for_object(self, aggregate_data=True)
        else:
            # The attribute may exist, but the annotated value could be None if there is no config context data
            config_context_data = self.config_context_data or []

        for context in config_context_data:
            data = deepmerge(data, context)

        # If the object has local config context data defined, merge it last
        if self.local_context_data:
            data = deepmerge(data, self.local_context_data)

        return data
","
        Compile all config data, overwriting lower-weight values with higher-weight values where a collision occurs.
        Return the rendered configuration context for a device or VM.
        ",24,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_config_context(self):
        
        data = {}

        if not hasattr(self, 'config_context_data'):
            # The annotation is not available, so we fall back to manually querying for the config context objects
            config_context_data = ConfigContext.objects.get_for_object(self, aggregate_data=True)
        else:
            # The attribute may exist, but the annotated value could be None if there is no config context data
            config_context_data = self.config_context_data or []

        for context in config_context_data:
            data = deepmerge(data, context)

        # If the object has local config context data defined, merge it last
        if self.local_context_data:
            data = deepmerge(data, self.local_context_data)

        return data

        ```
    ###Assistant : 
        Compile all config data, overwriting lower-weight values with higher-weight values where a collision occurs.
        Return the rendered configuration context for a device or VM.
        
    "
641,"def test_displayname_is_set_avatar_is_none(self) -> None:
        
        channel = self.make_request(
            ""POST"",
            self.url,
            access_token=self.admin_user_tok,
            content={
                ""user_id"": self.other_user,
                ""content"": {""msgtype"": ""m.text"", ""body"": ""test msg""},
            },
        )
        self.assertEqual(200, channel.code, msg=channel.json_body)

        # user has one invite
        self._check_invite_and_join_status(self.other_user, 1, 0)
","
        Tests that sending a server notices is successfully,
        if a display_name is set, avatar_url is `None` and
        ""check avatar size and mime type"" is set.
        ",25,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_displayname_is_set_avatar_is_none(self) -> None:
        
        channel = self.make_request(
            ""POST"",
            self.url,
            access_token=self.admin_user_tok,
            content={
                ""user_id"": self.other_user,
                ""content"": {""msgtype"": ""m.text"", ""body"": ""test msg""},
            },
        )
        self.assertEqual(200, channel.code, msg=channel.json_body)

        # user has one invite
        self._check_invite_and_join_status(self.other_user, 1, 0)

        ```
    ###Assistant : 
        Tests that sending a server notices is successfully,
        if a display_name is set, avatar_url is `None` and
        ""check avatar size and mime type"" is set.
        
    "
642,"def cur_iter_done(self) -> bool:
        
        return all(
            self._get_result_time(result) >= self._cumul_r
            for result in self._live_trials.values()
        )
","Checks if all iterations have completed.

        TODO(rliaw): also check that `t.iterations == self._r`",13,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cur_iter_done(self) -> bool:
        
        return all(
            self._get_result_time(result) >= self._cumul_r
            for result in self._live_trials.values()
        )

        ```
    ###Assistant : Checks if all iterations have completed.

        TODO(rliaw): also check that `t.iterations == self._r`
    "
643,"def get_attendance_years():
	year_list = frappe.db.sql_list(
		
	)
	if not year_list:
		year_list = [getdate().year]
	return ""\n"".join(str(year) for year in year_list)

",select distinct YEAR(date) from `tabStudent Attendance` ORDER BY YEAR(date) DESC,10,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_attendance_years():
	year_list = frappe.db.sql_list(
		
	)
	if not year_list:
		year_list = [getdate().year]
	return ""\n"".join(str(year) for year in year_list)


        ```
    ###Assistant : select distinct YEAR(date) from `tabStudent Attendance` ORDER BY YEAR(date) DESC
    "
644,"async def test_discovery_notification(hass):
    
    mock_integration(hass, MockModule(""test""))
    mock_entity_platform(hass, ""config_flow.test"", None)

    with patch.dict(config_entries.HANDLERS):
",Test that we create/dismiss a notification when source is discovery.,10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_discovery_notification(hass):
    
    mock_integration(hass, MockModule(""test""))
    mock_entity_platform(hass, ""config_flow.test"", None)

    with patch.dict(config_entries.HANDLERS):

        ```
    ###Assistant : Test that we create/dismiss a notification when source is discovery.
    "
645,"def get_preference(self, identifier, resolutions, candidates, information):
        
        raise NotImplementedError
","Produce a sort key for given requirement based on preference.

        The preference is defined as ""I think this requirement should be
        resolved first"". The lower the return value is, the more preferred
        this group of arguments is.

        :param identifier: An identifier as returned by ``identify()``. This
            identifies the dependency matches of which should be returned.
        :param resolutions: Mapping of candidates currently pinned by the
            resolver. Each key is an identifier, and the value a candidate.
            The candidate may conflict with requirements from ``information``.
        :param candidates: Mapping of each dependency's possible candidates.
            Each value is an iterator of candidates.
        :param information: Mapping of requirement information of each package.
            Each value is an iterator of *requirement information*.

        A *requirement information* instance is a named tuple with two members:

        * ``requirement`` specifies a requirement contributing to the current
          list of candidates.
        * ``parent`` specifies the candidate that provides (dependend on) the
          requirement, or ``None`` to indicate a root requirement.

        The preference could depend on a various of issues, including (not
        necessarily in this order):

        * Is this package pinned in the current resolution result?
        * How relaxed is the requirement? Stricter ones should probably be
          worked on first? (I don't know, actually.)
        * How many possibilities are there to satisfy this requirement? Those
          with few left should likely be worked on first, I guess?
        * Are there any known conflicts for this requirement? We should
          probably work on those with the most known conflicts.

        A sortable value should be returned (this will be used as the ``key``
        parameter of the built-in sorting function). The smaller the value is,
        the more preferred this requirement is (i.e. the sorting function
        is called with ``reverse=False``).
        ",279,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_preference(self, identifier, resolutions, candidates, information):
        
        raise NotImplementedError

        ```
    ###Assistant : Produce a sort key for given requirement based on preference.

        The preference is defined as ""I think this requirement should be
        resolved first"". The lower the return value is, the more preferred
        this group of arguments is.

        :param identifier: An identifier as returned by ``identify()``. This
            identifies the dependency matches of which should be returned.
        :param resolutions: Mapping of candidates currently pinned by the
            resolver. Each key is an identifier, and the value a candidate.
            The candidate may conflict with requirements from ``information``.
        :param candidates: Mapping of each dependency's possible candidates.
            Each value is an iterator of candidates.
        :param information: Mapping of requirement information of each package.
            Each value is an iterator of *requirement information*.

        A *requirement information* instance is a named tuple with two members:

        * ``requirement`` specifies a requirement contributing to the current
          list of candidates.
        * ``parent`` specifies the candidate that provides (dependend on) the
          requirement, or ``None`` to indicate a root requirement.

        The preference could depend on a various of issues, including (not
        necessarily in this order):

        * Is this package pinned in the current resolution result?
        * How relaxed is the requirement? Stricter ones should probably be
          worked on first? (I don't know, actually.)
        * How many possibilities are there to satisfy this requirement? Those
          with few left should likely be worked on first, I guess?
        * Are there any known conflicts for this requirement? We should
          probably work on those with the most known conflicts.

        A sortable value should be returned (this will be used as the ``key``
        parameter of the built-in sorting function). The smaller the value is,
        the more preferred this requirement is (i.e. the sorting function
        is called with ``reverse=False``).
        
    "
646,"def prepare_metadata(self) -> None:
        
        assert self.source_dir
        details = self.name or f""from {self.link}""

        if self.use_pep517:
            assert self.pep517_backend is not None
            if (
                self.editable
                and self.permit_editable_wheels
                and self.supports_pyproject_editable()
            ):
                self.metadata_directory = generate_editable_metadata(
                    build_env=self.build_env,
                    backend=self.pep517_backend,
                    details=details,
                )
            else:
                self.metadata_directory = generate_metadata(
                    build_env=self.build_env,
                    backend=self.pep517_backend,
                    details=details,
                )
        else:
            self.metadata_directory = generate_metadata_legacy(
                build_env=self.build_env,
                setup_py_path=self.setup_py_path,
                source_dir=self.unpacked_source_directory,
                isolated=self.isolated,
                details=details,
            )

        # Act on the newly generated metadata, based on the name and version.
        if not self.name:
            self._set_requirement()
        else:
            self.warn_on_mismatching_name()

        self.assert_source_matches_version()
","Ensure that project metadata is available.

        Under PEP 517 and PEP 660, call the backend hook to prepare the metadata.
        Under legacy processing, call setup.py egg-info.
        ",26,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def prepare_metadata(self) -> None:
        
        assert self.source_dir
        details = self.name or f""from {self.link}""

        if self.use_pep517:
            assert self.pep517_backend is not None
            if (
                self.editable
                and self.permit_editable_wheels
                and self.supports_pyproject_editable()
            ):
                self.metadata_directory = generate_editable_metadata(
                    build_env=self.build_env,
                    backend=self.pep517_backend,
                    details=details,
                )
            else:
                self.metadata_directory = generate_metadata(
                    build_env=self.build_env,
                    backend=self.pep517_backend,
                    details=details,
                )
        else:
            self.metadata_directory = generate_metadata_legacy(
                build_env=self.build_env,
                setup_py_path=self.setup_py_path,
                source_dir=self.unpacked_source_directory,
                isolated=self.isolated,
                details=details,
            )

        # Act on the newly generated metadata, based on the name and version.
        if not self.name:
            self._set_requirement()
        else:
            self.warn_on_mismatching_name()

        self.assert_source_matches_version()

        ```
    ###Assistant : Ensure that project metadata is available.

        Under PEP 517 and PEP 660, call the backend hook to prepare the metadata.
        Under legacy processing, call setup.py egg-info.
        
    "
647,"def get_image_filename(self, image, filterspec):
        
        name, ext = os.path.splitext(os.path.basename(image.file.name))
        return ""{}images/{}.{}{}"".format(settings.MEDIA_URL, name, filterspec, ext)
","
        Get the generated filename for a resized image
        ",8,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_image_filename(self, image, filterspec):
        
        name, ext = os.path.splitext(os.path.basename(image.file.name))
        return ""{}images/{}.{}{}"".format(settings.MEDIA_URL, name, filterspec, ext)

        ```
    ###Assistant : 
        Get the generated filename for a resized image
        
    "
648,"def min(self, other, context=None):
        
        other = _convert_other(other, raiseit=True)

        if context is None:
            context = getcontext()

        if self._is_special or other._is_special:
            # If one operand is a quiet NaN and the other is number, then the
            # number is always returned
            sn = self._isnan()
            on = other._isnan()
            if sn or on:
                if on == 1 and sn == 0:
                    return self._fix(context)
                if sn == 1 and on == 0:
                    return other._fix(context)
                return self._check_nans(other, context)

        c = self._cmp(other)
        if c == 0:
            c = self.compare_total(other)

        if c == -1:
            ans = self
        else:
            ans = other

        return ans._fix(context)
","Returns the smaller value.

        Like min(self, other) except if one is not a number, returns
        NaN (and signals if one is sNaN).  Also rounds.
        ",24,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def min(self, other, context=None):
        
        other = _convert_other(other, raiseit=True)

        if context is None:
            context = getcontext()

        if self._is_special or other._is_special:
            # If one operand is a quiet NaN and the other is number, then the
            # number is always returned
            sn = self._isnan()
            on = other._isnan()
            if sn or on:
                if on == 1 and sn == 0:
                    return self._fix(context)
                if sn == 1 and on == 0:
                    return other._fix(context)
                return self._check_nans(other, context)

        c = self._cmp(other)
        if c == 0:
            c = self.compare_total(other)

        if c == -1:
            ans = self
        else:
            ans = other

        return ans._fix(context)

        ```
    ###Assistant : Returns the smaller value.

        Like min(self, other) except if one is not a number, returns
        NaN (and signals if one is sNaN).  Also rounds.
        
    "
649,"def test_simplelistfilter_with_none_returning_lookups(self):
        
        modeladmin = DecadeFilterBookAdminWithNoneReturningLookups(Book, site)
        request = self.request_factory.get(""/"", {})
        request.user = self.alfred
        changelist = modeladmin.get_changelist_instance(request)
        filterspec = changelist.get_filters(request)[0]
        self.assertEqual(len(filterspec), 0)
","
        A SimpleListFilter lookups method can return None but disables the
        filter completely.
        ",12,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_simplelistfilter_with_none_returning_lookups(self):
        
        modeladmin = DecadeFilterBookAdminWithNoneReturningLookups(Book, site)
        request = self.request_factory.get(""/"", {})
        request.user = self.alfred
        changelist = modeladmin.get_changelist_instance(request)
        filterspec = changelist.get_filters(request)[0]
        self.assertEqual(len(filterspec), 0)

        ```
    ###Assistant : 
        A SimpleListFilter lookups method can return None but disables the
        filter completely.
        
    "
650,"def precompute_fill_value(dataset_cols, feature, preprocessing_parameters, backend):
    
    missing_value_strategy = preprocessing_parameters[""missing_value_strategy""]
    if missing_value_strategy == FILL_WITH_CONST:
        return preprocessing_parameters[""fill_value""]
    elif missing_value_strategy == FILL_WITH_MODE:
        return dataset_cols[feature[COLUMN]].value_counts().index[0]
    elif missing_value_strategy == FILL_WITH_MEAN:
        if feature[TYPE] != NUMBER:
            raise ValueError(
                f""Filling missing values with mean is supported ""
                f""only for number types, not for type {feature[TYPE]}."",
            )
        return backend.df_engine.compute(dataset_cols[feature[COLUMN]].mean())
    elif missing_value_strategy == FILL_WITH_FALSE:
        distinct_values = backend.df_engine.compute(
            dataset_cols[feature[COLUMN]].drop_duplicates().dropna()
        ).values.tolist()
        if len(distinct_values) > 2:
            raise ValueError(
                f""Missing value strategy `fill_with_false` ""
                f""for column {feature[COLUMN]} expects 2 distinct values, ""
                f""found: {len(distinct_values)} (ex: {distinct_values[:10]})""
            )

        # Determine the False label.
        # Distinct values are sorted in reverse to mirror the selection of the default fallback_true_label (in
        # binary_feature.get_feature_meta) for binary columns with unconventional boolean values, ""human""/""bot"".
        for v in sorted(distinct_values, reverse=True):
            fallback_true_label = preprocessing_parameters.get(""fallback_true_label"", ""true"")
            if strings_utils.str2bool(v, fallback_true_label) is False:
                return v
        raise ValueError(
            f""Unable to determine False value for column {feature[COLUMN]} with distinct values: {distinct_values}.""
        )
    # Otherwise, we cannot precompute the fill value for this dataset
    return None

","Precomputes the fill value for a feature.

    NOTE: this is called before NaNs are removed from the dataset. Modifications here must handle NaNs gracefully.
    ",24,157,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def precompute_fill_value(dataset_cols, feature, preprocessing_parameters, backend):
    
    missing_value_strategy = preprocessing_parameters[""missing_value_strategy""]
    if missing_value_strategy == FILL_WITH_CONST:
        return preprocessing_parameters[""fill_value""]
    elif missing_value_strategy == FILL_WITH_MODE:
        return dataset_cols[feature[COLUMN]].value_counts().index[0]
    elif missing_value_strategy == FILL_WITH_MEAN:
        if feature[TYPE] != NUMBER:
            raise ValueError(
                f""Filling missing values with mean is supported ""
                f""only for number types, not for type {feature[TYPE]}."",
            )
        return backend.df_engine.compute(dataset_cols[feature[COLUMN]].mean())
    elif missing_value_strategy == FILL_WITH_FALSE:
        distinct_values = backend.df_engine.compute(
            dataset_cols[feature[COLUMN]].drop_duplicates().dropna()
        ).values.tolist()
        if len(distinct_values) > 2:
            raise ValueError(
                f""Missing value strategy `fill_with_false` ""
                f""for column {feature[COLUMN]} expects 2 distinct values, ""
                f""found: {len(distinct_values)} (ex: {distinct_values[:10]})""
            )

        # Determine the False label.
        # Distinct values are sorted in reverse to mirror the selection of the default fallback_true_label (in
        # binary_feature.get_feature_meta) for binary columns with unconventional boolean values, ""human""/""bot"".
        for v in sorted(distinct_values, reverse=True):
            fallback_true_label = preprocessing_parameters.get(""fallback_true_label"", ""true"")
            if strings_utils.str2bool(v, fallback_true_label) is False:
                return v
        raise ValueError(
            f""Unable to determine False value for column {feature[COLUMN]} with distinct values: {distinct_values}.""
        )
    # Otherwise, we cannot precompute the fill value for this dataset
    return None


        ```
    ###Assistant : Precomputes the fill value for a feature.

    NOTE: this is called before NaNs are removed from the dataset. Modifications here must handle NaNs gracefully.
    
    "
651,"def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
        
        if ""logits"" not in outputs:
            raise ValueError(""No logits were found in the outputs"")

        source_logits = outputs[""logits""]

        idx = self._get_source_permutation_idx(indices)
        target_classes_o = torch.cat([t[""class_labels""][J] for t, (_, J) in zip(targets, indices)])
        target_classes = torch.full(
            source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
        )
        target_classes[idx] = target_classes_o

        target_classes_onehot = torch.zeros(
            [source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1],
            dtype=source_logits.dtype,
            layout=source_logits.layout,
            device=source_logits.device,
        )
        target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)

        target_classes_onehot = target_classes_onehot[:, :, :-1]
        loss_ce = (
            sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2)
            * source_logits.shape[1]
        )
        losses = {""loss_ce"": loss_ce}

        return losses
","Classification loss (NLL)
        targets dicts must contain the key ""labels"" containing a tensor of dim [nb_target_boxes]
        ",16,84,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
        
        if ""logits"" not in outputs:
            raise ValueError(""No logits were found in the outputs"")

        source_logits = outputs[""logits""]

        idx = self._get_source_permutation_idx(indices)
        target_classes_o = torch.cat([t[""class_labels""][J] for t, (_, J) in zip(targets, indices)])
        target_classes = torch.full(
            source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
        )
        target_classes[idx] = target_classes_o

        target_classes_onehot = torch.zeros(
            [source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1],
            dtype=source_logits.dtype,
            layout=source_logits.layout,
            device=source_logits.device,
        )
        target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)

        target_classes_onehot = target_classes_onehot[:, :, :-1]
        loss_ce = (
            sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2)
            * source_logits.shape[1]
        )
        losses = {""loss_ce"": loss_ce}

        return losses

        ```
    ###Assistant : Classification loss (NLL)
        targets dicts must contain the key ""labels"" containing a tensor of dim [nb_target_boxes]
        
    "
652,"def test_stacking_classifier_multilabel_predict_proba(estimator):
    
    X_train, X_test, y_train, y_test = train_test_split(
        X_multilabel, y_multilabel, stratify=y_multilabel, random_state=42
    )
    n_outputs = 3

    estimators = [(""est"", estimator)]
    stacker = StackingClassifier(
        estimators=estimators,
        final_estimator=KNeighborsClassifier(),
        stack_method=""predict_proba"",
    ).fit(X_train, y_train)

    X_trans = stacker.transform(X_test)
    assert X_trans.shape == (X_test.shape[0], n_outputs)
    # we should not have any collinear classes and thus nothing should sum to 1
    assert not any(np.isclose(X_trans.sum(axis=1), 1.0))

    y_pred = stacker.predict(X_test)
    assert y_pred.shape == y_test.shape

","Check the behaviour for the multilabel classification case and the
    `predict_proba` stacking method.

    Estimators are not consistent with the output arrays and we need to ensure that
    we handle all cases.
    ",31,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stacking_classifier_multilabel_predict_proba(estimator):
    
    X_train, X_test, y_train, y_test = train_test_split(
        X_multilabel, y_multilabel, stratify=y_multilabel, random_state=42
    )
    n_outputs = 3

    estimators = [(""est"", estimator)]
    stacker = StackingClassifier(
        estimators=estimators,
        final_estimator=KNeighborsClassifier(),
        stack_method=""predict_proba"",
    ).fit(X_train, y_train)

    X_trans = stacker.transform(X_test)
    assert X_trans.shape == (X_test.shape[0], n_outputs)
    # we should not have any collinear classes and thus nothing should sum to 1
    assert not any(np.isclose(X_trans.sum(axis=1), 1.0))

    y_pred = stacker.predict(X_test)
    assert y_pred.shape == y_test.shape


        ```
    ###Assistant : Check the behaviour for the multilabel classification case and the
    `predict_proba` stacking method.

    Estimators are not consistent with the output arrays and we need to ensure that
    we handle all cases.
    
    "
653,"def get_page(self, url):
        
        # http://peak.telecommunity.com/DevCenter/EasyInstall#package-index-api
        scheme, netloc, path, _, _, _ = urlparse(url)
        if scheme == 'file' and os.path.isdir(url2pathname(path)):
            url = urljoin(ensure_slash(url), 'index.html')

        if url in self._page_cache:
            result = self._page_cache[url]
            logger.debug('Returning %s from cache: %s', url, result)
        else:
            host = netloc.split(':', 1)[0]
            result = None
            if host in self._bad_hosts:
                logger.debug('Skipping %s due to bad host %s', url, host)
            else:
                req = Request(url, headers={'Accept-encoding': 'identity'})
                try:
                    logger.debug('Fetching %s', url)
                    resp = self.opener.open(req, timeout=self.timeout)
                    logger.debug('Fetched %s', url)
                    headers = resp.info()
                    content_type = headers.get('Content-Type', '')
                    if HTML_CONTENT_TYPE.match(content_type):
                        final_url = resp.geturl()
                        data = resp.read()
                        encoding = headers.get('Content-Encoding')
                        if encoding:
                            decoder = self.decoders[encoding]   # fail if not found
                            data = decoder(data)
                        encoding = 'utf-8'
                        m = CHARSET.search(content_type)
                        if m:
                            encoding = m.group(1)
                        try:
                            data = data.decode(encoding)
                        except UnicodeError:  # pragma: no cover
                            data = data.decode('latin-1')    # fallback
                        result = Page(data, final_url)
                        self._page_cache[final_url] = result
                except HTTPError as e:
                    if e.code != 404:
                        logger.exception('Fetch failed: %s: %s', url, e)
                except URLError as e:  # pragma: no cover
                    logger.exception('Fetch failed: %s: %s', url, e)
                    with self._lock:
                        self._bad_hosts.add(host)
                except Exception as e:  # pragma: no cover
                    logger.exception('Fetch failed: %s: %s', url, e)
                finally:
                    self._page_cache[url] = result   # even if None (failure)
        return result

    _distname_re = re.compile('<a href=[^>]*>([^<]+)<')
","
        Get the HTML for an URL, possibly from an in-memory cache.

        XXX TODO Note: this cache is never actually cleared. It's assumed that
        the data won't get stale over the lifetime of a locator instance (not
        necessarily true for the default_locator).
        ",41,199,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_page(self, url):
        
        # http://peak.telecommunity.com/DevCenter/EasyInstall#package-index-api
        scheme, netloc, path, _, _, _ = urlparse(url)
        if scheme == 'file' and os.path.isdir(url2pathname(path)):
            url = urljoin(ensure_slash(url), 'index.html')

        if url in self._page_cache:
            result = self._page_cache[url]
            logger.debug('Returning %s from cache: %s', url, result)
        else:
            host = netloc.split(':', 1)[0]
            result = None
            if host in self._bad_hosts:
                logger.debug('Skipping %s due to bad host %s', url, host)
            else:
                req = Request(url, headers={'Accept-encoding': 'identity'})
                try:
                    logger.debug('Fetching %s', url)
                    resp = self.opener.open(req, timeout=self.timeout)
                    logger.debug('Fetched %s', url)
                    headers = resp.info()
                    content_type = headers.get('Content-Type', '')
                    if HTML_CONTENT_TYPE.match(content_type):
                        final_url = resp.geturl()
                        data = resp.read()
                        encoding = headers.get('Content-Encoding')
                        if encoding:
                            decoder = self.decoders[encoding]   # fail if not found
                            data = decoder(data)
                        encoding = 'utf-8'
                        m = CHARSET.search(content_type)
                        if m:
                            encoding = m.group(1)
                        try:
                            data = data.decode(encoding)
                        except UnicodeError:  # pragma: no cover
                            data = data.decode('latin-1')    # fallback
                        result = Page(data, final_url)
                        self._page_cache[final_url] = result
                except HTTPError as e:
                    if e.code != 404:
                        logger.exception('Fetch failed: %s: %s', url, e)
                except URLError as e:  # pragma: no cover
                    logger.exception('Fetch failed: %s: %s', url, e)
                    with self._lock:
                        self._bad_hosts.add(host)
                except Exception as e:  # pragma: no cover
                    logger.exception('Fetch failed: %s: %s', url, e)
                finally:
                    self._page_cache[url] = result   # even if None (failure)
        return result

    _distname_re = re.compile('<a href=[^>]*>([^<]+)<')

        ```
    ###Assistant : 
        Get the HTML for an URL, possibly from an in-memory cache.

        XXX TODO Note: this cache is never actually cleared. It's assumed that
        the data won't get stale over the lifetime of a locator instance (not
        necessarily true for the default_locator).
        
    "
654,"def enable_all_warnings() -> None:
    
    __diag__.enable_all_warnings()


# hide abstract class
del __config_flags

","
    Enable all global pyparsing diagnostic warnings (see :class:`Diagnostics`).
    ",8,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def enable_all_warnings() -> None:
    
    __diag__.enable_all_warnings()


# hide abstract class
del __config_flags


        ```
    ###Assistant : 
    Enable all global pyparsing diagnostic warnings (see :class:`Diagnostics`).
    
    "
655,"def export_yaml(self):
        
        yaml_data = [obj.to_yaml() for obj in self.queryset]

        return '---\n'.join(yaml_data)
","
        Export the queryset of objects as concatenated YAML documents.
        ",9,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def export_yaml(self):
        
        yaml_data = [obj.to_yaml() for obj in self.queryset]

        return '---\n'.join(yaml_data)

        ```
    ###Assistant : 
        Export the queryset of objects as concatenated YAML documents.
        
    "
656,"def url_has_allowed_host_and_scheme(url, allowed_hosts, require_https=False):
    
    if url is not None:
        url = url.strip()
    if not url:
        return False
    if allowed_hosts is None:
        allowed_hosts = set()
    elif isinstance(allowed_hosts, str):
        allowed_hosts = {allowed_hosts}
    # Chrome treats \ completely as / in paths but it could be part of some
    # basic auth credentials so we need to check both URLs.
    return _url_has_allowed_host_and_scheme(
        url, allowed_hosts, require_https=require_https
    ) and _url_has_allowed_host_and_scheme(
        url.replace(""\\"", ""/""), allowed_hosts, require_https=require_https
    )


# Copied from urllib.parse.urlparse() but uses fixed urlsplit() function.","
    Return ``True`` if the url uses an allowed host and a safe scheme.

    Always return ``False`` on an empty url.

    If ``require_https`` is ``True``, only 'https' will be considered a valid
    scheme, as opposed to 'http' and 'https' with the default, ``False``.

    Note: ""True"" doesn't entail that a URL is ""safe"". It may still be e.g.
    quoted incorrectly. Ensure to also use django.utils.encoding.iri_to_uri()
    on the path component of untrusted URLs.
    ",70,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def url_has_allowed_host_and_scheme(url, allowed_hosts, require_https=False):
    
    if url is not None:
        url = url.strip()
    if not url:
        return False
    if allowed_hosts is None:
        allowed_hosts = set()
    elif isinstance(allowed_hosts, str):
        allowed_hosts = {allowed_hosts}
    # Chrome treats \ completely as / in paths but it could be part of some
    # basic auth credentials so we need to check both URLs.
    return _url_has_allowed_host_and_scheme(
        url, allowed_hosts, require_https=require_https
    ) and _url_has_allowed_host_and_scheme(
        url.replace(""\\"", ""/""), allowed_hosts, require_https=require_https
    )


# Copied from urllib.parse.urlparse() but uses fixed urlsplit() function.
        ```
    ###Assistant : 
    Return ``True`` if the url uses an allowed host and a safe scheme.

    Always return ``False`` on an empty url.

    If ``require_https`` is ``True``, only 'https' will be considered a valid
    scheme, as opposed to 'http' and 'https' with the default, ``False``.

    Note: ""True"" doesn't entail that a URL is ""safe"". It may still be e.g.
    quoted incorrectly. Ensure to also use django.utils.encoding.iri_to_uri()
    on the path component of untrusted URLs.
    
    "
657,"def get_ps(module, pattern):
    
    found = False
    if platform.system() == 'SunOS':
        flags = '-ef'
    else:
        flags = 'auxww'
    psbin = module.get_bin_path('ps', True)

    (rc, psout, pserr) = module.run_command([psbin, flags])
    if rc == 0:
        for line in psout.splitlines():
            if pattern in line:
                # FIXME: should add logic to prevent matching 'self', though that should be extremely rare
                found = True
                break
    return found

","
    Last resort to find a service by trying to match pattern to programs in memory
    ",15,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ps(module, pattern):
    
    found = False
    if platform.system() == 'SunOS':
        flags = '-ef'
    else:
        flags = 'auxww'
    psbin = module.get_bin_path('ps', True)

    (rc, psout, pserr) = module.run_command([psbin, flags])
    if rc == 0:
        for line in psout.splitlines():
            if pattern in line:
                # FIXME: should add logic to prevent matching 'self', though that should be extremely rare
                found = True
                break
    return found


        ```
    ###Assistant : 
    Last resort to find a service by trying to match pattern to programs in memory
    
    "
658,"def apply(self, sample, context=None):
        
        im = sample['image']
        im = im.astype(np.float32, copy=False)
        if self.is_scale:
            scale = 1.0 / 255.0
            im *= scale

        if self.norm_type == 'mean_std':
            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
            std = np.array(self.std)[np.newaxis, np.newaxis, :]
            im -= mean
            im /= std
        sample['image'] = im
        return sample


@register_op","Normalize the image.
        Operators:
            1.(optional) Scale the pixel to [0,1]
            2.(optional) Each pixel minus mean and is divided by std
        ",20,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply(self, sample, context=None):
        
        im = sample['image']
        im = im.astype(np.float32, copy=False)
        if self.is_scale:
            scale = 1.0 / 255.0
            im *= scale

        if self.norm_type == 'mean_std':
            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
            std = np.array(self.std)[np.newaxis, np.newaxis, :]
            im -= mean
            im /= std
        sample['image'] = im
        return sample


@register_op
        ```
    ###Assistant : Normalize the image.
        Operators:
            1.(optional) Scale the pixel to [0,1]
            2.(optional) Each pixel minus mean and is divided by std
        
    "
659,"def _check_m2m_through_same_relationship(cls):
        

        errors = []
        seen_intermediary_signatures = []

        fields = cls._meta.local_many_to_many

        # Skip when the target model wasn't found.
        fields = (f for f in fields if isinstance(f.remote_field.model, ModelBase))

        # Skip when the relationship model wasn't found.
        fields = (f for f in fields if isinstance(f.remote_field.through, ModelBase))

        for f in fields:
            signature = (
                f.remote_field.model,
                cls,
                f.remote_field.through,
                f.remote_field.through_fields,
            )
            if signature in seen_intermediary_signatures:
                errors.append(
                    checks.Error(
                        ""The model has two identical many-to-many relations ""
                        ""through the intermediate model '%s'.""
                        % f.remote_field.through._meta.label,
                        obj=cls,
                        id=""models.E003"",
                    )
                )
            else:
                seen_intermediary_signatures.append(signature)
        return errors
",Check if no relationship model is used by more than one m2m field.,13,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_m2m_through_same_relationship(cls):
        

        errors = []
        seen_intermediary_signatures = []

        fields = cls._meta.local_many_to_many

        # Skip when the target model wasn't found.
        fields = (f for f in fields if isinstance(f.remote_field.model, ModelBase))

        # Skip when the relationship model wasn't found.
        fields = (f for f in fields if isinstance(f.remote_field.through, ModelBase))

        for f in fields:
            signature = (
                f.remote_field.model,
                cls,
                f.remote_field.through,
                f.remote_field.through_fields,
            )
            if signature in seen_intermediary_signatures:
                errors.append(
                    checks.Error(
                        ""The model has two identical many-to-many relations ""
                        ""through the intermediate model '%s'.""
                        % f.remote_field.through._meta.label,
                        obj=cls,
                        id=""models.E003"",
                    )
                )
            else:
                seen_intermediary_signatures.append(signature)
        return errors

        ```
    ###Assistant : Check if no relationship model is used by more than one m2m field.
    "
660,"def image(self) -> ""np.ndarray"":
        
        assert self._image is not None
        return self._image
", :class:`numpy.ndarray`: The source frame for this object. ,7,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def image(self) -> ""np.ndarray"":
        
        assert self._image is not None
        return self._image

        ```
    ###Assistant :  :class:`numpy.ndarray`: The source frame for this object. 
    "
661,"def test_sends_deployment_notification(self, record_analytics):
        

        release = self.create_release()
        version_parsed = self.version_parsed = parse_release(release.version)[""description""]
        url = f""/api/0/organizations/{self.organization.slug}/releases/{release.version}/deploys/""
        with self.tasks():
            response = self.client.post(
                url, format=""json"", data={""environment"": self.environment.name}
            )
        assert response.status_code == 201, response.content

        msg = mail.outbox[0]
        # check the txt version
        assert f""Version {version_parsed} was deployed to {self.environment.name} on"" in msg.body
        # check the html version
        assert (
            f""Version {version_parsed} was deployed to {self.environment.name}\n    </h2>\n""
            in msg.alternatives[0][0]
        )

        attachment, text = get_attachment()

        assert (
            text
            == f""Release {version_parsed} was deployed to {self.environment.name} for this project""
        )
        assert (
            attachment[""actions""][0][""url""]
            == f""http://testserver/organizations/{self.organization.slug}/releases/{release.version}/?project={self.project.id}&unselectedSeries=Healthy/""
        )
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/deploy/?referrer=release_activity-slack-user|Notification Settings>""
        )
        assert analytics_called_with_args(
            record_analytics,
            ""integrations.email.notification_sent"",
            user_id=self.user.id,
            actor_id=self.user.actor_id,
            organization_id=self.organization.id,
        )
        assert analytics_called_with_args(
            record_analytics,
            ""integrations.slack.notification_sent"",
            user_id=self.user.id,
            actor_id=self.user.actor_id,
            organization_id=self.organization.id,
        )
","
        Test that an email AND Slack notification are sent with
        the expected values when a release is deployed.
        ",18,113,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_sends_deployment_notification(self, record_analytics):
        

        release = self.create_release()
        version_parsed = self.version_parsed = parse_release(release.version)[""description""]
        url = f""/api/0/organizations/{self.organization.slug}/releases/{release.version}/deploys/""
        with self.tasks():
            response = self.client.post(
                url, format=""json"", data={""environment"": self.environment.name}
            )
        assert response.status_code == 201, response.content

        msg = mail.outbox[0]
        # check the txt version
        assert f""Version {version_parsed} was deployed to {self.environment.name} on"" in msg.body
        # check the html version
        assert (
            f""Version {version_parsed} was deployed to {self.environment.name}\n    </h2>\n""
            in msg.alternatives[0][0]
        )

        attachment, text = get_attachment()

        assert (
            text
            == f""Release {version_parsed} was deployed to {self.environment.name} for this project""
        )
        assert (
            attachment[""actions""][0][""url""]
            == f""http://testserver/organizations/{self.organization.slug}/releases/{release.version}/?project={self.project.id}&unselectedSeries=Healthy/""
        )
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/deploy/?referrer=release_activity-slack-user|Notification Settings>""
        )
        assert analytics_called_with_args(
            record_analytics,
            ""integrations.email.notification_sent"",
            user_id=self.user.id,
            actor_id=self.user.actor_id,
            organization_id=self.organization.id,
        )
        assert analytics_called_with_args(
            record_analytics,
            ""integrations.slack.notification_sent"",
            user_id=self.user.id,
            actor_id=self.user.actor_id,
            organization_id=self.organization.id,
        )

        ```
    ###Assistant : 
        Test that an email AND Slack notification are sent with
        the expected values when a release is deployed.
        
    "
662,"def get_all_mode_of_payments(doc):
	return frappe.db.sql(
		,
		{""company"": doc.company},
		as_dict=1,
	)

","
		select mpa.default_account, mpa.parent, mp.type as type
		from `tabMode of Payment Account` mpa,`tabMode of Payment` mp
		where mpa.parent = mp.name and mpa.company = %(company)s and mp.enabled = 1",27,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all_mode_of_payments(doc):
	return frappe.db.sql(
		,
		{""company"": doc.company},
		as_dict=1,
	)


        ```
    ###Assistant : 
		select mpa.default_account, mpa.parent, mp.type as type
		from `tabMode of Payment Account` mpa,`tabMode of Payment` mp
		where mpa.parent = mp.name and mpa.company = %(company)s and mp.enabled = 1
    "
663,"def _on_move(self, event):
        

        if not self.button_pressed:
            return

        if self.get_navigate_mode() is not None:
            # we don't want to rotate if we are zooming/panning
            # from the toolbar
            return

        if self.M is None:
            return

        x, y = event.xdata, event.ydata
        # In case the mouse is out of bounds.
        if x is None or event.inaxes != self:
            return

        dx, dy = x - self._sx, y - self._sy
        w = self._pseudo_w
        h = self._pseudo_h

        # Rotation
        if self.button_pressed in self._rotate_btn:
            # rotate viewing point
            # get the x and y pixel coords
            if dx == 0 and dy == 0:
                return

            roll = np.deg2rad(self.roll)
            delev = -(dy/h)*180*np.cos(roll) + (dx/w)*180*np.sin(roll)
            dazim = -(dy/h)*180*np.sin(roll) - (dx/w)*180*np.cos(roll)
            self.elev = self.elev + delev
            self.azim = self.azim + dazim
            self.stale = True

        elif self.button_pressed in self._pan_btn:
            # Start the pan event with pixel coordinates
            px, py = self.transData.transform([self._sx, self._sy])
            self.start_pan(px, py, 2)
            # pan view (takes pixel coordinate input)
            self.drag_pan(2, None, event.x, event.y)
            self.end_pan()

        # Zoom
        elif self.button_pressed in self._zoom_btn:
            # zoom view (dragging down zooms in)
            scale = h/(h - dy)
            self._scale_axis_limits(scale, scale, scale)

        # Store the event coordinates for the next time through.
        self._sx, self._sy = x, y
        # Always request a draw update at the end of interaction
        self.figure.canvas.draw_idle()
","
        Mouse moving.

        By default, button-1 rotates, button-2 pans, and button-3 zooms;
        these buttons can be modified via `mouse_init`.
        ",18,203,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _on_move(self, event):
        

        if not self.button_pressed:
            return

        if self.get_navigate_mode() is not None:
            # we don't want to rotate if we are zooming/panning
            # from the toolbar
            return

        if self.M is None:
            return

        x, y = event.xdata, event.ydata
        # In case the mouse is out of bounds.
        if x is None or event.inaxes != self:
            return

        dx, dy = x - self._sx, y - self._sy
        w = self._pseudo_w
        h = self._pseudo_h

        # Rotation
        if self.button_pressed in self._rotate_btn:
            # rotate viewing point
            # get the x and y pixel coords
            if dx == 0 and dy == 0:
                return

            roll = np.deg2rad(self.roll)
            delev = -(dy/h)*180*np.cos(roll) + (dx/w)*180*np.sin(roll)
            dazim = -(dy/h)*180*np.sin(roll) - (dx/w)*180*np.cos(roll)
            self.elev = self.elev + delev
            self.azim = self.azim + dazim
            self.stale = True

        elif self.button_pressed in self._pan_btn:
            # Start the pan event with pixel coordinates
            px, py = self.transData.transform([self._sx, self._sy])
            self.start_pan(px, py, 2)
            # pan view (takes pixel coordinate input)
            self.drag_pan(2, None, event.x, event.y)
            self.end_pan()

        # Zoom
        elif self.button_pressed in self._zoom_btn:
            # zoom view (dragging down zooms in)
            scale = h/(h - dy)
            self._scale_axis_limits(scale, scale, scale)

        # Store the event coordinates for the next time through.
        self._sx, self._sy = x, y
        # Always request a draw update at the end of interaction
        self.figure.canvas.draw_idle()

        ```
    ###Assistant : 
        Mouse moving.

        By default, button-1 rotates, button-2 pans, and button-3 zooms;
        these buttons can be modified via `mouse_init`.
        
    "
664,"def install(name=None, refresh=False, pkgs=None, version=None, test=False, **kwargs):
    
    targets = salt.utils.args.split_input(pkgs) if pkgs else [name]
    if not targets:
        return {}

    if pkgs:
        log.debug(""Removing these fileset(s)/rpm package(s) %s: %s"", name, targets)

    # Get a list of the currently installed pkgs.
    old = list_pkgs()

    # Install the fileset (normally ends with bff or rte) or rpm package(s)
    errors = []
    for target in targets:
        filename = os.path.basename(target)
        if filename.endswith("".bff"") or filename.endswith("".rte""):
            if _is_installed(target):
                continue

            cmd = ""/usr/sbin/installp -acYXg""
            if test:
                cmd += ""p""
            cmd += "" -d ""
            dirpath = os.path.dirname(target)
            cmd += dirpath + "" "" + filename
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)
        else:
            if _is_installed_rpm(filename.split("".aix"")[0]):
                continue

            # assume use dnf or yum
            cmdflags = ""  install --allowerasing ""
            if pathlib.Path(""/opt/freeware/bin/dnf"").is_file():
                cmdexe = ""/opt/freeware/bin/dnf""
                if test:
                    cmdflags += "" --assumeno""
                else:
                    cmdflags += "" --assumeyes""
                if refresh:
                    cmdflags += "" --refresh""

            elif pathlib.Path(""/opt/freeware/bin/yum"").is_file():
                cmdexe = ""/opt/freeware/bin/yum""
                if test:
                    cmdflags += "" --assumeno""
                else:
                    cmdflags += "" --assumeyes""
                if refresh:
                    cmdflags += "" --refresh""

            elif pathlib.Path(""/usr/bin/yum"").is_file():
                cmdexe = ""/usr/bin/yum""
                if test:
                    cmdflags += "" --assumeno""
                else:
                    cmdflags += "" --assumeyes""
            else:
                cmdexe = ""/usr/bin/rpm""
                cmdflags = "" -Uivh ""
                if test:
                    cmdflags += "" --test""

            cmd = [cmdexe, cmdflags, target]
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)

        if 0 != out[""retcode""]:
            errors.append(out[""stderr""])

    # Get a list of the packages after the uninstall
    __context__.pop(""pkg.list_pkgs"", None)
    new = list_pkgs()
    ret = salt.utils.data.compare_dicts(old, new)

    if errors:
        raise CommandExecutionError(
            ""Problems encountered installing filesets(s)/package(s)"",
            info={""changes"": ret, ""errors"": errors},
        )

    # No error occurred
    if test:
        return ""Test succeeded.""

    return ret

","
    Install the named fileset(s)/rpm package(s).

    .. versionadded:: 3005

        preference to install rpm packages are to use in the following order:
            /opt/freeware/bin/dnf
            /opt/freeware/bin/yum
            /usr/bin/yum
            /usr/bin/rpm

        Note: use of rpm to install implies that rpm's dependencies must have been previously installed.
            dnf and yum automatically install rpm's dependencies as part of the install process

    name
        The name of the fileset or rpm package to be installed.

    refresh
        Whether or not to update the yum database before executing.


    Multiple Package Installation Options:

    pkgs
        A list of filesets and/or rpm packages to install.
        Must be passed as a python list. The ``name`` parameter will be
        ignored if this option is passed.

    version
        Install a specific version of a fileset/rpm package.
        (Unused at present).

    test
        Verify that command functions correctly:

    Returns a dict containing the new fileset(s)/rpm package(s) names and versions:

        {'<package>': {'old': '<old-version>',
                       'new': '<new-version>'}}

    CLI Example:

    .. code-block:: bash

        salt '*' pkg.install /stage/middleware/AIX/bash-4.2-3.aix6.1.ppc.rpm
        salt '*' pkg.install /stage/middleware/AIX/bash-4.2-3.aix6.1.ppc.rpm refresh=True
        salt '*' pkg.install /stage/middleware/AIX/VIOS2211_update/tpc_4.1.1.85.bff
        salt '*' pkg.install /stage/middleware/AIX/Xlc/usr/sys/inst.images/xlC.rte
        salt '*' pkg.install /stage/middleware/AIX/Firefox/ppc-AIX53/Firefox.base
        salt '*' pkg.install pkgs='[""foo"", ""bar""]'
    ",172,248,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def install(name=None, refresh=False, pkgs=None, version=None, test=False, **kwargs):
    
    targets = salt.utils.args.split_input(pkgs) if pkgs else [name]
    if not targets:
        return {}

    if pkgs:
        log.debug(""Removing these fileset(s)/rpm package(s) %s: %s"", name, targets)

    # Get a list of the currently installed pkgs.
    old = list_pkgs()

    # Install the fileset (normally ends with bff or rte) or rpm package(s)
    errors = []
    for target in targets:
        filename = os.path.basename(target)
        if filename.endswith("".bff"") or filename.endswith("".rte""):
            if _is_installed(target):
                continue

            cmd = ""/usr/sbin/installp -acYXg""
            if test:
                cmd += ""p""
            cmd += "" -d ""
            dirpath = os.path.dirname(target)
            cmd += dirpath + "" "" + filename
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)
        else:
            if _is_installed_rpm(filename.split("".aix"")[0]):
                continue

            # assume use dnf or yum
            cmdflags = ""  install --allowerasing ""
            if pathlib.Path(""/opt/freeware/bin/dnf"").is_file():
                cmdexe = ""/opt/freeware/bin/dnf""
                if test:
                    cmdflags += "" --assumeno""
                else:
                    cmdflags += "" --assumeyes""
                if refresh:
                    cmdflags += "" --refresh""

            elif pathlib.Path(""/opt/freeware/bin/yum"").is_file():
                cmdexe = ""/opt/freeware/bin/yum""
                if test:
                    cmdflags += "" --assumeno""
                else:
                    cmdflags += "" --assumeyes""
                if refresh:
                    cmdflags += "" --refresh""

            elif pathlib.Path(""/usr/bin/yum"").is_file():
                cmdexe = ""/usr/bin/yum""
                if test:
                    cmdflags += "" --assumeno""
                else:
                    cmdflags += "" --assumeyes""
            else:
                cmdexe = ""/usr/bin/rpm""
                cmdflags = "" -Uivh ""
                if test:
                    cmdflags += "" --test""

            cmd = [cmdexe, cmdflags, target]
            out = __salt__[""cmd.run_all""](cmd, python_shell=False)

        if 0 != out[""retcode""]:
            errors.append(out[""stderr""])

    # Get a list of the packages after the uninstall
    __context__.pop(""pkg.list_pkgs"", None)
    new = list_pkgs()
    ret = salt.utils.data.compare_dicts(old, new)

    if errors:
        raise CommandExecutionError(
            ""Problems encountered installing filesets(s)/package(s)"",
            info={""changes"": ret, ""errors"": errors},
        )

    # No error occurred
    if test:
        return ""Test succeeded.""

    return ret


        ```
    ###Assistant : 
    Install the named fileset(s)/rpm package(s).

    .. versionadded:: 3005

        preference to install rpm packages are to use in the following order:
            /opt/freeware/bin/dnf
            /opt/freeware/bin/yum
            /usr/bin/yum
            /usr/bin/rpm

        Note: use of rpm to install implies that rpm's dependencies must have been previously installed.
            dnf and yum automatically install rpm's dependencies as part of the install process

    name
        The name of the fileset or rpm package to be installed.

    refresh
        Whether or not to update the yum database before executing.


    Multiple Package Installation Options:

    pkgs
        A list of filesets and/or rpm packages to install.
        Must be passed as a python list. The ``name`` parameter will be
        ignored if this option is passed.

    version
        Install a specific version of a fileset/rpm package.
        (Unused at present).

    test
        Verify that command functions correctly:

    Returns a dict containing the new fileset(s)/rpm package(s) names and versions:

        {'<package>': {'old': '<old-version>',
                       'new': '<new-version>'}}

    CLI Example:

    .. code-block:: bash

        salt '*' pkg.install /stage/middleware/AIX/bash-4.2-3.aix6.1.ppc.rpm
        salt '*' pkg.install /stage/middleware/AIX/bash-4.2-3.aix6.1.ppc.rpm refresh=True
        salt '*' pkg.install /stage/middleware/AIX/VIOS2211_update/tpc_4.1.1.85.bff
        salt '*' pkg.install /stage/middleware/AIX/Xlc/usr/sys/inst.images/xlC.rte
        salt '*' pkg.install /stage/middleware/AIX/Firefox/ppc-AIX53/Firefox.base
        salt '*' pkg.install pkgs='[""foo"", ""bar""]'
    
    "
665,"def is_false(self, ds_key_long):
        
        value = self.get_value(ds_key_long)
        return False if value is None else not bool(value)
","
        Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very
        specific question of whether the value is set to `False` (and it's not set to `True`` or isn't set).
        ",38,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_false(self, ds_key_long):
        
        value = self.get_value(ds_key_long)
        return False if value is None else not bool(value)

        ```
    ###Assistant : 
        Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very
        specific question of whether the value is set to `False` (and it's not set to `True`` or isn't set).
        
    "
666,"def extract_pytorch_structures():
    
    for opt in lmo.optimizer_registry:
        # Get the torch class:
        optimizer_class = lmo.optimizer_registry[opt][0]

        # Parse and clean the class structure:
        path = get_fully_qualified_class_name(optimizer_class)
        opt_struct = get_pytkdocs_structure_for_path(path, ""google"")[""objects""][0]
        prune_pytorch_structures(opt_struct)

        # Write it to a file:
        parent_dir = str(Path(__file__).parent.parent)
        filename = os.path.join(parent_dir, ""ludwig/validation/generated/torch/"", optimizer_class.__name__) + "".json""
        os.makedirs(os.path.dirname(filename), exist_ok=True)
        with open(filename, ""w"") as outfile:
            json.dump(
                opt_struct,
                outfile,
                indent=4,
                sort_keys=True,
                separators=("","", "": ""),
            )
            outfile.write(""\n"")

","Extracts and saves the parsed structure of all pytorch classes referenced in
    `ludwig.modules.optimization_modules.optimizer_registry` as JSON files under
    `ludwig/validation/generated/torch/`.",18,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extract_pytorch_structures():
    
    for opt in lmo.optimizer_registry:
        # Get the torch class:
        optimizer_class = lmo.optimizer_registry[opt][0]

        # Parse and clean the class structure:
        path = get_fully_qualified_class_name(optimizer_class)
        opt_struct = get_pytkdocs_structure_for_path(path, ""google"")[""objects""][0]
        prune_pytorch_structures(opt_struct)

        # Write it to a file:
        parent_dir = str(Path(__file__).parent.parent)
        filename = os.path.join(parent_dir, ""ludwig/validation/generated/torch/"", optimizer_class.__name__) + "".json""
        os.makedirs(os.path.dirname(filename), exist_ok=True)
        with open(filename, ""w"") as outfile:
            json.dump(
                opt_struct,
                outfile,
                indent=4,
                sort_keys=True,
                separators=("","", "": ""),
            )
            outfile.write(""\n"")


        ```
    ###Assistant : Extracts and saves the parsed structure of all pytorch classes referenced in
    `ludwig.modules.optimization_modules.optimizer_registry` as JSON files under
    `ludwig/validation/generated/torch/`.
    "
667,"def test_hf_classification_bin(self, mock_handler):


        # create predictor
        create_sql = 

        model_name = 'spam_classifier'

        predict_sql = 
        self.hf_test_run(mock_handler, model_name, create_sql, predict_sql)
","
            CREATE PREDICTOR huggingface.spam_classifier
            predict PRED
            USING
                task='text-classification',
                model_name= ""mrm8488/bert-tiny-finetuned-sms-spam-detection"",
                input_column = 'text_spammy',
                labels=['ham','spam']
        
            SELECT h.*
            FROM pg.df as t 
            JOIN huggingface.spam_classifier as h
        ",23,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_hf_classification_bin(self, mock_handler):


        # create predictor
        create_sql = 

        model_name = 'spam_classifier'

        predict_sql = 
        self.hf_test_run(mock_handler, model_name, create_sql, predict_sql)

        ```
    ###Assistant : 
            CREATE PREDICTOR huggingface.spam_classifier
            predict PRED
            USING
                task='text-classification',
                model_name= ""mrm8488/bert-tiny-finetuned-sms-spam-detection"",
                input_column = 'text_spammy',
                labels=['ham','spam']
        
            SELECT h.*
            FROM pg.df as t 
            JOIN huggingface.spam_classifier as h
        
    "
668,"def match_submerged_margins(layoutgrids, fig):
    

    for sfig in fig.subfigs:
        match_submerged_margins(layoutgrids, sfig)

    axs = [a for a in fig.get_axes()
           if a.get_subplotspec() is not None and a.get_in_layout()]

    for ax1 in axs:
        ss1 = ax1.get_subplotspec()
        if ss1.get_gridspec() not in layoutgrids:
            axs.remove(ax1)
            continue
        lg1 = layoutgrids[ss1.get_gridspec()]

        # interior columns:
        if len(ss1.colspan) > 1:
            maxsubl = np.max(
                lg1.margin_vals['left'][ss1.colspan[1:]] +
                lg1.margin_vals['leftcb'][ss1.colspan[1:]]
            )
            maxsubr = np.max(
                lg1.margin_vals['right'][ss1.colspan[:-1]] +
                lg1.margin_vals['rightcb'][ss1.colspan[:-1]]
            )
            for ax2 in axs:
                ss2 = ax2.get_subplotspec()
                lg2 = layoutgrids[ss2.get_gridspec()]
                if lg2 is not None and len(ss2.colspan) > 1:
                    maxsubl2 = np.max(
                        lg2.margin_vals['left'][ss2.colspan[1:]] +
                        lg2.margin_vals['leftcb'][ss2.colspan[1:]])
                    if maxsubl2 > maxsubl:
                        maxsubl = maxsubl2
                    maxsubr2 = np.max(
                        lg2.margin_vals['right'][ss2.colspan[:-1]] +
                        lg2.margin_vals['rightcb'][ss2.colspan[:-1]])
                    if maxsubr2 > maxsubr:
                        maxsubr = maxsubr2
            for i in ss1.colspan[1:]:
                lg1.edit_margin_min('left', maxsubl, cell=i)
            for i in ss1.colspan[:-1]:
                lg1.edit_margin_min('right', maxsubr, cell=i)

        # interior rows:
        if len(ss1.rowspan) > 1:
            maxsubt = np.max(
                lg1.margin_vals['top'][ss1.rowspan[1:]] +
                lg1.margin_vals['topcb'][ss1.rowspan[1:]]
            )
            maxsubb = np.max(
                lg1.margin_vals['bottom'][ss1.rowspan[:-1]] +
                lg1.margin_vals['bottomcb'][ss1.rowspan[:-1]]
            )

            for ax2 in axs:
                ss2 = ax2.get_subplotspec()
                lg2 = layoutgrids[ss2.get_gridspec()]
                if lg2 is not None:
                    if len(ss2.rowspan) > 1:
                        maxsubt = np.max([np.max(
                            lg2.margin_vals['top'][ss2.rowspan[1:]] +
                            lg2.margin_vals['topcb'][ss2.rowspan[1:]]
                        ), maxsubt])
                        maxsubb = np.max([np.max(
                            lg2.margin_vals['bottom'][ss2.rowspan[:-1]] +
                            lg2.margin_vals['bottomcb'][ss2.rowspan[:-1]]
                        ), maxsubb])
            for i in ss1.rowspan[1:]:
                lg1.edit_margin_min('top', maxsubt, cell=i)
            for i in ss1.rowspan[:-1]:
                lg1.edit_margin_min('bottom', maxsubb, cell=i)

","
    Make the margins that are submerged inside an Axes the same size.

    This allows axes that span two columns (or rows) that are offset
    from one another to have the same size.

    This gives the proper layout for something like::
        fig = plt.figure(constrained_layout=True)
        axs = fig.subplot_mosaic(""AAAB\nCCDD"")

    Without this routine, the axes D will be wider than C, because the
    margin width between the two columns in C has no width by default,
    whereas the margins between the two columns of D are set by the
    width of the margin between A and B. However, obviously the user would
    like C and D to be the same size, so we need to add constraints to these
    ""submerged"" margins.

    This routine makes all the interior margins the same, and the spacing
    between the three columns in A and the two column in C are all set to the
    margins between the two columns of D.

    See test_constrained_layout::test_constrained_layout12 for an example.
    ",158,190,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def match_submerged_margins(layoutgrids, fig):
    

    for sfig in fig.subfigs:
        match_submerged_margins(layoutgrids, sfig)

    axs = [a for a in fig.get_axes()
           if a.get_subplotspec() is not None and a.get_in_layout()]

    for ax1 in axs:
        ss1 = ax1.get_subplotspec()
        if ss1.get_gridspec() not in layoutgrids:
            axs.remove(ax1)
            continue
        lg1 = layoutgrids[ss1.get_gridspec()]

        # interior columns:
        if len(ss1.colspan) > 1:
            maxsubl = np.max(
                lg1.margin_vals['left'][ss1.colspan[1:]] +
                lg1.margin_vals['leftcb'][ss1.colspan[1:]]
            )
            maxsubr = np.max(
                lg1.margin_vals['right'][ss1.colspan[:-1]] +
                lg1.margin_vals['rightcb'][ss1.colspan[:-1]]
            )
            for ax2 in axs:
                ss2 = ax2.get_subplotspec()
                lg2 = layoutgrids[ss2.get_gridspec()]
                if lg2 is not None and len(ss2.colspan) > 1:
                    maxsubl2 = np.max(
                        lg2.margin_vals['left'][ss2.colspan[1:]] +
                        lg2.margin_vals['leftcb'][ss2.colspan[1:]])
                    if maxsubl2 > maxsubl:
                        maxsubl = maxsubl2
                    maxsubr2 = np.max(
                        lg2.margin_vals['right'][ss2.colspan[:-1]] +
                        lg2.margin_vals['rightcb'][ss2.colspan[:-1]])
                    if maxsubr2 > maxsubr:
                        maxsubr = maxsubr2
            for i in ss1.colspan[1:]:
                lg1.edit_margin_min('left', maxsubl, cell=i)
            for i in ss1.colspan[:-1]:
                lg1.edit_margin_min('right', maxsubr, cell=i)

        # interior rows:
        if len(ss1.rowspan) > 1:
            maxsubt = np.max(
                lg1.margin_vals['top'][ss1.rowspan[1:]] +
                lg1.margin_vals['topcb'][ss1.rowspan[1:]]
            )
            maxsubb = np.max(
                lg1.margin_vals['bottom'][ss1.rowspan[:-1]] +
                lg1.margin_vals['bottomcb'][ss1.rowspan[:-1]]
            )

            for ax2 in axs:
                ss2 = ax2.get_subplotspec()
                lg2 = layoutgrids[ss2.get_gridspec()]
                if lg2 is not None:
                    if len(ss2.rowspan) > 1:
                        maxsubt = np.max([np.max(
                            lg2.margin_vals['top'][ss2.rowspan[1:]] +
                            lg2.margin_vals['topcb'][ss2.rowspan[1:]]
                        ), maxsubt])
                        maxsubb = np.max([np.max(
                            lg2.margin_vals['bottom'][ss2.rowspan[:-1]] +
                            lg2.margin_vals['bottomcb'][ss2.rowspan[:-1]]
                        ), maxsubb])
            for i in ss1.rowspan[1:]:
                lg1.edit_margin_min('top', maxsubt, cell=i)
            for i in ss1.rowspan[:-1]:
                lg1.edit_margin_min('bottom', maxsubb, cell=i)


        ```
    ###Assistant : 
    Make the margins that are submerged inside an Axes the same size.

    This allows axes that span two columns (or rows) that are offset
    from one another to have the same size.

    This gives the proper layout for something like::
        fig = plt.figure(constrained_layout=True)
        axs = fig.subplot_mosaic(""AAAB\nCCDD"")

    Without this routine, the axes D will be wider than C, because the
    margin width between the two columns in C has no width by default,
    whereas the margins between the two columns of D are set by the
    width of the margin between A and B. However, obviously the user would
    like C and D to be the same size, so we need to add constraints to these
    ""submerged"" margins.

    This routine makes all the interior margins the same, and the spacing
    between the three columns in A and the two column in C are all set to the
    margins between the two columns of D.

    See test_constrained_layout::test_constrained_layout12 for an example.
    
    "
669,"def parse_version_info(version_str):
    
    version_info = []
    for x in version_str.split('.'):
        if x.isdigit():
            version_info.append(int(x))
        elif x.find('rc') != -1:
            patch_version = x.split('rc')
            version_info.append(int(patch_version[0]))
            version_info.append(f'rc{patch_version[1]}')
    return tuple(version_info)


version_info = parse_version_info(__version__)
","Parse a version string into a tuple.

    Args:
        version_str (str): The version string.
    Returns:
        tuple[int | str]: The version info, e.g., ""1.3.0"" is parsed into
            (1, 3, 0), and ""2.0.0rc1"" is parsed into (2, 0, 0, 'rc1').
    ",37,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_version_info(version_str):
    
    version_info = []
    for x in version_str.split('.'):
        if x.isdigit():
            version_info.append(int(x))
        elif x.find('rc') != -1:
            patch_version = x.split('rc')
            version_info.append(int(patch_version[0]))
            version_info.append(f'rc{patch_version[1]}')
    return tuple(version_info)


version_info = parse_version_info(__version__)

        ```
    ###Assistant : Parse a version string into a tuple.

    Args:
        version_str (str): The version string.
    Returns:
        tuple[int | str]: The version info, e.g., ""1.3.0"" is parsed into
            (1, 3, 0), and ""2.0.0rc1"" is parsed into (2, 0, 0, 'rc1').
    
    "
670,"def adjust_settings_for_relay_tests(settings):
    
    settings.ALLOWED_HOSTS = [
        ""localhost"",
        ""testserver"",
        ""host.docker.internal"",
        ""0.0.0.0"",
        ""127.0.0.1"",
    ]
    settings.KAFKA_CLUSTERS = {
        ""default"": {
            ""common"": {""bootstrap.servers"": ""127.0.0.1:9092""},
            ""producers"": {
                ""compression.type"": ""lz4"",
                ""message.max.bytes"": 50000000,  # 50MB, default is 1MB
            },
        }
    }
    settings.SENTRY_RELAY_WHITELIST_PK = [""SMSesqan65THCV6M4qs4kBzPai60LzuDn-xNsvYpuP8""]
    settings.SENTRY_USE_RELAY = True


@pytest.fixture","
    Adjusts the application settings to accept calls from a Relay instance running inside a
    docker container.

    :param settings: the app settings
    ",21,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def adjust_settings_for_relay_tests(settings):
    
    settings.ALLOWED_HOSTS = [
        ""localhost"",
        ""testserver"",
        ""host.docker.internal"",
        ""0.0.0.0"",
        ""127.0.0.1"",
    ]
    settings.KAFKA_CLUSTERS = {
        ""default"": {
            ""common"": {""bootstrap.servers"": ""127.0.0.1:9092""},
            ""producers"": {
                ""compression.type"": ""lz4"",
                ""message.max.bytes"": 50000000,  # 50MB, default is 1MB
            },
        }
    }
    settings.SENTRY_RELAY_WHITELIST_PK = [""SMSesqan65THCV6M4qs4kBzPai60LzuDn-xNsvYpuP8""]
    settings.SENTRY_USE_RELAY = True


@pytest.fixture
        ```
    ###Assistant : 
    Adjusts the application settings to accept calls from a Relay instance running inside a
    docker container.

    :param settings: the app settings
    
    "
671,"def clear_backends():
  

  if xc._version < 79:
    raise RuntimeError(""clear_backends is not supported in the jaxlib used.""
                       ""Please update your jaxlib package."")

  xb._clear_backends()
  jax.lib.xla_bridge._backends = {}
  dispatch.xla_callable.cache_clear()  # type: ignore
  dispatch.xla_primitive_callable.cache_clear()
  _cpp_jit_cache.clear()
  jax_jit.CompiledFunctionCache.clear_all()
","
  Clear all backend clients so that new backend clients can be created later.
  ",13,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clear_backends():
  

  if xc._version < 79:
    raise RuntimeError(""clear_backends is not supported in the jaxlib used.""
                       ""Please update your jaxlib package."")

  xb._clear_backends()
  jax.lib.xla_bridge._backends = {}
  dispatch.xla_callable.cache_clear()  # type: ignore
  dispatch.xla_primitive_callable.cache_clear()
  _cpp_jit_cache.clear()
  jax_jit.CompiledFunctionCache.clear_all()

        ```
    ###Assistant : 
  Clear all backend clients so that new backend clients can be created later.
  
    "
672,"def get_power_utilization(self):
        
        powerfeeds = PowerFeed.objects.filter(rack=self)
        available_power_total = sum(pf.available_power for pf in powerfeeds)
        print(f'available_power_total: {available_power_total}')
        if not available_power_total:
            return 0

        powerports = []
        for powerfeed in powerfeeds:
            powerports.extend([
                peer for peer in powerfeed.link_peers if isinstance(peer, PowerPort)
            ])

        allocated_draw = 0
        for powerport in powerports:
            allocated_draw += powerport.get_power_draw()['allocated']
        print(f'allocated_draw: {allocated_draw}')

        return int(allocated_draw / available_power_total * 100)

","
        Determine the utilization rate of power in the rack and return it as a percentage.
        ",15,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_power_utilization(self):
        
        powerfeeds = PowerFeed.objects.filter(rack=self)
        available_power_total = sum(pf.available_power for pf in powerfeeds)
        print(f'available_power_total: {available_power_total}')
        if not available_power_total:
            return 0

        powerports = []
        for powerfeed in powerfeeds:
            powerports.extend([
                peer for peer in powerfeed.link_peers if isinstance(peer, PowerPort)
            ])

        allocated_draw = 0
        for powerport in powerports:
            allocated_draw += powerport.get_power_draw()['allocated']
        print(f'allocated_draw: {allocated_draw}')

        return int(allocated_draw / available_power_total * 100)


        ```
    ###Assistant : 
        Determine the utilization rate of power in the rack and return it as a percentage.
        
    "
673,"def cleanup(self):
        
        orphaned = []
        for w in self.workers[::]:
            if not w.alive:
                # the worker process has exited
                # 1. take the task it was running and enqueue the error
                #    callbacks
                # 2. take any pending tasks delivered to its queue and
                #    send them to another worker
                logger.error('worker pid:{} is gone (exit={})'.format(w.pid, w.exitcode))
                if w.current_task:
                    if w.current_task != 'QUIT':
                        try:
                            for j in UnifiedJob.objects.filter(celery_task_id=w.current_task['uuid']):
                                reaper.reap_job(j, 'failed')
                        except Exception:
                            logger.exception('failed to reap job UUID {}'.format(w.current_task['uuid']))
                orphaned.extend(w.orphaned_tasks)
                self.workers.remove(w)
            elif (len(self.workers) > self.min_workers) and w.ready_to_scale_down:
                # the process has an empty queue (it's idle) and we have
                # more processes in the pool than we need (> min)
                # send this process a message so it will exit gracefully
                # at the next opportunity
                logger.info(f'scaling down worker pid:{w.pid} prior total:{len(self.workers)}')
                w.quit()
                self.workers.remove(w)
            if w.alive:
                # if we discover a task manager invocation that's been running
                # too long, reap it (because otherwise it'll just hold the postgres
                # advisory lock forever); the goal of this code is to discover
                # deadlocks or other serious issues in the task manager that cause
                # the task manager to never do more work
                current_task = w.current_task
                if current_task and isinstance(current_task, dict):
                    endings = ['tasks.task_manager', 'tasks.dependency_manager', 'tasks.workflow_manager']
                    current_task_name = current_task.get('task', '')
                    if any(current_task_name.endswith(e) for e in endings):
                        if 'started' not in current_task:
                            w.managed_tasks[current_task['uuid']]['started'] = time.time()
                        age = time.time() - current_task['started']
                        w.managed_tasks[current_task['uuid']]['age'] = age
                        if age > self.task_manager_timeout:
                            logger.error(f'{current_task_name} has held the advisory lock for {age}, sending SIGTERM to {w.pid}')
                            os.kill(w.pid, signal.SIGTERM)

        for m in orphaned:
            # if all the workers are dead, spawn at least one
            if not len(self.workers):
                self.up()
            idx = random.choice(range(len(self.workers)))
            self.write(idx, m)
","
        Perform some internal account and cleanup.  This is run on
        every cluster node heartbeat:

        1.  Discover worker processes that exited, and recover messages they
            were handling.
        2.  Clean up unnecessary, idle workers.

        IMPORTANT: this function is one of the few places in the dispatcher
        (aside from setting lookups) where we talk to the database.  As such,
        if there's an outage, this method _can_ throw various
        django.db.utils.Error exceptions.  Act accordingly.
        ",69,270,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cleanup(self):
        
        orphaned = []
        for w in self.workers[::]:
            if not w.alive:
                # the worker process has exited
                # 1. take the task it was running and enqueue the error
                #    callbacks
                # 2. take any pending tasks delivered to its queue and
                #    send them to another worker
                logger.error('worker pid:{} is gone (exit={})'.format(w.pid, w.exitcode))
                if w.current_task:
                    if w.current_task != 'QUIT':
                        try:
                            for j in UnifiedJob.objects.filter(celery_task_id=w.current_task['uuid']):
                                reaper.reap_job(j, 'failed')
                        except Exception:
                            logger.exception('failed to reap job UUID {}'.format(w.current_task['uuid']))
                orphaned.extend(w.orphaned_tasks)
                self.workers.remove(w)
            elif (len(self.workers) > self.min_workers) and w.ready_to_scale_down:
                # the process has an empty queue (it's idle) and we have
                # more processes in the pool than we need (> min)
                # send this process a message so it will exit gracefully
                # at the next opportunity
                logger.info(f'scaling down worker pid:{w.pid} prior total:{len(self.workers)}')
                w.quit()
                self.workers.remove(w)
            if w.alive:
                # if we discover a task manager invocation that's been running
                # too long, reap it (because otherwise it'll just hold the postgres
                # advisory lock forever); the goal of this code is to discover
                # deadlocks or other serious issues in the task manager that cause
                # the task manager to never do more work
                current_task = w.current_task
                if current_task and isinstance(current_task, dict):
                    endings = ['tasks.task_manager', 'tasks.dependency_manager', 'tasks.workflow_manager']
                    current_task_name = current_task.get('task', '')
                    if any(current_task_name.endswith(e) for e in endings):
                        if 'started' not in current_task:
                            w.managed_tasks[current_task['uuid']]['started'] = time.time()
                        age = time.time() - current_task['started']
                        w.managed_tasks[current_task['uuid']]['age'] = age
                        if age > self.task_manager_timeout:
                            logger.error(f'{current_task_name} has held the advisory lock for {age}, sending SIGTERM to {w.pid}')
                            os.kill(w.pid, signal.SIGTERM)

        for m in orphaned:
            # if all the workers are dead, spawn at least one
            if not len(self.workers):
                self.up()
            idx = random.choice(range(len(self.workers)))
            self.write(idx, m)

        ```
    ###Assistant : 
        Perform some internal account and cleanup.  This is run on
        every cluster node heartbeat:

        1.  Discover worker processes that exited, and recover messages they
            were handling.
        2.  Clean up unnecessary, idle workers.

        IMPORTANT: this function is one of the few places in the dispatcher
        (aside from setting lookups) where we talk to the database.  As such,
        if there's an outage, this method _can_ throw various
        django.db.utils.Error exceptions.  Act accordingly.
        
    "
674,"def test_token_node_empty_csrf_cookie(self):
        
        req = self._get_request(cookie="""")
        mw = CsrfViewMiddleware(token_view)
        mw.process_view(req, token_view, (), {})
        resp = token_view(req)

        token = get_token(req)
        self.assertIsNotNone(token)
        csrf_secret = _unmask_cipher_token(token)
        self._check_token_present(resp, csrf_secret)
","
        A new token is sent if the csrf_cookie is the empty string.
        ",12,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_token_node_empty_csrf_cookie(self):
        
        req = self._get_request(cookie="""")
        mw = CsrfViewMiddleware(token_view)
        mw.process_view(req, token_view, (), {})
        resp = token_view(req)

        token = get_token(req)
        self.assertIsNotNone(token)
        csrf_secret = _unmask_cipher_token(token)
        self._check_token_present(resp, csrf_secret)

        ```
    ###Assistant : 
        A new token is sent if the csrf_cookie is the empty string.
        
    "
675,"def save(self, envs):
        
        assert isinstance(envs, list), ""envs should be a list""
        if len(envs) > 0:
            for env in envs:
                assert isstr(env), ""env should be a string""

        return self._send(
            {
                ""data"": envs,
            },
            ""save"",
        )
","
        This function allows the user to save envs that are alive on the
        Tornado server. The envs can be specified as a list of env ids.
        ",26,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save(self, envs):
        
        assert isinstance(envs, list), ""envs should be a list""
        if len(envs) > 0:
            for env in envs:
                assert isstr(env), ""env should be a string""

        return self._send(
            {
                ""data"": envs,
            },
            ""save"",
        )

        ```
    ###Assistant : 
        This function allows the user to save envs that are alive on the
        Tornado server. The envs can be specified as a list of env ids.
        
    "
676,"def test_has_related_field_in_list_display_o2o(self):
        
        media = Media.objects.create(name=""Foo"")
        Vodcast.objects.create(media=media)
        response = self.client.get(reverse(""admin:admin_views_vodcast_changelist""), {})

        response.context[""cl""].list_display = [""media""]
        self.assertIs(response.context[""cl""].has_related_field_in_list_display(), True)

        response.context[""cl""].list_display = [""media_id""]
        self.assertIs(response.context[""cl""].has_related_field_in_list_display(), False)
",Joins shouldn't be performed for <O2O>_id fields in list display.,10,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_has_related_field_in_list_display_o2o(self):
        
        media = Media.objects.create(name=""Foo"")
        Vodcast.objects.create(media=media)
        response = self.client.get(reverse(""admin:admin_views_vodcast_changelist""), {})

        response.context[""cl""].list_display = [""media""]
        self.assertIs(response.context[""cl""].has_related_field_in_list_display(), True)

        response.context[""cl""].list_display = [""media_id""]
        self.assertIs(response.context[""cl""].has_related_field_in_list_display(), False)

        ```
    ###Assistant : Joins shouldn't be performed for <O2O>_id fields in list display.
    "
677,"def binary_crossentropy(target, output, from_logits=False):
    
    target = tf.convert_to_tensor(target)
    output = tf.convert_to_tensor(output)

    # Use logits whenever they are available. `softmax` and `sigmoid`
    # activations cache logits on the `output` Tensor.
    if hasattr(output, ""_keras_logits""):
        output = output._keras_logits  # pylint: disable=protected-access
        if from_logits:
            warnings.warn(
                '""`binary_crossentropy` received `from_logits=True`, but the `output`'
                "" argument was produced by a sigmoid or softmax activation and thus ""
                'does not represent logits. Was this intended?""',
                stacklevel=2,
            )
        from_logits = True

    if from_logits:
        return tf.nn.sigmoid_cross_entropy_with_logits(
            labels=target, logits=output
        )

    if (
        not isinstance(output, (tf.__internal__.EagerTensor, tf.Variable))
        and output.op.type == ""Sigmoid""
    ) and not hasattr(output, ""_keras_history""):
        # When sigmoid activation function is used for output operation, we
        # use logits from the sigmoid function directly to compute loss in order
        # to prevent collapsing zero when training.
        assert len(output.op.inputs) == 1
        output = output.op.inputs[0]
        return tf.nn.sigmoid_cross_entropy_with_logits(
            labels=target, logits=output
        )

    epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)
    output = tf.clip_by_value(output, epsilon_, 1.0 - epsilon_)

    # Compute cross entropy from probabilities.
    bce = target * tf.math.log(output + epsilon())
    bce += (1 - target) * tf.math.log(1 - output + epsilon())
    return -bce


@keras_export(""keras.backend.binary_focal_crossentropy"")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs","Binary crossentropy between an output tensor and a target tensor.

    Args:
        target: A tensor with the same shape as `output`.
        output: A tensor.
        from_logits: Whether `output` is expected to be a logits tensor.
            By default, we consider that `output`
            encodes a probability distribution.

    Returns:
        A tensor.
    ",46,176,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def binary_crossentropy(target, output, from_logits=False):
    
    target = tf.convert_to_tensor(target)
    output = tf.convert_to_tensor(output)

    # Use logits whenever they are available. `softmax` and `sigmoid`
    # activations cache logits on the `output` Tensor.
    if hasattr(output, ""_keras_logits""):
        output = output._keras_logits  # pylint: disable=protected-access
        if from_logits:
            warnings.warn(
                '""`binary_crossentropy` received `from_logits=True`, but the `output`'
                "" argument was produced by a sigmoid or softmax activation and thus ""
                'does not represent logits. Was this intended?""',
                stacklevel=2,
            )
        from_logits = True

    if from_logits:
        return tf.nn.sigmoid_cross_entropy_with_logits(
            labels=target, logits=output
        )

    if (
        not isinstance(output, (tf.__internal__.EagerTensor, tf.Variable))
        and output.op.type == ""Sigmoid""
    ) and not hasattr(output, ""_keras_history""):
        # When sigmoid activation function is used for output operation, we
        # use logits from the sigmoid function directly to compute loss in order
        # to prevent collapsing zero when training.
        assert len(output.op.inputs) == 1
        output = output.op.inputs[0]
        return tf.nn.sigmoid_cross_entropy_with_logits(
            labels=target, logits=output
        )

    epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)
    output = tf.clip_by_value(output, epsilon_, 1.0 - epsilon_)

    # Compute cross entropy from probabilities.
    bce = target * tf.math.log(output + epsilon())
    bce += (1 - target) * tf.math.log(1 - output + epsilon())
    return -bce


@keras_export(""keras.backend.binary_focal_crossentropy"")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
        ```
    ###Assistant : Binary crossentropy between an output tensor and a target tensor.

    Args:
        target: A tensor with the same shape as `output`.
        output: A tensor.
        from_logits: Whether `output` is expected to be a logits tensor.
            By default, we consider that `output`
            encodes a probability distribution.

    Returns:
        A tensor.
    
    "
678,"def test_process_pulled_event_with_missing_state(self) -> None:
        
        return self._test_process_pulled_event_with_missing_state(False)
","Ensure that we correctly handle pulled events with lots of missing state

        In this test, we pretend we are processing a ""pulled"" event (eg, via backfill
        or get_missing_events). The pulled event has a prev_event we haven't previously
        seen, so the server requests the state at that prev_event. There is a lot
        of state we don't have, so we expect the server to make a /state request.

        We check that the pulled event is correctly persisted, and that the state is
        as we expect.
        ",83,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_process_pulled_event_with_missing_state(self) -> None:
        
        return self._test_process_pulled_event_with_missing_state(False)

        ```
    ###Assistant : Ensure that we correctly handle pulled events with lots of missing state

        In this test, we pretend we are processing a ""pulled"" event (eg, via backfill
        or get_missing_events). The pulled event has a prev_event we haven't previously
        seen, so the server requests the state at that prev_event. There is a lot
        of state we don't have, so we expect the server to make a /state request.

        We check that the pulled event is correctly persisted, and that the state is
        as we expect.
        
    "
679,"def test_resolved_in_release(self, mock_func):
        
        notification = ResolvedInReleaseActivityNotification(
            Activity(
                project=self.project,
                group=self.group,
                user=self.user,
                type=ActivityType.SET_RESOLVED_IN_RELEASE,
                data={""version"": ""meow""},
            )
        )
        with self.tasks():
            notification.send()

        attachment, text = get_attachment()
        release_name = notification.activity.data[""version""]
        assert text == f""Issue marked as resolved in {release_name} by {self.name}""
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=resolved_in_release_activity-slack-user|Notification Settings>""
        )
","
        Test that a Slack message is sent with the expected payload when an issue is resolved in a release
        ",19,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_resolved_in_release(self, mock_func):
        
        notification = ResolvedInReleaseActivityNotification(
            Activity(
                project=self.project,
                group=self.group,
                user=self.user,
                type=ActivityType.SET_RESOLVED_IN_RELEASE,
                data={""version"": ""meow""},
            )
        )
        with self.tasks():
            notification.send()

        attachment, text = get_attachment()
        release_name = notification.activity.data[""version""]
        assert text == f""Issue marked as resolved in {release_name} by {self.name}""
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=resolved_in_release_activity-slack-user|Notification Settings>""
        )

        ```
    ###Assistant : 
        Test that a Slack message is sent with the expected payload when an issue is resolved in a release
        
    "
680,"def get_vocabulary(self, include_special_tokens=True):
        
        # The lookup table data will not be sorted, so we will create a inverted
        # lookup here, and use that to lookup a range of indices [0,
        # vocab_size).
        if self.lookup_table.size() == 0:
            vocab, indices = [], []
        else:
            keys, values = self.lookup_table.export()
            vocab, indices = (values, keys) if self.invert else (keys, values)
            vocab, indices = (
                self._tensor_vocab_to_numpy(vocab),
                indices.numpy(),
            )
        lookup = collections.defaultdict(
            lambda: self.oov_token, zip(indices, vocab)
        )
        vocab = [lookup[x] for x in range(self.vocabulary_size())]
        if self.mask_token is not None and self.output_mode == INT:
            vocab[0] = self.mask_token
        if not include_special_tokens:
            vocab = vocab[self._token_start_index() :]
        return vocab
","Returns the current vocabulary of the layer.

        Args:
          include_special_tokens: If True, the returned vocabulary will include
            mask and OOV tokens, and a term's index in the vocabulary will equal
            the term's index when calling the layer. If False, the returned
            vocabulary will not include any mask or OOV tokens.
        ",49,100,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_vocabulary(self, include_special_tokens=True):
        
        # The lookup table data will not be sorted, so we will create a inverted
        # lookup here, and use that to lookup a range of indices [0,
        # vocab_size).
        if self.lookup_table.size() == 0:
            vocab, indices = [], []
        else:
            keys, values = self.lookup_table.export()
            vocab, indices = (values, keys) if self.invert else (keys, values)
            vocab, indices = (
                self._tensor_vocab_to_numpy(vocab),
                indices.numpy(),
            )
        lookup = collections.defaultdict(
            lambda: self.oov_token, zip(indices, vocab)
        )
        vocab = [lookup[x] for x in range(self.vocabulary_size())]
        if self.mask_token is not None and self.output_mode == INT:
            vocab[0] = self.mask_token
        if not include_special_tokens:
            vocab = vocab[self._token_start_index() :]
        return vocab

        ```
    ###Assistant : Returns the current vocabulary of the layer.

        Args:
          include_special_tokens: If True, the returned vocabulary will include
            mask and OOV tokens, and a term's index in the vocabulary will equal
            the term's index when calling the layer. If False, the returned
            vocabulary will not include any mask or OOV tokens.
        
    "
681,"def test_error_raised_with_float_limited_eval_batches():
    
    model = BoringModel()
    dl_size = len(model.val_dataloader())
    limit_val_batches = 1 / (dl_size + 2)
    trainer = Trainer(limit_val_batches=limit_val_batches)
    trainer._data_connector.attach_data(model)
    with pytest.raises(
        MisconfigurationException,
        match=fr""{limit_val_batches} \* {dl_size} < 1. Please increase the `limit_val_batches`"",
    ):
        trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING, model)


@pytest.mark.parametrize(
    ""val_dl"",
    [
        DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
        CombinedLoader(DataLoader(dataset=RandomDataset(32, 64), shuffle=True)),
        CombinedLoader(
            [DataLoader(dataset=RandomDataset(32, 64)), DataLoader(dataset=RandomDataset(32, 64), shuffle=True)]
        ),
        CombinedLoader(
            {
                ""dl1"": DataLoader(dataset=RandomDataset(32, 64)),
                ""dl2"": DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
            }
        ),
    ],
)","Test that an error is raised if there are not enough batches when passed with float value of
    limit_eval_batches.",19,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_error_raised_with_float_limited_eval_batches():
    
    model = BoringModel()
    dl_size = len(model.val_dataloader())
    limit_val_batches = 1 / (dl_size + 2)
    trainer = Trainer(limit_val_batches=limit_val_batches)
    trainer._data_connector.attach_data(model)
    with pytest.raises(
        MisconfigurationException,
        match=fr""{limit_val_batches} \* {dl_size} < 1. Please increase the `limit_val_batches`"",
    ):
        trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING, model)


@pytest.mark.parametrize(
    ""val_dl"",
    [
        DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
        CombinedLoader(DataLoader(dataset=RandomDataset(32, 64), shuffle=True)),
        CombinedLoader(
            [DataLoader(dataset=RandomDataset(32, 64)), DataLoader(dataset=RandomDataset(32, 64), shuffle=True)]
        ),
        CombinedLoader(
            {
                ""dl1"": DataLoader(dataset=RandomDataset(32, 64)),
                ""dl2"": DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
            }
        ),
    ],
)
        ```
    ###Assistant : Test that an error is raised if there are not enough batches when passed with float value of
    limit_eval_batches.
    "
682,"def not_none_device_or_backend_on_jit(backend, device, num_ins):
  
  # TODO(yashkatariya): Remove this entire function when backend and device are
  # removed as arguments on jit.

  from jax.experimental import sharding

  if device is not None and backend is not None:
    raise ValueError(""can't specify both a device and a backend for jit, ""
                     ""got device={} and backend={}"".format(device, backend))

  if backend is not None:
    da = [xb.get_backend(backend).get_default_device_assignment(1)[0]]
  else:
    assert device is not None
    da = [device]

  assert len(da) == 1
  # Set committed to True for this path because it simulates a device_put on
  # behalf of a user.
  committed = True
  # in_shardings will be marked as replicated regardless of whatever the input
  # had. Given that only a single device is allowed above, this is correct.
  in_shardings = [sharding.OpShardingSharding.get_replicated(da)] * num_ins
  return committed, da, in_shardings

","This is to support the backend and device argument on jit. It's a feature
  that's deprecated but needs to be supported for feature parity and so that we
  can delete the non-Array paths when Array is switched on.
  ",38,130,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def not_none_device_or_backend_on_jit(backend, device, num_ins):
  
  # TODO(yashkatariya): Remove this entire function when backend and device are
  # removed as arguments on jit.

  from jax.experimental import sharding

  if device is not None and backend is not None:
    raise ValueError(""can't specify both a device and a backend for jit, ""
                     ""got device={} and backend={}"".format(device, backend))

  if backend is not None:
    da = [xb.get_backend(backend).get_default_device_assignment(1)[0]]
  else:
    assert device is not None
    da = [device]

  assert len(da) == 1
  # Set committed to True for this path because it simulates a device_put on
  # behalf of a user.
  committed = True
  # in_shardings will be marked as replicated regardless of whatever the input
  # had. Given that only a single device is allowed above, this is correct.
  in_shardings = [sharding.OpShardingSharding.get_replicated(da)] * num_ins
  return committed, da, in_shardings


        ```
    ###Assistant : This is to support the backend and device argument on jit. It's a feature
  that's deprecated but needs to be supported for feature parity and so that we
  can delete the non-Array paths when Array is switched on.
  
    "
683,"def is_mouse_scrolling(self, *args):
        
        return 'button' in self.profile and 'scroll' in self.button
","Returns True if the touch event is a mousewheel scrolling

        .. versionadded:: 1.6.0
        ",13,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_mouse_scrolling(self, *args):
        
        return 'button' in self.profile and 'scroll' in self.button

        ```
    ###Assistant : Returns True if the touch event is a mousewheel scrolling

        .. versionadded:: 1.6.0
        
    "
684,"def easy_print(*args, size=(None, None), end=None, sep=None, location=(None, None), relative_location=(None, None), font=None, no_titlebar=False,
               no_button=False, grab_anywhere=False, keep_on_top=None, do_not_reroute_stdout=True, echo_stdout=False, text_color=None, background_color=None, colors=None, c=None,
               erase_all=False, resizable=True, blocking=None):
    
    if _DebugWin.debug_window is None:
        _DebugWin.debug_window = _DebugWin(size=size, location=location, relative_location=relative_location, font=font, no_titlebar=no_titlebar,
                                           no_button=no_button, grab_anywhere=grab_anywhere, keep_on_top=keep_on_top,
                                           do_not_reroute_stdout=do_not_reroute_stdout, echo_stdout=echo_stdout, resizable=resizable, blocking=blocking)
    txt_color, bg_color = _parse_colors_parm(c or colors)
    _DebugWin.debug_window.Print(*args, end=end, sep=sep, text_color=text_color or txt_color, background_color=background_color or bg_color,
                                 erase_all=erase_all, font=font, blocking=blocking)

","
    Works like a ""print"" statement but with windowing options.  Routes output to the ""Debug Window""

    In addition to the normal text and background colors, you can use a ""colors"" tuple/string
    The ""colors"" or ""c"" parameter defines both the text and background in a single parm.
    It can be a tuple or a single single. Both text and background colors need to be specified
    colors -(str, str) or str.  A combined text/background color definition in a single parameter
    c - (str, str) - Colors tuple has format (foreground, backgrouned)
    c - str - can also be a string of the format ""foreground on background""  (""white on red"")

    :param *args:                 stuff to output
    :type *args:                  (Any)
    :param size:                  (w,h) w=characters-wide, h=rows-high
    :type size:                   (int, int)
    :param end:                   end character
    :type end:                    (str)
    :param sep:                   separator character
    :type sep:                    (str)
    :param location:              Location of upper left corner of the window
    :type location:               (int, int)
    :param relative_location:     (x,y) location relative to the default location of the window, in pixels. Normally the window centers.  This location is relative to the location the window would be created. Note they can be negative.
    :type relative_location:      (int, int)
    :param font:                  specifies the  font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike
    :type font:                   (str or (str, int[, str]) or None)
    :param no_titlebar:           If True no titlebar will be shown
    :type no_titlebar:            (bool)
    :param no_button:             don't show button
    :type no_button:              (bool)
    :param grab_anywhere:         If True: can grab anywhere to move the window (Default = False)
    :type grab_anywhere:          (bool)
    :param background_color:      color of background
    :type background_color:       (str)
    :param text_color:            color of the text
    :type text_color:             (str)
    :param keep_on_top:           If True the window will remain above all current windows
    :type keep_on_top:            (bool)
    :param location:              Location of upper left corner of the window
    :type location:               (int, int)
    :param do_not_reroute_stdout: do not reroute stdout and stderr. If False, both stdout and stderr will reroute to here
    :type do_not_reroute_stdout:  (bool)
    :param echo_stdout:           If True stdout is sent to both the console and the debug window
    :type echo_stdout:            (bool)
    :param colors:                Either a tuple or a string that has both the text and background colors
    :type colors:                 (str) or (str, str)
    :param c:                     Either a tuple or a string that has both the text and background colors
    :type c:                      (str) or (str, str)
    :param resizable:             if True, the user can resize the debug window. Default is True
    :type resizable:              (bool)
    :param erase_all:             If True when erase the output before printing
    :type erase_all:              (bool)
    :param blocking:              if True, makes the window block instead of returning immediately. The ""Quit"" button changers to ""More""
    :type blocking:               (bool | None)
    :return:
    :rtype:
    ",444,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def easy_print(*args, size=(None, None), end=None, sep=None, location=(None, None), relative_location=(None, None), font=None, no_titlebar=False,
               no_button=False, grab_anywhere=False, keep_on_top=None, do_not_reroute_stdout=True, echo_stdout=False, text_color=None, background_color=None, colors=None, c=None,
               erase_all=False, resizable=True, blocking=None):
    
    if _DebugWin.debug_window is None:
        _DebugWin.debug_window = _DebugWin(size=size, location=location, relative_location=relative_location, font=font, no_titlebar=no_titlebar,
                                           no_button=no_button, grab_anywhere=grab_anywhere, keep_on_top=keep_on_top,
                                           do_not_reroute_stdout=do_not_reroute_stdout, echo_stdout=echo_stdout, resizable=resizable, blocking=blocking)
    txt_color, bg_color = _parse_colors_parm(c or colors)
    _DebugWin.debug_window.Print(*args, end=end, sep=sep, text_color=text_color or txt_color, background_color=background_color or bg_color,
                                 erase_all=erase_all, font=font, blocking=blocking)


        ```
    ###Assistant : 
    Works like a ""print"" statement but with windowing options.  Routes output to the ""Debug Window""

    In addition to the normal text and background colors, you can use a ""colors"" tuple/string
    The ""colors"" or ""c"" parameter defines both the text and background in a single parm.
    It can be a tuple or a single single. Both text and background colors need to be specified
    colors -(str, str) or str.  A combined text/background color definition in a single parameter
    c - (str, str) - Colors tuple has format (foreground, backgrouned)
    c - str - can also be a string of the format ""foreground on background""  (""white on red"")

    :param *args:                 stuff to output
    :type *args:                  (Any)
    :param size:                  (w,h) w=characters-wide, h=rows-high
    :type size:                   (int, int)
    :param end:                   end character
    :type end:                    (str)
    :param sep:                   separator character
    :type sep:                    (str)
    :param location:              Location of upper left corner of the window
    :type location:               (int, int)
    :param relative_location:     (x,y) location relative to the default location of the window, in pixels. Normally the window centers.  This location is relative to the location the window would be created. Note they can be negative.
    :type relative_location:      (int, int)
    :param font:                  specifies the  font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike
    :type font:                   (str or (str, int[, str]) or None)
    :param no_titlebar:           If True no titlebar will be shown
    :type no_titlebar:            (bool)
    :param no_button:             don't show button
    :type no_button:              (bool)
    :param grab_anywhere:         If True: can grab anywhere to move the window (Default = False)
    :type grab_anywhere:          (bool)
    :param background_color:      color of background
    :type background_color:       (str)
    :param text_color:            color of the text
    :type text_color:             (str)
    :param keep_on_top:           If True the window will remain above all current windows
    :type keep_on_top:            (bool)
    :param location:              Location of upper left corner of the window
    :type location:               (int, int)
    :param do_not_reroute_stdout: do not reroute stdout and stderr. If False, both stdout and stderr will reroute to here
    :type do_not_reroute_stdout:  (bool)
    :param echo_stdout:           If True stdout is sent to both the console and the debug window
    :type echo_stdout:            (bool)
    :param colors:                Either a tuple or a string that has both the text and background colors
    :type colors:                 (str) or (str, str)
    :param c:                     Either a tuple or a string that has both the text and background colors
    :type c:                      (str) or (str, str)
    :param resizable:             if True, the user can resize the debug window. Default is True
    :type resizable:              (bool)
    :param erase_all:             If True when erase the output before printing
    :type erase_all:              (bool)
    :param blocking:              if True, makes the window block instead of returning immediately. The ""Quit"" button changers to ""More""
    :type blocking:               (bool | None)
    :return:
    :rtype:
    
    "
685,"def wait_start_success(self):
        
        _timeout = self.args.timeout_ready
        if _timeout <= 0:
            _timeout = None
        else:
            _timeout /= 1e3
        if self._wait_for_ready_or_shutdown(_timeout):
            self._check_failed_to_start()
            self.logger.debug(__ready_msg__)
        else:
            self._fail_start_timeout(_timeout)
","Block until all pods starts successfully.

        If not success, it will raise an error hoping the outer function to catch it
        ",21,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wait_start_success(self):
        
        _timeout = self.args.timeout_ready
        if _timeout <= 0:
            _timeout = None
        else:
            _timeout /= 1e3
        if self._wait_for_ready_or_shutdown(_timeout):
            self._check_failed_to_start()
            self.logger.debug(__ready_msg__)
        else:
            self._fail_start_timeout(_timeout)

        ```
    ###Assistant : Block until all pods starts successfully.

        If not success, it will raise an error hoping the outer function to catch it
        
    "
686,"def _rank_decomposition(M, iszerofunc=_iszero, simplify=False):
    r

    F, pivot_cols = M.rref(simplify=simplify, iszerofunc=iszerofunc,
            pivots=True)
    rank = len(pivot_cols)

    C = M.extract(range(M.rows), pivot_cols)
    F = F[:rank, :]

    return C, F

","Returns a pair of matrices (`C`, `F`) with matching rank
    such that `A = C F`.

    Parameters
    ==========

    iszerofunc : Function, optional
        A function used for detecting whether an element can
        act as a pivot.  ``lambda x: x.is_zero`` is used by default.

    simplify : Bool or Function, optional
        A function used to simplify elements when looking for a
        pivot. By default SymPy's ``simplify`` is used.

    Returns
    =======

    (C, F) : Matrices
        `C` and `F` are full-rank matrices with rank as same as `A`,
        whose product gives `A`.

        See Notes for additional mathematical details.

    Examples
    ========

    >>> from sympy import Matrix
    >>> A = Matrix([
    ...     [1, 3, 1, 4],
    ...     [2, 7, 3, 9],
    ...     [1, 5, 3, 1],
    ...     [1, 2, 0, 8]
    ... ])
    >>> C, F = A.rank_decomposition()
    >>> C
    Matrix([
    [1, 3, 4],
    [2, 7, 9],
    [1, 5, 1],
    [1, 2, 8]])
    >>> F
    Matrix([
    [1, 0, -2, 0],
    [0, 1,  1, 0],
    [0, 0,  0, 1]])
    >>> C * F == A
    True

    Notes
    =====

    Obtaining `F`, an RREF of `A`, is equivalent to creating a
    product

    .. math::
        E_n E_{n-1} ... E_1 A = F

    where `E_n, E_{n-1}, \dots, E_1` are the elimination matrices or
    permutation matrices equivalent to each row-reduction step.

    The inverse of the same product of elimination matrices gives
    `C`:

    .. math::
        C = \left(E_n E_{n-1} \dots E_1\right)^{-1}

    It is not necessary, however, to actually compute the inverse:
    the columns of `C` are those from the original matrix with the
    same column indices as the indices of the pivot columns of `F`.

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Rank_factorization

    .. [2] Piziak, R.; Odell, P. L. (1 June 1999).
        ""Full Rank Factorization of Matrices"".
        Mathematics Magazine. 72 (3): 193. doi:10.2307/2690882

    See Also
    ========

    sympy.matrices.matrices.MatrixReductions.rref
    ",291,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _rank_decomposition(M, iszerofunc=_iszero, simplify=False):
    r

    F, pivot_cols = M.rref(simplify=simplify, iszerofunc=iszerofunc,
            pivots=True)
    rank = len(pivot_cols)

    C = M.extract(range(M.rows), pivot_cols)
    F = F[:rank, :]

    return C, F


        ```
    ###Assistant : Returns a pair of matrices (`C`, `F`) with matching rank
    such that `A = C F`.

    Parameters
    ==========

    iszerofunc : Function, optional
        A function used for detecting whether an element can
        act as a pivot.  ``lambda x: x.is_zero`` is used by default.

    simplify : Bool or Function, optional
        A function used to simplify elements when looking for a
        pivot. By default SymPy's ``simplify`` is used.

    Returns
    =======

    (C, F) : Matrices
        `C` and `F` are full-rank matrices with rank as same as `A`,
        whose product gives `A`.

        See Notes for additional mathematical details.

    Examples
    ========

    >>> from sympy import Matrix
    >>> A = Matrix([
    ...     [1, 3, 1, 4],
    ...     [2, 7, 3, 9],
    ...     [1, 5, 3, 1],
    ...     [1, 2, 0, 8]
    ... ])
    >>> C, F = A.rank_decomposition()
    >>> C
    Matrix([
    [1, 3, 4],
    [2, 7, 9],
    [1, 5, 1],
    [1, 2, 8]])
    >>> F
    Matrix([
    [1, 0, -2, 0],
    [0, 1,  1, 0],
    [0, 0,  0, 1]])
    >>> C * F == A
    True

    Notes
    =====

    Obtaining `F`, an RREF of `A`, is equivalent to creating a
    product

    .. math::
        E_n E_{n-1} ... E_1 A = F

    where `E_n, E_{n-1}, \dots, E_1` are the elimination matrices or
    permutation matrices equivalent to each row-reduction step.

    The inverse of the same product of elimination matrices gives
    `C`:

    .. math::
        C = \left(E_n E_{n-1} \dots E_1\right)^{-1}

    It is not necessary, however, to actually compute the inverse:
    the columns of `C` are those from the original matrix with the
    same column indices as the indices of the pivot columns of `F`.

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Rank_factorization

    .. [2] Piziak, R.; Odell, P. L. (1 June 1999).
        ""Full Rank Factorization of Matrices"".
        Mathematics Magazine. 72 (3): 193. doi:10.2307/2690882

    See Also
    ========

    sympy.matrices.matrices.MatrixReductions.rref
    
    "
687,"def normalize(X, norm=""l2"", *, axis=1, copy=True, return_norm=False):
    
    if norm not in (""l1"", ""l2"", ""max""):
        raise ValueError(""'%s' is not a supported norm"" % norm)

    if axis == 0:
        sparse_format = ""csc""
    elif axis == 1:
        sparse_format = ""csr""
    else:
        raise ValueError(""'%d' is not a supported axis"" % axis)

    X = check_array(
        X,
        accept_sparse=sparse_format,
        copy=copy,
        estimator=""the normalize function"",
        dtype=FLOAT_DTYPES,
    )
    if axis == 0:
        X = X.T

    if sparse.issparse(X):
        if return_norm and norm in (""l1"", ""l2""):
            raise NotImplementedError(
                ""return_norm=True is not implemented ""
                ""for sparse matrices with norm 'l1' ""
                ""or norm 'l2'""
            )
        if norm == ""l1"":
            inplace_csr_row_normalize_l1(X)
        elif norm == ""l2"":
            inplace_csr_row_normalize_l2(X)
        elif norm == ""max"":
            mins, maxes = min_max_axis(X, 1)
            norms = np.maximum(abs(mins), maxes)
            norms_elementwise = norms.repeat(np.diff(X.indptr))
            mask = norms_elementwise != 0
            X.data[mask] /= norms_elementwise[mask]
    else:
        if norm == ""l1"":
            norms = np.abs(X).sum(axis=1)
        elif norm == ""l2"":
            norms = row_norms(X)
        elif norm == ""max"":
            norms = np.max(abs(X), axis=1)
        norms = _handle_zeros_in_scale(norms, copy=False)
        X /= norms[:, np.newaxis]

    if axis == 0:
        X = X.T

    if return_norm:
        return X, norms
    else:
        return X

","Scale input vectors individually to unit norm (vector length).

    Read more in the :ref:`User Guide <preprocessing_normalization>`.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The data to normalize, element by element.
        scipy.sparse matrices should be in CSR format to avoid an
        un-necessary copy.

    norm : {'l1', 'l2', 'max'}, default='l2'
        The norm to use to normalize each non zero sample (or each non-zero
        feature if axis is 0).

    axis : {0, 1}, default=1
        axis used to normalize the data along. If 1, independently normalize
        each sample, otherwise (if 0) normalize each feature.

    copy : bool, default=True
        Set to False to perform inplace row normalization and avoid a
        copy (if the input is already a numpy array or a scipy.sparse
        CSR matrix and if axis is 1).

    return_norm : bool, default=False
        Whether to return the computed norms.

    Returns
    -------
    X : {ndarray, sparse matrix} of shape (n_samples, n_features)
        Normalized input X.

    norms : ndarray of shape (n_samples, ) if axis=1 else (n_features, )
        An array of norms along given axis for X.
        When X is sparse, a NotImplementedError will be raised
        for norm 'l1' or 'l2'.

    See Also
    --------
    Normalizer : Performs normalization using the Transformer API
        (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`).

    Notes
    -----
    For a comparison of the different scalers, transformers, and normalizers,
    see :ref:`examples/preprocessing/plot_all_scaling.py
    <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
    ",220,172,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def normalize(X, norm=""l2"", *, axis=1, copy=True, return_norm=False):
    
    if norm not in (""l1"", ""l2"", ""max""):
        raise ValueError(""'%s' is not a supported norm"" % norm)

    if axis == 0:
        sparse_format = ""csc""
    elif axis == 1:
        sparse_format = ""csr""
    else:
        raise ValueError(""'%d' is not a supported axis"" % axis)

    X = check_array(
        X,
        accept_sparse=sparse_format,
        copy=copy,
        estimator=""the normalize function"",
        dtype=FLOAT_DTYPES,
    )
    if axis == 0:
        X = X.T

    if sparse.issparse(X):
        if return_norm and norm in (""l1"", ""l2""):
            raise NotImplementedError(
                ""return_norm=True is not implemented ""
                ""for sparse matrices with norm 'l1' ""
                ""or norm 'l2'""
            )
        if norm == ""l1"":
            inplace_csr_row_normalize_l1(X)
        elif norm == ""l2"":
            inplace_csr_row_normalize_l2(X)
        elif norm == ""max"":
            mins, maxes = min_max_axis(X, 1)
            norms = np.maximum(abs(mins), maxes)
            norms_elementwise = norms.repeat(np.diff(X.indptr))
            mask = norms_elementwise != 0
            X.data[mask] /= norms_elementwise[mask]
    else:
        if norm == ""l1"":
            norms = np.abs(X).sum(axis=1)
        elif norm == ""l2"":
            norms = row_norms(X)
        elif norm == ""max"":
            norms = np.max(abs(X), axis=1)
        norms = _handle_zeros_in_scale(norms, copy=False)
        X /= norms[:, np.newaxis]

    if axis == 0:
        X = X.T

    if return_norm:
        return X, norms
    else:
        return X


        ```
    ###Assistant : Scale input vectors individually to unit norm (vector length).

    Read more in the :ref:`User Guide <preprocessing_normalization>`.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The data to normalize, element by element.
        scipy.sparse matrices should be in CSR format to avoid an
        un-necessary copy.

    norm : {'l1', 'l2', 'max'}, default='l2'
        The norm to use to normalize each non zero sample (or each non-zero
        feature if axis is 0).

    axis : {0, 1}, default=1
        axis used to normalize the data along. If 1, independently normalize
        each sample, otherwise (if 0) normalize each feature.

    copy : bool, default=True
        Set to False to perform inplace row normalization and avoid a
        copy (if the input is already a numpy array or a scipy.sparse
        CSR matrix and if axis is 1).

    return_norm : bool, default=False
        Whether to return the computed norms.

    Returns
    -------
    X : {ndarray, sparse matrix} of shape (n_samples, n_features)
        Normalized input X.

    norms : ndarray of shape (n_samples, ) if axis=1 else (n_features, )
        An array of norms along given axis for X.
        When X is sparse, a NotImplementedError will be raised
        for norm 'l1' or 'l2'.

    See Also
    --------
    Normalizer : Performs normalization using the Transformer API
        (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`).

    Notes
    -----
    For a comparison of the different scalers, transformers, and normalizers,
    see :ref:`examples/preprocessing/plot_all_scaling.py
    <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
    
    "
688,"def find_version_to_install(self, name):
        
        version = Version.parse(name)
        if version.patch is not None:
            return name
        try:
            best_match = max(
                (
                    inst_version
                    for inst_version in self.iter_installable_versions()
                    if inst_version.matches_minor(version)
                ),
                key=operator.attrgetter(""cmpkey""),
            )
        except ValueError:
            raise ValueError(
                f""no installable version found for {name!r}"",
            )
        return best_match
","Find a version in the installer from the version supplied.

        A ValueError is raised if a matching version cannot be found.
        ",21,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_version_to_install(self, name):
        
        version = Version.parse(name)
        if version.patch is not None:
            return name
        try:
            best_match = max(
                (
                    inst_version
                    for inst_version in self.iter_installable_versions()
                    if inst_version.matches_minor(version)
                ),
                key=operator.attrgetter(""cmpkey""),
            )
        except ValueError:
            raise ValueError(
                f""no installable version found for {name!r}"",
            )
        return best_match

        ```
    ###Assistant : Find a version in the installer from the version supplied.

        A ValueError is raised if a matching version cannot be found.
        
    "
689,"def copy_safe_request(request):
    
    meta = {
        k: request.META[k]
        for k in HTTP_REQUEST_META_SAFE_COPY
        if k in request.META and isinstance(request.META[k], str)
    }
    return NetBoxFakeRequest({
        'META': meta,
        'COOKIES': request.COOKIES,
        'POST': request.POST,
        'GET': request.GET,
        'FILES': request.FILES,
        'user': request.user,
        'path': request.path,
        'id': getattr(request, 'id', None),  # UUID assigned by middleware
    })

","
    Copy selected attributes from a request object into a new fake request object. This is needed in places where
    thread safe pickling of the useful request data is needed.
    ",29,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copy_safe_request(request):
    
    meta = {
        k: request.META[k]
        for k in HTTP_REQUEST_META_SAFE_COPY
        if k in request.META and isinstance(request.META[k], str)
    }
    return NetBoxFakeRequest({
        'META': meta,
        'COOKIES': request.COOKIES,
        'POST': request.POST,
        'GET': request.GET,
        'FILES': request.FILES,
        'user': request.user,
        'path': request.path,
        'id': getattr(request, 'id', None),  # UUID assigned by middleware
    })


        ```
    ###Assistant : 
    Copy selected attributes from a request object into a new fake request object. This is needed in places where
    thread safe pickling of the useful request data is needed.
    
    "
690,"def batchify(self, obs_batch, sort=False):
        
        batch = super().batchify(obs_batch, sort=sort)

        if batch.valid_indices is None:
            return batch

        batch.classifier_label = torch.tensor(
            [
                [obs_batch[i].get('classifier_label_idx', -1)]
                for i in batch.valid_indices
            ]
        )
        batch.is_ltr = torch.tensor(
            [[obs_batch[i].get('is_ltr', False)] for i in batch.valid_indices]
        )
        return batch
","
        This method calls the parent class's batchify method and then add
        classifier_label and is_ltr property to the the batch.
        ",19,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def batchify(self, obs_batch, sort=False):
        
        batch = super().batchify(obs_batch, sort=sort)

        if batch.valid_indices is None:
            return batch

        batch.classifier_label = torch.tensor(
            [
                [obs_batch[i].get('classifier_label_idx', -1)]
                for i in batch.valid_indices
            ]
        )
        batch.is_ltr = torch.tensor(
            [[obs_batch[i].get('is_ltr', False)] for i in batch.valid_indices]
        )
        return batch

        ```
    ###Assistant : 
        This method calls the parent class's batchify method and then add
        classifier_label and is_ltr property to the the batch.
        
    "
691,"def calculate_post_conv_height(height, kernel_size, stride, pad, n_convs):
        
        for _ in range(n_convs):
            height = (height - kernel_size + 2 * pad) // stride + 1
        return height

",Height of spec after n convolutions with fixed kernel/stride/pad.,9,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def calculate_post_conv_height(height, kernel_size, stride, pad, n_convs):
        
        for _ in range(n_convs):
            height = (height - kernel_size + 2 * pad) // stride + 1
        return height


        ```
    ###Assistant : Height of spec after n convolutions with fixed kernel/stride/pad.
    "
692,"async def test_hls_playlist_view(hass, setup_component, hls_stream, stream_worker_sync):
    
    stream = create_stream(hass, STREAM_SOURCE, {}, dynamic_stream_settings())
    stream_worker_sync.pause()
    hls = stream.add_provider(HLS_PROVIDER)
    for i in range(2):
        segment = Segment(sequence=i, duration=SEGMENT_DURATION)
        hls.put(segment)
    await hass.async_block_till_done()

    hls_client = await hls_stream(stream)

    resp = await hls_client.get(""/playlist.m3u8"")
    assert resp.status == HTTPStatus.OK
    assert await resp.text() == make_playlist(
        sequence=0, segments=[make_segment(0), make_segment(1)]
    )

    segment = Segment(sequence=2, duration=SEGMENT_DURATION)
    hls.put(segment)
    await hass.async_block_till_done()
    resp = await hls_client.get(""/playlist.m3u8"")
    assert resp.status == HTTPStatus.OK
    assert await resp.text() == make_playlist(
        sequence=0, segments=[make_segment(0), make_segment(1), make_segment(2)]
    )

    stream_worker_sync.resume()
    await stream.stop()

",Test rendering the hls playlist with 1 and 2 output segments.,11,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_hls_playlist_view(hass, setup_component, hls_stream, stream_worker_sync):
    
    stream = create_stream(hass, STREAM_SOURCE, {}, dynamic_stream_settings())
    stream_worker_sync.pause()
    hls = stream.add_provider(HLS_PROVIDER)
    for i in range(2):
        segment = Segment(sequence=i, duration=SEGMENT_DURATION)
        hls.put(segment)
    await hass.async_block_till_done()

    hls_client = await hls_stream(stream)

    resp = await hls_client.get(""/playlist.m3u8"")
    assert resp.status == HTTPStatus.OK
    assert await resp.text() == make_playlist(
        sequence=0, segments=[make_segment(0), make_segment(1)]
    )

    segment = Segment(sequence=2, duration=SEGMENT_DURATION)
    hls.put(segment)
    await hass.async_block_till_done()
    resp = await hls_client.get(""/playlist.m3u8"")
    assert resp.status == HTTPStatus.OK
    assert await resp.text() == make_playlist(
        sequence=0, segments=[make_segment(0), make_segment(1), make_segment(2)]
    )

    stream_worker_sync.resume()
    await stream.stop()


        ```
    ###Assistant : Test rendering the hls playlist with 1 and 2 output segments.
    "
693,"def _create_local_rank_map(self) -> Dict:
        
        rank_mapping = {}
        ip_dict = defaultdict(int)
        for world_rank in range(len(self.worker_group)):
            worker = self.worker_group.workers[world_rank]
            node_ip = worker.metadata.node_ip
            rank_mapping[world_rank] = ip_dict[node_ip]
            ip_dict[node_ip] += 1
        return rank_mapping
","Create mapping from worker world_rank to local_rank.

        Example:
            Worker 0: 0.0.0.0
            Worker 1: 0.0.0.0
            Worker 2: 0.0.0.1
            Worker 3: 0.0.0.0
            Worker 4: 0.0.0.1

            Workers 0, 1, 3 are on 0.0.0.0.
            Workers 2, 4 are on 0.0.0.1.

            Expected Output:
            {
                0 -> 0,
                1 -> 1,
                2 -> 0,
                3 -> 2,
                4 -> 1
            }
        ",55,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_local_rank_map(self) -> Dict:
        
        rank_mapping = {}
        ip_dict = defaultdict(int)
        for world_rank in range(len(self.worker_group)):
            worker = self.worker_group.workers[world_rank]
            node_ip = worker.metadata.node_ip
            rank_mapping[world_rank] = ip_dict[node_ip]
            ip_dict[node_ip] += 1
        return rank_mapping

        ```
    ###Assistant : Create mapping from worker world_rank to local_rank.

        Example:
            Worker 0: 0.0.0.0
            Worker 1: 0.0.0.0
            Worker 2: 0.0.0.1
            Worker 3: 0.0.0.0
            Worker 4: 0.0.0.1

            Workers 0, 1, 3 are on 0.0.0.0.
            Workers 2, 4 are on 0.0.0.1.

            Expected Output:
            {
                0 -> 0,
                1 -> 1,
                2 -> 0,
                3 -> 2,
                4 -> 1
            }
        
    "
694,"def _output_groups(self) -> None:
        
        is_rename = self._args.sort_method != ""none""

        logger.info(""Creating %s group folders in '%s'."",
                    len(self._sorter.binned), self._args.output_dir)
        bin_names = [f""_{b}"" for b in self._sorter.bin_names]
        if is_rename:
            bin_names = [f""{name}_by_{self._args.sort_method}"" for name in bin_names]
        for name in bin_names:
            folder = os.path.join(self._args.output_dir, name)
            if os.path.exists(folder):
                rmtree(folder)
            os.makedirs(folder)

        description = f""{'Copying' if self._args.keep_original else 'Moving'} into groups""
        description += "" and renaming"" if is_rename else """"

        pbar = tqdm(range(len(self._sorter.sorted_filelist)),
                    desc=description,
                    file=sys.stdout,
                    leave=False)
        idx = 0
        for bin_id, bin_ in enumerate(self._sorter.binned):
            pbar.set_description(f""{description}: Bin {bin_id + 1} of {len(self._sorter.binned)}"")
            output_path = os.path.join(self._args.output_dir, bin_names[bin_id])
            if not bin_:
                logger.debug(""Removing empty bin: %s"", output_path)
                os.rmdir(output_path)
            for source in bin_:
                basename = os.path.basename(source)
                dst_name = f""{idx:06d}_{basename}"" if is_rename else basename
                dest = os.path.join(output_path, dst_name)
                self._sort_file(source, dest)
                idx += 1
                pbar.update(1)

    # Output methods"," Move the files to folders.

        Obtains the bins and original filenames from :attr:`_sorter` and outputs into appropriate
        bins in the output location
        ",22,124,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _output_groups(self) -> None:
        
        is_rename = self._args.sort_method != ""none""

        logger.info(""Creating %s group folders in '%s'."",
                    len(self._sorter.binned), self._args.output_dir)
        bin_names = [f""_{b}"" for b in self._sorter.bin_names]
        if is_rename:
            bin_names = [f""{name}_by_{self._args.sort_method}"" for name in bin_names]
        for name in bin_names:
            folder = os.path.join(self._args.output_dir, name)
            if os.path.exists(folder):
                rmtree(folder)
            os.makedirs(folder)

        description = f""{'Copying' if self._args.keep_original else 'Moving'} into groups""
        description += "" and renaming"" if is_rename else """"

        pbar = tqdm(range(len(self._sorter.sorted_filelist)),
                    desc=description,
                    file=sys.stdout,
                    leave=False)
        idx = 0
        for bin_id, bin_ in enumerate(self._sorter.binned):
            pbar.set_description(f""{description}: Bin {bin_id + 1} of {len(self._sorter.binned)}"")
            output_path = os.path.join(self._args.output_dir, bin_names[bin_id])
            if not bin_:
                logger.debug(""Removing empty bin: %s"", output_path)
                os.rmdir(output_path)
            for source in bin_:
                basename = os.path.basename(source)
                dst_name = f""{idx:06d}_{basename}"" if is_rename else basename
                dest = os.path.join(output_path, dst_name)
                self._sort_file(source, dest)
                idx += 1
                pbar.update(1)

    # Output methods
        ```
    ###Assistant :  Move the files to folders.

        Obtains the bins and original filenames from :attr:`_sorter` and outputs into appropriate
        bins in the output location
        
    "
695,"def _setSharedLibraryRPATHElf(filename, rpath):
    # TODO: Might write something that makes a shell script replacement
    # in case no rpath is present, or use patchelf, for now our use
    # case seems to use rpaths for executables.

    # patchelf --set-rpath ""$ORIGIN/path/to/library"" <executable>
    with withEnvironmentVarOverriden(""LANG"", ""C""):
        executeToolChecked(
            logger=postprocessing_logger,
            command=(""patchelf"", ""--set-rpath"", rpath, filename),
            stderr_filter=_filterPatchelfErrorOutput,
            absence_message=,
        )

","\
Error, needs 'patchelf' on your system, due to 'RPATH' settings that need to be
set.",16,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _setSharedLibraryRPATHElf(filename, rpath):
    # TODO: Might write something that makes a shell script replacement
    # in case no rpath is present, or use patchelf, for now our use
    # case seems to use rpaths for executables.

    # patchelf --set-rpath ""$ORIGIN/path/to/library"" <executable>
    with withEnvironmentVarOverriden(""LANG"", ""C""):
        executeToolChecked(
            logger=postprocessing_logger,
            command=(""patchelf"", ""--set-rpath"", rpath, filename),
            stderr_filter=_filterPatchelfErrorOutput,
            absence_message=,
        )


        ```
    ###Assistant : \
Error, needs 'patchelf' on your system, due to 'RPATH' settings that need to be
set.
    "
696,"def _get_columns(self):
        
        if self._columns_cache is None:
            self._columns_cache, column_widths = self._compute_axis_labels_and_lengths(
                1
            )
            if self._column_widths_cache is None:
                self._column_widths_cache = column_widths
        return self._columns_cache
","
        Get the columns from the cache object.

        Returns
        -------
        pandas.Index
            An index object containing the column labels.
        ",17,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_columns(self):
        
        if self._columns_cache is None:
            self._columns_cache, column_widths = self._compute_axis_labels_and_lengths(
                1
            )
            if self._column_widths_cache is None:
                self._column_widths_cache = column_widths
        return self._columns_cache

        ```
    ###Assistant : 
        Get the columns from the cache object.

        Returns
        -------
        pandas.Index
            An index object containing the column labels.
        
    "
697,"async def async_update(self) -> None:
        
        # Update values from controller's device dictionary
        self._connected = self._controller.is_connected
        self._current_temp = self._controller.get_temperature(self._device_id)
        self._fan_speed = self._controller.get_fan_speed(self._device_id)
        self._power = self._controller.is_on(self._device_id)
        self._min_temp = self._controller.get_min_setpoint(self._device_id)
        self._max_temp = self._controller.get_max_setpoint(self._device_id)
        self._rssi = self._controller.get_rssi(self._device_id)
        self._run_hours = self._controller.get_run_hours(self._device_id)
        self._target_temp = self._controller.get_setpoint(self._device_id)
        self._outdoor_temp = self._controller.get_outdoor_temperature(self._device_id)

        # Operation mode
        mode = self._controller.get_mode(self._device_id)
        self._hvac_mode = MAP_IH_TO_HVAC_MODE.get(mode)

        # Preset mode
        preset = self._controller.get_preset_mode(self._device_id)
        self._preset = MAP_IH_TO_PRESET_MODE.get(preset)

        # Swing mode
        # Climate module only supports one swing setting.
        self._vvane = self._controller.get_vertical_swing(self._device_id)
        self._hvane = self._controller.get_horizontal_swing(self._device_id)

        # Power usage
        self._power_consumption_heat = self._controller.get_heat_power_consumption(
            self._device_id
        )
        self._power_consumption_cool = self._controller.get_cool_power_consumption(
            self._device_id
        )
",Copy values from controller dictionary to climate device.,8,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_update(self) -> None:
        
        # Update values from controller's device dictionary
        self._connected = self._controller.is_connected
        self._current_temp = self._controller.get_temperature(self._device_id)
        self._fan_speed = self._controller.get_fan_speed(self._device_id)
        self._power = self._controller.is_on(self._device_id)
        self._min_temp = self._controller.get_min_setpoint(self._device_id)
        self._max_temp = self._controller.get_max_setpoint(self._device_id)
        self._rssi = self._controller.get_rssi(self._device_id)
        self._run_hours = self._controller.get_run_hours(self._device_id)
        self._target_temp = self._controller.get_setpoint(self._device_id)
        self._outdoor_temp = self._controller.get_outdoor_temperature(self._device_id)

        # Operation mode
        mode = self._controller.get_mode(self._device_id)
        self._hvac_mode = MAP_IH_TO_HVAC_MODE.get(mode)

        # Preset mode
        preset = self._controller.get_preset_mode(self._device_id)
        self._preset = MAP_IH_TO_PRESET_MODE.get(preset)

        # Swing mode
        # Climate module only supports one swing setting.
        self._vvane = self._controller.get_vertical_swing(self._device_id)
        self._hvane = self._controller.get_horizontal_swing(self._device_id)

        # Power usage
        self._power_consumption_heat = self._controller.get_heat_power_consumption(
            self._device_id
        )
        self._power_consumption_cool = self._controller.get_cool_power_consumption(
            self._device_id
        )

        ```
    ###Assistant : Copy values from controller dictionary to climate device.
    "
698,"def test_kb_valid_entities(nlp):
    
    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)

    # adding entities
    mykb.add_entity(entity=""Q1"", freq=19, entity_vector=[8, 4, 3])
    mykb.add_entity(entity=""Q2"", freq=5, entity_vector=[2, 1, 0])
    mykb.add_entity(entity=""Q3"", freq=25, entity_vector=[-1, -6, 5])

    # adding aliases
    mykb.add_alias(alias=""douglas"", entities=[""Q2"", ""Q3""], probabilities=[0.8, 0.2])
    mykb.add_alias(alias=""adam"", entities=[""Q2""], probabilities=[0.9])

    # test the size of the corresponding KB
    assert mykb.get_size_entities() == 3
    assert mykb.get_size_aliases() == 2

    # test retrieval of the entity vectors
    assert mykb.get_vector(""Q1"") == [8, 4, 3]
    assert mykb.get_vector(""Q2"") == [2, 1, 0]
    assert mykb.get_vector(""Q3"") == [-1, -6, 5]

    # test retrieval of prior probabilities
    assert_almost_equal(mykb.get_prior_prob(entity=""Q2"", alias=""douglas""), 0.8)
    assert_almost_equal(mykb.get_prior_prob(entity=""Q3"", alias=""douglas""), 0.2)
    assert_almost_equal(mykb.get_prior_prob(entity=""Q342"", alias=""douglas""), 0.0)
    assert_almost_equal(mykb.get_prior_prob(entity=""Q3"", alias=""douglassssss""), 0.0)

",Test the valid construction of a KB with 3 entities and two aliases,13,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_kb_valid_entities(nlp):
    
    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)

    # adding entities
    mykb.add_entity(entity=""Q1"", freq=19, entity_vector=[8, 4, 3])
    mykb.add_entity(entity=""Q2"", freq=5, entity_vector=[2, 1, 0])
    mykb.add_entity(entity=""Q3"", freq=25, entity_vector=[-1, -6, 5])

    # adding aliases
    mykb.add_alias(alias=""douglas"", entities=[""Q2"", ""Q3""], probabilities=[0.8, 0.2])
    mykb.add_alias(alias=""adam"", entities=[""Q2""], probabilities=[0.9])

    # test the size of the corresponding KB
    assert mykb.get_size_entities() == 3
    assert mykb.get_size_aliases() == 2

    # test retrieval of the entity vectors
    assert mykb.get_vector(""Q1"") == [8, 4, 3]
    assert mykb.get_vector(""Q2"") == [2, 1, 0]
    assert mykb.get_vector(""Q3"") == [-1, -6, 5]

    # test retrieval of prior probabilities
    assert_almost_equal(mykb.get_prior_prob(entity=""Q2"", alias=""douglas""), 0.8)
    assert_almost_equal(mykb.get_prior_prob(entity=""Q3"", alias=""douglas""), 0.2)
    assert_almost_equal(mykb.get_prior_prob(entity=""Q342"", alias=""douglas""), 0.0)
    assert_almost_equal(mykb.get_prior_prob(entity=""Q3"", alias=""douglassssss""), 0.0)


        ```
    ###Assistant : Test the valid construction of a KB with 3 entities and two aliases
    "
699,"def eye(N, M=None, k=0, dtype=float, order='C', *, like=None):
    
    if like is not None:
        return _eye_with_like(N, M=M, k=k, dtype=dtype, order=order, like=like)
    if M is None:
        M = N
    m = zeros((N, M), dtype=dtype, order=order)
    if k >= M:
        return m
    # Ensure M and k are integers, so we don't get any surprise casting
    # results in the expressions `M-k` and `M+1` used below.  This avoids
    # a problem with inputs with type (for example) np.uint64.
    M = operator.index(M)
    k = operator.index(k)
    if k >= 0:
        i = k
    else:
        i = (-k) * M
    m[:M-k].flat[i::M+1] = 1
    return m


_eye_with_like = array_function_dispatch(
    _eye_dispatcher
)(eye)

","
    Return a 2-D array with ones on the diagonal and zeros elsewhere.

    Parameters
    ----------
    N : int
      Number of rows in the output.
    M : int, optional
      Number of columns in the output. If None, defaults to `N`.
    k : int, optional
      Index of the diagonal: 0 (the default) refers to the main diagonal,
      a positive value refers to an upper diagonal, and a negative value
      to a lower diagonal.
    dtype : data-type, optional
      Data-type of the returned array.
    order : {'C', 'F'}, optional
        Whether the output should be stored in row-major (C-style) or
        column-major (Fortran-style) order in memory.

        .. versionadded:: 1.14.0
    ${ARRAY_FUNCTION_LIKE}

        .. versionadded:: 1.20.0

    Returns
    -------
    I : ndarray of shape (N,M)
      An array where all elements are equal to zero, except for the `k`-th
      diagonal, whose values are equal to one.

    See Also
    --------
    identity : (almost) equivalent function
    diag : diagonal 2-D array from a 1-D array specified by the user.

    Examples
    --------
    >>> np.eye(2, dtype=int)
    array([[1, 0],
           [0, 1]])
    >>> np.eye(3, k=1)
    array([[0.,  1.,  0.],
           [0.,  0.,  1.],
           [0.,  0.,  0.]])

    ",176,104,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def eye(N, M=None, k=0, dtype=float, order='C', *, like=None):
    
    if like is not None:
        return _eye_with_like(N, M=M, k=k, dtype=dtype, order=order, like=like)
    if M is None:
        M = N
    m = zeros((N, M), dtype=dtype, order=order)
    if k >= M:
        return m
    # Ensure M and k are integers, so we don't get any surprise casting
    # results in the expressions `M-k` and `M+1` used below.  This avoids
    # a problem with inputs with type (for example) np.uint64.
    M = operator.index(M)
    k = operator.index(k)
    if k >= 0:
        i = k
    else:
        i = (-k) * M
    m[:M-k].flat[i::M+1] = 1
    return m


_eye_with_like = array_function_dispatch(
    _eye_dispatcher
)(eye)


        ```
    ###Assistant : 
    Return a 2-D array with ones on the diagonal and zeros elsewhere.

    Parameters
    ----------
    N : int
      Number of rows in the output.
    M : int, optional
      Number of columns in the output. If None, defaults to `N`.
    k : int, optional
      Index of the diagonal: 0 (the default) refers to the main diagonal,
      a positive value refers to an upper diagonal, and a negative value
      to a lower diagonal.
    dtype : data-type, optional
      Data-type of the returned array.
    order : {'C', 'F'}, optional
        Whether the output should be stored in row-major (C-style) or
        column-major (Fortran-style) order in memory.

        .. versionadded:: 1.14.0
    ${ARRAY_FUNCTION_LIKE}

        .. versionadded:: 1.20.0

    Returns
    -------
    I : ndarray of shape (N,M)
      An array where all elements are equal to zero, except for the `k`-th
      diagonal, whose values are equal to one.

    See Also
    --------
    identity : (almost) equivalent function
    diag : diagonal 2-D array from a 1-D array specified by the user.

    Examples
    --------
    >>> np.eye(2, dtype=int)
    array([[1, 0],
           [0, 1]])
    >>> np.eye(3, k=1)
    array([[0.,  1.,  0.],
           [0.,  0.,  1.],
           [0.,  0.,  0.]])

    
    "
700,"def _clean_configuration_value(cls, item_type, new_value):
        
        if (
            item_type == ConfigurationTypeField.BOOLEAN
            and new_value
            and not isinstance(new_value, bool)
        ):
            new_value = new_value.lower() == ""true""
        if item_type == ConfigurationTypeField.OUTPUT:
            # OUTPUT field is read only. No need to update it
            return
        return new_value
","Clean the value that is saved in plugin configuration.

        Change the string provided as boolean into the bool value.
        Return None for Output type, as it's read only field.
        ",29,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _clean_configuration_value(cls, item_type, new_value):
        
        if (
            item_type == ConfigurationTypeField.BOOLEAN
            and new_value
            and not isinstance(new_value, bool)
        ):
            new_value = new_value.lower() == ""true""
        if item_type == ConfigurationTypeField.OUTPUT:
            # OUTPUT field is read only. No need to update it
            return
        return new_value

        ```
    ###Assistant : Clean the value that is saved in plugin configuration.

        Change the string provided as boolean into the bool value.
        Return None for Output type, as it's read only field.
        
    "
701,"def coord_map_from_to(top_from, top_to):
    
    # We need to find a common ancestor of top_from and top_to.
    # We'll assume that all ancestors are equivalent here (otherwise the graph
    # is an inconsistent state (which we could improve this to check for)).
    # For now use a brute-force algorithm.
","
    Determine the coordinate mapping betweeen a top (from) and a top (to).
    Walk the graph to find a common ancestor while composing the coord maps for
    from and to until they meet. As a last step the from map is inverted.
    ",41,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def coord_map_from_to(top_from, top_to):
    
    # We need to find a common ancestor of top_from and top_to.
    # We'll assume that all ancestors are equivalent here (otherwise the graph
    # is an inconsistent state (which we could improve this to check for)).
    # For now use a brute-force algorithm.

        ```
    ###Assistant : 
    Determine the coordinate mapping betweeen a top (from) and a top (to).
    Walk the graph to find a common ancestor while composing the coord maps for
    from and to until they meet. As a last step the from map is inverted.
    
    "
702,"def get_conditions_to_validate_future_sle(sl_entries):
	warehouse_items_map = {}
	for entry in sl_entries:
		if entry.warehouse not in warehouse_items_map:
			warehouse_items_map[entry.warehouse] = set()

		warehouse_items_map[entry.warehouse].add(entry.item_code)

	or_conditions = []
	for warehouse, items in warehouse_items_map.items():
		or_conditions.append(
			f
		)

	return or_conditions

","warehouse = {frappe.db.escape(warehouse)}
				and item_code in ({', '.join(frappe.db.escape(item) for item in items)})",12,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_conditions_to_validate_future_sle(sl_entries):
	warehouse_items_map = {}
	for entry in sl_entries:
		if entry.warehouse not in warehouse_items_map:
			warehouse_items_map[entry.warehouse] = set()

		warehouse_items_map[entry.warehouse].add(entry.item_code)

	or_conditions = []
	for warehouse, items in warehouse_items_map.items():
		or_conditions.append(
			f
		)

	return or_conditions


        ```
    ###Assistant : warehouse = {frappe.db.escape(warehouse)}
				and item_code in ({', '.join(frappe.db.escape(item) for item in items)})
    "
703,"def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
    
    speakers = [item[""speaker_name""] for item in items]
    is_multi_speaker = len(set(speakers)) > 1
    if eval_split_size > 1:
        eval_split_size = int(eval_split_size)
    else:
        if eval_split_max_size:
            eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size))
        else:
            eval_split_size = int(len(items) * eval_split_size)

    assert (
        eval_split_size > 0
    ), "" [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}"".format(
        1 / len(items)
    )
    np.random.seed(0)
    np.random.shuffle(items)
    if is_multi_speaker:
        items_eval = []
        speakers = [item[""speaker_name""] for item in items]
        speaker_counter = Counter(speakers)
        while len(items_eval) < eval_split_size:
            item_idx = np.random.randint(0, len(items))
            speaker_to_be_removed = items[item_idx][""speaker_name""]
            if speaker_counter[speaker_to_be_removed] > 1:
                items_eval.append(items[item_idx])
                speaker_counter[speaker_to_be_removed] -= 1
                del items[item_idx]
        return items_eval, items
    return items[:eval_split_size], items[eval_split_size:]

","Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.

        Args:
    <<<<<<< HEAD
            items (List[List]):
                A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.

            eval_split_max_size (int):
                Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).

            eval_split_size (float):
                If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
                If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
    =======
            items (List[List]): A list of samples. Each sample is a list of `[text, audio_path, speaker_id]`.
    >>>>>>> Fix docstring
    ",101,118,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
    
    speakers = [item[""speaker_name""] for item in items]
    is_multi_speaker = len(set(speakers)) > 1
    if eval_split_size > 1:
        eval_split_size = int(eval_split_size)
    else:
        if eval_split_max_size:
            eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size))
        else:
            eval_split_size = int(len(items) * eval_split_size)

    assert (
        eval_split_size > 0
    ), "" [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}"".format(
        1 / len(items)
    )
    np.random.seed(0)
    np.random.shuffle(items)
    if is_multi_speaker:
        items_eval = []
        speakers = [item[""speaker_name""] for item in items]
        speaker_counter = Counter(speakers)
        while len(items_eval) < eval_split_size:
            item_idx = np.random.randint(0, len(items))
            speaker_to_be_removed = items[item_idx][""speaker_name""]
            if speaker_counter[speaker_to_be_removed] > 1:
                items_eval.append(items[item_idx])
                speaker_counter[speaker_to_be_removed] -= 1
                del items[item_idx]
        return items_eval, items
    return items[:eval_split_size], items[eval_split_size:]


        ```
    ###Assistant : Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.

        Args:
    <<<<<<< HEAD
            items (List[List]):
                A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.

            eval_split_max_size (int):
                Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).

            eval_split_size (float):
                If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
                If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
    =======
            items (List[List]): A list of samples. Each sample is a list of `[text, audio_path, speaker_id]`.
    >>>>>>> Fix docstring
    
    "
704,"def test_quarantine_media(self) -> None:
        

        media_info = self.get_success(self.store.get_local_media(self.media_id))
        assert media_info is not None
        self.assertFalse(media_info[""quarantined_by""])

        # quarantining
        channel = self.make_request(
            ""POST"",
            self.url % (""quarantine"", self.server_name, self.media_id),
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertFalse(channel.json_body)

        media_info = self.get_success(self.store.get_local_media(self.media_id))
        assert media_info is not None
        self.assertTrue(media_info[""quarantined_by""])

        # remove from quarantine
        channel = self.make_request(
            ""POST"",
            self.url % (""unquarantine"", self.server_name, self.media_id),
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertFalse(channel.json_body)

        media_info = self.get_success(self.store.get_local_media(self.media_id))
        assert media_info is not None
        self.assertFalse(media_info[""quarantined_by""])
","
        Tests that quarantining and remove from quarantine a media is successfully
        ",11,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_quarantine_media(self) -> None:
        

        media_info = self.get_success(self.store.get_local_media(self.media_id))
        assert media_info is not None
        self.assertFalse(media_info[""quarantined_by""])

        # quarantining
        channel = self.make_request(
            ""POST"",
            self.url % (""quarantine"", self.server_name, self.media_id),
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertFalse(channel.json_body)

        media_info = self.get_success(self.store.get_local_media(self.media_id))
        assert media_info is not None
        self.assertTrue(media_info[""quarantined_by""])

        # remove from quarantine
        channel = self.make_request(
            ""POST"",
            self.url % (""unquarantine"", self.server_name, self.media_id),
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertFalse(channel.json_body)

        media_info = self.get_success(self.store.get_local_media(self.media_id))
        assert media_info is not None
        self.assertFalse(media_info[""quarantined_by""])

        ```
    ###Assistant : 
        Tests that quarantining and remove from quarantine a media is successfully
        
    "
705,"def naive_greedy_modularity_communities(G, resolution=1, weight=None):
    r
    # First create one community for each node
    communities = list(frozenset([u]) for u in G.nodes())
    # Track merges
    merges = []
    # Greedily merge communities until no improvement is possible
    old_modularity = None
    new_modularity = modularity(G, communities, resolution=resolution, weight=weight)
    while old_modularity is None or new_modularity > old_modularity:
        # Save modularity for comparison
        old_modularity = new_modularity
        # Find best pair to merge
        trial_communities = list(communities)
        to_merge = None
        for i, u in enumerate(communities):
            for j, v in enumerate(communities):
                # Skip i==j and empty communities
                if j <= i or len(u) == 0 or len(v) == 0:
                    continue
                # Merge communities u and v
                trial_communities[j] = u | v
                trial_communities[i] = frozenset([])
                trial_modularity = modularity(
                    G, trial_communities, resolution=resolution, weight=weight
                )
                if trial_modularity >= new_modularity:
                    # Check if strictly better or tie
                    if trial_modularity > new_modularity:
                        # Found new best, save modularity and group indexes
                        new_modularity = trial_modularity
                        to_merge = (i, j, new_modularity - old_modularity)
                    elif to_merge and min(i, j) < min(to_merge[0], to_merge[1]):
                        # Break ties by choosing pair with lowest min id
                        new_modularity = trial_modularity
                        to_merge = (i, j, new_modularity - old_modularity)
                # Un-merge
                trial_communities[i] = u
                trial_communities[j] = v
        if to_merge is not None:
            # If the best merge improves modularity, use it
            merges.append(to_merge)
            i, j, dq = to_merge
            u, v = communities[i], communities[j]
            communities[j] = u | v
            communities[i] = frozenset([])
    # Remove empty communities and sort
    return sorted((c for c in communities if len(c) > 0), key=len, reverse=True)


# old name
_naive_greedy_modularity_communities = naive_greedy_modularity_communities
","Find communities in G using greedy modularity maximization.

    This implementation is O(n^4), much slower than alternatives, but it is
    provided as an easy-to-understand reference implementation.

    Greedy modularity maximization begins with each node in its own community
    and joins the pair of communities that most increases modularity until no
    such pair exists.

    This function maximizes the generalized modularity, where `resolution`
    is the resolution parameter, often expressed as $\gamma$.
    See :func:`~networkx.algorithms.community.quality.modularity`.

    Parameters
    ----------
    G : NetworkX graph

    resolution : float (default=1)
        If resolution is less than 1, modularity favors larger communities.
        Greater than 1 favors smaller communities.

    weight : string or None, optional (default=None)
        The name of an edge attribute that holds the numerical value used
        as a weight.  If None, then each edge has weight 1.
        The degree is the sum of the edge weights adjacent to the node.

    Returns
    -------
    list
        A list of sets of nodes, one for each community.
        Sorted by length with largest communities first.

    Examples
    --------
    >>> from networkx.algorithms.community import \
    ... naive_greedy_modularity_communities
    >>> G = nx.karate_club_graph()
    >>> c = naive_greedy_modularity_communities(G)
    >>> sorted(c[0])
    [8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]

    See Also
    --------
    greedy_modularity_communities
    modularity
    ",199,250,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def naive_greedy_modularity_communities(G, resolution=1, weight=None):
    r
    # First create one community for each node
    communities = list(frozenset([u]) for u in G.nodes())
    # Track merges
    merges = []
    # Greedily merge communities until no improvement is possible
    old_modularity = None
    new_modularity = modularity(G, communities, resolution=resolution, weight=weight)
    while old_modularity is None or new_modularity > old_modularity:
        # Save modularity for comparison
        old_modularity = new_modularity
        # Find best pair to merge
        trial_communities = list(communities)
        to_merge = None
        for i, u in enumerate(communities):
            for j, v in enumerate(communities):
                # Skip i==j and empty communities
                if j <= i or len(u) == 0 or len(v) == 0:
                    continue
                # Merge communities u and v
                trial_communities[j] = u | v
                trial_communities[i] = frozenset([])
                trial_modularity = modularity(
                    G, trial_communities, resolution=resolution, weight=weight
                )
                if trial_modularity >= new_modularity:
                    # Check if strictly better or tie
                    if trial_modularity > new_modularity:
                        # Found new best, save modularity and group indexes
                        new_modularity = trial_modularity
                        to_merge = (i, j, new_modularity - old_modularity)
                    elif to_merge and min(i, j) < min(to_merge[0], to_merge[1]):
                        # Break ties by choosing pair with lowest min id
                        new_modularity = trial_modularity
                        to_merge = (i, j, new_modularity - old_modularity)
                # Un-merge
                trial_communities[i] = u
                trial_communities[j] = v
        if to_merge is not None:
            # If the best merge improves modularity, use it
            merges.append(to_merge)
            i, j, dq = to_merge
            u, v = communities[i], communities[j]
            communities[j] = u | v
            communities[i] = frozenset([])
    # Remove empty communities and sort
    return sorted((c for c in communities if len(c) > 0), key=len, reverse=True)


# old name
_naive_greedy_modularity_communities = naive_greedy_modularity_communities

        ```
    ###Assistant : Find communities in G using greedy modularity maximization.

    This implementation is O(n^4), much slower than alternatives, but it is
    provided as an easy-to-understand reference implementation.

    Greedy modularity maximization begins with each node in its own community
    and joins the pair of communities that most increases modularity until no
    such pair exists.

    This function maximizes the generalized modularity, where `resolution`
    is the resolution parameter, often expressed as $\gamma$.
    See :func:`~networkx.algorithms.community.quality.modularity`.

    Parameters
    ----------
    G : NetworkX graph

    resolution : float (default=1)
        If resolution is less than 1, modularity favors larger communities.
        Greater than 1 favors smaller communities.

    weight : string or None, optional (default=None)
        The name of an edge attribute that holds the numerical value used
        as a weight.  If None, then each edge has weight 1.
        The degree is the sum of the edge weights adjacent to the node.

    Returns
    -------
    list
        A list of sets of nodes, one for each community.
        Sorted by length with largest communities first.

    Examples
    --------
    >>> from networkx.algorithms.community import \
    ... naive_greedy_modularity_communities
    >>> G = nx.karate_club_graph()
    >>> c = naive_greedy_modularity_communities(G)
    >>> sorted(c[0])
    [8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]

    See Also
    --------
    greedy_modularity_communities
    modularity
    
    "
706,"def invoke(self) -> Generator[PowerShell, None, None]:
        
        logger = copy(self.log)
        logger.setLevel(self._logging_level)
        local_context = self._conn is None
        if local_context:
            self.__enter__()
        try:
            assert self._conn is not None
            ps = PowerShell(self._conn)
            yield ps
            ps.begin_invoke()

            streams = [
                ps.output,
                ps.streams.debug,
                ps.streams.error,
                ps.streams.information,
                ps.streams.progress,
                ps.streams.verbose,
                ps.streams.warning,
            ]
            offsets = [0 for _ in streams]

            # We're using polling to make sure output and streams are
            # handled while the process is running.
            while ps.state == PSInvocationState.RUNNING:
                ps.poll_invoke(timeout=self._operation_timeout)

                for i, stream in enumerate(streams):
                    offset = offsets[i]
                    while len(stream) > offset:
                        record = stream[offset]

                        # Records received on the output stream during job
                        # status polling are handled via an optional callback,
                        # while the other streams are simply logged.
                        if stream is ps.output:
                            if self._on_output_callback is not None:
                                self._on_output_callback(record)
                        else:
                            self._log_record(logger.log, record)
                        offset += 1
                    offsets[i] = offset

            # For good measure, we'll make sure the process has
            # stopped running in any case.
            ps.end_invoke()

            self.log.info(""Invocation state: %s"", str(PSInvocationState(ps.state)))
            if ps.streams.error:
                raise AirflowException(""Process had one or more errors"")
        finally:
            if local_context:
                self.__exit__(None, None, None)
","
        Context manager that yields a PowerShell object to which commands can be
        added. Upon exit, the commands will be invoked.
        ",20,167,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def invoke(self) -> Generator[PowerShell, None, None]:
        
        logger = copy(self.log)
        logger.setLevel(self._logging_level)
        local_context = self._conn is None
        if local_context:
            self.__enter__()
        try:
            assert self._conn is not None
            ps = PowerShell(self._conn)
            yield ps
            ps.begin_invoke()

            streams = [
                ps.output,
                ps.streams.debug,
                ps.streams.error,
                ps.streams.information,
                ps.streams.progress,
                ps.streams.verbose,
                ps.streams.warning,
            ]
            offsets = [0 for _ in streams]

            # We're using polling to make sure output and streams are
            # handled while the process is running.
            while ps.state == PSInvocationState.RUNNING:
                ps.poll_invoke(timeout=self._operation_timeout)

                for i, stream in enumerate(streams):
                    offset = offsets[i]
                    while len(stream) > offset:
                        record = stream[offset]

                        # Records received on the output stream during job
                        # status polling are handled via an optional callback,
                        # while the other streams are simply logged.
                        if stream is ps.output:
                            if self._on_output_callback is not None:
                                self._on_output_callback(record)
                        else:
                            self._log_record(logger.log, record)
                        offset += 1
                    offsets[i] = offset

            # For good measure, we'll make sure the process has
            # stopped running in any case.
            ps.end_invoke()

            self.log.info(""Invocation state: %s"", str(PSInvocationState(ps.state)))
            if ps.streams.error:
                raise AirflowException(""Process had one or more errors"")
        finally:
            if local_context:
                self.__exit__(None, None, None)

        ```
    ###Assistant : 
        Context manager that yields a PowerShell object to which commands can be
        added. Upon exit, the commands will be invoked.
        
    "
707,"def util_call_before_task_publish_handler(self, headers_to_use, body_to_use):
        
        self.assertEqual(PaperlessTask.objects.all().count(), 0)

        before_task_publish_handler(headers=headers_to_use, body=body_to_use)

        self.assertEqual(PaperlessTask.objects.all().count(), 1)
","
        Simple utility to call the pre-run handle and ensure it created a single task
        instance
        ",15,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def util_call_before_task_publish_handler(self, headers_to_use, body_to_use):
        
        self.assertEqual(PaperlessTask.objects.all().count(), 0)

        before_task_publish_handler(headers=headers_to_use, body=body_to_use)

        self.assertEqual(PaperlessTask.objects.all().count(), 1)

        ```
    ###Assistant : 
        Simple utility to call the pre-run handle and ensure it created a single task
        instance
        
    "
708,"def test_form(self):
        
        form = self.EventPageForm(instance=self.event_page)

        self.assertIn(""comments"", form.formsets)

        comments_formset = form.formsets[""comments""]
        self.assertEqual(len(comments_formset.forms), 1)
        self.assertEqual(comments_formset.forms[0].user, self.commenting_user)

        replies_formset = comments_formset.forms[0].formsets[""replies""]
        self.assertEqual(len(replies_formset.forms), 2)
        self.assertEqual(replies_formset.forms[0].user, self.commenting_user)
","
        Check that the form has the comments/replies formsets, and that the
        user has been set on each CommentForm/CommentReplyForm subclass
        ",19,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_form(self):
        
        form = self.EventPageForm(instance=self.event_page)

        self.assertIn(""comments"", form.formsets)

        comments_formset = form.formsets[""comments""]
        self.assertEqual(len(comments_formset.forms), 1)
        self.assertEqual(comments_formset.forms[0].user, self.commenting_user)

        replies_formset = comments_formset.forms[0].formsets[""replies""]
        self.assertEqual(len(replies_formset.forms), 2)
        self.assertEqual(replies_formset.forms[0].user, self.commenting_user)

        ```
    ###Assistant : 
        Check that the form has the comments/replies formsets, and that the
        user has been set on each CommentForm/CommentReplyForm subclass
        
    "
709,"def _doc(self, doc_type, default, lang=""eng""):
        
        corpus = self._wordnet_corpus_reader
        if lang not in corpus.langs():
            return None
        elif lang == ""eng"":
            return default
        else:
            corpus._load_lang_data(lang)
            of = corpus.ss2of(self)
            i = corpus.lg_attrs.index(doc_type)
            if of in corpus._lang_data[lang][i]:
                return corpus._lang_data[lang][i][of]
            else:
                return None
",Helper method for Synset.definition and Synset.examples,6,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _doc(self, doc_type, default, lang=""eng""):
        
        corpus = self._wordnet_corpus_reader
        if lang not in corpus.langs():
            return None
        elif lang == ""eng"":
            return default
        else:
            corpus._load_lang_data(lang)
            of = corpus.ss2of(self)
            i = corpus.lg_attrs.index(doc_type)
            if of in corpus._lang_data[lang][i]:
                return corpus._lang_data[lang][i][of]
            else:
                return None

        ```
    ###Assistant : Helper method for Synset.definition and Synset.examples
    "
710,"def map(self, mapper):
        
        new_categories = self.categories.map(mapper)
        try:
            return self.from_codes(
                self._codes.copy(), categories=new_categories, ordered=self.ordered
            )
        except ValueError:
            # NA values are represented in self._codes with -1
            # np.take causes NA values to take final element in new_categories
            if np.any(self._codes == -1):
                new_categories = new_categories.insert(len(new_categories), np.nan)
            return np.take(new_categories, self._codes)

    __eq__ = _cat_compare_op(operator.eq)
    __ne__ = _cat_compare_op(operator.ne)
    __lt__ = _cat_compare_op(operator.lt)
    __gt__ = _cat_compare_op(operator.gt)
    __le__ = _cat_compare_op(operator.le)
    __ge__ = _cat_compare_op(operator.ge)

    # -------------------------------------------------------------
    # Validators; ideally these can be de-duplicated
","
        Map categories using an input mapping or function.

        Maps the categories to new categories. If the mapping correspondence is
        one-to-one the result is a :class:`~pandas.Categorical` which has the
        same order property as the original, otherwise a :class:`~pandas.Index`
        is returned. NaN values are unaffected.

        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
        mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
        will be returned.

        Parameters
        ----------
        mapper : function, dict, or Series
            Mapping correspondence.

        Returns
        -------
        pandas.Categorical or pandas.Index
            Mapped categorical.

        See Also
        --------
        CategoricalIndex.map : Apply a mapping correspondence on a
            :class:`~pandas.CategoricalIndex`.
        Index.map : Apply a mapping correspondence on an
            :class:`~pandas.Index`.
        Series.map : Apply a mapping correspondence on a
            :class:`~pandas.Series`.
        Series.apply : Apply more complex functions on a
            :class:`~pandas.Series`.

        Examples
        --------
        >>> cat = pd.Categorical(['a', 'b', 'c'])
        >>> cat
        ['a', 'b', 'c']
        Categories (3, object): ['a', 'b', 'c']
        >>> cat.map(lambda x: x.upper())
        ['A', 'B', 'C']
        Categories (3, object): ['A', 'B', 'C']
        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'third'})
        ['first', 'second', 'third']
        Categories (3, object): ['first', 'second', 'third']

        If the mapping is one-to-one the ordering of the categories is
        preserved:

        >>> cat = pd.Categorical(['a', 'b', 'c'], ordered=True)
        >>> cat
        ['a', 'b', 'c']
        Categories (3, object): ['a' < 'b' < 'c']
        >>> cat.map({'a': 3, 'b': 2, 'c': 1})
        [3, 2, 1]
        Categories (3, int64): [3 < 2 < 1]

        If the mapping is not one-to-one an :class:`~pandas.Index` is returned:

        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'first'})
        Index(['first', 'second', 'first'], dtype='object')

        If a `dict` is used, all unmapped categories are mapped to `NaN` and
        the result is an :class:`~pandas.Index`:

        >>> cat.map({'a': 'first', 'b': 'second'})
        Index(['first', 'second', nan], dtype='object')
        ",269,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def map(self, mapper):
        
        new_categories = self.categories.map(mapper)
        try:
            return self.from_codes(
                self._codes.copy(), categories=new_categories, ordered=self.ordered
            )
        except ValueError:
            # NA values are represented in self._codes with -1
            # np.take causes NA values to take final element in new_categories
            if np.any(self._codes == -1):
                new_categories = new_categories.insert(len(new_categories), np.nan)
            return np.take(new_categories, self._codes)

    __eq__ = _cat_compare_op(operator.eq)
    __ne__ = _cat_compare_op(operator.ne)
    __lt__ = _cat_compare_op(operator.lt)
    __gt__ = _cat_compare_op(operator.gt)
    __le__ = _cat_compare_op(operator.le)
    __ge__ = _cat_compare_op(operator.ge)

    # -------------------------------------------------------------
    # Validators; ideally these can be de-duplicated

        ```
    ###Assistant : 
        Map categories using an input mapping or function.

        Maps the categories to new categories. If the mapping correspondence is
        one-to-one the result is a :class:`~pandas.Categorical` which has the
        same order property as the original, otherwise a :class:`~pandas.Index`
        is returned. NaN values are unaffected.

        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
        mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
        will be returned.

        Parameters
        ----------
        mapper : function, dict, or Series
            Mapping correspondence.

        Returns
        -------
        pandas.Categorical or pandas.Index
            Mapped categorical.

        See Also
        --------
        CategoricalIndex.map : Apply a mapping correspondence on a
            :class:`~pandas.CategoricalIndex`.
        Index.map : Apply a mapping correspondence on an
            :class:`~pandas.Index`.
        Series.map : Apply a mapping correspondence on a
            :class:`~pandas.Series`.
        Series.apply : Apply more complex functions on a
            :class:`~pandas.Series`.

        Examples
        --------
        >>> cat = pd.Categorical(['a', 'b', 'c'])
        >>> cat
        ['a', 'b', 'c']
        Categories (3, object): ['a', 'b', 'c']
        >>> cat.map(lambda x: x.upper())
        ['A', 'B', 'C']
        Categories (3, object): ['A', 'B', 'C']
        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'third'})
        ['first', 'second', 'third']
        Categories (3, object): ['first', 'second', 'third']

        If the mapping is one-to-one the ordering of the categories is
        preserved:

        >>> cat = pd.Categorical(['a', 'b', 'c'], ordered=True)
        >>> cat
        ['a', 'b', 'c']
        Categories (3, object): ['a' < 'b' < 'c']
        >>> cat.map({'a': 3, 'b': 2, 'c': 1})
        [3, 2, 1]
        Categories (3, int64): [3 < 2 < 1]

        If the mapping is not one-to-one an :class:`~pandas.Index` is returned:

        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'first'})
        Index(['first', 'second', 'first'], dtype='object')

        If a `dict` is used, all unmapped categories are mapped to `NaN` and
        the result is an :class:`~pandas.Index`:

        >>> cat.map({'a': 'first', 'b': 'second'})
        Index(['first', 'second', nan], dtype='object')
        
    "
711,"def to_pickle_distributed(cls, qc, **kwargs):
        
        if not (
            isinstance(kwargs[""filepath_or_buffer""], str)
            and ""*"" in kwargs[""filepath_or_buffer""]
        ) or not isinstance(qc, PandasQueryCompiler):
            warnings.warn(""Defaulting to Modin core implementation"")
            return PandasOnUnidistIO.to_pickle(qc, **kwargs)
","
        When `*` in the filename all partitions are written to their own separate file.

        The filenames is determined as follows:
        - if `*` in the filename then it will be replaced by the increasing sequence 0, 1, 2, …
        - if `*` is not the filename, then will be used default implementation.

        Examples #1: 4 partitions and input filename=""partition*.pkl.gz"", then filenames will be:
        `partition0.pkl.gz`, `partition1.pkl.gz`, `partition2.pkl.gz`, `partition3.pkl.gz`.

        Parameters
        ----------
        qc : BaseQueryCompiler
            The query compiler of the Modin dataframe that we want
            to run ``to_pickle_distributed`` on.
        **kwargs : dict
            Parameters for ``pandas.to_pickle(**kwargs)``.
        ",92,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_pickle_distributed(cls, qc, **kwargs):
        
        if not (
            isinstance(kwargs[""filepath_or_buffer""], str)
            and ""*"" in kwargs[""filepath_or_buffer""]
        ) or not isinstance(qc, PandasQueryCompiler):
            warnings.warn(""Defaulting to Modin core implementation"")
            return PandasOnUnidistIO.to_pickle(qc, **kwargs)

        ```
    ###Assistant : 
        When `*` in the filename all partitions are written to their own separate file.

        The filenames is determined as follows:
        - if `*` in the filename then it will be replaced by the increasing sequence 0, 1, 2, …
        - if `*` is not the filename, then will be used default implementation.

        Examples #1: 4 partitions and input filename=""partition*.pkl.gz"", then filenames will be:
        `partition0.pkl.gz`, `partition1.pkl.gz`, `partition2.pkl.gz`, `partition3.pkl.gz`.

        Parameters
        ----------
        qc : BaseQueryCompiler
            The query compiler of the Modin dataframe that we want
            to run ``to_pickle_distributed`` on.
        **kwargs : dict
            Parameters for ``pandas.to_pickle(**kwargs)``.
        
    "
712,"def project_state(self, nodes=None, at_end=True):
        
        return self.graph.make_state(
            nodes=nodes, at_end=at_end, real_apps=self.unmigrated_apps
        )
","
        Return a ProjectState object representing the most recent state
        that the loaded migrations represent.

        See graph.make_state() for the meaning of ""nodes"" and ""at_end"".
        ",23,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def project_state(self, nodes=None, at_end=True):
        
        return self.graph.make_state(
            nodes=nodes, at_end=at_end, real_apps=self.unmigrated_apps
        )

        ```
    ###Assistant : 
        Return a ProjectState object representing the most recent state
        that the loaded migrations represent.

        See graph.make_state() for the meaning of ""nodes"" and ""at_end"".
        
    "
713,"def center(self, frequency=1000):
        
        equal_energy_fr = self.__class__(name='equal_energy', frequency=self.frequency.copy(), raw=self.raw.copy())
        equal_energy_fr.interpolate()
        interpolator = InterpolatedUnivariateSpline(np.log10(equal_energy_fr.frequency), equal_energy_fr.raw, k=1)
        if type(frequency) in [list, np.ndarray] and len(frequency) > 1:
            # Use the average of the gain values between the given frequencies as the difference to be subtracted
            diff = np.mean(equal_energy_fr.raw[np.logical_and(
                equal_energy_fr.frequency >= frequency[0],
                equal_energy_fr.frequency <= frequency[1]
            )])
        else:
            if type(frequency) in [list, np.ndarray]:
                # List or array with only one element
                frequency = frequency[0]
            # Use the gain value at the given frequency as the difference to be subtracted
            diff = interpolator(np.log10(frequency))

        self.raw -= diff
        if len(self.smoothed):
            self.smoothed -= diff
        if len(self.error):
            self.error += diff
        if len(self.error_smoothed):
            self.error_smoothed += diff

        # Everything but raw, smoothed, errors and target is affected by centering, reset them
        self.reset(raw=False, smoothed=False, error=False, error_smoothed=False, target=False)

        return -diff
","Removed bias from frequency response.

        Args:
            frequency: Frequency which is set to 0 dB. If this is a list with two values then an average between the two
                       frequencies is set to 0 dB.

        Returns:
            Gain shifted
        ",37,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def center(self, frequency=1000):
        
        equal_energy_fr = self.__class__(name='equal_energy', frequency=self.frequency.copy(), raw=self.raw.copy())
        equal_energy_fr.interpolate()
        interpolator = InterpolatedUnivariateSpline(np.log10(equal_energy_fr.frequency), equal_energy_fr.raw, k=1)
        if type(frequency) in [list, np.ndarray] and len(frequency) > 1:
            # Use the average of the gain values between the given frequencies as the difference to be subtracted
            diff = np.mean(equal_energy_fr.raw[np.logical_and(
                equal_energy_fr.frequency >= frequency[0],
                equal_energy_fr.frequency <= frequency[1]
            )])
        else:
            if type(frequency) in [list, np.ndarray]:
                # List or array with only one element
                frequency = frequency[0]
            # Use the gain value at the given frequency as the difference to be subtracted
            diff = interpolator(np.log10(frequency))

        self.raw -= diff
        if len(self.smoothed):
            self.smoothed -= diff
        if len(self.error):
            self.error += diff
        if len(self.error_smoothed):
            self.error_smoothed += diff

        # Everything but raw, smoothed, errors and target is affected by centering, reset them
        self.reset(raw=False, smoothed=False, error=False, error_smoothed=False, target=False)

        return -diff

        ```
    ###Assistant : Removed bias from frequency response.

        Args:
            frequency: Frequency which is set to 0 dB. If this is a list with two values then an average between the two
                       frequencies is set to 0 dB.

        Returns:
            Gain shifted
        
    "
714,"def _i18n_cache_key_suffix(request, cache_key):
    
    if settings.USE_I18N:
        # first check if LocaleMiddleware or another middleware added
        # LANGUAGE_CODE to request, then fall back to the active language
        # which in turn can also fall back to settings.LANGUAGE_CODE
        cache_key += "".%s"" % getattr(request, ""LANGUAGE_CODE"", get_language())
    if settings.USE_TZ:
        cache_key += "".%s"" % get_current_timezone_name()
    return cache_key

","If necessary, add the current locale or time zone to the cache key.",13,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _i18n_cache_key_suffix(request, cache_key):
    
    if settings.USE_I18N:
        # first check if LocaleMiddleware or another middleware added
        # LANGUAGE_CODE to request, then fall back to the active language
        # which in turn can also fall back to settings.LANGUAGE_CODE
        cache_key += "".%s"" % getattr(request, ""LANGUAGE_CODE"", get_language())
    if settings.USE_TZ:
        cache_key += "".%s"" % get_current_timezone_name()
    return cache_key


        ```
    ###Assistant : If necessary, add the current locale or time zone to the cache key.
    "
715,"def test_pandas_arff_parser_strip_double_quotes(parser_func):
    
    pd = pytest.importorskip(""pandas"")

    arff_file = BytesIO(
        textwrap.dedent(
            ",Check that we properly strip double quotes from the data.,10,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pandas_arff_parser_strip_double_quotes(parser_func):
    
    pd = pytest.importorskip(""pandas"")

    arff_file = BytesIO(
        textwrap.dedent(
            
        ```
    ###Assistant : Check that we properly strip double quotes from the data.
    "
716,"def get_tokens_unprocessed(self, text=None, context=None):
        
        tokendefs = self._tokens
        if not context:
            ctx = LexerContext(text, 0)
            statetokens = tokendefs['root']
        else:
            ctx = context
            statetokens = tokendefs[ctx.stack[-1]]
            text = ctx.text
        while 1:
            for rexmatch, action, new_state in statetokens:
                m = rexmatch(text, ctx.pos, ctx.end)
                if m:
                    if action is not None:
                        if type(action) is _TokenType:
                            yield ctx.pos, action, m.group()
                            ctx.pos = m.end()
                        else:
                            yield from action(self, m, ctx)
                            if not new_state:
                                # altered the state stack?
                                statetokens = tokendefs[ctx.stack[-1]]
                    # CAUTION: callback must set ctx.pos!
                    if new_state is not None:
                        # state transition
                        if isinstance(new_state, tuple):
                            for state in new_state:
                                if state == '#pop':
                                    if len(ctx.stack) > 1:
                                        ctx.stack.pop()
                                elif state == '#push':
                                    ctx.stack.append(ctx.stack[-1])
                                else:
                                    ctx.stack.append(state)
                        elif isinstance(new_state, int):
                            # see RegexLexer for why this check is made
                            if abs(new_state) >= len(ctx.stack):
                                del ctx.state[1:]
                            else:
                                del ctx.stack[new_state:]
                        elif new_state == '#push':
                            ctx.stack.append(ctx.stack[-1])
                        else:
                            assert False, ""wrong state def: %r"" % new_state
                        statetokens = tokendefs[ctx.stack[-1]]
                    break
            else:
                try:
                    if ctx.pos >= ctx.end:
                        break
                    if text[ctx.pos] == '\n':
                        # at EOL, reset state to ""root""
                        ctx.stack = ['root']
                        statetokens = tokendefs['root']
                        yield ctx.pos, Text, '\n'
                        ctx.pos += 1
                        continue
                    yield ctx.pos, Error, text[ctx.pos]
                    ctx.pos += 1
                except IndexError:
                    break

","
        Split ``text`` into (tokentype, text) pairs.
        If ``context`` is given, use this lexer context instead.
        ",15,193,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_tokens_unprocessed(self, text=None, context=None):
        
        tokendefs = self._tokens
        if not context:
            ctx = LexerContext(text, 0)
            statetokens = tokendefs['root']
        else:
            ctx = context
            statetokens = tokendefs[ctx.stack[-1]]
            text = ctx.text
        while 1:
            for rexmatch, action, new_state in statetokens:
                m = rexmatch(text, ctx.pos, ctx.end)
                if m:
                    if action is not None:
                        if type(action) is _TokenType:
                            yield ctx.pos, action, m.group()
                            ctx.pos = m.end()
                        else:
                            yield from action(self, m, ctx)
                            if not new_state:
                                # altered the state stack?
                                statetokens = tokendefs[ctx.stack[-1]]
                    # CAUTION: callback must set ctx.pos!
                    if new_state is not None:
                        # state transition
                        if isinstance(new_state, tuple):
                            for state in new_state:
                                if state == '#pop':
                                    if len(ctx.stack) > 1:
                                        ctx.stack.pop()
                                elif state == '#push':
                                    ctx.stack.append(ctx.stack[-1])
                                else:
                                    ctx.stack.append(state)
                        elif isinstance(new_state, int):
                            # see RegexLexer for why this check is made
                            if abs(new_state) >= len(ctx.stack):
                                del ctx.state[1:]
                            else:
                                del ctx.stack[new_state:]
                        elif new_state == '#push':
                            ctx.stack.append(ctx.stack[-1])
                        else:
                            assert False, ""wrong state def: %r"" % new_state
                        statetokens = tokendefs[ctx.stack[-1]]
                    break
            else:
                try:
                    if ctx.pos >= ctx.end:
                        break
                    if text[ctx.pos] == '\n':
                        # at EOL, reset state to ""root""
                        ctx.stack = ['root']
                        statetokens = tokendefs['root']
                        yield ctx.pos, Text, '\n'
                        ctx.pos += 1
                        continue
                    yield ctx.pos, Error, text[ctx.pos]
                    ctx.pos += 1
                except IndexError:
                    break


        ```
    ###Assistant : 
        Split ``text`` into (tokentype, text) pairs.
        If ``context`` is given, use this lexer context instead.
        
    "
717,"def get_policy_data_from_agent_data(agent_data, policy_map_fn):
    
    policy_data = {}
    for agent_id, data in agent_data.items():
        policy_id = policy_map_fn(agent_id)
        policy_data.setdefault(policy_id, {})
        policy_data[policy_id].setdefault(""agent_id"", [])

        if data[""obs""].ndim == 1:
            policy_data[policy_id][""agent_id""].append(agent_id)
        else:
            policy_data[policy_id][""agent_id""] += [agent_id] * len(data[""obs""])

        for k, v in data.items():
            policy_data[policy_id].setdefault(k, [])
            if v.ndim == 1:
                v = v[None]
            policy_data[policy_id][k].append(v)

    for policy_id in policy_data:
        policy_data[policy_id] = {
            k: np.concatenate(v) if k != ""agent_id"" else v
            for k, v in policy_data[policy_id].items()
        }

    return policy_data

","Utility function to get policy data from agent data and policy map function.

    It also keeps track of agent_id for each row so that we can retreive the agent
    level information after the forward pass.

    Returns:
        dict of module_id to module data
    ",42,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_policy_data_from_agent_data(agent_data, policy_map_fn):
    
    policy_data = {}
    for agent_id, data in agent_data.items():
        policy_id = policy_map_fn(agent_id)
        policy_data.setdefault(policy_id, {})
        policy_data[policy_id].setdefault(""agent_id"", [])

        if data[""obs""].ndim == 1:
            policy_data[policy_id][""agent_id""].append(agent_id)
        else:
            policy_data[policy_id][""agent_id""] += [agent_id] * len(data[""obs""])

        for k, v in data.items():
            policy_data[policy_id].setdefault(k, [])
            if v.ndim == 1:
                v = v[None]
            policy_data[policy_id][k].append(v)

    for policy_id in policy_data:
        policy_data[policy_id] = {
            k: np.concatenate(v) if k != ""agent_id"" else v
            for k, v in policy_data[policy_id].items()
        }

    return policy_data


        ```
    ###Assistant : Utility function to get policy data from agent data and policy map function.

    It also keeps track of agent_id for each row so that we can retreive the agent
    level information after the forward pass.

    Returns:
        dict of module_id to module data
    
    "
718,"def _update_dimensions(self) -> None:
        
        total_width = sum(column.width for column in self.columns)
        self.virtual_size = Size(
            total_width,
            len(self._y_offsets) + (self.header_height if self.show_header else 0),
        )
",Called to recalculate the virtual (scrollable) size.,7,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _update_dimensions(self) -> None:
        
        total_width = sum(column.width for column in self.columns)
        self.virtual_size = Size(
            total_width,
            len(self._y_offsets) + (self.header_height if self.show_header else 0),
        )

        ```
    ###Assistant : Called to recalculate the virtual (scrollable) size.
    "
719,"def _store(self, messages, response, *args, **kwargs):
        
        raise NotImplementedError(
            ""subclasses of BaseStorage must provide a _store() method""
        )
","
        Store a list of messages and return a list of any messages which could
        not be stored.

        One type of object must be able to be stored, ``Message``.

        **This method must be implemented by a subclass.**
        ",36,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _store(self, messages, response, *args, **kwargs):
        
        raise NotImplementedError(
            ""subclasses of BaseStorage must provide a _store() method""
        )

        ```
    ###Assistant : 
        Store a list of messages and return a list of any messages which could
        not be stored.

        One type of object must be able to be stored, ``Message``.

        **This method must be implemented by a subclass.**
        
    "
720,"def test_from_is_negative(self) -> None:
        

        channel = self.make_request(
            ""GET"",
            self.url + ""?from=-5"",
            access_token=self.admin_user_tok,
        )

        self.assertEqual(400, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.INVALID_PARAM, channel.json_body[""errcode""])
","
        Testing that a negative from parameter returns a 400
        ",9,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_from_is_negative(self) -> None:
        

        channel = self.make_request(
            ""GET"",
            self.url + ""?from=-5"",
            access_token=self.admin_user_tok,
        )

        self.assertEqual(400, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.INVALID_PARAM, channel.json_body[""errcode""])

        ```
    ###Assistant : 
        Testing that a negative from parameter returns a 400
        
    "
721,"async def test_focused_child_widget_no_inherit_empty_bindings_with_movement_bindings_on_screen() -> None:
    ","A focused child widget, that doesn't inherit bindings and sets BINDINGS empty, with movement bindings in the screen, should trigger screen actions.",22,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_focused_child_widget_no_inherit_empty_bindings_with_movement_bindings_on_screen() -> None:
    
        ```
    ###Assistant : A focused child widget, that doesn't inherit bindings and sets BINDINGS empty, with movement bindings in the screen, should trigger screen actions.
    "
722,"def addtoken(self, type, value, context):
        
        # Map from token to label
        ilabel = self.classify(type, value, context)
        # Loop until the token is shifted; may raise exceptions
        while True:
            dfa, state, node = self.stack[-1]
            states, first = dfa
            arcs = states[state]
            # Look for a state with this label
            for i, newstate in arcs:
                t, v = self.grammar.labels[i]
                if ilabel == i:
                    # Look it up in the list of labels
                    assert t < 256
                    # Shift a token; we're done with it
                    self.shift(type, value, newstate, context)
                    # Pop while we are in an accept-only state
                    state = newstate
                    while states[state] == [(0, state)]:
                        self.pop()
                        if not self.stack:
                            # Done parsing!
                            return True
                        dfa, state, node = self.stack[-1]
                        states, first = dfa
                    # Done with this token
                    return False
                elif t >= 256:
                    # See if it's a symbol and if we're in its first set
                    itsdfa = self.grammar.dfas[t]
                    itsstates, itsfirst = itsdfa
                    if ilabel in itsfirst:
                        # Push a symbol
                        self.push(t, self.grammar.dfas[t], newstate, context)
                        break # To continue the outer while loop
            else:
                if (0, state) in arcs:
                    # An accepting state, pop it and try something else
                    self.pop()
                    if not self.stack:
                        # Done parsing, but another token is input
                        raise ParseError(""too much input"",
                                         type, value, context)
                else:
                    # No success finding a transition
                    raise ParseError(""bad input"", type, value, context)
",Add a token; return True iff this is the end of the program.,13,220,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def addtoken(self, type, value, context):
        
        # Map from token to label
        ilabel = self.classify(type, value, context)
        # Loop until the token is shifted; may raise exceptions
        while True:
            dfa, state, node = self.stack[-1]
            states, first = dfa
            arcs = states[state]
            # Look for a state with this label
            for i, newstate in arcs:
                t, v = self.grammar.labels[i]
                if ilabel == i:
                    # Look it up in the list of labels
                    assert t < 256
                    # Shift a token; we're done with it
                    self.shift(type, value, newstate, context)
                    # Pop while we are in an accept-only state
                    state = newstate
                    while states[state] == [(0, state)]:
                        self.pop()
                        if not self.stack:
                            # Done parsing!
                            return True
                        dfa, state, node = self.stack[-1]
                        states, first = dfa
                    # Done with this token
                    return False
                elif t >= 256:
                    # See if it's a symbol and if we're in its first set
                    itsdfa = self.grammar.dfas[t]
                    itsstates, itsfirst = itsdfa
                    if ilabel in itsfirst:
                        # Push a symbol
                        self.push(t, self.grammar.dfas[t], newstate, context)
                        break # To continue the outer while loop
            else:
                if (0, state) in arcs:
                    # An accepting state, pop it and try something else
                    self.pop()
                    if not self.stack:
                        # Done parsing, but another token is input
                        raise ParseError(""too much input"",
                                         type, value, context)
                else:
                    # No success finding a transition
                    raise ParseError(""bad input"", type, value, context)

        ```
    ###Assistant : Add a token; return True iff this is the end of the program.
    "
723,"def import_local_settings():
    
    try:
        import airflow_local_settings

        if hasattr(airflow_local_settings, ""__all__""):
            for i in airflow_local_settings.__all__:
                globals()[i] = getattr(airflow_local_settings, i)
        else:
            for k, v in airflow_local_settings.__dict__.items():
                if not k.startswith(""__""):
                    globals()[k] = v

        # TODO: Remove once deprecated
        if ""policy"" in globals() and ""task_policy"" not in globals():
            warnings.warn(
                ""Using `policy` in airflow_local_settings.py is deprecated. ""
                ""Please rename your `policy` to `task_policy`."",
                DeprecationWarning,
                stacklevel=2,
            )
            globals()[""task_policy""] = globals()[""policy""]
            del globals()[""policy""]

        if not hasattr(task_instance_mutation_hook, 'is_noop'):
            task_instance_mutation_hook.is_noop = False

        log.info(""Loaded airflow_local_settings from %s ."", airflow_local_settings.__file__)
    except ModuleNotFoundError as e:
        if e.name == ""airflow_local_settings"":
            log.debug(""No airflow_local_settings to import."", exc_info=True)
        else:
            log.critical(
                ""Failed to import airflow_local_settings due to a transitive module not found error."",
                exc_info=True,
            )
            raise
    except ImportError:
        log.critical(""Failed to import airflow_local_settings."", exc_info=True)
        raise

",Import airflow_local_settings.py files to allow overriding any configs in settings.py file,11,115,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def import_local_settings():
    
    try:
        import airflow_local_settings

        if hasattr(airflow_local_settings, ""__all__""):
            for i in airflow_local_settings.__all__:
                globals()[i] = getattr(airflow_local_settings, i)
        else:
            for k, v in airflow_local_settings.__dict__.items():
                if not k.startswith(""__""):
                    globals()[k] = v

        # TODO: Remove once deprecated
        if ""policy"" in globals() and ""task_policy"" not in globals():
            warnings.warn(
                ""Using `policy` in airflow_local_settings.py is deprecated. ""
                ""Please rename your `policy` to `task_policy`."",
                DeprecationWarning,
                stacklevel=2,
            )
            globals()[""task_policy""] = globals()[""policy""]
            del globals()[""policy""]

        if not hasattr(task_instance_mutation_hook, 'is_noop'):
            task_instance_mutation_hook.is_noop = False

        log.info(""Loaded airflow_local_settings from %s ."", airflow_local_settings.__file__)
    except ModuleNotFoundError as e:
        if e.name == ""airflow_local_settings"":
            log.debug(""No airflow_local_settings to import."", exc_info=True)
        else:
            log.critical(
                ""Failed to import airflow_local_settings due to a transitive module not found error."",
                exc_info=True,
            )
            raise
    except ImportError:
        log.critical(""Failed to import airflow_local_settings."", exc_info=True)
        raise


        ```
    ###Assistant : Import airflow_local_settings.py files to allow overriding any configs in settings.py file
    "
724,"def unpolarify(eq, subs=None, exponents_only=False):
    
    if isinstance(eq, bool):
        return eq

    eq = sympify(eq)
    if subs is not None:
        return unpolarify(eq.subs(subs))
    changed = True
    pause = False
    if exponents_only:
        pause = True
    while changed:
        changed = False
        res = _unpolarify(eq, exponents_only, pause)
        if res != eq:
            changed = True
            eq = res
        if isinstance(res, bool):
            return res
    # Finally, replacing Exp(0) by 1 is always correct.
    # So is polar_lift(0) -> 0.
    return res.subs({exp_polar(0): 1, polar_lift(0): 0})
","
    If `p` denotes the projection from the Riemann surface of the logarithm to
    the complex line, return a simplified version `eq'` of `eq` such that
    `p(eq') = p(eq)`.
    Also apply the substitution subs in the end. (This is a convenience, since
    ``unpolarify``, in a certain sense, undoes :func:`polarify`.)

    Examples
    ========

    >>> from sympy import unpolarify, polar_lift, sin, I
    >>> unpolarify(polar_lift(I + 2))
    2 + I
    >>> unpolarify(sin(polar_lift(I + 7)))
    sin(7 + I)
    ",72,75,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unpolarify(eq, subs=None, exponents_only=False):
    
    if isinstance(eq, bool):
        return eq

    eq = sympify(eq)
    if subs is not None:
        return unpolarify(eq.subs(subs))
    changed = True
    pause = False
    if exponents_only:
        pause = True
    while changed:
        changed = False
        res = _unpolarify(eq, exponents_only, pause)
        if res != eq:
            changed = True
            eq = res
        if isinstance(res, bool):
            return res
    # Finally, replacing Exp(0) by 1 is always correct.
    # So is polar_lift(0) -> 0.
    return res.subs({exp_polar(0): 1, polar_lift(0): 0})

        ```
    ###Assistant : 
    If `p` denotes the projection from the Riemann surface of the logarithm to
    the complex line, return a simplified version `eq'` of `eq` such that
    `p(eq') = p(eq)`.
    Also apply the substitution subs in the end. (This is a convenience, since
    ``unpolarify``, in a certain sense, undoes :func:`polarify`.)

    Examples
    ========

    >>> from sympy import unpolarify, polar_lift, sin, I
    >>> unpolarify(polar_lift(I + 2))
    2 + I
    >>> unpolarify(sin(polar_lift(I + 7)))
    sin(7 + I)
    
    "
725,"def check_and_raise_error(self) -> None:
        
        for thread in self._threads:
            thread.check_and_raise_error()
"," Check all threads for errors

        Exposed for :mod:`~plugins.extract.pipeline` to check plugin's threads for errors
        ",14,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_and_raise_error(self) -> None:
        
        for thread in self._threads:
            thread.check_and_raise_error()

        ```
    ###Assistant :  Check all threads for errors

        Exposed for :mod:`~plugins.extract.pipeline` to check plugin's threads for errors
        
    "
726,"def recorder_or_dbworker(self) -> bool:
        
        thread_name = threading.current_thread().name
        return bool(
            thread_name == ""Recorder"" or thread_name.startswith(DB_WORKER_PREFIX)
        )
",Check if the thread is a recorder or dbworker thread.,10,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def recorder_or_dbworker(self) -> bool:
        
        thread_name = threading.current_thread().name
        return bool(
            thread_name == ""Recorder"" or thread_name.startswith(DB_WORKER_PREFIX)
        )

        ```
    ###Assistant : Check if the thread is a recorder or dbworker thread.
    "
727,"def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
    
    mapping = []
    for old_item in old_list:
        new_item = old_item.replace('in_layers.0', 'norm1')
        new_item = new_item.replace('in_layers.2', 'conv1')

        new_item = new_item.replace('out_layers.0', 'norm2')
        new_item = new_item.replace('out_layers.3', 'conv2')

        new_item = new_item.replace('emb_layers.1', 'time_emb_proj')
        new_item = new_item.replace('skip_connection', 'conv_shortcut')

        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({'old': old_item, 'new': new_item})

    return mapping

","
    Updates paths inside resnets to the new naming scheme (local renaming)
    ",11,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
    
    mapping = []
    for old_item in old_list:
        new_item = old_item.replace('in_layers.0', 'norm1')
        new_item = new_item.replace('in_layers.2', 'conv1')

        new_item = new_item.replace('out_layers.0', 'norm2')
        new_item = new_item.replace('out_layers.3', 'conv2')

        new_item = new_item.replace('emb_layers.1', 'time_emb_proj')
        new_item = new_item.replace('skip_connection', 'conv_shortcut')

        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({'old': old_item, 'new': new_item})

    return mapping


        ```
    ###Assistant : 
    Updates paths inside resnets to the new naming scheme (local renaming)
    
    "
728,"def _assert_splits_match(nested_splits_lists):
    
    error_msg = (
        ""Inputs must have identical ragged splits. ""
        f""Input received: {nested_splits_lists}""
    )
    for splits_list in nested_splits_lists:
        if len(splits_list) != len(nested_splits_lists[0]):
            raise ValueError(error_msg)
    return [
        tf.debugging.assert_equal(s1, s2, message=error_msg)
        for splits_list in nested_splits_lists[1:]
        for (s1, s2) in zip(nested_splits_lists[0], splits_list)
    ]

","Checks that the given splits lists are identical.

    Performs static tests to ensure that the given splits lists are identical,
    and returns a list of control dependency op tensors that check that they are
    fully identical.

    Args:
      nested_splits_lists: A list of nested_splits_lists, where each split_list
        is a list of `splits` tensors from a `RaggedTensor`, ordered from
        outermost ragged dimension to innermost ragged dimension.

    Returns:
      A list of control dependency op tensors.
    Raises:
      ValueError: If the splits are not identical.
    ",79,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _assert_splits_match(nested_splits_lists):
    
    error_msg = (
        ""Inputs must have identical ragged splits. ""
        f""Input received: {nested_splits_lists}""
    )
    for splits_list in nested_splits_lists:
        if len(splits_list) != len(nested_splits_lists[0]):
            raise ValueError(error_msg)
    return [
        tf.debugging.assert_equal(s1, s2, message=error_msg)
        for splits_list in nested_splits_lists[1:]
        for (s1, s2) in zip(nested_splits_lists[0], splits_list)
    ]


        ```
    ###Assistant : Checks that the given splits lists are identical.

    Performs static tests to ensure that the given splits lists are identical,
    and returns a list of control dependency op tensors that check that they are
    fully identical.

    Args:
      nested_splits_lists: A list of nested_splits_lists, where each split_list
        is a list of `splits` tensors from a `RaggedTensor`, ordered from
        outermost ragged dimension to innermost ragged dimension.

    Returns:
      A list of control dependency op tensors.
    Raises:
      ValueError: If the splits are not identical.
    
    "
729,"def _iteration_limit_callback(self, *args) -> None:
        
        try:
            limit = self.vars[""display_iterations""].get()
        except tk.TclError:
            # Don't update when there is no value in the variable
            return
        logger.debug(""Updating graph iteration limit: (new_value: %s, args: %s)"",
                     limit, args)
        for graph in self.subnotebook.children.values():
            graph.calcs.set_iterations_limit(limit)
"," Limit the amount of data displayed in the live graph on a iteration slider
        variable change. ",16,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _iteration_limit_callback(self, *args) -> None:
        
        try:
            limit = self.vars[""display_iterations""].get()
        except tk.TclError:
            # Don't update when there is no value in the variable
            return
        logger.debug(""Updating graph iteration limit: (new_value: %s, args: %s)"",
                     limit, args)
        for graph in self.subnotebook.children.values():
            graph.calcs.set_iterations_limit(limit)

        ```
    ###Assistant :  Limit the amount of data displayed in the live graph on a iteration slider
        variable change. 
    "
730,"def test_queued_dagruns_stops_creating_when_max_active_is_reached(self, dag_maker):
        
        with dag_maker(max_active_runs=10) as dag:
            EmptyOperator(task_id='mytask')

        session = settings.Session()
        self.scheduler_job = SchedulerJob(subdir=os.devnull)
        self.scheduler_job.executor = MockExecutor()
        self.scheduler_job.processor_agent = mock.MagicMock()

        self.scheduler_job.dagbag = dag_maker.dagbag

        session = settings.Session()
        orm_dag = session.query(DagModel).get(dag.dag_id)
        assert orm_dag is not None
        for _ in range(20):
            self.scheduler_job._create_dag_runs([orm_dag], session)
        drs = session.query(DagRun).all()
        assert len(drs) == 10

        for dr in drs:
            dr.state = State.RUNNING
            session.merge(dr)
        session.commit()
        assert session.query(DagRun.state).filter(DagRun.state == State.RUNNING).count() == 10
        for _ in range(20):
            self.scheduler_job._create_dag_runs([orm_dag], session)
        assert session.query(DagRun).count() == 10
        assert session.query(DagRun.state).filter(DagRun.state == State.RUNNING).count() == 10
        assert session.query(DagRun.state).filter(DagRun.state == State.QUEUED).count() == 0
        assert orm_dag.next_dagrun_create_after is None
",This tests that queued dagruns stops creating once max_active_runs is reached,11,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_queued_dagruns_stops_creating_when_max_active_is_reached(self, dag_maker):
        
        with dag_maker(max_active_runs=10) as dag:
            EmptyOperator(task_id='mytask')

        session = settings.Session()
        self.scheduler_job = SchedulerJob(subdir=os.devnull)
        self.scheduler_job.executor = MockExecutor()
        self.scheduler_job.processor_agent = mock.MagicMock()

        self.scheduler_job.dagbag = dag_maker.dagbag

        session = settings.Session()
        orm_dag = session.query(DagModel).get(dag.dag_id)
        assert orm_dag is not None
        for _ in range(20):
            self.scheduler_job._create_dag_runs([orm_dag], session)
        drs = session.query(DagRun).all()
        assert len(drs) == 10

        for dr in drs:
            dr.state = State.RUNNING
            session.merge(dr)
        session.commit()
        assert session.query(DagRun.state).filter(DagRun.state == State.RUNNING).count() == 10
        for _ in range(20):
            self.scheduler_job._create_dag_runs([orm_dag], session)
        assert session.query(DagRun).count() == 10
        assert session.query(DagRun.state).filter(DagRun.state == State.RUNNING).count() == 10
        assert session.query(DagRun.state).filter(DagRun.state == State.QUEUED).count() == 0
        assert orm_dag.next_dagrun_create_after is None

        ```
    ###Assistant : This tests that queued dagruns stops creating once max_active_runs is reached
    "
731,"def test_escape_sequence_resulting_in_multiple_keypresses(parser):
    
    events = list(parser.feed(""\x1b[2;4~""))
    assert len(events) == 2
    assert events[0].key == ""escape""
    assert events[1].key == ""shift+insert""
",Some sequences are interpreted as more than 1 keypress,9,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_escape_sequence_resulting_in_multiple_keypresses(parser):
    
    events = list(parser.feed(""\x1b[2;4~""))
    assert len(events) == 2
    assert events[0].key == ""escape""
    assert events[1].key == ""shift+insert""

        ```
    ###Assistant : Some sequences are interpreted as more than 1 keypress
    "
732,"def generate_altered_options(self):
        
        models_to_check = self.kept_model_keys.union(
            self.kept_proxy_keys,
            self.kept_unmanaged_keys,
            # unmanaged converted to managed
            self.old_unmanaged_keys & self.new_model_keys,
            # managed converted to unmanaged
            self.old_model_keys & self.new_unmanaged_keys,
        )

        for app_label, model_name in sorted(models_to_check):
            old_model_name = self.renamed_models.get(
                (app_label, model_name), model_name
            )
            old_model_state = self.from_state.models[app_label, old_model_name]
            new_model_state = self.to_state.models[app_label, model_name]
            old_options = {
                key: value
                for key, value in old_model_state.options.items()
                if key in AlterModelOptions.ALTER_OPTION_KEYS
            }
            new_options = {
                key: value
                for key, value in new_model_state.options.items()
                if key in AlterModelOptions.ALTER_OPTION_KEYS
            }
            if old_options != new_options:
                self.add_operation(
                    app_label,
                    operations.AlterModelOptions(
                        name=model_name,
                        options=new_options,
                    ),
                )
","
        Work out if any non-schema-affecting options have changed and make an
        operation to represent them in state changes (in case Python code in
        migrations needs them).
        ",26,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_altered_options(self):
        
        models_to_check = self.kept_model_keys.union(
            self.kept_proxy_keys,
            self.kept_unmanaged_keys,
            # unmanaged converted to managed
            self.old_unmanaged_keys & self.new_model_keys,
            # managed converted to unmanaged
            self.old_model_keys & self.new_unmanaged_keys,
        )

        for app_label, model_name in sorted(models_to_check):
            old_model_name = self.renamed_models.get(
                (app_label, model_name), model_name
            )
            old_model_state = self.from_state.models[app_label, old_model_name]
            new_model_state = self.to_state.models[app_label, model_name]
            old_options = {
                key: value
                for key, value in old_model_state.options.items()
                if key in AlterModelOptions.ALTER_OPTION_KEYS
            }
            new_options = {
                key: value
                for key, value in new_model_state.options.items()
                if key in AlterModelOptions.ALTER_OPTION_KEYS
            }
            if old_options != new_options:
                self.add_operation(
                    app_label,
                    operations.AlterModelOptions(
                        name=model_name,
                        options=new_options,
                    ),
                )

        ```
    ###Assistant : 
        Work out if any non-schema-affecting options have changed and make an
        operation to represent them in state changes (in case Python code in
        migrations needs them).
        
    "
733,"def _check_readonly_fields(self, obj):
        

        if obj.readonly_fields == ():
            return []
        elif not isinstance(obj.readonly_fields, (list, tuple)):
            return must_be(
                ""a list or tuple"", option=""readonly_fields"", obj=obj, id=""admin.E034""
            )
        else:
            return list(
                chain.from_iterable(
                    self._check_readonly_fields_item(
                        obj, field_name, ""readonly_fields[%d]"" % index
                    )
                    for index, field_name in enumerate(obj.readonly_fields)
                )
            )
",Check that readonly_fields refers to proper attribute or field.,9,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_readonly_fields(self, obj):
        

        if obj.readonly_fields == ():
            return []
        elif not isinstance(obj.readonly_fields, (list, tuple)):
            return must_be(
                ""a list or tuple"", option=""readonly_fields"", obj=obj, id=""admin.E034""
            )
        else:
            return list(
                chain.from_iterable(
                    self._check_readonly_fields_item(
                        obj, field_name, ""readonly_fields[%d]"" % index
                    )
                    for index, field_name in enumerate(obj.readonly_fields)
                )
            )

        ```
    ###Assistant : Check that readonly_fields refers to proper attribute or field.
    "
734,"def querystring(context, **kwargs):
    
    request = context[""request""]
    querydict = request.GET.copy()
    # Can't do querydict.update(kwargs), because QueryDict.update() appends to
    # the list of values, instead of replacing the values.
    for key, value in kwargs.items():
        if value is None:
            # Remove the key if the value is None
            querydict.pop(key, None)
        else:
            # Set the key otherwise
            querydict[key] = str(value)

    return ""?"" + querydict.urlencode()


@register.simple_tag(takes_context=True)","
    Print out the current querystring. Any keyword arguments to this template
    tag will be added to the querystring before it is printed out.

        <a href=""/page/{% querystring key='value' %}"">

    Will result in something like:

        <a href=""/page/?foo=bar&key=value"">
    ",35,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def querystring(context, **kwargs):
    
    request = context[""request""]
    querydict = request.GET.copy()
    # Can't do querydict.update(kwargs), because QueryDict.update() appends to
    # the list of values, instead of replacing the values.
    for key, value in kwargs.items():
        if value is None:
            # Remove the key if the value is None
            querydict.pop(key, None)
        else:
            # Set the key otherwise
            querydict[key] = str(value)

    return ""?"" + querydict.urlencode()


@register.simple_tag(takes_context=True)
        ```
    ###Assistant : 
    Print out the current querystring. Any keyword arguments to this template
    tag will be added to the querystring before it is printed out.

        <a href=""/page/{% querystring key='value' %}"">

    Will result in something like:

        <a href=""/page/?foo=bar&key=value"">
    
    "
735,"def _get_fingerprint_of_schema_without_irrelevant_keys(self) -> Text:
        
        graph_schema = self._execution_context.graph_schema
        schema_as_dict = graph_schema.as_dict()
        for node_name, node_dict in schema_as_dict[""nodes""].items():
            config_copy = copy.deepcopy(node_dict[""config""])
            config_copy.pop(EPOCHS, None)
            # ignore default values since they're filled in anyway later and can
            # end up in configs (or not) in mysterious ways
            defaults = graph_schema.nodes[node_name].uses.get_default_config()
            for key, default_value in defaults.items():
                if key in config_copy and config_copy[key] == default_value:
                    config_copy.pop(key)
            node_dict[""config""] = config_copy
            node_dict.pop(""eager"")
            node_dict.pop(""constructor_name"")
        return rasa.shared.utils.io.deep_container_fingerprint(schema_as_dict)
","Returns a fingerprint of the given schema with certain items removed.

        These items include specifications that do not influence actual training
        results such as ""eager"" mode. The only configuration (in your config) that is
        allowed to change is the number of `epochs`.

        Returns:
            fingerprint
        ",44,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_fingerprint_of_schema_without_irrelevant_keys(self) -> Text:
        
        graph_schema = self._execution_context.graph_schema
        schema_as_dict = graph_schema.as_dict()
        for node_name, node_dict in schema_as_dict[""nodes""].items():
            config_copy = copy.deepcopy(node_dict[""config""])
            config_copy.pop(EPOCHS, None)
            # ignore default values since they're filled in anyway later and can
            # end up in configs (or not) in mysterious ways
            defaults = graph_schema.nodes[node_name].uses.get_default_config()
            for key, default_value in defaults.items():
                if key in config_copy and config_copy[key] == default_value:
                    config_copy.pop(key)
            node_dict[""config""] = config_copy
            node_dict.pop(""eager"")
            node_dict.pop(""constructor_name"")
        return rasa.shared.utils.io.deep_container_fingerprint(schema_as_dict)

        ```
    ###Assistant : Returns a fingerprint of the given schema with certain items removed.

        These items include specifications that do not influence actual training
        results such as ""eager"" mode. The only configuration (in your config) that is
        allowed to change is the number of `epochs`.

        Returns:
            fingerprint
        
    "
736,"def get_gi_typelibs(module, version):
    
    module_info = GiModuleInfo(module, version)
    return module_info.collect_typelib_data()

","
    Return a tuple of (binaries, datas, hiddenimports) to be used by PyGObject related hooks. Searches for and adds
    dependencies recursively.

    :param module: GI module name, as passed to 'gi.require_version()'
    :param version: GI module version, as passed to 'gi.require_version()'
    ",38,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_gi_typelibs(module, version):
    
    module_info = GiModuleInfo(module, version)
    return module_info.collect_typelib_data()


        ```
    ###Assistant : 
    Return a tuple of (binaries, datas, hiddenimports) to be used by PyGObject related hooks. Searches for and adds
    dependencies recursively.

    :param module: GI module name, as passed to 'gi.require_version()'
    :param version: GI module version, as passed to 'gi.require_version()'
    
    "
737,"def _sync_dag_view_permissions(self, dag_id, access_control):
        

        dag_resource_name = permissions.resource_name_for_dag(dag_id)
","
        Set the access policy on the given DAG's ViewModel.

        :param dag_id: the ID of the DAG whose permissions should be updated
        :param access_control: a dict where each key is a rolename and
            each value is a set() of action names (e.g. {'can_read'})
        ",42,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _sync_dag_view_permissions(self, dag_id, access_control):
        

        dag_resource_name = permissions.resource_name_for_dag(dag_id)

        ```
    ###Assistant : 
        Set the access policy on the given DAG's ViewModel.

        :param dag_id: the ID of the DAG whose permissions should be updated
        :param access_control: a dict where each key is a rolename and
            each value is a set() of action names (e.g. {'can_read'})
        
    "
738,"def test_series_equal_datetime_values_mismatch(rtol):
    msg = 

    s1 = Series(pd.date_range(""2018-01-01"", periods=3, freq=""D""))
    s2 = Series(pd.date_range(""2019-02-02"", periods=3, freq=""D""))

    with pytest.raises(AssertionError, match=msg):
        tm.assert_series_equal(s1, s2, rtol=rtol)

","Series are different

Series values are different \\(100.0 %\\)
\\[index\\]: \\[0, 1, 2\\]
\\[left\\]:  \\[1514764800000000000, 1514851200000000000, 1514937600000000000\\]
\\[right\\]: \\[1549065600000000000, 1549152000000000000, 1549238400000000000\\]",21,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_series_equal_datetime_values_mismatch(rtol):
    msg = 

    s1 = Series(pd.date_range(""2018-01-01"", periods=3, freq=""D""))
    s2 = Series(pd.date_range(""2019-02-02"", periods=3, freq=""D""))

    with pytest.raises(AssertionError, match=msg):
        tm.assert_series_equal(s1, s2, rtol=rtol)


        ```
    ###Assistant : Series are different

Series values are different \\(100.0 %\\)
\\[index\\]: \\[0, 1, 2\\]
\\[left\\]:  \\[1514764800000000000, 1514851200000000000, 1514937600000000000\\]
\\[right\\]: \\[1549065600000000000, 1549152000000000000, 1549238400000000000\\]
    "
739,"def test_decrypt_pillar_invalid_renderer(salt_master, grains, pillar_homedir):
    
    opts = salt_master.config.copy()
    opts[""decrypt_pillar""] = [{""secrets:vault"": ""gpg""}]
    opts[""decrypt_pillar_default""] = ""foo""
    opts[""decrypt_pillar_renderers""] = [""foo"", ""bar""]
    pillar_obj = salt.pillar.Pillar(opts, grains, ""test"", ""base"")
    ret = pillar_obj.compile_pillar()
    expected = copy.deepcopy(GPG_PILLAR_ENCRYPTED)
    expected[""_errors""] = [
        ""Failed to decrypt pillar key 'secrets:vault': 'gpg' is not a valid decryption""
        "" renderer. Valid choices are: foo, bar""
    ]
    assert ret[""_errors""] == expected[""_errors""]
    assert ret[""secrets""][""vault""][""foo""] == expected[""secrets""][""vault""][""foo""]
    assert ret[""secrets""][""vault""][""bar""] == expected[""secrets""][""vault""][""bar""]
    assert ret[""secrets""][""vault""][""baz""] == expected[""secrets""][""vault""][""baz""]
    assert ret[""secrets""][""vault""][""qux""] == expected[""secrets""][""vault""][""qux""]

","
    Test decryption using a renderer which is not permitted. It should
    fail, leaving the encrypted keys intact, and add an error to the pillar
    dictionary.

        decrypt_pillar_default: foo
        decrypt_pillar_renderers:
          - foo
          - bar
        decrypt_pillar:
          - 'secrets:vault': gpg
    ",36,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_decrypt_pillar_invalid_renderer(salt_master, grains, pillar_homedir):
    
    opts = salt_master.config.copy()
    opts[""decrypt_pillar""] = [{""secrets:vault"": ""gpg""}]
    opts[""decrypt_pillar_default""] = ""foo""
    opts[""decrypt_pillar_renderers""] = [""foo"", ""bar""]
    pillar_obj = salt.pillar.Pillar(opts, grains, ""test"", ""base"")
    ret = pillar_obj.compile_pillar()
    expected = copy.deepcopy(GPG_PILLAR_ENCRYPTED)
    expected[""_errors""] = [
        ""Failed to decrypt pillar key 'secrets:vault': 'gpg' is not a valid decryption""
        "" renderer. Valid choices are: foo, bar""
    ]
    assert ret[""_errors""] == expected[""_errors""]
    assert ret[""secrets""][""vault""][""foo""] == expected[""secrets""][""vault""][""foo""]
    assert ret[""secrets""][""vault""][""bar""] == expected[""secrets""][""vault""][""bar""]
    assert ret[""secrets""][""vault""][""baz""] == expected[""secrets""][""vault""][""baz""]
    assert ret[""secrets""][""vault""][""qux""] == expected[""secrets""][""vault""][""qux""]


        ```
    ###Assistant : 
    Test decryption using a renderer which is not permitted. It should
    fail, leaving the encrypted keys intact, and add an error to the pillar
    dictionary.

        decrypt_pillar_default: foo
        decrypt_pillar_renderers:
          - foo
          - bar
        decrypt_pillar:
          - 'secrets:vault': gpg
    
    "
740,"def get_trial_name():
    
    warnings.warn(
        _deprecation_msg,
        DeprecationWarning,
    )
    _session = get_session()
    if _session:
        return _session.trial_name


@DeveloperAPI","Trial name for the corresponding trial.

    For function API use only.
    ",11,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_trial_name():
    
    warnings.warn(
        _deprecation_msg,
        DeprecationWarning,
    )
    _session = get_session()
    if _session:
        return _session.trial_name


@DeveloperAPI
        ```
    ###Assistant : Trial name for the corresponding trial.

    For function API use only.
    
    "
741,"def get_scheduler_lock(collection=None, scheduler=None):
    
    from dask import multiprocessing
    from dask.base import get_scheduler

    actual_get = get_scheduler(collections=[collection], scheduler=scheduler)

    if actual_get == multiprocessing.get:
        return multiprocessing.get_context().Manager().Lock()

    return SerializableLock()

","Get an instance of the appropriate lock for a certain situation based on
    scheduler used.",15,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_scheduler_lock(collection=None, scheduler=None):
    
    from dask import multiprocessing
    from dask.base import get_scheduler

    actual_get = get_scheduler(collections=[collection], scheduler=scheduler)

    if actual_get == multiprocessing.get:
        return multiprocessing.get_context().Manager().Lock()

    return SerializableLock()


        ```
    ###Assistant : Get an instance of the appropriate lock for a certain situation based on
    scheduler used.
    "
742,"def verify_dataset_shuffled(x):
    
    assert isinstance(x, tf.data.Dataset)
    graph_def = get_dataset_graph_def(x)
    for node in graph_def.node:
        if node.op.startswith(""ShuffleDataset""):
            return True
    # Also check graph_def.library.function for ds.interleave or ds.flat_map
    for function in graph_def.library.function:
        for node in function.node_def:
            if node.op.startswith(""ShuffleDataset""):
                return True
    logging.warning(
        ""Expected a shuffled dataset but input dataset `x` is ""
        ""not shuffled. Please invoke `shuffle()` on input dataset.""
    )
    return False

","Verifies that the dataset is shuffled.

    Args:
      x: Dataset passed as an input to the model.

    Returns:
      boolean, whether the input dataset is shuffled or not.
    ",26,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def verify_dataset_shuffled(x):
    
    assert isinstance(x, tf.data.Dataset)
    graph_def = get_dataset_graph_def(x)
    for node in graph_def.node:
        if node.op.startswith(""ShuffleDataset""):
            return True
    # Also check graph_def.library.function for ds.interleave or ds.flat_map
    for function in graph_def.library.function:
        for node in function.node_def:
            if node.op.startswith(""ShuffleDataset""):
                return True
    logging.warning(
        ""Expected a shuffled dataset but input dataset `x` is ""
        ""not shuffled. Please invoke `shuffle()` on input dataset.""
    )
    return False


        ```
    ###Assistant : Verifies that the dataset is shuffled.

    Args:
      x: Dataset passed as an input to the model.

    Returns:
      boolean, whether the input dataset is shuffled or not.
    
    "
743,"def get_binance_available_quotes_for_each_coin() -> dict:
    
    trading_pairs = _get_trading_pairs()
    results = defaultdict(list)
    for pair in trading_pairs:
        results[pair[""baseAsset""]].append(pair[""quoteAsset""])
    return results


@log_start_end(log=logger)","Helper methods that for every coin available on Binance add all quote assets. [Source: Binance]

    Returns
    -------
    dict:
        All quote assets for given coin
        {'ETH' : ['BTC', 'USDT' ...], 'UNI' : ['ETH', 'BTC','BUSD', ...]

    ",34,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_binance_available_quotes_for_each_coin() -> dict:
    
    trading_pairs = _get_trading_pairs()
    results = defaultdict(list)
    for pair in trading_pairs:
        results[pair[""baseAsset""]].append(pair[""quoteAsset""])
    return results


@log_start_end(log=logger)
        ```
    ###Assistant : Helper methods that for every coin available on Binance add all quote assets. [Source: Binance]

    Returns
    -------
    dict:
        All quote assets for given coin
        {'ETH' : ['BTC', 'USDT' ...], 'UNI' : ['ETH', 'BTC','BUSD', ...]

    
    "
744,"def update_qty_in_future_sle(args, allow_negative_stock=False):
	
	datetime_limit_condition = """"
	qty_shift = args.actual_qty

	# find difference/shift in qty caused by stock reconciliation
	if args.voucher_type == ""Stock Reconciliation"":
		qty_shift = get_stock_reco_qty_shift(args)

	# find the next nearest stock reco so that we only recalculate SLEs till that point
	next_stock_reco_detail = get_next_stock_reco(args)
	if next_stock_reco_detail:
		detail = next_stock_reco_detail[0]
		# add condition to update SLEs before this date & time
		datetime_limit_condition = get_datetime_limit_condition(detail)

	frappe.db.sql(
		.format(
			qty_shift=qty_shift, datetime_limit_condition=datetime_limit_condition
		),
		args,
	)

	validate_negative_qty_in_future_sle(args, allow_negative_stock)

","Recalculate Qty after Transaction in future SLEs based on current SLE.
		update `tabStock Ledger Entry`
		set qty_after_transaction = qty_after_transaction + {qty_shift}
		where
			item_code = %(item_code)s
			and warehouse = %(warehouse)s
			and voucher_no != %(voucher_no)s
			and is_cancelled = 0
			and (timestamp(posting_date, posting_time) > timestamp(%(posting_date)s, %(posting_time)s)
				or (
					timestamp(posting_date, posting_time) = timestamp(%(posting_date)s, %(posting_time)s)
					and creation > %(creation)s
				)
			)
		{datetime_limit_condition}
		",57,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_qty_in_future_sle(args, allow_negative_stock=False):
	
	datetime_limit_condition = """"
	qty_shift = args.actual_qty

	# find difference/shift in qty caused by stock reconciliation
	if args.voucher_type == ""Stock Reconciliation"":
		qty_shift = get_stock_reco_qty_shift(args)

	# find the next nearest stock reco so that we only recalculate SLEs till that point
	next_stock_reco_detail = get_next_stock_reco(args)
	if next_stock_reco_detail:
		detail = next_stock_reco_detail[0]
		# add condition to update SLEs before this date & time
		datetime_limit_condition = get_datetime_limit_condition(detail)

	frappe.db.sql(
		.format(
			qty_shift=qty_shift, datetime_limit_condition=datetime_limit_condition
		),
		args,
	)

	validate_negative_qty_in_future_sle(args, allow_negative_stock)


        ```
    ###Assistant : Recalculate Qty after Transaction in future SLEs based on current SLE.
		update `tabStock Ledger Entry`
		set qty_after_transaction = qty_after_transaction + {qty_shift}
		where
			item_code = %(item_code)s
			and warehouse = %(warehouse)s
			and voucher_no != %(voucher_no)s
			and is_cancelled = 0
			and (timestamp(posting_date, posting_time) > timestamp(%(posting_date)s, %(posting_time)s)
				or (
					timestamp(posting_date, posting_time) = timestamp(%(posting_date)s, %(posting_time)s)
					and creation > %(creation)s
				)
			)
		{datetime_limit_condition}
		
    "
745,"def get_current_timezone_tag(parser, token):
    
    # token.split_contents() isn't useful here because this tag doesn't accept variable as arguments
    args = token.contents.split()
    if len(args) != 3 or args[1] != 'as':
        raise TemplateSyntaxError(
            ""'get_current_timezone' requires 'as variable' (got %r)"" % args
        )
    return GetCurrentTimezoneNode(args[2])
","
    Store the name of the current time zone in the context.

    Usage::

        {% get_current_timezone as TIME_ZONE %}

    This will fetch the currently active time zone and put its name
    into the ``TIME_ZONE`` context variable.
    ",34,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_current_timezone_tag(parser, token):
    
    # token.split_contents() isn't useful here because this tag doesn't accept variable as arguments
    args = token.contents.split()
    if len(args) != 3 or args[1] != 'as':
        raise TemplateSyntaxError(
            ""'get_current_timezone' requires 'as variable' (got %r)"" % args
        )
    return GetCurrentTimezoneNode(args[2])

        ```
    ###Assistant : 
    Store the name of the current time zone in the context.

    Usage::

        {% get_current_timezone as TIME_ZONE %}

    This will fetch the currently active time zone and put its name
    into the ``TIME_ZONE`` context variable.
    
    "
746,"def test_save_multiple_world_logs_mutator(self):
        
        with testing_utils.tempdir() as tmpdir:
            log_report = os.path.join(tmpdir, 'world_logs.jsonl')
            multitask = 'integration_tests:mutators=flatten,integration_tests:ReverseTeacher:mutator=reverse'
            valid, test = testing_utils.train_model(
                {
                    'task': multitask,
                    'validation_max_exs': 10,
                    'model': 'repeat_label',
                    'short_final_eval': True,
                    'num_epochs': 1.0,
                    'world_logs': log_report,
                }
            )

            for task in multitask.split(','):
                task_log_report = get_task_world_logs(
                    task, log_report, is_multitask=True
                )
                with PathManager.open(task_log_report) as f:
                    json_lines = f.readlines()
                assert len(json_lines) == 5


@register_agent(""fake_report"")","
        Test that we can save multiple world_logs from train model on multiple tasks
        with mutators present.
        ",16,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_save_multiple_world_logs_mutator(self):
        
        with testing_utils.tempdir() as tmpdir:
            log_report = os.path.join(tmpdir, 'world_logs.jsonl')
            multitask = 'integration_tests:mutators=flatten,integration_tests:ReverseTeacher:mutator=reverse'
            valid, test = testing_utils.train_model(
                {
                    'task': multitask,
                    'validation_max_exs': 10,
                    'model': 'repeat_label',
                    'short_final_eval': True,
                    'num_epochs': 1.0,
                    'world_logs': log_report,
                }
            )

            for task in multitask.split(','):
                task_log_report = get_task_world_logs(
                    task, log_report, is_multitask=True
                )
                with PathManager.open(task_log_report) as f:
                    json_lines = f.readlines()
                assert len(json_lines) == 5


@register_agent(""fake_report"")
        ```
    ###Assistant : 
        Test that we can save multiple world_logs from train model on multiple tasks
        with mutators present.
        
    "
747,"def testDotsInLogdir(self):
        
        local_dir_path = Path(""/tmp/test_rel_dots"")
        local_dir = str(local_dir_path)
        if local_dir_path.exists():
            local_dir = tempfile.mkdtemp(prefix=str(local_dir_path) + ""_"")
        trial = Trial(trainable_name=""rel_logdir"", local_dir=local_dir)

        with self.assertRaises(ValueError):
            trial.logdir = ""/tmp/test_rel/../dots""
        with self.assertRaises(ValueError):
            trial.logdir = local_dir + ""/../""

        if shutil.rmtree.avoids_symlink_attacks:
            if local_dir_path.exists():
                shutil.rmtree(local_dir)
",This should result in errors as dots in paths are not allowed.,12,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def testDotsInLogdir(self):
        
        local_dir_path = Path(""/tmp/test_rel_dots"")
        local_dir = str(local_dir_path)
        if local_dir_path.exists():
            local_dir = tempfile.mkdtemp(prefix=str(local_dir_path) + ""_"")
        trial = Trial(trainable_name=""rel_logdir"", local_dir=local_dir)

        with self.assertRaises(ValueError):
            trial.logdir = ""/tmp/test_rel/../dots""
        with self.assertRaises(ValueError):
            trial.logdir = local_dir + ""/../""

        if shutil.rmtree.avoids_symlink_attacks:
            if local_dir_path.exists():
                shutil.rmtree(local_dir)

        ```
    ###Assistant : This should result in errors as dots in paths are not allowed.
    "
748,"def test_delete_post(self):
        
        # Send request
        response = self.client.post(
            reverse(""wagtailimages:delete_multiple"", args=(self.image.id,))
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertEqual(response[""Content-Type""], ""application/json"")

        # Make sure the image is deleted
        self.assertFalse(Image.objects.filter(id=self.image.id).exists())

        # Check JSON
        response_json = json.loads(response.content.decode())
        self.assertIn(""image_id"", response_json)
        self.assertIn(""success"", response_json)
        self.assertEqual(response_json[""image_id""], self.image.id)
        self.assertTrue(response_json[""success""])


@override_settings(WAGTAILIMAGES_IMAGE_MODEL=""tests.CustomImage"")","
        This tests that a POST request to the delete view deletes the image
        ",13,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_post(self):
        
        # Send request
        response = self.client.post(
            reverse(""wagtailimages:delete_multiple"", args=(self.image.id,))
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertEqual(response[""Content-Type""], ""application/json"")

        # Make sure the image is deleted
        self.assertFalse(Image.objects.filter(id=self.image.id).exists())

        # Check JSON
        response_json = json.loads(response.content.decode())
        self.assertIn(""image_id"", response_json)
        self.assertIn(""success"", response_json)
        self.assertEqual(response_json[""image_id""], self.image.id)
        self.assertTrue(response_json[""success""])


@override_settings(WAGTAILIMAGES_IMAGE_MODEL=""tests.CustomImage"")
        ```
    ###Assistant : 
        This tests that a POST request to the delete view deletes the image
        
    "
749,"def _show_diff_helper(self, frame_data, expected_frame_data):
        
        import matplotlib.gridspec as gridspec  # type: ignore
        import matplotlib.pyplot as plt

        gs = gridspec.GridSpec(2, 2)
        fig = plt.figure()
        fig.suptitle(f""Test for {str(self.scene).replace('Test', '')}"", fontsize=16)

        ax = fig.add_subplot(gs[0, 0])
        ax.imshow(frame_data)
        ax.set_title(""Generated :"")

        ax = fig.add_subplot(gs[0, 1])
        ax.imshow(expected_frame_data)
        ax.set_title(""Expected :"")

        ax = fig.add_subplot(gs[1, :])
        diff_im = expected_frame_data.copy()
        diff_im = np.where(
            frame_data != np.array([0, 0, 0, 255]),
            np.array([0, 255, 0, 255], dtype=""uint8""),
            np.array([0, 0, 0, 255], dtype=""uint8""),
        )  # Set any non-black pixels to green
        np.putmask(
            diff_im,
            expected_frame_data != frame_data,
            np.array([255, 0, 0, 255], dtype=""uint8""),
        )  # Set any different pixels to red
        ax.imshow(diff_im, interpolation=""nearest"")
        ax.set_title(""Differences summary : (green = same, red = different)"")

        plt.show()
        plt.savefig(f""{self.scene}.png"")
",Will visually display with matplotlib differences between frame generated and the one expected.,13,106,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _show_diff_helper(self, frame_data, expected_frame_data):
        
        import matplotlib.gridspec as gridspec  # type: ignore
        import matplotlib.pyplot as plt

        gs = gridspec.GridSpec(2, 2)
        fig = plt.figure()
        fig.suptitle(f""Test for {str(self.scene).replace('Test', '')}"", fontsize=16)

        ax = fig.add_subplot(gs[0, 0])
        ax.imshow(frame_data)
        ax.set_title(""Generated :"")

        ax = fig.add_subplot(gs[0, 1])
        ax.imshow(expected_frame_data)
        ax.set_title(""Expected :"")

        ax = fig.add_subplot(gs[1, :])
        diff_im = expected_frame_data.copy()
        diff_im = np.where(
            frame_data != np.array([0, 0, 0, 255]),
            np.array([0, 255, 0, 255], dtype=""uint8""),
            np.array([0, 0, 0, 255], dtype=""uint8""),
        )  # Set any non-black pixels to green
        np.putmask(
            diff_im,
            expected_frame_data != frame_data,
            np.array([255, 0, 0, 255], dtype=""uint8""),
        )  # Set any different pixels to red
        ax.imshow(diff_im, interpolation=""nearest"")
        ax.set_title(""Differences summary : (green = same, red = different)"")

        plt.show()
        plt.savefig(f""{self.scene}.png"")

        ```
    ###Assistant : Will visually display with matplotlib differences between frame generated and the one expected.
    "
750,"def consume_capacity(self, task):
        
        if self.is_container_group:
            self.container_group_jobs += 1
            self.container_group_consumed_forks += task.task_impact
        else:
            raise RuntimeError(""We only track capacity for container groups at the instance group level. Otherwise, consume capacity on instances."")
",We only consume capacity on an instance group level if it is a container group. Otherwise we consume capacity on an instance level.,23,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def consume_capacity(self, task):
        
        if self.is_container_group:
            self.container_group_jobs += 1
            self.container_group_consumed_forks += task.task_impact
        else:
            raise RuntimeError(""We only track capacity for container groups at the instance group level. Otherwise, consume capacity on instances."")

        ```
    ###Assistant : We only consume capacity on an instance group level if it is a container group. Otherwise we consume capacity on an instance level.
    "
751,"def get_avail_mem_per_ray_worker_node(spark, object_store_memory_per_node):
    
    num_cpus_per_spark_task = int(
        spark.sparkContext.getConf().get(""spark.task.cpus"", ""1"")
    )
","
    Return the available heap memory and object store memory for each ray worker.
    NB: We have one ray node per spark task.
    ",22,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_avail_mem_per_ray_worker_node(spark, object_store_memory_per_node):
    
    num_cpus_per_spark_task = int(
        spark.sparkContext.getConf().get(""spark.task.cpus"", ""1"")
    )

        ```
    ###Assistant : 
    Return the available heap memory and object store memory for each ray worker.
    NB: We have one ray node per spark task.
    
    "
752,"def _iter_egg_info_dependencies(self) -> Iterable[str]:
        
        for entry in self._iter_requires_txt_entries():
            if entry.extra and entry.marker:
                marker = f'({entry.marker}) and extra == ""{safe_extra(entry.extra)}""'
            elif entry.extra:
                marker = f'extra == ""{safe_extra(entry.extra)}""'
            elif entry.marker:
                marker = entry.marker
            else:
                marker = """"
            if marker:
                yield f""{entry.requirement} ; {marker}""
            else:
                yield entry.requirement
","Get distribution dependencies from the egg-info directory.

        To ease parsing, this converts a legacy dependency entry into a PEP 508
        requirement string. Like ``_iter_requires_txt_entries()``, there is code
        in ``importlib.metadata`` that does mostly the same, but not do exactly
        what we need.

        Namely, ``importlib.metadata`` does not normalize the extra name before
        putting it into the requirement string, which causes marker comparison
        to fail because the dist-info format do normalize. This is consistent in
        all currently available PEP 517 backends, although not standardized.
        ",81,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _iter_egg_info_dependencies(self) -> Iterable[str]:
        
        for entry in self._iter_requires_txt_entries():
            if entry.extra and entry.marker:
                marker = f'({entry.marker}) and extra == ""{safe_extra(entry.extra)}""'
            elif entry.extra:
                marker = f'extra == ""{safe_extra(entry.extra)}""'
            elif entry.marker:
                marker = entry.marker
            else:
                marker = """"
            if marker:
                yield f""{entry.requirement} ; {marker}""
            else:
                yield entry.requirement

        ```
    ###Assistant : Get distribution dependencies from the egg-info directory.

        To ease parsing, this converts a legacy dependency entry into a PEP 508
        requirement string. Like ``_iter_requires_txt_entries()``, there is code
        in ``importlib.metadata`` that does mostly the same, but not do exactly
        what we need.

        Namely, ``importlib.metadata`` does not normalize the extra name before
        putting it into the requirement string, which causes marker comparison
        to fail because the dist-info format do normalize. This is consistent in
        all currently available PEP 517 backends, although not standardized.
        
    "
753,"def cache_from_source(path, debug_override=None):
    
    with warnings.catch_warnings():
        warnings.simplefilter('ignore')
        return util.cache_from_source(path, debug_override)

","**DEPRECATED**

    Given the path to a .py file, return the path to its .pyc file.

    The .py file does not need to exist; this simply returns the path to the
    .pyc file calculated as if the .py file were imported.

    If debug_override is not None, then it must be a boolean and is used in
    place of sys.flags.optimize.

    If sys.implementation.cache_tag is None then NotImplementedError is raised.

    ",66,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cache_from_source(path, debug_override=None):
    
    with warnings.catch_warnings():
        warnings.simplefilter('ignore')
        return util.cache_from_source(path, debug_override)


        ```
    ###Assistant : **DEPRECATED**

    Given the path to a .py file, return the path to its .pyc file.

    The .py file does not need to exist; this simply returns the path to the
    .pyc file calculated as if the .py file were imported.

    If debug_override is not None, then it must be a boolean and is used in
    place of sys.flags.optimize.

    If sys.implementation.cache_tag is None then NotImplementedError is raised.

    
    "
754,"def _url_collapse_path(path):
    
    # Query component should not be involved.
    path, _, query = path.partition('?')
    path = urllib.parse.unquote(path)

    # Similar to os.path.split(os.path.normpath(path)) but specific to URL
    # path semantics rather than local operating system semantics.
    path_parts = path.split('/')
    head_parts = []
    for part in path_parts[:-1]:
        if part == '..':
            head_parts.pop() # IndexError if more '..' than prior parts
        elif part and part != '.':
            head_parts.append( part )
    if path_parts:
        tail_part = path_parts.pop()
        if tail_part:
            if tail_part == '..':
                head_parts.pop()
                tail_part = ''
            elif tail_part == '.':
                tail_part = ''
    else:
        tail_part = ''

    if query:
        tail_part = '?'.join((tail_part, query))

    splitpath = ('/' + '/'.join(head_parts), tail_part)
    collapsed_path = ""/"".join(splitpath)

    return collapsed_path



nobody = None
","
    Given a URL path, remove extra '/'s and '.' path elements and collapse
    any '..' references and returns a collapsed path.

    Implements something akin to RFC-2396 5.2 step 6 to parse relative paths.
    The utility of this function is limited to is_cgi method and helps
    preventing some security attacks.

    Returns: The reconstituted URL, which will always start with a '/'.

    Raises: IndexError if too many '..' occur within the path.

    ",70,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _url_collapse_path(path):
    
    # Query component should not be involved.
    path, _, query = path.partition('?')
    path = urllib.parse.unquote(path)

    # Similar to os.path.split(os.path.normpath(path)) but specific to URL
    # path semantics rather than local operating system semantics.
    path_parts = path.split('/')
    head_parts = []
    for part in path_parts[:-1]:
        if part == '..':
            head_parts.pop() # IndexError if more '..' than prior parts
        elif part and part != '.':
            head_parts.append( part )
    if path_parts:
        tail_part = path_parts.pop()
        if tail_part:
            if tail_part == '..':
                head_parts.pop()
                tail_part = ''
            elif tail_part == '.':
                tail_part = ''
    else:
        tail_part = ''

    if query:
        tail_part = '?'.join((tail_part, query))

    splitpath = ('/' + '/'.join(head_parts), tail_part)
    collapsed_path = ""/"".join(splitpath)

    return collapsed_path



nobody = None

        ```
    ###Assistant : 
    Given a URL path, remove extra '/'s and '.' path elements and collapse
    any '..' references and returns a collapsed path.

    Implements something akin to RFC-2396 5.2 step 6 to parse relative paths.
    The utility of this function is limited to is_cgi method and helps
    preventing some security attacks.

    Returns: The reconstituted URL, which will always start with a '/'.

    Raises: IndexError if too many '..' occur within the path.

    
    "
755,"def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False):
        
        from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo

        A, rows, columns = sparse_series_to_coo(
            self._parent, row_levels, column_levels, sort_labels=sort_labels
        )
        return A, rows, columns
","
        Create a scipy.sparse.coo_matrix from a Series with MultiIndex.

        Use row_levels and column_levels to determine the row and column
        coordinates respectively. row_levels and column_levels are the names
        (labels) or numbers of the levels. {row_levels, column_levels} must be
        a partition of the MultiIndex level names (or numbers).

        Parameters
        ----------
        row_levels : tuple/list
        column_levels : tuple/list
        sort_labels : bool, default False
            Sort the row and column labels before forming the sparse matrix.
            When `row_levels` and/or `column_levels` refer to a single level,
            set to `True` for a faster execution.

        Returns
        -------
        y : scipy.sparse.coo_matrix
        rows : list (row labels)
        columns : list (column labels)

        Examples
        --------
        >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
        >>> s.index = pd.MultiIndex.from_tuples(
        ...     [
        ...         (1, 2, ""a"", 0),
        ...         (1, 2, ""a"", 1),
        ...         (1, 1, ""b"", 0),
        ...         (1, 1, ""b"", 1),
        ...         (2, 1, ""b"", 0),
        ...         (2, 1, ""b"", 1)
        ...     ],
        ...     names=[""A"", ""B"", ""C"", ""D""],
        ... )
        >>> s
        A  B  C  D
        1  2  a  0    3.0
                 1    NaN
           1  b  0    1.0
                 1    3.0
        2  1  b  0    NaN
                 1    NaN
        dtype: float64

        >>> ss = s.astype(""Sparse"")
        >>> ss
        A  B  C  D
        1  2  a  0    3.0
                 1    NaN
           1  b  0    1.0
                 1    3.0
        2  1  b  0    NaN
                 1    NaN
        dtype: Sparse[float64, nan]

        >>> A, rows, columns = ss.sparse.to_coo(
        ...     row_levels=[""A"", ""B""], column_levels=[""C"", ""D""], sort_labels=True
        ... )
        >>> A
        <3x4 sparse matrix of type '<class 'numpy.float64'>'
        with 3 stored elements in COOrdinate format>
        >>> A.todense()
        matrix([[0., 0., 1., 3.],
        [3., 0., 0., 0.],
        [0., 0., 0., 0.]])

        >>> rows
        [(1, 1), (1, 2), (2, 1)]
        >>> columns
        [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
        ",279,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False):
        
        from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo

        A, rows, columns = sparse_series_to_coo(
            self._parent, row_levels, column_levels, sort_labels=sort_labels
        )
        return A, rows, columns

        ```
    ###Assistant : 
        Create a scipy.sparse.coo_matrix from a Series with MultiIndex.

        Use row_levels and column_levels to determine the row and column
        coordinates respectively. row_levels and column_levels are the names
        (labels) or numbers of the levels. {row_levels, column_levels} must be
        a partition of the MultiIndex level names (or numbers).

        Parameters
        ----------
        row_levels : tuple/list
        column_levels : tuple/list
        sort_labels : bool, default False
            Sort the row and column labels before forming the sparse matrix.
            When `row_levels` and/or `column_levels` refer to a single level,
            set to `True` for a faster execution.

        Returns
        -------
        y : scipy.sparse.coo_matrix
        rows : list (row labels)
        columns : list (column labels)

        Examples
        --------
        >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
        >>> s.index = pd.MultiIndex.from_tuples(
        ...     [
        ...         (1, 2, ""a"", 0),
        ...         (1, 2, ""a"", 1),
        ...         (1, 1, ""b"", 0),
        ...         (1, 1, ""b"", 1),
        ...         (2, 1, ""b"", 0),
        ...         (2, 1, ""b"", 1)
        ...     ],
        ...     names=[""A"", ""B"", ""C"", ""D""],
        ... )
        >>> s
        A  B  C  D
        1  2  a  0    3.0
                 1    NaN
           1  b  0    1.0
                 1    3.0
        2  1  b  0    NaN
                 1    NaN
        dtype: float64

        >>> ss = s.astype(""Sparse"")
        >>> ss
        A  B  C  D
        1  2  a  0    3.0
                 1    NaN
           1  b  0    1.0
                 1    3.0
        2  1  b  0    NaN
                 1    NaN
        dtype: Sparse[float64, nan]

        >>> A, rows, columns = ss.sparse.to_coo(
        ...     row_levels=[""A"", ""B""], column_levels=[""C"", ""D""], sort_labels=True
        ... )
        >>> A
        <3x4 sparse matrix of type '<class 'numpy.float64'>'
        with 3 stored elements in COOrdinate format>
        >>> A.todense()
        matrix([[0., 0., 1., 3.],
        [3., 0., 0., 0.],
        [0., 0., 0., 0.]])

        >>> rows
        [(1, 1), (1, 2), (2, 1)]
        >>> columns
        [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
        
    "
756,"def _get_memory_heuristic_values(self) -> Dict[str, Union[str, float, bool]]:
        
        return {
            'ignore_in_session_memories': self.opt.get(
                'ignore_in_session_memories_mkm', False
            ),
            'memory_overlap_threshold': self.opt.get('memory_overlap_threshold', 0.0),
            'memory_hard_block_for_n_turns': self.opt.get(
                'memory_hard_block_for_n_turns', 0
            ),
            'memory_soft_block_decay_factor': self.opt.get(
                'memory_soft_block_decay_factor', 0.0
            ),
        }
","
        Extract heuristics from self.opt.
        ",4,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_memory_heuristic_values(self) -> Dict[str, Union[str, float, bool]]:
        
        return {
            'ignore_in_session_memories': self.opt.get(
                'ignore_in_session_memories_mkm', False
            ),
            'memory_overlap_threshold': self.opt.get('memory_overlap_threshold', 0.0),
            'memory_hard_block_for_n_turns': self.opt.get(
                'memory_hard_block_for_n_turns', 0
            ),
            'memory_soft_block_decay_factor': self.opt.get(
                'memory_soft_block_decay_factor', 0.0
            ),
        }

        ```
    ###Assistant : 
        Extract heuristics from self.opt.
        
    "
757,"def plugin_list_buttons(context, model):
    
    return _get_registered_content(model, 'list_buttons', context)
","
    Render all list buttons registered by plugins
    ",7,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def plugin_list_buttons(context, model):
    
    return _get_registered_content(model, 'list_buttons', context)

        ```
    ###Assistant : 
    Render all list buttons registered by plugins
    
    "
758,"def drop_path(self, inputs):
        
        # if prob is 0 or eval mode, return original input
        if self.drop_prob == 0. or not self.training:
            return inputs
        keep_prob = 1 - self.drop_prob
        keep_prob = paddle.to_tensor(keep_prob, dtype='float32')
        shape = (inputs.shape[0], ) + (1, ) * (inputs.ndim - 1)  # shape=(N, 1, 1, 1)
        random_tensor = keep_prob + paddle.rand(shape, dtype=inputs.dtype)
        random_tensor = random_tensor.floor()  # mask
        output = inputs.divide(keep_prob) * random_tensor  #divide is to keep same output expectation
        return output
","drop path op
        Args:
            input: tensor with arbitrary shape
            drop_prob: float number of drop path probability, default: 0.0
            training: bool, if current mode is training, default: False
        Returns:
            output: output tensor after drop path
        ",34,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def drop_path(self, inputs):
        
        # if prob is 0 or eval mode, return original input
        if self.drop_prob == 0. or not self.training:
            return inputs
        keep_prob = 1 - self.drop_prob
        keep_prob = paddle.to_tensor(keep_prob, dtype='float32')
        shape = (inputs.shape[0], ) + (1, ) * (inputs.ndim - 1)  # shape=(N, 1, 1, 1)
        random_tensor = keep_prob + paddle.rand(shape, dtype=inputs.dtype)
        random_tensor = random_tensor.floor()  # mask
        output = inputs.divide(keep_prob) * random_tensor  #divide is to keep same output expectation
        return output

        ```
    ###Assistant : drop path op
        Args:
            input: tensor with arbitrary shape
            drop_prob: float number of drop path probability, default: 0.0
            training: bool, if current mode is training, default: False
        Returns:
            output: output tensor after drop path
        
    "
759,"def set_style(style=None, rc=None):
    
    style_object = axes_style(style, rc)
    mpl.rcParams.update(style_object)

","
    Set the parameters that control the general style of the plots.

    The style parameters control properties like the color of the background and
    whether a grid is enabled by default. This is accomplished using the
    matplotlib rcParams system.

    The options are illustrated in the
    :doc:`aesthetics tutorial <../tutorial/aesthetics>`.

    See :func:`axes_style` to get the parameter values.

    Parameters
    ----------
    style : dict, or one of {darkgrid, whitegrid, dark, white, ticks}
        A dictionary of parameters or the name of a preconfigured style.
    rc : dict, optional
        Parameter mappings to override the values in the preset seaborn
        style dictionaries. This only updates parameters that are
        considered part of the style definition.

    Examples
    --------

    .. include:: ../docstrings/set_style.rst

    ",111,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_style(style=None, rc=None):
    
    style_object = axes_style(style, rc)
    mpl.rcParams.update(style_object)


        ```
    ###Assistant : 
    Set the parameters that control the general style of the plots.

    The style parameters control properties like the color of the background and
    whether a grid is enabled by default. This is accomplished using the
    matplotlib rcParams system.

    The options are illustrated in the
    :doc:`aesthetics tutorial <../tutorial/aesthetics>`.

    See :func:`axes_style` to get the parameter values.

    Parameters
    ----------
    style : dict, or one of {darkgrid, whitegrid, dark, white, ticks}
        A dictionary of parameters or the name of a preconfigured style.
    rc : dict, optional
        Parameter mappings to override the values in the preset seaborn
        style dictionaries. This only updates parameters that are
        considered part of the style definition.

    Examples
    --------

    .. include:: ../docstrings/set_style.rst

    
    "
760,"def _resize(self, image, shorter=800, longer=1333, size_divisor=32, resample=Image.BICUBIC):
        
        if not isinstance(image, Image.Image):
            image = self.to_pil_image(image)

        w, h = image.size
        min_size = shorter
        max_size = longer
        scale = min_size / min(w, h)
        if h < w:
            newh, neww = min_size, scale * w
        else:
            newh, neww = scale * h, min_size

        if max(newh, neww) > max_size:
            scale = max_size / max(newh, neww)
            newh = newh * scale
            neww = neww * scale

        newh, neww = int(newh + 0.5), int(neww + 0.5)
        newh, neww = newh // size_divisor * size_divisor, neww // size_divisor * size_divisor

        return self.resize(image, size=(neww, newh), resample=resample)
","
        Resizes the shorter edge of `image` to `shorter` and limits the longer edge to under `longer`, while preserving
        the aspect ratio. Also makes sure that both the height and width can be divided by `size_divisor`.

        Based on original implementation:
        https://github.com/dandelin/ViLT/blob/3db8b5035464afee84d951bf6322e1b27f1d072d/vilt/transforms/utils.py#L5

        Args:
            image (`PIL.Image`):
                The image to resize.
            shorter (`int`, *optional*, defaults to `800`):
                The size to which to resize the shorter side of the image.
            longer (`int`, *optional*, defaults to `1333`):
                The size by which to limit the longer side of the image, while preserving the aspect ratio.
            size_divisor (`int`, *optional*, defaults to `32`):
                The size by which both the height and the width must be divisible.
            resample (`int`, *optional*, defaults to `PIL.Image.BICUBIC`):
                An optional resampling filter.
        ",117,97,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _resize(self, image, shorter=800, longer=1333, size_divisor=32, resample=Image.BICUBIC):
        
        if not isinstance(image, Image.Image):
            image = self.to_pil_image(image)

        w, h = image.size
        min_size = shorter
        max_size = longer
        scale = min_size / min(w, h)
        if h < w:
            newh, neww = min_size, scale * w
        else:
            newh, neww = scale * h, min_size

        if max(newh, neww) > max_size:
            scale = max_size / max(newh, neww)
            newh = newh * scale
            neww = neww * scale

        newh, neww = int(newh + 0.5), int(neww + 0.5)
        newh, neww = newh // size_divisor * size_divisor, neww // size_divisor * size_divisor

        return self.resize(image, size=(neww, newh), resample=resample)

        ```
    ###Assistant : 
        Resizes the shorter edge of `image` to `shorter` and limits the longer edge to under `longer`, while preserving
        the aspect ratio. Also makes sure that both the height and width can be divided by `size_divisor`.

        Based on original implementation:
        https://github.com/dandelin/ViLT/blob/3db8b5035464afee84d951bf6322e1b27f1d072d/vilt/transforms/utils.py#L5

        Args:
            image (`PIL.Image`):
                The image to resize.
            shorter (`int`, *optional*, defaults to `800`):
                The size to which to resize the shorter side of the image.
            longer (`int`, *optional*, defaults to `1333`):
                The size by which to limit the longer side of the image, while preserving the aspect ratio.
            size_divisor (`int`, *optional*, defaults to `32`):
                The size by which both the height and the width must be divisible.
            resample (`int`, *optional*, defaults to `PIL.Image.BICUBIC`):
                An optional resampling filter.
        
    "
761,"def extra_action_out_fn(self) -> Dict[str, TensorType]:
        
        extra_action_fetches = super().extra_action_out_fn()
        extra_action_fetches.update(self._policy_extra_action_fetches)
        return extra_action_fetches
","Extra values to fetch and return from compute_actions().

        Returns:
             Dict[str, TensorType]: An extra fetch-dict to be passed to and
                returned from the compute_actions() call.
        ",24,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extra_action_out_fn(self) -> Dict[str, TensorType]:
        
        extra_action_fetches = super().extra_action_out_fn()
        extra_action_fetches.update(self._policy_extra_action_fetches)
        return extra_action_fetches

        ```
    ###Assistant : Extra values to fetch and return from compute_actions().

        Returns:
             Dict[str, TensorType]: An extra fetch-dict to be passed to and
                returned from the compute_actions() call.
        
    "
762,"def get_all_exported_dataset_infos(cls) -> dict:
        
        dset_infos_file_path = os.path.join(cls.get_imported_module_dir(), config.DATASETDICT_INFOS_FILENAME)
        if os.path.exists(dset_infos_file_path):
            return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
        return {}
","Empty dict if doesn't exist

        Example:

        ```py
        >>> from datasets import load_dataset_builder
        >>> ds_builder = load_dataset_builder('rotten_tomatoes')
        >>> ds_builder.get_all_exported_dataset_infos()
        {'default': DatasetInfo(description=""Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\nsentiment categorization with respect to rating scales.'', Proceedings of the\nACL, 2005.\n"", citation='@InProceedings{Pang+Lee:05a,\n  author =       {Bo Pang and Lillian Lee},\n  title =        {Seeing stars: Exploiting class relationships for sentiment\n                  categorization with respect to rating scales},\n  booktitle =    {Proceedings of the ACL},\n  year =         2005\n}\n', homepage='http://www.cs.cornell.edu/people/pabo/movie-review-data/', license='', features={'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input='', output=''), task_templates=[TextClassification(task='text-classification', text_column='text', label_column='label')], builder_name='rotten_tomatoes_movie_review', config_name='default', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=1074810, num_examples=8530, dataset_name='rotten_tomatoes_movie_review'), 'validation': SplitInfo(name='validation', num_bytes=134679, num_examples=1066, dataset_name='rotten_tomatoes_movie_review'), 'test': SplitInfo(name='test', num_bytes=135972, num_examples=1066, dataset_name='rotten_tomatoes_movie_review')}, download_checksums={'https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz': {'num_bytes': 487770, 'checksum': 'a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9'}}, download_size=487770, post_processing_size=None, dataset_size=1345461, size_in_bytes=1833231)}
        ```
        ",140,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all_exported_dataset_infos(cls) -> dict:
        
        dset_infos_file_path = os.path.join(cls.get_imported_module_dir(), config.DATASETDICT_INFOS_FILENAME)
        if os.path.exists(dset_infos_file_path):
            return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
        return {}

        ```
    ###Assistant : Empty dict if doesn't exist

        Example:

        ```py
        >>> from datasets import load_dataset_builder
        >>> ds_builder = load_dataset_builder('rotten_tomatoes')
        >>> ds_builder.get_all_exported_dataset_infos()
        {'default': DatasetInfo(description=""Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\nsentiment categorization with respect to rating scales.'', Proceedings of the\nACL, 2005.\n"", citation='@InProceedings{Pang+Lee:05a,\n  author =       {Bo Pang and Lillian Lee},\n  title =        {Seeing stars: Exploiting class relationships for sentiment\n                  categorization with respect to rating scales},\n  booktitle =    {Proceedings of the ACL},\n  year =         2005\n}\n', homepage='http://www.cs.cornell.edu/people/pabo/movie-review-data/', license='', features={'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input='', output=''), task_templates=[TextClassification(task='text-classification', text_column='text', label_column='label')], builder_name='rotten_tomatoes_movie_review', config_name='default', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=1074810, num_examples=8530, dataset_name='rotten_tomatoes_movie_review'), 'validation': SplitInfo(name='validation', num_bytes=134679, num_examples=1066, dataset_name='rotten_tomatoes_movie_review'), 'test': SplitInfo(name='test', num_bytes=135972, num_examples=1066, dataset_name='rotten_tomatoes_movie_review')}, download_checksums={'https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz': {'num_bytes': 487770, 'checksum': 'a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9'}}, download_size=487770, post_processing_size=None, dataset_size=1345461, size_in_bytes=1833231)}
        ```
        
    "
763,"def to_sanitized_dict(self) -> Dict[str, Any]:
        
        d = self.to_dict()
        d = {
            ** d, ** {
                ""train_batch_size"": self.train_batch_size,
                ""eval_batch_size"": self.eval_batch_size
            }
        }

        valid_types = [bool, int, float, str]
        valid_types.append(paddle.Tensor)

        return {
            k: v if type(v) in valid_types else str(v)
            for k, v in d.items()
        }
","
        Sanitized serialization to use with TensorBoard’s hparams
        ",7,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_sanitized_dict(self) -> Dict[str, Any]:
        
        d = self.to_dict()
        d = {
            ** d, ** {
                ""train_batch_size"": self.train_batch_size,
                ""eval_batch_size"": self.eval_batch_size
            }
        }

        valid_types = [bool, int, float, str]
        valid_types.append(paddle.Tensor)

        return {
            k: v if type(v) in valid_types else str(v)
            for k, v in d.items()
        }

        ```
    ###Assistant : 
        Sanitized serialization to use with TensorBoard’s hparams
        
    "
764,"def readinto(self, b):
        
        self._check_can_read()
        return self._buffer.readinto(b)
","Read bytes into b.

        Returns the number of bytes read (0 for EOF).
        ",13,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def readinto(self, b):
        
        self._check_can_read()
        return self._buffer.readinto(b)

        ```
    ###Assistant : Read bytes into b.

        Returns the number of bytes read (0 for EOF).
        
    "
765,"def _output_type_handler(cursor, name, defaultType, length, precision, scale):
        
        if defaultType == Database.NUMBER:
            if scale == -127:
                if precision == 0:
                    # NUMBER column: decimal-precision floating point.
                    # This will normally be an integer from a sequence,
                    # but it could be a decimal value.
                    outconverter = FormatStylePlaceholderCursor._output_number_converter
                else:
                    # FLOAT column: binary-precision floating point.
                    # This comes from FloatField columns.
                    outconverter = float
            elif precision > 0:
                # NUMBER(p,s) column: decimal-precision fixed point.
                # This comes from IntegerField and DecimalField columns.
                outconverter = FormatStylePlaceholderCursor._get_decimal_converter(
                    precision, scale
                )
            else:
                # No type information. This normally comes from a
                # mathematical expression in the SELECT list. Guess int
                # or Decimal based on whether it has a decimal point.
                outconverter = FormatStylePlaceholderCursor._output_number_converter
            return cursor.var(
                Database.STRING,
                size=255,
                arraysize=cursor.arraysize,
                outconverter=outconverter,
            )
","
        Called for each db column fetched from cursors. Return numbers as the
        appropriate Python type.
        ",15,126,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _output_type_handler(cursor, name, defaultType, length, precision, scale):
        
        if defaultType == Database.NUMBER:
            if scale == -127:
                if precision == 0:
                    # NUMBER column: decimal-precision floating point.
                    # This will normally be an integer from a sequence,
                    # but it could be a decimal value.
                    outconverter = FormatStylePlaceholderCursor._output_number_converter
                else:
                    # FLOAT column: binary-precision floating point.
                    # This comes from FloatField columns.
                    outconverter = float
            elif precision > 0:
                # NUMBER(p,s) column: decimal-precision fixed point.
                # This comes from IntegerField and DecimalField columns.
                outconverter = FormatStylePlaceholderCursor._get_decimal_converter(
                    precision, scale
                )
            else:
                # No type information. This normally comes from a
                # mathematical expression in the SELECT list. Guess int
                # or Decimal based on whether it has a decimal point.
                outconverter = FormatStylePlaceholderCursor._output_number_converter
            return cursor.var(
                Database.STRING,
                size=255,
                arraysize=cursor.arraysize,
                outconverter=outconverter,
            )

        ```
    ###Assistant : 
        Called for each db column fetched from cursors. Return numbers as the
        appropriate Python type.
        
    "
766,"def decode_nested_example(schema, obj):
    
    # Nested structures: we allow dict, list/tuples, sequences
    if isinstance(schema, dict):
        return {
            k: decode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
        }
    elif isinstance(schema, (list, tuple)):
        sub_schema = schema[0]
        if obj is None:
            return None
        else:
            if len(obj) > 0:
                for first_elmt in obj:
                    if _check_non_null_non_empty_recursive(first_elmt, sub_schema):
                        break
                if decode_nested_example(sub_schema, first_elmt) != first_elmt:
                    return [decode_nested_example(sub_schema, o) for o in obj]
            return list(obj)
    elif isinstance(schema, Sequence):
        # We allow to reverse list of dict => dict of list for compatiblity with tfds
        if isinstance(schema.feature, dict):
            return {k: decode_nested_example([schema.feature[k]], obj[k]) for k in schema.feature}
        else:
            return decode_nested_example([schema.feature], obj)
    # Object with special decoding:
    elif isinstance(schema, (Audio, Image)):
        return schema.decode_example(obj) if obj is not None else None
    return obj

","Decode a nested example.
    This is used since some features (in particular Audio and Image) have some logic during decoding.

    To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be decoded.
    If the first element needs to be decoded, then all the elements of the list will be decoded, otherwise they'll stay the same.
    ",73,121,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def decode_nested_example(schema, obj):
    
    # Nested structures: we allow dict, list/tuples, sequences
    if isinstance(schema, dict):
        return {
            k: decode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
        }
    elif isinstance(schema, (list, tuple)):
        sub_schema = schema[0]
        if obj is None:
            return None
        else:
            if len(obj) > 0:
                for first_elmt in obj:
                    if _check_non_null_non_empty_recursive(first_elmt, sub_schema):
                        break
                if decode_nested_example(sub_schema, first_elmt) != first_elmt:
                    return [decode_nested_example(sub_schema, o) for o in obj]
            return list(obj)
    elif isinstance(schema, Sequence):
        # We allow to reverse list of dict => dict of list for compatiblity with tfds
        if isinstance(schema.feature, dict):
            return {k: decode_nested_example([schema.feature[k]], obj[k]) for k in schema.feature}
        else:
            return decode_nested_example([schema.feature], obj)
    # Object with special decoding:
    elif isinstance(schema, (Audio, Image)):
        return schema.decode_example(obj) if obj is not None else None
    return obj


        ```
    ###Assistant : Decode a nested example.
    This is used since some features (in particular Audio and Image) have some logic during decoding.

    To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be decoded.
    If the first element needs to be decoded, then all the elements of the list will be decoded, otherwise they'll stay the same.
    
    "
767,"def running_under_virtualenv() -> bool:
    
    return _running_under_venv() or _running_under_legacy_virtualenv()

","True if we're running inside a virtual environment, False otherwise.",10,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def running_under_virtualenv() -> bool:
    
    return _running_under_venv() or _running_under_legacy_virtualenv()


        ```
    ###Assistant : True if we're running inside a virtual environment, False otherwise.
    "
768,"def forward_train(self, x, data_samples, proposal_cfg=None, **kwargs):
        
        img_metas = [data_sample['meta'] for data_sample in data_samples]
        outs = self(x)
        gt_bboxes = [
            data_sample.gt_instances.bboxes for data_sample in data_samples
        ]

        if hasattr(data_samples[0].gt_instances, 'labels'):
            gt_labels = [
                data_sample.gt_instances.labels for data_sample in data_samples
            ]
        else:
            # RPN
            gt_labels = None

        if hasattr(data_samples[0], 'instances_ignore'):
            gt_bboxes_ignore = [
                data_sample.ignored_instances.bboxes
                for data_sample in data_samples
            ]
        else:
            gt_bboxes_ignore = None

        if gt_labels is None:
            loss_inputs = outs + (gt_bboxes, img_metas)
        else:
            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
        if proposal_cfg is None:
            return losses
        else:
            results_list = self.get_results(
                *outs, img_metas=img_metas, cfg=proposal_cfg)
            return losses, results_list
","
        Args:
            x (list[Tensor]): Features from FPN.
            data_samples (list[:obj:`GeneralData`]): Each item contains
                the meta information of each image and corresponding
                annotations.
            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
                if None, test_cfg would be used

        Returns:
            tuple or Tensor: When `proposal_cfg` is None, the detector is a \
            normal one-stage detector, The return value is the losses.

            - losses: (dict[str, Tensor]): A dictionary of loss components.

            When the `proposal_cfg` is not None, the head is used as a
            `rpn_head`, the return value is a tuple contains:

            - losses: (dict[str, Tensor]): A dictionary of loss components.
            - results_list (list[:obj:`InstanceData`]): Detection
              results of each image after the post process.
              Each item usually contains following keys.

                - scores (Tensor): Classification scores, has a shape
                  (num_instance,)
                - labels (Tensor): Labels of bboxes, has a shape
                  (num_instances,).
                - bboxes (Tensor): Has a shape (num_instances, 4),
                  the last dimension 4 arrange as (x1, y1, x2, y2).
        ",147,97,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward_train(self, x, data_samples, proposal_cfg=None, **kwargs):
        
        img_metas = [data_sample['meta'] for data_sample in data_samples]
        outs = self(x)
        gt_bboxes = [
            data_sample.gt_instances.bboxes for data_sample in data_samples
        ]

        if hasattr(data_samples[0].gt_instances, 'labels'):
            gt_labels = [
                data_sample.gt_instances.labels for data_sample in data_samples
            ]
        else:
            # RPN
            gt_labels = None

        if hasattr(data_samples[0], 'instances_ignore'):
            gt_bboxes_ignore = [
                data_sample.ignored_instances.bboxes
                for data_sample in data_samples
            ]
        else:
            gt_bboxes_ignore = None

        if gt_labels is None:
            loss_inputs = outs + (gt_bboxes, img_metas)
        else:
            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
        if proposal_cfg is None:
            return losses
        else:
            results_list = self.get_results(
                *outs, img_metas=img_metas, cfg=proposal_cfg)
            return losses, results_list

        ```
    ###Assistant : 
        Args:
            x (list[Tensor]): Features from FPN.
            data_samples (list[:obj:`GeneralData`]): Each item contains
                the meta information of each image and corresponding
                annotations.
            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
                if None, test_cfg would be used

        Returns:
            tuple or Tensor: When `proposal_cfg` is None, the detector is a \
            normal one-stage detector, The return value is the losses.

            - losses: (dict[str, Tensor]): A dictionary of loss components.

            When the `proposal_cfg` is not None, the head is used as a
            `rpn_head`, the return value is a tuple contains:

            - losses: (dict[str, Tensor]): A dictionary of loss components.
            - results_list (list[:obj:`InstanceData`]): Detection
              results of each image after the post process.
              Each item usually contains following keys.

                - scores (Tensor): Classification scores, has a shape
                  (num_instance,)
                - labels (Tensor): Labels of bboxes, has a shape
                  (num_instances,).
                - bboxes (Tensor): Has a shape (num_instances, 4),
                  the last dimension 4 arrange as (x1, y1, x2, y2).
        
    "
769,"def cf(self):
        
        return {
            cf.name: cf.deserialize(self.custom_field_data.get(cf.name))
            for cf in self.custom_fields
        }
","
        Return a dictionary mapping each custom field for this instance to its deserialized value.

        ```python
        >>> tenant = Tenant.objects.first()
        >>> tenant.cf
        {'primary_site': <Site: DM-NYC>, 'cust_id': 'DMI01', 'is_active': True}
        ```
        ",29,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cf(self):
        
        return {
            cf.name: cf.deserialize(self.custom_field_data.get(cf.name))
            for cf in self.custom_fields
        }

        ```
    ###Assistant : 
        Return a dictionary mapping each custom field for this instance to its deserialized value.

        ```python
        >>> tenant = Tenant.objects.first()
        >>> tenant.cf
        {'primary_site': <Site: DM-NYC>, 'cust_id': 'DMI01', 'is_active': True}
        ```
        
    "
770,"def test_arf_layout_negative_a_check(self):
        
        G = self.Gs
        pytest.raises(ValueError, nx.arf_layout, G=G, a=-1)

","
        Checks input parameters correctly raises errors. For example,  `a` should be larger than 1
        ",14,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_arf_layout_negative_a_check(self):
        
        G = self.Gs
        pytest.raises(ValueError, nx.arf_layout, G=G, a=-1)


        ```
    ###Assistant : 
        Checks input parameters correctly raises errors. For example,  `a` should be larger than 1
        
    "
771,"async def test_connected_device_registered(hass):
    

    registry = mock_registry(hass)
    dispatches = []
",Test dispatch on connected device being registered.,7,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_connected_device_registered(hass):
    

    registry = mock_registry(hass)
    dispatches = []

        ```
    ###Assistant : Test dispatch on connected device being registered.
    "
772,"def test_ddp_sharded_strategy_checkpoint_multi_gpu(tmpdir):
    
    model = BoringModel()
    trainer = Trainer(gpus=2, strategy=""ddp_sharded_spawn"", fast_dev_run=True)

    trainer.fit(model)

    checkpoint_path = os.path.join(tmpdir, ""model.pt"")
    trainer.save_checkpoint(checkpoint_path)
    saved_model = BoringModel.load_from_checkpoint(checkpoint_path)

    # Assert model parameters are identical after loading
    for ddp_param, shard_param in zip(model.parameters(), saved_model.parameters()):
        assert torch.equal(ddp_param.to(""cpu""), shard_param)


@RunIf(min_gpus=2, skip_windows=True, fairscale=True)",Test to ensure that checkpoint is saved correctly when using multiple GPUs.,12,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ddp_sharded_strategy_checkpoint_multi_gpu(tmpdir):
    
    model = BoringModel()
    trainer = Trainer(gpus=2, strategy=""ddp_sharded_spawn"", fast_dev_run=True)

    trainer.fit(model)

    checkpoint_path = os.path.join(tmpdir, ""model.pt"")
    trainer.save_checkpoint(checkpoint_path)
    saved_model = BoringModel.load_from_checkpoint(checkpoint_path)

    # Assert model parameters are identical after loading
    for ddp_param, shard_param in zip(model.parameters(), saved_model.parameters()):
        assert torch.equal(ddp_param.to(""cpu""), shard_param)


@RunIf(min_gpus=2, skip_windows=True, fairscale=True)
        ```
    ###Assistant : Test to ensure that checkpoint is saved correctly when using multiple GPUs.
    "
773,"def autoscale(self) -> None:
        
        for deployment_name, (
            deployment_info,
            route_prefix,
        ) in self.list_deployments().items():
            deployment_config = deployment_info.deployment_config
            autoscaling_policy = deployment_info.autoscaling_policy

            if autoscaling_policy is None:
                continue

            replicas = self.deployment_state_manager._deployment_states[
                deployment_name
            ]._replicas
            running_replicas = replicas.get([ReplicaState.RUNNING])

            current_num_ongoing_requests = []
            for replica in running_replicas:
                replica_tag = replica.replica_tag
                num_ongoing_requests = self.autoscaling_metrics_store.window_average(
                    replica_tag,
                    time.time() - autoscaling_policy.config.look_back_period_s,
                )
                if num_ongoing_requests is not None:
                    current_num_ongoing_requests.append(num_ongoing_requests)

            if len(current_num_ongoing_requests) == 0:
                continue

            new_deployment_config = deployment_config.copy()

            decision_num_replicas = autoscaling_policy.get_decision_num_replicas(
                current_num_ongoing_requests=current_num_ongoing_requests,
                curr_target_num_replicas=deployment_config.num_replicas,
            )
            new_deployment_config.num_replicas = decision_num_replicas

            new_deployment_info = copy(deployment_info)
            new_deployment_info.deployment_config = new_deployment_config

            goal_id, updating = self.deployment_state_manager.deploy(
                deployment_name, new_deployment_info
            )
",Updates autoscaling deployments with calculated num_replicas.,6,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def autoscale(self) -> None:
        
        for deployment_name, (
            deployment_info,
            route_prefix,
        ) in self.list_deployments().items():
            deployment_config = deployment_info.deployment_config
            autoscaling_policy = deployment_info.autoscaling_policy

            if autoscaling_policy is None:
                continue

            replicas = self.deployment_state_manager._deployment_states[
                deployment_name
            ]._replicas
            running_replicas = replicas.get([ReplicaState.RUNNING])

            current_num_ongoing_requests = []
            for replica in running_replicas:
                replica_tag = replica.replica_tag
                num_ongoing_requests = self.autoscaling_metrics_store.window_average(
                    replica_tag,
                    time.time() - autoscaling_policy.config.look_back_period_s,
                )
                if num_ongoing_requests is not None:
                    current_num_ongoing_requests.append(num_ongoing_requests)

            if len(current_num_ongoing_requests) == 0:
                continue

            new_deployment_config = deployment_config.copy()

            decision_num_replicas = autoscaling_policy.get_decision_num_replicas(
                current_num_ongoing_requests=current_num_ongoing_requests,
                curr_target_num_replicas=deployment_config.num_replicas,
            )
            new_deployment_config.num_replicas = decision_num_replicas

            new_deployment_info = copy(deployment_info)
            new_deployment_info.deployment_config = new_deployment_config

            goal_id, updating = self.deployment_state_manager.deploy(
                deployment_name, new_deployment_info
            )

        ```
    ###Assistant : Updates autoscaling deployments with calculated num_replicas.
    "
774,"def match_state_dict(model_state_dict, weight_state_dict):
    

    model_keys = sorted(model_state_dict.keys())
    weight_keys = sorted(weight_state_dict.keys())
","
    Match between the model state dict and pretrained weight state dict.
    Return the matched state dict.

    The method supposes that all the names in pretrained weight state dict are
    subclass of the names in models`, if the prefix 'backbone.' in pretrained weight
    keys is stripped. And we could get the candidates for each model key. Then we
    select the name with the longest matched size as the final match result. For
    example, the model state dict has the name of
    'backbone.res2.res2a.branch2a.conv.weight' and the pretrained weight as
    name of 'res2.res2a.branch2a.conv.weight' and 'branch2a.conv.weight'. We
    match the 'res2.res2a.branch2a.conv.weight' to the model key.
    ",99,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def match_state_dict(model_state_dict, weight_state_dict):
    

    model_keys = sorted(model_state_dict.keys())
    weight_keys = sorted(weight_state_dict.keys())

        ```
    ###Assistant : 
    Match between the model state dict and pretrained weight state dict.
    Return the matched state dict.

    The method supposes that all the names in pretrained weight state dict are
    subclass of the names in models`, if the prefix 'backbone.' in pretrained weight
    keys is stripped. And we could get the candidates for each model key. Then we
    select the name with the longest matched size as the final match result. For
    example, the model state dict has the name of
    'backbone.res2.res2a.branch2a.conv.weight' and the pretrained weight as
    name of 'res2.res2a.branch2a.conv.weight' and 'branch2a.conv.weight'. We
    match the 'res2.res2a.branch2a.conv.weight' to the model key.
    
    "
775,"def local_node_connectivity(G, source, target, cutoff=None):
    
    if target == source:
        raise nx.NetworkXError(""source and target have to be different nodes."")

    # Maximum possible node independent paths
    if G.is_directed():
        possible = min(G.out_degree(source), G.in_degree(target))
    else:
        possible = min(G.degree(source), G.degree(target))

    K = 0
    if not possible:
        return K

    if cutoff is None:
        cutoff = float(""inf"")

    exclude = set()
    for i in range(min(possible, cutoff)):
        try:
            path = _bidirectional_shortest_path(G, source, target, exclude)
            exclude.update(set(path))
            K += 1
        except nx.NetworkXNoPath:
            break

    return K

","Compute node connectivity between source and target.

    Pairwise or local node connectivity between two distinct and nonadjacent
    nodes is the minimum number of nodes that must be removed (minimum
    separating cutset) to disconnect them. By Menger's theorem, this is equal
    to the number of node independent paths (paths that share no nodes other
    than source and target). Which is what we compute in this function.

    This algorithm is a fast approximation that gives an strict lower
    bound on the actual number of node independent paths between two nodes [1]_.
    It works for both directed and undirected graphs.

    Parameters
    ----------

    G : NetworkX graph

    source : node
        Starting node for node connectivity

    target : node
        Ending node for node connectivity

    cutoff : integer
        Maximum node connectivity to consider. If None, the minimum degree
        of source or target is used as a cutoff. Default value None.

    Returns
    -------
    k: integer
       pairwise node connectivity

    Examples
    --------
    >>> # Platonic octahedral graph has node connectivity 4
    >>> # for each non adjacent node pair
    >>> from networkx.algorithms import approximation as approx
    >>> G = nx.octahedral_graph()
    >>> approx.local_node_connectivity(G, 0, 5)
    4

    Notes
    -----
    This algorithm [1]_ finds node independents paths between two nodes by
    computing their shortest path using BFS, marking the nodes of the path
    found as 'used' and then searching other shortest paths excluding the
    nodes marked as used until no more paths exist. It is not exact because
    a shortest path could use nodes that, if the path were longer, may belong
    to two different node independent paths. Thus it only guarantees an
    strict lower bound on node connectivity.

    Note that the authors propose a further refinement, losing accuracy and
    gaining speed, which is not implemented yet.

    See also
    --------
    all_pairs_node_connectivity
    node_connectivity

    References
    ----------
    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
        http://eclectic.ss.uci.edu/~drwhite/working.pdf

    ",314,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def local_node_connectivity(G, source, target, cutoff=None):
    
    if target == source:
        raise nx.NetworkXError(""source and target have to be different nodes."")

    # Maximum possible node independent paths
    if G.is_directed():
        possible = min(G.out_degree(source), G.in_degree(target))
    else:
        possible = min(G.degree(source), G.degree(target))

    K = 0
    if not possible:
        return K

    if cutoff is None:
        cutoff = float(""inf"")

    exclude = set()
    for i in range(min(possible, cutoff)):
        try:
            path = _bidirectional_shortest_path(G, source, target, exclude)
            exclude.update(set(path))
            K += 1
        except nx.NetworkXNoPath:
            break

    return K


        ```
    ###Assistant : Compute node connectivity between source and target.

    Pairwise or local node connectivity between two distinct and nonadjacent
    nodes is the minimum number of nodes that must be removed (minimum
    separating cutset) to disconnect them. By Menger's theorem, this is equal
    to the number of node independent paths (paths that share no nodes other
    than source and target). Which is what we compute in this function.

    This algorithm is a fast approximation that gives an strict lower
    bound on the actual number of node independent paths between two nodes [1]_.
    It works for both directed and undirected graphs.

    Parameters
    ----------

    G : NetworkX graph

    source : node
        Starting node for node connectivity

    target : node
        Ending node for node connectivity

    cutoff : integer
        Maximum node connectivity to consider. If None, the minimum degree
        of source or target is used as a cutoff. Default value None.

    Returns
    -------
    k: integer
       pairwise node connectivity

    Examples
    --------
    >>> # Platonic octahedral graph has node connectivity 4
    >>> # for each non adjacent node pair
    >>> from networkx.algorithms import approximation as approx
    >>> G = nx.octahedral_graph()
    >>> approx.local_node_connectivity(G, 0, 5)
    4

    Notes
    -----
    This algorithm [1]_ finds node independents paths between two nodes by
    computing their shortest path using BFS, marking the nodes of the path
    found as 'used' and then searching other shortest paths excluding the
    nodes marked as used until no more paths exist. It is not exact because
    a shortest path could use nodes that, if the path were longer, may belong
    to two different node independent paths. Thus it only guarantees an
    strict lower bound on node connectivity.

    Note that the authors propose a further refinement, losing accuracy and
    gaining speed, which is not implemented yet.

    See also
    --------
    all_pairs_node_connectivity
    node_connectivity

    References
    ----------
    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
        http://eclectic.ss.uci.edu/~drwhite/working.pdf

    
    "
776,"def _copartition(self, axis, other, how, sort, force_repartition=False):
        
        if isinstance(other, type(self)):
            other = [other]

        self_index = self.axes[axis]
        others_index = [o.axes[axis] for o in other]
        joined_index, make_reindexer = self._join_index_objects(
            axis, [self_index] + others_index, how, sort
        )

        frames = [self] + other
        non_empty_frames_idx = [
            i for i, o in enumerate(frames) if o._partitions.size != 0
        ]

        # If all frames are empty
        if len(non_empty_frames_idx) == 0:
            return (
                self._partitions,
                [o._partitions for o in other],
                joined_index,
                # There are no partition sizes because the resulting dataframe
                # has no partitions.
                [],
            )

        base_frame_idx = non_empty_frames_idx[0]
        other_frames = frames[base_frame_idx + 1 :]

        # Picking first non-empty frame
        base_frame = frames[non_empty_frames_idx[0]]
        base_index = base_frame.axes[axis]

        # define conditions for reindexing and repartitioning `self` frame
        do_reindex_base = not base_index.equals(joined_index)
        do_repartition_base = force_repartition or do_reindex_base

        # Perform repartitioning and reindexing for `base_frame` if needed.
        # Also define length of base and frames. We will need to know the
        # lengths for alignment.
        if do_repartition_base:
            reindexed_base = base_frame._partition_mgr_cls.map_axis_partitions(
                axis,
                base_frame._partitions,
                make_reindexer(do_reindex_base, base_frame_idx),
            )
            if axis:
                base_lengths = [obj.width() for obj in reindexed_base[0]]
            else:
                base_lengths = [obj.length() for obj in reindexed_base.T[0]]
        else:
            reindexed_base = base_frame._partitions
            base_lengths = self._column_widths if axis else self._row_lengths

        others_lengths = [o._axes_lengths[axis] for o in other_frames]

        # define conditions for reindexing and repartitioning `other` frames
        do_reindex_others = [
            not o.axes[axis].equals(joined_index) for o in other_frames
        ]

        do_repartition_others = [None] * len(other_frames)
        for i in range(len(other_frames)):
            do_repartition_others[i] = (
                force_repartition
                or do_reindex_others[i]
                or others_lengths[i] != base_lengths
            )

        # perform repartitioning and reindexing for `other_frames` if needed
        reindexed_other_list = [None] * len(other_frames)
        for i in range(len(other_frames)):
            if do_repartition_others[i]:
                # indices of others frame start from `base_frame_idx` + 1
                reindexed_other_list[i] = other_frames[
                    i
                ]._partition_mgr_cls.map_axis_partitions(
                    axis,
                    other_frames[i]._partitions,
                    make_reindexer(do_repartition_others[i], base_frame_idx + 1 + i),
                    lengths=base_lengths,
                )
            else:
                reindexed_other_list[i] = other_frames[i]._partitions
        reindexed_frames = (
            [frames[i]._partitions for i in range(base_frame_idx)]
            + [reindexed_base]
            + reindexed_other_list
        )
        return (reindexed_frames[0], reindexed_frames[1:], joined_index, base_lengths)
","
        Copartition two Modin DataFrames.

        Perform aligning of partitions, index and partition blocks.

        Parameters
        ----------
        axis : {0, 1}
            Axis to copartition along (0 - rows, 1 - columns).
        other : PandasDataframe
            Other Modin DataFrame(s) to copartition against.
        how : str
            How to manage joining the index object (""left"", ""right"", etc.).
        sort : bool
            Whether sort the joined index or not.
        force_repartition : bool, default: False
            Whether force the repartitioning or not. By default,
            this method will skip repartitioning if it is possible. This is because
            reindexing is extremely inefficient. Because this method is used to
            `join` or `append`, it is vital that the internal indices match.

        Returns
        -------
        tuple
            Tuple containing:
                1) 2-d NumPy array of aligned left partitions
                2) list of 2-d NumPy arrays of aligned right partitions
                3) joined index along ``axis``
                4) List with sizes of partitions along axis that partitioning
                   was done on. This list will be empty if and only if all
                   the frames are empty.
        ",161,304,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _copartition(self, axis, other, how, sort, force_repartition=False):
        
        if isinstance(other, type(self)):
            other = [other]

        self_index = self.axes[axis]
        others_index = [o.axes[axis] for o in other]
        joined_index, make_reindexer = self._join_index_objects(
            axis, [self_index] + others_index, how, sort
        )

        frames = [self] + other
        non_empty_frames_idx = [
            i for i, o in enumerate(frames) if o._partitions.size != 0
        ]

        # If all frames are empty
        if len(non_empty_frames_idx) == 0:
            return (
                self._partitions,
                [o._partitions for o in other],
                joined_index,
                # There are no partition sizes because the resulting dataframe
                # has no partitions.
                [],
            )

        base_frame_idx = non_empty_frames_idx[0]
        other_frames = frames[base_frame_idx + 1 :]

        # Picking first non-empty frame
        base_frame = frames[non_empty_frames_idx[0]]
        base_index = base_frame.axes[axis]

        # define conditions for reindexing and repartitioning `self` frame
        do_reindex_base = not base_index.equals(joined_index)
        do_repartition_base = force_repartition or do_reindex_base

        # Perform repartitioning and reindexing for `base_frame` if needed.
        # Also define length of base and frames. We will need to know the
        # lengths for alignment.
        if do_repartition_base:
            reindexed_base = base_frame._partition_mgr_cls.map_axis_partitions(
                axis,
                base_frame._partitions,
                make_reindexer(do_reindex_base, base_frame_idx),
            )
            if axis:
                base_lengths = [obj.width() for obj in reindexed_base[0]]
            else:
                base_lengths = [obj.length() for obj in reindexed_base.T[0]]
        else:
            reindexed_base = base_frame._partitions
            base_lengths = self._column_widths if axis else self._row_lengths

        others_lengths = [o._axes_lengths[axis] for o in other_frames]

        # define conditions for reindexing and repartitioning `other` frames
        do_reindex_others = [
            not o.axes[axis].equals(joined_index) for o in other_frames
        ]

        do_repartition_others = [None] * len(other_frames)
        for i in range(len(other_frames)):
            do_repartition_others[i] = (
                force_repartition
                or do_reindex_others[i]
                or others_lengths[i] != base_lengths
            )

        # perform repartitioning and reindexing for `other_frames` if needed
        reindexed_other_list = [None] * len(other_frames)
        for i in range(len(other_frames)):
            if do_repartition_others[i]:
                # indices of others frame start from `base_frame_idx` + 1
                reindexed_other_list[i] = other_frames[
                    i
                ]._partition_mgr_cls.map_axis_partitions(
                    axis,
                    other_frames[i]._partitions,
                    make_reindexer(do_repartition_others[i], base_frame_idx + 1 + i),
                    lengths=base_lengths,
                )
            else:
                reindexed_other_list[i] = other_frames[i]._partitions
        reindexed_frames = (
            [frames[i]._partitions for i in range(base_frame_idx)]
            + [reindexed_base]
            + reindexed_other_list
        )
        return (reindexed_frames[0], reindexed_frames[1:], joined_index, base_lengths)

        ```
    ###Assistant : 
        Copartition two Modin DataFrames.

        Perform aligning of partitions, index and partition blocks.

        Parameters
        ----------
        axis : {0, 1}
            Axis to copartition along (0 - rows, 1 - columns).
        other : PandasDataframe
            Other Modin DataFrame(s) to copartition against.
        how : str
            How to manage joining the index object (""left"", ""right"", etc.).
        sort : bool
            Whether sort the joined index or not.
        force_repartition : bool, default: False
            Whether force the repartitioning or not. By default,
            this method will skip repartitioning if it is possible. This is because
            reindexing is extremely inefficient. Because this method is used to
            `join` or `append`, it is vital that the internal indices match.

        Returns
        -------
        tuple
            Tuple containing:
                1) 2-d NumPy array of aligned left partitions
                2) list of 2-d NumPy arrays of aligned right partitions
                3) joined index along ``axis``
                4) List with sizes of partitions along axis that partitioning
                   was done on. This list will be empty if and only if all
                   the frames are empty.
        
    "
777,"def size(self) -> int:
        
        # override Index.size to avoid materializing _values
        return len(self)

    # --------------------------------------------------------------------
    # Levels Methods
","
        Return the number of elements in the underlying data.
        ",9,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def size(self) -> int:
        
        # override Index.size to avoid materializing _values
        return len(self)

    # --------------------------------------------------------------------
    # Levels Methods

        ```
    ###Assistant : 
        Return the number of elements in the underlying data.
        
    "
778,"def groupby_agg(self, by, axis, agg, groupby_args, **kwargs):
        
        # Currently we only expect 'by' to be a projection of the same frame.
        # If 'by' holds a list of columns/series, then we create such projection
        # to re-use code.
        if not isinstance(by, DFAlgQueryCompiler):
            if is_list_like(by):
                by_cols = []
                by_frames = []
                for obj in by:
                    if isinstance(obj, str):
                        by_cols.append(obj)
                    elif hasattr(obj, ""_modin_frame""):
                        by_frames.append(obj._modin_frame)
                    else:
                        raise NotImplementedError(""unsupported groupby args"")
                by_cols = Index.__new__(Index, data=by_cols, dtype=self.columns.dtype)
                by_frame = self.mask(col_labels=by_cols)
                if by_frames:
                    by_frame = by_frame.concat(
                        axis=1, other_modin_frames=by_frames, ignore_index=True
                    )
            else:
                raise NotImplementedError(""unsupported groupby args"")
        else:
            by_frame = by._modin_frame

        if axis != 0:
            raise NotImplementedError(""groupby is supported for axis = 0 only"")

        base = by_frame._find_common_projections_base(self)
        if base is None:
            raise NotImplementedError(""unsupported groupby args"")

        if groupby_args[""level""] is not None:
            raise NotImplementedError(""levels are not supported for groupby"")

        drop = kwargs.get(""drop"", True)
        as_index = groupby_args.get(""as_index"", True)
        groupby_cols = by_frame.columns
        if isinstance(agg, dict):
            agg_cols = agg.keys()
        elif not drop:
            # If 'by' data came from a different frame then 'self-aggregation'
            # columns are more prioritized.
            agg_cols = self.columns
        else:
            agg_cols = [col for col in self.columns if col not in groupby_cols]

        # Mimic pandas behaviour: pandas does not allow for aggregation to be empty
        # in case of multi-column 'by'.
        if not as_index and len(agg_cols) == 0 and len(groupby_cols) > 1:
            agg_cols = self.columns

        # Create new base where all required columns are computed. We don't allow
        # complex expressions to be a group key or an aggeregate operand.
        allowed_nodes = (FrameNode, TransformNode)
        if not isinstance(by_frame._op, allowed_nodes):
            raise NotImplementedError(
                ""OmniSci doesn't allow complex expression to be a group key. ""
                f""The only allowed frame nodes are: {tuple(o.__name__ for o in allowed_nodes)}, ""
                f""met '{type(by_frame._op).__name__}'.""
            )

        col_to_delete_template = ""__delete_me_{name}""
","
        Groupby with aggregation operation.

        Parameters
        ----------
        by : DFAlgQueryCompiler or list-like of str
            Grouping keys.
        axis : {0, 1}
            Only rows groupby is supported, so should be 0.
        agg : str or dict
            Aggregates to compute.
        groupby_args : dict
            Additional groupby args.
        **kwargs : dict
            Keyword args. Currently ignored.

        Returns
        -------
        OmnisciOnNativeDataframe
            The new frame.
        ",55,278,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def groupby_agg(self, by, axis, agg, groupby_args, **kwargs):
        
        # Currently we only expect 'by' to be a projection of the same frame.
        # If 'by' holds a list of columns/series, then we create such projection
        # to re-use code.
        if not isinstance(by, DFAlgQueryCompiler):
            if is_list_like(by):
                by_cols = []
                by_frames = []
                for obj in by:
                    if isinstance(obj, str):
                        by_cols.append(obj)
                    elif hasattr(obj, ""_modin_frame""):
                        by_frames.append(obj._modin_frame)
                    else:
                        raise NotImplementedError(""unsupported groupby args"")
                by_cols = Index.__new__(Index, data=by_cols, dtype=self.columns.dtype)
                by_frame = self.mask(col_labels=by_cols)
                if by_frames:
                    by_frame = by_frame.concat(
                        axis=1, other_modin_frames=by_frames, ignore_index=True
                    )
            else:
                raise NotImplementedError(""unsupported groupby args"")
        else:
            by_frame = by._modin_frame

        if axis != 0:
            raise NotImplementedError(""groupby is supported for axis = 0 only"")

        base = by_frame._find_common_projections_base(self)
        if base is None:
            raise NotImplementedError(""unsupported groupby args"")

        if groupby_args[""level""] is not None:
            raise NotImplementedError(""levels are not supported for groupby"")

        drop = kwargs.get(""drop"", True)
        as_index = groupby_args.get(""as_index"", True)
        groupby_cols = by_frame.columns
        if isinstance(agg, dict):
            agg_cols = agg.keys()
        elif not drop:
            # If 'by' data came from a different frame then 'self-aggregation'
            # columns are more prioritized.
            agg_cols = self.columns
        else:
            agg_cols = [col for col in self.columns if col not in groupby_cols]

        # Mimic pandas behaviour: pandas does not allow for aggregation to be empty
        # in case of multi-column 'by'.
        if not as_index and len(agg_cols) == 0 and len(groupby_cols) > 1:
            agg_cols = self.columns

        # Create new base where all required columns are computed. We don't allow
        # complex expressions to be a group key or an aggeregate operand.
        allowed_nodes = (FrameNode, TransformNode)
        if not isinstance(by_frame._op, allowed_nodes):
            raise NotImplementedError(
                ""OmniSci doesn't allow complex expression to be a group key. ""
                f""The only allowed frame nodes are: {tuple(o.__name__ for o in allowed_nodes)}, ""
                f""met '{type(by_frame._op).__name__}'.""
            )

        col_to_delete_template = ""__delete_me_{name}""

        ```
    ###Assistant : 
        Groupby with aggregation operation.

        Parameters
        ----------
        by : DFAlgQueryCompiler or list-like of str
            Grouping keys.
        axis : {0, 1}
            Only rows groupby is supported, so should be 0.
        agg : str or dict
            Aggregates to compute.
        groupby_args : dict
            Additional groupby args.
        **kwargs : dict
            Keyword args. Currently ignored.

        Returns
        -------
        OmnisciOnNativeDataframe
            The new frame.
        
    "
779,"def delete_subscription_from_snuba(query_subscription_id, **kwargs):
    
    try:
        subscription = QuerySubscription.objects.get(id=query_subscription_id)
    except QuerySubscription.DoesNotExist:
        metrics.incr(""snuba.subscriptions.delete.subscription_does_not_exist"")
        return

    if subscription.status not in [
        QuerySubscription.Status.DELETING.value,
        QuerySubscription.Status.DISABLED.value,
    ]:
        metrics.incr(""snuba.subscriptions.delete.incorrect_status"")
        return

    if subscription.subscription_id is not None:
        query_dataset = QueryDatasets(subscription.snuba_query.dataset)
        entity_key = get_entity_key_from_snuba_query(
            subscription.snuba_query, subscription.project.organization_id, subscription.project_id
        )
        _delete_from_snuba(
            query_dataset,
            subscription.subscription_id,
            entity_key,
        )

    if subscription.status == QuerySubscription.Status.DELETING.value:
        subscription.delete()
    else:
        subscription.update(subscription_id=None)

","
    Task to delete a corresponding subscription in Snuba from a `QuerySubscription` in
    Sentry.
    If the local subscription is marked for deletion (as opposed to disabled),
    then we delete the local subscription once we've successfully removed from Snuba.
    ",37,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_subscription_from_snuba(query_subscription_id, **kwargs):
    
    try:
        subscription = QuerySubscription.objects.get(id=query_subscription_id)
    except QuerySubscription.DoesNotExist:
        metrics.incr(""snuba.subscriptions.delete.subscription_does_not_exist"")
        return

    if subscription.status not in [
        QuerySubscription.Status.DELETING.value,
        QuerySubscription.Status.DISABLED.value,
    ]:
        metrics.incr(""snuba.subscriptions.delete.incorrect_status"")
        return

    if subscription.subscription_id is not None:
        query_dataset = QueryDatasets(subscription.snuba_query.dataset)
        entity_key = get_entity_key_from_snuba_query(
            subscription.snuba_query, subscription.project.organization_id, subscription.project_id
        )
        _delete_from_snuba(
            query_dataset,
            subscription.subscription_id,
            entity_key,
        )

    if subscription.status == QuerySubscription.Status.DELETING.value:
        subscription.delete()
    else:
        subscription.update(subscription_id=None)


        ```
    ###Assistant : 
    Task to delete a corresponding subscription in Snuba from a `QuerySubscription` in
    Sentry.
    If the local subscription is marked for deletion (as opposed to disabled),
    then we delete the local subscription once we've successfully removed from Snuba.
    
    "
780,"def test_changing_timer_with_messages_shown(qtbot, view, config_stub):
    
    config_stub.val.messages.timeout = 900000  # 15s
    view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    with qtbot.wait_signal(view._clear_timer.timeout):
        config_stub.val.messages.timeout = 100


@pytest.mark.parametrize('count, expected', [(1, 100), (3, 300),
                                             (5, 500), (7, 500)])","When we change messages.timeout, the timer should be restarted.",9,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_changing_timer_with_messages_shown(qtbot, view, config_stub):
    
    config_stub.val.messages.timeout = 900000  # 15s
    view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    with qtbot.wait_signal(view._clear_timer.timeout):
        config_stub.val.messages.timeout = 100


@pytest.mark.parametrize('count, expected', [(1, 100), (3, 300),
                                             (5, 500), (7, 500)])
        ```
    ###Assistant : When we change messages.timeout, the timer should be restarted.
    "
781,"def get_events(start, end, filters=None):
	
	from frappe.desk.calendar import get_event_conditions

	events = []

	event_color = {
		""Pending"": ""#fff4f0"",
		""Under Review"": ""#d3e8fc"",
		""Cleared"": ""#eaf5ed"",
		""Rejected"": ""#fce7e7"",
	}

	conditions = get_event_conditions(""Interview"", filters)

	interviews = frappe.db.sql(
		.format(
			conditions=conditions
		),
		{""start"": start, ""end"": end},
		as_dict=True,
		update={""allDay"": 0},
	)

	for d in interviews:
		subject_data = []
		for field in [""name"", ""job_applicant"", ""interview_round""]:
			if not d.get(field):
				continue
			subject_data.append(d.get(field))

		color = event_color.get(d.status)
		interview_data = {
			""from"": get_datetime(""%s %s"" % (d.scheduled_on, d.from_time or ""00:00:00"")),
			""to"": get_datetime(""%s %s"" % (d.scheduled_on, d.to_time or ""00:00:00"")),
			""name"": d.name,
			""subject"": ""\n"".join(subject_data),
			""color"": color if color else ""#89bcde"",
		}

		events.append(interview_data)

	return events
","Returns events for Gantt / Calendar view rendering.

	:param start: Start date-time.
	:param end: End date-time.
	:param filters: Filters (JSON).
	
			SELECT DISTINCT
				`tabInterview`.name, `tabInterview`.job_applicant, `tabInterview`.interview_round,
				`tabInterview`.scheduled_on, `tabInterview`.status, `tabInterview`.from_time as from_time,
				`tabInterview`.to_time as to_time
			from
				`tabInterview`
			where
				(`tabInterview`.scheduled_on between %(start)s and %(end)s)
				and docstatus != 2
				{conditions}
			",46,96,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_events(start, end, filters=None):
	
	from frappe.desk.calendar import get_event_conditions

	events = []

	event_color = {
		""Pending"": ""#fff4f0"",
		""Under Review"": ""#d3e8fc"",
		""Cleared"": ""#eaf5ed"",
		""Rejected"": ""#fce7e7"",
	}

	conditions = get_event_conditions(""Interview"", filters)

	interviews = frappe.db.sql(
		.format(
			conditions=conditions
		),
		{""start"": start, ""end"": end},
		as_dict=True,
		update={""allDay"": 0},
	)

	for d in interviews:
		subject_data = []
		for field in [""name"", ""job_applicant"", ""interview_round""]:
			if not d.get(field):
				continue
			subject_data.append(d.get(field))

		color = event_color.get(d.status)
		interview_data = {
			""from"": get_datetime(""%s %s"" % (d.scheduled_on, d.from_time or ""00:00:00"")),
			""to"": get_datetime(""%s %s"" % (d.scheduled_on, d.to_time or ""00:00:00"")),
			""name"": d.name,
			""subject"": ""\n"".join(subject_data),
			""color"": color if color else ""#89bcde"",
		}

		events.append(interview_data)

	return events

        ```
    ###Assistant : Returns events for Gantt / Calendar view rendering.

	:param start: Start date-time.
	:param end: End date-time.
	:param filters: Filters (JSON).
	
			SELECT DISTINCT
				`tabInterview`.name, `tabInterview`.job_applicant, `tabInterview`.interview_round,
				`tabInterview`.scheduled_on, `tabInterview`.status, `tabInterview`.from_time as from_time,
				`tabInterview`.to_time as to_time
			from
				`tabInterview`
			where
				(`tabInterview`.scheduled_on between %(start)s and %(end)s)
				and docstatus != 2
				{conditions}
			
    "
782,"def testFuncTrainableCheckpointConfigValidation(self):
        
        with self.assertRaises(ValueError):
            Experiment(
                name=""foo"",
                run=""f1"",  # Will point to a wrapped function trainable
                checkpoint_config=CheckpointConfig(checkpoint_at_end=True),
            )
        with self.assertRaises(ValueError):
            Experiment(
                name=""foo"",
                run=""f1"",
                checkpoint_config=CheckpointConfig(checkpoint_frequency=1),
            )
        with self.assertRaises(ValueError):
            Experiment(
                name=""foo"",
                run=lambda config: 1,
                checkpoint_config=CheckpointConfig(checkpoint_at_end=True),
            )

","Raise an error when trying to specify checkpoint_at_end/checkpoint_frequency
        with a function trainable.",12,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def testFuncTrainableCheckpointConfigValidation(self):
        
        with self.assertRaises(ValueError):
            Experiment(
                name=""foo"",
                run=""f1"",  # Will point to a wrapped function trainable
                checkpoint_config=CheckpointConfig(checkpoint_at_end=True),
            )
        with self.assertRaises(ValueError):
            Experiment(
                name=""foo"",
                run=""f1"",
                checkpoint_config=CheckpointConfig(checkpoint_frequency=1),
            )
        with self.assertRaises(ValueError):
            Experiment(
                name=""foo"",
                run=lambda config: 1,
                checkpoint_config=CheckpointConfig(checkpoint_at_end=True),
            )


        ```
    ###Assistant : Raise an error when trying to specify checkpoint_at_end/checkpoint_frequency
        with a function trainable.
    "
783,"def create_perspective_transform(src, dst, round=False, splat_args=False):
    
    try:
        transform_matrix = create_perspective_transform_matrix(src, dst)
        error = None
    except np.linalg.LinAlgError as e:
        transform_matrix = np.identity(3, dtype=np.float)
        error = ""invalid input quads (%s and %s): %s"" %(src, dst, e)
        error = error.replace(""\n"", """")

    to_eval = ""def perspective_transform(%s):\n"" %(
        splat_args and ""*pt"" or ""pt"",
    )
    to_eval += ""  res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n""
    to_eval += ""  res = res / res[2]\n""
    if round:
        to_eval += ""  return (int(round(res[0][0])), int(round(res[1][0])))\n""
    else:
        to_eval += ""  return (res[0][0], res[1][0])\n""
    locals = {
        ""transform_matrix"": transform_matrix,
    }
    locals.update(globals())
    exec(to_eval,locals,locals)
    res = locals[""perspective_transform""]
    res.matrix = transform_matrix
    res.error = error
    return res

"," Returns a function which will transform points in quadrilateral
        ``src`` to the corresponding points on quadrilateral ``dst``::

            >>> transform = create_perspective_transform(
            ...     [(0, 0), (10, 0), (10, 10), (0, 10)],
            ...     [(50, 50), (100, 50), (100, 100), (50, 100)],
            ... )
            >>> transform((5, 5))
            (74.99999999999639, 74.999999999999957)

        If ``round`` is ``True`` then points will be rounded to the nearest
        integer and integer values will be returned.

            >>> transform = create_perspective_transform(
            ...     [(0, 0), (10, 0), (10, 10), (0, 10)],
            ...     [(50, 50), (100, 50), (100, 100), (50, 100)],
            ...     round=True,
            ... )
            >>> transform((5, 5))
            (75, 75)

        If ``splat_args`` is ``True`` the function will accept two arguments
        instead of a tuple.

            >>> transform = create_perspective_transform(
            ...     [(0, 0), (10, 0), (10, 10), (0, 10)],
            ...     [(50, 50), (100, 50), (100, 100), (50, 100)],
            ...     splat_args=True,
            ... )
            >>> transform(5, 5)
            (74.99999999999639, 74.999999999999957)

        If the input values yield an invalid transformation matrix an identity
        function will be returned and the ``error`` attribute will be set to a
        description of the error::

            >>> tranform = create_perspective_transform(
            ...     np.zeros((4, 2)),
            ...     np.zeros((4, 2)),
            ... )
            >>> transform((5, 5))
            (5.0, 5.0)
            >>> transform.error
            'invalid input quads (...): Singular matrix
        ",194,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_perspective_transform(src, dst, round=False, splat_args=False):
    
    try:
        transform_matrix = create_perspective_transform_matrix(src, dst)
        error = None
    except np.linalg.LinAlgError as e:
        transform_matrix = np.identity(3, dtype=np.float)
        error = ""invalid input quads (%s and %s): %s"" %(src, dst, e)
        error = error.replace(""\n"", """")

    to_eval = ""def perspective_transform(%s):\n"" %(
        splat_args and ""*pt"" or ""pt"",
    )
    to_eval += ""  res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n""
    to_eval += ""  res = res / res[2]\n""
    if round:
        to_eval += ""  return (int(round(res[0][0])), int(round(res[1][0])))\n""
    else:
        to_eval += ""  return (res[0][0], res[1][0])\n""
    locals = {
        ""transform_matrix"": transform_matrix,
    }
    locals.update(globals())
    exec(to_eval,locals,locals)
    res = locals[""perspective_transform""]
    res.matrix = transform_matrix
    res.error = error
    return res


        ```
    ###Assistant :  Returns a function which will transform points in quadrilateral
        ``src`` to the corresponding points on quadrilateral ``dst``::

            >>> transform = create_perspective_transform(
            ...     [(0, 0), (10, 0), (10, 10), (0, 10)],
            ...     [(50, 50), (100, 50), (100, 100), (50, 100)],
            ... )
            >>> transform((5, 5))
            (74.99999999999639, 74.999999999999957)

        If ``round`` is ``True`` then points will be rounded to the nearest
        integer and integer values will be returned.

            >>> transform = create_perspective_transform(
            ...     [(0, 0), (10, 0), (10, 10), (0, 10)],
            ...     [(50, 50), (100, 50), (100, 100), (50, 100)],
            ...     round=True,
            ... )
            >>> transform((5, 5))
            (75, 75)

        If ``splat_args`` is ``True`` the function will accept two arguments
        instead of a tuple.

            >>> transform = create_perspective_transform(
            ...     [(0, 0), (10, 0), (10, 10), (0, 10)],
            ...     [(50, 50), (100, 50), (100, 100), (50, 100)],
            ...     splat_args=True,
            ... )
            >>> transform(5, 5)
            (74.99999999999639, 74.999999999999957)

        If the input values yield an invalid transformation matrix an identity
        function will be returned and the ``error`` attribute will be set to a
        description of the error::

            >>> tranform = create_perspective_transform(
            ...     np.zeros((4, 2)),
            ...     np.zeros((4, 2)),
            ... )
            >>> transform((5, 5))
            (5.0, 5.0)
            >>> transform.error
            'invalid input quads (...): Singular matrix
        
    "
784,"def get(self, url, cache=True, **kwargs):
        
        if not url.isValid():
            urlutils.invalid_url_error(url, ""start download"")
            return None

        req = QNetworkRequest(url)
        user_agent = websettings.user_agent(url)
        req.setHeader(QNetworkRequest.KnownHeaders.UserAgentHeader, user_agent)

        if not cache:
            req.setAttribute(QNetworkRequest.Attribute.CacheSaveControlAttribute, False)

        return self.get_request(req, **kwargs)
","Start a download with a link URL.

        Args:
            url: The URL to get, as QUrl
            cache: If set to False, don't cache the response.
            **kwargs: passed to get_request().

        Return:
            The created DownloadItem.
        ",32,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get(self, url, cache=True, **kwargs):
        
        if not url.isValid():
            urlutils.invalid_url_error(url, ""start download"")
            return None

        req = QNetworkRequest(url)
        user_agent = websettings.user_agent(url)
        req.setHeader(QNetworkRequest.KnownHeaders.UserAgentHeader, user_agent)

        if not cache:
            req.setAttribute(QNetworkRequest.Attribute.CacheSaveControlAttribute, False)

        return self.get_request(req, **kwargs)

        ```
    ###Assistant : Start a download with a link URL.

        Args:
            url: The URL to get, as QUrl
            cache: If set to False, don't cache the response.
            **kwargs: passed to get_request().

        Return:
            The created DownloadItem.
        
    "
785,"def test_send_join_partial_state(self):
        
        joining_user = ""@misspiggy:"" + self.OTHER_SERVER_NAME
        join_result = self._make_join(joining_user)

        join_event_dict = join_result[""event""]
        add_hashes_and_signatures(
            KNOWN_ROOM_VERSIONS[DEFAULT_ROOM_VERSION],
            join_event_dict,
            signature_name=self.OTHER_SERVER_NAME,
            signing_key=self.OTHER_SERVER_SIGNATURE_KEY,
        )
        channel = self.make_signed_federation_request(
            ""PUT"",
            f""/_matrix/federation/v2/send_join/{self._room_id}/x?org.matrix.msc3706.partial_state=true"",
            content=join_event_dict,
        )
        self.assertEquals(channel.code, 200, channel.json_body)

        # expect a reduced room state
        returned_state = [
            (ev[""type""], ev[""state_key""]) for ev in channel.json_body[""state""]
        ]
        self.assertCountEqual(
            returned_state,
            [
                (""m.room.create"", """"),
                (""m.room.power_levels"", """"),
                (""m.room.join_rules"", """"),
                (""m.room.history_visibility"", """"),
            ],
        )

        # the auth chain should not include anything already in ""state""
        returned_auth_chain_events = [
            (ev[""type""], ev[""state_key""]) for ev in channel.json_body[""auth_chain""]
        ]
        self.assertCountEqual(
            returned_auth_chain_events,
            [
                (""m.room.member"", ""@kermit:test""),
            ],
        )

        # the room should show that the new user is a member
        r = self.get_success(
            self.hs.get_state_handler().get_current_state(self._room_id)
        )
        self.assertEqual(r[(""m.room.member"", joining_user)].membership, ""join"")

","When MSC3706 support is enabled, /send_join should return partial state",10,106,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_send_join_partial_state(self):
        
        joining_user = ""@misspiggy:"" + self.OTHER_SERVER_NAME
        join_result = self._make_join(joining_user)

        join_event_dict = join_result[""event""]
        add_hashes_and_signatures(
            KNOWN_ROOM_VERSIONS[DEFAULT_ROOM_VERSION],
            join_event_dict,
            signature_name=self.OTHER_SERVER_NAME,
            signing_key=self.OTHER_SERVER_SIGNATURE_KEY,
        )
        channel = self.make_signed_federation_request(
            ""PUT"",
            f""/_matrix/federation/v2/send_join/{self._room_id}/x?org.matrix.msc3706.partial_state=true"",
            content=join_event_dict,
        )
        self.assertEquals(channel.code, 200, channel.json_body)

        # expect a reduced room state
        returned_state = [
            (ev[""type""], ev[""state_key""]) for ev in channel.json_body[""state""]
        ]
        self.assertCountEqual(
            returned_state,
            [
                (""m.room.create"", """"),
                (""m.room.power_levels"", """"),
                (""m.room.join_rules"", """"),
                (""m.room.history_visibility"", """"),
            ],
        )

        # the auth chain should not include anything already in ""state""
        returned_auth_chain_events = [
            (ev[""type""], ev[""state_key""]) for ev in channel.json_body[""auth_chain""]
        ]
        self.assertCountEqual(
            returned_auth_chain_events,
            [
                (""m.room.member"", ""@kermit:test""),
            ],
        )

        # the room should show that the new user is a member
        r = self.get_success(
            self.hs.get_state_handler().get_current_state(self._room_id)
        )
        self.assertEqual(r[(""m.room.member"", joining_user)].membership, ""join"")


        ```
    ###Assistant : When MSC3706 support is enabled, /send_join should return partial state
    "
786,"async def drain(self):
        
        if self._reader is not None:
            exc = self._reader.exception()
            if exc is not None:
                raise exc
        if self._transport.is_closing():
            # Wait for protocol.connection_lost() call
            # Raise connection closing error if any,
            # ConnectionResetError otherwise
            # Yield to the event loop so connection_lost() may be
            # called.  Without this, _drain_helper() would return
            # immediately, and code that calls
            #     write(...); await drain()
            # in a loop would never call connection_lost(), so it
            # would not see an error when the socket is closed.
            await sleep(0)
        await self._protocol._drain_helper()

","Flush the write buffer.

        The intended use is to write

          w.write(data)
          await w.drain()
        ",13,87,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def drain(self):
        
        if self._reader is not None:
            exc = self._reader.exception()
            if exc is not None:
                raise exc
        if self._transport.is_closing():
            # Wait for protocol.connection_lost() call
            # Raise connection closing error if any,
            # ConnectionResetError otherwise
            # Yield to the event loop so connection_lost() may be
            # called.  Without this, _drain_helper() would return
            # immediately, and code that calls
            #     write(...); await drain()
            # in a loop would never call connection_lost(), so it
            # would not see an error when the socket is closed.
            await sleep(0)
        await self._protocol._drain_helper()


        ```
    ###Assistant : Flush the write buffer.

        The intended use is to write

          w.write(data)
          await w.drain()
        
    "
787,"def on_chord_header_start(self, chord, **header) -> dict:
        
        if not isinstance(chord.tasks, group):
            chord.tasks = group(chord.tasks)
        return self.on_group_start(chord.tasks, **header)
","Method that is called on сhord header stamping start.

         Arguments:
             chord (chord): chord that is stamped.
             headers (Dict): Partial headers that could be merged with existing headers.
         Returns:
             Dict: headers to update.
         ",32,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def on_chord_header_start(self, chord, **header) -> dict:
        
        if not isinstance(chord.tasks, group):
            chord.tasks = group(chord.tasks)
        return self.on_group_start(chord.tasks, **header)

        ```
    ###Assistant : Method that is called on сhord header stamping start.

         Arguments:
             chord (chord): chord that is stamped.
             headers (Dict): Partial headers that could be merged with existing headers.
         Returns:
             Dict: headers to update.
         
    "
788,"def load_historic_predictions_from_disk(self):
        
        exists = self.historic_predictions_path.is_file()
        if exists:
            try:
                with open(self.historic_predictions_path, ""rb"") as fp:
                    self.historic_predictions = cloudpickle.load(fp)
                logger.info(
                    f""Found existing historic predictions at {self.full_path}, but beware ""
                    ""that statistics may be inaccurate if the bot has been offline for ""
                    ""an extended period of time.""
                )
            except EOFError:
                logger.warning(
                    'Historical prediction file was corrupted. Trying to load backup file.')
                with open(self.historic_predictions_bkp_path, ""rb"") as fp:
                    self.historic_predictions = cloudpickle.load(fp)
                logger.warning('FreqAI successfully loaded the backup historical predictions file.')

        elif not self.follow_mode:
            logger.info(""Could not find existing historic_predictions, starting from scratch"")
        else:
            logger.warning(
                f""Follower could not find historic predictions at {self.full_path} ""
                ""sending null values back to strategy""
            )

        return exists
","
        Locate and load a previously saved historic predictions.
        :return: bool - whether or not the drawer was located
        ",18,105,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_historic_predictions_from_disk(self):
        
        exists = self.historic_predictions_path.is_file()
        if exists:
            try:
                with open(self.historic_predictions_path, ""rb"") as fp:
                    self.historic_predictions = cloudpickle.load(fp)
                logger.info(
                    f""Found existing historic predictions at {self.full_path}, but beware ""
                    ""that statistics may be inaccurate if the bot has been offline for ""
                    ""an extended period of time.""
                )
            except EOFError:
                logger.warning(
                    'Historical prediction file was corrupted. Trying to load backup file.')
                with open(self.historic_predictions_bkp_path, ""rb"") as fp:
                    self.historic_predictions = cloudpickle.load(fp)
                logger.warning('FreqAI successfully loaded the backup historical predictions file.')

        elif not self.follow_mode:
            logger.info(""Could not find existing historic_predictions, starting from scratch"")
        else:
            logger.warning(
                f""Follower could not find historic predictions at {self.full_path} ""
                ""sending null values back to strategy""
            )

        return exists

        ```
    ###Assistant : 
        Locate and load a previously saved historic predictions.
        :return: bool - whether or not the drawer was located
        
    "
789,"def test_background(self):
        css = 
        stylesheet = Stylesheet()
        stylesheet.parse(css)

        styles = stylesheet.rules[0].styles
        assert styles.text_background == Color(""red"", type=ColorType.STANDARD, number=1)

","#some-widget {
            text: on red;
        }
        ",6,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_background(self):
        css = 
        stylesheet = Stylesheet()
        stylesheet.parse(css)

        styles = stylesheet.rules[0].styles
        assert styles.text_background == Color(""red"", type=ColorType.STANDARD, number=1)


        ```
    ###Assistant : #some-widget {
            text: on red;
        }
        
    "
790,"def update(self) -> None:
        
        try:
            response = requests.get(self._url, timeout=5)
        except (requests.exceptions.RequestException, ValueError):
            _LOGGER.warning(
                ""Could not update status for DTE Energy Bridge (%s)"", self._attr_name
            )
            return

        if response.status_code != HTTPStatus.OK:
            _LOGGER.warning(
                ""Invalid status_code from DTE Energy Bridge: %s (%s)"",
                response.status_code,
                self._attr_name,
            )
            return

        response_split = response.text.split()

        if len(response_split) != 2:
            _LOGGER.warning(
                'Invalid response from DTE Energy Bridge: ""%s"" (%s)',
                response.text,
                self._attr_name,
            )
            return

        val = float(response_split[0])

        # A workaround for a bug in the DTE energy bridge.
        # The returned value can randomly be in W or kW.  Checking for a
        # a decimal seems to be a reliable way to determine the units.
        # Limiting to version 1 because version 2 apparently always returns
        # values in the format 000000.000 kW, but the scaling is Watts
        # NOT kWatts
        if self._version == 1 and ""."" in response_split[0]:
            self._attr_native_value = val
        else:
            self._attr_native_value = val / 1000
",Get the energy usage data from the DTE energy bridge.,10,146,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self) -> None:
        
        try:
            response = requests.get(self._url, timeout=5)
        except (requests.exceptions.RequestException, ValueError):
            _LOGGER.warning(
                ""Could not update status for DTE Energy Bridge (%s)"", self._attr_name
            )
            return

        if response.status_code != HTTPStatus.OK:
            _LOGGER.warning(
                ""Invalid status_code from DTE Energy Bridge: %s (%s)"",
                response.status_code,
                self._attr_name,
            )
            return

        response_split = response.text.split()

        if len(response_split) != 2:
            _LOGGER.warning(
                'Invalid response from DTE Energy Bridge: ""%s"" (%s)',
                response.text,
                self._attr_name,
            )
            return

        val = float(response_split[0])

        # A workaround for a bug in the DTE energy bridge.
        # The returned value can randomly be in W or kW.  Checking for a
        # a decimal seems to be a reliable way to determine the units.
        # Limiting to version 1 because version 2 apparently always returns
        # values in the format 000000.000 kW, but the scaling is Watts
        # NOT kWatts
        if self._version == 1 and ""."" in response_split[0]:
            self._attr_native_value = val
        else:
            self._attr_native_value = val / 1000

        ```
    ###Assistant : Get the energy usage data from the DTE energy bridge.
    "
791,"def ndependencies(dependencies, dependents):
    
    num_needed = {}
    result = {}
    for k, v in dependencies.items():
        num_needed[k] = len(v)
        if not v:
            result[k] = 1

    num_dependencies = num_needed.copy()
    current = []
    current_pop = current.pop
    current_append = current.append

    for key in result:
        for parent in dependents[key]:
            num_needed[parent] -= 1
            if not num_needed[parent]:
                current_append(parent)
    while current:
        key = current_pop()
        result[key] = 1 + sum(result[child] for child in dependencies[key])
        for parent in dependents[key]:
            num_needed[parent] -= 1
            if not num_needed[parent]:
                current_append(parent)
    return num_dependencies, result

","Number of total data elements on which this key depends

    For each key we return the number of tasks that must be run for us to run
    this task.

    Examples
    --------
    >>> inc = lambda x: x + 1
    >>> dsk = {'a': 1, 'b': (inc, 'a'), 'c': (inc, 'b')}
    >>> dependencies, dependents = get_deps(dsk)
    >>> num_dependencies, total_dependencies = ndependencies(dependencies, dependents)
    >>> sorted(total_dependencies.items())
    [('a', 1), ('b', 2), ('c', 3)]

    Returns
    -------
    num_dependencies: Dict[key, int]
    total_dependencies: Dict[key, int]
    ",77,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ndependencies(dependencies, dependents):
    
    num_needed = {}
    result = {}
    for k, v in dependencies.items():
        num_needed[k] = len(v)
        if not v:
            result[k] = 1

    num_dependencies = num_needed.copy()
    current = []
    current_pop = current.pop
    current_append = current.append

    for key in result:
        for parent in dependents[key]:
            num_needed[parent] -= 1
            if not num_needed[parent]:
                current_append(parent)
    while current:
        key = current_pop()
        result[key] = 1 + sum(result[child] for child in dependencies[key])
        for parent in dependents[key]:
            num_needed[parent] -= 1
            if not num_needed[parent]:
                current_append(parent)
    return num_dependencies, result


        ```
    ###Assistant : Number of total data elements on which this key depends

    For each key we return the number of tasks that must be run for us to run
    this task.

    Examples
    --------
    >>> inc = lambda x: x + 1
    >>> dsk = {'a': 1, 'b': (inc, 'a'), 'c': (inc, 'b')}
    >>> dependencies, dependents = get_deps(dsk)
    >>> num_dependencies, total_dependencies = ndependencies(dependencies, dependents)
    >>> sorted(total_dependencies.items())
    [('a', 1), ('b', 2), ('c', 3)]

    Returns
    -------
    num_dependencies: Dict[key, int]
    total_dependencies: Dict[key, int]
    
    "
792,"def validate_attr(self, append) -> None:
        
        if append:
            existing_fields = getattr(self.attrs, self.kind_attr, None)
            if existing_fields is not None and existing_fields != list(self.values):
                raise ValueError(""appended items do not match existing items in table!"")

            existing_dtype = getattr(self.attrs, self.dtype_attr, None)
            if existing_dtype is not None and existing_dtype != self.dtype:
                raise ValueError(
                    ""appended items dtype do not match existing items dtype in table!""
                )
",validate that we have the same order as the existing & same dtype,13,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_attr(self, append) -> None:
        
        if append:
            existing_fields = getattr(self.attrs, self.kind_attr, None)
            if existing_fields is not None and existing_fields != list(self.values):
                raise ValueError(""appended items do not match existing items in table!"")

            existing_dtype = getattr(self.attrs, self.dtype_attr, None)
            if existing_dtype is not None and existing_dtype != self.dtype:
                raise ValueError(
                    ""appended items dtype do not match existing items dtype in table!""
                )

        ```
    ###Assistant : validate that we have the same order as the existing & same dtype
    "
793,"def __getitem__(self, parameters):
            item = typing._type_check(parameters,
                                      f'{self._name} accepts only single type')
            return typing._GenericAlias(self, (item,))

    Final = _FinalForm('Final',
                       doc=","A special typing construct to indicate that a name
                       cannot be re-assigned or overridden in a subclass.
                       For example:

                           MAX_SIZE: Final = 9000
                           MAX_SIZE += 1  # Error reported by type checker",32,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __getitem__(self, parameters):
            item = typing._type_check(parameters,
                                      f'{self._name} accepts only single type')
            return typing._GenericAlias(self, (item,))

    Final = _FinalForm('Final',
                       doc=
        ```
    ###Assistant : A special typing construct to indicate that a name
                       cannot be re-assigned or overridden in a subclass.
                       For example:

                           MAX_SIZE: Final = 9000
                           MAX_SIZE += 1  # Error reported by type checker
    "
794,"def type_spec_from_value(value):
    
    if is_extension_type(value):
        return value._type_spec  # pylint: disable=protected-access
    # Get a TensorSpec for array-like data without
    # converting the data to a Tensor
    if hasattr(value, ""shape"") and hasattr(value, ""dtype""):
        return tf.TensorSpec(value.shape, value.dtype)
    else:
        return tf.type_spec_from_value(value)

",Grab type_spec without converting array-likes to tensors.,7,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def type_spec_from_value(value):
    
    if is_extension_type(value):
        return value._type_spec  # pylint: disable=protected-access
    # Get a TensorSpec for array-like data without
    # converting the data to a Tensor
    if hasattr(value, ""shape"") and hasattr(value, ""dtype""):
        return tf.TensorSpec(value.shape, value.dtype)
    else:
        return tf.type_spec_from_value(value)


        ```
    ###Assistant : Grab type_spec without converting array-likes to tensors.
    "
795,"def make_window():
    

    sg.theme(settings.get('-theme-', 'DarkBlue2'))  # set the theme

    layout = [[sg.Text('Settings Window')],
              [sg.Input(settings.get('-input-', ''), k='-IN-')],
              [sg.Listbox(sg.theme_list(), default_values=[settings['-theme-'],], size=(15, 10), k='-LISTBOX-')],
              [sg.CB('Option 1', settings.get('-option1-', True), k='-CB1-')],
              [sg.CB('Option 2', settings.get('-option2-', False), k='-CB2-')],
              [sg.T('Settings file = ' + settings.get_filename())],
              [sg.Button('Save'), sg.Button('Settings Dictionary'), sg.Button('Exit without saving', k='Exit')]]

    window =  sg.Window('A Settings Window', layout)

","
    Creates a new window.  The default values for some elements are pulled directly from the
    ""User Settings"" without the use of temp variables.

    Some get_entry calls don't have a default value, such as theme, because there was an initial call
    that would have set the default value if the setting wasn't present.  Could still put the default
    value if you wanted but it would be 2 places to change if you wanted a different default value.

    Use of a lookup table to map between element keys and user settings could be aded. This demo
    is intentionally done without one to show how to use the settings APIs in the most basic,
    straightforward way.

    If your application allows changing the theme, then a make_window function is good to have
    so that you can close and re-create a window easily.

    :return: (sg.Window)  The window that was created
    ",145,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_window():
    

    sg.theme(settings.get('-theme-', 'DarkBlue2'))  # set the theme

    layout = [[sg.Text('Settings Window')],
              [sg.Input(settings.get('-input-', ''), k='-IN-')],
              [sg.Listbox(sg.theme_list(), default_values=[settings['-theme-'],], size=(15, 10), k='-LISTBOX-')],
              [sg.CB('Option 1', settings.get('-option1-', True), k='-CB1-')],
              [sg.CB('Option 2', settings.get('-option2-', False), k='-CB2-')],
              [sg.T('Settings file = ' + settings.get_filename())],
              [sg.Button('Save'), sg.Button('Settings Dictionary'), sg.Button('Exit without saving', k='Exit')]]

    window =  sg.Window('A Settings Window', layout)


        ```
    ###Assistant : 
    Creates a new window.  The default values for some elements are pulled directly from the
    ""User Settings"" without the use of temp variables.

    Some get_entry calls don't have a default value, such as theme, because there was an initial call
    that would have set the default value if the setting wasn't present.  Could still put the default
    value if you wanted but it would be 2 places to change if you wanted a different default value.

    Use of a lookup table to map between element keys and user settings could be aded. This demo
    is intentionally done without one to show how to use the settings APIs in the most basic,
    straightforward way.

    If your application allows changing the theme, then a make_window function is good to have
    so that you can close and re-create a window easily.

    :return: (sg.Window)  The window that was created
    
    "
796,"async def _collect(self) -> CommonUsageMetrics:
        
        dau_count = await self._store.count_daily_users()

        return CommonUsageMetrics(
            daily_active_users=dau_count,
        )
","Collect the common metrics and either create the CommonUsageMetrics object to
        use if it doesn't exist yet, or update it.
        ",20,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _collect(self) -> CommonUsageMetrics:
        
        dau_count = await self._store.count_daily_users()

        return CommonUsageMetrics(
            daily_active_users=dau_count,
        )

        ```
    ###Assistant : Collect the common metrics and either create the CommonUsageMetrics object to
        use if it doesn't exist yet, or update it.
        
    "
797,"def getPreprocessorSymbols(cls):
        

        if cls.preprocessor_symbols is None:
            cls.preprocessor_symbols = OrderedDict()

            for plugin in getActivePlugins():
                value = plugin.getPreprocessorSymbols()

                if value is not None:
                    assert type(value) is dict, value

                    # We order per plugin, but from the plugins, lets just take a dict
                    # and achieve determinism by ordering the defines by name.
                    for key, value in sorted(value.items()):
                        # False alarm, pylint: disable=I0021,unsupported-assignment-operation
                        cls.preprocessor_symbols[key] = value

        return cls.preprocessor_symbols
","Let plugins provide C defines to be used in compilation.

        Notes:
            The plugins can each contribute, but are hopefully using
            a namespace for their defines.

        Returns:
            OrderedDict(), where None value indicates no define value,
            i.e. ""-Dkey=value"" vs. ""-Dkey""
        ",38,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getPreprocessorSymbols(cls):
        

        if cls.preprocessor_symbols is None:
            cls.preprocessor_symbols = OrderedDict()

            for plugin in getActivePlugins():
                value = plugin.getPreprocessorSymbols()

                if value is not None:
                    assert type(value) is dict, value

                    # We order per plugin, but from the plugins, lets just take a dict
                    # and achieve determinism by ordering the defines by name.
                    for key, value in sorted(value.items()):
                        # False alarm, pylint: disable=I0021,unsupported-assignment-operation
                        cls.preprocessor_symbols[key] = value

        return cls.preprocessor_symbols

        ```
    ###Assistant : Let plugins provide C defines to be used in compilation.

        Notes:
            The plugins can each contribute, but are hopefully using
            a namespace for their defines.

        Returns:
            OrderedDict(), where None value indicates no define value,
            i.e. ""-Dkey=value"" vs. ""-Dkey""
        
    "
798,"def _check_xy(self, renderer=None):
        
        if renderer is None:
            renderer = self.figure._get_renderer()
        b = self.get_annotation_clip()
        if b or (b is None and self.xycoords == ""data""):
            # check if self.xy is inside the axes.
            xy_pixel = self._get_position_xy(renderer)
            return self.axes.contains_point(xy_pixel)
        return True
",Check whether the annotation at *xy_pixel* should be drawn.,9,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_xy(self, renderer=None):
        
        if renderer is None:
            renderer = self.figure._get_renderer()
        b = self.get_annotation_clip()
        if b or (b is None and self.xycoords == ""data""):
            # check if self.xy is inside the axes.
            xy_pixel = self._get_position_xy(renderer)
            return self.axes.contains_point(xy_pixel)
        return True

        ```
    ###Assistant : Check whether the annotation at *xy_pixel* should be drawn.
    "
799,"def _split_ssh_args(argstring):
        
        # In Python3, shlex.split doesn't work on a byte string.
        return [to_text(x.strip()) for x in shlex.split(argstring) if x.strip()]
","
        Takes a string like '-o Foo=1 -o Bar=""foo bar""' and returns a
        list ['-o', 'Foo=1', '-o', 'Bar=foo bar'] that can be added to
        the argument list. The list will not contain any empty elements.
        ",34,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _split_ssh_args(argstring):
        
        # In Python3, shlex.split doesn't work on a byte string.
        return [to_text(x.strip()) for x in shlex.split(argstring) if x.strip()]

        ```
    ###Assistant : 
        Takes a string like '-o Foo=1 -o Bar=""foo bar""' and returns a
        list ['-o', 'Foo=1', '-o', 'Bar=foo bar'] that can be added to
        the argument list. The list will not contain any empty elements.
        
    "
800,"def is_empty_indexer(indexer) -> bool:
    
    if is_list_like(indexer) and not len(indexer):
        return True
    if not isinstance(indexer, tuple):
        indexer = (indexer,)
    return any(isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer)


# -----------------------------------------------------------
# Indexer Validation

","
    Check if we have an empty indexer.

    Parameters
    ----------
    indexer : object

    Returns
    -------
    bool
    ",15,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_empty_indexer(indexer) -> bool:
    
    if is_list_like(indexer) and not len(indexer):
        return True
    if not isinstance(indexer, tuple):
        indexer = (indexer,)
    return any(isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer)


# -----------------------------------------------------------
# Indexer Validation


        ```
    ###Assistant : 
    Check if we have an empty indexer.

    Parameters
    ----------
    indexer : object

    Returns
    -------
    bool
    
    "
801,"def make_layoutgrids_gs(layoutgrids, gs):
    

    if gs in layoutgrids or gs.figure is None:
        return layoutgrids
    # in order to do constrained_layout there has to be at least *one*
    # gridspec in the tree:
    layoutgrids['hasgrids'] = True
    if not hasattr(gs, '_subplot_spec'):
        # normal gridspec
        parent = layoutgrids[gs.figure]
        layoutgrids[gs] = mlayoutgrid.LayoutGrid(
                parent=parent,
                parent_inner=True,
                name='gridspec',
                ncols=gs._ncols, nrows=gs._nrows,
                width_ratios=gs.get_width_ratios(),
                height_ratios=gs.get_height_ratios())
    else:
        # this is a gridspecfromsubplotspec:
        subplot_spec = gs._subplot_spec
        parentgs = subplot_spec.get_gridspec()
        # if a nested gridspec it is possible the parent is not in there yet:
        if parentgs not in layoutgrids:
            layoutgrids = make_layoutgrids_gs(layoutgrids, parentgs)
        subspeclb = layoutgrids[parentgs]
        # get a unique representation:
        rep = object.__repr__(gs) + 'top'
        # gridspecfromsubplotspec need an outer container:
        if rep not in layoutgrids:
            layoutgrids[rep] = mlayoutgrid.LayoutGrid(
                parent=subspeclb,
                name='top',
                nrows=1, ncols=1,
                parent_pos=(subplot_spec.rowspan, subplot_spec.colspan))
        layoutgrids[gs] = mlayoutgrid.LayoutGrid(
                parent=layoutgrids[rep],
                name='gridspec',
                nrows=gs._nrows, ncols=gs._ncols,
                width_ratios=gs.get_width_ratios(),
                height_ratios=gs.get_height_ratios())
    return layoutgrids

","
    Make the layoutgrid for a gridspec (and anything nested in the gridspec)
    ",12,134,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_layoutgrids_gs(layoutgrids, gs):
    

    if gs in layoutgrids or gs.figure is None:
        return layoutgrids
    # in order to do constrained_layout there has to be at least *one*
    # gridspec in the tree:
    layoutgrids['hasgrids'] = True
    if not hasattr(gs, '_subplot_spec'):
        # normal gridspec
        parent = layoutgrids[gs.figure]
        layoutgrids[gs] = mlayoutgrid.LayoutGrid(
                parent=parent,
                parent_inner=True,
                name='gridspec',
                ncols=gs._ncols, nrows=gs._nrows,
                width_ratios=gs.get_width_ratios(),
                height_ratios=gs.get_height_ratios())
    else:
        # this is a gridspecfromsubplotspec:
        subplot_spec = gs._subplot_spec
        parentgs = subplot_spec.get_gridspec()
        # if a nested gridspec it is possible the parent is not in there yet:
        if parentgs not in layoutgrids:
            layoutgrids = make_layoutgrids_gs(layoutgrids, parentgs)
        subspeclb = layoutgrids[parentgs]
        # get a unique representation:
        rep = object.__repr__(gs) + 'top'
        # gridspecfromsubplotspec need an outer container:
        if rep not in layoutgrids:
            layoutgrids[rep] = mlayoutgrid.LayoutGrid(
                parent=subspeclb,
                name='top',
                nrows=1, ncols=1,
                parent_pos=(subplot_spec.rowspan, subplot_spec.colspan))
        layoutgrids[gs] = mlayoutgrid.LayoutGrid(
                parent=layoutgrids[rep],
                name='gridspec',
                nrows=gs._nrows, ncols=gs._ncols,
                width_ratios=gs.get_width_ratios(),
                height_ratios=gs.get_height_ratios())
    return layoutgrids


        ```
    ###Assistant : 
    Make the layoutgrid for a gridspec (and anything nested in the gridspec)
    
    "
802,"def test_sitemap_published_titles(self):
        
        sitemap = CMSSitemap()
        locations = []
        urlset = sitemap.get_urls()
        for item in urlset:
            locations.append(item['location'])
        for title in Title.objects.public():
            page = title.page.get_public_object()
            if title.path:
                url = f'http://example.com/{title.language}/{title.path}/'
            else:
                url = f'http://example.com/{title.language}/{title.path}'
            if page.is_published('en') and not page.publisher_is_draft:
                self.assertTrue(url in locations)
            else:
                self.assertFalse(url in locations)
","
        Check that published titles are in the urls
        ",8,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_sitemap_published_titles(self):
        
        sitemap = CMSSitemap()
        locations = []
        urlset = sitemap.get_urls()
        for item in urlset:
            locations.append(item['location'])
        for title in Title.objects.public():
            page = title.page.get_public_object()
            if title.path:
                url = f'http://example.com/{title.language}/{title.path}/'
            else:
                url = f'http://example.com/{title.language}/{title.path}'
            if page.is_published('en') and not page.publisher_is_draft:
                self.assertTrue(url in locations)
            else:
                self.assertFalse(url in locations)

        ```
    ###Assistant : 
        Check that published titles are in the urls
        
    "
803,"def eye(N, chunks=""auto"", M=None, k=0, dtype=float):
    
    eye = {}
    if M is None:
        M = N
    if dtype is None:
        dtype = float

    if not isinstance(chunks, (int, str)):
        raise ValueError(""chunks must be an int or string"")

    vchunks, hchunks = normalize_chunks(chunks, shape=(N, M), dtype=dtype)
    chunks = vchunks[0]

    token = tokenize(N, chunks, M, k, dtype)
    name_eye = ""eye-"" + token

    for i, vchunk in enumerate(vchunks):
        for j, hchunk in enumerate(hchunks):
            if (j - i - 1) * chunks <= k <= (j - i + 1) * chunks:
                eye[name_eye, i, j] = (
                    np.eye,
                    vchunk,
                    hchunk,
                    k - (j - i) * chunks,
                    dtype,
                )
            else:
                eye[name_eye, i, j] = (np.zeros, (vchunk, hchunk), dtype)
    return Array(eye, name_eye, shape=(N, M), chunks=(chunks, chunks), dtype=dtype)


@derived_from(np)","
    Return a 2-D Array with ones on the diagonal and zeros elsewhere.

    Parameters
    ----------
    N : int
      Number of rows in the output.
    chunks : int, str
        How to chunk the array. Must be one of the following forms:

        -   A blocksize like 1000.
        -   A size in bytes, like ""100 MiB"" which will choose a uniform
            block-like shape
        -   The word ""auto"" which acts like the above, but uses a configuration
            value ``array.chunk-size`` for the chunk size
    M : int, optional
      Number of columns in the output. If None, defaults to `N`.
    k : int, optional
      Index of the diagonal: 0 (the default) refers to the main diagonal,
      a positive value refers to an upper diagonal, and a negative value
      to a lower diagonal.
    dtype : data-type, optional
      Data-type of the returned array.

    Returns
    -------
    I : Array of shape (N,M)
      An array where all elements are equal to zero, except for the `k`-th
      diagonal, whose values are equal to one.
    ",162,121,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def eye(N, chunks=""auto"", M=None, k=0, dtype=float):
    
    eye = {}
    if M is None:
        M = N
    if dtype is None:
        dtype = float

    if not isinstance(chunks, (int, str)):
        raise ValueError(""chunks must be an int or string"")

    vchunks, hchunks = normalize_chunks(chunks, shape=(N, M), dtype=dtype)
    chunks = vchunks[0]

    token = tokenize(N, chunks, M, k, dtype)
    name_eye = ""eye-"" + token

    for i, vchunk in enumerate(vchunks):
        for j, hchunk in enumerate(hchunks):
            if (j - i - 1) * chunks <= k <= (j - i + 1) * chunks:
                eye[name_eye, i, j] = (
                    np.eye,
                    vchunk,
                    hchunk,
                    k - (j - i) * chunks,
                    dtype,
                )
            else:
                eye[name_eye, i, j] = (np.zeros, (vchunk, hchunk), dtype)
    return Array(eye, name_eye, shape=(N, M), chunks=(chunks, chunks), dtype=dtype)


@derived_from(np)
        ```
    ###Assistant : 
    Return a 2-D Array with ones on the diagonal and zeros elsewhere.

    Parameters
    ----------
    N : int
      Number of rows in the output.
    chunks : int, str
        How to chunk the array. Must be one of the following forms:

        -   A blocksize like 1000.
        -   A size in bytes, like ""100 MiB"" which will choose a uniform
            block-like shape
        -   The word ""auto"" which acts like the above, but uses a configuration
            value ``array.chunk-size`` for the chunk size
    M : int, optional
      Number of columns in the output. If None, defaults to `N`.
    k : int, optional
      Index of the diagonal: 0 (the default) refers to the main diagonal,
      a positive value refers to an upper diagonal, and a negative value
      to a lower diagonal.
    dtype : data-type, optional
      Data-type of the returned array.

    Returns
    -------
    I : Array of shape (N,M)
      An array where all elements are equal to zero, except for the `k`-th
      diagonal, whose values are equal to one.
    
    "
804,"def is_composite_or_composite_value(tensor):
    
    # TODO(b/125094323): This should be isinstance(CompositeTensor) or
    # isinstance(CompositeTensorValue) once we support that.
    return isinstance(
        tensor,
        (
            tf.__internal__.CompositeTensor,
            tf.compat.v1.SparseTensorValue,
            tf.compat.v1.ragged.RaggedTensorValue,
        ),
    )

",Returns true if 'tensor' is a CompositeTensor or a CT Value object.,12,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_composite_or_composite_value(tensor):
    
    # TODO(b/125094323): This should be isinstance(CompositeTensor) or
    # isinstance(CompositeTensorValue) once we support that.
    return isinstance(
        tensor,
        (
            tf.__internal__.CompositeTensor,
            tf.compat.v1.SparseTensorValue,
            tf.compat.v1.ragged.RaggedTensorValue,
        ),
    )


        ```
    ###Assistant : Returns true if 'tensor' is a CompositeTensor or a CT Value object.
    "
805,"def create_partition(tblname, start=None, end=None, partition_label=None, minutely=False):
    
    current_time = now()
    if not start:
        if minutely:
            start = current_time.replace(microsecond=0, second=0)
        else:
            start = current_time.replace(microsecond=0, second=0, minute=0)
    if not end:
        if minutely:
            end = start.replace(microsecond=0, second=0) + timedelta(minutes=1)
        else:
            end = start.replace(microsecond=0, second=0, minute=0) + timedelta(hours=1)
    start_timestamp = str(start)
    end_timestamp = str(end)

    if not partition_label:
        if minutely:
            partition_label = start.strftime('%Y%m%d_%H%M')
        else:
            partition_label = start.strftime('%Y%m%d_%H')

    try:
        with transaction.atomic():
            with connection.cursor() as cursor:
                cursor.execute(
                    f'CREATE TABLE IF NOT EXISTS {tblname}_{partition_label} '
                    f'PARTITION OF {tblname} '
                    f'FOR VALUES FROM (\'{start_timestamp}\') to (\'{end_timestamp}\');'
                )
    except ProgrammingError as e:
        logger.debug(f'Caught known error due to existing partition: {e}')

","Creates new partition table for events.
    - start defaults to beginning of current hour
    - end defaults to end of current hour
    - partition_label defaults to YYYYMMDD_HH

    - minutely will create partitions that span _a single minute_ for testing purposes
    ",40,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_partition(tblname, start=None, end=None, partition_label=None, minutely=False):
    
    current_time = now()
    if not start:
        if minutely:
            start = current_time.replace(microsecond=0, second=0)
        else:
            start = current_time.replace(microsecond=0, second=0, minute=0)
    if not end:
        if minutely:
            end = start.replace(microsecond=0, second=0) + timedelta(minutes=1)
        else:
            end = start.replace(microsecond=0, second=0, minute=0) + timedelta(hours=1)
    start_timestamp = str(start)
    end_timestamp = str(end)

    if not partition_label:
        if minutely:
            partition_label = start.strftime('%Y%m%d_%H%M')
        else:
            partition_label = start.strftime('%Y%m%d_%H')

    try:
        with transaction.atomic():
            with connection.cursor() as cursor:
                cursor.execute(
                    f'CREATE TABLE IF NOT EXISTS {tblname}_{partition_label} '
                    f'PARTITION OF {tblname} '
                    f'FOR VALUES FROM (\'{start_timestamp}\') to (\'{end_timestamp}\');'
                )
    except ProgrammingError as e:
        logger.debug(f'Caught known error due to existing partition: {e}')


        ```
    ###Assistant : Creates new partition table for events.
    - start defaults to beginning of current hour
    - end defaults to end of current hour
    - partition_label defaults to YYYYMMDD_HH

    - minutely will create partitions that span _a single minute_ for testing purposes
    
    "
806,"def rbf_kernel(X, Y=None, gamma=None):
    
    X, Y = check_pairwise_arrays(X, Y)
    if gamma is None:
        gamma = 1.0 / X.shape[1]

    K = euclidean_distances(X, Y, squared=True)
    K *= -gamma
    np.exp(K, K)  # exponentiate K in-place
    return K

","Compute the rbf (gaussian) kernel between X and Y.

        K(x, y) = exp(-gamma ||x-y||^2)

    for each pair of rows x in X and y in Y.

    Read more in the :ref:`User Guide <rbf_kernel>`.

    Parameters
    ----------
    X : ndarray of shape (n_samples_X, n_features)
        A feature array.

    Y : ndarray of shape (n_samples_Y, n_features), default=None
        An optional second feature array. If `None`, uses `Y=X`.

    gamma : float, default=None
        If None, defaults to 1.0 / n_features.

    Returns
    -------
    kernel_matrix : ndarray of shape (n_samples_X, n_samples_Y)
        The RBF kernel.
    ",85,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rbf_kernel(X, Y=None, gamma=None):
    
    X, Y = check_pairwise_arrays(X, Y)
    if gamma is None:
        gamma = 1.0 / X.shape[1]

    K = euclidean_distances(X, Y, squared=True)
    K *= -gamma
    np.exp(K, K)  # exponentiate K in-place
    return K


        ```
    ###Assistant : Compute the rbf (gaussian) kernel between X and Y.

        K(x, y) = exp(-gamma ||x-y||^2)

    for each pair of rows x in X and y in Y.

    Read more in the :ref:`User Guide <rbf_kernel>`.

    Parameters
    ----------
    X : ndarray of shape (n_samples_X, n_features)
        A feature array.

    Y : ndarray of shape (n_samples_Y, n_features), default=None
        An optional second feature array. If `None`, uses `Y=X`.

    gamma : float, default=None
        If None, defaults to 1.0 / n_features.

    Returns
    -------
    kernel_matrix : ndarray of shape (n_samples_X, n_samples_Y)
        The RBF kernel.
    
    "
807,"def _validate_argument_values(argument_spec, parameters, options_context=None, errors=None):
    

    if errors is None:
        errors = AnsibleValidationErrorMultiple()

    for param, spec in argument_spec.items():
        choices = spec.get('choices')
        if choices is None:
            continue

        if isinstance(choices, (frozenset, KeysView, Sequence)) and not isinstance(choices, (binary_type, text_type)):
            if param in parameters:
                # Allow one or more when type='list' param with choices
                if isinstance(parameters[param], list):
                    diff_list = [item for item in parameters[param] if item not in choices]
                    if diff_list:
                        choices_str = "", "".join([to_native(c) for c in choices])
                        diff_str = "", "".join(diff_list)
                        msg = ""value of %s must be one or more of: %s. Got no match for: %s"" % (param, choices_str, diff_str)
                        if options_context:
                            msg = ""{0} found in {1}"".format(msg, "" -> "".join(options_context))
                        errors.append(ArgumentValueError(msg))
                elif parameters[param] not in choices:
                    # PyYaml converts certain strings to bools. If we can unambiguously convert back, do so before checking
                    # the value. If we can't figure this out, module author is responsible.
                    if parameters[param] == 'False':
                        overlap = BOOLEANS_FALSE.intersection(choices)
                        if len(overlap) == 1:
                            # Extract from a set
                            (parameters[param],) = overlap

                    if parameters[param] == 'True':
                        overlap = BOOLEANS_TRUE.intersection(choices)
                        if len(overlap) == 1:
                            (parameters[param],) = overlap

                    if parameters[param] not in choices:
                        choices_str = "", "".join([to_native(c) for c in choices])
                        msg = ""value of %s must be one of: %s, got: %s"" % (param, choices_str, parameters[param])
                        if options_context:
                            msg = ""{0} found in {1}"".format(msg, "" -> "".join(options_context))
                        errors.append(ArgumentValueError(msg))
        else:
            msg = ""internal error: choices for argument %s are not iterable: %s"" % (param, choices)
            if options_context:
                msg = ""{0} found in {1}"".format(msg, "" -> "".join(options_context))
            errors.append(ArgumentTypeError(msg))

","Ensure all arguments have the requested values, and there are no stray arguments",13,248,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate_argument_values(argument_spec, parameters, options_context=None, errors=None):
    

    if errors is None:
        errors = AnsibleValidationErrorMultiple()

    for param, spec in argument_spec.items():
        choices = spec.get('choices')
        if choices is None:
            continue

        if isinstance(choices, (frozenset, KeysView, Sequence)) and not isinstance(choices, (binary_type, text_type)):
            if param in parameters:
                # Allow one or more when type='list' param with choices
                if isinstance(parameters[param], list):
                    diff_list = [item for item in parameters[param] if item not in choices]
                    if diff_list:
                        choices_str = "", "".join([to_native(c) for c in choices])
                        diff_str = "", "".join(diff_list)
                        msg = ""value of %s must be one or more of: %s. Got no match for: %s"" % (param, choices_str, diff_str)
                        if options_context:
                            msg = ""{0} found in {1}"".format(msg, "" -> "".join(options_context))
                        errors.append(ArgumentValueError(msg))
                elif parameters[param] not in choices:
                    # PyYaml converts certain strings to bools. If we can unambiguously convert back, do so before checking
                    # the value. If we can't figure this out, module author is responsible.
                    if parameters[param] == 'False':
                        overlap = BOOLEANS_FALSE.intersection(choices)
                        if len(overlap) == 1:
                            # Extract from a set
                            (parameters[param],) = overlap

                    if parameters[param] == 'True':
                        overlap = BOOLEANS_TRUE.intersection(choices)
                        if len(overlap) == 1:
                            (parameters[param],) = overlap

                    if parameters[param] not in choices:
                        choices_str = "", "".join([to_native(c) for c in choices])
                        msg = ""value of %s must be one of: %s, got: %s"" % (param, choices_str, parameters[param])
                        if options_context:
                            msg = ""{0} found in {1}"".format(msg, "" -> "".join(options_context))
                        errors.append(ArgumentValueError(msg))
        else:
            msg = ""internal error: choices for argument %s are not iterable: %s"" % (param, choices)
            if options_context:
                msg = ""{0} found in {1}"".format(msg, "" -> "".join(options_context))
            errors.append(ArgumentTypeError(msg))


        ```
    ###Assistant : Ensure all arguments have the requested values, and there are no stray arguments
    "
808,"def content(self):
        
        if self._content is None:
            self._load()
        return self._content
","
        The content of the artifact (representation varies)
        ",7,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def content(self):
        
        if self._content is None:
            self._load()
        return self._content

        ```
    ###Assistant : 
        The content of the artifact (representation varies)
        
    "
809,"def get_product_filter_data(query_args=None):
	
	if isinstance(query_args, str):
		query_args = json.loads(query_args)

	query_args = frappe._dict(query_args)
	if query_args:
		search = query_args.get(""search"")
		field_filters = query_args.get(""field_filters"", {})
		attribute_filters = query_args.get(""attribute_filters"", {})
		start = cint(query_args.start) if query_args.get(""start"") else 0
		item_group = query_args.get(""item_group"")
		from_filters = query_args.get(""from_filters"")
	else:
		search, attribute_filters, item_group, from_filters = None, None, None, None
		field_filters = {}
		start = 0

	# if new filter is checked, reset start to show filtered items from page 1
	if from_filters:
		start = 0

	sub_categories = []
	if item_group:
		sub_categories = get_child_groups_for_website(item_group, immediate=True)

	engine = ProductQuery()
	try:
		result = engine.query(
			attribute_filters,
			field_filters,
			search_term=search,
			start=start,
			item_group=item_group
		)
	except Exception:
		traceback = frappe.get_traceback()
		frappe.log_error(traceback, frappe._(""Product Engine Error""))
		return {""exc"": ""Something went wrong!""}

	# discount filter data
	filters = {}
	discounts = result[""discounts""]

	if discounts:
		filter_engine = ProductFiltersBuilder()
		filters[""discount_filters""] = filter_engine.get_discount_filters(discounts)

	return {
		""items"": result[""items""] or [],
		""filters"": filters,
		""settings"": engine.settings,
		""sub_categories"": sub_categories,
		""items_count"": result[""items_count""]
	}

@frappe.whitelist(allow_guest=True)","
		Returns filtered products and discount filters.
		:param query_args (dict): contains filters to get products list

		Query Args filters:
		search (str): Search Term.
		field_filters (dict): Keys include item_group, brand, etc.
		attribute_filters(dict): Keys include Color, Size, etc.
		start (int): Offset items by
		item_group (str): Valid Item Group
		from_filters (bool): Set as True to jump to page 1
	",55,143,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_product_filter_data(query_args=None):
	
	if isinstance(query_args, str):
		query_args = json.loads(query_args)

	query_args = frappe._dict(query_args)
	if query_args:
		search = query_args.get(""search"")
		field_filters = query_args.get(""field_filters"", {})
		attribute_filters = query_args.get(""attribute_filters"", {})
		start = cint(query_args.start) if query_args.get(""start"") else 0
		item_group = query_args.get(""item_group"")
		from_filters = query_args.get(""from_filters"")
	else:
		search, attribute_filters, item_group, from_filters = None, None, None, None
		field_filters = {}
		start = 0

	# if new filter is checked, reset start to show filtered items from page 1
	if from_filters:
		start = 0

	sub_categories = []
	if item_group:
		sub_categories = get_child_groups_for_website(item_group, immediate=True)

	engine = ProductQuery()
	try:
		result = engine.query(
			attribute_filters,
			field_filters,
			search_term=search,
			start=start,
			item_group=item_group
		)
	except Exception:
		traceback = frappe.get_traceback()
		frappe.log_error(traceback, frappe._(""Product Engine Error""))
		return {""exc"": ""Something went wrong!""}

	# discount filter data
	filters = {}
	discounts = result[""discounts""]

	if discounts:
		filter_engine = ProductFiltersBuilder()
		filters[""discount_filters""] = filter_engine.get_discount_filters(discounts)

	return {
		""items"": result[""items""] or [],
		""filters"": filters,
		""settings"": engine.settings,
		""sub_categories"": sub_categories,
		""items_count"": result[""items_count""]
	}

@frappe.whitelist(allow_guest=True)
        ```
    ###Assistant : 
		Returns filtered products and discount filters.
		:param query_args (dict): contains filters to get products list

		Query Args filters:
		search (str): Search Term.
		field_filters (dict): Keys include item_group, brand, etc.
		attribute_filters(dict): Keys include Color, Size, etc.
		start (int): Offset items by
		item_group (str): Valid Item Group
		from_filters (bool): Set as True to jump to page 1
	
    "
810,"async def async_turn_on(self, **kwargs):  # noqa: C901
        
        should_update = False
        on_command_type = self._config[CONF_ON_COMMAND_TYPE]
","Turn the device on.

        This method is a coroutine.
        ",9,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_turn_on(self, **kwargs):  # noqa: C901
        
        should_update = False
        on_command_type = self._config[CONF_ON_COMMAND_TYPE]

        ```
    ###Assistant : Turn the device on.

        This method is a coroutine.
        
    "
811,"def test_fake_mac(self, modifiers, expected):
        
        seq = keyutils.KeySequence()
        info = keyutils.KeyInfo(key=Qt.Key.Key_A, modifiers=modifiers)
        new = seq.append_event(info.to_event())
        assert new[0] == keyutils.KeyInfo(Qt.Key.Key_A, expected)
",Make sure Control/Meta are swapped with a simulated Mac.,9,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_fake_mac(self, modifiers, expected):
        
        seq = keyutils.KeySequence()
        info = keyutils.KeyInfo(key=Qt.Key.Key_A, modifiers=modifiers)
        new = seq.append_event(info.to_event())
        assert new[0] == keyutils.KeyInfo(Qt.Key.Key_A, expected)

        ```
    ###Assistant : Make sure Control/Meta are swapped with a simulated Mac.
    "
812,"def default_params(self) -> dict:
        
        return {""order"": ""asc"", ""sort"": self.sort_key, ""limit"": self.limit}
","
        Returns the parameters to be sent together with the API call to Recurly
        ",13,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def default_params(self) -> dict:
        
        return {""order"": ""asc"", ""sort"": self.sort_key, ""limit"": self.limit}

        ```
    ###Assistant : 
        Returns the parameters to be sent together with the API call to Recurly
        
    "
813,"def _should_start_new_health_check(self) -> bool:
        
        if self._health_check_ref is not None:
            # There's already an active health check.
            return False

        # If there's no active health check, kick off another and reset
        # the timer if it's been long enough since the last health
        # check. Add some randomness to avoid synchronizing across all
        # replicas.
        time_since_last = time.time() - self._last_health_check_time
        randomized_period = self._health_check_period_s * random.uniform(0.9, 1.1)
        return time_since_last > randomized_period
","Determines if a new health check should be kicked off.

        A health check will be started if:
            1) There is not already an active health check.
            2) It has been more than self._health_check_period_s since the
               previous health check was *started*.

        This assumes that self._health_check_ref is reset to `None` when an
        active health check succeeds or fails (due to returning or timeout).
        ",61,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _should_start_new_health_check(self) -> bool:
        
        if self._health_check_ref is not None:
            # There's already an active health check.
            return False

        # If there's no active health check, kick off another and reset
        # the timer if it's been long enough since the last health
        # check. Add some randomness to avoid synchronizing across all
        # replicas.
        time_since_last = time.time() - self._last_health_check_time
        randomized_period = self._health_check_period_s * random.uniform(0.9, 1.1)
        return time_since_last > randomized_period

        ```
    ###Assistant : Determines if a new health check should be kicked off.

        A health check will be started if:
            1) There is not already an active health check.
            2) It has been more than self._health_check_period_s since the
               previous health check was *started*.

        This assumes that self._health_check_ref is reset to `None` when an
        active health check succeeds or fails (due to returning or timeout).
        
    "
814,"def numpy_pad_and_concatenate(array1, array2, padding_index=-100):
    
    array1 = atleast_1d(array1)
    array2 = atleast_1d(array2)

    if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]:
        return np.concatenate((array1, array2), axis=0)

    # Let's figure out the new shape
    new_shape = (array1.shape[0] + array2.shape[0], max(array1.shape[1], array2.shape[1])) + array1.shape[2:]

    # Now let's fill the result tensor
    result = np.full_like(array1, padding_index, shape=new_shape)
    result[: array1.shape[0], : array1.shape[1]] = array1
    result[array1.shape[0] :, : array2.shape[1]] = array2
    return result

","Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary.",14,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def numpy_pad_and_concatenate(array1, array2, padding_index=-100):
    
    array1 = atleast_1d(array1)
    array2 = atleast_1d(array2)

    if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]:
        return np.concatenate((array1, array2), axis=0)

    # Let's figure out the new shape
    new_shape = (array1.shape[0] + array2.shape[0], max(array1.shape[1], array2.shape[1])) + array1.shape[2:]

    # Now let's fill the result tensor
    result = np.full_like(array1, padding_index, shape=new_shape)
    result[: array1.shape[0], : array1.shape[1]] = array1
    result[array1.shape[0] :, : array2.shape[1]] = array2
    return result


        ```
    ###Assistant : Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary.
    "
815,"def _ray(self) -> ""ray"":
        
        global ray

        if ray is None:
            try:
                import ray
            except ImportError as exc:
                raise RuntimeError(
                    ""Using the `RayTaskRunner` requires `ray` to be installed.""
                ) from exc

        return ray
","
        Delayed import of `ray` allowing configuration of the task runner
        without the extra installed and improves `prefect` import times.
        ",19,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _ray(self) -> ""ray"":
        
        global ray

        if ray is None:
            try:
                import ray
            except ImportError as exc:
                raise RuntimeError(
                    ""Using the `RayTaskRunner` requires `ray` to be installed.""
                ) from exc

        return ray

        ```
    ###Assistant : 
        Delayed import of `ray` allowing configuration of the task runner
        without the extra installed and improves `prefect` import times.
        
    "
816,"def temperature_unit(self) -> str:
        
        if (
            self._unit_value
            and self._unit_value.metadata.unit
            and ""f"" in self._unit_value.metadata.unit.lower()
        ):
            return UnitOfTemperature.FAHRENHEIT
        return UnitOfTemperature.CELSIUS
",Return the unit of measurement used by the platform.,9,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def temperature_unit(self) -> str:
        
        if (
            self._unit_value
            and self._unit_value.metadata.unit
            and ""f"" in self._unit_value.metadata.unit.lower()
        ):
            return UnitOfTemperature.FAHRENHEIT
        return UnitOfTemperature.CELSIUS

        ```
    ###Assistant : Return the unit of measurement used by the platform.
    "
817,"def get_point_of_rotated_vertical(origin, line_length, degrees):
    
    rad = np.deg2rad(-degrees)
    return [origin[0] + line_length * np.sin(rad),
            origin[1] + line_length * np.cos(rad)]


fig, ax = plt.subplots(figsize=(8, 7))
ax.set(xlim=(0, 6), ylim=(-1, 4))
ax.set_title(""Orientation of the bracket arrows relative to angleA and angleB"")

for i, style in enumerate([""]-["", ""|-|""]):
    for j, angle in enumerate([-40, 60]):
        y = 2*i + j
        arrow_centers = ((1, y), (5, y))
        vlines = ((1, y + 0.5), (5, y + 0.5))
        anglesAB = (angle, -angle)
        bracketstyle = f""{style}, angleA={anglesAB[0]}, angleB={anglesAB[1]}""
        bracket = FancyArrowPatch(*arrow_centers, arrowstyle=bracketstyle,
                                  mutation_scale=42)
        ax.add_patch(bracket)
        ax.text(3, y + 0.05, bracketstyle, ha=""center"", va=""bottom"")
        ax.vlines([i[0] for i in vlines], [y, y], [i[1] for i in vlines],
                  linestyles=""--"", color=""C0"")
        # Get the top coordinates for the drawn patches at A and B
        patch_tops = [get_point_of_rotated_vertical(center, 0.5, angle)
                      for center, angle in zip(arrow_centers, anglesAB)]
        # Define the connection directions for the annotation arrows
        connection_dirs = (1, -1) if angle > 0 else (-1, 1)
        # Add arrows and annotation text
        arrowstyle = ""Simple, tail_width=0.5, head_width=4, head_length=8""
        for vline, dir, patch_top, angle in zip(vlines, connection_dirs,
                                                patch_tops, anglesAB):
            kw = dict(connectionstyle=f""arc3,rad={dir * 0.5}"",
                      arrowstyle=arrowstyle, color=""C0"")
            ax.add_patch(FancyArrowPatch(vline, patch_top, **kw))
            ax.text(vline[0] - dir * 0.15, y + 0.3, f'{angle}°', ha=""center"",
                    va=""center"")

#############################################################################
#
# .. admonition:: References
#
#    The use of the following functions, methods, classes and modules is shown
#    in this example:
#
#    - `matplotlib.patches.ArrowStyle`
",Return xy coordinates of the vertical line end rotated by degrees.,11,221,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_point_of_rotated_vertical(origin, line_length, degrees):
    
    rad = np.deg2rad(-degrees)
    return [origin[0] + line_length * np.sin(rad),
            origin[1] + line_length * np.cos(rad)]


fig, ax = plt.subplots(figsize=(8, 7))
ax.set(xlim=(0, 6), ylim=(-1, 4))
ax.set_title(""Orientation of the bracket arrows relative to angleA and angleB"")

for i, style in enumerate([""]-["", ""|-|""]):
    for j, angle in enumerate([-40, 60]):
        y = 2*i + j
        arrow_centers = ((1, y), (5, y))
        vlines = ((1, y + 0.5), (5, y + 0.5))
        anglesAB = (angle, -angle)
        bracketstyle = f""{style}, angleA={anglesAB[0]}, angleB={anglesAB[1]}""
        bracket = FancyArrowPatch(*arrow_centers, arrowstyle=bracketstyle,
                                  mutation_scale=42)
        ax.add_patch(bracket)
        ax.text(3, y + 0.05, bracketstyle, ha=""center"", va=""bottom"")
        ax.vlines([i[0] for i in vlines], [y, y], [i[1] for i in vlines],
                  linestyles=""--"", color=""C0"")
        # Get the top coordinates for the drawn patches at A and B
        patch_tops = [get_point_of_rotated_vertical(center, 0.5, angle)
                      for center, angle in zip(arrow_centers, anglesAB)]
        # Define the connection directions for the annotation arrows
        connection_dirs = (1, -1) if angle > 0 else (-1, 1)
        # Add arrows and annotation text
        arrowstyle = ""Simple, tail_width=0.5, head_width=4, head_length=8""
        for vline, dir, patch_top, angle in zip(vlines, connection_dirs,
                                                patch_tops, anglesAB):
            kw = dict(connectionstyle=f""arc3,rad={dir * 0.5}"",
                      arrowstyle=arrowstyle, color=""C0"")
            ax.add_patch(FancyArrowPatch(vline, patch_top, **kw))
            ax.text(vline[0] - dir * 0.15, y + 0.3, f'{angle}°', ha=""center"",
                    va=""center"")

#############################################################################
#
# .. admonition:: References
#
#    The use of the following functions, methods, classes and modules is shown
#    in this example:
#
#    - `matplotlib.patches.ArrowStyle`

        ```
    ###Assistant : Return xy coordinates of the vertical line end rotated by degrees.
    "
818,"def processEntityInAttribute(self, allowedChar):
        
        self.consumeEntity(allowedChar=allowedChar, fromAttribute=True)
","This method replaces the need for ""entityInAttributeValueState"".
        ",7,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def processEntityInAttribute(self, allowedChar):
        
        self.consumeEntity(allowedChar=allowedChar, fromAttribute=True)

        ```
    ###Assistant : This method replaces the need for ""entityInAttributeValueState"".
        
    "
819,"def _get_ordered_dummies(mul, verbose=False):
    
    # setup dicts to avoid repeated calculations in key()
    args = Mul.make_args(mul)
    fac_dum = { fac: fac.atoms(Dummy) for fac in args }
    fac_repr = { fac: __kprint(fac) for fac in args }
    all_dums = set().union(*fac_dum.values())
    mask = {}
    for d in all_dums:
        if d.assumptions0.get('below_fermi'):
            mask[d] = '0'
        elif d.assumptions0.get('above_fermi'):
            mask[d] = '1'
        else:
            mask[d] = '2'
    dum_repr = {d: __kprint(d) for d in all_dums}
","Returns all dummies in the mul sorted in canonical order.

    Explanation
    ===========

    The purpose of the canonical ordering is that dummies can be substituted
    consistently across terms with the result that equivalent terms can be
    simplified.

    It is not possible to determine if two terms are equivalent based solely on
    the dummy order.  However, a consistent substitution guided by the ordered
    dummies should lead to trivially (non-)equivalent terms, thereby revealing
    the equivalence.  This also means that if two terms have identical sequences of
    dummies, the (non-)equivalence should already be apparent.

    Strategy
    --------

    The canonical order is given by an arbitrary sorting rule.  A sort key
    is determined for each dummy as a tuple that depends on all factors where
    the index is present.  The dummies are thereby sorted according to the
    contraction structure of the term, instead of sorting based solely on the
    dummy symbol itself.

    After all dummies in the term has been assigned a key, we check for identical
    keys, i.e. unorderable dummies.  If any are found, we call a specialized
    method, _determine_ambiguous(), that will determine a unique order based
    on recursive calls to _get_ordered_dummies().

    Key description
    ---------------

    A high level description of the sort key:

        1. Range of the dummy index
        2. Relation to external (non-dummy) indices
        3. Position of the index in the first factor
        4. Position of the index in the second factor

    The sort key is a tuple with the following components:

        1. A single character indicating the range of the dummy (above, below
           or general.)
        2. A list of strings with fully masked string representations of all
           factors where the dummy is present.  By masked, we mean that dummies
           are represented by a symbol to indicate either below fermi, above or
           general.  No other information is displayed about the dummies at
           this point.  The list is sorted stringwise.
        3. An integer number indicating the position of the index, in the first
           factor as sorted in 2.
        4. An integer number indicating the position of the index, in the second
           factor as sorted in 2.

    If a factor is either of type AntiSymmetricTensor or SqOperator, the index
    position in items 3 and 4 is indicated as 'upper' or 'lower' only.
    (Creation operators are considered upper and annihilation operators lower.)

    If the masked factors are identical, the two factors cannot be ordered
    unambiguously in item 2.  In this case, items 3, 4 are left out.  If several
    indices are contracted between the unorderable factors, it will be handled by
    _determine_ambiguous()


    ",415,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_ordered_dummies(mul, verbose=False):
    
    # setup dicts to avoid repeated calculations in key()
    args = Mul.make_args(mul)
    fac_dum = { fac: fac.atoms(Dummy) for fac in args }
    fac_repr = { fac: __kprint(fac) for fac in args }
    all_dums = set().union(*fac_dum.values())
    mask = {}
    for d in all_dums:
        if d.assumptions0.get('below_fermi'):
            mask[d] = '0'
        elif d.assumptions0.get('above_fermi'):
            mask[d] = '1'
        else:
            mask[d] = '2'
    dum_repr = {d: __kprint(d) for d in all_dums}

        ```
    ###Assistant : Returns all dummies in the mul sorted in canonical order.

    Explanation
    ===========

    The purpose of the canonical ordering is that dummies can be substituted
    consistently across terms with the result that equivalent terms can be
    simplified.

    It is not possible to determine if two terms are equivalent based solely on
    the dummy order.  However, a consistent substitution guided by the ordered
    dummies should lead to trivially (non-)equivalent terms, thereby revealing
    the equivalence.  This also means that if two terms have identical sequences of
    dummies, the (non-)equivalence should already be apparent.

    Strategy
    --------

    The canonical order is given by an arbitrary sorting rule.  A sort key
    is determined for each dummy as a tuple that depends on all factors where
    the index is present.  The dummies are thereby sorted according to the
    contraction structure of the term, instead of sorting based solely on the
    dummy symbol itself.

    After all dummies in the term has been assigned a key, we check for identical
    keys, i.e. unorderable dummies.  If any are found, we call a specialized
    method, _determine_ambiguous(), that will determine a unique order based
    on recursive calls to _get_ordered_dummies().

    Key description
    ---------------

    A high level description of the sort key:

        1. Range of the dummy index
        2. Relation to external (non-dummy) indices
        3. Position of the index in the first factor
        4. Position of the index in the second factor

    The sort key is a tuple with the following components:

        1. A single character indicating the range of the dummy (above, below
           or general.)
        2. A list of strings with fully masked string representations of all
           factors where the dummy is present.  By masked, we mean that dummies
           are represented by a symbol to indicate either below fermi, above or
           general.  No other information is displayed about the dummies at
           this point.  The list is sorted stringwise.
        3. An integer number indicating the position of the index, in the first
           factor as sorted in 2.
        4. An integer number indicating the position of the index, in the second
           factor as sorted in 2.

    If a factor is either of type AntiSymmetricTensor or SqOperator, the index
    position in items 3 and 4 is indicated as 'upper' or 'lower' only.
    (Creation operators are considered upper and annihilation operators lower.)

    If the masked factors are identical, the two factors cannot be ordered
    unambiguously in item 2.  In this case, items 3, 4 are left out.  If several
    indices are contracted between the unorderable factors, it will be handled by
    _determine_ambiguous()


    
    "
820,"def read_results_from_s3(query_execution_id):
    s3_hook = S3Hook()
    file_obj = s3_hook.get_conn().get_object(Bucket=S3_BUCKET, Key=f'{S3_KEY}/{query_execution_id}.csv')
    file_content = file_obj['Body'].read().decode('utf-8')
    print(file_content)


QUERY_CREATE_TABLE = f

QUERY_READ_TABLE = f

QUERY_DROP_TABLE = f

with DAG(
    dag_id='example_athena',
    schedule_interval=None,
    start_date=datetime(2021, 1, 1),
    tags=['example'],
    catchup=False,
) as dag:

    upload_sample_data = S3CreateObjectOperator(
        task_id='upload_sample_data',
        s3_bucket=S3_BUCKET,
        s3_key=f'{S3_KEY}/{ATHENA_TABLE}/{SAMPLE_FILENAME}',
        data=SAMPLE_DATA,
        replace=True,
    )

    create_table = AthenaOperator(
        task_id='create_table',
        query=QUERY_CREATE_TABLE,
        database=ATHENA_DATABASE,
        output_location=f's3://{S3_BUCKET}/{S3_KEY}',
    )

    # [START howto_athena_operator]
    read_table = AthenaOperator(
        task_id='read_table',
        query=QUERY_READ_TABLE,
        database=ATHENA_DATABASE,
        output_location=f's3://{S3_BUCKET}/{S3_KEY}',
    )
    # [END howto_athena_operator]

    # [START howto_athena_sensor]
    await_query = AthenaSensor(
        task_id='await_query',
        query_execution_id=read_table.output,
    )
    # [END howto_athena_sensor]

    drop_table = AthenaOperator(
        task_id='drop_table',
        query=QUERY_DROP_TABLE,
        database=ATHENA_DATABASE,
        output_location=f's3://{S3_BUCKET}/{S3_KEY}',
    )

    remove_s3_files = S3DeleteObjectsOperator(
        task_id='remove_s3_files',
        bucket=S3_BUCKET,
        prefix=S3_KEY,
    )

    (
        upload_sample_data
        >> create_table
        >> read_table
        >> await_query
        >> read_results_from_s3(read_table.output)
        >> drop_table
        >> remove_s3_files
    )
","
CREATE EXTERNAL TABLE IF NOT EXISTS {ATHENA_DATABASE}.{ATHENA_TABLE} ( `name` string, `age` int )
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES ( 'serialization.format' = ',', 'field.delim' = ','
) LOCATION 's3://{S3_BUCKET}/{S3_KEY}/{ATHENA_TABLE}'
TBLPROPERTIES ('has_encrypted_data'='false')

SELECT * from {ATHENA_DATABASE}.{ATHENA_TABLE}

DROP TABLE IF EXISTS {ATHENA_DATABASE}.{ATHENA_TABLE}
",40,107,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read_results_from_s3(query_execution_id):
    s3_hook = S3Hook()
    file_obj = s3_hook.get_conn().get_object(Bucket=S3_BUCKET, Key=f'{S3_KEY}/{query_execution_id}.csv')
    file_content = file_obj['Body'].read().decode('utf-8')
    print(file_content)


QUERY_CREATE_TABLE = f

QUERY_READ_TABLE = f

QUERY_DROP_TABLE = f

with DAG(
    dag_id='example_athena',
    schedule_interval=None,
    start_date=datetime(2021, 1, 1),
    tags=['example'],
    catchup=False,
) as dag:

    upload_sample_data = S3CreateObjectOperator(
        task_id='upload_sample_data',
        s3_bucket=S3_BUCKET,
        s3_key=f'{S3_KEY}/{ATHENA_TABLE}/{SAMPLE_FILENAME}',
        data=SAMPLE_DATA,
        replace=True,
    )

    create_table = AthenaOperator(
        task_id='create_table',
        query=QUERY_CREATE_TABLE,
        database=ATHENA_DATABASE,
        output_location=f's3://{S3_BUCKET}/{S3_KEY}',
    )

    # [START howto_athena_operator]
    read_table = AthenaOperator(
        task_id='read_table',
        query=QUERY_READ_TABLE,
        database=ATHENA_DATABASE,
        output_location=f's3://{S3_BUCKET}/{S3_KEY}',
    )
    # [END howto_athena_operator]

    # [START howto_athena_sensor]
    await_query = AthenaSensor(
        task_id='await_query',
        query_execution_id=read_table.output,
    )
    # [END howto_athena_sensor]

    drop_table = AthenaOperator(
        task_id='drop_table',
        query=QUERY_DROP_TABLE,
        database=ATHENA_DATABASE,
        output_location=f's3://{S3_BUCKET}/{S3_KEY}',
    )

    remove_s3_files = S3DeleteObjectsOperator(
        task_id='remove_s3_files',
        bucket=S3_BUCKET,
        prefix=S3_KEY,
    )

    (
        upload_sample_data
        >> create_table
        >> read_table
        >> await_query
        >> read_results_from_s3(read_table.output)
        >> drop_table
        >> remove_s3_files
    )

        ```
    ###Assistant : 
CREATE EXTERNAL TABLE IF NOT EXISTS {ATHENA_DATABASE}.{ATHENA_TABLE} ( `name` string, `age` int )
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES ( 'serialization.format' = ',', 'field.delim' = ','
) LOCATION 's3://{S3_BUCKET}/{S3_KEY}/{ATHENA_TABLE}'
TBLPROPERTIES ('has_encrypted_data'='false')

SELECT * from {ATHENA_DATABASE}.{ATHENA_TABLE}

DROP TABLE IF EXISTS {ATHENA_DATABASE}.{ATHENA_TABLE}

    "
821,"def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
    
    if arr_or_dtype is None:
        return False
    try:
        tipo = get_dtype(arr_or_dtype)
    except TypeError:
        if is_datetime64tz_dtype(arr_or_dtype):
            tipo = get_dtype(arr_or_dtype.dtype)
        else:
            return False
    return tipo == DT64NS_DTYPE or (
        isinstance(tipo, DatetimeTZDtype) and tipo._unit == ""ns""
    )

","
    Check whether the provided array or dtype is of the datetime64[ns] dtype.

    Parameters
    ----------
    arr_or_dtype : array-like or dtype
        The array or dtype to check.

    Returns
    -------
    bool
        Whether or not the array or dtype is of the datetime64[ns] dtype.

    Examples
    --------
    >>> is_datetime64_ns_dtype(str)
    False
    >>> is_datetime64_ns_dtype(int)
    False
    >>> is_datetime64_ns_dtype(np.datetime64)  # no unit
    False
    >>> is_datetime64_ns_dtype(DatetimeTZDtype(""ns"", ""US/Eastern""))
    True
    >>> is_datetime64_ns_dtype(np.array(['a', 'b']))
    False
    >>> is_datetime64_ns_dtype(np.array([1, 2]))
    False
    >>> is_datetime64_ns_dtype(np.array([], dtype=""datetime64""))  # no unit
    False
    >>> is_datetime64_ns_dtype(np.array([], dtype=""datetime64[ps]""))  # wrong unit
    False
    >>> is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3], dtype=""datetime64[ns]""))
    True
    ",86,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
    
    if arr_or_dtype is None:
        return False
    try:
        tipo = get_dtype(arr_or_dtype)
    except TypeError:
        if is_datetime64tz_dtype(arr_or_dtype):
            tipo = get_dtype(arr_or_dtype.dtype)
        else:
            return False
    return tipo == DT64NS_DTYPE or (
        isinstance(tipo, DatetimeTZDtype) and tipo._unit == ""ns""
    )


        ```
    ###Assistant : 
    Check whether the provided array or dtype is of the datetime64[ns] dtype.

    Parameters
    ----------
    arr_or_dtype : array-like or dtype
        The array or dtype to check.

    Returns
    -------
    bool
        Whether or not the array or dtype is of the datetime64[ns] dtype.

    Examples
    --------
    >>> is_datetime64_ns_dtype(str)
    False
    >>> is_datetime64_ns_dtype(int)
    False
    >>> is_datetime64_ns_dtype(np.datetime64)  # no unit
    False
    >>> is_datetime64_ns_dtype(DatetimeTZDtype(""ns"", ""US/Eastern""))
    True
    >>> is_datetime64_ns_dtype(np.array(['a', 'b']))
    False
    >>> is_datetime64_ns_dtype(np.array([1, 2]))
    False
    >>> is_datetime64_ns_dtype(np.array([], dtype=""datetime64""))  # no unit
    False
    >>> is_datetime64_ns_dtype(np.array([], dtype=""datetime64[ps]""))  # wrong unit
    False
    >>> is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3], dtype=""datetime64[ns]""))
    True
    
    "
822,"def regroup(parser, token):
    
    bits = token.split_contents()
    if len(bits) != 6:
        raise TemplateSyntaxError(""'regroup' tag takes five arguments"")
    target = parser.compile_filter(bits[1])
    if bits[2] != 'by':
        raise TemplateSyntaxError(""second argument to 'regroup' tag must be 'by'"")
    if bits[4] != 'as':
        raise TemplateSyntaxError(
            ""next-to-last argument to 'regroup' tag must be 'as'""
        )
    var_name = bits[5]
    # RegroupNode will take each item in 'target', put it in the context under
    # 'var_name', evaluate 'var_name'.'expression' in the current context, and
    # group by the resulting value. After all items are processed, it will
    # save the final result in the context under 'var_name', thus clearing the
    # temporary values. This hack is necessary because the template engine
    # doesn't provide a context-aware equivalent of Python's getattr.
    expression = parser.compile_filter(var_name +
                                       VARIABLE_ATTRIBUTE_SEPARATOR +
                                       bits[3])
    return RegroupNode(target, expression, var_name)


@register.tag","
    Regroup a list of alike objects by a common attribute.

    This complex tag is best illustrated by use of an example: say that
    ``musicians`` is a list of ``Musician`` objects that have ``name`` and
    ``instrument`` attributes, and you'd like to display a list that
    looks like:

        * Guitar:
            * Django Reinhardt
            * Emily Remler
        * Piano:
            * Lovie Austin
            * Bud Powell
        * Trumpet:
            * Duke Ellington

    The following snippet of template code would accomplish this dubious task::

        {% regroup musicians by instrument as grouped %}
        <ul>
        {% for group in grouped %}
            <li>{{ group.grouper }}
            <ul>
                {% for musician in group.list %}
                <li>{{ musician.name }}</li>
                {% endfor %}
            </ul>
        {% endfor %}
        </ul>

    As you can see, ``{% regroup %}`` populates a variable with a list of
    objects with ``grouper`` and ``list`` attributes. ``grouper`` contains the
    item that was grouped by; ``list`` contains the list of objects that share
    that ``grouper``. In this case, ``grouper`` would be ``Guitar``, ``Piano``
    and ``Trumpet``, and ``list`` is the list of musicians who play this
    instrument.

    Note that ``{% regroup %}`` does not work when the list to be grouped is not
    sorted by the key you are grouping by! This means that if your list of
    musicians was not sorted by instrument, you'd need to make sure it is sorted
    before using it, i.e.::

        {% regroup musicians|dictsort:""instrument"" by instrument as grouped %}
    ",230,131,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def regroup(parser, token):
    
    bits = token.split_contents()
    if len(bits) != 6:
        raise TemplateSyntaxError(""'regroup' tag takes five arguments"")
    target = parser.compile_filter(bits[1])
    if bits[2] != 'by':
        raise TemplateSyntaxError(""second argument to 'regroup' tag must be 'by'"")
    if bits[4] != 'as':
        raise TemplateSyntaxError(
            ""next-to-last argument to 'regroup' tag must be 'as'""
        )
    var_name = bits[5]
    # RegroupNode will take each item in 'target', put it in the context under
    # 'var_name', evaluate 'var_name'.'expression' in the current context, and
    # group by the resulting value. After all items are processed, it will
    # save the final result in the context under 'var_name', thus clearing the
    # temporary values. This hack is necessary because the template engine
    # doesn't provide a context-aware equivalent of Python's getattr.
    expression = parser.compile_filter(var_name +
                                       VARIABLE_ATTRIBUTE_SEPARATOR +
                                       bits[3])
    return RegroupNode(target, expression, var_name)


@register.tag
        ```
    ###Assistant : 
    Regroup a list of alike objects by a common attribute.

    This complex tag is best illustrated by use of an example: say that
    ``musicians`` is a list of ``Musician`` objects that have ``name`` and
    ``instrument`` attributes, and you'd like to display a list that
    looks like:

        * Guitar:
            * Django Reinhardt
            * Emily Remler
        * Piano:
            * Lovie Austin
            * Bud Powell
        * Trumpet:
            * Duke Ellington

    The following snippet of template code would accomplish this dubious task::

        {% regroup musicians by instrument as grouped %}
        <ul>
        {% for group in grouped %}
            <li>{{ group.grouper }}
            <ul>
                {% for musician in group.list %}
                <li>{{ musician.name }}</li>
                {% endfor %}
            </ul>
        {% endfor %}
        </ul>

    As you can see, ``{% regroup %}`` populates a variable with a list of
    objects with ``grouper`` and ``list`` attributes. ``grouper`` contains the
    item that was grouped by; ``list`` contains the list of objects that share
    that ``grouper``. In this case, ``grouper`` would be ``Guitar``, ``Piano``
    and ``Trumpet``, and ``list`` is the list of musicians who play this
    instrument.

    Note that ``{% regroup %}`` does not work when the list to be grouped is not
    sorted by the key you are grouping by! This means that if your list of
    musicians was not sorted by instrument, you'd need to make sure it is sorted
    before using it, i.e.::

        {% regroup musicians|dictsort:""instrument"" by instrument as grouped %}
    
    "
823,"def create_basic_list(cls) -> ""Saved"":
        

        metadata = cls.get_metadata(""saved"")
        urls = cls.get_urls(""saved"")

        return cls(**metadata, urls=urls, songs=[])
","
        Create a basic list with only the required metadata and urls.

        ### Returns
        - The Saved object.
        ",17,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_basic_list(cls) -> ""Saved"":
        

        metadata = cls.get_metadata(""saved"")
        urls = cls.get_urls(""saved"")

        return cls(**metadata, urls=urls, songs=[])

        ```
    ###Assistant : 
        Create a basic list with only the required metadata and urls.

        ### Returns
        - The Saved object.
        
    "
824,"def assert_lists_same(a, b):
    
    assert len(a) == len(b)
    for i in a:
        assert i in b
    for i in b:
        assert i in a

","Compare two lists, ignoring order.

    Check both that all items in a are in b and that all items in b are in a,
    otherwise assert_lists_same([""1"", ""1""], [""1"", ""2""]) could be True.
    ",32,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def assert_lists_same(a, b):
    
    assert len(a) == len(b)
    for i in a:
        assert i in b
    for i in b:
        assert i in a


        ```
    ###Assistant : Compare two lists, ignoring order.

    Check both that all items in a are in b and that all items in b are in a,
    otherwise assert_lists_same([""1"", ""1""], [""1"", ""2""]) could be True.
    
    "
825,"def test_dataframe_format_with_index():
    pytest.importorskip(""jinja2"")
    df = pd.DataFrame(
        {
            ""A"": [1, 2, 3, 4, 5, 6, 7, 8],
            ""B"": list(""ABCDEFGH""),
            ""C"": pd.Categorical(list(""AAABBBCC"")),
        },
        index=list(""ABCDEFGH""),
    )
    ddf = dd.from_pandas(df, 3)
    exp = (
        ""Dask DataFrame Structure:\n""
        ""                   A       B                C\n""
        ""npartitions=3                                \n""
        ""A              int64  object  category[known]\n""
        ""D                ...     ...              ...\n""
        ""G                ...     ...              ...\n""
        ""H                ...     ...              ...\n""
        ""Dask Name: from_pandas, 1 graph layer""
    )
    assert repr(ddf) == exp
    assert str(ddf) == exp

    exp_table = 

    exp = .format(
        exp_table=exp_table
    )
    assert ddf.to_html() == exp

    # table is boxed with div and has style
    exp = .format(
        style=style, exp_table=exp_table
    )
    assert ddf._repr_html_() == exp

","<table border=""1"" class=""dataframe"">
  <thead>
    <tr style=""text-align: right;"">
      <th></th>
      <th>A</th>
      <th>B</th>
      <th>C</th>
    </tr>
    <tr>
      <th>npartitions=3</th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>A</th>
      <td>int64</td>
      <td>object</td>
      <td>category[known]</td>
    </tr>
    <tr>
      <th>D</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>G</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>H</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
  </tbody>
</table><div><strong>Dask DataFrame Structure:</strong></div>
{exp_table}
<div>Dask Name: from_pandas, 1 graph layer</div><div><strong>Dask DataFrame Structure:</strong></div>
<div>
{style}{exp_table}
</div>
<div>Dask Name: from_pandas, 1 graph layer</div>",66,100,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dataframe_format_with_index():
    pytest.importorskip(""jinja2"")
    df = pd.DataFrame(
        {
            ""A"": [1, 2, 3, 4, 5, 6, 7, 8],
            ""B"": list(""ABCDEFGH""),
            ""C"": pd.Categorical(list(""AAABBBCC"")),
        },
        index=list(""ABCDEFGH""),
    )
    ddf = dd.from_pandas(df, 3)
    exp = (
        ""Dask DataFrame Structure:\n""
        ""                   A       B                C\n""
        ""npartitions=3                                \n""
        ""A              int64  object  category[known]\n""
        ""D                ...     ...              ...\n""
        ""G                ...     ...              ...\n""
        ""H                ...     ...              ...\n""
        ""Dask Name: from_pandas, 1 graph layer""
    )
    assert repr(ddf) == exp
    assert str(ddf) == exp

    exp_table = 

    exp = .format(
        exp_table=exp_table
    )
    assert ddf.to_html() == exp

    # table is boxed with div and has style
    exp = .format(
        style=style, exp_table=exp_table
    )
    assert ddf._repr_html_() == exp


        ```
    ###Assistant : <table border=""1"" class=""dataframe"">
  <thead>
    <tr style=""text-align: right;"">
      <th></th>
      <th>A</th>
      <th>B</th>
      <th>C</th>
    </tr>
    <tr>
      <th>npartitions=3</th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>A</th>
      <td>int64</td>
      <td>object</td>
      <td>category[known]</td>
    </tr>
    <tr>
      <th>D</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>G</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>H</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
  </tbody>
</table><div><strong>Dask DataFrame Structure:</strong></div>
{exp_table}
<div>Dask Name: from_pandas, 1 graph layer</div><div><strong>Dask DataFrame Structure:</strong></div>
<div>
{style}{exp_table}
</div>
<div>Dask Name: from_pandas, 1 graph layer</div>
    "
826,"def test_run_from_argv_closes_connections(self):
        
        command = BaseCommand()
        command.check = lambda: []
        command.handle = lambda *args, **kwargs: args
        with mock.patch(""django.core.management.base.connections"") as mock_connections:
            command.run_from_argv(["""", """"])
        # Test connections have been closed
        self.assertTrue(mock_connections.close_all.called)
","
        A command called from the command line should close connections after
        being executed (#21255).
        ",14,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_run_from_argv_closes_connections(self):
        
        command = BaseCommand()
        command.check = lambda: []
        command.handle = lambda *args, **kwargs: args
        with mock.patch(""django.core.management.base.connections"") as mock_connections:
            command.run_from_argv(["""", """"])
        # Test connections have been closed
        self.assertTrue(mock_connections.close_all.called)

        ```
    ###Assistant : 
        A command called from the command line should close connections after
        being executed (#21255).
        
    "
827,"def test_version_managing(self, data_handler):
        # set up

        df = pd.DataFrame([
            {'a': 1, 'b': dt.datetime(2020, 1, 1)},
            {'a': 2, 'b': dt.datetime(2020, 1, 2)},
            {'a': 1, 'b': dt.datetime(2020, 1, 3)},
        ])
        self.set_handler(data_handler, name='pg', tables={'tasks': df})

        # ================= retrain cycles =====================

        # create folder
        self.run_sql('create database proj')

        # -- create model --
        self.run_sql(
            
        )
        self.wait_predictor('proj', 'task_model')

        assert data_handler().native_query.call_args[0][0] == 'select * from tasks'

        # tag works in create model
        ret = self.run_sql('select * from proj.models')
        assert ret['TAG'][0] == 'first'

        # use model
        ret = self.run_sql()

        assert len(ret) == 3
        assert ret.predicted[0] == 42

        # -- retrain predictor with tag --
        data_handler.reset_mock()
        self.run_sql(
            
        )
        self.wait_predictor('proj', 'task_model', {'tag': 'second'})

        # get current model
        ret = self.run_sql('select * from proj.models')

        # check target
        assert ret['PREDICT'][0] == 'b'

        # check label
        assert ret['TAG'][0] == 'second'

        # check integration sql
        assert data_handler().native_query.call_args[0][0] == 'select * from tasks where a=2'

        # use model
        ret = self.run_sql()
        assert ret.predicted[0] == 42

        # used model has tag 'second'
        models = self.get_models()
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'second'

        # -- retrain again with active=0 --
        data_handler.reset_mock()
        self.run_sql(
            
        )
        self.wait_predictor('proj', 'task_model', {'tag': 'third'})

        ret = self.run_sql('select * from proj.models')

        # check target is from previous retrain
        assert ret['PREDICT'][0] == 'b'

        # use model
        ret = self.run_sql()

        # used model has tag 'second' (previous)
        models = self.get_models()
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'second'

        # ================ working with inactive versions =================

        # run 3st version model and check used model version
        ret = self.run_sql()

        models = self.get_models()
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'third'

        # one-line query model by version
        ret = self.run_sql('SELECT * from proj.task_model.3 where a=1 and b=2')
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'third'

        # not existing version
        with pytest.raises(Exception) as exc_info:
            self.run_sql(
                'SELECT * from proj.task_model.4 where a=1 and b=2',
            )
        assert 'does not exists' in str(exc_info.value)

        # ================== managing versions =========================

        # show models command
        # Show models <from | in> <project> where <expr>
        ret = self.run_sql('Show models')
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql('Show models from proj')
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql('Show models in proj')
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql(""Show models where name='task_model'"")
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql(""Show models from proj where name='xxx'"")
        assert len(ret) == 0

        # ----------------

        # See all versions
        ret = self.run_sql('select * from proj.models_versions')
        # we have all tags in versions
        assert set(ret['TAG']) == {'first', 'second', 'third'}

        # Set active selected version
        self.run_sql()

        # get active version
        ret = self.run_sql('select * from proj.models_versions where active = 1')
        assert ret['TAG'][0] == 'first'

        # use active version ?

        # Delete specific version
        self.run_sql()

        # deleted version not in list
        ret = self.run_sql('select * from proj.models_versions')
        assert len(ret) == 2
        assert 'second' not in ret['TAG']

        # try to use deleted version
        with pytest.raises(Exception) as exc_info:
            self.run_sql(
                'SELECT * from proj.task_model.2 where a=1',
            )
        assert 'does not exists' in str(exc_info.value)

        # exception with deleting active version
        with pytest.raises(Exception) as exc_info:
            self.run_sql()
        assert 'is not found' in str(exc_info.value)

        # drop predictor and check model is deleted and no versions
        self.run_sql('drop predictor proj.task_model')
        ret = self.run_sql('select * from proj.models')
        assert len(ret) == 0

        ret = self.run_sql('select * from proj.models_versions')
        assert len(ret) == 0


","
                CREATE PREDICTOR proj.task_model
                from pg (select * from tasks)
                PREDICT a
                using engine='dummy_ml',  tag = 'first'
            
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model as m
        
                retrain proj.task_model
                from pg (select * from tasks where a=2)
                PREDICT b
                using tag = 'second'
            
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model as m
        
                retrain proj.task_model
                from pg (select * from tasks where a=2)
                PREDICT a
                using tag='third', active=0
            
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model as m
        
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model.3 as m
        
           update proj.models_versions 
           set active=1
           where version=1 and name='task_model' 
        
           delete from proj.models_versions 
           where version=2 
           and name='task_model'
        
               delete from proj.models_versions 
               where version=3 
               and model='task_model'
            ",109,536,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_version_managing(self, data_handler):
        # set up

        df = pd.DataFrame([
            {'a': 1, 'b': dt.datetime(2020, 1, 1)},
            {'a': 2, 'b': dt.datetime(2020, 1, 2)},
            {'a': 1, 'b': dt.datetime(2020, 1, 3)},
        ])
        self.set_handler(data_handler, name='pg', tables={'tasks': df})

        # ================= retrain cycles =====================

        # create folder
        self.run_sql('create database proj')

        # -- create model --
        self.run_sql(
            
        )
        self.wait_predictor('proj', 'task_model')

        assert data_handler().native_query.call_args[0][0] == 'select * from tasks'

        # tag works in create model
        ret = self.run_sql('select * from proj.models')
        assert ret['TAG'][0] == 'first'

        # use model
        ret = self.run_sql()

        assert len(ret) == 3
        assert ret.predicted[0] == 42

        # -- retrain predictor with tag --
        data_handler.reset_mock()
        self.run_sql(
            
        )
        self.wait_predictor('proj', 'task_model', {'tag': 'second'})

        # get current model
        ret = self.run_sql('select * from proj.models')

        # check target
        assert ret['PREDICT'][0] == 'b'

        # check label
        assert ret['TAG'][0] == 'second'

        # check integration sql
        assert data_handler().native_query.call_args[0][0] == 'select * from tasks where a=2'

        # use model
        ret = self.run_sql()
        assert ret.predicted[0] == 42

        # used model has tag 'second'
        models = self.get_models()
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'second'

        # -- retrain again with active=0 --
        data_handler.reset_mock()
        self.run_sql(
            
        )
        self.wait_predictor('proj', 'task_model', {'tag': 'third'})

        ret = self.run_sql('select * from proj.models')

        # check target is from previous retrain
        assert ret['PREDICT'][0] == 'b'

        # use model
        ret = self.run_sql()

        # used model has tag 'second' (previous)
        models = self.get_models()
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'second'

        # ================ working with inactive versions =================

        # run 3st version model and check used model version
        ret = self.run_sql()

        models = self.get_models()
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'third'

        # one-line query model by version
        ret = self.run_sql('SELECT * from proj.task_model.3 where a=1 and b=2')
        model_id = ret.predictor_id[0]
        assert models[model_id].label == 'third'

        # not existing version
        with pytest.raises(Exception) as exc_info:
            self.run_sql(
                'SELECT * from proj.task_model.4 where a=1 and b=2',
            )
        assert 'does not exists' in str(exc_info.value)

        # ================== managing versions =========================

        # show models command
        # Show models <from | in> <project> where <expr>
        ret = self.run_sql('Show models')
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql('Show models from proj')
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql('Show models in proj')
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql(""Show models where name='task_model'"")
        assert len(ret) == 1 and ret['NAME'][0] == 'task_model'

        ret = self.run_sql(""Show models from proj where name='xxx'"")
        assert len(ret) == 0

        # ----------------

        # See all versions
        ret = self.run_sql('select * from proj.models_versions')
        # we have all tags in versions
        assert set(ret['TAG']) == {'first', 'second', 'third'}

        # Set active selected version
        self.run_sql()

        # get active version
        ret = self.run_sql('select * from proj.models_versions where active = 1')
        assert ret['TAG'][0] == 'first'

        # use active version ?

        # Delete specific version
        self.run_sql()

        # deleted version not in list
        ret = self.run_sql('select * from proj.models_versions')
        assert len(ret) == 2
        assert 'second' not in ret['TAG']

        # try to use deleted version
        with pytest.raises(Exception) as exc_info:
            self.run_sql(
                'SELECT * from proj.task_model.2 where a=1',
            )
        assert 'does not exists' in str(exc_info.value)

        # exception with deleting active version
        with pytest.raises(Exception) as exc_info:
            self.run_sql()
        assert 'is not found' in str(exc_info.value)

        # drop predictor and check model is deleted and no versions
        self.run_sql('drop predictor proj.task_model')
        ret = self.run_sql('select * from proj.models')
        assert len(ret) == 0

        ret = self.run_sql('select * from proj.models_versions')
        assert len(ret) == 0



        ```
    ###Assistant : 
                CREATE PREDICTOR proj.task_model
                from pg (select * from tasks)
                PREDICT a
                using engine='dummy_ml',  tag = 'first'
            
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model as m
        
                retrain proj.task_model
                from pg (select * from tasks where a=2)
                PREDICT b
                using tag = 'second'
            
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model as m
        
                retrain proj.task_model
                from pg (select * from tasks where a=2)
                PREDICT a
                using tag='third', active=0
            
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model as m
        
             SELECT m.*
               FROM pg.tasks as t
               JOIN proj.task_model.3 as m
        
           update proj.models_versions 
           set active=1
           where version=1 and name='task_model' 
        
           delete from proj.models_versions 
           where version=2 
           and name='task_model'
        
               delete from proj.models_versions 
               where version=3 
               and model='task_model'
            
    "
828,"def _looks_like_red_hat_lib() -> bool:
    
    from distutils.command.install import INSTALL_SCHEMES  # type: ignore

    return all(
        k in INSTALL_SCHEMES
        and _looks_like_red_hat_patched_platlib_purelib(INSTALL_SCHEMES[k])
        for k in (""unix_prefix"", ""unix_home"")
    )


@functools.lru_cache(maxsize=None)","Red Hat patches platlib in unix_prefix and unix_home, but not purelib.

    This is the only way I can see to tell a Red Hat-patched Python.
    ",25,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _looks_like_red_hat_lib() -> bool:
    
    from distutils.command.install import INSTALL_SCHEMES  # type: ignore

    return all(
        k in INSTALL_SCHEMES
        and _looks_like_red_hat_patched_platlib_purelib(INSTALL_SCHEMES[k])
        for k in (""unix_prefix"", ""unix_home"")
    )


@functools.lru_cache(maxsize=None)
        ```
    ###Assistant : Red Hat patches platlib in unix_prefix and unix_home, but not purelib.

    This is the only way I can see to tell a Red Hat-patched Python.
    
    "
829,"def get_valid_parent_pages(self, user):
        
        # Get queryset of pages where this page type can be added
        allowed_parent_page_content_types = list(
            ContentType.objects.get_for_models(
                *self.model.allowed_parent_page_models()
            ).values()
        )
        allowed_parent_pages = Page.objects.filter(
            content_type__in=allowed_parent_page_content_types
        )

        # Get queryset of pages where the user has permission to add subpages
        if user.is_superuser:
            pages_where_user_can_add = Page.objects.all()
        else:
            pages_where_user_can_add = Page.objects.none()
            user_perms = UserPagePermissionsProxy(user)

            for perm in user_perms.permissions.filter(permission_type=""add""):
                # user has add permission on any subpage of perm.page
                # (including perm.page itself)
                pages_where_user_can_add |= Page.objects.descendant_of(
                    perm.page, inclusive=True
                )

        # Combine them
        return allowed_parent_pages & pages_where_user_can_add
","
        Identifies possible parent pages for the current user by first looking
        at allowed_parent_page_models() on self.model to limit options to the
        correct type of page, then checking permissions on those individual
        pages to make sure we have permission to add a subpage to it.
        ",43,83,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_valid_parent_pages(self, user):
        
        # Get queryset of pages where this page type can be added
        allowed_parent_page_content_types = list(
            ContentType.objects.get_for_models(
                *self.model.allowed_parent_page_models()
            ).values()
        )
        allowed_parent_pages = Page.objects.filter(
            content_type__in=allowed_parent_page_content_types
        )

        # Get queryset of pages where the user has permission to add subpages
        if user.is_superuser:
            pages_where_user_can_add = Page.objects.all()
        else:
            pages_where_user_can_add = Page.objects.none()
            user_perms = UserPagePermissionsProxy(user)

            for perm in user_perms.permissions.filter(permission_type=""add""):
                # user has add permission on any subpage of perm.page
                # (including perm.page itself)
                pages_where_user_can_add |= Page.objects.descendant_of(
                    perm.page, inclusive=True
                )

        # Combine them
        return allowed_parent_pages & pages_where_user_can_add

        ```
    ###Assistant : 
        Identifies possible parent pages for the current user by first looking
        at allowed_parent_page_models() on self.model to limit options to the
        correct type of page, then checking permissions on those individual
        pages to make sure we have permission to add a subpage to it.
        
    "
830,"def get_attendance_list(conditions, filters):
	attendance_list = frappe.db.sql(
		
		% conditions,
		filters,
		as_dict=1,
	)

	if not attendance_list:
		msgprint(_(""No attendance record found""), alert=True, indicator=""orange"")

	att_map = {}
	for d in attendance_list:
		att_map.setdefault(d.employee, frappe._dict()).setdefault(d.day_of_month, """")
		att_map[d.employee][d.day_of_month] = d.status

	return att_map

","select employee, day(attendance_date) as day_of_month,
		status from tabAttendance where docstatus = 1 %s order by employee, attendance_date",17,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_attendance_list(conditions, filters):
	attendance_list = frappe.db.sql(
		
		% conditions,
		filters,
		as_dict=1,
	)

	if not attendance_list:
		msgprint(_(""No attendance record found""), alert=True, indicator=""orange"")

	att_map = {}
	for d in attendance_list:
		att_map.setdefault(d.employee, frappe._dict()).setdefault(d.day_of_month, """")
		att_map[d.employee][d.day_of_month] = d.status

	return att_map


        ```
    ###Assistant : select employee, day(attendance_date) as day_of_month,
		status from tabAttendance where docstatus = 1 %s order by employee, attendance_date
    "
831,"def _reorder_cache(past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past),)
        return reordered_past


@add_start_docstrings(
    ,
    REMBERT_START_DOCSTRING,
)","
    RemBERT Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks.
    ",14,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _reorder_cache(past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past),)
        return reordered_past


@add_start_docstrings(
    ,
    REMBERT_START_DOCSTRING,
)
        ```
    ###Assistant : 
    RemBERT Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks.
    
    "
832,"def test_remove_as_admin_not_in_team(self):
        

        # an org with closed membership (byproduct of flags=0)
        org = self.create_organization(owner=self.user, flags=0)
        team = self.create_team(organization=org)
        admin_user = self.create_user(email=""foo@example.com"", is_superuser=False)

        self.create_member(
            organization=org,
            user=admin_user,
            role=""admin"",
            teams=[],  # note that admin_user isn't a member of `team`
        )

        self.login_as(admin_user)

        # first, try deleting the team with open membership off
        self.get_error_response(team.organization.slug, team.slug, status_code=403)
        self.assert_team_not_deleted(team.id)

        # now, with open membership on
        org.flags.allow_joinleave = True
        org.save()

        self.get_success_response(team.organization.slug, team.slug, status_code=204)
        self.assert_team_deleted(team.id)
","Admins can't remove teams of which they're not a part, unless
        open membership is on.",15,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_remove_as_admin_not_in_team(self):
        

        # an org with closed membership (byproduct of flags=0)
        org = self.create_organization(owner=self.user, flags=0)
        team = self.create_team(organization=org)
        admin_user = self.create_user(email=""foo@example.com"", is_superuser=False)

        self.create_member(
            organization=org,
            user=admin_user,
            role=""admin"",
            teams=[],  # note that admin_user isn't a member of `team`
        )

        self.login_as(admin_user)

        # first, try deleting the team with open membership off
        self.get_error_response(team.organization.slug, team.slug, status_code=403)
        self.assert_team_not_deleted(team.id)

        # now, with open membership on
        org.flags.allow_joinleave = True
        org.save()

        self.get_success_response(team.organization.slug, team.slug, status_code=204)
        self.assert_team_deleted(team.id)

        ```
    ###Assistant : Admins can't remove teams of which they're not a part, unless
        open membership is on.
    "
833,"def batch_test(num_threads, delay):
    
    with mock.patch(
        ""ray.autoscaler._private.aws.node_provider.make_ec2_client""
    ), mock.patch.object(AWSNodeProvider, ""_create_tags"", mock_create_tags):
        provider = AWSNodeProvider(
            provider_config={""region"": ""nowhere""}, cluster_name=""default""
        )
        provider.batch_counter = 0
        provider.tag_update_counter = 0
        provider.tag_cache = {str(x): {} for x in range(num_threads)}

        threads = []
        for x in range(num_threads):
            thread = threading.Thread(
                target=provider.set_node_tags, args=(str(x), {""foo"": ""bar""})
            )
            threads.append(thread)

        for thread in threads:
            thread.start()
            time.sleep(delay)
        for thread in threads:
            thread.join()

        return provider.batch_counter, provider.tag_update_counter

","Run AWSNodeProvider.set_node_tags in several threads, with a
    specified delay between thread launches.

    Return the number of batches of tag updates and the number of tags
    updated.
    ",26,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def batch_test(num_threads, delay):
    
    with mock.patch(
        ""ray.autoscaler._private.aws.node_provider.make_ec2_client""
    ), mock.patch.object(AWSNodeProvider, ""_create_tags"", mock_create_tags):
        provider = AWSNodeProvider(
            provider_config={""region"": ""nowhere""}, cluster_name=""default""
        )
        provider.batch_counter = 0
        provider.tag_update_counter = 0
        provider.tag_cache = {str(x): {} for x in range(num_threads)}

        threads = []
        for x in range(num_threads):
            thread = threading.Thread(
                target=provider.set_node_tags, args=(str(x), {""foo"": ""bar""})
            )
            threads.append(thread)

        for thread in threads:
            thread.start()
            time.sleep(delay)
        for thread in threads:
            thread.join()

        return provider.batch_counter, provider.tag_update_counter


        ```
    ###Assistant : Run AWSNodeProvider.set_node_tags in several threads, with a
    specified delay between thread launches.

    Return the number of batches of tag updates and the number of tags
    updated.
    
    "
834,"def forward(self, feats, img_metas):
        
        y = self.last_feat_conv(feats[-1])
        for i in range(self.num_inputs - 2, -1, -1):
            x = feats[i]
            cur_fpn = self.lateral_convs[i](x)
            y = cur_fpn + \
                F.interpolate(y, size=cur_fpn.shape[-2:], mode='nearest')
            y = self.output_convs[i](y)

        mask_feature = self.mask_feature(y)
        memory = feats[-1]
        return mask_feature, memory


@PLUGIN_LAYERS.register_module()","
        Args:
            feats (list[Tensor]): Feature maps of each level. Each has
                shape of (batch_size, c, h, w).
            img_metas (list[dict]): List of image information. Pass in
                for creating more accurate padding mask. Not used here.

        Returns:
            tuple: a tuple containing the following:

                - mask_feature (Tensor): Shape (batch_size, c, h, w).
                - memory (Tensor): Output of last stage of backbone.\
                    Shape (batch_size, c, h, w).
        ",62,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, feats, img_metas):
        
        y = self.last_feat_conv(feats[-1])
        for i in range(self.num_inputs - 2, -1, -1):
            x = feats[i]
            cur_fpn = self.lateral_convs[i](x)
            y = cur_fpn + \
                F.interpolate(y, size=cur_fpn.shape[-2:], mode='nearest')
            y = self.output_convs[i](y)

        mask_feature = self.mask_feature(y)
        memory = feats[-1]
        return mask_feature, memory


@PLUGIN_LAYERS.register_module()
        ```
    ###Assistant : 
        Args:
            feats (list[Tensor]): Feature maps of each level. Each has
                shape of (batch_size, c, h, w).
            img_metas (list[dict]): List of image information. Pass in
                for creating more accurate padding mask. Not used here.

        Returns:
            tuple: a tuple containing the following:

                - mask_feature (Tensor): Shape (batch_size, c, h, w).
                - memory (Tensor): Output of last stage of backbone.\
                    Shape (batch_size, c, h, w).
        
    "
835,"async def test_remote_scanner_expires_non_connectable(hass):
    
    manager = _get_manager()

    switchbot_device = BLEDevice(
        ""44:44:33:11:23:45"",
        ""wohand"",
        {},
        rssi=-100,
    )
    switchbot_device_adv = generate_advertisement_data(
        local_name=""wohand"",
        service_uuids=[],
        manufacturer_data={1: b""\x01""},
        rssi=-100,
    )
",Test the remote scanner expires stale non connectable data.,9,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_remote_scanner_expires_non_connectable(hass):
    
    manager = _get_manager()

    switchbot_device = BLEDevice(
        ""44:44:33:11:23:45"",
        ""wohand"",
        {},
        rssi=-100,
    )
    switchbot_device_adv = generate_advertisement_data(
        local_name=""wohand"",
        service_uuids=[],
        manufacturer_data={1: b""\x01""},
        rssi=-100,
    )

        ```
    ###Assistant : Test the remote scanner expires stale non connectable data.
    "
836,"def convert_yaml_objects_to_native(obj):
    
    if isinstance(obj, dict):
        return dict((k, convert_yaml_objects_to_native(v)) for k, v in obj.items())
    elif isinstance(obj, list):
        return [convert_yaml_objects_to_native(v) for v in obj]
    elif isinstance(obj, text_type):
        return text_type(obj)
    else:
        return obj

","Older versions of the ``toml`` python library, and tomllib, don't have
    a pluggable way to tell the encoder about custom types, so we need to
    ensure objects that we pass are native types.

    Used with:
      - ``toml<0.10.0`` where ``toml.TomlEncoder`` is missing
      - ``tomli`` or ``tomllib``

    This function recurses an object and ensures we cast any of the types from
    ``ansible.parsing.yaml.objects`` into their native types, effectively cleansing
    the data before we hand it over to the toml library.

    This function doesn't directly check for the types from ``ansible.parsing.yaml.objects``
    but instead checks for the types those objects inherit from, to offer more flexibility.
    ",101,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_yaml_objects_to_native(obj):
    
    if isinstance(obj, dict):
        return dict((k, convert_yaml_objects_to_native(v)) for k, v in obj.items())
    elif isinstance(obj, list):
        return [convert_yaml_objects_to_native(v) for v in obj]
    elif isinstance(obj, text_type):
        return text_type(obj)
    else:
        return obj


        ```
    ###Assistant : Older versions of the ``toml`` python library, and tomllib, don't have
    a pluggable way to tell the encoder about custom types, so we need to
    ensure objects that we pass are native types.

    Used with:
      - ``toml<0.10.0`` where ``toml.TomlEncoder`` is missing
      - ``tomli`` or ``tomllib``

    This function recurses an object and ensures we cast any of the types from
    ``ansible.parsing.yaml.objects`` into their native types, effectively cleansing
    the data before we hand it over to the toml library.

    This function doesn't directly check for the types from ``ansible.parsing.yaml.objects``
    but instead checks for the types those objects inherit from, to offer more flexibility.
    
    "
837,"def make_future_dataframe(self, periods, freq='D', include_history=True):
        
        if self.history_dates is None:
            raise Exception('Model has not been fit.')
        if freq is None:
            # taking the tail makes freq inference more reliable
            freq = pd.infer_freq(self.history_dates.tail(5))
            # returns None if inference failed
            if freq is None:
                raise Exception('Unable to infer `freq`')
        last_date = self.history_dates.max()
        dates = pd.date_range(
            start=last_date,
            periods=periods + 1,  # An extra in case we include start
            freq=freq)
        dates = dates[dates > last_date]  # Drop start if equals last_date
        dates = dates[:periods]  # Return correct number of periods

        if include_history:
            dates = np.concatenate((np.array(self.history_dates), dates))

        return pd.DataFrame({'ds': dates})
","Simulate the trend using the extrapolated generative model.

        Parameters
        ----------
        periods: Int number of periods to forecast forward.
        freq: Any valid frequency for pd.date_range, such as 'D' or 'M'.
        include_history: Boolean to include the historical dates in the data
            frame for predictions.

        Returns
        -------
        pd.Dataframe that extends forward from the end of self.history for the
        requested number of periods.
        ",59,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_future_dataframe(self, periods, freq='D', include_history=True):
        
        if self.history_dates is None:
            raise Exception('Model has not been fit.')
        if freq is None:
            # taking the tail makes freq inference more reliable
            freq = pd.infer_freq(self.history_dates.tail(5))
            # returns None if inference failed
            if freq is None:
                raise Exception('Unable to infer `freq`')
        last_date = self.history_dates.max()
        dates = pd.date_range(
            start=last_date,
            periods=periods + 1,  # An extra in case we include start
            freq=freq)
        dates = dates[dates > last_date]  # Drop start if equals last_date
        dates = dates[:periods]  # Return correct number of periods

        if include_history:
            dates = np.concatenate((np.array(self.history_dates), dates))

        return pd.DataFrame({'ds': dates})

        ```
    ###Assistant : Simulate the trend using the extrapolated generative model.

        Parameters
        ----------
        periods: Int number of periods to forecast forward.
        freq: Any valid frequency for pd.date_range, such as 'D' or 'M'.
        include_history: Boolean to include the historical dates in the data
            frame for predictions.

        Returns
        -------
        pd.Dataframe that extends forward from the end of self.history for the
        requested number of periods.
        
    "
838,"def array_to_blobproto(arr, diff=None):
    
    blob = caffe_pb2.BlobProto()
    blob.shape.dim.extend(arr.shape)
    blob.data.extend(arr.astype(float).flat)
    if diff is not None:
        blob.diff.extend(diff.astype(float).flat)
    return blob

","Converts a N-dimensional array to blob proto. If diff is given, also
    convert the diff. You need to make sure that arr and diff have the same
    shape, and this function does not do sanity check.
    ",36,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def array_to_blobproto(arr, diff=None):
    
    blob = caffe_pb2.BlobProto()
    blob.shape.dim.extend(arr.shape)
    blob.data.extend(arr.astype(float).flat)
    if diff is not None:
        blob.diff.extend(diff.astype(float).flat)
    return blob


        ```
    ###Assistant : Converts a N-dimensional array to blob proto. If diff is given, also
    convert the diff. You need to make sure that arr and diff have the same
    shape, and this function does not do sanity check.
    
    "
839,"def test_cross_signing_keys_retry(self):
        
        remote_user_id = ""@john:test_remote""
        remote_master_key = ""85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY""
        remote_self_signing_key = ""QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ""

        # Register mock device list retrieval on the federation client.
        federation_client = self.homeserver.get_federation_client()
        federation_client.query_user_devices = Mock(
            return_value=make_awaitable(
                {
                    ""user_id"": remote_user_id,
                    ""stream_id"": 1,
                    ""devices"": [],
                    ""master_key"": {
                        ""user_id"": remote_user_id,
                        ""usage"": [""master""],
                        ""keys"": {""ed25519:"" + remote_master_key: remote_master_key},
                    },
                    ""self_signing_key"": {
                        ""user_id"": remote_user_id,
                        ""usage"": [""self_signing""],
                        ""keys"": {
                            ""ed25519:""
                            + remote_self_signing_key: remote_self_signing_key
                        },
                    },
                }
            )
        )

        # Resync the device list.
        device_handler = self.homeserver.get_device_handler()
        self.get_success(
            device_handler.device_list_updater.user_device_resync(remote_user_id),
        )

        # Retrieve the cross-signing keys for this user.
        keys = self.get_success(
            self.store.get_e2e_cross_signing_keys_bulk(user_ids=[remote_user_id]),
        )
        self.assertTrue(remote_user_id in keys)

        # Check that the master key is the one returned by the mock.
        master_key = keys[remote_user_id][""master""]
        self.assertEqual(len(master_key[""keys""]), 1)
        self.assertTrue(""ed25519:"" + remote_master_key in master_key[""keys""].keys())
        self.assertTrue(remote_master_key in master_key[""keys""].values())

        # Check that the self-signing key is the one returned by the mock.
        self_signing_key = keys[remote_user_id][""self_signing""]
        self.assertEqual(len(self_signing_key[""keys""]), 1)
        self.assertTrue(
            ""ed25519:"" + remote_self_signing_key in self_signing_key[""keys""].keys(),
        )
        self.assertTrue(remote_self_signing_key in self_signing_key[""keys""].values())

","Tests that resyncing a device list correctly processes cross-signing keys from
        the remote server.
        ",14,145,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_cross_signing_keys_retry(self):
        
        remote_user_id = ""@john:test_remote""
        remote_master_key = ""85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY""
        remote_self_signing_key = ""QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ""

        # Register mock device list retrieval on the federation client.
        federation_client = self.homeserver.get_federation_client()
        federation_client.query_user_devices = Mock(
            return_value=make_awaitable(
                {
                    ""user_id"": remote_user_id,
                    ""stream_id"": 1,
                    ""devices"": [],
                    ""master_key"": {
                        ""user_id"": remote_user_id,
                        ""usage"": [""master""],
                        ""keys"": {""ed25519:"" + remote_master_key: remote_master_key},
                    },
                    ""self_signing_key"": {
                        ""user_id"": remote_user_id,
                        ""usage"": [""self_signing""],
                        ""keys"": {
                            ""ed25519:""
                            + remote_self_signing_key: remote_self_signing_key
                        },
                    },
                }
            )
        )

        # Resync the device list.
        device_handler = self.homeserver.get_device_handler()
        self.get_success(
            device_handler.device_list_updater.user_device_resync(remote_user_id),
        )

        # Retrieve the cross-signing keys for this user.
        keys = self.get_success(
            self.store.get_e2e_cross_signing_keys_bulk(user_ids=[remote_user_id]),
        )
        self.assertTrue(remote_user_id in keys)

        # Check that the master key is the one returned by the mock.
        master_key = keys[remote_user_id][""master""]
        self.assertEqual(len(master_key[""keys""]), 1)
        self.assertTrue(""ed25519:"" + remote_master_key in master_key[""keys""].keys())
        self.assertTrue(remote_master_key in master_key[""keys""].values())

        # Check that the self-signing key is the one returned by the mock.
        self_signing_key = keys[remote_user_id][""self_signing""]
        self.assertEqual(len(self_signing_key[""keys""]), 1)
        self.assertTrue(
            ""ed25519:"" + remote_self_signing_key in self_signing_key[""keys""].keys(),
        )
        self.assertTrue(remote_self_signing_key in self_signing_key[""keys""].values())


        ```
    ###Assistant : Tests that resyncing a device list correctly processes cross-signing keys from
        the remote server.
        
    "
840,"def test_exec_success(self, db_mock_class):
        
        run = {
            'new_cluster': NEW_CLUSTER,
            'notebook_task': NOTEBOOK_TASK,
        }
        op = DatabricksSubmitRunOperator(task_id=TASK_ID, json=run)
        db_mock = db_mock_class.return_value
        db_mock.submit_run.return_value = 1
        db_mock.get_run_state.return_value = RunState('TERMINATED', 'SUCCESS', '')

        op.execute(None)

        expected = databricks_operator._deep_string_coerce(
            {'new_cluster': NEW_CLUSTER, 'notebook_task': NOTEBOOK_TASK, 'run_name': TASK_ID}
        )
        db_mock_class.assert_called_once_with(
            DEFAULT_CONN_ID,
            retry_limit=op.databricks_retry_limit,
            retry_delay=op.databricks_retry_delay,
            retry_args=None,
        )

        db_mock.submit_run.assert_called_once_with(expected)
        db_mock.get_run_page_url.assert_called_once_with(RUN_ID)
        db_mock.get_run_state.assert_called_once_with(RUN_ID)
        assert RUN_ID == op.run_id
","
        Test the execute function in case where the run is successful.
        ",11,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_exec_success(self, db_mock_class):
        
        run = {
            'new_cluster': NEW_CLUSTER,
            'notebook_task': NOTEBOOK_TASK,
        }
        op = DatabricksSubmitRunOperator(task_id=TASK_ID, json=run)
        db_mock = db_mock_class.return_value
        db_mock.submit_run.return_value = 1
        db_mock.get_run_state.return_value = RunState('TERMINATED', 'SUCCESS', '')

        op.execute(None)

        expected = databricks_operator._deep_string_coerce(
            {'new_cluster': NEW_CLUSTER, 'notebook_task': NOTEBOOK_TASK, 'run_name': TASK_ID}
        )
        db_mock_class.assert_called_once_with(
            DEFAULT_CONN_ID,
            retry_limit=op.databricks_retry_limit,
            retry_delay=op.databricks_retry_delay,
            retry_args=None,
        )

        db_mock.submit_run.assert_called_once_with(expected)
        db_mock.get_run_page_url.assert_called_once_with(RUN_ID)
        db_mock.get_run_state.assert_called_once_with(RUN_ID)
        assert RUN_ID == op.run_id

        ```
    ###Assistant : 
        Test the execute function in case where the run is successful.
        
    "
841,"def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input=""rgb""):
    
    rgb = _color_to_rgb(color, input)
    h, s, l = husl.rgb_to_husl(*rgb)
    gray_s, gray_l = .15 * s, 15
    gray = _color_to_rgb((h, gray_s, gray_l), input=""husl"")
    colors = [rgb, gray] if reverse else [gray, rgb]
    return blend_palette(colors, n_colors, as_cmap)

","Make a sequential palette that blends from dark to ``color``.

    This kind of palette is good for data that range between relatively
    uninteresting low values and interesting high values.

    The ``color`` parameter can be specified in a number of ways, including
    all options for defining a color in matplotlib and several additional
    color spaces that are handled by seaborn. You can also use the database
    of named colors from the XKCD color survey.

    If you are using the IPython notebook, you can also choose this palette
    interactively with the :func:`choose_dark_palette` function.

    Parameters
    ----------
    color : base color for high values
        hex, rgb-tuple, or html color name
    n_colors : int, optional
        number of colors in the palette
    reverse : bool, optional
        if True, reverse the direction of the blend
    as_cmap : bool, optional
        If True, return a :class:`matplotlib.colors.ListedColormap`.
    input : {'rgb', 'hls', 'husl', xkcd'}
        Color space to interpret the input color. The first three options
        apply to tuple inputs and the latter applies to string inputs.

    Returns
    -------
    palette
        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`

    See Also
    --------
    light_palette : Create a sequential palette with bright low values.
    diverging_palette : Create a diverging palette with two colors.

    Examples
    --------
    .. include:: ../docstrings/dark_palette.rst

    ",201,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input=""rgb""):
    
    rgb = _color_to_rgb(color, input)
    h, s, l = husl.rgb_to_husl(*rgb)
    gray_s, gray_l = .15 * s, 15
    gray = _color_to_rgb((h, gray_s, gray_l), input=""husl"")
    colors = [rgb, gray] if reverse else [gray, rgb]
    return blend_palette(colors, n_colors, as_cmap)


        ```
    ###Assistant : Make a sequential palette that blends from dark to ``color``.

    This kind of palette is good for data that range between relatively
    uninteresting low values and interesting high values.

    The ``color`` parameter can be specified in a number of ways, including
    all options for defining a color in matplotlib and several additional
    color spaces that are handled by seaborn. You can also use the database
    of named colors from the XKCD color survey.

    If you are using the IPython notebook, you can also choose this palette
    interactively with the :func:`choose_dark_palette` function.

    Parameters
    ----------
    color : base color for high values
        hex, rgb-tuple, or html color name
    n_colors : int, optional
        number of colors in the palette
    reverse : bool, optional
        if True, reverse the direction of the blend
    as_cmap : bool, optional
        If True, return a :class:`matplotlib.colors.ListedColormap`.
    input : {'rgb', 'hls', 'husl', xkcd'}
        Color space to interpret the input color. The first three options
        apply to tuple inputs and the latter applies to string inputs.

    Returns
    -------
    palette
        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`

    See Also
    --------
    light_palette : Create a sequential palette with bright low values.
    diverging_palette : Create a diverging palette with two colors.

    Examples
    --------
    .. include:: ../docstrings/dark_palette.rst

    
    "
842,"def contour(self, X, win=None, env=None, opts=None):
        

        return self._surface(X=X, stype=""contour"", opts=opts, win=win, env=env)
","
        This function draws a contour plot. It takes as input an `NxM` tensor
        `X` that specifies the value at each location in the contour plot.

        The following `opts` are supported:

        - `opts.colormap`: colormap (`string`; default = `'Viridis'`)
        - `opts.xmin`    : clip minimum value (`number`; default = `X:min()`)
        - `opts.xmax`    : clip maximum value (`number`; default = `X:max()`)
        ",57,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def contour(self, X, win=None, env=None, opts=None):
        

        return self._surface(X=X, stype=""contour"", opts=opts, win=win, env=env)

        ```
    ###Assistant : 
        This function draws a contour plot. It takes as input an `NxM` tensor
        `X` that specifies the value at each location in the contour plot.

        The following `opts` are supported:

        - `opts.colormap`: colormap (`string`; default = `'Viridis'`)
        - `opts.xmin`    : clip minimum value (`number`; default = `X:min()`)
        - `opts.xmax`    : clip maximum value (`number`; default = `X:max()`)
        
    "
843,"def get_themes():
    

    themes = {}
    eps = set(importlib_metadata.entry_points(group='mkdocs.themes'))
    builtins = {ep.name for ep in eps if ep.dist.name == 'mkdocs'}

    for theme in eps:

        if theme.name in builtins and theme.dist.name != 'mkdocs':
            raise exceptions.ConfigurationError(
                f""The theme '{theme.name}' is a builtin theme but the package '{theme.dist.name}' ""
                ""attempts to provide a theme with the same name.""
            )
        elif theme.name in themes:
            log.warning(
                f""A theme named '{theme.name}' is provided by the Python packages '{theme.dist.name}' ""
                f""and '{themes[theme.name].dist.name}'. The one in '{theme.dist.name}' will be used.""
            )

        themes[theme.name] = theme

    return themes

",Return a dict of all installed themes as {name: EntryPoint}.,10,87,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_themes():
    

    themes = {}
    eps = set(importlib_metadata.entry_points(group='mkdocs.themes'))
    builtins = {ep.name for ep in eps if ep.dist.name == 'mkdocs'}

    for theme in eps:

        if theme.name in builtins and theme.dist.name != 'mkdocs':
            raise exceptions.ConfigurationError(
                f""The theme '{theme.name}' is a builtin theme but the package '{theme.dist.name}' ""
                ""attempts to provide a theme with the same name.""
            )
        elif theme.name in themes:
            log.warning(
                f""A theme named '{theme.name}' is provided by the Python packages '{theme.dist.name}' ""
                f""and '{themes[theme.name].dist.name}'. The one in '{theme.dist.name}' will be used.""
            )

        themes[theme.name] = theme

    return themes


        ```
    ###Assistant : Return a dict of all installed themes as {name: EntryPoint}.
    "
844,"def test_subscribe_by_default(self):
        
        NotificationSetting.objects.update_settings(
            ExternalProviders.EMAIL,
            NotificationSettingTypes.ISSUE_ALERTS,
            NotificationSettingOptionValues.NEVER,
            user=self.user,
        )

        response = self.get_success_response(""me"")
        assert response.data.get(""subscribeByDefault"") is False

","
        Test that we expect project-independent issue alert preferences to be
        returned as `subscribe_by_default`.
        ",13,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_subscribe_by_default(self):
        
        NotificationSetting.objects.update_settings(
            ExternalProviders.EMAIL,
            NotificationSettingTypes.ISSUE_ALERTS,
            NotificationSettingOptionValues.NEVER,
            user=self.user,
        )

        response = self.get_success_response(""me"")
        assert response.data.get(""subscribeByDefault"") is False


        ```
    ###Assistant : 
        Test that we expect project-independent issue alert preferences to be
        returned as `subscribe_by_default`.
        
    "
845,"def _get_users_with_any_permission_codenames_filter(self, permission_codenames):
        
        permissions = Permission.objects.filter(
            content_type=self._content_type, codename__in=permission_codenames
        )
        return (
            Q(is_superuser=True)
            | Q(user_permissions__in=permissions)
            | Q(groups__permissions__in=permissions)
        ) & Q(is_active=True)
","
        Given a list of permission codenames, return a filter expression which
        will find all users which have any of those permissions - either
        through group permissions, user permissions, or implicitly through
        being a superuser.
        ",34,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_users_with_any_permission_codenames_filter(self, permission_codenames):
        
        permissions = Permission.objects.filter(
            content_type=self._content_type, codename__in=permission_codenames
        )
        return (
            Q(is_superuser=True)
            | Q(user_permissions__in=permissions)
            | Q(groups__permissions__in=permissions)
        ) & Q(is_active=True)

        ```
    ###Assistant : 
        Given a list of permission codenames, return a filter expression which
        will find all users which have any of those permissions - either
        through group permissions, user permissions, or implicitly through
        being a superuser.
        
    "
846,"def _async_update_rssi_and_transients(self) -> None:
        
        for (
            unique_id,
            ibeacon_advertisement,
        ) in self._last_ibeacon_advertisement_by_unique_id.items():
            address = unique_id.split(""_"")[-1]
            service_info = bluetooth.async_last_service_info(
                self.hass, address, connectable=False
            )
            if not service_info:
                continue

            if address in self._transient_seen_count:
                self._transient_seen_count[address] += 1
                if self._transient_seen_count[address] == MIN_SEEN_TRANSIENT_NEW:
                    self._transient_seen_count.pop(address)
                    _async_dispatch_update(
                        self.hass,
                        unique_id,
                        service_info,
                        ibeacon_advertisement,
                        True,
                        True,
                    )
                    continue

            if service_info.rssi != ibeacon_advertisement.rssi:
                ibeacon_advertisement.update_rssi(service_info.rssi)
                async_dispatcher_send(
                    self.hass,
                    signal_seen(unique_id),
                    ibeacon_advertisement,
                )
","Check to see if the rssi has changed and update any devices.

        We don't callback on RSSI changes so we need to check them
        here and send them over the dispatcher periodically to
        ensure the distance calculation is update.

        If the transient flag is set we also need to check to see
        if the device is still transmitting and increment the counter
        ",62,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _async_update_rssi_and_transients(self) -> None:
        
        for (
            unique_id,
            ibeacon_advertisement,
        ) in self._last_ibeacon_advertisement_by_unique_id.items():
            address = unique_id.split(""_"")[-1]
            service_info = bluetooth.async_last_service_info(
                self.hass, address, connectable=False
            )
            if not service_info:
                continue

            if address in self._transient_seen_count:
                self._transient_seen_count[address] += 1
                if self._transient_seen_count[address] == MIN_SEEN_TRANSIENT_NEW:
                    self._transient_seen_count.pop(address)
                    _async_dispatch_update(
                        self.hass,
                        unique_id,
                        service_info,
                        ibeacon_advertisement,
                        True,
                        True,
                    )
                    continue

            if service_info.rssi != ibeacon_advertisement.rssi:
                ibeacon_advertisement.update_rssi(service_info.rssi)
                async_dispatcher_send(
                    self.hass,
                    signal_seen(unique_id),
                    ibeacon_advertisement,
                )

        ```
    ###Assistant : Check to see if the rssi has changed and update any devices.

        We don't callback on RSSI changes so we need to check them
        here and send them over the dispatcher periodically to
        ensure the distance calculation is update.

        If the transient flag is set we also need to check to see
        if the device is still transmitting and increment the counter
        
    "
847,"def setup_app_for_worker(app, loglevel, logfile) -> None:
    # type: (Celery, Union[str, int], str) -> None
    
    app.finalize()
    app.set_current()
    app.set_default()
    type(app.log)._setup = False
    app.log.setup(loglevel=loglevel, logfile=logfile)
",Setup the app to be used for starting an embedded worker.,11,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setup_app_for_worker(app, loglevel, logfile) -> None:
    # type: (Celery, Union[str, int], str) -> None
    
    app.finalize()
    app.set_current()
    app.set_default()
    type(app.log)._setup = False
    app.log.setup(loglevel=loglevel, logfile=logfile)

        ```
    ###Assistant : Setup the app to be used for starting an embedded worker.
    "
848,"def rank(self):
        
        if self._rank is None:
            self._rank = int(gray_to_bin(self.current), 2)
        return self._rank
","
        Ranks the Gray code.

        A ranking algorithm determines the position (or rank)
        of a combinatorial object among all the objects w.r.t.
        a given order. For example, the 4 bit binary reflected
        Gray code (BRGC) '0101' has a rank of 6 as it appears in
        the 6th position in the canonical ordering of the family
        of 4 bit Gray codes.

        Examples
        ========

        >>> from sympy.combinatorics import GrayCode
        >>> a = GrayCode(3)
        >>> list(a.generate_gray())
        ['000', '001', '011', '010', '110', '111', '101', '100']
        >>> GrayCode(3, start='100').rank
        7
        >>> GrayCode(3, rank=7).current
        '100'

        See Also
        ========

        unrank

        References
        ==========

        .. [1] http://statweb.stanford.edu/~susan/courses/s208/node12.html

        ",97,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rank(self):
        
        if self._rank is None:
            self._rank = int(gray_to_bin(self.current), 2)
        return self._rank

        ```
    ###Assistant : 
        Ranks the Gray code.

        A ranking algorithm determines the position (or rank)
        of a combinatorial object among all the objects w.r.t.
        a given order. For example, the 4 bit binary reflected
        Gray code (BRGC) '0101' has a rank of 6 as it appears in
        the 6th position in the canonical ordering of the family
        of 4 bit Gray codes.

        Examples
        ========

        >>> from sympy.combinatorics import GrayCode
        >>> a = GrayCode(3)
        >>> list(a.generate_gray())
        ['000', '001', '011', '010', '110', '111', '101', '100']
        >>> GrayCode(3, start='100').rank
        7
        >>> GrayCode(3, rank=7).current
        '100'

        See Also
        ========

        unrank

        References
        ==========

        .. [1] http://statweb.stanford.edu/~susan/courses/s208/node12.html

        
    "
849,"async def async_update(self, log_errors=True):
        
        if not self._async_client:
            self._async_client = get_async_client(
                self._hass, verify_ssl=self._verify_ssl
            )

        rendered_headers = template.render_complex(self._headers, parse_result=False)
        rendered_params = template.render_complex(self._params)

        _LOGGER.debug(""Updating from %s"", self._resource)
        try:
            response = await self._async_client.request(
                self._method,
                self._resource,
                headers=rendered_headers,
                params=rendered_params,
                auth=self._auth,
                content=self._request_data,
                timeout=self._timeout,
                follow_redirects=True,
            )
            self.data = response.text
            self.headers = response.headers
        except httpx.TimeoutException as ex:
            if log_errors:
                _LOGGER.error(""Timeout while fetching data: %s"", self._resource)
            self.last_exception = ex
            self.data = None
            self.headers = None
        except httpx.RequestError as ex:
            if log_errors:
                _LOGGER.error(
                    ""Error fetching data: %s failed with %s"", self._resource, ex
                )
            self.last_exception = ex
            self.data = None
            self.headers = None
",Get the latest data from REST service with provided method.,10,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_update(self, log_errors=True):
        
        if not self._async_client:
            self._async_client = get_async_client(
                self._hass, verify_ssl=self._verify_ssl
            )

        rendered_headers = template.render_complex(self._headers, parse_result=False)
        rendered_params = template.render_complex(self._params)

        _LOGGER.debug(""Updating from %s"", self._resource)
        try:
            response = await self._async_client.request(
                self._method,
                self._resource,
                headers=rendered_headers,
                params=rendered_params,
                auth=self._auth,
                content=self._request_data,
                timeout=self._timeout,
                follow_redirects=True,
            )
            self.data = response.text
            self.headers = response.headers
        except httpx.TimeoutException as ex:
            if log_errors:
                _LOGGER.error(""Timeout while fetching data: %s"", self._resource)
            self.last_exception = ex
            self.data = None
            self.headers = None
        except httpx.RequestError as ex:
            if log_errors:
                _LOGGER.error(
                    ""Error fetching data: %s failed with %s"", self._resource, ex
                )
            self.last_exception = ex
            self.data = None
            self.headers = None

        ```
    ###Assistant : Get the latest data from REST service with provided method.
    "
850,"def set_default_timezone() -> None:
    
    dotenv.load_dotenv(USER_ENV_FILE)
    user_tz = os.getenv(""OPENBB_TIMEZONE"")
    if not user_tz:
        dotenv.set_key(USER_ENV_FILE, ""OPENBB_TIMEZONE"", ""America/New_York"")

",Set a default (America/New_York) timezone if one doesn't exist.,9,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_default_timezone() -> None:
    
    dotenv.load_dotenv(USER_ENV_FILE)
    user_tz = os.getenv(""OPENBB_TIMEZONE"")
    if not user_tz:
        dotenv.set_key(USER_ENV_FILE, ""OPENBB_TIMEZONE"", ""America/New_York"")


        ```
    ###Assistant : Set a default (America/New_York) timezone if one doesn't exist.
    "
851,"def return_stopped_response(self) -> dict:
        
        return {k: ""Agent stopped due to max iterations."" for k in self.return_values}

",Return response when agent has been stopped due to max iterations.,11,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def return_stopped_response(self) -> dict:
        
        return {k: ""Agent stopped due to max iterations."" for k in self.return_values}


        ```
    ###Assistant : Return response when agent has been stopped due to max iterations.
    "
852,"def clean(self, value):
        
        if self.null_option is not None and value == settings.FILTERS_NULL_CHOICE_VALUE:
            return None
        return super().clean(value)

","
        When null option is enabled and ""None"" is sent as part of a form to be submitted, it is sent as the
        string 'null'.  This will check for that condition and gracefully handle the conversion to a NoneType.
        ",38,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clean(self, value):
        
        if self.null_option is not None and value == settings.FILTERS_NULL_CHOICE_VALUE:
            return None
        return super().clean(value)


        ```
    ###Assistant : 
        When null option is enabled and ""None"" is sent as part of a form to be submitted, it is sent as the
        string 'null'.  This will check for that condition and gracefully handle the conversion to a NoneType.
        
    "
853,"def test_unexpected_auth_events(self):
        
        creator = ""@creator:example.com""

        create_event = _create_event(RoomVersions.V9, creator)
        join_event = _join_event(RoomVersions.V9, creator)
        pl_event = _power_levels_event(
            RoomVersions.V9,
            creator,
            {""state_default"": 30, ""users"": {""creator"": 100}},
        )
        join_rules_event = _join_rules_event(RoomVersions.V9, creator, ""public"")

        event_store = _StubEventSourceStore()
        event_store.add_events([create_event, join_event, pl_event, join_rules_event])

        good_event = _random_state_event(
            RoomVersions.V9, creator, [create_event, join_event, pl_event]
        )
        # join rules should *not* be included in the auth events.
        bad_event = _random_state_event(
            RoomVersions.V9,
            creator,
            [create_event, join_event, pl_event, join_rules_event],
        )

        get_awaitable_result(
            event_auth.check_state_independent_auth_rules(event_store, good_event)
        )
        with self.assertRaises(AuthError):
            get_awaitable_result(
                event_auth.check_state_independent_auth_rules(event_store, bad_event)
            )
","Events with excess auth_events should be rejected

        https://spec.matrix.org/v1.3/rooms/v9/#authorization-rules
        2. Reject if event has auth_events that:
           2. have entries whose type and state_key don’t match those specified by the
              auth events selection algorithm described in the server specification.
        ",37,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unexpected_auth_events(self):
        
        creator = ""@creator:example.com""

        create_event = _create_event(RoomVersions.V9, creator)
        join_event = _join_event(RoomVersions.V9, creator)
        pl_event = _power_levels_event(
            RoomVersions.V9,
            creator,
            {""state_default"": 30, ""users"": {""creator"": 100}},
        )
        join_rules_event = _join_rules_event(RoomVersions.V9, creator, ""public"")

        event_store = _StubEventSourceStore()
        event_store.add_events([create_event, join_event, pl_event, join_rules_event])

        good_event = _random_state_event(
            RoomVersions.V9, creator, [create_event, join_event, pl_event]
        )
        # join rules should *not* be included in the auth events.
        bad_event = _random_state_event(
            RoomVersions.V9,
            creator,
            [create_event, join_event, pl_event, join_rules_event],
        )

        get_awaitable_result(
            event_auth.check_state_independent_auth_rules(event_store, good_event)
        )
        with self.assertRaises(AuthError):
            get_awaitable_result(
                event_auth.check_state_independent_auth_rules(event_store, bad_event)
            )

        ```
    ###Assistant : Events with excess auth_events should be rejected

        https://spec.matrix.org/v1.3/rooms/v9/#authorization-rules
        2. Reject if event has auth_events that:
           2. have entries whose type and state_key don’t match those specified by the
              auth events selection algorithm described in the server specification.
        
    "
854,"async def _consume_incoming(self) -> None:
        
        while True:
            message_json = await self.incoming_queue.get()
            if message_json is None:
                self.incoming_queue.task_done()
                break

            type = message_json[""type""]
            if type == ""client_log"":
                path = message_json[""payload""][""path""]
                line_number = message_json[""payload""][""line_number""]
                timestamp = message_json[""payload""][""timestamp""]
                encoded_segments = message_json[""payload""][""encoded_segments""]
                decoded_segments = base64.b64decode(encoded_segments)
                segments = pickle.loads(decoded_segments)
                self.service.console.print(
                    DevtoolsLogMessage(
                        segments=segments,
                        path=path,
                        line_number=line_number,
                        unix_timestamp=timestamp,
                    )
                )
            elif type == ""client_spillover"":
                spillover = int(message_json[""payload""][""spillover""])
                info_renderable = DevtoolsInternalMessage(
                    f""Discarded {spillover} messages"", level=""warning""
                )
                self.service.console.print(info_renderable)
            self.incoming_queue.task_done()
","Consume messages from the incoming (client -> server) Queue, and print
        the corresponding renderables to the console for each message.
        ",20,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _consume_incoming(self) -> None:
        
        while True:
            message_json = await self.incoming_queue.get()
            if message_json is None:
                self.incoming_queue.task_done()
                break

            type = message_json[""type""]
            if type == ""client_log"":
                path = message_json[""payload""][""path""]
                line_number = message_json[""payload""][""line_number""]
                timestamp = message_json[""payload""][""timestamp""]
                encoded_segments = message_json[""payload""][""encoded_segments""]
                decoded_segments = base64.b64decode(encoded_segments)
                segments = pickle.loads(decoded_segments)
                self.service.console.print(
                    DevtoolsLogMessage(
                        segments=segments,
                        path=path,
                        line_number=line_number,
                        unix_timestamp=timestamp,
                    )
                )
            elif type == ""client_spillover"":
                spillover = int(message_json[""payload""][""spillover""])
                info_renderable = DevtoolsInternalMessage(
                    f""Discarded {spillover} messages"", level=""warning""
                )
                self.service.console.print(info_renderable)
            self.incoming_queue.task_done()

        ```
    ###Assistant : Consume messages from the incoming (client -> server) Queue, and print
        the corresponding renderables to the console for each message.
        
    "
855,"def _get_basic_ray_cr() -> dict:
    
    cr_path = str(
        Path(__file__).resolve().parents[2]
        / ""python""
        / ""ray""
        / ""autoscaler""
        / ""kuberay""
        / ""ray-cluster.complete.yaml""
    )
    return yaml.safe_load(open(cr_path).read())

",Returns the example Ray CR included in the Ray documentation.,10,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_basic_ray_cr() -> dict:
    
    cr_path = str(
        Path(__file__).resolve().parents[2]
        / ""python""
        / ""ray""
        / ""autoscaler""
        / ""kuberay""
        / ""ray-cluster.complete.yaml""
    )
    return yaml.safe_load(open(cr_path).read())


        ```
    ###Assistant : Returns the example Ray CR included in the Ray documentation.
    "
856,"def spectral_graph_forge(G, alpha, transformation=""identity"", seed=None):
    
    import numpy as np
    import scipy as sp
    import scipy.stats  # call as sp.stats

    available_transformations = [""identity"", ""modularity""]
    alpha = np.clip(alpha, 0, 1)
    A = nx.to_numpy_array(G)
    n = A.shape[1]
    level = int(round(n * alpha))

    if transformation not in available_transformations:
        msg = f""{transformation!r} is not a valid transformation. ""
        msg += f""Transformations: {available_transformations}""
        raise nx.NetworkXError(msg)

    K = np.ones((1, n)) @ A

    B = A
    if transformation == ""modularity"":
        B -= K.T @ K / K.sum()

    # Compute low-rank approximation of B
    evals, evecs = np.linalg.eigh(B)
    k = np.argsort(np.abs(evals))[::-1]  # indices of evals in descending order
    evecs[:, k[np.arange(level, n)]] = 0  # set smallest eigenvectors to 0
    B = evecs @ np.diag(evals) @ evecs.T

    if transformation == ""modularity"":
        B += K.T @ K / K.sum()

    B = np.clip(B, 0, 1)
    np.fill_diagonal(B, 0)

    for i in range(n - 1):
        B[i, i + 1 :] = sp.stats.bernoulli.rvs(B[i, i + 1 :], random_state=seed)
        B[i + 1 :, i] = np.transpose(B[i, i + 1 :])

    H = nx.from_numpy_array(B)

    return H
","Returns a random simple graph with spectrum resembling that of `G`

    This algorithm, called Spectral Graph Forge (SGF), computes the
    eigenvectors of a given graph adjacency matrix, filters them and
    builds a random graph with a similar eigenstructure.
    SGF has been proved to be particularly useful for synthesizing
    realistic social networks and it can also be used to anonymize
    graph sensitive data.

    Parameters
    ----------
    G : Graph
    alpha :  float
        Ratio representing the percentage of eigenvectors of G to consider,
        values in [0,1].
    transformation : string, optional
        Represents the intended matrix linear transformation, possible values
        are 'identity' and 'modularity'
    seed : integer, random_state, or None (default)
        Indicator of numpy random number generation state.
        See :ref:`Randomness<randomness>`.

    Returns
    -------
    H : Graph
        A graph with a similar eigenvector structure of the input one.

    Raises
    ------
    NetworkXError
        If transformation has a value different from 'identity' or 'modularity'

    Notes
    -----
    Spectral Graph Forge (SGF) generates a random simple graph resembling the
    global properties of the given one.
    It leverages the low-rank approximation of the associated adjacency matrix
    driven by the *alpha* precision parameter.
    SGF preserves the number of nodes of the input graph and their ordering.
    This way, nodes of output graphs resemble the properties of the input one
    and attributes can be directly mapped.

    It considers the graph adjacency matrices which can optionally be
    transformed to other symmetric real matrices (currently transformation
    options include *identity* and *modularity*).
    The *modularity* transformation, in the sense of Newman's modularity matrix
    allows the focusing on community structure related properties of the graph.

    SGF applies a low-rank approximation whose fixed rank is computed from the
    ratio *alpha* of the input graph adjacency matrix dimension.
    This step performs a filtering on the input eigenvectors similar to the low
    pass filtering common in telecommunications.

    The filtered values (after truncation) are used as input to a Bernoulli
    sampling for constructing a random adjacency matrix.

    References
    ----------
    ..  [1] L. Baldesi, C. T. Butts, A. Markopoulou, ""Spectral Graph Forge:
        Graph Generation Targeting Modularity"", IEEE Infocom, '18.
        https://arxiv.org/abs/1801.01715
    ..  [2] M. Newman, ""Networks: an introduction"", Oxford university press,
        2010

    Examples
    --------
    >>> G = nx.karate_club_graph()
    >>> H = nx.spectral_graph_forge(G, 0.3)
    >>>
    ",358,169,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def spectral_graph_forge(G, alpha, transformation=""identity"", seed=None):
    
    import numpy as np
    import scipy as sp
    import scipy.stats  # call as sp.stats

    available_transformations = [""identity"", ""modularity""]
    alpha = np.clip(alpha, 0, 1)
    A = nx.to_numpy_array(G)
    n = A.shape[1]
    level = int(round(n * alpha))

    if transformation not in available_transformations:
        msg = f""{transformation!r} is not a valid transformation. ""
        msg += f""Transformations: {available_transformations}""
        raise nx.NetworkXError(msg)

    K = np.ones((1, n)) @ A

    B = A
    if transformation == ""modularity"":
        B -= K.T @ K / K.sum()

    # Compute low-rank approximation of B
    evals, evecs = np.linalg.eigh(B)
    k = np.argsort(np.abs(evals))[::-1]  # indices of evals in descending order
    evecs[:, k[np.arange(level, n)]] = 0  # set smallest eigenvectors to 0
    B = evecs @ np.diag(evals) @ evecs.T

    if transformation == ""modularity"":
        B += K.T @ K / K.sum()

    B = np.clip(B, 0, 1)
    np.fill_diagonal(B, 0)

    for i in range(n - 1):
        B[i, i + 1 :] = sp.stats.bernoulli.rvs(B[i, i + 1 :], random_state=seed)
        B[i + 1 :, i] = np.transpose(B[i, i + 1 :])

    H = nx.from_numpy_array(B)

    return H

        ```
    ###Assistant : Returns a random simple graph with spectrum resembling that of `G`

    This algorithm, called Spectral Graph Forge (SGF), computes the
    eigenvectors of a given graph adjacency matrix, filters them and
    builds a random graph with a similar eigenstructure.
    SGF has been proved to be particularly useful for synthesizing
    realistic social networks and it can also be used to anonymize
    graph sensitive data.

    Parameters
    ----------
    G : Graph
    alpha :  float
        Ratio representing the percentage of eigenvectors of G to consider,
        values in [0,1].
    transformation : string, optional
        Represents the intended matrix linear transformation, possible values
        are 'identity' and 'modularity'
    seed : integer, random_state, or None (default)
        Indicator of numpy random number generation state.
        See :ref:`Randomness<randomness>`.

    Returns
    -------
    H : Graph
        A graph with a similar eigenvector structure of the input one.

    Raises
    ------
    NetworkXError
        If transformation has a value different from 'identity' or 'modularity'

    Notes
    -----
    Spectral Graph Forge (SGF) generates a random simple graph resembling the
    global properties of the given one.
    It leverages the low-rank approximation of the associated adjacency matrix
    driven by the *alpha* precision parameter.
    SGF preserves the number of nodes of the input graph and their ordering.
    This way, nodes of output graphs resemble the properties of the input one
    and attributes can be directly mapped.

    It considers the graph adjacency matrices which can optionally be
    transformed to other symmetric real matrices (currently transformation
    options include *identity* and *modularity*).
    The *modularity* transformation, in the sense of Newman's modularity matrix
    allows the focusing on community structure related properties of the graph.

    SGF applies a low-rank approximation whose fixed rank is computed from the
    ratio *alpha* of the input graph adjacency matrix dimension.
    This step performs a filtering on the input eigenvectors similar to the low
    pass filtering common in telecommunications.

    The filtered values (after truncation) are used as input to a Bernoulli
    sampling for constructing a random adjacency matrix.

    References
    ----------
    ..  [1] L. Baldesi, C. T. Butts, A. Markopoulou, ""Spectral Graph Forge:
        Graph Generation Targeting Modularity"", IEEE Infocom, '18.
        https://arxiv.org/abs/1801.01715
    ..  [2] M. Newman, ""Networks: an introduction"", Oxford university press,
        2010

    Examples
    --------
    >>> G = nx.karate_club_graph()
    >>> H = nx.spectral_graph_forge(G, 0.3)
    >>>
    
    "
857,"def encode_example(self, example):
        
        example = cast_to_python_objects(example)
        return encode_nested_example(self, example)
","
        Encode example into a format for Arrow.

        Args:
            example (`dict[str, Any]`):
                Data in a Dataset row.

        Returns:
            `dict[str, Any]`
        ",19,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def encode_example(self, example):
        
        example = cast_to_python_objects(example)
        return encode_nested_example(self, example)

        ```
    ###Assistant : 
        Encode example into a format for Arrow.

        Args:
            example (`dict[str, Any]`):
                Data in a Dataset row.

        Returns:
            `dict[str, Any]`
        
    "
858,"def _add_unique_metric_name(self, metric_name, metric_fn, output_index):
        
        # For multi-output models, prepend the output names to the metric name.
        if len(self.output_names) > 1:
            # If we're loading from an already-serialized model, we've already
            # prepended the output name, and we don't want to do it again.
            #
            # Alternatively, we may be receiving a stateless metric (e.g. the string
            # ""accuracy"") rather than a `Metric` object, in which case we want to
            # prepend the output name even if we are loading a serialized model.
            if not getattr(metric_fn, ""_from_serialized"", False):
                metric_name = ""%s_%s"" % (
                    self.output_names[output_index],
                    metric_name,
                )

        j = 1
        base_metric_name = metric_name
        while metric_name in self.metrics_names:
            metric_name = ""%s_%d"" % (base_metric_name, j)
            j += 1

        return metric_name
","Makes the metric name unique.

          If there are multiple outputs for which the metrics are calculated, the
          metric names have to be made unique by appending an integer.

        Args:
          metric_name: Metric name that corresponds to the metric specified by the
              user. For example: 'acc'.
          metric_fn: The Metric object.
          output_index: The index of the model output for which the metric name is
            being added.

        Returns:
          string, name of the model's unique metric name
        ",72,117,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _add_unique_metric_name(self, metric_name, metric_fn, output_index):
        
        # For multi-output models, prepend the output names to the metric name.
        if len(self.output_names) > 1:
            # If we're loading from an already-serialized model, we've already
            # prepended the output name, and we don't want to do it again.
            #
            # Alternatively, we may be receiving a stateless metric (e.g. the string
            # ""accuracy"") rather than a `Metric` object, in which case we want to
            # prepend the output name even if we are loading a serialized model.
            if not getattr(metric_fn, ""_from_serialized"", False):
                metric_name = ""%s_%s"" % (
                    self.output_names[output_index],
                    metric_name,
                )

        j = 1
        base_metric_name = metric_name
        while metric_name in self.metrics_names:
            metric_name = ""%s_%d"" % (base_metric_name, j)
            j += 1

        return metric_name

        ```
    ###Assistant : Makes the metric name unique.

          If there are multiple outputs for which the metrics are calculated, the
          metric names have to be made unique by appending an integer.

        Args:
          metric_name: Metric name that corresponds to the metric specified by the
              user. For example: 'acc'.
          metric_fn: The Metric object.
          output_index: The index of the model output for which the metric name is
            being added.

        Returns:
          string, name of the model's unique metric name
        
    "
859,"def strongly_connected_components(G):
    
    preorder = {}
    lowlink = {}
    scc_found = set()
    scc_queue = []
    i = 0  # Preorder counter
    neighbors = {v: iter(G[v]) for v in G}
    for source in G:
        if source not in scc_found:
            queue = [source]
            while queue:
                v = queue[-1]
                if v not in preorder:
                    i = i + 1
                    preorder[v] = i
                done = True
                for w in neighbors[v]:
                    if w not in preorder:
                        queue.append(w)
                        done = False
                        break
                if done:
                    lowlink[v] = preorder[v]
                    for w in G[v]:
                        if w not in scc_found:
                            if preorder[w] > preorder[v]:
                                lowlink[v] = min([lowlink[v], lowlink[w]])
                            else:
                                lowlink[v] = min([lowlink[v], preorder[w]])
                    queue.pop()
                    if lowlink[v] == preorder[v]:
                        scc = {v}
                        while scc_queue and preorder[scc_queue[-1]] > preorder[v]:
                            k = scc_queue.pop()
                            scc.add(k)
                        scc_found.update(scc)
                        yield scc
                    else:
                        scc_queue.append(v)


@not_implemented_for(""undirected"")","Generate nodes in strongly connected components of graph.

    Parameters
    ----------
    G : NetworkX Graph
        A directed graph.

    Returns
    -------
    comp : generator of sets
        A generator of sets of nodes, one for each strongly connected
        component of G.

    Raises
    ------
    NetworkXNotImplemented
        If G is undirected.

    Examples
    --------
    Generate a sorted list of strongly connected components, largest first.

    >>> G = nx.cycle_graph(4, create_using=nx.DiGraph())
    >>> nx.add_cycle(G, [10, 11, 12])
    >>> [
    ...     len(c)
    ...     for c in sorted(nx.strongly_connected_components(G), key=len, reverse=True)
    ... ]
    [4, 3]

    If you only want the largest component, it's more efficient to
    use max instead of sort.

    >>> largest = max(nx.strongly_connected_components(G), key=len)

    See Also
    --------
    connected_components
    weakly_connected_components
    kosaraju_strongly_connected_components

    Notes
    -----
    Uses Tarjan's algorithm[1]_ with Nuutila's modifications[2]_.
    Nonrecursive version of algorithm.

    References
    ----------
    .. [1] Depth-first search and linear graph algorithms, R. Tarjan
       SIAM Journal of Computing 1(2):146-160, (1972).

    .. [2] On finding the strongly connected components in a directed graph.
       E. Nuutila and E. Soisalon-Soinen
       Information Processing Letters 49(1): 9-14, (1994)..

    ",162,126,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def strongly_connected_components(G):
    
    preorder = {}
    lowlink = {}
    scc_found = set()
    scc_queue = []
    i = 0  # Preorder counter
    neighbors = {v: iter(G[v]) for v in G}
    for source in G:
        if source not in scc_found:
            queue = [source]
            while queue:
                v = queue[-1]
                if v not in preorder:
                    i = i + 1
                    preorder[v] = i
                done = True
                for w in neighbors[v]:
                    if w not in preorder:
                        queue.append(w)
                        done = False
                        break
                if done:
                    lowlink[v] = preorder[v]
                    for w in G[v]:
                        if w not in scc_found:
                            if preorder[w] > preorder[v]:
                                lowlink[v] = min([lowlink[v], lowlink[w]])
                            else:
                                lowlink[v] = min([lowlink[v], preorder[w]])
                    queue.pop()
                    if lowlink[v] == preorder[v]:
                        scc = {v}
                        while scc_queue and preorder[scc_queue[-1]] > preorder[v]:
                            k = scc_queue.pop()
                            scc.add(k)
                        scc_found.update(scc)
                        yield scc
                    else:
                        scc_queue.append(v)


@not_implemented_for(""undirected"")
        ```
    ###Assistant : Generate nodes in strongly connected components of graph.

    Parameters
    ----------
    G : NetworkX Graph
        A directed graph.

    Returns
    -------
    comp : generator of sets
        A generator of sets of nodes, one for each strongly connected
        component of G.

    Raises
    ------
    NetworkXNotImplemented
        If G is undirected.

    Examples
    --------
    Generate a sorted list of strongly connected components, largest first.

    >>> G = nx.cycle_graph(4, create_using=nx.DiGraph())
    >>> nx.add_cycle(G, [10, 11, 12])
    >>> [
    ...     len(c)
    ...     for c in sorted(nx.strongly_connected_components(G), key=len, reverse=True)
    ... ]
    [4, 3]

    If you only want the largest component, it's more efficient to
    use max instead of sort.

    >>> largest = max(nx.strongly_connected_components(G), key=len)

    See Also
    --------
    connected_components
    weakly_connected_components
    kosaraju_strongly_connected_components

    Notes
    -----
    Uses Tarjan's algorithm[1]_ with Nuutila's modifications[2]_.
    Nonrecursive version of algorithm.

    References
    ----------
    .. [1] Depth-first search and linear graph algorithms, R. Tarjan
       SIAM Journal of Computing 1(2):146-160, (1972).

    .. [2] On finding the strongly connected components in a directed graph.
       E. Nuutila and E. Soisalon-Soinen
       Information Processing Letters 49(1): 9-14, (1994)..

    
    "
860,"def score(self, X, y, sample_weight=None):
        
        # TODO: Adapt link to User Guide in the docstring, once
        # https://github.com/scikit-learn/scikit-learn/pull/22118 is merged.
        #
        # Note, default score defined in RegressorMixin is R^2 score.
        # TODO: make D^2 a score function in module metrics (and thereby get
        #       input validation and so on)
        raw_prediction = self._linear_predictor(X)  # validates X
        # required by losses
        y = check_array(y, dtype=raw_prediction.dtype, order=""C"", ensure_2d=False)

        if sample_weight is not None:
            # Note that _check_sample_weight calls check_array(order=""C"") required by
            # losses.
            sample_weight = _check_sample_weight(sample_weight, X, dtype=y.dtype)

        base_loss = self._linear_loss.base_loss

        if not base_loss.in_y_true_range(y):
            raise ValueError(
                ""Some value(s) of y are out of the valid range of the loss""
                f"" {self._base_loss.__name__}.""
            )

        # Note that constant_to_optimal_zero is already multiplied by sample_weight.
        constant = np.mean(base_loss.constant_to_optimal_zero(y_true=y))
        if sample_weight is not None:
            constant *= sample_weight.shape[0] / np.sum(sample_weight)

        # Missing factor of 2 in deviance cancels out.
        deviance = base_loss(
            y_true=y,
            raw_prediction=raw_prediction,
            sample_weight=sample_weight,
            n_threads=1,
        )
        y_mean = base_loss.link.link(np.average(y, weights=sample_weight))
        deviance_null = base_loss(
            y_true=y,
            raw_prediction=np.tile(y_mean, y.shape[0]),
            sample_weight=sample_weight,
            n_threads=1,
        )
        return 1 - (deviance + constant) / (deviance_null + constant)
","Compute D^2, the percentage of deviance explained.

        D^2 is a generalization of the coefficient of determination R^2.
        R^2 uses squared error and D^2 uses the deviance of this GLM, see the
        :ref:`User Guide <regression_metrics>`.

        D^2 is defined as
        :math:`D^2 = 1-\\frac{D(y_{true},y_{pred})}{D_{null}}`,
        :math:`D_{null}` is the null deviance, i.e. the deviance of a model
        with intercept alone, which corresponds to :math:`y_{pred} = \\bar{y}`.
        The mean :math:`\\bar{y}` is averaged by sample_weight.
        Best possible score is 1.0 and it can be negative (because the model
        can be arbitrarily worse).

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Test samples.

        y : array-like of shape (n_samples,)
            True values of target.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        Returns
        -------
        score : float
            D^2 of self.predict(X) w.r.t. y.
        ",127,172,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def score(self, X, y, sample_weight=None):
        
        # TODO: Adapt link to User Guide in the docstring, once
        # https://github.com/scikit-learn/scikit-learn/pull/22118 is merged.
        #
        # Note, default score defined in RegressorMixin is R^2 score.
        # TODO: make D^2 a score function in module metrics (and thereby get
        #       input validation and so on)
        raw_prediction = self._linear_predictor(X)  # validates X
        # required by losses
        y = check_array(y, dtype=raw_prediction.dtype, order=""C"", ensure_2d=False)

        if sample_weight is not None:
            # Note that _check_sample_weight calls check_array(order=""C"") required by
            # losses.
            sample_weight = _check_sample_weight(sample_weight, X, dtype=y.dtype)

        base_loss = self._linear_loss.base_loss

        if not base_loss.in_y_true_range(y):
            raise ValueError(
                ""Some value(s) of y are out of the valid range of the loss""
                f"" {self._base_loss.__name__}.""
            )

        # Note that constant_to_optimal_zero is already multiplied by sample_weight.
        constant = np.mean(base_loss.constant_to_optimal_zero(y_true=y))
        if sample_weight is not None:
            constant *= sample_weight.shape[0] / np.sum(sample_weight)

        # Missing factor of 2 in deviance cancels out.
        deviance = base_loss(
            y_true=y,
            raw_prediction=raw_prediction,
            sample_weight=sample_weight,
            n_threads=1,
        )
        y_mean = base_loss.link.link(np.average(y, weights=sample_weight))
        deviance_null = base_loss(
            y_true=y,
            raw_prediction=np.tile(y_mean, y.shape[0]),
            sample_weight=sample_weight,
            n_threads=1,
        )
        return 1 - (deviance + constant) / (deviance_null + constant)

        ```
    ###Assistant : Compute D^2, the percentage of deviance explained.

        D^2 is a generalization of the coefficient of determination R^2.
        R^2 uses squared error and D^2 uses the deviance of this GLM, see the
        :ref:`User Guide <regression_metrics>`.

        D^2 is defined as
        :math:`D^2 = 1-\\frac{D(y_{true},y_{pred})}{D_{null}}`,
        :math:`D_{null}` is the null deviance, i.e. the deviance of a model
        with intercept alone, which corresponds to :math:`y_{pred} = \\bar{y}`.
        The mean :math:`\\bar{y}` is averaged by sample_weight.
        Best possible score is 1.0 and it can be negative (because the model
        can be arbitrarily worse).

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Test samples.

        y : array-like of shape (n_samples,)
            True values of target.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        Returns
        -------
        score : float
            D^2 of self.predict(X) w.r.t. y.
        
    "
861,"def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
    r

    assert isinstance(factor, int) and factor >= 1

    # Check weight shape.
    w = tf.convert_to_tensor(w)
    assert w.shape.rank == 4
    convH = w.shape[0].value
    convW = w.shape[1].value
    inC = _shape(w, 2)
    outC = _shape(w, 3)
    assert convW == convH

    # Setup filter kernel.
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * (gain * (factor ** 2))
    p = (k.shape[0] - factor) - (convW - 1)

    # Determine data dimensions.
    if data_format == 'NCHW':
        stride = [1, 1, factor, factor]
        output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW]
        num_groups = _shape(x, 1) // inC
    else:
        stride = [1, factor, factor, 1]
        output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC]
        num_groups = _shape(x, 3) // inC

    # Transpose weights.
    w = tf.reshape(w, [convH, convW, inC, num_groups, -1])
    w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2])
    w = tf.reshape(w, [convH, convW, -1, num_groups * inC])

    # Execute.
    x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format)
    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2+factor-1, pad1=p//2+1, data_format=data_format, impl=impl)

#----------------------------------------------------------------------------
","Fused `upsample_2d()` followed by `tf.nn.conv2d()`.

    Padding is performed only once at the beginning, not between the operations.
    The fused op is considerably more efficient than performing the same calculation
    using standard TensorFlow ops. It supports gradients of arbitrary order.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to nearest-neighbor
                      upsampling.
        factor:       Integer upsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `""ref""` or `""cuda""` (default).

    Returns:
        Tensor of the shape `[N, C, H * factor, W * factor]` or
        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
    ",158,198,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
    r

    assert isinstance(factor, int) and factor >= 1

    # Check weight shape.
    w = tf.convert_to_tensor(w)
    assert w.shape.rank == 4
    convH = w.shape[0].value
    convW = w.shape[1].value
    inC = _shape(w, 2)
    outC = _shape(w, 3)
    assert convW == convH

    # Setup filter kernel.
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * (gain * (factor ** 2))
    p = (k.shape[0] - factor) - (convW - 1)

    # Determine data dimensions.
    if data_format == 'NCHW':
        stride = [1, 1, factor, factor]
        output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW]
        num_groups = _shape(x, 1) // inC
    else:
        stride = [1, factor, factor, 1]
        output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC]
        num_groups = _shape(x, 3) // inC

    # Transpose weights.
    w = tf.reshape(w, [convH, convW, inC, num_groups, -1])
    w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2])
    w = tf.reshape(w, [convH, convW, -1, num_groups * inC])

    # Execute.
    x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format)
    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2+factor-1, pad1=p//2+1, data_format=data_format, impl=impl)

#----------------------------------------------------------------------------

        ```
    ###Assistant : Fused `upsample_2d()` followed by `tf.nn.conv2d()`.

    Padding is performed only once at the beginning, not between the operations.
    The fused op is considerably more efficient than performing the same calculation
    using standard TensorFlow ops. It supports gradients of arbitrary order.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to nearest-neighbor
                      upsampling.
        factor:       Integer upsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `""ref""` or `""cuda""` (default).

    Returns:
        Tensor of the shape `[N, C, H * factor, W * factor]` or
        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
    
    "
862,"def normalize_config(config):
        
        return json.loads(json.dumps(config, cls=NumpyEncoder))
",Convert to json string and back again to remove numpy types.,11,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def normalize_config(config):
        
        return json.loads(json.dumps(config, cls=NumpyEncoder))

        ```
    ###Assistant : Convert to json string and back again to remove numpy types.
    "
863,"def ensure_srgb(img, srgb_profile=None):
    
    img_info = dict(img.info)
    icc = img_info.pop(""icc_profile"", None)
    if not icc:
        return img

    if ImageCms is None:
        raise RuntimeError(""ImageCms is required for color profile utilities"")

    if srgb_profile is not None:
        srgb_profile = ImageCms.ImageCmsProfile(srgb_profile)
    else:
        srgb_profile = DEFAULT_SRGB_PROFILE

    buf = BytesIO(icc)
    try:
        orig_profile = ImageCms.ImageCmsProfile(buf)
        color_space = orig_profile.profile.xcolor_space
    except (AttributeError, OSError, TypeError, ValueError):
        return None
    finally:
        buf.close()

    if color_space == ""RGB "":
        logger.debug(""Returning img (RGB)"")
        return img

    if color_space not in (""GRAY"", ""CMYK""):
        # Other color spaces are rare, but best not to try to convert them.
        # Upstream understands a None return as meaning it should not
        # use it for the target encoder.
        logger.debug(
            ""Cannot convert to sRGB; color space = %s"",
            (color_space.strip()),
        )
        return None

    # Probably not possible to have an animated image with CMYK or GRAY icc
    # profile, but best leave it alone if we have one
    if getattr(img, ""is_animated"", False):
        return None

    if color_space == ""GRAY"":
        pil_mode = ""L""
    else:
        pil_mode = ""CMYK""

    logger.debug(""Converting from %s to sRGB"", color_space)

    transform = ImageCms.ImageCmsTransform(
        orig_profile,
        srgb_profile,
        pil_mode,
        ""RGBA"",
        intent=ImageCms.INTENT_RELATIVE_COLORIMETRIC,
        flags=TRANSFORM_FLAGS,
    )

    src_im = Image.new(pil_mode, img.size, ""white"")
    src_im.paste(img)

    dst_im = Image.new(""RGBA"", img.size, ""white"")
    dst_im.info = img_info
    dst_im = transform.apply(src_im, dst_im)
    dst_im = dst_im.convert(""RGB"")
    dst_im.info = img_info
    return dst_im
","
    Ensures that an image either has no ICC profile (and so is implicitly
    sRGB) or has an sRGB color profile. If the image is sRGB, it is returned
    unchanged. If it has a CMYK or Gray color profile, this function will
    return an image converted to sRGB. Any color profiles in other color
    spaces will return None.
    ",57,203,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ensure_srgb(img, srgb_profile=None):
    
    img_info = dict(img.info)
    icc = img_info.pop(""icc_profile"", None)
    if not icc:
        return img

    if ImageCms is None:
        raise RuntimeError(""ImageCms is required for color profile utilities"")

    if srgb_profile is not None:
        srgb_profile = ImageCms.ImageCmsProfile(srgb_profile)
    else:
        srgb_profile = DEFAULT_SRGB_PROFILE

    buf = BytesIO(icc)
    try:
        orig_profile = ImageCms.ImageCmsProfile(buf)
        color_space = orig_profile.profile.xcolor_space
    except (AttributeError, OSError, TypeError, ValueError):
        return None
    finally:
        buf.close()

    if color_space == ""RGB "":
        logger.debug(""Returning img (RGB)"")
        return img

    if color_space not in (""GRAY"", ""CMYK""):
        # Other color spaces are rare, but best not to try to convert them.
        # Upstream understands a None return as meaning it should not
        # use it for the target encoder.
        logger.debug(
            ""Cannot convert to sRGB; color space = %s"",
            (color_space.strip()),
        )
        return None

    # Probably not possible to have an animated image with CMYK or GRAY icc
    # profile, but best leave it alone if we have one
    if getattr(img, ""is_animated"", False):
        return None

    if color_space == ""GRAY"":
        pil_mode = ""L""
    else:
        pil_mode = ""CMYK""

    logger.debug(""Converting from %s to sRGB"", color_space)

    transform = ImageCms.ImageCmsTransform(
        orig_profile,
        srgb_profile,
        pil_mode,
        ""RGBA"",
        intent=ImageCms.INTENT_RELATIVE_COLORIMETRIC,
        flags=TRANSFORM_FLAGS,
    )

    src_im = Image.new(pil_mode, img.size, ""white"")
    src_im.paste(img)

    dst_im = Image.new(""RGBA"", img.size, ""white"")
    dst_im.info = img_info
    dst_im = transform.apply(src_im, dst_im)
    dst_im = dst_im.convert(""RGB"")
    dst_im.info = img_info
    return dst_im

        ```
    ###Assistant : 
    Ensures that an image either has no ICC profile (and so is implicitly
    sRGB) or has an sRGB color profile. If the image is sRGB, it is returned
    unchanged. If it has a CMYK or Gray color profile, this function will
    return an image converted to sRGB. Any color profiles in other color
    spaces will return None.
    
    "
864,"def _cast_single_input(self, x):
        
        if self._should_cast_single_input(x):
            return tf.cast(x, self._compute_dtype_object)
        else:
            return x

    # _dtype used to be an attribute set in the constructor. We still expose it
    # because some clients still use it.
    # TODO(reedwm): Deprecate, then remove the _dtype property.",Cast a single Tensor or TensorSpec to the compute dtype.,10,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _cast_single_input(self, x):
        
        if self._should_cast_single_input(x):
            return tf.cast(x, self._compute_dtype_object)
        else:
            return x

    # _dtype used to be an attribute set in the constructor. We still expose it
    # because some clients still use it.
    # TODO(reedwm): Deprecate, then remove the _dtype property.
        ```
    ###Assistant : Cast a single Tensor or TensorSpec to the compute dtype.
    "
865,"def queryset_in_batches(queryset):
    
    start_pk = 0

    while True:
        qs = queryset.filter(pk__gt=start_pk)[:BATCH_SIZE]
        pks = list(qs.values_list(""pk"", flat=True))

        if not pks:
            break

        yield pks

        start_pk = pks[-1]

","Slice a queryset into batches.

    Input queryset should be sorted be pk.
    ",12,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def queryset_in_batches(queryset):
    
    start_pk = 0

    while True:
        qs = queryset.filter(pk__gt=start_pk)[:BATCH_SIZE]
        pks = list(qs.values_list(""pk"", flat=True))

        if not pks:
            break

        yield pks

        start_pk = pks[-1]


        ```
    ###Assistant : Slice a queryset into batches.

    Input queryset should be sorted be pk.
    
    "
866,"def execute():
	frappe.reload_doc(""stock"", ""doctype"", ""purchase_receipt"")
	frappe.reload_doc(""stock"", ""doctype"", ""purchase_receipt_item"")
	frappe.reload_doc(""stock"", ""doctype"", ""delivery_note"")
	frappe.reload_doc(""stock"", ""doctype"", ""delivery_note_item"")
	frappe.reload_doc(""stock"", ""doctype"", ""stock_settings"")

	def update_from_return_docs(doctype):
		for return_doc in frappe.get_all(
			doctype, filters={""is_return"": 1, ""docstatus"": 1, ""return_against"": (""!="", """")}
		):
			# Update original receipt/delivery document from return
			return_doc = frappe.get_cached_doc(doctype, return_doc.name)
			try:
				return_doc.update_prevdoc_status()
			except OverAllowanceError:
				frappe.db.rollback()
				continue

			return_against = frappe.get_doc(doctype, return_doc.return_against)
			return_against.update_billing_status()
			frappe.db.commit()

	# Set received qty in stock uom in PR, as returned qty is checked against it
	frappe.db.sql(
		
	)

	for doctype in (""Purchase Receipt"", ""Delivery Note""):
		update_from_return_docs(doctype)
"," update `tabPurchase Receipt Item`
		set received_stock_qty = received_qty * conversion_factor
		where docstatus = 1 ",14,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	frappe.reload_doc(""stock"", ""doctype"", ""purchase_receipt"")
	frappe.reload_doc(""stock"", ""doctype"", ""purchase_receipt_item"")
	frappe.reload_doc(""stock"", ""doctype"", ""delivery_note"")
	frappe.reload_doc(""stock"", ""doctype"", ""delivery_note_item"")
	frappe.reload_doc(""stock"", ""doctype"", ""stock_settings"")

	def update_from_return_docs(doctype):
		for return_doc in frappe.get_all(
			doctype, filters={""is_return"": 1, ""docstatus"": 1, ""return_against"": (""!="", """")}
		):
			# Update original receipt/delivery document from return
			return_doc = frappe.get_cached_doc(doctype, return_doc.name)
			try:
				return_doc.update_prevdoc_status()
			except OverAllowanceError:
				frappe.db.rollback()
				continue

			return_against = frappe.get_doc(doctype, return_doc.return_against)
			return_against.update_billing_status()
			frappe.db.commit()

	# Set received qty in stock uom in PR, as returned qty is checked against it
	frappe.db.sql(
		
	)

	for doctype in (""Purchase Receipt"", ""Delivery Note""):
		update_from_return_docs(doctype)

        ```
    ###Assistant :  update `tabPurchase Receipt Item`
		set received_stock_qty = received_qty * conversion_factor
		where docstatus = 1 
    "
867,"def call_dex(self, other_args):
        
        parser = argparse.ArgumentParser(
            prog=""dex"",
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            description=,
        )

        parser.add_argument(
            ""-l"",
            ""--limit"",
            dest=""limit"",
            type=check_positive,
            help=""Number of records to display"",
            default=15,
        )
        parser.add_argument(
            ""-s"",
            ""--sort"",
            dest=""sortby"",
            nargs=""+"",
            help=""Sort by given column. Default: Daily Volume [$]"",
            default=""Daily Volume [$]"",
        )
        ns_parser = self.parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
        )
        if ns_parser:
            dappradar_view.display_top_dexes(
                sortby="" "".join(ns_parser.sortby),
                limit=ns_parser.limit,
                export=ns_parser.export,
            )
","Process dex command
            Shows top decentralized exchanges [Source: https://dappradar.com/]
            Accepts --sort {Name,Daily Users,Daily Volume [$]}
            to sort by column
            ",19,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_dex(self, other_args):
        
        parser = argparse.ArgumentParser(
            prog=""dex"",
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            description=,
        )

        parser.add_argument(
            ""-l"",
            ""--limit"",
            dest=""limit"",
            type=check_positive,
            help=""Number of records to display"",
            default=15,
        )
        parser.add_argument(
            ""-s"",
            ""--sort"",
            dest=""sortby"",
            nargs=""+"",
            help=""Sort by given column. Default: Daily Volume [$]"",
            default=""Daily Volume [$]"",
        )
        ns_parser = self.parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
        )
        if ns_parser:
            dappradar_view.display_top_dexes(
                sortby="" "".join(ns_parser.sortby),
                limit=ns_parser.limit,
                export=ns_parser.export,
            )

        ```
    ###Assistant : Process dex command
            Shows top decentralized exchanges [Source: https://dappradar.com/]
            Accepts --sort {Name,Daily Users,Daily Volume [$]}
            to sort by column
            
    "
868,"def get_backend_for_dir(self, location):
        # type: (str) -> Optional[VersionControl]
        
        vcs_backends = {}
        for vcs_backend in self._registry.values():
            repo_path = vcs_backend.get_repository_root(location)
            if not repo_path:
                continue
            logger.debug('Determine that %s uses VCS: %s',
                         location, vcs_backend.name)
            vcs_backends[repo_path] = vcs_backend

        if not vcs_backends:
            return None

        # Choose the VCS in the inner-most directory. Since all repository
        # roots found here would be either `location` or one of its
        # parents, the longest path should have the most path components,
        # i.e. the backend representing the inner-most repository.
        inner_most_repo_path = max(vcs_backends, key=len)
        return vcs_backends[inner_most_repo_path]
","
        Return a VersionControl object if a repository of that type is found
        at the given directory.
        ",16,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_backend_for_dir(self, location):
        # type: (str) -> Optional[VersionControl]
        
        vcs_backends = {}
        for vcs_backend in self._registry.values():
            repo_path = vcs_backend.get_repository_root(location)
            if not repo_path:
                continue
            logger.debug('Determine that %s uses VCS: %s',
                         location, vcs_backend.name)
            vcs_backends[repo_path] = vcs_backend

        if not vcs_backends:
            return None

        # Choose the VCS in the inner-most directory. Since all repository
        # roots found here would be either `location` or one of its
        # parents, the longest path should have the most path components,
        # i.e. the backend representing the inner-most repository.
        inner_most_repo_path = max(vcs_backends, key=len)
        return vcs_backends[inner_most_repo_path]

        ```
    ###Assistant : 
        Return a VersionControl object if a repository of that type is found
        at the given directory.
        
    "
869,"def has_access(self, action_name, resource_name, user=None) -> bool:
        
        if not user:
            user = g.user

        if user.is_anonymous:
            user.roles = self.get_user_roles(user)

        has_access = self._has_access(user, action_name, resource_name)
        # FAB built-in view access method. Won't work for AllDag access.
        if self.is_dag_resource(resource_name):
            if action_name == permissions.ACTION_CAN_READ:
                has_access |= self.can_read_dag(resource_name, user)
            elif action_name == permissions.ACTION_CAN_EDIT:
                has_access |= self.can_edit_dag(resource_name, user)

        return has_access
","
        Verify whether a given user could perform a certain action
        (e.g can_read, can_write) on the given resource.

        :param action_name: action_name on resource (e.g can_read, can_edit).
        :param resource_name: name of view-menu or resource.
        :param user: user name
        :return: Whether user could perform certain action on the resource.
        :rtype bool
        ",48,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def has_access(self, action_name, resource_name, user=None) -> bool:
        
        if not user:
            user = g.user

        if user.is_anonymous:
            user.roles = self.get_user_roles(user)

        has_access = self._has_access(user, action_name, resource_name)
        # FAB built-in view access method. Won't work for AllDag access.
        if self.is_dag_resource(resource_name):
            if action_name == permissions.ACTION_CAN_READ:
                has_access |= self.can_read_dag(resource_name, user)
            elif action_name == permissions.ACTION_CAN_EDIT:
                has_access |= self.can_edit_dag(resource_name, user)

        return has_access

        ```
    ###Assistant : 
        Verify whether a given user could perform a certain action
        (e.g can_read, can_write) on the given resource.

        :param action_name: action_name on resource (e.g can_read, can_edit).
        :param resource_name: name of view-menu or resource.
        :param user: user name
        :return: Whether user could perform certain action on the resource.
        :rtype bool
        
    "
870,"def has_refs(self) -> bool:
            
            return len(self._session_report_run_counts) > 0
","True if this Entry has references from any AppSession.

            If not, it can be removed from the cache.
            ",18,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def has_refs(self) -> bool:
            
            return len(self._session_report_run_counts) > 0

        ```
    ###Assistant : True if this Entry has references from any AppSession.

            If not, it can be removed from the cache.
            
    "
871,"def _reshape_to_record_metrics(self, batch, losses, num_target_tokens, indices):
        
        val_id_shape = batch.valid_indices.shape
        reshaped_losses = torch.zeros(
            val_id_shape, device=losses.device, dtype=losses.dtype
        )
        reshaped_num_target_tokens = torch.zeros(
            val_id_shape, device=num_target_tokens.device, dtype=num_target_tokens.dtype
        )

        reshaped_losses[indices] = losses
        reshaped_num_target_tokens[indices] = num_target_tokens

        return (reshaped_losses, reshaped_num_target_tokens)
","
        MultitaskAgent shuffles and combines examples from both classifier and the
        generator tasks in a single batch. We compute losses only for those exs in the
        batch resulting in losses and num_target_tokens vectors that are smaller than
        the.

        This method reshapes the losses and num_target_tokens vectors back to the batch size. This is needed to record local metrics as the metrics need to be of batch size.

        Args:
            batch: batch being processed in this iteration.
            losses: classifier or generator loss vector (shape: b' X 1), where b' <= b.
            num_target_tokens: number of tokens in each examples for classification or generation tasks. (shape: b' X 1), where b' <= b.
            indices: indices of (either classification or generation) exs for which the loss was computed.

        Returns:
            A tuple of reshaped losses and num_target_tokens, both of shape: b X 1.
        ",136,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _reshape_to_record_metrics(self, batch, losses, num_target_tokens, indices):
        
        val_id_shape = batch.valid_indices.shape
        reshaped_losses = torch.zeros(
            val_id_shape, device=losses.device, dtype=losses.dtype
        )
        reshaped_num_target_tokens = torch.zeros(
            val_id_shape, device=num_target_tokens.device, dtype=num_target_tokens.dtype
        )

        reshaped_losses[indices] = losses
        reshaped_num_target_tokens[indices] = num_target_tokens

        return (reshaped_losses, reshaped_num_target_tokens)

        ```
    ###Assistant : 
        MultitaskAgent shuffles and combines examples from both classifier and the
        generator tasks in a single batch. We compute losses only for those exs in the
        batch resulting in losses and num_target_tokens vectors that are smaller than
        the.

        This method reshapes the losses and num_target_tokens vectors back to the batch size. This is needed to record local metrics as the metrics need to be of batch size.

        Args:
            batch: batch being processed in this iteration.
            losses: classifier or generator loss vector (shape: b' X 1), where b' <= b.
            num_target_tokens: number of tokens in each examples for classification or generation tasks. (shape: b' X 1), where b' <= b.
            indices: indices of (either classification or generation) exs for which the loss was computed.

        Returns:
            A tuple of reshaped losses and num_target_tokens, both of shape: b X 1.
        
    "
872,"def add_support(self, location, type):
        
        if location not in self._node_labels:
            raise ValueError(""Support must be added on a known node"")

        else:
            self._supports[location] = type
","
        This method adds a pinned or roller support at a particular node

        Parameters
        ==========

        location: String or Symbol
            Label of the Node at which support is added.

        type: String
            Type of the support being provided at the node.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> from sympy import symbols
        >>> t = Truss()
        >>> t.add_node('A', 0, 0)
        >>> t.add_node('B', 3, 0)
        >>> t.add_support('A', 'pinned')
        >>> t.supports
        {'A': 'pinned', 'B': 'none'}
        ",71,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_support(self, location, type):
        
        if location not in self._node_labels:
            raise ValueError(""Support must be added on a known node"")

        else:
            self._supports[location] = type

        ```
    ###Assistant : 
        This method adds a pinned or roller support at a particular node

        Parameters
        ==========

        location: String or Symbol
            Label of the Node at which support is added.

        type: String
            Type of the support being provided at the node.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> from sympy import symbols
        >>> t = Truss()
        >>> t.add_node('A', 0, 0)
        >>> t.add_node('B', 3, 0)
        >>> t.add_support('A', 'pinned')
        >>> t.supports
        {'A': 'pinned', 'B': 'none'}
        
    "
873,"def makeport(self):
        
        sock = socket.create_server(("""", 0), family=self.af, backlog=1)
        port = sock.getsockname()[1] # Get proper port
        host = self.sock.getsockname()[0] # Get proper host
        if self.af == socket.AF_INET:
            resp = self.sendport(host, port)
        else:
            resp = self.sendeprt(host, port)
        if self.timeout is not _GLOBAL_DEFAULT_TIMEOUT:
            sock.settimeout(self.timeout)
        return sock
",Create a new socket and send a PORT command for it.,11,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def makeport(self):
        
        sock = socket.create_server(("""", 0), family=self.af, backlog=1)
        port = sock.getsockname()[1] # Get proper port
        host = self.sock.getsockname()[0] # Get proper host
        if self.af == socket.AF_INET:
            resp = self.sendport(host, port)
        else:
            resp = self.sendeprt(host, port)
        if self.timeout is not _GLOBAL_DEFAULT_TIMEOUT:
            sock.settimeout(self.timeout)
        return sock

        ```
    ###Assistant : Create a new socket and send a PORT command for it.
    "
874,"def test_all(gm_manager):
    
    _save_script(test_gm_script, 'test.user.js')
    gm_manager.load_scripts()

    assert (gm_manager.all_scripts()[0].name ==
            ""qutebrowser test userscript"")


@pytest.mark.parametrize(""url, expected_matches"", [
    # included
    ('http://trolol.com/', 1),
    # neither included nor excluded
    ('http://aaaaaaaaaa.com/', 0),
    # excluded
    ('https://badhost.xxx/', 0),
])","Test that a script gets read from file, parsed and returned.",11,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_all(gm_manager):
    
    _save_script(test_gm_script, 'test.user.js')
    gm_manager.load_scripts()

    assert (gm_manager.all_scripts()[0].name ==
            ""qutebrowser test userscript"")


@pytest.mark.parametrize(""url, expected_matches"", [
    # included
    ('http://trolol.com/', 1),
    # neither included nor excluded
    ('http://aaaaaaaaaa.com/', 0),
    # excluded
    ('https://badhost.xxx/', 0),
])
        ```
    ###Assistant : Test that a script gets read from file, parsed and returned.
    "
875,"def split_path_msys(path):
    if path.startswith(('/', '\\')) and not path.startswith(('//', '\\\\')):
        global msysroot
        if not msysroot:
            msysroot = subprocess.check_output(['cygpath', '-w', '/']).decode(sys.stdout.encoding or 'latin-1')
            msysroot = msysroot.strip()
        path = os.path.normpath(msysroot + os.sep + path)
    return split_path_win32(path)


if sys.platform == 'cygwin':
    split_path = split_path_cygwin
elif is_win32:
    if os.environ.get('MSYSTEM') and sys.executable.startswith('/'):
        split_path = split_path_msys
    else:
        split_path = split_path_win32
else:
    split_path = split_path_unix
split_path.__doc__ = 

","
Splits a path by / or \\; do not confuse this function with with ``os.path.split``

:type  path: string
:param path: path to split
:return:     list of string
",27,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def split_path_msys(path):
    if path.startswith(('/', '\\')) and not path.startswith(('//', '\\\\')):
        global msysroot
        if not msysroot:
            msysroot = subprocess.check_output(['cygpath', '-w', '/']).decode(sys.stdout.encoding or 'latin-1')
            msysroot = msysroot.strip()
        path = os.path.normpath(msysroot + os.sep + path)
    return split_path_win32(path)


if sys.platform == 'cygwin':
    split_path = split_path_cygwin
elif is_win32:
    if os.environ.get('MSYSTEM') and sys.executable.startswith('/'):
        split_path = split_path_msys
    else:
        split_path = split_path_win32
else:
    split_path = split_path_unix
split_path.__doc__ = 


        ```
    ###Assistant : 
Splits a path by / or \\; do not confuse this function with with ``os.path.split``

:type  path: string
:param path: path to split
:return:     list of string

    "
876,"def find_image_duplicates(image, user, permission_policy):
    

    instances = permission_policy.instances_user_has_permission_for(user, ""choose"")
    return instances.exclude(pk=image.pk).filter(file_hash=image.file_hash)
","
    Finds all the duplicates of a given image.
    To keep things simple, two images are considered to be duplicates if they have the same `file_hash` value.
    This function also ensures that the `user` can choose one of the duplicate images returned (if any).
    ",43,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_image_duplicates(image, user, permission_policy):
    

    instances = permission_policy.instances_user_has_permission_for(user, ""choose"")
    return instances.exclude(pk=image.pk).filter(file_hash=image.file_hash)

        ```
    ###Assistant : 
    Finds all the duplicates of a given image.
    To keep things simple, two images are considered to be duplicates if they have the same `file_hash` value.
    This function also ensures that the `user` can choose one of the duplicate images returned (if any).
    
    "
877,"def _trim_arity(func, maxargs=2):
    
    global _trim_arity_call_line

    if func in _single_arg_builtins:
        return lambda s, l, t: func(t)

    limit = 0
    found_arity = False
",decorator to trim function calls to match the arity of the target,12,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _trim_arity(func, maxargs=2):
    
    global _trim_arity_call_line

    if func in _single_arg_builtins:
        return lambda s, l, t: func(t)

    limit = 0
    found_arity = False

        ```
    ###Assistant : decorator to trim function calls to match the arity of the target
    "
878,"def call_exmarkets(self, other_args):
        
        parser = argparse.ArgumentParser(
            prog=""exmarkets"",
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            description=,
        )

        parser.add_argument(
            ""-e"",
            ""--exchange"",
            help=""Identifier of exchange e.g for Binance Exchange -> binance"",
            dest=""exchange"",
            default=""binance"",
            type=str,
        )

        parser.add_argument(
            ""-l"",
            ""--limit"",
            dest=""limit"",
            type=check_positive,
            help=""display N number records"",
            default=10,
        )

        parser.add_argument(
            ""-s"",
            ""--sortby"",
            dest=""sortby"",
            type=str,
            help=""Sort by given column. Default: reported_volume_24h_share"",
            default=""reported_volume_24h_share"",
            choices=coinpaprika_model.EXMARKETS_FILTERS,
        )

        parser.add_argument(
            ""--descend"",
            action=""store_false"",
            help=""Flag to sort in descending order (lowest first)"",
            dest=""descend"",
            default=False,
        )

        parser.add_argument(
            ""-u"",
            ""--urls"",
            dest=""urls"",
            action=""store_true"",
            help=,
            default=False,
        )

        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-e"")

        ns_parser = self.parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
        )
        if ns_parser:
            coinpaprika_view.display_exchange_markets(
                exchange=ns_parser.exchange,
                limit=ns_parser.limit,
                export=ns_parser.export,
                sortby=ns_parser.sortby,
                ascend=not ns_parser.descend,
                links=ns_parser.urls,
            )
","Process exmarkets commandGet all exchange markets found for given exchange
                You can display only N number of records with --limit parameter.
                You can sort data by pair, base_currency_name, quote_currency_name, market_url, category,
                reported_volume_24h_share, trust_score --sortby parameter and also with --descend flag to sort descending.
                You can use additional flag --urls to see urls for each market
                Displays:
                    exchange_id, pair, base_currency_name, quote_currency_name, market_url,
                    category, reported_volume_24h_share, trust_score,Flag to show urls. If you will use that flag you will see only:
                exchange, pair, trust_score, market_url columns",82,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_exmarkets(self, other_args):
        
        parser = argparse.ArgumentParser(
            prog=""exmarkets"",
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            description=,
        )

        parser.add_argument(
            ""-e"",
            ""--exchange"",
            help=""Identifier of exchange e.g for Binance Exchange -> binance"",
            dest=""exchange"",
            default=""binance"",
            type=str,
        )

        parser.add_argument(
            ""-l"",
            ""--limit"",
            dest=""limit"",
            type=check_positive,
            help=""display N number records"",
            default=10,
        )

        parser.add_argument(
            ""-s"",
            ""--sortby"",
            dest=""sortby"",
            type=str,
            help=""Sort by given column. Default: reported_volume_24h_share"",
            default=""reported_volume_24h_share"",
            choices=coinpaprika_model.EXMARKETS_FILTERS,
        )

        parser.add_argument(
            ""--descend"",
            action=""store_false"",
            help=""Flag to sort in descending order (lowest first)"",
            dest=""descend"",
            default=False,
        )

        parser.add_argument(
            ""-u"",
            ""--urls"",
            dest=""urls"",
            action=""store_true"",
            help=,
            default=False,
        )

        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-e"")

        ns_parser = self.parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
        )
        if ns_parser:
            coinpaprika_view.display_exchange_markets(
                exchange=ns_parser.exchange,
                limit=ns_parser.limit,
                export=ns_parser.export,
                sortby=ns_parser.sortby,
                ascend=not ns_parser.descend,
                links=ns_parser.urls,
            )

        ```
    ###Assistant : Process exmarkets commandGet all exchange markets found for given exchange
                You can display only N number of records with --limit parameter.
                You can sort data by pair, base_currency_name, quote_currency_name, market_url, category,
                reported_volume_24h_share, trust_score --sortby parameter and also with --descend flag to sort descending.
                You can use additional flag --urls to see urls for each market
                Displays:
                    exchange_id, pair, base_currency_name, quote_currency_name, market_url,
                    category, reported_volume_24h_share, trust_score,Flag to show urls. If you will use that flag you will see only:
                exchange, pair, trust_score, market_url columns
    "
879,"async def test_unload_config_entry(hass, entry, lcn_connection):
    
    await hass.config_entries.async_unload(entry.entry_id)
    assert hass.states.get(""cover.cover_outputs"").state == STATE_UNAVAILABLE
    assert hass.states.get(""cover.cover_relays"").state == STATE_UNAVAILABLE
",Test the cover is removed when the config entry is unloaded.,11,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_unload_config_entry(hass, entry, lcn_connection):
    
    await hass.config_entries.async_unload(entry.entry_id)
    assert hass.states.get(""cover.cover_outputs"").state == STATE_UNAVAILABLE
    assert hass.states.get(""cover.cover_relays"").state == STATE_UNAVAILABLE

        ```
    ###Assistant : Test the cover is removed when the config entry is unloaded.
    "
880,"def topk(self, k, axis=-1, split_every=None):
        
        from dask.array.reductions import topk

        return topk(self, k, axis=axis, split_every=split_every)
","The top k elements of an array.

        See :func:`dask.array.topk` for docstring.

        ",11,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def topk(self, k, axis=-1, split_every=None):
        
        from dask.array.reductions import topk

        return topk(self, k, axis=axis, split_every=split_every)

        ```
    ###Assistant : The top k elements of an array.

        See :func:`dask.array.topk` for docstring.

        
    "
881,"def for_each_ternary(self, fn, selector=None, row=None, col=None) -> ""Figure"":
        
        for obj in self.select_ternaries(selector=selector, row=row, col=col):
            fn(obj)

        return self
","
        Apply a function to all ternary objects that satisfy the
        specified selection criteria

        Parameters
        ----------
        fn:
            Function that inputs a single ternary object.
        selector: dict, function, or None (default None)
            Dict to use as selection criteria.
            ternary objects will be selected if they contain
            properties corresponding to all of the dictionary's keys, with
            values that exactly match the supplied values. If None
            (the default), all ternary objects are selected. If a
            function, it must be a function accepting a single argument and
            returning a boolean. The function will be called on each
            ternary and those for which the function returned True will
            be in the selection.
        row, col: int or None (default None)
            Subplot row and column index of ternary objects to select.
            To select ternary objects by row and column, the Figure
            must have been created using plotly.subplots.make_subplots.
            If None (the default), all ternary objects are selected.
        Returns
        -------
        self
            Returns the Figure object that the method was called on
        ",161,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def for_each_ternary(self, fn, selector=None, row=None, col=None) -> ""Figure"":
        
        for obj in self.select_ternaries(selector=selector, row=row, col=col):
            fn(obj)

        return self

        ```
    ###Assistant : 
        Apply a function to all ternary objects that satisfy the
        specified selection criteria

        Parameters
        ----------
        fn:
            Function that inputs a single ternary object.
        selector: dict, function, or None (default None)
            Dict to use as selection criteria.
            ternary objects will be selected if they contain
            properties corresponding to all of the dictionary's keys, with
            values that exactly match the supplied values. If None
            (the default), all ternary objects are selected. If a
            function, it must be a function accepting a single argument and
            returning a boolean. The function will be called on each
            ternary and those for which the function returned True will
            be in the selection.
        row, col: int or None (default None)
            Subplot row and column index of ternary objects to select.
            To select ternary objects by row and column, the Figure
            must have been created using plotly.subplots.make_subplots.
            If None (the default), all ternary objects are selected.
        Returns
        -------
        self
            Returns the Figure object that the method was called on
        
    "
882,"def call(self, *args, **kwargs):
        
        warnings.warn(
            ""'call()' method is deprecated. "" + ""Use '__call__()' instead"",
            DeprecationWarning,
        )
        return self.__call__(*args, **kwargs)

",Use ``__call__`` instead because this method is deprecated.,8,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call(self, *args, **kwargs):
        
        warnings.warn(
            ""'call()' method is deprecated. "" + ""Use '__call__()' instead"",
            DeprecationWarning,
        )
        return self.__call__(*args, **kwargs)


        ```
    ###Assistant : Use ``__call__`` instead because this method is deprecated.
    "
883,"def validate_per_replica_inputs(distribution_strategy, x):
  
  # Convert the inputs and targets into a list of PerReplica objects.
  per_replica_list = tf.nest.flatten(x)
  x_values_list = []
  for x in per_replica_list:
    # At this point x should contain only tensors.
    x_values = distribution_strategy.unwrap(x)
    for value in x_values:
      if not tf.is_tensor(value):
        raise ValueError('Dataset input to the model should be tensors instead '
                         'they are of type {}'.format(type(value)))

    if not tf.executing_eagerly():
      # Validate that the shape and dtype of all the elements in x are the same.
      validate_all_tensor_shapes(x, x_values)
    validate_all_tensor_types(x, x_values)

    x_values_list.append(x_values[0])
  return x_values_list

","Validates PerReplica dataset input list.

  Args:
    distribution_strategy: The current DistributionStrategy used to call
      `fit`, `evaluate` and `predict`.
    x: A list of PerReplica objects that represent the input or
      target values.

  Returns:
    List containing the first element of each of the PerReplica objects in
    the input list.

  Raises:
    ValueError: If any of the objects in the `per_replica_list` is not a tensor.

  ",60,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_per_replica_inputs(distribution_strategy, x):
  
  # Convert the inputs and targets into a list of PerReplica objects.
  per_replica_list = tf.nest.flatten(x)
  x_values_list = []
  for x in per_replica_list:
    # At this point x should contain only tensors.
    x_values = distribution_strategy.unwrap(x)
    for value in x_values:
      if not tf.is_tensor(value):
        raise ValueError('Dataset input to the model should be tensors instead '
                         'they are of type {}'.format(type(value)))

    if not tf.executing_eagerly():
      # Validate that the shape and dtype of all the elements in x are the same.
      validate_all_tensor_shapes(x, x_values)
    validate_all_tensor_types(x, x_values)

    x_values_list.append(x_values[0])
  return x_values_list


        ```
    ###Assistant : Validates PerReplica dataset input list.

  Args:
    distribution_strategy: The current DistributionStrategy used to call
      `fit`, `evaluate` and `predict`.
    x: A list of PerReplica objects that represent the input or
      target values.

  Returns:
    List containing the first element of each of the PerReplica objects in
    the input list.

  Raises:
    ValueError: If any of the objects in the `per_replica_list` is not a tensor.

  
    "
884,"def get_mac_addr(self):
        
        if (
            self.bulb.host_firmware_version
            and AwesomeVersion(self.bulb.host_firmware_version) >= FIX_MAC_FW
        ):
            octets = [int(octet, 16) for octet in self.mac_addr.split("":"")]
            octets[5] = (octets[5] + 1) % 256
            return "":"".join(f""{octet:02x}"" for octet in octets)
        return self.mac_addr
",Increment the last byte of the mac address by one for FW>3.70.,12,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_mac_addr(self):
        
        if (
            self.bulb.host_firmware_version
            and AwesomeVersion(self.bulb.host_firmware_version) >= FIX_MAC_FW
        ):
            octets = [int(octet, 16) for octet in self.mac_addr.split("":"")]
            octets[5] = (octets[5] + 1) % 256
            return "":"".join(f""{octet:02x}"" for octet in octets)
        return self.mac_addr

        ```
    ###Assistant : Increment the last byte of the mac address by one for FW>3.70.
    "
885,"def test_positive_integer_or_none_4():
    
    assert positive_integer_or_none('none') is None
    assert positive_integer_or_none('None') is None

",Assert that the TPOT CLI interface's positive_integer_or_none parsing return None when value is string 'None' or 'none'.,17,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_positive_integer_or_none_4():
    
    assert positive_integer_or_none('none') is None
    assert positive_integer_or_none('None') is None


        ```
    ###Assistant : Assert that the TPOT CLI interface's positive_integer_or_none parsing return None when value is string 'None' or 'none'.
    "
886,"def get_local_part(value):
    
    local_part = LocalPart()
    leader = None
    if value[0] in CFWS_LEADER:
        leader, value = get_cfws(value)
    if not value:
        raise errors.HeaderParseError(
            ""expected local-part but found '{}'"".format(value))
    try:
        token, value = get_dot_atom(value)
    except errors.HeaderParseError:
        try:
            token, value = get_word(value)
        except errors.HeaderParseError:
            if value[0] != '\\' and value[0] in PHRASE_ENDS:
                raise
            token = TokenList()
    if leader is not None:
        token[:0] = [leader]
    local_part.append(token)
    if value and (value[0]=='\\' or value[0] not in PHRASE_ENDS):
        obs_local_part, value = get_obs_local_part(str(local_part) + value)
        if obs_local_part.token_type == 'invalid-obs-local-part':
            local_part.defects.append(errors.InvalidHeaderDefect(
                ""local-part is not dot-atom, quoted-string, or obs-local-part""))
        else:
            local_part.defects.append(errors.ObsoleteHeaderDefect(
                ""local-part is not a dot-atom (contains CFWS)""))
        local_part[0] = obs_local_part
    try:
        local_part.value.encode('ascii')
    except UnicodeEncodeError:
        local_part.defects.append(errors.NonASCIILocalPartDefect(
                ""local-part contains non-ASCII characters)""))
    return local_part, value
"," local-part = dot-atom / quoted-string / obs-local-part

    ",7,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_local_part(value):
    
    local_part = LocalPart()
    leader = None
    if value[0] in CFWS_LEADER:
        leader, value = get_cfws(value)
    if not value:
        raise errors.HeaderParseError(
            ""expected local-part but found '{}'"".format(value))
    try:
        token, value = get_dot_atom(value)
    except errors.HeaderParseError:
        try:
            token, value = get_word(value)
        except errors.HeaderParseError:
            if value[0] != '\\' and value[0] in PHRASE_ENDS:
                raise
            token = TokenList()
    if leader is not None:
        token[:0] = [leader]
    local_part.append(token)
    if value and (value[0]=='\\' or value[0] not in PHRASE_ENDS):
        obs_local_part, value = get_obs_local_part(str(local_part) + value)
        if obs_local_part.token_type == 'invalid-obs-local-part':
            local_part.defects.append(errors.InvalidHeaderDefect(
                ""local-part is not dot-atom, quoted-string, or obs-local-part""))
        else:
            local_part.defects.append(errors.ObsoleteHeaderDefect(
                ""local-part is not a dot-atom (contains CFWS)""))
        local_part[0] = obs_local_part
    try:
        local_part.value.encode('ascii')
    except UnicodeEncodeError:
        local_part.defects.append(errors.NonASCIILocalPartDefect(
                ""local-part contains non-ASCII characters)""))
    return local_part, value

        ```
    ###Assistant :  local-part = dot-atom / quoted-string / obs-local-part

    
    "
887,"def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
    

    assert not (numpify and torchify), ""You cannot specify both numpy and PyTorch tensors at the same time""

    video_inputs = []
    for i in range(feature_extract_tester.batch_size):
        if equal_resolution:
            width = height = feature_extract_tester.max_resolution
        else:
            width, height = np.random.choice(
                np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
            )
            video = prepare_video(
                feature_extract_tester=feature_extract_tester,
                width=width,
                height=height,
                numpify=numpify,
                torchify=torchify,
            )
        video_inputs.append(video)

    return video_inputs

","This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
    one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.

    One can specify whether the videos are of the same resolution or not.
    ",51,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
    

    assert not (numpify and torchify), ""You cannot specify both numpy and PyTorch tensors at the same time""

    video_inputs = []
    for i in range(feature_extract_tester.batch_size):
        if equal_resolution:
            width = height = feature_extract_tester.max_resolution
        else:
            width, height = np.random.choice(
                np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
            )
            video = prepare_video(
                feature_extract_tester=feature_extract_tester,
                width=width,
                height=height,
                numpify=numpify,
                torchify=torchify,
            )
        video_inputs.append(video)

    return video_inputs


        ```
    ###Assistant : This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
    one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.

    One can specify whether the videos are of the same resolution or not.
    
    "
888,"def _verify_no_matching_http_header(self, ssl_vhost, header_substring):
        
        header_path = self.parser.find_dir(""Header"", None,
                                           start=ssl_vhost.path)
        if header_path:
            # ""Existing Header directive for virtualhost""
            pat = '(?:[ ""]|^)(%s)(?:[ ""]|$)' % (header_substring.lower())
            for match in header_path:
                if re.search(pat, self.parser.aug.get(match).lower()):
                    raise errors.PluginEnhancementAlreadyPresent(
                        ""Existing %s header"" % header_substring)
","Checks to see if there is an existing Header directive that
        contains the string header_substring.

        :param ssl_vhost: vhost to check
        :type vhost: :class:`~certbot_apache._internal.obj.VirtualHost`

        :param header_substring: string that uniquely identifies a header.
                e.g: Strict-Transport-Security, Upgrade-Insecure-Requests.
        :type str

        :returns: boolean
        :rtype: (bool)

        :raises errors.PluginEnhancementAlreadyPresent When header
                header_substring exists

        ",46,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _verify_no_matching_http_header(self, ssl_vhost, header_substring):
        
        header_path = self.parser.find_dir(""Header"", None,
                                           start=ssl_vhost.path)
        if header_path:
            # ""Existing Header directive for virtualhost""
            pat = '(?:[ ""]|^)(%s)(?:[ ""]|$)' % (header_substring.lower())
            for match in header_path:
                if re.search(pat, self.parser.aug.get(match).lower()):
                    raise errors.PluginEnhancementAlreadyPresent(
                        ""Existing %s header"" % header_substring)

        ```
    ###Assistant : Checks to see if there is an existing Header directive that
        contains the string header_substring.

        :param ssl_vhost: vhost to check
        :type vhost: :class:`~certbot_apache._internal.obj.VirtualHost`

        :param header_substring: string that uniquely identifies a header.
                e.g: Strict-Transport-Security, Upgrade-Insecure-Requests.
        :type str

        :returns: boolean
        :rtype: (bool)

        :raises errors.PluginEnhancementAlreadyPresent When header
                header_substring exists

        
    "
889,"def indices(dimensions, dtype=int32, sparse=False):
  dimensions = tuple(
      core.concrete_or_error(operator.index, d, ""dimensions argument of jnp.indices"")
      for d in dimensions)
  N = len(dimensions)
  output = []
  s = dimensions
  for i, dim in enumerate(dimensions):
    idx = lax.iota(dtype, dim)
    if sparse:
      s = (1,)*i + (dim,) + (1,)*(N - i - 1)
    output.append(lax.broadcast_in_dim(idx, s, (i,)))
  if sparse:
    return tuple(output)
  return stack(output, 0) if output else array([], dtype=dtype)


_TOTAL_REPEAT_LENGTH_DOC = 


@_wraps(np.repeat, lax_description=_TOTAL_REPEAT_LENGTH_DOC)","\
Jax adds the optional `total_repeat_length` parameter which specifies the total
number of repeat, and defaults to sum(repeats). It must be specified for repeat
to be compilable. If `sum(repeats)` is larger than the specified
`total_repeat_length` the remaining values will be discarded. In the case of
`sum(repeats)` being smaller than the specified target length, the final value
will be repeated.
",59,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def indices(dimensions, dtype=int32, sparse=False):
  dimensions = tuple(
      core.concrete_or_error(operator.index, d, ""dimensions argument of jnp.indices"")
      for d in dimensions)
  N = len(dimensions)
  output = []
  s = dimensions
  for i, dim in enumerate(dimensions):
    idx = lax.iota(dtype, dim)
    if sparse:
      s = (1,)*i + (dim,) + (1,)*(N - i - 1)
    output.append(lax.broadcast_in_dim(idx, s, (i,)))
  if sparse:
    return tuple(output)
  return stack(output, 0) if output else array([], dtype=dtype)


_TOTAL_REPEAT_LENGTH_DOC = 


@_wraps(np.repeat, lax_description=_TOTAL_REPEAT_LENGTH_DOC)
        ```
    ###Assistant : \
Jax adds the optional `total_repeat_length` parameter which specifies the total
number of repeat, and defaults to sum(repeats). It must be specified for repeat
to be compilable. If `sum(repeats)` is larger than the specified
`total_repeat_length` the remaining values will be discarded. In the case of
`sum(repeats)` being smaller than the specified target length, the final value
will be repeated.

    "
890,"def test_open_connection(tctx):
    
    assert Playbook(tcp.TCPLayer(tctx, True)) << OpenConnection(tctx.server)

    tctx.server.timestamp_start = 1624544785
    assert Playbook(tcp.TCPLayer(tctx, True)) << None

","
    If there is no server connection yet, establish one,
    because the server may send data first.
    ",16,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_open_connection(tctx):
    
    assert Playbook(tcp.TCPLayer(tctx, True)) << OpenConnection(tctx.server)

    tctx.server.timestamp_start = 1624544785
    assert Playbook(tcp.TCPLayer(tctx, True)) << None


        ```
    ###Assistant : 
    If there is no server connection yet, establish one,
    because the server may send data first.
    
    "
891,"def get_connected_endpoints(self, obj):
        
        endpoints = obj.connected_endpoints
        if endpoints:
            serializer = get_serializer_for_model(endpoints[0], prefix='Nested')
            context = {'request': self.context['request']}
            return serializer(endpoints, many=True, context=context).data
","
        Return the appropriate serializer for the type of connected object.
        ",10,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_connected_endpoints(self, obj):
        
        endpoints = obj.connected_endpoints
        if endpoints:
            serializer = get_serializer_for_model(endpoints[0], prefix='Nested')
            context = {'request': self.context['request']}
            return serializer(endpoints, many=True, context=context).data

        ```
    ###Assistant : 
        Return the appropriate serializer for the type of connected object.
        
    "
892,"def test_basic(self):
        
        trees = [(nx.full_rary_tree(2, 2**2 - 1), 0) for i in range(2)]
        actual = nx.join(trees)
        expected = nx.full_rary_tree(2, 2**3 - 1)
        assert nx.is_isomorphic(actual, expected)
",Tests for joining multiple subtrees at a root node.,9,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_basic(self):
        
        trees = [(nx.full_rary_tree(2, 2**2 - 1), 0) for i in range(2)]
        actual = nx.join(trees)
        expected = nx.full_rary_tree(2, 2**3 - 1)
        assert nx.is_isomorphic(actual, expected)

        ```
    ###Assistant : Tests for joining multiple subtrees at a root node.
    "
893,"def test_result_list_html(self):
        
        new_parent = Parent.objects.create(name=""parent"")
        new_child = Child.objects.create(name=""name"", parent=new_parent)
        request = self.factory.get(""/child/"")
        request.user = self.superuser
        m = ChildAdmin(Child, custom_site)
        cl = m.get_changelist_instance(request)
        cl.formset = None
        template = Template(
            ""{% load admin_list %}{% spaceless %}{% result_list cl %}{% endspaceless %}""
        )
        context = Context({""cl"": cl, ""opts"": Child._meta})
        table_output = template.render(context)
        link = reverse(""admin:admin_changelist_child_change"", args=(new_child.id,))
        row_html = build_tbody_html(
            new_child.id, link, '<td class=""field-parent nowrap"">%s</td>' % new_parent
        )
        self.assertNotEqual(
            table_output.find(row_html),
            -1,
            ""Failed to find expected row element: %s"" % table_output,
        )
","
        Inclusion tag result_list generates a table when with default
        ModelAdmin settings.
        ",11,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_result_list_html(self):
        
        new_parent = Parent.objects.create(name=""parent"")
        new_child = Child.objects.create(name=""name"", parent=new_parent)
        request = self.factory.get(""/child/"")
        request.user = self.superuser
        m = ChildAdmin(Child, custom_site)
        cl = m.get_changelist_instance(request)
        cl.formset = None
        template = Template(
            ""{% load admin_list %}{% spaceless %}{% result_list cl %}{% endspaceless %}""
        )
        context = Context({""cl"": cl, ""opts"": Child._meta})
        table_output = template.render(context)
        link = reverse(""admin:admin_changelist_child_change"", args=(new_child.id,))
        row_html = build_tbody_html(
            new_child.id, link, '<td class=""field-parent nowrap"">%s</td>' % new_parent
        )
        self.assertNotEqual(
            table_output.find(row_html),
            -1,
            ""Failed to find expected row element: %s"" % table_output,
        )

        ```
    ###Assistant : 
        Inclusion tag result_list generates a table when with default
        ModelAdmin settings.
        
    "
894,"def test_delete_uploaded_image(self):
        
        # Send request
        response = self.client.post(
            reverse(
                ""wagtailimages:delete_upload_multiple"", args=(self.uploaded_image.id,)
            )
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertEqual(response[""Content-Type""], ""application/json"")

        # Make sure the image is deleted
        self.assertFalse(
            UploadedImage.objects.filter(id=self.uploaded_image.id).exists()
        )

        # Check JSON
        response_json = json.loads(response.content.decode())
        self.assertTrue(response_json[""success""])

","
        This tests that a POST request to the delete view deletes the UploadedImage
        ",13,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_uploaded_image(self):
        
        # Send request
        response = self.client.post(
            reverse(
                ""wagtailimages:delete_upload_multiple"", args=(self.uploaded_image.id,)
            )
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertEqual(response[""Content-Type""], ""application/json"")

        # Make sure the image is deleted
        self.assertFalse(
            UploadedImage.objects.filter(id=self.uploaded_image.id).exists()
        )

        # Check JSON
        response_json = json.loads(response.content.decode())
        self.assertTrue(response_json[""success""])


        ```
    ###Assistant : 
        This tests that a POST request to the delete view deletes the UploadedImage
        
    "
895,"def matrix(self) -> np.ndarray:
        
        if not np.any(self._matrices[self._centering]):
            matrix = self._matrices[""legacy""].copy()
            matrix[:, 2] -= self.pose.offset[self._centering]
            self._matrices[self._centering] = matrix
            logger.trace(""original matrix: %s, new matrix: %s"",  # type: ignore
                         self._matrices[""legacy""], matrix)
        return self._matrices[self._centering]
"," :class:`numpy.ndarray`: The 3x2 transformation matrix for extracting and aligning the
        core face area out of the original frame, with no padding or sizing applied. The returned
        matrix is offset for the given :attr:`centering`. ",33,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def matrix(self) -> np.ndarray:
        
        if not np.any(self._matrices[self._centering]):
            matrix = self._matrices[""legacy""].copy()
            matrix[:, 2] -= self.pose.offset[self._centering]
            self._matrices[self._centering] = matrix
            logger.trace(""original matrix: %s, new matrix: %s"",  # type: ignore
                         self._matrices[""legacy""], matrix)
        return self._matrices[self._centering]

        ```
    ###Assistant :  :class:`numpy.ndarray`: The 3x2 transformation matrix for extracting and aligning the
        core face area out of the original frame, with no padding or sizing applied. The returned
        matrix is offset for the given :attr:`centering`. 
    "
896,"def read_docstub(filename):
    

    in_documentation = False
    capturing = False
    indent_detection = ''
    doc_stub = []

    with open(filename, 'r') as t_module_data:
        for line in t_module_data:
            if in_documentation:
                # start capturing the stub until indentation returns
                if capturing and line.startswith(indent_detection):
                    doc_stub.append(line)

                elif capturing and not line.startswith(indent_detection):
                    break

                elif line.lstrip().startswith('short_description:'):
                    capturing = True
                    # Detect that the short_description continues on the next line if it's indented more
                    # than short_description itself.
                    indent_detection = ' ' * (len(line) - len(line.lstrip()) + 1)
                    doc_stub.append(line)

            elif line.startswith('DOCUMENTATION') and ('=' in line or ':' in line):
                in_documentation = True

    short_description = r''.join(doc_stub).strip().rstrip('.')
    data = AnsibleLoader(short_description, file_name=filename).get_single_data()

    return data
","
    Quickly find short_description using string methods instead of node parsing.
    This does not return a full set of documentation strings and is intended for
    operations like ansible-doc -l.
    ",28,100,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read_docstub(filename):
    

    in_documentation = False
    capturing = False
    indent_detection = ''
    doc_stub = []

    with open(filename, 'r') as t_module_data:
        for line in t_module_data:
            if in_documentation:
                # start capturing the stub until indentation returns
                if capturing and line.startswith(indent_detection):
                    doc_stub.append(line)

                elif capturing and not line.startswith(indent_detection):
                    break

                elif line.lstrip().startswith('short_description:'):
                    capturing = True
                    # Detect that the short_description continues on the next line if it's indented more
                    # than short_description itself.
                    indent_detection = ' ' * (len(line) - len(line.lstrip()) + 1)
                    doc_stub.append(line)

            elif line.startswith('DOCUMENTATION') and ('=' in line or ':' in line):
                in_documentation = True

    short_description = r''.join(doc_stub).strip().rstrip('.')
    data = AnsibleLoader(short_description, file_name=filename).get_single_data()

    return data

        ```
    ###Assistant : 
    Quickly find short_description using string methods instead of node parsing.
    This does not return a full set of documentation strings and is intended for
    operations like ansible-doc -l.
    
    "
897,"def test_02_train_predictor(self):
        query = f
        response = self.handler.native_query(query)
        self.assertTrue(response.type == RESPONSE_TYPE.OK)

    # def test_03_retrain_predictor(self):
    #     query = f""RETRAIN {self.test_model_name_1}""
    #     response = self.handler.native_query(query)
    #     self.assertTrue(response.type == RESPONSE_TYPE.OK)
","
            CREATE PREDICTOR {self.test_model_name_1}
            FROM {PG_HANDLER_NAME} (SELECT * FROM demo_data.home_rentals limit 50)
            PREDICT rental_price
        ",13,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_02_train_predictor(self):
        query = f
        response = self.handler.native_query(query)
        self.assertTrue(response.type == RESPONSE_TYPE.OK)

    # def test_03_retrain_predictor(self):
    #     query = f""RETRAIN {self.test_model_name_1}""
    #     response = self.handler.native_query(query)
    #     self.assertTrue(response.type == RESPONSE_TYPE.OK)

        ```
    ###Assistant : 
            CREATE PREDICTOR {self.test_model_name_1}
            FROM {PG_HANDLER_NAME} (SELECT * FROM demo_data.home_rentals limit 50)
            PREDICT rental_price
        
    "
898,"def get_dependencies(dsk, key=None, task=no_default, as_list=False):
    
    if key is not None:
        arg = dsk[key]
    elif task is not no_default:
        arg = task
    else:
        raise ValueError(""Provide either key or task"")

    return keys_in_tasks(dsk, [arg], as_list=as_list)

","Get the immediate tasks on which this task depends

    Examples
    --------
    >>> inc = lambda x: x + 1
    >>> add = lambda x, y: x + y
    >>> dsk = {'x': 1,
    ...        'y': (inc, 'x'),
    ...        'z': (add, 'x', 'y'),
    ...        'w': (inc, 'z'),
    ...        'a': (add, (inc, 'x'), 1)}

    >>> get_dependencies(dsk, 'x')
    set()

    >>> get_dependencies(dsk, 'y')
    {'x'}

    >>> get_dependencies(dsk, 'z')  # doctest: +SKIP
    {'x', 'y'}

    >>> get_dependencies(dsk, 'w')  # Only direct dependencies
    {'z'}

    >>> get_dependencies(dsk, 'a')  # Ignore non-keys
    {'x'}

    >>> get_dependencies(dsk, task=(inc, 'x'))  # provide tasks directly
    {'x'}
    ",92,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_dependencies(dsk, key=None, task=no_default, as_list=False):
    
    if key is not None:
        arg = dsk[key]
    elif task is not no_default:
        arg = task
    else:
        raise ValueError(""Provide either key or task"")

    return keys_in_tasks(dsk, [arg], as_list=as_list)


        ```
    ###Assistant : Get the immediate tasks on which this task depends

    Examples
    --------
    >>> inc = lambda x: x + 1
    >>> add = lambda x, y: x + y
    >>> dsk = {'x': 1,
    ...        'y': (inc, 'x'),
    ...        'z': (add, 'x', 'y'),
    ...        'w': (inc, 'z'),
    ...        'a': (add, (inc, 'x'), 1)}

    >>> get_dependencies(dsk, 'x')
    set()

    >>> get_dependencies(dsk, 'y')
    {'x'}

    >>> get_dependencies(dsk, 'z')  # doctest: +SKIP
    {'x', 'y'}

    >>> get_dependencies(dsk, 'w')  # Only direct dependencies
    {'z'}

    >>> get_dependencies(dsk, 'a')  # Ignore non-keys
    {'x'}

    >>> get_dependencies(dsk, task=(inc, 'x'))  # provide tasks directly
    {'x'}
    
    "
899,"def test_dict_checkpoint_fs(self):
        
        checkpoint = self._prepare_dict_checkpoint()

        # Convert into fs checkpoint
        path = checkpoint.to_directory()
        self.assertIsInstance(path, str)

        # Create from path
        checkpoint = Checkpoint.from_directory(path)
        self.assertTrue(checkpoint._local_path)

        self._assert_dict_checkpoint(checkpoint)
",Test conversion from dict to FS checkpoint and back.,9,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dict_checkpoint_fs(self):
        
        checkpoint = self._prepare_dict_checkpoint()

        # Convert into fs checkpoint
        path = checkpoint.to_directory()
        self.assertIsInstance(path, str)

        # Create from path
        checkpoint = Checkpoint.from_directory(path)
        self.assertTrue(checkpoint._local_path)

        self._assert_dict_checkpoint(checkpoint)

        ```
    ###Assistant : Test conversion from dict to FS checkpoint and back.
    "
900,"def box2corners(box):
    
    B = box.shape[0]
    x, y, w, h, alpha = paddle.split(box, 5, axis=-1)
    x4 = paddle.to_tensor(
        [0.5, 0.5, -0.5, -0.5], dtype=paddle.float32).reshape(
            (1, 1, 4))  # (1,1,4)
    x4 = x4 * w  # (B, N, 4)
    y4 = paddle.to_tensor(
        [-0.5, 0.5, 0.5, -0.5], dtype=paddle.float32).reshape((1, 1, 4))
    y4 = y4 * h  # (B, N, 4)
    corners = paddle.stack([x4, y4], axis=-1)  # (B, N, 4, 2)
    sin = paddle.sin(alpha)
    cos = paddle.cos(alpha)
    row1 = paddle.concat([cos, sin], axis=-1)
    row2 = paddle.concat([-sin, cos], axis=-1)  # (B, N, 2)
    rot_T = paddle.stack([row1, row2], axis=-2)  # (B, N, 2, 2)
    rotated = paddle.bmm(corners.reshape([-1, 4, 2]), rot_T.reshape([-1, 2, 2]))
    rotated = rotated.reshape([B, -1, 4, 2])  # (B*N, 4, 2) -> (B, N, 4, 2)
    rotated[..., 0] += x
    rotated[..., 1] += y
    return rotated

","convert box coordinate to corners
    Args:
        box (Tensor): (B, N, 5) with (x, y, w, h, alpha) angle is in [0, 90)
    Returns:
        corners (Tensor): (B, N, 4, 2) with (x1, y1, x2, y2, x3, y3, x4, y4)
    ",38,128,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def box2corners(box):
    
    B = box.shape[0]
    x, y, w, h, alpha = paddle.split(box, 5, axis=-1)
    x4 = paddle.to_tensor(
        [0.5, 0.5, -0.5, -0.5], dtype=paddle.float32).reshape(
            (1, 1, 4))  # (1,1,4)
    x4 = x4 * w  # (B, N, 4)
    y4 = paddle.to_tensor(
        [-0.5, 0.5, 0.5, -0.5], dtype=paddle.float32).reshape((1, 1, 4))
    y4 = y4 * h  # (B, N, 4)
    corners = paddle.stack([x4, y4], axis=-1)  # (B, N, 4, 2)
    sin = paddle.sin(alpha)
    cos = paddle.cos(alpha)
    row1 = paddle.concat([cos, sin], axis=-1)
    row2 = paddle.concat([-sin, cos], axis=-1)  # (B, N, 2)
    rot_T = paddle.stack([row1, row2], axis=-2)  # (B, N, 2, 2)
    rotated = paddle.bmm(corners.reshape([-1, 4, 2]), rot_T.reshape([-1, 2, 2]))
    rotated = rotated.reshape([B, -1, 4, 2])  # (B*N, 4, 2) -> (B, N, 4, 2)
    rotated[..., 0] += x
    rotated[..., 1] += y
    return rotated


        ```
    ###Assistant : convert box coordinate to corners
    Args:
        box (Tensor): (B, N, 5) with (x, y, w, h, alpha) angle is in [0, 90)
    Returns:
        corners (Tensor): (B, N, 4, 2) with (x1, y1, x2, y2, x3, y3, x4, y4)
    
    "
901,"def get_crash_rate_alert_metrics_aggregation_value(self, subscription_update):
        
        rows = subscription_update[""values""][""data""]
        if BaseMetricsEntitySubscription.is_crash_rate_format_v2(rows):
            version = ""v2""
            result = self._get_crash_rate_alert_metrics_aggregation_value_v2(subscription_update)
        else:
            version = ""v1""
            result = self._get_crash_rate_alert_metrics_aggregation_value_v1(subscription_update)

        metrics.incr(
            ""incidents.alert_rules.get_crash_rate_alert_metrics_aggregation_value"",
            tags={""format"": version},
            sample_rate=1.0,
        )
        return result
","Handle both update formats. Once all subscriptions have been updated
        to v2, we can remove v1 and replace this function with current v2.
        ",23,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_crash_rate_alert_metrics_aggregation_value(self, subscription_update):
        
        rows = subscription_update[""values""][""data""]
        if BaseMetricsEntitySubscription.is_crash_rate_format_v2(rows):
            version = ""v2""
            result = self._get_crash_rate_alert_metrics_aggregation_value_v2(subscription_update)
        else:
            version = ""v1""
            result = self._get_crash_rate_alert_metrics_aggregation_value_v1(subscription_update)

        metrics.incr(
            ""incidents.alert_rules.get_crash_rate_alert_metrics_aggregation_value"",
            tags={""format"": version},
            sample_rate=1.0,
        )
        return result

        ```
    ###Assistant : Handle both update formats. Once all subscriptions have been updated
        to v2, we can remove v1 and replace this function with current v2.
        
    "
902,"def strict_promotion_if_dtypes_match(dtypes):
  
  if all(dtype == dtypes[0] for dtype in dtypes):
    return jax.numpy_dtype_promotion('strict')
  return jax.numpy_dtype_promotion('standard')
","
  Context manager to enable strict promotion if all dtypes match,
  and enable standard dtype promotion otherwise.
  ",16,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def strict_promotion_if_dtypes_match(dtypes):
  
  if all(dtype == dtypes[0] for dtype in dtypes):
    return jax.numpy_dtype_promotion('strict')
  return jax.numpy_dtype_promotion('standard')

        ```
    ###Assistant : 
  Context manager to enable strict promotion if all dtypes match,
  and enable standard dtype promotion otherwise.
  
    "
903,"def test_stroptions_deprecated_subset():
    
    with pytest.raises(ValueError, match=""deprecated options must be a subset""):
        StrOptions({""a"", ""b"", ""c""}, deprecated={""a"", ""d""})

",Check that the deprecated parameter must be a subset of options.,11,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stroptions_deprecated_subset():
    
    with pytest.raises(ValueError, match=""deprecated options must be a subset""):
        StrOptions({""a"", ""b"", ""c""}, deprecated={""a"", ""d""})


        ```
    ###Assistant : Check that the deprecated parameter must be a subset of options.
    "
904,"def _validate_target_and_loss(self, y, loss):
        

        # `self.loss` references the loss added via `compile` call. If users have
        # provided such, the target must be provided; otherwise it's a user error.
        # Note that `self.loss` does not include losses added via `add_loss`, and it
        # is a valid use when such loss from `add_loss` exists and target does not.
        if self.loss and y is None:
            raise ValueError(
                ""Target data is missing. Your model was compiled with ""
                f""loss={self.loss}, ""
                ""and therefore expects target data to be provided in `fit()`.""
            )

        # For training, there must be compiled loss or regularization loss to exist
        # in order to apply the gradients. If one is not found, it means no loss
        # was supplied via `compile` or `add_loss`.
        elif loss is None:
            raise ValueError(
                ""No loss found. You may have forgotten to provide a `loss` argument ""
                ""in the `compile()` method.""
            )
","Raises error if target or loss is not found.

        This method verifies that the target and loss are properly populated
        when applicable, or raises errors.

        Args:
          y: the target for training.
          loss: the total loss tensor including loss added via `compile` and
            `add_loss`.
        ",43,148,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate_target_and_loss(self, y, loss):
        

        # `self.loss` references the loss added via `compile` call. If users have
        # provided such, the target must be provided; otherwise it's a user error.
        # Note that `self.loss` does not include losses added via `add_loss`, and it
        # is a valid use when such loss from `add_loss` exists and target does not.
        if self.loss and y is None:
            raise ValueError(
                ""Target data is missing. Your model was compiled with ""
                f""loss={self.loss}, ""
                ""and therefore expects target data to be provided in `fit()`.""
            )

        # For training, there must be compiled loss or regularization loss to exist
        # in order to apply the gradients. If one is not found, it means no loss
        # was supplied via `compile` or `add_loss`.
        elif loss is None:
            raise ValueError(
                ""No loss found. You may have forgotten to provide a `loss` argument ""
                ""in the `compile()` method.""
            )

        ```
    ###Assistant : Raises error if target or loss is not found.

        This method verifies that the target and loss are properly populated
        when applicable, or raises errors.

        Args:
          y: the target for training.
          loss: the total loss tensor including loss added via `compile` and
            `add_loss`.
        
    "
905,"def _validate_datetimelike_monotonic(self):
        
        # GH 46061
        if self._on.hasnans:
            self._raise_monotonic_error(""values must not have NaT"")
        for group_indices in self._grouper.indices.values():
            group_on = self._on.take(group_indices)
            if not (
                group_on.is_monotonic_increasing or group_on.is_monotonic_decreasing
            ):
                on = ""index"" if self.on is None else self.on
                raise ValueError(
                    f""Each group within {on} must be monotonic. ""
                    f""Sort the values in {on} first.""
                )
","
        Validate that each group in self._on is monotonic
        ",8,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate_datetimelike_monotonic(self):
        
        # GH 46061
        if self._on.hasnans:
            self._raise_monotonic_error(""values must not have NaT"")
        for group_indices in self._grouper.indices.values():
            group_on = self._on.take(group_indices)
            if not (
                group_on.is_monotonic_increasing or group_on.is_monotonic_decreasing
            ):
                on = ""index"" if self.on is None else self.on
                raise ValueError(
                    f""Each group within {on} must be monotonic. ""
                    f""Sort the values in {on} first.""
                )

        ```
    ###Assistant : 
        Validate that each group in self._on is monotonic
        
    "
906,"def framework_info(filename):
    
    is_framework = STRICT_FRAMEWORK_RE.match(filename)
    if not is_framework:
        return None
    return is_framework.groupdict()
","
    A framework name can take one of the following four forms:
        Location/Name.framework/Versions/SomeVersion/Name_Suffix
        Location/Name.framework/Versions/SomeVersion/Name
        Location/Name.framework/Name_Suffix
        Location/Name.framework/Name

    returns None if not found, or a mapping equivalent to:
        dict(
            location='Location',
            name='Name.framework/Versions/SomeVersion/Name_Suffix',
            shortname='Name',
            version='SomeVersion',
            suffix='Suffix',
        )

    Note that SomeVersion and Suffix are optional and may be None
    if not present
    ",46,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def framework_info(filename):
    
    is_framework = STRICT_FRAMEWORK_RE.match(filename)
    if not is_framework:
        return None
    return is_framework.groupdict()

        ```
    ###Assistant : 
    A framework name can take one of the following four forms:
        Location/Name.framework/Versions/SomeVersion/Name_Suffix
        Location/Name.framework/Versions/SomeVersion/Name
        Location/Name.framework/Name_Suffix
        Location/Name.framework/Name

    returns None if not found, or a mapping equivalent to:
        dict(
            location='Location',
            name='Name.framework/Versions/SomeVersion/Name_Suffix',
            shortname='Name',
            version='SomeVersion',
            suffix='Suffix',
        )

    Note that SomeVersion and Suffix are optional and may be None
    if not present
    
    "
907,"def contained_in(filename, directory):
    
    filename = os.path.normcase(os.path.abspath(filename))
    directory = os.path.normcase(os.path.abspath(directory))
    return os.path.commonprefix([filename, directory]) == directory

",Test if a file is located within the given directory.,10,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def contained_in(filename, directory):
    
    filename = os.path.normcase(os.path.abspath(filename))
    directory = os.path.normcase(os.path.abspath(directory))
    return os.path.commonprefix([filename, directory]) == directory


        ```
    ###Assistant : Test if a file is located within the given directory.
    "
908,"def glu(x, axis=-1):
    
    a, b = tf.split(x, 2, axis=axis)
    return a * tf.math.sigmoid(b)


if version.parse(tf.version.VERSION) >= version.parse(""2.4""):
","
    Gated Linear Unit. Implementation as defined in the original paper (see https://arxiv.org/abs/1612.08083), where
    the input `x` is split in two halves across a dimension (`axis`), A and B, returning A * sigmoid(B).

    Args:
        `x`: float Tensor to perform activation
        `axis`: dimension across which `x` be split in half

    Returns:
        `x` with the GLU activation applied (with its size halved across the dimension `axis`).
    ",63,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def glu(x, axis=-1):
    
    a, b = tf.split(x, 2, axis=axis)
    return a * tf.math.sigmoid(b)


if version.parse(tf.version.VERSION) >= version.parse(""2.4""):

        ```
    ###Assistant : 
    Gated Linear Unit. Implementation as defined in the original paper (see https://arxiv.org/abs/1612.08083), where
    the input `x` is split in two halves across a dimension (`axis`), A and B, returning A * sigmoid(B).

    Args:
        `x`: float Tensor to perform activation
        `axis`: dimension across which `x` be split in half

    Returns:
        `x` with the GLU activation applied (with its size halved across the dimension `axis`).
    
    "
909,"def matplot(self, plot, opts=None, env=None, win=None):
        
        opts = {} if opts is None else opts
        _title2str(opts)
        _assert_opts(opts)

        # write plot to SVG buffer:
        buffer = StringIO()
        plot.savefig(buffer, format=""svg"")
        buffer.seek(0)
        svg = buffer.read()
        buffer.close()

        if opts.get(""resizable"", False):
            if not BS4_AVAILABLE:
                raise ImportError(""No module named 'bs4'"")
            else:
                try:
                    soup = bs4.BeautifulSoup(svg, ""xml"")
                except bs4.FeatureNotFound as e:
                    import six

                    six.raise_from(ImportError(""No module named 'lxml'""), e)
                height = soup.svg.attrs.pop(""height"", None)
                width = soup.svg.attrs.pop(""width"", None)
                svg = str(soup)
        else:
            height = None
            width = None

        # show SVG:
        if ""height"" not in opts:
            height = height or re.search(r'height\=""([0-9\.]*)pt""', svg)
            if height is not None:
                if not isstr(height):
                    height = height.group(1)
                height = height.replace(""pt"", ""00"")
                opts[""height""] = 1.4 * int(math.ceil(float(height)))
        if ""width"" not in opts:
            width = width or re.search(r'width\=""([0-9\.]*)pt""', svg)
            if width is not None:
                if not isstr(width):
                    width = width.group(1)
                width = width.replace(""pt"", ""00"")
                opts[""width""] = 1.35 * int(math.ceil(float(width)))
        return self.svg(svgstr=svg, opts=opts, env=env, win=win)
","
        This function draws a Matplotlib `plot`. The function supports
        one plot-specific option: `resizable`. When set to `True` the plot
        is resized with the pane. You need `beautifulsoup4` and `lxml`
        packages installed to use this option.
        ",35,149,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def matplot(self, plot, opts=None, env=None, win=None):
        
        opts = {} if opts is None else opts
        _title2str(opts)
        _assert_opts(opts)

        # write plot to SVG buffer:
        buffer = StringIO()
        plot.savefig(buffer, format=""svg"")
        buffer.seek(0)
        svg = buffer.read()
        buffer.close()

        if opts.get(""resizable"", False):
            if not BS4_AVAILABLE:
                raise ImportError(""No module named 'bs4'"")
            else:
                try:
                    soup = bs4.BeautifulSoup(svg, ""xml"")
                except bs4.FeatureNotFound as e:
                    import six

                    six.raise_from(ImportError(""No module named 'lxml'""), e)
                height = soup.svg.attrs.pop(""height"", None)
                width = soup.svg.attrs.pop(""width"", None)
                svg = str(soup)
        else:
            height = None
            width = None

        # show SVG:
        if ""height"" not in opts:
            height = height or re.search(r'height\=""([0-9\.]*)pt""', svg)
            if height is not None:
                if not isstr(height):
                    height = height.group(1)
                height = height.replace(""pt"", ""00"")
                opts[""height""] = 1.4 * int(math.ceil(float(height)))
        if ""width"" not in opts:
            width = width or re.search(r'width\=""([0-9\.]*)pt""', svg)
            if width is not None:
                if not isstr(width):
                    width = width.group(1)
                width = width.replace(""pt"", ""00"")
                opts[""width""] = 1.35 * int(math.ceil(float(width)))
        return self.svg(svgstr=svg, opts=opts, env=env, win=win)

        ```
    ###Assistant : 
        This function draws a Matplotlib `plot`. The function supports
        one plot-specific option: `resizable`. When set to `True` the plot
        is resized with the pane. You need `beautifulsoup4` and `lxml`
        packages installed to use this option.
        
    "
910,"def acl_clone(consul_url=None, token=None, **kwargs):
    
    ret = {}
    data = {}
    if not consul_url:
        consul_url = _get_config()
        if not consul_url:
            log.error(""No Consul URL found."")
            ret[""message""] = ""No Consul URL found.""
            ret[""res""] = False
            return ret

    if ""id"" not in kwargs:
        ret[""message""] = 'Required parameter ""id"" is missing.'
        ret[""res""] = False
        return ret

    function = ""acl/clone/{}"".format(kwargs[""id""])
    res = _query(
        consul_url=consul_url, token=token, data=data, method=""PUT"", function=function
    )
    if res[""res""]:
        ret[""res""] = True
        ret[""message""] = ""ACL {} cloned."".format(kwargs[""name""])
        ret[""ID""] = res[""data""]
    else:
        ret[""res""] = False
        ret[""message""] = ""Cloning ACL item {} failed."".format(kwargs[""name""])
    return ret

","
    Information about an ACL token.

    :param consul_url: The Consul server URL.
    :param id: Unique identifier for the ACL to update.
    :return: Boolean, message of success or
             failure, and new ID of cloned ACL.

    CLI Example:

    .. code-block:: bash

        salt '*' consul.acl_info id='c1c4d223-91cb-3d1f-1ee8-f2af9e7b6716'

    ",42,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def acl_clone(consul_url=None, token=None, **kwargs):
    
    ret = {}
    data = {}
    if not consul_url:
        consul_url = _get_config()
        if not consul_url:
            log.error(""No Consul URL found."")
            ret[""message""] = ""No Consul URL found.""
            ret[""res""] = False
            return ret

    if ""id"" not in kwargs:
        ret[""message""] = 'Required parameter ""id"" is missing.'
        ret[""res""] = False
        return ret

    function = ""acl/clone/{}"".format(kwargs[""id""])
    res = _query(
        consul_url=consul_url, token=token, data=data, method=""PUT"", function=function
    )
    if res[""res""]:
        ret[""res""] = True
        ret[""message""] = ""ACL {} cloned."".format(kwargs[""name""])
        ret[""ID""] = res[""data""]
    else:
        ret[""res""] = False
        ret[""message""] = ""Cloning ACL item {} failed."".format(kwargs[""name""])
    return ret


        ```
    ###Assistant : 
    Information about an ACL token.

    :param consul_url: The Consul server URL.
    :param id: Unique identifier for the ACL to update.
    :return: Boolean, message of success or
             failure, and new ID of cloned ACL.

    CLI Example:

    .. code-block:: bash

        salt '*' consul.acl_info id='c1c4d223-91cb-3d1f-1ee8-f2af9e7b6716'

    
    "
911,"def slice_indexer(self, start=None, end=None, step=None, kind=lib.no_default):
        
        self._deprecated_arg(kind, ""kind"", ""slice_indexer"")

        # For historical reasons DatetimeIndex supports slices between two
        # instances of datetime.time as if it were applying a slice mask to
        # an array of (self.hour, self.minute, self.seconds, self.microsecond).
        if isinstance(start, time) and isinstance(end, time):
            if step is not None and step != 1:
                raise ValueError(""Must have step size of 1 with time slices"")
            return self.indexer_between_time(start, end)

        if isinstance(start, time) or isinstance(end, time):
            raise KeyError(""Cannot mix time and non-time slice keys"")
","
        Return indexer for specified label slice.
        Index.slice_indexer, customized to handle time slicing.

        In addition to functionality provided by Index.slice_indexer, does the
        following:

        - if both `start` and `end` are instances of `datetime.time`, it
          invokes `indexer_between_time`
        - if `start` and `end` are both either string or None perform
          value-based selection in non-monotonic cases.

        ",52,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def slice_indexer(self, start=None, end=None, step=None, kind=lib.no_default):
        
        self._deprecated_arg(kind, ""kind"", ""slice_indexer"")

        # For historical reasons DatetimeIndex supports slices between two
        # instances of datetime.time as if it were applying a slice mask to
        # an array of (self.hour, self.minute, self.seconds, self.microsecond).
        if isinstance(start, time) and isinstance(end, time):
            if step is not None and step != 1:
                raise ValueError(""Must have step size of 1 with time slices"")
            return self.indexer_between_time(start, end)

        if isinstance(start, time) or isinstance(end, time):
            raise KeyError(""Cannot mix time and non-time slice keys"")

        ```
    ###Assistant : 
        Return indexer for specified label slice.
        Index.slice_indexer, customized to handle time slicing.

        In addition to functionality provided by Index.slice_indexer, does the
        following:

        - if both `start` and `end` are instances of `datetime.time`, it
          invokes `indexer_between_time`
        - if `start` and `end` are both either string or None perform
          value-based selection in non-monotonic cases.

        
    "
912,"def fix_old_dry_orders(engine):
    with engine.begin() as connection:
        connection.execute(
            text(
                
            )
        )
        connection.execute(
            text(
                
            )
        )

","
                update orders
                set ft_is_open = 0
                where ft_is_open = 1 and (ft_trade_id, order_id) not in (
                    select id, stoploss_order_id from trades where stoploss_order_id is not null
                ) and ft_order_side = 'stoploss'
                and order_id like 'dry_%'
                
                update orders
                set ft_is_open = 0
                where ft_is_open = 1
                and (ft_trade_id, order_id) not in (
                    select id, open_order_id from trades where open_order_id is not null
                ) and ft_order_side != 'stoploss'
                and order_id like 'dry_%'
                ",70,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fix_old_dry_orders(engine):
    with engine.begin() as connection:
        connection.execute(
            text(
                
            )
        )
        connection.execute(
            text(
                
            )
        )


        ```
    ###Assistant : 
                update orders
                set ft_is_open = 0
                where ft_is_open = 1 and (ft_trade_id, order_id) not in (
                    select id, stoploss_order_id from trades where stoploss_order_id is not null
                ) and ft_order_side = 'stoploss'
                and order_id like 'dry_%'
                
                update orders
                set ft_is_open = 0
                where ft_is_open = 1
                and (ft_trade_id, order_id) not in (
                    select id, open_order_id from trades where open_order_id is not null
                ) and ft_order_side != 'stoploss'
                and order_id like 'dry_%'
                
    "
913,"def build_data_frame(self, data, flags=None, stream_id=1, padding_len=0):
        
        flags = set(flags) if flags is not None else set()
        f = DataFrame(stream_id)
        f.data = data
        f.flags = flags

        if padding_len:
            flags.add(""PADDED"")
            f.pad_length = padding_len

        return f
","
        Builds a single data frame out of a chunk of data.
        ",11,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def build_data_frame(self, data, flags=None, stream_id=1, padding_len=0):
        
        flags = set(flags) if flags is not None else set()
        f = DataFrame(stream_id)
        f.data = data
        f.flags = flags

        if padding_len:
            flags.add(""PADDED"")
            f.pad_length = padding_len

        return f

        ```
    ###Assistant : 
        Builds a single data frame out of a chunk of data.
        
    "
914,"def set_dryrun_parser(parser=None):
    
    if not parser:
        parser = set_base_parser()

    parser.add_argument(
        'host',
        type=str,
        help='The full host address of the Gateway, e.g. grpc://localhost:12345',
    )

    parser.add_argument(
        '--timeout',
        type=int,
        default=3000,
        help=,
    )

    return parser
","Set the parser for `dryrun`

    :param parser: an existing parser to build upon
    :return: the parser
    
Timeout in millisecond of one check
-1 for waiting forever
",26,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_dryrun_parser(parser=None):
    
    if not parser:
        parser = set_base_parser()

    parser.add_argument(
        'host',
        type=str,
        help='The full host address of the Gateway, e.g. grpc://localhost:12345',
    )

    parser.add_argument(
        '--timeout',
        type=int,
        default=3000,
        help=,
    )

    return parser

        ```
    ###Assistant : Set the parser for `dryrun`

    :param parser: an existing parser to build upon
    :return: the parser
    
Timeout in millisecond of one check
-1 for waiting forever

    "
915,"def wheel_dist_info_dir(source, name):
    # type: (ZipFile, str) -> str
    
    # Zip file path separators must be /
    subdirs = {p.split(""/"", 1)[0] for p in source.namelist()}

    info_dirs = [s for s in subdirs if s.endswith("".dist-info"")]

    if not info_dirs:
        raise UnsupportedWheel("".dist-info directory not found"")

    if len(info_dirs) > 1:
        raise UnsupportedWheel(
            ""multiple .dist-info directories found: {}"".format("", "".join(info_dirs))
        )

    info_dir = info_dirs[0]

    info_dir_name = canonicalize_name(info_dir)
    canonical_name = canonicalize_name(name)
    if not info_dir_name.startswith(canonical_name):
        raise UnsupportedWheel(
            "".dist-info directory {!r} does not start with {!r}"".format(
                info_dir, canonical_name
            )
        )

    return info_dir

","Returns the name of the contained .dist-info directory.

    Raises AssertionError or UnsupportedWheel if not found, >1 found, or
    it doesn't match the provided name.
    ",24,83,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wheel_dist_info_dir(source, name):
    # type: (ZipFile, str) -> str
    
    # Zip file path separators must be /
    subdirs = {p.split(""/"", 1)[0] for p in source.namelist()}

    info_dirs = [s for s in subdirs if s.endswith("".dist-info"")]

    if not info_dirs:
        raise UnsupportedWheel("".dist-info directory not found"")

    if len(info_dirs) > 1:
        raise UnsupportedWheel(
            ""multiple .dist-info directories found: {}"".format("", "".join(info_dirs))
        )

    info_dir = info_dirs[0]

    info_dir_name = canonicalize_name(info_dir)
    canonical_name = canonicalize_name(name)
    if not info_dir_name.startswith(canonical_name):
        raise UnsupportedWheel(
            "".dist-info directory {!r} does not start with {!r}"".format(
                info_dir, canonical_name
            )
        )

    return info_dir


        ```
    ###Assistant : Returns the name of the contained .dist-info directory.

    Raises AssertionError or UnsupportedWheel if not found, >1 found, or
    it doesn't match the provided name.
    
    "
916,"def in1d(ar1, ar2, assume_unique=False, invert=False):
    
    # Ravel both arrays, behavior for the first array could be different
    ar1 = np.asarray(ar1).ravel()
    ar2 = np.asarray(ar2).ravel()

    # Ensure that iteration through object arrays yields size-1 arrays
    if ar2.dtype == object:
        ar2 = ar2.reshape(-1, 1)
    # Check if we can use a fast integer algorithm:
    integer_arrays = (np.issubdtype(ar1.dtype, np.integer) and
                      np.issubdtype(ar2.dtype, np.integer))

    if integer_arrays:
        ar2_min = np.min(ar2)
        ar2_max = np.max(ar2)
        ar2_range = ar2_max - ar2_min
        ar2_size = ar2.size

        # Optimal performance is for approximately
        # log10(size) > (log10(range) - 2.27) / 0.927, see discussion on
        # https://github.com/numpy/numpy/pull/12065
        optimal_parameters = (
                np.log10(ar2_size + 1) >
                ((np.log10(ar2_range + 1) - 2.27) / 0.927)
            )

        if optimal_parameters:

            if invert:
                outgoing_array = np.ones_like(ar1, dtype=np.bool_)
            else:
                outgoing_array = np.zeros_like(ar1, dtype=np.bool_)

            # Make elements 1 where the integer exists in ar2
            if invert:
                isin_helper_ar = np.ones(ar2_range + 1, dtype=np.bool_)
                isin_helper_ar[ar2 - ar2_min] = 0
            else:
                isin_helper_ar = np.zeros(ar2_range + 1, dtype=np.bool_)
                isin_helper_ar[ar2 - ar2_min] = 1

            # Mask out elements we know won't work
            basic_mask = (ar1 <= ar2_max) & (ar1 >= ar2_min)
            outgoing_array[basic_mask] = isin_helper_ar[ar1[basic_mask] -
                                                        ar2_min]

            return outgoing_array


    # Check if one of the arrays may contain arbitrary objects
    contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject

    # This code is run when
    # a) the first condition is true, making the code significantly faster
    # b) the second condition is true (i.e. `ar1` or `ar2` may contain
    #    arbitrary objects), since then sorting is not guaranteed to work
    if len(ar2) < 10 * len(ar1) ** 0.145 or contains_object:
        if invert:
            mask = np.ones(len(ar1), dtype=bool)
            for a in ar2:
                mask &= (ar1 != a)
        else:
            mask = np.zeros(len(ar1), dtype=bool)
            for a in ar2:
                mask |= (ar1 == a)
        return mask

    # Otherwise use sorting
    if not assume_unique:
        ar1, rev_idx = np.unique(ar1, return_inverse=True)
        ar2 = np.unique(ar2)

    ar = np.concatenate((ar1, ar2))
    # We need this to be a stable sort, so always use 'mergesort'
    # here. The values from the first array should always come before
    # the values from the second array.
    order = ar.argsort(kind='mergesort')
    sar = ar[order]
    if invert:
        bool_ar = (sar[1:] != sar[:-1])
    else:
        bool_ar = (sar[1:] == sar[:-1])
    flag = np.concatenate((bool_ar, [invert]))
    ret = np.empty(ar.shape, dtype=bool)
    ret[order] = flag

    if assume_unique:
        return ret[:len(ar1)]
    else:
        return ret[rev_idx]

","
    Test whether each element of a 1-D array is also present in a second array.

    Returns a boolean array the same length as `ar1` that is True
    where an element of `ar1` is in `ar2` and False otherwise.

    We recommend using :func:`isin` instead of `in1d` for new code.

    Parameters
    ----------
    ar1 : (M,) array_like
        Input array.
    ar2 : array_like
        The values against which to test each value of `ar1`.
    assume_unique : bool, optional
        If True, the input arrays are both assumed to be unique, which
        can speed up the calculation.  Default is False.
    invert : bool, optional
        If True, the values in the returned array are inverted (that is,
        False where an element of `ar1` is in `ar2` and True otherwise).
        Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
        to (but is faster than) ``np.invert(in1d(a, b))``.

        .. versionadded:: 1.8.0

    Returns
    -------
    in1d : (M,) ndarray, bool
        The values `ar1[in1d]` are in `ar2`.

    See Also
    --------
    isin                  : Version of this function that preserves the
                            shape of ar1.
    numpy.lib.arraysetops : Module with a number of other functions for
                            performing set operations on arrays.

    Notes
    -----
    `in1d` can be considered as an element-wise function version of the
    python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
    equivalent to ``np.array([item in b for item in a])``.
    However, this idea fails if `ar2` is a set, or similar (non-sequence)
    container:  As ``ar2`` is converted to an array, in those cases
    ``asarray(ar2)`` is an object array rather than the expected array of
    contained values.

    .. versionadded:: 1.4.0

    Examples
    --------
    >>> test = np.array([0, 1, 2, 5, 0])
    >>> states = [0, 2]
    >>> mask = np.in1d(test, states)
    >>> mask
    array([ True, False,  True, False,  True])
    >>> test[mask]
    array([0, 2, 0])
    >>> mask = np.in1d(test, states, invert=True)
    >>> mask
    array([False,  True, False,  True, False])
    >>> test[mask]
    array([1, 5])
    ",303,367,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def in1d(ar1, ar2, assume_unique=False, invert=False):
    
    # Ravel both arrays, behavior for the first array could be different
    ar1 = np.asarray(ar1).ravel()
    ar2 = np.asarray(ar2).ravel()

    # Ensure that iteration through object arrays yields size-1 arrays
    if ar2.dtype == object:
        ar2 = ar2.reshape(-1, 1)
    # Check if we can use a fast integer algorithm:
    integer_arrays = (np.issubdtype(ar1.dtype, np.integer) and
                      np.issubdtype(ar2.dtype, np.integer))

    if integer_arrays:
        ar2_min = np.min(ar2)
        ar2_max = np.max(ar2)
        ar2_range = ar2_max - ar2_min
        ar2_size = ar2.size

        # Optimal performance is for approximately
        # log10(size) > (log10(range) - 2.27) / 0.927, see discussion on
        # https://github.com/numpy/numpy/pull/12065
        optimal_parameters = (
                np.log10(ar2_size + 1) >
                ((np.log10(ar2_range + 1) - 2.27) / 0.927)
            )

        if optimal_parameters:

            if invert:
                outgoing_array = np.ones_like(ar1, dtype=np.bool_)
            else:
                outgoing_array = np.zeros_like(ar1, dtype=np.bool_)

            # Make elements 1 where the integer exists in ar2
            if invert:
                isin_helper_ar = np.ones(ar2_range + 1, dtype=np.bool_)
                isin_helper_ar[ar2 - ar2_min] = 0
            else:
                isin_helper_ar = np.zeros(ar2_range + 1, dtype=np.bool_)
                isin_helper_ar[ar2 - ar2_min] = 1

            # Mask out elements we know won't work
            basic_mask = (ar1 <= ar2_max) & (ar1 >= ar2_min)
            outgoing_array[basic_mask] = isin_helper_ar[ar1[basic_mask] -
                                                        ar2_min]

            return outgoing_array


    # Check if one of the arrays may contain arbitrary objects
    contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject

    # This code is run when
    # a) the first condition is true, making the code significantly faster
    # b) the second condition is true (i.e. `ar1` or `ar2` may contain
    #    arbitrary objects), since then sorting is not guaranteed to work
    if len(ar2) < 10 * len(ar1) ** 0.145 or contains_object:
        if invert:
            mask = np.ones(len(ar1), dtype=bool)
            for a in ar2:
                mask &= (ar1 != a)
        else:
            mask = np.zeros(len(ar1), dtype=bool)
            for a in ar2:
                mask |= (ar1 == a)
        return mask

    # Otherwise use sorting
    if not assume_unique:
        ar1, rev_idx = np.unique(ar1, return_inverse=True)
        ar2 = np.unique(ar2)

    ar = np.concatenate((ar1, ar2))
    # We need this to be a stable sort, so always use 'mergesort'
    # here. The values from the first array should always come before
    # the values from the second array.
    order = ar.argsort(kind='mergesort')
    sar = ar[order]
    if invert:
        bool_ar = (sar[1:] != sar[:-1])
    else:
        bool_ar = (sar[1:] == sar[:-1])
    flag = np.concatenate((bool_ar, [invert]))
    ret = np.empty(ar.shape, dtype=bool)
    ret[order] = flag

    if assume_unique:
        return ret[:len(ar1)]
    else:
        return ret[rev_idx]


        ```
    ###Assistant : 
    Test whether each element of a 1-D array is also present in a second array.

    Returns a boolean array the same length as `ar1` that is True
    where an element of `ar1` is in `ar2` and False otherwise.

    We recommend using :func:`isin` instead of `in1d` for new code.

    Parameters
    ----------
    ar1 : (M,) array_like
        Input array.
    ar2 : array_like
        The values against which to test each value of `ar1`.
    assume_unique : bool, optional
        If True, the input arrays are both assumed to be unique, which
        can speed up the calculation.  Default is False.
    invert : bool, optional
        If True, the values in the returned array are inverted (that is,
        False where an element of `ar1` is in `ar2` and True otherwise).
        Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
        to (but is faster than) ``np.invert(in1d(a, b))``.

        .. versionadded:: 1.8.0

    Returns
    -------
    in1d : (M,) ndarray, bool
        The values `ar1[in1d]` are in `ar2`.

    See Also
    --------
    isin                  : Version of this function that preserves the
                            shape of ar1.
    numpy.lib.arraysetops : Module with a number of other functions for
                            performing set operations on arrays.

    Notes
    -----
    `in1d` can be considered as an element-wise function version of the
    python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
    equivalent to ``np.array([item in b for item in a])``.
    However, this idea fails if `ar2` is a set, or similar (non-sequence)
    container:  As ``ar2`` is converted to an array, in those cases
    ``asarray(ar2)`` is an object array rather than the expected array of
    contained values.

    .. versionadded:: 1.4.0

    Examples
    --------
    >>> test = np.array([0, 1, 2, 5, 0])
    >>> states = [0, 2]
    >>> mask = np.in1d(test, states)
    >>> mask
    array([ True, False,  True, False,  True])
    >>> test[mask]
    array([0, 2, 0])
    >>> mask = np.in1d(test, states, invert=True)
    >>> mask
    array([False,  True, False,  True, False])
    >>> test[mask]
    array([1, 5])
    
    "
917,"def out_degree_centrality(G):
    
    if len(G) <= 1:
        return {n: 1 for n in G}

    s = 1.0 / (len(G) - 1.0)
    centrality = {n: d * s for n, d in G.out_degree()}
    return centrality
","Compute the out-degree centrality for nodes.

    The out-degree centrality for a node v is the fraction of nodes its
    outgoing edges are connected to.

    Parameters
    ----------
    G : graph
        A NetworkX graph

    Returns
    -------
    nodes : dictionary
        Dictionary of nodes with out-degree centrality as values.

    Raises
    ------
    NetworkXNotImplemented
        If G is undirected.

    Examples
    --------
    >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
    >>> nx.out_degree_centrality(G)
    {0: 1.0, 1: 0.6666666666666666, 2: 0.0, 3: 0.0}

    See Also
    --------
    degree_centrality, in_degree_centrality

    Notes
    -----
    The degree centrality values are normalized by dividing by the maximum
    possible degree in a simple graph n-1 where n is the number of nodes in G.

    For multigraphs or graphs with self loops the maximum degree might
    be higher than n-1 and values of degree centrality greater than 1
    are possible.
    ",136,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def out_degree_centrality(G):
    
    if len(G) <= 1:
        return {n: 1 for n in G}

    s = 1.0 / (len(G) - 1.0)
    centrality = {n: d * s for n, d in G.out_degree()}
    return centrality

        ```
    ###Assistant : Compute the out-degree centrality for nodes.

    The out-degree centrality for a node v is the fraction of nodes its
    outgoing edges are connected to.

    Parameters
    ----------
    G : graph
        A NetworkX graph

    Returns
    -------
    nodes : dictionary
        Dictionary of nodes with out-degree centrality as values.

    Raises
    ------
    NetworkXNotImplemented
        If G is undirected.

    Examples
    --------
    >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
    >>> nx.out_degree_centrality(G)
    {0: 1.0, 1: 0.6666666666666666, 2: 0.0, 3: 0.0}

    See Also
    --------
    degree_centrality, in_degree_centrality

    Notes
    -----
    The degree centrality values are normalized by dividing by the maximum
    possible degree in a simple graph n-1 where n is the number of nodes in G.

    For multigraphs or graphs with self loops the maximum degree might
    be higher than n-1 and values of degree centrality greater than 1
    are possible.
    
    "
918,"def _read_html(self, file_url):
        
        with open(file_url.replace(""file://"", """").replace("" "", """")) as f:
            return f.read()

    if matplotlylib:
","Read and return the HTML contents from a file_url in the
        form e.g. file:///Users/chriddyp/Repos/plotly.py/plotly-temp.html
        ",14,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _read_html(self, file_url):
        
        with open(file_url.replace(""file://"", """").replace("" "", """")) as f:
            return f.read()

    if matplotlylib:

        ```
    ###Assistant : Read and return the HTML contents from a file_url in the
        form e.g. file:///Users/chriddyp/Repos/plotly.py/plotly-temp.html
        
    "
919,"def set_omp_num_threads_if_unset() -> bool:
    
    num_threads_from_env = os.environ.get(""OMP_NUM_THREADS"")
    if num_threads_from_env is not None:
        # No ops if it's set
        return False

    # If unset, try setting the correct CPU count assigned.
    runtime_ctx = ray.get_runtime_context()
    if runtime_ctx.worker.mode != ray._private.worker.WORKER_MODE:
        # Non worker mode, no ops.
        return False

    num_assigned_cpus = runtime_ctx.get_assigned_resources().get(""CPU"")

    if num_assigned_cpus is None:
        # This is an actor task w/o any num_cpus specified, set it to 1
        logger.debug(
            ""[ray] Forcing OMP_NUM_THREADS=1 to avoid performance ""
            ""degradation with many workers (issue #6998). You can override this ""
            ""by explicitly setting OMP_NUM_THREADS, or changing num_cpus.""
        )
        num_assigned_cpus = 1

    import math

    # For num_cpu < 1: Set to 1.
    # For num_cpus >= 1: Set to the floor of the actual assigned cpus.
    omp_num_threads = max(math.floor(num_assigned_cpus), 1)
    os.environ[""OMP_NUM_THREADS""] = str(omp_num_threads)
    return True

","Set the OMP_NUM_THREADS to default to num cpus assigned to the worker

    This function sets the environment variable OMP_NUM_THREADS for the worker,
    if the env is not previously set and it's running in worker (WORKER_MODE).

    Returns True if OMP_NUM_THREADS is set in this function.

    ",44,129,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_omp_num_threads_if_unset() -> bool:
    
    num_threads_from_env = os.environ.get(""OMP_NUM_THREADS"")
    if num_threads_from_env is not None:
        # No ops if it's set
        return False

    # If unset, try setting the correct CPU count assigned.
    runtime_ctx = ray.get_runtime_context()
    if runtime_ctx.worker.mode != ray._private.worker.WORKER_MODE:
        # Non worker mode, no ops.
        return False

    num_assigned_cpus = runtime_ctx.get_assigned_resources().get(""CPU"")

    if num_assigned_cpus is None:
        # This is an actor task w/o any num_cpus specified, set it to 1
        logger.debug(
            ""[ray] Forcing OMP_NUM_THREADS=1 to avoid performance ""
            ""degradation with many workers (issue #6998). You can override this ""
            ""by explicitly setting OMP_NUM_THREADS, or changing num_cpus.""
        )
        num_assigned_cpus = 1

    import math

    # For num_cpu < 1: Set to 1.
    # For num_cpus >= 1: Set to the floor of the actual assigned cpus.
    omp_num_threads = max(math.floor(num_assigned_cpus), 1)
    os.environ[""OMP_NUM_THREADS""] = str(omp_num_threads)
    return True


        ```
    ###Assistant : Set the OMP_NUM_THREADS to default to num cpus assigned to the worker

    This function sets the environment variable OMP_NUM_THREADS for the worker,
    if the env is not previously set and it's running in worker (WORKER_MODE).

    Returns True if OMP_NUM_THREADS is set in this function.

    
    "
920,"async def test_binary_device_classes(hass, hk_driver):
    
    entity_id = ""binary_sensor.demo""
    aid = 1

    for device_class, (service, char, _) in BINARY_SENSOR_SERVICE_MAP.items():
        hass.states.async_set(entity_id, STATE_OFF, {ATTR_DEVICE_CLASS: device_class})
        await hass.async_block_till_done()

        aid += 1
        acc = BinarySensor(hass, hk_driver, ""Binary Sensor"", entity_id, aid, None)
        assert acc.get_service(service).display_name == service
        assert acc.char_detected.display_name == char

",Test if services and characteristics are assigned correctly.,8,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_binary_device_classes(hass, hk_driver):
    
    entity_id = ""binary_sensor.demo""
    aid = 1

    for device_class, (service, char, _) in BINARY_SENSOR_SERVICE_MAP.items():
        hass.states.async_set(entity_id, STATE_OFF, {ATTR_DEVICE_CLASS: device_class})
        await hass.async_block_till_done()

        aid += 1
        acc = BinarySensor(hass, hk_driver, ""Binary Sensor"", entity_id, aid, None)
        assert acc.get_service(service).display_name == service
        assert acc.char_detected.display_name == char


        ```
    ###Assistant : Test if services and characteristics are assigned correctly.
    "
921,"def Internaldate2tuple(resp):
    

    mo = InternalDate.match(resp)
    if not mo:
        return None

    mon = Mon2num[mo.group('mon')]
    zonen = mo.group('zonen')

    day = int(mo.group('day'))
    year = int(mo.group('year'))
    hour = int(mo.group('hour'))
    min = int(mo.group('min'))
    sec = int(mo.group('sec'))
    zoneh = int(mo.group('zoneh'))
    zonem = int(mo.group('zonem'))

    # INTERNALDATE timezone must be subtracted to get UT

    zone = (zoneh*60 + zonem)*60
    if zonen == b'-':
        zone = -zone

    tt = (year, mon, day, hour, min, sec, -1, -1, -1)
    utc = calendar.timegm(tt) - zone

    return time.localtime(utc)


","Parse an IMAP4 INTERNALDATE string.

    Return corresponding local time.  The return value is a
    time.struct_time tuple or None if the string has wrong format.
    ",24,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def Internaldate2tuple(resp):
    

    mo = InternalDate.match(resp)
    if not mo:
        return None

    mon = Mon2num[mo.group('mon')]
    zonen = mo.group('zonen')

    day = int(mo.group('day'))
    year = int(mo.group('year'))
    hour = int(mo.group('hour'))
    min = int(mo.group('min'))
    sec = int(mo.group('sec'))
    zoneh = int(mo.group('zoneh'))
    zonem = int(mo.group('zonem'))

    # INTERNALDATE timezone must be subtracted to get UT

    zone = (zoneh*60 + zonem)*60
    if zonen == b'-':
        zone = -zone

    tt = (year, mon, day, hour, min, sec, -1, -1, -1)
    utc = calendar.timegm(tt) - zone

    return time.localtime(utc)



        ```
    ###Assistant : Parse an IMAP4 INTERNALDATE string.

    Return corresponding local time.  The return value is a
    time.struct_time tuple or None if the string has wrong format.
    
    "
922,"def test_delete_get(self):
        
        # Send request
        response = self.client.get(
            reverse(""wagtaildocs:delete_multiple"", args=(self.doc.id,))
        )

        # Check response
        self.assertEqual(response.status_code, 405)
","
        This tests that a GET request to the delete view returns a 405 ""METHOD NOT ALLOWED"" response
        ",17,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_get(self):
        
        # Send request
        response = self.client.get(
            reverse(""wagtaildocs:delete_multiple"", args=(self.doc.id,))
        )

        # Check response
        self.assertEqual(response.status_code, 405)

        ```
    ###Assistant : 
        This tests that a GET request to the delete view returns a 405 ""METHOD NOT ALLOWED"" response
        
    "
923,"def test_stream_admin_remove_others_from_public_stream(self) -> None:
        
        result = self.attempt_unsubscribe_of_principal(
            query_count=15,
            target_users=[self.example_user(""cordelia"")],
            is_realm_admin=False,
            is_stream_admin=True,
            is_subbed=True,
            invite_only=False,
            target_users_subbed=True,
        )
        json = self.assert_json_success(result)
        self.assert_length(json[""removed""], 1)
        self.assert_length(json[""not_removed""], 0)
","
        You can remove others from public streams you're a stream administrator of.
        ",12,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stream_admin_remove_others_from_public_stream(self) -> None:
        
        result = self.attempt_unsubscribe_of_principal(
            query_count=15,
            target_users=[self.example_user(""cordelia"")],
            is_realm_admin=False,
            is_stream_admin=True,
            is_subbed=True,
            invite_only=False,
            target_users_subbed=True,
        )
        json = self.assert_json_success(result)
        self.assert_length(json[""removed""], 1)
        self.assert_length(json[""not_removed""], 0)

        ```
    ###Assistant : 
        You can remove others from public streams you're a stream administrator of.
        
    "
924,"def _mosaic_combine(self, loc, center_position_xy, img_shape_wh):
        
        assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
        if loc == 'top_left':
            # index0 to top left part of image
            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
                max(center_position_xy[1] - img_shape_wh[1], 0), \
                center_position_xy[0], \
                center_position_xy[1]
            crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
                y2 - y1), img_shape_wh[0], img_shape_wh[1]

        elif loc == 'top_right':
            # index1 to top right part of image
            x1, y1, x2, y2 = center_position_xy[0], \
                max(center_position_xy[1] - img_shape_wh[1], 0), \
                min(center_position_xy[0] + img_shape_wh[0],
                    self.img_scale[1] * 2), \
                center_position_xy[1]
            crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
                img_shape_wh[0], x2 - x1), img_shape_wh[1]

        elif loc == 'bottom_left':
            # index2 to bottom left part of image
            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
                center_position_xy[1], \
                center_position_xy[0], \
                min(self.img_scale[0] * 2, center_position_xy[1] +
                    img_shape_wh[1])
            crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
                y2 - y1, img_shape_wh[1])

        else:
            # index3 to bottom right part of image
            x1, y1, x2, y2 = center_position_xy[0], \
                center_position_xy[1], \
                min(center_position_xy[0] + img_shape_wh[0],
                    self.img_scale[1] * 2), \
                min(self.img_scale[0] * 2, center_position_xy[1] +
                    img_shape_wh[1])
            crop_coord = 0, 0, min(img_shape_wh[0],
                                   x2 - x1), min(y2 - y1, img_shape_wh[1])

        paste_coord = x1, y1, x2, y2
        return paste_coord, crop_coord
","Calculate global coordinate of mosaic image and local coordinate of
        cropped sub-image.

        Args:
            loc (str): Index for the sub-image, loc in ('top_left',
              'top_right', 'bottom_left', 'bottom_right').
            center_position_xy (Sequence[float]): Mixing center for 4 images,
                (x, y).
            img_shape_wh (Sequence[int]): Width and height of sub-image

        Returns:
            tuple[tuple[float]]: Corresponding coordinate of pasting and
                cropping
                - paste_coord (tuple): paste corner coordinate in mosaic image.
                - crop_coord (tuple): crop corner coordinate in mosaic image.
        ",67,201,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _mosaic_combine(self, loc, center_position_xy, img_shape_wh):
        
        assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
        if loc == 'top_left':
            # index0 to top left part of image
            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
                max(center_position_xy[1] - img_shape_wh[1], 0), \
                center_position_xy[0], \
                center_position_xy[1]
            crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
                y2 - y1), img_shape_wh[0], img_shape_wh[1]

        elif loc == 'top_right':
            # index1 to top right part of image
            x1, y1, x2, y2 = center_position_xy[0], \
                max(center_position_xy[1] - img_shape_wh[1], 0), \
                min(center_position_xy[0] + img_shape_wh[0],
                    self.img_scale[1] * 2), \
                center_position_xy[1]
            crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
                img_shape_wh[0], x2 - x1), img_shape_wh[1]

        elif loc == 'bottom_left':
            # index2 to bottom left part of image
            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
                center_position_xy[1], \
                center_position_xy[0], \
                min(self.img_scale[0] * 2, center_position_xy[1] +
                    img_shape_wh[1])
            crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
                y2 - y1, img_shape_wh[1])

        else:
            # index3 to bottom right part of image
            x1, y1, x2, y2 = center_position_xy[0], \
                center_position_xy[1], \
                min(center_position_xy[0] + img_shape_wh[0],
                    self.img_scale[1] * 2), \
                min(self.img_scale[0] * 2, center_position_xy[1] +
                    img_shape_wh[1])
            crop_coord = 0, 0, min(img_shape_wh[0],
                                   x2 - x1), min(y2 - y1, img_shape_wh[1])

        paste_coord = x1, y1, x2, y2
        return paste_coord, crop_coord

        ```
    ###Assistant : Calculate global coordinate of mosaic image and local coordinate of
        cropped sub-image.

        Args:
            loc (str): Index for the sub-image, loc in ('top_left',
              'top_right', 'bottom_left', 'bottom_right').
            center_position_xy (Sequence[float]): Mixing center for 4 images,
                (x, y).
            img_shape_wh (Sequence[int]): Width and height of sub-image

        Returns:
            tuple[tuple[float]]: Corresponding coordinate of pasting and
                cropping
                - paste_coord (tuple): paste corner coordinate in mosaic image.
                - crop_coord (tuple): crop corner coordinate in mosaic image.
        
    "
925,"def permute(self, perm, orientation='rows', direction='forward'):
        r
        from sympy.combinatorics import Permutation

        # allow british variants and `columns`
        if direction == 'forwards':
            direction = 'forward'
        if direction == 'backwards':
            direction = 'backward'
        if orientation == 'columns':
            orientation = 'cols'

        if direction not in ('forward', 'backward'):
            raise TypeError(""direction='{}' is an invalid kwarg. ""
                            ""Try 'forward' or 'backward'"".format(direction))
        if orientation not in ('rows', 'cols'):
            raise TypeError(""orientation='{}' is an invalid kwarg. ""
                            ""Try 'rows' or 'cols'"".format(orientation))

        if not isinstance(perm, (Permutation, Iterable)):
            raise ValueError(
                ""{} must be a list, a list of lists, ""
                ""or a SymPy permutation object."".format(perm))

        # ensure all swaps are in range
        max_index = self.rows if orientation == 'rows' else self.cols
        if not all(0 <= t <= max_index for t in flatten(list(perm))):
            raise IndexError(""`swap` indices out of range."")

        if perm and not isinstance(perm, Permutation) and \
            isinstance(perm[0], Iterable):
            if direction == 'forward':
                perm = list(reversed(perm))
            perm = Permutation(perm, size=max_index+1)
        else:
            perm = Permutation(perm, size=max_index+1)

        if orientation == 'rows':
            return self._eval_permute_rows(perm)
        if orientation == 'cols':
            return self._eval_permute_cols(perm)
","Permute the rows or columns of a matrix by the given list of
        swaps.

        Parameters
        ==========

        perm : Permutation, list, or list of lists
            A representation for the permutation.

            If it is ``Permutation``, it is used directly with some
            resizing with respect to the matrix size.

            If it is specified as list of lists,
            (e.g., ``[[0, 1], [0, 2]]``), then the permutation is formed
            from applying the product of cycles. The direction how the
            cyclic product is applied is described in below.

            If it is specified as a list, the list should represent
            an array form of a permutation. (e.g., ``[1, 2, 0]``) which
            would would form the swapping function
            `0 \mapsto 1, 1 \mapsto 2, 2\mapsto 0`.

        orientation : 'rows', 'cols'
            A flag to control whether to permute the rows or the columns

        direction : 'forward', 'backward'
            A flag to control whether to apply the permutations from
            the start of the list first, or from the back of the list
            first.

            For example, if the permutation specification is
            ``[[0, 1], [0, 2]]``,

            If the flag is set to ``'forward'``, the cycle would be
            formed as `0 \mapsto 2, 2 \mapsto 1, 1 \mapsto 0`.

            If the flag is set to ``'backward'``, the cycle would be
            formed as `0 \mapsto 1, 1 \mapsto 2, 2 \mapsto 0`.

            If the argument ``perm`` is not in a form of list of lists,
            this flag takes no effect.

        Examples
        ========

        >>> from sympy import eye
        >>> M = eye(3)
        >>> M.permute([[0, 1], [0, 2]], orientation='rows', direction='forward')
        Matrix([
        [0, 0, 1],
        [1, 0, 0],
        [0, 1, 0]])

        >>> from sympy import eye
        >>> M = eye(3)
        >>> M.permute([[0, 1], [0, 2]], orientation='rows', direction='backward')
        Matrix([
        [0, 1, 0],
        [0, 0, 1],
        [1, 0, 0]])

        Notes
        =====

        If a bijective function
        `\sigma : \mathbb{N}_0 \rightarrow \mathbb{N}_0` denotes the
        permutation.

        If the matrix `A` is the matrix to permute, represented as
        a horizontal or a vertical stack of vectors:

        .. math::
            A =
            \begin{bmatrix}
            a_0 \\ a_1 \\ \vdots \\ a_{n-1}
            \end{bmatrix} =
            \begin{bmatrix}
            \alpha_0 & \alpha_1 & \cdots & \alpha_{n-1}
            \end{bmatrix}

        If the matrix `B` is the result, the permutation of matrix rows
        is defined as:

        .. math::
            B := \begin{bmatrix}
            a_{\sigma(0)} \\ a_{\sigma(1)} \\ \vdots \\ a_{\sigma(n-1)}
            \end{bmatrix}

        And the permutation of matrix columns is defined as:

        .. math::
            B := \begin{bmatrix}
            \alpha_{\sigma(0)} & \alpha_{\sigma(1)} &
            \cdots & \alpha_{\sigma(n-1)}
            \end{bmatrix}
        ",395,164,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def permute(self, perm, orientation='rows', direction='forward'):
        r
        from sympy.combinatorics import Permutation

        # allow british variants and `columns`
        if direction == 'forwards':
            direction = 'forward'
        if direction == 'backwards':
            direction = 'backward'
        if orientation == 'columns':
            orientation = 'cols'

        if direction not in ('forward', 'backward'):
            raise TypeError(""direction='{}' is an invalid kwarg. ""
                            ""Try 'forward' or 'backward'"".format(direction))
        if orientation not in ('rows', 'cols'):
            raise TypeError(""orientation='{}' is an invalid kwarg. ""
                            ""Try 'rows' or 'cols'"".format(orientation))

        if not isinstance(perm, (Permutation, Iterable)):
            raise ValueError(
                ""{} must be a list, a list of lists, ""
                ""or a SymPy permutation object."".format(perm))

        # ensure all swaps are in range
        max_index = self.rows if orientation == 'rows' else self.cols
        if not all(0 <= t <= max_index for t in flatten(list(perm))):
            raise IndexError(""`swap` indices out of range."")

        if perm and not isinstance(perm, Permutation) and \
            isinstance(perm[0], Iterable):
            if direction == 'forward':
                perm = list(reversed(perm))
            perm = Permutation(perm, size=max_index+1)
        else:
            perm = Permutation(perm, size=max_index+1)

        if orientation == 'rows':
            return self._eval_permute_rows(perm)
        if orientation == 'cols':
            return self._eval_permute_cols(perm)

        ```
    ###Assistant : Permute the rows or columns of a matrix by the given list of
        swaps.

        Parameters
        ==========

        perm : Permutation, list, or list of lists
            A representation for the permutation.

            If it is ``Permutation``, it is used directly with some
            resizing with respect to the matrix size.

            If it is specified as list of lists,
            (e.g., ``[[0, 1], [0, 2]]``), then the permutation is formed
            from applying the product of cycles. The direction how the
            cyclic product is applied is described in below.

            If it is specified as a list, the list should represent
            an array form of a permutation. (e.g., ``[1, 2, 0]``) which
            would would form the swapping function
            `0 \mapsto 1, 1 \mapsto 2, 2\mapsto 0`.

        orientation : 'rows', 'cols'
            A flag to control whether to permute the rows or the columns

        direction : 'forward', 'backward'
            A flag to control whether to apply the permutations from
            the start of the list first, or from the back of the list
            first.

            For example, if the permutation specification is
            ``[[0, 1], [0, 2]]``,

            If the flag is set to ``'forward'``, the cycle would be
            formed as `0 \mapsto 2, 2 \mapsto 1, 1 \mapsto 0`.

            If the flag is set to ``'backward'``, the cycle would be
            formed as `0 \mapsto 1, 1 \mapsto 2, 2 \mapsto 0`.

            If the argument ``perm`` is not in a form of list of lists,
            this flag takes no effect.

        Examples
        ========

        >>> from sympy import eye
        >>> M = eye(3)
        >>> M.permute([[0, 1], [0, 2]], orientation='rows', direction='forward')
        Matrix([
        [0, 0, 1],
        [1, 0, 0],
        [0, 1, 0]])

        >>> from sympy import eye
        >>> M = eye(3)
        >>> M.permute([[0, 1], [0, 2]], orientation='rows', direction='backward')
        Matrix([
        [0, 1, 0],
        [0, 0, 1],
        [1, 0, 0]])

        Notes
        =====

        If a bijective function
        `\sigma : \mathbb{N}_0 \rightarrow \mathbb{N}_0` denotes the
        permutation.

        If the matrix `A` is the matrix to permute, represented as
        a horizontal or a vertical stack of vectors:

        .. math::
            A =
            \begin{bmatrix}
            a_0 \\ a_1 \\ \vdots \\ a_{n-1}
            \end{bmatrix} =
            \begin{bmatrix}
            \alpha_0 & \alpha_1 & \cdots & \alpha_{n-1}
            \end{bmatrix}

        If the matrix `B` is the result, the permutation of matrix rows
        is defined as:

        .. math::
            B := \begin{bmatrix}
            a_{\sigma(0)} \\ a_{\sigma(1)} \\ \vdots \\ a_{\sigma(n-1)}
            \end{bmatrix}

        And the permutation of matrix columns is defined as:

        .. math::
            B := \begin{bmatrix}
            \alpha_{\sigma(0)} & \alpha_{\sigma(1)} &
            \cdots & \alpha_{\sigma(n-1)}
            \end{bmatrix}
        
    "
926,"def apply(self, func, *args, **kwargs):
        
        func(self, *args, **kwargs)
        return self
","
        Pass the grid to a user-supplied function and return self.

        The `func` must accept an object of this type for its first
        positional argument. Additional arguments are passed through.
        The return value of `func` is ignored; this method returns self.
        See the `pipe` method if you want the return value.

        Added in v0.12.0.

        ",53,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply(self, func, *args, **kwargs):
        
        func(self, *args, **kwargs)
        return self

        ```
    ###Assistant : 
        Pass the grid to a user-supplied function and return self.

        The `func` must accept an object of this type for its first
        positional argument. Additional arguments are passed through.
        The return value of `func` is ignored; this method returns self.
        See the `pipe` method if you want the return value.

        Added in v0.12.0.

        
    "
927,"def copy_m2m_relationships(obj1, obj2, fields, kwargs=None):
    
    for field_name in fields:
        if hasattr(obj1, field_name):
            try:
                field_obj = obj1._meta.get_field(field_name)
            except FieldDoesNotExist:
                continue
            if isinstance(field_obj, ManyToManyField):
                # Many to Many can be specified as field_name
                src_field_value = getattr(obj1, field_name)
                if kwargs and field_name in kwargs:
                    override_field_val = kwargs[field_name]
                    # TODO: Should we spike this our or just put the for loop inside the next if and make everything respect order?
                    if field_name == 'instance_groups':
                        # instance_groups are a list but we need to preserve the order
                        for ig_id in override_field_val:
                            getattr(obj2, field_name).add(ig_id)
                        continue
                    if isinstance(override_field_val, (set, list, QuerySet)):
                        getattr(obj2, field_name).add(*override_field_val)
                        continue
                    if override_field_val.__class__.__name__ == 'ManyRelatedManager':
                        src_field_value = override_field_val
                dest_field = getattr(obj2, field_name)
                dest_field.add(*list(src_field_value.all().values_list('id', flat=True)))

","
    In-place operation.
    Given two saved objects, copies related objects from obj1
    to obj2 to field of same name, if field occurs in `fields`
    ",23,110,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copy_m2m_relationships(obj1, obj2, fields, kwargs=None):
    
    for field_name in fields:
        if hasattr(obj1, field_name):
            try:
                field_obj = obj1._meta.get_field(field_name)
            except FieldDoesNotExist:
                continue
            if isinstance(field_obj, ManyToManyField):
                # Many to Many can be specified as field_name
                src_field_value = getattr(obj1, field_name)
                if kwargs and field_name in kwargs:
                    override_field_val = kwargs[field_name]
                    # TODO: Should we spike this our or just put the for loop inside the next if and make everything respect order?
                    if field_name == 'instance_groups':
                        # instance_groups are a list but we need to preserve the order
                        for ig_id in override_field_val:
                            getattr(obj2, field_name).add(ig_id)
                        continue
                    if isinstance(override_field_val, (set, list, QuerySet)):
                        getattr(obj2, field_name).add(*override_field_val)
                        continue
                    if override_field_val.__class__.__name__ == 'ManyRelatedManager':
                        src_field_value = override_field_val
                dest_field = getattr(obj2, field_name)
                dest_field.add(*list(src_field_value.all().values_list('id', flat=True)))


        ```
    ###Assistant : 
    In-place operation.
    Given two saved objects, copies related objects from obj1
    to obj2 to field of same name, if field occurs in `fields`
    
    "
928,"def load_from_pipeline(pipeline):
    
    try:
        import transformers
    except ImportError:
        raise ImportError(
            ""transformers not installed. Please try `pip install transformers`""
        )
    if not isinstance(pipeline, transformers.Pipeline):
        raise ValueError(""pipeline must be a transformers.Pipeline"")

    # Handle the different pipelines. The has_attr() checks to make sure the pipeline exists in the
    # version of the transformers library that the user has installed.
    if hasattr(transformers, ""AudioClassificationPipeline"") and isinstance(
        pipeline, transformers.AudioClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Audio(label=""Input"", source=""microphone"", type=""filepath""),
            ""outputs"": outputs.Label(label=""Class"", type=""confidences""),
            ""preprocess"": lambda i: {""inputs"": i},
            ""postprocess"": lambda r: {i[""label""].split("", "")[0]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""AutomaticSpeechRecognitionPipeline"") and isinstance(
        pipeline, transformers.AutomaticSpeechRecognitionPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Audio(label=""Input"", source=""microphone"", type=""filepath""),
            ""outputs"": outputs.Textbox(label=""Output""),
            ""preprocess"": lambda i: {""inputs"": i},
            ""postprocess"": lambda r: r[""text""],
        }
    elif hasattr(transformers, ""FeatureExtractionPipeline"") and isinstance(
        pipeline, transformers.FeatureExtractionPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Dataframe(label=""Output""),
            ""preprocess"": lambda x: {""inputs"": x},
            ""postprocess"": lambda r: r[0],
        }
    elif hasattr(transformers, ""FillMaskPipeline"") and isinstance(
        pipeline, transformers.FillMaskPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda x: {""inputs"": x},
            ""postprocess"": lambda r: {i[""token_str""]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""ImageClassificationPipeline"") and isinstance(
        pipeline, transformers.ImageClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Image(label=""Input Image"", type=""filepath""),
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda i: {""images"": i},
            ""postprocess"": lambda r: {i[""label""].split("", "")[0]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""QuestionAnsweringPipeline"") and isinstance(
        pipeline, transformers.QuestionAnsweringPipeline
    ):
        pipeline_info = {
            ""inputs"": [
                inputs.Textbox(label=""Context"", lines=7),
                inputs.Textbox(label=""Question""),
            ],
            ""outputs"": [outputs.Textbox(label=""Answer""), outputs.Label(label=""Score"")],
            ""preprocess"": lambda c, q: {""context"": c, ""question"": q},
            ""postprocess"": lambda r: (r[""answer""], r[""score""]),
        }
    elif hasattr(transformers, ""SummarizationPipeline"") and isinstance(
        pipeline, transformers.SummarizationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input"", lines=7),
            ""outputs"": outputs.Textbox(label=""Summary""),
            ""preprocess"": lambda x: {""inputs"": x},
            ""postprocess"": lambda r: r[0][""summary_text""],
        }
    elif hasattr(transformers, ""TextClassificationPipeline"") and isinstance(
        pipeline, transformers.TextClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda x: [x],
            ""postprocess"": lambda r: {i[""label""].split("", "")[0]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""TextGenerationPipeline"") and isinstance(
        pipeline, transformers.TextGenerationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Textbox(label=""Output""),
            ""preprocess"": lambda x: {""text_inputs"": x},
            ""postprocess"": lambda r: r[0][""generated_text""],
        }
    elif hasattr(transformers, ""TranslationPipeline"") and isinstance(
        pipeline, transformers.TranslationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Textbox(label=""Translation""),
            ""preprocess"": lambda x: [x],
            ""postprocess"": lambda r: r[0][""translation_text""],
        }
    elif hasattr(transformers, ""Text2TextGenerationPipeline"") and isinstance(
        pipeline, transformers.Text2TextGenerationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Textbox(label=""Generated Text""),
            ""preprocess"": lambda x: [x],
            ""postprocess"": lambda r: r[0][""generated_text""],
        }
    elif hasattr(transformers, ""ZeroShotClassificationPipeline"") and isinstance(
        pipeline, transformers.ZeroShotClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": [
                inputs.Textbox(label=""Input""),
                inputs.Textbox(label=""Possible class names ("" ""comma-separated)""),
                inputs.Checkbox(label=""Allow multiple true classes""),
            ],
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda i, c, m: {
                ""sequences"": i,
                ""candidate_labels"": c,
                ""multi_label"": m,
            },
            ""postprocess"": lambda r: {
                r[""labels""][i]: r[""scores""][i] for i in range(len(r[""labels""]))
            },
        }
    else:
        raise ValueError(""Unsupported pipeline type: {}"".format(type(pipeline)))

    # define the function that will be called by the Interface","
    Gets the appropriate Interface kwargs for a given Hugging Face transformers.Pipeline.
    pipeline (transformers.Pipeline): the transformers.Pipeline from which to create an interface
    Returns:
    (dict): a dictionary of kwargs that can be used to construct an Interface object
    ",36,440,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_from_pipeline(pipeline):
    
    try:
        import transformers
    except ImportError:
        raise ImportError(
            ""transformers not installed. Please try `pip install transformers`""
        )
    if not isinstance(pipeline, transformers.Pipeline):
        raise ValueError(""pipeline must be a transformers.Pipeline"")

    # Handle the different pipelines. The has_attr() checks to make sure the pipeline exists in the
    # version of the transformers library that the user has installed.
    if hasattr(transformers, ""AudioClassificationPipeline"") and isinstance(
        pipeline, transformers.AudioClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Audio(label=""Input"", source=""microphone"", type=""filepath""),
            ""outputs"": outputs.Label(label=""Class"", type=""confidences""),
            ""preprocess"": lambda i: {""inputs"": i},
            ""postprocess"": lambda r: {i[""label""].split("", "")[0]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""AutomaticSpeechRecognitionPipeline"") and isinstance(
        pipeline, transformers.AutomaticSpeechRecognitionPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Audio(label=""Input"", source=""microphone"", type=""filepath""),
            ""outputs"": outputs.Textbox(label=""Output""),
            ""preprocess"": lambda i: {""inputs"": i},
            ""postprocess"": lambda r: r[""text""],
        }
    elif hasattr(transformers, ""FeatureExtractionPipeline"") and isinstance(
        pipeline, transformers.FeatureExtractionPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Dataframe(label=""Output""),
            ""preprocess"": lambda x: {""inputs"": x},
            ""postprocess"": lambda r: r[0],
        }
    elif hasattr(transformers, ""FillMaskPipeline"") and isinstance(
        pipeline, transformers.FillMaskPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda x: {""inputs"": x},
            ""postprocess"": lambda r: {i[""token_str""]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""ImageClassificationPipeline"") and isinstance(
        pipeline, transformers.ImageClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Image(label=""Input Image"", type=""filepath""),
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda i: {""images"": i},
            ""postprocess"": lambda r: {i[""label""].split("", "")[0]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""QuestionAnsweringPipeline"") and isinstance(
        pipeline, transformers.QuestionAnsweringPipeline
    ):
        pipeline_info = {
            ""inputs"": [
                inputs.Textbox(label=""Context"", lines=7),
                inputs.Textbox(label=""Question""),
            ],
            ""outputs"": [outputs.Textbox(label=""Answer""), outputs.Label(label=""Score"")],
            ""preprocess"": lambda c, q: {""context"": c, ""question"": q},
            ""postprocess"": lambda r: (r[""answer""], r[""score""]),
        }
    elif hasattr(transformers, ""SummarizationPipeline"") and isinstance(
        pipeline, transformers.SummarizationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input"", lines=7),
            ""outputs"": outputs.Textbox(label=""Summary""),
            ""preprocess"": lambda x: {""inputs"": x},
            ""postprocess"": lambda r: r[0][""summary_text""],
        }
    elif hasattr(transformers, ""TextClassificationPipeline"") and isinstance(
        pipeline, transformers.TextClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda x: [x],
            ""postprocess"": lambda r: {i[""label""].split("", "")[0]: i[""score""] for i in r},
        }
    elif hasattr(transformers, ""TextGenerationPipeline"") and isinstance(
        pipeline, transformers.TextGenerationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Textbox(label=""Output""),
            ""preprocess"": lambda x: {""text_inputs"": x},
            ""postprocess"": lambda r: r[0][""generated_text""],
        }
    elif hasattr(transformers, ""TranslationPipeline"") and isinstance(
        pipeline, transformers.TranslationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Textbox(label=""Translation""),
            ""preprocess"": lambda x: [x],
            ""postprocess"": lambda r: r[0][""translation_text""],
        }
    elif hasattr(transformers, ""Text2TextGenerationPipeline"") and isinstance(
        pipeline, transformers.Text2TextGenerationPipeline
    ):
        pipeline_info = {
            ""inputs"": inputs.Textbox(label=""Input""),
            ""outputs"": outputs.Textbox(label=""Generated Text""),
            ""preprocess"": lambda x: [x],
            ""postprocess"": lambda r: r[0][""generated_text""],
        }
    elif hasattr(transformers, ""ZeroShotClassificationPipeline"") and isinstance(
        pipeline, transformers.ZeroShotClassificationPipeline
    ):
        pipeline_info = {
            ""inputs"": [
                inputs.Textbox(label=""Input""),
                inputs.Textbox(label=""Possible class names ("" ""comma-separated)""),
                inputs.Checkbox(label=""Allow multiple true classes""),
            ],
            ""outputs"": outputs.Label(label=""Classification"", type=""confidences""),
            ""preprocess"": lambda i, c, m: {
                ""sequences"": i,
                ""candidate_labels"": c,
                ""multi_label"": m,
            },
            ""postprocess"": lambda r: {
                r[""labels""][i]: r[""scores""][i] for i in range(len(r[""labels""]))
            },
        }
    else:
        raise ValueError(""Unsupported pipeline type: {}"".format(type(pipeline)))

    # define the function that will be called by the Interface
        ```
    ###Assistant : 
    Gets the appropriate Interface kwargs for a given Hugging Face transformers.Pipeline.
    pipeline (transformers.Pipeline): the transformers.Pipeline from which to create an interface
    Returns:
    (dict): a dictionary of kwargs that can be used to construct an Interface object
    
    "
929,"def calculate_bounds_for_mechanism(value_array, min_val_array, max_val_array):
    
    

    # TODO: Double check whether the iDPGaussianMechanism class squares its squared_l2_norm values!!
    worst_case_l2_norm = np.sqrt(np.sum(np.square(max_val_array - min_val_array))) * np.ones_like(value_array)
    l2_norm = np.sqrt(np.sum(np.square(value_array))) * np.ones_like(value_array)
    # print(l2_norm.shape, worst_case_l2_norm.shape)
    # print(l2_norm.shape)
    return l2_norm, worst_case_l2_norm

","Calculates the squared L2 norm values needed to create a Mechanism, and calculate privacy budget + spend If you calculate the privacy budget spend with the worst case bound, you can show this number to the D.S.
    If you calculate it with the regular value (the value computed below when public_only = False, you cannot show the 
    privacy budget to the DS because this violates privacy.
    ",66,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def calculate_bounds_for_mechanism(value_array, min_val_array, max_val_array):
    
    

    # TODO: Double check whether the iDPGaussianMechanism class squares its squared_l2_norm values!!
    worst_case_l2_norm = np.sqrt(np.sum(np.square(max_val_array - min_val_array))) * np.ones_like(value_array)
    l2_norm = np.sqrt(np.sum(np.square(value_array))) * np.ones_like(value_array)
    # print(l2_norm.shape, worst_case_l2_norm.shape)
    # print(l2_norm.shape)
    return l2_norm, worst_case_l2_norm


        ```
    ###Assistant : Calculates the squared L2 norm values needed to create a Mechanism, and calculate privacy budget + spend If you calculate the privacy budget spend with the worst case bound, you can show this number to the D.S.
    If you calculate it with the regular value (the value computed below when public_only = False, you cannot show the 
    privacy budget to the DS because this violates privacy.
    
    "
930,"def get_po_entries(conditions):
	return frappe.db.sql(
		.format(
			conditions=conditions
		),
		as_dict=1,
	)  # nosec
","
		SELECT
			child.name,
			child.parent,
			child.cost_center,
			child.project,
			child.warehouse,
			child.material_request,
			child.material_request_item,
			child.item_code,
			child.stock_uom,
			child.qty,
			child.amount,
			child.base_amount,
			child.schedule_date,
			parent.transaction_date,
			parent.supplier,
			parent.status,
			parent.owner
		FROM `tabPurchase Order` parent, `tabPurchase Order Item` child
		WHERE
			parent.docstatus = 1
			AND parent.name = child.parent
			AND parent.status not in  (""Closed"",""Completed"",""Cancelled"")
			{conditions}
		GROUP BY
			parent.name, child.item_code
		",44,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_po_entries(conditions):
	return frappe.db.sql(
		.format(
			conditions=conditions
		),
		as_dict=1,
	)  # nosec

        ```
    ###Assistant : 
		SELECT
			child.name,
			child.parent,
			child.cost_center,
			child.project,
			child.warehouse,
			child.material_request,
			child.material_request_item,
			child.item_code,
			child.stock_uom,
			child.qty,
			child.amount,
			child.base_amount,
			child.schedule_date,
			parent.transaction_date,
			parent.supplier,
			parent.status,
			parent.owner
		FROM `tabPurchase Order` parent, `tabPurchase Order Item` child
		WHERE
			parent.docstatus = 1
			AND parent.name = child.parent
			AND parent.status not in  (""Closed"",""Completed"",""Cancelled"")
			{conditions}
		GROUP BY
			parent.name, child.item_code
		
    "
931,"def get_year(self):
        
        year = self.year
        if year is None:
            try:
                year = self.kwargs[""year""]
            except KeyError:
                try:
                    year = self.request.GET[""year""]
                except KeyError:
                    raise Http404(_(""No year specified""))
        return year
",Return the year for which this view should display data.,10,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_year(self):
        
        year = self.year
        if year is None:
            try:
                year = self.kwargs[""year""]
            except KeyError:
                try:
                    year = self.request.GET[""year""]
                except KeyError:
                    raise Http404(_(""No year specified""))
        return year

        ```
    ###Assistant : Return the year for which this view should display data.
    "
932,"def test_overwrite_storage_path(self):
        
        call_command(""document_retagger"", ""--storage_path"", ""--overwrite"")
        d_first, d_second, d_unrelated, d_auto = self.get_updated_docs()

        self.assertEqual(d_first.storage_path, self.sp2)
        self.assertEqual(d_auto.storage_path, self.sp1)
        self.assertIsNone(d_second.storage_path)
        self.assertEqual(d_unrelated.storage_path, self.sp2)
","
        GIVEN:
            - 2 storage paths with documents which match them
            - 1 document which matches but has a storage path
        WHEN:
            - document retagger is called with overwrite
        THEN:
            - Matching document's storage paths updated
            - Non-matching documents have no storage path
            - Existing storage patch overwritten
        ",47,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_overwrite_storage_path(self):
        
        call_command(""document_retagger"", ""--storage_path"", ""--overwrite"")
        d_first, d_second, d_unrelated, d_auto = self.get_updated_docs()

        self.assertEqual(d_first.storage_path, self.sp2)
        self.assertEqual(d_auto.storage_path, self.sp1)
        self.assertIsNone(d_second.storage_path)
        self.assertEqual(d_unrelated.storage_path, self.sp2)

        ```
    ###Assistant : 
        GIVEN:
            - 2 storage paths with documents which match them
            - 1 document which matches but has a storage path
        WHEN:
            - document retagger is called with overwrite
        THEN:
            - Matching document's storage paths updated
            - Non-matching documents have no storage path
            - Existing storage patch overwritten
        
    "
933,"def invert(self):
        
        return DataFrameDefault.register(pandas.DataFrame.__invert__)(self)
","
        Apply bitwise inversion for each element of the QueryCompiler.

        Returns
        -------
        BaseQueryCompiler
            New QueryCompiler containing bitwise inversion for each value.
        ",20,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def invert(self):
        
        return DataFrameDefault.register(pandas.DataFrame.__invert__)(self)

        ```
    ###Assistant : 
        Apply bitwise inversion for each element of the QueryCompiler.

        Returns
        -------
        BaseQueryCompiler
            New QueryCompiler containing bitwise inversion for each value.
        
    "
934,"def test_center_head_loss(self):
        
        s = 256
        img_metas = [{'batch_input_shape': (s, s, 3)}]
        test_cfg = dict(topK=100, max_per_img=100)
        centernet_head = CenterNetHead(
            num_classes=4, in_channels=1, feat_channels=4, test_cfg=test_cfg)

        feat = [torch.rand(1, 1, s, s)]
        center_out, wh_out, offset_out = centernet_head.forward(feat)
        # Test that empty ground truth encourages the network to
        # predict background
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.empty((0, 4))
        gt_instances.labels = torch.LongTensor([])
        empty_gt_losses = centernet_head.loss(center_out, wh_out, offset_out,
                                              [gt_instances], img_metas)
        loss_center = empty_gt_losses['loss_center_heatmap']
        loss_wh = empty_gt_losses['loss_wh']
        loss_offset = empty_gt_losses['loss_offset']
        assert loss_center.item() > 0, 'loss_center should be non-zero'
        assert loss_wh.item() == 0, (
            'there should be no loss_wh when there are no true boxes')
        assert loss_offset.item() == 0, (
            'there should be no loss_offset when there are no true boxes')

        # When truth is non-empty then both cls and box loss
        # should be nonzero for random inputs
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.Tensor(
            [[23.6667, 23.8757, 238.6326, 151.8874]])
        gt_instances.labels = torch.LongTensor([2])
        one_gt_losses = centernet_head.loss(center_out, wh_out, offset_out,
                                            [gt_instances], img_metas)
        loss_center = one_gt_losses['loss_center_heatmap']
        loss_wh = one_gt_losses['loss_wh']
        loss_offset = one_gt_losses['loss_offset']
        assert loss_center.item() > 0, 'loss_center should be non-zero'
        assert loss_wh.item() > 0, 'loss_wh should be non-zero'
        assert loss_offset.item() > 0, 'loss_offset should be non-zero'
",Tests center head loss when truth is empty and non-empty.,10,183,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_center_head_loss(self):
        
        s = 256
        img_metas = [{'batch_input_shape': (s, s, 3)}]
        test_cfg = dict(topK=100, max_per_img=100)
        centernet_head = CenterNetHead(
            num_classes=4, in_channels=1, feat_channels=4, test_cfg=test_cfg)

        feat = [torch.rand(1, 1, s, s)]
        center_out, wh_out, offset_out = centernet_head.forward(feat)
        # Test that empty ground truth encourages the network to
        # predict background
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.empty((0, 4))
        gt_instances.labels = torch.LongTensor([])
        empty_gt_losses = centernet_head.loss(center_out, wh_out, offset_out,
                                              [gt_instances], img_metas)
        loss_center = empty_gt_losses['loss_center_heatmap']
        loss_wh = empty_gt_losses['loss_wh']
        loss_offset = empty_gt_losses['loss_offset']
        assert loss_center.item() > 0, 'loss_center should be non-zero'
        assert loss_wh.item() == 0, (
            'there should be no loss_wh when there are no true boxes')
        assert loss_offset.item() == 0, (
            'there should be no loss_offset when there are no true boxes')

        # When truth is non-empty then both cls and box loss
        # should be nonzero for random inputs
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.Tensor(
            [[23.6667, 23.8757, 238.6326, 151.8874]])
        gt_instances.labels = torch.LongTensor([2])
        one_gt_losses = centernet_head.loss(center_out, wh_out, offset_out,
                                            [gt_instances], img_metas)
        loss_center = one_gt_losses['loss_center_heatmap']
        loss_wh = one_gt_losses['loss_wh']
        loss_offset = one_gt_losses['loss_offset']
        assert loss_center.item() > 0, 'loss_center should be non-zero'
        assert loss_wh.item() > 0, 'loss_wh should be non-zero'
        assert loss_offset.item() > 0, 'loss_offset should be non-zero'

        ```
    ###Assistant : Tests center head loss when truth is empty and non-empty.
    "
935,"def from_package(package):
    
    spec = wrap_spec(package)
    reader = spec.loader.get_resource_reader(spec.name)
    return reader.files()


@contextlib.contextmanager","
    Return a Traversable object for the given package.

    ",8,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_package(package):
    
    spec = wrap_spec(package)
    reader = spec.loader.get_resource_reader(spec.name)
    return reader.files()


@contextlib.contextmanager
        ```
    ###Assistant : 
    Return a Traversable object for the given package.

    
    "
936,"def _is_refund_ongoing(payment):
    
    return (
        payment.transactions.filter(
            kind=TransactionKind.REFUND_ONGOING, is_success=True
        ).exists()
        if payment
        else False
    )

",Return True if refund is ongoing for given payment.,9,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _is_refund_ongoing(payment):
    
    return (
        payment.transactions.filter(
            kind=TransactionKind.REFUND_ONGOING, is_success=True
        ).exists()
        if payment
        else False
    )


        ```
    ###Assistant : Return True if refund is ongoing for given payment.
    "
937,"def installed(name, updates=None):
    
    if isinstance(updates, str):
        updates = [updates]

    if not updates:
        updates = name

    ret = {""name"": name, ""changes"": {}, ""result"": True, ""comment"": """"}

    wua = salt.utils.win_update.WindowsUpdateAgent()

    # Search for updates
    install_list = wua.search(updates)

    # No updates found
    if install_list.count() == 0:
        ret[""comment""] = ""No updates found""
        return ret

    # List of updates to download
    download = salt.utils.win_update.Updates()
    for item in install_list.updates:
        if not salt.utils.data.is_true(item.IsDownloaded):
            download.updates.Add(item)

    # List of updates to install
    install = salt.utils.win_update.Updates()
    installed_updates = []
    for item in install_list.updates:
        if not salt.utils.data.is_true(item.IsInstalled):
            install.updates.Add(item)
        else:
            installed_updates.extend(""KB"" + kb for kb in item.KBArticleIDs)

    if install.count() == 0:
        ret[""comment""] = ""Updates already installed: ""
        ret[""comment""] += ""\n - "".join(installed_updates)
        return ret

    # Return comment of changes if test.
    if __opts__[""test""]:
        ret[""result""] = None
        ret[""comment""] = ""Updates will be installed:""
        for update in install.updates:
            ret[""comment""] += ""\n""
            ret[""comment""] += "": "".join([update.Identity.UpdateID, update.Title])
        return ret

    # Download updates
    wua.download(download)

    # Install updates
    wua.install(install)

    # Refresh windows update info
    wua.refresh()
    post_info = wua.updates().list()

    # Verify the installation
    for item in install.list():
        if not salt.utils.data.is_true(post_info[item][""Installed""]):
            ret[""changes""][""failed""] = {
                item: {
                    ""Title"": post_info[item][""Title""],
                    ""KBs"": post_info[item][""KBs""],
                }
            }
            ret[""result""] = False
        else:
            ret[""changes""][""installed""] = {
                item: {
                    ""Title"": post_info[item][""Title""],
                    ""NeedsReboot"": post_info[item][""NeedsReboot""],
                    ""KBs"": post_info[item][""KBs""],
                }
            }

    if ret[""changes""].get(""failed"", False):
        ret[""comment""] = ""Updates failed""
    else:
        ret[""comment""] = ""Updates installed successfully""

    return ret

","
    Ensure Microsoft Updates are installed. Updates will be downloaded if
    needed.

    Args:

        name (str):
            The identifier of a single update to install.

        updates (list):
            A list of identifiers for updates to be installed. Overrides
            ``name``. Default is None.

    .. note:: Identifiers can be the GUID, the KB number, or any part of the
       Title of the Microsoft update. GUIDs and KBs are the preferred method
       to ensure you're installing the correct update.

    .. warning:: Using a partial KB number or a partial Title could result in
       more than one update being installed.

    Returns:
        dict: A dictionary containing the results of the update

    CLI Example:

    .. code-block:: yaml

        # using a GUID
        install_update:
          wua.installed:
            - name: 28cf1b09-2b1a-458c-9bd1-971d1b26b211

        # using a KB
        install_update:
          wua.installed:
            - name: KB3194343

        # using the full Title
        install_update:
          wua.installed:
            - name: Security Update for Adobe Flash Player for Windows 10 Version 1607 (for x64-based Systems) (KB3194343)

        # Install multiple updates
        install_updates:
          wua.installed:
            - updates:
              - KB3194343
              - 28cf1b09-2b1a-458c-9bd1-971d1b26b211
    ",161,215,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def installed(name, updates=None):
    
    if isinstance(updates, str):
        updates = [updates]

    if not updates:
        updates = name

    ret = {""name"": name, ""changes"": {}, ""result"": True, ""comment"": """"}

    wua = salt.utils.win_update.WindowsUpdateAgent()

    # Search for updates
    install_list = wua.search(updates)

    # No updates found
    if install_list.count() == 0:
        ret[""comment""] = ""No updates found""
        return ret

    # List of updates to download
    download = salt.utils.win_update.Updates()
    for item in install_list.updates:
        if not salt.utils.data.is_true(item.IsDownloaded):
            download.updates.Add(item)

    # List of updates to install
    install = salt.utils.win_update.Updates()
    installed_updates = []
    for item in install_list.updates:
        if not salt.utils.data.is_true(item.IsInstalled):
            install.updates.Add(item)
        else:
            installed_updates.extend(""KB"" + kb for kb in item.KBArticleIDs)

    if install.count() == 0:
        ret[""comment""] = ""Updates already installed: ""
        ret[""comment""] += ""\n - "".join(installed_updates)
        return ret

    # Return comment of changes if test.
    if __opts__[""test""]:
        ret[""result""] = None
        ret[""comment""] = ""Updates will be installed:""
        for update in install.updates:
            ret[""comment""] += ""\n""
            ret[""comment""] += "": "".join([update.Identity.UpdateID, update.Title])
        return ret

    # Download updates
    wua.download(download)

    # Install updates
    wua.install(install)

    # Refresh windows update info
    wua.refresh()
    post_info = wua.updates().list()

    # Verify the installation
    for item in install.list():
        if not salt.utils.data.is_true(post_info[item][""Installed""]):
            ret[""changes""][""failed""] = {
                item: {
                    ""Title"": post_info[item][""Title""],
                    ""KBs"": post_info[item][""KBs""],
                }
            }
            ret[""result""] = False
        else:
            ret[""changes""][""installed""] = {
                item: {
                    ""Title"": post_info[item][""Title""],
                    ""NeedsReboot"": post_info[item][""NeedsReboot""],
                    ""KBs"": post_info[item][""KBs""],
                }
            }

    if ret[""changes""].get(""failed"", False):
        ret[""comment""] = ""Updates failed""
    else:
        ret[""comment""] = ""Updates installed successfully""

    return ret


        ```
    ###Assistant : 
    Ensure Microsoft Updates are installed. Updates will be downloaded if
    needed.

    Args:

        name (str):
            The identifier of a single update to install.

        updates (list):
            A list of identifiers for updates to be installed. Overrides
            ``name``. Default is None.

    .. note:: Identifiers can be the GUID, the KB number, or any part of the
       Title of the Microsoft update. GUIDs and KBs are the preferred method
       to ensure you're installing the correct update.

    .. warning:: Using a partial KB number or a partial Title could result in
       more than one update being installed.

    Returns:
        dict: A dictionary containing the results of the update

    CLI Example:

    .. code-block:: yaml

        # using a GUID
        install_update:
          wua.installed:
            - name: 28cf1b09-2b1a-458c-9bd1-971d1b26b211

        # using a KB
        install_update:
          wua.installed:
            - name: KB3194343

        # using the full Title
        install_update:
          wua.installed:
            - name: Security Update for Adobe Flash Player for Windows 10 Version 1607 (for x64-based Systems) (KB3194343)

        # Install multiple updates
        install_updates:
          wua.installed:
            - updates:
              - KB3194343
              - 28cf1b09-2b1a-458c-9bd1-971d1b26b211
    
    "
938,"def _getKivyInformation(self):
        setup_codes = r
        info = self.queryRuntimeInformationMultiple(
            info_name=""kivy_info"",
            setup_codes=setup_codes,
            values=(
                (""libs_loaded"", ""kivy.core.image.libs_loaded""),
                (""window_impl"", ""kivy.core.window.window_impl""),
                (""label_libs"", ""kivy.core.text.label_libs""),
                (""sdl2_dep_bins"", ""sdl2_dep_bins""),
                (""glew_dep_bins"", ""glew_dep_bins""),
            ),
        )

        if info is None:
            self.sysexit(""Error, it seems Kivy is not installed."")

        return info
","
import kivy.core.image
import kivy.core.text
# Prevent Window from being created at compile time.
kivy.core.core_select_lib=(lambda *args, **kwargs: None)
import kivy.core.window

# Kivy has packages designed to provide these on Windows
try:
    from kivy_deps.sdl2 import dep_bins as sdl2_dep_bins
except ImportError:
    sdl2_dep_bins = []
try:
    from kivy_deps.glew import dep_bins as glew_dep_bins
except ImportError:
    glew_dep_bins = []
",53,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _getKivyInformation(self):
        setup_codes = r
        info = self.queryRuntimeInformationMultiple(
            info_name=""kivy_info"",
            setup_codes=setup_codes,
            values=(
                (""libs_loaded"", ""kivy.core.image.libs_loaded""),
                (""window_impl"", ""kivy.core.window.window_impl""),
                (""label_libs"", ""kivy.core.text.label_libs""),
                (""sdl2_dep_bins"", ""sdl2_dep_bins""),
                (""glew_dep_bins"", ""glew_dep_bins""),
            ),
        )

        if info is None:
            self.sysexit(""Error, it seems Kivy is not installed."")

        return info

        ```
    ###Assistant : 
import kivy.core.image
import kivy.core.text
# Prevent Window from being created at compile time.
kivy.core.core_select_lib=(lambda *args, **kwargs: None)
import kivy.core.window

# Kivy has packages designed to provide these on Windows
try:
    from kivy_deps.sdl2 import dep_bins as sdl2_dep_bins
except ImportError:
    sdl2_dep_bins = []
try:
    from kivy_deps.glew import dep_bins as glew_dep_bins
except ImportError:
    glew_dep_bins = []

    "
939,"def __iter__(self) -> Iterator:
        
        return iter(self._info_axis)

    # can we get a better explanation of this?","
        Iterate over info axis.

        Returns
        -------
        iterator
            Info axis as iterator.
        ",11,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __iter__(self) -> Iterator:
        
        return iter(self._info_axis)

    # can we get a better explanation of this?
        ```
    ###Assistant : 
        Iterate over info axis.

        Returns
        -------
        iterator
            Info axis as iterator.
        
    "
940,"def cast(self, target_schema, *args, **kwargs):
        
        table = table_cast(self.table, target_schema, *args, **kwargs)
        blocks = []
        for subtables in self.blocks:
            new_tables = []
            fields = list(target_schema)
            for subtable in subtables:
                subfields = []
                for name in subtable.column_names:
                    subfields.append(fields.pop(next(i for i, field in enumerate(fields) if field.name == name)))
                subschema = pa.schema(subfields)
                new_tables.append(subtable.cast(subschema, *args, **kwargs))
            blocks.append(new_tables)
        return ConcatenationTable(table, blocks)
","
        Cast table values to another schema

        Args:
            target_schema (:obj:`Schema`):
                Schema to cast to, the names and order of fields must match
            safe (:obj:`bool`, defaults to :obj:`True`):
                Check for overflows or other unsafe conversions

        Returns:
            :class:`datasets.table.Table`:
        ",35,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cast(self, target_schema, *args, **kwargs):
        
        table = table_cast(self.table, target_schema, *args, **kwargs)
        blocks = []
        for subtables in self.blocks:
            new_tables = []
            fields = list(target_schema)
            for subtable in subtables:
                subfields = []
                for name in subtable.column_names:
                    subfields.append(fields.pop(next(i for i, field in enumerate(fields) if field.name == name)))
                subschema = pa.schema(subfields)
                new_tables.append(subtable.cast(subschema, *args, **kwargs))
            blocks.append(new_tables)
        return ConcatenationTable(table, blocks)

        ```
    ###Assistant : 
        Cast table values to another schema

        Args:
            target_schema (:obj:`Schema`):
                Schema to cast to, the names and order of fields must match
            safe (:obj:`bool`, defaults to :obj:`True`):
                Check for overflows or other unsafe conversions

        Returns:
            :class:`datasets.table.Table`:
        
    "
941,"def softmax(p, axis=None, temperature=1):
  
  if axis is None:
    axis = p.ndim - 1
  if temperature == 0.:
    # NOTE: in case of multiple equal maxima, returns uniform distribution.
    p = p == np.max(p, axis=axis, keepdims=True)
  else:
    # oldp = p
    logp = np.log(p)
    logp /= temperature
    logp -= logp.max(axis=axis, keepdims=True)
    p = np.exp(logp)
  p /= p.sum(axis=axis, keepdims=True)
  if np.isnan(p).any():
    pdb.set_trace()  # pylint: disable=forgotten-debug-statement
  return p

","Apply the softmax transform to an array of categorical distributions.

  Args:
    p: an array of categorical probability vectors, possibly unnormalized.
    axis: the axis that spans the categories (default: -1).
    temperature: if not 1, transform the distribution by dividing the log
        probabilities and renormalizing. Values greater than 1 increase entropy,
        values less than 1 decrease entropy. A value of 0 yields a deterministic
        distribution that chooses the mode.

  Returns:
    An array of categorical probability vectors, like `p` but tempered and
    normalized.
  ",80,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def softmax(p, axis=None, temperature=1):
  
  if axis is None:
    axis = p.ndim - 1
  if temperature == 0.:
    # NOTE: in case of multiple equal maxima, returns uniform distribution.
    p = p == np.max(p, axis=axis, keepdims=True)
  else:
    # oldp = p
    logp = np.log(p)
    logp /= temperature
    logp -= logp.max(axis=axis, keepdims=True)
    p = np.exp(logp)
  p /= p.sum(axis=axis, keepdims=True)
  if np.isnan(p).any():
    pdb.set_trace()  # pylint: disable=forgotten-debug-statement
  return p


        ```
    ###Assistant : Apply the softmax transform to an array of categorical distributions.

  Args:
    p: an array of categorical probability vectors, possibly unnormalized.
    axis: the axis that spans the categories (default: -1).
    temperature: if not 1, transform the distribution by dividing the log
        probabilities and renormalizing. Values greater than 1 increase entropy,
        values less than 1 decrease entropy. A value of 0 yields a deterministic
        distribution that chooses the mode.

  Returns:
    An array of categorical probability vectors, like `p` but tempered and
    normalized.
  
    "
942,"def _populate_static_information(self) -> None:
        
        self.info[""ludwig_version""] = LUDWIG_VERSION
        self.info[""start_disk_usage""] = shutil.disk_usage(os.path.expanduser(""~"")).used

        # CPU information
        cpu_info = get_my_cpu_info()
        self.info[""cpu_architecture""] = cpu_info[""arch""]
        self.info[""num_cpu""] = psutil.cpu_count()
        self.info[""cpu_name""] = cpu_info[""brand_raw""]
        self.info[""total_cpu_memory_size""] = psutil.virtual_memory().total

        # GPU information
        if self.cuda_is_available:
            gpu_infos = get_gpu_info()
            for i, gpu_info in enumerate(gpu_infos):
                gpu_key = f""cuda_{i}""
                self.info[f""{gpu_key}_memory_used""] = []
                self.info[f""{gpu_key}_name""] = gpu_info[""name""]
                self.info[f""{gpu_key}_total_memory""] = gpu_info[""total_memory""]
                self.info[f""{gpu_key}_driver_version""] = gpu_info[""driver_version""]
                self.info[f""{gpu_key}_cuda_version""] = gpu_info[""cuda_version""]

        # recording in microseconds to be in line with torch profiler time recording.
        self.info[""start_time""] = time.perf_counter_ns() / 1000
",Populate the report with static software and hardware information.,9,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _populate_static_information(self) -> None:
        
        self.info[""ludwig_version""] = LUDWIG_VERSION
        self.info[""start_disk_usage""] = shutil.disk_usage(os.path.expanduser(""~"")).used

        # CPU information
        cpu_info = get_my_cpu_info()
        self.info[""cpu_architecture""] = cpu_info[""arch""]
        self.info[""num_cpu""] = psutil.cpu_count()
        self.info[""cpu_name""] = cpu_info[""brand_raw""]
        self.info[""total_cpu_memory_size""] = psutil.virtual_memory().total

        # GPU information
        if self.cuda_is_available:
            gpu_infos = get_gpu_info()
            for i, gpu_info in enumerate(gpu_infos):
                gpu_key = f""cuda_{i}""
                self.info[f""{gpu_key}_memory_used""] = []
                self.info[f""{gpu_key}_name""] = gpu_info[""name""]
                self.info[f""{gpu_key}_total_memory""] = gpu_info[""total_memory""]
                self.info[f""{gpu_key}_driver_version""] = gpu_info[""driver_version""]
                self.info[f""{gpu_key}_cuda_version""] = gpu_info[""cuda_version""]

        # recording in microseconds to be in line with torch profiler time recording.
        self.info[""start_time""] = time.perf_counter_ns() / 1000

        ```
    ###Assistant : Populate the report with static software and hardware information.
    "
943,"def assertCanNotCreateAt(self, parent_model, child_model, msg=None):
        
        if self._testCanCreateAt(parent_model, child_model):
            msg = self._formatMessage(
                msg,
                ""Can create a %s.%s under a %s.%s""
                % (
                    child_model._meta.app_label,
                    child_model._meta.model_name,
                    parent_model._meta.app_label,
                    parent_model._meta.model_name,
                ),
            )
            raise self.failureException(msg)
","
        Assert a particular child Page type can not be created under a parent
        Page type. ``parent_model`` and ``child_model`` should be the Page
        classes being tested.
        ",25,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def assertCanNotCreateAt(self, parent_model, child_model, msg=None):
        
        if self._testCanCreateAt(parent_model, child_model):
            msg = self._formatMessage(
                msg,
                ""Can create a %s.%s under a %s.%s""
                % (
                    child_model._meta.app_label,
                    child_model._meta.model_name,
                    parent_model._meta.app_label,
                    parent_model._meta.model_name,
                ),
            )
            raise self.failureException(msg)

        ```
    ###Assistant : 
        Assert a particular child Page type can not be created under a parent
        Page type. ``parent_model`` and ``child_model`` should be the Page
        classes being tested.
        
    "
944,"def get_feature_objects(self) -> Mapping[Project, Feature]:
        

        cls = self._manager._get_feature_class(self.feature_name)
        return {obj: cls(self.feature_name, obj) for obj in self.objects}
","
        Iterate over individual Feature objects.

        This is a fallback mode for applying a FeatureHandler that doesn't
        support checking the entire batch at once.
        ",23,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_feature_objects(self) -> Mapping[Project, Feature]:
        

        cls = self._manager._get_feature_class(self.feature_name)
        return {obj: cls(self.feature_name, obj) for obj in self.objects}

        ```
    ###Assistant : 
        Iterate over individual Feature objects.

        This is a fallback mode for applying a FeatureHandler that doesn't
        support checking the entire batch at once.
        
    "
945,"def require_ffmpeg(test_case):
    
    import subprocess

    try:
        subprocess.check_output([""ffmpeg"", ""-h""], stderr=subprocess.DEVNULL)
        return test_case
    except Exception:
        return unittest.skip(""test requires ffmpeg"")(test_case)

","
    Decorator marking a test that requires FFmpeg.

    These tests are skipped when FFmpeg isn't installed.

    ",15,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def require_ffmpeg(test_case):
    
    import subprocess

    try:
        subprocess.check_output([""ffmpeg"", ""-h""], stderr=subprocess.DEVNULL)
        return test_case
    except Exception:
        return unittest.skip(""test requires ffmpeg"")(test_case)


        ```
    ###Assistant : 
    Decorator marking a test that requires FFmpeg.

    These tests are skipped when FFmpeg isn't installed.

    
    "
946,"def generate_random_string():
        
        import random
        import string

        return """".join(random.choices(string.ascii_uppercase + string.digits, k=8))

    random_string = generate_random_string()

    # [START create_queue]
    create_queue = CloudTasksQueueCreateOperator(
        location=LOCATION,
        task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=0.5)),
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        retry=Retry(maximum=10.0),
        timeout=5,
        task_id=""create_queue"",
    )
    # [END create_queue]

    # [START delete_queue]
    delete_queue = CloudTasksQueueDeleteOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""delete_queue"",
    )
    # [END delete_queue]
    delete_queue.trigger_rule = TriggerRule.ALL_DONE

    # [START resume_queue]
    resume_queue = CloudTasksQueueResumeOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""resume_queue"",
    )
    # [END resume_queue]

    # [START pause_queue]
    pause_queue = CloudTasksQueuePauseOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""pause_queue"",
    )
    # [END pause_queue]

    # [START purge_queue]
    purge_queue = CloudTasksQueuePurgeOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""purge_queue"",
    )
    # [END purge_queue]

    # [START get_queue]
    get_queue = CloudTasksQueueGetOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""get_queue"",
    )

    get_queue_result = BashOperator(
        task_id=""get_queue_result"",
        bash_command=f""echo {get_queue.output}"",
    )
    # [END get_queue]

    # [START update_queue]
    update_queue = CloudTasksQueueUpdateOperator(
        task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=1)),
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        update_mask=FieldMask(paths=[""stackdriver_logging_config.sampling_ratio""]),
        task_id=""update_queue"",
    )
    # [END update_queue]

    # [START list_queue]
    list_queue = CloudTasksQueuesListOperator(location=LOCATION, task_id=""list_queue"")
    # [END list_queue]

    chain(
        random_string,
        create_queue,
        update_queue,
        pause_queue,
        resume_queue,
        purge_queue,
        get_queue,
        get_queue_result,
        list_queue,
        delete_queue,
    )

    from tests.system.utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when ""tearDown"" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()


from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
test_run = get_test_run(dag)
","
        Generate random string for queue and task names.
        Queue name cannot be repeated in preceding 7 days and
        task name in the last 1 hour.
        ",25,221,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_random_string():
        
        import random
        import string

        return """".join(random.choices(string.ascii_uppercase + string.digits, k=8))

    random_string = generate_random_string()

    # [START create_queue]
    create_queue = CloudTasksQueueCreateOperator(
        location=LOCATION,
        task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=0.5)),
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        retry=Retry(maximum=10.0),
        timeout=5,
        task_id=""create_queue"",
    )
    # [END create_queue]

    # [START delete_queue]
    delete_queue = CloudTasksQueueDeleteOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""delete_queue"",
    )
    # [END delete_queue]
    delete_queue.trigger_rule = TriggerRule.ALL_DONE

    # [START resume_queue]
    resume_queue = CloudTasksQueueResumeOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""resume_queue"",
    )
    # [END resume_queue]

    # [START pause_queue]
    pause_queue = CloudTasksQueuePauseOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""pause_queue"",
    )
    # [END pause_queue]

    # [START purge_queue]
    purge_queue = CloudTasksQueuePurgeOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""purge_queue"",
    )
    # [END purge_queue]

    # [START get_queue]
    get_queue = CloudTasksQueueGetOperator(
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        task_id=""get_queue"",
    )

    get_queue_result = BashOperator(
        task_id=""get_queue_result"",
        bash_command=f""echo {get_queue.output}"",
    )
    # [END get_queue]

    # [START update_queue]
    update_queue = CloudTasksQueueUpdateOperator(
        task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=1)),
        location=LOCATION,
        queue_name=QUEUE_ID + ""{{ task_instance.xcom_pull(task_ids='random_string') }}"",
        update_mask=FieldMask(paths=[""stackdriver_logging_config.sampling_ratio""]),
        task_id=""update_queue"",
    )
    # [END update_queue]

    # [START list_queue]
    list_queue = CloudTasksQueuesListOperator(location=LOCATION, task_id=""list_queue"")
    # [END list_queue]

    chain(
        random_string,
        create_queue,
        update_queue,
        pause_queue,
        resume_queue,
        purge_queue,
        get_queue,
        get_queue_result,
        list_queue,
        delete_queue,
    )

    from tests.system.utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when ""tearDown"" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()


from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
test_run = get_test_run(dag)

        ```
    ###Assistant : 
        Generate random string for queue and task names.
        Queue name cannot be repeated in preceding 7 days and
        task name in the last 1 hour.
        
    "
947,"def make_gradient_clipvalue_fn(clipvalue):
    
    if clipvalue is None:
        return lambda grads_and_vars: grads_and_vars
",Creates a gradient transformation function for clipping by value.,9,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_gradient_clipvalue_fn(clipvalue):
    
    if clipvalue is None:
        return lambda grads_and_vars: grads_and_vars

        ```
    ###Assistant : Creates a gradient transformation function for clipping by value.
    "
948,"def odd_ext(x, n, axis=-1):
  
  if n < 1:
    return x
  if n > x.shape[axis] - 1:
    raise ValueError(
        f""The extension length n ({n}) is too big. ""
        f""It must not exceed x.shape[axis]-1, which is {x.shape[axis] - 1}."")
  left_end = lax.slice_in_dim(x, 0, 1, axis=axis)
  left_ext = jnp.flip(lax.slice_in_dim(x, 1, n + 1, axis=axis), axis=axis)
  right_end = lax.slice_in_dim(x, -1, None, axis=axis)
  right_ext = jnp.flip(lax.slice_in_dim(x, -(n + 1), -1, axis=axis), axis=axis)
  ext = jnp.concatenate((2 * left_end - left_ext,
                         x,
                         2 * right_end - right_ext),
                         axis=axis)
  return ext

","Extends `x` along with `axis` by odd-extension.

  This function was previously a part of ""scipy.signal.signaltools"" but is no
  longer exposed.

  Args:
    x : input array
    n : the number of points to be added to the both end
    axis: the axis to be extended
  ",44,83,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def odd_ext(x, n, axis=-1):
  
  if n < 1:
    return x
  if n > x.shape[axis] - 1:
    raise ValueError(
        f""The extension length n ({n}) is too big. ""
        f""It must not exceed x.shape[axis]-1, which is {x.shape[axis] - 1}."")
  left_end = lax.slice_in_dim(x, 0, 1, axis=axis)
  left_ext = jnp.flip(lax.slice_in_dim(x, 1, n + 1, axis=axis), axis=axis)
  right_end = lax.slice_in_dim(x, -1, None, axis=axis)
  right_ext = jnp.flip(lax.slice_in_dim(x, -(n + 1), -1, axis=axis), axis=axis)
  ext = jnp.concatenate((2 * left_end - left_ext,
                         x,
                         2 * right_end - right_ext),
                         axis=axis)
  return ext


        ```
    ###Assistant : Extends `x` along with `axis` by odd-extension.

  This function was previously a part of ""scipy.signal.signaltools"" but is no
  longer exposed.

  Args:
    x : input array
    n : the number of points to be added to the both end
    axis: the axis to be extended
  
    "
949,"def filter_on_submodules(all_modules, submodule):
  

  filtered_modules = [
      mod for mod in all_modules if PACKAGE + submodule in mod.__name__
  ]
  return filtered_modules

","Filters all the modules based on the module flag.

  The module flag has to be relative to the core package imported.
  For example, if `submodule=keras.layers` then, this function will return
  all the modules in the submodule.

  Args:
    all_modules: All the modules in the core package.
    submodule: Submodule to filter from all the modules.

  Returns:
    All the modules in the submodule.
  ",60,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def filter_on_submodules(all_modules, submodule):
  

  filtered_modules = [
      mod for mod in all_modules if PACKAGE + submodule in mod.__name__
  ]
  return filtered_modules


        ```
    ###Assistant : Filters all the modules based on the module flag.

  The module flag has to be relative to the core package imported.
  For example, if `submodule=keras.layers` then, this function will return
  all the modules in the submodule.

  Args:
    all_modules: All the modules in the core package.
    submodule: Submodule to filter from all the modules.

  Returns:
    All the modules in the submodule.
  
    "
950,"def get_pe_matching_query(amount_condition, account_from_to, transaction):
	# get matching payment entries query
	from_date = frappe.db.get_single_value(""Bank Reconciliation Tool"", ""bank_statement_from_date"")
	to_date = frappe.db.get_single_value(""Bank Reconciliation Tool"", ""bank_statement_to_date"")
	from_reference_date = frappe.db.get_single_value(
		""Bank Reconciliation Tool"", ""from_reference_date""
	)
	to_reference_date = frappe.db.get_single_value(""Bank Reconciliation Tool"", ""to_reference_date"")
	filtered_by_reference_date = frappe.db.get_single_value(
		""Bank Reconciliation Tool"", ""filtered_by_reference_date""
	)
	if transaction.deposit > 0:
		currency_field = ""paid_to_account_currency as currency""
	else:
		currency_field = ""paid_from_account_currency as currency""
	cond_filtered_from_ref_date = """"
	cond_filtered_to_ref_date = """"
	cond_filtered_from_posting_date = """"
	cond_filtered_to_posting_date = """"
	from_ref_date  =""""
	to_ref_date =""""
	from_post_date = """"
	to_post_date = """"
	if(filtered_by_reference_date):
		cond_filtered_from_ref_date = "" AND reference_date >=""
		cond_filtered_to_ref_date = "" AND reference_date <=""
		from_ref_date = from_reference_date
		to_ref_date = to_reference_date
	elif(not filtered_by_reference_date):
		cond_filtered_from_posting_date = "" AND posting_date >=""
		cond_filtered_to_posting_date = "" AND posting_date <=""
		from_post_date = from_date
		to_post_date = to_date
		
	pe_data=  f	
	return pe_data

","
		SELECT
			(CASE WHEN reference_no=%(reference_no)s THEN 1 ELSE 0 END
			+ CASE WHEN (party_type = %(party_type)s AND party = %(party)s ) THEN 1 ELSE 0  END
			+ 1 ) AS rank,
			'Payment Entry' as doctype,
			name,
			paid_amount,
			reference_no,
			reference_date,
			party,
			party_type,
			posting_date,
			{currency_field}
		FROM
			`tabPayment Entry`
		WHERE
			paid_amount {amount_condition} %(amount)s
			AND docstatus = 1
			AND payment_type IN (%(payment_type)s, 'Internal Transfer')
			AND ifnull(clearance_date, '') = """"
			AND {account_from_to} = %(bank_account)s
			AND reference_no = '{transaction.reference_number}'
			{cond_filtered_from_ref_date} ""{from_ref_date}""
			{cond_filtered_to_ref_date} ""{to_ref_date}""
			{cond_filtered_from_posting_date} ""{from_post_date}""
			{cond_filtered_to_posting_date} ""{to_post_date}""
		",80,124,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_pe_matching_query(amount_condition, account_from_to, transaction):
	# get matching payment entries query
	from_date = frappe.db.get_single_value(""Bank Reconciliation Tool"", ""bank_statement_from_date"")
	to_date = frappe.db.get_single_value(""Bank Reconciliation Tool"", ""bank_statement_to_date"")
	from_reference_date = frappe.db.get_single_value(
		""Bank Reconciliation Tool"", ""from_reference_date""
	)
	to_reference_date = frappe.db.get_single_value(""Bank Reconciliation Tool"", ""to_reference_date"")
	filtered_by_reference_date = frappe.db.get_single_value(
		""Bank Reconciliation Tool"", ""filtered_by_reference_date""
	)
	if transaction.deposit > 0:
		currency_field = ""paid_to_account_currency as currency""
	else:
		currency_field = ""paid_from_account_currency as currency""
	cond_filtered_from_ref_date = """"
	cond_filtered_to_ref_date = """"
	cond_filtered_from_posting_date = """"
	cond_filtered_to_posting_date = """"
	from_ref_date  =""""
	to_ref_date =""""
	from_post_date = """"
	to_post_date = """"
	if(filtered_by_reference_date):
		cond_filtered_from_ref_date = "" AND reference_date >=""
		cond_filtered_to_ref_date = "" AND reference_date <=""
		from_ref_date = from_reference_date
		to_ref_date = to_reference_date
	elif(not filtered_by_reference_date):
		cond_filtered_from_posting_date = "" AND posting_date >=""
		cond_filtered_to_posting_date = "" AND posting_date <=""
		from_post_date = from_date
		to_post_date = to_date
		
	pe_data=  f	
	return pe_data


        ```
    ###Assistant : 
		SELECT
			(CASE WHEN reference_no=%(reference_no)s THEN 1 ELSE 0 END
			+ CASE WHEN (party_type = %(party_type)s AND party = %(party)s ) THEN 1 ELSE 0  END
			+ 1 ) AS rank,
			'Payment Entry' as doctype,
			name,
			paid_amount,
			reference_no,
			reference_date,
			party,
			party_type,
			posting_date,
			{currency_field}
		FROM
			`tabPayment Entry`
		WHERE
			paid_amount {amount_condition} %(amount)s
			AND docstatus = 1
			AND payment_type IN (%(payment_type)s, 'Internal Transfer')
			AND ifnull(clearance_date, '') = """"
			AND {account_from_to} = %(bank_account)s
			AND reference_no = '{transaction.reference_number}'
			{cond_filtered_from_ref_date} ""{from_ref_date}""
			{cond_filtered_to_ref_date} ""{to_ref_date}""
			{cond_filtered_from_posting_date} ""{from_post_date}""
			{cond_filtered_to_posting_date} ""{to_post_date}""
		
    "
951,"def closed(self) -> IntervalInclusiveType:
        
        warnings.warn(
            ""Attribute `closed` is deprecated in favor of `inclusive`."",
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        return self.dtype.inclusive

    _interval_shared_docs[""set_closed""] = textwrap.dedent(
        
    )
","
        String describing the inclusive side the intervals.

        Either ``left``, ``right``, ``both`` or ``neither`.
        
        Return an identical %(klass)s closed on the specified side.

        .. deprecated:: 1.5.0

        Parameters
        ----------
        closed : {'left', 'right', 'both', 'neither'}
            Whether the intervals are closed on the left-side, right-side, both
            or neither.

        Returns
        -------
        new_index : %(klass)s

        %(examples)s\
        ",51,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def closed(self) -> IntervalInclusiveType:
        
        warnings.warn(
            ""Attribute `closed` is deprecated in favor of `inclusive`."",
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        return self.dtype.inclusive

    _interval_shared_docs[""set_closed""] = textwrap.dedent(
        
    )

        ```
    ###Assistant : 
        String describing the inclusive side the intervals.

        Either ``left``, ``right``, ``both`` or ``neither`.
        
        Return an identical %(klass)s closed on the specified side.

        .. deprecated:: 1.5.0

        Parameters
        ----------
        closed : {'left', 'right', 'both', 'neither'}
            Whether the intervals are closed on the left-side, right-side, both
            or neither.

        Returns
        -------
        new_index : %(klass)s

        %(examples)s\
        
    "
952,"def as_real_imag(self, deep=True, **hints):
        
        from sympy.functions.elementary.trigonometric import cos, sin
        re, im = self.args[0].as_real_imag()
        if deep:
            re = re.expand(deep, **hints)
            im = im.expand(deep, **hints)
        cos, sin = cos(im), sin(im)
        return (exp(re)*cos, exp(re)*sin)
","
        Returns this function as a 2-tuple representing a complex number.

        Examples
        ========

        >>> from sympy import I, exp
        >>> from sympy.abc import x
        >>> exp(x).as_real_imag()
        (exp(re(x))*cos(im(x)), exp(re(x))*sin(im(x)))
        >>> exp(1).as_real_imag()
        (E, 0)
        >>> exp(I).as_real_imag()
        (cos(1), sin(1))
        >>> exp(1+I).as_real_imag()
        (E*cos(1), E*sin(1))

        See Also
        ========

        sympy.functions.elementary.complexes.re
        sympy.functions.elementary.complexes.im
        ",44,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def as_real_imag(self, deep=True, **hints):
        
        from sympy.functions.elementary.trigonometric import cos, sin
        re, im = self.args[0].as_real_imag()
        if deep:
            re = re.expand(deep, **hints)
            im = im.expand(deep, **hints)
        cos, sin = cos(im), sin(im)
        return (exp(re)*cos, exp(re)*sin)

        ```
    ###Assistant : 
        Returns this function as a 2-tuple representing a complex number.

        Examples
        ========

        >>> from sympy import I, exp
        >>> from sympy.abc import x
        >>> exp(x).as_real_imag()
        (exp(re(x))*cos(im(x)), exp(re(x))*sin(im(x)))
        >>> exp(1).as_real_imag()
        (E, 0)
        >>> exp(I).as_real_imag()
        (cos(1), sin(1))
        >>> exp(1+I).as_real_imag()
        (E*cos(1), E*sin(1))

        See Also
        ========

        sympy.functions.elementary.complexes.re
        sympy.functions.elementary.complexes.im
        
    "
953,"async def _get_conversation_ids_to_process(self) -> Set[Text]:
        
        conversation_ids_in_tracker_store = (
            await self._get_conversation_ids_in_tracker()
        )

        if not self.requested_conversation_ids:
            return conversation_ids_in_tracker_store

        self._validate_all_requested_ids_exist(conversation_ids_in_tracker_store)

        conversation_ids_to_process = conversation_ids_in_tracker_store & set(
            self.requested_conversation_ids
        )

        if not conversation_ids_to_process:
            raise NoEventsToMigrateError(
                ""Could not find an overlap between the requested ""
                ""conversation IDs and those found in the tracker store. Exiting.""
            )

        return conversation_ids_to_process
","Get conversation IDs that are good for processing.

        Finds the intersection of events that are contained in the tracker store with
        those events requested as a command-line argument.

        Returns:
            Conversation IDs that are both requested and contained in the tracker
            store. If no conversation IDs are requested, all conversation IDs in the
            tracker store are returned.

        ",56,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _get_conversation_ids_to_process(self) -> Set[Text]:
        
        conversation_ids_in_tracker_store = (
            await self._get_conversation_ids_in_tracker()
        )

        if not self.requested_conversation_ids:
            return conversation_ids_in_tracker_store

        self._validate_all_requested_ids_exist(conversation_ids_in_tracker_store)

        conversation_ids_to_process = conversation_ids_in_tracker_store & set(
            self.requested_conversation_ids
        )

        if not conversation_ids_to_process:
            raise NoEventsToMigrateError(
                ""Could not find an overlap between the requested ""
                ""conversation IDs and those found in the tracker store. Exiting.""
            )

        return conversation_ids_to_process

        ```
    ###Assistant : Get conversation IDs that are good for processing.

        Finds the intersection of events that are contained in the tracker store with
        those events requested as a command-line argument.

        Returns:
            Conversation IDs that are both requested and contained in the tracker
            store. If no conversation IDs are requested, all conversation IDs in the
            tracker store are returned.

        
    "
954,"def call(self, inputs, training=None, mask=None):
        
        raise NotImplementedError(
            ""Unimplemented `tf.keras.Model.call()`: if you ""
            ""intend to create a `Model` with the Functional ""
            ""API, please provide `inputs` and `outputs` ""
            ""arguments. Otherwise, subclass `Model` with an ""
            ""overridden `call()` method.""
        )
","Calls the model on new inputs and returns the outputs as tensors.

        In this case `call()` just reapplies
        all ops in the graph to the new inputs
        (e.g. build a new computational graph from the provided inputs).

        Note: This method should not be called directly. It is only meant to be
        overridden when subclassing `tf.keras.Model`.
        To call a model on an input, always use the `__call__()` method,
        i.e. `model(inputs)`, which relies on the underlying `call()` method.

        Args:
            inputs: Input tensor, or dict/list/tuple of input tensors.
            training: Boolean or boolean scalar tensor, indicating whether to run
              the `Network` in training mode or inference mode.
            mask: A mask or list of masks. A mask can be either a boolean tensor or
              None (no mask). For more details, check the guide
                [here](https://www.tensorflow.org/guide/keras/masking_and_padding).

        Returns:
            A tensor if there is a single output, or
            a list of tensors if there are more than one outputs.
        ",150,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call(self, inputs, training=None, mask=None):
        
        raise NotImplementedError(
            ""Unimplemented `tf.keras.Model.call()`: if you ""
            ""intend to create a `Model` with the Functional ""
            ""API, please provide `inputs` and `outputs` ""
            ""arguments. Otherwise, subclass `Model` with an ""
            ""overridden `call()` method.""
        )

        ```
    ###Assistant : Calls the model on new inputs and returns the outputs as tensors.

        In this case `call()` just reapplies
        all ops in the graph to the new inputs
        (e.g. build a new computational graph from the provided inputs).

        Note: This method should not be called directly. It is only meant to be
        overridden when subclassing `tf.keras.Model`.
        To call a model on an input, always use the `__call__()` method,
        i.e. `model(inputs)`, which relies on the underlying `call()` method.

        Args:
            inputs: Input tensor, or dict/list/tuple of input tensors.
            training: Boolean or boolean scalar tensor, indicating whether to run
              the `Network` in training mode or inference mode.
            mask: A mask or list of masks. A mask can be either a boolean tensor or
              None (no mask). For more details, check the guide
                [here](https://www.tensorflow.org/guide/keras/masking_and_padding).

        Returns:
            A tensor if there is a single output, or
            a list of tensors if there are more than one outputs.
        
    "
955,"def _read(cls, path_or_buf, **kwargs):
        
        path_or_buf = cls.get_path_or_buffer(path_or_buf)
        if isinstance(path_or_buf, str):
            if not cls.file_exists(path_or_buf):
                return cls.single_worker_read(path_or_buf, **kwargs)
            path_or_buf = cls.get_path(path_or_buf)
        elif not cls.pathlib_or_pypath(path_or_buf):
            return cls.single_worker_read(path_or_buf, **kwargs)
        if not kwargs.get(""lines"", False):
            return cls.single_worker_read(path_or_buf, **kwargs)
        with OpenFile(path_or_buf, ""rb"") as f:
            columns = pandas.read_json(BytesIO(b"""" + f.readline()), lines=True).columns
        kwargs[""columns""] = columns
        empty_pd_df = pandas.DataFrame(columns=columns)

        with OpenFile(path_or_buf, ""rb"", kwargs.get(""compression"", ""infer"")) as f:
            partition_ids = []
            index_ids = []
            dtypes_ids = []

            column_widths, num_splits = cls._define_metadata(empty_pd_df, columns)

            args = {""fname"": path_or_buf, ""num_splits"": num_splits, **kwargs}

            splits = cls.partitioned_file(
                f,
                num_partitions=NPartitions.get(),
            )
            for start, end in splits:
                args.update({""start"": start, ""end"": end})
                partition_id = cls.deploy(cls.parse, num_returns=num_splits + 3, **args)
                partition_ids.append(partition_id[:-3])
                index_ids.append(partition_id[-3])
                dtypes_ids.append(partition_id[-2])

        # partition_id[-1] contains the columns for each partition, which will be useful
        # for implementing when `lines=False`.
        row_lengths = cls.materialize(index_ids)
        new_index = pandas.RangeIndex(sum(row_lengths))

        dtypes = cls.get_dtypes(dtypes_ids)
        partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths)

        if isinstance(dtypes, pandas.Series):
            dtypes.index = columns
        else:
            dtypes = pandas.Series(dtypes, index=columns)

        new_frame = cls.frame_cls(
            np.array(partition_ids),
            new_index,
            columns,
            row_lengths,
            column_widths,
            dtypes=dtypes,
        )
        new_frame.synchronize_labels(axis=0)
        return cls.query_compiler_cls(new_frame)
","
        Read data from `path_or_buf` according to the passed `read_json` `kwargs` parameters.

        Parameters
        ----------
        path_or_buf : str, path object or file-like object
            `path_or_buf` parameter of `read_json` function.
        **kwargs : dict
            Parameters of `read_json` function.

        Returns
        -------
        BaseQueryCompiler
            Query compiler with imported data for further processing.
        ",44,157,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _read(cls, path_or_buf, **kwargs):
        
        path_or_buf = cls.get_path_or_buffer(path_or_buf)
        if isinstance(path_or_buf, str):
            if not cls.file_exists(path_or_buf):
                return cls.single_worker_read(path_or_buf, **kwargs)
            path_or_buf = cls.get_path(path_or_buf)
        elif not cls.pathlib_or_pypath(path_or_buf):
            return cls.single_worker_read(path_or_buf, **kwargs)
        if not kwargs.get(""lines"", False):
            return cls.single_worker_read(path_or_buf, **kwargs)
        with OpenFile(path_or_buf, ""rb"") as f:
            columns = pandas.read_json(BytesIO(b"""" + f.readline()), lines=True).columns
        kwargs[""columns""] = columns
        empty_pd_df = pandas.DataFrame(columns=columns)

        with OpenFile(path_or_buf, ""rb"", kwargs.get(""compression"", ""infer"")) as f:
            partition_ids = []
            index_ids = []
            dtypes_ids = []

            column_widths, num_splits = cls._define_metadata(empty_pd_df, columns)

            args = {""fname"": path_or_buf, ""num_splits"": num_splits, **kwargs}

            splits = cls.partitioned_file(
                f,
                num_partitions=NPartitions.get(),
            )
            for start, end in splits:
                args.update({""start"": start, ""end"": end})
                partition_id = cls.deploy(cls.parse, num_returns=num_splits + 3, **args)
                partition_ids.append(partition_id[:-3])
                index_ids.append(partition_id[-3])
                dtypes_ids.append(partition_id[-2])

        # partition_id[-1] contains the columns for each partition, which will be useful
        # for implementing when `lines=False`.
        row_lengths = cls.materialize(index_ids)
        new_index = pandas.RangeIndex(sum(row_lengths))

        dtypes = cls.get_dtypes(dtypes_ids)
        partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths)

        if isinstance(dtypes, pandas.Series):
            dtypes.index = columns
        else:
            dtypes = pandas.Series(dtypes, index=columns)

        new_frame = cls.frame_cls(
            np.array(partition_ids),
            new_index,
            columns,
            row_lengths,
            column_widths,
            dtypes=dtypes,
        )
        new_frame.synchronize_labels(axis=0)
        return cls.query_compiler_cls(new_frame)

        ```
    ###Assistant : 
        Read data from `path_or_buf` according to the passed `read_json` `kwargs` parameters.

        Parameters
        ----------
        path_or_buf : str, path object or file-like object
            `path_or_buf` parameter of `read_json` function.
        **kwargs : dict
            Parameters of `read_json` function.

        Returns
        -------
        BaseQueryCompiler
            Query compiler with imported data for further processing.
        
    "
956,"def stop_ambient_camera_rotation(self, about=""theta""):
        
        about: str = about.lower()
        try:
            if config.renderer == RendererType.CAIRO:
                trackers = {
                    ""theta"": self.camera.theta_tracker,
                    ""phi"": self.camera.phi_tracker,
                    ""gamma"": self.camera.gamma_tracker,
                }
                x: ValueTracker = trackers[about]
                x.clear_updaters()
                self.remove(x)
            elif config.renderer == RendererType.OPENGL:
                self.camera.clear_updaters()
        except Exception:
            raise ValueError(""Invalid ambient rotation angle."")
","
        This method stops all ambient camera rotation.
        ",7,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def stop_ambient_camera_rotation(self, about=""theta""):
        
        about: str = about.lower()
        try:
            if config.renderer == RendererType.CAIRO:
                trackers = {
                    ""theta"": self.camera.theta_tracker,
                    ""phi"": self.camera.phi_tracker,
                    ""gamma"": self.camera.gamma_tracker,
                }
                x: ValueTracker = trackers[about]
                x.clear_updaters()
                self.remove(x)
            elif config.renderer == RendererType.OPENGL:
                self.camera.clear_updaters()
        except Exception:
            raise ValueError(""Invalid ambient rotation angle."")

        ```
    ###Assistant : 
        This method stops all ambient camera rotation.
        
    "
957,"def _get_bundled_aggregations(self) -> JsonDict:
        
        # Fetch the bundled aggregations of the event.
        channel = self.make_request(
            ""GET"",
            f""/_matrix/client/unstable/rooms/{self.room}/event/{self.parent_id}"",
            access_token=self.user_token,
        )
        self.assertEquals(200, channel.code, channel.json_body)
        return channel.json_body[""unsigned""].get(""m.relations"", {})
","
        Requests /event on the parent ID and returns the m.relations field (from unsigned), if it exists.
        ",16,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_bundled_aggregations(self) -> JsonDict:
        
        # Fetch the bundled aggregations of the event.
        channel = self.make_request(
            ""GET"",
            f""/_matrix/client/unstable/rooms/{self.room}/event/{self.parent_id}"",
            access_token=self.user_token,
        )
        self.assertEquals(200, channel.code, channel.json_body)
        return channel.json_body[""unsigned""].get(""m.relations"", {})

        ```
    ###Assistant : 
        Requests /event on the parent ID and returns the m.relations field (from unsigned), if it exists.
        
    "
958,"def get_memos(self) -> Dict[bytes32, List[bytes]]:
        
        memos: Dict[bytes32, List[bytes]] = {}
        for coin_spend in self.coin_spends:
            result = Program.from_bytes(bytes(coin_spend.puzzle_reveal)).run(
                Program.from_bytes(bytes(coin_spend.solution))
            )
            for condition in result.as_python():
                if condition[0] == ConditionOpcode.CREATE_COIN and len(condition) >= 4:
                    # If only 3 elements (opcode + 2 args), there is no memo, this is ph, amount
                    coin_added = Coin(coin_spend.coin.name(), bytes32(condition[1]), int_from_bytes(condition[2]))
                    if type(condition[3]) != list:
                        # If it's not a list, it's not the correct format
                        continue
                    memos[coin_added.name()] = condition[3]
        return memos

    # Note that `coin_spends` used to have the bad name `coin_solutions`.
    # Some API still expects this name. For now, we accept both names.
    #
    # TODO: continue this deprecation. Eventually, all code below here should be removed.
    #  1. set `exclude_modern_keys` to `False` (and manually set to `True` where necessary)
    #  2. set `include_legacy_keys` to `False` (and manually set to `False` where necessary)
    #  3. remove all references to `include_legacy_keys=True`
    #  4. remove all code below this point
","
        Retrieves the memos for additions in this spend_bundle, which are formatted as a list in the 3rd parameter of
        CREATE_COIN. If there are no memos, the addition coin_id is not included. If they are not formatted as a list
        of bytes, they are not included. This is expensive to call, it should not be used in full node code.
        ",59,153,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_memos(self) -> Dict[bytes32, List[bytes]]:
        
        memos: Dict[bytes32, List[bytes]] = {}
        for coin_spend in self.coin_spends:
            result = Program.from_bytes(bytes(coin_spend.puzzle_reveal)).run(
                Program.from_bytes(bytes(coin_spend.solution))
            )
            for condition in result.as_python():
                if condition[0] == ConditionOpcode.CREATE_COIN and len(condition) >= 4:
                    # If only 3 elements (opcode + 2 args), there is no memo, this is ph, amount
                    coin_added = Coin(coin_spend.coin.name(), bytes32(condition[1]), int_from_bytes(condition[2]))
                    if type(condition[3]) != list:
                        # If it's not a list, it's not the correct format
                        continue
                    memos[coin_added.name()] = condition[3]
        return memos

    # Note that `coin_spends` used to have the bad name `coin_solutions`.
    # Some API still expects this name. For now, we accept both names.
    #
    # TODO: continue this deprecation. Eventually, all code below here should be removed.
    #  1. set `exclude_modern_keys` to `False` (and manually set to `True` where necessary)
    #  2. set `include_legacy_keys` to `False` (and manually set to `False` where necessary)
    #  3. remove all references to `include_legacy_keys=True`
    #  4. remove all code below this point

        ```
    ###Assistant : 
        Retrieves the memos for additions in this spend_bundle, which are formatted as a list in the 3rd parameter of
        CREATE_COIN. If there are no memos, the addition coin_id is not included. If they are not formatted as a list
        of bytes, they are not included. This is expensive to call, it should not be used in full node code.
        
    "
959,"def _clean_url_path_part(part):
    # type: (str) -> str
    
    # We unquote prior to quoting to make sure nothing is double quoted.
    return urllib.parse.quote(urllib.parse.unquote(part))

","
    Clean a ""part"" of a URL path (i.e. after splitting on ""@"" characters).
    ",13,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _clean_url_path_part(part):
    # type: (str) -> str
    
    # We unquote prior to quoting to make sure nothing is double quoted.
    return urllib.parse.quote(urllib.parse.unquote(part))


        ```
    ###Assistant : 
    Clean a ""part"" of a URL path (i.e. after splitting on ""@"" characters).
    
    "
960,"def CheckAltTokens(filename, clean_lines, linenum, error):
  
  line = clean_lines.elided[linenum]

  # Avoid preprocessor lines
  if Match(r'^\s*#', line):
    return

  # Last ditch effort to avoid multi-line comments.  This will not help
  # if the comment started before the current line or ended after the
  # current line, but it catches most of the false positives.  At least,
  # it provides a way to workaround this warning for people who use
  # multi-line comments in preprocessor macros.
  #
  # TODO(unknown): remove this once cpplint has better support for
  # multi-line comments.
  if line.find('/*') >= 0 or line.find('*/') >= 0:
    return

  for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line):
    error(filename, linenum, 'readability/alt_tokens', 2,
          'Use operator %s instead of %s' % (
              _ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1)))

","Check alternative keywords being used in boolean expressions.

  Args:
    filename: The name of the current file.
    clean_lines: A CleansedLines instance containing the file.
    linenum: The number of the line to check.
    error: The function to call with any errors found.
  ",40,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def CheckAltTokens(filename, clean_lines, linenum, error):
  
  line = clean_lines.elided[linenum]

  # Avoid preprocessor lines
  if Match(r'^\s*#', line):
    return

  # Last ditch effort to avoid multi-line comments.  This will not help
  # if the comment started before the current line or ended after the
  # current line, but it catches most of the false positives.  At least,
  # it provides a way to workaround this warning for people who use
  # multi-line comments in preprocessor macros.
  #
  # TODO(unknown): remove this once cpplint has better support for
  # multi-line comments.
  if line.find('/*') >= 0 or line.find('*/') >= 0:
    return

  for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line):
    error(filename, linenum, 'readability/alt_tokens', 2,
          'Use operator %s instead of %s' % (
              _ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1)))


        ```
    ###Assistant : Check alternative keywords being used in boolean expressions.

  Args:
    filename: The name of the current file.
    clean_lines: A CleansedLines instance containing the file.
    linenum: The number of the line to check.
    error: The function to call with any errors found.
  
    "
961,"def register_ray():
    
    try:
        from ray.util.joblib.ray_backend import RayBackend

        register_parallel_backend(""ray"", RayBackend)
    except ImportError:
        msg = (
            ""To use the ray backend you must install ray.""
            ""Try running 'pip install ray'.""
            ""See https://docs.ray.io/en/master/installation.html""
            ""for more information.""
        )
        raise ImportError(msg)


__all__ = [""register_ray""]
","Register Ray Backend to be called with parallel_backend(""ray"").",8,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def register_ray():
    
    try:
        from ray.util.joblib.ray_backend import RayBackend

        register_parallel_backend(""ray"", RayBackend)
    except ImportError:
        msg = (
            ""To use the ray backend you must install ray.""
            ""Try running 'pip install ray'.""
            ""See https://docs.ray.io/en/master/installation.html""
            ""for more information.""
        )
        raise ImportError(msg)


__all__ = [""register_ray""]

        ```
    ###Assistant : Register Ray Backend to be called with parallel_backend(""ray"").
    "
962,"def get_group_permissions(self, user_obj, obj=None):
        
        return self._get_permissions(user_obj, obj, ""group"")
","
        Return a set of permission strings the user `user_obj` has from the
        groups they belong.
        ",15,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_group_permissions(self, user_obj, obj=None):
        
        return self._get_permissions(user_obj, obj, ""group"")

        ```
    ###Assistant : 
        Return a set of permission strings the user `user_obj` has from the
        groups they belong.
        
    "
963,"def get_evaluation_sets(self) -> List[dict]:
        
        return self.evaluation_set_client.get_evaluation_sets()
","
        Returns a list of uploaded evaluation sets to deepset cloud.

        :return: list of evaluation sets as dicts
                 These contain (""name"", ""evaluation_set_id"", ""created_at"", ""matched_labels"", ""total_labels"") as fields.
        ",26,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_evaluation_sets(self) -> List[dict]:
        
        return self.evaluation_set_client.get_evaluation_sets()

        ```
    ###Assistant : 
        Returns a list of uploaded evaluation sets to deepset cloud.

        :return: list of evaluation sets as dicts
                 These contain (""name"", ""evaluation_set_id"", ""created_at"", ""matched_labels"", ""total_labels"") as fields.
        
    "
964,"def allowlist_svg(dirty_xml):
    
    from lxml.html import clean

    allow_tags = [
            'xml',
            'svg',
            'circle',
            'ellipse',
            'line',
            'path',
            'polygon',
            'polyline',
            'rect'
    ]

    cleaner = clean.Cleaner(
            allow_tags=allow_tags,
            style=True,
            links=True,
            add_nofollow=False,
            page_structure=True,
            safe_attrs_only=False,
            remove_unknown_tags=False)

    clean_xml = cleaner.clean_html(dirty_xml)
    return clean_xml

","Filter out malicious/harmful content from SVG files
    by defining allowed tags
    ",11,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def allowlist_svg(dirty_xml):
    
    from lxml.html import clean

    allow_tags = [
            'xml',
            'svg',
            'circle',
            'ellipse',
            'line',
            'path',
            'polygon',
            'polyline',
            'rect'
    ]

    cleaner = clean.Cleaner(
            allow_tags=allow_tags,
            style=True,
            links=True,
            add_nofollow=False,
            page_structure=True,
            safe_attrs_only=False,
            remove_unknown_tags=False)

    clean_xml = cleaner.clean_html(dirty_xml)
    return clean_xml


        ```
    ###Assistant : Filter out malicious/harmful content from SVG files
    by defining allowed tags
    
    "
965,"def _sort_filelist(self) -> None:
        
        for filename, image, alignments in self._iterator():
            self.score_image(filename, image, alignments)

        self.sort()
        logger.debug(""sorted list: %s"",
                     [r[0] if isinstance(r, (tuple, list)) else r for r in self._result])
"," Call the sort method's logic to populate the :attr:`_results` attribute.

        Put logic for scoring an individual frame in in :attr:`score_image` of the child

        Returns
        -------
        list
            The sorted file. A list of tuples with the filename in the first position and score in
            the second position
        ",46,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _sort_filelist(self) -> None:
        
        for filename, image, alignments in self._iterator():
            self.score_image(filename, image, alignments)

        self.sort()
        logger.debug(""sorted list: %s"",
                     [r[0] if isinstance(r, (tuple, list)) else r for r in self._result])

        ```
    ###Assistant :  Call the sort method's logic to populate the :attr:`_results` attribute.

        Put logic for scoring an individual frame in in :attr:`score_image` of the child

        Returns
        -------
        list
            The sorted file. A list of tuples with the filename in the first position and score in
            the second position
        
    "
966,"def finalize_variable_values(self, var_list):
        
        if self.use_ema:
            # If the optimizer uses EMA, then when finalizing, we replace the model
            # variable value with its moving average stored inside optimizer.
            self._overwrite_model_variables_with_average_value(var_list)
","Set the final value of model's trainable variables.

        Sometimes there are some extra steps before ending the variable updates,
        such as overriding the model variables with its average value.

        Args:
          var_list: list of model variables.
        ",35,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def finalize_variable_values(self, var_list):
        
        if self.use_ema:
            # If the optimizer uses EMA, then when finalizing, we replace the model
            # variable value with its moving average stored inside optimizer.
            self._overwrite_model_variables_with_average_value(var_list)

        ```
    ###Assistant : Set the final value of model's trainable variables.

        Sometimes there are some extra steps before ending the variable updates,
        such as overriding the model variables with its average value.

        Args:
          var_list: list of model variables.
        
    "
967,"async def wait_floating_requests_end(self):
        
        while self.total_num_floating_tasks_alive > 0:
            await asyncio.sleep(0)
","
        Await this coroutine to make sure that all the floating tasks that the request handler may bring are properly consumed
        ",20,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def wait_floating_requests_end(self):
        
        while self.total_num_floating_tasks_alive > 0:
            await asyncio.sleep(0)

        ```
    ###Assistant : 
        Await this coroutine to make sure that all the floating tasks that the request handler may bring are properly consumed
        
    "
968,"def _lu_impl(A, pivot=True, get_infos=False, out=None):
    # type: (Tensor, bool, bool, Any) -> Tuple[Tensor, Tensor, Tensor]
    r
    # If get_infos is True, then we don't need to check for errors and vice versa
    return torch._lu_with_info(A, pivot=pivot, check_errors=(not get_infos))

if TYPE_CHECKING:
    _ListOrSeq = Sequence[Tensor]
else:
    _ListOrSeq = List[Tensor]
","Computes the LU factorization of a matrix or batches of matrices
    :attr:`A`. Returns a tuple containing the LU factorization and
    pivots of :attr:`A`.  Pivoting is done if :attr:`pivot` is set to
    ``True``.

    .. note::
        * The returned permutation matrix for every matrix in the batch is
          represented by a 1-indexed vector of size ``min(A.shape[-2], A.shape[-1])``.
          ``pivots[i] == j`` represents that in the ``i``-th step of the algorithm,
          the ``i``-th row was permuted with the ``j-1``-th row.
        * LU factorization with :attr:`pivot` = ``False`` is not available
          for CPU, and attempting to do so will throw an error. However,
          LU factorization with :attr:`pivot` = ``False`` is available for
          CUDA.
        * This function does not check if the factorization was successful
          or not if :attr:`get_infos` is ``True`` since the status of the
          factorization is present in the third element of the return tuple.
        * In the case of batches of square matrices with size less or equal
          to 32 on a CUDA device, the LU factorization is repeated for
          singular matrices due to the bug in the MAGMA library
          (see magma issue 13).
        * ``L``, ``U``, and ``P`` can be derived using :func:`torch.lu_unpack`.

    .. warning::
        The gradients of this function will only be finite when :attr:`A` is full rank.
        This is because the LU decomposition is just differentiable at full rank matrices.
        Furthermore, if :attr:`A` is close to not being full rank,
        the gradient will be numerically unstable as it depends on the computation of :math:`L^{-1}` and :math:`U^{-1}`.

    Args:
        A (Tensor): the tensor to factor of size :math:`(*, m, n)`
        pivot (bool, optional): controls whether pivoting is done. Default: ``True``
        get_infos (bool, optional): if set to ``True``, returns an info IntTensor.
                                    Default: ``False``
        out (tuple, optional): optional output tuple. If :attr:`get_infos` is ``True``,
                               then the elements in the tuple are Tensor, IntTensor,
                               and IntTensor. If :attr:`get_infos` is ``False``, then the
                               elements in the tuple are Tensor, IntTensor. Default: ``None``

    Returns:
        (Tensor, IntTensor, IntTensor (optional)): A tuple of tensors containing

            - **factorization** (*Tensor*): the factorization of size :math:`(*, m, n)`

            - **pivots** (*IntTensor*): the pivots of size :math:`(*, \text{min}(m, n))`.
              ``pivots`` stores all the intermediate transpositions of rows.
              The final permutation ``perm`` could be reconstructed by
              applying ``swap(perm[i], perm[pivots[i] - 1])`` for ``i = 0, ..., pivots.size(-1) - 1``,
              where ``perm`` is initially the identity permutation of :math:`m` elements
              (essentially this is what :func:`torch.lu_unpack` is doing).

            - **infos** (*IntTensor*, *optional*): if :attr:`get_infos` is ``True``, this is a tensor of
              size :math:`(*)` where non-zero values indicate whether factorization for the matrix or
              each minibatch has succeeded or failed

    Example::

        >>> A = torch.randn(2, 3, 3)
        >>> A_LU, pivots = torch.lu(A)
        >>> A_LU
        tensor([[[ 1.3506,  2.5558, -0.0816],
                 [ 0.1684,  1.1551,  0.1940],
                 [ 0.1193,  0.6189, -0.5497]],

                [[ 0.4526,  1.2526, -0.3285],
                 [-0.7988,  0.7175, -0.9701],
                 [ 0.2634, -0.9255, -0.3459]]])
        >>> pivots
        tensor([[ 3,  3,  3],
                [ 3,  3,  3]], dtype=torch.int32)
        >>> A_LU, pivots, info = torch.lu(A, get_infos=True)
        >>> if info.nonzero().size(0) == 0:
        ...   print('LU factorization succeeded for all samples!')
        LU factorization succeeded for all samples!
    ",497,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _lu_impl(A, pivot=True, get_infos=False, out=None):
    # type: (Tensor, bool, bool, Any) -> Tuple[Tensor, Tensor, Tensor]
    r
    # If get_infos is True, then we don't need to check for errors and vice versa
    return torch._lu_with_info(A, pivot=pivot, check_errors=(not get_infos))

if TYPE_CHECKING:
    _ListOrSeq = Sequence[Tensor]
else:
    _ListOrSeq = List[Tensor]

        ```
    ###Assistant : Computes the LU factorization of a matrix or batches of matrices
    :attr:`A`. Returns a tuple containing the LU factorization and
    pivots of :attr:`A`.  Pivoting is done if :attr:`pivot` is set to
    ``True``.

    .. note::
        * The returned permutation matrix for every matrix in the batch is
          represented by a 1-indexed vector of size ``min(A.shape[-2], A.shape[-1])``.
          ``pivots[i] == j`` represents that in the ``i``-th step of the algorithm,
          the ``i``-th row was permuted with the ``j-1``-th row.
        * LU factorization with :attr:`pivot` = ``False`` is not available
          for CPU, and attempting to do so will throw an error. However,
          LU factorization with :attr:`pivot` = ``False`` is available for
          CUDA.
        * This function does not check if the factorization was successful
          or not if :attr:`get_infos` is ``True`` since the status of the
          factorization is present in the third element of the return tuple.
        * In the case of batches of square matrices with size less or equal
          to 32 on a CUDA device, the LU factorization is repeated for
          singular matrices due to the bug in the MAGMA library
          (see magma issue 13).
        * ``L``, ``U``, and ``P`` can be derived using :func:`torch.lu_unpack`.

    .. warning::
        The gradients of this function will only be finite when :attr:`A` is full rank.
        This is because the LU decomposition is just differentiable at full rank matrices.
        Furthermore, if :attr:`A` is close to not being full rank,
        the gradient will be numerically unstable as it depends on the computation of :math:`L^{-1}` and :math:`U^{-1}`.

    Args:
        A (Tensor): the tensor to factor of size :math:`(*, m, n)`
        pivot (bool, optional): controls whether pivoting is done. Default: ``True``
        get_infos (bool, optional): if set to ``True``, returns an info IntTensor.
                                    Default: ``False``
        out (tuple, optional): optional output tuple. If :attr:`get_infos` is ``True``,
                               then the elements in the tuple are Tensor, IntTensor,
                               and IntTensor. If :attr:`get_infos` is ``False``, then the
                               elements in the tuple are Tensor, IntTensor. Default: ``None``

    Returns:
        (Tensor, IntTensor, IntTensor (optional)): A tuple of tensors containing

            - **factorization** (*Tensor*): the factorization of size :math:`(*, m, n)`

            - **pivots** (*IntTensor*): the pivots of size :math:`(*, \text{min}(m, n))`.
              ``pivots`` stores all the intermediate transpositions of rows.
              The final permutation ``perm`` could be reconstructed by
              applying ``swap(perm[i], perm[pivots[i] - 1])`` for ``i = 0, ..., pivots.size(-1) - 1``,
              where ``perm`` is initially the identity permutation of :math:`m` elements
              (essentially this is what :func:`torch.lu_unpack` is doing).

            - **infos** (*IntTensor*, *optional*): if :attr:`get_infos` is ``True``, this is a tensor of
              size :math:`(*)` where non-zero values indicate whether factorization for the matrix or
              each minibatch has succeeded or failed

    Example::

        >>> A = torch.randn(2, 3, 3)
        >>> A_LU, pivots = torch.lu(A)
        >>> A_LU
        tensor([[[ 1.3506,  2.5558, -0.0816],
                 [ 0.1684,  1.1551,  0.1940],
                 [ 0.1193,  0.6189, -0.5497]],

                [[ 0.4526,  1.2526, -0.3285],
                 [-0.7988,  0.7175, -0.9701],
                 [ 0.2634, -0.9255, -0.3459]]])
        >>> pivots
        tensor([[ 3,  3,  3],
                [ 3,  3,  3]], dtype=torch.int32)
        >>> A_LU, pivots, info = torch.lu(A, get_infos=True)
        >>> if info.nonzero().size(0) == 0:
        ...   print('LU factorization succeeded for all samples!')
        LU factorization succeeded for all samples!
    
    "
969,"def test_new_configs_appservice_worker(self) -> None:
        
        appservice_worker_config = self._make_worker_config(
            worker_app=""synapse.app.generic_worker"", worker_name=""worker1""
        )

        self.assertTrue(
            appservice_worker_config._should_this_worker_perform_duty(
                {
                    ""notify_appservices_from_worker"": ""worker1"",
                },
                ""notify_appservices"",
                ""synapse.app.appservice"",
                ""notify_appservices_from_worker"",
            )
        )

        self.assertFalse(
            appservice_worker_config._should_this_worker_perform_duty(
                {
                    ""notify_appservices_from_worker"": ""worker2"",
                },
                ""notify_appservices"",
                ""synapse.app.appservice"",
                ""notify_appservices_from_worker"",
            )
        )
","
        Tests new config options. This is for the worker's config.
        ",10,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_new_configs_appservice_worker(self) -> None:
        
        appservice_worker_config = self._make_worker_config(
            worker_app=""synapse.app.generic_worker"", worker_name=""worker1""
        )

        self.assertTrue(
            appservice_worker_config._should_this_worker_perform_duty(
                {
                    ""notify_appservices_from_worker"": ""worker1"",
                },
                ""notify_appservices"",
                ""synapse.app.appservice"",
                ""notify_appservices_from_worker"",
            )
        )

        self.assertFalse(
            appservice_worker_config._should_this_worker_perform_duty(
                {
                    ""notify_appservices_from_worker"": ""worker2"",
                },
                ""notify_appservices"",
                ""synapse.app.appservice"",
                ""notify_appservices_from_worker"",
            )
        )

        ```
    ###Assistant : 
        Tests new config options. This is for the worker's config.
        
    "
970,"def E_nl(n, Z=1):
    
    n, Z = S(n), S(Z)
    if n.is_integer and (n < 1):
        raise ValueError(""'n' must be positive integer"")
    return -Z**2/(2*n**2)

","
    Returns the energy of the state (n, l) in Hartree atomic units.

    The energy does not depend on ""l"".

    Parameters
    ==========

    n : integer
        Principal Quantum Number which is
        an integer with possible values as 1, 2, 3, 4,...
    Z :
        Atomic number (1 for Hydrogen, 2 for Helium, ...)

    Examples
    ========

    >>> from sympy.physics.hydrogen import E_nl
    >>> from sympy.abc import n, Z
    >>> E_nl(n, Z)
    -Z**2/(2*n**2)
    >>> E_nl(1)
    -1/2
    >>> E_nl(2)
    -1/8
    >>> E_nl(3)
    -1/18
    >>> E_nl(3, 47)
    -2209/18

    ",80,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def E_nl(n, Z=1):
    
    n, Z = S(n), S(Z)
    if n.is_integer and (n < 1):
        raise ValueError(""'n' must be positive integer"")
    return -Z**2/(2*n**2)


        ```
    ###Assistant : 
    Returns the energy of the state (n, l) in Hartree atomic units.

    The energy does not depend on ""l"".

    Parameters
    ==========

    n : integer
        Principal Quantum Number which is
        an integer with possible values as 1, 2, 3, 4,...
    Z :
        Atomic number (1 for Hydrogen, 2 for Helium, ...)

    Examples
    ========

    >>> from sympy.physics.hydrogen import E_nl
    >>> from sympy.abc import n, Z
    >>> E_nl(n, Z)
    -Z**2/(2*n**2)
    >>> E_nl(1)
    -1/2
    >>> E_nl(2)
    -1/8
    >>> E_nl(3)
    -1/18
    >>> E_nl(3, 47)
    -2209/18

    
    "
971,"def test_model_checkpoint_no_extraneous_invocations(tmpdir):
    
    model = LogInTwoMethods()
    num_epochs = 4
    model_checkpoint = ModelCheckpointTestInvocations(monitor=""early_stop_on"", expected_count=num_epochs, save_top_k=-1)
    trainer = Trainer(
        strategy=""ddp_spawn"",
        accelerator=""cpu"",
        devices=2,
        default_root_dir=tmpdir,
        callbacks=[model_checkpoint],
        max_epochs=num_epochs,
    )
    trainer.fit(model)
    assert trainer.state.finished, f""Training failed with {trainer.state}""

",Test to ensure that the model callback saves the checkpoints only once in distributed mode.,15,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_model_checkpoint_no_extraneous_invocations(tmpdir):
    
    model = LogInTwoMethods()
    num_epochs = 4
    model_checkpoint = ModelCheckpointTestInvocations(monitor=""early_stop_on"", expected_count=num_epochs, save_top_k=-1)
    trainer = Trainer(
        strategy=""ddp_spawn"",
        accelerator=""cpu"",
        devices=2,
        default_root_dir=tmpdir,
        callbacks=[model_checkpoint],
        max_epochs=num_epochs,
    )
    trainer.fit(model)
    assert trainer.state.finished, f""Training failed with {trainer.state}""


        ```
    ###Assistant : Test to ensure that the model callback saves the checkpoints only once in distributed mode.
    "
972,"def find_module(self, fullname, path):
        
        warnings.warn(""MetaPathFinder.find_module() is deprecated since Python ""
                      ""3.4 in favor of MetaPathFinder.find_spec() and is ""
                      ""slated for removal in Python 3.12"",
                      DeprecationWarning,
                      stacklevel=2)
        if not hasattr(self, 'find_spec'):
            return None
        found = self.find_spec(fullname, path)
        return found.loader if found is not None else None
","Return a loader for the module.

        If no module is found, return None.  The fullname is a str and
        the path is a list of strings or None.

        This method is deprecated since Python 3.4 in favor of
        finder.find_spec(). If find_spec() exists then backwards-compatible
        functionality is provided for this method.

        ",50,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_module(self, fullname, path):
        
        warnings.warn(""MetaPathFinder.find_module() is deprecated since Python ""
                      ""3.4 in favor of MetaPathFinder.find_spec() and is ""
                      ""slated for removal in Python 3.12"",
                      DeprecationWarning,
                      stacklevel=2)
        if not hasattr(self, 'find_spec'):
            return None
        found = self.find_spec(fullname, path)
        return found.loader if found is not None else None

        ```
    ###Assistant : Return a loader for the module.

        If no module is found, return None.  The fullname is a str and
        the path is a list of strings or None.

        This method is deprecated since Python 3.4 in favor of
        finder.find_spec(). If find_spec() exists then backwards-compatible
        functionality is provided for this method.

        
    "
973,"def _generate_individual(self, parameter_id):
        
        pos = -1

        for i in range(len(self.population)):
            if self.population[i].result is None:
                pos = i
                break

        if pos != -1:
            indiv = copy.deepcopy(self.population[pos])
            self.population.pop(pos)
        else:
            random.shuffle(self.population)
            # avoid only 1 individual has result
            if len(self.population) > 1 and self.population[0].result < self.population[1].result:
                self.population[0] = self.population[1]

            # mutation on the worse individual
            space = json2space(self.searchspace_json,
                               self.population[0].config)
            is_rand = dict()
            mutation_pos = space[random.randint(0, len(space)-1)]

            for i in range(len(self.space)):
                is_rand[self.space[i]] = (self.space[i] == mutation_pos)
            config = json2parameter(
                self.searchspace_json, is_rand, self.random_state, self.population[0].config)

            if len(self.population) > 1:
                self.population.pop(1)

            indiv = Individual(config=config)

        # remove ""_index"" from config and save params-id
        self.running_trials[parameter_id] = indiv
        config = split_index(indiv.config)
        return config

","
        This function will generate the config for a trial.
        If at the first generation, randomly generates individuals to satisfy self.population_size.
        Otherwise, random choose a pair of individuals and compare their fitnesses.
        The worst of the pair will be removed. Copy the best of the pair and mutate it to generate a new individual.

        Parameters
        ----------

        parameter_id : int

        Returns
        -------
        dict
            A group of candidate parameters that evolution tuner generated.
        ",70,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _generate_individual(self, parameter_id):
        
        pos = -1

        for i in range(len(self.population)):
            if self.population[i].result is None:
                pos = i
                break

        if pos != -1:
            indiv = copy.deepcopy(self.population[pos])
            self.population.pop(pos)
        else:
            random.shuffle(self.population)
            # avoid only 1 individual has result
            if len(self.population) > 1 and self.population[0].result < self.population[1].result:
                self.population[0] = self.population[1]

            # mutation on the worse individual
            space = json2space(self.searchspace_json,
                               self.population[0].config)
            is_rand = dict()
            mutation_pos = space[random.randint(0, len(space)-1)]

            for i in range(len(self.space)):
                is_rand[self.space[i]] = (self.space[i] == mutation_pos)
            config = json2parameter(
                self.searchspace_json, is_rand, self.random_state, self.population[0].config)

            if len(self.population) > 1:
                self.population.pop(1)

            indiv = Individual(config=config)

        # remove ""_index"" from config and save params-id
        self.running_trials[parameter_id] = indiv
        config = split_index(indiv.config)
        return config


        ```
    ###Assistant : 
        This function will generate the config for a trial.
        If at the first generation, randomly generates individuals to satisfy self.population_size.
        Otherwise, random choose a pair of individuals and compare their fitnesses.
        The worst of the pair will be removed. Copy the best of the pair and mutate it to generate a new individual.

        Parameters
        ----------

        parameter_id : int

        Returns
        -------
        dict
            A group of candidate parameters that evolution tuner generated.
        
    "
974,"async def connect(self):
        

        connection = {""client_id"": self.client_id, ""websocket"": self.websocket}
        logging.info(f""Connecting WebSocket: {connection}"")
        await self.websocket.accept()
        WSProgressHandler.instances.append(self)
","
        Called when a new client connects to the websocket.
        ",9,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def connect(self):
        

        connection = {""client_id"": self.client_id, ""websocket"": self.websocket}
        logging.info(f""Connecting WebSocket: {connection}"")
        await self.websocket.accept()
        WSProgressHandler.instances.append(self)

        ```
    ###Assistant : 
        Called when a new client connects to the websocket.
        
    "
975,"def prepare_test_img(self, idx):
        

        img_info = self.data_infos[idx]
        results = dict(img_info=img_info)
        if self.proposals is not None:
            results['proposals'] = self.proposals[idx]
        self.pre_pipeline(results)
        return self.pipeline(results)
","Get testing data after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Testing data after pipeline with new keys introduced by \
                pipeline.
        ",24,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def prepare_test_img(self, idx):
        

        img_info = self.data_infos[idx]
        results = dict(img_info=img_info)
        if self.proposals is not None:
            results['proposals'] = self.proposals[idx]
        self.pre_pipeline(results)
        return self.pipeline(results)

        ```
    ###Assistant : Get testing data after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Testing data after pipeline with new keys introduced by \
                pipeline.
        
    "
976,"def get_data(filters):
	data = []

	conditions = get_conditions(filters)

	salary_slips = frappe.db.sql(
		
		% (conditions),
		as_dict=1,
	)

	component_type_dict = frappe._dict(
		frappe.db.sql(
			
		)
	)

	if not len(component_type_dict):
		return []

	entry = frappe.db.sql(
		
		% (conditions, "", "".join([""%s""] * len(component_type_dict))),
		tuple(component_type_dict.keys()),
		as_dict=1,
	)

	data_list = prepare_data(entry, component_type_dict)

	for d in salary_slips:
		total = 0
		if data_list.get(d.name):
			employee = {
				""employee"": data_list.get(d.name).get(""employee""),
				""employee_name"": data_list.get(d.name).get(""employee_name""),
				""pf_account"": data_list.get(d.name).get(""pf_account""),
			}

			if data_list.get(d.name).get(""Provident Fund""):
				employee[""pf_amount""] = data_list.get(d.name).get(""Provident Fund"")
				total += data_list.get(d.name).get(""Provident Fund"")

			if data_list.get(d.name).get(""Additional Provident Fund""):
				employee[""additional_pf""] = data_list.get(d.name).get(""Additional Provident Fund"")
				total += data_list.get(d.name).get(""Additional Provident Fund"")

			if data_list.get(d.name).get(""Provident Fund Loan""):
				employee[""pf_loan""] = data_list.get(d.name).get(""Provident Fund Loan"")
				total += data_list.get(d.name).get(""Provident Fund Loan"")

			employee[""total""] = total

			data.append(employee)

	return data


@frappe.whitelist()"," select sal.name from `tabSalary Slip` sal
		where docstatus = 1 %s
		 select name, component_type from `tabSalary Component`
		where component_type in ('Provident Fund', 'Additional Provident Fund', 'Provident Fund Loan') select sal.name, sal.employee, sal.employee_name, ded.salary_component, ded.amount
		from `tabSalary Slip` sal, `tabSalary Detail` ded
		where sal.name = ded.parent
		and ded.parentfield = 'deductions'
		and ded.parenttype = 'Salary Slip'
		and sal.docstatus = 1 %s
		and ded.salary_component in (%s)
	",63,107,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_data(filters):
	data = []

	conditions = get_conditions(filters)

	salary_slips = frappe.db.sql(
		
		% (conditions),
		as_dict=1,
	)

	component_type_dict = frappe._dict(
		frappe.db.sql(
			
		)
	)

	if not len(component_type_dict):
		return []

	entry = frappe.db.sql(
		
		% (conditions, "", "".join([""%s""] * len(component_type_dict))),
		tuple(component_type_dict.keys()),
		as_dict=1,
	)

	data_list = prepare_data(entry, component_type_dict)

	for d in salary_slips:
		total = 0
		if data_list.get(d.name):
			employee = {
				""employee"": data_list.get(d.name).get(""employee""),
				""employee_name"": data_list.get(d.name).get(""employee_name""),
				""pf_account"": data_list.get(d.name).get(""pf_account""),
			}

			if data_list.get(d.name).get(""Provident Fund""):
				employee[""pf_amount""] = data_list.get(d.name).get(""Provident Fund"")
				total += data_list.get(d.name).get(""Provident Fund"")

			if data_list.get(d.name).get(""Additional Provident Fund""):
				employee[""additional_pf""] = data_list.get(d.name).get(""Additional Provident Fund"")
				total += data_list.get(d.name).get(""Additional Provident Fund"")

			if data_list.get(d.name).get(""Provident Fund Loan""):
				employee[""pf_loan""] = data_list.get(d.name).get(""Provident Fund Loan"")
				total += data_list.get(d.name).get(""Provident Fund Loan"")

			employee[""total""] = total

			data.append(employee)

	return data


@frappe.whitelist()
        ```
    ###Assistant :  select sal.name from `tabSalary Slip` sal
		where docstatus = 1 %s
		 select name, component_type from `tabSalary Component`
		where component_type in ('Provident Fund', 'Additional Provident Fund', 'Provident Fund Loan') select sal.name, sal.employee, sal.employee_name, ded.salary_component, ded.amount
		from `tabSalary Slip` sal, `tabSalary Detail` ded
		where sal.name = ded.parent
		and ded.parentfield = 'deductions'
		and ded.parenttype = 'Salary Slip'
		and sal.docstatus = 1 %s
		and ded.salary_component in (%s)
	
    "
977,"def as_dict(self) -> dict[str, Any]:
        
        return {
            ""extended_dict"": self.as_extended_dict(),
            ""short_dict"": self.as_short_dict(),
        }
",Return an dictionary version of this ActionTrace for saving.,9,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def as_dict(self) -> dict[str, Any]:
        
        return {
            ""extended_dict"": self.as_extended_dict(),
            ""short_dict"": self.as_short_dict(),
        }

        ```
    ###Assistant : Return an dictionary version of this ActionTrace for saving.
    "
978,"def deserialize(config, custom_objects=None, **kwargs):
    
    # loss_scale_optimizer has a direct dependency of optimizer, import here
    # rather than top to avoid the cyclic dependency.
    from keras.mixed_precision import (
        loss_scale_optimizer,
    )

    use_legacy_optimizer = kwargs.pop(""use_legacy_optimizer"", False)
    if len(config[""config""]) > 0:
        # If the optimizer config is not empty, then we use the value of
        # `is_legacy_optimizer` to override `use_legacy_optimizer`. If
        # `is_legacy_optimizer` does not exist in config, it means we are
        # using the legacy optimzier.
        use_legacy_optimizer = config[""config""].get(""is_legacy_optimizer"", True)
    if (
        tf.__internal__.tf2.enabled()
        and tf.executing_eagerly()
        and not use_legacy_optimizer
    ):
        all_classes = {
            ""adadelta"": adadelta_experimental.Adadelta,
            ""adagrad"": adagrad_experimental.Adagrad,
            ""adam"": adam_experimental.Adam,
            ""adamax"": adamax_experimental.Adamax,
            ""experimentaladadelta"": adadelta_experimental.Adadelta,
            ""experimentaladagrad"": adagrad_experimental.Adagrad,
            ""experimentaladam"": adam_experimental.Adam,
            ""experimentalsgd"": sgd_experimental.SGD,
            ""nadam"": nadam_experimental.Nadam,
            ""rmsprop"": rmsprop_experimental.RMSprop,
            ""sgd"": sgd_experimental.SGD,
            ""ftrl"": ftrl_experimental.Ftrl,
            ""lossscaleoptimizer"": loss_scale_optimizer.LossScaleOptimizerV3,
            ""lossscaleoptimizerv3"": loss_scale_optimizer.LossScaleOptimizerV3,
            # LossScaleOptimizerV1 was an old version of LSO that was removed.
            # Deserializing it turns it into a LossScaleOptimizer
            ""lossscaleoptimizerv1"": loss_scale_optimizer.LossScaleOptimizer,
        }
    else:
        all_classes = {
            ""adadelta"": adadelta_v2.Adadelta,
            ""adagrad"": adagrad_v2.Adagrad,
            ""adam"": adam_v2.Adam,
            ""adamax"": adamax_v2.Adamax,
            ""experimentaladadelta"": adadelta_experimental.Adadelta,
            ""experimentaladagrad"": adagrad_experimental.Adagrad,
            ""experimentaladam"": adam_experimental.Adam,
            ""experimentalsgd"": sgd_experimental.SGD,
            ""nadam"": nadam_v2.Nadam,
            ""rmsprop"": rmsprop_v2.RMSprop,
            ""sgd"": gradient_descent_v2.SGD,
            ""ftrl"": ftrl_v2.Ftrl,
            ""lossscaleoptimizer"": loss_scale_optimizer.LossScaleOptimizer,
            ""lossscaleoptimizerv3"": loss_scale_optimizer.LossScaleOptimizerV3,
            # LossScaleOptimizerV1 was an old version of LSO that was removed.
            # Deserializing it turns it into a LossScaleOptimizer
            ""lossscaleoptimizerv1"": loss_scale_optimizer.LossScaleOptimizer,
        }

    # Make deserialization case-insensitive for built-in optimizers.
    if config[""class_name""].lower() in all_classes:
        config[""class_name""] = config[""class_name""].lower()
    return deserialize_keras_object(
        config,
        module_objects=all_classes,
        custom_objects=custom_objects,
        printable_module_name=""optimizer"",
    )


@keras_export(
    ""keras.__internal__.optimizers.convert_to_legacy_optimizer"", v1=[]
)","Inverse of the `serialize` function.

    Args:
        config: Optimizer configuration dictionary.
        custom_objects: Optional dictionary mapping names (strings) to custom
            objects (classes and functions) to be considered during
            deserialization.

    Returns:
        A Keras Optimizer instance.
    ",32,218,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deserialize(config, custom_objects=None, **kwargs):
    
    # loss_scale_optimizer has a direct dependency of optimizer, import here
    # rather than top to avoid the cyclic dependency.
    from keras.mixed_precision import (
        loss_scale_optimizer,
    )

    use_legacy_optimizer = kwargs.pop(""use_legacy_optimizer"", False)
    if len(config[""config""]) > 0:
        # If the optimizer config is not empty, then we use the value of
        # `is_legacy_optimizer` to override `use_legacy_optimizer`. If
        # `is_legacy_optimizer` does not exist in config, it means we are
        # using the legacy optimzier.
        use_legacy_optimizer = config[""config""].get(""is_legacy_optimizer"", True)
    if (
        tf.__internal__.tf2.enabled()
        and tf.executing_eagerly()
        and not use_legacy_optimizer
    ):
        all_classes = {
            ""adadelta"": adadelta_experimental.Adadelta,
            ""adagrad"": adagrad_experimental.Adagrad,
            ""adam"": adam_experimental.Adam,
            ""adamax"": adamax_experimental.Adamax,
            ""experimentaladadelta"": adadelta_experimental.Adadelta,
            ""experimentaladagrad"": adagrad_experimental.Adagrad,
            ""experimentaladam"": adam_experimental.Adam,
            ""experimentalsgd"": sgd_experimental.SGD,
            ""nadam"": nadam_experimental.Nadam,
            ""rmsprop"": rmsprop_experimental.RMSprop,
            ""sgd"": sgd_experimental.SGD,
            ""ftrl"": ftrl_experimental.Ftrl,
            ""lossscaleoptimizer"": loss_scale_optimizer.LossScaleOptimizerV3,
            ""lossscaleoptimizerv3"": loss_scale_optimizer.LossScaleOptimizerV3,
            # LossScaleOptimizerV1 was an old version of LSO that was removed.
            # Deserializing it turns it into a LossScaleOptimizer
            ""lossscaleoptimizerv1"": loss_scale_optimizer.LossScaleOptimizer,
        }
    else:
        all_classes = {
            ""adadelta"": adadelta_v2.Adadelta,
            ""adagrad"": adagrad_v2.Adagrad,
            ""adam"": adam_v2.Adam,
            ""adamax"": adamax_v2.Adamax,
            ""experimentaladadelta"": adadelta_experimental.Adadelta,
            ""experimentaladagrad"": adagrad_experimental.Adagrad,
            ""experimentaladam"": adam_experimental.Adam,
            ""experimentalsgd"": sgd_experimental.SGD,
            ""nadam"": nadam_v2.Nadam,
            ""rmsprop"": rmsprop_v2.RMSprop,
            ""sgd"": gradient_descent_v2.SGD,
            ""ftrl"": ftrl_v2.Ftrl,
            ""lossscaleoptimizer"": loss_scale_optimizer.LossScaleOptimizer,
            ""lossscaleoptimizerv3"": loss_scale_optimizer.LossScaleOptimizerV3,
            # LossScaleOptimizerV1 was an old version of LSO that was removed.
            # Deserializing it turns it into a LossScaleOptimizer
            ""lossscaleoptimizerv1"": loss_scale_optimizer.LossScaleOptimizer,
        }

    # Make deserialization case-insensitive for built-in optimizers.
    if config[""class_name""].lower() in all_classes:
        config[""class_name""] = config[""class_name""].lower()
    return deserialize_keras_object(
        config,
        module_objects=all_classes,
        custom_objects=custom_objects,
        printable_module_name=""optimizer"",
    )


@keras_export(
    ""keras.__internal__.optimizers.convert_to_legacy_optimizer"", v1=[]
)
        ```
    ###Assistant : Inverse of the `serialize` function.

    Args:
        config: Optimizer configuration dictionary.
        custom_objects: Optional dictionary mapping names (strings) to custom
            objects (classes and functions) to be considered during
            deserialization.

    Returns:
        A Keras Optimizer instance.
    
    "
979,"def multiply(self, a, b):
        
        a = _convert_other(a, raiseit=True)
        r = a.__mul__(b, context=self)
        if r is NotImplemented:
            raise TypeError(""Unable to convert %s to Decimal"" % b)
        else:
            return r
","multiply multiplies two operands.

        If either operand is a special value then the general rules apply.
        Otherwise, the operands are multiplied together
        ('long multiplication'), resulting in a number which may be as long as
        the sum of the lengths of the two operands.

        >>> ExtendedContext.multiply(Decimal('1.20'), Decimal('3'))
        Decimal('3.60')
        >>> ExtendedContext.multiply(Decimal('7'), Decimal('3'))
        Decimal('21')
        >>> ExtendedContext.multiply(Decimal('0.9'), Decimal('0.8'))
        Decimal('0.72')
        >>> ExtendedContext.multiply(Decimal('0.9'), Decimal('-0'))
        Decimal('-0.0')
        >>> ExtendedContext.multiply(Decimal('654321'), Decimal('654321'))
        Decimal('4.28135971E+11')
        >>> ExtendedContext.multiply(7, 7)
        Decimal('49')
        >>> ExtendedContext.multiply(Decimal(7), 7)
        Decimal('49')
        >>> ExtendedContext.multiply(7, Decimal(7))
        Decimal('49')
        ",75,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def multiply(self, a, b):
        
        a = _convert_other(a, raiseit=True)
        r = a.__mul__(b, context=self)
        if r is NotImplemented:
            raise TypeError(""Unable to convert %s to Decimal"" % b)
        else:
            return r

        ```
    ###Assistant : multiply multiplies two operands.

        If either operand is a special value then the general rules apply.
        Otherwise, the operands are multiplied together
        ('long multiplication'), resulting in a number which may be as long as
        the sum of the lengths of the two operands.

        >>> ExtendedContext.multiply(Decimal('1.20'), Decimal('3'))
        Decimal('3.60')
        >>> ExtendedContext.multiply(Decimal('7'), Decimal('3'))
        Decimal('21')
        >>> ExtendedContext.multiply(Decimal('0.9'), Decimal('0.8'))
        Decimal('0.72')
        >>> ExtendedContext.multiply(Decimal('0.9'), Decimal('-0'))
        Decimal('-0.0')
        >>> ExtendedContext.multiply(Decimal('654321'), Decimal('654321'))
        Decimal('4.28135971E+11')
        >>> ExtendedContext.multiply(7, 7)
        Decimal('49')
        >>> ExtendedContext.multiply(Decimal(7), 7)
        Decimal('49')
        >>> ExtendedContext.multiply(7, Decimal(7))
        Decimal('49')
        
    "
980,"def test_run_cleanup_skip_archive(self, cleanup_table_mock, kwargs, should_skip):
        
        run_cleanup(
            clean_before_timestamp=None,
            table_names=['log'],
            dry_run=None,
            verbose=None,
            confirm=False,
            **kwargs,
        )
        assert cleanup_table_mock.call_args[1]['skip_archive'] is should_skip
",test that delete confirmation input is called when appropriate,9,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_run_cleanup_skip_archive(self, cleanup_table_mock, kwargs, should_skip):
        
        run_cleanup(
            clean_before_timestamp=None,
            table_names=['log'],
            dry_run=None,
            verbose=None,
            confirm=False,
            **kwargs,
        )
        assert cleanup_table_mock.call_args[1]['skip_archive'] is should_skip

        ```
    ###Assistant : test that delete confirmation input is called when appropriate
    "
981,"def process(self) -> None:
        
        if not self.is_downloaded():
            self.download()
        self.process_downloaded_dataset()
",Process the dataset into a dataframe and save it at self.processed_dataset_path.,11,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def process(self) -> None:
        
        if not self.is_downloaded():
            self.download()
        self.process_downloaded_dataset()

        ```
    ###Assistant : Process the dataset into a dataframe and save it at self.processed_dataset_path.
    "
982,"def test_healthy_only_works_for_list_of_functions(self):
        
        actors = [Actor.remote(i) for i in range(4)]
        manager = FaultTolerantActorManager(actors=actors)

        # Mark first and second actor as unhealthy.
        manager.set_actor_state(1, False)
        manager.set_actor_state(2, False)
",Test healthy only mode works when a list of funcs are provided.,12,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_healthy_only_works_for_list_of_functions(self):
        
        actors = [Actor.remote(i) for i in range(4)]
        manager = FaultTolerantActorManager(actors=actors)

        # Mark first and second actor as unhealthy.
        manager.set_actor_state(1, False)
        manager.set_actor_state(2, False)

        ```
    ###Assistant : Test healthy only mode works when a list of funcs are provided.
    "
983,"def test_unassignment(self, mock_func):
        
        notification = UnassignedActivityNotification(
            Activity(
                project=self.project,
                group=self.group,
                user=self.user,
                type=ActivityType.ASSIGNED,
                data={""assignee"": """"},
            )
        )
        with self.tasks():
            notification.send()

        attachment, text = get_attachment()
        assert text == f""Issue unassigned by {self.name}""
        assert attachment[""title""] == self.group.title
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=unassigned_activity-slack-user|Notification Settings>""
        )
","
        Test that a Slack message is sent with the expected payload when an issue is unassigned
        ",16,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unassignment(self, mock_func):
        
        notification = UnassignedActivityNotification(
            Activity(
                project=self.project,
                group=self.group,
                user=self.user,
                type=ActivityType.ASSIGNED,
                data={""assignee"": """"},
            )
        )
        with self.tasks():
            notification.send()

        attachment, text = get_attachment()
        assert text == f""Issue unassigned by {self.name}""
        assert attachment[""title""] == self.group.title
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=unassigned_activity-slack-user|Notification Settings>""
        )

        ```
    ###Assistant : 
        Test that a Slack message is sent with the expected payload when an issue is unassigned
        
    "
984,"def print_help(self):
        
        has_ticker_start = ""[unvl]"" if not self.ticker else """"
        has_ticker_end = ""[/unvl]"" if not self.ticker else """"

        help_text = f
        console.print(text=help_text, menu=""Stocks - Insider Trading"")
","Print help[cmds]
    view          view available presets
    set           set one of the available presets[/cmds]

[param]PRESET: [/param]{self.preset}[cmds]

    filter        filter insiders based on preset [src][Open Insider][/src]


    load          load a specific stock ticker for analysis[/cmds]
{has_ticker_start}
[param]Ticker: [/param]{self.ticker}

    stats         insider stats of the company [src][Open Insider][/src]
    act           insider activity over time [src][Business Insider][/src]
    lins          last insider trading of the company [src][Finviz][/src]
{has_ticker_end}

[info]Latest Insiders[/info] [src][Open Insider][/src][cmds]
    lcb           latest cluster boys
    lpsb          latest penny stock buys
    lit           latest insider trading (all filings)
    lip           latest insider purchases
    blip          big latest insider purchases ($25k+)
    blop          big latest officer purchases ($25k+)
    blcp          big latest CEO/CFO purchases ($25k+)
    lis           latest insider sales
    blis          big latest insider sales ($100k+)
    blos          big latest officer sales ($100k+)
    blcs          big latest CEO/CFO sales ($100k+)
[info]Top Insiders [src][Open Insider][/src][/info]
    topt          top officer purchases today
    toppw         top officer purchases past week
    toppm         top officer purchases past month
    tipt          top insider purchases today
    tippw         top insider purchases past week
    tippm         top insider purchases past month
    tist          top insider sales today
    tispw         top insider sales past week
    tispm         top insider sales past month[/cmds]
",176,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        
        has_ticker_start = ""[unvl]"" if not self.ticker else """"
        has_ticker_end = ""[/unvl]"" if not self.ticker else """"

        help_text = f
        console.print(text=help_text, menu=""Stocks - Insider Trading"")

        ```
    ###Assistant : Print help[cmds]
    view          view available presets
    set           set one of the available presets[/cmds]

[param]PRESET: [/param]{self.preset}[cmds]

    filter        filter insiders based on preset [src][Open Insider][/src]


    load          load a specific stock ticker for analysis[/cmds]
{has_ticker_start}
[param]Ticker: [/param]{self.ticker}

    stats         insider stats of the company [src][Open Insider][/src]
    act           insider activity over time [src][Business Insider][/src]
    lins          last insider trading of the company [src][Finviz][/src]
{has_ticker_end}

[info]Latest Insiders[/info] [src][Open Insider][/src][cmds]
    lcb           latest cluster boys
    lpsb          latest penny stock buys
    lit           latest insider trading (all filings)
    lip           latest insider purchases
    blip          big latest insider purchases ($25k+)
    blop          big latest officer purchases ($25k+)
    blcp          big latest CEO/CFO purchases ($25k+)
    lis           latest insider sales
    blis          big latest insider sales ($100k+)
    blos          big latest officer sales ($100k+)
    blcs          big latest CEO/CFO sales ($100k+)
[info]Top Insiders [src][Open Insider][/src][/info]
    topt          top officer purchases today
    toppw         top officer purchases past week
    toppm         top officer purchases past month
    tipt          top insider purchases today
    tippw         top insider purchases past week
    tippm         top insider purchases past month
    tist          top insider sales today
    tispw         top insider sales past week
    tispm         top insider sales past month[/cmds]

    "
985,"def test_project_config_dynamic_sampling_is_none(default_project):
    
    default_project.update_option(""sentry:dynamic_sampling"", None)

    with Feature({""organizations:server-side-sampling"": True}):
        cfg = get_project_config(default_project)

    cfg = cfg.to_dict()
    dynamic_sampling = get_path(cfg, ""config"", ""dynamicSampling"")

    assert dynamic_sampling is None


@pytest.mark.django_db","
    Tests test check inc-237 that dynamic sampling is None,
    so it's pass when we have fix and fails when we dont
    ",21,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_project_config_dynamic_sampling_is_none(default_project):
    
    default_project.update_option(""sentry:dynamic_sampling"", None)

    with Feature({""organizations:server-side-sampling"": True}):
        cfg = get_project_config(default_project)

    cfg = cfg.to_dict()
    dynamic_sampling = get_path(cfg, ""config"", ""dynamicSampling"")

    assert dynamic_sampling is None


@pytest.mark.django_db
        ```
    ###Assistant : 
    Tests test check inc-237 that dynamic sampling is None,
    so it's pass when we have fix and fails when we dont
    
    "
986,"def seek(self, offset, whence=io.SEEK_SET):
        
        self._check_can_seek()
        return self._buffer.seek(offset, whence)
","Change the file position.

        The new position is specified by offset, relative to the
        position indicated by whence. Values for whence are:

            0: start of stream (default); offset must not be negative
            1: current stream position
            2: end of stream; offset must not be positive

        Returns the new file position.

        Note that seeking is emulated, so depending on the parameters,
        this operation may be extremely slow.
        ",66,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def seek(self, offset, whence=io.SEEK_SET):
        
        self._check_can_seek()
        return self._buffer.seek(offset, whence)

        ```
    ###Assistant : Change the file position.

        The new position is specified by offset, relative to the
        position indicated by whence. Values for whence are:

            0: start of stream (default); offset must not be negative
            1: current stream position
            2: end of stream; offset must not be positive

        Returns the new file position.

        Note that seeking is emulated, so depending on the parameters,
        this operation may be extremely slow.
        
    "
987,"def savepoint(self):
        
        if not self._savepoint_allowed():
            return

        thread_ident = _thread.get_ident()
        tid = str(thread_ident).replace(""-"", """")

        self.savepoint_state += 1
        sid = ""s%s_x%d"" % (tid, self.savepoint_state)

        self.validate_thread_sharing()
        self._savepoint(sid)

        return sid
","
        Create a savepoint inside the current transaction. Return an
        identifier for the savepoint that will be used for the subsequent
        rollback or commit. Do nothing if savepoints are not supported.
        ",30,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def savepoint(self):
        
        if not self._savepoint_allowed():
            return

        thread_ident = _thread.get_ident()
        tid = str(thread_ident).replace(""-"", """")

        self.savepoint_state += 1
        sid = ""s%s_x%d"" % (tid, self.savepoint_state)

        self.validate_thread_sharing()
        self._savepoint(sid)

        return sid

        ```
    ###Assistant : 
        Create a savepoint inside the current transaction. Return an
        identifier for the savepoint that will be used for the subsequent
        rollback or commit. Do nothing if savepoints are not supported.
        
    "
988,"def get_unclaimed_expese_claims(filters):
	cond = ""1=1""
	if filters.get(""employee""):
		cond = ""ec.employee = %(employee)s""

	return frappe.db.sql(
		.format(
			cond=cond
		),
		filters,
		as_list=1,
	)
","
		select
			ec.employee, ec.employee_name, ec.name, ec.total_sanctioned_amount, ec.total_amount_reimbursed,
			sum(gle.credit_in_account_currency - gle.debit_in_account_currency) as outstanding_amt
		from
			`tabExpense Claim` ec, `tabGL Entry` gle
		where
			gle.against_voucher_type = ""Expense Claim"" and gle.against_voucher = ec.name
			and gle.party is not null and ec.docstatus = 1 and ec.is_paid = 0 and {cond} group by ec.name
		having
			outstanding_amt > 0
	",49,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_unclaimed_expese_claims(filters):
	cond = ""1=1""
	if filters.get(""employee""):
		cond = ""ec.employee = %(employee)s""

	return frappe.db.sql(
		.format(
			cond=cond
		),
		filters,
		as_list=1,
	)

        ```
    ###Assistant : 
		select
			ec.employee, ec.employee_name, ec.name, ec.total_sanctioned_amount, ec.total_amount_reimbursed,
			sum(gle.credit_in_account_currency - gle.debit_in_account_currency) as outstanding_amt
		from
			`tabExpense Claim` ec, `tabGL Entry` gle
		where
			gle.against_voucher_type = ""Expense Claim"" and gle.against_voucher = ec.name
			and gle.party is not null and ec.docstatus = 1 and ec.is_paid = 0 and {cond} group by ec.name
		having
			outstanding_amt > 0
	
    "
989,"def site_config_dir(self) -> str:
        
        # XDG default for $XDG_CONFIG_DIRS only first, if multipath is False
        path = os.environ.get(""XDG_CONFIG_DIRS"", """")
        if not path.strip():
            path = ""/etc/xdg""
        return self._with_multi_path(path)
","
        :return: config directories shared by users (if `multipath <platformdirs.api.PlatformDirsABC.multipath>`
         is enabled and ``XDG_DATA_DIR`` is set and a multi path the response is also a multi path separated by the OS
         path separator), e.g. ``/etc/xdg/$appname/$version``
        ",34,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def site_config_dir(self) -> str:
        
        # XDG default for $XDG_CONFIG_DIRS only first, if multipath is False
        path = os.environ.get(""XDG_CONFIG_DIRS"", """")
        if not path.strip():
            path = ""/etc/xdg""
        return self._with_multi_path(path)

        ```
    ###Assistant : 
        :return: config directories shared by users (if `multipath <platformdirs.api.PlatformDirsABC.multipath>`
         is enabled and ``XDG_DATA_DIR`` is set and a multi path the response is also a multi path separated by the OS
         path separator), e.g. ``/etc/xdg/$appname/$version``
        
    "
990,"def fit_transform(self, X, y=None):
        
        self._validate_params()

        return self._fit_transform(X, compute_sources=True)
","Fit the model and recover the sources from X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data, where `n_samples` is the number of samples
            and `n_features` is the number of features.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components)
            Estimated sources obtained by transforming the data with the
            estimated unmixing matrix.
        ",66,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit_transform(self, X, y=None):
        
        self._validate_params()

        return self._fit_transform(X, compute_sources=True)

        ```
    ###Assistant : Fit the model and recover the sources from X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data, where `n_samples` is the number of samples
            and `n_features` is the number of features.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components)
            Estimated sources obtained by transforming the data with the
            estimated unmixing matrix.
        
    "
991,"def __fetch_randomly_sampled_transactions(self, project, query, sample_size, query_time_range):
        
        sampling_factor = self.__generate_transactions_sampling_factor(
            project=project,
            query=query,
            sample_size=sample_size,
            query_time_range=query_time_range,
        )
        builder = QueryBuilder(
            Dataset.Discover,
            params={
                ""start"": query_time_range.start_time,
                ""end"": query_time_range.end_time,
                ""project_id"": [project.id],
                ""organization_id"": project.organization.id,
            },
            query=f""{query} event.type:transaction"",
            selected_columns=[
                ""id"",
                ""trace"",
                ""random_number() as rand_num"",
                f""modulo(rand_num, {sampling_factor}) as modulo_num"",
            ],
            equations=[],
            orderby=None,
            auto_fields=True,
            auto_aggregations=True,
            use_aggregate_conditions=True,
            functions_acl=[""random_number"", ""modulo""],
            limit=sample_size,
            offset=0,
            equation_config={""auto_add"": False},
        )
        builder.add_conditions([Condition(lhs=Column(""modulo_num""), op=Op.EQ, rhs=0)])
        snuba_query = builder.get_snql_query().query

        snuba_query = snuba_query.set_select(
            snuba_query.select
            + [
                Function(
                    ""not"",
                    [Function(""has"", [Column(""contexts.key""), TRACE_PARENT_SPAN_CONTEXT])],
                    alias=""is_root"",
                )
            ]
        )
        snuba_query = snuba_query.set_groupby(
            snuba_query.groupby + [Column(""modulo_num""), Column(""contexts.key"")]
        )

        data = raw_snql_query(
            SnubaRequest(dataset=Dataset.Discover.value, app_id=""default"", query=snuba_query),
            referrer=Referrer.DYNAMIC_SAMPLING_DISTRIBUTION_FETCH_TRANSACTIONS.value,
        )[""data""]
        return data
","
        Fetches a random sample of transactions of size `sample_size` in the last period
        defined by `stats_period`. The random sample is fetched by generating a random number by
        for every row, and then doing a modulo operation on it, and if that number is divisible
        by the sampling factor then its kept, otherwise is discarded. This is an alternative to
        sampling the query before applying the conditions. The goal here is to fetch the
        transaction ids, their sample rates and their trace ids.
        ",82,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __fetch_randomly_sampled_transactions(self, project, query, sample_size, query_time_range):
        
        sampling_factor = self.__generate_transactions_sampling_factor(
            project=project,
            query=query,
            sample_size=sample_size,
            query_time_range=query_time_range,
        )
        builder = QueryBuilder(
            Dataset.Discover,
            params={
                ""start"": query_time_range.start_time,
                ""end"": query_time_range.end_time,
                ""project_id"": [project.id],
                ""organization_id"": project.organization.id,
            },
            query=f""{query} event.type:transaction"",
            selected_columns=[
                ""id"",
                ""trace"",
                ""random_number() as rand_num"",
                f""modulo(rand_num, {sampling_factor}) as modulo_num"",
            ],
            equations=[],
            orderby=None,
            auto_fields=True,
            auto_aggregations=True,
            use_aggregate_conditions=True,
            functions_acl=[""random_number"", ""modulo""],
            limit=sample_size,
            offset=0,
            equation_config={""auto_add"": False},
        )
        builder.add_conditions([Condition(lhs=Column(""modulo_num""), op=Op.EQ, rhs=0)])
        snuba_query = builder.get_snql_query().query

        snuba_query = snuba_query.set_select(
            snuba_query.select
            + [
                Function(
                    ""not"",
                    [Function(""has"", [Column(""contexts.key""), TRACE_PARENT_SPAN_CONTEXT])],
                    alias=""is_root"",
                )
            ]
        )
        snuba_query = snuba_query.set_groupby(
            snuba_query.groupby + [Column(""modulo_num""), Column(""contexts.key"")]
        )

        data = raw_snql_query(
            SnubaRequest(dataset=Dataset.Discover.value, app_id=""default"", query=snuba_query),
            referrer=Referrer.DYNAMIC_SAMPLING_DISTRIBUTION_FETCH_TRANSACTIONS.value,
        )[""data""]
        return data

        ```
    ###Assistant : 
        Fetches a random sample of transactions of size `sample_size` in the last period
        defined by `stats_period`. The random sample is fetched by generating a random number by
        for every row, and then doing a modulo operation on it, and if that number is divisible
        by the sampling factor then its kept, otherwise is discarded. This is an alternative to
        sampling the query before applying the conditions. The goal here is to fetch the
        transaction ids, their sample rates and their trace ids.
        
    "
992,"def item_query(doctype, txt, searchfield, start, page_len, filters, as_dict=False):
	conditions = []

	if isinstance(filters, str):
		filters = json.loads(filters)

	#Get searchfields from meta and use in Item Link field query
	meta = frappe.get_meta(""Item"", cached=True)
	searchfields = meta.get_search_fields()

	# these are handled separately
	ignored_search_fields = (""item_name"", ""description"")
	for ignored_field in ignored_search_fields:
		if ignored_field in searchfields:
			searchfields.remove(ignored_field)

	columns = ''
	extra_searchfields = [field for field in searchfields
		if not field in [""name"", ""item_group"", ""description"", ""item_name""]]

	if extra_searchfields:
		columns = "", "" + "", "".join(extra_searchfields)

	searchfields = searchfields + [field for field in[searchfield or ""name"", ""item_code"", ""item_group"", ""item_name""]
		if not field in searchfields]
	searchfields = "" or "".join([field + "" like %(txt)s"" for field in searchfields])

	if filters and isinstance(filters, dict):
		if filters.get('customer') or filters.get('supplier'):
			party = filters.get('customer') or filters.get('supplier')
			item_rules_list = frappe.get_all('Party Specific Item',
				filters = {'party': party}, fields = ['restrict_based_on', 'based_on_value'])

			filters_dict = {}
			for rule in item_rules_list:
				if rule['restrict_based_on'] == 'Item':
					rule['restrict_based_on'] = 'name'
				filters_dict[rule.restrict_based_on] = []

			for rule in item_rules_list:
				filters_dict[rule.restrict_based_on].append(rule.based_on_value)

			for filter in filters_dict:
				filters[scrub(filter)] = ['in', filters_dict[filter]]

			if filters.get('customer'):
				del filters['customer']
			else:
				del filters['supplier']
		else:
			filters.pop('customer', None)
			filters.pop('supplier', None)


	description_cond = ''
	if frappe.db.count('Item', cache=True) < 50000:
		# scan description only if items are less than 50000
		description_cond = 'or tabItem.description LIKE %(txt)s'
	return frappe.db.sql(.format(
			columns=columns,
			scond=searchfields,
			fcond=get_filters_cond(doctype, filters, conditions).replace('%', '%%'),
			mcond=get_match_cond(doctype).replace('%', '%%'),
			description_cond = description_cond),
			{
				""today"": nowdate(),
				""txt"": ""%%%s%%"" % txt,
				""_txt"": txt.replace(""%"", """"),
				""start"": start,
				""page_len"": page_len
			}, as_dict=as_dict)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs","select
			tabItem.name, tabItem.item_name, tabItem.item_group,
		if(length(tabItem.description) > 40, \
			concat(substr(tabItem.description, 1, 40), ""...""), description) as description
		{columns}
		from tabItem
		where tabItem.docstatus < 2
			and tabItem.disabled=0
			and tabItem.has_variants=0
			and (tabItem.end_of_life > %(today)s or ifnull(tabItem.end_of_life, '0000-00-00')='0000-00-00')
			and ({scond} or tabItem.item_code IN (select parent from `tabItem Barcode` where barcode LIKE %(txt)s)
				{description_cond})
			{fcond} {mcond}
		order by
			if(locate(%(_txt)s, name), locate(%(_txt)s, name), 99999),
			if(locate(%(_txt)s, item_name), locate(%(_txt)s, item_name), 99999),
			idx desc,
			name, item_name
		limit %(start)s, %(page_len)s ",69,235,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def item_query(doctype, txt, searchfield, start, page_len, filters, as_dict=False):
	conditions = []

	if isinstance(filters, str):
		filters = json.loads(filters)

	#Get searchfields from meta and use in Item Link field query
	meta = frappe.get_meta(""Item"", cached=True)
	searchfields = meta.get_search_fields()

	# these are handled separately
	ignored_search_fields = (""item_name"", ""description"")
	for ignored_field in ignored_search_fields:
		if ignored_field in searchfields:
			searchfields.remove(ignored_field)

	columns = ''
	extra_searchfields = [field for field in searchfields
		if not field in [""name"", ""item_group"", ""description"", ""item_name""]]

	if extra_searchfields:
		columns = "", "" + "", "".join(extra_searchfields)

	searchfields = searchfields + [field for field in[searchfield or ""name"", ""item_code"", ""item_group"", ""item_name""]
		if not field in searchfields]
	searchfields = "" or "".join([field + "" like %(txt)s"" for field in searchfields])

	if filters and isinstance(filters, dict):
		if filters.get('customer') or filters.get('supplier'):
			party = filters.get('customer') or filters.get('supplier')
			item_rules_list = frappe.get_all('Party Specific Item',
				filters = {'party': party}, fields = ['restrict_based_on', 'based_on_value'])

			filters_dict = {}
			for rule in item_rules_list:
				if rule['restrict_based_on'] == 'Item':
					rule['restrict_based_on'] = 'name'
				filters_dict[rule.restrict_based_on] = []

			for rule in item_rules_list:
				filters_dict[rule.restrict_based_on].append(rule.based_on_value)

			for filter in filters_dict:
				filters[scrub(filter)] = ['in', filters_dict[filter]]

			if filters.get('customer'):
				del filters['customer']
			else:
				del filters['supplier']
		else:
			filters.pop('customer', None)
			filters.pop('supplier', None)


	description_cond = ''
	if frappe.db.count('Item', cache=True) < 50000:
		# scan description only if items are less than 50000
		description_cond = 'or tabItem.description LIKE %(txt)s'
	return frappe.db.sql(.format(
			columns=columns,
			scond=searchfields,
			fcond=get_filters_cond(doctype, filters, conditions).replace('%', '%%'),
			mcond=get_match_cond(doctype).replace('%', '%%'),
			description_cond = description_cond),
			{
				""today"": nowdate(),
				""txt"": ""%%%s%%"" % txt,
				""_txt"": txt.replace(""%"", """"),
				""start"": start,
				""page_len"": page_len
			}, as_dict=as_dict)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs
        ```
    ###Assistant : select
			tabItem.name, tabItem.item_name, tabItem.item_group,
		if(length(tabItem.description) > 40, \
			concat(substr(tabItem.description, 1, 40), ""...""), description) as description
		{columns}
		from tabItem
		where tabItem.docstatus < 2
			and tabItem.disabled=0
			and tabItem.has_variants=0
			and (tabItem.end_of_life > %(today)s or ifnull(tabItem.end_of_life, '0000-00-00')='0000-00-00')
			and ({scond} or tabItem.item_code IN (select parent from `tabItem Barcode` where barcode LIKE %(txt)s)
				{description_cond})
			{fcond} {mcond}
		order by
			if(locate(%(_txt)s, name), locate(%(_txt)s, name), 99999),
			if(locate(%(_txt)s, item_name), locate(%(_txt)s, item_name), 99999),
			idx desc,
			name, item_name
		limit %(start)s, %(page_len)s 
    "
993,"def apply_and_enforce(*args, **kwargs):
    
    func = kwargs.pop(""_func"")
    expected_ndim = kwargs.pop(""expected_ndim"")
    out = func(*args, **kwargs)
    if getattr(out, ""ndim"", 0) != expected_ndim:
        out_ndim = getattr(out, ""ndim"", 0)
        raise ValueError(
            f""Dimension mismatch: expected output of {func} ""
            f""to have dims = {expected_ndim}.  Got {out_ndim} instead.""
        )
    return out

","Apply a function, and enforce the output.ndim to match expected_ndim

    Ensures the output has the expected dimensionality.",17,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply_and_enforce(*args, **kwargs):
    
    func = kwargs.pop(""_func"")
    expected_ndim = kwargs.pop(""expected_ndim"")
    out = func(*args, **kwargs)
    if getattr(out, ""ndim"", 0) != expected_ndim:
        out_ndim = getattr(out, ""ndim"", 0)
        raise ValueError(
            f""Dimension mismatch: expected output of {func} ""
            f""to have dims = {expected_ndim}.  Got {out_ndim} instead.""
        )
    return out


        ```
    ###Assistant : Apply a function, and enforce the output.ndim to match expected_ndim

    Ensures the output has the expected dimensionality.
    "
994,"def _sanitize_non_ordered(data) -> None:
    
    if isinstance(data, (set, frozenset)):
        raise TypeError(f""'{type(data).__name__}' type is unordered"")

","
    Raise only for unordered sets, e.g., not for dict_keys
    ",9,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _sanitize_non_ordered(data) -> None:
    
    if isinstance(data, (set, frozenset)):
        raise TypeError(f""'{type(data).__name__}' type is unordered"")


        ```
    ###Assistant : 
    Raise only for unordered sets, e.g., not for dict_keys
    
    "
995,"def dis(x=None, *, file=None, depth=None):
    
    if x is None:
        distb(file=file)
        return
    # Extract functions from methods.
    if hasattr(x, '__func__'):
        x = x.__func__
    # Extract compiled code objects from...
    if hasattr(x, '__code__'):  # ...a function, or
        x = x.__code__
    elif hasattr(x, 'gi_code'):  #...a generator object, or
        x = x.gi_code
    elif hasattr(x, 'ag_code'):  #...an asynchronous generator object, or
        x = x.ag_code
    elif hasattr(x, 'cr_code'):  #...a coroutine.
        x = x.cr_code
    # Perform the disassembly.
    if hasattr(x, '__dict__'):  # Class or module
        items = sorted(x.__dict__.items())
        for name, x1 in items:
            if isinstance(x1, _have_code):
                print(""Disassembly of %s:"" % name, file=file)
                try:
                    dis(x1, file=file, depth=depth)
                except TypeError as msg:
                    print(""Sorry:"", msg, file=file)
                print(file=file)
    elif hasattr(x, 'co_code'): # Code object
        _disassemble_recursive(x, file=file, depth=depth)
    elif isinstance(x, (bytes, bytearray)): # Raw bytecode
        _disassemble_bytes(x, file=file)
    elif isinstance(x, str):    # Source code
        _disassemble_str(x, file=file, depth=depth)
    else:
        raise TypeError(""don't know how to disassemble %s objects"" %
                        type(x).__name__)
","Disassemble classes, methods, functions, and other compiled objects.

    With no argument, disassemble the last traceback.

    Compiled objects currently include generator objects, async generator
    objects, and coroutine objects, all of which store their code object
    in a special attribute.
    ",38,145,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dis(x=None, *, file=None, depth=None):
    
    if x is None:
        distb(file=file)
        return
    # Extract functions from methods.
    if hasattr(x, '__func__'):
        x = x.__func__
    # Extract compiled code objects from...
    if hasattr(x, '__code__'):  # ...a function, or
        x = x.__code__
    elif hasattr(x, 'gi_code'):  #...a generator object, or
        x = x.gi_code
    elif hasattr(x, 'ag_code'):  #...an asynchronous generator object, or
        x = x.ag_code
    elif hasattr(x, 'cr_code'):  #...a coroutine.
        x = x.cr_code
    # Perform the disassembly.
    if hasattr(x, '__dict__'):  # Class or module
        items = sorted(x.__dict__.items())
        for name, x1 in items:
            if isinstance(x1, _have_code):
                print(""Disassembly of %s:"" % name, file=file)
                try:
                    dis(x1, file=file, depth=depth)
                except TypeError as msg:
                    print(""Sorry:"", msg, file=file)
                print(file=file)
    elif hasattr(x, 'co_code'): # Code object
        _disassemble_recursive(x, file=file, depth=depth)
    elif isinstance(x, (bytes, bytearray)): # Raw bytecode
        _disassemble_bytes(x, file=file)
    elif isinstance(x, str):    # Source code
        _disassemble_str(x, file=file, depth=depth)
    else:
        raise TypeError(""don't know how to disassemble %s objects"" %
                        type(x).__name__)

        ```
    ###Assistant : Disassemble classes, methods, functions, and other compiled objects.

    With no argument, disassemble the last traceback.

    Compiled objects currently include generator objects, async generator
    objects, and coroutine objects, all of which store their code object
    in a special attribute.
    
    "
996,"def bernoulli_poly(n, x=None, polys=False):
    r
    return named_poly(n, dup_bernoulli, QQ, ""Bernoulli polynomial"", (x,), polys)

","Generates the Bernoulli polynomial `\operatorname{B}_n(x)`.

    `\operatorname{B}_n(x)` is the unique polynomial satisfying

    .. math :: \int_{x}^{x+1} \operatorname{B}_n(t) \,dt = x^n.

    Based on this, we have for nonnegative integer `s` and integer
    `a` and `b`

    .. math :: \sum_{k=a}^{b} k^s = \frac{\operatorname{B}_{s+1}(b+1) -
            \operatorname{B}_{s+1}(a)}{s+1}

    which is related to Jakob Bernoulli's original motivation for introducing
    the Bernoulli numbers, the values of these polynomials at `x = 1`.

    Examples
    ========

    >>> from sympy import summation
    >>> from sympy.abc import x
    >>> from sympy.polys import bernoulli_poly
    >>> bernoulli_poly(5, x)
    x**5 - 5*x**4/2 + 5*x**3/3 - x/6

    >>> def psum(p, a, b):
    ...     return (bernoulli_poly(p+1,b+1) - bernoulli_poly(p+1,a)) / (p+1)
    >>> psum(4, -6, 27)
    3144337
    >>> summation(x**4, (x, -6, 27))
    3144337

    >>> psum(1, 1, x).factor()
    x*(x + 1)/2
    >>> psum(2, 1, x).factor()
    x*(x + 1)*(2*x + 1)/6
    >>> psum(3, 1, x).factor()
    x**2*(x + 1)**2/4

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Bernoulli_polynomials
    ",168,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bernoulli_poly(n, x=None, polys=False):
    r
    return named_poly(n, dup_bernoulli, QQ, ""Bernoulli polynomial"", (x,), polys)


        ```
    ###Assistant : Generates the Bernoulli polynomial `\operatorname{B}_n(x)`.

    `\operatorname{B}_n(x)` is the unique polynomial satisfying

    .. math :: \int_{x}^{x+1} \operatorname{B}_n(t) \,dt = x^n.

    Based on this, we have for nonnegative integer `s` and integer
    `a` and `b`

    .. math :: \sum_{k=a}^{b} k^s = \frac{\operatorname{B}_{s+1}(b+1) -
            \operatorname{B}_{s+1}(a)}{s+1}

    which is related to Jakob Bernoulli's original motivation for introducing
    the Bernoulli numbers, the values of these polynomials at `x = 1`.

    Examples
    ========

    >>> from sympy import summation
    >>> from sympy.abc import x
    >>> from sympy.polys import bernoulli_poly
    >>> bernoulli_poly(5, x)
    x**5 - 5*x**4/2 + 5*x**3/3 - x/6

    >>> def psum(p, a, b):
    ...     return (bernoulli_poly(p+1,b+1) - bernoulli_poly(p+1,a)) / (p+1)
    >>> psum(4, -6, 27)
    3144337
    >>> summation(x**4, (x, -6, 27))
    3144337

    >>> psum(1, 1, x).factor()
    x*(x + 1)/2
    >>> psum(2, 1, x).factor()
    x*(x + 1)*(2*x + 1)/6
    >>> psum(3, 1, x).factor()
    x**2*(x + 1)**2/4

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Bernoulli_polynomials
    
    "
997,"def after_log(logger, log_level, sec_format=""%0.3f""):
    
    log_tpl = (
        ""Finished call to '%s' after "" + str(sec_format) + ""(s), ""
        ""this was the %s time calling it.""
    )
",After call strategy that logs to some logger the finished attempt.,11,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def after_log(logger, log_level, sec_format=""%0.3f""):
    
    log_tpl = (
        ""Finished call to '%s' after "" + str(sec_format) + ""(s), ""
        ""this was the %s time calling it.""
    )

        ```
    ###Assistant : After call strategy that logs to some logger the finished attempt.
    "
998,"def _compat_get_offset(meth):
    
    sigs = [lambda self, width, height, xdescent, ydescent, renderer: locals(),
            lambda self, bbox, renderer: locals()]
","
    Decorator for the get_offset method of OffsetBox and subclasses, that
    allows supporting both the new signature (self, bbox, renderer) and the old
    signature (self, width, height, xdescent, ydescent, renderer).
    ",29,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _compat_get_offset(meth):
    
    sigs = [lambda self, width, height, xdescent, ydescent, renderer: locals(),
            lambda self, bbox, renderer: locals()]

        ```
    ###Assistant : 
    Decorator for the get_offset method of OffsetBox and subclasses, that
    allows supporting both the new signature (self, bbox, renderer) and the old
    signature (self, width, height, xdescent, ydescent, renderer).
    
    "
999,"def sixtofour(self):
        
        if (self._ip >> 112) != 0x2002:
            return None
        return IPv4Address((self._ip >> 80) & 0xFFFFFFFF)

","Return the IPv4 6to4 embedded address.

        Returns:
            The IPv4 6to4-embedded address if present or None if the
            address doesn't appear to contain a 6to4 embedded address.

        ",26,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sixtofour(self):
        
        if (self._ip >> 112) != 0x2002:
            return None
        return IPv4Address((self._ip >> 80) & 0xFFFFFFFF)


        ```
    ###Assistant : Return the IPv4 6to4 embedded address.

        Returns:
            The IPv4 6to4-embedded address if present or None if the
            address doesn't appear to contain a 6to4 embedded address.

        
    "
1000,"def reload_markets(self) -> None:
        
        # Check whether markets have to be reloaded
        if (self._last_markets_refresh > 0) and (
                self._last_markets_refresh + self.markets_refresh_interval
                > arrow.utcnow().int_timestamp):
            return None
        logger.debug(""Performing scheduled market reload.."")
        try:
            self._markets = self._api.load_markets(reload=True)
            # Also reload async markets to avoid issues with newly listed pairs
            self._load_async_markets(reload=True)
            self._last_markets_refresh = arrow.utcnow().int_timestamp
            self.fill_leverage_tiers()
        except ccxt.BaseError:
            logger.exception(""Could not reload markets."")
",Reload markets both sync and async if refresh interval has passed ,11,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reload_markets(self) -> None:
        
        # Check whether markets have to be reloaded
        if (self._last_markets_refresh > 0) and (
                self._last_markets_refresh + self.markets_refresh_interval
                > arrow.utcnow().int_timestamp):
            return None
        logger.debug(""Performing scheduled market reload.."")
        try:
            self._markets = self._api.load_markets(reload=True)
            # Also reload async markets to avoid issues with newly listed pairs
            self._load_async_markets(reload=True)
            self._last_markets_refresh = arrow.utcnow().int_timestamp
            self.fill_leverage_tiers()
        except ccxt.BaseError:
            logger.exception(""Could not reload markets."")

        ```
    ###Assistant : Reload markets both sync and async if refresh interval has passed 
    "
1001,"def is_strongly_diagonally_dominant(self):
        r
        if not self.is_square:
            return False

        rows, cols = self.shape
","Tests if the matrix is row strongly diagonally dominant.

        Explanation
        ===========

        A $n, n$ matrix $A$ is row strongly diagonally dominant if

        .. math::
            \left|A_{i, i}\right| > \sum_{j = 0, j \neq i}^{n-1}
            \left|A_{i, j}\right| \quad {\text{for all }}
            i \in \{ 0, ..., n-1 \}

        Examples
        ========

        >>> from sympy import Matrix
        >>> A = Matrix([[3, -2, 1], [1, -3, 2], [-1, 2, 4]])
        >>> A.is_strongly_diagonally_dominant
        False

        >>> A = Matrix([[-2, 2, 1], [1, 3, 2], [1, -2, 0]])
        >>> A.is_strongly_diagonally_dominant
        False

        >>> A = Matrix([[-4, 2, 1], [1, 6, 2], [1, -2, 5]])
        >>> A.is_strongly_diagonally_dominant
        True

        Notes
        =====

        If you want to test whether a matrix is column diagonally
        dominant, you can apply the test after transposing the matrix.
        ",121,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_strongly_diagonally_dominant(self):
        r
        if not self.is_square:
            return False

        rows, cols = self.shape

        ```
    ###Assistant : Tests if the matrix is row strongly diagonally dominant.

        Explanation
        ===========

        A $n, n$ matrix $A$ is row strongly diagonally dominant if

        .. math::
            \left|A_{i, i}\right| > \sum_{j = 0, j \neq i}^{n-1}
            \left|A_{i, j}\right| \quad {\text{for all }}
            i \in \{ 0, ..., n-1 \}

        Examples
        ========

        >>> from sympy import Matrix
        >>> A = Matrix([[3, -2, 1], [1, -3, 2], [-1, 2, 4]])
        >>> A.is_strongly_diagonally_dominant
        False

        >>> A = Matrix([[-2, 2, 1], [1, 3, 2], [1, -2, 0]])
        >>> A.is_strongly_diagonally_dominant
        False

        >>> A = Matrix([[-4, 2, 1], [1, 6, 2], [1, -2, 5]])
        >>> A.is_strongly_diagonally_dominant
        True

        Notes
        =====

        If you want to test whether a matrix is column diagonally
        dominant, you can apply the test after transposing the matrix.
        
    "
1002,"def create_calibration_module(self, feature) -> torch.nn.Module:
        
        if feature.get(""calibration""):
            calibration_cls = calibration.get_calibration_cls(BINARY, ""temperature_scaling"")
            return calibration_cls(binary=True)
        return None
","Creates the appropriate calibration module based on the feature config.

        Today, only one type of calibration (""temperature_scaling"") is available, but more options may be supported in
        the future.
        ",28,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_calibration_module(self, feature) -> torch.nn.Module:
        
        if feature.get(""calibration""):
            calibration_cls = calibration.get_calibration_cls(BINARY, ""temperature_scaling"")
            return calibration_cls(binary=True)
        return None

        ```
    ###Assistant : Creates the appropriate calibration module based on the feature config.

        Today, only one type of calibration (""temperature_scaling"") is available, but more options may be supported in
        the future.
        
    "
1003,"def _download(url, path, md5sum=None):
    
    if not osp.exists(path):
        os.makedirs(path)

    fname = osp.split(url)[-1]
    fullname = osp.join(path, fname)
    retry_cnt = 0

    while not (osp.exists(fullname) and _check_exist_file_md5(fullname, md5sum,
                                                              url)):
        if retry_cnt < DOWNLOAD_RETRY_LIMIT:
            retry_cnt += 1
        else:
            raise RuntimeError(""Download from {} failed. ""
                               ""Retry limit reached"".format(url))

        logger.info(""Downloading {} from {}"".format(fname, url))

        # NOTE: windows path join may incur \, which is invalid in url
        if sys.platform == ""win32"":
            url = url.replace('\\', '/')

        req = requests.get(url, stream=True)
        if req.status_code != 200:
            raise RuntimeError(""Downloading from {} failed with code ""
                               ""{}!"".format(url, req.status_code))

        # For protecting download interupted, download to
        # tmp_fullname firstly, move tmp_fullname to fullname
        # after download finished
        tmp_fullname = fullname + ""_tmp""
        total_size = req.headers.get('content-length')
        with open(tmp_fullname, 'wb') as f:
            if total_size:
                for chunk in tqdm.tqdm(
                        req.iter_content(chunk_size=1024),
                        total=(int(total_size) + 1023) // 1024,
                        unit='KB'):
                    f.write(chunk)
            else:
                for chunk in req.iter_content(chunk_size=1024):
                    if chunk:
                        f.write(chunk)
        shutil.move(tmp_fullname, fullname)
    return fullname

","
    Download from url, save to path.

    url (str): download url
    path (str): download to given path
    ",16,143,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _download(url, path, md5sum=None):
    
    if not osp.exists(path):
        os.makedirs(path)

    fname = osp.split(url)[-1]
    fullname = osp.join(path, fname)
    retry_cnt = 0

    while not (osp.exists(fullname) and _check_exist_file_md5(fullname, md5sum,
                                                              url)):
        if retry_cnt < DOWNLOAD_RETRY_LIMIT:
            retry_cnt += 1
        else:
            raise RuntimeError(""Download from {} failed. ""
                               ""Retry limit reached"".format(url))

        logger.info(""Downloading {} from {}"".format(fname, url))

        # NOTE: windows path join may incur \, which is invalid in url
        if sys.platform == ""win32"":
            url = url.replace('\\', '/')

        req = requests.get(url, stream=True)
        if req.status_code != 200:
            raise RuntimeError(""Downloading from {} failed with code ""
                               ""{}!"".format(url, req.status_code))

        # For protecting download interupted, download to
        # tmp_fullname firstly, move tmp_fullname to fullname
        # after download finished
        tmp_fullname = fullname + ""_tmp""
        total_size = req.headers.get('content-length')
        with open(tmp_fullname, 'wb') as f:
            if total_size:
                for chunk in tqdm.tqdm(
                        req.iter_content(chunk_size=1024),
                        total=(int(total_size) + 1023) // 1024,
                        unit='KB'):
                    f.write(chunk)
            else:
                for chunk in req.iter_content(chunk_size=1024):
                    if chunk:
                        f.write(chunk)
        shutil.move(tmp_fullname, fullname)
    return fullname


        ```
    ###Assistant : 
    Download from url, save to path.

    url (str): download url
    path (str): download to given path
    
    "
1004,"def related_objects(self):
        
        all_related_fields = self._get_fields(
            forward=False, reverse=True, include_hidden=True
        )
        return make_immutable_fields_list(
            ""related_objects"",
            (
                obj
                for obj in all_related_fields
                if not obj.hidden or obj.field.many_to_many
            ),
        )
","
        Return all related objects pointing to the current model. The related
        objects can come from a one-to-one, one-to-many, or many-to-many field
        relation type.

        Private API intended only to be used by Django itself; get_fields()
        combined with filtering of field properties is the public API for
        obtaining this field list.
        ",49,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def related_objects(self):
        
        all_related_fields = self._get_fields(
            forward=False, reverse=True, include_hidden=True
        )
        return make_immutable_fields_list(
            ""related_objects"",
            (
                obj
                for obj in all_related_fields
                if not obj.hidden or obj.field.many_to_many
            ),
        )

        ```
    ###Assistant : 
        Return all related objects pointing to the current model. The related
        objects can come from a one-to-one, one-to-many, or many-to-many field
        relation type.

        Private API intended only to be used by Django itself; get_fields()
        combined with filtering of field properties is the public API for
        obtaining this field list.
        
    "
1005,"def test_https_good_referer(self):
        
        req = self._get_POST_request_with_token()
        req._is_secure_override = True
        req.META[""HTTP_HOST""] = ""www.example.com""
        req.META[""HTTP_REFERER""] = ""https://www.example.com/somepage""
        mw = CsrfViewMiddleware(post_form_view)
        mw.process_request(req)
        resp = mw.process_view(req, post_form_view, (), {})
        self.assertIsNone(resp)
","
        A POST HTTPS request with a good referer is accepted.
        ",10,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_https_good_referer(self):
        
        req = self._get_POST_request_with_token()
        req._is_secure_override = True
        req.META[""HTTP_HOST""] = ""www.example.com""
        req.META[""HTTP_REFERER""] = ""https://www.example.com/somepage""
        mw = CsrfViewMiddleware(post_form_view)
        mw.process_request(req)
        resp = mw.process_view(req, post_form_view, (), {})
        self.assertIsNone(resp)

        ```
    ###Assistant : 
        A POST HTTPS request with a good referer is accepted.
        
    "
1006,"def test_state_policy(self) -> None:
        
        room_id = self.helper.create_room_as(self.user_id, tok=self.token)

        # Set the maximum lifetime to 35 days so that the first event gets expired but not
        # the second one.
        self.helper.send_state(
            room_id=room_id,
            event_type=EventTypes.Retention,
            body={""max_lifetime"": one_day_ms * 35},
            tok=self.token,
        )

        self._test_retention(room_id, expected_code_for_first_event=404)
","Tests that an event gets correctly expired if there is no default retention
        policy but there's a policy specific to the room.
        ",22,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_state_policy(self) -> None:
        
        room_id = self.helper.create_room_as(self.user_id, tok=self.token)

        # Set the maximum lifetime to 35 days so that the first event gets expired but not
        # the second one.
        self.helper.send_state(
            room_id=room_id,
            event_type=EventTypes.Retention,
            body={""max_lifetime"": one_day_ms * 35},
            tok=self.token,
        )

        self._test_retention(room_id, expected_code_for_first_event=404)

        ```
    ###Assistant : Tests that an event gets correctly expired if there is no default retention
        policy but there's a policy specific to the room.
        
    "
1007,"def insertion_sort(list, n):
    
    for i in range(0, n):
        key = list[i]
        j = i - 1
        # Swap elements witth key iff they are
        # greater than key
        while j >= 0 and list[j] > key:
            list[j + 1] = list[j]
            j = j - 1
        list[j + 1] = key
    return list

","
    sort list in assending order

    INPUT:
        list=list of values to be sorted
        n=size of list that contains values to be sorted

    OUTPUT:
        list of sorted values in assending order
    ",29,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def insertion_sort(list, n):
    
    for i in range(0, n):
        key = list[i]
        j = i - 1
        # Swap elements witth key iff they are
        # greater than key
        while j >= 0 and list[j] > key:
            list[j + 1] = list[j]
            j = j - 1
        list[j + 1] = key
    return list


        ```
    ###Assistant : 
    sort list in assending order

    INPUT:
        list=list of values to be sorted
        n=size of list that contains values to be sorted

    OUTPUT:
        list of sorted values in assending order
    
    "
1008,"def get_template_context(self):
        
        return {""name"": self.__class__.__name__.lower(), ""label"": self.label}
","
        :return: a dictionary with context variables for the javascript file associated with the context
        ",14,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_template_context(self):
        
        return {""name"": self.__class__.__name__.lower(), ""label"": self.label}

        ```
    ###Assistant : 
        :return: a dictionary with context variables for the javascript file associated with the context
        
    "
1009,"def connect(self):
        

        if self.is_connected is True:
            return self.connection

        connection = teradatasql.connect(
            **self.connection_data
        )

        self.is_connected = True
        self.connection = connection
        return self.connection
","
        Handles the connection to a Teradata database insance.
        ",8,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def connect(self):
        

        if self.is_connected is True:
            return self.connection

        connection = teradatasql.connect(
            **self.connection_data
        )

        self.is_connected = True
        self.connection = connection
        return self.connection

        ```
    ###Assistant : 
        Handles the connection to a Teradata database insance.
        
    "
1010,"def unregister_event_manager(self, manager):
        
        self.event_managers.remove(manager)
        for type_id in manager.type_ids:
            self.event_managers_dict[type_id].remove(manager)
        manager.stop()
        manager.window = None
","Unregister and stop an event manager previously registered with
        :meth:`register_event_manager`.

        .. versionadded:: 2.1.0

        .. warning::
            This is an experimental method and it remains so until this warning
            is present as it can be changed or removed in the next versions of
            Kivy.
        ",42,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unregister_event_manager(self, manager):
        
        self.event_managers.remove(manager)
        for type_id in manager.type_ids:
            self.event_managers_dict[type_id].remove(manager)
        manager.stop()
        manager.window = None

        ```
    ###Assistant : Unregister and stop an event manager previously registered with
        :meth:`register_event_manager`.

        .. versionadded:: 2.1.0

        .. warning::
            This is an experimental method and it remains so until this warning
            is present as it can be changed or removed in the next versions of
            Kivy.
        
    "
1011,"def _c3_mro(cls, abcs=None):
    
    for i, base in enumerate(reversed(cls.__bases__)):
        if hasattr(base, '__abstractmethods__'):
            boundary = len(cls.__bases__) - i
            break   # Bases up to the last explicit ABC are considered first.
    else:
        boundary = 0
    abcs = list(abcs) if abcs else []
    explicit_bases = list(cls.__bases__[:boundary])
    abstract_bases = []
    other_bases = list(cls.__bases__[boundary:])
    for base in abcs:
        if issubclass(cls, base) and not any(
                issubclass(b, base) for b in cls.__bases__
            ):
            # If *cls* is the class that introduces behaviour described by
            # an ABC *base*, insert said ABC to its MRO.
            abstract_bases.append(base)
    for base in abstract_bases:
        abcs.remove(base)
    explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases]
    abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases]
    other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases]
    return _c3_merge(
        [[cls]] +
        explicit_c3_mros + abstract_c3_mros + other_c3_mros +
        [explicit_bases] + [abstract_bases] + [other_bases]
    )
","Computes the method resolution order using extended C3 linearization.

    If no *abcs* are given, the algorithm works exactly like the built-in C3
    linearization used for method resolution.

    If given, *abcs* is a list of abstract base classes that should be inserted
    into the resulting MRO. Unrelated ABCs are ignored and don't end up in the
    result. The algorithm inserts ABCs where their functionality is introduced,
    i.e. issubclass(cls, abc) returns True for the class itself but returns
    False for all its direct base classes. Implicit ABCs for a given class
    (either registered or inferred from the presence of a special method like
    __len__) are inserted directly after the last ABC explicitly listed in the
    MRO of said class. If two implicit ABCs end up next to each other in the
    resulting MRO, their ordering depends on the order of types in *abcs*.

    ",141,132,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _c3_mro(cls, abcs=None):
    
    for i, base in enumerate(reversed(cls.__bases__)):
        if hasattr(base, '__abstractmethods__'):
            boundary = len(cls.__bases__) - i
            break   # Bases up to the last explicit ABC are considered first.
    else:
        boundary = 0
    abcs = list(abcs) if abcs else []
    explicit_bases = list(cls.__bases__[:boundary])
    abstract_bases = []
    other_bases = list(cls.__bases__[boundary:])
    for base in abcs:
        if issubclass(cls, base) and not any(
                issubclass(b, base) for b in cls.__bases__
            ):
            # If *cls* is the class that introduces behaviour described by
            # an ABC *base*, insert said ABC to its MRO.
            abstract_bases.append(base)
    for base in abstract_bases:
        abcs.remove(base)
    explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases]
    abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases]
    other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases]
    return _c3_merge(
        [[cls]] +
        explicit_c3_mros + abstract_c3_mros + other_c3_mros +
        [explicit_bases] + [abstract_bases] + [other_bases]
    )

        ```
    ###Assistant : Computes the method resolution order using extended C3 linearization.

    If no *abcs* are given, the algorithm works exactly like the built-in C3
    linearization used for method resolution.

    If given, *abcs* is a list of abstract base classes that should be inserted
    into the resulting MRO. Unrelated ABCs are ignored and don't end up in the
    result. The algorithm inserts ABCs where their functionality is introduced,
    i.e. issubclass(cls, abc) returns True for the class itself but returns
    False for all its direct base classes. Implicit ABCs for a given class
    (either registered or inferred from the presence of a special method like
    __len__) are inserted directly after the last ABC explicitly listed in the
    MRO of said class. If two implicit ABCs end up next to each other in the
    resulting MRO, their ordering depends on the order of types in *abcs*.

    
    "
1012,"async def async_start_charging(self) -> None:
        
        await self.hass.async_add_executor_job(self.leaf.start_charging)
        self.schedule_update()
",Request to start charging the car. Used by the button platform.,11,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_start_charging(self) -> None:
        
        await self.hass.async_add_executor_job(self.leaf.start_charging)
        self.schedule_update()

        ```
    ###Assistant : Request to start charging the car. Used by the button platform.
    "
1013,"def test_multiple_server_connections(tctx):
    
    server1 = Placeholder(Server)
    server2 = Placeholder(Server)
    playbook = Playbook(http.HttpLayer(tctx, HTTPMode.regular), hooks=False)
",Test multiple requests being rewritten to different targets.,8,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_multiple_server_connections(tctx):
    
    server1 = Placeholder(Server)
    server2 = Placeholder(Server)
    playbook = Playbook(http.HttpLayer(tctx, HTTPMode.regular), hooks=False)

        ```
    ###Assistant : Test multiple requests being rewritten to different targets.
    "
1014,"def test_overlapping_output_names(self) -> None:
        
        self._test_overlapping_names(
            outputs0=['o0', 'o1'], outputs1=['o1', 'o2'])
","
        Tests error checking when the name of the output overlaps
        ",10,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_overlapping_output_names(self) -> None:
        
        self._test_overlapping_names(
            outputs0=['o0', 'o1'], outputs1=['o1', 'o2'])

        ```
    ###Assistant : 
        Tests error checking when the name of the output overlaps
        
    "
1015,"def test_write_profiles_does_not_include_default(self, temporary_profiles_path):
        
        write_profiles({})
        assert ""profiles.default"" not in temporary_profiles_path.read_text()
","
        Including the default has a tendency to bake in settings the user may not want, and
        can prevent them from gaining new defaults.
        ",23,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_write_profiles_does_not_include_default(self, temporary_profiles_path):
        
        write_profiles({})
        assert ""profiles.default"" not in temporary_profiles_path.read_text()

        ```
    ###Assistant : 
        Including the default has a tendency to bake in settings the user may not want, and
        can prevent them from gaining new defaults.
        
    "
1016,"def phase_retarder(theta=0, delta=0):
    
    R = Matrix([[cos(theta)**2 + exp(I*delta)*sin(theta)**2,
                (1-exp(I*delta))*cos(theta)*sin(theta)],
                [(1-exp(I*delta))*cos(theta)*sin(theta),
                sin(theta)**2 + exp(I*delta)*cos(theta)**2]])
    return R*exp(-I*delta/2)

","A phase retarder Jones matrix with retardance `delta` at angle `theta`.

    Parameters
    ==========

    theta : numeric type or SymPy Symbol
        The angle of the fast axis relative to the horizontal plane.
    delta : numeric type or SymPy Symbol
        The phase difference between the fast and slow axes of the
        transmitted light.

    Returns
    =======

    SymPy Matrix :
        A Jones matrix representing the retarder.

    Examples
    ========

    A generic retarder.

    >>> from sympy import pprint, symbols
    >>> from sympy.physics.optics.polarization import phase_retarder
    >>> theta, delta = symbols(""theta, delta"", real=True)
    >>> R = phase_retarder(theta, delta)
    >>> pprint(R, use_unicode=True)
    ⎡                          -ⅈ⋅δ               -ⅈ⋅δ               ⎤
    ⎢                          ─────              ─────              ⎥
    ⎢⎛ ⅈ⋅δ    2         2   ⎞    2    ⎛     ⅈ⋅δ⎞    2                ⎥
    ⎢⎝ℯ   ⋅sin (θ) + cos (θ)⎠⋅ℯ       ⎝1 - ℯ   ⎠⋅ℯ     ⋅sin(θ)⋅cos(θ)⎥
    ⎢                                                                ⎥
    ⎢            -ⅈ⋅δ                                           -ⅈ⋅δ ⎥
    ⎢            ─────                                          ─────⎥
    ⎢⎛     ⅈ⋅δ⎞    2                  ⎛ ⅈ⋅δ    2         2   ⎞    2  ⎥
    ⎣⎝1 - ℯ   ⎠⋅ℯ     ⋅sin(θ)⋅cos(θ)  ⎝ℯ   ⋅cos (θ) + sin (θ)⎠⋅ℯ     ⎦

    ",153,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def phase_retarder(theta=0, delta=0):
    
    R = Matrix([[cos(theta)**2 + exp(I*delta)*sin(theta)**2,
                (1-exp(I*delta))*cos(theta)*sin(theta)],
                [(1-exp(I*delta))*cos(theta)*sin(theta),
                sin(theta)**2 + exp(I*delta)*cos(theta)**2]])
    return R*exp(-I*delta/2)


        ```
    ###Assistant : A phase retarder Jones matrix with retardance `delta` at angle `theta`.

    Parameters
    ==========

    theta : numeric type or SymPy Symbol
        The angle of the fast axis relative to the horizontal plane.
    delta : numeric type or SymPy Symbol
        The phase difference between the fast and slow axes of the
        transmitted light.

    Returns
    =======

    SymPy Matrix :
        A Jones matrix representing the retarder.

    Examples
    ========

    A generic retarder.

    >>> from sympy import pprint, symbols
    >>> from sympy.physics.optics.polarization import phase_retarder
    >>> theta, delta = symbols(""theta, delta"", real=True)
    >>> R = phase_retarder(theta, delta)
    >>> pprint(R, use_unicode=True)
    ⎡                          -ⅈ⋅δ               -ⅈ⋅δ               ⎤
    ⎢                          ─────              ─────              ⎥
    ⎢⎛ ⅈ⋅δ    2         2   ⎞    2    ⎛     ⅈ⋅δ⎞    2                ⎥
    ⎢⎝ℯ   ⋅sin (θ) + cos (θ)⎠⋅ℯ       ⎝1 - ℯ   ⎠⋅ℯ     ⋅sin(θ)⋅cos(θ)⎥
    ⎢                                                                ⎥
    ⎢            -ⅈ⋅δ                                           -ⅈ⋅δ ⎥
    ⎢            ─────                                          ─────⎥
    ⎢⎛     ⅈ⋅δ⎞    2                  ⎛ ⅈ⋅δ    2         2   ⎞    2  ⎥
    ⎣⎝1 - ℯ   ⎠⋅ℯ     ⋅sin(θ)⋅cos(θ)  ⎝ℯ   ⋅cos (θ) + sin (θ)⎠⋅ℯ     ⎦

    
    "
1017,"def wrapCommandForDebuggerForExec(*args):
    

    gdb_path = getExecutablePath(""gdb"")

    # Windows extra ball, attempt the downloaded one.
    if isWin32Windows() and gdb_path is None:
        from nuitka.Options import assumeYesForDownloads

        mingw64_gcc_path = getCachedDownloadedMinGW64(
            target_arch=getArchitecture(),
            assume_yes_for_downloads=assumeYesForDownloads(),
        )

        with withEnvironmentPathAdded(""PATH"", os.path.dirname(mingw64_gcc_path)):
            lldb_path = getExecutablePath(""lldb"")

    if gdb_path is None and lldb_path is None:
        lldb_path = getExecutablePath(""lldb"")

        if lldb_path is None:
            general.sysexit(""Error, no 'gdb' or 'lldb' binary found in path."")

    if gdb_path is not None:
        args = (gdb_path, ""gdb"", ""-ex=run"", ""-ex=where"", ""-ex=quit"", ""--args"") + args
    else:
        args = (lldb_path, ""lldb"", ""-o"", ""run"", ""-o"", ""bt"", ""-o"", ""quit"", ""--"") + args

    return args

","Wrap a command for system debugger to call exec

    Args:
        args: (list of str) args for call to be debugged
    Returns:
        args tuple with debugger command inserted

    Notes:
        Currently only gdb and lldb are supported, but adding more
        debuggers would be very welcome.
    ",43,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wrapCommandForDebuggerForExec(*args):
    

    gdb_path = getExecutablePath(""gdb"")

    # Windows extra ball, attempt the downloaded one.
    if isWin32Windows() and gdb_path is None:
        from nuitka.Options import assumeYesForDownloads

        mingw64_gcc_path = getCachedDownloadedMinGW64(
            target_arch=getArchitecture(),
            assume_yes_for_downloads=assumeYesForDownloads(),
        )

        with withEnvironmentPathAdded(""PATH"", os.path.dirname(mingw64_gcc_path)):
            lldb_path = getExecutablePath(""lldb"")

    if gdb_path is None and lldb_path is None:
        lldb_path = getExecutablePath(""lldb"")

        if lldb_path is None:
            general.sysexit(""Error, no 'gdb' or 'lldb' binary found in path."")

    if gdb_path is not None:
        args = (gdb_path, ""gdb"", ""-ex=run"", ""-ex=where"", ""-ex=quit"", ""--args"") + args
    else:
        args = (lldb_path, ""lldb"", ""-o"", ""run"", ""-o"", ""bt"", ""-o"", ""quit"", ""--"") + args

    return args


        ```
    ###Assistant : Wrap a command for system debugger to call exec

    Args:
        args: (list of str) args for call to be debugged
    Returns:
        args tuple with debugger command inserted

    Notes:
        Currently only gdb and lldb are supported, but adding more
        debuggers would be very welcome.
    
    "
1018,"def test_commands_with_invalid_settings(self):
        
        args = [""startproject""]
        out, err = self.run_django_admin(args, settings_file=""bad_settings"")
        self.assertNoOutput(out)
        self.assertOutput(err, ""You must provide a project name"", regex=True)

","
        Commands that don't require settings succeed if the settings file
        doesn't exist.
        ",12,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_commands_with_invalid_settings(self):
        
        args = [""startproject""]
        out, err = self.run_django_admin(args, settings_file=""bad_settings"")
        self.assertNoOutput(out)
        self.assertOutput(err, ""You must provide a project name"", regex=True)


        ```
    ###Assistant : 
        Commands that don't require settings succeed if the settings file
        doesn't exist.
        
    "
1019,"def read(self, size=-1):
        

        if self.closed:
            raise ValueError(""I/O operation on closed file"")
        if self.size_read >= self.chunksize:
            return b''
        if size < 0:
            size = self.chunksize - self.size_read
        if size > self.chunksize - self.size_read:
            size = self.chunksize - self.size_read
        data = self.file.read(size)
        self.size_read = self.size_read + len(data)
        if self.size_read == self.chunksize and \
           self.align and \
           (self.chunksize & 1):
            dummy = self.file.read(1)
            self.size_read = self.size_read + len(dummy)
        return data
","Read at most size bytes from the chunk.
        If size is omitted or negative, read until the end
        of the chunk.
        ",21,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read(self, size=-1):
        

        if self.closed:
            raise ValueError(""I/O operation on closed file"")
        if self.size_read >= self.chunksize:
            return b''
        if size < 0:
            size = self.chunksize - self.size_read
        if size > self.chunksize - self.size_read:
            size = self.chunksize - self.size_read
        data = self.file.read(size)
        self.size_read = self.size_read + len(data)
        if self.size_read == self.chunksize and \
           self.align and \
           (self.chunksize & 1):
            dummy = self.file.read(1)
            self.size_read = self.size_read + len(dummy)
        return data

        ```
    ###Assistant : Read at most size bytes from the chunk.
        If size is omitted or negative, read until the end
        of the chunk.
        
    "
1020,"def slicing_plan(chunks, index):
    
    from dask.array.utils import asarray_safe

    if not is_arraylike(index):
        index = np.asanyarray(index)
    cum_chunks = cached_cumsum(chunks)

    cum_chunks = asarray_safe(cum_chunks, like=index)
    # this dispactches to the array library
    chunk_locations = np.searchsorted(cum_chunks, index, side=""right"")

    # but we need chunk_locations as python ints for getitem calls downstream
    chunk_locations = chunk_locations.tolist()
    where = np.where(np.diff(chunk_locations))[0] + 1

    extra = asarray_safe([0], like=where)
    c_loc = asarray_safe([len(chunk_locations)], like=where)
    where = np.concatenate([extra, where, c_loc])

    out = []
    for i in range(len(where) - 1):
        sub_index = index[where[i] : where[i + 1]]
        chunk = chunk_locations[where[i]]
        if chunk > 0:
            sub_index = sub_index - cum_chunks[chunk - 1]
        out.append((chunk, sub_index))

    return out

","Construct a plan to slice chunks with the given index

    Parameters
    ----------
    chunks : Tuple[int]
        One dimensions worth of chunking information
    index : np.ndarray[int]
        The index passed to slice on that dimension

    Returns
    -------
    out : List[Tuple[int, np.ndarray]]
        A list of chunk/sub-index pairs corresponding to each output chunk
    ",48,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def slicing_plan(chunks, index):
    
    from dask.array.utils import asarray_safe

    if not is_arraylike(index):
        index = np.asanyarray(index)
    cum_chunks = cached_cumsum(chunks)

    cum_chunks = asarray_safe(cum_chunks, like=index)
    # this dispactches to the array library
    chunk_locations = np.searchsorted(cum_chunks, index, side=""right"")

    # but we need chunk_locations as python ints for getitem calls downstream
    chunk_locations = chunk_locations.tolist()
    where = np.where(np.diff(chunk_locations))[0] + 1

    extra = asarray_safe([0], like=where)
    c_loc = asarray_safe([len(chunk_locations)], like=where)
    where = np.concatenate([extra, where, c_loc])

    out = []
    for i in range(len(where) - 1):
        sub_index = index[where[i] : where[i + 1]]
        chunk = chunk_locations[where[i]]
        if chunk > 0:
            sub_index = sub_index - cum_chunks[chunk - 1]
        out.append((chunk, sub_index))

    return out


        ```
    ###Assistant : Construct a plan to slice chunks with the given index

    Parameters
    ----------
    chunks : Tuple[int]
        One dimensions worth of chunking information
    index : np.ndarray[int]
        The index passed to slice on that dimension

    Returns
    -------
    out : List[Tuple[int, np.ndarray]]
        A list of chunk/sub-index pairs corresponding to each output chunk
    
    "
1021,"def _add_conv_branch(self) -> None:
        
        branch_convs = ModuleList()
        for i in range(self.num_convs):
            branch_convs.append(
                Bottleneck(
                    inplanes=self.conv_out_channels,
                    planes=self.conv_out_channels // 4,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg))
        return branch_convs
",Add the fc branch which consists of a sequential of conv layers.,12,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _add_conv_branch(self) -> None:
        
        branch_convs = ModuleList()
        for i in range(self.num_convs):
            branch_convs.append(
                Bottleneck(
                    inplanes=self.conv_out_channels,
                    planes=self.conv_out_channels // 4,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg))
        return branch_convs

        ```
    ###Assistant : Add the fc branch which consists of a sequential of conv layers.
    "
1022,"def match_files(patterns, files):
	
	all_files = files if isinstance(files, Collection) else list(files)
	return_files = set()
	for pattern in patterns:
		if pattern.include is not None:
			result_files = pattern.match(all_files)
			if pattern.include:
				return_files.update(result_files)
			else:
				return_files.difference_update(result_files)
	return return_files

","
	Matches the files to the patterns.

	*patterns* (:class:`~collections.abc.Iterable` of :class:`~pathspec.pattern.Pattern`)
	contains the patterns to use.

	*files* (:class:`~collections.abc.Iterable` of :class:`str`) contains
	the normalized file paths to be matched against *patterns*.

	Returns the matched files (:class:`set` of :class:`str`).
	",36,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def match_files(patterns, files):
	
	all_files = files if isinstance(files, Collection) else list(files)
	return_files = set()
	for pattern in patterns:
		if pattern.include is not None:
			result_files = pattern.match(all_files)
			if pattern.include:
				return_files.update(result_files)
			else:
				return_files.difference_update(result_files)
	return return_files


        ```
    ###Assistant : 
	Matches the files to the patterns.

	*patterns* (:class:`~collections.abc.Iterable` of :class:`~pathspec.pattern.Pattern`)
	contains the patterns to use.

	*files* (:class:`~collections.abc.Iterable` of :class:`str`) contains
	the normalized file paths to be matched against *patterns*.

	Returns the matched files (:class:`set` of :class:`str`).
	
    "
1023,"def _get_offsets_buffer(self) -> Tuple[PandasBuffer, Any]:
        
        if self.dtype[0] == DtypeKind.STRING:
            # For each string, we need to manually determine the next offset
            values = self._col.to_numpy()
            ptr = 0
            offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64)
            for i, v in enumerate(values):
                # For missing values (in this case, `np.nan` values)
                # we don't increment the pointer
                if isinstance(v, str):
                    b = v.encode(encoding=""utf-8"")
                    ptr += len(b)

                offsets[i + 1] = ptr

            # Convert the offsets to a Pandas ""buffer"" using
            # the NumPy array as the backing store
            buffer = PandasBuffer(offsets)

            # Assemble the buffer dtype info
            dtype = (
                DtypeKind.INT,
                64,
                ArrowCTypes.INT64,
                Endianness.NATIVE,
            )  # note: currently only support native endianness
        else:
            raise NoBufferPresent(
                ""This column has a fixed-length dtype so ""
                ""it does not have an offsets buffer""
            )

        return buffer, dtype
","
        Return the buffer containing the offset values for variable-size binary
        data (e.g., variable-length strings) and the buffer's associated dtype.
        Raises NoBufferPresent if the data buffer does not have an associated
        offsets buffer.
        ",32,130,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_offsets_buffer(self) -> Tuple[PandasBuffer, Any]:
        
        if self.dtype[0] == DtypeKind.STRING:
            # For each string, we need to manually determine the next offset
            values = self._col.to_numpy()
            ptr = 0
            offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64)
            for i, v in enumerate(values):
                # For missing values (in this case, `np.nan` values)
                # we don't increment the pointer
                if isinstance(v, str):
                    b = v.encode(encoding=""utf-8"")
                    ptr += len(b)

                offsets[i + 1] = ptr

            # Convert the offsets to a Pandas ""buffer"" using
            # the NumPy array as the backing store
            buffer = PandasBuffer(offsets)

            # Assemble the buffer dtype info
            dtype = (
                DtypeKind.INT,
                64,
                ArrowCTypes.INT64,
                Endianness.NATIVE,
            )  # note: currently only support native endianness
        else:
            raise NoBufferPresent(
                ""This column has a fixed-length dtype so ""
                ""it does not have an offsets buffer""
            )

        return buffer, dtype

        ```
    ###Assistant : 
        Return the buffer containing the offset values for variable-size binary
        data (e.g., variable-length strings) and the buffer's associated dtype.
        Raises NoBufferPresent if the data buffer does not have an associated
        offsets buffer.
        
    "
1024,"def _unschedule_refresh(self) -> None:
        
        if self._unsub_refresh:
            self._unsub_refresh()
            self._unsub_refresh = None
",Unschedule any pending refresh since there is no longer any listeners.,11,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _unschedule_refresh(self) -> None:
        
        if self._unsub_refresh:
            self._unsub_refresh()
            self._unsub_refresh = None

        ```
    ###Assistant : Unschedule any pending refresh since there is no longer any listeners.
    "
1025,"def test_connect_and_rollback(self):
        
        new_connection = connection.copy()
        try:
            # Ensure the database default time zone is different than
            # the time zone in new_connection.settings_dict. We can
            # get the default time zone by reset & show.
            with new_connection.cursor() as cursor:
                cursor.execute(""RESET TIMEZONE"")
                cursor.execute(""SHOW TIMEZONE"")
                db_default_tz = cursor.fetchone()[0]
            new_tz = ""Europe/Paris"" if db_default_tz == ""UTC"" else ""UTC""
            new_connection.close()

            # Invalidate timezone name cache, because the setting_changed
            # handler cannot know about new_connection.
            del new_connection.timezone_name

            # Fetch a new connection with the new_tz as default
            # time zone, run a query and rollback.
            with self.settings(TIME_ZONE=new_tz):
                new_connection.set_autocommit(False)
                new_connection.rollback()

                # Now let's see if the rollback rolled back the SET TIME ZONE.
                with new_connection.cursor() as cursor:
                    cursor.execute(""SHOW TIMEZONE"")
                    tz = cursor.fetchone()[0]
                self.assertEqual(new_tz, tz)

        finally:
            new_connection.close()
","
        PostgreSQL shouldn't roll back SET TIME ZONE, even if the first
        transaction is rolled back (#17062).
        ",16,119,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_connect_and_rollback(self):
        
        new_connection = connection.copy()
        try:
            # Ensure the database default time zone is different than
            # the time zone in new_connection.settings_dict. We can
            # get the default time zone by reset & show.
            with new_connection.cursor() as cursor:
                cursor.execute(""RESET TIMEZONE"")
                cursor.execute(""SHOW TIMEZONE"")
                db_default_tz = cursor.fetchone()[0]
            new_tz = ""Europe/Paris"" if db_default_tz == ""UTC"" else ""UTC""
            new_connection.close()

            # Invalidate timezone name cache, because the setting_changed
            # handler cannot know about new_connection.
            del new_connection.timezone_name

            # Fetch a new connection with the new_tz as default
            # time zone, run a query and rollback.
            with self.settings(TIME_ZONE=new_tz):
                new_connection.set_autocommit(False)
                new_connection.rollback()

                # Now let's see if the rollback rolled back the SET TIME ZONE.
                with new_connection.cursor() as cursor:
                    cursor.execute(""SHOW TIMEZONE"")
                    tz = cursor.fetchone()[0]
                self.assertEqual(new_tz, tz)

        finally:
            new_connection.close()

        ```
    ###Assistant : 
        PostgreSQL shouldn't roll back SET TIME ZONE, even if the first
        transaction is rolled back (#17062).
        
    "
1026,"def test_readback_tfrecords(ray_start_regular_shared, tmp_path):
    

    # The dataset we will write to a .tfrecords file.
    ds = ray.data.from_items(
        [
            # Row one.
            {
                ""int_item"": 1,
                ""int_list"": [2, 2, 3],
                ""float_item"": 1.0,
                ""float_list"": [2.0, 3.0, 4.0],
                ""bytes_item"": b""abc"",
                ""bytes_list"": [b""abc"", b""1234""],
            },
            # Row two.
            {
                ""int_item"": 2,
                ""int_list"": [3, 3, 4],
                ""float_item"": 2.0,
                ""float_list"": [2.0, 2.0, 3.0],
                ""bytes_item"": b""def"",
                ""bytes_list"": [b""def"", b""1234""],
            },
        ]
    )

    # Write the TFRecords.
    ds.write_tfrecords(tmp_path)

    # Read the TFRecords.
    readback_ds = ray.data.read_tfrecords(tmp_path)
    assert ds.take() == readback_ds.take()

","
    Test reading back TFRecords written using datasets.
    The dataset we read back should be the same that we wrote.
    ",19,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_readback_tfrecords(ray_start_regular_shared, tmp_path):
    

    # The dataset we will write to a .tfrecords file.
    ds = ray.data.from_items(
        [
            # Row one.
            {
                ""int_item"": 1,
                ""int_list"": [2, 2, 3],
                ""float_item"": 1.0,
                ""float_list"": [2.0, 3.0, 4.0],
                ""bytes_item"": b""abc"",
                ""bytes_list"": [b""abc"", b""1234""],
            },
            # Row two.
            {
                ""int_item"": 2,
                ""int_list"": [3, 3, 4],
                ""float_item"": 2.0,
                ""float_list"": [2.0, 2.0, 3.0],
                ""bytes_item"": b""def"",
                ""bytes_list"": [b""def"", b""1234""],
            },
        ]
    )

    # Write the TFRecords.
    ds.write_tfrecords(tmp_path)

    # Read the TFRecords.
    readback_ds = ray.data.read_tfrecords(tmp_path)
    assert ds.take() == readback_ds.take()


        ```
    ###Assistant : 
    Test reading back TFRecords written using datasets.
    The dataset we read back should be the same that we wrote.
    
    "
1027,"def call(self, inputs, state):
        
        cur_state_pos = 0
        cur_inp = inputs
        new_states = []
        for i, cell in enumerate(self._cells):
            with tf.compat.v1.variable_scope(""cell_%d"" % i):
                if self._state_is_tuple:
                    if not tf.nest.is_nested(state):
                        raise ValueError(
                            ""Expected state to be a tuple of length ""
                            f""{len(self.state_size)}""
                            f"", but received: {state}""
                        )
                    cur_state = state[i]
                else:
                    cur_state = tf.slice(
                        state, [0, cur_state_pos], [-1, cell.state_size]
                    )
                    cur_state_pos += cell.state_size
                cur_inp, new_state = cell(cur_inp, cur_state)
                new_states.append(new_state)

        new_states = (
            tuple(new_states)
            if self._state_is_tuple
            else tf.concat(new_states, 1)
        )

        return cur_inp, new_states

","Run this multi-layer cell on inputs, starting from state.",9,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call(self, inputs, state):
        
        cur_state_pos = 0
        cur_inp = inputs
        new_states = []
        for i, cell in enumerate(self._cells):
            with tf.compat.v1.variable_scope(""cell_%d"" % i):
                if self._state_is_tuple:
                    if not tf.nest.is_nested(state):
                        raise ValueError(
                            ""Expected state to be a tuple of length ""
                            f""{len(self.state_size)}""
                            f"", but received: {state}""
                        )
                    cur_state = state[i]
                else:
                    cur_state = tf.slice(
                        state, [0, cur_state_pos], [-1, cell.state_size]
                    )
                    cur_state_pos += cell.state_size
                cur_inp, new_state = cell(cur_inp, cur_state)
                new_states.append(new_state)

        new_states = (
            tuple(new_states)
            if self._state_is_tuple
            else tf.concat(new_states, 1)
        )

        return cur_inp, new_states


        ```
    ###Assistant : Run this multi-layer cell on inputs, starting from state.
    "
1028,"def test_basic(push_channel):
    
    msgs = [
        {""foo"": ""bar""},
        {""bar"": ""baz""},
        {""baz"": ""qux"", ""list"": [1, 2, 3]},
    ]
    for msg in msgs:
        ret = push_channel.send(msg, timeout=5, tries=1)
        assert ret[""load""] == msg

","
    Test a variety of messages, make sure we get the expected responses
    ",12,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_basic(push_channel):
    
    msgs = [
        {""foo"": ""bar""},
        {""bar"": ""baz""},
        {""baz"": ""qux"", ""list"": [1, 2, 3]},
    ]
    for msg in msgs:
        ret = push_channel.send(msg, timeout=5, tries=1)
        assert ret[""load""] == msg


        ```
    ###Assistant : 
    Test a variety of messages, make sure we get the expected responses
    
    "
1029,"def test_put_global(self) -> None:
        

        self.get_success(
            self._module_api.account_data_manager.put_global(
                self.user_id, ""test.data"", {""wombat"": True}
            )
        )

        # Request that account data from the normal store; check it's as we expect.
        self.assertEqual(
            self.get_success(
                self._store.get_global_account_data_by_type_for_user(
                    self.user_id, ""test.data""
                )
            ),
            {""wombat"": True},
        )
","
        Tests that written account data using `put_global` can be read out again later.
        ",13,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_put_global(self) -> None:
        

        self.get_success(
            self._module_api.account_data_manager.put_global(
                self.user_id, ""test.data"", {""wombat"": True}
            )
        )

        # Request that account data from the normal store; check it's as we expect.
        self.assertEqual(
            self.get_success(
                self._store.get_global_account_data_by_type_for_user(
                    self.user_id, ""test.data""
                )
            ),
            {""wombat"": True},
        )

        ```
    ###Assistant : 
        Tests that written account data using `put_global` can be read out again later.
        
    "
1030,"def get_conda_environment_content(build_metadata):
    template = environment.from_string(
        .strip()
    )
    return template.render(build_metadata=build_metadata)

","
# DO NOT EDIT: this file is generated from the specification found in the
# following script to centralize the configuration for all Azure CI builds:
# build_tools/azure/update_environments_and_lock_files.py
channels:
  - {{ build_metadata['channel'] }}
dependencies:
  {% for conda_dep in build_metadata['conda_dependencies'] %}
  - {{ conda_dep | get_package_with_constraint(build_metadata) }}
  {% endfor %}
  {% if build_metadata['pip_dependencies'] %}
  - pip
  - pip:
  {% for pip_dep in build_metadata.get('pip_dependencies', []) %}
    - {{ pip_dep | get_package_with_constraint(build_metadata, uses_pip=True) }}
  {% endfor %}
  {% endif %}",77,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_conda_environment_content(build_metadata):
    template = environment.from_string(
        .strip()
    )
    return template.render(build_metadata=build_metadata)


        ```
    ###Assistant : 
# DO NOT EDIT: this file is generated from the specification found in the
# following script to centralize the configuration for all Azure CI builds:
# build_tools/azure/update_environments_and_lock_files.py
channels:
  - {{ build_metadata['channel'] }}
dependencies:
  {% for conda_dep in build_metadata['conda_dependencies'] %}
  - {{ conda_dep | get_package_with_constraint(build_metadata) }}
  {% endfor %}
  {% if build_metadata['pip_dependencies'] %}
  - pip
  - pip:
  {% for pip_dep in build_metadata.get('pip_dependencies', []) %}
    - {{ pip_dep | get_package_with_constraint(build_metadata, uses_pip=True) }}
  {% endfor %}
  {% endif %}
    "
1031,"def preprocess(self, image, image_format):
        
        format = self.format or image_format
        save_kwargs = {""format"": format}

        # Ensuring image is properly rotated
        if hasattr(image, ""_getexif""):
            exif_datadict = image._getexif()  # returns None if no EXIF data
            if exif_datadict is not None:
                exif = dict(exif_datadict.items())
                orientation = exif.get(self.EXIF_ORIENTATION_KEY, None)
                if orientation == 3:
                    image = image.transpose(Image.ROTATE_180)
                elif orientation == 6:
                    image = image.transpose(Image.ROTATE_270)
                elif orientation == 8:
                    image = image.transpose(Image.ROTATE_90)

        # Ensure any embedded ICC profile is preserved
        save_kwargs[""icc_profile""] = image.info.get(""icc_profile"")

        if hasattr(self, ""preprocess_%s"" % format):
            image, addl_save_kwargs = getattr(self, ""preprocess_%s"" % format)(
                image=image
            )
            save_kwargs.update(addl_save_kwargs)

        return image, save_kwargs
","Preprocess an image.

        An API hook for image pre-processing. Calls any image format specific
        pre-processors (if defined). I.E. If `image_format` is 'JPEG', this
        method will look for a method named `preprocess_JPEG`, if found
        `image` will be passed to it.

        Arguments:
            image: a PIL Image instance
            image_format: str, a valid PIL format (i.e. 'JPEG' or 'WEBP')

        Subclasses should return a 2-tuple:
            * [0]: A PIL Image instance.
            * [1]: A dictionary of additional keyword arguments to be used
                    when the instance is saved. If no additional keyword
                    arguments, return an empty dict ({}).

        ",92,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def preprocess(self, image, image_format):
        
        format = self.format or image_format
        save_kwargs = {""format"": format}

        # Ensuring image is properly rotated
        if hasattr(image, ""_getexif""):
            exif_datadict = image._getexif()  # returns None if no EXIF data
            if exif_datadict is not None:
                exif = dict(exif_datadict.items())
                orientation = exif.get(self.EXIF_ORIENTATION_KEY, None)
                if orientation == 3:
                    image = image.transpose(Image.ROTATE_180)
                elif orientation == 6:
                    image = image.transpose(Image.ROTATE_270)
                elif orientation == 8:
                    image = image.transpose(Image.ROTATE_90)

        # Ensure any embedded ICC profile is preserved
        save_kwargs[""icc_profile""] = image.info.get(""icc_profile"")

        if hasattr(self, ""preprocess_%s"" % format):
            image, addl_save_kwargs = getattr(self, ""preprocess_%s"" % format)(
                image=image
            )
            save_kwargs.update(addl_save_kwargs)

        return image, save_kwargs

        ```
    ###Assistant : Preprocess an image.

        An API hook for image pre-processing. Calls any image format specific
        pre-processors (if defined). I.E. If `image_format` is 'JPEG', this
        method will look for a method named `preprocess_JPEG`, if found
        `image` will be passed to it.

        Arguments:
            image: a PIL Image instance
            image_format: str, a valid PIL format (i.e. 'JPEG' or 'WEBP')

        Subclasses should return a 2-tuple:
            * [0]: A PIL Image instance.
            * [1]: A dictionary of additional keyword arguments to be used
                    when the instance is saved. If no additional keyword
                    arguments, return an empty dict ({}).

        
    "
1032,"def get_package_paths(package):
    
    pkg_paths = get_all_package_paths(package)
    if not pkg_paths:
        raise ValueError(f""Package '{package}' does not exist or is not a package!"")

    if len(pkg_paths) > 1:
        logger.warning(
            ""get_package_paths - package %s has multiple paths (%r); returning only first one!"", package, pkg_paths
        )

    pkg_dir = pkg_paths[0]
    pkg_base = package_base_path(pkg_dir, package)

    return pkg_base, pkg_dir

","
    Given a package, return the path to packages stored on this machine and also returns the path to this particular
    package. For example, if pkg.subpkg lives in /abs/path/to/python/libs, then this function returns
    ``(/abs/path/to/python/libs, /abs/path/to/python/libs/pkg/subpkg)``.

    NOTE: due to backwards compatibility, this function returns only one package path along with its base directory.
    In case of PEP 420 namespace package with multiple location, only first location is returned. To obtain all
    package paths, use the ``get_all_package_paths`` function and obtain corresponding base directories using the
    ``package_base_path`` helper.
    ",84,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_package_paths(package):
    
    pkg_paths = get_all_package_paths(package)
    if not pkg_paths:
        raise ValueError(f""Package '{package}' does not exist or is not a package!"")

    if len(pkg_paths) > 1:
        logger.warning(
            ""get_package_paths - package %s has multiple paths (%r); returning only first one!"", package, pkg_paths
        )

    pkg_dir = pkg_paths[0]
    pkg_base = package_base_path(pkg_dir, package)

    return pkg_base, pkg_dir


        ```
    ###Assistant : 
    Given a package, return the path to packages stored on this machine and also returns the path to this particular
    package. For example, if pkg.subpkg lives in /abs/path/to/python/libs, then this function returns
    ``(/abs/path/to/python/libs, /abs/path/to/python/libs/pkg/subpkg)``.

    NOTE: due to backwards compatibility, this function returns only one package path along with its base directory.
    In case of PEP 420 namespace package with multiple location, only first location is returned. To obtain all
    package paths, use the ``get_all_package_paths`` function and obtain corresponding base directories using the
    ``package_base_path`` helper.
    
    "
1033,"def get_address_territory(address_name):
	
	territory = None

	if address_name:
		address_fields = frappe.db.get_value(""Address"", address_name, [""city"", ""state"", ""country""])
		for value in address_fields:
			territory = frappe.db.get_value(""Territory"", value)
			if territory:
				break

	return territory

","Tries to match city, state and country of address to existing territory",12,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_address_territory(address_name):
	
	territory = None

	if address_name:
		address_fields = frappe.db.get_value(""Address"", address_name, [""city"", ""state"", ""country""])
		for value in address_fields:
			territory = frappe.db.get_value(""Territory"", value)
			if territory:
				break

	return territory


        ```
    ###Assistant : Tries to match city, state and country of address to existing territory
    "
1034,"def _get_device_coords(self, position, height):
        
        x = self.legend_width + RACK_ELEVATION_BORDER_WIDTH
        y = RACK_ELEVATION_BORDER_WIDTH
        if self.rack.desc_units:
            y += int((position - 1) * self.unit_height)
        else:
            y += int((self.rack.u_height - position + 1) * self.unit_height) - int(height * self.unit_height)

        return x, y
","
        Return the X, Y coordinates of the top left corner for a device in the specified rack unit.
        ",18,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_device_coords(self, position, height):
        
        x = self.legend_width + RACK_ELEVATION_BORDER_WIDTH
        y = RACK_ELEVATION_BORDER_WIDTH
        if self.rack.desc_units:
            y += int((position - 1) * self.unit_height)
        else:
            y += int((self.rack.u_height - position + 1) * self.unit_height) - int(height * self.unit_height)

        return x, y

        ```
    ###Assistant : 
        Return the X, Y coordinates of the top left corner for a device in the specified rack unit.
        
    "
1035,"def igcd(*args):
    
    if len(args) < 2:
        raise TypeError(
            'igcd() takes at least 2 arguments (%s given)' % len(args))
    args_temp = [abs(as_int(i)) for i in args]
    if 1 in args_temp:
        return 1
    a = args_temp.pop()
    if HAS_GMPY: # Using gmpy if present to speed up.
        for b in args_temp:
            a = gmpy.gcd(a, b) if b else a
        return as_int(a)
    for b in args_temp:
        a = math.gcd(a, b)
    return a


igcd2 = math.gcd

","Computes nonnegative integer greatest common divisor.

    Explanation
    ===========

    The algorithm is based on the well known Euclid's algorithm [1]_. To
    improve speed, ``igcd()`` has its own caching mechanism.

    Examples
    ========

    >>> from sympy import igcd
    >>> igcd(2, 4)
    2
    >>> igcd(5, 10, 15)
    5

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Euclidean_algorithm

    ",49,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def igcd(*args):
    
    if len(args) < 2:
        raise TypeError(
            'igcd() takes at least 2 arguments (%s given)' % len(args))
    args_temp = [abs(as_int(i)) for i in args]
    if 1 in args_temp:
        return 1
    a = args_temp.pop()
    if HAS_GMPY: # Using gmpy if present to speed up.
        for b in args_temp:
            a = gmpy.gcd(a, b) if b else a
        return as_int(a)
    for b in args_temp:
        a = math.gcd(a, b)
    return a


igcd2 = math.gcd


        ```
    ###Assistant : Computes nonnegative integer greatest common divisor.

    Explanation
    ===========

    The algorithm is based on the well known Euclid's algorithm [1]_. To
    improve speed, ``igcd()`` has its own caching mechanism.

    Examples
    ========

    >>> from sympy import igcd
    >>> igcd(2, 4)
    2
    >>> igcd(5, 10, 15)
    5

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Euclidean_algorithm

    
    "
1036,"def _prev_next_cb(self, found, *, going_up, callback):
        
        if found:
            result = browsertab.SearchNavigationResult.found
            # Check if the match count change is opposite to the search direction
            if self._old_match.current > 0:
                if not going_up and self._old_match.current > self.match.current:
                    result = browsertab.SearchNavigationResult.wrapped_bottom
                elif going_up and self._old_match.current < self.match.current:
                    result = browsertab.SearchNavigationResult.wrapped_top
        else:
            result = browsertab.SearchNavigationResult.not_found

        callback(result)
",Call the prev/next callback based on the search result.,9,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _prev_next_cb(self, found, *, going_up, callback):
        
        if found:
            result = browsertab.SearchNavigationResult.found
            # Check if the match count change is opposite to the search direction
            if self._old_match.current > 0:
                if not going_up and self._old_match.current > self.match.current:
                    result = browsertab.SearchNavigationResult.wrapped_bottom
                elif going_up and self._old_match.current < self.match.current:
                    result = browsertab.SearchNavigationResult.wrapped_top
        else:
            result = browsertab.SearchNavigationResult.not_found

        callback(result)

        ```
    ###Assistant : Call the prev/next callback based on the search result.
    "
1037,"def onModuleSourceCode(self, module_name, source_code):
        
        if module_name != ""tensorflow"":
            return source_code

        source_lines = source_code.splitlines()
        found_insert = False
        for i, l in enumerate(source_lines):
            if l.startswith(""def "") and ""_running_from_pip_package():"" in l:
                source_lines.insert(i, ""_site_packages_dirs = []"")
                source_lines.insert(i, ""from tensorflow.python import keras"")
                found_insert = True
                break

        if found_insert is True:
            self.info(""Patched 'running-from-pip' path magic."")
        else:
            self.sysexit(""Did not find 'running-from-pip' path magic code."")

        return ""\n"".join(source_lines)
","Neutralize some path magic in tensorflow.

        Notes:
            Make sure tensorflow understands, we are not running as a PIP
            installed application.
        ",20,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def onModuleSourceCode(self, module_name, source_code):
        
        if module_name != ""tensorflow"":
            return source_code

        source_lines = source_code.splitlines()
        found_insert = False
        for i, l in enumerate(source_lines):
            if l.startswith(""def "") and ""_running_from_pip_package():"" in l:
                source_lines.insert(i, ""_site_packages_dirs = []"")
                source_lines.insert(i, ""from tensorflow.python import keras"")
                found_insert = True
                break

        if found_insert is True:
            self.info(""Patched 'running-from-pip' path magic."")
        else:
            self.sysexit(""Did not find 'running-from-pip' path magic code."")

        return ""\n"".join(source_lines)

        ```
    ###Assistant : Neutralize some path magic in tensorflow.

        Notes:
            Make sure tensorflow understands, we are not running as a PIP
            installed application.
        
    "
1038,"def cast_scalar_indexer(val):
    
    # assumes lib.is_scalar(val)
    if lib.is_float(val) and val.is_integer():
        raise IndexError(
            # GH#34193
            ""Indexing with a float is no longer supported. Manually convert ""
            ""to an integer key instead.""
        )
    return val

","
    Disallow indexing with a float key, even if that key is a round number.

    Parameters
    ----------
    val : scalar

    Returns
    -------
    outval : scalar
    ",24,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cast_scalar_indexer(val):
    
    # assumes lib.is_scalar(val)
    if lib.is_float(val) and val.is_integer():
        raise IndexError(
            # GH#34193
            ""Indexing with a float is no longer supported. Manually convert ""
            ""to an integer key instead.""
        )
    return val


        ```
    ###Assistant : 
    Disallow indexing with a float key, even if that key is a round number.

    Parameters
    ----------
    val : scalar

    Returns
    -------
    outval : scalar
    
    "
1039,"def load_plugins(base_type, database):  # type: (t.Type[C], t.Dict[str, t.Type[C]]) -> None
    
    plugins: t.Dict[str, t.Type[C]] = dict((sc.__module__.rsplit('.', 1)[1], sc) for sc in get_subclasses(base_type))

    for plugin in plugins:
        database[plugin] = plugins[plugin]

","
    Load plugins of the specified type and track them in the specified database.
    Only plugins which have already been imported will be loaded.
    ",23,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_plugins(base_type, database):  # type: (t.Type[C], t.Dict[str, t.Type[C]]) -> None
    
    plugins: t.Dict[str, t.Type[C]] = dict((sc.__module__.rsplit('.', 1)[1], sc) for sc in get_subclasses(base_type))

    for plugin in plugins:
        database[plugin] = plugins[plugin]


        ```
    ###Assistant : 
    Load plugins of the specified type and track them in the specified database.
    Only plugins which have already been imported will be loaded.
    
    "
1040,"def _undetermined_coefficients_match(expr, x, func=None, eq_homogeneous=S.Zero):
    r
    a = Wild('a', exclude=[x])
    b = Wild('b', exclude=[x])
    expr = powsimp(expr, combine='exp')  # exp(x)*exp(2*x + 1) => exp(3*x + 1)
    retdict = {}
","
    Returns a trial function match if undetermined coefficients can be applied
    to ``expr``, and ``None`` otherwise.

    A trial expression can be found for an expression for use with the method
    of undetermined coefficients if the expression is an
    additive/multiplicative combination of constants, polynomials in `x` (the
    independent variable of expr), `\sin(a x + b)`, `\cos(a x + b)`, and
    `e^{a x}` terms (in other words, it has a finite number of linearly
    independent derivatives).

    Note that you may still need to multiply each term returned here by
    sufficient `x` to make it linearly independent with the solutions to the
    homogeneous equation.

    This is intended for internal use by ``undetermined_coefficients`` hints.

    SymPy currently has no way to convert `\sin^n(x) \cos^m(y)` into a sum of
    only `\sin(a x)` and `\cos(b x)` terms, so these are not implemented.  So,
    for example, you will need to manually convert `\sin^2(x)` into `[1 +
    \cos(2 x)]/2` to properly apply the method of undetermined coefficients on
    it.

    Examples
    ========

    >>> from sympy import log, exp
    >>> from sympy.solvers.ode.nonhomogeneous import _undetermined_coefficients_match
    >>> from sympy.abc import x
    >>> _undetermined_coefficients_match(9*x*exp(x) + exp(-x), x)
    {'test': True, 'trialset': {x*exp(x), exp(-x), exp(x)}}
    >>> _undetermined_coefficients_match(log(x), x)
    {'test': False}

    ",194,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _undetermined_coefficients_match(expr, x, func=None, eq_homogeneous=S.Zero):
    r
    a = Wild('a', exclude=[x])
    b = Wild('b', exclude=[x])
    expr = powsimp(expr, combine='exp')  # exp(x)*exp(2*x + 1) => exp(3*x + 1)
    retdict = {}

        ```
    ###Assistant : 
    Returns a trial function match if undetermined coefficients can be applied
    to ``expr``, and ``None`` otherwise.

    A trial expression can be found for an expression for use with the method
    of undetermined coefficients if the expression is an
    additive/multiplicative combination of constants, polynomials in `x` (the
    independent variable of expr), `\sin(a x + b)`, `\cos(a x + b)`, and
    `e^{a x}` terms (in other words, it has a finite number of linearly
    independent derivatives).

    Note that you may still need to multiply each term returned here by
    sufficient `x` to make it linearly independent with the solutions to the
    homogeneous equation.

    This is intended for internal use by ``undetermined_coefficients`` hints.

    SymPy currently has no way to convert `\sin^n(x) \cos^m(y)` into a sum of
    only `\sin(a x)` and `\cos(b x)` terms, so these are not implemented.  So,
    for example, you will need to manually convert `\sin^2(x)` into `[1 +
    \cos(2 x)]/2` to properly apply the method of undetermined coefficients on
    it.

    Examples
    ========

    >>> from sympy import log, exp
    >>> from sympy.solvers.ode.nonhomogeneous import _undetermined_coefficients_match
    >>> from sympy.abc import x
    >>> _undetermined_coefficients_match(9*x*exp(x) + exp(-x), x)
    {'test': True, 'trialset': {x*exp(x), exp(-x), exp(x)}}
    >>> _undetermined_coefficients_match(log(x), x)
    {'test': False}

    
    "
1041,"def _remove_raw(self) -> None:
        
        if ""raw"" in self._selections:
            return
        logger.debug(""Removing Raw Data from output"")
        for key in list(self._stats.keys()):
            if key.startswith(""raw""):
                del self._stats[key]
        logger.debug(""Removed Raw Data from output"")
", Remove raw values from :attr:`stats` if they are not requested. ,10,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _remove_raw(self) -> None:
        
        if ""raw"" in self._selections:
            return
        logger.debug(""Removing Raw Data from output"")
        for key in list(self._stats.keys()):
            if key.startswith(""raw""):
                del self._stats[key]
        logger.debug(""Removed Raw Data from output"")

        ```
    ###Assistant :  Remove raw values from :attr:`stats` if they are not requested. 
    "
1042,"def get_keras_blocks(keras_weight_names):
    
    # example: 'block1a_dwconv/depthwise_kernel:0' -> 'block1a'
    keras_blocks = {x.split(""_"")[0] for x in keras_weight_names if ""block"" in x}
    return sorted(keras_blocks)

",Extract the block names from list of full weight names.,10,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_keras_blocks(keras_weight_names):
    
    # example: 'block1a_dwconv/depthwise_kernel:0' -> 'block1a'
    keras_blocks = {x.split(""_"")[0] for x in keras_weight_names if ""block"" in x}
    return sorted(keras_blocks)


        ```
    ###Assistant : Extract the block names from list of full weight names.
    "
1043,"def get_dashboard_info(party_type, party, loyalty_program=None):
	current_fiscal_year = get_fiscal_year(nowdate(), as_dict=True)

	doctype = ""Sales Invoice"" if party_type == ""Customer"" else ""Purchase Invoice""

	companies = frappe.get_all(
		doctype, filters={""docstatus"": 1, party_type.lower(): party}, distinct=1, fields=[""company""]
	)

	company_wise_info = []

	company_wise_grand_total = frappe.get_all(
		doctype,
		filters={
			""docstatus"": 1,
			party_type.lower(): party,
			""posting_date"": (
				""between"",
				[current_fiscal_year.year_start_date, current_fiscal_year.year_end_date],
			),
		},
		group_by=""company"",
		fields=[
			""company"",
			""sum(grand_total) as grand_total"",
			""sum(base_grand_total) as base_grand_total"",
		],
	)

	loyalty_point_details = []

	if party_type == ""Customer"":
		loyalty_point_details = frappe._dict(
			frappe.get_all(
				""Loyalty Point Entry"",
				filters={
					""customer"": party,
					""expiry_date"": ("">="", getdate()),
				},
				group_by=""company"",
				fields=[""company"", ""sum(loyalty_points) as loyalty_points""],
				as_list=1,
			)
		)

	company_wise_billing_this_year = frappe._dict()

	for d in company_wise_grand_total:
		company_wise_billing_this_year.setdefault(
			d.company, {""grand_total"": d.grand_total, ""base_grand_total"": d.base_grand_total}
		)

	company_wise_total_unpaid = frappe._dict(
		frappe.db.sql(
			,
			(party_type, party),
		)
	)

	for d in companies:
		company_default_currency = frappe.db.get_value(""Company"", d.company, ""default_currency"")
		party_account_currency = get_party_account_currency(party_type, party, d.company)

		if party_account_currency == company_default_currency:
			billing_this_year = flt(
				company_wise_billing_this_year.get(d.company, {}).get(""base_grand_total"")
			)
		else:
			billing_this_year = flt(company_wise_billing_this_year.get(d.company, {}).get(""grand_total""))

		total_unpaid = flt(company_wise_total_unpaid.get(d.company))

		if loyalty_point_details:
			loyalty_points = loyalty_point_details.get(d.company)

		info = {}
		info[""billing_this_year""] = flt(billing_this_year) if billing_this_year else 0
		info[""currency""] = party_account_currency
		info[""total_unpaid""] = flt(total_unpaid) if total_unpaid else 0
		info[""company""] = d.company

		if party_type == ""Customer"" and loyalty_point_details:
			info[""loyalty_points""] = loyalty_points

		if party_type == ""Supplier"":
			info[""total_unpaid""] = -1 * info[""total_unpaid""]

		company_wise_info.append(info)

	return company_wise_info

","
		select company, sum(debit_in_account_currency) - sum(credit_in_account_currency)
		from `tabGL Entry`
		where party_type = %s and party=%s
		and is_cancelled = 0
		group by company",21,193,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_dashboard_info(party_type, party, loyalty_program=None):
	current_fiscal_year = get_fiscal_year(nowdate(), as_dict=True)

	doctype = ""Sales Invoice"" if party_type == ""Customer"" else ""Purchase Invoice""

	companies = frappe.get_all(
		doctype, filters={""docstatus"": 1, party_type.lower(): party}, distinct=1, fields=[""company""]
	)

	company_wise_info = []

	company_wise_grand_total = frappe.get_all(
		doctype,
		filters={
			""docstatus"": 1,
			party_type.lower(): party,
			""posting_date"": (
				""between"",
				[current_fiscal_year.year_start_date, current_fiscal_year.year_end_date],
			),
		},
		group_by=""company"",
		fields=[
			""company"",
			""sum(grand_total) as grand_total"",
			""sum(base_grand_total) as base_grand_total"",
		],
	)

	loyalty_point_details = []

	if party_type == ""Customer"":
		loyalty_point_details = frappe._dict(
			frappe.get_all(
				""Loyalty Point Entry"",
				filters={
					""customer"": party,
					""expiry_date"": ("">="", getdate()),
				},
				group_by=""company"",
				fields=[""company"", ""sum(loyalty_points) as loyalty_points""],
				as_list=1,
			)
		)

	company_wise_billing_this_year = frappe._dict()

	for d in company_wise_grand_total:
		company_wise_billing_this_year.setdefault(
			d.company, {""grand_total"": d.grand_total, ""base_grand_total"": d.base_grand_total}
		)

	company_wise_total_unpaid = frappe._dict(
		frappe.db.sql(
			,
			(party_type, party),
		)
	)

	for d in companies:
		company_default_currency = frappe.db.get_value(""Company"", d.company, ""default_currency"")
		party_account_currency = get_party_account_currency(party_type, party, d.company)

		if party_account_currency == company_default_currency:
			billing_this_year = flt(
				company_wise_billing_this_year.get(d.company, {}).get(""base_grand_total"")
			)
		else:
			billing_this_year = flt(company_wise_billing_this_year.get(d.company, {}).get(""grand_total""))

		total_unpaid = flt(company_wise_total_unpaid.get(d.company))

		if loyalty_point_details:
			loyalty_points = loyalty_point_details.get(d.company)

		info = {}
		info[""billing_this_year""] = flt(billing_this_year) if billing_this_year else 0
		info[""currency""] = party_account_currency
		info[""total_unpaid""] = flt(total_unpaid) if total_unpaid else 0
		info[""company""] = d.company

		if party_type == ""Customer"" and loyalty_point_details:
			info[""loyalty_points""] = loyalty_points

		if party_type == ""Supplier"":
			info[""total_unpaid""] = -1 * info[""total_unpaid""]

		company_wise_info.append(info)

	return company_wise_info


        ```
    ###Assistant : 
		select company, sum(debit_in_account_currency) - sum(credit_in_account_currency)
		from `tabGL Entry`
		where party_type = %s and party=%s
		and is_cancelled = 0
		group by company
    "
1044,"def print_help(self):
        
        help_text = 
        console.print(text=help_text, menu=""Portfolio - Brokers - Robinhood"")
","Print help[cmds]
    login       login to robinhood

    holdings    show account holdings in stocks
    history     show equity history of your account
[/cmds]",20,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        
        help_text = 
        console.print(text=help_text, menu=""Portfolio - Brokers - Robinhood"")

        ```
    ###Assistant : Print help[cmds]
    login       login to robinhood

    holdings    show account holdings in stocks
    history     show equity history of your account
[/cmds]
    "
1045,"def test_page_with_inline_model_with_tabbed_panel_only(self):
        

        EventPageSpeaker.settings_panels = [
            FieldPanel(""first_name""),
            FieldPanel(""last_name""),
        ]

        warning = checks.Warning(
            ""EventPageSpeaker.settings_panels will have no effect on InlinePanel model editing"",
            hint=,
            obj=EventPageSpeaker,
            id=self.warning_id,
        )

        checks_results = self.get_checks_result()

        self.assertIn(warning, checks_results)

        delattr(EventPageSpeaker, ""settings_panels"")
","Test that checks will warn against setting single tabbed panel on InlinePanel modelEnsure that EventPageSpeaker uses `panels` instead of `settings_panels`.
There are no tabs on non-Page model editing within InlinePanels.",30,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_page_with_inline_model_with_tabbed_panel_only(self):
        

        EventPageSpeaker.settings_panels = [
            FieldPanel(""first_name""),
            FieldPanel(""last_name""),
        ]

        warning = checks.Warning(
            ""EventPageSpeaker.settings_panels will have no effect on InlinePanel model editing"",
            hint=,
            obj=EventPageSpeaker,
            id=self.warning_id,
        )

        checks_results = self.get_checks_result()

        self.assertIn(warning, checks_results)

        delattr(EventPageSpeaker, ""settings_panels"")

        ```
    ###Assistant : Test that checks will warn against setting single tabbed panel on InlinePanel modelEnsure that EventPageSpeaker uses `panels` instead of `settings_panels`.
There are no tabs on non-Page model editing within InlinePanels.
    "
1046,"def to_kwargs(self):
        
        default_dict = self.__class__().to_dict()
        this_dict = self.to_dict()
        return {k: v for k, v in this_dict.items() if default_dict[k] != v}


@dataclass","
        Returns a dictionary containing the attributes with values different from the default of this class.
        ",15,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_kwargs(self):
        
        default_dict = self.__class__().to_dict()
        this_dict = self.to_dict()
        return {k: v for k, v in this_dict.items() if default_dict[k] != v}


@dataclass
        ```
    ###Assistant : 
        Returns a dictionary containing the attributes with values different from the default of this class.
        
    "
1047,"def update_document_archive_file(document_id):
    
    document = Document.objects.get(id=document_id)

    mime_type = document.mime_type

    parser_class: Type[DocumentParser] = get_parser_class_for_mime_type(mime_type)

    if not parser_class:
        logger.error(
            f""No parser found for mime type {mime_type}, cannot ""
            f""archive document {document} (ID: {document_id})"",
        )
        return

    parser: DocumentParser = parser_class(logging_group=uuid.uuid4())

    try:
        parser.parse(document.source_path, mime_type, document.get_public_filename())

        thumbnail = parser.get_thumbnail(
            document.source_path,
            mime_type,
            document.get_public_filename(),
        )

        if parser.get_archive_path():
            with transaction.atomic():
                with open(parser.get_archive_path(), ""rb"") as f:
                    checksum = hashlib.md5(f.read()).hexdigest()
                # I'm going to save first so that in case the file move
                # fails, the database is rolled back.
                # We also don't use save() since that triggers the filehandling
                # logic, and we don't want that yet (file not yet in place)
                document.archive_filename = generate_unique_filename(
                    document,
                    archive_filename=True,
                )
                Document.objects.filter(pk=document.pk).update(
                    archive_checksum=checksum,
                    content=parser.get_text(),
                    archive_filename=document.archive_filename,
                )
                with FileLock(settings.MEDIA_LOCK):
                    create_source_path_directory(document.archive_path)
                    shutil.move(parser.get_archive_path(), document.archive_path)
                    shutil.move(thumbnail, document.thumbnail_path)

            with index.open_index_writer() as writer:
                index.update_document(writer, document)

    except Exception:
        logger.exception(
            f""Error while parsing document {document} "" f""(ID: {document_id})"",
        )
    finally:
        parser.cleanup()
","
    Re-creates the archive file of a document, including new OCR content and thumbnail
    ",13,141,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_document_archive_file(document_id):
    
    document = Document.objects.get(id=document_id)

    mime_type = document.mime_type

    parser_class: Type[DocumentParser] = get_parser_class_for_mime_type(mime_type)

    if not parser_class:
        logger.error(
            f""No parser found for mime type {mime_type}, cannot ""
            f""archive document {document} (ID: {document_id})"",
        )
        return

    parser: DocumentParser = parser_class(logging_group=uuid.uuid4())

    try:
        parser.parse(document.source_path, mime_type, document.get_public_filename())

        thumbnail = parser.get_thumbnail(
            document.source_path,
            mime_type,
            document.get_public_filename(),
        )

        if parser.get_archive_path():
            with transaction.atomic():
                with open(parser.get_archive_path(), ""rb"") as f:
                    checksum = hashlib.md5(f.read()).hexdigest()
                # I'm going to save first so that in case the file move
                # fails, the database is rolled back.
                # We also don't use save() since that triggers the filehandling
                # logic, and we don't want that yet (file not yet in place)
                document.archive_filename = generate_unique_filename(
                    document,
                    archive_filename=True,
                )
                Document.objects.filter(pk=document.pk).update(
                    archive_checksum=checksum,
                    content=parser.get_text(),
                    archive_filename=document.archive_filename,
                )
                with FileLock(settings.MEDIA_LOCK):
                    create_source_path_directory(document.archive_path)
                    shutil.move(parser.get_archive_path(), document.archive_path)
                    shutil.move(thumbnail, document.thumbnail_path)

            with index.open_index_writer() as writer:
                index.update_document(writer, document)

    except Exception:
        logger.exception(
            f""Error while parsing document {document} "" f""(ID: {document_id})"",
        )
    finally:
        parser.cleanup()

        ```
    ###Assistant : 
    Re-creates the archive file of a document, including new OCR content and thumbnail
    
    "
1048,"def execute():
	
	frappe.reload_doc(""Selling"", ""doctype"", ""Customer Credit Limit"")
	frappe.reload_doc(""Selling"", ""doctype"", ""Customer"")
	frappe.reload_doc(""Setup"", ""doctype"", ""Customer Group"")

	if frappe.db.a_row_exists(""Customer Credit Limit""):
		return

	move_credit_limit_to_child_table()

",Move credit limit and bypass credit limit to the child table of customer credit limit,15,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	
	frappe.reload_doc(""Selling"", ""doctype"", ""Customer Credit Limit"")
	frappe.reload_doc(""Selling"", ""doctype"", ""Customer"")
	frappe.reload_doc(""Setup"", ""doctype"", ""Customer Group"")

	if frappe.db.a_row_exists(""Customer Credit Limit""):
		return

	move_credit_limit_to_child_table()


        ```
    ###Assistant : Move credit limit and bypass credit limit to the child table of customer credit limit
    "
1049,"def forceexit(self, tradeid, ordertype=None, amount=None):
        

        return self._post(""forceexit"", data={
            ""tradeid"": tradeid,
            ""ordertype"": ordertype,
            ""amount"": amount,
            })
","Force-exit a trade.

        :param tradeid: Id of the trade (can be received via status command)
        :param ordertype: Order type to use (must be market or limit)
        :param amount: Amount to sell. Full sell if not given
        :return: json object
        ",39,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forceexit(self, tradeid, ordertype=None, amount=None):
        

        return self._post(""forceexit"", data={
            ""tradeid"": tradeid,
            ""ordertype"": ordertype,
            ""amount"": amount,
            })

        ```
    ###Assistant : Force-exit a trade.

        :param tradeid: Id of the trade (can be received via status command)
        :param ordertype: Order type to use (must be market or limit)
        :param amount: Amount to sell. Full sell if not given
        :return: json object
        
    "
1050,"def _abi3_applies(python_version):
    # type: (PythonVersion) -> bool
    
    return len(python_version) > 1 and tuple(python_version) >= (3, 2)

","
    Determine if the Python version supports abi3.

    PEP 384 was first implemented in Python 3.2.
    ",15,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _abi3_applies(python_version):
    # type: (PythonVersion) -> bool
    
    return len(python_version) > 1 and tuple(python_version) >= (3, 2)


        ```
    ###Assistant : 
    Determine if the Python version supports abi3.

    PEP 384 was first implemented in Python 3.2.
    
    "
1051,"def variable(value, dtype=None, name=None, constraint=None):
    
    if dtype is None:
        dtype = floatx()
    if hasattr(value, ""tocoo""):
        sparse_coo = value.tocoo()
        indices = np.concatenate(
            (
                np.expand_dims(sparse_coo.row, 1),
                np.expand_dims(sparse_coo.col, 1),
            ),
            1,
        )
        v = tf.SparseTensor(
            indices=indices,
            values=sparse_coo.data,
            dense_shape=sparse_coo.shape,
        )
        v._keras_shape = sparse_coo.shape
        return v
    v = tf.Variable(
        value, dtype=tf.as_dtype(dtype), name=name, constraint=constraint
    )
    if isinstance(value, np.ndarray):
        v._keras_shape = value.shape
    elif hasattr(value, ""shape""):
        v._keras_shape = int_shape(value)
    track_variable(v)
    return v

","Instantiates a variable and returns it.

    Args:
        value: Numpy array, initial value of the tensor.
        dtype: Tensor type.
        name: Optional name string for the tensor.
        constraint: Optional projection function to be
            applied to the variable after an optimizer update.

    Returns:
        A variable instance (with Keras metadata included).

    Examples:

    >>> val = np.array([[1, 2], [3, 4]])
    >>> kvar = tf.keras.backend.variable(value=val, dtype='float64',
    ...                                  name='example_var')
    >>> tf.keras.backend.dtype(kvar)
    'float64'
    >>> print(kvar)
    <tf.Variable 'example_var:...' shape=(2, 2) dtype=float64, numpy=
      array([[1., 2.],
             [3., 4.]])>

    ",77,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def variable(value, dtype=None, name=None, constraint=None):
    
    if dtype is None:
        dtype = floatx()
    if hasattr(value, ""tocoo""):
        sparse_coo = value.tocoo()
        indices = np.concatenate(
            (
                np.expand_dims(sparse_coo.row, 1),
                np.expand_dims(sparse_coo.col, 1),
            ),
            1,
        )
        v = tf.SparseTensor(
            indices=indices,
            values=sparse_coo.data,
            dense_shape=sparse_coo.shape,
        )
        v._keras_shape = sparse_coo.shape
        return v
    v = tf.Variable(
        value, dtype=tf.as_dtype(dtype), name=name, constraint=constraint
    )
    if isinstance(value, np.ndarray):
        v._keras_shape = value.shape
    elif hasattr(value, ""shape""):
        v._keras_shape = int_shape(value)
    track_variable(v)
    return v


        ```
    ###Assistant : Instantiates a variable and returns it.

    Args:
        value: Numpy array, initial value of the tensor.
        dtype: Tensor type.
        name: Optional name string for the tensor.
        constraint: Optional projection function to be
            applied to the variable after an optimizer update.

    Returns:
        A variable instance (with Keras metadata included).

    Examples:

    >>> val = np.array([[1, 2], [3, 4]])
    >>> kvar = tf.keras.backend.variable(value=val, dtype='float64',
    ...                                  name='example_var')
    >>> tf.keras.backend.dtype(kvar)
    'float64'
    >>> print(kvar)
    <tf.Variable 'example_var:...' shape=(2, 2) dtype=float64, numpy=
      array([[1., 2.],
             [3., 4.]])>

    
    "
1052,"def get_cost_to_borrow() -> pd.DataFrame:
    
    ftp = ftplib.FTP(""ftp3.interactivebrokers.com"", ""shortstock"")

    flo = BytesIO()
    ftp.retrbinary(""RETR usa.txt"", flo.write)
    flo.seek(0)

    data = pd.read_csv(flo, sep=""|"", skiprows=1)
    data = data[[""#SYM"", ""FEERATE"", ""AVAILABLE""]]
    data[""AVAILABLE""] = data[""AVAILABLE""].replace("">10000000"", 10000000)
    data.fillna(0, inplace=True)
    data[""AVAILABLE""] = data[""AVAILABLE""].astype(int)
    data.sort_values(by=[""FEERATE""], ascending=False, inplace=True)
    data[""FEERATE""] = data[""FEERATE""].apply(lambda x: str(x) + ""%"")
    data.columns = [""Symbol"", ""Fees"", ""Available""]
    return data
","Get stocks with highest cost to borrow [Source: Interactive Broker]

    Returns
    -------
    pd.DataFrame
        Cost to borrow
    ",16,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_cost_to_borrow() -> pd.DataFrame:
    
    ftp = ftplib.FTP(""ftp3.interactivebrokers.com"", ""shortstock"")

    flo = BytesIO()
    ftp.retrbinary(""RETR usa.txt"", flo.write)
    flo.seek(0)

    data = pd.read_csv(flo, sep=""|"", skiprows=1)
    data = data[[""#SYM"", ""FEERATE"", ""AVAILABLE""]]
    data[""AVAILABLE""] = data[""AVAILABLE""].replace("">10000000"", 10000000)
    data.fillna(0, inplace=True)
    data[""AVAILABLE""] = data[""AVAILABLE""].astype(int)
    data.sort_values(by=[""FEERATE""], ascending=False, inplace=True)
    data[""FEERATE""] = data[""FEERATE""].apply(lambda x: str(x) + ""%"")
    data.columns = [""Symbol"", ""Fees"", ""Available""]
    return data

        ```
    ###Assistant : Get stocks with highest cost to borrow [Source: Interactive Broker]

    Returns
    -------
    pd.DataFrame
        Cost to borrow
    
    "
1053,"def _enable_task_listeners():
        
        if get_listener_manager().has_listeners:
            register_task_instance_state_events()
","
        Check if we have any registered listeners, then register sqlalchemy hooks for
        TI state change if we do.
        ",18,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _enable_task_listeners():
        
        if get_listener_manager().has_listeners:
            register_task_instance_state_events()

        ```
    ###Assistant : 
        Check if we have any registered listeners, then register sqlalchemy hooks for
        TI state change if we do.
        
    "
1054,"def has_computed_output(self) -> bool:
        
        return self._snapshot_blocks is not None and not self._stages_after_snapshot

","Whether this plan has a computed snapshot for the final stage, i.e. for the
        output of this plan.
        ",18,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def has_computed_output(self) -> bool:
        
        return self._snapshot_blocks is not None and not self._stages_after_snapshot


        ```
    ###Assistant : Whether this plan has a computed snapshot for the final stage, i.e. for the
        output of this plan.
        
    "
1055,"def get_conditions(filters):
	conditions = """"

	accounting_dimensions = get_accounting_dimensions(as_list=False) or []
	accounting_dimensions_list = [d.fieldname for d in accounting_dimensions]

	if filters.get(""company""):
		conditions += "" and company=%(company)s""

	if filters.get(""customer"") and ""customer"" not in accounting_dimensions_list:
		conditions += "" and customer = %(customer)s""

	if filters.get(""from_date""):
		conditions += "" and posting_date >= %(from_date)s""
	if filters.get(""to_date""):
		conditions += "" and posting_date <= %(to_date)s""

	if filters.get(""owner""):
		conditions += "" and owner = %(owner)s""

	def get_sales_invoice_item_field_condition(field, table=""Sales Invoice Item"") -> str:
		if not filters.get(field) or field in accounting_dimensions_list:
			return """"
		return f

	conditions += get_sales_invoice_item_field_condition(""mode_of_payments"", ""Sales Invoice Payment"")
	conditions += get_sales_invoice_item_field_condition(""cost_center"")
	conditions += get_sales_invoice_item_field_condition(""warehouse"")
	conditions += get_sales_invoice_item_field_condition(""brand"")
	conditions += get_sales_invoice_item_field_condition(""item_group"")

	if accounting_dimensions:
		common_condition = 
		for dimension in accounting_dimensions:
			if filters.get(dimension.fieldname):
				if frappe.get_cached_value(""DocType"", dimension.document_type, ""is_tree""):
					filters[dimension.fieldname] = get_dimension_with_children(
						dimension.document_type, filters.get(dimension.fieldname)
					)

					conditions += (
						common_condition
						+ ""and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)"".format(dimension.fieldname)
					)
				else:
					conditions += (
						common_condition
						+ ""and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)"".format(dimension.fieldname)
					)

	return conditions

"," and exists(select name from `tab{table}`
			 where parent=`tabSales Invoice`.name
			 	and ifnull(`tab{table}`.{field}, '') = %({field})s)
			and exists(select name from `tabSales Invoice Item`
				where parent=`tabSales Invoice`.name
			",23,150,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_conditions(filters):
	conditions = """"

	accounting_dimensions = get_accounting_dimensions(as_list=False) or []
	accounting_dimensions_list = [d.fieldname for d in accounting_dimensions]

	if filters.get(""company""):
		conditions += "" and company=%(company)s""

	if filters.get(""customer"") and ""customer"" not in accounting_dimensions_list:
		conditions += "" and customer = %(customer)s""

	if filters.get(""from_date""):
		conditions += "" and posting_date >= %(from_date)s""
	if filters.get(""to_date""):
		conditions += "" and posting_date <= %(to_date)s""

	if filters.get(""owner""):
		conditions += "" and owner = %(owner)s""

	def get_sales_invoice_item_field_condition(field, table=""Sales Invoice Item"") -> str:
		if not filters.get(field) or field in accounting_dimensions_list:
			return """"
		return f

	conditions += get_sales_invoice_item_field_condition(""mode_of_payments"", ""Sales Invoice Payment"")
	conditions += get_sales_invoice_item_field_condition(""cost_center"")
	conditions += get_sales_invoice_item_field_condition(""warehouse"")
	conditions += get_sales_invoice_item_field_condition(""brand"")
	conditions += get_sales_invoice_item_field_condition(""item_group"")

	if accounting_dimensions:
		common_condition = 
		for dimension in accounting_dimensions:
			if filters.get(dimension.fieldname):
				if frappe.get_cached_value(""DocType"", dimension.document_type, ""is_tree""):
					filters[dimension.fieldname] = get_dimension_with_children(
						dimension.document_type, filters.get(dimension.fieldname)
					)

					conditions += (
						common_condition
						+ ""and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)"".format(dimension.fieldname)
					)
				else:
					conditions += (
						common_condition
						+ ""and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)"".format(dimension.fieldname)
					)

	return conditions


        ```
    ###Assistant :  and exists(select name from `tab{table}`
			 where parent=`tabSales Invoice`.name
			 	and ifnull(`tab{table}`.{field}, '') = %({field})s)
			and exists(select name from `tabSales Invoice Item`
				where parent=`tabSales Invoice`.name
			
    "
1056,"def _get_classifier_artifacts(fitted_estimator, prefix, X, y_true, sample_weight):
    
    import sklearn

    if not _is_plotting_supported():
        return []
","
    Draw and record various common artifacts for classifier

    For all classifiers, we always log:
    (1) confusion matrix:
    https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html

    For only binary classifiers, we will log:
    (2) precision recall curve:
    https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_precision_recall_curve.html
    (3) roc curve:
    https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

    Steps:
    1. Extract X and y_true from fit_args and fit_kwargs, and split into train & test datasets.
    2. If the sample_weight argument exists in fit_func (accuracy_score by default
    has sample_weight), extract it from fit_args or fit_kwargs as
    (y_true, y_pred, sample_weight, multioutput), otherwise as (y_true, y_pred, multioutput)
    3. return a list of artifacts path to be logged

    :param fitted_estimator: The already fitted regressor
    :param fit_args: Positional arguments given to fit_func.
    :param fit_kwargs: Keyword arguments given to fit_func.
    :return: List of artifacts to be logged
    ",117,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_classifier_artifacts(fitted_estimator, prefix, X, y_true, sample_weight):
    
    import sklearn

    if not _is_plotting_supported():
        return []

        ```
    ###Assistant : 
    Draw and record various common artifacts for classifier

    For all classifiers, we always log:
    (1) confusion matrix:
    https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html

    For only binary classifiers, we will log:
    (2) precision recall curve:
    https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_precision_recall_curve.html
    (3) roc curve:
    https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

    Steps:
    1. Extract X and y_true from fit_args and fit_kwargs, and split into train & test datasets.
    2. If the sample_weight argument exists in fit_func (accuracy_score by default
    has sample_weight), extract it from fit_args or fit_kwargs as
    (y_true, y_pred, sample_weight, multioutput), otherwise as (y_true, y_pred, multioutput)
    3. return a list of artifacts path to be logged

    :param fitted_estimator: The already fitted regressor
    :param fit_args: Positional arguments given to fit_func.
    :param fit_kwargs: Keyword arguments given to fit_func.
    :return: List of artifacts to be logged
    
    "
1057,"def double_edge_swap(G, nswap=1, max_tries=100, seed=None):
    
    if G.is_directed():
        raise nx.NetworkXError(
            ""double_edge_swap() not defined for directed graphs. Use directed_edge_swap instead.""
        )
    if nswap > max_tries:
        raise nx.NetworkXError(""Number of swaps > number of tries allowed."")
    if len(G) < 4:
        raise nx.NetworkXError(""Graph has less than four nodes."")
    # Instead of choosing uniformly at random from a generated edge list,
    # this algorithm chooses nonuniformly from the set of nodes with
    # probability weighted by degree.
    n = 0
    swapcount = 0
    keys, degrees = zip(*G.degree())  # keys, degree
    cdf = nx.utils.cumulative_distribution(degrees)  # cdf of degree
    discrete_sequence = nx.utils.discrete_sequence
    while swapcount < nswap:
        #        if random.random() < 0.5: continue # trick to avoid periodicities?
        # pick two random edges without creating edge list
        # choose source node indices from discrete distribution
        (ui, xi) = discrete_sequence(2, cdistribution=cdf, seed=seed)
        if ui == xi:
            continue  # same source, skip
        u = keys[ui]  # convert index to label
        x = keys[xi]
        # choose target uniformly from neighbors
        v = seed.choice(list(G[u]))
        y = seed.choice(list(G[x]))
        if v == y:
            continue  # same target, skip
        if (x not in G[u]) and (y not in G[v]):  # don't create parallel edges
            G.add_edge(u, x)
            G.add_edge(v, y)
            G.remove_edge(u, v)
            G.remove_edge(x, y)
            swapcount += 1
        if n >= max_tries:
            e = (
                f""Maximum number of swap attempts ({n}) exceeded ""
                f""before desired swaps achieved ({nswap}).""
            )
            raise nx.NetworkXAlgorithmError(e)
        n += 1
    return G


@py_random_state(3)","Swap two edges in the graph while keeping the node degrees fixed.

    A double-edge swap removes two randomly chosen edges u-v and x-y
    and creates the new edges u-x and v-y::

     u--v            u  v
            becomes  |  |
     x--y            x  y

    If either the edge u-x or v-y already exist no swap is performed
    and another attempt is made to find a suitable edge pair.

    Parameters
    ----------
    G : graph
       An undirected graph

    nswap : integer (optional, default=1)
       Number of double-edge swaps to perform

    max_tries : integer (optional)
       Maximum number of attempts to swap edges

    seed : integer, random_state, or None (default)
        Indicator of random number generation state.
        See :ref:`Randomness<randomness>`.

    Returns
    -------
    G : graph
       The graph after double edge swaps.

    Notes
    -----
    Does not enforce any connectivity constraints.

    The graph G is modified in place.
    ",135,228,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def double_edge_swap(G, nswap=1, max_tries=100, seed=None):
    
    if G.is_directed():
        raise nx.NetworkXError(
            ""double_edge_swap() not defined for directed graphs. Use directed_edge_swap instead.""
        )
    if nswap > max_tries:
        raise nx.NetworkXError(""Number of swaps > number of tries allowed."")
    if len(G) < 4:
        raise nx.NetworkXError(""Graph has less than four nodes."")
    # Instead of choosing uniformly at random from a generated edge list,
    # this algorithm chooses nonuniformly from the set of nodes with
    # probability weighted by degree.
    n = 0
    swapcount = 0
    keys, degrees = zip(*G.degree())  # keys, degree
    cdf = nx.utils.cumulative_distribution(degrees)  # cdf of degree
    discrete_sequence = nx.utils.discrete_sequence
    while swapcount < nswap:
        #        if random.random() < 0.5: continue # trick to avoid periodicities?
        # pick two random edges without creating edge list
        # choose source node indices from discrete distribution
        (ui, xi) = discrete_sequence(2, cdistribution=cdf, seed=seed)
        if ui == xi:
            continue  # same source, skip
        u = keys[ui]  # convert index to label
        x = keys[xi]
        # choose target uniformly from neighbors
        v = seed.choice(list(G[u]))
        y = seed.choice(list(G[x]))
        if v == y:
            continue  # same target, skip
        if (x not in G[u]) and (y not in G[v]):  # don't create parallel edges
            G.add_edge(u, x)
            G.add_edge(v, y)
            G.remove_edge(u, v)
            G.remove_edge(x, y)
            swapcount += 1
        if n >= max_tries:
            e = (
                f""Maximum number of swap attempts ({n}) exceeded ""
                f""before desired swaps achieved ({nswap}).""
            )
            raise nx.NetworkXAlgorithmError(e)
        n += 1
    return G


@py_random_state(3)
        ```
    ###Assistant : Swap two edges in the graph while keeping the node degrees fixed.

    A double-edge swap removes two randomly chosen edges u-v and x-y
    and creates the new edges u-x and v-y::

     u--v            u  v
            becomes  |  |
     x--y            x  y

    If either the edge u-x or v-y already exist no swap is performed
    and another attempt is made to find a suitable edge pair.

    Parameters
    ----------
    G : graph
       An undirected graph

    nswap : integer (optional, default=1)
       Number of double-edge swaps to perform

    max_tries : integer (optional)
       Maximum number of attempts to swap edges

    seed : integer, random_state, or None (default)
        Indicator of random number generation state.
        See :ref:`Randomness<randomness>`.

    Returns
    -------
    G : graph
       The graph after double edge swaps.

    Notes
    -----
    Does not enforce any connectivity constraints.

    The graph G is modified in place.
    
    "
1058,"def test_dataset_shard_with_task_parallelization(self):
        
        config = {
            ""input"": ""dataset"",
            ""input_config"": {
                ""format"": ""json"",
                ""paths"": self.dset_path,
                ""parallelism"": 10,
            },
        }
        NUM_WORKERS = 4

        _, shards = get_dataset_and_shards(config, num_workers=NUM_WORKERS)

        assert len(shards) == NUM_WORKERS + 1
        assert shards[0] is None
        assert all(
            isinstance(remote_shard, ray.data.Dataset) for remote_shard in shards[1:]
        )
","Tests whether the dataset_shard function works correctly with parallelism
        for reading the dataset.",13,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dataset_shard_with_task_parallelization(self):
        
        config = {
            ""input"": ""dataset"",
            ""input_config"": {
                ""format"": ""json"",
                ""paths"": self.dset_path,
                ""parallelism"": 10,
            },
        }
        NUM_WORKERS = 4

        _, shards = get_dataset_and_shards(config, num_workers=NUM_WORKERS)

        assert len(shards) == NUM_WORKERS + 1
        assert shards[0] is None
        assert all(
            isinstance(remote_shard, ray.data.Dataset) for remote_shard in shards[1:]
        )

        ```
    ###Assistant : Tests whether the dataset_shard function works correctly with parallelism
        for reading the dataset.
    "
1059,"def test_image_comparison_expect_rms(im1, im2, tol, expect_rms):
    
    baseline_dir, result_dir = map(Path, _image_directories(lambda: ""dummy""))
    # Copy both ""baseline"" and ""test"" image to result_dir, so that 1)
    # compare_images writes the diff to result_dir, rather than to the source
    # tree and 2) the baseline image doesn't appear missing to triage_tests.py.
    uid = str(uuid.uuid4())
    result_im1 = make_test_filename(result_dir / (uid + im1), ""expected"")
    shutil.copyfile(baseline_dir / im1, result_im1)
    result_im2 = result_dir / im1
    shutil.copyfile(baseline_dir / im2, result_im2)
    results = compare_images(
        result_im1, result_im2, tol=tol, in_decorator=True)

    if expect_rms is None:
        assert results is None
    else:
        assert results is not None
        assert results['rms'] == approx(expect_rms, abs=1e-4)
","
    Compare two images, expecting a particular RMS error.

    im1 and im2 are filenames relative to the baseline_dir directory.

    tol is the tolerance to pass to compare_images.

    expect_rms is the expected RMS value, or None. If None, the test will
    succeed if compare_images succeeds. Otherwise, the test will succeed if
    compare_images fails and returns an RMS error almost equal to this value.
    ",61,97,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_image_comparison_expect_rms(im1, im2, tol, expect_rms):
    
    baseline_dir, result_dir = map(Path, _image_directories(lambda: ""dummy""))
    # Copy both ""baseline"" and ""test"" image to result_dir, so that 1)
    # compare_images writes the diff to result_dir, rather than to the source
    # tree and 2) the baseline image doesn't appear missing to triage_tests.py.
    uid = str(uuid.uuid4())
    result_im1 = make_test_filename(result_dir / (uid + im1), ""expected"")
    shutil.copyfile(baseline_dir / im1, result_im1)
    result_im2 = result_dir / im1
    shutil.copyfile(baseline_dir / im2, result_im2)
    results = compare_images(
        result_im1, result_im2, tol=tol, in_decorator=True)

    if expect_rms is None:
        assert results is None
    else:
        assert results is not None
        assert results['rms'] == approx(expect_rms, abs=1e-4)

        ```
    ###Assistant : 
    Compare two images, expecting a particular RMS error.

    im1 and im2 are filenames relative to the baseline_dir directory.

    tol is the tolerance to pass to compare_images.

    expect_rms is the expected RMS value, or None. If None, the test will
    succeed if compare_images succeeds. Otherwise, the test will succeed if
    compare_images fails and returns an RMS error almost equal to this value.
    
    "
1060,"def test_versioned_symbols_reserialization(self):
        
        module_v2 = torch.jit.load(pytorch_test_dir + ""/jit/fixtures/_test_serialization_subcmul_v2.pt"")
        buffer = io.BytesIO()
        torch.jit.save(module_v2, buffer)
        buffer.seek(0)
        module_reserialized = torch.jit.load(buffer)

        subcmul_nodes = sum(""subcmul"" in n.kind() for
                            n in module_reserialized.graph.nodes())
        self.assertEqual(subcmul_nodes, 0)
","
        Tests that loading and saving serialized Torchscript with a versioned
        symbol won't persist the original function and will inline the
        versioned builtin.
        ",22,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_versioned_symbols_reserialization(self):
        
        module_v2 = torch.jit.load(pytorch_test_dir + ""/jit/fixtures/_test_serialization_subcmul_v2.pt"")
        buffer = io.BytesIO()
        torch.jit.save(module_v2, buffer)
        buffer.seek(0)
        module_reserialized = torch.jit.load(buffer)

        subcmul_nodes = sum(""subcmul"" in n.kind() for
                            n in module_reserialized.graph.nodes())
        self.assertEqual(subcmul_nodes, 0)

        ```
    ###Assistant : 
        Tests that loading and saving serialized Torchscript with a versioned
        symbol won't persist the original function and will inline the
        versioned builtin.
        
    "
1061,"def test_stylesheet_apply_takes_final_rule_in_specificity_clash():
    
    css = "".a {background: red; color: lime;} .b {background: blue;}""
    stylesheet = _make_stylesheet(css)
    node = DOMNode(classes=""a b"", id=""c"")
    stylesheet.apply(node)

    assert node.styles.color == Color(0, 255, 0)  # color: lime
    assert node.styles.background == Color(0, 0, 255)  # background: blue

",".a and .b both contain background and have same specificity, so .b wins
    since it was declared last - the background should be blue.",24,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stylesheet_apply_takes_final_rule_in_specificity_clash():
    
    css = "".a {background: red; color: lime;} .b {background: blue;}""
    stylesheet = _make_stylesheet(css)
    node = DOMNode(classes=""a b"", id=""c"")
    stylesheet.apply(node)

    assert node.styles.color == Color(0, 255, 0)  # color: lime
    assert node.styles.background == Color(0, 0, 255)  # background: blue


        ```
    ###Assistant : .a and .b both contain background and have same specificity, so .b wins
    since it was declared last - the background should be blue.
    "
1062,"def upgrade():
    
    conn = op.get_bind()
    if conn.dialect.name == 'sqlite':
        op.execute('PRAGMA foreign_keys=OFF')
        with op.batch_alter_table('ab_view_menu', schema=None) as batch_op:
            batch_op.create_unique_constraint(batch_op.f('ab_view_menu_name_uq'), ['name'])
        op.execute('PRAGMA foreign_keys=ON')
    elif conn.dialect.name == 'mysql':
        with op.batch_alter_table('ab_register_user', schema=None) as batch_op:
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
        with op.batch_alter_table('ab_user', schema=None) as batch_op:
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
    elif conn.dialect.name == 'mssql':
        with op.batch_alter_table('ab_register_user') as batch_op:
            # Drop the unique constraint on username and email
            constraints = get_mssql_table_constraints(conn, 'ab_register_user')
            for k, _ in constraints.get('UNIQUE').items():
                batch_op.drop_constraint(k, type_='unique')
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.create_unique_constraint(None, ['username'])
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
        with op.batch_alter_table('ab_user') as batch_op:
            # Drop the unique constraint on username and email
            constraints = get_mssql_table_constraints(conn, 'ab_user')
            for k, _ in constraints.get('UNIQUE').items():
                batch_op.drop_constraint(k, type_='unique')
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.create_unique_constraint(None, ['username'])
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
            batch_op.create_unique_constraint(None, ['email'])

",Apply Update migration for FAB tables to add missing constraints,10,116,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def upgrade():
    
    conn = op.get_bind()
    if conn.dialect.name == 'sqlite':
        op.execute('PRAGMA foreign_keys=OFF')
        with op.batch_alter_table('ab_view_menu', schema=None) as batch_op:
            batch_op.create_unique_constraint(batch_op.f('ab_view_menu_name_uq'), ['name'])
        op.execute('PRAGMA foreign_keys=ON')
    elif conn.dialect.name == 'mysql':
        with op.batch_alter_table('ab_register_user', schema=None) as batch_op:
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
        with op.batch_alter_table('ab_user', schema=None) as batch_op:
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
    elif conn.dialect.name == 'mssql':
        with op.batch_alter_table('ab_register_user') as batch_op:
            # Drop the unique constraint on username and email
            constraints = get_mssql_table_constraints(conn, 'ab_register_user')
            for k, _ in constraints.get('UNIQUE').items():
                batch_op.drop_constraint(k, type_='unique')
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.create_unique_constraint(None, ['username'])
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
        with op.batch_alter_table('ab_user') as batch_op:
            # Drop the unique constraint on username and email
            constraints = get_mssql_table_constraints(conn, 'ab_user')
            for k, _ in constraints.get('UNIQUE').items():
                batch_op.drop_constraint(k, type_='unique')
            batch_op.alter_column('username', existing_type=sa.String(256), nullable=False)
            batch_op.create_unique_constraint(None, ['username'])
            batch_op.alter_column('email', existing_type=sa.String(256), nullable=False)
            batch_op.create_unique_constraint(None, ['email'])


        ```
    ###Assistant : Apply Update migration for FAB tables to add missing constraints
    "
1063,"def upgrade():
    op.drop_table('ai_table')

    conn = op.get_bind()

    # views was created with unnamed fk. Therefore need recreate it
    op.create_table(
        'view_tmp',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('name', sa.String(), nullable=False),
        sa.Column('company_id', sa.Integer(), nullable=True),
        sa.Column('query', sa.String(), nullable=False),
        sa.Column('integration_id', sa.Integer(), nullable=False),
        sa.ForeignKeyConstraint(['integration_id'], ['integration.id'], name='fk_integration_id'),
        sa.PrimaryKeyConstraint('id'),
        sa.UniqueConstraint('name', 'company_id', name='unique_name_company_id')
    )
    conn.execute(text())
    op.drop_table('view')
    op.rename_table('view_tmp', 'view')

    op.create_table(
        'analysis',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('analysis', mindsdb.interfaces.storage.db.Json(), nullable=False),
        sa.Column('created_at', sa.DateTime(), nullable=True),
        sa.Column('updated_at', sa.DateTime(), nullable=True),
        sa.PrimaryKeyConstraint('id')
    )

    with op.batch_alter_table('datasource', schema=None) as batch_op:
        batch_op.add_column(sa.Column('analysis_id', sa.Integer(), nullable=True))
        batch_op.create_foreign_key('fk_analysis_id', 'analysis', ['analysis_id'], ['id'])
        batch_op.add_column(sa.Column('ds_class', sa.String(), nullable=True))

    session = sa.orm.Session(bind=conn)
    dsatasources = conn.execute('select id, analysis from datasource').fetchall()
    for row in dsatasources:
        if row['analysis'] is not None:
            # NOTE 'returning' is relatively new in sqlite, so better will be use select after insert.
            conn.execute(
                text(), {
                    'id': row['id']
                }
            )
            analysis_id = conn.execute(text()).fetchall()
            conn.execute(
                text(), {
                    'analysis_id': analysis_id[0][0],
                    'id': row['id']
                }
            )

    with op.batch_alter_table('datasource', schema=None) as batch_op:
        batch_op.drop_column('analysis')

    op.create_table(
        'file',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('name', sa.String(), nullable=False),
        sa.Column('company_id', sa.Integer(), nullable=True),
        sa.Column('source_file_path', sa.String(), nullable=False),
        sa.Column('file_path', sa.String(), nullable=False),
        sa.Column('row_count', sa.Integer(), nullable=False),
        sa.Column('columns', mindsdb.interfaces.storage.db.Json(), nullable=False),
        # sa.Column('created_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp()),  # ?????
        # sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp(), server_onupdate=sa.func.current_timestamp()),  # ????? erver_default=func.now()
        # sa.Column('created_at', sa.DateTime(), nullable=True, server_default=datetime.datetime.now),  # ?????
        # sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=datetime.datetime.now, server_onupdate=datetime.datetime.now),  # ????? erver_default=func.now()
        sa.Column('created_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp()),  # ?????
        sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp(), server_onupdate=sa.func.current_timestamp()),  # ????? erver_default=func.now()
        sa.Column('analysis_id', sa.Integer(), nullable=True),
        sa.ForeignKeyConstraint(['analysis_id'], ['analysis.id'], name='fk_analysis_id'),
        sa.PrimaryKeyConstraint('id'),
        sa.UniqueConstraint('name', 'company_id', name='unique_name_company_id')
    )

    # delete ds where data is none
    dsatasources = conn.execute(text('select * from datasource')).fetchall()
    for ds in dsatasources:
        if ds['data'] is None:
            conn.execute(text('delete from datasource where id = :id'), {'id': ds['id']})
            continue
        ds_data = json.loads(ds['data'])
        creation_info = json.loads(ds['creation_info'])
        datasource_name = ds_data.get('source_type')
        if datasource_name == 'file':
            created_at = None
            if isinstance(ds['created_at'], str):
                created_at = datetime.datetime.fromisoformat(ds['created_at'])
            elif isinstance(ds['created_at'], [float, int]):
                created_at = datetime.fromtimestamp(ds['created_at'])

            updated_at = None
            if isinstance(ds['updated_at'], str):
                updated_at = datetime.datetime.fromisoformat(ds['updated_at'])
            elif isinstance(ds['updated_at'], [float, int]):
                updated_at = datetime.fromtimestamp(ds['updated_at'])

            file = mindsdb.interfaces.storage.db.File(
                name=ds['name'],
                company_id=ds['company_id'],
                source_file_path=ds_data['source'],
                file_path=creation_info['args'][0],
                row_count=ds_data['row_count'],
                columns=ds_data['columns'],
                created_at=created_at,
                updated_at=updated_at,
                analysis_id=ds['analysis_id']
            )
            session.add(file)

        conn.execute(
            text(), {
                'datasource_name': datasource_name,
                'company_id': ds['company_id'],
                'ds_class': creation_info['class'],
                'id': ds['id']
            }
        )

    session.commit()

    op.rename_table('datasource', 'dataset')

    with op.batch_alter_table('dataset', schema=None) as batch_op:
        batch_op.create_foreign_key('fk_integration_id', 'integration', ['integration_id'], ['id'])

    # NOTE two different 'batch' is necessary, in other way FK is not creating
    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.alter_column('datasource_id', new_column_name='dataset_id')
    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.create_foreign_key('fk_dataset_id', 'dataset', ['dataset_id'], ['id'])
    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id'])

    with op.batch_alter_table('integration', schema=None) as batch_op:
        batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id'])

    with op.batch_alter_table('dataset', schema=None) as batch_op:
        batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id'])

","
        insert into view_tmp (id, name, company_id, query, integration_id)
        select id, name, company_id, query, datasource_id from view;
    
                    insert into analysis (analysis) select analysis from datasource where id = :id;
                
                select id from analysis order by id desc limit 1;
            
                    update datasource set analysis_id = :analysis_id where id = :id
                
                update datasource
                set integration_id = (select id from integration where name = :datasource_name and company_id = :company_id),
                    ds_class = :ds_class
                where id = :id
            ",72,386,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def upgrade():
    op.drop_table('ai_table')

    conn = op.get_bind()

    # views was created with unnamed fk. Therefore need recreate it
    op.create_table(
        'view_tmp',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('name', sa.String(), nullable=False),
        sa.Column('company_id', sa.Integer(), nullable=True),
        sa.Column('query', sa.String(), nullable=False),
        sa.Column('integration_id', sa.Integer(), nullable=False),
        sa.ForeignKeyConstraint(['integration_id'], ['integration.id'], name='fk_integration_id'),
        sa.PrimaryKeyConstraint('id'),
        sa.UniqueConstraint('name', 'company_id', name='unique_name_company_id')
    )
    conn.execute(text())
    op.drop_table('view')
    op.rename_table('view_tmp', 'view')

    op.create_table(
        'analysis',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('analysis', mindsdb.interfaces.storage.db.Json(), nullable=False),
        sa.Column('created_at', sa.DateTime(), nullable=True),
        sa.Column('updated_at', sa.DateTime(), nullable=True),
        sa.PrimaryKeyConstraint('id')
    )

    with op.batch_alter_table('datasource', schema=None) as batch_op:
        batch_op.add_column(sa.Column('analysis_id', sa.Integer(), nullable=True))
        batch_op.create_foreign_key('fk_analysis_id', 'analysis', ['analysis_id'], ['id'])
        batch_op.add_column(sa.Column('ds_class', sa.String(), nullable=True))

    session = sa.orm.Session(bind=conn)
    dsatasources = conn.execute('select id, analysis from datasource').fetchall()
    for row in dsatasources:
        if row['analysis'] is not None:
            # NOTE 'returning' is relatively new in sqlite, so better will be use select after insert.
            conn.execute(
                text(), {
                    'id': row['id']
                }
            )
            analysis_id = conn.execute(text()).fetchall()
            conn.execute(
                text(), {
                    'analysis_id': analysis_id[0][0],
                    'id': row['id']
                }
            )

    with op.batch_alter_table('datasource', schema=None) as batch_op:
        batch_op.drop_column('analysis')

    op.create_table(
        'file',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('name', sa.String(), nullable=False),
        sa.Column('company_id', sa.Integer(), nullable=True),
        sa.Column('source_file_path', sa.String(), nullable=False),
        sa.Column('file_path', sa.String(), nullable=False),
        sa.Column('row_count', sa.Integer(), nullable=False),
        sa.Column('columns', mindsdb.interfaces.storage.db.Json(), nullable=False),
        # sa.Column('created_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp()),  # ?????
        # sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp(), server_onupdate=sa.func.current_timestamp()),  # ????? erver_default=func.now()
        # sa.Column('created_at', sa.DateTime(), nullable=True, server_default=datetime.datetime.now),  # ?????
        # sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=datetime.datetime.now, server_onupdate=datetime.datetime.now),  # ????? erver_default=func.now()
        sa.Column('created_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp()),  # ?????
        sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp(), server_onupdate=sa.func.current_timestamp()),  # ????? erver_default=func.now()
        sa.Column('analysis_id', sa.Integer(), nullable=True),
        sa.ForeignKeyConstraint(['analysis_id'], ['analysis.id'], name='fk_analysis_id'),
        sa.PrimaryKeyConstraint('id'),
        sa.UniqueConstraint('name', 'company_id', name='unique_name_company_id')
    )

    # delete ds where data is none
    dsatasources = conn.execute(text('select * from datasource')).fetchall()
    for ds in dsatasources:
        if ds['data'] is None:
            conn.execute(text('delete from datasource where id = :id'), {'id': ds['id']})
            continue
        ds_data = json.loads(ds['data'])
        creation_info = json.loads(ds['creation_info'])
        datasource_name = ds_data.get('source_type')
        if datasource_name == 'file':
            created_at = None
            if isinstance(ds['created_at'], str):
                created_at = datetime.datetime.fromisoformat(ds['created_at'])
            elif isinstance(ds['created_at'], [float, int]):
                created_at = datetime.fromtimestamp(ds['created_at'])

            updated_at = None
            if isinstance(ds['updated_at'], str):
                updated_at = datetime.datetime.fromisoformat(ds['updated_at'])
            elif isinstance(ds['updated_at'], [float, int]):
                updated_at = datetime.fromtimestamp(ds['updated_at'])

            file = mindsdb.interfaces.storage.db.File(
                name=ds['name'],
                company_id=ds['company_id'],
                source_file_path=ds_data['source'],
                file_path=creation_info['args'][0],
                row_count=ds_data['row_count'],
                columns=ds_data['columns'],
                created_at=created_at,
                updated_at=updated_at,
                analysis_id=ds['analysis_id']
            )
            session.add(file)

        conn.execute(
            text(), {
                'datasource_name': datasource_name,
                'company_id': ds['company_id'],
                'ds_class': creation_info['class'],
                'id': ds['id']
            }
        )

    session.commit()

    op.rename_table('datasource', 'dataset')

    with op.batch_alter_table('dataset', schema=None) as batch_op:
        batch_op.create_foreign_key('fk_integration_id', 'integration', ['integration_id'], ['id'])

    # NOTE two different 'batch' is necessary, in other way FK is not creating
    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.alter_column('datasource_id', new_column_name='dataset_id')
    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.create_foreign_key('fk_dataset_id', 'dataset', ['dataset_id'], ['id'])
    with op.batch_alter_table('predictor', schema=None) as batch_op:
        batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id'])

    with op.batch_alter_table('integration', schema=None) as batch_op:
        batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id'])

    with op.batch_alter_table('dataset', schema=None) as batch_op:
        batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id'])


        ```
    ###Assistant : 
        insert into view_tmp (id, name, company_id, query, integration_id)
        select id, name, company_id, query, datasource_id from view;
    
                    insert into analysis (analysis) select analysis from datasource where id = :id;
                
                select id from analysis order by id desc limit 1;
            
                    update datasource set analysis_id = :analysis_id where id = :id
                
                update datasource
                set integration_id = (select id from integration where name = :datasource_name and company_id = :company_id),
                    ds_class = :ds_class
                where id = :id
            
    "
1064,"def _predict_recursive(self, X, sample_weight, cluster_node):
        
        if cluster_node.left is None:
            # This cluster has no subcluster. Labels are just the label of the cluster.
            return np.full(X.shape[0], cluster_node.label, dtype=np.int32)

        # Determine if data points belong to the left or right subcluster
        centers = np.vstack((cluster_node.left.center, cluster_node.right.center))
        if hasattr(self, ""_X_mean""):
            centers += self._X_mean

        cluster_labels = _labels_inertia_threadpool_limit(
            X,
            sample_weight,
            centers,
            self._n_threads,
            return_inertia=False,
        )
        mask = cluster_labels == 0

        # Compute the labels for each subset of the data points.
        labels = np.full(X.shape[0], -1, dtype=np.int32)

        labels[mask] = self._predict_recursive(
            X[mask], sample_weight[mask], cluster_node.left
        )

        labels[~mask] = self._predict_recursive(
            X[~mask], sample_weight[~mask], cluster_node.right
        )

        return labels
","Predict recursively by going down the hierarchical tree.

        Parameters
        ----------
        X : {ndarray, csr_matrix} of shape (n_samples, n_features)
            The data points, currently assigned to `cluster_node`, to predict between
            the subclusters of this node.

        sample_weight : ndarray of shape (n_samples,)
            The weights for each observation in X.

        cluster_node : _BisectingTree node object
            The cluster node of the hierarchical tree.

        Returns
        -------
        labels : ndarray of shape (n_samples,)
            Index of the cluster each sample belongs to.
        ",74,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _predict_recursive(self, X, sample_weight, cluster_node):
        
        if cluster_node.left is None:
            # This cluster has no subcluster. Labels are just the label of the cluster.
            return np.full(X.shape[0], cluster_node.label, dtype=np.int32)

        # Determine if data points belong to the left or right subcluster
        centers = np.vstack((cluster_node.left.center, cluster_node.right.center))
        if hasattr(self, ""_X_mean""):
            centers += self._X_mean

        cluster_labels = _labels_inertia_threadpool_limit(
            X,
            sample_weight,
            centers,
            self._n_threads,
            return_inertia=False,
        )
        mask = cluster_labels == 0

        # Compute the labels for each subset of the data points.
        labels = np.full(X.shape[0], -1, dtype=np.int32)

        labels[mask] = self._predict_recursive(
            X[mask], sample_weight[mask], cluster_node.left
        )

        labels[~mask] = self._predict_recursive(
            X[~mask], sample_weight[~mask], cluster_node.right
        )

        return labels

        ```
    ###Assistant : Predict recursively by going down the hierarchical tree.

        Parameters
        ----------
        X : {ndarray, csr_matrix} of shape (n_samples, n_features)
            The data points, currently assigned to `cluster_node`, to predict between
            the subclusters of this node.

        sample_weight : ndarray of shape (n_samples,)
            The weights for each observation in X.

        cluster_node : _BisectingTree node object
            The cluster node of the hierarchical tree.

        Returns
        -------
        labels : ndarray of shape (n_samples,)
            Index of the cluster each sample belongs to.
        
    "
1065,"def inference_voice_conversion(self, reference_wav, speaker_id=None, d_vector=None, reference_speaker_id=None, reference_d_vector=None):
        
        # compute spectrograms
        y = wav_to_spec(reference_wav, self.config.audio.fft_size, self.config.audio.hop_length, self.config.audio.win_length, center=False).transpose(1, 2)
        y_lengths = torch.tensor([y.size(-1)]).to(y.device)
        speaker_cond_src = reference_speaker_id if reference_speaker_id is not None else reference_d_vector
        speaker_cond_tgt = speaker_id if speaker_id is not None else d_vector
        # print(y.shape, y_lengths.shape)
        wav, _, _ = self.voice_conversion(y, y_lengths, speaker_cond_src, speaker_cond_tgt)
        return wav
","Inference for voice conversion

        Args:
            reference_wav (Tensor): Reference wavform. Tensor of shape [B, T]
            speaker_id (Tensor): speaker_id of the target speaker. Tensor of shape [B]
            d_vector (Tensor): d_vector embedding of target speaker. Tensor of shape `[B, C]`
            reference_speaker_id (Tensor): speaker_id of the reference_wav speaker. Tensor of shape [B]
            reference_d_vector (Tensor): d_vector embedding of the reference_wav speaker. Tensor of shape `[B, C]`
        ",61,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def inference_voice_conversion(self, reference_wav, speaker_id=None, d_vector=None, reference_speaker_id=None, reference_d_vector=None):
        
        # compute spectrograms
        y = wav_to_spec(reference_wav, self.config.audio.fft_size, self.config.audio.hop_length, self.config.audio.win_length, center=False).transpose(1, 2)
        y_lengths = torch.tensor([y.size(-1)]).to(y.device)
        speaker_cond_src = reference_speaker_id if reference_speaker_id is not None else reference_d_vector
        speaker_cond_tgt = speaker_id if speaker_id is not None else d_vector
        # print(y.shape, y_lengths.shape)
        wav, _, _ = self.voice_conversion(y, y_lengths, speaker_cond_src, speaker_cond_tgt)
        return wav

        ```
    ###Assistant : Inference for voice conversion

        Args:
            reference_wav (Tensor): Reference wavform. Tensor of shape [B, T]
            speaker_id (Tensor): speaker_id of the target speaker. Tensor of shape [B]
            d_vector (Tensor): d_vector embedding of target speaker. Tensor of shape `[B, C]`
            reference_speaker_id (Tensor): speaker_id of the reference_wav speaker. Tensor of shape [B]
            reference_d_vector (Tensor): d_vector embedding of the reference_wav speaker. Tensor of shape `[B, C]`
        
    "
1066,"def test_nonconflicting_specified_basename(self):
        
        self.router.register(r'notes', NoteViewSet, basename='notes')
        self.router.register(r'notes_kwduplicate', KWargedNoteViewSet, basename='notes_kwduplicate')
        self.router.register(r'notes_duplicate', NoteViewSet, basename='notes_duplicate')
","
        Ensure 2 routers with the same model, and a distinct basename specified
        on each does not throw an exception
        ",19,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_nonconflicting_specified_basename(self):
        
        self.router.register(r'notes', NoteViewSet, basename='notes')
        self.router.register(r'notes_kwduplicate', KWargedNoteViewSet, basename='notes_kwduplicate')
        self.router.register(r'notes_duplicate', NoteViewSet, basename='notes_duplicate')

        ```
    ###Assistant : 
        Ensure 2 routers with the same model, and a distinct basename specified
        on each does not throw an exception
        
    "
1067,"async def test_state(hass, setup_comp):
    
    state = hass.states.get(COVER_GROUP)
    # No entity has a valid state -> group state unknown
    assert state.state == STATE_UNKNOWN
    assert state.attributes[ATTR_FRIENDLY_NAME] == DEFAULT_NAME
    assert state.attributes[ATTR_ENTITY_ID] == [
        DEMO_COVER,
        DEMO_COVER_POS,
        DEMO_COVER_TILT,
        DEMO_TILT,
    ]
    assert ATTR_ASSUMED_STATE not in state.attributes
    assert state.attributes[ATTR_SUPPORTED_FEATURES] == 0
    assert ATTR_CURRENT_POSITION not in state.attributes
    assert ATTR_CURRENT_TILT_POSITION not in state.attributes

    # The group state is unknown if all group members are unknown or unavailable.
    for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_UNAVAILABLE, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_UNKNOWN

    for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_UNKNOWN, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_UNKNOWN

    # At least one member opening -> group opening
    for state_1 in (
        STATE_CLOSED,
        STATE_CLOSING,
        STATE_OPEN,
        STATE_OPENING,
        STATE_UNAVAILABLE,
        STATE_UNKNOWN,
    ):
        for state_2 in (
            STATE_CLOSED,
            STATE_CLOSING,
            STATE_OPEN,
            STATE_OPENING,
            STATE_UNAVAILABLE,
            STATE_UNKNOWN,
        ):
            for state_3 in (
                STATE_CLOSED,
                STATE_CLOSING,
                STATE_OPEN,
                STATE_OPENING,
                STATE_UNAVAILABLE,
                STATE_UNKNOWN,
            ):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_OPENING, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_OPENING

    # At least one member closing -> group closing
    for state_1 in (
        STATE_CLOSED,
        STATE_CLOSING,
        STATE_OPEN,
        STATE_UNAVAILABLE,
        STATE_UNKNOWN,
    ):
        for state_2 in (
            STATE_CLOSED,
            STATE_CLOSING,
            STATE_OPEN,
            STATE_UNAVAILABLE,
            STATE_UNKNOWN,
        ):
            for state_3 in (
                STATE_CLOSED,
                STATE_CLOSING,
                STATE_OPEN,
                STATE_UNAVAILABLE,
                STATE_UNKNOWN,
            ):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_CLOSING, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_CLOSING

    # At least one member open -> group open
    for state_1 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_OPEN, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_OPEN

    # At least one member closed -> group closed
    for state_1 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_CLOSED, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_CLOSED

    # All group members removed from the state machine -> unknown
    hass.states.async_remove(DEMO_COVER)
    hass.states.async_remove(DEMO_COVER_POS)
    hass.states.async_remove(DEMO_COVER_TILT)
    hass.states.async_remove(DEMO_TILT)
    await hass.async_block_till_done()
    state = hass.states.get(COVER_GROUP)
    assert state.state == STATE_UNKNOWN


@pytest.mark.parametrize(""config_count"", [(CONFIG_ATTRIBUTES, 1)])","Test handling of state.

    The group state is unknown if all group members are unknown or unavailable.
    Otherwise, the group state is opening if at least one group member is opening.
    Otherwise, the group state is closing if at least one group member is closing.
    Otherwise, the group state is open if at least one group member is open.
    Otherwise, the group state is closed.
    ",65,389,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_state(hass, setup_comp):
    
    state = hass.states.get(COVER_GROUP)
    # No entity has a valid state -> group state unknown
    assert state.state == STATE_UNKNOWN
    assert state.attributes[ATTR_FRIENDLY_NAME] == DEFAULT_NAME
    assert state.attributes[ATTR_ENTITY_ID] == [
        DEMO_COVER,
        DEMO_COVER_POS,
        DEMO_COVER_TILT,
        DEMO_TILT,
    ]
    assert ATTR_ASSUMED_STATE not in state.attributes
    assert state.attributes[ATTR_SUPPORTED_FEATURES] == 0
    assert ATTR_CURRENT_POSITION not in state.attributes
    assert ATTR_CURRENT_TILT_POSITION not in state.attributes

    # The group state is unknown if all group members are unknown or unavailable.
    for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_UNAVAILABLE, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_UNKNOWN

    for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_UNKNOWN, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_UNKNOWN

    # At least one member opening -> group opening
    for state_1 in (
        STATE_CLOSED,
        STATE_CLOSING,
        STATE_OPEN,
        STATE_OPENING,
        STATE_UNAVAILABLE,
        STATE_UNKNOWN,
    ):
        for state_2 in (
            STATE_CLOSED,
            STATE_CLOSING,
            STATE_OPEN,
            STATE_OPENING,
            STATE_UNAVAILABLE,
            STATE_UNKNOWN,
        ):
            for state_3 in (
                STATE_CLOSED,
                STATE_CLOSING,
                STATE_OPEN,
                STATE_OPENING,
                STATE_UNAVAILABLE,
                STATE_UNKNOWN,
            ):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_OPENING, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_OPENING

    # At least one member closing -> group closing
    for state_1 in (
        STATE_CLOSED,
        STATE_CLOSING,
        STATE_OPEN,
        STATE_UNAVAILABLE,
        STATE_UNKNOWN,
    ):
        for state_2 in (
            STATE_CLOSED,
            STATE_CLOSING,
            STATE_OPEN,
            STATE_UNAVAILABLE,
            STATE_UNKNOWN,
        ):
            for state_3 in (
                STATE_CLOSED,
                STATE_CLOSING,
                STATE_OPEN,
                STATE_UNAVAILABLE,
                STATE_UNKNOWN,
            ):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_CLOSING, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_CLOSING

    # At least one member open -> group open
    for state_1 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_OPEN, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_OPEN

    # At least one member closed -> group closed
    for state_1 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN):
        for state_2 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN):
            for state_3 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN):
                hass.states.async_set(DEMO_COVER, state_1, {})
                hass.states.async_set(DEMO_COVER_POS, state_2, {})
                hass.states.async_set(DEMO_COVER_TILT, state_3, {})
                hass.states.async_set(DEMO_TILT, STATE_CLOSED, {})
                await hass.async_block_till_done()
                state = hass.states.get(COVER_GROUP)
                assert state.state == STATE_CLOSED

    # All group members removed from the state machine -> unknown
    hass.states.async_remove(DEMO_COVER)
    hass.states.async_remove(DEMO_COVER_POS)
    hass.states.async_remove(DEMO_COVER_TILT)
    hass.states.async_remove(DEMO_TILT)
    await hass.async_block_till_done()
    state = hass.states.get(COVER_GROUP)
    assert state.state == STATE_UNKNOWN


@pytest.mark.parametrize(""config_count"", [(CONFIG_ATTRIBUTES, 1)])
        ```
    ###Assistant : Test handling of state.

    The group state is unknown if all group members are unknown or unavailable.
    Otherwise, the group state is opening if at least one group member is opening.
    Otherwise, the group state is closing if at least one group member is closing.
    Otherwise, the group state is open if at least one group member is open.
    Otherwise, the group state is closed.
    
    "
1068,"def connect(self, signal, func):
        
        if self._signals is not None:
            _api.check_in_list(self._signals, signal=signal)
        self._func_cid_map.setdefault(signal, {})
        proxy = _weak_or_strong_ref(func, self._remove_proxy)
        if proxy in self._func_cid_map[signal]:
            return self._func_cid_map[signal][proxy]
        cid = next(self._cid_gen)
        self._func_cid_map[signal][proxy] = cid
        self.callbacks.setdefault(signal, {})
        self.callbacks[signal][cid] = proxy
        return cid
",Register *func* to be called when signal *signal* is generated.,10,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def connect(self, signal, func):
        
        if self._signals is not None:
            _api.check_in_list(self._signals, signal=signal)
        self._func_cid_map.setdefault(signal, {})
        proxy = _weak_or_strong_ref(func, self._remove_proxy)
        if proxy in self._func_cid_map[signal]:
            return self._func_cid_map[signal][proxy]
        cid = next(self._cid_gen)
        self._func_cid_map[signal][proxy] = cid
        self.callbacks.setdefault(signal, {})
        self.callbacks[signal][cid] = proxy
        return cid

        ```
    ###Assistant : Register *func* to be called when signal *signal* is generated.
    "
1069,"def get_or_create_account(company_name, account):
	
	default_root_type = ""Liability""
	root_type = account.get(""root_type"", default_root_type)

	existing_accounts = frappe.get_all(
		""Account"",
		filters={""company"": company_name, ""root_type"": root_type},
		or_filters={
			""account_name"": account.get(""account_name""),
			""account_number"": account.get(""account_number""),
		},
	)

	if existing_accounts:
		return frappe.get_doc(""Account"", existing_accounts[0].name)

	tax_group = get_or_create_tax_group(company_name, root_type)

	account[""doctype""] = ""Account""
	account[""company""] = company_name
	account[""parent_account""] = tax_group
	account[""report_type""] = ""Balance Sheet""
	account[""account_type""] = ""Tax""
	account[""root_type""] = root_type
	account[""is_group""] = 0

	doc = frappe.get_doc(account)
	doc.flags.ignore_links = True
	doc.flags.ignore_validate = True
	doc.insert(ignore_permissions=True, ignore_mandatory=True)
	return doc

","
	Check if account already exists. If not, create it.
	Return a tax account or None.
	",15,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_or_create_account(company_name, account):
	
	default_root_type = ""Liability""
	root_type = account.get(""root_type"", default_root_type)

	existing_accounts = frappe.get_all(
		""Account"",
		filters={""company"": company_name, ""root_type"": root_type},
		or_filters={
			""account_name"": account.get(""account_name""),
			""account_number"": account.get(""account_number""),
		},
	)

	if existing_accounts:
		return frappe.get_doc(""Account"", existing_accounts[0].name)

	tax_group = get_or_create_tax_group(company_name, root_type)

	account[""doctype""] = ""Account""
	account[""company""] = company_name
	account[""parent_account""] = tax_group
	account[""report_type""] = ""Balance Sheet""
	account[""account_type""] = ""Tax""
	account[""root_type""] = root_type
	account[""is_group""] = 0

	doc = frappe.get_doc(account)
	doc.flags.ignore_links = True
	doc.flags.ignore_validate = True
	doc.insert(ignore_permissions=True, ignore_mandatory=True)
	return doc


        ```
    ###Assistant : 
	Check if account already exists. If not, create it.
	Return a tax account or None.
	
    "
1070,"def set_default_options(self) -> None:
        
        default = self.cli_opts.get_option_values()
        logger.debug(default)
        self._gui_objects.default_options = default
        self.project.set_default_options()
"," Set the default options for :mod:`lib.gui.projects`

        The Default GUI options are stored on Faceswap startup.

        Exposed as the :attr:`_default_opts` for a project cannot be set until after the main
        Command Tabs have been loaded.
        ",34,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_default_options(self) -> None:
        
        default = self.cli_opts.get_option_values()
        logger.debug(default)
        self._gui_objects.default_options = default
        self.project.set_default_options()

        ```
    ###Assistant :  Set the default options for :mod:`lib.gui.projects`

        The Default GUI options are stored on Faceswap startup.

        Exposed as the :attr:`_default_opts` for a project cannot be set until after the main
        Command Tabs have been loaded.
        
    "
1071,"def _format_lines(self, tokensource):
        
        nocls = self.noclasses
        lsep = self.lineseparator
        tagsfile = self.tagsfile

        lspan = ''
        line = []
        for ttype, value in tokensource:
            try:
                cspan = self.span_element_openers[ttype]
            except KeyError:
                title = ' title=""%s""' % '.'.join(ttype) if self.debug_token_types else ''
                if nocls:
                    css_style = self._get_css_inline_styles(ttype)
                    if css_style:
                        css_style = self.class2style[css_style][0]
                        cspan = '<span style=""%s""%s>' % (css_style, title)
                    else:
                        cspan = ''
                else:
                    css_class = self._get_css_classes(ttype)
                    if css_class:
                        cspan = '<span class=""%s""%s>' % (css_class, title)
                    else:
                        cspan = ''
                self.span_element_openers[ttype] = cspan

            parts = self._translate_parts(value)

            if tagsfile and ttype in Token.Name:
                filename, linenumber = self._lookup_ctag(value)
                if linenumber:
                    base, filename = os.path.split(filename)
                    if base:
                        base += '/'
                    filename, extension = os.path.splitext(filename)
                    url = self.tagurlformat % {'path': base, 'fname': filename,
                                               'fext': extension}
                    parts[0] = ""<a href=\""%s#%s-%d\"">%s"" % \
                        (url, self.lineanchors, linenumber, parts[0])
                    parts[-1] = parts[-1] + ""</a>""

            # for all but the last line
            for part in parts[:-1]:
                if line:
                    if lspan != cspan:
                        line.extend(((lspan and '</span>'), cspan, part,
                                     (cspan and '</span>'), lsep))
                    else:  # both are the same
                        line.extend((part, (lspan and '</span>'), lsep))
                    yield 1, ''.join(line)
                    line = []
                elif part:
                    yield 1, ''.join((cspan, part, (cspan and '</span>'), lsep))
                else:
                    yield 1, lsep
            # for the last line
            if line and parts[-1]:
                if lspan != cspan:
                    line.extend(((lspan and '</span>'), cspan, parts[-1]))
                    lspan = cspan
                else:
                    line.append(parts[-1])
            elif parts[-1]:
                line = [cspan, parts[-1]]
                lspan = cspan
            # else we neither have to open a new span nor set lspan

        if line:
            line.extend(((lspan and '</span>'), lsep))
            yield 1, ''.join(line)
","
        Just format the tokens, without any wrapping tags.
        Yield individual lines.
        ",11,244,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _format_lines(self, tokensource):
        
        nocls = self.noclasses
        lsep = self.lineseparator
        tagsfile = self.tagsfile

        lspan = ''
        line = []
        for ttype, value in tokensource:
            try:
                cspan = self.span_element_openers[ttype]
            except KeyError:
                title = ' title=""%s""' % '.'.join(ttype) if self.debug_token_types else ''
                if nocls:
                    css_style = self._get_css_inline_styles(ttype)
                    if css_style:
                        css_style = self.class2style[css_style][0]
                        cspan = '<span style=""%s""%s>' % (css_style, title)
                    else:
                        cspan = ''
                else:
                    css_class = self._get_css_classes(ttype)
                    if css_class:
                        cspan = '<span class=""%s""%s>' % (css_class, title)
                    else:
                        cspan = ''
                self.span_element_openers[ttype] = cspan

            parts = self._translate_parts(value)

            if tagsfile and ttype in Token.Name:
                filename, linenumber = self._lookup_ctag(value)
                if linenumber:
                    base, filename = os.path.split(filename)
                    if base:
                        base += '/'
                    filename, extension = os.path.splitext(filename)
                    url = self.tagurlformat % {'path': base, 'fname': filename,
                                               'fext': extension}
                    parts[0] = ""<a href=\""%s#%s-%d\"">%s"" % \
                        (url, self.lineanchors, linenumber, parts[0])
                    parts[-1] = parts[-1] + ""</a>""

            # for all but the last line
            for part in parts[:-1]:
                if line:
                    if lspan != cspan:
                        line.extend(((lspan and '</span>'), cspan, part,
                                     (cspan and '</span>'), lsep))
                    else:  # both are the same
                        line.extend((part, (lspan and '</span>'), lsep))
                    yield 1, ''.join(line)
                    line = []
                elif part:
                    yield 1, ''.join((cspan, part, (cspan and '</span>'), lsep))
                else:
                    yield 1, lsep
            # for the last line
            if line and parts[-1]:
                if lspan != cspan:
                    line.extend(((lspan and '</span>'), cspan, parts[-1]))
                    lspan = cspan
                else:
                    line.append(parts[-1])
            elif parts[-1]:
                line = [cspan, parts[-1]]
                lspan = cspan
            # else we neither have to open a new span nor set lspan

        if line:
            line.extend(((lspan and '</span>'), lsep))
            yield 1, ''.join(line)

        ```
    ###Assistant : 
        Just format the tokens, without any wrapping tags.
        Yield individual lines.
        
    "
1072,"def _get_remote_resource(self) -> Optional[Union[SourceRead, DestinationRead, ConnectionRead]]:
        
        search_results = self._search().get(f""{self.resource_type}s"", [])
        if len(search_results) > 1:
            raise DuplicateResourceError(""Two or more ressources exist with the same name."")
        if len(search_results) == 1:
            return search_results[0]
        else:
            return None
","Find the remote resource on the Airbyte instance associated with the current resource.

        Raises:
            DuplicateResourceError: raised if the search results return multiple resources.

        Returns:
            Optional[Union[SourceRead, DestinationRead, ConnectionRead]]: The remote resource found.
        ",31,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_remote_resource(self) -> Optional[Union[SourceRead, DestinationRead, ConnectionRead]]:
        
        search_results = self._search().get(f""{self.resource_type}s"", [])
        if len(search_results) > 1:
            raise DuplicateResourceError(""Two or more ressources exist with the same name."")
        if len(search_results) == 1:
            return search_results[0]
        else:
            return None

        ```
    ###Assistant : Find the remote resource on the Airbyte instance associated with the current resource.

        Raises:
            DuplicateResourceError: raised if the search results return multiple resources.

        Returns:
            Optional[Union[SourceRead, DestinationRead, ConnectionRead]]: The remote resource found.
        
    "
1073,"def _get_compile_args(self, user_metrics=True):
        
        self._assert_compile_was_called()
        # pylint: disable=protected-access

        saved_metrics = self.compiled_metrics._user_metrics
        saved_weighted_metrics = self.compiled_metrics._user_weighted_metrics

        if not user_metrics:
            if saved_metrics is not None:
                saved_metrics = self.compiled_metrics._metrics
            if saved_weighted_metrics is not None:
                saved_weighted_metrics = self.compiled_metrics._weighted_metrics

        compile_args = {
            ""optimizer"": self.optimizer,
            ""loss"": self.compiled_loss._user_losses,
            ""metrics"": saved_metrics,
            ""weighted_metrics"": saved_weighted_metrics,
            ""loss_weights"": self.compiled_loss._user_loss_weights,
        }
        # pylint: enable=protected-access
        return compile_args
","Used for saving or cloning a Model.

        Args:
          user_metrics: Whether to return user-supplied metrics or `Metric` objects.
            Defaults to returning the user-supplied metrics.

        Returns:
          Dictionary of arguments that were used when compiling the model.
        ",34,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_compile_args(self, user_metrics=True):
        
        self._assert_compile_was_called()
        # pylint: disable=protected-access

        saved_metrics = self.compiled_metrics._user_metrics
        saved_weighted_metrics = self.compiled_metrics._user_weighted_metrics

        if not user_metrics:
            if saved_metrics is not None:
                saved_metrics = self.compiled_metrics._metrics
            if saved_weighted_metrics is not None:
                saved_weighted_metrics = self.compiled_metrics._weighted_metrics

        compile_args = {
            ""optimizer"": self.optimizer,
            ""loss"": self.compiled_loss._user_losses,
            ""metrics"": saved_metrics,
            ""weighted_metrics"": saved_weighted_metrics,
            ""loss_weights"": self.compiled_loss._user_loss_weights,
        }
        # pylint: enable=protected-access
        return compile_args

        ```
    ###Assistant : Used for saving or cloning a Model.

        Args:
          user_metrics: Whether to return user-supplied metrics or `Metric` objects.
            Defaults to returning the user-supplied metrics.

        Returns:
          Dictionary of arguments that were used when compiling the model.
        
    "
1074,"def test_norestexdoc(capfd, hello_world_f90, monkeypatch):
    
    ipath = Path(hello_world_f90)
    mname = ""blah""
    monkeypatch.setattr(sys, ""argv"",
                        f'f2py -m {mname} {ipath} --no-rest-doc'.split())

    with util.switchdir(ipath.parent):
        f2pycli()
        out, _ = capfd.readouterr()
        assert ""ReST Documentation is saved to file"" not in out

","Ensures that TeX documentation is written out

    CLI :: --no-rest-doc
    ",10,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_norestexdoc(capfd, hello_world_f90, monkeypatch):
    
    ipath = Path(hello_world_f90)
    mname = ""blah""
    monkeypatch.setattr(sys, ""argv"",
                        f'f2py -m {mname} {ipath} --no-rest-doc'.split())

    with util.switchdir(ipath.parent):
        f2pycli()
        out, _ = capfd.readouterr()
        assert ""ReST Documentation is saved to file"" not in out


        ```
    ###Assistant : Ensures that TeX documentation is written out

    CLI :: --no-rest-doc
    
    "
1075,"def set_weights(self, weights):
        
        params = self.weights
        if len(params) != len(weights):
            raise ValueError(
                ""Length of the specified weight list (""
                + str(len(weights))
                + "") does not match the number of weights ""
                ""of the optimizer ("" + str(len(params)) + "")""
            )
        weight_value_tuples = []
        param_values = backend.batch_get_value(params)
        for pv, p, w in zip(param_values, params, weights):
            if pv.shape != w.shape:
                raise ValueError(
                    ""Optimizer weight shape ""
                    + str(pv.shape)
                    + "" not compatible with ""
                    ""provided weight shape "" + str(w.shape)
                )
            weight_value_tuples.append((p, w))
        backend.batch_set_value(weight_value_tuples)
","Sets the weights of the optimizer, from Numpy arrays.

        Should only be called after computing the gradients
        (otherwise the optimizer has no weights).

        Args:
            weights: a list of Numpy arrays. The number of arrays and their shape
              must match number of the dimensions of the weights of the optimizer
              (i.e. it should match the output of `get_weights`).

        Raises:
            ValueError: in case of incompatible weight shapes.
        ",65,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_weights(self, weights):
        
        params = self.weights
        if len(params) != len(weights):
            raise ValueError(
                ""Length of the specified weight list (""
                + str(len(weights))
                + "") does not match the number of weights ""
                ""of the optimizer ("" + str(len(params)) + "")""
            )
        weight_value_tuples = []
        param_values = backend.batch_get_value(params)
        for pv, p, w in zip(param_values, params, weights):
            if pv.shape != w.shape:
                raise ValueError(
                    ""Optimizer weight shape ""
                    + str(pv.shape)
                    + "" not compatible with ""
                    ""provided weight shape "" + str(w.shape)
                )
            weight_value_tuples.append((p, w))
        backend.batch_set_value(weight_value_tuples)

        ```
    ###Assistant : Sets the weights of the optimizer, from Numpy arrays.

        Should only be called after computing the gradients
        (otherwise the optimizer has no weights).

        Args:
            weights: a list of Numpy arrays. The number of arrays and their shape
              must match number of the dimensions of the weights of the optimizer
              (i.e. it should match the output of `get_weights`).

        Raises:
            ValueError: in case of incompatible weight shapes.
        
    "
1076,"def extract_tensors_from_dataset(dataset):
    
    iterator = get_iterator(dataset)
    inputs, targets, sample_weight = unpack_iterator_input(iterator)
    return inputs, targets, sample_weight

","Extract a tuple of tensors `inputs, targets, sample_weight` from a dataset.

    Args:
      dataset: Dataset instance.

    Returns:
      Tuple of tensors `x, y, weights`. `y` and `weights` entry may be None.
    ",29,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extract_tensors_from_dataset(dataset):
    
    iterator = get_iterator(dataset)
    inputs, targets, sample_weight = unpack_iterator_input(iterator)
    return inputs, targets, sample_weight


        ```
    ###Assistant : Extract a tuple of tensors `inputs, targets, sample_weight` from a dataset.

    Args:
      dataset: Dataset instance.

    Returns:
      Tuple of tensors `x, y, weights`. `y` and `weights` entry may be None.
    
    "
1077,"def _set_autocommit(self, autocommit):
        
        raise NotImplementedError(
            ""subclasses of BaseDatabaseWrapper may require a _set_autocommit() method""
        )

    # ##### Generic transaction management methods #####
","
        Backend-specific implementation to enable or disable autocommit.
        ",7,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_autocommit(self, autocommit):
        
        raise NotImplementedError(
            ""subclasses of BaseDatabaseWrapper may require a _set_autocommit() method""
        )

    # ##### Generic transaction management methods #####

        ```
    ###Assistant : 
        Backend-specific implementation to enable or disable autocommit.
        
    "
1078,"def get_edit_upload_form_context_data(self):
        
        edit_form_class = self.get_edit_form_class()
        return {
            self.context_upload_name: self.upload_object,
            ""edit_action"": reverse(
                self.edit_upload_url_name, args=(self.upload_object.id,)
            ),
            ""delete_action"": reverse(
                self.delete_upload_url_name, args=(self.upload_object.id,)
            ),
            ""form"": edit_form_class(
                instance=self.object,
                prefix=""%s-%d"" % (self.edit_upload_form_prefix, self.upload_object.id),
                user=self.request.user,
            ),
        }
","
        Return the context data necessary for rendering the HTML form for supplying the
        metadata to turn an upload object into a final object
        ",23,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_edit_upload_form_context_data(self):
        
        edit_form_class = self.get_edit_form_class()
        return {
            self.context_upload_name: self.upload_object,
            ""edit_action"": reverse(
                self.edit_upload_url_name, args=(self.upload_object.id,)
            ),
            ""delete_action"": reverse(
                self.delete_upload_url_name, args=(self.upload_object.id,)
            ),
            ""form"": edit_form_class(
                instance=self.object,
                prefix=""%s-%d"" % (self.edit_upload_form_prefix, self.upload_object.id),
                user=self.request.user,
            ),
        }

        ```
    ###Assistant : 
        Return the context data necessary for rendering the HTML form for supplying the
        metadata to turn an upload object into a final object
        
    "
1079,"def test_overriding_has_module_permission(self):
        
        articles = Article._meta.verbose_name_plural.title()
        sections = Section._meta.verbose_name_plural.title()
        index_url = reverse(""admin7:index"")

        self.client.force_login(self.superuser)
        response = self.client.get(index_url)
        self.assertContains(response, sections)
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.viewuser)
        response = self.client.get(index_url)
        self.assertNotContains(response, ""admin_views"")
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.adduser)
        response = self.client.get(index_url)
        self.assertNotContains(response, ""admin_views"")
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.changeuser)
        response = self.client.get(index_url)
        self.assertNotContains(response, ""admin_views"")
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.deleteuser)
        response = self.client.get(index_url)
        self.assertNotContains(response, articles)

        # The app list displays Sections but not Articles as the latter has
        # ModelAdmin.has_module_permission() = False.
        self.client.force_login(self.superuser)
        response = self.client.get(reverse(""admin7:app_list"", args=(""admin_views"",)))
        self.assertContains(response, sections)
        self.assertNotContains(response, articles)
","
        If has_module_permission() always returns False, the module shouldn't
        be displayed on the admin index page for any users.
        ",18,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_overriding_has_module_permission(self):
        
        articles = Article._meta.verbose_name_plural.title()
        sections = Section._meta.verbose_name_plural.title()
        index_url = reverse(""admin7:index"")

        self.client.force_login(self.superuser)
        response = self.client.get(index_url)
        self.assertContains(response, sections)
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.viewuser)
        response = self.client.get(index_url)
        self.assertNotContains(response, ""admin_views"")
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.adduser)
        response = self.client.get(index_url)
        self.assertNotContains(response, ""admin_views"")
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.changeuser)
        response = self.client.get(index_url)
        self.assertNotContains(response, ""admin_views"")
        self.assertNotContains(response, articles)
        self.client.logout()

        self.client.force_login(self.deleteuser)
        response = self.client.get(index_url)
        self.assertNotContains(response, articles)

        # The app list displays Sections but not Articles as the latter has
        # ModelAdmin.has_module_permission() = False.
        self.client.force_login(self.superuser)
        response = self.client.get(reverse(""admin7:app_list"", args=(""admin_views"",)))
        self.assertContains(response, sections)
        self.assertNotContains(response, articles)

        ```
    ###Assistant : 
        If has_module_permission() always returns False, the module shouldn't
        be displayed on the admin index page for any users.
        
    "
1080,"def non_field_errors(self):
        
        return self.errors.get(
            NON_FIELD_ERRORS,
            self.error_class(error_class=""nonfield"", renderer=self.renderer),
        )
","
        Return an ErrorList of errors that aren't associated with a particular
        field -- i.e., from Form.clean(). Return an empty ErrorList if there
        are none.
        ",24,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def non_field_errors(self):
        
        return self.errors.get(
            NON_FIELD_ERRORS,
            self.error_class(error_class=""nonfield"", renderer=self.renderer),
        )

        ```
    ###Assistant : 
        Return an ErrorList of errors that aren't associated with a particular
        field -- i.e., from Form.clean(). Return an empty ErrorList if there
        are none.
        
    "
1081,"def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, VanModel):
            module.gradient_checkpointing = value


VAN_START_DOCSTRING = r

VAN_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare VAN model outputting raw features without any specific head on top. Note, VAN does not have an embedding layer."",
    VAN_START_DOCSTRING,
)","
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`VanConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See
            [`AutoFeatureExtractor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all stages. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
",128,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, VanModel):
            module.gradient_checkpointing = value


VAN_START_DOCSTRING = r

VAN_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare VAN model outputting raw features without any specific head on top. Note, VAN does not have an embedding layer."",
    VAN_START_DOCSTRING,
)
        ```
    ###Assistant : 
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`VanConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See
            [`AutoFeatureExtractor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all stages. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.

    "
1082,"def update(self, dest, url, rev_options):
        # type: (str, HiddenText, RevOptions) -> None
        
        raise NotImplementedError
","
        Update an already-existing repo to the given ``rev_options``.

        Args:
          rev_options: a RevOptions object.
        ",13,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self, dest, url, rev_options):
        # type: (str, HiddenText, RevOptions) -> None
        
        raise NotImplementedError

        ```
    ###Assistant : 
        Update an already-existing repo to the given ``rev_options``.

        Args:
          rev_options: a RevOptions object.
        
    "
1083,"def get_nested_field(value, field):
    
    if field == '__self__':
        return value
    fields = field.split('__')
    for fld in fields:
        if isinstance(value, list):
            value = [getattr(v, fld) for v in value]
        else:
            value = getattr(value, fld)
    return value","
    Get nested field from list of objects or single instance
    :param value: Single instance or list to look up field
    :param field: Field to lookup
    :return: List or single instance of looked up field
    ",34,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_nested_field(value, field):
    
    if field == '__self__':
        return value
    fields = field.split('__')
    for fld in fields:
        if isinstance(value, list):
            value = [getattr(v, fld) for v in value]
        else:
            value = getattr(value, fld)
    return value
        ```
    ###Assistant : 
    Get nested field from list of objects or single instance
    :param value: Single instance or list to look up field
    :param field: Field to lookup
    :return: List or single instance of looked up field
    
    "
1084,"def test_stylesheet_many_classes_dont_overrule_id():
    
    css = ""#id {color: red;} .a.b.c.d {color: blue;}""
    stylesheet = _make_stylesheet(css)
    node = DOMNode(classes=""a b c d"", id=""id"")
    stylesheet.apply(node)

    assert node.styles.color == Color(255, 0, 0)

","#id is further to the left in the specificity tuple than class, and
    a selector containing multiple classes cannot take priority over even a
    single class.",26,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stylesheet_many_classes_dont_overrule_id():
    
    css = ""#id {color: red;} .a.b.c.d {color: blue;}""
    stylesheet = _make_stylesheet(css)
    node = DOMNode(classes=""a b c d"", id=""id"")
    stylesheet.apply(node)

    assert node.styles.color == Color(255, 0, 0)


        ```
    ###Assistant : #id is further to the left in the specificity tuple than class, and
    a selector containing multiple classes cannot take priority over even a
    single class.
    "
1085,"def site_data_dir(self) -> str:
        
        return self._append_app_name_and_version(""/Library/Application Support"")
",":return: data directory shared by users, e.g. ``/Library/Application Support/$appname/$version``",9,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def site_data_dir(self) -> str:
        
        return self._append_app_name_and_version(""/Library/Application Support"")

        ```
    ###Assistant : :return: data directory shared by users, e.g. ``/Library/Application Support/$appname/$version``
    "
1086,"def test_warm_start():
    
    tpot_obj = TPOTClassifier(
        random_state=42,
        population_size=1,
        offspring_size=2,
        generations=1,
        verbosity=0,
        config_dict='TPOT light',
        warm_start=True)
    tpot_obj.fit(pretest_X, pretest_y)

    assert tpot_obj._pop is not None
    assert tpot_obj._pareto_front is not None

    first_pop = tpot_obj._pop
    tpot_obj.random_state = 21
    tpot_obj.fit(pretest_X, pretest_y)

    assert tpot_obj._pop == first_pop


",Assert that the TPOT warm_start flag stores the pop and pareto_front from the first run.,15,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_warm_start():
    
    tpot_obj = TPOTClassifier(
        random_state=42,
        population_size=1,
        offspring_size=2,
        generations=1,
        verbosity=0,
        config_dict='TPOT light',
        warm_start=True)
    tpot_obj.fit(pretest_X, pretest_y)

    assert tpot_obj._pop is not None
    assert tpot_obj._pareto_front is not None

    first_pop = tpot_obj._pop
    tpot_obj.random_state = 21
    tpot_obj.fit(pretest_X, pretest_y)

    assert tpot_obj._pop == first_pop



        ```
    ###Assistant : Assert that the TPOT warm_start flag stores the pop and pareto_front from the first run.
    "
1087,"def load_data_wiki(batch_size, max_len):
    
    num_workers = d2l.get_dataloader_workers()
    data_dir = d2l.download_extract('wikitext-2', 'wikitext-2')
    paragraphs = _read_wiki(data_dir)
    train_set = _WikiTextDataset(paragraphs, max_len)
    train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
                                       num_workers=num_workers)
    return train_iter, train_set.vocab
","Load the WikiText-2 dataset.

    Defined in :numref:`subsec_prepare_mlm_data`",7,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_data_wiki(batch_size, max_len):
    
    num_workers = d2l.get_dataloader_workers()
    data_dir = d2l.download_extract('wikitext-2', 'wikitext-2')
    paragraphs = _read_wiki(data_dir)
    train_set = _WikiTextDataset(paragraphs, max_len)
    train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
                                       num_workers=num_workers)
    return train_iter, train_set.vocab

        ```
    ###Assistant : Load the WikiText-2 dataset.

    Defined in :numref:`subsec_prepare_mlm_data`
    "
1088,"def save(self, loc, **kwargs) -> Plot:
        
        # TODO expose important keyword arguments in our signature?
        with theme_context(self._theme_with_defaults()):
            self._plot().save(loc, **kwargs)
        return self
","
        Compile the plot and write it to a buffer or file on disk.

        Parameters
        ----------
        loc : str, path, or buffer
            Location on disk to save the figure, or a buffer to write into.
        kwargs
            Other keyword arguments are passed through to
            :meth:`matplotlib.figure.Figure.savefig`.

        ",43,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save(self, loc, **kwargs) -> Plot:
        
        # TODO expose important keyword arguments in our signature?
        with theme_context(self._theme_with_defaults()):
            self._plot().save(loc, **kwargs)
        return self

        ```
    ###Assistant : 
        Compile the plot and write it to a buffer or file on disk.

        Parameters
        ----------
        loc : str, path, or buffer
            Location on disk to save the figure, or a buffer to write into.
        kwargs
            Other keyword arguments are passed through to
            :meth:`matplotlib.figure.Figure.savefig`.

        
    "
1089,"def test_create_realm_no_creation_key(self) -> None:
        
        email = ""user1@test.com""

        with self.settings(OPEN_REALM_CREATION=False):
            # Create new realm with the email, but no creation key.
            result = self.client_post(""/new/"", {""email"": email})
            self.assertEqual(result.status_code, 200)
            self.assert_in_response(""Organization creation link required"", result)
","
        Trying to create a realm without a creation_key should fail when
        OPEN_REALM_CREATION is false.
        ",14,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_create_realm_no_creation_key(self) -> None:
        
        email = ""user1@test.com""

        with self.settings(OPEN_REALM_CREATION=False):
            # Create new realm with the email, but no creation key.
            result = self.client_post(""/new/"", {""email"": email})
            self.assertEqual(result.status_code, 200)
            self.assert_in_response(""Organization creation link required"", result)

        ```
    ###Assistant : 
        Trying to create a realm without a creation_key should fail when
        OPEN_REALM_CREATION is false.
        
    "
1090,"def _get_device_names(self) -> List[str]:
        
        names = [pynvml.nvmlDeviceGetName(handle).decode(""utf-8"")
                 for handle in self._handles]
        self._log(""debug"", f""GPU Devices: {names}"")
        return names
"," Obtain the list of names of connected Nvidia GPUs as identified in :attr:`_handles`.

        Returns
        -------
        list
            The list of connected Nvidia GPU names
        ",23,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_device_names(self) -> List[str]:
        
        names = [pynvml.nvmlDeviceGetName(handle).decode(""utf-8"")
                 for handle in self._handles]
        self._log(""debug"", f""GPU Devices: {names}"")
        return names

        ```
    ###Assistant :  Obtain the list of names of connected Nvidia GPUs as identified in :attr:`_handles`.

        Returns
        -------
        list
            The list of connected Nvidia GPU names
        
    "
1091,"def test_delayed_message(self) -> None:
        
        user1 = UserID.from_string(self.user_id1)

        # Send a message before user2 joins
        event_id1 = self.create_and_send_event(self.room_id, user1)

        # Have user2 join the room
        self.helper.join(self.room_id, self.user_id2, tok=self.tok2)

        # They start off with no notifications
        self.assertEqual(self.get_notif_count(self.user_id2), 0)

        # Send another message that references the event before the join to
        # simulate a ""delayed"" event
        self.create_and_send_event(self.room_id, user1, prev_event_ids=[event_id1])

        # user2 should not be notified about it, because they can't see it.
        self.assertEqual(self.get_notif_count(self.user_id2), 0)
","Test that a delayed message that was from before a user joined
        doesn't cause a notification for the joined user.
        ",20,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delayed_message(self) -> None:
        
        user1 = UserID.from_string(self.user_id1)

        # Send a message before user2 joins
        event_id1 = self.create_and_send_event(self.room_id, user1)

        # Have user2 join the room
        self.helper.join(self.room_id, self.user_id2, tok=self.tok2)

        # They start off with no notifications
        self.assertEqual(self.get_notif_count(self.user_id2), 0)

        # Send another message that references the event before the join to
        # simulate a ""delayed"" event
        self.create_and_send_event(self.room_id, user1, prev_event_ids=[event_id1])

        # user2 should not be notified about it, because they can't see it.
        self.assertEqual(self.get_notif_count(self.user_id2), 0)

        ```
    ###Assistant : Test that a delayed message that was from before a user joined
        doesn't cause a notification for the joined user.
        
    "
1092,"def test_glm_regression_unpenalized_hstacked_X(solver, fit_intercept, glm_dataset):
    
    model, X, y, coef, _, _, _ = glm_dataset
    n_samples, n_features = X.shape
    alpha = 0  # unpenalized
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    if fit_intercept:
        intercept = coef[-1]
        coef = coef[:-1]
        if n_samples > n_features:
            X = X[:, :-1]  # remove intercept
            X = 0.5 * np.concatenate((X, X), axis=1)
        else:
            # To know the minimum norm solution, we keep one intercept column and do
            # not divide by 2. Later on, we must take special care.
            X = np.c_[X[:, :-1], X[:, :-1], X[:, -1]]
    else:
        intercept = 0
        X = 0.5 * np.concatenate((X, X), axis=1)
    assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)

    with warnings.catch_warnings():
        if fit_intercept and n_samples <= n_features:
            # XXX: Investigate if the lack of convergence in this case should be
            # considered a bug or not.
            warnings.filterwarnings(""ignore"", category=ConvergenceWarning)
        model.fit(X, y)

    if fit_intercept and n_samples <= n_features:
        # Here we take special care.
        model_intercept = 2 * model.intercept_
        model_coef = 2 * model.coef_[:-1]  # exclude the other intercept term.
        # For minimum norm solution, we would have
        # assert model.intercept_ == pytest.approx(model.coef_[-1])
    else:
        model_intercept = model.intercept_
        model_coef = model.coef_

    rtol = 6e-5
    if n_samples > n_features:
        assert model_intercept == pytest.approx(intercept)
        assert_allclose(model_coef, np.r_[coef, coef], rtol=1e-4)
    else:
        # As it is an underdetermined problem, prediction = y. The following shows that
        # we get a solution, i.e. a (non-unique) minimum of the objective function ...
        assert_allclose(model.predict(X), y, rtol=1e-6)
        if fit_intercept:
            # Same as in test_glm_regression_unpenalized.
            # But it is not the minimum norm solution. Otherwise the norms would be
            # equal.
            norm_solution = np.linalg.norm(
                0.5 * np.r_[intercept, intercept, coef, coef]
            )
            norm_model = np.linalg.norm(np.r_[model.intercept_, model.coef_])
            assert norm_model > (1 + 1e-12) * norm_solution
            # For minimum norm solution, we would have
            # assert model.intercept_ == pytest.approx(model.coef_[-1])
        else:
            assert model_intercept == pytest.approx(intercept)
            assert_allclose(model_coef, np.r_[coef, coef], rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])","Test that unpenalized GLM converges for all solvers to correct solution.

    We work with a simple constructed data set with known solution.
    GLM fit on [X] is the same as fit on [X, X]/2.
    For long X, [X, X] is a singular matrix and we check against the minimum norm
    solution:
        min ||w||_2 subject to w = argmin deviance(X, y, w)
    ",61,314,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_glm_regression_unpenalized_hstacked_X(solver, fit_intercept, glm_dataset):
    
    model, X, y, coef, _, _, _ = glm_dataset
    n_samples, n_features = X.shape
    alpha = 0  # unpenalized
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    if fit_intercept:
        intercept = coef[-1]
        coef = coef[:-1]
        if n_samples > n_features:
            X = X[:, :-1]  # remove intercept
            X = 0.5 * np.concatenate((X, X), axis=1)
        else:
            # To know the minimum norm solution, we keep one intercept column and do
            # not divide by 2. Later on, we must take special care.
            X = np.c_[X[:, :-1], X[:, :-1], X[:, -1]]
    else:
        intercept = 0
        X = 0.5 * np.concatenate((X, X), axis=1)
    assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)

    with warnings.catch_warnings():
        if fit_intercept and n_samples <= n_features:
            # XXX: Investigate if the lack of convergence in this case should be
            # considered a bug or not.
            warnings.filterwarnings(""ignore"", category=ConvergenceWarning)
        model.fit(X, y)

    if fit_intercept and n_samples <= n_features:
        # Here we take special care.
        model_intercept = 2 * model.intercept_
        model_coef = 2 * model.coef_[:-1]  # exclude the other intercept term.
        # For minimum norm solution, we would have
        # assert model.intercept_ == pytest.approx(model.coef_[-1])
    else:
        model_intercept = model.intercept_
        model_coef = model.coef_

    rtol = 6e-5
    if n_samples > n_features:
        assert model_intercept == pytest.approx(intercept)
        assert_allclose(model_coef, np.r_[coef, coef], rtol=1e-4)
    else:
        # As it is an underdetermined problem, prediction = y. The following shows that
        # we get a solution, i.e. a (non-unique) minimum of the objective function ...
        assert_allclose(model.predict(X), y, rtol=1e-6)
        if fit_intercept:
            # Same as in test_glm_regression_unpenalized.
            # But it is not the minimum norm solution. Otherwise the norms would be
            # equal.
            norm_solution = np.linalg.norm(
                0.5 * np.r_[intercept, intercept, coef, coef]
            )
            norm_model = np.linalg.norm(np.r_[model.intercept_, model.coef_])
            assert norm_model > (1 + 1e-12) * norm_solution
            # For minimum norm solution, we would have
            # assert model.intercept_ == pytest.approx(model.coef_[-1])
        else:
            assert model_intercept == pytest.approx(intercept)
            assert_allclose(model_coef, np.r_[coef, coef], rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])
        ```
    ###Assistant : Test that unpenalized GLM converges for all solvers to correct solution.

    We work with a simple constructed data set with known solution.
    GLM fit on [X] is the same as fit on [X, X]/2.
    For long X, [X, X] is a singular matrix and we check against the minimum norm
    solution:
        min ||w||_2 subject to w = argmin deviance(X, y, w)
    
    "
1093,"def test_count_aggregation_threads(self) -> None:
        

        user_id, token, _, other_token, room_id = self._create_users_and_room()
        thread_id: str

        last_event_id: str
","
        This is essentially the same test as test_count_aggregation, but adds
        events to the main timeline and to a thread.
        ",19,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_count_aggregation_threads(self) -> None:
        

        user_id, token, _, other_token, room_id = self._create_users_and_room()
        thread_id: str

        last_event_id: str

        ```
    ###Assistant : 
        This is essentially the same test as test_count_aggregation, but adds
        events to the main timeline and to a thread.
        
    "
1094,"def test_global_instantiated_before_config_load(self):
        
        cache = LruCache(100)
        add_resizable_cache(""foo"", cache_resize_callback=cache.set_cache_factor)
        self.assertEqual(cache.max_size, 50)

        config = {""caches"": {""global_factor"": 4}}
        self.config.read_config(config, config_dir_path="""", data_dir_path="""")
        self.config.resize_all_caches()

        self.assertEqual(cache.max_size, 400)
","
        If a cache is instantiated before the config is read, it will be given
        the default cache size in the interim, and then resized to the new
        default cache size once the config is loaded.
        ",35,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_global_instantiated_before_config_load(self):
        
        cache = LruCache(100)
        add_resizable_cache(""foo"", cache_resize_callback=cache.set_cache_factor)
        self.assertEqual(cache.max_size, 50)

        config = {""caches"": {""global_factor"": 4}}
        self.config.read_config(config, config_dir_path="""", data_dir_path="""")
        self.config.resize_all_caches()

        self.assertEqual(cache.max_size, 400)

        ```
    ###Assistant : 
        If a cache is instantiated before the config is read, it will be given
        the default cache size in the interim, and then resized to the new
        default cache size once the config is loaded.
        
    "
1095,"def in_top_k(predictions, targets, k):
    
    return tf.compat.v1.math.in_top_k(predictions, targets, k)


# CONVOLUTIONS

","Returns whether the `targets` are in the top `k` `predictions`.

    Args:
        predictions: A tensor of shape `(batch_size, classes)` and type `float32`.
        targets: A 1D tensor of length `batch_size` and type `int32` or `int64`.
        k: An `int`, number of top elements to consider.

    Returns:
        A 1D tensor of length `batch_size` and type `bool`.
        `output[i]` is `True` if `predictions[i, targets[i]]` is within top-`k`
        values of `predictions[i]`.
    ",64,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def in_top_k(predictions, targets, k):
    
    return tf.compat.v1.math.in_top_k(predictions, targets, k)


# CONVOLUTIONS


        ```
    ###Assistant : Returns whether the `targets` are in the top `k` `predictions`.

    Args:
        predictions: A tensor of shape `(batch_size, classes)` and type `float32`.
        targets: A 1D tensor of length `batch_size` and type `int32` or `int64`.
        k: An `int`, number of top elements to consider.

    Returns:
        A 1D tensor of length `batch_size` and type `bool`.
        `output[i]` is `True` if `predictions[i, targets[i]]` is within top-`k`
        values of `predictions[i]`.
    
    "
1096,"def test_update_job(self, parent_job, grouped_jobs, api, batch):
        
        parent_job.update_job()

        # assert
        for job in grouped_jobs:
            job.update_job.assert_called_once_with(batch=batch)
",Checks jobs status in advance and restart if some failed.,10,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_update_job(self, parent_job, grouped_jobs, api, batch):
        
        parent_job.update_job()

        # assert
        for job in grouped_jobs:
            job.update_job.assert_called_once_with(batch=batch)

        ```
    ###Assistant : Checks jobs status in advance and restart if some failed.
    "
1097,"def list_master_symlinks(saltenv=None, prefix=""""):
    
    if not saltenv:
        saltenv = __opts__[""saltenv""] or ""base""
    return _client().symlink_list(saltenv, prefix)

","
    .. versionchanged:: 3005
        ``saltenv`` will use value from config if not explicitly set

    List all of the symlinks stored on the master

    CLI Example:

    .. code-block:: bash

        salt '*' cp.list_master_symlinks
    ",30,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list_master_symlinks(saltenv=None, prefix=""""):
    
    if not saltenv:
        saltenv = __opts__[""saltenv""] or ""base""
    return _client().symlink_list(saltenv, prefix)


        ```
    ###Assistant : 
    .. versionchanged:: 3005
        ``saltenv`` will use value from config if not explicitly set

    List all of the symlinks stored on the master

    CLI Example:

    .. code-block:: bash

        salt '*' cp.list_master_symlinks
    
    "
1098,"def print_help(self):
        
        has_screen_tickers_start = """" if self.screen_tickers else ""[unvl]""
        has_screen_tickers_end = """" if self.screen_tickers else ""[/unvl]""
        help_text = f
        console.print(text=help_text, menu=""Stocks - Options - Screener"")
","Print help[cmds]
    view          view available presets (or one in particular)
    set           set one of the available presets
[/cmds]
[param]PRESET: [/param]{self.preset}[cmds]

    scr            screen data from this preset[/cmds]
{has_screen_tickers_start}
[param]Last screened tickers: [/param]{', '.join(self.screen_tickers)}[menu]
>   ca             take these to comparison analysis menu
>   po             take these to portoflio optimization menu{has_screen_tickers_end}
        ",48,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        
        has_screen_tickers_start = """" if self.screen_tickers else ""[unvl]""
        has_screen_tickers_end = """" if self.screen_tickers else ""[/unvl]""
        help_text = f
        console.print(text=help_text, menu=""Stocks - Options - Screener"")

        ```
    ###Assistant : Print help[cmds]
    view          view available presets (or one in particular)
    set           set one of the available presets
[/cmds]
[param]PRESET: [/param]{self.preset}[cmds]

    scr            screen data from this preset[/cmds]
{has_screen_tickers_start}
[param]Last screened tickers: [/param]{', '.join(self.screen_tickers)}[menu]
>   ca             take these to comparison analysis menu
>   po             take these to portoflio optimization menu{has_screen_tickers_end}
        
    "
1099,"def _find(self, tests, obj, name, module, source_lines, globs, seen):
        
        if self._verbose:
            print('Finding tests in %s' % name)

        # If we've already processed this object, then ignore it.
        if id(obj) in seen:
            return
        seen[id(obj)] = 1

        # Make sure we don't run doctests for classes outside of sympy, such
        # as in numpy or scipy.
        if inspect.isclass(obj):
            if obj.__module__.split('.')[0] != 'sympy':
                return

        # Find a test for this object, and add it to the list of tests.
        test = self._get_test(obj, name, module, globs, source_lines)
        if test is not None:
            tests.append(test)

        if not self._recurse:
            return

        # Look for tests in a module's contained objects.
        if inspect.ismodule(obj):
            for rawname, val in obj.__dict__.items():
                # Recurse to functions & classes.
                if inspect.isfunction(val) or inspect.isclass(val):
                    # Make sure we don't run doctests functions or classes
                    # from different modules
                    if val.__module__ != module.__name__:
                        continue

                    assert self._from_module(module, val), \
                        ""%s is not in module %s (rawname %s)"" % (val, module, rawname)

                    try:
                        valname = '%s.%s' % (name, rawname)
                        self._find(tests, val, valname, module,
                                   source_lines, globs, seen)
                    except KeyboardInterrupt:
                        raise

            # Look for tests in a module's __test__ dictionary.
            for valname, val in getattr(obj, '__test__', {}).items():
                if not isinstance(valname, str):
                    raise ValueError(""SymPyDocTestFinder.find: __test__ keys ""
                                     ""must be strings: %r"" %
                                     (type(valname),))
                if not (inspect.isfunction(val) or inspect.isclass(val) or
                        inspect.ismethod(val) or inspect.ismodule(val) or
                        isinstance(val, str)):
                    raise ValueError(""SymPyDocTestFinder.find: __test__ values ""
                                     ""must be strings, functions, methods, ""
                                     ""classes, or modules: %r"" %
                                     (type(val),))
                valname = '%s.__test__.%s' % (name, valname)
                self._find(tests, val, valname, module, source_lines,
                           globs, seen)


        # Look for tests in a class's contained objects.
        if inspect.isclass(obj):
            for valname, val in obj.__dict__.items():
                # Special handling for staticmethod/classmethod.
                if isinstance(val, staticmethod):
                    val = getattr(obj, valname)
                if isinstance(val, classmethod):
                    val = getattr(obj, valname).__func__


                # Recurse to methods, properties, and nested classes.
                if ((inspect.isfunction(unwrap(val)) or
                        inspect.isclass(val) or
                        isinstance(val, property)) and
                    self._from_module(module, val)):
                    # Make sure we don't run doctests functions or classes
                    # from different modules
                    if isinstance(val, property):
                        if hasattr(val.fget, '__module__'):
                            if val.fget.__module__ != module.__name__:
                                continue
                    else:
                        if val.__module__ != module.__name__:
                            continue

                    assert self._from_module(module, val), \
                        ""%s is not in module %s (valname %s)"" % (
                            val, module, valname)

                    valname = '%s.%s' % (name, valname)
                    self._find(tests, val, valname, module, source_lines,
                               globs, seen)
","
        Find tests for the given object and any contained objects, and
        add them to ``tests``.
        ",15,358,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _find(self, tests, obj, name, module, source_lines, globs, seen):
        
        if self._verbose:
            print('Finding tests in %s' % name)

        # If we've already processed this object, then ignore it.
        if id(obj) in seen:
            return
        seen[id(obj)] = 1

        # Make sure we don't run doctests for classes outside of sympy, such
        # as in numpy or scipy.
        if inspect.isclass(obj):
            if obj.__module__.split('.')[0] != 'sympy':
                return

        # Find a test for this object, and add it to the list of tests.
        test = self._get_test(obj, name, module, globs, source_lines)
        if test is not None:
            tests.append(test)

        if not self._recurse:
            return

        # Look for tests in a module's contained objects.
        if inspect.ismodule(obj):
            for rawname, val in obj.__dict__.items():
                # Recurse to functions & classes.
                if inspect.isfunction(val) or inspect.isclass(val):
                    # Make sure we don't run doctests functions or classes
                    # from different modules
                    if val.__module__ != module.__name__:
                        continue

                    assert self._from_module(module, val), \
                        ""%s is not in module %s (rawname %s)"" % (val, module, rawname)

                    try:
                        valname = '%s.%s' % (name, rawname)
                        self._find(tests, val, valname, module,
                                   source_lines, globs, seen)
                    except KeyboardInterrupt:
                        raise

            # Look for tests in a module's __test__ dictionary.
            for valname, val in getattr(obj, '__test__', {}).items():
                if not isinstance(valname, str):
                    raise ValueError(""SymPyDocTestFinder.find: __test__ keys ""
                                     ""must be strings: %r"" %
                                     (type(valname),))
                if not (inspect.isfunction(val) or inspect.isclass(val) or
                        inspect.ismethod(val) or inspect.ismodule(val) or
                        isinstance(val, str)):
                    raise ValueError(""SymPyDocTestFinder.find: __test__ values ""
                                     ""must be strings, functions, methods, ""
                                     ""classes, or modules: %r"" %
                                     (type(val),))
                valname = '%s.__test__.%s' % (name, valname)
                self._find(tests, val, valname, module, source_lines,
                           globs, seen)


        # Look for tests in a class's contained objects.
        if inspect.isclass(obj):
            for valname, val in obj.__dict__.items():
                # Special handling for staticmethod/classmethod.
                if isinstance(val, staticmethod):
                    val = getattr(obj, valname)
                if isinstance(val, classmethod):
                    val = getattr(obj, valname).__func__


                # Recurse to methods, properties, and nested classes.
                if ((inspect.isfunction(unwrap(val)) or
                        inspect.isclass(val) or
                        isinstance(val, property)) and
                    self._from_module(module, val)):
                    # Make sure we don't run doctests functions or classes
                    # from different modules
                    if isinstance(val, property):
                        if hasattr(val.fget, '__module__'):
                            if val.fget.__module__ != module.__name__:
                                continue
                    else:
                        if val.__module__ != module.__name__:
                            continue

                    assert self._from_module(module, val), \
                        ""%s is not in module %s (valname %s)"" % (
                            val, module, valname)

                    valname = '%s.%s' % (name, valname)
                    self._find(tests, val, valname, module, source_lines,
                               globs, seen)

        ```
    ###Assistant : 
        Find tests for the given object and any contained objects, and
        add them to ``tests``.
        
    "
1100,"def get_mapped_key_strings_to_ints(self) -> MutableMapping[str, int]:
        
        cache_key_results: MutableMapping[str, int] = {}
        for org_id, result_dict in self.results.items():
            for string, id in result_dict.items():
                key = f""{org_id}:{string}""
                if id is not None:
                    cache_key_results[key] = id

        return cache_key_results
","
        Return the results, but formatted as the following:
            {
                ""1:a"": 10,
                ""1:b"": 11,
                ""1:c"", 12,
                ""2:e"": 13
            }
        This is for when we use indexer_cache.set_many()
        ",25,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_mapped_key_strings_to_ints(self) -> MutableMapping[str, int]:
        
        cache_key_results: MutableMapping[str, int] = {}
        for org_id, result_dict in self.results.items():
            for string, id in result_dict.items():
                key = f""{org_id}:{string}""
                if id is not None:
                    cache_key_results[key] = id

        return cache_key_results

        ```
    ###Assistant : 
        Return the results, but formatted as the following:
            {
                ""1:a"": 10,
                ""1:b"": 11,
                ""1:c"", 12,
                ""2:e"": 13
            }
        This is for when we use indexer_cache.set_many()
        
    "
1101,"def get_atext(value):
    
    m = _non_atom_end_matcher(value)
    if not m:
        raise errors.HeaderParseError(
            ""expected atext but found '{}'"".format(value))
    atext = m.group()
    value = value[len(atext):]
    atext = ValueTerminal(atext, 'atext')
    _validate_xtext(atext)
    return atext, value
","atext = <matches _atext_matcher>

    We allow any non-ATOM_ENDS in atext, but add an InvalidATextDefect to
    the token's defects list if we find non-atext characters.
    ",24,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_atext(value):
    
    m = _non_atom_end_matcher(value)
    if not m:
        raise errors.HeaderParseError(
            ""expected atext but found '{}'"".format(value))
    atext = m.group()
    value = value[len(atext):]
    atext = ValueTerminal(atext, 'atext')
    _validate_xtext(atext)
    return atext, value

        ```
    ###Assistant : atext = <matches _atext_matcher>

    We allow any non-ATOM_ENDS in atext, but add an InvalidATextDefect to
    the token's defects list if we find non-atext characters.
    
    "
1102,"def _get_linenumber_pos(self, lineno):
        
        return (self.image_pad, self._get_line_y(lineno))
","
        Get the actual position for the start of a line number.
        ",11,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_linenumber_pos(self, lineno):
        
        return (self.image_pad, self._get_line_y(lineno))

        ```
    ###Assistant : 
        Get the actual position for the start of a line number.
        
    "
1103,"def test_image_inside_paragraph(self):
        # In Draftail's data model, images are block-level elements and therefore
        # split up preceding / following text into their own paragraphs
        converter = ContentstateConverter(features=[""image""])
        result = json.loads(
            converter.from_database_format(
                
            )
        )
        self.assertContentStateEqual(
            result,
            {
                ""blocks"": [
                    {
                        ""key"": ""00000"",
                        ""inlineStyleRanges"": [],
                        ""entityRanges"": [],
                        ""depth"": 0,
                        ""text"": ""before"",
                        ""type"": ""unstyled"",
                    },
                    {
                        ""key"": ""00000"",
                        ""inlineStyleRanges"": [],
                        ""entityRanges"": [{""key"": 0, ""offset"": 0, ""length"": 1}],
                        ""depth"": 0,
                        ""text"": "" "",
                        ""type"": ""atomic"",
                    },
                    {
                        ""key"": ""00000"",
                        ""inlineStyleRanges"": [],
                        ""entityRanges"": [],
                        ""depth"": 0,
                        ""text"": ""after"",
                        ""type"": ""unstyled"",
                    },
                ],
                ""entityMap"": {
                    ""0"": {
                        ""data"": {
                            ""format"": ""left"",
                            ""alt"": ""an image"",
                            ""id"": ""1"",
                            ""src"": ""/media/not-found"",
                        },
                        ""mutability"": ""IMMUTABLE"",
                        ""type"": ""IMAGE"",
                    }
                },
            },
        )
","
            <p>before <embed embedtype=""image"" alt=""an image"" id=""1"" format=""left"" /> after</p>
            ",9,111,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_image_inside_paragraph(self):
        # In Draftail's data model, images are block-level elements and therefore
        # split up preceding / following text into their own paragraphs
        converter = ContentstateConverter(features=[""image""])
        result = json.loads(
            converter.from_database_format(
                
            )
        )
        self.assertContentStateEqual(
            result,
            {
                ""blocks"": [
                    {
                        ""key"": ""00000"",
                        ""inlineStyleRanges"": [],
                        ""entityRanges"": [],
                        ""depth"": 0,
                        ""text"": ""before"",
                        ""type"": ""unstyled"",
                    },
                    {
                        ""key"": ""00000"",
                        ""inlineStyleRanges"": [],
                        ""entityRanges"": [{""key"": 0, ""offset"": 0, ""length"": 1}],
                        ""depth"": 0,
                        ""text"": "" "",
                        ""type"": ""atomic"",
                    },
                    {
                        ""key"": ""00000"",
                        ""inlineStyleRanges"": [],
                        ""entityRanges"": [],
                        ""depth"": 0,
                        ""text"": ""after"",
                        ""type"": ""unstyled"",
                    },
                ],
                ""entityMap"": {
                    ""0"": {
                        ""data"": {
                            ""format"": ""left"",
                            ""alt"": ""an image"",
                            ""id"": ""1"",
                            ""src"": ""/media/not-found"",
                        },
                        ""mutability"": ""IMMUTABLE"",
                        ""type"": ""IMAGE"",
                    }
                },
            },
        )

        ```
    ###Assistant : 
            <p>before <embed embedtype=""image"" alt=""an image"" id=""1"" format=""left"" /> after</p>
            
    "
1104,"def l1_min_c(X, y, *, loss=""squared_hinge"", fit_intercept=True, intercept_scaling=1.0):
    
    if loss not in (""squared_hinge"", ""log""):
        raise ValueError('loss type not in (""squared_hinge"", ""log"")')

    X = check_array(X, accept_sparse=""csc"")
    check_consistent_length(X, y)

    Y = LabelBinarizer(neg_label=-1).fit_transform(y).T
    # maximum absolute value over classes and features
    den = np.max(np.abs(safe_sparse_dot(Y, X)))
    if fit_intercept:
        bias = np.full(
            (np.size(y), 1), intercept_scaling, dtype=np.array(intercept_scaling).dtype
        )
        den = max(den, abs(np.dot(Y, bias)).max())

    if den == 0.0:
        raise ValueError(
            ""Ill-posed l1_min_c calculation: l1 will always ""
            ""select zero coefficients for this data""
        )
    if loss == ""squared_hinge"":
        return 0.5 / den
    else:  # loss == 'log':
        return 2.0 / den
","Return the lowest bound for C.

    The lower bound for C is computed such that for C in (l1_min_C, infinity)
    the model is guaranteed not to be empty. This applies to l1 penalized
    classifiers, such as LinearSVC with penalty='l1' and
    linear_model.LogisticRegression with penalty='l1'.

    This value is valid if class_weight parameter in fit() is not set.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Training vector, where `n_samples` is the number of samples and
        `n_features` is the number of features.

    y : array-like of shape (n_samples,)
        Target vector relative to X.

    loss : {'squared_hinge', 'log'}, default='squared_hinge'
        Specifies the loss function.
        With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss).
        With 'log' it is the loss of logistic regression models.

    fit_intercept : bool, default=True
        Specifies if the intercept should be fitted by the model.
        It must match the fit() method parameter.

    intercept_scaling : float, default=1.0
        When fit_intercept is True, instance vector x becomes
        [x, intercept_scaling],
        i.e. a ""synthetic"" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        It must match the fit() method parameter.

    Returns
    -------
    l1_min_c : float
        Minimum value for C.
    ",190,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def l1_min_c(X, y, *, loss=""squared_hinge"", fit_intercept=True, intercept_scaling=1.0):
    
    if loss not in (""squared_hinge"", ""log""):
        raise ValueError('loss type not in (""squared_hinge"", ""log"")')

    X = check_array(X, accept_sparse=""csc"")
    check_consistent_length(X, y)

    Y = LabelBinarizer(neg_label=-1).fit_transform(y).T
    # maximum absolute value over classes and features
    den = np.max(np.abs(safe_sparse_dot(Y, X)))
    if fit_intercept:
        bias = np.full(
            (np.size(y), 1), intercept_scaling, dtype=np.array(intercept_scaling).dtype
        )
        den = max(den, abs(np.dot(Y, bias)).max())

    if den == 0.0:
        raise ValueError(
            ""Ill-posed l1_min_c calculation: l1 will always ""
            ""select zero coefficients for this data""
        )
    if loss == ""squared_hinge"":
        return 0.5 / den
    else:  # loss == 'log':
        return 2.0 / den

        ```
    ###Assistant : Return the lowest bound for C.

    The lower bound for C is computed such that for C in (l1_min_C, infinity)
    the model is guaranteed not to be empty. This applies to l1 penalized
    classifiers, such as LinearSVC with penalty='l1' and
    linear_model.LogisticRegression with penalty='l1'.

    This value is valid if class_weight parameter in fit() is not set.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Training vector, where `n_samples` is the number of samples and
        `n_features` is the number of features.

    y : array-like of shape (n_samples,)
        Target vector relative to X.

    loss : {'squared_hinge', 'log'}, default='squared_hinge'
        Specifies the loss function.
        With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss).
        With 'log' it is the loss of logistic regression models.

    fit_intercept : bool, default=True
        Specifies if the intercept should be fitted by the model.
        It must match the fit() method parameter.

    intercept_scaling : float, default=1.0
        When fit_intercept is True, instance vector x becomes
        [x, intercept_scaling],
        i.e. a ""synthetic"" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        It must match the fit() method parameter.

    Returns
    -------
    l1_min_c : float
        Minimum value for C.
    
    "
1105,"def _mysql_tables_where_indexes_already_present(conn):
    
    to_check = [
        ('xcom', 'idx_xcom_task_instance'),
        ('task_reschedule', 'idx_task_reschedule_dag_run'),
        ('task_fail', 'idx_task_fail_task_instance'),
    ]
    tables = set()
    for tbl, idx in to_check:
        if conn.execute(f""show indexes from {tbl} where Key_name = '{idx}'"").first():
            tables.add(tbl)
    return tables

","
    If user downgraded and is upgrading again, we have to check for existing
    indexes on mysql because we can't (and don't) drop them as part of the
    downgrade.
    ",28,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _mysql_tables_where_indexes_already_present(conn):
    
    to_check = [
        ('xcom', 'idx_xcom_task_instance'),
        ('task_reschedule', 'idx_task_reschedule_dag_run'),
        ('task_fail', 'idx_task_fail_task_instance'),
    ]
    tables = set()
    for tbl, idx in to_check:
        if conn.execute(f""show indexes from {tbl} where Key_name = '{idx}'"").first():
            tables.add(tbl)
    return tables


        ```
    ###Assistant : 
    If user downgraded and is upgrading again, we have to check for existing
    indexes on mysql because we can't (and don't) drop them as part of the
    downgrade.
    
    "
1106,"def test_webclient_resolves_with_client_resource(self):
        
        for resource_name_order_list in [
            [""webclient"", ""client""],
            [""client"", ""webclient""],
        ]:
            # Create a dictionary from path regex -> resource
            resource_dict: Dict[str, Resource] = {}

            for resource_name in resource_name_order_list:
                resource_dict.update(
                    SynapseHomeServer._configure_named_resource(self.hs, resource_name)
                )

            # Create a root resource which ties the above resources together into one
            root_resource = Resource()
            create_resource_tree(resource_dict, root_resource)

            # Create a site configured with this resource to make HTTP requests against
            listener_config = ListenerConfig(
                port=8008,
                bind_addresses=[""127.0.0.1""],
                type=""http"",
                http_options=HttpListenerConfig(
                    resources=[HttpResourceConfig(names=resource_name_order_list)]
                ),
            )
            test_site = SynapseSite(
                logger_name=""synapse.access.http.fake"",
                site_tag=self.hs.config.server.server_name,
                config=listener_config,
                resource=root_resource,
                server_version_string=""1"",
                max_request_body_size=1234,
                reactor=self.reactor,
            )

            # Attempt to make requests to endpoints on both the webclient and client resources
            # on test_site.
            self._request_client_and_webclient_resources(test_site)
","
        Tests that both client and webclient resources can be accessed simultaneously.

        This is a regression test created in response to https://github.com/matrix-org/synapse/issues/11763.
        ",21,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_webclient_resolves_with_client_resource(self):
        
        for resource_name_order_list in [
            [""webclient"", ""client""],
            [""client"", ""webclient""],
        ]:
            # Create a dictionary from path regex -> resource
            resource_dict: Dict[str, Resource] = {}

            for resource_name in resource_name_order_list:
                resource_dict.update(
                    SynapseHomeServer._configure_named_resource(self.hs, resource_name)
                )

            # Create a root resource which ties the above resources together into one
            root_resource = Resource()
            create_resource_tree(resource_dict, root_resource)

            # Create a site configured with this resource to make HTTP requests against
            listener_config = ListenerConfig(
                port=8008,
                bind_addresses=[""127.0.0.1""],
                type=""http"",
                http_options=HttpListenerConfig(
                    resources=[HttpResourceConfig(names=resource_name_order_list)]
                ),
            )
            test_site = SynapseSite(
                logger_name=""synapse.access.http.fake"",
                site_tag=self.hs.config.server.server_name,
                config=listener_config,
                resource=root_resource,
                server_version_string=""1"",
                max_request_body_size=1234,
                reactor=self.reactor,
            )

            # Attempt to make requests to endpoints on both the webclient and client resources
            # on test_site.
            self._request_client_and_webclient_resources(test_site)

        ```
    ###Assistant : 
        Tests that both client and webclient resources can be accessed simultaneously.

        This is a regression test created in response to https://github.com/matrix-org/synapse/issues/11763.
        
    "
1107,"def unregister_pickle_by_value(module):
    
    if not isinstance(module, types.ModuleType):
        raise ValueError(f""Input should be a module object, got {str(module)} instead"")
    if module.__name__ not in _PICKLE_BY_VALUE_MODULES:
        raise ValueError(f""{module} is not registered for pickle by value"")
    else:
        _PICKLE_BY_VALUE_MODULES.remove(module.__name__)

",Unregister that the input module should be pickled by value.,10,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unregister_pickle_by_value(module):
    
    if not isinstance(module, types.ModuleType):
        raise ValueError(f""Input should be a module object, got {str(module)} instead"")
    if module.__name__ not in _PICKLE_BY_VALUE_MODULES:
        raise ValueError(f""{module} is not registered for pickle by value"")
    else:
        _PICKLE_BY_VALUE_MODULES.remove(module.__name__)


        ```
    ###Assistant : Unregister that the input module should be pickled by value.
    "
1108,"def test_get_bad_permissions(self):
        
        # Remove privileges from user
        self.user.is_superuser = False
        self.user.user_permissions.add(
            Permission.objects.get(
                content_type__app_label=""wagtailadmin"", codename=""access_admin""
            )
        )
        self.user.save()

        # Get
        response = self.client.get(
            reverse(""wagtailimages:url_generator"", args=(self.image.id,))
        )

        # Check response
        self.assertRedirects(response, reverse(""wagtailadmin_home""))

","
        This tests that the view returns a ""permission denied"" redirect if a user without correct
        permissions attempts to access it
        ",20,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_bad_permissions(self):
        
        # Remove privileges from user
        self.user.is_superuser = False
        self.user.user_permissions.add(
            Permission.objects.get(
                content_type__app_label=""wagtailadmin"", codename=""access_admin""
            )
        )
        self.user.save()

        # Get
        response = self.client.get(
            reverse(""wagtailimages:url_generator"", args=(self.image.id,))
        )

        # Check response
        self.assertRedirects(response, reverse(""wagtailadmin_home""))


        ```
    ###Assistant : 
        This tests that the view returns a ""permission denied"" redirect if a user without correct
        permissions attempts to access it
        
    "
1109,"def get_archive_formats():
    
    formats = [(name, registry[2]) for name, registry in
               _ARCHIVE_FORMATS.items()]
    formats.sort()
    return formats
","Returns a list of supported formats for archiving and unarchiving.

    Each element of the returned sequence is a tuple (name, description)
    ",21,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_archive_formats():
    
    formats = [(name, registry[2]) for name, registry in
               _ARCHIVE_FORMATS.items()]
    formats.sort()
    return formats

        ```
    ###Assistant : Returns a list of supported formats for archiving and unarchiving.

    Each element of the returned sequence is a tuple (name, description)
    
    "
1110,"def _handle_transforms(self, element, mobject):
        

        if element.hasAttribute(""x"") and element.hasAttribute(""y""):
            x = self._attribute_to_float(element.getAttribute(""x""))
            # Flip y
            y = -self._attribute_to_float(element.getAttribute(""y""))
            mobject.shift(x * RIGHT + y * UP)

        transform_attr_value = element.getAttribute(""transform"")

        # parse the various transforms in the attribute value
        transform_names = [""matrix"", ""translate"", ""scale"", ""rotate"", ""skewX"", ""skewY""]

        # Borrowed/Inspired from:
        # https://github.com/cjlano/svg/blob/3ea3384457c9780fa7d67837c9c5fd4ebc42cb3b/svg/svg.py#L75

        # match any SVG transformation with its parameter (until final parenthesis)
        # [^)]*    == anything but a closing parenthesis
        # '|'.join == OR-list of SVG transformations
        transform_regex = ""|"".join([x + r""[^)]*\)"" for x in transform_names])
        transforms = re.findall(transform_regex, transform_attr_value)

        number_regex = r""[-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][-+]?\d+)?""

        for t in transforms:
            op_name, op_args = t.split(""("")
            op_name = op_name.strip()
            op_args = [float(x) for x in re.findall(number_regex, op_args)]

            if op_name == ""matrix"":
                transform_args = np.array(op_args).reshape([3, 2])
                x = transform_args[2][0]
                y = -transform_args[2][1]
                matrix = np.identity(self.dim)
                matrix[:2, :2] = transform_args[:2, :]
                matrix[1] *= -1
                matrix[:, 1] *= -1

                for mob in mobject.family_members_with_points():
                    if config[""renderer""] == ""opengl"":
                        mob.points = np.dot(mob.points, matrix)
                    else:
                        mob.points = np.dot(mob.points, matrix)
                mobject.shift(x * RIGHT + y * UP)

            elif op_name == ""scale"":
                scale_values = op_args
                if len(scale_values) == 2:
                    scale_x, scale_y = scale_values
                    mobject.scale(np.array([scale_x, scale_y, 1]), about_point=ORIGIN)
                elif len(scale_values) == 1:
                    scale = scale_values[0]
                    mobject.scale(np.array([scale, scale, 1]), about_point=ORIGIN)

            elif op_name == ""translate"":
                if len(op_args) == 2:
                    x, y = op_args
                else:
                    x = op_args
                    y = 0
                mobject.shift(x * RIGHT + y * DOWN)

            else:
                # TODO: handle rotate, skewX and skewY
                # for now adding a warning message
                logger.warning(
                    ""Handling of %s transform is not supported yet!"",
                    op_name,
                )
","Applies the SVG transform to the specified mobject. Transforms include:
        ``matrix``, ``translate``, and ``scale``.

        Parameters
        ----------
        element : :class:`minidom.Element`
            The transform command to perform

        mobject : :class:`Mobject`
            The Mobject to transform.
        ",31,245,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _handle_transforms(self, element, mobject):
        

        if element.hasAttribute(""x"") and element.hasAttribute(""y""):
            x = self._attribute_to_float(element.getAttribute(""x""))
            # Flip y
            y = -self._attribute_to_float(element.getAttribute(""y""))
            mobject.shift(x * RIGHT + y * UP)

        transform_attr_value = element.getAttribute(""transform"")

        # parse the various transforms in the attribute value
        transform_names = [""matrix"", ""translate"", ""scale"", ""rotate"", ""skewX"", ""skewY""]

        # Borrowed/Inspired from:
        # https://github.com/cjlano/svg/blob/3ea3384457c9780fa7d67837c9c5fd4ebc42cb3b/svg/svg.py#L75

        # match any SVG transformation with its parameter (until final parenthesis)
        # [^)]*    == anything but a closing parenthesis
        # '|'.join == OR-list of SVG transformations
        transform_regex = ""|"".join([x + r""[^)]*\)"" for x in transform_names])
        transforms = re.findall(transform_regex, transform_attr_value)

        number_regex = r""[-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][-+]?\d+)?""

        for t in transforms:
            op_name, op_args = t.split(""("")
            op_name = op_name.strip()
            op_args = [float(x) for x in re.findall(number_regex, op_args)]

            if op_name == ""matrix"":
                transform_args = np.array(op_args).reshape([3, 2])
                x = transform_args[2][0]
                y = -transform_args[2][1]
                matrix = np.identity(self.dim)
                matrix[:2, :2] = transform_args[:2, :]
                matrix[1] *= -1
                matrix[:, 1] *= -1

                for mob in mobject.family_members_with_points():
                    if config[""renderer""] == ""opengl"":
                        mob.points = np.dot(mob.points, matrix)
                    else:
                        mob.points = np.dot(mob.points, matrix)
                mobject.shift(x * RIGHT + y * UP)

            elif op_name == ""scale"":
                scale_values = op_args
                if len(scale_values) == 2:
                    scale_x, scale_y = scale_values
                    mobject.scale(np.array([scale_x, scale_y, 1]), about_point=ORIGIN)
                elif len(scale_values) == 1:
                    scale = scale_values[0]
                    mobject.scale(np.array([scale, scale, 1]), about_point=ORIGIN)

            elif op_name == ""translate"":
                if len(op_args) == 2:
                    x, y = op_args
                else:
                    x = op_args
                    y = 0
                mobject.shift(x * RIGHT + y * DOWN)

            else:
                # TODO: handle rotate, skewX and skewY
                # for now adding a warning message
                logger.warning(
                    ""Handling of %s transform is not supported yet!"",
                    op_name,
                )

        ```
    ###Assistant : Applies the SVG transform to the specified mobject. Transforms include:
        ``matrix``, ``translate``, and ``scale``.

        Parameters
        ----------
        element : :class:`minidom.Element`
            The transform command to perform

        mobject : :class:`Mobject`
            The Mobject to transform.
        
    "
1111,"def pro_data_fixture():
    
    return json.loads(load_fixture(""data.json"", ""airvisual_pro""))


@pytest.fixture(name=""pro"")",Define an update coordinator data example for the Pro.,9,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pro_data_fixture():
    
    return json.loads(load_fixture(""data.json"", ""airvisual_pro""))


@pytest.fixture(name=""pro"")
        ```
    ###Assistant : Define an update coordinator data example for the Pro.
    "
1112,"def fetch_species_distributions(*, data_home=None, download_if_missing=True):
    
    data_home = get_data_home(data_home)
    if not exists(data_home):
        makedirs(data_home)

    # Define parameters for the data files.  These should not be changed
    # unless the data model changes.  They will be saved in the npz file
    # with the downloaded data.
    extra_params = dict(
        x_left_lower_corner=-94.8,
        Nx=1212,
        y_left_lower_corner=-56.05,
        Ny=1592,
        grid_size=0.05,
    )
    dtype = np.int16

    archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)

    if not exists(archive_path):
        if not download_if_missing:
            raise IOError(""Data not found and `download_if_missing` is False"")
        logger.info(""Downloading species data from %s to %s"" % (SAMPLES.url, data_home))
        samples_path = _fetch_remote(SAMPLES, dirname=data_home)
        with np.load(samples_path) as X:  # samples.zip is a valid npz
            for f in X.files:
                fhandle = BytesIO(X[f])
                if ""train"" in f:
                    train = _load_csv(fhandle)
                if ""test"" in f:
                    test = _load_csv(fhandle)
        remove(samples_path)

        logger.info(
            ""Downloading coverage data from %s to %s"" % (COVERAGES.url, data_home)
        )
        coverages_path = _fetch_remote(COVERAGES, dirname=data_home)
        with np.load(coverages_path) as X:  # coverages.zip is a valid npz
            coverages = []
            for f in X.files:
                fhandle = BytesIO(X[f])
                logger.debug("" - converting {}"".format(f))
                coverages.append(_load_coverage(fhandle))
            coverages = np.asarray(coverages, dtype=dtype)
        remove(coverages_path)

        bunch = Bunch(coverages=coverages, test=test, train=train, **extra_params)
        joblib.dump(bunch, archive_path, compress=9)
    else:
        bunch = joblib.load(archive_path)

    return bunch
","Loader for species distribution dataset from Phillips et. al. (2006).

    Read more in the :ref:`User Guide <datasets>`.

    Parameters
    ----------
    data_home : str, default=None
        Specify another download and cache folder for the datasets. By default
        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

    download_if_missing : bool, default=True
        If False, raise a IOError if the data is not locally available
        instead of trying to download the data from the source site.

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        coverages : array, shape = [14, 1592, 1212]
            These represent the 14 features measured
            at each point of the map grid.
            The latitude/longitude values for the grid are discussed below.
            Missing data is represented by the value -9999.
        train : record array, shape = (1624,)
            The training points for the data.  Each point has three fields:

            - train['species'] is the species name
            - train['dd long'] is the longitude, in degrees
            - train['dd lat'] is the latitude, in degrees
        test : record array, shape = (620,)
            The test points for the data.  Same format as the training data.
        Nx, Ny : integers
            The number of longitudes (x) and latitudes (y) in the grid
        x_left_lower_corner, y_left_lower_corner : floats
            The (x,y) position of the lower-left corner, in degrees
        grid_size : float
            The spacing between points of the grid, in degrees

    Notes
    -----

    This dataset represents the geographic distribution of species.
    The dataset is provided by Phillips et. al. (2006).

    The two species are:

    - `""Bradypus variegatus""
      <http://www.iucnredlist.org/details/3038/0>`_ ,
      the Brown-throated Sloth.

    - `""Microryzomys minutus""
      <http://www.iucnredlist.org/details/13408/0>`_ ,
      also known as the Forest Small Rice Rat, a rodent that lives in Peru,
      Colombia, Ecuador, Peru, and Venezuela.

    - For an example of using this dataset with scikit-learn, see
      :ref:`examples/applications/plot_species_distribution_modeling.py
      <sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.

    References
    ----------

    * `""Maximum entropy modeling of species geographic distributions""
      <http://rob.schapire.net/papers/ecolmod.pdf>`_
      S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
      190:231-259, 2006.
    ",310,179,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fetch_species_distributions(*, data_home=None, download_if_missing=True):
    
    data_home = get_data_home(data_home)
    if not exists(data_home):
        makedirs(data_home)

    # Define parameters for the data files.  These should not be changed
    # unless the data model changes.  They will be saved in the npz file
    # with the downloaded data.
    extra_params = dict(
        x_left_lower_corner=-94.8,
        Nx=1212,
        y_left_lower_corner=-56.05,
        Ny=1592,
        grid_size=0.05,
    )
    dtype = np.int16

    archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)

    if not exists(archive_path):
        if not download_if_missing:
            raise IOError(""Data not found and `download_if_missing` is False"")
        logger.info(""Downloading species data from %s to %s"" % (SAMPLES.url, data_home))
        samples_path = _fetch_remote(SAMPLES, dirname=data_home)
        with np.load(samples_path) as X:  # samples.zip is a valid npz
            for f in X.files:
                fhandle = BytesIO(X[f])
                if ""train"" in f:
                    train = _load_csv(fhandle)
                if ""test"" in f:
                    test = _load_csv(fhandle)
        remove(samples_path)

        logger.info(
            ""Downloading coverage data from %s to %s"" % (COVERAGES.url, data_home)
        )
        coverages_path = _fetch_remote(COVERAGES, dirname=data_home)
        with np.load(coverages_path) as X:  # coverages.zip is a valid npz
            coverages = []
            for f in X.files:
                fhandle = BytesIO(X[f])
                logger.debug("" - converting {}"".format(f))
                coverages.append(_load_coverage(fhandle))
            coverages = np.asarray(coverages, dtype=dtype)
        remove(coverages_path)

        bunch = Bunch(coverages=coverages, test=test, train=train, **extra_params)
        joblib.dump(bunch, archive_path, compress=9)
    else:
        bunch = joblib.load(archive_path)

    return bunch

        ```
    ###Assistant : Loader for species distribution dataset from Phillips et. al. (2006).

    Read more in the :ref:`User Guide <datasets>`.

    Parameters
    ----------
    data_home : str, default=None
        Specify another download and cache folder for the datasets. By default
        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

    download_if_missing : bool, default=True
        If False, raise a IOError if the data is not locally available
        instead of trying to download the data from the source site.

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        coverages : array, shape = [14, 1592, 1212]
            These represent the 14 features measured
            at each point of the map grid.
            The latitude/longitude values for the grid are discussed below.
            Missing data is represented by the value -9999.
        train : record array, shape = (1624,)
            The training points for the data.  Each point has three fields:

            - train['species'] is the species name
            - train['dd long'] is the longitude, in degrees
            - train['dd lat'] is the latitude, in degrees
        test : record array, shape = (620,)
            The test points for the data.  Same format as the training data.
        Nx, Ny : integers
            The number of longitudes (x) and latitudes (y) in the grid
        x_left_lower_corner, y_left_lower_corner : floats
            The (x,y) position of the lower-left corner, in degrees
        grid_size : float
            The spacing between points of the grid, in degrees

    Notes
    -----

    This dataset represents the geographic distribution of species.
    The dataset is provided by Phillips et. al. (2006).

    The two species are:

    - `""Bradypus variegatus""
      <http://www.iucnredlist.org/details/3038/0>`_ ,
      the Brown-throated Sloth.

    - `""Microryzomys minutus""
      <http://www.iucnredlist.org/details/13408/0>`_ ,
      also known as the Forest Small Rice Rat, a rodent that lives in Peru,
      Colombia, Ecuador, Peru, and Venezuela.

    - For an example of using this dataset with scikit-learn, see
      :ref:`examples/applications/plot_species_distribution_modeling.py
      <sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.

    References
    ----------

    * `""Maximum entropy modeling of species geographic distributions""
      <http://rob.schapire.net/papers/ecolmod.pdf>`_
      S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
      190:231-259, 2006.
    
    "
1113,"def set_split_factor(factor, dev=None):
    
    assert 0 <= factor
    global split_factors
    dev = ivy.default(dev, default_device())
    split_factors[dev] = factor


# noinspection PyShadowingNames","
    Set the global split factor for a given device, which can be used to scale batch splitting chunk sizes for the
    device across the codebase.

    :param factor: The factor to set the device-specific split factor to.
    :type factor: float
    :param dev: The device to set the split factor for. Sets the default device by default.
    :type dev: str, optional
    ",59,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_split_factor(factor, dev=None):
    
    assert 0 <= factor
    global split_factors
    dev = ivy.default(dev, default_device())
    split_factors[dev] = factor


# noinspection PyShadowingNames
        ```
    ###Assistant : 
    Set the global split factor for a given device, which can be used to scale batch splitting chunk sizes for the
    device across the codebase.

    :param factor: The factor to set the device-specific split factor to.
    :type factor: float
    :param dev: The device to set the split factor for. Sets the default device by default.
    :type dev: str, optional
    
    "
1114,"def run(self, test, compileflags=None, out=None, clear_globs=True):
        
        self.test = test

        # Remove ``` from the end of example, which may appear in Markdown
        # files
        for example in test.examples:
            example.want = example.want.replace('```\n', '')
            example.exc_msg = example.exc_msg and example.exc_msg.replace('```\n', '')


        if compileflags is None:
            compileflags = pdoctest._extract_future_flags(test.globs)

        save_stdout = sys.stdout
        if out is None:
            out = save_stdout.write
        sys.stdout = self._fakeout

        # Patch pdb.set_trace to restore sys.stdout during interactive
        # debugging (so it's not still redirected to self._fakeout).
        # Note that the interactive output will go to *our*
        # save_stdout, even if that's not the real sys.stdout; this
        # allows us to write test cases for the set_trace behavior.
        save_set_trace = pdb.set_trace
        self.debugger = pdoctest._OutputRedirectingPdb(save_stdout)
        self.debugger.reset()
        pdb.set_trace = self.debugger.set_trace

        # Patch linecache.getlines, so we can see the example's source
        # when we're inside the debugger.
        self.save_linecache_getlines = pdoctest.linecache.getlines
        linecache.getlines = self.__patched_linecache_getlines

        # Fail for deprecation warnings
        with raise_on_deprecated():
            try:
                return self.__run(test, compileflags, out)
            finally:
                sys.stdout = save_stdout
                pdb.set_trace = save_set_trace
                linecache.getlines = self.save_linecache_getlines
                if clear_globs:
                    test.globs.clear()


# We have to override the name mangled methods.
monkeypatched_methods = [
    'patched_linecache_getlines',
    'run',
    'record_outcome'
]
for method in monkeypatched_methods:
    oldname = '_DocTestRunner__' + method
    newname = '_SymPyDocTestRunner__' + method
    setattr(SymPyDocTestRunner, newname, getattr(DocTestRunner, oldname))

","
        Run the examples in ``test``, and display the results using the
        writer function ``out``.

        The examples are run in the namespace ``test.globs``.  If
        ``clear_globs`` is true (the default), then this namespace will
        be cleared after the test runs, to help with garbage
        collection.  If you would like to examine the namespace after
        the test completes, then use ``clear_globs=False``.

        ``compileflags`` gives the set of flags that should be used by
        the Python compiler when running the examples.  If not
        specified, then it will default to the set of future-import
        flags that apply to ``globs``.

        The output of each example is checked using
        ``SymPyDocTestRunner.check_output``, and the results are
        formatted by the ``SymPyDocTestRunner.report_*`` methods.
        ",111,197,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def run(self, test, compileflags=None, out=None, clear_globs=True):
        
        self.test = test

        # Remove ``` from the end of example, which may appear in Markdown
        # files
        for example in test.examples:
            example.want = example.want.replace('```\n', '')
            example.exc_msg = example.exc_msg and example.exc_msg.replace('```\n', '')


        if compileflags is None:
            compileflags = pdoctest._extract_future_flags(test.globs)

        save_stdout = sys.stdout
        if out is None:
            out = save_stdout.write
        sys.stdout = self._fakeout

        # Patch pdb.set_trace to restore sys.stdout during interactive
        # debugging (so it's not still redirected to self._fakeout).
        # Note that the interactive output will go to *our*
        # save_stdout, even if that's not the real sys.stdout; this
        # allows us to write test cases for the set_trace behavior.
        save_set_trace = pdb.set_trace
        self.debugger = pdoctest._OutputRedirectingPdb(save_stdout)
        self.debugger.reset()
        pdb.set_trace = self.debugger.set_trace

        # Patch linecache.getlines, so we can see the example's source
        # when we're inside the debugger.
        self.save_linecache_getlines = pdoctest.linecache.getlines
        linecache.getlines = self.__patched_linecache_getlines

        # Fail for deprecation warnings
        with raise_on_deprecated():
            try:
                return self.__run(test, compileflags, out)
            finally:
                sys.stdout = save_stdout
                pdb.set_trace = save_set_trace
                linecache.getlines = self.save_linecache_getlines
                if clear_globs:
                    test.globs.clear()


# We have to override the name mangled methods.
monkeypatched_methods = [
    'patched_linecache_getlines',
    'run',
    'record_outcome'
]
for method in monkeypatched_methods:
    oldname = '_DocTestRunner__' + method
    newname = '_SymPyDocTestRunner__' + method
    setattr(SymPyDocTestRunner, newname, getattr(DocTestRunner, oldname))


        ```
    ###Assistant : 
        Run the examples in ``test``, and display the results using the
        writer function ``out``.

        The examples are run in the namespace ``test.globs``.  If
        ``clear_globs`` is true (the default), then this namespace will
        be cleared after the test runs, to help with garbage
        collection.  If you would like to examine the namespace after
        the test completes, then use ``clear_globs=False``.

        ``compileflags`` gives the set of flags that should be used by
        the Python compiler when running the examples.  If not
        specified, then it will default to the set of future-import
        flags that apply to ``globs``.

        The output of each example is checked using
        ``SymPyDocTestRunner.check_output``, and the results are
        formatted by the ``SymPyDocTestRunner.report_*`` methods.
        
    "
1115,"def _fetch_all_variants(client, variables={}, permissions=None):
    query = 
    response = client.post_graphql(
        query, variables, permissions=permissions, check_no_permissions=False
    )
    content = get_graphql_content(response)
    return content[""data""][""productVariants""]

","
        query fetchAllVariants($channel: String) {
            productVariants(first: 10, channel: $channel) {
                totalCount
                edges {
                    node {
                        id
                    }
                }
            }
        }
    ",19,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _fetch_all_variants(client, variables={}, permissions=None):
    query = 
    response = client.post_graphql(
        query, variables, permissions=permissions, check_no_permissions=False
    )
    content = get_graphql_content(response)
    return content[""data""][""productVariants""]


        ```
    ###Assistant : 
        query fetchAllVariants($channel: String) {
            productVariants(first: 10, channel: $channel) {
                totalCount
                edges {
                    node {
                        id
                    }
                }
            }
        }
    
    "
1116,"def astar_torus(grid, start_node, goal_node):
    
    colors = ['white', 'black', 'red', 'pink', 'yellow', 'green', 'orange']
    levels = [0, 1, 2, 3, 4, 5, 6, 7]
    cmap, norm = from_levels_and_colors(levels, colors)

    grid[start_node] = 4
    grid[goal_node] = 5

    parent_map = [[() for _ in range(M)] for _ in range(M)]

    heuristic_map = calc_heuristic_map(M, goal_node)

    explored_heuristic_map = np.full((M, M), np.inf)
    distance_map = np.full((M, M), np.inf)
    explored_heuristic_map[start_node] = heuristic_map[start_node]
    distance_map[start_node] = 0
    while True:
        grid[start_node] = 4
        grid[goal_node] = 5

        current_node = np.unravel_index(
            np.argmin(explored_heuristic_map, axis=None), explored_heuristic_map.shape)
        min_distance = np.min(explored_heuristic_map)
        if (current_node == goal_node) or np.isinf(min_distance):
            break

        grid[current_node] = 2
        explored_heuristic_map[current_node] = np.inf

        i, j = current_node[0], current_node[1]

        neighbors = find_neighbors(i, j)

        for neighbor in neighbors:
            if grid[neighbor] == 0 or grid[neighbor] == 5:
                distance_map[neighbor] = distance_map[current_node] + 1
                explored_heuristic_map[neighbor] = heuristic_map[neighbor]
                parent_map[neighbor[0]][neighbor[1]] = current_node
                grid[neighbor] = 3

    if np.isinf(explored_heuristic_map[goal_node]):
        route = []
        print(""No route found."")
    else:
        route = [goal_node]
        while parent_map[route[0][0]][route[0][1]] != ():
            route.insert(0, parent_map[route[0][0]][route[0][1]])

        print(""The route found covers %d grid cells."" % len(route))
        for i in range(1, len(route)):
            grid[route[i]] = 6
            plt.cla()
            # for stopping simulation with the esc key.
            plt.gcf().canvas.mpl_connect('key_release_event',
                    lambda event: [exit(0) if event.key == 'escape' else None])
            plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None)
            plt.show()
            plt.pause(1e-2)

    return route

","
    Finds a path between an initial and goal joint configuration using
    the A* Algorithm on a tororiadal grid.

    Args:
        grid: An occupancy grid (ndarray)
        start_node: Initial joint configuration (tuple)
        goal_node: Goal joint configuration (tuple)

    Returns:
        Obstacle-free route in joint space from start_node to goal_node
    ",44,192,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def astar_torus(grid, start_node, goal_node):
    
    colors = ['white', 'black', 'red', 'pink', 'yellow', 'green', 'orange']
    levels = [0, 1, 2, 3, 4, 5, 6, 7]
    cmap, norm = from_levels_and_colors(levels, colors)

    grid[start_node] = 4
    grid[goal_node] = 5

    parent_map = [[() for _ in range(M)] for _ in range(M)]

    heuristic_map = calc_heuristic_map(M, goal_node)

    explored_heuristic_map = np.full((M, M), np.inf)
    distance_map = np.full((M, M), np.inf)
    explored_heuristic_map[start_node] = heuristic_map[start_node]
    distance_map[start_node] = 0
    while True:
        grid[start_node] = 4
        grid[goal_node] = 5

        current_node = np.unravel_index(
            np.argmin(explored_heuristic_map, axis=None), explored_heuristic_map.shape)
        min_distance = np.min(explored_heuristic_map)
        if (current_node == goal_node) or np.isinf(min_distance):
            break

        grid[current_node] = 2
        explored_heuristic_map[current_node] = np.inf

        i, j = current_node[0], current_node[1]

        neighbors = find_neighbors(i, j)

        for neighbor in neighbors:
            if grid[neighbor] == 0 or grid[neighbor] == 5:
                distance_map[neighbor] = distance_map[current_node] + 1
                explored_heuristic_map[neighbor] = heuristic_map[neighbor]
                parent_map[neighbor[0]][neighbor[1]] = current_node
                grid[neighbor] = 3

    if np.isinf(explored_heuristic_map[goal_node]):
        route = []
        print(""No route found."")
    else:
        route = [goal_node]
        while parent_map[route[0][0]][route[0][1]] != ():
            route.insert(0, parent_map[route[0][0]][route[0][1]])

        print(""The route found covers %d grid cells."" % len(route))
        for i in range(1, len(route)):
            grid[route[i]] = 6
            plt.cla()
            # for stopping simulation with the esc key.
            plt.gcf().canvas.mpl_connect('key_release_event',
                    lambda event: [exit(0) if event.key == 'escape' else None])
            plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None)
            plt.show()
            plt.pause(1e-2)

    return route


        ```
    ###Assistant : 
    Finds a path between an initial and goal joint configuration using
    the A* Algorithm on a tororiadal grid.

    Args:
        grid: An occupancy grid (ndarray)
        start_node: Initial joint configuration (tuple)
        goal_node: Goal joint configuration (tuple)

    Returns:
        Obstacle-free route in joint space from start_node to goal_node
    
    "
1117,"def root_node(self) -> Optional[str]:
        
        if len(self.graph.nodes) < 1:
            return None
        return list(self.graph.nodes)[0]  # List conversion is required, see networkx docs
","
        Returns the root node of the pipeline's graph.
        ",8,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def root_node(self) -> Optional[str]:
        
        if len(self.graph.nodes) < 1:
            return None
        return list(self.graph.nodes)[0]  # List conversion is required, see networkx docs

        ```
    ###Assistant : 
        Returns the root node of the pipeline's graph.
        
    "
1118,"def as_coefficients_dict(self, *syms):
        
        if not syms:
            d = defaultdict(list)
            for ai in self.args:
                c, m = ai.as_coeff_Mul()
                d[m].append(c)
            for k, v in d.items():
                if len(v) == 1:
                    d[k] = v[0]
                else:
                    d[k] = Add(*v)
            di = defaultdict(int)
            di.update(d)
            return di
        else:
            d = defaultdict(list)
            ind, dep = self.as_independent(*syms, as_Add=True)
            for i in Add.make_args(dep):
                c, x = i.as_independent(*syms, as_Add=False)
                d[x].append(c)
            d = {k: Add(*d[k]) for k in d}
            d.update({S.One: ind})
            return d

","Return a dictionary mapping terms to their Rational coefficient.
        Since the dictionary is a defaultdict, inquiries about terms which
        were not present will return a coefficient of 0. If an expression is
        not an Add it is considered to have a single term.

        If symbols `syms` are provided, any multiplicative terms
        independent of them will be considered a coefficient and a
        regular dictionary of syms-dependent generators as keys and
        their corresponding coefficients as values will be returned.

        Examples
        ========

        >>> from sympy import exp
        >>> from sympy.abc import a, x
        >>> (3*x + a*x + 4).as_coefficients_dict()
        {1: 4, x: 3, a*x: 1}
        >>> _[a]
        0
        >>> (3*a*x).as_coefficients_dict()
        {a*x: 3}

        >>> (3*exp(x)*x + a/x + 2).as_coefficients_dict(x)
        {1: 2, 1/x: a, x*exp(x): 3}
        ",121,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def as_coefficients_dict(self, *syms):
        
        if not syms:
            d = defaultdict(list)
            for ai in self.args:
                c, m = ai.as_coeff_Mul()
                d[m].append(c)
            for k, v in d.items():
                if len(v) == 1:
                    d[k] = v[0]
                else:
                    d[k] = Add(*v)
            di = defaultdict(int)
            di.update(d)
            return di
        else:
            d = defaultdict(list)
            ind, dep = self.as_independent(*syms, as_Add=True)
            for i in Add.make_args(dep):
                c, x = i.as_independent(*syms, as_Add=False)
                d[x].append(c)
            d = {k: Add(*d[k]) for k in d}
            d.update({S.One: ind})
            return d


        ```
    ###Assistant : Return a dictionary mapping terms to their Rational coefficient.
        Since the dictionary is a defaultdict, inquiries about terms which
        were not present will return a coefficient of 0. If an expression is
        not an Add it is considered to have a single term.

        If symbols `syms` are provided, any multiplicative terms
        independent of them will be considered a coefficient and a
        regular dictionary of syms-dependent generators as keys and
        their corresponding coefficients as values will be returned.

        Examples
        ========

        >>> from sympy import exp
        >>> from sympy.abc import a, x
        >>> (3*x + a*x + 4).as_coefficients_dict()
        {1: 4, x: 3, a*x: 1}
        >>> _[a]
        0
        >>> (3*a*x).as_coefficients_dict()
        {a*x: 3}

        >>> (3*exp(x)*x + a/x + 2).as_coefficients_dict(x)
        {1: 2, 1/x: a, x*exp(x): 3}
        
    "
1119,"def verify_emoji_code_foreign_keys(self) -> None:
        
        dct = {}

        for row in RealmEmoji.objects.all():
            dct[row.id] = row

        if not dct:
            raise AssertionError(""test needs RealmEmoji rows"")

        count = 0
        for row in Reaction.objects.filter(reaction_type=Reaction.REALM_EMOJI):
            realm_emoji_id = int(row.emoji_code)
            assert realm_emoji_id in dct
            self.assertEqual(dct[realm_emoji_id].name, row.emoji_name)
            self.assertEqual(dct[realm_emoji_id].realm_id, row.user_profile.realm_id)
            count += 1

        for row in UserStatus.objects.filter(reaction_type=UserStatus.REALM_EMOJI):
            realm_emoji_id = int(row.emoji_code)
            assert realm_emoji_id in dct
            self.assertEqual(dct[realm_emoji_id].name, row.emoji_name)
            self.assertEqual(dct[realm_emoji_id].realm_id, row.user_profile.realm_id)
            count += 1

        if count == 0:
            raise AssertionError(""test is meaningless without any pertinent rows"")

","
        DB tables that refer to RealmEmoji use int(emoji_code) as the
        foreign key. Those tables tend to de-normalize emoji_name due
        to our inheritance-based setup. This helper makes sure those
        invariants are intact, which is particularly tricky during
        the import/export process (or during conversions from things
        like Slack/RocketChat/MatterMost/etc.).
        ",46,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def verify_emoji_code_foreign_keys(self) -> None:
        
        dct = {}

        for row in RealmEmoji.objects.all():
            dct[row.id] = row

        if not dct:
            raise AssertionError(""test needs RealmEmoji rows"")

        count = 0
        for row in Reaction.objects.filter(reaction_type=Reaction.REALM_EMOJI):
            realm_emoji_id = int(row.emoji_code)
            assert realm_emoji_id in dct
            self.assertEqual(dct[realm_emoji_id].name, row.emoji_name)
            self.assertEqual(dct[realm_emoji_id].realm_id, row.user_profile.realm_id)
            count += 1

        for row in UserStatus.objects.filter(reaction_type=UserStatus.REALM_EMOJI):
            realm_emoji_id = int(row.emoji_code)
            assert realm_emoji_id in dct
            self.assertEqual(dct[realm_emoji_id].name, row.emoji_name)
            self.assertEqual(dct[realm_emoji_id].realm_id, row.user_profile.realm_id)
            count += 1

        if count == 0:
            raise AssertionError(""test is meaningless without any pertinent rows"")


        ```
    ###Assistant : 
        DB tables that refer to RealmEmoji use int(emoji_code) as the
        foreign key. Those tables tend to de-normalize emoji_name due
        to our inheritance-based setup. This helper makes sure those
        invariants are intact, which is particularly tricky during
        the import/export process (or during conversions from things
        like Slack/RocketChat/MatterMost/etc.).
        
    "
1120,"def format_string_to_json(balance_info):
	Working Account|KES|481000.00|481000.00|0.00|0.00
	balance_dict = frappe._dict()
	for account_info in balance_info.split(""&""):
		account_info = account_info.split(""|"")
		balance_dict[account_info[0]] = dict(
			current_balance=fmt_money(account_info[2], currency=""KES""),
			available_balance=fmt_money(account_info[3], currency=""KES""),
			reserved_balance=fmt_money(account_info[4], currency=""KES""),
			uncleared_balance=fmt_money(account_info[5], currency=""KES""),
		)
	return dumps(balance_dict)

","
	Format string to json.

	e.g: 
	=> {'Working Account': {'current_balance': '481000.00',
	        'available_balance': '481000.00',
	        'reserved_balance': '0.00',
	        'uncleared_balance': '0.00'}}
	",16,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def format_string_to_json(balance_info):
	Working Account|KES|481000.00|481000.00|0.00|0.00
	balance_dict = frappe._dict()
	for account_info in balance_info.split(""&""):
		account_info = account_info.split(""|"")
		balance_dict[account_info[0]] = dict(
			current_balance=fmt_money(account_info[2], currency=""KES""),
			available_balance=fmt_money(account_info[3], currency=""KES""),
			reserved_balance=fmt_money(account_info[4], currency=""KES""),
			uncleared_balance=fmt_money(account_info[5], currency=""KES""),
		)
	return dumps(balance_dict)


        ```
    ###Assistant : 
	Format string to json.

	e.g: 
	=> {'Working Account': {'current_balance': '481000.00',
	        'available_balance': '481000.00',
	        'reserved_balance': '0.00',
	        'uncleared_balance': '0.00'}}
	
    "
1121,"def _validate_path(self) -> list[Any]:
        

        msg = (
            ""xpath does not return any nodes or attributes. ""
            ""Be sure to specify in `xpath` the parent nodes of ""
            ""children and attributes to parse. ""
            ""If document uses namespaces denoted with ""
            ""xmlns, be sure to define namespaces and ""
            ""use them in xpath.""
        )
        try:
            elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces)
            children = [ch for el in elems for ch in el.findall(""*"")]
            attrs = {k: v for el in elems for k, v in el.attrib.items()}

            if elems is None:
                raise ValueError(msg)

            if elems is not None:
                if self.elems_only and children == []:
                    raise ValueError(msg)
                if self.attrs_only and attrs == {}:
                    raise ValueError(msg)
                if children == [] and attrs == {}:
                    raise ValueError(msg)

        except (KeyError, SyntaxError):
            raise SyntaxError(
                ""You have used an incorrect or unsupported XPath ""
                ""expression for etree library or you used an ""
                ""undeclared namespace prefix.""
            )

        return elems
","
        Notes
        -----
        `etree` supports limited XPath. If user attempts a more complex
        expression syntax error will raise.
        ",17,148,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate_path(self) -> list[Any]:
        

        msg = (
            ""xpath does not return any nodes or attributes. ""
            ""Be sure to specify in `xpath` the parent nodes of ""
            ""children and attributes to parse. ""
            ""If document uses namespaces denoted with ""
            ""xmlns, be sure to define namespaces and ""
            ""use them in xpath.""
        )
        try:
            elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces)
            children = [ch for el in elems for ch in el.findall(""*"")]
            attrs = {k: v for el in elems for k, v in el.attrib.items()}

            if elems is None:
                raise ValueError(msg)

            if elems is not None:
                if self.elems_only and children == []:
                    raise ValueError(msg)
                if self.attrs_only and attrs == {}:
                    raise ValueError(msg)
                if children == [] and attrs == {}:
                    raise ValueError(msg)

        except (KeyError, SyntaxError):
            raise SyntaxError(
                ""You have used an incorrect or unsupported XPath ""
                ""expression for etree library or you used an ""
                ""undeclared namespace prefix.""
            )

        return elems

        ```
    ###Assistant : 
        Notes
        -----
        `etree` supports limited XPath. If user attempts a more complex
        expression syntax error will raise.
        
    "
1122,"def mask(self, row_labels, col_labels):
        
        new_obj = super().mask(row_labels, col_labels)
        if isinstance(row_labels, slice) and isinstance(
            self._length_cache, ObjectIDType
        ):
            new_obj._length_cache = compute_sliced_len.remote(
                row_labels, self._length_cache
            )
        if isinstance(col_labels, slice) and isinstance(
            self._width_cache, ObjectIDType
        ):
            new_obj._width_cache = compute_sliced_len.remote(
                col_labels, self._width_cache
            )
        return new_obj
","
        Lazily create a mask that extracts the indices provided.

        Parameters
        ----------
        row_labels : list-like, slice or label
            The row labels for the rows to extract.
        col_labels : list-like, slice or label
            The column labels for the columns to extract.

        Returns
        -------
        PandasOnRayDataframePartition
            A new ``PandasOnRayDataframePartition`` object.
        ",46,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mask(self, row_labels, col_labels):
        
        new_obj = super().mask(row_labels, col_labels)
        if isinstance(row_labels, slice) and isinstance(
            self._length_cache, ObjectIDType
        ):
            new_obj._length_cache = compute_sliced_len.remote(
                row_labels, self._length_cache
            )
        if isinstance(col_labels, slice) and isinstance(
            self._width_cache, ObjectIDType
        ):
            new_obj._width_cache = compute_sliced_len.remote(
                col_labels, self._width_cache
            )
        return new_obj

        ```
    ###Assistant : 
        Lazily create a mask that extracts the indices provided.

        Parameters
        ----------
        row_labels : list-like, slice or label
            The row labels for the rows to extract.
        col_labels : list-like, slice or label
            The column labels for the columns to extract.

        Returns
        -------
        PandasOnRayDataframePartition
            A new ``PandasOnRayDataframePartition`` object.
        
    "
1123,"def postprocess(data_out, label_list, top_k):
    
    output = []
    for result in data_out:
        result_i = softmax(result)
        output_i = {}
        indexs = np.argsort(result_i)[::-1][0:top_k]
        for index in indexs:
            label = label_list[index].split(',')[0]
            output_i[label] = float(result_i[index])
        output.append(output_i)
    return output
","
    Postprocess output of network, one image at a time.

    Args:
        data_out (numpy.ndarray): output data of network.
        label_list (list): list of label.
        top_k (int): Return top k results.
    ",27,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def postprocess(data_out, label_list, top_k):
    
    output = []
    for result in data_out:
        result_i = softmax(result)
        output_i = {}
        indexs = np.argsort(result_i)[::-1][0:top_k]
        for index in indexs:
            label = label_list[index].split(',')[0]
            output_i[label] = float(result_i[index])
        output.append(output_i)
    return output

        ```
    ###Assistant : 
    Postprocess output of network, one image at a time.

    Args:
        data_out (numpy.ndarray): output data of network.
        label_list (list): list of label.
        top_k (int): Return top k results.
    
    "
1124,"def requests(self):
        
        if hasattr(self, '_requests'):
            return self._requests
        else:
            if not hasattr(self, 'requests_by_class'):
                self.requests_by_class = {}
            if self.__class__.__name__ not in self.requests_by_class:
                self.requests_by_class[self.__class__.__name__] = {}
            # we need to copy so that different instances with different (requests) in input do not disturb one another
            self._requests = copy.copy(self.requests_by_class[self.__class__.__name__])
            return self._requests
","
        Get the request dictionary corresponding to this specific class

        :return: Returns the requests corresponding to the specific Executor instance class
        ",20,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def requests(self):
        
        if hasattr(self, '_requests'):
            return self._requests
        else:
            if not hasattr(self, 'requests_by_class'):
                self.requests_by_class = {}
            if self.__class__.__name__ not in self.requests_by_class:
                self.requests_by_class[self.__class__.__name__] = {}
            # we need to copy so that different instances with different (requests) in input do not disturb one another
            self._requests = copy.copy(self.requests_by_class[self.__class__.__name__])
            return self._requests

        ```
    ###Assistant : 
        Get the request dictionary corresponding to this specific class

        :return: Returns the requests corresponding to the specific Executor instance class
        
    "
1125,"def __call__(self, *args, **kwargs):
        r

        result = super().__call__(*args, **kwargs)
        if isinstance(args[0], list) and all(isinstance(el, str) for el in args[0]):
            return [res[0] for res in result]
        return result
","
        Generate the output text(s) using text(s) given as inputs.

        Args:
            args (`str` or `List[str]`):
                Input text for the encoder.
            return_tensors (`bool`, *optional*, defaults to `False`):
                Whether or not to include the tensors of predictions (as token indices) in the outputs.
            return_text (`bool`, *optional*, defaults to `True`):
                Whether or not to include the decoded texts in the outputs.
            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                Whether or not to clean up the potential extra spaces in the text output.
            truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`):
                The truncation strategy for the tokenization within the pipeline. `TruncationStrategy.DO_NOT_TRUNCATE`
                (default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's
                max_length instead of throwing an error down the line.
            generate_kwargs:
                Additional keyword arguments to pass along to the generate method of the model (see the generate method
                corresponding to your framework [here](./model#generative-models)).

        Return:
            A list or a list of list of `dict`: Each result comes as a dictionary with the following keys:

            - **generated_text** (`str`, present when `return_text=True`) -- The generated text.
            - **generated_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token
              ids of the generated text.
        ",188,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self, *args, **kwargs):
        r

        result = super().__call__(*args, **kwargs)
        if isinstance(args[0], list) and all(isinstance(el, str) for el in args[0]):
            return [res[0] for res in result]
        return result

        ```
    ###Assistant : 
        Generate the output text(s) using text(s) given as inputs.

        Args:
            args (`str` or `List[str]`):
                Input text for the encoder.
            return_tensors (`bool`, *optional*, defaults to `False`):
                Whether or not to include the tensors of predictions (as token indices) in the outputs.
            return_text (`bool`, *optional*, defaults to `True`):
                Whether or not to include the decoded texts in the outputs.
            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                Whether or not to clean up the potential extra spaces in the text output.
            truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`):
                The truncation strategy for the tokenization within the pipeline. `TruncationStrategy.DO_NOT_TRUNCATE`
                (default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's
                max_length instead of throwing an error down the line.
            generate_kwargs:
                Additional keyword arguments to pass along to the generate method of the model (see the generate method
                corresponding to your framework [here](./model#generative-models)).

        Return:
            A list or a list of list of `dict`: Each result comes as a dictionary with the following keys:

            - **generated_text** (`str`, present when `return_text=True`) -- The generated text.
            - **generated_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token
              ids of the generated text.
        
    "
1126,"def rewind_body(prepared_request):
    
    body_seek = getattr(prepared_request.body, ""seek"", None)
    if body_seek is not None and isinstance(
        prepared_request._body_position, integer_types
    ):
        try:
            body_seek(prepared_request._body_position)
        except OSError:
            raise UnrewindableBodyError(
                ""An error occurred when rewinding request body for redirect.""
            )
    else:
        raise UnrewindableBodyError(""Unable to rewind request body for redirect."")
","Move file pointer back to its recorded starting position
    so it can be read again on redirect.
    ",17,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rewind_body(prepared_request):
    
    body_seek = getattr(prepared_request.body, ""seek"", None)
    if body_seek is not None and isinstance(
        prepared_request._body_position, integer_types
    ):
        try:
            body_seek(prepared_request._body_position)
        except OSError:
            raise UnrewindableBodyError(
                ""An error occurred when rewinding request body for redirect.""
            )
    else:
        raise UnrewindableBodyError(""Unable to rewind request body for redirect."")

        ```
    ###Assistant : Move file pointer back to its recorded starting position
    so it can be read again on redirect.
    
    "
1127,"def nested_concat(tensors, new_tensors, padding_index=-100):
    
    assert type(tensors) == type(
        new_tensors
    ), f""Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}.""
    if isinstance(tensors, (list, tuple)):
        return type(tensors)(nested_concat(
            t, n, padding_index=padding_index)
                             for t, n in zip(tensors, new_tensors))
    elif isinstance(tensors, paddle.Tensor):
        return paddle_pad_and_concatenate(
            tensors, new_tensors, padding_index=padding_index)
    elif isinstance(tensors, np.ndarray):
        return numpy_pad_and_concatenate(
            tensors, new_tensors, padding_index=padding_index)
    else:
        raise TypeError(
            f""Unsupported type for concatenation: got {type(tensors)}"")

","
    Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
    nested list/tuples of tensors.
    ",25,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def nested_concat(tensors, new_tensors, padding_index=-100):
    
    assert type(tensors) == type(
        new_tensors
    ), f""Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}.""
    if isinstance(tensors, (list, tuple)):
        return type(tensors)(nested_concat(
            t, n, padding_index=padding_index)
                             for t, n in zip(tensors, new_tensors))
    elif isinstance(tensors, paddle.Tensor):
        return paddle_pad_and_concatenate(
            tensors, new_tensors, padding_index=padding_index)
    elif isinstance(tensors, np.ndarray):
        return numpy_pad_and_concatenate(
            tensors, new_tensors, padding_index=padding_index)
    else:
        raise TypeError(
            f""Unsupported type for concatenation: got {type(tensors)}"")


        ```
    ###Assistant : 
    Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
    nested list/tuples of tensors.
    
    "
1128,"def kubernetes_manifest():
    

    template = Template(
        (prefect.__module_path__ / ""cli"" / ""templates"" / ""kubernetes.yaml"").read_text()
    )
    manifest = template.substitute(
        {
            ""image_name"": get_prefect_image_name(),
        }
    )
    print(manifest)
","
    Generates a kubernetes manifest for to deploy Orion to a cluster.

    Example:
        $ prefect orion kubernetes-manifest | kubectl apply -f -
    ",21,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def kubernetes_manifest():
    

    template = Template(
        (prefect.__module_path__ / ""cli"" / ""templates"" / ""kubernetes.yaml"").read_text()
    )
    manifest = template.substitute(
        {
            ""image_name"": get_prefect_image_name(),
        }
    )
    print(manifest)

        ```
    ###Assistant : 
    Generates a kubernetes manifest for to deploy Orion to a cluster.

    Example:
        $ prefect orion kubernetes-manifest | kubectl apply -f -
    
    "
1129,"def test_queries_when_requested_project_is_head_of_trace(self, mock_query, mock_querybuilder):
        
        # Case A: Head of trace project
        self.login_as(self.user)
        heart = self.create_project(
            name=""Heart"", slug=""heart"", teams=[self.team], fire_project_created=True
        )
        mock_query.side_effect = [
            {""data"": [{""count()"": 1000}]},
        ]
        mock_querybuilder.side_effect = [
            {
                ""data"": [
                    {
                        ""trace"": ""6503ee33b7bc43aead1facaa625a5dba"",
                        ""id"": ""6ddc83ee612b4e89b95b5278c8fd188f"",
                        ""random_number() AS random_number"": 4255299100,
                        ""is_root"": 1,
                    },
                    {
                        ""trace"": ""6503ee33b7bc43aead1facaa625a5dba"",
                        ""id"": ""0b127a578f8440c793f9ba1de595229f"",
                        ""random_number() AS random_number"": 3976019453,
                        ""is_root"": 1,
                    },
                ]
            },
            {
                ""data"": [
                    {
                        ""project"": self.project.slug,
                        ""project_id"": self.project.id,
                        ""count"": 2,
                        ""root_count"": 2,
                    },
                    {
                        ""project"": heart.slug,
                        ""project_id"": heart.id,
                        ""count"": 1,
                        ""root_count"": 0,
                    },
                ]
            },
        ]
        end_time = timezone.now()
        start_time = end_time - timedelta(hours=1)
        query = ""environment:dev""
        requested_sample_size = 2

        calls = self.generate_fetch_transactions_count_query(
            query, start_time, end_time, requested_sample_size
        )

        snuba_query_random_transactions = random_transactions_snuba_query(
            query, requested_sample_size, start_time, end_time, self.project
        )
        snuba_query_project_stats = project_stats_snuba_query(
            query,
            start_time,
            end_time,
            self.project,
            trace_ids=[""6503ee33b7bc43aead1facaa625a5dba""] * 2,
        )

        with Feature({""organizations:server-side-sampling"": True}):
            response = self.client.get(
                f""{self.endpoint}?sampleSize={requested_sample_size}&query={query}""
            )
            assert response.status_code == 200
            assert mock_query.mock_calls == calls
            assert len(mock_querybuilder.call_args_list) == 2
            self.assert_mocked_query_calls(
                snuba_query_random_transactions, snuba_query_project_stats, mock_querybuilder
            )

            response_data = response.json()
            assert response_data[""projectBreakdown""] == [
                {""project_id"": self.project.id, ""project"": self.project.slug, ""count()"": 2},
                {""project_id"": heart.id, ""project"": heart.slug, ""count()"": 1},
            ]
            assert response_data[""parentProjectBreakdown""] == [
                {""project"": self.project.slug, ""projectId"": self.project.id, ""percentage"": 1.0}
            ]
","
        Case A: Requesting for a project (bar) that is root but is a head of distributed traces
        Example of smart query response (DYNAMIC_SAMPLING_DISTRIBUTION_FETCH_PROJECT_STATS):
        |---------+-------+------|
        | project | count | root |
        |---------+-------+------|
        | bar     | 100   | 100  |
        | heart   | 5     | 0    |
        |---------+-------+------|
        ",47,183,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_queries_when_requested_project_is_head_of_trace(self, mock_query, mock_querybuilder):
        
        # Case A: Head of trace project
        self.login_as(self.user)
        heart = self.create_project(
            name=""Heart"", slug=""heart"", teams=[self.team], fire_project_created=True
        )
        mock_query.side_effect = [
            {""data"": [{""count()"": 1000}]},
        ]
        mock_querybuilder.side_effect = [
            {
                ""data"": [
                    {
                        ""trace"": ""6503ee33b7bc43aead1facaa625a5dba"",
                        ""id"": ""6ddc83ee612b4e89b95b5278c8fd188f"",
                        ""random_number() AS random_number"": 4255299100,
                        ""is_root"": 1,
                    },
                    {
                        ""trace"": ""6503ee33b7bc43aead1facaa625a5dba"",
                        ""id"": ""0b127a578f8440c793f9ba1de595229f"",
                        ""random_number() AS random_number"": 3976019453,
                        ""is_root"": 1,
                    },
                ]
            },
            {
                ""data"": [
                    {
                        ""project"": self.project.slug,
                        ""project_id"": self.project.id,
                        ""count"": 2,
                        ""root_count"": 2,
                    },
                    {
                        ""project"": heart.slug,
                        ""project_id"": heart.id,
                        ""count"": 1,
                        ""root_count"": 0,
                    },
                ]
            },
        ]
        end_time = timezone.now()
        start_time = end_time - timedelta(hours=1)
        query = ""environment:dev""
        requested_sample_size = 2

        calls = self.generate_fetch_transactions_count_query(
            query, start_time, end_time, requested_sample_size
        )

        snuba_query_random_transactions = random_transactions_snuba_query(
            query, requested_sample_size, start_time, end_time, self.project
        )
        snuba_query_project_stats = project_stats_snuba_query(
            query,
            start_time,
            end_time,
            self.project,
            trace_ids=[""6503ee33b7bc43aead1facaa625a5dba""] * 2,
        )

        with Feature({""organizations:server-side-sampling"": True}):
            response = self.client.get(
                f""{self.endpoint}?sampleSize={requested_sample_size}&query={query}""
            )
            assert response.status_code == 200
            assert mock_query.mock_calls == calls
            assert len(mock_querybuilder.call_args_list) == 2
            self.assert_mocked_query_calls(
                snuba_query_random_transactions, snuba_query_project_stats, mock_querybuilder
            )

            response_data = response.json()
            assert response_data[""projectBreakdown""] == [
                {""project_id"": self.project.id, ""project"": self.project.slug, ""count()"": 2},
                {""project_id"": heart.id, ""project"": heart.slug, ""count()"": 1},
            ]
            assert response_data[""parentProjectBreakdown""] == [
                {""project"": self.project.slug, ""projectId"": self.project.id, ""percentage"": 1.0}
            ]

        ```
    ###Assistant : 
        Case A: Requesting for a project (bar) that is root but is a head of distributed traces
        Example of smart query response (DYNAMIC_SAMPLING_DISTRIBUTION_FETCH_PROJECT_STATS):
        |---------+-------+------|
        | project | count | root |
        |---------+-------+------|
        | bar     | 100   | 100  |
        | heart   | 5     | 0    |
        |---------+-------+------|
        
    "
1130,"def validate_leave_allocation_against_leave_application(ledger):
	
	leave_application_records = frappe.db.sql_list(
		,
		(ledger.employee, ledger.leave_type, ledger.from_date, ledger.to_date),
	)

	if leave_application_records:
		frappe.throw(
			_(""Leave allocation {0} is linked with the Leave Application {1}"").format(
				ledger.transaction_name, "", "".join(leave_application_records)
			)
		)

","Checks that leave allocation has no leave application against it
		SELECT transaction_name
		FROM `tabLeave Ledger Entry`
		WHERE
			employee=%s
			AND leave_type=%s
			AND transaction_type='Leave Application'
			AND from_date>=%s
			AND to_date<=%s
	",27,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_leave_allocation_against_leave_application(ledger):
	
	leave_application_records = frappe.db.sql_list(
		,
		(ledger.employee, ledger.leave_type, ledger.from_date, ledger.to_date),
	)

	if leave_application_records:
		frappe.throw(
			_(""Leave allocation {0} is linked with the Leave Application {1}"").format(
				ledger.transaction_name, "", "".join(leave_application_records)
			)
		)


        ```
    ###Assistant : Checks that leave allocation has no leave application against it
		SELECT transaction_name
		FROM `tabLeave Ledger Entry`
		WHERE
			employee=%s
			AND leave_type=%s
			AND transaction_type='Leave Application'
			AND from_date>=%s
			AND to_date<=%s
	
    "
1131,"def dce_rpc_endianess(pkt):
    
    try:
        endianness = pkt.underlayer.endian
    except AttributeError:
        # handle the case where a PNIO class is
        # built without its DCE-RPC under-layer
        # i.e there is no endianness indication
        return ""!""
    if endianness == 0:  # big endian
        return "">""
    elif endianness == 1:  # little endian
        return ""<""
    else:
        return ""!""

",determine the symbol for the endianness of a the DCE/RPC,10,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dce_rpc_endianess(pkt):
    
    try:
        endianness = pkt.underlayer.endian
    except AttributeError:
        # handle the case where a PNIO class is
        # built without its DCE-RPC under-layer
        # i.e there is no endianness indication
        return ""!""
    if endianness == 0:  # big endian
        return "">""
    elif endianness == 1:  # little endian
        return ""<""
    else:
        return ""!""


        ```
    ###Assistant : determine the symbol for the endianness of a the DCE/RPC
    "
1132,"def _get_extra_hosts(self, docker_client) -> Dict[str, str]:
        
        if sys.platform == ""linux"" and (
            # Do not warn if the user has specified a host manually that does not use
            # a local address
            ""PREFECT_API_URL"" not in self.env
            or re.search(
                "".*(localhost)|(127.0.0.1)|(host.docker.internal).*"",
                self.env[""PREFECT_API_URL""],
            )
        ):
            user_version = packaging.version.parse(docker_client.version()[""Version""])
            required_version = packaging.version.parse(""20.10.0"")

            if user_version < required_version:
                warnings.warn(
                    ""`host.docker.internal` could not be automatically resolved to your ""
                    ""local ip address. This feature is not supported on Docker Engine ""
                    f""v{user_version}, upgrade to v{required_version}+ if you ""
                    ""encounter issues.""
                )
                return {}
            else:
                # Compatibility for linux -- https://github.com/docker/cli/issues/2290
                # Only supported by Docker v20.10.0+ which is our minimum recommend version
                return {""host.docker.internal"": ""host-gateway""}
","
        A host.docker.internal -> host-gateway mapping is necessary for communicating
        with the API on Linux machines. Docker Desktop on macOS will automatically
        already have this mapping.
        ",25,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_extra_hosts(self, docker_client) -> Dict[str, str]:
        
        if sys.platform == ""linux"" and (
            # Do not warn if the user has specified a host manually that does not use
            # a local address
            ""PREFECT_API_URL"" not in self.env
            or re.search(
                "".*(localhost)|(127.0.0.1)|(host.docker.internal).*"",
                self.env[""PREFECT_API_URL""],
            )
        ):
            user_version = packaging.version.parse(docker_client.version()[""Version""])
            required_version = packaging.version.parse(""20.10.0"")

            if user_version < required_version:
                warnings.warn(
                    ""`host.docker.internal` could not be automatically resolved to your ""
                    ""local ip address. This feature is not supported on Docker Engine ""
                    f""v{user_version}, upgrade to v{required_version}+ if you ""
                    ""encounter issues.""
                )
                return {}
            else:
                # Compatibility for linux -- https://github.com/docker/cli/issues/2290
                # Only supported by Docker v20.10.0+ which is our minimum recommend version
                return {""host.docker.internal"": ""host-gateway""}

        ```
    ###Assistant : 
        A host.docker.internal -> host-gateway mapping is necessary for communicating
        with the API on Linux machines. Docker Desktop on macOS will automatically
        already have this mapping.
        
    "
1133,"def test_del_store():
    
    with patch(""salt.modules.win_certutil.get_cert_serial"") as cert_serial_mock:
        cmd_mock = MagicMock(
            return_value=(
                ""CertInfo\r\n""
                ""================ Certificate 0 ================\r\n""
                ""Serial Number: 180720d39cd2db3244ba037417241e90\r\n""
                ""OtherStuff""
            )
        )
        cache_mock = MagicMock(return_value=""/tmp/cert.cer"")
        cert_serial_mock.return_value = ""ABCDEF""
        with patch.dict(
            certutil.__salt__, {""cmd.run"": cmd_mock, ""cp.cache_file"": cache_mock}
        ), patch(""os.path.exists"", MagicMock(return_value=True)):
            certutil.del_store(""salt://path/to/file"", ""TrustedPublisher"")
            cmd_mock.assert_called_once_with(
                'certutil.exe -delstore TrustedPublisher ""ABCDEF""'
            )
            cache_mock.assert_called_once_with(""salt://path/to/file"", ""base"")
","
    Test removing a certificate to a specific store
    ",8,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_del_store():
    
    with patch(""salt.modules.win_certutil.get_cert_serial"") as cert_serial_mock:
        cmd_mock = MagicMock(
            return_value=(
                ""CertInfo\r\n""
                ""================ Certificate 0 ================\r\n""
                ""Serial Number: 180720d39cd2db3244ba037417241e90\r\n""
                ""OtherStuff""
            )
        )
        cache_mock = MagicMock(return_value=""/tmp/cert.cer"")
        cert_serial_mock.return_value = ""ABCDEF""
        with patch.dict(
            certutil.__salt__, {""cmd.run"": cmd_mock, ""cp.cache_file"": cache_mock}
        ), patch(""os.path.exists"", MagicMock(return_value=True)):
            certutil.del_store(""salt://path/to/file"", ""TrustedPublisher"")
            cmd_mock.assert_called_once_with(
                'certutil.exe -delstore TrustedPublisher ""ABCDEF""'
            )
            cache_mock.assert_called_once_with(""salt://path/to/file"", ""base"")

        ```
    ###Assistant : 
    Test removing a certificate to a specific store
    
    "
1134,"def _select_backend(config):
    
    backend_arg = config.getoption('--qute-backend')
    backend_env = os.environ.get('QUTE_TESTS_BACKEND')

    backend = backend_arg or backend_env or _auto_select_backend()

    # Fail early if selected backend is not available
    # pylint: disable=unused-import
    if backend == 'webkit':
        import PyQt5.QtWebKitWidgets
    elif backend == 'webengine':
        import PyQt5.QtWebEngineWidgets
    else:
        raise utils.Unreachable(backend)

    return backend

","Select the backend for running tests.

    The backend is auto-selected in the following manner:
    1. Use QtWebKit if available
    2. Otherwise use QtWebEngine as a fallback

    Auto-selection is overridden by either passing a backend via
    `--qute-backend=<backend>` or setting the environment variable
    `QUTE_TESTS_BACKEND=<backend>`.

    Args:
        config: pytest config

    Raises:
        ImportError if the selected backend is not available.

    Returns:
        The selected backend as a string (e.g. 'webkit').
    ",64,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _select_backend(config):
    
    backend_arg = config.getoption('--qute-backend')
    backend_env = os.environ.get('QUTE_TESTS_BACKEND')

    backend = backend_arg or backend_env or _auto_select_backend()

    # Fail early if selected backend is not available
    # pylint: disable=unused-import
    if backend == 'webkit':
        import PyQt5.QtWebKitWidgets
    elif backend == 'webengine':
        import PyQt5.QtWebEngineWidgets
    else:
        raise utils.Unreachable(backend)

    return backend


        ```
    ###Assistant : Select the backend for running tests.

    The backend is auto-selected in the following manner:
    1. Use QtWebKit if available
    2. Otherwise use QtWebEngine as a fallback

    Auto-selection is overridden by either passing a backend via
    `--qute-backend=<backend>` or setting the environment variable
    `QUTE_TESTS_BACKEND=<backend>`.

    Args:
        config: pytest config

    Raises:
        ImportError if the selected backend is not available.

    Returns:
        The selected backend as a string (e.g. 'webkit').
    
    "
1135,"def equals(self, other):
        
        from sympy.logic.inference import satisfiable
        from sympy.core.relational import Relational

        if self.has(Relational) or other.has(Relational):
            raise NotImplementedError('handling of relationals')
        return self.atoms() == other.atoms() and \
            not satisfiable(Not(Equivalent(self, other)))
","
        Returns True if the given formulas have the same truth table.
        For two formulas to be equal they must have the same literals.

        Examples
        ========

        >>> from sympy.abc import A, B, C
        >>> from sympy import And, Or, Not
        >>> (A >> B).equals(~B >> ~A)
        True
        >>> Not(And(A, B, C)).equals(And(Not(A), Not(B), Not(C)))
        False
        >>> Not(And(A, Not(A))).equals(Or(B, Not(B)))
        False

        ",58,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def equals(self, other):
        
        from sympy.logic.inference import satisfiable
        from sympy.core.relational import Relational

        if self.has(Relational) or other.has(Relational):
            raise NotImplementedError('handling of relationals')
        return self.atoms() == other.atoms() and \
            not satisfiable(Not(Equivalent(self, other)))

        ```
    ###Assistant : 
        Returns True if the given formulas have the same truth table.
        For two formulas to be equal they must have the same literals.

        Examples
        ========

        >>> from sympy.abc import A, B, C
        >>> from sympy import And, Or, Not
        >>> (A >> B).equals(~B >> ~A)
        True
        >>> Not(And(A, B, C)).equals(And(Not(A), Not(B), Not(C)))
        False
        >>> Not(And(A, Not(A))).equals(Or(B, Not(B)))
        False

        
    "
1136,"def get_latest_stock_qty(item_code, warehouse=None):
	values, condition = [item_code], """"
	if warehouse:
		lft, rgt, is_group = frappe.db.get_value(""Warehouse"", warehouse, [""lft"", ""rgt"", ""is_group""])

		if is_group:
			values.extend([lft, rgt])
			condition += ""and exists (\
				select name from `tabWarehouse` wh where wh.name = tabBin.warehouse\
				and wh.lft >= %s and wh.rgt <= %s)""

		else:
			values.append(warehouse)
			condition += "" AND warehouse = %s""

	actual_qty = frappe.db.sql(
		.format(
			condition
		),
		values,
	)[0][0]

	return actual_qty

","select sum(actual_qty) from tabBin
		where item_code=%s {0}",7,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_latest_stock_qty(item_code, warehouse=None):
	values, condition = [item_code], """"
	if warehouse:
		lft, rgt, is_group = frappe.db.get_value(""Warehouse"", warehouse, [""lft"", ""rgt"", ""is_group""])

		if is_group:
			values.extend([lft, rgt])
			condition += ""and exists (\
				select name from `tabWarehouse` wh where wh.name = tabBin.warehouse\
				and wh.lft >= %s and wh.rgt <= %s)""

		else:
			values.append(warehouse)
			condition += "" AND warehouse = %s""

	actual_qty = frappe.db.sql(
		.format(
			condition
		),
		values,
	)[0][0]

	return actual_qty


        ```
    ###Assistant : select sum(actual_qty) from tabBin
		where item_code=%s {0}
    "
1137,"def get_feature_names_out(self, input_features=None):
        
        input_features = _check_feature_names_in(
            self, input_features, generate_names=True
        )
        est_name = self.__class__.__name__.lower()

        names_list = [f""{est_name}_{name}_sqrt"" for name in input_features]

        for j in range(1, self.sample_steps):
            cos_names = [f""{est_name}_{name}_cos{j}"" for name in input_features]
            sin_names = [f""{est_name}_{name}_sin{j}"" for name in input_features]
            names_list.extend(cos_names + sin_names)

        return np.asarray(names_list, dtype=object)
","Get output feature names for transformation.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Only used to validate feature names with the names seen in :meth:`fit`.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Transformed feature names.
        ",39,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_feature_names_out(self, input_features=None):
        
        input_features = _check_feature_names_in(
            self, input_features, generate_names=True
        )
        est_name = self.__class__.__name__.lower()

        names_list = [f""{est_name}_{name}_sqrt"" for name in input_features]

        for j in range(1, self.sample_steps):
            cos_names = [f""{est_name}_{name}_cos{j}"" for name in input_features]
            sin_names = [f""{est_name}_{name}_sin{j}"" for name in input_features]
            names_list.extend(cos_names + sin_names)

        return np.asarray(names_list, dtype=object)

        ```
    ###Assistant : Get output feature names for transformation.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Only used to validate feature names with the names seen in :meth:`fit`.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Transformed feature names.
        
    "
1138,"def test_render_valid_image_as_context_variable(self):
        
        context = {""image"": self.image, ""image_node"": ""fake value""}
        node = ImageNode(Variable(""image""), ""original"", ""image_node"")

        rendered = node.render(context)

        self.assertEqual(rendered, """")
        self.assertIsInstance(context[""image_node""], Rendition)
","
        Tests that an ImageNode with a valid image and a context variable name
        renders an empty string and puts a rendition in the context variable
        ",25,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_render_valid_image_as_context_variable(self):
        
        context = {""image"": self.image, ""image_node"": ""fake value""}
        node = ImageNode(Variable(""image""), ""original"", ""image_node"")

        rendered = node.render(context)

        self.assertEqual(rendered, """")
        self.assertIsInstance(context[""image_node""], Rendition)

        ```
    ###Assistant : 
        Tests that an ImageNode with a valid image and a context variable name
        renders an empty string and puts a rendition in the context variable
        
    "
1139,"def dup_cauchy_lower_bound(f, K):
    
    g = dup_reverse(f)
    if len(g) < 2:
        raise PolynomialError('Polynomial has no non-zero roots.')
    if K.is_ZZ:
        K = K.get_field()
    b = dup_cauchy_upper_bound(g, K)
    return K.one / b
","Compute the Cauchy lower bound on the absolute value of all non-zero
       roots of f, real or complex.",18,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dup_cauchy_lower_bound(f, K):
    
    g = dup_reverse(f)
    if len(g) < 2:
        raise PolynomialError('Polynomial has no non-zero roots.')
    if K.is_ZZ:
        K = K.get_field()
    b = dup_cauchy_upper_bound(g, K)
    return K.one / b

        ```
    ###Assistant : Compute the Cauchy lower bound on the absolute value of all non-zero
       roots of f, real or complex.
    "
1140,"def call_deploy(cls, fname, col_partitions, storage_options, **kwargs):
        
        from pyarrow.parquet import ParquetFile
        from modin.core.storage_formats.pandas.parsers import ParquetFileToRead

        # If we don't have any columns to read, we should just return an empty
        # set of references.
        if len(col_partitions) == 0:
            return []

        filesystem, parquet_files = cls.get_fsspec_files(fname, storage_options)

        row_groups_per_file = []
        num_row_groups = 0
        # Count up the total number of row groups across all files and
        # keep track of row groups per file to use later.
        for file in parquet_files:
            with filesystem.open(file) as f:
                row_groups = ParquetFile(f).num_row_groups
                row_groups_per_file.append(row_groups)
                num_row_groups += row_groups

        # step determines how many row groups are going to be in a partition
        step = compute_chunksize(
            num_row_groups,
            NPartitions.get(),
            min_block_size=1,
        )
        current_partition_size = 0
        file_index = 0
        partition_files = []  # 2D array - each element contains list of chunks to read
        row_groups_used_in_current_file = 0
        total_row_groups_added = 0
        # On each iteration, we add a chunk of one file. That will
        # take us either to the end of a partition, or to the end
        # of a file.
        while total_row_groups_added < num_row_groups:
            if current_partition_size == 0:
                partition_files.append([])
            partition_file = partition_files[-1]
            file_path = parquet_files[file_index]
            row_group_start = row_groups_used_in_current_file
            row_groups_left_in_file = (
                row_groups_per_file[file_index] - row_groups_used_in_current_file
            )
            row_groups_left_for_this_partition = step - current_partition_size
            if row_groups_left_for_this_partition <= row_groups_left_in_file:
                # File has at least what we need to finish partition
                # So finish this partition and start a new one.
                num_row_groups_to_add = row_groups_left_for_this_partition
                current_partition_size = 0
            else:
                # File doesn't have enough to complete this partition. Add
                # it into current partition and go to next file.
                num_row_groups_to_add = row_groups_left_in_file
                current_partition_size += num_row_groups_to_add
            if num_row_groups_to_add == row_groups_left_in_file:
                file_index += 1
                row_groups_used_in_current_file = 0
            else:
                row_groups_used_in_current_file += num_row_groups_to_add
            partition_file.append(
                ParquetFileToRead(
                    file_path, row_group_start, row_group_start + num_row_groups_to_add
                )
            )
            total_row_groups_added += num_row_groups_to_add

        assert (
            total_row_groups_added == num_row_groups
        ), ""row groups added does not match total num of row groups across parquet files""

        all_partitions = []
        for files_to_read in partition_files:
            all_partitions.append(
                [
                    cls.deploy(
                        cls.parse,
                        files_for_parser=files_to_read,
                        columns=cols,
                        num_returns=3,
                        storage_options=storage_options,
                        **kwargs,
                    )
                    for cols in col_partitions
                ]
            )
        return all_partitions
","
        Deploy remote tasks to the workers with passed parameters.

        Parameters
        ----------
        fname : str, path object or file-like object
            Name of the file to read.
        col_partitions : list
            List of arrays with columns names that should be read
            by each partition.
        storage_options : dict
            Parameters for specific storage engine.
        **kwargs : dict
            Parameters of deploying read_* function.

        Returns
        -------
        List
            Array with references to the task deploy result for each partition.
        ",71,327,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_deploy(cls, fname, col_partitions, storage_options, **kwargs):
        
        from pyarrow.parquet import ParquetFile
        from modin.core.storage_formats.pandas.parsers import ParquetFileToRead

        # If we don't have any columns to read, we should just return an empty
        # set of references.
        if len(col_partitions) == 0:
            return []

        filesystem, parquet_files = cls.get_fsspec_files(fname, storage_options)

        row_groups_per_file = []
        num_row_groups = 0
        # Count up the total number of row groups across all files and
        # keep track of row groups per file to use later.
        for file in parquet_files:
            with filesystem.open(file) as f:
                row_groups = ParquetFile(f).num_row_groups
                row_groups_per_file.append(row_groups)
                num_row_groups += row_groups

        # step determines how many row groups are going to be in a partition
        step = compute_chunksize(
            num_row_groups,
            NPartitions.get(),
            min_block_size=1,
        )
        current_partition_size = 0
        file_index = 0
        partition_files = []  # 2D array - each element contains list of chunks to read
        row_groups_used_in_current_file = 0
        total_row_groups_added = 0
        # On each iteration, we add a chunk of one file. That will
        # take us either to the end of a partition, or to the end
        # of a file.
        while total_row_groups_added < num_row_groups:
            if current_partition_size == 0:
                partition_files.append([])
            partition_file = partition_files[-1]
            file_path = parquet_files[file_index]
            row_group_start = row_groups_used_in_current_file
            row_groups_left_in_file = (
                row_groups_per_file[file_index] - row_groups_used_in_current_file
            )
            row_groups_left_for_this_partition = step - current_partition_size
            if row_groups_left_for_this_partition <= row_groups_left_in_file:
                # File has at least what we need to finish partition
                # So finish this partition and start a new one.
                num_row_groups_to_add = row_groups_left_for_this_partition
                current_partition_size = 0
            else:
                # File doesn't have enough to complete this partition. Add
                # it into current partition and go to next file.
                num_row_groups_to_add = row_groups_left_in_file
                current_partition_size += num_row_groups_to_add
            if num_row_groups_to_add == row_groups_left_in_file:
                file_index += 1
                row_groups_used_in_current_file = 0
            else:
                row_groups_used_in_current_file += num_row_groups_to_add
            partition_file.append(
                ParquetFileToRead(
                    file_path, row_group_start, row_group_start + num_row_groups_to_add
                )
            )
            total_row_groups_added += num_row_groups_to_add

        assert (
            total_row_groups_added == num_row_groups
        ), ""row groups added does not match total num of row groups across parquet files""

        all_partitions = []
        for files_to_read in partition_files:
            all_partitions.append(
                [
                    cls.deploy(
                        cls.parse,
                        files_for_parser=files_to_read,
                        columns=cols,
                        num_returns=3,
                        storage_options=storage_options,
                        **kwargs,
                    )
                    for cols in col_partitions
                ]
            )
        return all_partitions

        ```
    ###Assistant : 
        Deploy remote tasks to the workers with passed parameters.

        Parameters
        ----------
        fname : str, path object or file-like object
            Name of the file to read.
        col_partitions : list
            List of arrays with columns names that should be read
            by each partition.
        storage_options : dict
            Parameters for specific storage engine.
        **kwargs : dict
            Parameters of deploying read_* function.

        Returns
        -------
        List
            Array with references to the task deploy result for each partition.
        
    "
1141,"def get_connection(self):
        
        if self.lib == _PSYCOPG_LIB_NAME:
            import psycopg2

            return psycopg2.connect(*self.args, **self.kwargs)
        if self.lib == _SQLALCHEMY_LIB_NAME:
            from sqlalchemy import create_engine

            return create_engine(*self.args, **self.kwargs).connect()

        raise UnsupportedDatabaseException(""Unsupported database library"")
","
        Make the database connection and get it.

        For psycopg2, pass all arguments to psycopg2.connect() and return the
        result of psycopg2.connect(). For sqlalchemy, pass all arguments to
        sqlalchemy.create_engine() and return the result of calling connect()
        on the engine.

        Returns
        -------
        Any
            The open database connection.
        ",44,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_connection(self):
        
        if self.lib == _PSYCOPG_LIB_NAME:
            import psycopg2

            return psycopg2.connect(*self.args, **self.kwargs)
        if self.lib == _SQLALCHEMY_LIB_NAME:
            from sqlalchemy import create_engine

            return create_engine(*self.args, **self.kwargs).connect()

        raise UnsupportedDatabaseException(""Unsupported database library"")

        ```
    ###Assistant : 
        Make the database connection and get it.

        For psycopg2, pass all arguments to psycopg2.connect() and return the
        result of psycopg2.connect(). For sqlalchemy, pass all arguments to
        sqlalchemy.create_engine() and return the result of calling connect()
        on the engine.

        Returns
        -------
        Any
            The open database connection.
        
    "
1142,"def argmax(x, axis=-1):
    
    return tf.argmax(x, axis)


@keras_export(""keras.backend.argmin"")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs","Returns the index of the maximum value along an axis.

    Args:
        x: Tensor or variable.
        axis: axis along which to perform the reduction.

    Returns:
        A tensor.
    ",26,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def argmax(x, axis=-1):
    
    return tf.argmax(x, axis)


@keras_export(""keras.backend.argmin"")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
        ```
    ###Assistant : Returns the index of the maximum value along an axis.

    Args:
        x: Tensor or variable.
        axis: axis along which to perform the reduction.

    Returns:
        A tensor.
    
    "
1143,"def print_rules(self) -> Iterator[str]:
        
        yield from self._defined_facts_lines()
        yield ''
        yield ''
        yield from self._full_implications_lines()
        yield ''
        yield ''
        yield from self._prereq_lines()
        yield ''
        yield ''
        yield from self._beta_rules_lines()
        yield ''
        yield ''
        yield ""generated_assumptions = {'defined_facts': defined_facts, 'full_implications': full_implications,""
        yield ""               'prereq': prereq, 'beta_rules': beta_rules, 'beta_triggers': beta_triggers}""
        yield ''
        yield ''

", Returns a generator with lines to represent the facts and rules ,11,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_rules(self) -> Iterator[str]:
        
        yield from self._defined_facts_lines()
        yield ''
        yield ''
        yield from self._full_implications_lines()
        yield ''
        yield ''
        yield from self._prereq_lines()
        yield ''
        yield ''
        yield from self._beta_rules_lines()
        yield ''
        yield ''
        yield ""generated_assumptions = {'defined_facts': defined_facts, 'full_implications': full_implications,""
        yield ""               'prereq': prereq, 'beta_rules': beta_rules, 'beta_triggers': beta_triggers}""
        yield ''
        yield ''


        ```
    ###Assistant :  Returns a generator with lines to represent the facts and rules 
    "
1144,"def ensure_string(self, option, default=None):
        
        self._ensure_stringlike(option, ""string"", default)
","Ensure that 'option' is a string; if not defined, set it to
        'default'.
        ",13,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ensure_string(self, option, default=None):
        
        self._ensure_stringlike(option, ""string"", default)

        ```
    ###Assistant : Ensure that 'option' is a string; if not defined, set it to
        'default'.
        
    "
1145,"def update_ema(target_params, source_params, rate=0.99):
    
    for targ, src in zip(target_params, source_params):
        targ.detach().mul_(rate).add_(src, alpha=1 - rate)

","
    Update target parameters to be closer to those of source parameters using
    an exponential moving average.

    :param target_params: the target parameter sequence.
    :param source_params: the source parameter sequence.
    :param rate: the EMA rate (closer to 1 means slower).
    ",38,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_ema(target_params, source_params, rate=0.99):
    
    for targ, src in zip(target_params, source_params):
        targ.detach().mul_(rate).add_(src, alpha=1 - rate)


        ```
    ###Assistant : 
    Update target parameters to be closer to those of source parameters using
    an exponential moving average.

    :param target_params: the target parameter sequence.
    :param source_params: the source parameter sequence.
    :param rate: the EMA rate (closer to 1 means slower).
    
    "
1146,"def _android_folder() -> str | None:
    
    try:
        # First try to get path to android app via pyjnius
        from jnius import autoclass

        Context = autoclass(""android.content.Context"")  # noqa: N806
        result: str | None = Context.getFilesDir().getParentFile().getAbsolutePath()
    except Exception:
        # if fails find an android folder looking path on the sys.path
        pattern = re.compile(r""/data/(data|user/\d+)/(.+)/files"")
        for path in sys.path:
            if pattern.match(path):
                result = path.split(""/files"")[0]
                break
        else:
            result = None
    return result


@lru_cache(maxsize=1)",:return: base folder for the Android OS or None if cannot be found,13,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _android_folder() -> str | None:
    
    try:
        # First try to get path to android app via pyjnius
        from jnius import autoclass

        Context = autoclass(""android.content.Context"")  # noqa: N806
        result: str | None = Context.getFilesDir().getParentFile().getAbsolutePath()
    except Exception:
        # if fails find an android folder looking path on the sys.path
        pattern = re.compile(r""/data/(data|user/\d+)/(.+)/files"")
        for path in sys.path:
            if pattern.match(path):
                result = path.split(""/files"")[0]
                break
        else:
            result = None
    return result


@lru_cache(maxsize=1)
        ```
    ###Assistant : :return: base folder for the Android OS or None if cannot be found
    "
1147,"def de_bruijn(charset, n, maxlen):
        # type: (str, int, int) -> str
        
        k = len(charset)
        a = [0] * k * n
        sequence = []  # type: List[str]
","
        Generate the De Bruijn Sequence up to `maxlen` characters
        for the charset `charset` and subsequences of length `n`.
        Algorithm modified from wikipedia
        https://en.wikipedia.org/wiki/De_Bruijn_sequence
        ",23,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def de_bruijn(charset, n, maxlen):
        # type: (str, int, int) -> str
        
        k = len(charset)
        a = [0] * k * n
        sequence = []  # type: List[str]

        ```
    ###Assistant : 
        Generate the De Bruijn Sequence up to `maxlen` characters
        for the charset `charset` and subsequences of length `n`.
        Algorithm modified from wikipedia
        https://en.wikipedia.org/wiki/De_Bruijn_sequence
        
    "
1148,"def _add_option_refresh(self) -> None:
        
        logger.debug(""Adding refresh option"")
        btnrefresh = ttk.Button(self.optsframe,
                                image=get_images().icons[""reload""],
                                command=lambda x=""update"": preview_trigger().set(x))  # type:ignore
        btnrefresh.pack(padx=2, side=tk.RIGHT)
        Tooltip(btnrefresh,
                text=_(""Preview updates at every model save. Click to refresh now.""),
                wrap_length=200)
        logger.debug(""Added refresh option"")
", Add refresh button to refresh preview immediately ,7,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _add_option_refresh(self) -> None:
        
        logger.debug(""Adding refresh option"")
        btnrefresh = ttk.Button(self.optsframe,
                                image=get_images().icons[""reload""],
                                command=lambda x=""update"": preview_trigger().set(x))  # type:ignore
        btnrefresh.pack(padx=2, side=tk.RIGHT)
        Tooltip(btnrefresh,
                text=_(""Preview updates at every model save. Click to refresh now.""),
                wrap_length=200)
        logger.debug(""Added refresh option"")

        ```
    ###Assistant :  Add refresh button to refresh preview immediately 
    "
1149,"def close(self):
        
        try:
            if hasattr(self, ""_close__fp""):
                self._close__fp()
            if self.fp:
                self.fp.close()
            self.fp = None
        except Exception as msg:
            logger.debug(""Error closing: %s"", msg)

        if getattr(self, ""map"", None):
            self.map = None

        # Instead of simply setting to None, we're setting up a
        # deferred error that will better explain that the core image
        # object is gone.
        self.im = DeferredError(ValueError(""Operation on closed image""))
","
        Closes the file pointer, if possible.

        This operation will destroy the image core and release its memory.
        The image data will be unusable afterward.

        This function is required to close images that have multiple frames or
        have not had their file read and closed by the
        :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for
        more information.
        ",53,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def close(self):
        
        try:
            if hasattr(self, ""_close__fp""):
                self._close__fp()
            if self.fp:
                self.fp.close()
            self.fp = None
        except Exception as msg:
            logger.debug(""Error closing: %s"", msg)

        if getattr(self, ""map"", None):
            self.map = None

        # Instead of simply setting to None, we're setting up a
        # deferred error that will better explain that the core image
        # object is gone.
        self.im = DeferredError(ValueError(""Operation on closed image""))

        ```
    ###Assistant : 
        Closes the file pointer, if possible.

        This operation will destroy the image core and release its memory.
        The image data will be unusable afterward.

        This function is required to close images that have multiple frames or
        have not had their file read and closed by the
        :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for
        more information.
        
    "
1150,"def diop_general_sum_of_squares(eq, limit=1):
    r
    var, coeff, diop_type = classify_diop(eq, _dict=False)

    if diop_type == GeneralSumOfSquares.name:
        return set(GeneralSumOfSquares(eq).solve(limit=limit))

","
    Solves the equation `x_{1}^2 + x_{2}^2 + . . . + x_{n}^2 - k = 0`.

    Returns at most ``limit`` number of solutions.

    Usage
    =====

    ``general_sum_of_squares(eq, limit)`` : Here ``eq`` is an expression which
    is assumed to be zero. Also, ``eq`` should be in the form,
    `x_{1}^2 + x_{2}^2 + . . . + x_{n}^2 - k = 0`.

    Details
    =======

    When `n = 3` if `k = 4^a(8m + 7)` for some `a, m \in Z` then there will be
    no solutions. Refer to [1]_ for more details.

    Examples
    ========

    >>> from sympy.solvers.diophantine.diophantine import diop_general_sum_of_squares
    >>> from sympy.abc import a, b, c, d, e
    >>> diop_general_sum_of_squares(a**2 + b**2 + c**2 + d**2 + e**2 - 2345)
    {(15, 22, 22, 24, 24)}

    Reference
    =========

    .. [1] Representing an integer as a sum of three squares, [online],
        Available:
        http://www.proofwiki.org/wiki/Integer_as_Sum_of_Three_Squares
    ",138,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def diop_general_sum_of_squares(eq, limit=1):
    r
    var, coeff, diop_type = classify_diop(eq, _dict=False)

    if diop_type == GeneralSumOfSquares.name:
        return set(GeneralSumOfSquares(eq).solve(limit=limit))


        ```
    ###Assistant : 
    Solves the equation `x_{1}^2 + x_{2}^2 + . . . + x_{n}^2 - k = 0`.

    Returns at most ``limit`` number of solutions.

    Usage
    =====

    ``general_sum_of_squares(eq, limit)`` : Here ``eq`` is an expression which
    is assumed to be zero. Also, ``eq`` should be in the form,
    `x_{1}^2 + x_{2}^2 + . . . + x_{n}^2 - k = 0`.

    Details
    =======

    When `n = 3` if `k = 4^a(8m + 7)` for some `a, m \in Z` then there will be
    no solutions. Refer to [1]_ for more details.

    Examples
    ========

    >>> from sympy.solvers.diophantine.diophantine import diop_general_sum_of_squares
    >>> from sympy.abc import a, b, c, d, e
    >>> diop_general_sum_of_squares(a**2 + b**2 + c**2 + d**2 + e**2 - 2345)
    {(15, 22, 22, 24, 24)}

    Reference
    =========

    .. [1] Representing an integer as a sum of three squares, [online],
        Available:
        http://www.proofwiki.org/wiki/Integer_as_Sum_of_Three_Squares
    
    "
1151,"def resolved_combinations(self) -> Tuple[Tuple[ChallengeBody, ...], ...]:
        
        warnings.warn(
            ""acme.messages.Authorization.resolved_combinations is deprecated and will be ""
            ""removed in a future release."", DeprecationWarning)
        return tuple(tuple(self.challenges[idx] for idx in combo)
                     for combo in self.combinations)  # pylint: disable=not-an-iterable


@Directory.register","Combinations with challenges instead of indices.

        .. deprecated: 1.30.0

        ",9,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resolved_combinations(self) -> Tuple[Tuple[ChallengeBody, ...], ...]:
        
        warnings.warn(
            ""acme.messages.Authorization.resolved_combinations is deprecated and will be ""
            ""removed in a future release."", DeprecationWarning)
        return tuple(tuple(self.challenges[idx] for idx in combo)
                     for combo in self.combinations)  # pylint: disable=not-an-iterable


@Directory.register
        ```
    ###Assistant : Combinations with challenges instead of indices.

        .. deprecated: 1.30.0

        
    "
1152,"def test_tweedie_log_identity_consistency(p):
    
    half_tweedie_log = HalfTweedieLoss(power=p)
    half_tweedie_identity = HalfTweedieLossIdentity(power=p)
    n_samples = 10
    y_true, raw_prediction = random_y_true_raw_prediction(
        loss=half_tweedie_log, n_samples=n_samples, seed=42
    )
    y_pred = half_tweedie_log.link.inverse(raw_prediction)  # exp(raw_prediction)

    # Let's compare the loss values, up to some constant term that is dropped
    # in HalfTweedieLoss but not in HalfTweedieLossIdentity.
    loss_log = half_tweedie_log.loss(
        y_true=y_true, raw_prediction=raw_prediction
    ) + half_tweedie_log.constant_to_optimal_zero(y_true)
    loss_identity = half_tweedie_identity.loss(
        y_true=y_true, raw_prediction=y_pred
    ) + half_tweedie_identity.constant_to_optimal_zero(y_true)
    # Note that HalfTweedieLoss ignores different constant terms than
    # HalfTweedieLossIdentity. Constant terms means terms not depending on
    # raw_prediction. By adding these terms, `constant_to_optimal_zero`, both losses
    # give the same values.
    assert_allclose(loss_log, loss_identity)

    # For gradients and hessians, the constant terms do not matter. We have, however,
    # to account for the chain rule, i.e. with x=raw_prediction
    #     gradient_log(x) = d/dx loss_log(x)
    #                     = d/dx loss_identity(exp(x))
    #                     = exp(x) * gradient_identity(exp(x))
    # Similarly,
    #     hessian_log(x) = exp(x) * gradient_identity(exp(x))
    #                    + exp(x)**2 * hessian_identity(x)
    gradient_log, hessian_log = half_tweedie_log.gradient_hessian(
        y_true=y_true, raw_prediction=raw_prediction
    )
    gradient_identity, hessian_identity = half_tweedie_identity.gradient_hessian(
        y_true=y_true, raw_prediction=y_pred
    )
    assert_allclose(gradient_log, y_pred * gradient_identity)
    assert_allclose(
        hessian_log, y_pred * gradient_identity + y_pred**2 * hessian_identity
    )
",Test for identical losses when only the link function is different.,11,174,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_tweedie_log_identity_consistency(p):
    
    half_tweedie_log = HalfTweedieLoss(power=p)
    half_tweedie_identity = HalfTweedieLossIdentity(power=p)
    n_samples = 10
    y_true, raw_prediction = random_y_true_raw_prediction(
        loss=half_tweedie_log, n_samples=n_samples, seed=42
    )
    y_pred = half_tweedie_log.link.inverse(raw_prediction)  # exp(raw_prediction)

    # Let's compare the loss values, up to some constant term that is dropped
    # in HalfTweedieLoss but not in HalfTweedieLossIdentity.
    loss_log = half_tweedie_log.loss(
        y_true=y_true, raw_prediction=raw_prediction
    ) + half_tweedie_log.constant_to_optimal_zero(y_true)
    loss_identity = half_tweedie_identity.loss(
        y_true=y_true, raw_prediction=y_pred
    ) + half_tweedie_identity.constant_to_optimal_zero(y_true)
    # Note that HalfTweedieLoss ignores different constant terms than
    # HalfTweedieLossIdentity. Constant terms means terms not depending on
    # raw_prediction. By adding these terms, `constant_to_optimal_zero`, both losses
    # give the same values.
    assert_allclose(loss_log, loss_identity)

    # For gradients and hessians, the constant terms do not matter. We have, however,
    # to account for the chain rule, i.e. with x=raw_prediction
    #     gradient_log(x) = d/dx loss_log(x)
    #                     = d/dx loss_identity(exp(x))
    #                     = exp(x) * gradient_identity(exp(x))
    # Similarly,
    #     hessian_log(x) = exp(x) * gradient_identity(exp(x))
    #                    + exp(x)**2 * hessian_identity(x)
    gradient_log, hessian_log = half_tweedie_log.gradient_hessian(
        y_true=y_true, raw_prediction=raw_prediction
    )
    gradient_identity, hessian_identity = half_tweedie_identity.gradient_hessian(
        y_true=y_true, raw_prediction=y_pred
    )
    assert_allclose(gradient_log, y_pred * gradient_identity)
    assert_allclose(
        hessian_log, y_pred * gradient_identity + y_pred**2 * hessian_identity
    )

        ```
    ###Assistant : Test for identical losses when only the link function is different.
    "
1153,"def laplace_transform(f, t, s, legacy_matrix=True, **hints):
    r

    debug('\n***** laplace_transform(%s, %s, %s)'%(f, t, s))

    if isinstance(f, MatrixBase) and hasattr(f, 'applyfunc'):

        conds = not hints.get('noconds', False)

        if conds and legacy_matrix:
            SymPyDeprecationWarning(
                feature=""laplace_transform of a Matrix with noconds=False (default)"",
                useinstead=""the option legacy_matrix=False to get the new behaviour"",
                issue=21504,
                deprecated_since_version=""1.9""
            ).warn()
            return f.applyfunc(lambda fij: laplace_transform(fij, t, s, **hints))
        else:
            elements_trans = [laplace_transform(fij, t, s, **hints) for fij in f]
            if conds:
                elements, avals, conditions = zip(*elements_trans)
                f_laplace = type(f)(*f.shape, elements)
                return f_laplace, Max(*avals), And(*conditions)
            else:
                return type(f)(*f.shape, elements_trans)

    return LaplaceTransform(f, t, s).doit(**hints)


@_noconds_(True)","
    Compute the Laplace Transform `F(s)` of `f(t)`,

    .. math :: F(s) = \int_{0^{-}}^\infty e^{-st} f(t) \mathrm{d}t.

    Explanation
    ===========

    For all sensible functions, this converges absolutely in a
    half-plane

    .. math :: a < \operatorname{Re}(s)

    This function returns ``(F, a, cond)`` where ``F`` is the Laplace
    transform of ``f``, `a` is the half-plane of convergence, and `cond` are
    auxiliary convergence conditions.

    The implementation is rule-based, and if you are interested in which
    rules are applied, and whether integration is attemped, you can switch
    debug information on by setting ``sympy.SYMPY_DEBUG=True``.

    The lower bound is `0-`, meaning that this bound should be approached
    from the lower side. This is only necessary if distributions are involved.
    At present, it is only done if `f(t)` contains ``DiracDelta``, in which
    case the Laplace transform is computed implicitly as

    .. math :: F(s) = \lim_{\tau\to 0^{-}} \int_{\tau}^\infty e^{-st} f(t) \mathrm{d}t

    by applying rules.

    If the integral cannot be fully computed in closed form, this function
    returns an unevaluated :class:`LaplaceTransform` object.

    For a description of possible hints, refer to the docstring of
    :func:`sympy.integrals.transforms.IntegralTransform.doit`. If ``noconds=True``,
    only `F` will be returned (i.e. not ``cond``, and also not the plane ``a``).

    .. deprecated:: 1.9
        Legacy behavior for matrices where ``laplace_transform`` with
        ``noconds=False`` (the default) returns a Matrix whose elements are
        tuples. The behavior of ``laplace_transform`` for matrices will change
        in a future release of SymPy to return a tuple of the transformed
        Matrix and the convergence conditions for the matrix as a whole. Use
        ``legacy_matrix=False`` to enable the new behavior.

    Examples
    ========

    >>> from sympy import DiracDelta, exp, laplace_transform
    >>> from sympy.abc import t, s, a
    >>> laplace_transform(t**4, t, s)
    (24/s**5, 0, True)
    >>> laplace_transform(t**a, t, s)
    (gamma(a + 1)/(s*s**a), 0, re(a) > -1)
    >>> laplace_transform(DiracDelta(t)-a*exp(-a*t),t,s)
    (s/(a + s), Max(0, -a), True)

    See Also
    ========

    inverse_laplace_transform, mellin_transform, fourier_transform
    hankel_transform, inverse_hankel_transform

    ",300,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def laplace_transform(f, t, s, legacy_matrix=True, **hints):
    r

    debug('\n***** laplace_transform(%s, %s, %s)'%(f, t, s))

    if isinstance(f, MatrixBase) and hasattr(f, 'applyfunc'):

        conds = not hints.get('noconds', False)

        if conds and legacy_matrix:
            SymPyDeprecationWarning(
                feature=""laplace_transform of a Matrix with noconds=False (default)"",
                useinstead=""the option legacy_matrix=False to get the new behaviour"",
                issue=21504,
                deprecated_since_version=""1.9""
            ).warn()
            return f.applyfunc(lambda fij: laplace_transform(fij, t, s, **hints))
        else:
            elements_trans = [laplace_transform(fij, t, s, **hints) for fij in f]
            if conds:
                elements, avals, conditions = zip(*elements_trans)
                f_laplace = type(f)(*f.shape, elements)
                return f_laplace, Max(*avals), And(*conditions)
            else:
                return type(f)(*f.shape, elements_trans)

    return LaplaceTransform(f, t, s).doit(**hints)


@_noconds_(True)
        ```
    ###Assistant : 
    Compute the Laplace Transform `F(s)` of `f(t)`,

    .. math :: F(s) = \int_{0^{-}}^\infty e^{-st} f(t) \mathrm{d}t.

    Explanation
    ===========

    For all sensible functions, this converges absolutely in a
    half-plane

    .. math :: a < \operatorname{Re}(s)

    This function returns ``(F, a, cond)`` where ``F`` is the Laplace
    transform of ``f``, `a` is the half-plane of convergence, and `cond` are
    auxiliary convergence conditions.

    The implementation is rule-based, and if you are interested in which
    rules are applied, and whether integration is attemped, you can switch
    debug information on by setting ``sympy.SYMPY_DEBUG=True``.

    The lower bound is `0-`, meaning that this bound should be approached
    from the lower side. This is only necessary if distributions are involved.
    At present, it is only done if `f(t)` contains ``DiracDelta``, in which
    case the Laplace transform is computed implicitly as

    .. math :: F(s) = \lim_{\tau\to 0^{-}} \int_{\tau}^\infty e^{-st} f(t) \mathrm{d}t

    by applying rules.

    If the integral cannot be fully computed in closed form, this function
    returns an unevaluated :class:`LaplaceTransform` object.

    For a description of possible hints, refer to the docstring of
    :func:`sympy.integrals.transforms.IntegralTransform.doit`. If ``noconds=True``,
    only `F` will be returned (i.e. not ``cond``, and also not the plane ``a``).

    .. deprecated:: 1.9
        Legacy behavior for matrices where ``laplace_transform`` with
        ``noconds=False`` (the default) returns a Matrix whose elements are
        tuples. The behavior of ``laplace_transform`` for matrices will change
        in a future release of SymPy to return a tuple of the transformed
        Matrix and the convergence conditions for the matrix as a whole. Use
        ``legacy_matrix=False`` to enable the new behavior.

    Examples
    ========

    >>> from sympy import DiracDelta, exp, laplace_transform
    >>> from sympy.abc import t, s, a
    >>> laplace_transform(t**4, t, s)
    (24/s**5, 0, True)
    >>> laplace_transform(t**a, t, s)
    (gamma(a + 1)/(s*s**a), 0, re(a) > -1)
    >>> laplace_transform(DiracDelta(t)-a*exp(-a*t),t,s)
    (s/(a + s), Max(0, -a), True)

    See Also
    ========

    inverse_laplace_transform, mellin_transform, fourier_transform
    hankel_transform, inverse_hankel_transform

    
    "
1154,"def reduce_annotations(anno_types, answers):
    
    for at in set(anno_types):
        assert at in (""no_answer"", ""short_answer"")
    if anno_types.count(""short_answer"") >= anno_types.count(""no_answer""):
        majority = ""short_answer""
        is_impossible = False
    else:
        majority = ""no_answer""
        is_impossible = True
    answers = [a for at, a in zip(anno_types, answers) if at == majority]
    reduction = len(anno_types) - len(answers)
    assert reduction < 3
    if not is_impossible:
        global n_no_ans
        n_no_ans += reduction
    else:
        global n_short
        n_short += reduction
        answers = []
    return answers, is_impossible

","
    In cases where there is annotator disagreement, this fn picks either only the short_answers or only the no_answers,
    depending on which is more numerous, with a bias towards picking short_answers.

    Note: By this stage, all long_answer annotations and all samples with yes/no answer have been removed.
    This leaves just no_answer and short_answers",52,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reduce_annotations(anno_types, answers):
    
    for at in set(anno_types):
        assert at in (""no_answer"", ""short_answer"")
    if anno_types.count(""short_answer"") >= anno_types.count(""no_answer""):
        majority = ""short_answer""
        is_impossible = False
    else:
        majority = ""no_answer""
        is_impossible = True
    answers = [a for at, a in zip(anno_types, answers) if at == majority]
    reduction = len(anno_types) - len(answers)
    assert reduction < 3
    if not is_impossible:
        global n_no_ans
        n_no_ans += reduction
    else:
        global n_short
        n_short += reduction
        answers = []
    return answers, is_impossible


        ```
    ###Assistant : 
    In cases where there is annotator disagreement, this fn picks either only the short_answers or only the no_answers,
    depending on which is more numerous, with a bias towards picking short_answers.

    Note: By this stage, all long_answer annotations and all samples with yes/no answer have been removed.
    This leaves just no_answer and short_answers
    "
1155,"async def async_open_cover(self, **kwargs):
        
        await mqtt.async_publish(
            self.hass,
            self._config.get(CONF_COMMAND_TOPIC),
            self._config[CONF_PAYLOAD_OPEN],
            self._config[CONF_QOS],
            self._config[CONF_RETAIN],
            self._config[CONF_ENCODING],
        )
        if self._optimistic:
            # Optimistically assume that cover has changed state.
            self._state = STATE_OPEN
            if self._config.get(CONF_GET_POSITION_TOPIC):
                self._position = self.find_percentage_in_range(
                    self._config[CONF_POSITION_OPEN], COVER_PAYLOAD
                )
            self.async_write_ha_state()
","Move the cover up.

        This method is a coroutine.
        ",9,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_open_cover(self, **kwargs):
        
        await mqtt.async_publish(
            self.hass,
            self._config.get(CONF_COMMAND_TOPIC),
            self._config[CONF_PAYLOAD_OPEN],
            self._config[CONF_QOS],
            self._config[CONF_RETAIN],
            self._config[CONF_ENCODING],
        )
        if self._optimistic:
            # Optimistically assume that cover has changed state.
            self._state = STATE_OPEN
            if self._config.get(CONF_GET_POSITION_TOPIC):
                self._position = self.find_percentage_in_range(
                    self._config[CONF_POSITION_OPEN], COVER_PAYLOAD
                )
            self.async_write_ha_state()

        ```
    ###Assistant : Move the cover up.

        This method is a coroutine.
        
    "
1156,"def _parse_distro_release_content(line):
        # type: (str) -> Dict[str, str]
        
        matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(line.strip()[::-1])
        distro_info = {}
        if matches:
            # regexp ensures non-None
            distro_info[""name""] = matches.group(3)[::-1]
            if matches.group(2):
                distro_info[""version_id""] = matches.group(2)[::-1]
            if matches.group(1):
                distro_info[""codename""] = matches.group(1)[::-1]
        elif line:
            distro_info[""name""] = line.strip()
        return distro_info


_distro = LinuxDistribution()

","
        Parse a line from a distro release file.

        Parameters:
        * line: Line from the distro release file. Must be a unicode string
                or a UTF-8 encoded byte string.

        Returns:
            A dictionary containing all information items.
        ",35,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_distro_release_content(line):
        # type: (str) -> Dict[str, str]
        
        matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(line.strip()[::-1])
        distro_info = {}
        if matches:
            # regexp ensures non-None
            distro_info[""name""] = matches.group(3)[::-1]
            if matches.group(2):
                distro_info[""version_id""] = matches.group(2)[::-1]
            if matches.group(1):
                distro_info[""codename""] = matches.group(1)[::-1]
        elif line:
            distro_info[""name""] = line.strip()
        return distro_info


_distro = LinuxDistribution()


        ```
    ###Assistant : 
        Parse a line from a distro release file.

        Parameters:
        * line: Line from the distro release file. Must be a unicode string
                or a UTF-8 encoded byte string.

        Returns:
            A dictionary containing all information items.
        
    "
1157,"def load_sharded_checkpoint(model, folder, strict=True):
    
    # Load the index
    index_file = os.path.join(folder, WEIGHTS_INDEX_NAME)
    if not os.path.isfile(index_file):
        raise ValueError(f""Can't find a checkpoint index ({WEIGHTS_INDEX_NAME}) in {folder}."")

    with open(index_file, ""r"", encoding=""utf-8"") as f:
        index = json.load(f)

    shard_files = list(set(index[""weight_map""].values()))

    # If strict=True, error before loading any of the state dicts.
    loaded_keys = index[""weight_map""].keys()
    model_keys = model.state_dict().keys()
    missing_keys = [key for key in model_keys if key not in loaded_keys]
    unexpected_keys = [key for key in loaded_keys if key not in model_keys]
    if strict and (len(missing_keys) > 0 or len(unexpected_keys) > 0):
        error_message = f""Error(s) in loading state_dict for {model.__class__.__name__}""
        if len(missing_keys) > 0:
            str_missing_keys = "","".join([f'""{k}""' for k in missing_keys])
            error_message += f""\nMissing key(s): {str_missing_keys}.""
        if len(unexpected_keys) > 0:
            str_unexpected_keys = "","".join([f'""{k}""' for k in unexpected_keys])
            error_message += f""\nMissing key(s): {str_unexpected_keys}.""
        raise RuntimeError(error_message)

    for shard_file in shard_files:
        state_dict = torch.load(os.path.join(folder, shard_file))
        model.load_state_dict(state_dict, strict=False)

        # Make sure memory is fred before we load the next state dict.
        del state_dict
        gc.collect()

    # Return the same thing as PyTorch load_state_dict function.
    return torch.nn.modules.module._IncompatibleKeys(missing_keys, unexpected_keys)

","
    This is the same as
    [`torch.nn.Module.load_state_dict`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=load_state_dict#torch.nn.Module.load_state_dict)
    but for a sharded checkpoint.

    This load is performed efficiently: each checkpoint shard is loaded one by one in RAM and deleted after being
    loaded in the model.

    Args:
        model (`torch.nn.Module`): The model in which to load the checkpoint.
        folder (`str` or `os.PathLike`): A path to a folder containing the sharded checkpoint.
        strict (`bool`, *optional`, defaults to `True`):
            Whether to strictly enforce that the keys in the model state dict match the keys in the sharded checkpoint.

    Returns:
        `NamedTuple`: A named tuple with `missing_keys` and `unexpected_keys` fields
            - `missing_keys` is a list of str containing the missing keys
            - `unexpected_keys` is a list of str containing the unexpected keys
    ",115,167,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_sharded_checkpoint(model, folder, strict=True):
    
    # Load the index
    index_file = os.path.join(folder, WEIGHTS_INDEX_NAME)
    if not os.path.isfile(index_file):
        raise ValueError(f""Can't find a checkpoint index ({WEIGHTS_INDEX_NAME}) in {folder}."")

    with open(index_file, ""r"", encoding=""utf-8"") as f:
        index = json.load(f)

    shard_files = list(set(index[""weight_map""].values()))

    # If strict=True, error before loading any of the state dicts.
    loaded_keys = index[""weight_map""].keys()
    model_keys = model.state_dict().keys()
    missing_keys = [key for key in model_keys if key not in loaded_keys]
    unexpected_keys = [key for key in loaded_keys if key not in model_keys]
    if strict and (len(missing_keys) > 0 or len(unexpected_keys) > 0):
        error_message = f""Error(s) in loading state_dict for {model.__class__.__name__}""
        if len(missing_keys) > 0:
            str_missing_keys = "","".join([f'""{k}""' for k in missing_keys])
            error_message += f""\nMissing key(s): {str_missing_keys}.""
        if len(unexpected_keys) > 0:
            str_unexpected_keys = "","".join([f'""{k}""' for k in unexpected_keys])
            error_message += f""\nMissing key(s): {str_unexpected_keys}.""
        raise RuntimeError(error_message)

    for shard_file in shard_files:
        state_dict = torch.load(os.path.join(folder, shard_file))
        model.load_state_dict(state_dict, strict=False)

        # Make sure memory is fred before we load the next state dict.
        del state_dict
        gc.collect()

    # Return the same thing as PyTorch load_state_dict function.
    return torch.nn.modules.module._IncompatibleKeys(missing_keys, unexpected_keys)


        ```
    ###Assistant : 
    This is the same as
    [`torch.nn.Module.load_state_dict`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=load_state_dict#torch.nn.Module.load_state_dict)
    but for a sharded checkpoint.

    This load is performed efficiently: each checkpoint shard is loaded one by one in RAM and deleted after being
    loaded in the model.

    Args:
        model (`torch.nn.Module`): The model in which to load the checkpoint.
        folder (`str` or `os.PathLike`): A path to a folder containing the sharded checkpoint.
        strict (`bool`, *optional`, defaults to `True`):
            Whether to strictly enforce that the keys in the model state dict match the keys in the sharded checkpoint.

    Returns:
        `NamedTuple`: A named tuple with `missing_keys` and `unexpected_keys` fields
            - `missing_keys` is a list of str containing the missing keys
            - `unexpected_keys` is a list of str containing the unexpected keys
    
    "
1158,"def customer_query(doctype, txt, searchfield, start, page_len, filters):
	conditions = []
	cust_master_name = frappe.defaults.get_user_default(""cust_master_name"")

	if cust_master_name == ""Customer Name"":
		fields = [""name"", ""customer_group"", ""territory""]
	else:
		fields = [""name"", ""customer_name"", ""customer_group"", ""territory""]

	fields = get_fields(""Customer"", fields)

	searchfields = frappe.get_meta(""Customer"").get_search_fields()
	searchfields = "" or "".join(field + "" like %(txt)s"" for field in searchfields)

	return frappe.db.sql(
		.format(
			**{
				""fields"": "", "".join(fields),
				""scond"": searchfields,
				""mcond"": get_match_cond(doctype),
				""fcond"": get_filters_cond(doctype, filters, conditions).replace(""%"", ""%%""),
			}
		),
		{""txt"": ""%%%s%%"" % txt, ""_txt"": txt.replace(""%"", """"), ""start"": start, ""page_len"": page_len},
	)


# searches for supplier
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs","select {fields} from `tabCustomer`
		where docstatus < 2
			and ({scond}) and disabled=0
			{fcond} {mcond}
		order by
			if(locate(%(_txt)s, name), locate(%(_txt)s, name), 99999),
			if(locate(%(_txt)s, customer_name), locate(%(_txt)s, customer_name), 99999),
			idx desc,
			name, customer_name
		limit %(start)s, %(page_len)s",33,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def customer_query(doctype, txt, searchfield, start, page_len, filters):
	conditions = []
	cust_master_name = frappe.defaults.get_user_default(""cust_master_name"")

	if cust_master_name == ""Customer Name"":
		fields = [""name"", ""customer_group"", ""territory""]
	else:
		fields = [""name"", ""customer_name"", ""customer_group"", ""territory""]

	fields = get_fields(""Customer"", fields)

	searchfields = frappe.get_meta(""Customer"").get_search_fields()
	searchfields = "" or "".join(field + "" like %(txt)s"" for field in searchfields)

	return frappe.db.sql(
		.format(
			**{
				""fields"": "", "".join(fields),
				""scond"": searchfields,
				""mcond"": get_match_cond(doctype),
				""fcond"": get_filters_cond(doctype, filters, conditions).replace(""%"", ""%%""),
			}
		),
		{""txt"": ""%%%s%%"" % txt, ""_txt"": txt.replace(""%"", """"), ""start"": start, ""page_len"": page_len},
	)


# searches for supplier
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs
        ```
    ###Assistant : select {fields} from `tabCustomer`
		where docstatus < 2
			and ({scond}) and disabled=0
			{fcond} {mcond}
		order by
			if(locate(%(_txt)s, name), locate(%(_txt)s, name), 99999),
			if(locate(%(_txt)s, customer_name), locate(%(_txt)s, customer_name), 99999),
			idx desc,
			name, customer_name
		limit %(start)s, %(page_len)s
    "
1159,"def get_group_tag_value_count(self, group, environment_id, key):
        
        raise NotImplementedError
","
        >>> get_group_tag_value_count(group, 3, 'key1')
        ",4,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_group_tag_value_count(self, group, environment_id, key):
        
        raise NotImplementedError

        ```
    ###Assistant : 
        >>> get_group_tag_value_count(group, 3, 'key1')
        
    "
1160,"def test_enqueue_task_instances_sets_ti_state_to_None_if_dagrun_in_finish_state(self, state, dag_maker):
        
        dag_id = 'SchedulerJobTest.test_enqueue_task_instances_with_queued_state'
        task_id_1 = 'dummy'
        session = settings.Session()
        with dag_maker(dag_id=dag_id, start_date=DEFAULT_DATE, session=session):
            task1 = EmptyOperator(task_id=task_id_1)

        self.scheduler_job = SchedulerJob(subdir=os.devnull)

        dr1 = dag_maker.create_dagrun(state=state)
        ti = dr1.get_task_instance(task1.task_id, session)
        ti.state = State.SCHEDULED
        session.merge(ti)
        session.commit()

        with patch.object(BaseExecutor, 'queue_command') as mock_queue_command:
            self.scheduler_job._enqueue_task_instances_with_queued_state([ti])
        ti.refresh_from_db()
        assert ti.state == State.NONE
        mock_queue_command.assert_not_called()
",This tests that task instances whose dagrun is in finished state are not queued,14,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_enqueue_task_instances_sets_ti_state_to_None_if_dagrun_in_finish_state(self, state, dag_maker):
        
        dag_id = 'SchedulerJobTest.test_enqueue_task_instances_with_queued_state'
        task_id_1 = 'dummy'
        session = settings.Session()
        with dag_maker(dag_id=dag_id, start_date=DEFAULT_DATE, session=session):
            task1 = EmptyOperator(task_id=task_id_1)

        self.scheduler_job = SchedulerJob(subdir=os.devnull)

        dr1 = dag_maker.create_dagrun(state=state)
        ti = dr1.get_task_instance(task1.task_id, session)
        ti.state = State.SCHEDULED
        session.merge(ti)
        session.commit()

        with patch.object(BaseExecutor, 'queue_command') as mock_queue_command:
            self.scheduler_job._enqueue_task_instances_with_queued_state([ti])
        ti.refresh_from_db()
        assert ti.state == State.NONE
        mock_queue_command.assert_not_called()

        ```
    ###Assistant : This tests that task instances whose dagrun is in finished state are not queued
    "
1161,"def match_hostname(cert, hostname):
        
        if not cert:
            raise ValueError(""empty or no certificate, match_hostname needs a ""
                             ""SSL socket or SSL context with either ""
                             ""CERT_OPTIONAL or CERT_REQUIRED"")
        dnsnames = []
        san = cert.get('subjectAltName', ())
        for key, value in san:
            if key == 'DNS':
                if _dnsname_match(value, hostname):
                    return
                dnsnames.append(value)
        if not dnsnames:
            # The subject is only checked when there is no dNSName entry
            # in subjectAltName
            for sub in cert.get('subject', ()):
                for key, value in sub:
                    # XXX according to RFC 2818, the most specific Common Name
                    # must be used.
                    if key == 'commonName':
                        if _dnsname_match(value, hostname):
                            return
                        dnsnames.append(value)
        if len(dnsnames) > 1:
            raise CertificateError(""hostname %r ""
                ""doesn't match either of %s""
                % (hostname, ', '.join(map(repr, dnsnames))))
        elif len(dnsnames) == 1:
            raise CertificateError(""hostname %r ""
                ""doesn't match %r""
                % (hostname, dnsnames[0]))
        else:
            raise CertificateError(""no appropriate commonName or ""
                ""subjectAltName fields were found"")


try:
    from types import SimpleNamespace as Container
except ImportError:  # pragma: no cover","Verify that *cert* (in decoded format as returned by
        SSLSocket.getpeercert()) matches the *hostname*.  RFC 2818 and RFC 6125
        rules are followed, but IP addresses are not accepted for *hostname*.

        CertificateError is raised on failure. On success, the function
        returns nothing.
        ",40,155,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def match_hostname(cert, hostname):
        
        if not cert:
            raise ValueError(""empty or no certificate, match_hostname needs a ""
                             ""SSL socket or SSL context with either ""
                             ""CERT_OPTIONAL or CERT_REQUIRED"")
        dnsnames = []
        san = cert.get('subjectAltName', ())
        for key, value in san:
            if key == 'DNS':
                if _dnsname_match(value, hostname):
                    return
                dnsnames.append(value)
        if not dnsnames:
            # The subject is only checked when there is no dNSName entry
            # in subjectAltName
            for sub in cert.get('subject', ()):
                for key, value in sub:
                    # XXX according to RFC 2818, the most specific Common Name
                    # must be used.
                    if key == 'commonName':
                        if _dnsname_match(value, hostname):
                            return
                        dnsnames.append(value)
        if len(dnsnames) > 1:
            raise CertificateError(""hostname %r ""
                ""doesn't match either of %s""
                % (hostname, ', '.join(map(repr, dnsnames))))
        elif len(dnsnames) == 1:
            raise CertificateError(""hostname %r ""
                ""doesn't match %r""
                % (hostname, dnsnames[0]))
        else:
            raise CertificateError(""no appropriate commonName or ""
                ""subjectAltName fields were found"")


try:
    from types import SimpleNamespace as Container
except ImportError:  # pragma: no cover
        ```
    ###Assistant : Verify that *cert* (in decoded format as returned by
        SSLSocket.getpeercert()) matches the *hostname*.  RFC 2818 and RFC 6125
        rules are followed, but IP addresses are not accepted for *hostname*.

        CertificateError is raised on failure. On success, the function
        returns nothing.
        
    "
1162,"def take(self, n) -> ""IterableDataset"":
        
        ex_iterable = TakeExamplesIterable(self._ex_iterable, n)
        return iterable_dataset(
            ex_iterable=ex_iterable,
            info=self._info.copy(),
            split=self._split,
            format_type=self._format_type,
            shuffling=copy.deepcopy(self._shuffling),
            token_per_repo_id=self._token_per_repo_id,
        )
","
        Create a new IterableDataset with only the first ``n`` elements.

        Args:
            n (:obj:`int`): number of elements to take.

        Example:

        ```py
        >>> from datasets import load_dataset
        >>> ds = load_dataset(""rotten_tomatoes"", split=""train"", streaming=True)
        >>> small_ds = ds.take(2)
        >>> list(small_ds)
        [{'label': 1,
         'text': 'the rock is destined to be the 21st century\'s new "" conan "" and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'},
         {'label': 1,
         'text': 'the gorgeously elaborate continuation of "" the lord of the rings "" trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .'}]
        ```
        ",117,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def take(self, n) -> ""IterableDataset"":
        
        ex_iterable = TakeExamplesIterable(self._ex_iterable, n)
        return iterable_dataset(
            ex_iterable=ex_iterable,
            info=self._info.copy(),
            split=self._split,
            format_type=self._format_type,
            shuffling=copy.deepcopy(self._shuffling),
            token_per_repo_id=self._token_per_repo_id,
        )

        ```
    ###Assistant : 
        Create a new IterableDataset with only the first ``n`` elements.

        Args:
            n (:obj:`int`): number of elements to take.

        Example:

        ```py
        >>> from datasets import load_dataset
        >>> ds = load_dataset(""rotten_tomatoes"", split=""train"", streaming=True)
        >>> small_ds = ds.take(2)
        >>> list(small_ds)
        [{'label': 1,
         'text': 'the rock is destined to be the 21st century\'s new "" conan "" and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'},
         {'label': 1,
         'text': 'the gorgeously elaborate continuation of "" the lord of the rings "" trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .'}]
        ```
        
    "
1163,"def test_category_match_group(self):
        
        from sentry.grouping.enhancer import Enhancements

        enhancement = Enhancements.from_config_string(
            ,
        )

        event = make_event(
            platform=""native"",
            exception={
                ""values"": [
                    {
                        ""type"": ""Hello"",
                        ""stacktrace"": {
                            ""frames"": [
                                {
                                    ""function"": ""foo"",
                                },
                                {
                                    ""function"": ""bar"",
                                },
                            ]
                        },
                    }
                ]
            },
        )

        manager = EventManager(event)
        manager.normalize()

        grouping_config = {
            ""enhancements"": enhancement.dumps(),
            ""id"": ""mobile:2021-02-12"",
        }

        manager.get_data()[""grouping_config""] = grouping_config
        event1 = manager.save(self.project.id)

        event2 = Event(event1.project_id, event1.event_id, data=event1.data)

        assert event1.get_hashes().hashes == event2.get_hashes(grouping_config).hashes
","
        Regression test to ensure categories are applied consistently and don't
        produce hash mismatches.
        
            function:foo category=foo_like
            category:foo_like -group
            ",17,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_category_match_group(self):
        
        from sentry.grouping.enhancer import Enhancements

        enhancement = Enhancements.from_config_string(
            ,
        )

        event = make_event(
            platform=""native"",
            exception={
                ""values"": [
                    {
                        ""type"": ""Hello"",
                        ""stacktrace"": {
                            ""frames"": [
                                {
                                    ""function"": ""foo"",
                                },
                                {
                                    ""function"": ""bar"",
                                },
                            ]
                        },
                    }
                ]
            },
        )

        manager = EventManager(event)
        manager.normalize()

        grouping_config = {
            ""enhancements"": enhancement.dumps(),
            ""id"": ""mobile:2021-02-12"",
        }

        manager.get_data()[""grouping_config""] = grouping_config
        event1 = manager.save(self.project.id)

        event2 = Event(event1.project_id, event1.event_id, data=event1.data)

        assert event1.get_hashes().hashes == event2.get_hashes(grouping_config).hashes

        ```
    ###Assistant : 
        Regression test to ensure categories are applied consistently and don't
        produce hash mismatches.
        
            function:foo category=foo_like
            category:foo_like -group
            
    "
1164,"def notify(self, notification, raise_exception=False):
        
        event = notification.event
        try:
            return self.notify_users(
                event.group, event, triggering_rules=[r.label for r in notification.rules]
            )
        except (
            ApiError,
            HTTPError,
            InvalidIdentity,
            PluginError,
            SSLError,
            UrllibHTTPError,
        ) as err:
            self.logger.info(
                ""notification-plugin.notify-failed"",
                extra={
                    ""error"": str(err),
                    ""plugin"": self.slug,
                    ""project_id"": event.group.project_id,
                    ""organization_id"": event.group.project.organization_id,
                },
            )
            if raise_exception:
                raise err
            return False
","
        This calls the notify_users method of the plugin.
        Normally this method eats the error and logs it but if we
        set raise_exception=True like we do for the test plugin button,
        the exception is raised
        ",34,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def notify(self, notification, raise_exception=False):
        
        event = notification.event
        try:
            return self.notify_users(
                event.group, event, triggering_rules=[r.label for r in notification.rules]
            )
        except (
            ApiError,
            HTTPError,
            InvalidIdentity,
            PluginError,
            SSLError,
            UrllibHTTPError,
        ) as err:
            self.logger.info(
                ""notification-plugin.notify-failed"",
                extra={
                    ""error"": str(err),
                    ""plugin"": self.slug,
                    ""project_id"": event.group.project_id,
                    ""organization_id"": event.group.project.organization_id,
                },
            )
            if raise_exception:
                raise err
            return False

        ```
    ###Assistant : 
        This calls the notify_users method of the plugin.
        Normally this method eats the error and logs it but if we
        set raise_exception=True like we do for the test plugin button,
        the exception is raised
        
    "
1165,"def test_processors(self):
        
        from djangocms_text_ckeditor.cms_plugins import TextPlugin

        from cms.plugin_pool import plugin_pool

        instance = CMSPlugin.objects.all()[0].get_plugin_instance()[0]

        load_from_string = self.load_template_from_string
","
        Tests that plugin processors and plugin context processors can be defined
        in settings and are working and that extra plugin context processors can be
        passed to PluginContext.
        ",27,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_processors(self):
        
        from djangocms_text_ckeditor.cms_plugins import TextPlugin

        from cms.plugin_pool import plugin_pool

        instance = CMSPlugin.objects.all()[0].get_plugin_instance()[0]

        load_from_string = self.load_template_from_string

        ```
    ###Assistant : 
        Tests that plugin processors and plugin context processors can be defined
        in settings and are working and that extra plugin context processors can be
        passed to PluginContext.
        
    "
1166,"async def test_unique_id_ignore(hass, manager):
    
    async_setup_entry = AsyncMock(return_value=False)
    mock_integration(hass, MockModule(""comp"", async_setup_entry=async_setup_entry))
    mock_entity_platform(hass, ""config_flow.comp"", None)
",Test that we can ignore flows that are in progress and have a unique ID.,15,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_unique_id_ignore(hass, manager):
    
    async_setup_entry = AsyncMock(return_value=False)
    mock_integration(hass, MockModule(""comp"", async_setup_entry=async_setup_entry))
    mock_entity_platform(hass, ""config_flow.comp"", None)

        ```
    ###Assistant : Test that we can ignore flows that are in progress and have a unique ID.
    "
1167,"def bind(self, bind_string, key, propagate=True):
        
        if not self._is_window_created('tried Window.bind'):
            return
        self.TKroot.bind(bind_string, lambda evt: self._user_bind_callback(bind_string, evt, propagate))
        self.user_bind_dict[bind_string] = key
","
        Used to add tkinter events to a Window.
        The tkinter specific data is in the Window's member variable user_bind_event
        :param bind_string: The string tkinter expected in its bind function
        :type bind_string:  (str)
        :param key:         The event that will be generated when the tkinter event occurs
        :type key:          str | int | tuple | object
        :param propagate:   If True then tkinter will be told to propagate the event
        :type propagate:    (bool)
        ",70,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bind(self, bind_string, key, propagate=True):
        
        if not self._is_window_created('tried Window.bind'):
            return
        self.TKroot.bind(bind_string, lambda evt: self._user_bind_callback(bind_string, evt, propagate))
        self.user_bind_dict[bind_string] = key

        ```
    ###Assistant : 
        Used to add tkinter events to a Window.
        The tkinter specific data is in the Window's member variable user_bind_event
        :param bind_string: The string tkinter expected in its bind function
        :type bind_string:  (str)
        :param key:         The event that will be generated when the tkinter event occurs
        :type key:          str | int | tuple | object
        :param propagate:   If True then tkinter will be told to propagate the event
        :type propagate:    (bool)
        
    "
1168,"def forward(self, body_feats=None, rois=None, rois_num=None, inputs=None):
        
        targets = []
        if self.training:
            rois, rois_num, targets = self.bbox_assigner(rois, rois_num, inputs)
            targets_list = [targets]
            self.assigned_rois = (rois, rois_num)
            self.assigned_targets = targets

        pred_bbox = None
        head_out_list = []
        for i in range(self.num_cascade_stages):
            if i > 0:
                rois, rois_num = self._get_rois_from_boxes(pred_bbox,
                                                           inputs['im_shape'])
                if self.training:
                    rois, rois_num, targets = self.bbox_assigner(
                        rois, rois_num, inputs, i, is_cascade=True)
                    targets_list.append(targets)

            rois_feat = self.roi_extractor(body_feats, rois, rois_num)
            bbox_feat = self.head(rois_feat, i)
            scores = self.bbox_score_list[i](bbox_feat)
            deltas = self.bbox_delta_list[i](bbox_feat)

            # TODO (lyuwenyu) Is it correct for only one class ?
            if not self.reg_class_agnostic and i < self.num_cascade_stages - 1:
                deltas = deltas.reshape([-1, self.num_classes, 4])
                labels = scores[:, :-1].argmax(axis=-1)
                deltas = deltas[paddle.arange(deltas.shape[0]), labels]

            head_out_list.append([scores, deltas, rois])
            pred_bbox = self._get_pred_bbox(deltas, rois, self.bbox_weight[i])

        if self.training:
            loss = {}
            for stage, value in enumerate(zip(head_out_list, targets_list)):
                (scores, deltas, rois), targets = value
                loss_stage = self.get_loss(scores, deltas, targets, rois,
                                           self.bbox_weight[stage])
                for k, v in loss_stage.items():
                    loss[k + ""_stage{}"".format(
                        stage)] = v / self.num_cascade_stages

            return loss, bbox_feat
        else:
            scores, deltas, self.refined_rois = self.get_prediction(
                head_out_list)
            return (deltas, scores), self.head
","
        body_feats (list[Tensor]): Feature maps from backbone
        rois (Tensor): RoIs generated from RPN module
        rois_num (Tensor): The number of RoIs in each image
        inputs (dict{Tensor}): The ground-truth of image
        ",28,167,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, body_feats=None, rois=None, rois_num=None, inputs=None):
        
        targets = []
        if self.training:
            rois, rois_num, targets = self.bbox_assigner(rois, rois_num, inputs)
            targets_list = [targets]
            self.assigned_rois = (rois, rois_num)
            self.assigned_targets = targets

        pred_bbox = None
        head_out_list = []
        for i in range(self.num_cascade_stages):
            if i > 0:
                rois, rois_num = self._get_rois_from_boxes(pred_bbox,
                                                           inputs['im_shape'])
                if self.training:
                    rois, rois_num, targets = self.bbox_assigner(
                        rois, rois_num, inputs, i, is_cascade=True)
                    targets_list.append(targets)

            rois_feat = self.roi_extractor(body_feats, rois, rois_num)
            bbox_feat = self.head(rois_feat, i)
            scores = self.bbox_score_list[i](bbox_feat)
            deltas = self.bbox_delta_list[i](bbox_feat)

            # TODO (lyuwenyu) Is it correct for only one class ?
            if not self.reg_class_agnostic and i < self.num_cascade_stages - 1:
                deltas = deltas.reshape([-1, self.num_classes, 4])
                labels = scores[:, :-1].argmax(axis=-1)
                deltas = deltas[paddle.arange(deltas.shape[0]), labels]

            head_out_list.append([scores, deltas, rois])
            pred_bbox = self._get_pred_bbox(deltas, rois, self.bbox_weight[i])

        if self.training:
            loss = {}
            for stage, value in enumerate(zip(head_out_list, targets_list)):
                (scores, deltas, rois), targets = value
                loss_stage = self.get_loss(scores, deltas, targets, rois,
                                           self.bbox_weight[stage])
                for k, v in loss_stage.items():
                    loss[k + ""_stage{}"".format(
                        stage)] = v / self.num_cascade_stages

            return loss, bbox_feat
        else:
            scores, deltas, self.refined_rois = self.get_prediction(
                head_out_list)
            return (deltas, scores), self.head

        ```
    ###Assistant : 
        body_feats (list[Tensor]): Feature maps from backbone
        rois (Tensor): RoIs generated from RPN module
        rois_num (Tensor): The number of RoIs in each image
        inputs (dict{Tensor}): The ground-truth of image
        
    "
1169,"def get_fws(value):
    
    newvalue = value.lstrip()
    fws = WhiteSpaceTerminal(value[:len(value)-len(newvalue)], 'fws')
    return fws, newvalue
","FWS = 1*WSP

    This isn't the RFC definition.  We're using fws to represent tokens where
    folding can be done, but when we are parsing the *un*folding has already
    been done so we don't need to watch out for CRLF.

    ",39,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_fws(value):
    
    newvalue = value.lstrip()
    fws = WhiteSpaceTerminal(value[:len(value)-len(newvalue)], 'fws')
    return fws, newvalue

        ```
    ###Assistant : FWS = 1*WSP

    This isn't the RFC definition.  We're using fws to represent tokens where
    folding can be done, but when we are parsing the *un*folding has already
    been done so we don't need to watch out for CRLF.

    
    "
1170,"def load_data_snli(batch_size, num_steps=50):
    
    num_workers = d2l.get_dataloader_workers()
    data_dir = d2l.download_extract('SNLI')
    train_data = read_snli(data_dir, True)
    test_data = read_snli(data_dir, False)
    train_set = SNLIDataset(train_data, num_steps)
    test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
    train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
                                       num_workers=num_workers)
    test_iter = gluon.data.DataLoader(test_set, batch_size, shuffle=False,
                                      num_workers=num_workers)
    return train_iter, test_iter, train_set.vocab
","Download the SNLI dataset and return data iterators and vocabulary.

    Defined in :numref:`sec_natural-language-inference-and-dataset`",13,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_data_snli(batch_size, num_steps=50):
    
    num_workers = d2l.get_dataloader_workers()
    data_dir = d2l.download_extract('SNLI')
    train_data = read_snli(data_dir, True)
    test_data = read_snli(data_dir, False)
    train_set = SNLIDataset(train_data, num_steps)
    test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
    train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
                                       num_workers=num_workers)
    test_iter = gluon.data.DataLoader(test_set, batch_size, shuffle=False,
                                      num_workers=num_workers)
    return train_iter, test_iter, train_set.vocab

        ```
    ###Assistant : Download the SNLI dataset and return data iterators and vocabulary.

    Defined in :numref:`sec_natural-language-inference-and-dataset`
    "
1171,"def plot_feature_importance(model, feature_names, pair, train_dir, count_max=50) -> None:
    
    try:
        import plotly.graph_objects as go
        from plotly.subplots import make_subplots
    except ImportError:
        logger.exception(""Module plotly not found \n Please install using `pip3 install plotly`"")
        exit(1)

    from freqtrade.plot.plotting import store_plot_file

    # Gather feature importance from model
    if ""catboost.core"" in str(model.__class__):
        feature_importance = model.get_feature_importance()
    elif ""lightgbm.sklearn"" in str(model.__class__):
        feature_importance = model.feature_importances_
    else:
        raise NotImplementedError(f""Cannot extract feature importance for {model.__class__}"")

    # Data preparation
    fi_df = pd.DataFrame({
        ""feature_names"": np.array(feature_names),
        ""feature_importance"": np.array(feature_importance)
    })
    fi_df_top = fi_df.nlargest(count_max, ""feature_importance"")[::-1]
    fi_df_worst = fi_df.nsmallest(count_max, ""feature_importance"")[::-1]

    # Plotting","
        Plot Best and Worst Features by importance for CatBoost model.
        Called once per sub-train.
        Usage: plot_feature_importance(
            model=model,
            feature_names=dk.training_features_list,
            pair=pair,
            train_dir=dk.data_path)
    ",20,84,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def plot_feature_importance(model, feature_names, pair, train_dir, count_max=50) -> None:
    
    try:
        import plotly.graph_objects as go
        from plotly.subplots import make_subplots
    except ImportError:
        logger.exception(""Module plotly not found \n Please install using `pip3 install plotly`"")
        exit(1)

    from freqtrade.plot.plotting import store_plot_file

    # Gather feature importance from model
    if ""catboost.core"" in str(model.__class__):
        feature_importance = model.get_feature_importance()
    elif ""lightgbm.sklearn"" in str(model.__class__):
        feature_importance = model.feature_importances_
    else:
        raise NotImplementedError(f""Cannot extract feature importance for {model.__class__}"")

    # Data preparation
    fi_df = pd.DataFrame({
        ""feature_names"": np.array(feature_names),
        ""feature_importance"": np.array(feature_importance)
    })
    fi_df_top = fi_df.nlargest(count_max, ""feature_importance"")[::-1]
    fi_df_worst = fi_df.nsmallest(count_max, ""feature_importance"")[::-1]

    # Plotting
        ```
    ###Assistant : 
        Plot Best and Worst Features by importance for CatBoost model.
        Called once per sub-train.
        Usage: plot_feature_importance(
            model=model,
            feature_names=dk.training_features_list,
            pair=pair,
            train_dir=dk.data_path)
    
    "
1172,"def get_primary_key_column(self, cursor, table_name):
        
        cursor.execute(
            ""PRAGMA table_info(%s)"" % self.connection.ops.quote_name(table_name)
        )
        for _, name, *_, pk in cursor.fetchall():
            if pk:
                return name
        return None
",Return the column name of the primary key for the given table.,12,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_primary_key_column(self, cursor, table_name):
        
        cursor.execute(
            ""PRAGMA table_info(%s)"" % self.connection.ops.quote_name(table_name)
        )
        for _, name, *_, pk in cursor.fetchall():
            if pk:
                return name
        return None

        ```
    ###Assistant : Return the column name of the primary key for the given table.
    "
1173,"def validate_settings():
    
    try:
        django_backend = [x for x in settings.TEMPLATES
                          if x['BACKEND'] == 'django.template.backends.django.DjangoTemplates'][0]
    except IndexError:
        raise ImproperlyConfigured(
            ""django CMS requires django.template.context_processors.request in ""
            ""'django.template.backends.django.DjangoTemplates' context processors.""
        )

    context_processors = django_backend.get('OPTIONS', {}).get('context_processors', [])
    if ('django.core.context_processors.request' not in context_processors and  # noqa: W504
            'django.template.context_processors.request' not in context_processors):
        raise ImproperlyConfigured(""django CMS requires django.template.context_processors.request in ""
                                   ""'django.template.backends.django.DjangoTemplates' context processors."")

    if (
        hasattr(settings, ""SEND_BROKEN_LINK_EMAILS"") and  # noqa: W504
        ""django.middleware.common.BrokenLinkEmailsMiddleware"" not in getattr(settings, ""MIDDLEWARE"", [])
    ):
        warnings.warn('The setting ""SEND_BROKEN_LINK_EMAILS"" will not be honored by django CMS as of version 4.1. '
                      'Add ""django.middleware.common.BrokenLinkEmailsMiddleware"" to your MIDDLEWARE settings '
                      'instead.', DeprecationWarning)

","
    Check project settings file for required options
    ",7,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_settings():
    
    try:
        django_backend = [x for x in settings.TEMPLATES
                          if x['BACKEND'] == 'django.template.backends.django.DjangoTemplates'][0]
    except IndexError:
        raise ImproperlyConfigured(
            ""django CMS requires django.template.context_processors.request in ""
            ""'django.template.backends.django.DjangoTemplates' context processors.""
        )

    context_processors = django_backend.get('OPTIONS', {}).get('context_processors', [])
    if ('django.core.context_processors.request' not in context_processors and  # noqa: W504
            'django.template.context_processors.request' not in context_processors):
        raise ImproperlyConfigured(""django CMS requires django.template.context_processors.request in ""
                                   ""'django.template.backends.django.DjangoTemplates' context processors."")

    if (
        hasattr(settings, ""SEND_BROKEN_LINK_EMAILS"") and  # noqa: W504
        ""django.middleware.common.BrokenLinkEmailsMiddleware"" not in getattr(settings, ""MIDDLEWARE"", [])
    ):
        warnings.warn('The setting ""SEND_BROKEN_LINK_EMAILS"" will not be honored by django CMS as of version 4.1. '
                      'Add ""django.middleware.common.BrokenLinkEmailsMiddleware"" to your MIDDLEWARE settings '
                      'instead.', DeprecationWarning)


        ```
    ###Assistant : 
    Check project settings file for required options
    
    "
1174,"def get(cls):
        
        min_partition_size = super().get()
        assert min_partition_size > 0, ""`min_partition_size` should be > 0""
        return min_partition_size

","
        Get ``MinPartitionSize`` with extra checks.

        Returns
        -------
        int
        ",8,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get(cls):
        
        min_partition_size = super().get()
        assert min_partition_size > 0, ""`min_partition_size` should be > 0""
        return min_partition_size


        ```
    ###Assistant : 
        Get ``MinPartitionSize`` with extra checks.

        Returns
        -------
        int
        
    "
1175,"def pbt_function(config):
    
    lr = config[""lr""]
    accuracy = 0.0  # end = 1000
    start = 0
    if session.get_checkpoint():
        state = session.get_checkpoint().to_dict()
        accuracy = state[""acc""]
        start = state[""step""]

    midpoint = 100  # lr starts decreasing after acc > midpoint
    q_tolerance = 3  # penalize exceeding lr by more than this multiple
    noise_level = 2  # add gaussian noise to the acc increase
    # triangle wave:
    #  - start at 0.001 @ t=0,
    #  - peak at 0.01 @ t=midpoint,
    #  - end at 0.001 @ t=midpoint * 2,
    for step in range(start, 100):
        if accuracy < midpoint:
            optimal_lr = 0.01 * accuracy / midpoint
        else:
            optimal_lr = 0.01 - 0.01 * (accuracy - midpoint) / midpoint
        optimal_lr = min(0.01, max(0.001, optimal_lr))

        # compute accuracy increase
        q_err = max(lr, optimal_lr) / min(lr, optimal_lr)
        if q_err < q_tolerance:
            accuracy += (1.0 / q_err) * random.random()
        elif lr > optimal_lr:
            accuracy -= (q_err - q_tolerance) * random.random()
        accuracy += noise_level * np.random.normal()
        accuracy = max(0, accuracy)

        checkpoint = None
        if step % 3 == 0:
            checkpoint = Checkpoint.from_dict({""acc"": accuracy, ""step"": start})

        session.report(
            {
                ""mean_accuracy"": accuracy,
                ""cur_lr"": lr,
                ""optimal_lr"": optimal_lr,  # for debugging
                ""q_err"": q_err,  # for debugging
                ""done"": accuracy > midpoint * 2,  # this stops the training process
            },
            checkpoint=checkpoint,
        )

","Toy PBT problem for benchmarking adaptive learning rate.

    The goal is to optimize this trainable's accuracy. The accuracy increases
    fastest at the optimal lr, which is a function of the current accuracy.

    The optimal lr schedule for this problem is the triangle wave as follows.
    Note that many lr schedules for real models also follow this shape:

     best lr
      ^
      |    /\
      |   /  \
      |  /    \
      | /      \
      ------------> accuracy

    In this problem, using PBT with a population of 2-4 is sufficient to
    roughly approximate this lr schedule. Higher population sizes will yield
    faster convergence. Training will not converge without PBT.
    ",104,207,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pbt_function(config):
    
    lr = config[""lr""]
    accuracy = 0.0  # end = 1000
    start = 0
    if session.get_checkpoint():
        state = session.get_checkpoint().to_dict()
        accuracy = state[""acc""]
        start = state[""step""]

    midpoint = 100  # lr starts decreasing after acc > midpoint
    q_tolerance = 3  # penalize exceeding lr by more than this multiple
    noise_level = 2  # add gaussian noise to the acc increase
    # triangle wave:
    #  - start at 0.001 @ t=0,
    #  - peak at 0.01 @ t=midpoint,
    #  - end at 0.001 @ t=midpoint * 2,
    for step in range(start, 100):
        if accuracy < midpoint:
            optimal_lr = 0.01 * accuracy / midpoint
        else:
            optimal_lr = 0.01 - 0.01 * (accuracy - midpoint) / midpoint
        optimal_lr = min(0.01, max(0.001, optimal_lr))

        # compute accuracy increase
        q_err = max(lr, optimal_lr) / min(lr, optimal_lr)
        if q_err < q_tolerance:
            accuracy += (1.0 / q_err) * random.random()
        elif lr > optimal_lr:
            accuracy -= (q_err - q_tolerance) * random.random()
        accuracy += noise_level * np.random.normal()
        accuracy = max(0, accuracy)

        checkpoint = None
        if step % 3 == 0:
            checkpoint = Checkpoint.from_dict({""acc"": accuracy, ""step"": start})

        session.report(
            {
                ""mean_accuracy"": accuracy,
                ""cur_lr"": lr,
                ""optimal_lr"": optimal_lr,  # for debugging
                ""q_err"": q_err,  # for debugging
                ""done"": accuracy > midpoint * 2,  # this stops the training process
            },
            checkpoint=checkpoint,
        )


        ```
    ###Assistant : Toy PBT problem for benchmarking adaptive learning rate.

    The goal is to optimize this trainable's accuracy. The accuracy increases
    fastest at the optimal lr, which is a function of the current accuracy.

    The optimal lr schedule for this problem is the triangle wave as follows.
    Note that many lr schedules for real models also follow this shape:

     best lr
      ^
      |    /\
      |   /  \
      |  /    \
      | /      \
      ------------> accuracy

    In this problem, using PBT with a population of 2-4 is sufficient to
    roughly approximate this lr schedule. Higher population sizes will yield
    faster convergence. Training will not converge without PBT.
    
    "
1176,"def add_hedge_option(price, implied_volatility, strike, days, side):
    
    # Determine delta position given the option
    delta = calc_delta(price, implied_volatility, strike, days, 0, side)

    # Determine gamma position given the option
    gamma = calc_gamma(price, implied_volatility, strike, days, 0)

    # Determine vega position given the option
    vega = calc_vega(price, implied_volatility, strike, days, 0)

    return delta, gamma, vega

","Determine the delta, gamma and vega value of the portfolio and/or options.

    Parameters
    ----------
    price: int
        The price.
    implied_volatility: float
        The implied volatility.
    strike: float
        The strike price.
    days: float
        The amount of days until expiration. Use annual notation thus a month would be 30 / 360.
    sign: int
        Whether you have a long (1) or short (-1) position

    Returns
    -------
    delta: float
    gamma: float
    portfolio: float
    ",67,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_hedge_option(price, implied_volatility, strike, days, side):
    
    # Determine delta position given the option
    delta = calc_delta(price, implied_volatility, strike, days, 0, side)

    # Determine gamma position given the option
    gamma = calc_gamma(price, implied_volatility, strike, days, 0)

    # Determine vega position given the option
    vega = calc_vega(price, implied_volatility, strike, days, 0)

    return delta, gamma, vega


        ```
    ###Assistant : Determine the delta, gamma and vega value of the portfolio and/or options.

    Parameters
    ----------
    price: int
        The price.
    implied_volatility: float
        The implied volatility.
    strike: float
        The strike price.
    days: float
        The amount of days until expiration. Use annual notation thus a month would be 30 / 360.
    sign: int
        Whether you have a long (1) or short (-1) position

    Returns
    -------
    delta: float
    gamma: float
    portfolio: float
    
    "
1177,"def header_encode(self, string):
        
        codec = self.output_codec or 'us-ascii'
        header_bytes = _encode(string, codec)
        # 7bit/8bit encodings return the string unchanged (modulo conversions)
        encoder_module = self._get_encoder(header_bytes)
        if encoder_module is None:
            return string
        return encoder_module.header_encode(header_bytes, codec)
","Header-encode a string by converting it first to bytes.

        The type of encoding (base64 or quoted-printable) will be based on
        this charset's `header_encoding`.

        :param string: A unicode string for the header.  It must be possible
            to encode this string to bytes using the character set's
            output codec.
        :return: The encoded string, with RFC 2047 chrome.
        ",55,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def header_encode(self, string):
        
        codec = self.output_codec or 'us-ascii'
        header_bytes = _encode(string, codec)
        # 7bit/8bit encodings return the string unchanged (modulo conversions)
        encoder_module = self._get_encoder(header_bytes)
        if encoder_module is None:
            return string
        return encoder_module.header_encode(header_bytes, codec)

        ```
    ###Assistant : Header-encode a string by converting it first to bytes.

        The type of encoding (base64 or quoted-printable) will be based on
        this charset's `header_encoding`.

        :param string: A unicode string for the header.  It must be possible
            to encode this string to bytes using the character set's
            output codec.
        :return: The encoded string, with RFC 2047 chrome.
        
    "
1178,"def data_dict(self, records):
        
        self.version = records[0].replace(""File-Date:"", """").strip()
        dic = {}
        dic[""deprecated""] = {}
        for label in [
            ""language"",
            ""extlang"",
            ""script"",
            ""region"",
            ""variant"",
            ""redundant"",
            ""grandfathered"",
        ]:
            dic[""deprecated""][label] = {}
        for record in records[1:]:
            fields = [field.split("": "") for field in record.strip().split(""\n"")]
            typ = fields[0][1]
            tag = fields[1][1]
            if typ not in dic:
                dic[typ] = {}
            subfields = {}
            for field in fields[2:]:
                if len(field) == 2:
                    [key, val] = field
                    if key not in subfields:
                        subfields[key] = [val]
                    else:  # multiple value
                        subfields[key].append(val)
                else:  # multiline field
                    subfields[key][-1] += "" "" + field[0].strip()
                if (
                    ""Deprecated"" not in record
                    and typ == ""language""
                    and key == ""Description""
                ):
                    self.langcode[subfields[key][-1]] = tag
            for key in subfields:
                if len(subfields[key]) == 1:  # single value
                    subfields[key] = subfields[key][0]
            if ""Deprecated"" in record:
                dic[""deprecated""][typ][tag] = subfields
            else:
                dic[typ][tag] = subfields
        return dic
",Convert the BCP-47 language subtag registry to a dictionary,9,137,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def data_dict(self, records):
        
        self.version = records[0].replace(""File-Date:"", """").strip()
        dic = {}
        dic[""deprecated""] = {}
        for label in [
            ""language"",
            ""extlang"",
            ""script"",
            ""region"",
            ""variant"",
            ""redundant"",
            ""grandfathered"",
        ]:
            dic[""deprecated""][label] = {}
        for record in records[1:]:
            fields = [field.split("": "") for field in record.strip().split(""\n"")]
            typ = fields[0][1]
            tag = fields[1][1]
            if typ not in dic:
                dic[typ] = {}
            subfields = {}
            for field in fields[2:]:
                if len(field) == 2:
                    [key, val] = field
                    if key not in subfields:
                        subfields[key] = [val]
                    else:  # multiple value
                        subfields[key].append(val)
                else:  # multiline field
                    subfields[key][-1] += "" "" + field[0].strip()
                if (
                    ""Deprecated"" not in record
                    and typ == ""language""
                    and key == ""Description""
                ):
                    self.langcode[subfields[key][-1]] = tag
            for key in subfields:
                if len(subfields[key]) == 1:  # single value
                    subfields[key] = subfields[key][0]
            if ""Deprecated"" in record:
                dic[""deprecated""][typ][tag] = subfields
            else:
                dic[typ][tag] = subfields
        return dic

        ```
    ###Assistant : Convert the BCP-47 language subtag registry to a dictionary
    "
1179,"def test_async_add_hass_job_schedule_partial_coroutinefunction(event_loop):
    
    hass = MagicMock(loop=MagicMock(wraps=event_loop))
",Test that we schedule partial coros and add jobs to the job pool.,13,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_async_add_hass_job_schedule_partial_coroutinefunction(event_loop):
    
    hass = MagicMock(loop=MagicMock(wraps=event_loop))

        ```
    ###Assistant : Test that we schedule partial coros and add jobs to the job pool.
    "
1180,"def to_label_objs(self, answer_type=""generative""):
        
        df_labels = self.df[[""id"", ""question"", ""answer_text"", ""answer_start"", ""context"", ""document_id""]]
        record_dicts = df_labels.to_dict(""records"")
        labels = [
            Label(
                query=record[""question""],
                answer=Answer(answer=record[""answer_text""], answer_type=answer_type),
                is_correct_answer=True,
                is_correct_document=True,
                id=record[""id""],
                origin=record.get(""origin"", ""gold-label""),
                document=Document(content=record.get(""context""), id=str(record[""document_id""])),
            )
            for record in record_dicts
        ]
        return labels
",Export all labels stored in this object to haystack.Label objects,10,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_label_objs(self, answer_type=""generative""):
        
        df_labels = self.df[[""id"", ""question"", ""answer_text"", ""answer_start"", ""context"", ""document_id""]]
        record_dicts = df_labels.to_dict(""records"")
        labels = [
            Label(
                query=record[""question""],
                answer=Answer(answer=record[""answer_text""], answer_type=answer_type),
                is_correct_answer=True,
                is_correct_document=True,
                id=record[""id""],
                origin=record.get(""origin"", ""gold-label""),
                document=Document(content=record.get(""context""), id=str(record[""document_id""])),
            )
            for record in record_dicts
        ]
        return labels

        ```
    ###Assistant : Export all labels stored in this object to haystack.Label objects
    "
1181,"def test_api_get_storage_path(self):
        
        response = self.client.get(""/api/storage_paths/"", format=""json"")
        self.assertEqual(response.status_code, 200)

        self.assertEqual(response.status_code, 200)
        self.assertEqual(response.data[""count""], 1)

        resp_storage_path = response.data[""results""][0]
        self.assertEqual(resp_storage_path[""id""], self.sp1.id)
        self.assertEqual(resp_storage_path[""path""], self.sp1.path)
","
        GIVEN:
            - API request to get all storage paths
        WHEN:
            - API is called
        THEN:
            - Existing storage paths are returned
        ",21,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_api_get_storage_path(self):
        
        response = self.client.get(""/api/storage_paths/"", format=""json"")
        self.assertEqual(response.status_code, 200)

        self.assertEqual(response.status_code, 200)
        self.assertEqual(response.data[""count""], 1)

        resp_storage_path = response.data[""results""][0]
        self.assertEqual(resp_storage_path[""id""], self.sp1.id)
        self.assertEqual(resp_storage_path[""path""], self.sp1.path)

        ```
    ###Assistant : 
        GIVEN:
            - API request to get all storage paths
        WHEN:
            - API is called
        THEN:
            - Existing storage paths are returned
        
    "
1182,"def test_normalize_metric_warning():
    
    msg = ""Normalized stress is not supported""
    sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
    with pytest.raises(ValueError, match=msg):
        mds.smacof(sim, metric=True, normalized_stress=True)
","
    Test that a UserWarning is emitted when using normalized stress with
    metric-MDS.
    ",12,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_normalize_metric_warning():
    
    msg = ""Normalized stress is not supported""
    sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]])
    with pytest.raises(ValueError, match=msg):
        mds.smacof(sim, metric=True, normalized_stress=True)

        ```
    ###Assistant : 
    Test that a UserWarning is emitted when using normalized stress with
    metric-MDS.
    
    "
1183,"def delete_and_patch_duplicate_bins():

	duplicate_bins = frappe.db.sql(, as_dict=1)

	for duplicate_bin in duplicate_bins:
		existing_bins = frappe.get_list(""Bin"",
				filters={
					""item_code"": duplicate_bin.item_code,
					""warehouse"": duplicate_bin.warehouse
					},
				fields=[""name""],
				order_by=""creation"",)

		# keep last one
		existing_bins.pop()

		for broken_bin in existing_bins:
			frappe.delete_doc(""Bin"", broken_bin.name)

		qty_dict = {
			""reserved_qty"": get_reserved_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""indented_qty"": get_indented_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""ordered_qty"": get_ordered_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""planned_qty"": get_planned_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""actual_qty"": get_balance_qty_from_sle(duplicate_bin.item_code, duplicate_bin.warehouse)
		}

		update_bin_qty(duplicate_bin.item_code, duplicate_bin.warehouse, qty_dict)
","
		SELECT
			item_code, warehouse, count(*) as bin_count
		FROM
			tabBin
		GROUP BY
			item_code, warehouse
		HAVING
			bin_count > 1
	",16,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_and_patch_duplicate_bins():

	duplicate_bins = frappe.db.sql(, as_dict=1)

	for duplicate_bin in duplicate_bins:
		existing_bins = frappe.get_list(""Bin"",
				filters={
					""item_code"": duplicate_bin.item_code,
					""warehouse"": duplicate_bin.warehouse
					},
				fields=[""name""],
				order_by=""creation"",)

		# keep last one
		existing_bins.pop()

		for broken_bin in existing_bins:
			frappe.delete_doc(""Bin"", broken_bin.name)

		qty_dict = {
			""reserved_qty"": get_reserved_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""indented_qty"": get_indented_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""ordered_qty"": get_ordered_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""planned_qty"": get_planned_qty(duplicate_bin.item_code, duplicate_bin.warehouse),
			""actual_qty"": get_balance_qty_from_sle(duplicate_bin.item_code, duplicate_bin.warehouse)
		}

		update_bin_qty(duplicate_bin.item_code, duplicate_bin.warehouse, qty_dict)

        ```
    ###Assistant : 
		SELECT
			item_code, warehouse, count(*) as bin_count
		FROM
			tabBin
		GROUP BY
			item_code, warehouse
		HAVING
			bin_count > 1
	
    "
1184,"async def test_stop_long_running_job(job_sdk_client):
    
    agent_client, head_client = job_sdk_client

    with tempfile.TemporaryDirectory() as tmp_dir:
        path = Path(tmp_dir)
        driver_script = 
        test_script_file = path / ""test_script.py""
        with open(test_script_file, ""w+"") as file:
            file.write(driver_script)

        runtime_env = {""working_dir"": tmp_dir}
        runtime_env = upload_working_dir_if_needed(runtime_env, tmp_dir, logger=logger)
        runtime_env = RuntimeEnv(**runtime_env).to_dict()

        request = validate_request_type(
            {""runtime_env"": runtime_env, ""entrypoint"": ""python test_script.py""},
            JobSubmitRequest,
        )
        submit_result = await agent_client.submit_job_internal(request)
        job_id = submit_result.submission_id

        resp = await agent_client.stop_job_internal(job_id)
        assert resp.stopped is True

        wait_for_condition(
            partial(
                _check_job, client=head_client, job_id=job_id, status=JobStatus.STOPPED
            ),
            timeout=10,
        )


@pytest.mark.asyncio","
    Submit a job that runs for a while and stop it in the middle.
    
print('Hello !')
import time
time.sleep(300) # This should never finish
raise RuntimeError('Intentionally failed.')
        ",27,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_stop_long_running_job(job_sdk_client):
    
    agent_client, head_client = job_sdk_client

    with tempfile.TemporaryDirectory() as tmp_dir:
        path = Path(tmp_dir)
        driver_script = 
        test_script_file = path / ""test_script.py""
        with open(test_script_file, ""w+"") as file:
            file.write(driver_script)

        runtime_env = {""working_dir"": tmp_dir}
        runtime_env = upload_working_dir_if_needed(runtime_env, tmp_dir, logger=logger)
        runtime_env = RuntimeEnv(**runtime_env).to_dict()

        request = validate_request_type(
            {""runtime_env"": runtime_env, ""entrypoint"": ""python test_script.py""},
            JobSubmitRequest,
        )
        submit_result = await agent_client.submit_job_internal(request)
        job_id = submit_result.submission_id

        resp = await agent_client.stop_job_internal(job_id)
        assert resp.stopped is True

        wait_for_condition(
            partial(
                _check_job, client=head_client, job_id=job_id, status=JobStatus.STOPPED
            ),
            timeout=10,
        )


@pytest.mark.asyncio
        ```
    ###Assistant : 
    Submit a job that runs for a while and stop it in the middle.
    
print('Hello !')
import time
time.sleep(300) # This should never finish
raise RuntimeError('Intentionally failed.')
        
    "
1185,"def print_help(self):
        
        source_txt = CRYPTO_SOURCES.get(self.source, ""?"") if self.source != """" else """"
        help_text = f
        console.print(text=help_text, menu=""Stocks - Due Diligence"")
","Print help[cmds]
    load        load a specific cryptocurrency for analysis

[param]Coin: [/param]{self.current_coin}
[param]Source: [/param]{source_txt}

[src]Glassnode[/src]
   active          active addresses
   nonzero         addresses with non-zero balances
   change          30d change of supply held on exchange wallets
   eb              total balance held on exchanges (in percentage and units)
[src]Coinglass[/src]
   oi              open interest per exchange
[src]CoinPaprika[/src]
   basic           basic information about loaded coin
   ps              price and supply related metrics for loaded coin
   mkt             all markets for loaded coin
   ex              all exchanges where loaded coin is listed
   twitter         tweets for loaded coin
   events          events related to loaded coin
[src]CoinGecko[/src]
   info            basic information about loaded coin
   market          market stats about loaded coin
   ath             all time high related stats for loaded coin
   atl             all time low related stats for loaded coin
   web             found websites for loaded coin e.g forum, homepage
   social          social portals urls for loaded coin, e.g reddit, twitter
   score           different kind of scores for loaded coin, e.g developer score, sentiment score
   dev             github, bitbucket coin development statistics
   bc              links to blockchain explorers for loaded coin
[src]Binance[/src]
   binbook         show order book
   balance         show coin balance
[src]Coinbase[/src]
   cbbook          show order book
   trades          show last trades
   stats           show coin stats[/cmds]
",187,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        
        source_txt = CRYPTO_SOURCES.get(self.source, ""?"") if self.source != """" else """"
        help_text = f
        console.print(text=help_text, menu=""Stocks - Due Diligence"")

        ```
    ###Assistant : Print help[cmds]
    load        load a specific cryptocurrency for analysis

[param]Coin: [/param]{self.current_coin}
[param]Source: [/param]{source_txt}

[src]Glassnode[/src]
   active          active addresses
   nonzero         addresses with non-zero balances
   change          30d change of supply held on exchange wallets
   eb              total balance held on exchanges (in percentage and units)
[src]Coinglass[/src]
   oi              open interest per exchange
[src]CoinPaprika[/src]
   basic           basic information about loaded coin
   ps              price and supply related metrics for loaded coin
   mkt             all markets for loaded coin
   ex              all exchanges where loaded coin is listed
   twitter         tweets for loaded coin
   events          events related to loaded coin
[src]CoinGecko[/src]
   info            basic information about loaded coin
   market          market stats about loaded coin
   ath             all time high related stats for loaded coin
   atl             all time low related stats for loaded coin
   web             found websites for loaded coin e.g forum, homepage
   social          social portals urls for loaded coin, e.g reddit, twitter
   score           different kind of scores for loaded coin, e.g developer score, sentiment score
   dev             github, bitbucket coin development statistics
   bc              links to blockchain explorers for loaded coin
[src]Binance[/src]
   binbook         show order book
   balance         show coin balance
[src]Coinbase[/src]
   cbbook          show order book
   trades          show last trades
   stats           show coin stats[/cmds]

    "
1186,"def test_get_cached_repo_files_with_all_files(self):
        
        responses.add(
            method=responses.GET,
            url=f""https://api.github.com/repos/{self.repo.name}/git/trees/master?recursive=1"",
            status=200,
            json={
                ""tree"": [
                    {""type"": ""blob"", ""path"": ""src/foo.py""},
                    {""type"": ""blob"", ""path"": ""README""},
                ]
            },
        )
        repo_key = f""github:repo:{self.repo.name}:all""
        assert cache.get(repo_key) is None
        with mock.patch(""sentry.integrations.github.client.get_jwt"", return_value=b""jwt_token_1""):
            files = self.client.get_cached_repo_files(self.repo.name, ""master"")
            assert files == [""src/foo.py""]
",Fetch files for repo. All files rather than just source code files,12,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_cached_repo_files_with_all_files(self):
        
        responses.add(
            method=responses.GET,
            url=f""https://api.github.com/repos/{self.repo.name}/git/trees/master?recursive=1"",
            status=200,
            json={
                ""tree"": [
                    {""type"": ""blob"", ""path"": ""src/foo.py""},
                    {""type"": ""blob"", ""path"": ""README""},
                ]
            },
        )
        repo_key = f""github:repo:{self.repo.name}:all""
        assert cache.get(repo_key) is None
        with mock.patch(""sentry.integrations.github.client.get_jwt"", return_value=b""jwt_token_1""):
            files = self.client.get_cached_repo_files(self.repo.name, ""master"")
            assert files == [""src/foo.py""]

        ```
    ###Assistant : Fetch files for repo. All files rather than just source code files
    "
1187,"def check_migrations(self):
        
        from django.db.migrations.executor import MigrationExecutor

        try:
            executor = MigrationExecutor(connections[DEFAULT_DB_ALIAS])
        except ImproperlyConfigured:
            # No databases are configured (or the dummy one)
            return

        plan = executor.migration_plan(executor.loader.graph.leaf_nodes())
        if plan:
            apps_waiting_migration = sorted(
                {migration.app_label for migration, backwards in plan}
            )
            self.stdout.write(
                self.style.NOTICE(
                    ""\nYou have %(unapplied_migration_count)s unapplied migration(s). ""
                    ""Your project may not work properly until you apply the ""
                    ""migrations for app(s): %(apps_waiting_migration)s.""
                    % {
                        ""unapplied_migration_count"": len(plan),
                        ""apps_waiting_migration"": "", "".join(apps_waiting_migration),
                    }
                )
            )
            self.stdout.write(
                self.style.NOTICE(""Run 'python manage.py migrate' to apply them."")
            )
","
        Print a warning if the set of migrations on disk don't match the
        migrations in the database.
        ",17,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_migrations(self):
        
        from django.db.migrations.executor import MigrationExecutor

        try:
            executor = MigrationExecutor(connections[DEFAULT_DB_ALIAS])
        except ImproperlyConfigured:
            # No databases are configured (or the dummy one)
            return

        plan = executor.migration_plan(executor.loader.graph.leaf_nodes())
        if plan:
            apps_waiting_migration = sorted(
                {migration.app_label for migration, backwards in plan}
            )
            self.stdout.write(
                self.style.NOTICE(
                    ""\nYou have %(unapplied_migration_count)s unapplied migration(s). ""
                    ""Your project may not work properly until you apply the ""
                    ""migrations for app(s): %(apps_waiting_migration)s.""
                    % {
                        ""unapplied_migration_count"": len(plan),
                        ""apps_waiting_migration"": "", "".join(apps_waiting_migration),
                    }
                )
            )
            self.stdout.write(
                self.style.NOTICE(""Run 'python manage.py migrate' to apply them."")
            )

        ```
    ###Assistant : 
        Print a warning if the set of migrations on disk don't match the
        migrations in the database.
        
    "
1188,"def find_induced_nodes(G, s, t, treewidth_bound=sys.maxsize):
    
    if not is_chordal(G):
        raise nx.NetworkXError(""Input graph is not chordal."")

    H = nx.Graph(G)
    H.add_edge(s, t)
    induced_nodes = set()
    triplet = _find_chordality_breaker(H, s, treewidth_bound)
    while triplet:
        (u, v, w) = triplet
        induced_nodes.update(triplet)
        for n in triplet:
            if n != s:
                H.add_edge(s, n)
        triplet = _find_chordality_breaker(H, s, treewidth_bound)
    if induced_nodes:
        # Add t and the second node in the induced path from s to t.
        induced_nodes.add(t)
        for u in G[s]:
            if len(induced_nodes & set(G[u])) == 2:
                induced_nodes.add(u)
                break
    return induced_nodes

","Returns the set of induced nodes in the path from s to t.

    Parameters
    ----------
    G : graph
      A chordal NetworkX graph
    s : node
        Source node to look for induced nodes
    t : node
        Destination node to look for induced nodes
    treewidth_bound: float
        Maximum treewidth acceptable for the graph H. The search
        for induced nodes will end as soon as the treewidth_bound is exceeded.

    Returns
    -------
    induced_nodes : Set of nodes
        The set of induced nodes in the path from s to t in G

    Raises
    ------
    NetworkXError
        The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
        If the input graph is an instance of one of these classes, a
        :exc:`NetworkXError` is raised.
        The algorithm can only be applied to chordal graphs. If the input
        graph is found to be non-chordal, a :exc:`NetworkXError` is raised.

    Examples
    --------
    >>> G = nx.Graph()
    >>> G = nx.generators.classic.path_graph(10)
    >>> induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)
    >>> sorted(induced_nodes)
    [1, 2, 3, 4, 5, 6, 7, 8, 9]

    Notes
    -----
    G must be a chordal graph and (s,t) an edge that is not in G.

    If a treewidth_bound is provided, the search for induced nodes will end
    as soon as the treewidth_bound is exceeded.

    The algorithm is inspired by Algorithm 4 in [1]_.
    A formal definition of induced node can also be found on that reference.

    References
    ----------
    .. [1] Learning Bounded Treewidth Bayesian Networks.
       Gal Elidan, Stephen Gould; JMLR, 9(Dec):2699--2731, 2008.
       http://jmlr.csail.mit.edu/papers/volume9/elidan08a/elidan08a.pdf
    ",239,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_induced_nodes(G, s, t, treewidth_bound=sys.maxsize):
    
    if not is_chordal(G):
        raise nx.NetworkXError(""Input graph is not chordal."")

    H = nx.Graph(G)
    H.add_edge(s, t)
    induced_nodes = set()
    triplet = _find_chordality_breaker(H, s, treewidth_bound)
    while triplet:
        (u, v, w) = triplet
        induced_nodes.update(triplet)
        for n in triplet:
            if n != s:
                H.add_edge(s, n)
        triplet = _find_chordality_breaker(H, s, treewidth_bound)
    if induced_nodes:
        # Add t and the second node in the induced path from s to t.
        induced_nodes.add(t)
        for u in G[s]:
            if len(induced_nodes & set(G[u])) == 2:
                induced_nodes.add(u)
                break
    return induced_nodes


        ```
    ###Assistant : Returns the set of induced nodes in the path from s to t.

    Parameters
    ----------
    G : graph
      A chordal NetworkX graph
    s : node
        Source node to look for induced nodes
    t : node
        Destination node to look for induced nodes
    treewidth_bound: float
        Maximum treewidth acceptable for the graph H. The search
        for induced nodes will end as soon as the treewidth_bound is exceeded.

    Returns
    -------
    induced_nodes : Set of nodes
        The set of induced nodes in the path from s to t in G

    Raises
    ------
    NetworkXError
        The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
        If the input graph is an instance of one of these classes, a
        :exc:`NetworkXError` is raised.
        The algorithm can only be applied to chordal graphs. If the input
        graph is found to be non-chordal, a :exc:`NetworkXError` is raised.

    Examples
    --------
    >>> G = nx.Graph()
    >>> G = nx.generators.classic.path_graph(10)
    >>> induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)
    >>> sorted(induced_nodes)
    [1, 2, 3, 4, 5, 6, 7, 8, 9]

    Notes
    -----
    G must be a chordal graph and (s,t) an edge that is not in G.

    If a treewidth_bound is provided, the search for induced nodes will end
    as soon as the treewidth_bound is exceeded.

    The algorithm is inspired by Algorithm 4 in [1]_.
    A formal definition of induced node can also be found on that reference.

    References
    ----------
    .. [1] Learning Bounded Treewidth Bayesian Networks.
       Gal Elidan, Stephen Gould; JMLR, 9(Dec):2699--2731, 2008.
       http://jmlr.csail.mit.edu/papers/volume9/elidan08a/elidan08a.pdf
    
    "
1189,"def any_skipna_inferred_dtype(request):
    
    inferred_dtype, values = request.param
    values = np.array(values, dtype=object)  # object dtype to avoid casting

    # correctness of inference tested in tests/dtypes/test_inference.py
    return inferred_dtype, values


# ----------------------------------------------------------------
# Misc
# ----------------------------------------------------------------
@pytest.fixture","
    Fixture for all inferred dtypes from _libs.lib.infer_dtype

    The covered (inferred) types are:
    * 'string'
    * 'empty'
    * 'bytes'
    * 'mixed'
    * 'mixed-integer'
    * 'mixed-integer-float'
    * 'floating'
    * 'integer'
    * 'decimal'
    * 'boolean'
    * 'datetime64'
    * 'datetime'
    * 'date'
    * 'timedelta'
    * 'time'
    * 'period'
    * 'interval'

    Returns
    -------
    inferred_dtype : str
        The string for the inferred dtype from _libs.lib.infer_dtype
    values : np.ndarray
        An array of object dtype that will be inferred to have
        `inferred_dtype`

    Examples
    --------
    >>> from pandas._libs import lib
    >>>
    >>> def test_something(any_skipna_inferred_dtype):
    ...     inferred_dtype, values = any_skipna_inferred_dtype
    ...     # will pass
    ...     assert lib.infer_dtype(values, skipna=True) == inferred_dtype
    ",100,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def any_skipna_inferred_dtype(request):
    
    inferred_dtype, values = request.param
    values = np.array(values, dtype=object)  # object dtype to avoid casting

    # correctness of inference tested in tests/dtypes/test_inference.py
    return inferred_dtype, values


# ----------------------------------------------------------------
# Misc
# ----------------------------------------------------------------
@pytest.fixture
        ```
    ###Assistant : 
    Fixture for all inferred dtypes from _libs.lib.infer_dtype

    The covered (inferred) types are:
    * 'string'
    * 'empty'
    * 'bytes'
    * 'mixed'
    * 'mixed-integer'
    * 'mixed-integer-float'
    * 'floating'
    * 'integer'
    * 'decimal'
    * 'boolean'
    * 'datetime64'
    * 'datetime'
    * 'date'
    * 'timedelta'
    * 'time'
    * 'period'
    * 'interval'

    Returns
    -------
    inferred_dtype : str
        The string for the inferred dtype from _libs.lib.infer_dtype
    values : np.ndarray
        An array of object dtype that will be inferred to have
        `inferred_dtype`

    Examples
    --------
    >>> from pandas._libs import lib
    >>>
    >>> def test_something(any_skipna_inferred_dtype):
    ...     inferred_dtype, values = any_skipna_inferred_dtype
    ...     # will pass
    ...     assert lib.infer_dtype(values, skipna=True) == inferred_dtype
    
    "
1190,"def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
    r

    assert isinstance(factor, int) and factor >= 1
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * gain
    p = k.shape[0] - factor
    return _simple_upfirdn_2d(x, k, down=factor, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)

#----------------------------------------------------------------------------
","Downsample a batch of 2D images with the given filter.

    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
    and downsamples each image with the given filter. The filter is normalized so that
    if the input pixels are constant, they will be scaled by the specified `gain`.
    Pixels outside the image are assumed to be zero, and the filter is padded with
    zeros so that its shape is a multiple of the downsampling factor.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to average pooling.
        factor:       Integer downsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `""ref""` or `""cuda""` (default).

    Returns:
        Tensor of the shape `[N, C, H // factor, W // factor]` or
        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
    ",181,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
    r

    assert isinstance(factor, int) and factor >= 1
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * gain
    p = k.shape[0] - factor
    return _simple_upfirdn_2d(x, k, down=factor, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)

#----------------------------------------------------------------------------

        ```
    ###Assistant : Downsample a batch of 2D images with the given filter.

    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
    and downsamples each image with the given filter. The filter is normalized so that
    if the input pixels are constant, they will be scaled by the specified `gain`.
    Pixels outside the image are assumed to be zero, and the filter is padded with
    zeros so that its shape is a multiple of the downsampling factor.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to average pooling.
        factor:       Integer downsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `""ref""` or `""cuda""` (default).

    Returns:
        Tensor of the shape `[N, C, H // factor, W // factor]` or
        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
    
    "
1191,"def _check_deprecated_resample_kwargs(kwargs, origin):
    
    # Deprecation warning of `base` and `loffset` since v1.1.0:
    # we are raising the warning here to be able to set the `stacklevel`
    # properly since we need to raise the `base` and `loffset` deprecation
    # warning from three different cases:
    #   core/generic.py::NDFrame.resample
    #   core/groupby/groupby.py::GroupBy.resample
    #   core/groupby/grouper.py::Grouper
    # raising these warnings from TimeGrouper directly would fail the test:
    #   tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base

    if kwargs.get(""base"", None) is not None:
        warnings.warn(
            ""'base' in .resample() and in Grouper() is deprecated.\n""
            ""The new arguments that you should use are 'offset' or 'origin'.\n""
            '\n>>> df.resample(freq=""3s"", base=2)\n'
            ""\nbecomes:\n""
            '\n>>> df.resample(freq=""3s"", offset=""2s"")\n',
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
    if kwargs.get(""loffset"", None) is not None:
        warnings.warn(
            ""'loffset' in .resample() and in Grouper() is deprecated.\n""
            '\n>>> df.resample(freq=""3s"", loffset=""8H"")\n'
            ""\nbecomes:\n""
            ""\n>>> from pandas.tseries.frequencies import to_offset""
            '\n>>> df = df.resample(freq=""3s"").mean()'
            '\n>>> df.index = df.index.to_timestamp() + to_offset(""8H"")\n',
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
","
    Check for use of deprecated parameters in ``resample`` and related functions.

    Raises the appropriate warnings if these parameters are detected.
    Only sets an approximate ``stacklevel`` for the warnings (see #37603, #36629).

    Parameters
    ----------
    kwargs : dict
        Dictionary of keyword arguments to check for deprecated parameters.
    origin : object
        From where this function is being called; either Grouper or TimeGrouper. Used
        to determine an approximate stacklevel.
    ",65,136,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_deprecated_resample_kwargs(kwargs, origin):
    
    # Deprecation warning of `base` and `loffset` since v1.1.0:
    # we are raising the warning here to be able to set the `stacklevel`
    # properly since we need to raise the `base` and `loffset` deprecation
    # warning from three different cases:
    #   core/generic.py::NDFrame.resample
    #   core/groupby/groupby.py::GroupBy.resample
    #   core/groupby/grouper.py::Grouper
    # raising these warnings from TimeGrouper directly would fail the test:
    #   tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base

    if kwargs.get(""base"", None) is not None:
        warnings.warn(
            ""'base' in .resample() and in Grouper() is deprecated.\n""
            ""The new arguments that you should use are 'offset' or 'origin'.\n""
            '\n>>> df.resample(freq=""3s"", base=2)\n'
            ""\nbecomes:\n""
            '\n>>> df.resample(freq=""3s"", offset=""2s"")\n',
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )
    if kwargs.get(""loffset"", None) is not None:
        warnings.warn(
            ""'loffset' in .resample() and in Grouper() is deprecated.\n""
            '\n>>> df.resample(freq=""3s"", loffset=""8H"")\n'
            ""\nbecomes:\n""
            ""\n>>> from pandas.tseries.frequencies import to_offset""
            '\n>>> df = df.resample(freq=""3s"").mean()'
            '\n>>> df.index = df.index.to_timestamp() + to_offset(""8H"")\n',
            FutureWarning,
            stacklevel=find_stack_level(inspect.currentframe()),
        )

        ```
    ###Assistant : 
    Check for use of deprecated parameters in ``resample`` and related functions.

    Raises the appropriate warnings if these parameters are detected.
    Only sets an approximate ``stacklevel`` for the warnings (see #37603, #36629).

    Parameters
    ----------
    kwargs : dict
        Dictionary of keyword arguments to check for deprecated parameters.
    origin : object
        From where this function is being called; either Grouper or TimeGrouper. Used
        to determine an approximate stacklevel.
    
    "
1192,"def get_conn(self) -> DataCatalogClient:
        
        if not self._client:
            self._client = DataCatalogClient(credentials=self._get_credentials(), client_info=CLIENT_INFO)
        return self._client
",Retrieves client library object that allow access to Cloud Data Catalog service.,12,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_conn(self) -> DataCatalogClient:
        
        if not self._client:
            self._client = DataCatalogClient(credentials=self._get_credentials(), client_info=CLIENT_INFO)
        return self._client

        ```
    ###Assistant : Retrieves client library object that allow access to Cloud Data Catalog service.
    "
1193,"def get_api_client(self) -> ApiClient:
        
        try:
            return new_client_from_config_dict(
                config_dict=self.config, context=self.context
            )
        except ConfigException:
            raise
","
        Returns an instance of the kubernetes api client with a specific context
        ",12,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_api_client(self) -> ApiClient:
        
        try:
            return new_client_from_config_dict(
                config_dict=self.config, context=self.context
            )
        except ConfigException:
            raise

        ```
    ###Assistant : 
        Returns an instance of the kubernetes api client with a specific context
        
    "
1194,"def cuts(self) -> list[list[int]]:
        
        if self._cuts is not None:
            return self._cuts
        width = self.width
        height = self.height
        screen_region = Region(0, 0, width, height)
        cuts_sets = [{0, width} for _ in range(height)]

        if self.map is not None:
            for region, order, clip in self.map.values():
                region = region.intersection(clip)
                if region and (region in screen_region):
                    region_cuts = region.x_extents
                    for y in region.y_range:
                        cuts_sets[y].update(region_cuts)

        # Sort the cuts for each line
        self._cuts = [sorted(cut_set) for cut_set in cuts_sets]
        return self._cuts
","Get vertical cuts.

        A cut is every point on a line where a widget starts or ends.

        Returns:
            list[list[int]]: A list of cuts for every line.
        ",26,75,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cuts(self) -> list[list[int]]:
        
        if self._cuts is not None:
            return self._cuts
        width = self.width
        height = self.height
        screen_region = Region(0, 0, width, height)
        cuts_sets = [{0, width} for _ in range(height)]

        if self.map is not None:
            for region, order, clip in self.map.values():
                region = region.intersection(clip)
                if region and (region in screen_region):
                    region_cuts = region.x_extents
                    for y in region.y_range:
                        cuts_sets[y].update(region_cuts)

        # Sort the cuts for each line
        self._cuts = [sorted(cut_set) for cut_set in cuts_sets]
        return self._cuts

        ```
    ###Assistant : Get vertical cuts.

        A cut is every point on a line where a widget starts or ends.

        Returns:
            list[list[int]]: A list of cuts for every line.
        
    "
1195,"def is_subclassed(layer):
    
    return (
        layer.__module__.find(""keras.engine"") == -1
        and layer.__module__.find(""keras.layers"") == -1
    )

",Returns True if the object is a subclassed layer or subclassed model.,12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_subclassed(layer):
    
    return (
        layer.__module__.find(""keras.engine"") == -1
        and layer.__module__.find(""keras.layers"") == -1
    )


        ```
    ###Assistant : Returns True if the object is a subclassed layer or subclassed model.
    "
1196,"def _mat(self):
        sympy_deprecation_warning(
            ,
            deprecated_since_version=""1.9"",
            active_deprecations_target=""deprecated-private-matrix-attributes""
        )

        return self.flat()
","
            The private _mat attribute of Matrix is deprecated. Use the
            .flat() method instead.
            ",13,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _mat(self):
        sympy_deprecation_warning(
            ,
            deprecated_since_version=""1.9"",
            active_deprecations_target=""deprecated-private-matrix-attributes""
        )

        return self.flat()

        ```
    ###Assistant : 
            The private _mat attribute of Matrix is deprecated. Use the
            .flat() method instead.
            
    "
1197,"def test_missing_required_field(self):
        
        cf3 = CustomField(type=CustomFieldTypeChoices.TYPE_TEXT, name='baz', required=True)
        cf3.save()
        cf3.content_types.set([ContentType.objects.get_for_model(Site)])

        site = Site(name='Test Site', slug='test-site')

        # Set custom field data with a required field omitted
        site.custom_field_data['foo'] = 'abc'
        with self.assertRaises(ValidationError):
            site.clean()

        site.custom_field_data['baz'] = 'def'
        site.clean()

","
        Check that a ValidationError is raised if any required custom fields are not present.
        ",14,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_missing_required_field(self):
        
        cf3 = CustomField(type=CustomFieldTypeChoices.TYPE_TEXT, name='baz', required=True)
        cf3.save()
        cf3.content_types.set([ContentType.objects.get_for_model(Site)])

        site = Site(name='Test Site', slug='test-site')

        # Set custom field data with a required field omitted
        site.custom_field_data['foo'] = 'abc'
        with self.assertRaises(ValidationError):
            site.clean()

        site.custom_field_data['baz'] = 'def'
        site.clean()


        ```
    ###Assistant : 
        Check that a ValidationError is raised if any required custom fields are not present.
        
    "
1198,"def test_basic(self):
        context = Context({})

        template = 

        expected = 

        self.assertHTMLEqual(expected, Template(template).render(context))
","
            {% load wagtailadmin_tags %}
            {% fragment as my_fragment %}
            <p>Hello, World</p>
            {% endfragment %}
            Text coming after:
            {{ my_fragment }}
        
            Text coming after:
            <p>Hello, World</p>
        ",25,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_basic(self):
        context = Context({})

        template = 

        expected = 

        self.assertHTMLEqual(expected, Template(template).render(context))

        ```
    ###Assistant : 
            {% load wagtailadmin_tags %}
            {% fragment as my_fragment %}
            <p>Hello, World</p>
            {% endfragment %}
            Text coming after:
            {{ my_fragment }}
        
            Text coming after:
            <p>Hello, World</p>
        
    "
1199,"def to_dense(self) -> Series:
        
        from pandas import Series

        return Series(
            self._parent.array.to_dense(),
            index=self._parent.index,
            name=self._parent.name,
        )

","
        Convert a Series from sparse values to dense.

        .. versionadded:: 0.25.0

        Returns
        -------
        Series:
            A Series with the same values, stored as a dense array.

        Examples
        --------
        >>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))
        >>> series
        0    0
        1    1
        2    0
        dtype: Sparse[int64, 0]

        >>> series.sparse.to_dense()
        0    0
        1    1
        2    0
        dtype: int64
        ",54,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_dense(self) -> Series:
        
        from pandas import Series

        return Series(
            self._parent.array.to_dense(),
            index=self._parent.index,
            name=self._parent.name,
        )


        ```
    ###Assistant : 
        Convert a Series from sparse values to dense.

        .. versionadded:: 0.25.0

        Returns
        -------
        Series:
            A Series with the same values, stored as a dense array.

        Examples
        --------
        >>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))
        >>> series
        0    0
        1    1
        2    0
        dtype: Sparse[int64, 0]

        >>> series.sparse.to_dense()
        0    0
        1    1
        2    0
        dtype: int64
        
    "
1200,"def replaceHTMLEntity(t):
    
    return _htmlEntityMap.get(t.entity)

# it's easy to get these comment structures wrong - they're very common, so may as well make them available
cStyleComment = Combine(Regex(r""/\*(?:[^*]|\*(?!/))*"") + '*/').setName(""C style comment"")
""Comment of the form ``/* ... */``""

htmlComment = Regex(r""<!--[\s\S]*?-->"").setName(""HTML comment"")
""Comment of the form ``<!-- ... -->``""

restOfLine = Regex(r"".*"").leaveWhitespace().setName(""rest of line"")
dblSlashComment = Regex(r""//(?:\\\n|[^\n])*"").setName(""// comment"")
""Comment of the form ``// ... (to end of line)``""

cppStyleComment = Combine(Regex(r""/\*(?:[^*]|\*(?!/))*"") + '*/' | dblSlashComment).setName(""C++ style comment"")
""Comment of either form :class:`cStyleComment` or :class:`dblSlashComment`""

javaStyleComment = cppStyleComment
""Same as :class:`cppStyleComment`""

pythonStyleComment = Regex(r""#.*"").setName(""Python style comment"")
""Comment of the form ``# ... (to end of line)``""

_commasepitem = Combine(OneOrMore(Word(printables, excludeChars=',')
                                  + Optional(Word("" \t"")
                                             + ~Literal("","") + ~LineEnd()))).streamline().setName(""commaItem"")
commaSeparatedList = delimitedList(Optional(quotedString.copy() | _commasepitem, default="""")).setName(""commaSeparatedList"")


# some other useful expressions - using lower-case class name since we are really using this as a namespace","Helper parser action to replace common HTML entities with their special characters(Deprecated) Predefined expression of 1 or more printable words or
quoted strings, separated by commas.

This expression is deprecated in favor of :class:`pyparsing_common.comma_separated_list`.
",34,141,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def replaceHTMLEntity(t):
    
    return _htmlEntityMap.get(t.entity)

# it's easy to get these comment structures wrong - they're very common, so may as well make them available
cStyleComment = Combine(Regex(r""/\*(?:[^*]|\*(?!/))*"") + '*/').setName(""C style comment"")
""Comment of the form ``/* ... */``""

htmlComment = Regex(r""<!--[\s\S]*?-->"").setName(""HTML comment"")
""Comment of the form ``<!-- ... -->``""

restOfLine = Regex(r"".*"").leaveWhitespace().setName(""rest of line"")
dblSlashComment = Regex(r""//(?:\\\n|[^\n])*"").setName(""// comment"")
""Comment of the form ``// ... (to end of line)``""

cppStyleComment = Combine(Regex(r""/\*(?:[^*]|\*(?!/))*"") + '*/' | dblSlashComment).setName(""C++ style comment"")
""Comment of either form :class:`cStyleComment` or :class:`dblSlashComment`""

javaStyleComment = cppStyleComment
""Same as :class:`cppStyleComment`""

pythonStyleComment = Regex(r""#.*"").setName(""Python style comment"")
""Comment of the form ``# ... (to end of line)``""

_commasepitem = Combine(OneOrMore(Word(printables, excludeChars=',')
                                  + Optional(Word("" \t"")
                                             + ~Literal("","") + ~LineEnd()))).streamline().setName(""commaItem"")
commaSeparatedList = delimitedList(Optional(quotedString.copy() | _commasepitem, default="""")).setName(""commaSeparatedList"")


# some other useful expressions - using lower-case class name since we are really using this as a namespace
        ```
    ###Assistant : Helper parser action to replace common HTML entities with their special characters(Deprecated) Predefined expression of 1 or more printable words or
quoted strings, separated by commas.

This expression is deprecated in favor of :class:`pyparsing_common.comma_separated_list`.

    "
1201,"def read_csv_with_nan(path, nan_percent=0.0):
    
    df = pd.read_csv(path)
    if nan_percent > 0:
        num_rows = len(df)
        for col in df.columns:
            for row in random.sample(range(num_rows), int(round(nan_percent * num_rows))):
                df[col].iloc[row] = np.nan
    return df

",Converts `nan_percent` of samples in each row of the CSV at `path` to NaNs.,14,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read_csv_with_nan(path, nan_percent=0.0):
    
    df = pd.read_csv(path)
    if nan_percent > 0:
        num_rows = len(df)
        for col in df.columns:
            for row in random.sample(range(num_rows), int(round(nan_percent * num_rows))):
                df[col].iloc[row] = np.nan
    return df


        ```
    ###Assistant : Converts `nan_percent` of samples in each row of the CSV at `path` to NaNs.
    "
1202,"def _upsample_2d(self, x, w=None, k=None, factor=2, gain=1):
        

        assert isinstance(factor, int) and factor >= 1

        # Setup filter kernel.
        if k is None:
            k = [1] * factor

        # setup kernel
        k = np.asarray(k, dtype=np.float32)
        if k.ndim == 1:
            k = np.outer(k, k)
        k /= np.sum(k)

        k = k * (gain * (factor**2))

        if self.use_conv:
            convH = w.shape[2]
            convW = w.shape[3]
            inC = w.shape[1]

            p = (k.shape[0] - factor) - (convW - 1)

            stride = (factor, factor)
            # Determine data dimensions.
            stride = [1, 1, factor, factor]
            output_shape = ((x.shape[2] - 1) * factor + convH, (x.shape[3] - 1) * factor + convW)
            output_padding = (
                output_shape[0] - (x.shape[2] - 1) * stride[0] - convH,
                output_shape[1] - (x.shape[3] - 1) * stride[1] - convW,
            )
            assert output_padding[0] >= 0 and output_padding[1] >= 0
            inC = w.shape[1]
            num_groups = x.shape[1] // inC

            # Transpose weights.
            w = paddle.reshape(w, (num_groups, -1, inC, convH, convW))
            w = w[..., ::-1, ::-1].transpose([0, 2, 1, 3, 4])
            w = paddle.reshape(w, (num_groups * inC, -1, convH, convW))

            x = F.conv2d_transpose(x, w, stride=stride, output_padding=output_padding, padding=0)

            x = upfirdn2d_native(x, paddle.to_tensor(k), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
        else:
            p = k.shape[0] - factor
            x = upfirdn2d_native(x, paddle.to_tensor(k), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))

        return x
","Fused `upsample_2d()` followed by `Conv2d()`.

        Args:
        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
        order.
        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
            C]`.
        w: Weight tensor of the shape `[filterH, filterW, inChannels,
            outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
        k: FIR filter of the shape `[firH, firW]` or `[firN]`
            (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
        factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).

        Returns:
        Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as
        `x`.
        ",139,219,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _upsample_2d(self, x, w=None, k=None, factor=2, gain=1):
        

        assert isinstance(factor, int) and factor >= 1

        # Setup filter kernel.
        if k is None:
            k = [1] * factor

        # setup kernel
        k = np.asarray(k, dtype=np.float32)
        if k.ndim == 1:
            k = np.outer(k, k)
        k /= np.sum(k)

        k = k * (gain * (factor**2))

        if self.use_conv:
            convH = w.shape[2]
            convW = w.shape[3]
            inC = w.shape[1]

            p = (k.shape[0] - factor) - (convW - 1)

            stride = (factor, factor)
            # Determine data dimensions.
            stride = [1, 1, factor, factor]
            output_shape = ((x.shape[2] - 1) * factor + convH, (x.shape[3] - 1) * factor + convW)
            output_padding = (
                output_shape[0] - (x.shape[2] - 1) * stride[0] - convH,
                output_shape[1] - (x.shape[3] - 1) * stride[1] - convW,
            )
            assert output_padding[0] >= 0 and output_padding[1] >= 0
            inC = w.shape[1]
            num_groups = x.shape[1] // inC

            # Transpose weights.
            w = paddle.reshape(w, (num_groups, -1, inC, convH, convW))
            w = w[..., ::-1, ::-1].transpose([0, 2, 1, 3, 4])
            w = paddle.reshape(w, (num_groups * inC, -1, convH, convW))

            x = F.conv2d_transpose(x, w, stride=stride, output_padding=output_padding, padding=0)

            x = upfirdn2d_native(x, paddle.to_tensor(k), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
        else:
            p = k.shape[0] - factor
            x = upfirdn2d_native(x, paddle.to_tensor(k), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))

        return x

        ```
    ###Assistant : Fused `upsample_2d()` followed by `Conv2d()`.

        Args:
        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
        order.
        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
            C]`.
        w: Weight tensor of the shape `[filterH, filterW, inChannels,
            outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
        k: FIR filter of the shape `[firH, firW]` or `[firN]`
            (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
        factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).

        Returns:
        Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as
        `x`.
        
    "
1203,"def get_network_names(self) -> t.Optional[t.List[str]]:
        
        if self.networks is None:
            return None

        return sorted(self.networks)
",Return a list of the network names the container is attached to.,12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_network_names(self) -> t.Optional[t.List[str]]:
        
        if self.networks is None:
            return None

        return sorted(self.networks)

        ```
    ###Assistant : Return a list of the network names the container is attached to.
    "
1204,"def get_delivered_items_cost():
	dn_items = frappe.db.sql(
		,
		as_dict=1,
	)

	si_items = frappe.db.sql(
		,
		as_dict=1,
	)

	dn_item_map = {}
	for item in dn_items:
		dn_item_map.setdefault(item.project, item.amount)

	for item in si_items:
		dn_item_map.setdefault(item.project, item.amount)

	return dn_item_map
","select dn.project, sum(dn_item.base_net_amount) as amount
		from `tabDelivery Note` dn, `tabDelivery Note Item` dn_item
		where dn.name = dn_item.parent and dn.docstatus = 1 and ifnull(dn.project, '') != ''
		group by dn.projectselect si.project, sum(si_item.base_net_amount) as amount
		from `tabSales Invoice` si, `tabSales Invoice Item` si_item
		where si.name = si_item.parent and si.docstatus = 1 and si.update_stock = 1
		and si.is_pos = 1 and ifnull(si.project, '') != ''
		group by si.project",65,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_delivered_items_cost():
	dn_items = frappe.db.sql(
		,
		as_dict=1,
	)

	si_items = frappe.db.sql(
		,
		as_dict=1,
	)

	dn_item_map = {}
	for item in dn_items:
		dn_item_map.setdefault(item.project, item.amount)

	for item in si_items:
		dn_item_map.setdefault(item.project, item.amount)

	return dn_item_map

        ```
    ###Assistant : select dn.project, sum(dn_item.base_net_amount) as amount
		from `tabDelivery Note` dn, `tabDelivery Note Item` dn_item
		where dn.name = dn_item.parent and dn.docstatus = 1 and ifnull(dn.project, '') != ''
		group by dn.projectselect si.project, sum(si_item.base_net_amount) as amount
		from `tabSales Invoice` si, `tabSales Invoice Item` si_item
		where si.name = si_item.parent and si.docstatus = 1 and si.update_stock = 1
		and si.is_pos = 1 and ifnull(si.project, '') != ''
		group by si.project
    "
1205,"def __new__(cls, *args, **kw_args):
        is_canon_bp = kw_args.get('is_canon_bp', False)
        args = list(map(_sympify, args))

        
        free = [get_free_indices(arg) for arg in args]
        free = set(itertools.chain(*free)) #flatten free
        newargs = []
        for arg in args:
            dum_this = set(get_dummy_indices(arg))
            dum_other = [get_dummy_indices(a) for a in newargs]
            dum_other = set(itertools.chain(*dum_other)) #flatten dum_other
            free_this = set(get_free_indices(arg))
            if len(dum_this.intersection(free)) > 0:
                exclude = free_this.union(free, dum_other)
                newarg = TensMul._dedupe_indices(arg, exclude, arg._index_structure)
            else:
                newarg = arg
            newargs.append(newarg)

        args = newargs

        # Flatten:
        args = [i for arg in args for i in (arg.args if isinstance(arg, (TensMul, Mul)) else [arg])]

        args, indices, free, dum = TensMul._tensMul_contract_indices(args, replace_indices=False)

        # Data for indices:
        index_types = [i.tensor_index_type for i in indices]
        index_structure = _IndexStructure(free, dum, index_types, indices, canon_bp=is_canon_bp)

        obj = TensExpr.__new__(cls, *args)
        obj._indices = indices
        obj._index_types = index_types[:]
        obj._index_structure = index_structure
        obj._free = index_structure.free[:]
        obj._dum = index_structure.dum[:]
        obj._free_indices = {x[0] for x in obj.free}
        obj._rank = len(obj.free)
        obj._ext_rank = len(obj._index_structure.free) + 2*len(obj._index_structure.dum)
        obj._coeff = S.One
        obj._is_canon_bp = is_canon_bp
        return obj

    index_types = property(lambda self: self._index_types)
    free = property(lambda self: self._free)
    dum = property(lambda self: self._dum)
    free_indices = property(lambda self: self._free_indices)
    rank = property(lambda self: self._rank)
    ext_rank = property(lambda self: self._ext_rank)
","
        If the internal dummy indices in one arg conflict with the free indices of the remaining args, we need to rename those internal dummy indices.
        ",25,186,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __new__(cls, *args, **kw_args):
        is_canon_bp = kw_args.get('is_canon_bp', False)
        args = list(map(_sympify, args))

        
        free = [get_free_indices(arg) for arg in args]
        free = set(itertools.chain(*free)) #flatten free
        newargs = []
        for arg in args:
            dum_this = set(get_dummy_indices(arg))
            dum_other = [get_dummy_indices(a) for a in newargs]
            dum_other = set(itertools.chain(*dum_other)) #flatten dum_other
            free_this = set(get_free_indices(arg))
            if len(dum_this.intersection(free)) > 0:
                exclude = free_this.union(free, dum_other)
                newarg = TensMul._dedupe_indices(arg, exclude, arg._index_structure)
            else:
                newarg = arg
            newargs.append(newarg)

        args = newargs

        # Flatten:
        args = [i for arg in args for i in (arg.args if isinstance(arg, (TensMul, Mul)) else [arg])]

        args, indices, free, dum = TensMul._tensMul_contract_indices(args, replace_indices=False)

        # Data for indices:
        index_types = [i.tensor_index_type for i in indices]
        index_structure = _IndexStructure(free, dum, index_types, indices, canon_bp=is_canon_bp)

        obj = TensExpr.__new__(cls, *args)
        obj._indices = indices
        obj._index_types = index_types[:]
        obj._index_structure = index_structure
        obj._free = index_structure.free[:]
        obj._dum = index_structure.dum[:]
        obj._free_indices = {x[0] for x in obj.free}
        obj._rank = len(obj.free)
        obj._ext_rank = len(obj._index_structure.free) + 2*len(obj._index_structure.dum)
        obj._coeff = S.One
        obj._is_canon_bp = is_canon_bp
        return obj

    index_types = property(lambda self: self._index_types)
    free = property(lambda self: self._free)
    dum = property(lambda self: self._dum)
    free_indices = property(lambda self: self._free_indices)
    rank = property(lambda self: self._rank)
    ext_rank = property(lambda self: self._ext_rank)

        ```
    ###Assistant : 
        If the internal dummy indices in one arg conflict with the free indices of the remaining args, we need to rename those internal dummy indices.
        
    "
1206,"def test_context_for_crash_rate_alert(self):
        
        status = TriggerStatus.ACTIVE
        incident = self.create_incident()
        alert_rule = self.create_alert_rule(
            aggregate=""percentage(sessions_crashed, sessions) AS _crash_rate_alert_aggregate""
        )
        alert_rule_trigger = self.create_alert_rule_trigger(alert_rule)
        action = self.create_alert_rule_trigger_action(
            alert_rule_trigger=alert_rule_trigger, triggered_for_incident=incident
        )
        assert (
            generate_incident_trigger_email_context(
                self.project, incident, action.alert_rule_trigger, status, IncidentStatus.CRITICAL
            )[""aggregate""]
            == ""percentage(sessions_crashed, sessions)""
        )
","
        Test that ensures the metric name for Crash rate alerts excludes the alias
        ",13,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_context_for_crash_rate_alert(self):
        
        status = TriggerStatus.ACTIVE
        incident = self.create_incident()
        alert_rule = self.create_alert_rule(
            aggregate=""percentage(sessions_crashed, sessions) AS _crash_rate_alert_aggregate""
        )
        alert_rule_trigger = self.create_alert_rule_trigger(alert_rule)
        action = self.create_alert_rule_trigger_action(
            alert_rule_trigger=alert_rule_trigger, triggered_for_incident=incident
        )
        assert (
            generate_incident_trigger_email_context(
                self.project, incident, action.alert_rule_trigger, status, IncidentStatus.CRITICAL
            )[""aggregate""]
            == ""percentage(sessions_crashed, sessions)""
        )

        ```
    ###Assistant : 
        Test that ensures the metric name for Crash rate alerts excludes the alias
        
    "
1207,"def get_template(self, template_name, skip=None):
        
        tried = []

        for origin in self.get_template_sources(template_name):
            if skip is not None and origin in skip:
                tried.append((origin, ""Skipped to avoid recursion""))
                continue

            try:
                contents = self.get_contents(origin)
            except TemplateDoesNotExist:
                tried.append((origin, ""Source does not exist""))
                continue
            else:
                return Template(
                    contents,
                    origin,
                    origin.template_name,
                    self.engine,
                )

        raise TemplateDoesNotExist(template_name, tried=tried)
","
        Call self.get_template_sources() and return a Template object for
        the first template matching template_name. If skip is provided, ignore
        template origins in skip. This is used to avoid recursion during
        template extending.
        ",31,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_template(self, template_name, skip=None):
        
        tried = []

        for origin in self.get_template_sources(template_name):
            if skip is not None and origin in skip:
                tried.append((origin, ""Skipped to avoid recursion""))
                continue

            try:
                contents = self.get_contents(origin)
            except TemplateDoesNotExist:
                tried.append((origin, ""Source does not exist""))
                continue
            else:
                return Template(
                    contents,
                    origin,
                    origin.template_name,
                    self.engine,
                )

        raise TemplateDoesNotExist(template_name, tried=tried)

        ```
    ###Assistant : 
        Call self.get_template_sources() and return a Template object for
        the first template matching template_name. If skip is provided, ignore
        template origins in skip. This is used to avoid recursion during
        template extending.
        
    "
1208,"def from_config(cls, config):
        
        if ""learning_rate"" in config:
            if isinstance(config[""learning_rate""], dict):
                config[""learning_rate""] = learning_rate_schedule.deserialize(
                    config[""learning_rate""]
                )
        return cls(**config)


base_optimizer_keyword_args = 


# pylint: disable=g-classes-have-attributes
@keras_export(""keras.optimizers.experimental.Optimizer"", v1=[])","Creates an optimizer from its config.

        This method is the reverse of `get_config`, capable of instantiating the
        same optimizer from the config dictionary.

        Args:
            config: A Python dictionary, typically the output of get_config.

        Returns:
            An optimizer instance.
        name: String. The name to use
      for momentum accumulator weights created by
      the optimizer.
    clipnorm: Float. If set, the gradient of each weight is individually
      clipped so that its norm is no higher than this value.
    clipvalue: Float. If set, the gradient of each weight is clipped to be no
      higher than this value.
    global_clipnorm: Float. If set, the gradient of all weights is clipped so
      that their global norm is no higher than this value.
    use_ema: Boolean, defaults to False. If True, exponential moving average
      (EMA) is applied. EMA consists of computing an exponential moving
      average of the weights of the model (as the weight values change after
      each training batch), and periodically overwriting the weights with
      their moving average.
    ema_momentum: Float, defaults to 0.99. Only used if `use_ema=True`. This is
      the momentum to use when computing the EMA of the model's weights:
      `new_average = ema_momentum * old_average + (1 - ema_momentum) *
      current_variable_value`.
    ema_overwrite_frequency: Int or None, defaults to None. Only used if
      `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations, we
      overwrite the model variable by its moving average. If None, the optimizer
       does not overwrite model variables in the middle of training, and you
      need to explicitly overwrite the variables at the end of training
      by calling `optimizer.finalize_variable_values()` (which updates the model
      variables in-place). When using the built-in `fit()` training loop, this
      happens automatically after the last epoch, and you don't need to do
      anything.
    jit_compile: Boolean, defaults to True. If True, the optimizer will use XLA
      compilation. If no GPU device is found, this flag will be ignored.
    **kwargs: keyword arguments only used for backward compatibility.",306,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_config(cls, config):
        
        if ""learning_rate"" in config:
            if isinstance(config[""learning_rate""], dict):
                config[""learning_rate""] = learning_rate_schedule.deserialize(
                    config[""learning_rate""]
                )
        return cls(**config)


base_optimizer_keyword_args = 


# pylint: disable=g-classes-have-attributes
@keras_export(""keras.optimizers.experimental.Optimizer"", v1=[])
        ```
    ###Assistant : Creates an optimizer from its config.

        This method is the reverse of `get_config`, capable of instantiating the
        same optimizer from the config dictionary.

        Args:
            config: A Python dictionary, typically the output of get_config.

        Returns:
            An optimizer instance.
        name: String. The name to use
      for momentum accumulator weights created by
      the optimizer.
    clipnorm: Float. If set, the gradient of each weight is individually
      clipped so that its norm is no higher than this value.
    clipvalue: Float. If set, the gradient of each weight is clipped to be no
      higher than this value.
    global_clipnorm: Float. If set, the gradient of all weights is clipped so
      that their global norm is no higher than this value.
    use_ema: Boolean, defaults to False. If True, exponential moving average
      (EMA) is applied. EMA consists of computing an exponential moving
      average of the weights of the model (as the weight values change after
      each training batch), and periodically overwriting the weights with
      their moving average.
    ema_momentum: Float, defaults to 0.99. Only used if `use_ema=True`. This is
      the momentum to use when computing the EMA of the model's weights:
      `new_average = ema_momentum * old_average + (1 - ema_momentum) *
      current_variable_value`.
    ema_overwrite_frequency: Int or None, defaults to None. Only used if
      `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations, we
      overwrite the model variable by its moving average. If None, the optimizer
       does not overwrite model variables in the middle of training, and you
      need to explicitly overwrite the variables at the end of training
      by calling `optimizer.finalize_variable_values()` (which updates the model
      variables in-place). When using the built-in `fit()` training loop, this
      happens automatically after the last epoch, and you don't need to do
      anything.
    jit_compile: Boolean, defaults to True. If True, the optimizer will use XLA
      compilation. If no GPU device is found, this flag will be ignored.
    **kwargs: keyword arguments only used for backward compatibility.
    "
1209,"def close(self):
        
        # When application exit, system shuts down all handlers by
        # calling close method. Here we check if logger is already
        # closed to prevent uploading the log to remote storage multiple
        # times when `logging.shutdown` is called.
        if self.closed:
            return

        super().close()

        if not self.upload_on_close:
            return

        local_loc = os.path.join(self.local_base, self.log_relative_path)
        remote_loc = os.path.join(self.remote_base, self.log_relative_path)
        if os.path.exists(local_loc):
            # read log and remove old logs to get just the latest additions
            log = pathlib.Path(local_loc).read_text()
            self.s3_write(log, remote_loc)

        # Mark closed so we don't double write if close is called twice
        self.closed = True
",Close and upload local log file to remote storage S3.,10,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def close(self):
        
        # When application exit, system shuts down all handlers by
        # calling close method. Here we check if logger is already
        # closed to prevent uploading the log to remote storage multiple
        # times when `logging.shutdown` is called.
        if self.closed:
            return

        super().close()

        if not self.upload_on_close:
            return

        local_loc = os.path.join(self.local_base, self.log_relative_path)
        remote_loc = os.path.join(self.remote_base, self.log_relative_path)
        if os.path.exists(local_loc):
            # read log and remove old logs to get just the latest additions
            log = pathlib.Path(local_loc).read_text()
            self.s3_write(log, remote_loc)

        # Mark closed so we don't double write if close is called twice
        self.closed = True

        ```
    ###Assistant : Close and upload local log file to remote storage S3.
    "
1210,"def safestring_in_template_exception(request):
    
    template = Template('{% extends ""<script>alert(1);</script>"" %}')
    try:
        template.render(Context())
    except Exception:
        return technical_500_response(request, *sys.exc_info())

","
    Trigger an exception in the template machinery which causes a SafeString
    to be inserted as args[0] of the Exception.
    ",19,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def safestring_in_template_exception(request):
    
    template = Template('{% extends ""<script>alert(1);</script>"" %}')
    try:
        template.render(Context())
    except Exception:
        return technical_500_response(request, *sys.exc_info())


        ```
    ###Assistant : 
    Trigger an exception in the template machinery which causes a SafeString
    to be inserted as args[0] of the Exception.
    
    "
1211,"async def test_unique_id_in_progress(hass, manager):
    
    mock_integration(hass, MockModule(""comp""))
    mock_entity_platform(hass, ""config_flow.comp"", None)
",Test that we abort if there is already a flow in progress with same unique id.,16,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_unique_id_in_progress(hass, manager):
    
    mock_integration(hass, MockModule(""comp""))
    mock_entity_platform(hass, ""config_flow.comp"", None)

        ```
    ###Assistant : Test that we abort if there is already a flow in progress with same unique id.
    "
1212,"def test_simple(self) -> None:
        

        event_factory = self.hs.get_event_builder_factory()
        bob = ""@creator:test""
        alice = ""@alice:test""
        room_id = ""!room:test""

        # Ensure that we have a rooms entry so that we generate the chain index.
        self.get_success(
            self.store.store_room(
                room_id=room_id,
                room_creator_user_id="""",
                is_public=True,
                room_version=RoomVersions.V6,
            )
        )

        create = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Create,
                    ""state_key"": """",
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""create""},
                },
            ).build(prev_event_ids=[], auth_event_ids=[])
        )

        bob_join = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": bob,
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""bob_join""},
                },
            ).build(prev_event_ids=[], auth_event_ids=[create.event_id])
        )

        power = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.PowerLevels,
                    ""state_key"": """",
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""power""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id],
            )
        )

        alice_invite = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": alice,
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""alice_invite""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id, power.event_id],
            )
        )

        alice_join = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": alice,
                    ""sender"": alice,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""alice_join""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, alice_invite.event_id, power.event_id],
            )
        )

        power_2 = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.PowerLevels,
                    ""state_key"": """",
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""power_2""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id, power.event_id],
            )
        )

        bob_join_2 = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": bob,
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""bob_join_2""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id, power.event_id],
            )
        )

        alice_join2 = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": alice,
                    ""sender"": alice,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""alice_join2""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[
                    create.event_id,
                    alice_join.event_id,
                    power_2.event_id,
                ],
            )
        )

        events = [
            create,
            bob_join,
            power,
            alice_invite,
            alice_join,
            bob_join_2,
            power_2,
            alice_join2,
        ]

        expected_links = [
            (bob_join, create),
            (power, create),
            (power, bob_join),
            (alice_invite, create),
            (alice_invite, power),
            (alice_invite, bob_join),
            (bob_join_2, power),
            (alice_join2, power_2),
        ]

        self.persist(events)
        chain_map, link_map = self.fetch_chains(events)

        # Check that the expected links and only the expected links have been
        # added.
        self.assertEqual(len(expected_links), len(list(link_map.get_additions())))

        for start, end in expected_links:
            start_id, start_seq = chain_map[start.event_id]
            end_id, end_seq = chain_map[end.event_id]

            self.assertIn(
                (start_seq, end_seq), list(link_map.get_links_between(start_id, end_id))
            )

        # Test that everything can reach the create event, but the create event
        # can't reach anything.
        for event in events[1:]:
            self.assertTrue(
                link_map.exists_path_from(
                    chain_map[event.event_id], chain_map[create.event_id]
                ),
            )

            self.assertFalse(
                link_map.exists_path_from(
                    chain_map[create.event_id],
                    chain_map[event.event_id],
                ),
            )
","Test that the example in `docs/auth_chain_difference_algorithm.md`
        works.
        ",7,338,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_simple(self) -> None:
        

        event_factory = self.hs.get_event_builder_factory()
        bob = ""@creator:test""
        alice = ""@alice:test""
        room_id = ""!room:test""

        # Ensure that we have a rooms entry so that we generate the chain index.
        self.get_success(
            self.store.store_room(
                room_id=room_id,
                room_creator_user_id="""",
                is_public=True,
                room_version=RoomVersions.V6,
            )
        )

        create = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Create,
                    ""state_key"": """",
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""create""},
                },
            ).build(prev_event_ids=[], auth_event_ids=[])
        )

        bob_join = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": bob,
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""bob_join""},
                },
            ).build(prev_event_ids=[], auth_event_ids=[create.event_id])
        )

        power = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.PowerLevels,
                    ""state_key"": """",
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""power""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id],
            )
        )

        alice_invite = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": alice,
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""alice_invite""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id, power.event_id],
            )
        )

        alice_join = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": alice,
                    ""sender"": alice,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""alice_join""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, alice_invite.event_id, power.event_id],
            )
        )

        power_2 = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.PowerLevels,
                    ""state_key"": """",
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""power_2""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id, power.event_id],
            )
        )

        bob_join_2 = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": bob,
                    ""sender"": bob,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""bob_join_2""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[create.event_id, bob_join.event_id, power.event_id],
            )
        )

        alice_join2 = self.get_success(
            event_factory.for_room_version(
                RoomVersions.V6,
                {
                    ""type"": EventTypes.Member,
                    ""state_key"": alice,
                    ""sender"": alice,
                    ""room_id"": room_id,
                    ""content"": {""tag"": ""alice_join2""},
                },
            ).build(
                prev_event_ids=[],
                auth_event_ids=[
                    create.event_id,
                    alice_join.event_id,
                    power_2.event_id,
                ],
            )
        )

        events = [
            create,
            bob_join,
            power,
            alice_invite,
            alice_join,
            bob_join_2,
            power_2,
            alice_join2,
        ]

        expected_links = [
            (bob_join, create),
            (power, create),
            (power, bob_join),
            (alice_invite, create),
            (alice_invite, power),
            (alice_invite, bob_join),
            (bob_join_2, power),
            (alice_join2, power_2),
        ]

        self.persist(events)
        chain_map, link_map = self.fetch_chains(events)

        # Check that the expected links and only the expected links have been
        # added.
        self.assertEqual(len(expected_links), len(list(link_map.get_additions())))

        for start, end in expected_links:
            start_id, start_seq = chain_map[start.event_id]
            end_id, end_seq = chain_map[end.event_id]

            self.assertIn(
                (start_seq, end_seq), list(link_map.get_links_between(start_id, end_id))
            )

        # Test that everything can reach the create event, but the create event
        # can't reach anything.
        for event in events[1:]:
            self.assertTrue(
                link_map.exists_path_from(
                    chain_map[event.event_id], chain_map[create.event_id]
                ),
            )

            self.assertFalse(
                link_map.exists_path_from(
                    chain_map[create.event_id],
                    chain_map[event.event_id],
                ),
            )

        ```
    ###Assistant : Test that the example in `docs/auth_chain_difference_algorithm.md`
        works.
        
    "
1213,"async def _do_retry(self, func, attempts=3) -> Any:
        
        # pylint: disable=no-self-use
        exception = None
        for attempt in range(1, attempts + 1):
            _LOGGER.debug(""Attempt %s of %s"", attempt, attempts)
            try:
                return await func()
            except Exception as exception1:  # pylint: disable=broad-except
                _LOGGER.debug(
                    ""Failed attempt %s of %s (%s)"", attempt, attempts, exception1
                )
                # Make each backoff pause a little bit longer
                await asyncio.sleep(0.5 * attempt)
                exception = exception1
                continue

        if exception:
            raise exception
","Retry a function call.

        Withings' API occasionally and incorrectly throws errors. Retrying the call tends to work.
        ",17,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _do_retry(self, func, attempts=3) -> Any:
        
        # pylint: disable=no-self-use
        exception = None
        for attempt in range(1, attempts + 1):
            _LOGGER.debug(""Attempt %s of %s"", attempt, attempts)
            try:
                return await func()
            except Exception as exception1:  # pylint: disable=broad-except
                _LOGGER.debug(
                    ""Failed attempt %s of %s (%s)"", attempt, attempts, exception1
                )
                # Make each backoff pause a little bit longer
                await asyncio.sleep(0.5 * attempt)
                exception = exception1
                continue

        if exception:
            raise exception

        ```
    ###Assistant : Retry a function call.

        Withings' API occasionally and incorrectly throws errors. Retrying the call tends to work.
        
    "
1214,"def refactor_doctest(self, block, lineno, indent, filename):
        
        try:
            tree = self.parse_block(block, lineno, indent)
        except Exception as err:
            if self.logger.isEnabledFor(logging.DEBUG):
                for line in block:
                    self.log_debug(""Source: %s"", line.rstrip(""\n""))
            self.log_error(""Can't parse docstring in %s line %s: %s: %s"",
                           filename, lineno, err.__class__.__name__, err)
            return block
        if self.refactor_tree(tree, filename):
            new = str(tree).splitlines(keepends=True)
            # Undo the adjustment of the line numbers in wrap_toks() below.
            clipped, new = new[:lineno-1], new[lineno-1:]
            assert clipped == [""\n""] * (lineno-1), clipped
            if not new[-1].endswith(""\n""):
                new[-1] += ""\n""
            block = [indent + self.PS1 + new.pop(0)]
            if new:
                block += [indent + self.PS2 + line for line in new]
        return block
","Refactors one doctest.

        A doctest is given as a block of lines, the first of which starts
        with "">>>"" (possibly indented), while the remaining lines start
        with ""..."" (identically indented).

        ",30,97,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def refactor_doctest(self, block, lineno, indent, filename):
        
        try:
            tree = self.parse_block(block, lineno, indent)
        except Exception as err:
            if self.logger.isEnabledFor(logging.DEBUG):
                for line in block:
                    self.log_debug(""Source: %s"", line.rstrip(""\n""))
            self.log_error(""Can't parse docstring in %s line %s: %s: %s"",
                           filename, lineno, err.__class__.__name__, err)
            return block
        if self.refactor_tree(tree, filename):
            new = str(tree).splitlines(keepends=True)
            # Undo the adjustment of the line numbers in wrap_toks() below.
            clipped, new = new[:lineno-1], new[lineno-1:]
            assert clipped == [""\n""] * (lineno-1), clipped
            if not new[-1].endswith(""\n""):
                new[-1] += ""\n""
            block = [indent + self.PS1 + new.pop(0)]
            if new:
                block += [indent + self.PS2 + line for line in new]
        return block

        ```
    ###Assistant : Refactors one doctest.

        A doctest is given as a block of lines, the first of which starts
        with "">>>"" (possibly indented), while the remaining lines start
        with ""..."" (identically indented).

        
    "
1215,"def get_tables(self, dataset_id) -> Response:
        
        client = self.connect()
        result = client.list_tables(dataset_id)
        return result
","
        Get a list with all of the tabels in BigQuery
        ",10,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_tables(self, dataset_id) -> Response:
        
        client = self.connect()
        result = client.list_tables(dataset_id)
        return result

        ```
    ###Assistant : 
        Get a list with all of the tabels in BigQuery
        
    "
1216,"def mac_set_relative_dylib_deps(libname, distname):
    

    from macholib import util
    from macholib.MachO import MachO

    # Ignore bootloader; otherwise PyInstaller fails with exception like
    # 'ValueError: total_size > low_offset (288 > 0)'
    if os.path.basename(libname) in _BOOTLOADER_FNAMES:
        return

    # Determine how many directories up ('../') is the directory with shared dynamic libraries.
    # E.g., ./qt4_plugins/images/ -> ./../../
    parent_dir = ''
    # Check if distname is not only base filename.
    if os.path.dirname(distname):
        parent_level = len(os.path.dirname(distname).split(os.sep))
        parent_dir = parent_level * (os.pardir + os.sep)
","
    On Mac OS set relative paths to dynamic library dependencies of `libname`.

    Relative paths allow to avoid using environment variable DYLD_LIBRARY_PATH. There are known some issues with
    DYLD_LIBRARY_PATH. Relative paths is more flexible mechanism.

    Current location of dependent libraries is derived from the location of the library path (paths start with
    '@loader_path').

    'distname'  path of the library relative to dist directory of frozen executable. We need this to determine the level
                of directory level for @loader_path of binaries not found in dist directory.

                For example, Qt5 plugins are not in the same directory as Qt*.dylib files. Without using
                '@loader_path/../..' for Qt plugins, Mac OS would not be able to resolve shared library dependencies,
                and Qt plugins will not be loaded.
    ",120,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mac_set_relative_dylib_deps(libname, distname):
    

    from macholib import util
    from macholib.MachO import MachO

    # Ignore bootloader; otherwise PyInstaller fails with exception like
    # 'ValueError: total_size > low_offset (288 > 0)'
    if os.path.basename(libname) in _BOOTLOADER_FNAMES:
        return

    # Determine how many directories up ('../') is the directory with shared dynamic libraries.
    # E.g., ./qt4_plugins/images/ -> ./../../
    parent_dir = ''
    # Check if distname is not only base filename.
    if os.path.dirname(distname):
        parent_level = len(os.path.dirname(distname).split(os.sep))
        parent_dir = parent_level * (os.pardir + os.sep)

        ```
    ###Assistant : 
    On Mac OS set relative paths to dynamic library dependencies of `libname`.

    Relative paths allow to avoid using environment variable DYLD_LIBRARY_PATH. There are known some issues with
    DYLD_LIBRARY_PATH. Relative paths is more flexible mechanism.

    Current location of dependent libraries is derived from the location of the library path (paths start with
    '@loader_path').

    'distname'  path of the library relative to dist directory of frozen executable. We need this to determine the level
                of directory level for @loader_path of binaries not found in dist directory.

                For example, Qt5 plugins are not in the same directory as Qt*.dylib files. Without using
                '@loader_path/../..' for Qt plugins, Mac OS would not be able to resolve shared library dependencies,
                and Qt plugins will not be loaded.
    
    "
1217,"def get_v2_optimizer(name, **kwargs):
    
    try:
        return _V2_OPTIMIZER_MAP[name](**kwargs)
    except KeyError:
        raise ValueError(
            ""Could not find requested v2 optimizer: {}\nValid choices: {}"".format(
                name, list(_V2_OPTIMIZER_MAP.keys())
            )
        )

","Get the v2 optimizer requested.

    This is only necessary until v2 are the default, as we are testing in Eager,
    and Eager + v1 optimizers fail tests. When we are in v2, the strings alone
    should be sufficient, and this mapping can theoretically be removed.

    Args:
      name: string name of Keras v2 optimizer.
      **kwargs: any kwargs to pass to the optimizer constructor.

    Returns:
      Initialized Keras v2 optimizer.

    Raises:
      ValueError: if an unknown name was passed.
    ",75,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_v2_optimizer(name, **kwargs):
    
    try:
        return _V2_OPTIMIZER_MAP[name](**kwargs)
    except KeyError:
        raise ValueError(
            ""Could not find requested v2 optimizer: {}\nValid choices: {}"".format(
                name, list(_V2_OPTIMIZER_MAP.keys())
            )
        )


        ```
    ###Assistant : Get the v2 optimizer requested.

    This is only necessary until v2 are the default, as we are testing in Eager,
    and Eager + v1 optimizers fail tests. When we are in v2, the strings alone
    should be sufficient, and this mapping can theoretically be removed.

    Args:
      name: string name of Keras v2 optimizer.
      **kwargs: any kwargs to pass to the optimizer constructor.

    Returns:
      Initialized Keras v2 optimizer.

    Raises:
      ValueError: if an unknown name was passed.
    
    "
1218,"def _prefix_from_ip_int(cls, ip_int):
        
        trailing_zeroes = _count_righthand_zero_bits(ip_int,
                                                     cls._max_prefixlen)
        prefixlen = cls._max_prefixlen - trailing_zeroes
        leading_ones = ip_int >> trailing_zeroes
        all_ones = (1 << prefixlen) - 1
        if leading_ones != all_ones:
            byteslen = cls._max_prefixlen // 8
            details = ip_int.to_bytes(byteslen, 'big')
            msg = 'Netmask pattern %r mixes zeroes & ones'
            raise ValueError(msg % details)
        return prefixlen
","Return prefix length from the bitwise netmask.

        Args:
            ip_int: An integer, the netmask in expanded bitwise format

        Returns:
            An integer, the prefix length.

        Raises:
            ValueError: If the input intermingles zeroes & ones
        ",32,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _prefix_from_ip_int(cls, ip_int):
        
        trailing_zeroes = _count_righthand_zero_bits(ip_int,
                                                     cls._max_prefixlen)
        prefixlen = cls._max_prefixlen - trailing_zeroes
        leading_ones = ip_int >> trailing_zeroes
        all_ones = (1 << prefixlen) - 1
        if leading_ones != all_ones:
            byteslen = cls._max_prefixlen // 8
            details = ip_int.to_bytes(byteslen, 'big')
            msg = 'Netmask pattern %r mixes zeroes & ones'
            raise ValueError(msg % details)
        return prefixlen

        ```
    ###Assistant : Return prefix length from the bitwise netmask.

        Args:
            ip_int: An integer, the netmask in expanded bitwise format

        Returns:
            An integer, the prefix length.

        Raises:
            ValueError: If the input intermingles zeroes & ones
        
    "
1219,"def batch_has_learnable_example(self, examples):
        

        for eg in examples:
            for ent in eg.predicted.ents:
                candidates = list(self.get_candidates(self.kb, ent))
                if candidates:
                    return True

        return False
","Check if a batch contains a learnable example.

        If one isn't present, then the update step needs to be skipped.
        ",20,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def batch_has_learnable_example(self, examples):
        

        for eg in examples:
            for ent in eg.predicted.ents:
                candidates = list(self.get_candidates(self.kb, ent))
                if candidates:
                    return True

        return False

        ```
    ###Assistant : Check if a batch contains a learnable example.

        If one isn't present, then the update step needs to be skipped.
        
    "
1220,"def get_delayed_update_fields(self):
        
        self.extra_update_fields['emitted_events'] = self.event_ct
        if 'got an unexpected keyword argument' in self.extra_update_fields.get('result_traceback', ''):
            self.delay_update(result_traceback=ANSIBLE_RUNNER_NEEDS_UPDATE_MESSAGE)
        return self.extra_update_fields
",Return finalized dict of all fields that should be saved along with the job status change,16,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_delayed_update_fields(self):
        
        self.extra_update_fields['emitted_events'] = self.event_ct
        if 'got an unexpected keyword argument' in self.extra_update_fields.get('result_traceback', ''):
            self.delay_update(result_traceback=ANSIBLE_RUNNER_NEEDS_UPDATE_MESSAGE)
        return self.extra_update_fields

        ```
    ###Assistant : Return finalized dict of all fields that should be saved along with the job status change
    "
1221,"def get_checks_result(warning_id=None):
        
        checks_result = checks.run_checks()
        if warning_id:
            return [
                warning for warning in
                checks_result if warning.id == warning_id]
        return checks_result
",Run Django checks on any with the 'search' tag used when registering the check,14,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_checks_result(warning_id=None):
        
        checks_result = checks.run_checks()
        if warning_id:
            return [
                warning for warning in
                checks_result if warning.id == warning_id]
        return checks_result

        ```
    ###Assistant : Run Django checks on any with the 'search' tag used when registering the check
    "
1222,"def use_bottleneck_cb(key) -> None:
    from pandas.core import nanops

    nanops.set_use_bottleneck(cf.get_option(key))


use_numexpr_doc = 

","
: bool
    Use the numexpr library to accelerate computation if it is installed,
    the default is True
    Valid values: False,True
",20,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def use_bottleneck_cb(key) -> None:
    from pandas.core import nanops

    nanops.set_use_bottleneck(cf.get_option(key))


use_numexpr_doc = 


        ```
    ###Assistant : 
: bool
    Use the numexpr library to accelerate computation if it is installed,
    the default is True
    Valid values: False,True

    "
1223,"def test_repeated_column_labels(self, datapath):
        # GH 13923, 25772
        msg = 
        with pytest.raises(ValueError, match=msg):
            read_stata(
                datapath(""io"", ""data"", ""stata"", ""stata15.dta""),
                convert_categoricals=True,
            )
","
Value labels for column ethnicsn are not unique. These cannot be converted to
pandas categoricals.

Either read the file with `convert_categoricals` set to False or use the
low level interface in `StataReader` to separately read the values and the
value_labels.

The repeated labels are:\n-+\nwolof
",44,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_repeated_column_labels(self, datapath):
        # GH 13923, 25772
        msg = 
        with pytest.raises(ValueError, match=msg):
            read_stata(
                datapath(""io"", ""data"", ""stata"", ""stata15.dta""),
                convert_categoricals=True,
            )

        ```
    ###Assistant : 
Value labels for column ethnicsn are not unique. These cannot be converted to
pandas categoricals.

Either read the file with `convert_categoricals` set to False or use the
low level interface in `StataReader` to separately read the values and the
value_labels.

The repeated labels are:\n-+\nwolof

    "
1224,"def string_width_in_pixels(cls, font, string):
        

        # if no windows have been created (there is no hidden master root to rely on) then temporarily make a window so the measurement can happen
        if Window.NumOpenWindows == 0:
            root = tk.Tk()
        else:
            root = None

        size = 0
        try:
            size = tkinter.font.Font(font=font).measure(string)  # string's  width
        except Exception as e:
            _error_popup_with_traceback('Exception retrieving string width in pixels', e)

        if root is not None:
            root.destroy()

        return size
","
        Get the with of the supplied string in pixels for the font being passed in.
        If an error occurs, 0 will be returned
        :param font:   specifies the  font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike, to be measured
        :type font:    (str or (str, int[, str]) or None)
        :param string: the string to measure
        :type string:  str
        :return:       Width in pixels of string
        :rtype:        (int)
        ",76,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def string_width_in_pixels(cls, font, string):
        

        # if no windows have been created (there is no hidden master root to rely on) then temporarily make a window so the measurement can happen
        if Window.NumOpenWindows == 0:
            root = tk.Tk()
        else:
            root = None

        size = 0
        try:
            size = tkinter.font.Font(font=font).measure(string)  # string's  width
        except Exception as e:
            _error_popup_with_traceback('Exception retrieving string width in pixels', e)

        if root is not None:
            root.destroy()

        return size

        ```
    ###Assistant : 
        Get the with of the supplied string in pixels for the font being passed in.
        If an error occurs, 0 will be returned
        :param font:   specifies the  font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike, to be measured
        :type font:    (str or (str, int[, str]) or None)
        :param string: the string to measure
        :type string:  str
        :return:       Width in pixels of string
        :rtype:        (int)
        
    "
1225,"def model_call_inputs(model, keep_original_batch_size=False):
    
    input_specs = model.save_spec(dynamic_batch=not keep_original_batch_size)
    if input_specs is None:
        return None, None
    input_specs = _enforce_names_consistency(input_specs)
    return input_specs

","Inspect model to get its input signature.

    The model's input signature is a list with a single (possibly-nested) object.
    This is due to the Keras-enforced restriction that tensor inputs must be
    passed in as the first argument.

    For example, a model with input {'feature1': <Tensor>, 'feature2': <Tensor>}
    will have input signature: [{'feature1': TensorSpec, 'feature2': TensorSpec}]

    Args:
      model: Keras Model object.
      keep_original_batch_size: A boolean indicating whether we want to keep using
        the original batch size or set it to None. Default is `False`, which means
        that the batch dim of the returned input signature will always be set to
        `None`.

    Returns:
      A tuple containing `(args, kwargs)` TensorSpecs of the model call function
      inputs.
      `kwargs` does not contain the `training` argument.
    ",119,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def model_call_inputs(model, keep_original_batch_size=False):
    
    input_specs = model.save_spec(dynamic_batch=not keep_original_batch_size)
    if input_specs is None:
        return None, None
    input_specs = _enforce_names_consistency(input_specs)
    return input_specs


        ```
    ###Assistant : Inspect model to get its input signature.

    The model's input signature is a list with a single (possibly-nested) object.
    This is due to the Keras-enforced restriction that tensor inputs must be
    passed in as the first argument.

    For example, a model with input {'feature1': <Tensor>, 'feature2': <Tensor>}
    will have input signature: [{'feature1': TensorSpec, 'feature2': TensorSpec}]

    Args:
      model: Keras Model object.
      keep_original_batch_size: A boolean indicating whether we want to keep using
        the original batch size or set it to None. Default is `False`, which means
        that the batch dim of the returned input signature will always be set to
        `None`.

    Returns:
      A tuple containing `(args, kwargs)` TensorSpecs of the model call function
      inputs.
      `kwargs` does not contain the `training` argument.
    
    "
1226,"def _getSubDirectoryFolders(self, module, sub_dirs):
        

        module_dir = module.getCompileTimeDirectory()
        file_list = []

        data_dirs = [os.path.join(module_dir, subdir) for subdir in sub_dirs]

        # Gather the full file list, probably makes no sense to include bytecode files
        file_list = sum(
            (
                getFileList(
                    data_dir, ignore_dirs=(""__pycache__"",), ignore_suffixes=("".pyc"",)
                )
                for data_dir in data_dirs
            ),
            [],
        )

        if not file_list:
            msg = ""No files or folders found for '%s' in subfolder(s) %r (%r)."" % (
                module.getFullName(),
                sub_dirs,
                data_dirs,
            )
            self.warning(msg)

        is_package = (
            module.isCompiledPythonPackage() or module.isUncompiledPythonPackage()
        )

        # We need to preserve the package target path in the dist folder.
        if is_package:
            package_part = module.getFullName().asPath()
        else:
            package = module.getFullName().getPackageName()

            if package is None:
                package_part = """"
            else:
                package_part = package.asPath()

        item_set = OrderedSet()

        for f in file_list:
            target = os.path.join(package_part, os.path.relpath(f, module_dir))

            dir_name = os.path.dirname(target)
            item_set.add(dir_name)

        return self.makeIncludedEmptyDirectories(
            source_path=module_dir,
            dest_paths=item_set,
            reason=""Subdirectories of module %s"" % module.getFullName(),
            tags=""config"",
        )
","Get dirnames in given subdirs of the module.

        Notes:
            All dirnames in folders below one of the sub_dirs are recursively
            retrieved and returned shortened to begin with the string of subdir.
        Args:
            module: module object
            sub_dirs: sub folder name(s) - tuple
        Returns:
            makeIncludedEmptyDirectories of found dirnames.
        ",46,139,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _getSubDirectoryFolders(self, module, sub_dirs):
        

        module_dir = module.getCompileTimeDirectory()
        file_list = []

        data_dirs = [os.path.join(module_dir, subdir) for subdir in sub_dirs]

        # Gather the full file list, probably makes no sense to include bytecode files
        file_list = sum(
            (
                getFileList(
                    data_dir, ignore_dirs=(""__pycache__"",), ignore_suffixes=("".pyc"",)
                )
                for data_dir in data_dirs
            ),
            [],
        )

        if not file_list:
            msg = ""No files or folders found for '%s' in subfolder(s) %r (%r)."" % (
                module.getFullName(),
                sub_dirs,
                data_dirs,
            )
            self.warning(msg)

        is_package = (
            module.isCompiledPythonPackage() or module.isUncompiledPythonPackage()
        )

        # We need to preserve the package target path in the dist folder.
        if is_package:
            package_part = module.getFullName().asPath()
        else:
            package = module.getFullName().getPackageName()

            if package is None:
                package_part = """"
            else:
                package_part = package.asPath()

        item_set = OrderedSet()

        for f in file_list:
            target = os.path.join(package_part, os.path.relpath(f, module_dir))

            dir_name = os.path.dirname(target)
            item_set.add(dir_name)

        return self.makeIncludedEmptyDirectories(
            source_path=module_dir,
            dest_paths=item_set,
            reason=""Subdirectories of module %s"" % module.getFullName(),
            tags=""config"",
        )

        ```
    ###Assistant : Get dirnames in given subdirs of the module.

        Notes:
            All dirnames in folders below one of the sub_dirs are recursively
            retrieved and returned shortened to begin with the string of subdir.
        Args:
            module: module object
            sub_dirs: sub folder name(s) - tuple
        Returns:
            makeIncludedEmptyDirectories of found dirnames.
        
    "
1227,"def test_orderby_percentile_with_many_fields_one_entity_no_data(self):
        
        for metric in [
            TransactionMRI.MEASUREMENTS_FCP.value,
            ""transaction"",
        ]:
            perf_indexer_record(self.organization.id, metric)
        response = self.get_success_response(
            self.organization.slug,
            field=[
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
                f""p50({TransactionMetricKey.MEASUREMENTS_FCP.value})"",
            ],
            statsPeriod=""1h"",
            interval=""1h"",
            groupBy=[""project_id"", ""transaction""],
            orderBy=f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
            useCase=""performance"",
        )
        groups = response.data[""groups""]
        assert len(groups) == 0
","
        Test that ensures that when metrics data is available then an empty response is returned
        gracefully
        ",16,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_orderby_percentile_with_many_fields_one_entity_no_data(self):
        
        for metric in [
            TransactionMRI.MEASUREMENTS_FCP.value,
            ""transaction"",
        ]:
            perf_indexer_record(self.organization.id, metric)
        response = self.get_success_response(
            self.organization.slug,
            field=[
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
                f""p50({TransactionMetricKey.MEASUREMENTS_FCP.value})"",
            ],
            statsPeriod=""1h"",
            interval=""1h"",
            groupBy=[""project_id"", ""transaction""],
            orderBy=f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
            useCase=""performance"",
        )
        groups = response.data[""groups""]
        assert len(groups) == 0

        ```
    ###Assistant : 
        Test that ensures that when metrics data is available then an empty response is returned
        gracefully
        
    "
1228,"def _add_save_button(self) -> None:
        
        logger.debug(""Adding save button"")
        button = tk.Button(self,
                           text=""Save"",
                           cursor=""hand2"",
                           command=lambda: self.save_var.set(True))
        button.pack(side=tk.LEFT)
        logger.debug(""Added save burron: '%s'"", button)
", Add a save button for saving out original preview ,9,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _add_save_button(self) -> None:
        
        logger.debug(""Adding save button"")
        button = tk.Button(self,
                           text=""Save"",
                           cursor=""hand2"",
                           command=lambda: self.save_var.set(True))
        button.pack(side=tk.LEFT)
        logger.debug(""Added save burron: '%s'"", button)

        ```
    ###Assistant :  Add a save button for saving out original preview 
    "
1229,"def temporary_environ_defaults(**kwargs):
    
    old_env = os.environ.copy()

    try:
        for var in kwargs:
            # TODO: Consider warning on conflicts
            os.environ.setdefault(var, str(kwargs[var]))

        yield {var: os.environ[var] for var in kwargs}

    finally:
        for var in kwargs:
            if old_env.get(var):
                os.environ[var] = old_env[var]
            else:
                os.environ.pop(var, None)

","
    Temporarily override default values in os.environ.

    Yields a dictionary of the key/value pairs matching the provided keys.
    ",17,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def temporary_environ_defaults(**kwargs):
    
    old_env = os.environ.copy()

    try:
        for var in kwargs:
            # TODO: Consider warning on conflicts
            os.environ.setdefault(var, str(kwargs[var]))

        yield {var: os.environ[var] for var in kwargs}

    finally:
        for var in kwargs:
            if old_env.get(var):
                os.environ[var] = old_env[var]
            else:
                os.environ.pop(var, None)


        ```
    ###Assistant : 
    Temporarily override default values in os.environ.

    Yields a dictionary of the key/value pairs matching the provided keys.
    
    "
1230,"def test_same_entity_multiple_metric_ids_missing_data(self, mocked_derived_metrics):
        
        mocked_derived_metrics.return_value = MOCKED_DERIVED_METRICS_2
        _indexer_record(self.organization.id, ""metric_foo_doe"")
        self.store_session(
            self.build_session(
                project_id=self.project.id,
                started=(time.time() // 60) * 60,
                status=""ok"",
                release=""foobar@2.0"",
                errors=2,
            )
        )
        response = self.get_response(
            self.organization.slug,
            ""derived_metric.multiple_metrics"",
        )
        assert response.status_code == 404
        assert response.json()[""detail""] == (
            ""Not all the requested metrics or the constituent metrics in ""
            ""['derived_metric.multiple_metrics'] have data in the dataset""
        )
","
        Test when not requested metrics have data in the dataset
        ",10,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_same_entity_multiple_metric_ids_missing_data(self, mocked_derived_metrics):
        
        mocked_derived_metrics.return_value = MOCKED_DERIVED_METRICS_2
        _indexer_record(self.organization.id, ""metric_foo_doe"")
        self.store_session(
            self.build_session(
                project_id=self.project.id,
                started=(time.time() // 60) * 60,
                status=""ok"",
                release=""foobar@2.0"",
                errors=2,
            )
        )
        response = self.get_response(
            self.organization.slug,
            ""derived_metric.multiple_metrics"",
        )
        assert response.status_code == 404
        assert response.json()[""detail""] == (
            ""Not all the requested metrics or the constituent metrics in ""
            ""['derived_metric.multiple_metrics'] have data in the dataset""
        )

        ```
    ###Assistant : 
        Test when not requested metrics have data in the dataset
        
    "
1231,"def _align_matrices(x, y):
    
    x_matrix = _to_matrix(x)
    y_matrix = _to_matrix(y)
    x_shape = x_matrix.shape
    y_shape = y_matrix.shape
    if y_shape[1] != x_shape[1]:  # dimensions do not match.
        raise ValueError(
            ""The outermost dimensions of the input tensors should match. ""
            f""Received y = {y_shape[1]} vs x = {x_shape[1]}.""
        )

    x_tile = tf.tile(tf.expand_dims(x_matrix, 1), [1, y_shape[0], 1])
    y_tile = tf.tile(tf.expand_dims(y_matrix, 0), [x_shape[0], 1, 1])
    return x_tile, y_tile

",Aligns x and y tensors to allow computations over pairs of their rows.,13,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _align_matrices(x, y):
    
    x_matrix = _to_matrix(x)
    y_matrix = _to_matrix(y)
    x_shape = x_matrix.shape
    y_shape = y_matrix.shape
    if y_shape[1] != x_shape[1]:  # dimensions do not match.
        raise ValueError(
            ""The outermost dimensions of the input tensors should match. ""
            f""Received y = {y_shape[1]} vs x = {x_shape[1]}.""
        )

    x_tile = tf.tile(tf.expand_dims(x_matrix, 1), [1, y_shape[0], 1])
    y_tile = tf.tile(tf.expand_dims(y_matrix, 0), [x_shape[0], 1, 1])
    return x_tile, y_tile


        ```
    ###Assistant : Aligns x and y tensors to allow computations over pairs of their rows.
    "
1232,"def test_prune_gap_if_dummy_local(self):
        

        body = self.helper.send(self.room_id, body=""Test"", tok=self.token)

        body = self.helper.send_event(
            self.room_id, type=EventTypes.Dummy, content={}, tok=self.token
        )
        local_message_event_id = body[""event_id""]
        self.assert_extremities([local_message_event_id])

        # Advance the clock for many days to make the old extremity ""old"". We
        # also set the depth to ""lots"".
        self.reactor.advance(7 * 24 * 60 * 60)

        # Fudge a second event which points to an event we don't have. This is a
        # state event so that the state changes (otherwise we won't prune the
        # extremity as they'll have the same state group).
        remote_event_2 = event_from_pdu_json(
            {
                ""type"": EventTypes.Member,
                ""state_key"": ""@user:other2"",
                ""content"": {""membership"": Membership.JOIN},
                ""room_id"": self.room_id,
                ""sender"": ""@user:other2"",
                ""depth"": 10000,
                ""prev_events"": [""$some_unknown_message""],
                ""auth_events"": [],
                ""origin_server_ts"": self.clock.time_msec(),
            },
            RoomVersions.V6,
        )

        state_before_gap = self.get_success(
            self.state.get_current_state_ids(self.room_id)
        )

        self.persist_event(remote_event_2, state=state_before_gap)

        # Check the new extremity is just the new remote event.
        self.assert_extremities([remote_event_2.event_id, local_message_event_id])
","Test that we don't drop extremities after a gap when the previous
        extremity is a local dummy event and points to local events.
        ",23,131,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_prune_gap_if_dummy_local(self):
        

        body = self.helper.send(self.room_id, body=""Test"", tok=self.token)

        body = self.helper.send_event(
            self.room_id, type=EventTypes.Dummy, content={}, tok=self.token
        )
        local_message_event_id = body[""event_id""]
        self.assert_extremities([local_message_event_id])

        # Advance the clock for many days to make the old extremity ""old"". We
        # also set the depth to ""lots"".
        self.reactor.advance(7 * 24 * 60 * 60)

        # Fudge a second event which points to an event we don't have. This is a
        # state event so that the state changes (otherwise we won't prune the
        # extremity as they'll have the same state group).
        remote_event_2 = event_from_pdu_json(
            {
                ""type"": EventTypes.Member,
                ""state_key"": ""@user:other2"",
                ""content"": {""membership"": Membership.JOIN},
                ""room_id"": self.room_id,
                ""sender"": ""@user:other2"",
                ""depth"": 10000,
                ""prev_events"": [""$some_unknown_message""],
                ""auth_events"": [],
                ""origin_server_ts"": self.clock.time_msec(),
            },
            RoomVersions.V6,
        )

        state_before_gap = self.get_success(
            self.state.get_current_state_ids(self.room_id)
        )

        self.persist_event(remote_event_2, state=state_before_gap)

        # Check the new extremity is just the new remote event.
        self.assert_extremities([remote_event_2.event_id, local_message_event_id])

        ```
    ###Assistant : Test that we don't drop extremities after a gap when the previous
        extremity is a local dummy event and points to local events.
        
    "
1233,"def _reset_layer_losses(parent_layer):
  
  losses_dict = {}
  for layer in utils.list_all_layers_and_sublayers(parent_layer):
    losses_dict[layer] = {
        'losses': layer._losses[:],
        'eager_losses': layer._eager_losses[:]
    }
    with utils.no_automatic_dependency_tracking_scope(layer):
      layer._losses = []
      layer._eager_losses = []
  return losses_dict

","Resets losses of layer and its sublayers, and returns original losses.",11,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _reset_layer_losses(parent_layer):
  
  losses_dict = {}
  for layer in utils.list_all_layers_and_sublayers(parent_layer):
    losses_dict[layer] = {
        'losses': layer._losses[:],
        'eager_losses': layer._eager_losses[:]
    }
    with utils.no_automatic_dependency_tracking_scope(layer):
      layer._losses = []
      layer._eager_losses = []
  return losses_dict


        ```
    ###Assistant : Resets losses of layer and its sublayers, and returns original losses.
    "
1234,"def test_result_list_editable_html(self):
        
        new_parent = Parent.objects.create(name=""parent"")
        new_child = Child.objects.create(name=""name"", parent=new_parent)
        request = self.factory.get(""/child/"")
        request.user = self.superuser
        m = ChildAdmin(Child, custom_site)

        # Test with list_editable fields
        m.list_display = [""id"", ""name"", ""parent""]
        m.list_display_links = [""id""]
        m.list_editable = [""name""]
        cl = m.get_changelist_instance(request)
        FormSet = m.get_changelist_formset(request)
        cl.formset = FormSet(queryset=cl.result_list)
        template = Template(
            ""{% load admin_list %}{% spaceless %}{% result_list cl %}{% endspaceless %}""
        )
        context = Context({""cl"": cl, ""opts"": Child._meta})
        table_output = template.render(context)
        # make sure that hidden fields are in the correct place
        hiddenfields_div = (
            '<div class=""hiddenfields"">'
            '<input type=""hidden"" name=""form-0-id"" value=""%d"" id=""id_form-0-id"">'
            ""</div>""
        ) % new_child.id
        self.assertInHTML(
            hiddenfields_div, table_output, msg_prefix=""Failed to find hidden fields""
        )

        # make sure that list editable fields are rendered in divs correctly
        editable_name_field = (
            '<input name=""form-0-name"" value=""name"" class=""vTextField"" '
            'maxlength=""30"" type=""text"" id=""id_form-0-name"">'
        )
        self.assertInHTML(
            '<td class=""field-name"">%s</td>' % editable_name_field,
            table_output,
            msg_prefix='Failed to find ""name"" list_editable field',
        )
","
        Regression tests for #11791: Inclusion tag result_list generates a
        table and this checks that the items are nested within the table
        element tags.
        Also a regression test for #13599, verifies that hidden fields
        when list_editable is enabled are rendered in a div outside the
        table.
        ",45,139,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_result_list_editable_html(self):
        
        new_parent = Parent.objects.create(name=""parent"")
        new_child = Child.objects.create(name=""name"", parent=new_parent)
        request = self.factory.get(""/child/"")
        request.user = self.superuser
        m = ChildAdmin(Child, custom_site)

        # Test with list_editable fields
        m.list_display = [""id"", ""name"", ""parent""]
        m.list_display_links = [""id""]
        m.list_editable = [""name""]
        cl = m.get_changelist_instance(request)
        FormSet = m.get_changelist_formset(request)
        cl.formset = FormSet(queryset=cl.result_list)
        template = Template(
            ""{% load admin_list %}{% spaceless %}{% result_list cl %}{% endspaceless %}""
        )
        context = Context({""cl"": cl, ""opts"": Child._meta})
        table_output = template.render(context)
        # make sure that hidden fields are in the correct place
        hiddenfields_div = (
            '<div class=""hiddenfields"">'
            '<input type=""hidden"" name=""form-0-id"" value=""%d"" id=""id_form-0-id"">'
            ""</div>""
        ) % new_child.id
        self.assertInHTML(
            hiddenfields_div, table_output, msg_prefix=""Failed to find hidden fields""
        )

        # make sure that list editable fields are rendered in divs correctly
        editable_name_field = (
            '<input name=""form-0-name"" value=""name"" class=""vTextField"" '
            'maxlength=""30"" type=""text"" id=""id_form-0-name"">'
        )
        self.assertInHTML(
            '<td class=""field-name"">%s</td>' % editable_name_field,
            table_output,
            msg_prefix='Failed to find ""name"" list_editable field',
        )

        ```
    ###Assistant : 
        Regression tests for #11791: Inclusion tag result_list generates a
        table and this checks that the items are nested within the table
        element tags.
        Also a regression test for #13599, verifies that hidden fields
        when list_editable is enabled are rendered in a div outside the
        table.
        
    "
1235,"def test_title_column(self):
        root_page = Page.objects.filter(depth=2).first()
        blog = Site.objects.create(
            hostname=""blog.example.com"", site_name=""My blog"", root_page=root_page
        )
        gallery = Site.objects.create(
            hostname=""gallery.example.com"", site_name=""My gallery"", root_page=root_page
        )
        data = [blog, gallery]

        table = Table(
            [
                TitleColumn(
                    ""hostname"",
                    url_name=""wagtailsites:edit"",
                    link_classname=""choose-site"",
                    link_attrs={""data-chooser"": ""yes""},
                ),
                Column(""site_name"", label=""Site name""),
            ],
            data,
        )

        html = self.render_component(table)
        self.assertHTMLEqual(
            html,
            
            % (blog.pk, gallery.pk),
        )
","
            <table class=""listing"">
                <thead>
                    <tr><th>Hostname</th><th>Site name</th></tr>
                </thead>
                <tbody>
                    <tr>
                        <td class=""title"">
                            <div class=""title-wrapper"">
                                <a href=""/admin/sites/%d/"" class=""choose-site"" data-chooser=""yes"">blog.example.com</a>
                            </div>
                        </td>
                        <td>My blog</td>
                    </tr>
                    <tr>
                        <td class=""title"">
                            <div class=""title-wrapper"">
                                <a href=""/admin/sites/%d/"" class=""choose-site"" data-chooser=""yes"">gallery.example.com</a>
                            </div>
                        </td>
                        <td>My gallery</td>
                    </tr>
                </tbody>
            </table>
        ",37,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_title_column(self):
        root_page = Page.objects.filter(depth=2).first()
        blog = Site.objects.create(
            hostname=""blog.example.com"", site_name=""My blog"", root_page=root_page
        )
        gallery = Site.objects.create(
            hostname=""gallery.example.com"", site_name=""My gallery"", root_page=root_page
        )
        data = [blog, gallery]

        table = Table(
            [
                TitleColumn(
                    ""hostname"",
                    url_name=""wagtailsites:edit"",
                    link_classname=""choose-site"",
                    link_attrs={""data-chooser"": ""yes""},
                ),
                Column(""site_name"", label=""Site name""),
            ],
            data,
        )

        html = self.render_component(table)
        self.assertHTMLEqual(
            html,
            
            % (blog.pk, gallery.pk),
        )

        ```
    ###Assistant : 
            <table class=""listing"">
                <thead>
                    <tr><th>Hostname</th><th>Site name</th></tr>
                </thead>
                <tbody>
                    <tr>
                        <td class=""title"">
                            <div class=""title-wrapper"">
                                <a href=""/admin/sites/%d/"" class=""choose-site"" data-chooser=""yes"">blog.example.com</a>
                            </div>
                        </td>
                        <td>My blog</td>
                    </tr>
                    <tr>
                        <td class=""title"">
                            <div class=""title-wrapper"">
                                <a href=""/admin/sites/%d/"" class=""choose-site"" data-chooser=""yes"">gallery.example.com</a>
                            </div>
                        </td>
                        <td>My gallery</td>
                    </tr>
                </tbody>
            </table>
        
    "
1236,"def _clean_text(self, text):
        
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xFFFD or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append("" "")
            else:
                output.append(char)
        return """".join(output)

",Performs invalid character removal and whitespace cleanup on text.,9,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _clean_text(self, text):
        
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xFFFD or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append("" "")
            else:
                output.append(char)
        return """".join(output)


        ```
    ###Assistant : Performs invalid character removal and whitespace cleanup on text.
    "
1237,"def call_candle(self, other_args):
        
        if self.symbol:
            parser = argparse.ArgumentParser(
                add_help=False,
                formatter_class=argparse.ArgumentDefaultsHelpFormatter,
                prog=""candle"",
                description=,
            )

            ns_parser = parse_known_args_and_warn(
                parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES
            )

            if ns_parser:
                plot_chart(
                    symbol=self.symbol,
                    currency=self.current_currency,
                    prices_df=self.current_df,
                )
","Process candle commandDisplay chart for loaded coin. You can specify currency vs which you want
                to show chart and also number of days to get data for.",27,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_candle(self, other_args):
        
        if self.symbol:
            parser = argparse.ArgumentParser(
                add_help=False,
                formatter_class=argparse.ArgumentDefaultsHelpFormatter,
                prog=""candle"",
                description=,
            )

            ns_parser = parse_known_args_and_warn(
                parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES
            )

            if ns_parser:
                plot_chart(
                    symbol=self.symbol,
                    currency=self.current_currency,
                    prices_df=self.current_df,
                )

        ```
    ###Assistant : Process candle commandDisplay chart for loaded coin. You can specify currency vs which you want
                to show chart and also number of days to get data for.
    "
1238,"async def test_load_values_when_added_to_hass(hass):
    

    config = {
        ""binary_sensor"": {
            ""name"": ""Test_Binary"",
            ""platform"": ""bayesian"",
            ""unique_id"": ""3b4c9563-5e84-4167-8fe7-8f507e796d72"",
            ""device_class"": ""connectivity"",
            ""observations"": [
                {
                    ""platform"": ""state"",
                    ""entity_id"": ""sensor.test_monitored"",
                    ""to_state"": ""off"",
                    ""prob_given_true"": 0.8,
                    ""prob_given_false"": 0.4,
                }
            ],
            ""prior"": 0.2,
            ""probability_threshold"": 0.32,
        }
    }

    hass.states.async_set(""sensor.test_monitored"", ""off"")
    await hass.async_block_till_done()

    assert await async_setup_component(hass, ""binary_sensor"", config)
    await hass.async_block_till_done()

    entity_registry = async_get_entities(hass)
    assert (
        entity_registry.entities[""binary_sensor.test_binary""].unique_id
        == ""bayesian-3b4c9563-5e84-4167-8fe7-8f507e796d72""
    )

    state = hass.states.get(""binary_sensor.test_binary"")
    assert state.attributes.get(""device_class"") == ""connectivity""
    assert state.attributes.get(""observations"")[0][""prob_given_true""] == 0.8
    assert state.attributes.get(""observations"")[0][""prob_given_false""] == 0.4

",Test that sensor initializes with observations of relevant entities.,9,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_load_values_when_added_to_hass(hass):
    

    config = {
        ""binary_sensor"": {
            ""name"": ""Test_Binary"",
            ""platform"": ""bayesian"",
            ""unique_id"": ""3b4c9563-5e84-4167-8fe7-8f507e796d72"",
            ""device_class"": ""connectivity"",
            ""observations"": [
                {
                    ""platform"": ""state"",
                    ""entity_id"": ""sensor.test_monitored"",
                    ""to_state"": ""off"",
                    ""prob_given_true"": 0.8,
                    ""prob_given_false"": 0.4,
                }
            ],
            ""prior"": 0.2,
            ""probability_threshold"": 0.32,
        }
    }

    hass.states.async_set(""sensor.test_monitored"", ""off"")
    await hass.async_block_till_done()

    assert await async_setup_component(hass, ""binary_sensor"", config)
    await hass.async_block_till_done()

    entity_registry = async_get_entities(hass)
    assert (
        entity_registry.entities[""binary_sensor.test_binary""].unique_id
        == ""bayesian-3b4c9563-5e84-4167-8fe7-8f507e796d72""
    )

    state = hass.states.get(""binary_sensor.test_binary"")
    assert state.attributes.get(""device_class"") == ""connectivity""
    assert state.attributes.get(""observations"")[0][""prob_given_true""] == 0.8
    assert state.attributes.get(""observations"")[0][""prob_given_false""] == 0.4


        ```
    ###Assistant : Test that sensor initializes with observations of relevant entities.
    "
1239,"def test_copy_with_target_credential(self):
        expression = ""col1, col2""
        op = DatabricksCopyIntoOperator(
            file_location=COPY_FILE_LOCATION,
            file_format='CSV',
            table_name='test',
            task_id=TASK_ID,
            expression_list=expression,
            storage_credential='abc',
            credential={'AZURE_SAS_TOKEN': 'abc'},
        )
        assert (
            op._create_sql_query()
            == f.strip()
        )
","COPY INTO test WITH (CREDENTIAL abc)
FROM (SELECT {expression} FROM '{COPY_FILE_LOCATION}' WITH (CREDENTIAL (AZURE_SAS_TOKEN = 'abc') ))
FILEFORMAT = CSV
",20,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_copy_with_target_credential(self):
        expression = ""col1, col2""
        op = DatabricksCopyIntoOperator(
            file_location=COPY_FILE_LOCATION,
            file_format='CSV',
            table_name='test',
            task_id=TASK_ID,
            expression_list=expression,
            storage_credential='abc',
            credential={'AZURE_SAS_TOKEN': 'abc'},
        )
        assert (
            op._create_sql_query()
            == f.strip()
        )

        ```
    ###Assistant : COPY INTO test WITH (CREDENTIAL abc)
FROM (SELECT {expression} FROM '{COPY_FILE_LOCATION}' WITH (CREDENTIAL (AZURE_SAS_TOKEN = 'abc') ))
FILEFORMAT = CSV

    "
1240,"def show_actual_vendor_versions(vendor_txt_versions):
    # type: (Dict[str, str]) -> None
    
    for module_name, expected_version in vendor_txt_versions.items():
        extra_message = ''
        actual_version = get_vendor_version_from_module(module_name)
        if not actual_version:
            extra_message = ' (Unable to locate actual module version, using'\
                            ' vendor.txt specified version)'
            actual_version = expected_version
        elif parse_version(actual_version) != parse_version(expected_version):
            extra_message = ' (CONFLICT: vendor.txt suggests version should'\
                            ' be {})'.format(expected_version)
        logger.info('%s==%s%s', module_name, actual_version, extra_message)

","Log the actual version and print extra info if there is
    a conflict or if the actual version could not be imported.
    ",22,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def show_actual_vendor_versions(vendor_txt_versions):
    # type: (Dict[str, str]) -> None
    
    for module_name, expected_version in vendor_txt_versions.items():
        extra_message = ''
        actual_version = get_vendor_version_from_module(module_name)
        if not actual_version:
            extra_message = ' (Unable to locate actual module version, using'\
                            ' vendor.txt specified version)'
            actual_version = expected_version
        elif parse_version(actual_version) != parse_version(expected_version):
            extra_message = ' (CONFLICT: vendor.txt suggests version should'\
                            ' be {})'.format(expected_version)
        logger.info('%s==%s%s', module_name, actual_version, extra_message)


        ```
    ###Assistant : Log the actual version and print extra info if there is
    a conflict or if the actual version could not be imported.
    
    "
1241,"def escape_rfc3986(s):
    
    if sys.version_info < (3, 0) and isinstance(s, compat_str):
        s = s.encode('utf-8')
    # ensure unicode: after quoting, it can always be converted
    return compat_str(compat_urllib_parse.quote(s, b""%/;:@&=+$,!~*'()?#[]""))

",Escape non-ASCII characters as suggested by RFC 3986,8,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def escape_rfc3986(s):
    
    if sys.version_info < (3, 0) and isinstance(s, compat_str):
        s = s.encode('utf-8')
    # ensure unicode: after quoting, it can always be converted
    return compat_str(compat_urllib_parse.quote(s, b""%/;:@&=+$,!~*'()?#[]""))


        ```
    ###Assistant : Escape non-ASCII characters as suggested by RFC 3986
    "
1242,"def call_social(self, other_args):
        
        parser = argparse.ArgumentParser(
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            prog=""social"",
            description=,
        )
        ns_parser = parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
        )

        if ns_parser:
            pycoingecko_view.display_social(
                self.coin_map_df[""CoinGecko""], export=ns_parser.export
            )
","Process social commandShows social media corresponding to loaded coin. You can find there name of
            telegram channel, urls to twitter, reddit, bitcointalk, facebook and discord.",25,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_social(self, other_args):
        
        parser = argparse.ArgumentParser(
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            prog=""social"",
            description=,
        )
        ns_parser = parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
        )

        if ns_parser:
            pycoingecko_view.display_social(
                self.coin_map_df[""CoinGecko""], export=ns_parser.export
            )

        ```
    ###Assistant : Process social commandShows social media corresponding to loaded coin. You can find there name of
            telegram channel, urls to twitter, reddit, bitcointalk, facebook and discord.
    "
1243,"def test_ignores_different_group(self):
        
        url = reverse(
            ""sentry-api-0-project-event-details"",
            kwargs={
                ""event_id"": self.next_transaction_event.event_id,
                ""project_slug"": self.next_transaction_event.project.slug,
                ""organization_slug"": self.next_transaction_event.project.organization.slug,
            },
        )
        with self.feature(""organizations:performance-issues""):
            response = self.client.get(url, format=""json"", data={""group_id"": self.group.id})

        assert response.status_code == 200, response.content
        assert response.data[""id""] == str(self.next_transaction_event.event_id)
        assert response.data[""nextEventID""] is None
",Test that a different group's events aren't attributed to the one that was passed,14,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ignores_different_group(self):
        
        url = reverse(
            ""sentry-api-0-project-event-details"",
            kwargs={
                ""event_id"": self.next_transaction_event.event_id,
                ""project_slug"": self.next_transaction_event.project.slug,
                ""organization_slug"": self.next_transaction_event.project.organization.slug,
            },
        )
        with self.feature(""organizations:performance-issues""):
            response = self.client.get(url, format=""json"", data={""group_id"": self.group.id})

        assert response.status_code == 200, response.content
        assert response.data[""id""] == str(self.next_transaction_event.event_id)
        assert response.data[""nextEventID""] is None

        ```
    ###Assistant : Test that a different group's events aren't attributed to the one that was passed
    "
1244,"def _dedupe_indices(new, exclude, index_structure):
        
        inds_self = set(exclude)
        dums_new = set(get_dummy_indices(new))

        conflicts = dums_new.intersection(inds_self)
        if len(conflicts) == 0:
            return None

        
        inds_self.update(dums_new)
        self_args_free = [(i, None) for i in inds_self]
        gen = index_structure._get_generator_for_dummy_indices(self_args_free)
        repl = {}
        for d in conflicts:
            if -d in repl.keys():
                continue
            newname = gen(d.tensor_index_type)
            new_d = d.func(newname, *d.args[1:])
            repl[d] = new_d
            repl[-d] = -new_d

        if len(repl) == 0:
            return None

        new_renamed = new._replace_indices(repl)
        return new_renamed
","
        exclude: set
        new: TensExpr
        index_structure: _IndexStructure (required to generate new dummy indices)

        If ``new`` has any dummy indices that are in ``exclude``, return a version
        of new with those indices replaced. If no replacements are needed,
        return None
        
        ``self_args_free`` is to be passed to ``_IndexStructure._get_generator_for_dummy_indices()``.
        Since the latter does not use the index position for anything, we just
        set it as ``None`` here.
        ",63,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _dedupe_indices(new, exclude, index_structure):
        
        inds_self = set(exclude)
        dums_new = set(get_dummy_indices(new))

        conflicts = dums_new.intersection(inds_self)
        if len(conflicts) == 0:
            return None

        
        inds_self.update(dums_new)
        self_args_free = [(i, None) for i in inds_self]
        gen = index_structure._get_generator_for_dummy_indices(self_args_free)
        repl = {}
        for d in conflicts:
            if -d in repl.keys():
                continue
            newname = gen(d.tensor_index_type)
            new_d = d.func(newname, *d.args[1:])
            repl[d] = new_d
            repl[-d] = -new_d

        if len(repl) == 0:
            return None

        new_renamed = new._replace_indices(repl)
        return new_renamed

        ```
    ###Assistant : 
        exclude: set
        new: TensExpr
        index_structure: _IndexStructure (required to generate new dummy indices)

        If ``new`` has any dummy indices that are in ``exclude``, return a version
        of new with those indices replaced. If no replacements are needed,
        return None
        
        ``self_args_free`` is to be passed to ``_IndexStructure._get_generator_for_dummy_indices()``.
        Since the latter does not use the index position for anything, we just
        set it as ``None`` here.
        
    "
1245,"def get_window_extent(self, renderer=None):
        
        # make sure the location is updated so that transforms etc are correct:
        self._adjust_location()
        bb = super().get_window_extent(renderer=renderer)
        if self.axis is None or not self.axis.get_visible():
            return bb
        bboxes = [bb]
        drawn_ticks = self.axis._update_ticks()

        major_tick = next(iter({*drawn_ticks} & {*self.axis.majorTicks}), None)
        minor_tick = next(iter({*drawn_ticks} & {*self.axis.minorTicks}), None)
        for tick in [major_tick, minor_tick]:
            if tick is None:
                continue
            bb0 = bb.frozen()
            tickl = tick._size
            tickdir = tick._tickdir
            if tickdir == 'out':
                padout = 1
                padin = 0
            elif tickdir == 'in':
                padout = 0
                padin = 1
            else:
                padout = 0.5
                padin = 0.5
            padout = padout * tickl / 72 * self.figure.dpi
            padin = padin * tickl / 72 * self.figure.dpi

            if tick.tick1line.get_visible():
                if self.spine_type == 'left':
                    bb0.x0 = bb0.x0 - padout
                    bb0.x1 = bb0.x1 + padin
                elif self.spine_type == 'bottom':
                    bb0.y0 = bb0.y0 - padout
                    bb0.y1 = bb0.y1 + padin

            if tick.tick2line.get_visible():
                if self.spine_type == 'right':
                    bb0.x1 = bb0.x1 + padout
                    bb0.x0 = bb0.x0 - padin
                elif self.spine_type == 'top':
                    bb0.y1 = bb0.y1 + padout
                    bb0.y0 = bb0.y0 - padout
            bboxes.append(bb0)

        return mtransforms.Bbox.union(bboxes)
","
        Return the window extent of the spines in display space, including
        padding for ticks (but not their labels)

        See Also
        --------
        matplotlib.axes.Axes.get_tightbbox
        matplotlib.axes.Axes.get_window_extent
        ",23,174,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_window_extent(self, renderer=None):
        
        # make sure the location is updated so that transforms etc are correct:
        self._adjust_location()
        bb = super().get_window_extent(renderer=renderer)
        if self.axis is None or not self.axis.get_visible():
            return bb
        bboxes = [bb]
        drawn_ticks = self.axis._update_ticks()

        major_tick = next(iter({*drawn_ticks} & {*self.axis.majorTicks}), None)
        minor_tick = next(iter({*drawn_ticks} & {*self.axis.minorTicks}), None)
        for tick in [major_tick, minor_tick]:
            if tick is None:
                continue
            bb0 = bb.frozen()
            tickl = tick._size
            tickdir = tick._tickdir
            if tickdir == 'out':
                padout = 1
                padin = 0
            elif tickdir == 'in':
                padout = 0
                padin = 1
            else:
                padout = 0.5
                padin = 0.5
            padout = padout * tickl / 72 * self.figure.dpi
            padin = padin * tickl / 72 * self.figure.dpi

            if tick.tick1line.get_visible():
                if self.spine_type == 'left':
                    bb0.x0 = bb0.x0 - padout
                    bb0.x1 = bb0.x1 + padin
                elif self.spine_type == 'bottom':
                    bb0.y0 = bb0.y0 - padout
                    bb0.y1 = bb0.y1 + padin

            if tick.tick2line.get_visible():
                if self.spine_type == 'right':
                    bb0.x1 = bb0.x1 + padout
                    bb0.x0 = bb0.x0 - padin
                elif self.spine_type == 'top':
                    bb0.y1 = bb0.y1 + padout
                    bb0.y0 = bb0.y0 - padout
            bboxes.append(bb0)

        return mtransforms.Bbox.union(bboxes)

        ```
    ###Assistant : 
        Return the window extent of the spines in display space, including
        padding for ticks (but not their labels)

        See Also
        --------
        matplotlib.axes.Axes.get_tightbbox
        matplotlib.axes.Axes.get_window_extent
        
    "
1246,"def apply_support(self, location, type):
        
        if location not in self._node_labels:
            raise ValueError(""Support must be added on a known node"")

        else:
            self._supports[location] = type

        if type == ""pinned"":
            self._loads['R_'+str(location)+'_x']= []
            self._loads['R_'+str(location)+'_y']= []

        elif type == ""roller"":
            self._loads['R_'+str(location)+'_y']= []
            if 'R_'+str(location)+'_x' in list(self._loads):
                self._loads.pop('R_'+str(location)+'_x')
","
        This method adds a pinned or roller support at a particular node

        Parameters
        ==========

        location: String or Symbol
            Label of the Node at which support is added.

        type: String
            Type of the support being provided at the node.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> t = Truss()
        >>> t.add_node('A', 0, 0)
        >>> t.add_node('B', 3, 0)
        >>> t.apply_support('A', 'pinned')
        >>> t.supports
        {'A': 'pinned', 'B': 'none'}
        ",66,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply_support(self, location, type):
        
        if location not in self._node_labels:
            raise ValueError(""Support must be added on a known node"")

        else:
            self._supports[location] = type

        if type == ""pinned"":
            self._loads['R_'+str(location)+'_x']= []
            self._loads['R_'+str(location)+'_y']= []

        elif type == ""roller"":
            self._loads['R_'+str(location)+'_y']= []
            if 'R_'+str(location)+'_x' in list(self._loads):
                self._loads.pop('R_'+str(location)+'_x')

        ```
    ###Assistant : 
        This method adds a pinned or roller support at a particular node

        Parameters
        ==========

        location: String or Symbol
            Label of the Node at which support is added.

        type: String
            Type of the support being provided at the node.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> t = Truss()
        >>> t.add_node('A', 0, 0)
        >>> t.add_node('B', 3, 0)
        >>> t.apply_support('A', 'pinned')
        >>> t.supports
        {'A': 'pinned', 'B': 'none'}
        
    "
1247,"def timers(self) -> list[dict[str, Any]]:
        
        return [
            {
                ""enabled"": timer.enabled,
                ""cron"": timer.cron,
                ""next_schedule"": as_utc(timer.next_schedule),
            }
            for timer in self.coordinator.data.timers
        ]
",Get the list of added timers of the vacuum cleaner.,10,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def timers(self) -> list[dict[str, Any]]:
        
        return [
            {
                ""enabled"": timer.enabled,
                ""cron"": timer.cron,
                ""next_schedule"": as_utc(timer.next_schedule),
            }
            for timer in self.coordinator.data.timers
        ]

        ```
    ###Assistant : Get the list of added timers of the vacuum cleaner.
    "
1248,"def plot_avg_pitch(pitch, chars, fig_size=(30, 10), output_fig=False):
    
    old_fig_size = plt.rcParams[""figure.figsize""]
    if fig_size is not None:
        plt.rcParams[""figure.figsize""] = fig_size

    fig, ax = plt.subplots()

    x = np.array(range(len(chars)))
    my_xticks = [c for c in chars]
    plt.xticks(x, my_xticks)

    ax.set_xlabel(""characters"")
    ax.set_ylabel(""freq"")

    ax2 = ax.twinx()
    ax2.plot(pitch, linewidth=5.0, color=""red"")
    ax2.set_ylabel(""F0"")

    plt.rcParams[""figure.figsize""] = old_fig_size
    if not output_fig:
        plt.close()
    return fig

","Plot pitch curves on top of the input characters.

    Args:
        pitch (np.array): Pitch values.
        chars (str): Characters to place to the x-axis.

    Shapes:
        pitch: :math:`(T,)`
    ",25,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def plot_avg_pitch(pitch, chars, fig_size=(30, 10), output_fig=False):
    
    old_fig_size = plt.rcParams[""figure.figsize""]
    if fig_size is not None:
        plt.rcParams[""figure.figsize""] = fig_size

    fig, ax = plt.subplots()

    x = np.array(range(len(chars)))
    my_xticks = [c for c in chars]
    plt.xticks(x, my_xticks)

    ax.set_xlabel(""characters"")
    ax.set_ylabel(""freq"")

    ax2 = ax.twinx()
    ax2.plot(pitch, linewidth=5.0, color=""red"")
    ax2.set_ylabel(""F0"")

    plt.rcParams[""figure.figsize""] = old_fig_size
    if not output_fig:
        plt.close()
    return fig


        ```
    ###Assistant : Plot pitch curves on top of the input characters.

    Args:
        pitch (np.array): Pitch values.
        chars (str): Characters to place to the x-axis.

    Shapes:
        pitch: :math:`(T,)`
    
    "
1249,"def composite(nth):
    
    n = as_int(nth)
    if n < 1:
        raise ValueError(""nth must be a positive integer; composite(1) == 4"")
    composite_arr = [4, 6, 8, 9, 10, 12, 14, 15, 16, 18]
    if n <= 10:
        return composite_arr[n - 1]

    a, b = 4, sieve._list[-1]
    if n <= b - primepi(b) - 1:
        while a < b - 1:
            mid = (a + b) >> 1
            if mid - primepi(mid) - 1 > n:
                b = mid
            else:
                a = mid
        if isprime(a):
            a -= 1
        return a

    from sympy.functions.elementary.exponential import log
    from sympy.functions.special.error_functions import li
    a = 4 # Lower bound for binary search
    b = int(n*(log(n) + log(log(n)))) # Upper bound for the search.

    while a < b:
        mid = (a + b) >> 1
        if mid - li(mid) - 1 > n:
            b = mid
        else:
            a = mid + 1

    n_composites = a - primepi(a) - 1
    while n_composites > n:
        if not isprime(a):
            n_composites -= 1
        a -= 1
    if isprime(a):
        a -= 1
    return a

"," Return the nth composite number, with the composite numbers indexed as
        composite(1) = 4, composite(2) = 6, etc....

        Examples
        ========

        >>> from sympy import composite
        >>> composite(36)
        52
        >>> composite(1)
        4
        >>> composite(17737)
        20000

        See Also
        ========

        sympy.ntheory.primetest.isprime : Test if n is prime
        primerange : Generate all primes in a given range
        primepi : Return the number of primes less than or equal to n
        prime : Return the nth prime
        compositepi : Return the number of positive composite numbers less than or equal to n
    ",87,170,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def composite(nth):
    
    n = as_int(nth)
    if n < 1:
        raise ValueError(""nth must be a positive integer; composite(1) == 4"")
    composite_arr = [4, 6, 8, 9, 10, 12, 14, 15, 16, 18]
    if n <= 10:
        return composite_arr[n - 1]

    a, b = 4, sieve._list[-1]
    if n <= b - primepi(b) - 1:
        while a < b - 1:
            mid = (a + b) >> 1
            if mid - primepi(mid) - 1 > n:
                b = mid
            else:
                a = mid
        if isprime(a):
            a -= 1
        return a

    from sympy.functions.elementary.exponential import log
    from sympy.functions.special.error_functions import li
    a = 4 # Lower bound for binary search
    b = int(n*(log(n) + log(log(n)))) # Upper bound for the search.

    while a < b:
        mid = (a + b) >> 1
        if mid - li(mid) - 1 > n:
            b = mid
        else:
            a = mid + 1

    n_composites = a - primepi(a) - 1
    while n_composites > n:
        if not isprime(a):
            n_composites -= 1
        a -= 1
    if isprime(a):
        a -= 1
    return a


        ```
    ###Assistant :  Return the nth composite number, with the composite numbers indexed as
        composite(1) = 4, composite(2) = 6, etc....

        Examples
        ========

        >>> from sympy import composite
        >>> composite(36)
        52
        >>> composite(1)
        4
        >>> composite(17737)
        20000

        See Also
        ========

        sympy.ntheory.primetest.isprime : Test if n is prime
        primerange : Generate all primes in a given range
        primepi : Return the number of primes less than or equal to n
        prime : Return the nth prime
        compositepi : Return the number of positive composite numbers less than or equal to n
    
    "
1250,"def histogram2d(x, y, bins=10, range=None, density=None, weights=None):
    
    from numpy import histogramdd

    if len(x) != len(y):
        raise ValueError('x and y must have the same length.')

    try:
        N = len(bins)
    except TypeError:
        N = 1

    if N != 1 and N != 2:
        xedges = yedges = asarray(bins)
        bins = [xedges, yedges]
    hist, edges = histogramdd([x, y], bins, range, density, weights)
    return hist, edges[0], edges[1]


@set_module('numpy')","
    Compute the bi-dimensional histogram of two data samples.

    Parameters
    ----------
    x : array_like, shape (N,)
        An array containing the x coordinates of the points to be
        histogrammed.
    y : array_like, shape (N,)
        An array containing the y coordinates of the points to be
        histogrammed.
    bins : int or array_like or [int, int] or [array, array], optional
        The bin specification:

          * If int, the number of bins for the two dimensions (nx=ny=bins).
          * If array_like, the bin edges for the two dimensions
            (x_edges=y_edges=bins).
          * If [int, int], the number of bins in each dimension
            (nx, ny = bins).
          * If [array, array], the bin edges in each dimension
            (x_edges, y_edges = bins).
          * A combination [int, array] or [array, int], where int
            is the number of bins and array is the bin edges.

    range : array_like, shape(2,2), optional
        The leftmost and rightmost edges of the bins along each dimension
        (if not specified explicitly in the `bins` parameters):
        ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
        will be considered outliers and not tallied in the histogram.
    density : bool, optional
        If False, the default, returns the number of samples in each bin.
        If True, returns the probability *density* function at the bin,
        ``bin_count / sample_count / bin_area``.
    weights : array_like, shape(N,), optional
        An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.
        Weights are normalized to 1 if `density` is True. If `density` is
        False, the values of the returned histogram are equal to the sum of
        the weights belonging to the samples falling into each bin.

    Returns
    -------
    H : ndarray, shape(nx, ny)
        The bi-dimensional histogram of samples `x` and `y`. Values in `x`
        are histogrammed along the first dimension and values in `y` are
        histogrammed along the second dimension.
    xedges : ndarray, shape(nx+1,)
        The bin edges along the first dimension.
    yedges : ndarray, shape(ny+1,)
        The bin edges along the second dimension.

    See Also
    --------
    histogram : 1D histogram
    histogramdd : Multidimensional histogram

    Notes
    -----
    When `density` is True, then the returned histogram is the sample
    density, defined such that the sum over bins of the product
    ``bin_value * bin_area`` is 1.

    Please note that the histogram does not follow the Cartesian convention
    where `x` values are on the abscissa and `y` values on the ordinate
    axis.  Rather, `x` is histogrammed along the first dimension of the
    array (vertical), and `y` along the second dimension of the array
    (horizontal).  This ensures compatibility with `histogramdd`.

    Examples
    --------
    >>> from matplotlib.image import NonUniformImage
    >>> import matplotlib.pyplot as plt

    Construct a 2-D histogram with variable bin width. First define the bin
    edges:

    >>> xedges = [0, 1, 3, 5]
    >>> yedges = [0, 2, 3, 4, 6]

    Next we create a histogram H with random bin content:

    >>> x = np.random.normal(2, 1, 100)
    >>> y = np.random.normal(1, 1, 100)
    >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
    >>> # Histogram does not follow Cartesian convention (see Notes),
    >>> # therefore transpose H for visualization purposes.
    >>> H = H.T

    :func:`imshow <matplotlib.pyplot.imshow>` can only display square bins:

    >>> fig = plt.figure(figsize=(7, 3))
    >>> ax = fig.add_subplot(131, title='imshow: square bins')
    >>> plt.imshow(H, interpolation='nearest', origin='lower',
    ...         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
    <matplotlib.image.AxesImage object at 0x...>

    :func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:

    >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges',
    ...         aspect='equal')
    >>> X, Y = np.meshgrid(xedges, yedges)
    >>> ax.pcolormesh(X, Y, H)
    <matplotlib.collections.QuadMesh object at 0x...>

    :class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to
    display actual bin edges with interpolation:

    >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
    ...         aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
    >>> im = NonUniformImage(ax, interpolation='bilinear')
    >>> xcenters = (xedges[:-1] + xedges[1:]) / 2
    >>> ycenters = (yedges[:-1] + yedges[1:]) / 2
    >>> im.set_data(xcenters, ycenters, H)
    >>> ax.images.append(im)
    >>> plt.show()

    It is also possible to construct a 2-D histogram without specifying bin
    edges:

    >>> # Generate non-symmetric test data
    >>> n = 10000
    >>> x = np.linspace(1, 100, n)
    >>> y = 2*np.log(x) + np.random.rand(n) - 0.5
    >>> # Compute 2d histogram. Note the order of x/y and xedges/yedges
    >>> H, yedges, xedges = np.histogram2d(y, x, bins=20)

    Now we can plot the histogram using
    :func:`pcolormesh <matplotlib.pyplot.pcolormesh>`, and a
    :func:`hexbin <matplotlib.pyplot.hexbin>` for comparison.

    >>> # Plot histogram using pcolormesh
    >>> fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)
    >>> ax1.pcolormesh(xedges, yedges, H, cmap='rainbow')
    >>> ax1.plot(x, 2*np.log(x), 'k-')
    >>> ax1.set_xlim(x.min(), x.max())
    >>> ax1.set_ylim(y.min(), y.max())
    >>> ax1.set_xlabel('x')
    >>> ax1.set_ylabel('y')
    >>> ax1.set_title('histogram2d')
    >>> ax1.grid()

    >>> # Create hexbin plot for comparison
    >>> ax2.hexbin(x, y, gridsize=20, cmap='rainbow')
    >>> ax2.plot(x, 2*np.log(x), 'k-')
    >>> ax2.set_title('hexbin')
    >>> ax2.set_xlim(x.min(), x.max())
    >>> ax2.set_xlabel('x')
    >>> ax2.grid()

    >>> plt.show()
    ",747,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def histogram2d(x, y, bins=10, range=None, density=None, weights=None):
    
    from numpy import histogramdd

    if len(x) != len(y):
        raise ValueError('x and y must have the same length.')

    try:
        N = len(bins)
    except TypeError:
        N = 1

    if N != 1 and N != 2:
        xedges = yedges = asarray(bins)
        bins = [xedges, yedges]
    hist, edges = histogramdd([x, y], bins, range, density, weights)
    return hist, edges[0], edges[1]


@set_module('numpy')
        ```
    ###Assistant : 
    Compute the bi-dimensional histogram of two data samples.

    Parameters
    ----------
    x : array_like, shape (N,)
        An array containing the x coordinates of the points to be
        histogrammed.
    y : array_like, shape (N,)
        An array containing the y coordinates of the points to be
        histogrammed.
    bins : int or array_like or [int, int] or [array, array], optional
        The bin specification:

          * If int, the number of bins for the two dimensions (nx=ny=bins).
          * If array_like, the bin edges for the two dimensions
            (x_edges=y_edges=bins).
          * If [int, int], the number of bins in each dimension
            (nx, ny = bins).
          * If [array, array], the bin edges in each dimension
            (x_edges, y_edges = bins).
          * A combination [int, array] or [array, int], where int
            is the number of bins and array is the bin edges.

    range : array_like, shape(2,2), optional
        The leftmost and rightmost edges of the bins along each dimension
        (if not specified explicitly in the `bins` parameters):
        ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
        will be considered outliers and not tallied in the histogram.
    density : bool, optional
        If False, the default, returns the number of samples in each bin.
        If True, returns the probability *density* function at the bin,
        ``bin_count / sample_count / bin_area``.
    weights : array_like, shape(N,), optional
        An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.
        Weights are normalized to 1 if `density` is True. If `density` is
        False, the values of the returned histogram are equal to the sum of
        the weights belonging to the samples falling into each bin.

    Returns
    -------
    H : ndarray, shape(nx, ny)
        The bi-dimensional histogram of samples `x` and `y`. Values in `x`
        are histogrammed along the first dimension and values in `y` are
        histogrammed along the second dimension.
    xedges : ndarray, shape(nx+1,)
        The bin edges along the first dimension.
    yedges : ndarray, shape(ny+1,)
        The bin edges along the second dimension.

    See Also
    --------
    histogram : 1D histogram
    histogramdd : Multidimensional histogram

    Notes
    -----
    When `density` is True, then the returned histogram is the sample
    density, defined such that the sum over bins of the product
    ``bin_value * bin_area`` is 1.

    Please note that the histogram does not follow the Cartesian convention
    where `x` values are on the abscissa and `y` values on the ordinate
    axis.  Rather, `x` is histogrammed along the first dimension of the
    array (vertical), and `y` along the second dimension of the array
    (horizontal).  This ensures compatibility with `histogramdd`.

    Examples
    --------
    >>> from matplotlib.image import NonUniformImage
    >>> import matplotlib.pyplot as plt

    Construct a 2-D histogram with variable bin width. First define the bin
    edges:

    >>> xedges = [0, 1, 3, 5]
    >>> yedges = [0, 2, 3, 4, 6]

    Next we create a histogram H with random bin content:

    >>> x = np.random.normal(2, 1, 100)
    >>> y = np.random.normal(1, 1, 100)
    >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
    >>> # Histogram does not follow Cartesian convention (see Notes),
    >>> # therefore transpose H for visualization purposes.
    >>> H = H.T

    :func:`imshow <matplotlib.pyplot.imshow>` can only display square bins:

    >>> fig = plt.figure(figsize=(7, 3))
    >>> ax = fig.add_subplot(131, title='imshow: square bins')
    >>> plt.imshow(H, interpolation='nearest', origin='lower',
    ...         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
    <matplotlib.image.AxesImage object at 0x...>

    :func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:

    >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges',
    ...         aspect='equal')
    >>> X, Y = np.meshgrid(xedges, yedges)
    >>> ax.pcolormesh(X, Y, H)
    <matplotlib.collections.QuadMesh object at 0x...>

    :class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to
    display actual bin edges with interpolation:

    >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
    ...         aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
    >>> im = NonUniformImage(ax, interpolation='bilinear')
    >>> xcenters = (xedges[:-1] + xedges[1:]) / 2
    >>> ycenters = (yedges[:-1] + yedges[1:]) / 2
    >>> im.set_data(xcenters, ycenters, H)
    >>> ax.images.append(im)
    >>> plt.show()

    It is also possible to construct a 2-D histogram without specifying bin
    edges:

    >>> # Generate non-symmetric test data
    >>> n = 10000
    >>> x = np.linspace(1, 100, n)
    >>> y = 2*np.log(x) + np.random.rand(n) - 0.5
    >>> # Compute 2d histogram. Note the order of x/y and xedges/yedges
    >>> H, yedges, xedges = np.histogram2d(y, x, bins=20)

    Now we can plot the histogram using
    :func:`pcolormesh <matplotlib.pyplot.pcolormesh>`, and a
    :func:`hexbin <matplotlib.pyplot.hexbin>` for comparison.

    >>> # Plot histogram using pcolormesh
    >>> fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)
    >>> ax1.pcolormesh(xedges, yedges, H, cmap='rainbow')
    >>> ax1.plot(x, 2*np.log(x), 'k-')
    >>> ax1.set_xlim(x.min(), x.max())
    >>> ax1.set_ylim(y.min(), y.max())
    >>> ax1.set_xlabel('x')
    >>> ax1.set_ylabel('y')
    >>> ax1.set_title('histogram2d')
    >>> ax1.grid()

    >>> # Create hexbin plot for comparison
    >>> ax2.hexbin(x, y, gridsize=20, cmap='rainbow')
    >>> ax2.plot(x, 2*np.log(x), 'k-')
    >>> ax2.set_title('hexbin')
    >>> ax2.set_xlim(x.min(), x.max())
    >>> ax2.set_xlabel('x')
    >>> ax2.grid()

    >>> plt.show()
    
    "
1251,"def available(self) -> bool:
        
        return self._device is not None and self._device.profile_device.available
",Device is available when we have a connection to it.,10,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def available(self) -> bool:
        
        return self._device is not None and self._device.profile_device.available

        ```
    ###Assistant : Device is available when we have a connection to it.
    "
1252,"def _get_input_from_iterator(iterator, model):
    
    next_element = iterator.get_next()

    # `len(nest.flatten(x))` is going to not count empty elements such as {}.
    # len(nest.flatten([[0,1,2], {}])) is 3 and not 4.   The `next_element` is
    # going to get flattened in `_prepare_feed_values` to work around that. Empty
    # elements are going to get filtered out as part of the flattening.
    if len(tf.nest.flatten(next_element)) == len(model.inputs):
        x = next_element
        y = None
        sample_weights = None
    elif len(tf.nest.flatten(next_element)) == (
        len(model.inputs) + len(model.outputs)
    ):
        x, y = next_element
        sample_weights = None
    else:
        x, y, sample_weights = next_element

    # Validate that all the elements in x and y are of the same type and shape.
    validate_distributed_dataset_inputs(
        model._distribution_strategy, x, y, sample_weights
    )
    return x, y, sample_weights

",Get elements from the iterator and verify the input shape and type.,12,115,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_input_from_iterator(iterator, model):
    
    next_element = iterator.get_next()

    # `len(nest.flatten(x))` is going to not count empty elements such as {}.
    # len(nest.flatten([[0,1,2], {}])) is 3 and not 4.   The `next_element` is
    # going to get flattened in `_prepare_feed_values` to work around that. Empty
    # elements are going to get filtered out as part of the flattening.
    if len(tf.nest.flatten(next_element)) == len(model.inputs):
        x = next_element
        y = None
        sample_weights = None
    elif len(tf.nest.flatten(next_element)) == (
        len(model.inputs) + len(model.outputs)
    ):
        x, y = next_element
        sample_weights = None
    else:
        x, y, sample_weights = next_element

    # Validate that all the elements in x and y are of the same type and shape.
    validate_distributed_dataset_inputs(
        model._distribution_strategy, x, y, sample_weights
    )
    return x, y, sample_weights


        ```
    ###Assistant : Get elements from the iterator and verify the input shape and type.
    "
1253,"def inner(a, b):
    
    return (a, b)


@array_function_from_c_func_and_dispatcher(_multiarray_umath.where)","
    inner(a, b, /)

    Inner product of two arrays.

    Ordinary inner product of vectors for 1-D arrays (without complex
    conjugation), in higher dimensions a sum product over the last axes.

    Parameters
    ----------
    a, b : array_like
        If `a` and `b` are nonscalar, their last dimensions must match.

    Returns
    -------
    out : ndarray
        If `a` and `b` are both
        scalars or both 1-D arrays then a scalar is returned; otherwise
        an array is returned.
        ``out.shape = (*a.shape[:-1], *b.shape[:-1])``

    Raises
    ------
    ValueError
        If both `a` and `b` are nonscalar and their last dimensions have
        different sizes.

    See Also
    --------
    tensordot : Sum products over arbitrary axes.
    dot : Generalised matrix product, using second last dimension of `b`.
    einsum : Einstein summation convention.

    Notes
    -----
    For vectors (1-D arrays) it computes the ordinary inner-product::

        np.inner(a, b) = sum(a[:]*b[:])

    More generally, if ``ndim(a) = r > 0`` and ``ndim(b) = s > 0``::

        np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))

    or explicitly::

        np.inner(a, b)[i0,...,ir-2,j0,...,js-2]
             = sum(a[i0,...,ir-2,:]*b[j0,...,js-2,:])

    In addition `a` or `b` may be scalars, in which case::

       np.inner(a,b) = a*b

    Examples
    --------
    Ordinary inner product for vectors:

    >>> a = np.array([1,2,3])
    >>> b = np.array([0,1,0])
    >>> np.inner(a, b)
    2

    Some multidimensional examples:

    >>> a = np.arange(24).reshape((2,3,4))
    >>> b = np.arange(4)
    >>> c = np.inner(a, b)
    >>> c.shape
    (2, 3)
    >>> c
    array([[ 14,  38,  62],
           [ 86, 110, 134]])

    >>> a = np.arange(2).reshape((1,1,2))
    >>> b = np.arange(6).reshape((3,2))
    >>> c = np.inner(a, b)
    >>> c.shape
    (1, 1, 3)
    >>> c
    array([[[1, 3, 5]]])

    An example where `b` is a scalar:

    >>> np.inner(np.eye(2), 7)
    array([[7., 0.],
           [0., 7.]])

    ",260,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def inner(a, b):
    
    return (a, b)


@array_function_from_c_func_and_dispatcher(_multiarray_umath.where)
        ```
    ###Assistant : 
    inner(a, b, /)

    Inner product of two arrays.

    Ordinary inner product of vectors for 1-D arrays (without complex
    conjugation), in higher dimensions a sum product over the last axes.

    Parameters
    ----------
    a, b : array_like
        If `a` and `b` are nonscalar, their last dimensions must match.

    Returns
    -------
    out : ndarray
        If `a` and `b` are both
        scalars or both 1-D arrays then a scalar is returned; otherwise
        an array is returned.
        ``out.shape = (*a.shape[:-1], *b.shape[:-1])``

    Raises
    ------
    ValueError
        If both `a` and `b` are nonscalar and their last dimensions have
        different sizes.

    See Also
    --------
    tensordot : Sum products over arbitrary axes.
    dot : Generalised matrix product, using second last dimension of `b`.
    einsum : Einstein summation convention.

    Notes
    -----
    For vectors (1-D arrays) it computes the ordinary inner-product::

        np.inner(a, b) = sum(a[:]*b[:])

    More generally, if ``ndim(a) = r > 0`` and ``ndim(b) = s > 0``::

        np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))

    or explicitly::

        np.inner(a, b)[i0,...,ir-2,j0,...,js-2]
             = sum(a[i0,...,ir-2,:]*b[j0,...,js-2,:])

    In addition `a` or `b` may be scalars, in which case::

       np.inner(a,b) = a*b

    Examples
    --------
    Ordinary inner product for vectors:

    >>> a = np.array([1,2,3])
    >>> b = np.array([0,1,0])
    >>> np.inner(a, b)
    2

    Some multidimensional examples:

    >>> a = np.arange(24).reshape((2,3,4))
    >>> b = np.arange(4)
    >>> c = np.inner(a, b)
    >>> c.shape
    (2, 3)
    >>> c
    array([[ 14,  38,  62],
           [ 86, 110, 134]])

    >>> a = np.arange(2).reshape((1,1,2))
    >>> b = np.arange(6).reshape((3,2))
    >>> c = np.inner(a, b)
    >>> c.shape
    (1, 1, 3)
    >>> c
    array([[[1, 3, 5]]])

    An example where `b` is a scalar:

    >>> np.inner(np.eye(2), 7)
    array([[7., 0.],
           [0., 7.]])

    
    "
1254,"def contains(self, mouseevent):
        
        inside, info = self._default_contains(mouseevent)
        if inside is not None:
            return inside, info

        if not self.get_visible():
            return False, {}

        pickradius = (
            float(self._picker)
            if isinstance(self._picker, Number) and
               self._picker is not True  # the bool, not just nonzero or 1
            else self._pickradius)

        if self.axes:
            self.axes._unstale_viewLim()

        transform, offset_trf, offsets, paths = self._prepare_points()

        # Tests if the point is contained on one of the polygons formed
        # by the control points of each of the paths. A point is considered
        # ""on"" a path if it would lie within a stroke of width 2*pickradius
        # following the path. If pickradius <= 0, then we instead simply check
        # if the point is *inside* of the path instead.
        ind = _path.point_in_path_collection(
            mouseevent.x, mouseevent.y, pickradius,
            transform.frozen(), paths, self.get_transforms(),
            offsets, offset_trf, pickradius <= 0)

        return len(ind) > 0, dict(ind=ind)
","
        Test whether the mouse event occurred in the collection.

        Returns ``bool, dict(ind=itemlist)``, where every item in itemlist
        contains the event.
        ",20,135,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def contains(self, mouseevent):
        
        inside, info = self._default_contains(mouseevent)
        if inside is not None:
            return inside, info

        if not self.get_visible():
            return False, {}

        pickradius = (
            float(self._picker)
            if isinstance(self._picker, Number) and
               self._picker is not True  # the bool, not just nonzero or 1
            else self._pickradius)

        if self.axes:
            self.axes._unstale_viewLim()

        transform, offset_trf, offsets, paths = self._prepare_points()

        # Tests if the point is contained on one of the polygons formed
        # by the control points of each of the paths. A point is considered
        # ""on"" a path if it would lie within a stroke of width 2*pickradius
        # following the path. If pickradius <= 0, then we instead simply check
        # if the point is *inside* of the path instead.
        ind = _path.point_in_path_collection(
            mouseevent.x, mouseevent.y, pickradius,
            transform.frozen(), paths, self.get_transforms(),
            offsets, offset_trf, pickradius <= 0)

        return len(ind) > 0, dict(ind=ind)

        ```
    ###Assistant : 
        Test whether the mouse event occurred in the collection.

        Returns ``bool, dict(ind=itemlist)``, where every item in itemlist
        contains the event.
        
    "
1255,"def deployments(self) -> List[Dict]:
        
        return [
            {
                'name': self.name,
                'head_host': self.head_host,
                'head_port_in': self.head_port_in,
            }
        ]

","Get deployments of the deployment. The BaseDeployment just gives one deployment.

        :return: list of deployments
        ",15,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deployments(self) -> List[Dict]:
        
        return [
            {
                'name': self.name,
                'head_host': self.head_host,
                'head_port_in': self.head_port_in,
            }
        ]


        ```
    ###Assistant : Get deployments of the deployment. The BaseDeployment just gives one deployment.

        :return: list of deployments
        
    "
1256,"def parse_content_disposition(reply):
    
    is_inline = True
    filename = None
    content_disposition_header = b'Content-Disposition'
    # First check if the Content-Disposition header has a filename
    # attribute.
    if reply.hasRawHeader(content_disposition_header):
        # We use the unsafe variant of the filename as we sanitize it via
        # os.path.basename later.
        try:
            value = bytes(reply.rawHeader(content_disposition_header))
            log.network.debug(""Parsing Content-Disposition: {value!r}"")
            content_disposition = ContentDisposition.parse(value)
            filename = content_disposition.filename()
        except ContentDispositionError as e:
            log.network.error(f""Error while parsing filename: {e}"")
        else:
            is_inline = content_disposition.is_inline()
    # Then try to get filename from url
    if not filename:
        filename = reply.url().path().rstrip('/')
    # If that fails as well, use a fallback
    if not filename:
        filename = 'qutebrowser-download'
    return is_inline, os.path.basename(filename)

","Parse a content_disposition header.

    Args:
        reply: The QNetworkReply to get a filename for.

    Return:
        A (is_inline, filename) tuple.
    ",18,100,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_content_disposition(reply):
    
    is_inline = True
    filename = None
    content_disposition_header = b'Content-Disposition'
    # First check if the Content-Disposition header has a filename
    # attribute.
    if reply.hasRawHeader(content_disposition_header):
        # We use the unsafe variant of the filename as we sanitize it via
        # os.path.basename later.
        try:
            value = bytes(reply.rawHeader(content_disposition_header))
            log.network.debug(""Parsing Content-Disposition: {value!r}"")
            content_disposition = ContentDisposition.parse(value)
            filename = content_disposition.filename()
        except ContentDispositionError as e:
            log.network.error(f""Error while parsing filename: {e}"")
        else:
            is_inline = content_disposition.is_inline()
    # Then try to get filename from url
    if not filename:
        filename = reply.url().path().rstrip('/')
    # If that fails as well, use a fallback
    if not filename:
        filename = 'qutebrowser-download'
    return is_inline, os.path.basename(filename)


        ```
    ###Assistant : Parse a content_disposition header.

    Args:
        reply: The QNetworkReply to get a filename for.

    Return:
        A (is_inline, filename) tuple.
    
    "
1257,"def _get_categorical_mapping(self, scale, data):
        
        levels = categorical_order(data, scale.order)
        n = len(levels)
        values = scale.values

        if isinstance(values, dict):
            self._check_dict_entries(levels, values)
            # TODO where to ensure that dict values have consistent representation?
            colors = [values[x] for x in levels]
        elif isinstance(values, list):
            colors = self._check_list_length(levels, scale.values)
        elif isinstance(values, tuple):
            colors = blend_palette(values, n)
        elif isinstance(values, str):
            colors = color_palette(values, n)
        elif values is None:
            if n <= len(get_color_cycle()):
                # Use current (global) default palette
                colors = color_palette(n_colors=n)
            else:
                colors = color_palette(""husl"", n)
        else:
            scale_class = scale.__class__.__name__
            msg = "" "".join([
                f""Scale values for {self.variable} with a {scale_class} mapping"",
                f""must be string, list, tuple, or dict; not {type(scale.values)}.""
            ])
            raise TypeError(msg)

        # If color specified here has alpha channel, it will override alpha property
        colors = self._standardize_colors(colors)
",Define mapping as lookup in list of discrete color values.,10,124,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_categorical_mapping(self, scale, data):
        
        levels = categorical_order(data, scale.order)
        n = len(levels)
        values = scale.values

        if isinstance(values, dict):
            self._check_dict_entries(levels, values)
            # TODO where to ensure that dict values have consistent representation?
            colors = [values[x] for x in levels]
        elif isinstance(values, list):
            colors = self._check_list_length(levels, scale.values)
        elif isinstance(values, tuple):
            colors = blend_palette(values, n)
        elif isinstance(values, str):
            colors = color_palette(values, n)
        elif values is None:
            if n <= len(get_color_cycle()):
                # Use current (global) default palette
                colors = color_palette(n_colors=n)
            else:
                colors = color_palette(""husl"", n)
        else:
            scale_class = scale.__class__.__name__
            msg = "" "".join([
                f""Scale values for {self.variable} with a {scale_class} mapping"",
                f""must be string, list, tuple, or dict; not {type(scale.values)}.""
            ])
            raise TypeError(msg)

        # If color specified here has alpha channel, it will override alpha property
        colors = self._standardize_colors(colors)

        ```
    ###Assistant : Define mapping as lookup in list of discrete color values.
    "
1258,"def minorlocator(self, loc):
        
        self._long_axis().set_minor_locator(loc)
        self._minorlocator = loc
","
        Set minor locator being used for colorbar
        ",7,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def minorlocator(self, loc):
        
        self._long_axis().set_minor_locator(loc)
        self._minorlocator = loc

        ```
    ###Assistant : 
        Set minor locator being used for colorbar
        
    "
1259,"def test_process_datetime_to_timestamp_freeze_time(time_zone, hass):
    
    hass.config.set_time_zone(time_zone)
    utc_now = dt_util.utcnow()
    with freeze_time(utc_now):
        epoch = utc_now.timestamp()
        assert process_datetime_to_timestamp(dt_util.utcnow()) == epoch
        now = dt_util.now()
        assert process_datetime_to_timestamp(now) == epoch


@pytest.mark.parametrize(
    ""time_zone"", [""Europe/Berlin"", ""America/Chicago"", ""US/Hawaii"", ""UTC""]
)","Test we can handle processing database datatimes to timestamps.

    This test freezes time to make sure everything matches.
    ",18,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_process_datetime_to_timestamp_freeze_time(time_zone, hass):
    
    hass.config.set_time_zone(time_zone)
    utc_now = dt_util.utcnow()
    with freeze_time(utc_now):
        epoch = utc_now.timestamp()
        assert process_datetime_to_timestamp(dt_util.utcnow()) == epoch
        now = dt_util.now()
        assert process_datetime_to_timestamp(now) == epoch


@pytest.mark.parametrize(
    ""time_zone"", [""Europe/Berlin"", ""America/Chicago"", ""US/Hawaii"", ""UTC""]
)
        ```
    ###Assistant : Test we can handle processing database datatimes to timestamps.

    This test freezes time to make sure everything matches.
    
    "
1260,"def _spatially_filter(self) -> np.ndarray:
        
        logger.debug(""Spatially Filter"")
        assert self._shapes_model is not None
        landmarks_norm = self._normalized[""landmarks""]
        # Convert to matrix form
        landmarks_norm_table = np.reshape(landmarks_norm, [68 * 2, landmarks_norm.shape[2]]).T
        # Project onto shapes model and reconstruct
        landmarks_norm_table_rec = self._shapes_model.inverse_transform(
            self._shapes_model.transform(landmarks_norm_table))
        # Convert back to shapes (numKeypoint, num_dims, numFrames)
        landmarks_norm_rec = np.reshape(landmarks_norm_table_rec.T,
                                        [68, 2, landmarks_norm.shape[2]])
        # Transform back to image co-ordinates
        retval = self._normalized_to_original(landmarks_norm_rec,
                                              self._normalized[""scale_factors""],
                                              self._normalized[""mean_coords""])

        logger.debug(""Spatially Filtered: %s"", retval)
        return retval
"," interpret the shapes using our shape model (project and reconstruct)

        Returns
        -------
        :class:`numpy.ndarray`
            The filtered landmarks in original coordinate space
        ",20,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _spatially_filter(self) -> np.ndarray:
        
        logger.debug(""Spatially Filter"")
        assert self._shapes_model is not None
        landmarks_norm = self._normalized[""landmarks""]
        # Convert to matrix form
        landmarks_norm_table = np.reshape(landmarks_norm, [68 * 2, landmarks_norm.shape[2]]).T
        # Project onto shapes model and reconstruct
        landmarks_norm_table_rec = self._shapes_model.inverse_transform(
            self._shapes_model.transform(landmarks_norm_table))
        # Convert back to shapes (numKeypoint, num_dims, numFrames)
        landmarks_norm_rec = np.reshape(landmarks_norm_table_rec.T,
                                        [68, 2, landmarks_norm.shape[2]])
        # Transform back to image co-ordinates
        retval = self._normalized_to_original(landmarks_norm_rec,
                                              self._normalized[""scale_factors""],
                                              self._normalized[""mean_coords""])

        logger.debug(""Spatially Filtered: %s"", retval)
        return retval

        ```
    ###Assistant :  interpret the shapes using our shape model (project and reconstruct)

        Returns
        -------
        :class:`numpy.ndarray`
            The filtered landmarks in original coordinate space
        
    "
1261,"def letter_form(self):
        
        return tuple(flatten([(i,)*j if j > 0 else (-i,)*(-j)
                    for i, j in self.array_form]))
","
        The letter representation of a ``FreeGroupElement`` is a tuple
        of generator symbols, with each entry corresponding to a group
        generator. Inverses of the generators are represented by
        negative generator symbols.

        Examples
        ========

        >>> from sympy.combinatorics import free_group
        >>> f, a, b, c, d = free_group(""a b c d"")
        >>> (a**3).letter_form
        (a, a, a)
        >>> (a**2*d**-2*a*b**-4).letter_form
        (a, a, -d, -d, a, -b, -b, -b, -b)
        >>> (a**-2*b**3*d).letter_form
        (-a, -a, b, b, b, d)

        See Also
        ========

        array_form

        ",76,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def letter_form(self):
        
        return tuple(flatten([(i,)*j if j > 0 else (-i,)*(-j)
                    for i, j in self.array_form]))

        ```
    ###Assistant : 
        The letter representation of a ``FreeGroupElement`` is a tuple
        of generator symbols, with each entry corresponding to a group
        generator. Inverses of the generators are represented by
        negative generator symbols.

        Examples
        ========

        >>> from sympy.combinatorics import free_group
        >>> f, a, b, c, d = free_group(""a b c d"")
        >>> (a**3).letter_form
        (a, a, a)
        >>> (a**2*d**-2*a*b**-4).letter_form
        (a, a, -d, -d, a, -b, -b, -b, -b)
        >>> (a**-2*b**3*d).letter_form
        (-a, -a, b, b, b, d)

        See Also
        ========

        array_form

        
    "
1262,"def _handle_default_message(self, type, data):
        
        logger.debug(f""Received message from Leader of type {type}: {data}"")
","
        Default leader message handler, just logs it. We should never have to
        run this unless the leader sends us some weird message.
        ",22,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _handle_default_message(self, type, data):
        
        logger.debug(f""Received message from Leader of type {type}: {data}"")

        ```
    ###Assistant : 
        Default leader message handler, just logs it. We should never have to
        run this unless the leader sends us some weird message.
        
    "
1263,"def export_probs(self) -> dict[str, Any]:
        
        result = {}
        for module in self.nas_modules:
            try:
                result.update(module.export_probs(memo=result))
            except NotImplementedError:
                warnings.warn(
                    'Some super-modules you have used did not implement export_probs. You might find some logs are missing.',
                    UserWarning
                )
        return result
","
        Export the probability of every choice in the search space got chosen.

        .. note:: If such method of some modules is not implemented, they will be simply ignored.

        Returns
        -------
        dict
            In most cases, keys are names of ``nas_modules`` suffixed with ``/`` and choice name.
            Values are the probability / logits depending on the implementation.
        ",55,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def export_probs(self) -> dict[str, Any]:
        
        result = {}
        for module in self.nas_modules:
            try:
                result.update(module.export_probs(memo=result))
            except NotImplementedError:
                warnings.warn(
                    'Some super-modules you have used did not implement export_probs. You might find some logs are missing.',
                    UserWarning
                )
        return result

        ```
    ###Assistant : 
        Export the probability of every choice in the search space got chosen.

        .. note:: If such method of some modules is not implemented, they will be simply ignored.

        Returns
        -------
        dict
            In most cases, keys are names of ``nas_modules`` suffixed with ``/`` and choice name.
            Values are the probability / logits depending on the implementation.
        
    "
1264,"def debounce_update_release_health_data(organization, project_ids):
    
    # Figure out which projects need to get updates from the snuba.
    should_update = {}
    cache_keys = [""debounce-health:%d"" % id for id in project_ids]
    cache_data = cache.get_many(cache_keys)
    for project_id, cache_key in zip(project_ids, cache_keys):
        if cache_data.get(cache_key) is None:
            should_update[project_id] = cache_key

    if not should_update:
        return

    projects = {p.id: p for p in Project.objects.get_many_from_cache(should_update.keys())}

    # This gives us updates for all release-projects which have seen new
    # health data over the last days. It will miss releases where the last
    # date is longer than what `get_changed_project_release_model_adoptions`
    # considers recent.
    project_releases = release_health.get_changed_project_release_model_adoptions(
        should_update.keys()
    )

    # Check which we already have rows for.
    existing = set(
        ReleaseProject.objects.filter(
            project_id__in=[x[0] for x in project_releases],
            release__version__in=[x[1] for x in project_releases],
        ).values_list(""project_id"", ""release__version"")
    )
    to_upsert = []
    for key in project_releases:
        if key not in existing:
            to_upsert.append(key)

    if to_upsert:
        dates = release_health.get_oldest_health_data_for_releases(to_upsert)

        for project_id, version in to_upsert:
            project = projects.get(project_id)
            if project is None:
                # should not happen
                continue

            # Ignore versions that were saved with an empty string before validation was added
            if not Release.is_valid_version(version):
                continue

            # We might have never observed the release.  This for instance can
            # happen if the release only had health data so far.  For these cases
            # we want to create the release the first time we observed it on the
            # health side.
            release = Release.get_or_create(
                project=project, version=version, date_added=dates.get((project_id, version))
            )

            # Make sure that the release knows about this project.  Like we had before
            # the project might not have been associated with this release yet.
            release.add_project(project)

    # Debounce updates for a minute
    cache.set_many(dict(zip(should_update.values(), [True] * len(should_update))), 60)


@region_silo_endpoint","This causes a flush of snuba health data to the postgres tables once
    per minute for the given projects.
    ",19,265,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def debounce_update_release_health_data(organization, project_ids):
    
    # Figure out which projects need to get updates from the snuba.
    should_update = {}
    cache_keys = [""debounce-health:%d"" % id for id in project_ids]
    cache_data = cache.get_many(cache_keys)
    for project_id, cache_key in zip(project_ids, cache_keys):
        if cache_data.get(cache_key) is None:
            should_update[project_id] = cache_key

    if not should_update:
        return

    projects = {p.id: p for p in Project.objects.get_many_from_cache(should_update.keys())}

    # This gives us updates for all release-projects which have seen new
    # health data over the last days. It will miss releases where the last
    # date is longer than what `get_changed_project_release_model_adoptions`
    # considers recent.
    project_releases = release_health.get_changed_project_release_model_adoptions(
        should_update.keys()
    )

    # Check which we already have rows for.
    existing = set(
        ReleaseProject.objects.filter(
            project_id__in=[x[0] for x in project_releases],
            release__version__in=[x[1] for x in project_releases],
        ).values_list(""project_id"", ""release__version"")
    )
    to_upsert = []
    for key in project_releases:
        if key not in existing:
            to_upsert.append(key)

    if to_upsert:
        dates = release_health.get_oldest_health_data_for_releases(to_upsert)

        for project_id, version in to_upsert:
            project = projects.get(project_id)
            if project is None:
                # should not happen
                continue

            # Ignore versions that were saved with an empty string before validation was added
            if not Release.is_valid_version(version):
                continue

            # We might have never observed the release.  This for instance can
            # happen if the release only had health data so far.  For these cases
            # we want to create the release the first time we observed it on the
            # health side.
            release = Release.get_or_create(
                project=project, version=version, date_added=dates.get((project_id, version))
            )

            # Make sure that the release knows about this project.  Like we had before
            # the project might not have been associated with this release yet.
            release.add_project(project)

    # Debounce updates for a minute
    cache.set_many(dict(zip(should_update.values(), [True] * len(should_update))), 60)


@region_silo_endpoint
        ```
    ###Assistant : This causes a flush of snuba health data to the postgres tables once
    per minute for the given projects.
    
    "
1265,"def show_code(co, *, file=None):
    
    print(code_info(co), file=file)

_Instruction = collections.namedtuple(""_Instruction"",
     ""opname opcode arg argval argrepr offset starts_line is_jump_target"")

_Instruction.opname.__doc__ = ""Human readable name for operation""
_Instruction.opcode.__doc__ = ""Numeric code for operation""
_Instruction.arg.__doc__ = ""Numeric argument to operation (if any), otherwise None""
_Instruction.argval.__doc__ = ""Resolved arg value (if known), otherwise same as arg""
_Instruction.argrepr.__doc__ = ""Human readable description of operation argument""
_Instruction.offset.__doc__ = ""Start index of operation within bytecode sequence""
_Instruction.starts_line.__doc__ = ""Line started by this opcode (if any), otherwise None""
_Instruction.is_jump_target.__doc__ = ""True if other code jumps to here, otherwise False""

_OPNAME_WIDTH = 20
_OPARG_WIDTH = 5
","Print details of methods, functions, or code to *file*.

    If *file* is not provided, the output is printed on stdout.
    ",20,96,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def show_code(co, *, file=None):
    
    print(code_info(co), file=file)

_Instruction = collections.namedtuple(""_Instruction"",
     ""opname opcode arg argval argrepr offset starts_line is_jump_target"")

_Instruction.opname.__doc__ = ""Human readable name for operation""
_Instruction.opcode.__doc__ = ""Numeric code for operation""
_Instruction.arg.__doc__ = ""Numeric argument to operation (if any), otherwise None""
_Instruction.argval.__doc__ = ""Resolved arg value (if known), otherwise same as arg""
_Instruction.argrepr.__doc__ = ""Human readable description of operation argument""
_Instruction.offset.__doc__ = ""Start index of operation within bytecode sequence""
_Instruction.starts_line.__doc__ = ""Line started by this opcode (if any), otherwise None""
_Instruction.is_jump_target.__doc__ = ""True if other code jumps to here, otherwise False""

_OPNAME_WIDTH = 20
_OPARG_WIDTH = 5

        ```
    ###Assistant : Print details of methods, functions, or code to *file*.

    If *file* is not provided, the output is printed on stdout.
    
    "
1266,"def set_cmap(cmap):
    
    cmap = colormaps[cmap]

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap)


@_copy_docstring_and_deprecators(matplotlib.image.imread)","
    Set the default colormap, and applies it to the current image if any.

    Parameters
    ----------
    cmap : `~matplotlib.colors.Colormap` or str
        A colormap instance or the name of a registered colormap.

    See Also
    --------
    colormaps
    matplotlib.cm.register_cmap
    matplotlib.cm.get_cmap
    ",36,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_cmap(cmap):
    
    cmap = colormaps[cmap]

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap)


@_copy_docstring_and_deprecators(matplotlib.image.imread)
        ```
    ###Assistant : 
    Set the default colormap, and applies it to the current image if any.

    Parameters
    ----------
    cmap : `~matplotlib.colors.Colormap` or str
        A colormap instance or the name of a registered colormap.

    See Also
    --------
    colormaps
    matplotlib.cm.register_cmap
    matplotlib.cm.get_cmap
    
    "
1267,"def completion_item_focus(self, which, history=False):
        
        if history:
            if (self._cmd.text() == ':' or self._cmd.history.is_browsing() or
                    not self._active):
                if which == 'next':
                    self._cmd.command_history_next()
                    return
                elif which == 'prev':
                    self._cmd.command_history_prev()
                    return
                else:
                    raise cmdutils.CommandError(""Can't combine --history with ""
                                                ""{}!"".format(which))

        if not self._active:
            return

        selmodel = self.selectionModel()
        indices = {
            'next': lambda: self._next_idx(upwards=False),
            'prev': lambda: self._next_idx(upwards=True),
            'next-category': lambda: self._next_category_idx(upwards=False),
            'prev-category': lambda: self._next_category_idx(upwards=True),
            'next-page': lambda: self._next_page(upwards=False),
            'prev-page': lambda: self._next_page(upwards=True),
        }
        idx = indices[which]()

        if not idx.isValid():
            return

        selmodel.setCurrentIndex(
            idx,
            QItemSelectionModel.ClearAndSelect |
            QItemSelectionModel.Rows)

        # if the last item is focused, try to fetch more
        next_idx = self.indexBelow(idx)
        if not self.visualRect(next_idx).isValid():
            self.expandAll()

        count = self._model().count()
        if count == 0:
            self.hide()
        elif count == 1 and config.val.completion.quick:
            self.hide()
        elif config.val.completion.show == 'auto':
            self.show()
","Shift the focus of the completion menu to another item.

        Args:
            which: 'next', 'prev',
                   'next-category', 'prev-category',
                   'next-page', or 'prev-page'.
            history: Navigate through command history if no text was typed.
        ",29,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def completion_item_focus(self, which, history=False):
        
        if history:
            if (self._cmd.text() == ':' or self._cmd.history.is_browsing() or
                    not self._active):
                if which == 'next':
                    self._cmd.command_history_next()
                    return
                elif which == 'prev':
                    self._cmd.command_history_prev()
                    return
                else:
                    raise cmdutils.CommandError(""Can't combine --history with ""
                                                ""{}!"".format(which))

        if not self._active:
            return

        selmodel = self.selectionModel()
        indices = {
            'next': lambda: self._next_idx(upwards=False),
            'prev': lambda: self._next_idx(upwards=True),
            'next-category': lambda: self._next_category_idx(upwards=False),
            'prev-category': lambda: self._next_category_idx(upwards=True),
            'next-page': lambda: self._next_page(upwards=False),
            'prev-page': lambda: self._next_page(upwards=True),
        }
        idx = indices[which]()

        if not idx.isValid():
            return

        selmodel.setCurrentIndex(
            idx,
            QItemSelectionModel.ClearAndSelect |
            QItemSelectionModel.Rows)

        # if the last item is focused, try to fetch more
        next_idx = self.indexBelow(idx)
        if not self.visualRect(next_idx).isValid():
            self.expandAll()

        count = self._model().count()
        if count == 0:
            self.hide()
        elif count == 1 and config.val.completion.quick:
            self.hide()
        elif config.val.completion.show == 'auto':
            self.show()

        ```
    ###Assistant : Shift the focus of the completion menu to another item.

        Args:
            which: 'next', 'prev',
                   'next-category', 'prev-category',
                   'next-page', or 'prev-page'.
            history: Navigate through command history if no text was typed.
        
    "
1268,"def _background_extract(self, output_folder, progress_queue):
        
        _io = dict(saver=ImagesSaver(get_folder(output_folder), as_bytes=True),
                   loader=ImagesLoader(self._input_location, count=self._alignments.frames_count))

        for frame_idx, (filename, image) in enumerate(_io[""loader""].load()):
            logger.trace(""Outputting frame: %s: %s"", frame_idx, filename)
            src_filename = os.path.basename(filename)
            frame_name = os.path.splitext(src_filename)[0]
            progress_queue.put(1)

            for face_idx, face in enumerate(self._frame_faces[frame_idx]):
                output = f""{frame_name}_{face_idx}.png""
                aligned = AlignedFace(face.landmarks_xy,
                                      image=image,
                                      centering=""head"",
                                      size=512)  # TODO user selectable size
                meta = dict(alignments=face.to_png_meta(),
                            source=dict(alignments_version=self._alignments.version,
                                        original_filename=output,
                                        face_index=face_idx,
                                        source_filename=src_filename,
                                        source_is_video=self._globals.is_video,
                                        source_frame_dims=image.shape[:2]))

                b_image = encode_image(aligned.face, "".png"", metadata=meta)
                _io[""saver""].save(output, b_image)
        _io[""saver""].close()

"," Perform the background extraction in a thread so GUI doesn't become unresponsive.

        Parameters
        ----------
        output_folder: str
            The location to save the output faces to
        progress_queue: :class:`queue.Queue`
            The queue to place incremental counts to for updating the GUI's progress bar
        ",39,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _background_extract(self, output_folder, progress_queue):
        
        _io = dict(saver=ImagesSaver(get_folder(output_folder), as_bytes=True),
                   loader=ImagesLoader(self._input_location, count=self._alignments.frames_count))

        for frame_idx, (filename, image) in enumerate(_io[""loader""].load()):
            logger.trace(""Outputting frame: %s: %s"", frame_idx, filename)
            src_filename = os.path.basename(filename)
            frame_name = os.path.splitext(src_filename)[0]
            progress_queue.put(1)

            for face_idx, face in enumerate(self._frame_faces[frame_idx]):
                output = f""{frame_name}_{face_idx}.png""
                aligned = AlignedFace(face.landmarks_xy,
                                      image=image,
                                      centering=""head"",
                                      size=512)  # TODO user selectable size
                meta = dict(alignments=face.to_png_meta(),
                            source=dict(alignments_version=self._alignments.version,
                                        original_filename=output,
                                        face_index=face_idx,
                                        source_filename=src_filename,
                                        source_is_video=self._globals.is_video,
                                        source_frame_dims=image.shape[:2]))

                b_image = encode_image(aligned.face, "".png"", metadata=meta)
                _io[""saver""].save(output, b_image)
        _io[""saver""].close()


        ```
    ###Assistant :  Perform the background extraction in a thread so GUI doesn't become unresponsive.

        Parameters
        ----------
        output_folder: str
            The location to save the output faces to
        progress_queue: :class:`queue.Queue`
            The queue to place incremental counts to for updating the GUI's progress bar
        
    "
1269,"def test_multi_part_language(self, m):
        
        m.return_value = [""chi_sim"", ""eng""]

        msgs = check_default_language_available(None)

        self.assertEqual(len(msgs), 0)
","
        GIVEN:
            - An OCR language which is multi part (ie chi-sim)
            - The language is correctly formatted
        WHEN:
            - Installed packages are checked
        THEN:
            - No errors are reported
        ",29,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_multi_part_language(self, m):
        
        m.return_value = [""chi_sim"", ""eng""]

        msgs = check_default_language_available(None)

        self.assertEqual(len(msgs), 0)

        ```
    ###Assistant : 
        GIVEN:
            - An OCR language which is multi part (ie chi-sim)
            - The language is correctly formatted
        WHEN:
            - Installed packages are checked
        THEN:
            - No errors are reported
        
    "
1270,"def finished_callback(self, runner_obj):
        
        event_data = {
            'event': 'EOF',
            'final_counter': self.event_ct,
            'guid': self.guid,
        }
        event_data.setdefault(self.event_data_key, self.instance.id)
        self.dispatcher.dispatch(event_data)
","
        Ansible runner callback triggered on finished run
        ",7,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def finished_callback(self, runner_obj):
        
        event_data = {
            'event': 'EOF',
            'final_counter': self.event_ct,
            'guid': self.guid,
        }
        event_data.setdefault(self.event_data_key, self.instance.id)
        self.dispatcher.dispatch(event_data)

        ```
    ###Assistant : 
        Ansible runner callback triggered on finished run
        
    "
1271,"def test_anonymize_gql_operation_response_with_fragment_spread(gql_operation_factory):
    query = 
    result = {""data"": ""result""}
    sensitive_fields = {""Product"": {""name""}}
    operation_result = gql_operation_factory(query, result=result)

    anonymize_gql_operation_response(operation_result, sensitive_fields)

    assert operation_result.result[""data""] == MASK


@pytest.mark.parametrize(
    ""sensitive_fields"",
    [
        {""NonExistingType"": {}},
        {""Product"": {""nonExistingField""}},
        {""Node"": {""id""}},
    ],
)","
    fragment ProductFragment on Product {
      id
      name
    }
    query products($first: Int){
      products(channel: ""channel-pln"", first:$first){
        edges{
          node{
            ... ProductFragment
            variants {
                variantName: name
            }
          }
        }
      }
    }",27,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_anonymize_gql_operation_response_with_fragment_spread(gql_operation_factory):
    query = 
    result = {""data"": ""result""}
    sensitive_fields = {""Product"": {""name""}}
    operation_result = gql_operation_factory(query, result=result)

    anonymize_gql_operation_response(operation_result, sensitive_fields)

    assert operation_result.result[""data""] == MASK


@pytest.mark.parametrize(
    ""sensitive_fields"",
    [
        {""NonExistingType"": {}},
        {""Product"": {""nonExistingField""}},
        {""Node"": {""id""}},
    ],
)
        ```
    ###Assistant : 
    fragment ProductFragment on Product {
      id
      name
    }
    query products($first: Int){
      products(channel: ""channel-pln"", first:$first){
        edges{
          node{
            ... ProductFragment
            variants {
                variantName: name
            }
          }
        }
      }
    }
    "
1272,"def complete_bipartite_graph(n1, n2, create_using=None):
    
    G = nx.empty_graph(0, create_using)
    if G.is_directed():
        raise nx.NetworkXError(""Directed Graph not supported"")

    n1, top = n1
    n2, bottom = n2
    if isinstance(n1, numbers.Integral) and isinstance(n2, numbers.Integral):
        bottom = [n1 + i for i in bottom]
    G.add_nodes_from(top, bipartite=0)
    G.add_nodes_from(bottom, bipartite=1)
    if len(G) != len(top) + len(bottom):
        raise nx.NetworkXError(""Inputs n1 and n2 must contain distinct nodes"")
    G.add_edges_from((u, v) for u in top for v in bottom)
    G.graph[""name""] = f""complete_bipartite_graph({n1}, {n2})""
    return G


@py_random_state(3)","Returns the complete bipartite graph `K_{n_1,n_2}`.

    The graph is composed of two partitions with nodes 0 to (n1 - 1)
    in the first and nodes n1 to (n1 + n2 - 1) in the second.
    Each node in the first is connected to each node in the second.

    Parameters
    ----------
    n1, n2 : integer or iterable container of nodes
        If integers, nodes are from `range(n1)` and `range(n1, n1 + n2)`.
        If a container, the elements are the nodes.
    create_using : NetworkX graph instance, (default: nx.Graph)
       Return graph of this type.

    Notes
    -----
    Nodes are the integers 0 to `n1 + n2 - 1` unless either n1 or n2 are
    containers of nodes. If only one of n1 or n2 are integers, that
    integer is replaced by `range` of that integer.

    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
    to indicate which bipartite set the node belongs to.

    This function is not imported in the main namespace.
    To use it use nx.bipartite.complete_bipartite_graph
    ",166,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def complete_bipartite_graph(n1, n2, create_using=None):
    
    G = nx.empty_graph(0, create_using)
    if G.is_directed():
        raise nx.NetworkXError(""Directed Graph not supported"")

    n1, top = n1
    n2, bottom = n2
    if isinstance(n1, numbers.Integral) and isinstance(n2, numbers.Integral):
        bottom = [n1 + i for i in bottom]
    G.add_nodes_from(top, bipartite=0)
    G.add_nodes_from(bottom, bipartite=1)
    if len(G) != len(top) + len(bottom):
        raise nx.NetworkXError(""Inputs n1 and n2 must contain distinct nodes"")
    G.add_edges_from((u, v) for u in top for v in bottom)
    G.graph[""name""] = f""complete_bipartite_graph({n1}, {n2})""
    return G


@py_random_state(3)
        ```
    ###Assistant : Returns the complete bipartite graph `K_{n_1,n_2}`.

    The graph is composed of two partitions with nodes 0 to (n1 - 1)
    in the first and nodes n1 to (n1 + n2 - 1) in the second.
    Each node in the first is connected to each node in the second.

    Parameters
    ----------
    n1, n2 : integer or iterable container of nodes
        If integers, nodes are from `range(n1)` and `range(n1, n1 + n2)`.
        If a container, the elements are the nodes.
    create_using : NetworkX graph instance, (default: nx.Graph)
       Return graph of this type.

    Notes
    -----
    Nodes are the integers 0 to `n1 + n2 - 1` unless either n1 or n2 are
    containers of nodes. If only one of n1 or n2 are integers, that
    integer is replaced by `range` of that integer.

    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
    to indicate which bipartite set the node belongs to.

    This function is not imported in the main namespace.
    To use it use nx.bipartite.complete_bipartite_graph
    
    "
1273,"def shutdown(self, callback=None):
        
        if self._state == _UNWRAPPED:
            raise RuntimeError('no security layer present')
        if self._state == _SHUTDOWN:
            raise RuntimeError('shutdown in progress')
        assert self._state in (_WRAPPED, _DO_HANDSHAKE)
        self._state = _SHUTDOWN
        self._shutdown_cb = callback
        ssldata, appdata = self.feed_ssldata(b'')
        assert appdata == [] or appdata == [b'']
        return ssldata
","Start the SSL shutdown sequence.

        Return a list of ssldata. A ssldata element is a list of buffers

        The optional *callback* argument can be used to install a callback that
        will be called when the shutdown is complete. The callback will be
        called without arguments.
        ",45,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def shutdown(self, callback=None):
        
        if self._state == _UNWRAPPED:
            raise RuntimeError('no security layer present')
        if self._state == _SHUTDOWN:
            raise RuntimeError('shutdown in progress')
        assert self._state in (_WRAPPED, _DO_HANDSHAKE)
        self._state = _SHUTDOWN
        self._shutdown_cb = callback
        ssldata, appdata = self.feed_ssldata(b'')
        assert appdata == [] or appdata == [b'']
        return ssldata

        ```
    ###Assistant : Start the SSL shutdown sequence.

        Return a list of ssldata. A ssldata element is a list of buffers

        The optional *callback* argument can be used to install a callback that
        will be called when the shutdown is complete. The callback will be
        called without arguments.
        
    "
1274,"def preferred_ip(vm_, ips):
    
    proto = config.get_cloud_config_value(
        ""protocol"", vm_, __opts__, default=""ipv4"", search_global=False
    )

    family = socket.AF_INET
    if proto == ""ipv6"":
        family = socket.AF_INET6
    for ip in ips:
        ignore_ip = ignore_cidr(vm_, ip)
        if ignore_ip:
            continue
        try:
            socket.inet_pton(family, ip)
            return ip
        except Exception:  # pylint: disable=broad-except
            continue
    return False

","
    Return either an 'ipv4' (default) or 'ipv6' address depending on 'protocol' option.
    The list of 'ipv4' IPs is filtered by ignore_cidr() to remove any unreachable private addresses.
    ",27,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def preferred_ip(vm_, ips):
    
    proto = config.get_cloud_config_value(
        ""protocol"", vm_, __opts__, default=""ipv4"", search_global=False
    )

    family = socket.AF_INET
    if proto == ""ipv6"":
        family = socket.AF_INET6
    for ip in ips:
        ignore_ip = ignore_cidr(vm_, ip)
        if ignore_ip:
            continue
        try:
            socket.inet_pton(family, ip)
            return ip
        except Exception:  # pylint: disable=broad-except
            continue
    return False


        ```
    ###Assistant : 
    Return either an 'ipv4' (default) or 'ipv6' address depending on 'protocol' option.
    The list of 'ipv4' IPs is filtered by ignore_cidr() to remove any unreachable private addresses.
    
    "
1275,"def get_parent_account(doctype, txt, searchfield, start, page_len, filters):
	return frappe.db.sql(
		
		% (""%s"", searchfield, ""%s"", ""%s"", ""%s""),
		(filters[""company""], ""%%%s%%"" % txt, page_len, start),
		as_list=1,
	)

","select name from tabAccount
		where is_group = 1 and docstatus != 2 and company = %s
		and %s like %s order by name limit %s offset %s",27,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_parent_account(doctype, txt, searchfield, start, page_len, filters):
	return frappe.db.sql(
		
		% (""%s"", searchfield, ""%s"", ""%s"", ""%s""),
		(filters[""company""], ""%%%s%%"" % txt, page_len, start),
		as_list=1,
	)


        ```
    ###Assistant : select name from tabAccount
		where is_group = 1 and docstatus != 2 and company = %s
		and %s like %s order by name limit %s offset %s
    "
1276,"def exact_laplacian_kernel(x, y, stddev):
    r
    x_aligned, y_aligned = _align_matrices(x, y)
    diff_l1_norm = tf.reduce_sum(tf.abs(tf.subtract(x_aligned, y_aligned)), 2)
    return tf.exp(-diff_l1_norm / stddev)
","Computes exact Laplacian kernel value(s) for tensors x and y using stddev.

    The Laplacian kernel for vectors u, v is defined as follows:
         K(u, v) = exp(-||u-v|| / stddev)
    where the norm is the l1-norm. x, y can be either vectors or matrices. If they
    are vectors, they must have the same dimension. If they are matrices, they
    must have the same number of columns. In the latter case, the method returns
    (as a matrix) K(u, v) values for all pairs (u, v) where u is a row from x and
    v is a row from y.

    Args:
      x: a tensor of rank 1 or 2. It's shape should be either [dim] or [m, dim].
      y: a tensor of rank 1 or 2. It's shape should be either [dim] or [n, dim].
      stddev: The width of the Gaussian kernel.

    Returns:
      A single value (scalar) with shape (1, 1)  if x, y are vectors or a matrix
      of shape (m, n) with entries K(u, v) (where K is the Laplacian kernel) for
      all (u,v) pairs where u, v are rows from x and y respectively.

    Raises:
      ValueError: if the shapes of x, y are not compatible.
    ",195,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def exact_laplacian_kernel(x, y, stddev):
    r
    x_aligned, y_aligned = _align_matrices(x, y)
    diff_l1_norm = tf.reduce_sum(tf.abs(tf.subtract(x_aligned, y_aligned)), 2)
    return tf.exp(-diff_l1_norm / stddev)

        ```
    ###Assistant : Computes exact Laplacian kernel value(s) for tensors x and y using stddev.

    The Laplacian kernel for vectors u, v is defined as follows:
         K(u, v) = exp(-||u-v|| / stddev)
    where the norm is the l1-norm. x, y can be either vectors or matrices. If they
    are vectors, they must have the same dimension. If they are matrices, they
    must have the same number of columns. In the latter case, the method returns
    (as a matrix) K(u, v) values for all pairs (u, v) where u is a row from x and
    v is a row from y.

    Args:
      x: a tensor of rank 1 or 2. It's shape should be either [dim] or [m, dim].
      y: a tensor of rank 1 or 2. It's shape should be either [dim] or [n, dim].
      stddev: The width of the Gaussian kernel.

    Returns:
      A single value (scalar) with shape (1, 1)  if x, y are vectors or a matrix
      of shape (m, n) with entries K(u, v) (where K is the Laplacian kernel) for
      all (u,v) pairs where u, v are rows from x and y respectively.

    Raises:
      ValueError: if the shapes of x, y are not compatible.
    
    "
1277,"def transform(self, X):
        
        check_is_fitted(self)
        if self.n_neighbors is not None:
            distances, indices = self.nbrs_.kneighbors(X, return_distance=True)
        else:
            distances, indices = self.nbrs_.radius_neighbors(X, return_distance=True)

        # Create the graph of shortest distances from X to
        # training data via the nearest neighbors of X.
        # This can be done as a single array operation, but it potentially
        # takes a lot of memory.  To avoid that, use a loop:

        n_samples_fit = self.nbrs_.n_samples_fit_
        n_queries = distances.shape[0]
        G_X = np.zeros((n_queries, n_samples_fit))
        for i in range(n_queries):
            G_X[i] = np.min(self.dist_matrix_[indices[i]] + distances[i][:, None], 0)

        G_X **= 2
        G_X *= -0.5

        return self.kernel_pca_.transform(G_X)
","Transform X.

        This is implemented by linking the points X into the graph of geodesic
        distances of the training data. First the `n_neighbors` nearest
        neighbors of X are found in the training data, and from these the
        shortest geodesic distances from each point in X to each point in
        the training data are computed in order to construct the kernel.
        The embedding of X is the projection of this kernel onto the
        embedding vectors of the training set.

        Parameters
        ----------
        X : array-like, shape (n_queries, n_features)
            If neighbors_algorithm='precomputed', X is assumed to be a
            distance matrix or a sparse graph of shape
            (n_queries, n_samples_fit).

        Returns
        -------
        X_new : array-like, shape (n_queries, n_components)
            X transformed in the new space.
        ",118,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transform(self, X):
        
        check_is_fitted(self)
        if self.n_neighbors is not None:
            distances, indices = self.nbrs_.kneighbors(X, return_distance=True)
        else:
            distances, indices = self.nbrs_.radius_neighbors(X, return_distance=True)

        # Create the graph of shortest distances from X to
        # training data via the nearest neighbors of X.
        # This can be done as a single array operation, but it potentially
        # takes a lot of memory.  To avoid that, use a loop:

        n_samples_fit = self.nbrs_.n_samples_fit_
        n_queries = distances.shape[0]
        G_X = np.zeros((n_queries, n_samples_fit))
        for i in range(n_queries):
            G_X[i] = np.min(self.dist_matrix_[indices[i]] + distances[i][:, None], 0)

        G_X **= 2
        G_X *= -0.5

        return self.kernel_pca_.transform(G_X)

        ```
    ###Assistant : Transform X.

        This is implemented by linking the points X into the graph of geodesic
        distances of the training data. First the `n_neighbors` nearest
        neighbors of X are found in the training data, and from these the
        shortest geodesic distances from each point in X to each point in
        the training data are computed in order to construct the kernel.
        The embedding of X is the projection of this kernel onto the
        embedding vectors of the training set.

        Parameters
        ----------
        X : array-like, shape (n_queries, n_features)
            If neighbors_algorithm='precomputed', X is assumed to be a
            distance matrix or a sparse graph of shape
            (n_queries, n_samples_fit).

        Returns
        -------
        X_new : array-like, shape (n_queries, n_components)
            X transformed in the new space.
        
    "
1278,"def generate_config_style_dict(self) -> dict[str, str]:
        
        keys_converting_dict = {
            ""fill"": (""color"", ""fill_color""),
            ""fill-opacity"": (""opacity"", ""fill_opacity""),
            ""stroke"": (""color"", ""stroke_color""),
            ""stroke-opacity"": (""opacity"", ""stroke_opacity""),
            ""stroke-width"": (""stroke_width"",),
        }
        svg_default_dict = self.svg_default
        result = {}
        for svg_key, style_keys in keys_converting_dict.items():
            for style_key in style_keys:
                if svg_default_dict[style_key] is None:
                    continue
                result[svg_key] = str(svg_default_dict[style_key])
        return result
",Generate a dictionary holding the default style information.,8,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_config_style_dict(self) -> dict[str, str]:
        
        keys_converting_dict = {
            ""fill"": (""color"", ""fill_color""),
            ""fill-opacity"": (""opacity"", ""fill_opacity""),
            ""stroke"": (""color"", ""stroke_color""),
            ""stroke-opacity"": (""opacity"", ""stroke_opacity""),
            ""stroke-width"": (""stroke_width"",),
        }
        svg_default_dict = self.svg_default
        result = {}
        for svg_key, style_keys in keys_converting_dict.items():
            for style_key in style_keys:
                if svg_default_dict[style_key] is None:
                    continue
                result[svg_key] = str(svg_default_dict[style_key])
        return result

        ```
    ###Assistant : Generate a dictionary holding the default style information.
    "
1279,"def get_variables(self):  # type: () -> t.Dict[str, t.Union[str, t.List[str]]]
        
        return dict(
            bootstrap_type=self.bootstrap_type,
            controller='yes' if self.controller else '',
            python_versions=self.python_versions,
            ssh_key_type=self.ssh_key.KEY_TYPE,
            ssh_private_key=self.ssh_key.key_contents,
            ssh_public_key=self.ssh_key.pub_contents,
        )
",The variables to template in the bootstrapping script.,8,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_variables(self):  # type: () -> t.Dict[str, t.Union[str, t.List[str]]]
        
        return dict(
            bootstrap_type=self.bootstrap_type,
            controller='yes' if self.controller else '',
            python_versions=self.python_versions,
            ssh_key_type=self.ssh_key.KEY_TYPE,
            ssh_private_key=self.ssh_key.key_contents,
            ssh_public_key=self.ssh_key.pub_contents,
        )

        ```
    ###Assistant : The variables to template in the bootstrapping script.
    "
1280,"def _create_pseudo_member_(cls, value):
        
        pseudo_member = cls._value2member_map_.get(value, None)
        if pseudo_member is None:
            # verify all bits are accounted for
            _, extra_flags = _decompose(cls, value)
            if extra_flags:
                raise ValueError(""%r is not a valid %s"" % (value, cls.__qualname__))
            # construct a singleton enum pseudo-member
            pseudo_member = object.__new__(cls)
            pseudo_member._name_ = None
            pseudo_member._value_ = value
            # use setdefault in case another thread already created a composite
            # with this value
            pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member)
        return pseudo_member
","
        Create a composite member iff value contains only members.
        ",9,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_pseudo_member_(cls, value):
        
        pseudo_member = cls._value2member_map_.get(value, None)
        if pseudo_member is None:
            # verify all bits are accounted for
            _, extra_flags = _decompose(cls, value)
            if extra_flags:
                raise ValueError(""%r is not a valid %s"" % (value, cls.__qualname__))
            # construct a singleton enum pseudo-member
            pseudo_member = object.__new__(cls)
            pseudo_member._name_ = None
            pseudo_member._value_ = value
            # use setdefault in case another thread already created a composite
            # with this value
            pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member)
        return pseudo_member

        ```
    ###Assistant : 
        Create a composite member iff value contains only members.
        
    "
1281,"def _identify_infrequent(self, category_count, n_samples, col_idx):
        
        if isinstance(self.min_frequency, numbers.Integral):
            infrequent_mask = category_count < self.min_frequency
        elif isinstance(self.min_frequency, numbers.Real):
            min_frequency_abs = n_samples * self.min_frequency
            infrequent_mask = category_count < min_frequency_abs
        else:
            infrequent_mask = np.zeros(category_count.shape[0], dtype=bool)

        n_current_features = category_count.size - infrequent_mask.sum() + 1
        if self.max_categories is not None and self.max_categories < n_current_features:
            # stable sort to preserve original count order
            smallest_levels = np.argsort(category_count, kind=""mergesort"")[
                : -self.max_categories + 1
            ]
            infrequent_mask[smallest_levels] = True

        output = np.flatnonzero(infrequent_mask)
        return output if output.size > 0 else None
","Compute the infrequent indices.

        Parameters
        ----------
        category_count : ndarray of shape (n_cardinality,)
            Category counts.

        n_samples : int
            Number of samples.

        col_idx : int
            Index of the current category. Only used for the error message.

        Returns
        -------
        output : ndarray of shape (n_infrequent_categories,) or None
            If there are infrequent categories, indices of infrequent
            categories. Otherwise None.
        ",55,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _identify_infrequent(self, category_count, n_samples, col_idx):
        
        if isinstance(self.min_frequency, numbers.Integral):
            infrequent_mask = category_count < self.min_frequency
        elif isinstance(self.min_frequency, numbers.Real):
            min_frequency_abs = n_samples * self.min_frequency
            infrequent_mask = category_count < min_frequency_abs
        else:
            infrequent_mask = np.zeros(category_count.shape[0], dtype=bool)

        n_current_features = category_count.size - infrequent_mask.sum() + 1
        if self.max_categories is not None and self.max_categories < n_current_features:
            # stable sort to preserve original count order
            smallest_levels = np.argsort(category_count, kind=""mergesort"")[
                : -self.max_categories + 1
            ]
            infrequent_mask[smallest_levels] = True

        output = np.flatnonzero(infrequent_mask)
        return output if output.size > 0 else None

        ```
    ###Assistant : Compute the infrequent indices.

        Parameters
        ----------
        category_count : ndarray of shape (n_cardinality,)
            Category counts.

        n_samples : int
            Number of samples.

        col_idx : int
            Index of the current category. Only used for the error message.

        Returns
        -------
        output : ndarray of shape (n_infrequent_categories,) or None
            If there are infrequent categories, indices of infrequent
            categories. Otherwise None.
        
    "
1282,"def get_conn(self) -> container_v1.ClusterManagerClient:
        
        if self._client is None:
            credentials = self._get_credentials()
            self._client = container_v1.ClusterManagerClient(credentials=credentials, client_info=CLIENT_INFO)
        return self._client

    # To preserve backward compatibility
    # TODO: remove one day","
        Returns ClusterManagerCLinet object.

        :rtype: google.cloud.container_v1.ClusterManagerClient
        ",5,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_conn(self) -> container_v1.ClusterManagerClient:
        
        if self._client is None:
            credentials = self._get_credentials()
            self._client = container_v1.ClusterManagerClient(credentials=credentials, client_info=CLIENT_INFO)
        return self._client

    # To preserve backward compatibility
    # TODO: remove one day
        ```
    ###Assistant : 
        Returns ClusterManagerCLinet object.

        :rtype: google.cloud.container_v1.ClusterManagerClient
        
    "
1283,"def iscoroutinefunction(func):
    
    return (inspect.iscoroutinefunction(func) or
            getattr(func, '_is_coroutine', None) is _is_coroutine)


# Prioritize native coroutine check to speed-up
# asyncio.iscoroutine.
_COROUTINE_TYPES = (types.CoroutineType, types.GeneratorType,
                    collections.abc.Coroutine, CoroWrapper)
_iscoroutine_typecache = set()

",Return True if func is a decorated coroutine function.,9,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def iscoroutinefunction(func):
    
    return (inspect.iscoroutinefunction(func) or
            getattr(func, '_is_coroutine', None) is _is_coroutine)


# Prioritize native coroutine check to speed-up
# asyncio.iscoroutine.
_COROUTINE_TYPES = (types.CoroutineType, types.GeneratorType,
                    collections.abc.Coroutine, CoroWrapper)
_iscoroutine_typecache = set()


        ```
    ###Assistant : Return True if func is a decorated coroutine function.
    "
1284,"async def async_load(self) -> _T | None:
        
        if self._load_task is None:
            self._load_task = self.hass.async_create_task(self._async_load())

        return await self._load_task
","Load data.

        If the expected version and minor version do not match the given versions, the
        migrate function will be invoked with migrate_func(version, minor_version, config).

        Will ensure that when a call comes in while another one is in progress,
        the second call will wait and return the result of the first call.
        ",52,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_load(self) -> _T | None:
        
        if self._load_task is None:
            self._load_task = self.hass.async_create_task(self._async_load())

        return await self._load_task

        ```
    ###Assistant : Load data.

        If the expected version and minor version do not match the given versions, the
        migrate function will be invoked with migrate_func(version, minor_version, config).

        Will ensure that when a call comes in while another one is in progress,
        the second call will wait and return the result of the first call.
        
    "
1285,"def extra_state_attributes(self) -> dict[str, Any] | None:
        
        data = super().extra_state_attributes or {}

        last_user = self.vera_device.get_last_user_alert()
        if last_user is not None:
            data[ATTR_LAST_USER_NAME] = last_user[1]

        data[ATTR_LOW_BATTERY] = self.vera_device.get_low_battery_alert()
        return data
","Who unlocked the lock and did a low battery alert fire.

        Reports on the previous poll cycle.
        changed_by_name is a string like 'Bob'.
        low_battery is 1 if an alert fired, 0 otherwise.
        ",32,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extra_state_attributes(self) -> dict[str, Any] | None:
        
        data = super().extra_state_attributes or {}

        last_user = self.vera_device.get_last_user_alert()
        if last_user is not None:
            data[ATTR_LAST_USER_NAME] = last_user[1]

        data[ATTR_LOW_BATTERY] = self.vera_device.get_low_battery_alert()
        return data

        ```
    ###Assistant : Who unlocked the lock and did a low battery alert fire.

        Reports on the previous poll cycle.
        changed_by_name is a string like 'Bob'.
        low_battery is 1 if an alert fired, 0 otherwise.
        
    "
1286,"def magic(self, arg_s):
        
        warnings.warn(
            ""`magic(...)` is deprecated since IPython 0.13 (warning added in ""
            ""8.1), use run_line_magic(magic_name, parameter_s)."",
            DeprecationWarning,
            stacklevel=2,
        )
        # TODO: should we issue a loud deprecation warning here?
        magic_name, _, magic_arg_s = arg_s.partition(' ')
        magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
        return self.run_line_magic(magic_name, magic_arg_s, _stack_depth=2)

    #-------------------------------------------------------------------------
    # Things related to macros
    #-------------------------------------------------------------------------
","
        DEPRECATED

        Deprecated since IPython 0.13 (warning added in
        8.1), use run_line_magic(magic_name, parameter_s).

        Call a magic function by name.

        Input: a string containing the name of the magic function to call and
        any additional arguments to be passed to the magic.

        magic('name -opt foo bar') is equivalent to typing at the ipython
        prompt:

        In[1]: %name -opt foo bar

        To call a magic without arguments, simply use magic('name').

        This provides a proper Python function to call IPython's magics in any
        valid Python code you can type at the interpreter, including loops and
        compound statements.
        ",92,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def magic(self, arg_s):
        
        warnings.warn(
            ""`magic(...)` is deprecated since IPython 0.13 (warning added in ""
            ""8.1), use run_line_magic(magic_name, parameter_s)."",
            DeprecationWarning,
            stacklevel=2,
        )
        # TODO: should we issue a loud deprecation warning here?
        magic_name, _, magic_arg_s = arg_s.partition(' ')
        magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
        return self.run_line_magic(magic_name, magic_arg_s, _stack_depth=2)

    #-------------------------------------------------------------------------
    # Things related to macros
    #-------------------------------------------------------------------------

        ```
    ###Assistant : 
        DEPRECATED

        Deprecated since IPython 0.13 (warning added in
        8.1), use run_line_magic(magic_name, parameter_s).

        Call a magic function by name.

        Input: a string containing the name of the magic function to call and
        any additional arguments to be passed to the magic.

        magic('name -opt foo bar') is equivalent to typing at the ipython
        prompt:

        In[1]: %name -opt foo bar

        To call a magic without arguments, simply use magic('name').

        This provides a proper Python function to call IPython's magics in any
        valid Python code you can type at the interpreter, including loops and
        compound statements.
        
    "
1287,"def create_command(name, **kwargs):
    # type: (str, **Any) -> Command
    
    module_path, class_name, summary = commands_dict[name]
    module = importlib.import_module(module_path)
    command_class = getattr(module, class_name)
    command = command_class(name=name, summary=summary, **kwargs)

    return command

","
    Create an instance of the Command class with the given name.
    ",11,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_command(name, **kwargs):
    # type: (str, **Any) -> Command
    
    module_path, class_name, summary = commands_dict[name]
    module = importlib.import_module(module_path)
    command_class = getattr(module, class_name)
    command = command_class(name=name, summary=summary, **kwargs)

    return command


        ```
    ###Assistant : 
    Create an instance of the Command class with the given name.
    
    "
1288,"def to_pandas_refs(self) -> List[ObjectRef[""pandas.DataFrame""]]:
        

        block_to_df = cached_remote_fn(_block_to_df)
        return [block_to_df.remote(block) for block in self._blocks.get_blocks()]
","Convert this dataset into a distributed set of Pandas dataframes.

        This is only supported for datasets convertible to Arrow records.
        This function induces a copy of the data. For zero-copy access to the
        underlying data, consider using ``.to_arrow()`` or
        ``.get_internal_block_refs()``.

        Time complexity: O(dataset size / parallelism)

        Returns:
            A list of remote Pandas dataframes created from this dataset.
        ",57,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_pandas_refs(self) -> List[ObjectRef[""pandas.DataFrame""]]:
        

        block_to_df = cached_remote_fn(_block_to_df)
        return [block_to_df.remote(block) for block in self._blocks.get_blocks()]

        ```
    ###Assistant : Convert this dataset into a distributed set of Pandas dataframes.

        This is only supported for datasets convertible to Arrow records.
        This function induces a copy of the data. For zero-copy access to the
        underlying data, consider using ``.to_arrow()`` or
        ``.get_internal_block_refs()``.

        Time complexity: O(dataset size / parallelism)

        Returns:
            A list of remote Pandas dataframes created from this dataset.
        
    "
1289,"def hsplit(ary, indices_or_sections):
    
    if _nx.ndim(ary) == 0:
        raise ValueError('hsplit only works on arrays of 1 or more dimensions')
    if ary.ndim > 1:
        return split(ary, indices_or_sections, 1)
    else:
        return split(ary, indices_or_sections, 0)


@array_function_dispatch(_hvdsplit_dispatcher)","
    Split an array into multiple sub-arrays horizontally (column-wise).

    Please refer to the `split` documentation.  `hsplit` is equivalent
    to `split` with ``axis=1``, the array is always split along the second
    axis except for 1-D arrays, where it is split at ``axis=0``.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[ 0.,   1.,   2.,   3.],
           [ 4.,   5.,   6.,   7.],
           [ 8.,   9.,  10.,  11.],
           [12.,  13.,  14.,  15.]])
    >>> np.hsplit(x, 2)
    [array([[  0.,   1.],
           [  4.,   5.],
           [  8.,   9.],
           [12.,  13.]]),
     array([[  2.,   3.],
           [  6.,   7.],
           [10.,  11.],
           [14.,  15.]])]
    >>> np.hsplit(x, np.array([3, 6]))
    [array([[ 0.,   1.,   2.],
           [ 4.,   5.,   6.],
           [ 8.,   9.,  10.],
           [12.,  13.,  14.]]),
     array([[ 3.],
           [ 7.],
           [11.],
           [15.]]),
     array([], shape=(4, 0), dtype=float64)]

    With a higher dimensional array the split is still along the second axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[0.,  1.],
            [2.,  3.]],
           [[4.,  5.],
            [6.,  7.]]])
    >>> np.hsplit(x, 2)
    [array([[[0.,  1.]],
           [[4.,  5.]]]),
     array([[[2.,  3.]],
           [[6.,  7.]]])]

    With a 1-D array, the split is along axis 0.

    >>> x = np.array([0, 1, 2, 3, 4, 5])
    >>> np.hsplit(x, 2)
    [array([0, 1, 2]), array([3, 4, 5])]

    ",203,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def hsplit(ary, indices_or_sections):
    
    if _nx.ndim(ary) == 0:
        raise ValueError('hsplit only works on arrays of 1 or more dimensions')
    if ary.ndim > 1:
        return split(ary, indices_or_sections, 1)
    else:
        return split(ary, indices_or_sections, 0)


@array_function_dispatch(_hvdsplit_dispatcher)
        ```
    ###Assistant : 
    Split an array into multiple sub-arrays horizontally (column-wise).

    Please refer to the `split` documentation.  `hsplit` is equivalent
    to `split` with ``axis=1``, the array is always split along the second
    axis except for 1-D arrays, where it is split at ``axis=0``.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[ 0.,   1.,   2.,   3.],
           [ 4.,   5.,   6.,   7.],
           [ 8.,   9.,  10.,  11.],
           [12.,  13.,  14.,  15.]])
    >>> np.hsplit(x, 2)
    [array([[  0.,   1.],
           [  4.,   5.],
           [  8.,   9.],
           [12.,  13.]]),
     array([[  2.,   3.],
           [  6.,   7.],
           [10.,  11.],
           [14.,  15.]])]
    >>> np.hsplit(x, np.array([3, 6]))
    [array([[ 0.,   1.,   2.],
           [ 4.,   5.,   6.],
           [ 8.,   9.,  10.],
           [12.,  13.,  14.]]),
     array([[ 3.],
           [ 7.],
           [11.],
           [15.]]),
     array([], shape=(4, 0), dtype=float64)]

    With a higher dimensional array the split is still along the second axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[0.,  1.],
            [2.,  3.]],
           [[4.,  5.],
            [6.,  7.]]])
    >>> np.hsplit(x, 2)
    [array([[[0.,  1.]],
           [[4.,  5.]]]),
     array([[[2.,  3.]],
           [[6.,  7.]]])]

    With a 1-D array, the split is along axis 0.

    >>> x = np.array([0, 1, 2, 3, 4, 5])
    >>> np.hsplit(x, 2)
    [array([0, 1, 2]), array([3, 4, 5])]

    
    "
1290,"def get_points_earned(self):
	def get_returned_amount():
		returned_amount = frappe.db.sql(
			,
			self.name,
		)
		return abs(flt(returned_amount[0][0])) if returned_amount else 0

	lp_details = get_loyalty_program_details_with_points(
		self.customer,
		company=self.company,
		loyalty_program=self.loyalty_program,
		expiry_date=self.posting_date,
		include_expired_entry=True,
	)
	if (
		lp_details
		and getdate(lp_details.from_date) <= getdate(self.posting_date)
		and (not lp_details.to_date or getdate(lp_details.to_date) >= getdate(self.posting_date))
	):
		returned_amount = get_returned_amount()
		eligible_amount = flt(self.grand_total) - cint(self.loyalty_amount) - returned_amount
		points_earned = cint(eligible_amount / lp_details.collection_factor)

	return points_earned or 0

","
			select sum(grand_total)
			from `tabSales Invoice`
			where docstatus=1 and is_return=1 and ifnull(return_against, '')=%s
		",12,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_points_earned(self):
	def get_returned_amount():
		returned_amount = frappe.db.sql(
			,
			self.name,
		)
		return abs(flt(returned_amount[0][0])) if returned_amount else 0

	lp_details = get_loyalty_program_details_with_points(
		self.customer,
		company=self.company,
		loyalty_program=self.loyalty_program,
		expiry_date=self.posting_date,
		include_expired_entry=True,
	)
	if (
		lp_details
		and getdate(lp_details.from_date) <= getdate(self.posting_date)
		and (not lp_details.to_date or getdate(lp_details.to_date) >= getdate(self.posting_date))
	):
		returned_amount = get_returned_amount()
		eligible_amount = flt(self.grand_total) - cint(self.loyalty_amount) - returned_amount
		points_earned = cint(eligible_amount / lp_details.collection_factor)

	return points_earned or 0


        ```
    ###Assistant : 
			select sum(grand_total)
			from `tabSales Invoice`
			where docstatus=1 and is_return=1 and ifnull(return_against, '')=%s
		
    "
1291,"def _get_kernel(self) -> plaidml.tile.Value:
        
        coords = np.arange(self._filter_size, dtype=""float32"")
        coords -= (self._filter_size - 1) / 2.

        kernel = np.square(coords)
        kernel *= -0.5 / np.square(self._filter_sigma)
        kernel = np.reshape(kernel, (1, -1)) + np.reshape(kernel, (-1, 1))
        kernel = K.constant(np.reshape(kernel, (1, -1)))
        kernel = K.softmax(kernel)
        kernel = K.reshape(kernel, (self._filter_size, self._filter_size, 1, 1))
        return kernel
"," Obtain the base kernel for performing depthwise convolution.

        Returns
        -------
        :class:`plaidml.tile.Value`
            The gaussian kernel based on selected size and sigma
        ",20,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_kernel(self) -> plaidml.tile.Value:
        
        coords = np.arange(self._filter_size, dtype=""float32"")
        coords -= (self._filter_size - 1) / 2.

        kernel = np.square(coords)
        kernel *= -0.5 / np.square(self._filter_sigma)
        kernel = np.reshape(kernel, (1, -1)) + np.reshape(kernel, (-1, 1))
        kernel = K.constant(np.reshape(kernel, (1, -1)))
        kernel = K.softmax(kernel)
        kernel = K.reshape(kernel, (self._filter_size, self._filter_size, 1, 1))
        return kernel

        ```
    ###Assistant :  Obtain the base kernel for performing depthwise convolution.

        Returns
        -------
        :class:`plaidml.tile.Value`
            The gaussian kernel based on selected size and sigma
        
    "
1292,"def test_default_default(self):
        
        block = blocks.ListBlock(blocks.CharBlock(default='chocolate'))

        self.assertEqual(list(block.get_default()), ['chocolate'])

        block.set_name('test_shoppinglistblock')
        js_args = ListBlockAdapter().js_args(block)
        self.assertEqual(js_args[2], 'chocolate')
","
        if no explicit 'default' is set on the ListBlock, it should fall back on
        a single instance of the child block in its default state.
        ",25,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_default_default(self):
        
        block = blocks.ListBlock(blocks.CharBlock(default='chocolate'))

        self.assertEqual(list(block.get_default()), ['chocolate'])

        block.set_name('test_shoppinglistblock')
        js_args = ListBlockAdapter().js_args(block)
        self.assertEqual(js_args[2], 'chocolate')

        ```
    ###Assistant : 
        if no explicit 'default' is set on the ListBlock, it should fall back on
        a single instance of the child block in its default state.
        
    "
1293,"def convert_empty_str_key(self) -> None:
        

        if self.namespaces and """" in self.namespaces.keys():
            self.namespaces[None] = self.namespaces.pop("""", ""default"")
","
        Replace zero-length string in `namespaces`.

        This method will replace '' with None to align to `lxml`
        requirement that empty string prefixes are not allowed.
        ",24,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_empty_str_key(self) -> None:
        

        if self.namespaces and """" in self.namespaces.keys():
            self.namespaces[None] = self.namespaces.pop("""", ""default"")

        ```
    ###Assistant : 
        Replace zero-length string in `namespaces`.

        This method will replace '' with None to align to `lxml`
        requirement that empty string prefixes are not allowed.
        
    "
1294,"def test_get_with_custom_key_using_default_key(self):
        
        # Generate signature
        signature = generate_signature(self.image.id, ""fill-800x600"")

        # Get the image
        response = self.client.get(
            reverse(
                ""wagtailimages_serve_custom_key"",
                args=(signature, self.image.id, ""fill-800x600""),
            )
            + ""test.png""
        )

        # Check response
        self.assertEqual(response.status_code, 403)
","
        Test that that the key can be changed on the view

        This tests that the default key no longer works when the key is changed on the view
        ",28,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_with_custom_key_using_default_key(self):
        
        # Generate signature
        signature = generate_signature(self.image.id, ""fill-800x600"")

        # Get the image
        response = self.client.get(
            reverse(
                ""wagtailimages_serve_custom_key"",
                args=(signature, self.image.id, ""fill-800x600""),
            )
            + ""test.png""
        )

        # Check response
        self.assertEqual(response.status_code, 403)

        ```
    ###Assistant : 
        Test that that the key can be changed on the view

        This tests that the default key no longer works when the key is changed on the view
        
    "
1295,"async def test_timeouts_do_not_hide_crashes(self, flow_run, orion_client):
        
        started = anyio.Event()
","
        Since timeouts capture anyio cancellations, we want to ensure that something
        still ends up in a 'Crashed' state if it is cancelled independently from our
        timeout cancellation.
        ",27,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_timeouts_do_not_hide_crashes(self, flow_run, orion_client):
        
        started = anyio.Event()

        ```
    ###Assistant : 
        Since timeouts capture anyio cancellations, we want to ensure that something
        still ends up in a 'Crashed' state if it is cancelled independently from our
        timeout cancellation.
        
    "
1296,"def test_enable_disable_conflict_with_config():
    
    nlp = English()
    nlp.add_pipe(""tagger"")
    nlp.add_pipe(""senter"")
    nlp.add_pipe(""sentencizer"")

    with make_tempdir() as tmp_dir:
        nlp.to_disk(tmp_dir)
        # Expected to fail, as config and arguments conflict.
        with pytest.raises(ValueError):
            spacy.load(
                tmp_dir, enable=[""tagger""], config={""nlp"": {""disabled"": [""senter""]}}
            )
        # Expected to succeed without warning due to the lack of a conflicting config option.
        spacy.load(tmp_dir, enable=[""tagger""])
        # Expected to succeed with a warning, as disable=[] should override the config setting.
        with pytest.warns(UserWarning):
            spacy.load(
                tmp_dir,
                enable=[""tagger""],
                disable=[],
                config={""nlp"": {""disabled"": [""senter""]}},
            )

",Test conflict between enable/disable w.r.t. `nlp.disabled` set in the config.,10,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_enable_disable_conflict_with_config():
    
    nlp = English()
    nlp.add_pipe(""tagger"")
    nlp.add_pipe(""senter"")
    nlp.add_pipe(""sentencizer"")

    with make_tempdir() as tmp_dir:
        nlp.to_disk(tmp_dir)
        # Expected to fail, as config and arguments conflict.
        with pytest.raises(ValueError):
            spacy.load(
                tmp_dir, enable=[""tagger""], config={""nlp"": {""disabled"": [""senter""]}}
            )
        # Expected to succeed without warning due to the lack of a conflicting config option.
        spacy.load(tmp_dir, enable=[""tagger""])
        # Expected to succeed with a warning, as disable=[] should override the config setting.
        with pytest.warns(UserWarning):
            spacy.load(
                tmp_dir,
                enable=[""tagger""],
                disable=[],
                config={""nlp"": {""disabled"": [""senter""]}},
            )


        ```
    ###Assistant : Test conflict between enable/disable w.r.t. `nlp.disabled` set in the config.
    "
1297,"async def test_logs_streaming(job_manager):
    

    stream_logs_script = 

    stream_logs_cmd = f'python -c ""{stream_logs_script}""'

    job_id = await job_manager.submit_job(entrypoint=stream_logs_cmd)
    await async_wait_for_condition(
        lambda: ""STREAMED"" in job_manager.get_job_logs(job_id)
    )

    job_manager.stop_job(job_id)


@pytest.mark.asyncio","Test that logs are streamed during the job, not just at the end.
import time
print('STREAMED')
while True:
    time.sleep(1)
",19,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_logs_streaming(job_manager):
    

    stream_logs_script = 

    stream_logs_cmd = f'python -c ""{stream_logs_script}""'

    job_id = await job_manager.submit_job(entrypoint=stream_logs_cmd)
    await async_wait_for_condition(
        lambda: ""STREAMED"" in job_manager.get_job_logs(job_id)
    )

    job_manager.stop_job(job_id)


@pytest.mark.asyncio
        ```
    ###Assistant : Test that logs are streamed during the job, not just at the end.
import time
print('STREAMED')
while True:
    time.sleep(1)

    "
1298,"def _train_no_recompute(n_steps):
    
    img_dim, n_channels, batch_size = 256, 1, 4
    x, y = _get_dummy_data(img_dim, n_channels, batch_size)
    model = _get_big_cnn_model(
        img_dim, n_channels, num_partitions=3, blocks_per_partition=2
    )
    optimizer = optimizers.SGD()
    losses = []
    tr_vars = model.trainable_variables
    for _ in range(n_steps):
        with tf.GradientTape() as tape:
            logits = model(x)
            loss = _compute_loss(logits, y)
            losses.append(loss)
        grads = tape.gradient(loss, tr_vars)  # tr_vars
        optimizer.apply_gradients(zip(grads, tr_vars))
        del grads
    return losses

",Trains a single large model without gradient checkpointing.,8,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _train_no_recompute(n_steps):
    
    img_dim, n_channels, batch_size = 256, 1, 4
    x, y = _get_dummy_data(img_dim, n_channels, batch_size)
    model = _get_big_cnn_model(
        img_dim, n_channels, num_partitions=3, blocks_per_partition=2
    )
    optimizer = optimizers.SGD()
    losses = []
    tr_vars = model.trainable_variables
    for _ in range(n_steps):
        with tf.GradientTape() as tape:
            logits = model(x)
            loss = _compute_loss(logits, y)
            losses.append(loss)
        grads = tape.gradient(loss, tr_vars)  # tr_vars
        optimizer.apply_gradients(zip(grads, tr_vars))
        del grads
    return losses


        ```
    ###Assistant : Trains a single large model without gradient checkpointing.
    "
1299,"def _make_replica_execution_function(model, mode):
    
    if mode == ModeKeys.TRAIN:
        func = model.train_on_batch
    elif mode == ModeKeys.TEST:
        func = model.test_on_batch
    else:
",A single step of the distributed execution on a replica.,10,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _make_replica_execution_function(model, mode):
    
    if mode == ModeKeys.TRAIN:
        func = model.train_on_batch
    elif mode == ModeKeys.TEST:
        func = model.test_on_batch
    else:

        ```
    ###Assistant : A single step of the distributed execution on a replica.
    "
1300,"def monthdays2calendar(self, year, month):
        
        days = list(self.itermonthdays2(year, month))
        return [ days[i:i+7] for i in range(0, len(days), 7) ]
","
        Return a matrix representing a month's calendar.
        Each row represents a week; week entries are
        (day number, weekday number) tuples. Day numbers outside this month
        are zero.
        ",27,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def monthdays2calendar(self, year, month):
        
        days = list(self.itermonthdays2(year, month))
        return [ days[i:i+7] for i in range(0, len(days), 7) ]

        ```
    ###Assistant : 
        Return a matrix representing a month's calendar.
        Each row represents a week; week entries are
        (day number, weekday number) tuples. Day numbers outside this month
        are zero.
        
    "
1301,"async def predict_with_route(self, route_path, *args, **kwargs):
        
        if route_path not in self.dags:
            raise RayServeException(f""{route_path} does not exist in dags routes"")
        return await self.dags[route_path].remote(*args, **kwargs)
",Perform inference directly without HTTP for multi dags.,8,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def predict_with_route(self, route_path, *args, **kwargs):
        
        if route_path not in self.dags:
            raise RayServeException(f""{route_path} does not exist in dags routes"")
        return await self.dags[route_path].remote(*args, **kwargs)

        ```
    ###Assistant : Perform inference directly without HTTP for multi dags.
    "
1302,"def should_recover(self):
        
        return (
            self.num_failures < self.max_failures
            or self.max_failures < 0
            or (
                self.num_failures == self.max_failures
                and self.num_restore_failures
                < int(os.environ.get(""TUNE_RESTORE_RETRY_NUM"", 0))
            )
        )
","Returns whether the trial qualifies for retrying.

        This is if the trial has not failed more than max_failures. Note this
        may return true even when there is no checkpoint, either because
        `self.checkpoint_freq` is `0` or because the trial failed before
        a checkpoint has been made.
        ",45,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def should_recover(self):
        
        return (
            self.num_failures < self.max_failures
            or self.max_failures < 0
            or (
                self.num_failures == self.max_failures
                and self.num_restore_failures
                < int(os.environ.get(""TUNE_RESTORE_RETRY_NUM"", 0))
            )
        )

        ```
    ###Assistant : Returns whether the trial qualifies for retrying.

        This is if the trial has not failed more than max_failures. Note this
        may return true even when there is no checkpoint, either because
        `self.checkpoint_freq` is `0` or because the trial failed before
        a checkpoint has been made.
        
    "
1303,"def test_change_root_page_locale_on_locale_deletion(self):
        
        # change 'real' pages first
        Page.objects.filter(depth__gt=1).update(
            locale=Locale.objects.get(language_code=""fr"")
        )
        self.assertEqual(Page.get_first_root_node().locale.language_code, ""en"")
        Locale.objects.get(language_code=""en"").delete()
        self.assertEqual(Page.get_first_root_node().locale.language_code, ""fr"")
","
        On deleting the locale used for the root page (but no 'real' pages), the
        root page should be reassigned to a new locale (the default one, if possible)
        ",28,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_change_root_page_locale_on_locale_deletion(self):
        
        # change 'real' pages first
        Page.objects.filter(depth__gt=1).update(
            locale=Locale.objects.get(language_code=""fr"")
        )
        self.assertEqual(Page.get_first_root_node().locale.language_code, ""en"")
        Locale.objects.get(language_code=""en"").delete()
        self.assertEqual(Page.get_first_root_node().locale.language_code, ""fr"")

        ```
    ###Assistant : 
        On deleting the locale used for the root page (but no 'real' pages), the
        root page should be reassigned to a new locale (the default one, if possible)
        
    "
1304,"def _proc_pax(self, tarfile):
        
        # Read the header information.
        buf = tarfile.fileobj.read(self._block(self.size))

        # A pax header stores supplemental information for either
        # the following file (extended) or all following files
        # (global).
        if self.type == XGLTYPE:
            pax_headers = tarfile.pax_headers
        else:
            pax_headers = tarfile.pax_headers.copy()

        # Check if the pax header contains a hdrcharset field. This tells us
        # the encoding of the path, linkpath, uname and gname fields. Normally,
        # these fields are UTF-8 encoded but since POSIX.1-2008 tar
        # implementations are allowed to store them as raw binary strings if
        # the translation to UTF-8 fails.
        match = re.search(br""\d+ hdrcharset=([^\n]+)\n"", buf)
        if match is not None:
            pax_headers[""hdrcharset""] = match.group(1).decode(""utf8"")

        # For the time being, we don't care about anything other than ""BINARY"".
        # The only other value that is currently allowed by the standard is
        # ""ISO-IR 10646 2000 UTF-8"" in other words UTF-8.
        hdrcharset = pax_headers.get(""hdrcharset"")
        if hdrcharset == ""BINARY"":
            encoding = tarfile.encoding
        else:
            encoding = ""utf8""

        # Parse pax header information. A record looks like that:
        # ""%d %s=%s\n"" % (length, keyword, value). length is the size
        # of the complete record including the length field itself and
        # the newline. keyword and value are both UTF-8 encoded strings.
        regex = re.compile(br""(\d+) ([^=]+)="")
        pos = 0
        while True:
            match = regex.match(buf, pos)
            if not match:
                break

            length, keyword = match.groups()
            length = int(length)
            value = buf[match.end(2) + 1:match.start(1) + length - 1]

            # Normally, we could just use ""utf8"" as the encoding and ""strict""
            # as the error handler, but we better not take the risk. For
            # example, GNU tar <= 1.23 is known to store filenames it cannot
            # translate to UTF-8 as raw strings (unfortunately without a
            # hdrcharset=BINARY header).
            # We first try the strict standard encoding, and if that fails we
            # fall back on the user's encoding and error handler.
            keyword = self._decode_pax_field(keyword, ""utf8"", ""utf8"",
                    tarfile.errors)
            if keyword in PAX_NAME_FIELDS:
                value = self._decode_pax_field(value, encoding, tarfile.encoding,
                        tarfile.errors)
            else:
                value = self._decode_pax_field(value, ""utf8"", ""utf8"",
                        tarfile.errors)

            pax_headers[keyword] = value
            pos += length

        # Fetch the next header.
        try:
            next = self.fromtarfile(tarfile)
        except HeaderError:
            raise SubsequentHeaderError(""missing or bad subsequent header"")

        # Process GNU sparse information.
        if ""GNU.sparse.map"" in pax_headers:
            # GNU extended sparse format version 0.1.
            self._proc_gnusparse_01(next, pax_headers)

        elif ""GNU.sparse.size"" in pax_headers:
            # GNU extended sparse format version 0.0.
            self._proc_gnusparse_00(next, pax_headers, buf)

        elif pax_headers.get(""GNU.sparse.major"") == ""1"" and pax_headers.get(""GNU.sparse.minor"") == ""0"":
            # GNU extended sparse format version 1.0.
            self._proc_gnusparse_10(next, pax_headers, tarfile)

        if self.type in (XHDTYPE, SOLARIS_XHDTYPE):
            # Patch the TarInfo object with the extended header info.
            next._apply_pax_info(pax_headers, tarfile.encoding, tarfile.errors)
            next.offset = self.offset

            if ""size"" in pax_headers:
                # If the extended header replaces the size field,
                # we need to recalculate the offset where the next
                # header starts.
                offset = next.offset_data
                if next.isreg() or next.type not in SUPPORTED_TYPES:
                    offset += next._block(next.size)
                tarfile.offset = offset

        return next
","Process an extended or global header as described in
           POSIX.1-2008.
        ",10,468,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _proc_pax(self, tarfile):
        
        # Read the header information.
        buf = tarfile.fileobj.read(self._block(self.size))

        # A pax header stores supplemental information for either
        # the following file (extended) or all following files
        # (global).
        if self.type == XGLTYPE:
            pax_headers = tarfile.pax_headers
        else:
            pax_headers = tarfile.pax_headers.copy()

        # Check if the pax header contains a hdrcharset field. This tells us
        # the encoding of the path, linkpath, uname and gname fields. Normally,
        # these fields are UTF-8 encoded but since POSIX.1-2008 tar
        # implementations are allowed to store them as raw binary strings if
        # the translation to UTF-8 fails.
        match = re.search(br""\d+ hdrcharset=([^\n]+)\n"", buf)
        if match is not None:
            pax_headers[""hdrcharset""] = match.group(1).decode(""utf8"")

        # For the time being, we don't care about anything other than ""BINARY"".
        # The only other value that is currently allowed by the standard is
        # ""ISO-IR 10646 2000 UTF-8"" in other words UTF-8.
        hdrcharset = pax_headers.get(""hdrcharset"")
        if hdrcharset == ""BINARY"":
            encoding = tarfile.encoding
        else:
            encoding = ""utf8""

        # Parse pax header information. A record looks like that:
        # ""%d %s=%s\n"" % (length, keyword, value). length is the size
        # of the complete record including the length field itself and
        # the newline. keyword and value are both UTF-8 encoded strings.
        regex = re.compile(br""(\d+) ([^=]+)="")
        pos = 0
        while True:
            match = regex.match(buf, pos)
            if not match:
                break

            length, keyword = match.groups()
            length = int(length)
            value = buf[match.end(2) + 1:match.start(1) + length - 1]

            # Normally, we could just use ""utf8"" as the encoding and ""strict""
            # as the error handler, but we better not take the risk. For
            # example, GNU tar <= 1.23 is known to store filenames it cannot
            # translate to UTF-8 as raw strings (unfortunately without a
            # hdrcharset=BINARY header).
            # We first try the strict standard encoding, and if that fails we
            # fall back on the user's encoding and error handler.
            keyword = self._decode_pax_field(keyword, ""utf8"", ""utf8"",
                    tarfile.errors)
            if keyword in PAX_NAME_FIELDS:
                value = self._decode_pax_field(value, encoding, tarfile.encoding,
                        tarfile.errors)
            else:
                value = self._decode_pax_field(value, ""utf8"", ""utf8"",
                        tarfile.errors)

            pax_headers[keyword] = value
            pos += length

        # Fetch the next header.
        try:
            next = self.fromtarfile(tarfile)
        except HeaderError:
            raise SubsequentHeaderError(""missing or bad subsequent header"")

        # Process GNU sparse information.
        if ""GNU.sparse.map"" in pax_headers:
            # GNU extended sparse format version 0.1.
            self._proc_gnusparse_01(next, pax_headers)

        elif ""GNU.sparse.size"" in pax_headers:
            # GNU extended sparse format version 0.0.
            self._proc_gnusparse_00(next, pax_headers, buf)

        elif pax_headers.get(""GNU.sparse.major"") == ""1"" and pax_headers.get(""GNU.sparse.minor"") == ""0"":
            # GNU extended sparse format version 1.0.
            self._proc_gnusparse_10(next, pax_headers, tarfile)

        if self.type in (XHDTYPE, SOLARIS_XHDTYPE):
            # Patch the TarInfo object with the extended header info.
            next._apply_pax_info(pax_headers, tarfile.encoding, tarfile.errors)
            next.offset = self.offset

            if ""size"" in pax_headers:
                # If the extended header replaces the size field,
                # we need to recalculate the offset where the next
                # header starts.
                offset = next.offset_data
                if next.isreg() or next.type not in SUPPORTED_TYPES:
                    offset += next._block(next.size)
                tarfile.offset = offset

        return next

        ```
    ###Assistant : Process an extended or global header as described in
           POSIX.1-2008.
        
    "
1305,"def get_global_params():
        
        GlobalParams = namedtuple('GlobalParams', [
            'drop_connect_rate', 'width_coefficient', 'depth_coefficient',
            'depth_divisor', 'image_size'
        ])
        global_params = GlobalParams(
            drop_connect_rate=0.3,
            width_coefficient=1.2,
            depth_coefficient=1.4,
            depth_divisor=8,
            image_size=64)
        return global_params
","
        The fllowing are efficientnetb3's arch superparams, but to fit for scene 
        text recognition task, the resolution(image_size) here is changed 
        from 300 to 64.
        ",23,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_global_params():
        
        GlobalParams = namedtuple('GlobalParams', [
            'drop_connect_rate', 'width_coefficient', 'depth_coefficient',
            'depth_divisor', 'image_size'
        ])
        global_params = GlobalParams(
            drop_connect_rate=0.3,
            width_coefficient=1.2,
            depth_coefficient=1.4,
            depth_divisor=8,
            image_size=64)
        return global_params

        ```
    ###Assistant : 
        The fllowing are efficientnetb3's arch superparams, but to fit for scene 
        text recognition task, the resolution(image_size) here is changed 
        from 300 to 64.
        
    "
1306,"def _create_closed(cls, vertices):
        
        v = _to_unmasked_float_array(vertices)
        return cls(np.concatenate([v, v[:1]]), closed=True)
","
        Create a closed polygonal path going through *vertices*.

        Unlike ``Path(..., closed=True)``, *vertices* should **not** end with
        an entry for the CLOSEPATH; this entry is added by `._create_closed`.
        ",27,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_closed(cls, vertices):
        
        v = _to_unmasked_float_array(vertices)
        return cls(np.concatenate([v, v[:1]]), closed=True)

        ```
    ###Assistant : 
        Create a closed polygonal path going through *vertices*.

        Unlike ``Path(..., closed=True)``, *vertices* should **not** end with
        an entry for the CLOSEPATH; this entry is added by `._create_closed`.
        
    "
1307,"def test_predict_proba(loss, global_random_seed):
    
    n_samples = 20
    y_true, raw_prediction = random_y_true_raw_prediction(
        loss=loss,
        n_samples=n_samples,
        y_bound=(-100, 100),
        raw_bound=(-5, 5),
        seed=global_random_seed,
    )

    if hasattr(loss, ""predict_proba""):
        proba = loss.predict_proba(raw_prediction)
        assert proba.shape == (n_samples, loss.n_classes)
        assert np.sum(proba, axis=1) == approx(1, rel=1e-11)

    if hasattr(loss, ""gradient_proba""):
        for grad, proba in (
            (None, None),
            (None, np.empty_like(raw_prediction)),
            (np.empty_like(raw_prediction), None),
            (np.empty_like(raw_prediction), np.empty_like(raw_prediction)),
        ):
            grad, proba = loss.gradient_proba(
                y_true=y_true,
                raw_prediction=raw_prediction,
                sample_weight=None,
                gradient_out=grad,
                proba_out=proba,
            )
            assert proba.shape == (n_samples, loss.n_classes)
            assert np.sum(proba, axis=1) == approx(1, rel=1e-11)
            assert_allclose(
                grad,
                loss.gradient(
                    y_true=y_true,
                    raw_prediction=raw_prediction,
                    sample_weight=None,
                    gradient_out=None,
                ),
            )


@pytest.mark.parametrize(""loss"", ALL_LOSSES)
@pytest.mark.parametrize(""sample_weight"", [None, ""range""])
@pytest.mark.parametrize(""dtype"", (np.float32, np.float64))
@pytest.mark.parametrize(""order"", (""C"", ""F""))",Test that predict_proba and gradient_proba work as expected.,8,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_predict_proba(loss, global_random_seed):
    
    n_samples = 20
    y_true, raw_prediction = random_y_true_raw_prediction(
        loss=loss,
        n_samples=n_samples,
        y_bound=(-100, 100),
        raw_bound=(-5, 5),
        seed=global_random_seed,
    )

    if hasattr(loss, ""predict_proba""):
        proba = loss.predict_proba(raw_prediction)
        assert proba.shape == (n_samples, loss.n_classes)
        assert np.sum(proba, axis=1) == approx(1, rel=1e-11)

    if hasattr(loss, ""gradient_proba""):
        for grad, proba in (
            (None, None),
            (None, np.empty_like(raw_prediction)),
            (np.empty_like(raw_prediction), None),
            (np.empty_like(raw_prediction), np.empty_like(raw_prediction)),
        ):
            grad, proba = loss.gradient_proba(
                y_true=y_true,
                raw_prediction=raw_prediction,
                sample_weight=None,
                gradient_out=grad,
                proba_out=proba,
            )
            assert proba.shape == (n_samples, loss.n_classes)
            assert np.sum(proba, axis=1) == approx(1, rel=1e-11)
            assert_allclose(
                grad,
                loss.gradient(
                    y_true=y_true,
                    raw_prediction=raw_prediction,
                    sample_weight=None,
                    gradient_out=None,
                ),
            )


@pytest.mark.parametrize(""loss"", ALL_LOSSES)
@pytest.mark.parametrize(""sample_weight"", [None, ""range""])
@pytest.mark.parametrize(""dtype"", (np.float32, np.float64))
@pytest.mark.parametrize(""order"", (""C"", ""F""))
        ```
    ###Assistant : Test that predict_proba and gradient_proba work as expected.
    "
1308,"def get_late_shipments(scorecard):
	
	return get_total_shipments(scorecard) - get_on_time_shipments(scorecard)

",Gets the number of late shipments (counting each item) in the period (based on Purchase Receipts vs POs),18,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_late_shipments(scorecard):
	
	return get_total_shipments(scorecard) - get_on_time_shipments(scorecard)


        ```
    ###Assistant : Gets the number of late shipments (counting each item) in the period (based on Purchase Receipts vs POs)
    "
1309,"def eval_loss(self, targets, predictions):
        
        eval_loss = 0
        for of_name, of_obj in self.output_features.items():
            of_eval_loss = of_obj.eval_loss(targets[of_name], predictions[of_name])
            eval_loss += of_obj.loss[""weight""] * of_eval_loss

        additional_loss = 0
        additional_losses = self.losses()
        if additional_losses:
            additional_loss = torch.sum(torch.stack(additional_losses))  # other losses

        return eval_loss, additional_loss
","Computes all evaluation losses for the model given targets and predictions.

        Args:
            targets: A dictionary of target names to target tensors.
            predictions: A dictionary of output names to output tensors.

        Returns:
            A tuple of loss values for eval losses and additional losses.
        ",42,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def eval_loss(self, targets, predictions):
        
        eval_loss = 0
        for of_name, of_obj in self.output_features.items():
            of_eval_loss = of_obj.eval_loss(targets[of_name], predictions[of_name])
            eval_loss += of_obj.loss[""weight""] * of_eval_loss

        additional_loss = 0
        additional_losses = self.losses()
        if additional_losses:
            additional_loss = torch.sum(torch.stack(additional_losses))  # other losses

        return eval_loss, additional_loss

        ```
    ###Assistant : Computes all evaluation losses for the model given targets and predictions.

        Args:
            targets: A dictionary of target names to target tensors.
            predictions: A dictionary of output names to output tensors.

        Returns:
            A tuple of loss values for eval losses and additional losses.
        
    "
1310,"def partition_query(self, query, limit, offset):
        
        return (
            (
                f""SELECT * FROM ({query}) AS _ ORDER BY(SELECT NULL)""
                + f"" OFFSET {offset} ROWS FETCH NEXT {limit} ROWS ONLY""
            )
            if self._dialect_is_microsoft_sql()
            else f""SELECT * FROM ({query}) LIMIT {limit} OFFSET {offset}""
        )
","
        Get a query that partitions the original `query`.

        Parameters
        ----------
        query : str
            The SQL query to get a partition.
        limit : int
            The size of the partition.
        offset : int
            Where the partition begins.

        Returns
        -------
        str
        ",38,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def partition_query(self, query, limit, offset):
        
        return (
            (
                f""SELECT * FROM ({query}) AS _ ORDER BY(SELECT NULL)""
                + f"" OFFSET {offset} ROWS FETCH NEXT {limit} ROWS ONLY""
            )
            if self._dialect_is_microsoft_sql()
            else f""SELECT * FROM ({query}) LIMIT {limit} OFFSET {offset}""
        )

        ```
    ###Assistant : 
        Get a query that partitions the original `query`.

        Parameters
        ----------
        query : str
            The SQL query to get a partition.
        limit : int
            The size of the partition.
        offset : int
            Where the partition begins.

        Returns
        -------
        str
        
    "
1311,"def test_parameter_ends_with__in__or__isnull(self):
        
        # When it ends with '__in' -----------------------------------------
        modeladmin = DecadeFilterBookAdminParameterEndsWith__In(Book, site)
        request = self.request_factory.get(""/"", {""decade__in"": ""the 90s""})
        request.user = self.alfred
        changelist = modeladmin.get_changelist_instance(request)

        # Make sure the correct queryset is returned
        queryset = changelist.get_queryset(request)
        self.assertEqual(list(queryset), [self.bio_book])

        # Make sure the correct choice is selected
        filterspec = changelist.get_filters(request)[0][0]
        self.assertEqual(filterspec.title, ""publication decade"")
        choices = list(filterspec.choices(changelist))
        self.assertEqual(choices[2][""display""], ""the 1990's"")
        self.assertIs(choices[2][""selected""], True)
        self.assertEqual(choices[2][""query_string""], ""?decade__in=the+90s"")

        # When it ends with '__isnull' ---------------------------------------
        modeladmin = DecadeFilterBookAdminParameterEndsWith__Isnull(Book, site)
        request = self.request_factory.get(""/"", {""decade__isnull"": ""the 90s""})
        request.user = self.alfred
        changelist = modeladmin.get_changelist_instance(request)

        # Make sure the correct queryset is returned
        queryset = changelist.get_queryset(request)
        self.assertEqual(list(queryset), [self.bio_book])

        # Make sure the correct choice is selected
        filterspec = changelist.get_filters(request)[0][0]
        self.assertEqual(filterspec.title, ""publication decade"")
        choices = list(filterspec.choices(changelist))
        self.assertEqual(choices[2][""display""], ""the 1990's"")
        self.assertIs(choices[2][""selected""], True)
        self.assertEqual(choices[2][""query_string""], ""?decade__isnull=the+90s"")
","
        A SimpleListFilter's parameter name is not mistaken for a model field
        if it ends with '__isnull' or '__in' (#17091).
        ",19,122,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_parameter_ends_with__in__or__isnull(self):
        
        # When it ends with '__in' -----------------------------------------
        modeladmin = DecadeFilterBookAdminParameterEndsWith__In(Book, site)
        request = self.request_factory.get(""/"", {""decade__in"": ""the 90s""})
        request.user = self.alfred
        changelist = modeladmin.get_changelist_instance(request)

        # Make sure the correct queryset is returned
        queryset = changelist.get_queryset(request)
        self.assertEqual(list(queryset), [self.bio_book])

        # Make sure the correct choice is selected
        filterspec = changelist.get_filters(request)[0][0]
        self.assertEqual(filterspec.title, ""publication decade"")
        choices = list(filterspec.choices(changelist))
        self.assertEqual(choices[2][""display""], ""the 1990's"")
        self.assertIs(choices[2][""selected""], True)
        self.assertEqual(choices[2][""query_string""], ""?decade__in=the+90s"")

        # When it ends with '__isnull' ---------------------------------------
        modeladmin = DecadeFilterBookAdminParameterEndsWith__Isnull(Book, site)
        request = self.request_factory.get(""/"", {""decade__isnull"": ""the 90s""})
        request.user = self.alfred
        changelist = modeladmin.get_changelist_instance(request)

        # Make sure the correct queryset is returned
        queryset = changelist.get_queryset(request)
        self.assertEqual(list(queryset), [self.bio_book])

        # Make sure the correct choice is selected
        filterspec = changelist.get_filters(request)[0][0]
        self.assertEqual(filterspec.title, ""publication decade"")
        choices = list(filterspec.choices(changelist))
        self.assertEqual(choices[2][""display""], ""the 1990's"")
        self.assertIs(choices[2][""selected""], True)
        self.assertEqual(choices[2][""query_string""], ""?decade__isnull=the+90s"")

        ```
    ###Assistant : 
        A SimpleListFilter's parameter name is not mistaken for a model field
        if it ends with '__isnull' or '__in' (#17091).
        
    "
1312,"def v4_int_to_packed(address):
    
    try:
        return address.to_bytes(4, 'big')
    except OverflowError:
        raise ValueError(""Address negative or too large for IPv4"")

","Represent an address as 4 packed bytes in network (big-endian) order.

    Args:
        address: An integer representation of an IPv4 IP address.

    Returns:
        The integer address packed as 4 bytes in network (big-endian) order.

    Raises:
        ValueError: If the integer is negative or too large to be an
          IPv4 IP address.

    ",49,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def v4_int_to_packed(address):
    
    try:
        return address.to_bytes(4, 'big')
    except OverflowError:
        raise ValueError(""Address negative or too large for IPv4"")


        ```
    ###Assistant : Represent an address as 4 packed bytes in network (big-endian) order.

    Args:
        address: An integer representation of an IPv4 IP address.

    Returns:
        The integer address packed as 4 bytes in network (big-endian) order.

    Raises:
        ValueError: If the integer is negative or too large to be an
          IPv4 IP address.

    
    "
1313,"def test_delete_alias_not_allowed(self) -> None:
        
        self._create_alias(self.admin_user)
        self.get_failure(
            self.handler.delete_association(
                create_requester(self.test_user), self.room_alias
            ),
            synapse.api.errors.AuthError,
        )
",A user that doesn't meet the expected guidelines cannot delete an alias.,12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_alias_not_allowed(self) -> None:
        
        self._create_alias(self.admin_user)
        self.get_failure(
            self.handler.delete_association(
                create_requester(self.test_user), self.room_alias
            ),
            synapse.api.errors.AuthError,
        )

        ```
    ###Assistant : A user that doesn't meet the expected guidelines cannot delete an alias.
    "
1314,"def trigintegrate(f, x, conds='piecewise'):
    
    pat, a, n, m = _pat_sincos(x)

    f = f.rewrite('sincos')
    M = f.match(pat)

    if M is None:
        return

    n, m = M[n], M[m]
    if n.is_zero and m.is_zero:
        return x
    zz = x if n.is_zero else S.Zero

    a = M[a]

    if n.is_odd or m.is_odd:
        u = _u
        n_, m_ = n.is_odd, m.is_odd

        # take smallest n or m -- to choose simplest substitution
        if n_ and m_:

            # Make sure to choose the positive one
            # otherwise an incorrect integral can occur.
            if n < 0 and m > 0:
                m_ = True
                n_ = False
            elif m < 0 and n > 0:
                n_ = True
                m_ = False
            # Both are negative so choose the smallest n or m
            # in absolute value for simplest substitution.
            elif (n < 0 and m < 0):
                n_ = n > m
                m_ = not (n > m)

            # Both n and m are odd and positive
            else:
                n_ = (n < m)      # NB: careful here, one of the
                m_ = not (n < m)  # conditions *must* be true

        #  n      m       u=C        (n-1)/2    m
        # S(x) * C(x) dx  --> -(1-u^2)       * u  du
        if n_:
            ff = -(1 - u**2)**((n - 1)/2) * u**m
            uu = cos(a*x)

        #  n      m       u=S   n         (m-1)/2
        # S(x) * C(x) dx  -->  u  * (1-u^2)       du
        elif m_:
            ff = u**n * (1 - u**2)**((m - 1)/2)
            uu = sin(a*x)

        fi = integrate(ff, u)  # XXX cyclic deps
        fx = fi.subs(u, uu)
        if conds == 'piecewise':
            return Piecewise((fx / a, Ne(a, 0)), (zz, True))
        return fx / a

    # n & m are both even
    #
    #               2k      2m                         2l       2l
    # we transform S (x) * C (x) into terms with only S (x) or C (x)
    #
    # example:
    #  100     4       100        2    2    100          4         2
    # S (x) * C (x) = S (x) * (1-S (x))  = S (x) * (1 + S (x) - 2*S (x))
    #
    #                  104       102     100
    #               = S (x) - 2*S (x) + S (x)
    #       2k
    # then S   is integrated with recursive formula

    # take largest n or m -- to choose simplest substitution
    n_ = (Abs(n) > Abs(m))
    m_ = (Abs(m) > Abs(n))
    res = S.Zero

    if n_:
        #  2k         2 k             i             2i
        # C   = (1 - S )  = sum(i, (-) * B(k, i) * S  )
        if m > 0:
            for i in range(0, m//2 + 1):
                res += (S.NegativeOne**i * binomial(m//2, i) *
                        _sin_pow_integrate(n + 2*i, x))

        elif m == 0:
            res = _sin_pow_integrate(n, x)
        else:

            # m < 0 , |n| > |m|
            #  /
            # |
            # |    m       n
            # | cos (x) sin (x) dx =
            # |
            # |
            #/
            #                                      /
            #                                     |
            #   -1        m+1     n-1     n - 1   |     m+2     n-2
            # ________ cos (x) sin (x) + _______  |  cos (x) sin (x) dx
            #                                     |
            #   m + 1                     m + 1   |
            #                                    /

            res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) +
                   Rational(n - 1, m + 1) *
                   trigintegrate(cos(x)**(m + 2)*sin(x)**(n - 2), x))

    elif m_:
        #  2k         2 k            i             2i
        # S   = (1 - C ) = sum(i, (-) * B(k, i) * C  )
        if n > 0:

            #      /                            /
            #     |                            |
            #     |    m       n               |    -m         n
            #     | cos (x)*sin (x) dx  or     | cos (x) * sin (x) dx
            #     |                            |
            #    /                            /
            #
            #    |m| > |n| ; m, n >0 ; m, n belong to Z - {0}
            #       n                                         2
            #    sin (x) term is expanded here in terms of cos (x),
            #    and then integrated.
            #

            for i in range(0, n//2 + 1):
                res += (S.NegativeOne**i * binomial(n//2, i) *
                        _cos_pow_integrate(m + 2*i, x))

        elif n == 0:

            #   /
            #  |
            #  |  1
            #  | _ _ _
            #  |    m
            #  | cos (x)
            # /
            #

            res = _cos_pow_integrate(m, x)
        else:

            # n < 0 , |m| > |n|
            #  /
            # |
            # |    m       n
            # | cos (x) sin (x) dx =
            # |
            # |
            #/
            #                                      /
            #                                     |
            #    1        m-1     n+1     m - 1   |     m-2     n+2
            #  _______ cos (x) sin (x) + _______  |  cos (x) sin (x) dx
            #                                     |
            #   n + 1                     n + 1   |
            #                                    /

            res = (Rational(1, n + 1) * cos(x)**(m - 1)*sin(x)**(n + 1) +
                   Rational(m - 1, n + 1) *
                   trigintegrate(cos(x)**(m - 2)*sin(x)**(n + 2), x))

    else:
        if m == n:
            ##Substitute sin(2x)/2 for sin(x)cos(x) and then Integrate.
            res = integrate((sin(2*x)*S.Half)**m, x)
        elif (m == -n):
            if n < 0:
                # Same as the scheme described above.
                # the function argument to integrate in the end will
                # be 1, this cannot be integrated by trigintegrate.
                # Hence use sympy.integrals.integrate.
                res = (Rational(1, n + 1) * cos(x)**(m - 1) * sin(x)**(n + 1) +
                       Rational(m - 1, n + 1) *
                       integrate(cos(x)**(m - 2) * sin(x)**(n + 2), x))
            else:
                res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) +
                       Rational(n - 1, m + 1) *
                       integrate(cos(x)**(m + 2)*sin(x)**(n - 2), x))
    if conds == 'piecewise':
        return Piecewise((res.subs(x, a*x) / a, Ne(a, 0)), (zz, True))
    return res.subs(x, a*x) / a

","
    Integrate f = Mul(trig) over x.

    Examples
    ========

    >>> from sympy import sin, cos, tan, sec
    >>> from sympy.integrals.trigonometry import trigintegrate
    >>> from sympy.abc import x

    >>> trigintegrate(sin(x)*cos(x), x)
    sin(x)**2/2

    >>> trigintegrate(sin(x)**2, x)
    x/2 - sin(x)*cos(x)/2

    >>> trigintegrate(tan(x)*sec(x), x)
    1/cos(x)

    >>> trigintegrate(sin(x)*tan(x), x)
    -log(sin(x) - 1)/2 + log(sin(x) + 1)/2 - sin(x)

    References
    ==========

    .. [1] http://en.wikibooks.org/wiki/Calculus/Integration_techniques

    See Also
    ========

    sympy.integrals.integrals.Integral.doit
    sympy.integrals.integrals.Integral
    ",62,909,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def trigintegrate(f, x, conds='piecewise'):
    
    pat, a, n, m = _pat_sincos(x)

    f = f.rewrite('sincos')
    M = f.match(pat)

    if M is None:
        return

    n, m = M[n], M[m]
    if n.is_zero and m.is_zero:
        return x
    zz = x if n.is_zero else S.Zero

    a = M[a]

    if n.is_odd or m.is_odd:
        u = _u
        n_, m_ = n.is_odd, m.is_odd

        # take smallest n or m -- to choose simplest substitution
        if n_ and m_:

            # Make sure to choose the positive one
            # otherwise an incorrect integral can occur.
            if n < 0 and m > 0:
                m_ = True
                n_ = False
            elif m < 0 and n > 0:
                n_ = True
                m_ = False
            # Both are negative so choose the smallest n or m
            # in absolute value for simplest substitution.
            elif (n < 0 and m < 0):
                n_ = n > m
                m_ = not (n > m)

            # Both n and m are odd and positive
            else:
                n_ = (n < m)      # NB: careful here, one of the
                m_ = not (n < m)  # conditions *must* be true

        #  n      m       u=C        (n-1)/2    m
        # S(x) * C(x) dx  --> -(1-u^2)       * u  du
        if n_:
            ff = -(1 - u**2)**((n - 1)/2) * u**m
            uu = cos(a*x)

        #  n      m       u=S   n         (m-1)/2
        # S(x) * C(x) dx  -->  u  * (1-u^2)       du
        elif m_:
            ff = u**n * (1 - u**2)**((m - 1)/2)
            uu = sin(a*x)

        fi = integrate(ff, u)  # XXX cyclic deps
        fx = fi.subs(u, uu)
        if conds == 'piecewise':
            return Piecewise((fx / a, Ne(a, 0)), (zz, True))
        return fx / a

    # n & m are both even
    #
    #               2k      2m                         2l       2l
    # we transform S (x) * C (x) into terms with only S (x) or C (x)
    #
    # example:
    #  100     4       100        2    2    100          4         2
    # S (x) * C (x) = S (x) * (1-S (x))  = S (x) * (1 + S (x) - 2*S (x))
    #
    #                  104       102     100
    #               = S (x) - 2*S (x) + S (x)
    #       2k
    # then S   is integrated with recursive formula

    # take largest n or m -- to choose simplest substitution
    n_ = (Abs(n) > Abs(m))
    m_ = (Abs(m) > Abs(n))
    res = S.Zero

    if n_:
        #  2k         2 k             i             2i
        # C   = (1 - S )  = sum(i, (-) * B(k, i) * S  )
        if m > 0:
            for i in range(0, m//2 + 1):
                res += (S.NegativeOne**i * binomial(m//2, i) *
                        _sin_pow_integrate(n + 2*i, x))

        elif m == 0:
            res = _sin_pow_integrate(n, x)
        else:

            # m < 0 , |n| > |m|
            #  /
            # |
            # |    m       n
            # | cos (x) sin (x) dx =
            # |
            # |
            #/
            #                                      /
            #                                     |
            #   -1        m+1     n-1     n - 1   |     m+2     n-2
            # ________ cos (x) sin (x) + _______  |  cos (x) sin (x) dx
            #                                     |
            #   m + 1                     m + 1   |
            #                                    /

            res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) +
                   Rational(n - 1, m + 1) *
                   trigintegrate(cos(x)**(m + 2)*sin(x)**(n - 2), x))

    elif m_:
        #  2k         2 k            i             2i
        # S   = (1 - C ) = sum(i, (-) * B(k, i) * C  )
        if n > 0:

            #      /                            /
            #     |                            |
            #     |    m       n               |    -m         n
            #     | cos (x)*sin (x) dx  or     | cos (x) * sin (x) dx
            #     |                            |
            #    /                            /
            #
            #    |m| > |n| ; m, n >0 ; m, n belong to Z - {0}
            #       n                                         2
            #    sin (x) term is expanded here in terms of cos (x),
            #    and then integrated.
            #

            for i in range(0, n//2 + 1):
                res += (S.NegativeOne**i * binomial(n//2, i) *
                        _cos_pow_integrate(m + 2*i, x))

        elif n == 0:

            #   /
            #  |
            #  |  1
            #  | _ _ _
            #  |    m
            #  | cos (x)
            # /
            #

            res = _cos_pow_integrate(m, x)
        else:

            # n < 0 , |m| > |n|
            #  /
            # |
            # |    m       n
            # | cos (x) sin (x) dx =
            # |
            # |
            #/
            #                                      /
            #                                     |
            #    1        m-1     n+1     m - 1   |     m-2     n+2
            #  _______ cos (x) sin (x) + _______  |  cos (x) sin (x) dx
            #                                     |
            #   n + 1                     n + 1   |
            #                                    /

            res = (Rational(1, n + 1) * cos(x)**(m - 1)*sin(x)**(n + 1) +
                   Rational(m - 1, n + 1) *
                   trigintegrate(cos(x)**(m - 2)*sin(x)**(n + 2), x))

    else:
        if m == n:
            ##Substitute sin(2x)/2 for sin(x)cos(x) and then Integrate.
            res = integrate((sin(2*x)*S.Half)**m, x)
        elif (m == -n):
            if n < 0:
                # Same as the scheme described above.
                # the function argument to integrate in the end will
                # be 1, this cannot be integrated by trigintegrate.
                # Hence use sympy.integrals.integrate.
                res = (Rational(1, n + 1) * cos(x)**(m - 1) * sin(x)**(n + 1) +
                       Rational(m - 1, n + 1) *
                       integrate(cos(x)**(m - 2) * sin(x)**(n + 2), x))
            else:
                res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) +
                       Rational(n - 1, m + 1) *
                       integrate(cos(x)**(m + 2)*sin(x)**(n - 2), x))
    if conds == 'piecewise':
        return Piecewise((res.subs(x, a*x) / a, Ne(a, 0)), (zz, True))
    return res.subs(x, a*x) / a


        ```
    ###Assistant : 
    Integrate f = Mul(trig) over x.

    Examples
    ========

    >>> from sympy import sin, cos, tan, sec
    >>> from sympy.integrals.trigonometry import trigintegrate
    >>> from sympy.abc import x

    >>> trigintegrate(sin(x)*cos(x), x)
    sin(x)**2/2

    >>> trigintegrate(sin(x)**2, x)
    x/2 - sin(x)*cos(x)/2

    >>> trigintegrate(tan(x)*sec(x), x)
    1/cos(x)

    >>> trigintegrate(sin(x)*tan(x), x)
    -log(sin(x) - 1)/2 + log(sin(x) + 1)/2 - sin(x)

    References
    ==========

    .. [1] http://en.wikibooks.org/wiki/Calculus/Integration_techniques

    See Also
    ========

    sympy.integrals.integrals.Integral.doit
    sympy.integrals.integrals.Integral
    
    "
1315,"def get_pywin32_module_file_attribute(module_name):
    
    from PyInstaller.utils.win32 import winutils
    module = winutils.import_pywin32_module(module_name)
    return module.__file__
","
    Get the absolute path of the PyWin32 DLL specific to the PyWin32 module with the passed name.

    On import, each PyWin32 module:

    * Imports a DLL specific to that module.
    * Overwrites the values of all module attributes with values specific to that DLL. This includes that module's
      `__file__` attribute, which then provides the absolute path of that DLL.

    This function safely imports that module in a PyWin32-aware subprocess and returns the value of that module's
    `__file__` attribute.

    Parameters
    ----------
    module_name : str
        Fully-qualified name of that module.

    Returns
    ----------
    str
        Absolute path of that DLL.

    See Also
    ----------
    `PyInstaller.utils.win32.winutils.import_pywin32_module()`
        For further details.
    ",103,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_pywin32_module_file_attribute(module_name):
    
    from PyInstaller.utils.win32 import winutils
    module = winutils.import_pywin32_module(module_name)
    return module.__file__

        ```
    ###Assistant : 
    Get the absolute path of the PyWin32 DLL specific to the PyWin32 module with the passed name.

    On import, each PyWin32 module:

    * Imports a DLL specific to that module.
    * Overwrites the values of all module attributes with values specific to that DLL. This includes that module's
      `__file__` attribute, which then provides the absolute path of that DLL.

    This function safely imports that module in a PyWin32-aware subprocess and returns the value of that module's
    `__file__` attribute.

    Parameters
    ----------
    module_name : str
        Fully-qualified name of that module.

    Returns
    ----------
    str
        Absolute path of that DLL.

    See Also
    ----------
    `PyInstaller.utils.win32.winutils.import_pywin32_module()`
        For further details.
    
    "
1316,"def render_warning(self, message):
        
        context = {""error"": message}
        return render_to_response(""sentry/pipeline-provider-error.html"", context, self.request)
",For situations when we want to display an error without triggering an issue,13,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def render_warning(self, message):
        
        context = {""error"": message}
        return render_to_response(""sentry/pipeline-provider-error.html"", context, self.request)

        ```
    ###Assistant : For situations when we want to display an error without triggering an issue
    "
1317,"def test_context_filter_not_labels(self) -> None:
        
        event_id = self._send_labelled_messages_in_room()

        channel = self.make_request(
            ""GET"",
            ""/rooms/%s/context/%s?filter=%s""
            % (self.room_id, event_id, json.dumps(self.FILTER_NOT_LABELS)),
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 200, channel.result)

        events_before = channel.json_body[""events_before""]

        self.assertEqual(
            len(events_before), 1, [event[""content""] for event in events_before]
        )
        self.assertEqual(
            events_before[0][""content""][""body""], ""without label"", events_before[0]
        )

        events_after = channel.json_body[""events_after""]

        self.assertEqual(
            len(events_after), 2, [event[""content""] for event in events_after]
        )
        self.assertEqual(
            events_after[0][""content""][""body""], ""with wrong label"", events_after[0]
        )
        self.assertEqual(
            events_after[1][""content""][""body""], ""with two wrong labels"", events_after[1]
        )
",Test that we can filter by the absence of a label on a /context request.,15,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_context_filter_not_labels(self) -> None:
        
        event_id = self._send_labelled_messages_in_room()

        channel = self.make_request(
            ""GET"",
            ""/rooms/%s/context/%s?filter=%s""
            % (self.room_id, event_id, json.dumps(self.FILTER_NOT_LABELS)),
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 200, channel.result)

        events_before = channel.json_body[""events_before""]

        self.assertEqual(
            len(events_before), 1, [event[""content""] for event in events_before]
        )
        self.assertEqual(
            events_before[0][""content""][""body""], ""without label"", events_before[0]
        )

        events_after = channel.json_body[""events_after""]

        self.assertEqual(
            len(events_after), 2, [event[""content""] for event in events_after]
        )
        self.assertEqual(
            events_after[0][""content""][""body""], ""with wrong label"", events_after[0]
        )
        self.assertEqual(
            events_after[1][""content""][""body""], ""with two wrong labels"", events_after[1]
        )

        ```
    ###Assistant : Test that we can filter by the absence of a label on a /context request.
    "
1318,"def _consistent_PT(u, v, graph_params, state_params):
    
    G1, G2 = graph_params.G1, graph_params.G2
    mapping, reverse_mapping = state_params.mapping, state_params.reverse_mapping

    for neighbor in G1[u]:
        if neighbor in mapping:
            if G1.number_of_edges(u, neighbor) != G2.number_of_edges(
                v, mapping[neighbor]
            ):
                return False

    for neighbor in G2[v]:
        if neighbor in reverse_mapping:
            if G1.number_of_edges(u, reverse_mapping[neighbor]) != G2.number_of_edges(
                v, neighbor
            ):
                return False
    return True
","Checks the consistency of extending the mapping using the current node pair.

    Parameters
    ----------
    u, v: Graph node
        The two candidate nodes being examined.

    graph_params: namedtuple
        Contains all the Graph-related parameters:

        G1,G2: NetworkX Graph or MultiGraph instances.
            The two graphs to check for isomorphism or monomorphism

        G1_labels,G2_labels: dict
            The label of every node in G1 and G2 respectively

    state_params: namedtuple
        Contains all the State-related parameters:

        mapping: dict
            The mapping as extended so far. Maps nodes of G1 to nodes of G2

        reverse_mapping: dict
            The reverse mapping as extended so far. Maps nodes from G2 to nodes of G1. It's basically ""mapping"" reversed

        T1, T2: set
            Ti contains uncovered neighbors of covered nodes from Gi, i.e. nodes that are not in the mapping, but are
            neighbors of nodes that are.

        T1_out, T2_out: set
            Ti_out contains all the nodes from Gi, that are neither in the mapping nor in Ti

    Returns
    -------
    True if the pair passes all the consistency checks successfully. False otherwise.
    ",162,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _consistent_PT(u, v, graph_params, state_params):
    
    G1, G2 = graph_params.G1, graph_params.G2
    mapping, reverse_mapping = state_params.mapping, state_params.reverse_mapping

    for neighbor in G1[u]:
        if neighbor in mapping:
            if G1.number_of_edges(u, neighbor) != G2.number_of_edges(
                v, mapping[neighbor]
            ):
                return False

    for neighbor in G2[v]:
        if neighbor in reverse_mapping:
            if G1.number_of_edges(u, reverse_mapping[neighbor]) != G2.number_of_edges(
                v, neighbor
            ):
                return False
    return True

        ```
    ###Assistant : Checks the consistency of extending the mapping using the current node pair.

    Parameters
    ----------
    u, v: Graph node
        The two candidate nodes being examined.

    graph_params: namedtuple
        Contains all the Graph-related parameters:

        G1,G2: NetworkX Graph or MultiGraph instances.
            The two graphs to check for isomorphism or monomorphism

        G1_labels,G2_labels: dict
            The label of every node in G1 and G2 respectively

    state_params: namedtuple
        Contains all the State-related parameters:

        mapping: dict
            The mapping as extended so far. Maps nodes of G1 to nodes of G2

        reverse_mapping: dict
            The reverse mapping as extended so far. Maps nodes from G2 to nodes of G1. It's basically ""mapping"" reversed

        T1, T2: set
            Ti contains uncovered neighbors of covered nodes from Gi, i.e. nodes that are not in the mapping, but are
            neighbors of nodes that are.

        T1_out, T2_out: set
            Ti_out contains all the nodes from Gi, that are neither in the mapping nor in Ti

    Returns
    -------
    True if the pair passes all the consistency checks successfully. False otherwise.
    
    "
1319,"def get_variant_values_for(items):
	
	attribute_map = {}
	for attr in frappe.db.sql(
		
		% "", "".join([""%s""] * len(items)),
		tuple(items),
		as_dict=1,
	):
		attribute_map.setdefault(attr[""parent""], {})
		attribute_map[attr[""parent""]].update({attr[""attribute""]: attr[""attribute_value""]})

	return attribute_map
","Returns variant values for items.select parent, attribute, attribute_value
		from `tabItem Variant Attribute` where parent in (%s)
		",16,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_variant_values_for(items):
	
	attribute_map = {}
	for attr in frappe.db.sql(
		
		% "", "".join([""%s""] * len(items)),
		tuple(items),
		as_dict=1,
	):
		attribute_map.setdefault(attr[""parent""], {})
		attribute_map[attr[""parent""]].update({attr[""attribute""]: attr[""attribute_value""]})

	return attribute_map

        ```
    ###Assistant : Returns variant values for items.select parent, attribute, attribute_value
		from `tabItem Variant Attribute` where parent in (%s)
		
    "
1320,"def test_edit_post_locked_by_self(self):
        
        # Lock the snippet
        self.lock_snippet(self.user)

        # Try to edit the snippet
        response = self.client.post(
            self.get_url(""edit""),
            {""text"": ""Edited while locked""},
            follow=True,
        )
        self.refresh_snippet()

        # Should not show error message
        self.assertNotContains(
            response,
            f""The {self.model_name} could not be saved as it is locked"",
        )

        # Check that the snippet is still locked
        self.assertTrue(self.snippet.locked)

        # Check that the snippet is edited
        self.assertEqual(self.snippet.text, ""Edited while locked"")
",A user can edit a snippet that is locked by themselves.,11,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_edit_post_locked_by_self(self):
        
        # Lock the snippet
        self.lock_snippet(self.user)

        # Try to edit the snippet
        response = self.client.post(
            self.get_url(""edit""),
            {""text"": ""Edited while locked""},
            follow=True,
        )
        self.refresh_snippet()

        # Should not show error message
        self.assertNotContains(
            response,
            f""The {self.model_name} could not be saved as it is locked"",
        )

        # Check that the snippet is still locked
        self.assertTrue(self.snippet.locked)

        # Check that the snippet is edited
        self.assertEqual(self.snippet.text, ""Edited while locked"")

        ```
    ###Assistant : A user can edit a snippet that is locked by themselves.
    "
1321,"def update_metrics(self, targets, predictions):
        
        for of_name, of_obj in self.output_features.items():
            of_obj.update_metrics(targets[of_name], predictions[of_name])

        eval_loss, additional_losses = self.eval_loss(targets, predictions)
        self.eval_loss_metric.update(eval_loss)
        self.eval_additional_losses_metrics.update(additional_losses)
",Updates the model's metrics given targets and predictions.,8,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_metrics(self, targets, predictions):
        
        for of_name, of_obj in self.output_features.items():
            of_obj.update_metrics(targets[of_name], predictions[of_name])

        eval_loss, additional_losses = self.eval_loss(targets, predictions)
        self.eval_loss_metric.update(eval_loss)
        self.eval_additional_losses_metrics.update(additional_losses)

        ```
    ###Assistant : Updates the model's metrics given targets and predictions.
    "
1322,"def tick_right(self):
        
        label = True
        if 'label1On' in self._major_tick_kw:
            label = (self._major_tick_kw['label1On']
                     or self._major_tick_kw['label2On'])
        self.set_ticks_position('right')
        # if labels were turned off before this was called
        # leave them off
        self.set_tick_params(which='both', labelright=label)
","
        Move ticks and ticklabels (if present) to the right of the Axes.
        ",12,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tick_right(self):
        
        label = True
        if 'label1On' in self._major_tick_kw:
            label = (self._major_tick_kw['label1On']
                     or self._major_tick_kw['label2On'])
        self.set_ticks_position('right')
        # if labels were turned off before this was called
        # leave them off
        self.set_tick_params(which='both', labelright=label)

        ```
    ###Assistant : 
        Move ticks and ticklabels (if present) to the right of the Axes.
        
    "
1323,"def test_show_message_twice(view, info1, info2, count):
    
    view.show_message(info1)
    view.show_message(info2)
    assert len(view._messages) == count

",Show the exact same message twice -> only one should be shown.,12,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_show_message_twice(view, info1, info2, count):
    
    view.show_message(info1)
    view.show_message(info2)
    assert len(view._messages) == count


        ```
    ###Assistant : Show the exact same message twice -> only one should be shown.
    "
1324,"def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
        
        bboxes1 = get_box_tensor(bboxes1)
        bboxes2 = get_box_tensor(bboxes2)
        assert bboxes1.size(-1) in [0, 4, 5]
        assert bboxes2.size(-1) in [0, 4, 5]
        if bboxes2.size(-1) == 5:
            bboxes2 = bboxes2[..., :4]
        if bboxes1.size(-1) == 5:
            bboxes1 = bboxes1[..., :4]

        if self.dtype == 'fp16':
            # change tensor type to save cpu and cuda memory and keep speed
            bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype)
            bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype)
            overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
            if not overlaps.is_cuda and overlaps.dtype == torch.float16:
                # resume cpu float32
                overlaps = overlaps.float()
            return overlaps

        return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
","Calculate IoU between 2D bboxes.

        Args:
            bboxes1 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
                in <x1, y1, x2, y2> format, or shape (m, 5) in <x1, y1, x2,
                y2, score> format.
            bboxes2 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
                in <x1, y1, x2, y2> format, shape (m, 5) in <x1, y1, x2, y2,
                score> format, or be empty. If ``is_aligned `` is ``True``,
                then m and n must be equal.
            mode (str): ""iou"" (intersection over union), ""iof"" (intersection
                over foreground), or ""giou"" (generalized intersection over
                union).
            is_aligned (bool, optional): If True, then m and n must be equal.
                Default False.

        Returns:
            Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
        ",115,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
        
        bboxes1 = get_box_tensor(bboxes1)
        bboxes2 = get_box_tensor(bboxes2)
        assert bboxes1.size(-1) in [0, 4, 5]
        assert bboxes2.size(-1) in [0, 4, 5]
        if bboxes2.size(-1) == 5:
            bboxes2 = bboxes2[..., :4]
        if bboxes1.size(-1) == 5:
            bboxes1 = bboxes1[..., :4]

        if self.dtype == 'fp16':
            # change tensor type to save cpu and cuda memory and keep speed
            bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype)
            bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype)
            overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
            if not overlaps.is_cuda and overlaps.dtype == torch.float16:
                # resume cpu float32
                overlaps = overlaps.float()
            return overlaps

        return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)

        ```
    ###Assistant : Calculate IoU between 2D bboxes.

        Args:
            bboxes1 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
                in <x1, y1, x2, y2> format, or shape (m, 5) in <x1, y1, x2,
                y2, score> format.
            bboxes2 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
                in <x1, y1, x2, y2> format, shape (m, 5) in <x1, y1, x2, y2,
                score> format, or be empty. If ``is_aligned `` is ``True``,
                then m and n must be equal.
            mode (str): ""iou"" (intersection over union), ""iof"" (intersection
                over foreground), or ""giou"" (generalized intersection over
                union).
            is_aligned (bool, optional): If True, then m and n must be equal.
                Default False.

        Returns:
            Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
        
    "
1325,"def binning(self) -> List[List[str]]:
        
        return self._binning_linear_threshold(multiplier=100)

"," Create bins to split linearly from the lowest to the highest sample value

        Returns
        -------
        list
            List of bins of filenames
        ",21,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def binning(self) -> List[List[str]]:
        
        return self._binning_linear_threshold(multiplier=100)


        ```
    ###Assistant :  Create bins to split linearly from the lowest to the highest sample value

        Returns
        -------
        list
            List of bins of filenames
        
    "
1326,"def connect(self, publish_port, connect_callback=None, disconnect_callback=None):
        
        raise NotImplementedError
","
        Create a network connection to the the PublishServer or broker.
        ",10,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def connect(self, publish_port, connect_callback=None, disconnect_callback=None):
        
        raise NotImplementedError

        ```
    ###Assistant : 
        Create a network connection to the the PublishServer or broker.
        
    "
1327,"def contains(self, g):
        
        if not isinstance(g, FreeGroupElement):
            return False
        elif self != g.group:
            return False
        else:
            return True
","Tests if Free Group element ``g`` belong to self, ``G``.

        In mathematical terms any linear combination of generators
        of a Free Group is contained in it.

        Examples
        ========

        >>> from sympy.combinatorics import free_group
        >>> f, x, y, z = free_group(""x y z"")
        >>> f.contains(x**3*y**2)
        True

        ",45,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def contains(self, g):
        
        if not isinstance(g, FreeGroupElement):
            return False
        elif self != g.group:
            return False
        else:
            return True

        ```
    ###Assistant : Tests if Free Group element ``g`` belong to self, ``G``.

        In mathematical terms any linear combination of generators
        of a Free Group is contained in it.

        Examples
        ========

        >>> from sympy.combinatorics import free_group
        >>> f, x, y, z = free_group(""x y z"")
        >>> f.contains(x**3*y**2)
        True

        
    "
1328,"def get_pledged_security_qty(loan):

	current_pledges = {}

	unpledges = frappe._dict(
		frappe.db.sql(
			,
			(loan),
		)
	)

	pledges = frappe._dict(
		frappe.db.sql(
			,
			(loan),
		)
	)

	for security, qty in pledges.items():
		current_pledges.setdefault(security, qty)
		current_pledges[security] -= unpledges.get(security, 0.0)

	return current_pledges
","
		SELECT u.loan_security, sum(u.qty) as qty
		FROM `tabLoan Security Unpledge` up, `tabUnpledge` u
		WHERE up.loan = %s
		AND u.parent = up.name
		AND up.status = 'Approved'
		GROUP BY u.loan_security
	
		SELECT p.loan_security, sum(p.qty) as qty
		FROM `tabLoan Security Pledge` lp, `tabPledge`p
		WHERE lp.loan = %s
		AND p.parent = lp.name
		AND lp.status = 'Pledged'
		GROUP BY p.loan_security
	",53,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_pledged_security_qty(loan):

	current_pledges = {}

	unpledges = frappe._dict(
		frappe.db.sql(
			,
			(loan),
		)
	)

	pledges = frappe._dict(
		frappe.db.sql(
			,
			(loan),
		)
	)

	for security, qty in pledges.items():
		current_pledges.setdefault(security, qty)
		current_pledges[security] -= unpledges.get(security, 0.0)

	return current_pledges

        ```
    ###Assistant : 
		SELECT u.loan_security, sum(u.qty) as qty
		FROM `tabLoan Security Unpledge` up, `tabUnpledge` u
		WHERE up.loan = %s
		AND u.parent = up.name
		AND up.status = 'Approved'
		GROUP BY u.loan_security
	
		SELECT p.loan_security, sum(p.qty) as qty
		FROM `tabLoan Security Pledge` lp, `tabPledge`p
		WHERE lp.loan = %s
		AND p.parent = lp.name
		AND lp.status = 'Pledged'
		GROUP BY p.loan_security
	
    "
1329,"def get_select(self):
        
        select = []
        klass_info = None
        annotations = {}
        select_idx = 0
        for alias, (sql, params) in self.query.extra_select.items():
            annotations[alias] = select_idx
            select.append((RawSQL(sql, params), alias))
            select_idx += 1
        assert not (self.query.select and self.query.default_cols)
        if self.query.default_cols:
            cols = self.get_default_columns()
        else:
            # self.query.select is a special case. These columns never go to
            # any model.
            cols = self.query.select
        if cols:
            select_list = []
            for col in cols:
                select_list.append(select_idx)
                select.append((col, None))
                select_idx += 1
            klass_info = {
                ""model"": self.query.model,
                ""select_fields"": select_list,
            }
        for alias, annotation in self.query.annotation_select.items():
            annotations[alias] = select_idx
            select.append((annotation, alias))
            select_idx += 1

        if self.query.select_related:
            related_klass_infos = self.get_related_selections(select)
            klass_info[""related_klass_infos""] = related_klass_infos
","
        Return three values:
        - a list of 3-tuples of (expression, (sql, params), alias)
        - a klass_info structure,
        - a dictionary of annotations

        The (sql, params) is what the expression will produce, and alias is the
        ""AS alias"" for the column (possibly None).

        The klass_info structure contains the following information:
        - The base model of the query.
        - Which columns for that model are present in the query (by
          position of the select clause).
        - related_klass_infos: [f, klass_info] to descent into

        The annotations is a dictionary of {'attname': column position} values.
        ",90,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_select(self):
        
        select = []
        klass_info = None
        annotations = {}
        select_idx = 0
        for alias, (sql, params) in self.query.extra_select.items():
            annotations[alias] = select_idx
            select.append((RawSQL(sql, params), alias))
            select_idx += 1
        assert not (self.query.select and self.query.default_cols)
        if self.query.default_cols:
            cols = self.get_default_columns()
        else:
            # self.query.select is a special case. These columns never go to
            # any model.
            cols = self.query.select
        if cols:
            select_list = []
            for col in cols:
                select_list.append(select_idx)
                select.append((col, None))
                select_idx += 1
            klass_info = {
                ""model"": self.query.model,
                ""select_fields"": select_list,
            }
        for alias, annotation in self.query.annotation_select.items():
            annotations[alias] = select_idx
            select.append((annotation, alias))
            select_idx += 1

        if self.query.select_related:
            related_klass_infos = self.get_related_selections(select)
            klass_info[""related_klass_infos""] = related_klass_infos

        ```
    ###Assistant : 
        Return three values:
        - a list of 3-tuples of (expression, (sql, params), alias)
        - a klass_info structure,
        - a dictionary of annotations

        The (sql, params) is what the expression will produce, and alias is the
        ""AS alias"" for the column (possibly None).

        The klass_info structure contains the following information:
        - The base model of the query.
        - Which columns for that model are present in the query (by
          position of the select clause).
        - related_klass_infos: [f, klass_info] to descent into

        The annotations is a dictionary of {'attname': column position} values.
        
    "
1330,"def set_module_collection_mode(self, name, mode):
        
        if name is None:
            name = self.__name__
        if mode is None:
            self._module_collection_mode.pop(name)
        else:
            self._module_collection_mode[name] = mode
","""
        Set the package/module collection mode for the specified module
        name. If `name` is `None`, the hooked module/package name is used.
        Valid values for `mode` are: `'pyc'`, `'py'`, and `None`.
        ",30,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_module_collection_mode(self, name, mode):
        
        if name is None:
            name = self.__name__
        if mode is None:
            self._module_collection_mode.pop(name)
        else:
            self._module_collection_mode[name] = mode

        ```
    ###Assistant : ""
        Set the package/module collection mode for the specified module
        name. If `name` is `None`, the hooked module/package name is used.
        Valid values for `mode` are: `'pyc'`, `'py'`, and `None`.
        
    "
1331,"def write_file (filename, contents):
    
    f = open(filename, ""w"")
    try:
        for line in contents:
            f.write(line + ""\n"")
    finally:
        f.close()
","Create a file with the specified name and write 'contents' (a
    sequence of strings without line terminators) to it.
    ",19,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def write_file (filename, contents):
    
    f = open(filename, ""w"")
    try:
        for line in contents:
            f.write(line + ""\n"")
    finally:
        f.close()

        ```
    ###Assistant : Create a file with the specified name and write 'contents' (a
    sequence of strings without line terminators) to it.
    
    "
1332,"async def test_flow_run_policy_is_backwards_compatible(self):
        

        empty_new_policy = schemas.core.FlowRunPolicy()

        # should not raise an error
        self.OldFlowRunPolicy(**empty_new_policy.dict())
","
        In version 2.1.1 and prior, the FlowRunPolicy schema required two properties,
        `max_retries` and `retry_delay_seconds`. These properties are deprecated.

        This test ensures old clients can load new FlowRunPolicySchemas. It can be removed
        when the corresponding properties are removed.
        ",37,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_flow_run_policy_is_backwards_compatible(self):
        

        empty_new_policy = schemas.core.FlowRunPolicy()

        # should not raise an error
        self.OldFlowRunPolicy(**empty_new_policy.dict())

        ```
    ###Assistant : 
        In version 2.1.1 and prior, the FlowRunPolicy schema required two properties,
        `max_retries` and `retry_delay_seconds`. These properties are deprecated.

        This test ensures old clients can load new FlowRunPolicySchemas. It can be removed
        when the corresponding properties are removed.
        
    "
1333,"def test_help_text_examples_are_contextualized():
    
    rendered_inline = render(spacing_invalid_value(""padding"", ""inline""))
    assert ""widget.styles.padding"" in rendered_inline

    rendered_css = render(spacing_invalid_value(""padding"", ""css""))
    assert ""padding:"" in rendered_css

","Ensure that if the user is using CSS, they see CSS-specific examples
    and if they're using inline styles they see inline-specific examples.",22,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_help_text_examples_are_contextualized():
    
    rendered_inline = render(spacing_invalid_value(""padding"", ""inline""))
    assert ""widget.styles.padding"" in rendered_inline

    rendered_css = render(spacing_invalid_value(""padding"", ""css""))
    assert ""padding:"" in rendered_css


        ```
    ###Assistant : Ensure that if the user is using CSS, they see CSS-specific examples
    and if they're using inline styles they see inline-specific examples.
    "
1334,"def test_reverse_proxy(tctx, keep_host_header):
    
    server = Placeholder(Server)
    tctx.options.mode = ""reverse:http://localhost:8000""
    tctx.options.connection_strategy = ""lazy""
    tctx.options.keep_host_header = keep_host_header
    assert (
        Playbook(modes.ReverseProxy(tctx), hooks=False)
        >> DataReceived(
            tctx.client, b""GET /foo HTTP/1.1\r\n"" b""Host: example.com\r\n\r\n""
        )
        << NextLayerHook(Placeholder(NextLayer))
        >> reply_next_layer(lambda ctx: http.HttpLayer(ctx, HTTPMode.transparent))
        << OpenConnection(server)
        >> reply(None)
        << SendData(
            server,
            b""GET /foo HTTP/1.1\r\n""
            b""Host: ""
            + (b""example.com"" if keep_host_header else b""localhost:8000"")
            + b""\r\n\r\n"",
        )
        >> DataReceived(server, b""HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n"")
        << SendData(tctx.client, b""HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n"")
    )
    assert server().address == (""localhost"", 8000)


@pytest.mark.parametrize(""patch"", [True, False])
@pytest.mark.parametrize(""connection_strategy"", [""eager"", ""lazy""])","Test mitmproxy in reverse proxy mode.

    - make sure that we connect to the right host
    - make sure that we respect keep_host_header
    - make sure that we include non-standard ports in the host header (#4280)
    ",36,80,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_reverse_proxy(tctx, keep_host_header):
    
    server = Placeholder(Server)
    tctx.options.mode = ""reverse:http://localhost:8000""
    tctx.options.connection_strategy = ""lazy""
    tctx.options.keep_host_header = keep_host_header
    assert (
        Playbook(modes.ReverseProxy(tctx), hooks=False)
        >> DataReceived(
            tctx.client, b""GET /foo HTTP/1.1\r\n"" b""Host: example.com\r\n\r\n""
        )
        << NextLayerHook(Placeholder(NextLayer))
        >> reply_next_layer(lambda ctx: http.HttpLayer(ctx, HTTPMode.transparent))
        << OpenConnection(server)
        >> reply(None)
        << SendData(
            server,
            b""GET /foo HTTP/1.1\r\n""
            b""Host: ""
            + (b""example.com"" if keep_host_header else b""localhost:8000"")
            + b""\r\n\r\n"",
        )
        >> DataReceived(server, b""HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n"")
        << SendData(tctx.client, b""HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n"")
    )
    assert server().address == (""localhost"", 8000)


@pytest.mark.parametrize(""patch"", [True, False])
@pytest.mark.parametrize(""connection_strategy"", [""eager"", ""lazy""])
        ```
    ###Assistant : Test mitmproxy in reverse proxy mode.

    - make sure that we connect to the right host
    - make sure that we respect keep_host_header
    - make sure that we include non-standard ports in the host header (#4280)
    
    "
1335,"def _check_filter_horizontal(self, obj):
        
        if not isinstance(obj.filter_horizontal, (list, tuple)):
            return must_be(
                ""a list or tuple"", option=""filter_horizontal"", obj=obj, id=""admin.E018""
            )
        else:
            return list(
                chain.from_iterable(
                    self._check_filter_item(
                        obj, field_name, ""filter_horizontal[%d]"" % index
                    )
                    for index, field_name in enumerate(obj.filter_horizontal)
                )
            )
",Check that filter_horizontal is a sequence of field names.,9,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_filter_horizontal(self, obj):
        
        if not isinstance(obj.filter_horizontal, (list, tuple)):
            return must_be(
                ""a list or tuple"", option=""filter_horizontal"", obj=obj, id=""admin.E018""
            )
        else:
            return list(
                chain.from_iterable(
                    self._check_filter_item(
                        obj, field_name, ""filter_horizontal[%d]"" % index
                    )
                    for index, field_name in enumerate(obj.filter_horizontal)
                )
            )

        ```
    ###Assistant : Check that filter_horizontal is a sequence of field names.
    "
1336,"def poly_intersection(poly_det, poly_gt):
    
    assert isinstance(poly_det, plg.Polygon)
    assert isinstance(poly_gt, plg.Polygon)

    poly_inter = poly_det & poly_gt
    if len(poly_inter) == 0:
        return 0, poly_inter
    return poly_inter.area(), poly_inter

","Calculate the intersection area between two polygon.

    Args:
        poly_det (Polygon): A polygon predicted by detector.
        poly_gt (Polygon): A gt polygon.

    Returns:
        intersection_area (float): The intersection area between two polygons.
    ",29,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def poly_intersection(poly_det, poly_gt):
    
    assert isinstance(poly_det, plg.Polygon)
    assert isinstance(poly_gt, plg.Polygon)

    poly_inter = poly_det & poly_gt
    if len(poly_inter) == 0:
        return 0, poly_inter
    return poly_inter.area(), poly_inter


        ```
    ###Assistant : Calculate the intersection area between two polygon.

    Args:
        poly_det (Polygon): A polygon predicted by detector.
        poly_gt (Polygon): A gt polygon.

    Returns:
        intersection_area (float): The intersection area between two polygons.
    
    "
1337,"def size(self) -> int:
        
        return sum(len(x.data) for x in [*self.answers, *self.authorities, *self.additionals])
",Returns the cumulative data size of all resource record sections.,10,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def size(self) -> int:
        
        return sum(len(x.data) for x in [*self.answers, *self.authorities, *self.additionals])

        ```
    ###Assistant : Returns the cumulative data size of all resource record sections.
    "
1338,"async def get_job_submission_info(self):
        

        jobs = {}
        fetched_jobs = await self._job_info_client.get_all_jobs()
        for (
            job_submission_id,
            job_info,
        ) in fetched_jobs.items():
            if job_info is not None:
                entry = {
                    ""job_submission_id"": job_submission_id,
                    ""status"": job_info.status,
                    ""message"": job_info.message,
                    ""error_type"": job_info.error_type,
                    ""start_time"": job_info.start_time,
                    ""end_time"": job_info.end_time,
                    ""metadata"": job_info.metadata,
                    ""runtime_env"": job_info.runtime_env,
                    ""entrypoint"": job_info.entrypoint,
                }
                jobs[job_submission_id] = entry
        return jobs
",Info for Ray job submission.  Here a job can have 0 or many drivers.,14,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def get_job_submission_info(self):
        

        jobs = {}
        fetched_jobs = await self._job_info_client.get_all_jobs()
        for (
            job_submission_id,
            job_info,
        ) in fetched_jobs.items():
            if job_info is not None:
                entry = {
                    ""job_submission_id"": job_submission_id,
                    ""status"": job_info.status,
                    ""message"": job_info.message,
                    ""error_type"": job_info.error_type,
                    ""start_time"": job_info.start_time,
                    ""end_time"": job_info.end_time,
                    ""metadata"": job_info.metadata,
                    ""runtime_env"": job_info.runtime_env,
                    ""entrypoint"": job_info.entrypoint,
                }
                jobs[job_submission_id] = entry
        return jobs

        ```
    ###Assistant : Info for Ray job submission.  Here a job can have 0 or many drivers.
    "
1339,"def _gen_sieve_array(M, factor_base):
    
    sieve_array = [0]*(2*M + 1)
    for factor in factor_base:
        if factor.soln1 is None: #The prime does not divides a
            continue
        for idx in range((M + factor.soln1) % factor.prime, 2*M, factor.prime):
            sieve_array[idx] += factor.log_p
        if factor.prime == 2:
            continue
        #if prime is 2 then sieve only with soln_1_p
        for idx in range((M + factor.soln2) % factor.prime, 2*M, factor.prime):
            sieve_array[idx] += factor.log_p
    return sieve_array

","Sieve Stage of the Quadratic Sieve. For every prime in the factor_base
    that does not divide the coefficient `a` we add log_p over the sieve_array
    such that ``-M <= soln1 + i*p <=  M`` and ``-M <= soln2 + i*p <=  M`` where `i`
    is an integer. When p = 2 then log_p is only added using
    ``-M <= soln1 + i*p <=  M``.

    Parameters
    ==========

    M : sieve interval
    factor_base : factor_base primes
    ",74,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _gen_sieve_array(M, factor_base):
    
    sieve_array = [0]*(2*M + 1)
    for factor in factor_base:
        if factor.soln1 is None: #The prime does not divides a
            continue
        for idx in range((M + factor.soln1) % factor.prime, 2*M, factor.prime):
            sieve_array[idx] += factor.log_p
        if factor.prime == 2:
            continue
        #if prime is 2 then sieve only with soln_1_p
        for idx in range((M + factor.soln2) % factor.prime, 2*M, factor.prime):
            sieve_array[idx] += factor.log_p
    return sieve_array


        ```
    ###Assistant : Sieve Stage of the Quadratic Sieve. For every prime in the factor_base
    that does not divide the coefficient `a` we add log_p over the sieve_array
    such that ``-M <= soln1 + i*p <=  M`` and ``-M <= soln2 + i*p <=  M`` where `i`
    is an integer. When p = 2 then log_p is only added using
    ``-M <= soln1 + i*p <=  M``.

    Parameters
    ==========

    M : sieve interval
    factor_base : factor_base primes
    
    "
1340,"def rescue_docarray():
    
    try:
        import docarray as docarray

        __docarray_version__ = docarray.__version__

    except AttributeError:
        # Being here means docarray is not installed correctly, attempt to reinstall it
        # as recommended by pip https://pip.pypa.io/en/latest/user_guide/#using-pip-from-your-program
        import subprocess

        subprocess.check_call(
            [sys.executable, '-m', 'pip', 'uninstall', '--yes', 'docarray']
        )
        subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'docarray'])

","Upgrading from 2.x to 3.x is broken (https://github.com/jina-ai/jina/issues/4194)
    This function checks if docarray is broken and if so attempts to rescue it
    ",22,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rescue_docarray():
    
    try:
        import docarray as docarray

        __docarray_version__ = docarray.__version__

    except AttributeError:
        # Being here means docarray is not installed correctly, attempt to reinstall it
        # as recommended by pip https://pip.pypa.io/en/latest/user_guide/#using-pip-from-your-program
        import subprocess

        subprocess.check_call(
            [sys.executable, '-m', 'pip', 'uninstall', '--yes', 'docarray']
        )
        subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'docarray'])


        ```
    ###Assistant : Upgrading from 2.x to 3.x is broken (https://github.com/jina-ai/jina/issues/4194)
    This function checks if docarray is broken and if so attempts to rescue it
    
    "
1341,"def transform(self, X):
        
        msg = (
            ""%(name)s is not fitted. Call fit to set the parameters before""
            "" calling transform""
        )
        check_is_fitted(self, msg=msg)

        X = self._validate_data(X, accept_sparse=""csr"", reset=False)
        check_non_negative(X, ""X in AdditiveChi2Sampler.transform"")
        sparse = sp.issparse(X)

        # zeroth component
        # 1/cosh = sech
        # cosh(0) = 1.0

        transf = self._transform_sparse if sparse else self._transform_dense
        return transf(X)
","Apply approximate feature map to X.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            Training data, where `n_samples` is the number of samples
            and `n_features` is the number of features.

        Returns
        -------
        X_new : {ndarray, sparse matrix}, \
               shape = (n_samples, n_features * (2*sample_steps - 1))
            Whether the return value is an array or sparse matrix depends on
            the type of the input X.
        ",66,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transform(self, X):
        
        msg = (
            ""%(name)s is not fitted. Call fit to set the parameters before""
            "" calling transform""
        )
        check_is_fitted(self, msg=msg)

        X = self._validate_data(X, accept_sparse=""csr"", reset=False)
        check_non_negative(X, ""X in AdditiveChi2Sampler.transform"")
        sparse = sp.issparse(X)

        # zeroth component
        # 1/cosh = sech
        # cosh(0) = 1.0

        transf = self._transform_sparse if sparse else self._transform_dense
        return transf(X)

        ```
    ###Assistant : Apply approximate feature map to X.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            Training data, where `n_samples` is the number of samples
            and `n_features` is the number of features.

        Returns
        -------
        X_new : {ndarray, sparse matrix}, \
               shape = (n_samples, n_features * (2*sample_steps - 1))
            Whether the return value is an array or sparse matrix depends on
            the type of the input X.
        
    "
1342,"def get_repository_root(cls, location):
        # type: (str) -> Optional[str]
        
        if cls.is_repository_directory(location):
            return location
        return None
","
        Return the ""root"" (top-level) directory controlled by the vcs,
        or `None` if the directory is not in any.

        It is meant to be overridden to implement smarter detection
        mechanisms for specific vcs.

        This can do more than is_repository_directory() alone. For
        example, the Git override checks that Git is actually available.
        ",50,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_repository_root(cls, location):
        # type: (str) -> Optional[str]
        
        if cls.is_repository_directory(location):
            return location
        return None

        ```
    ###Assistant : 
        Return the ""root"" (top-level) directory controlled by the vcs,
        or `None` if the directory is not in any.

        It is meant to be overridden to implement smarter detection
        mechanisms for specific vcs.

        This can do more than is_repository_directory() alone. For
        example, the Git override checks that Git is actually available.
        
    "
1343,"def test_it_should_not_read_quotes_stream_if_it_does_not_exist_in_client(oauth_config, configured_catalog):
    
    source = SourceHubspot()

    all_records = list(source.read(logger, config=oauth_config, catalog=configured_catalog, state=None))
    records = [record for record in all_records if record.type == Type.RECORD]
    assert not records
","
    If 'quotes' stream is not in the client, it should skip it.
    ",12,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_it_should_not_read_quotes_stream_if_it_does_not_exist_in_client(oauth_config, configured_catalog):
    
    source = SourceHubspot()

    all_records = list(source.read(logger, config=oauth_config, catalog=configured_catalog, state=None))
    records = [record for record in all_records if record.type == Type.RECORD]
    assert not records

        ```
    ###Assistant : 
    If 'quotes' stream is not in the client, it should skip it.
    
    "
1344,"def get_conda_env_dir(env_name):
    
    conda_prefix = os.environ.get(""CONDA_PREFIX"")
    if conda_prefix is None:
        # The caller is neither in a conda env or in (base) env.  This is rare
        # because by default, new terminals start in (base), but we can still
        # support this case.
        conda_exe = os.environ.get(""CONDA_EXE"")
        if conda_exe is None:
            raise ValueError(
                ""Cannot find environment variables set by conda. ""
                ""Please verify conda is installed.""
            )
        # Example: CONDA_EXE=$HOME/anaconda3/bin/python
        # Strip out /bin/python by going up two parent directories.
        conda_prefix = str(Path(conda_exe).parent.parent)

    # There are two cases:
    # 1. We are in a conda (base) env: CONDA_DEFAULT_ENV=base and
    #    CONDA_PREFIX=$HOME/anaconda3
    # 2. We are in a user-created conda env: CONDA_DEFAULT_ENV=$env_name and
    #    CONDA_PREFIX=$HOME/anaconda3/envs/$current_env_name
    if os.environ.get(""CONDA_DEFAULT_ENV"") == ""base"":
        # Caller's curent environment is (base).
        # Not recommended by conda, but we can still support it.
        if env_name == ""base"":
            # Desired environment is (base), located at e.g. $HOME/anaconda3
            env_dir = conda_prefix
        else:
            # Desired environment is user-created, e.g.
            # $HOME/anaconda3/envs/$env_name
            env_dir = os.path.join(conda_prefix, ""envs"", env_name)
    else:
        # Now `conda_prefix` should be something like
        # $HOME/anaconda3/envs/$current_env_name
        # We want to replace the last component with the desired env name.
        conda_envs_dir = os.path.split(conda_prefix)[0]
        env_dir = os.path.join(conda_envs_dir, env_name)
    if not os.path.isdir(env_dir):
        raise ValueError(
            ""conda env ""
            + env_name
            + "" not found in conda envs directory. Run `conda env list` to ""
            + ""verify the name is correct.""
        )
    return env_dir

","Find and validate the conda directory for a given conda environment.

    For example, given the environment name `tf1`, this function checks
    the existence of the corresponding conda directory, e.g.
    `/Users/scaly/anaconda3/envs/tf1`, and returns it.
    ",33,226,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_conda_env_dir(env_name):
    
    conda_prefix = os.environ.get(""CONDA_PREFIX"")
    if conda_prefix is None:
        # The caller is neither in a conda env or in (base) env.  This is rare
        # because by default, new terminals start in (base), but we can still
        # support this case.
        conda_exe = os.environ.get(""CONDA_EXE"")
        if conda_exe is None:
            raise ValueError(
                ""Cannot find environment variables set by conda. ""
                ""Please verify conda is installed.""
            )
        # Example: CONDA_EXE=$HOME/anaconda3/bin/python
        # Strip out /bin/python by going up two parent directories.
        conda_prefix = str(Path(conda_exe).parent.parent)

    # There are two cases:
    # 1. We are in a conda (base) env: CONDA_DEFAULT_ENV=base and
    #    CONDA_PREFIX=$HOME/anaconda3
    # 2. We are in a user-created conda env: CONDA_DEFAULT_ENV=$env_name and
    #    CONDA_PREFIX=$HOME/anaconda3/envs/$current_env_name
    if os.environ.get(""CONDA_DEFAULT_ENV"") == ""base"":
        # Caller's curent environment is (base).
        # Not recommended by conda, but we can still support it.
        if env_name == ""base"":
            # Desired environment is (base), located at e.g. $HOME/anaconda3
            env_dir = conda_prefix
        else:
            # Desired environment is user-created, e.g.
            # $HOME/anaconda3/envs/$env_name
            env_dir = os.path.join(conda_prefix, ""envs"", env_name)
    else:
        # Now `conda_prefix` should be something like
        # $HOME/anaconda3/envs/$current_env_name
        # We want to replace the last component with the desired env name.
        conda_envs_dir = os.path.split(conda_prefix)[0]
        env_dir = os.path.join(conda_envs_dir, env_name)
    if not os.path.isdir(env_dir):
        raise ValueError(
            ""conda env ""
            + env_name
            + "" not found in conda envs directory. Run `conda env list` to ""
            + ""verify the name is correct.""
        )
    return env_dir


        ```
    ###Assistant : Find and validate the conda directory for a given conda environment.

    For example, given the environment name `tf1`, this function checks
    the existence of the corresponding conda directory, e.g.
    `/Users/scaly/anaconda3/envs/tf1`, and returns it.
    
    "
1345,"async def test_switch_change_outlet_state(hass, utcnow):
    
    helper = await setup_test_component(hass, create_switch_service)

    await hass.services.async_call(
        ""switch"", ""turn_on"", {""entity_id"": ""switch.testdevice""}, blocking=True
    )
    helper.async_assert_service_values(
        ServicesTypes.OUTLET,
        {
            CharacteristicsTypes.ON: 1,
        },
    )

    await hass.services.async_call(
        ""switch"", ""turn_off"", {""entity_id"": ""switch.testdevice""}, blocking=True
    )
    helper.async_assert_service_values(
        ServicesTypes.OUTLET,
        {
            CharacteristicsTypes.ON: 0,
        },
    )

",Test that we can turn a HomeKit outlet on and off again.,12,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_switch_change_outlet_state(hass, utcnow):
    
    helper = await setup_test_component(hass, create_switch_service)

    await hass.services.async_call(
        ""switch"", ""turn_on"", {""entity_id"": ""switch.testdevice""}, blocking=True
    )
    helper.async_assert_service_values(
        ServicesTypes.OUTLET,
        {
            CharacteristicsTypes.ON: 1,
        },
    )

    await hass.services.async_call(
        ""switch"", ""turn_off"", {""entity_id"": ""switch.testdevice""}, blocking=True
    )
    helper.async_assert_service_values(
        ServicesTypes.OUTLET,
        {
            CharacteristicsTypes.ON: 0,
        },
    )


        ```
    ###Assistant : Test that we can turn a HomeKit outlet on and off again.
    "
1346,"def _update_label_position(self, renderer):
        
        if not self._autolabelpos:
            return

        # get bounding boxes for this axis and any siblings
        # that have been set by `fig.align_xlabels()`
        bboxes, bboxes2 = self._get_tick_boxes_siblings(renderer=renderer)

        x, y = self.label.get_position()
        if self.label_position == 'bottom':
            try:
                spine = self.axes.spines['bottom']
                spinebbox = spine.get_window_extent()
            except KeyError:
                # use axes if spine doesn't exist
                spinebbox = self.axes.bbox
            bbox = mtransforms.Bbox.union(bboxes + [spinebbox])
            bottom = bbox.y0

            self.label.set_position(
                (x, bottom - self.labelpad * self.figure.dpi / 72)
            )
        else:
            try:
                spine = self.axes.spines['top']
                spinebbox = spine.get_window_extent()
            except KeyError:
                # use axes if spine doesn't exist
                spinebbox = self.axes.bbox
            bbox = mtransforms.Bbox.union(bboxes2 + [spinebbox])
            top = bbox.y1

            self.label.set_position(
                (x, top + self.labelpad * self.figure.dpi / 72)
            )
","
        Update the label position based on the bounding box enclosing
        all the ticklabels and axis spine
        ",16,111,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _update_label_position(self, renderer):
        
        if not self._autolabelpos:
            return

        # get bounding boxes for this axis and any siblings
        # that have been set by `fig.align_xlabels()`
        bboxes, bboxes2 = self._get_tick_boxes_siblings(renderer=renderer)

        x, y = self.label.get_position()
        if self.label_position == 'bottom':
            try:
                spine = self.axes.spines['bottom']
                spinebbox = spine.get_window_extent()
            except KeyError:
                # use axes if spine doesn't exist
                spinebbox = self.axes.bbox
            bbox = mtransforms.Bbox.union(bboxes + [spinebbox])
            bottom = bbox.y0

            self.label.set_position(
                (x, bottom - self.labelpad * self.figure.dpi / 72)
            )
        else:
            try:
                spine = self.axes.spines['top']
                spinebbox = spine.get_window_extent()
            except KeyError:
                # use axes if spine doesn't exist
                spinebbox = self.axes.bbox
            bbox = mtransforms.Bbox.union(bboxes2 + [spinebbox])
            top = bbox.y1

            self.label.set_position(
                (x, top + self.labelpad * self.figure.dpi / 72)
            )

        ```
    ###Assistant : 
        Update the label position based on the bounding box enclosing
        all the ticklabels and axis spine
        
    "
1347,"def poly_union(poly_det, poly_gt):
    
    assert isinstance(poly_det, plg.Polygon)
    assert isinstance(poly_gt, plg.Polygon)

    area_det = poly_det.area()
    area_gt = poly_gt.area()
    area_inters, _ = poly_intersection(poly_det, poly_gt)
    return area_det + area_gt - area_inters

","Calculate the union area between two polygon.

    Args:
        poly_det (Polygon): A polygon predicted by detector.
        poly_gt (Polygon): A gt polygon.

    Returns:
        union_area (float): The union area between two polygons.
    ",29,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def poly_union(poly_det, poly_gt):
    
    assert isinstance(poly_det, plg.Polygon)
    assert isinstance(poly_gt, plg.Polygon)

    area_det = poly_det.area()
    area_gt = poly_gt.area()
    area_inters, _ = poly_intersection(poly_det, poly_gt)
    return area_det + area_gt - area_inters


        ```
    ###Assistant : Calculate the union area between two polygon.

    Args:
        poly_det (Polygon): A polygon predicted by detector.
        poly_gt (Polygon): A gt polygon.

    Returns:
        union_area (float): The union area between two polygons.
    
    "
1348,"def test_whitelist_idna_result(self) -> None:
        
        config: JsonDict = {
            ""federation_certificate_verification_whitelist"": [
                ""example.com"",
                ""*.xn--eckwd4c7c.xn--zckzah"",
            ]
        }
        t = TestConfig()
        t.tls.read_config(config, config_dir_path="""", data_dir_path="""")

        cf = FederationPolicyForHTTPS(cast(HomeServerConfig, t))

        # Not in the whitelist
        opts = cf.get_options(b""notexample.com"")
        assert isinstance(opts, SSLClientConnectionCreator)
        self.assertTrue(opts._verifier._verify_certs)

        # Caught by the wildcard
        opts = cf.get_options(idna.encode(""テスト.ドメイン.テスト""))
        assert isinstance(opts, SSLClientConnectionCreator)
        self.assertFalse(opts._verifier._verify_certs)

","
        The federation certificate whitelist will match on IDNA encoded names.
        ",10,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_whitelist_idna_result(self) -> None:
        
        config: JsonDict = {
            ""federation_certificate_verification_whitelist"": [
                ""example.com"",
                ""*.xn--eckwd4c7c.xn--zckzah"",
            ]
        }
        t = TestConfig()
        t.tls.read_config(config, config_dir_path="""", data_dir_path="""")

        cf = FederationPolicyForHTTPS(cast(HomeServerConfig, t))

        # Not in the whitelist
        opts = cf.get_options(b""notexample.com"")
        assert isinstance(opts, SSLClientConnectionCreator)
        self.assertTrue(opts._verifier._verify_certs)

        # Caught by the wildcard
        opts = cf.get_options(idna.encode(""テスト.ドメイン.テスト""))
        assert isinstance(opts, SSLClientConnectionCreator)
        self.assertFalse(opts._verifier._verify_certs)


        ```
    ###Assistant : 
        The federation certificate whitelist will match on IDNA encoded names.
        
    "
1349,"def simple_test_rpn(self, x, img_metas):
        
        rpn_outs = self(x)
        proposal_list = self.get_results(*rpn_outs, img_metas=img_metas)
        return proposal_list
","Test without augmentation, only for ``RPNHead`` and its variants,
        e.g., ``GARPNHead``, etc.

        Args:
            x (tuple[Tensor]): Features from the upstream network, each is
                a 4D-tensor.
            img_metas (list[dict]): Meta info of each image.

        Returns:
            list[Tensor]: Proposals of each image, each item has shape (n, 5),
                where 5 represent (tl_x, tl_y, br_x, br_y, score).
        ",51,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def simple_test_rpn(self, x, img_metas):
        
        rpn_outs = self(x)
        proposal_list = self.get_results(*rpn_outs, img_metas=img_metas)
        return proposal_list

        ```
    ###Assistant : Test without augmentation, only for ``RPNHead`` and its variants,
        e.g., ``GARPNHead``, etc.

        Args:
            x (tuple[Tensor]): Features from the upstream network, each is
                a 4D-tensor.
            img_metas (list[dict]): Meta info of each image.

        Returns:
            list[Tensor]: Proposals of each image, each item has shape (n, 5),
                where 5 represent (tl_x, tl_y, br_x, br_y, score).
        
    "
1350,"def _get_tick_boxes_siblings(self, renderer):
        
        # Get the Grouper keeping track of x or y label groups for this figure.
        axis_names = [
            name for name, axis in self.axes._get_axis_map().items()
            if name in self.figure._align_label_groups and axis is self]
        if len(axis_names) != 1:
            return [], []
        axis_name, = axis_names
        grouper = self.figure._align_label_groups[axis_name]
        bboxes = []
        bboxes2 = []
        # If we want to align labels from other Axes:
        for ax in grouper.get_siblings(self.axes):
            axis = getattr(ax, f""{axis_name}axis"")
            ticks_to_draw = axis._update_ticks()
            tlb, tlb2 = axis._get_ticklabel_bboxes(ticks_to_draw, renderer)
            bboxes.extend(tlb)
            bboxes2.extend(tlb2)
        return bboxes, bboxes2
","
        Get the bounding boxes for this `.axis` and its siblings
        as set by `.Figure.align_xlabels` or  `.Figure.align_ylabels`.

        By default it just gets bboxes for self.
        ",24,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_tick_boxes_siblings(self, renderer):
        
        # Get the Grouper keeping track of x or y label groups for this figure.
        axis_names = [
            name for name, axis in self.axes._get_axis_map().items()
            if name in self.figure._align_label_groups and axis is self]
        if len(axis_names) != 1:
            return [], []
        axis_name, = axis_names
        grouper = self.figure._align_label_groups[axis_name]
        bboxes = []
        bboxes2 = []
        # If we want to align labels from other Axes:
        for ax in grouper.get_siblings(self.axes):
            axis = getattr(ax, f""{axis_name}axis"")
            ticks_to_draw = axis._update_ticks()
            tlb, tlb2 = axis._get_ticklabel_bboxes(ticks_to_draw, renderer)
            bboxes.extend(tlb)
            bboxes2.extend(tlb2)
        return bboxes, bboxes2

        ```
    ###Assistant : 
        Get the bounding boxes for this `.axis` and its siblings
        as set by `.Figure.align_xlabels` or  `.Figure.align_ylabels`.

        By default it just gets bboxes for self.
        
    "
1351,"def default(method):
    
    method._is_default = True  # pylint: disable=protected-access
    return method

",Decorates a method to detect overrides in subclasses.,8,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def default(method):
    
    method._is_default = True  # pylint: disable=protected-access
    return method


        ```
    ###Assistant : Decorates a method to detect overrides in subclasses.
    "
1352,"def test_help_tooltip(self):
        
        st.camera_input(""the label"", help=""help_label"")

        c = self.get_delta_from_queue().new_element.camera_input
        self.assertEqual(c.help, ""help_label"")
",Test that it can be called using a string for type parameter.,12,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_help_tooltip(self):
        
        st.camera_input(""the label"", help=""help_label"")

        c = self.get_delta_from_queue().new_element.camera_input
        self.assertEqual(c.help, ""help_label"")

        ```
    ###Assistant : Test that it can be called using a string for type parameter.
    "
1353,"async def test_multiple_event_images(hass, auth):
    
    subscriber = await async_setup_camera(hass, DEVICE_TRAITS, auth=auth)
    assert len(hass.states.async_all()) == 1
    assert hass.states.get(""camera.my_camera"")

    event_timestamp = utcnow()
    await subscriber.async_receive_event(
        make_motion_event(event_session_id=""event-session-1"", timestamp=event_timestamp)
    )
    await hass.async_block_till_done()

    auth.responses = [
        # Fake response from API that returns url image
        aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE),
        # Fake response for the image content fetch
        aiohttp.web.Response(body=IMAGE_BYTES_FROM_EVENT),
        # Image is refetched after being cleared by expiration alarm
        aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE),
        aiohttp.web.Response(body=b""updated image bytes""),
    ]

    image = await async_get_image(hass)
    assert image.content == IMAGE_BYTES_FROM_EVENT

    next_event_timestamp = event_timestamp + datetime.timedelta(seconds=25)
    await subscriber.async_receive_event(
        make_motion_event(
            event_id=""updated-event-id"",
            event_session_id=""event-session-2"",
            timestamp=next_event_timestamp,
        )
    )
    await hass.async_block_till_done()

    image = await async_get_image(hass)
    assert image.content == b""updated image bytes""

",Test fallback for an event event image that has been cleaned up on expiration.,14,96,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_multiple_event_images(hass, auth):
    
    subscriber = await async_setup_camera(hass, DEVICE_TRAITS, auth=auth)
    assert len(hass.states.async_all()) == 1
    assert hass.states.get(""camera.my_camera"")

    event_timestamp = utcnow()
    await subscriber.async_receive_event(
        make_motion_event(event_session_id=""event-session-1"", timestamp=event_timestamp)
    )
    await hass.async_block_till_done()

    auth.responses = [
        # Fake response from API that returns url image
        aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE),
        # Fake response for the image content fetch
        aiohttp.web.Response(body=IMAGE_BYTES_FROM_EVENT),
        # Image is refetched after being cleared by expiration alarm
        aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE),
        aiohttp.web.Response(body=b""updated image bytes""),
    ]

    image = await async_get_image(hass)
    assert image.content == IMAGE_BYTES_FROM_EVENT

    next_event_timestamp = event_timestamp + datetime.timedelta(seconds=25)
    await subscriber.async_receive_event(
        make_motion_event(
            event_id=""updated-event-id"",
            event_session_id=""event-session-2"",
            timestamp=next_event_timestamp,
        )
    )
    await hass.async_block_till_done()

    image = await async_get_image(hass)
    assert image.content == b""updated image bytes""


        ```
    ###Assistant : Test fallback for an event event image that has been cleaned up on expiration.
    "
1354,"def test_delete_same_room_twice(self) -> None:
        

        body = {""new_room_user_id"": self.admin_user}

        # first call to delete room
        # and do not wait for finish the task
        first_channel = self.make_request(
            ""DELETE"",
            self.url.encode(""ascii""),
            content=body,
            access_token=self.admin_user_tok,
            await_result=False,
        )

        # second call to delete room
        second_channel = self.make_request(
            ""DELETE"",
            self.url.encode(""ascii""),
            content=body,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(
            HTTPStatus.BAD_REQUEST, second_channel.code, msg=second_channel.json_body
        )
        self.assertEqual(Codes.UNKNOWN, second_channel.json_body[""errcode""])
        self.assertEqual(
            f""History purge already in progress for {self.room_id}"",
            second_channel.json_body[""error""],
        )

        # get result of first call
        first_channel.await_result()
        self.assertEqual(200, first_channel.code, msg=first_channel.json_body)
        self.assertIn(""delete_id"", first_channel.json_body)

        # check status after finish the task
        self._test_result(
            first_channel.json_body[""delete_id""],
            self.other_user,
            expect_new_room=True,
        )
",Test that the call for delete a room at second time gives an exception.,14,87,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_same_room_twice(self) -> None:
        

        body = {""new_room_user_id"": self.admin_user}

        # first call to delete room
        # and do not wait for finish the task
        first_channel = self.make_request(
            ""DELETE"",
            self.url.encode(""ascii""),
            content=body,
            access_token=self.admin_user_tok,
            await_result=False,
        )

        # second call to delete room
        second_channel = self.make_request(
            ""DELETE"",
            self.url.encode(""ascii""),
            content=body,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(
            HTTPStatus.BAD_REQUEST, second_channel.code, msg=second_channel.json_body
        )
        self.assertEqual(Codes.UNKNOWN, second_channel.json_body[""errcode""])
        self.assertEqual(
            f""History purge already in progress for {self.room_id}"",
            second_channel.json_body[""error""],
        )

        # get result of first call
        first_channel.await_result()
        self.assertEqual(200, first_channel.code, msg=first_channel.json_body)
        self.assertIn(""delete_id"", first_channel.json_body)

        # check status after finish the task
        self._test_result(
            first_channel.json_body[""delete_id""],
            self.other_user,
            expect_new_room=True,
        )

        ```
    ###Assistant : Test that the call for delete a room at second time gives an exception.
    "
1355,"async def _async_refresh_device_detail_by_ids(self, device_ids_list):
        
        for device_id in device_ids_list:
            try:
                await self._async_refresh_device_detail_by_id(device_id)
            except asyncio.TimeoutError:
                _LOGGER.warning(
                    ""Timed out calling august api during refresh of device: %s"",
                    device_id,
                )
            except (ClientResponseError, CannotConnect) as err:
                _LOGGER.warning(
                    ""Error from august api during refresh of device: %s"",
                    device_id,
                    exc_info=err,
                )
","Refresh each device in sequence.

        This used to be a gather but it was less reliable with august's
        recent api changes.

        The august api has been timing out for some devices so
        we want the ones that it isn't timing out for to keep working.
        ",45,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _async_refresh_device_detail_by_ids(self, device_ids_list):
        
        for device_id in device_ids_list:
            try:
                await self._async_refresh_device_detail_by_id(device_id)
            except asyncio.TimeoutError:
                _LOGGER.warning(
                    ""Timed out calling august api during refresh of device: %s"",
                    device_id,
                )
            except (ClientResponseError, CannotConnect) as err:
                _LOGGER.warning(
                    ""Error from august api during refresh of device: %s"",
                    device_id,
                    exc_info=err,
                )

        ```
    ###Assistant : Refresh each device in sequence.

        This used to be a gather but it was less reliable with august's
        recent api changes.

        The august api has been timing out for some devices so
        we want the ones that it isn't timing out for to keep working.
        
    "
1356,"def masks_to_boxes(masks):
    
    if masks.size == 0:
        return np.zeros((0, 4))

    h, w = masks.shape[-2:]

    y = np.arange(0, h, dtype=np.float32)
    x = np.arange(0, w, dtype=np.float32)
    # see https://github.com/pytorch/pytorch/issues/50276
    y, x = np.meshgrid(y, x, indexing=""ij"")

    x_mask = masks * np.expand_dims(x, axis=0)
    x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
    x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
    x_min = x.filled(fill_value=1e8)
    x_min = x_min.reshape(x_min.shape[0], -1).min(-1)

    y_mask = masks * np.expand_dims(y, axis=0)
    y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
    y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
    y_min = y.filled(fill_value=1e8)
    y_min = y_min.reshape(y_min.shape[0], -1).min(-1)

    return np.stack([x_min, y_min, x_max, y_max], 1)


# Copied from transformers.models.detr.feature_extraction_detr.rgb_to_id","
    Compute the bounding boxes around the provided panoptic segmentation masks.

    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.

    Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
    ",44,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def masks_to_boxes(masks):
    
    if masks.size == 0:
        return np.zeros((0, 4))

    h, w = masks.shape[-2:]

    y = np.arange(0, h, dtype=np.float32)
    x = np.arange(0, w, dtype=np.float32)
    # see https://github.com/pytorch/pytorch/issues/50276
    y, x = np.meshgrid(y, x, indexing=""ij"")

    x_mask = masks * np.expand_dims(x, axis=0)
    x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
    x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
    x_min = x.filled(fill_value=1e8)
    x_min = x_min.reshape(x_min.shape[0], -1).min(-1)

    y_mask = masks * np.expand_dims(y, axis=0)
    y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
    y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
    y_min = y.filled(fill_value=1e8)
    y_min = y_min.reshape(y_min.shape[0], -1).min(-1)

    return np.stack([x_min, y_min, x_max, y_max], 1)


# Copied from transformers.models.detr.feature_extraction_detr.rgb_to_id
        ```
    ###Assistant : 
    Compute the bounding boxes around the provided panoptic segmentation masks.

    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.

    Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
    
    "
1357,"def _roots_with_zeros(p, num_leading_zeros):
  # Avoid lapack errors when p is all zero
  p = _where(len(p) == num_leading_zeros, 1.0, p)
  # Roll any leading zeros to the end & compute the roots
  roots = _roots_no_zeros(roll(p, -num_leading_zeros))
  # Sort zero roots to the end.
  roots = lax.sort_key_val(roots == 0, roots)[1]
  # Set roots associated with num_leading_zeros to NaN
  return _where(arange(roots.size) < roots.size - num_leading_zeros, roots, complex(np.nan, np.nan))


@_wraps(np.roots, lax_description=,
extra_params=)","\
Unlike the numpy version of this function, the JAX version returns the roots in
a complex array regardless of the values of the roots. Additionally, the jax
version of this function adds the ``strip_zeros`` function which must be set to
False for the function to be compatible with JIT and other JAX transformations.
With ``strip_zeros=False``, if your coefficients have leading zeros, the
roots will be padded with NaN values:

>>> coeffs = jnp.array([0, 1, 2])

# The default behavior matches numpy and strips leading zeros:
>>> jnp.roots(coeffs)
DeviceArray([-2.+0.j], dtype=complex64)

# With strip_zeros=False, extra roots are set to NaN:
>>> jnp.roots(coeffs, strip_zeros=False)
DeviceArray([-2. +0.j, nan+nanj], dtype=complex64)

strip_zeros : bool, default=True
    If set to True, then leading zeros in the coefficients will be stripped, similar
    to :func:`numpy.roots`. If set to False, leading zeros will not be stripped, and
    undefined roots will be represented by NaN values in the function output.
    ``strip_zeros`` must be set to ``False`` for the function to be compatible with
    :func:`jax.jit` and other JAX transformations.
",167,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _roots_with_zeros(p, num_leading_zeros):
  # Avoid lapack errors when p is all zero
  p = _where(len(p) == num_leading_zeros, 1.0, p)
  # Roll any leading zeros to the end & compute the roots
  roots = _roots_no_zeros(roll(p, -num_leading_zeros))
  # Sort zero roots to the end.
  roots = lax.sort_key_val(roots == 0, roots)[1]
  # Set roots associated with num_leading_zeros to NaN
  return _where(arange(roots.size) < roots.size - num_leading_zeros, roots, complex(np.nan, np.nan))


@_wraps(np.roots, lax_description=,
extra_params=)
        ```
    ###Assistant : \
Unlike the numpy version of this function, the JAX version returns the roots in
a complex array regardless of the values of the roots. Additionally, the jax
version of this function adds the ``strip_zeros`` function which must be set to
False for the function to be compatible with JIT and other JAX transformations.
With ``strip_zeros=False``, if your coefficients have leading zeros, the
roots will be padded with NaN values:

>>> coeffs = jnp.array([0, 1, 2])

# The default behavior matches numpy and strips leading zeros:
>>> jnp.roots(coeffs)
DeviceArray([-2.+0.j], dtype=complex64)

# With strip_zeros=False, extra roots are set to NaN:
>>> jnp.roots(coeffs, strip_zeros=False)
DeviceArray([-2. +0.j, nan+nanj], dtype=complex64)

strip_zeros : bool, default=True
    If set to True, then leading zeros in the coefficients will be stripped, similar
    to :func:`numpy.roots`. If set to False, leading zeros will not be stripped, and
    undefined roots will be represented by NaN values in the function output.
    ``strip_zeros`` must be set to ``False`` for the function to be compatible with
    :func:`jax.jit` and other JAX transformations.

    "
1358,"def port_monitoring(self) -> int:
        
        if GATEWAY_NAME in self._deployment_nodes:
            return self[GATEWAY_NAME].args.port_monitoring
        else:
            return self._common_kwargs.get(
                'port_monitoring', __default_port_monitoring__
            )
","Return if the monitoring is enabled
        .. # noqa: DAR201
        ",10,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def port_monitoring(self) -> int:
        
        if GATEWAY_NAME in self._deployment_nodes:
            return self[GATEWAY_NAME].args.port_monitoring
        else:
            return self._common_kwargs.get(
                'port_monitoring', __default_port_monitoring__
            )

        ```
    ###Assistant : Return if the monitoring is enabled
        .. # noqa: DAR201
        
    "
1359,"def autodiscover_modules(*args, **kwargs):
    
    from django.apps import apps

    register_to = kwargs.get(""register_to"")
    for app_config in apps.get_app_configs():
        for module_to_search in args:
            # Attempt to import the app's module.
            try:
                if register_to:
                    before_import_registry = copy.copy(register_to._registry)

                import_module(""%s.%s"" % (app_config.name, module_to_search))
            except Exception:
                # Reset the registry to the state before the last import
                # as this import will have to reoccur on the next request and
                # this could raise NotRegistered and AlreadyRegistered
                # exceptions (see #8245).
                if register_to:
                    register_to._registry = before_import_registry

                # Decide whether to bubble up this error. If the app just
                # doesn't have the module in question, we can ignore the error
                # attempting to import it, otherwise we want it to bubble up.
                if module_has_submodule(app_config.module, module_to_search):
                    raise

","
    Auto-discover INSTALLED_APPS modules and fail silently when
    not present. This forces an import on them to register any admin bits they
    may want.

    You may provide a register_to keyword parameter as a way to access a
    registry. This register_to object must have a _registry instance variable
    to access it.
    ",49,117,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def autodiscover_modules(*args, **kwargs):
    
    from django.apps import apps

    register_to = kwargs.get(""register_to"")
    for app_config in apps.get_app_configs():
        for module_to_search in args:
            # Attempt to import the app's module.
            try:
                if register_to:
                    before_import_registry = copy.copy(register_to._registry)

                import_module(""%s.%s"" % (app_config.name, module_to_search))
            except Exception:
                # Reset the registry to the state before the last import
                # as this import will have to reoccur on the next request and
                # this could raise NotRegistered and AlreadyRegistered
                # exceptions (see #8245).
                if register_to:
                    register_to._registry = before_import_registry

                # Decide whether to bubble up this error. If the app just
                # doesn't have the module in question, we can ignore the error
                # attempting to import it, otherwise we want it to bubble up.
                if module_has_submodule(app_config.module, module_to_search):
                    raise


        ```
    ###Assistant : 
    Auto-discover INSTALLED_APPS modules and fail silently when
    not present. This forces an import on them to register any admin bits they
    may want.

    You may provide a register_to keyword parameter as a way to access a
    registry. This register_to object must have a _registry instance variable
    to access it.
    
    "
1360,"def ensure_future(coro_or_future, *, loop=None):
    
    return _ensure_future(coro_or_future, loop=loop)

","Wrap a coroutine or an awaitable in a future.

    If the argument is a Future, it is returned directly.
    ",19,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ensure_future(coro_or_future, *, loop=None):
    
    return _ensure_future(coro_or_future, loop=loop)


        ```
    ###Assistant : Wrap a coroutine or an awaitable in a future.

    If the argument is a Future, it is returned directly.
    
    "
1361,"def getpalette(self, rawmode=""RGB""):
        

        self.load()
        try:
            mode = self.im.getpalettemode()
        except ValueError:
            return None  # no palette
        if rawmode is None:
            rawmode = mode
        return list(self.im.getpalette(mode, rawmode))
","
        Returns the image palette as a list.

        :param rawmode: The mode in which to return the palette. ``None`` will
           return the palette in its current mode.
        :returns: A list of color values [r, g, b, ...], or None if the
           image has no palette.
        ",44,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getpalette(self, rawmode=""RGB""):
        

        self.load()
        try:
            mode = self.im.getpalettemode()
        except ValueError:
            return None  # no palette
        if rawmode is None:
            rawmode = mode
        return list(self.im.getpalette(mode, rawmode))

        ```
    ###Assistant : 
        Returns the image palette as a list.

        :param rawmode: The mode in which to return the palette. ``None`` will
           return the palette in its current mode.
        :returns: A list of color values [r, g, b, ...], or None if the
           image has no palette.
        
    "
1362,"def last_executor(self):
        
        if len(self.proto_wo_data.routes) > 0:
            return self.proto_wo_data.routes[-1].executor
","
        Returns the name of the last Executor that has processed this Request

        :return: the name of the last Executor that processed this Request
        ",23,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def last_executor(self):
        
        if len(self.proto_wo_data.routes) > 0:
            return self.proto_wo_data.routes[-1].executor

        ```
    ###Assistant : 
        Returns the name of the last Executor that has processed this Request

        :return: the name of the last Executor that processed this Request
        
    "
1363,"def custom_generator_multi_io_temporal(self, sample_weights=None):
        
        batch_size = 3
        num_samples = 3
        iteration = 0
        while True:
            batch_index = iteration * batch_size % num_samples
            iteration += 1
            start = batch_index
            end = start + batch_size
            x = [self.x[start:end], self.x[start:end]]
            y = [self.y1[start:end], self.y2[start:end]]
            if sample_weights:
                sw = tf.nest.map_structure(
                    lambda w: w[start:end], sample_weights
                )
            else:
                sw = None
            yield x, y, sw
","Generator for getting data for temporal multi io model.

        Args:
          sample_weights: List of sample_weights.

        Yields:
          Tuple of inputs, label, sample weights data.
        ",22,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def custom_generator_multi_io_temporal(self, sample_weights=None):
        
        batch_size = 3
        num_samples = 3
        iteration = 0
        while True:
            batch_index = iteration * batch_size % num_samples
            iteration += 1
            start = batch_index
            end = start + batch_size
            x = [self.x[start:end], self.x[start:end]]
            y = [self.y1[start:end], self.y2[start:end]]
            if sample_weights:
                sw = tf.nest.map_structure(
                    lambda w: w[start:end], sample_weights
                )
            else:
                sw = None
            yield x, y, sw

        ```
    ###Assistant : Generator for getting data for temporal multi io model.

        Args:
          sample_weights: List of sample_weights.

        Yields:
          Tuple of inputs, label, sample weights data.
        
    "
1364,"def _just_docs(self):
        
        try:
            for child in self.ast.body:
                if not isinstance(child, ast.Assign):
                    # allow string constant expressions (these are docstrings)
                    if isinstance(child, ast.Expr) and isinstance(child.value, ast.Constant) and isinstance(child.value.value, str):
                        continue

                    # allowed from __future__ imports
                    if isinstance(child, ast.ImportFrom) and child.module == '__future__':
                        for future_import in child.names:
                            if future_import.name not in self.ACCEPTLIST_FUTURE_IMPORTS:
                                break
                        else:
                            continue
                    return False
            return True
        except AttributeError:
            return False
","Module can contain just docs and from __future__ boilerplate
        ",9,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _just_docs(self):
        
        try:
            for child in self.ast.body:
                if not isinstance(child, ast.Assign):
                    # allow string constant expressions (these are docstrings)
                    if isinstance(child, ast.Expr) and isinstance(child.value, ast.Constant) and isinstance(child.value.value, str):
                        continue

                    # allowed from __future__ imports
                    if isinstance(child, ast.ImportFrom) and child.module == '__future__':
                        for future_import in child.names:
                            if future_import.name not in self.ACCEPTLIST_FUTURE_IMPORTS:
                                break
                        else:
                            continue
                    return False
            return True
        except AttributeError:
            return False

        ```
    ###Assistant : Module can contain just docs and from __future__ boilerplate
        
    "
1365,"def response_validator(self) -> RequestValidator:
        
        self.check_reload()
        assert self._response_validator is not None
        return self._response_validator

","Reload the OpenAPI file if it has been modified after the last time
        it was read, and then return the openapi_core validator object. Similar
        to preceding functions. Used for proper access to OpenAPI objects.
        ",34,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def response_validator(self) -> RequestValidator:
        
        self.check_reload()
        assert self._response_validator is not None
        return self._response_validator


        ```
    ###Assistant : Reload the OpenAPI file if it has been modified after the last time
        it was read, and then return the openapi_core validator object. Similar
        to preceding functions. Used for proper access to OpenAPI objects.
        
    "
1366,"def roots(p, *, strip_zeros=True):
  # ported from https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/polynomial.py#L168-L251
  p = atleast_1d(p)
  if p.ndim != 1:
    raise ValueError(""Input must be a rank-1 array."")

  # strip_zeros=False is unsafe because leading zeros aren't removed
  if not strip_zeros:
    if p.size > 1:
      return _roots_no_zeros(p)
    else:
      return array([])

  if all(p == 0):
    return array([])

  # factor out trivial roots
  start, end = _nonzero_range(p)
  # number of trailing zeros = number of roots at 0
  trailing_zeros = p.size - end

  # strip leading and trailing zeros
  p = p[start:end]

  if p.size < 2:
    return zeros(trailing_zeros, p.dtype)
  else:
    roots = _roots_no_zeros(p)
    # combine roots and zero roots
    roots = hstack((roots, zeros(trailing_zeros, p.dtype)))
    return roots


_POLYFIT_DOC = 
@_wraps(np.polyfit, lax_description=_POLYFIT_DOC)
@partial(jit, static_argnames=('deg', 'rcond', 'full', 'cov'))","\
Unlike NumPy's implementation of polyfit, :py:func:`jax.numpy.polyfit` will not warn on rank reduction, which indicates an ill conditioned matrix
Also, it works best on rcond <= 10e-3 values.
",28,116,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def roots(p, *, strip_zeros=True):
  # ported from https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/polynomial.py#L168-L251
  p = atleast_1d(p)
  if p.ndim != 1:
    raise ValueError(""Input must be a rank-1 array."")

  # strip_zeros=False is unsafe because leading zeros aren't removed
  if not strip_zeros:
    if p.size > 1:
      return _roots_no_zeros(p)
    else:
      return array([])

  if all(p == 0):
    return array([])

  # factor out trivial roots
  start, end = _nonzero_range(p)
  # number of trailing zeros = number of roots at 0
  trailing_zeros = p.size - end

  # strip leading and trailing zeros
  p = p[start:end]

  if p.size < 2:
    return zeros(trailing_zeros, p.dtype)
  else:
    roots = _roots_no_zeros(p)
    # combine roots and zero roots
    roots = hstack((roots, zeros(trailing_zeros, p.dtype)))
    return roots


_POLYFIT_DOC = 
@_wraps(np.polyfit, lax_description=_POLYFIT_DOC)
@partial(jit, static_argnames=('deg', 'rcond', 'full', 'cov'))
        ```
    ###Assistant : \
Unlike NumPy's implementation of polyfit, :py:func:`jax.numpy.polyfit` will not warn on rank reduction, which indicates an ill conditioned matrix
Also, it works best on rcond <= 10e-3 values.

    "
1367,"def _storage_path(self, local_path):
        
        rel_local_path = os.path.relpath(local_path, self.logdir)
        return os.path.join(self.remote_checkpoint_dir, rel_local_path)
","Converts a `local_path` to be based off of
        `self.remote_checkpoint_dir`.",9,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _storage_path(self, local_path):
        
        rel_local_path = os.path.relpath(local_path, self.logdir)
        return os.path.join(self.remote_checkpoint_dir, rel_local_path)

        ```
    ###Assistant : Converts a `local_path` to be based off of
        `self.remote_checkpoint_dir`.
    "
1368,"def get_time_since_last_update(self) -> float:
        
        return time.time() - self.last_update_time
",How much time has passed since the last call to update.,11,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_time_since_last_update(self) -> float:
        
        return time.time() - self.last_update_time

        ```
    ###Assistant : How much time has passed since the last call to update.
    "
1369,"def train_fixbn(self, mode=True, freeze_bn=True, freeze_bn_affine=False):
        r
        super(DeepLabv3_plus, self).train(mode)
        if freeze_bn:
            print(""Freezing Mean/Var of BatchNorm2D."")
            if freeze_bn_affine:
                print(""Freezing Weight/Bias of BatchNorm2D."")
        if freeze_bn:
            for m in self.xception_features.modules():
                if isinstance(m, nn.BatchNorm2d):
                    m.eval()
                    if freeze_bn_affine:
                        m.weight.requires_grad = False
                        m.bias.requires_grad = False
            # for m in self.aspp1.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.aspp2.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.aspp3.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.aspp4.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.global_avg_pool.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.concat_projection_bn1.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.feature_projection_bn1.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
","Sets the module in training mode.

        This has any effect only on certain modules. See documentations of
        particular modules for details of their behaviors in training/evaluation
        mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
        etc.

        Returns:
            Module: self
        ",38,192,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def train_fixbn(self, mode=True, freeze_bn=True, freeze_bn_affine=False):
        r
        super(DeepLabv3_plus, self).train(mode)
        if freeze_bn:
            print(""Freezing Mean/Var of BatchNorm2D."")
            if freeze_bn_affine:
                print(""Freezing Weight/Bias of BatchNorm2D."")
        if freeze_bn:
            for m in self.xception_features.modules():
                if isinstance(m, nn.BatchNorm2d):
                    m.eval()
                    if freeze_bn_affine:
                        m.weight.requires_grad = False
                        m.bias.requires_grad = False
            # for m in self.aspp1.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.aspp2.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.aspp3.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.aspp4.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.global_avg_pool.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.concat_projection_bn1.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False
            # for m in self.feature_projection_bn1.modules():
            #     if isinstance(m, nn.BatchNorm2d):
            #         m.eval()
            #         if freeze_bn_affine:
            #             m.weight.requires_grad = False
            #             m.bias.requires_grad = False

        ```
    ###Assistant : Sets the module in training mode.

        This has any effect only on certain modules. See documentations of
        particular modules for details of their behaviors in training/evaluation
        mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
        etc.

        Returns:
            Module: self
        
    "
1370,"def assert_params_all_zeros(module) -> bool:
    
    weight_data = module.weight.data
    is_weight_zero = weight_data.allclose(
        weight_data.new_zeros(weight_data.size()))

    if hasattr(module, 'bias') and module.bias is not None:
        bias_data = module.bias.data
        is_bias_zero = bias_data.allclose(
            bias_data.new_zeros(bias_data.size()))
    else:
        is_bias_zero = True

    return is_weight_zero and is_bias_zero

","Check if the parameters of the module is all zeros.

    Args:
        module (nn.Module): The module to be checked.

    Returns:
        bool: Whether the parameters of the module is all zeros.
    ",29,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def assert_params_all_zeros(module) -> bool:
    
    weight_data = module.weight.data
    is_weight_zero = weight_data.allclose(
        weight_data.new_zeros(weight_data.size()))

    if hasattr(module, 'bias') and module.bias is not None:
        bias_data = module.bias.data
        is_bias_zero = bias_data.allclose(
            bias_data.new_zeros(bias_data.size()))
    else:
        is_bias_zero = True

    return is_weight_zero and is_bias_zero


        ```
    ###Assistant : Check if the parameters of the module is all zeros.

    Args:
        module (nn.Module): The module to be checked.

    Returns:
        bool: Whether the parameters of the module is all zeros.
    
    "
1371,"def cleanup_state(self):
        # type: () -> None
        
        for f in self.cleanup_functions:
            if not callable(f):
                continue
            try:
                if not f(self.socket, self.configuration):
                    log_automotive.info(
                        ""Cleanup function %s failed"", repr(f))
            except (OSError, ValueError, Scapy_Exception) as e:
                log_automotive.critical(""Exception during cleanup: %s"", e)

        self.cleanup_functions = list()
","
        Executes all collected cleanup functions from a traversed path
        :return: None
        ",11,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cleanup_state(self):
        # type: () -> None
        
        for f in self.cleanup_functions:
            if not callable(f):
                continue
            try:
                if not f(self.socket, self.configuration):
                    log_automotive.info(
                        ""Cleanup function %s failed"", repr(f))
            except (OSError, ValueError, Scapy_Exception) as e:
                log_automotive.critical(""Exception during cleanup: %s"", e)

        self.cleanup_functions = list()

        ```
    ###Assistant : 
        Executes all collected cleanup functions from a traversed path
        :return: None
        
    "
1372,"def test_model_with_two_tabbed_panels_only(self):
        Publisher.settings_panels = [FieldPanel(""name"")]
        Publisher.promote_panels = [FieldPanel(""headquartered_in"")]

        warning_1 = checks.Warning(
            ""Publisher.promote_panels will have no effect on modeladmin editing"",
            hint=,
            obj=Publisher,
            id=""wagtailadmin.W002"",
        )

        warning_2 = checks.Warning(
            ""Publisher.settings_panels will have no effect on modeladmin editing"",
            hint=,
            obj=Publisher,
            id=""wagtailadmin.W002"",
        )

        checks_results = self.get_checks_result()

        self.assertIn(warning_1, checks_results)
        self.assertIn(warning_2, checks_results)

        # clean up for future checks
        delattr(Publisher, ""settings_panels"")
        delattr(Publisher, ""promote_panels"")
","Ensure that Publisher uses `panels` instead of `promote_panels`\
or set up an `edit_handler` if you want a tabbed editing interface.
There are no default tabs on non-Page models so there will be no\
 Promote tab for the promote_panels to render in.Ensure that Publisher uses `panels` instead of `settings_panels`\
or set up an `edit_handler` if you want a tabbed editing interface.
There are no default tabs on non-Page models so there will be no\
 Settings tab for the settings_panels to render in.",81,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_model_with_two_tabbed_panels_only(self):
        Publisher.settings_panels = [FieldPanel(""name"")]
        Publisher.promote_panels = [FieldPanel(""headquartered_in"")]

        warning_1 = checks.Warning(
            ""Publisher.promote_panels will have no effect on modeladmin editing"",
            hint=,
            obj=Publisher,
            id=""wagtailadmin.W002"",
        )

        warning_2 = checks.Warning(
            ""Publisher.settings_panels will have no effect on modeladmin editing"",
            hint=,
            obj=Publisher,
            id=""wagtailadmin.W002"",
        )

        checks_results = self.get_checks_result()

        self.assertIn(warning_1, checks_results)
        self.assertIn(warning_2, checks_results)

        # clean up for future checks
        delattr(Publisher, ""settings_panels"")
        delattr(Publisher, ""promote_panels"")

        ```
    ###Assistant : Ensure that Publisher uses `panels` instead of `promote_panels`\
or set up an `edit_handler` if you want a tabbed editing interface.
There are no default tabs on non-Page models so there will be no\
 Promote tab for the promote_panels to render in.Ensure that Publisher uses `panels` instead of `settings_panels`\
or set up an `edit_handler` if you want a tabbed editing interface.
There are no default tabs on non-Page models so there will be no\
 Settings tab for the settings_panels to render in.
    "
1373,"def get_builtin_layer(class_name):
    
    if not hasattr(LOCAL, ""ALL_OBJECTS""):
        populate_deserializable_objects()
    return LOCAL.ALL_OBJECTS.get(class_name)

","Returns class if `class_name` is registered, else returns None.",9,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_builtin_layer(class_name):
    
    if not hasattr(LOCAL, ""ALL_OBJECTS""):
        populate_deserializable_objects()
    return LOCAL.ALL_OBJECTS.get(class_name)


        ```
    ###Assistant : Returns class if `class_name` is registered, else returns None.
    "
1374,"def abelian_invariants(self):
        
        if self.is_trivial:
            return []
        gns = self.generators
        inv = []
        G = self
        H = G.derived_subgroup()
        Hgens = H.generators
        for p in primefactors(G.order()):
            ranks = []
            while True:
                pows = []
                for g in gns:
                    elm = g**p
                    if not H.contains(elm):
                        pows.append(elm)
                K = PermutationGroup(Hgens + pows) if pows else H
                r = G.order()//K.order()
                G = K
                gns = pows
                if r == 1:
                    break
                ranks.append(multiplicity(p, r))

            if ranks:
                pows = [1]*ranks[0]
                for i in ranks:
                    for j in range(0, i):
                        pows[j] = pows[j]*p
                inv.extend(pows)
        inv.sort()
        return inv
","
        Returns the abelian invariants for the given group.
        Let ``G`` be a nontrivial finite abelian group. Then G is isomorphic to
        the direct product of finitely many nontrivial cyclic groups of
        prime-power order.

        Explanation
        ===========

        The prime-powers that occur as the orders of the factors are uniquely
        determined by G. More precisely, the primes that occur in the orders of the
        factors in any such decomposition of ``G`` are exactly the primes that divide
        ``|G|`` and for any such prime ``p``, if the orders of the factors that are
        p-groups in one such decomposition of ``G`` are ``p^{t_1} >= p^{t_2} >= ... p^{t_r}``,
        then the orders of the factors that are p-groups in any such decomposition of ``G``
        are ``p^{t_1} >= p^{t_2} >= ... p^{t_r}``.

        The uniquely determined integers ``p^{t_1} >= p^{t_2} >= ... p^{t_r}``, taken
        for all primes that divide ``|G|`` are called the invariants of the nontrivial
        group ``G`` as suggested in ([14], p. 542).

        Notes
        =====

        We adopt the convention that the invariants of a trivial group are [].

        Examples
        ========

        >>> from sympy.combinatorics import Permutation, PermutationGroup
        >>> a = Permutation([0, 2, 1])
        >>> b = Permutation([1, 0, 2])
        >>> G = PermutationGroup([a, b])
        >>> G.abelian_invariants()
        [2]
        >>> from sympy.combinatorics import CyclicGroup
        >>> G = CyclicGroup(7)
        >>> G.abelian_invariants()
        [7]

        ",212,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def abelian_invariants(self):
        
        if self.is_trivial:
            return []
        gns = self.generators
        inv = []
        G = self
        H = G.derived_subgroup()
        Hgens = H.generators
        for p in primefactors(G.order()):
            ranks = []
            while True:
                pows = []
                for g in gns:
                    elm = g**p
                    if not H.contains(elm):
                        pows.append(elm)
                K = PermutationGroup(Hgens + pows) if pows else H
                r = G.order()//K.order()
                G = K
                gns = pows
                if r == 1:
                    break
                ranks.append(multiplicity(p, r))

            if ranks:
                pows = [1]*ranks[0]
                for i in ranks:
                    for j in range(0, i):
                        pows[j] = pows[j]*p
                inv.extend(pows)
        inv.sort()
        return inv

        ```
    ###Assistant : 
        Returns the abelian invariants for the given group.
        Let ``G`` be a nontrivial finite abelian group. Then G is isomorphic to
        the direct product of finitely many nontrivial cyclic groups of
        prime-power order.

        Explanation
        ===========

        The prime-powers that occur as the orders of the factors are uniquely
        determined by G. More precisely, the primes that occur in the orders of the
        factors in any such decomposition of ``G`` are exactly the primes that divide
        ``|G|`` and for any such prime ``p``, if the orders of the factors that are
        p-groups in one such decomposition of ``G`` are ``p^{t_1} >= p^{t_2} >= ... p^{t_r}``,
        then the orders of the factors that are p-groups in any such decomposition of ``G``
        are ``p^{t_1} >= p^{t_2} >= ... p^{t_r}``.

        The uniquely determined integers ``p^{t_1} >= p^{t_2} >= ... p^{t_r}``, taken
        for all primes that divide ``|G|`` are called the invariants of the nontrivial
        group ``G`` as suggested in ([14], p. 542).

        Notes
        =====

        We adopt the convention that the invariants of a trivial group are [].

        Examples
        ========

        >>> from sympy.combinatorics import Permutation, PermutationGroup
        >>> a = Permutation([0, 2, 1])
        >>> b = Permutation([1, 0, 2])
        >>> G = PermutationGroup([a, b])
        >>> G.abelian_invariants()
        [2]
        >>> from sympy.combinatorics import CyclicGroup
        >>> G = CyclicGroup(7)
        >>> G.abelian_invariants()
        [7]

        
    "
1375,"def clone_graph_nodes(inputs, outputs):
    
    nodes_to_clone = find_nodes_by_inputs_and_outputs(inputs, outputs)
    cloned_inputs = []
    cloned_outputs = []
    # We not only need to create copies of Nodes (mimic the calls), also need to
    # clone keras_tensors to avoid the override of _keras_history attached on the
    # keras_tensor. The following dict is used to track any keras tensor we cloned
    # The key is the string ID of the original keras tensor, and value is the
    # cloned keras_tensor instance.
    kt_id_mapping = {}

    for kt_input in tf.nest.flatten(inputs):
        if kt_input.node.is_input:
            # For any existing keras_tensor from tf.keras.Input, we leave them as is.
            cloned_inputs.append(kt_input)
            kt_id_mapping[id(kt_input)] = kt_input
        else:
            # We need to create a new tf.keras.Input for any intermediate keras_tensor
            cpy = _clone_keras_tensor(kt_input)
            cloned_input = input_layer_module.Input(tensor=cpy)
            cloned_inputs.append(cloned_input)
            kt_id_mapping[id(kt_input)] = cloned_input
    cloned_inputs = tf.nest.pack_sequence_as(inputs, cloned_inputs)

    for kt_output in tf.nest.flatten(outputs):
        cpy = _clone_keras_tensor(kt_output)
        # We reuse the _keras_history here, which contains the old information. It
        # is used in the Node constructor to check if the tensor ""is_keras_tensor()""
        # The history will be override by the Node constructor anyway for the
        # corresponding layer output anyway.
        cpy._keras_history = (
            kt_output._keras_history
        )  # pylint: disable=protected-access
        cloned_outputs.append(cpy)
        kt_id_mapping[id(kt_output)] = cpy
    cloned_outputs = tf.nest.pack_sequence_as(outputs, cloned_outputs)

    for node in nodes_to_clone:
        # Clone any keras_tensors to avoid override of _keras_history
        # Or reuse an existing keras_tensor if it has already been cloned.
        output_copy = clone_keras_tensors(node.output_tensors, kt_id_mapping)
        call_args_copy = clone_keras_tensors(node.call_args, kt_id_mapping)
        call_kwargs_copy = clone_keras_tensors(node.call_kwargs, kt_id_mapping)
        # Creating new nodes based on the existing node information.
        # Node wires itself to inbound and outbound layers.
        # The Node constructor actually updates this layer's self._inbound_nodes,
        # sets _keras_history on the outputs, and adds itself to the
        # `_outbound_nodes` of the layers that produced the inputs to this
        # layer call.
        node_module.Node(
            node.layer,
            call_args=call_args_copy,
            call_kwargs=call_kwargs_copy,
            outputs=output_copy,
        )
    return cloned_inputs, cloned_outputs

","Clone the `Node` between the inputs and output tensors.

    This function is used to create a new functional model from any intermediate
    keras tensors. The clone of the nodes mimic the behavior of reconstructing the
    functional graph network by re-executing all the __call__ methods. The cloned
    nodes will be appended to the layers.

    Note that a new tf.keras.Inputs will be created for any items in the `inputs`

    Args:
      inputs: A nested structure of keras_tensors.
      outputs: A nested structure of keras_tensors.

    Returns:
      A pair of inputs and outputs, with cloned keras_tensors. They can be used to
      create a new functional model.
    ",100,292,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clone_graph_nodes(inputs, outputs):
    
    nodes_to_clone = find_nodes_by_inputs_and_outputs(inputs, outputs)
    cloned_inputs = []
    cloned_outputs = []
    # We not only need to create copies of Nodes (mimic the calls), also need to
    # clone keras_tensors to avoid the override of _keras_history attached on the
    # keras_tensor. The following dict is used to track any keras tensor we cloned
    # The key is the string ID of the original keras tensor, and value is the
    # cloned keras_tensor instance.
    kt_id_mapping = {}

    for kt_input in tf.nest.flatten(inputs):
        if kt_input.node.is_input:
            # For any existing keras_tensor from tf.keras.Input, we leave them as is.
            cloned_inputs.append(kt_input)
            kt_id_mapping[id(kt_input)] = kt_input
        else:
            # We need to create a new tf.keras.Input for any intermediate keras_tensor
            cpy = _clone_keras_tensor(kt_input)
            cloned_input = input_layer_module.Input(tensor=cpy)
            cloned_inputs.append(cloned_input)
            kt_id_mapping[id(kt_input)] = cloned_input
    cloned_inputs = tf.nest.pack_sequence_as(inputs, cloned_inputs)

    for kt_output in tf.nest.flatten(outputs):
        cpy = _clone_keras_tensor(kt_output)
        # We reuse the _keras_history here, which contains the old information. It
        # is used in the Node constructor to check if the tensor ""is_keras_tensor()""
        # The history will be override by the Node constructor anyway for the
        # corresponding layer output anyway.
        cpy._keras_history = (
            kt_output._keras_history
        )  # pylint: disable=protected-access
        cloned_outputs.append(cpy)
        kt_id_mapping[id(kt_output)] = cpy
    cloned_outputs = tf.nest.pack_sequence_as(outputs, cloned_outputs)

    for node in nodes_to_clone:
        # Clone any keras_tensors to avoid override of _keras_history
        # Or reuse an existing keras_tensor if it has already been cloned.
        output_copy = clone_keras_tensors(node.output_tensors, kt_id_mapping)
        call_args_copy = clone_keras_tensors(node.call_args, kt_id_mapping)
        call_kwargs_copy = clone_keras_tensors(node.call_kwargs, kt_id_mapping)
        # Creating new nodes based on the existing node information.
        # Node wires itself to inbound and outbound layers.
        # The Node constructor actually updates this layer's self._inbound_nodes,
        # sets _keras_history on the outputs, and adds itself to the
        # `_outbound_nodes` of the layers that produced the inputs to this
        # layer call.
        node_module.Node(
            node.layer,
            call_args=call_args_copy,
            call_kwargs=call_kwargs_copy,
            outputs=output_copy,
        )
    return cloned_inputs, cloned_outputs


        ```
    ###Assistant : Clone the `Node` between the inputs and output tensors.

    This function is used to create a new functional model from any intermediate
    keras tensors. The clone of the nodes mimic the behavior of reconstructing the
    functional graph network by re-executing all the __call__ methods. The cloned
    nodes will be appended to the layers.

    Note that a new tf.keras.Inputs will be created for any items in the `inputs`

    Args:
      inputs: A nested structure of keras_tensors.
      outputs: A nested structure of keras_tensors.

    Returns:
      A pair of inputs and outputs, with cloned keras_tensors. They can be used to
      create a new functional model.
    
    "
1376,"def parallel_axis(self, point, frame=None):
        
        # circular import issue
        from sympy.physics.mechanics.functions import inertia_of_point_mass
        if frame is None:
            frame = self.frame
        return self.central_inertia.express(frame) + inertia_of_point_mass(
            self.mass, self.masscenter.pos_from(point), frame)
","Returns the inertia dyadic of the body with respect to another
        point.

        Parameters
        ==========

        point : sympy.physics.vector.Point
            The point to express the inertia dyadic about.
        frame : sympy.physics.vector.ReferenceFrame
            The reference frame used to construct the dyadic.

        Returns
        =======

        inertia : sympy.physics.vector.Dyadic
            The inertia dyadic of the rigid body expressed about the provided
            point.

        ",53,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parallel_axis(self, point, frame=None):
        
        # circular import issue
        from sympy.physics.mechanics.functions import inertia_of_point_mass
        if frame is None:
            frame = self.frame
        return self.central_inertia.express(frame) + inertia_of_point_mass(
            self.mass, self.masscenter.pos_from(point), frame)

        ```
    ###Assistant : Returns the inertia dyadic of the body with respect to another
        point.

        Parameters
        ==========

        point : sympy.physics.vector.Point
            The point to express the inertia dyadic about.
        frame : sympy.physics.vector.ReferenceFrame
            The reference frame used to construct the dyadic.

        Returns
        =======

        inertia : sympy.physics.vector.Dyadic
            The inertia dyadic of the rigid body expressed about the provided
            point.

        
    "
1377,"def test_class_weight_does_not_contains_more_classses():
    
    tree = DecisionTreeClassifier(class_weight={0: 1, 1: 10, 2: 20})

    # Does not raise
    tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1])
","Check that class_weight can contain more labels than in y.

    Non-regression test for #22413
    ",14,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_class_weight_does_not_contains_more_classses():
    
    tree = DecisionTreeClassifier(class_weight={0: 1, 1: 10, 2: 20})

    # Does not raise
    tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1])

        ```
    ###Assistant : Check that class_weight can contain more labels than in y.

    Non-regression test for #22413
    
    "
1378,"def parse(self, state):  # type: (ParserState) -> str
        
        if state.mode == ParserMode.PARSE:
            path = AnyParser().parse(state)

            if not os.path.isfile(path):
                raise ParserError(f'Not a file: {path}')
        else:
            path = ''

            with state.delimit(PATH_DELIMITER, required=False) as boundary:  # type: ParserBoundary
                while boundary.ready:
                    directory = path or '.'

                    try:
                        with os.scandir(directory) as scan:  # type: t.Iterator[os.DirEntry]
                            choices = [f'{item.name}{PATH_DELIMITER}' if item.is_dir() else item.name for item in scan]
                    except OSError:
                        choices = []

                    if not path:
                        choices.append(PATH_DELIMITER)  # allow absolute paths
                        choices.append('../')  # suggest relative paths

                    part = RelativePathNameParser(choices).parse(state)
                    path += f'{part}{boundary.match or """"}'

        return path

",Parse the input from the given state and return the result.,11,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse(self, state):  # type: (ParserState) -> str
        
        if state.mode == ParserMode.PARSE:
            path = AnyParser().parse(state)

            if not os.path.isfile(path):
                raise ParserError(f'Not a file: {path}')
        else:
            path = ''

            with state.delimit(PATH_DELIMITER, required=False) as boundary:  # type: ParserBoundary
                while boundary.ready:
                    directory = path or '.'

                    try:
                        with os.scandir(directory) as scan:  # type: t.Iterator[os.DirEntry]
                            choices = [f'{item.name}{PATH_DELIMITER}' if item.is_dir() else item.name for item in scan]
                    except OSError:
                        choices = []

                    if not path:
                        choices.append(PATH_DELIMITER)  # allow absolute paths
                        choices.append('../')  # suggest relative paths

                    part = RelativePathNameParser(choices).parse(state)
                    path += f'{part}{boundary.match or """"}'

        return path


        ```
    ###Assistant : Parse the input from the given state and return the result.
    "
1379,"def layout(self) -> Layout:
        
        # self.log(""I"", self._inline_styles)
        # self.log(""C"", self._css_styles)
        # self.log(""S"", self.styles)
        assert self.styles.layout
        return self.styles.layout

    # @layout.setter
    # def layout(self, new_value: Layout) -> None:
    #     
    #     self.styles.layout = new_value
","Convenience property for accessing ``self.styles.layout``.

        Returns: The Layout associated with this view
        Convenience property setter for setting ``view.styles.layout``.
    #     Args:
    #         new_value:

    #     Returns:
    #         None
    #     ",27,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def layout(self) -> Layout:
        
        # self.log(""I"", self._inline_styles)
        # self.log(""C"", self._css_styles)
        # self.log(""S"", self.styles)
        assert self.styles.layout
        return self.styles.layout

    # @layout.setter
    # def layout(self, new_value: Layout) -> None:
    #     
    #     self.styles.layout = new_value

        ```
    ###Assistant : Convenience property for accessing ``self.styles.layout``.

        Returns: The Layout associated with this view
        Convenience property setter for setting ``view.styles.layout``.
    #     Args:
    #         new_value:

    #     Returns:
    #         None
    #     
    "
1380,"def timeout_message(self, message):
        
        future = self.send_future_map.pop(message, None)
        # In a race condition the message might have been sent by the time
        # we're timing it out. Make sure the future is not None
        if future is not None:
            del self.send_timeout_map[message]
            if future.attempts < future.tries:
                future.attempts += 1
                log.info(
                    ""SaltReqTimeoutError, retrying. (%s/%s)"",
                    future.attempts,
                    future.tries,
                )
                self.send(
                    message,
                    timeout=future.timeout,
                    tries=future.tries,
                    future=future,
                )

            else:
                future.set_exception(SaltReqTimeoutError(""Message timed out""))
","
        Handle a message timeout by removing it from the sending queue
        and informing the caller

        :raises: SaltReqTimeoutError
        ",17,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def timeout_message(self, message):
        
        future = self.send_future_map.pop(message, None)
        # In a race condition the message might have been sent by the time
        # we're timing it out. Make sure the future is not None
        if future is not None:
            del self.send_timeout_map[message]
            if future.attempts < future.tries:
                future.attempts += 1
                log.info(
                    ""SaltReqTimeoutError, retrying. (%s/%s)"",
                    future.attempts,
                    future.tries,
                )
                self.send(
                    message,
                    timeout=future.timeout,
                    tries=future.tries,
                    future=future,
                )

            else:
                future.set_exception(SaltReqTimeoutError(""Message timed out""))

        ```
    ###Assistant : 
        Handle a message timeout by removing it from the sending queue
        and informing the caller

        :raises: SaltReqTimeoutError
        
    "
1381,"def test_not_all_records(self, requests_mock, authenticator, config, responses):
        

        expected_output = [
            {""id"": 1, ""updated_at"": ""2018-01-02T00:00:00Z""},
            {""id"": 2, ""updated_at"": ""2018-02-02T00:00:00Z""},
            {""id"": 2, ""updated_at"": ""2018-02-02T00:00:00Z""},  # duplicate
            {""id"": 3, ""updated_at"": ""2018-03-02T00:00:00Z""},
            {""id"": 3, ""updated_at"": ""2018-03-02T00:00:00Z""},  # duplicate
            {""id"": 4, ""updated_at"": ""2019-01-03T00:00:00Z""},
            {""id"": 4, ""updated_at"": ""2019-01-03T00:00:00Z""},  # duplicate
            {""id"": 5, ""updated_at"": ""2019-02-03T00:00:00Z""},
            {""id"": 5, ""updated_at"": ""2019-02-03T00:00:00Z""},  # duplicate
            {""id"": 6, ""updated_at"": ""2019-03-03T00:00:00Z""},
        ]

        # INT value of page number where the switch state should be triggered.
        # in this test case values from: 1 - 4, assuming we want to switch state on this page.
        ticket_paginate_limit = 2
        # This parameter mocks the ""per_page"" parameter in the API Call
        result_return_limit = 1

        # Create test_stream instance.
        test_stream = Tickets(authenticator=authenticator, config=config)
        test_stream.ticket_paginate_limit = ticket_paginate_limit
        test_stream.result_return_limit = result_return_limit

        # Mocking Request
        for response in responses:
            requests_mock.register_uri(
                ""GET"",
                response[""url""],
                json=response[""json""],
                headers=response.get(""headers"", {}),
            )

        records = list(test_stream.read_records(sync_mode=SyncMode.full_refresh))

        # We're expecting 6 records to return from the tickets_stream
        assert records == expected_output
","
        TEST 1 - not all records are retrieved

        During test1 the tickets_stream changes the state of parameters on page: 2,
        by updating the params:
        `params[""order_by""] = ""updated_at""`
        `params[""updated_since""] = last_record`
        continues to fetch records from the source, using new cycle, and so on.

        NOTE:
        After switch of the state on ticket_paginate_limit = 2, is this example, we will experience the
        records duplication, because of the last_record state, starting at the point
        where we stoped causes the duplication of the output. The solution for this is to add at least 1 second to the
        last_record state. The DBT normalization should handle this for the end user, so the duplication issue is not a
        blocker in such cases.
        Main pricipal here is: airbyte is at-least-once delivery, but skipping records is data loss.
        ",130,152,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_not_all_records(self, requests_mock, authenticator, config, responses):
        

        expected_output = [
            {""id"": 1, ""updated_at"": ""2018-01-02T00:00:00Z""},
            {""id"": 2, ""updated_at"": ""2018-02-02T00:00:00Z""},
            {""id"": 2, ""updated_at"": ""2018-02-02T00:00:00Z""},  # duplicate
            {""id"": 3, ""updated_at"": ""2018-03-02T00:00:00Z""},
            {""id"": 3, ""updated_at"": ""2018-03-02T00:00:00Z""},  # duplicate
            {""id"": 4, ""updated_at"": ""2019-01-03T00:00:00Z""},
            {""id"": 4, ""updated_at"": ""2019-01-03T00:00:00Z""},  # duplicate
            {""id"": 5, ""updated_at"": ""2019-02-03T00:00:00Z""},
            {""id"": 5, ""updated_at"": ""2019-02-03T00:00:00Z""},  # duplicate
            {""id"": 6, ""updated_at"": ""2019-03-03T00:00:00Z""},
        ]

        # INT value of page number where the switch state should be triggered.
        # in this test case values from: 1 - 4, assuming we want to switch state on this page.
        ticket_paginate_limit = 2
        # This parameter mocks the ""per_page"" parameter in the API Call
        result_return_limit = 1

        # Create test_stream instance.
        test_stream = Tickets(authenticator=authenticator, config=config)
        test_stream.ticket_paginate_limit = ticket_paginate_limit
        test_stream.result_return_limit = result_return_limit

        # Mocking Request
        for response in responses:
            requests_mock.register_uri(
                ""GET"",
                response[""url""],
                json=response[""json""],
                headers=response.get(""headers"", {}),
            )

        records = list(test_stream.read_records(sync_mode=SyncMode.full_refresh))

        # We're expecting 6 records to return from the tickets_stream
        assert records == expected_output

        ```
    ###Assistant : 
        TEST 1 - not all records are retrieved

        During test1 the tickets_stream changes the state of parameters on page: 2,
        by updating the params:
        `params[""order_by""] = ""updated_at""`
        `params[""updated_since""] = last_record`
        continues to fetch records from the source, using new cycle, and so on.

        NOTE:
        After switch of the state on ticket_paginate_limit = 2, is this example, we will experience the
        records duplication, because of the last_record state, starting at the point
        where we stoped causes the duplication of the output. The solution for this is to add at least 1 second to the
        last_record state. The DBT normalization should handle this for the end user, so the duplication issue is not a
        blocker in such cases.
        Main pricipal here is: airbyte is at-least-once delivery, but skipping records is data loss.
        
    "
1382,"def run_from_argv(self, argv):
        
        self.test_runner = get_command_line_option(argv, ""--testrunner"")
        super().run_from_argv(argv)
","
        Pre-parse the command line to extract the value of the --testrunner
        option. This allows a test runner to define additional command line
        arguments.
        ",23,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def run_from_argv(self, argv):
        
        self.test_runner = get_command_line_option(argv, ""--testrunner"")
        super().run_from_argv(argv)

        ```
    ###Assistant : 
        Pre-parse the command line to extract the value of the --testrunner
        option. This allows a test runner to define additional command line
        arguments.
        
    "
1383,"def test_new_processing_issue(self, mock_func):
        

        notification = NewProcessingIssuesActivityNotification(
            Activity(
                project=self.project,
                user=self.user,
                type=ActivityType.NEW_PROCESSING_ISSUES,
                data={
                    ""issues"": get_issues_data(),
                    ""reprocessing_active"": True,
                },
            )
        )
        with self.tasks():
            notification.send()

        attachment, text = get_attachment()
        assert (
            text
            == f""Processing issues on <http://testserver/settings/{self.organization.slug}/projects/{self.project.slug}/processing-issues/|{self.project.slug}>""
        )
        assert (
            attachment[""text""]
            == f""Some events failed to process in your project {self.project.slug}""
        )
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=new_processing_issues_activity-slack-user|Notification Settings>""
        )
","
        Test that a Slack message is sent with the expected payload when an issue is held back in reprocessing
        ",19,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_new_processing_issue(self, mock_func):
        

        notification = NewProcessingIssuesActivityNotification(
            Activity(
                project=self.project,
                user=self.user,
                type=ActivityType.NEW_PROCESSING_ISSUES,
                data={
                    ""issues"": get_issues_data(),
                    ""reprocessing_active"": True,
                },
            )
        )
        with self.tasks():
            notification.send()

        attachment, text = get_attachment()
        assert (
            text
            == f""Processing issues on <http://testserver/settings/{self.organization.slug}/projects/{self.project.slug}/processing-issues/|{self.project.slug}>""
        )
        assert (
            attachment[""text""]
            == f""Some events failed to process in your project {self.project.slug}""
        )
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=new_processing_issues_activity-slack-user|Notification Settings>""
        )

        ```
    ###Assistant : 
        Test that a Slack message is sent with the expected payload when an issue is held back in reprocessing
        
    "
1384,"def load_array(data_arrays, batch_size, is_train=True):
    
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)
","Construct a PyTorch data iterator.

    Defined in :numref:`sec_linear_concise`",8,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_array(data_arrays, batch_size, is_train=True):
    
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)

        ```
    ###Assistant : Construct a PyTorch data iterator.

    Defined in :numref:`sec_linear_concise`
    "
1385,"def is_permanent_redirect(self):
        
        return ""location"" in self.headers and self.status_code in (
            codes.moved_permanently,
            codes.permanent_redirect,
        )
",True if this Response one of the permanent versions of redirect.,11,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_permanent_redirect(self):
        
        return ""location"" in self.headers and self.status_code in (
            codes.moved_permanently,
            codes.permanent_redirect,
        )

        ```
    ###Assistant : True if this Response one of the permanent versions of redirect.
    "
1386,"def _estimate_step_duration(self, current, now):
        
        if current:
            # there are a few special scenarios here:
            # 1) somebody is calling the progress bar without ever supplying
            #    step 1
            # 2) somebody is calling the progress bar and supplies step one
            #    multiple times, e.g. as part of a finalizing call
            # in these cases, we just fall back to the simple calculation
            if self._time_after_first_step is not None and current > 1:
                time_per_unit = (now - self._time_after_first_step) / (
                    current - 1
                )
            else:
                time_per_unit = (now - self._start) / current

            if current == 1:
                self._time_after_first_step = now
            return time_per_unit
        else:
            return 0
","Estimate the duration of a single step.

        Given the step number `current` and the corresponding time `now` this
        function returns an estimate for how long a single step takes. If this
        is called before one step has been completed (i.e. `current == 0`) then
        zero is given as an estimate. The duration estimate ignores the duration
        of the (assumed to be non-representative) first step for estimates when
        more steps are available (i.e. `current>1`).

        Args:
          current: Index of current step.
          now: The current time.

        Returns: Estimate of the duration of a single step.
        ",92,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _estimate_step_duration(self, current, now):
        
        if current:
            # there are a few special scenarios here:
            # 1) somebody is calling the progress bar without ever supplying
            #    step 1
            # 2) somebody is calling the progress bar and supplies step one
            #    multiple times, e.g. as part of a finalizing call
            # in these cases, we just fall back to the simple calculation
            if self._time_after_first_step is not None and current > 1:
                time_per_unit = (now - self._time_after_first_step) / (
                    current - 1
                )
            else:
                time_per_unit = (now - self._start) / current

            if current == 1:
                self._time_after_first_step = now
            return time_per_unit
        else:
            return 0

        ```
    ###Assistant : Estimate the duration of a single step.

        Given the step number `current` and the corresponding time `now` this
        function returns an estimate for how long a single step takes. If this
        is called before one step has been completed (i.e. `current == 0`) then
        zero is given as an estimate. The duration estimate ignores the duration
        of the (assumed to be non-representative) first step for estimates when
        more steps are available (i.e. `current>1`).

        Args:
          current: Index of current step.
          now: The current time.

        Returns: Estimate of the duration of a single step.
        
    "
1387,"def _propagate_index_objs(self, axis=None):
        
        self._filter_empties()
        if axis is None or axis == 0:
            cum_row_lengths = np.cumsum([0] + self._row_lengths)
        if axis is None or axis == 1:
            cum_col_widths = np.cumsum([0] + self._column_widths)

        if axis is None:
","
        Synchronize labels by applying the index object for specific `axis` to the `self._partitions` lazily.

        Adds `set_axis` function to call-queue of each partition from `self._partitions`
        to apply new axis.

        Parameters
        ----------
        axis : int, default: None
            The axis to apply to. If it's None applies to both axes.
        ",47,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _propagate_index_objs(self, axis=None):
        
        self._filter_empties()
        if axis is None or axis == 0:
            cum_row_lengths = np.cumsum([0] + self._row_lengths)
        if axis is None or axis == 1:
            cum_col_widths = np.cumsum([0] + self._column_widths)

        if axis is None:

        ```
    ###Assistant : 
        Synchronize labels by applying the index object for specific `axis` to the `self._partitions` lazily.

        Adds `set_axis` function to call-queue of each partition from `self._partitions`
        to apply new axis.

        Parameters
        ----------
        axis : int, default: None
            The axis to apply to. If it's None applies to both axes.
        
    "
1388,"def test_query_devices_remote_no_sync(self) -> None:
        

        remote_user_id = ""@test:other""
        local_user_id = ""@test:test""

        remote_master_key = ""85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY""
        remote_self_signing_key = ""QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ""

        self.hs.get_federation_client().query_client_keys = mock.Mock(
            return_value=defer.succeed(
                {
                    ""device_keys"": {remote_user_id: {}},
                    ""master_keys"": {
                        remote_user_id: {
                            ""user_id"": remote_user_id,
                            ""usage"": [""master""],
                            ""keys"": {""ed25519:"" + remote_master_key: remote_master_key},
                        },
                    },
                    ""self_signing_keys"": {
                        remote_user_id: {
                            ""user_id"": remote_user_id,
                            ""usage"": [""self_signing""],
                            ""keys"": {
                                ""ed25519:""
                                + remote_self_signing_key: remote_self_signing_key
                            },
                        }
                    },
                }
            )
        )

        e2e_handler = self.hs.get_e2e_keys_handler()

        query_result = self.get_success(
            e2e_handler.query_devices(
                {
                    ""device_keys"": {remote_user_id: []},
                },
                timeout=10,
                from_user_id=local_user_id,
                from_device_id=""some_device_id"",
            )
        )

        self.assertEqual(query_result[""failures""], {})
        self.assertEqual(
            query_result[""master_keys""],
            {
                remote_user_id: {
                    ""user_id"": remote_user_id,
                    ""usage"": [""master""],
                    ""keys"": {""ed25519:"" + remote_master_key: remote_master_key},
                },
            },
        )
        self.assertEqual(
            query_result[""self_signing_keys""],
            {
                remote_user_id: {
                    ""user_id"": remote_user_id,
                    ""usage"": [""self_signing""],
                    ""keys"": {
                        ""ed25519:"" + remote_self_signing_key: remote_self_signing_key
                    },
                }
            },
        )
","Tests that querying keys for a remote user that we don't share a room
        with returns the cross signing keys correctly.
        ",21,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_query_devices_remote_no_sync(self) -> None:
        

        remote_user_id = ""@test:other""
        local_user_id = ""@test:test""

        remote_master_key = ""85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY""
        remote_self_signing_key = ""QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ""

        self.hs.get_federation_client().query_client_keys = mock.Mock(
            return_value=defer.succeed(
                {
                    ""device_keys"": {remote_user_id: {}},
                    ""master_keys"": {
                        remote_user_id: {
                            ""user_id"": remote_user_id,
                            ""usage"": [""master""],
                            ""keys"": {""ed25519:"" + remote_master_key: remote_master_key},
                        },
                    },
                    ""self_signing_keys"": {
                        remote_user_id: {
                            ""user_id"": remote_user_id,
                            ""usage"": [""self_signing""],
                            ""keys"": {
                                ""ed25519:""
                                + remote_self_signing_key: remote_self_signing_key
                            },
                        }
                    },
                }
            )
        )

        e2e_handler = self.hs.get_e2e_keys_handler()

        query_result = self.get_success(
            e2e_handler.query_devices(
                {
                    ""device_keys"": {remote_user_id: []},
                },
                timeout=10,
                from_user_id=local_user_id,
                from_device_id=""some_device_id"",
            )
        )

        self.assertEqual(query_result[""failures""], {})
        self.assertEqual(
            query_result[""master_keys""],
            {
                remote_user_id: {
                    ""user_id"": remote_user_id,
                    ""usage"": [""master""],
                    ""keys"": {""ed25519:"" + remote_master_key: remote_master_key},
                },
            },
        )
        self.assertEqual(
            query_result[""self_signing_keys""],
            {
                remote_user_id: {
                    ""user_id"": remote_user_id,
                    ""usage"": [""self_signing""],
                    ""keys"": {
                        ""ed25519:"" + remote_self_signing_key: remote_self_signing_key
                    },
                }
            },
        )

        ```
    ###Assistant : Tests that querying keys for a remote user that we don't share a room
        with returns the cross signing keys correctly.
        
    "
1389,"def _matches_get_other_nodes(dictionary, nodes, node_ind):
        
        ind_node = nodes[node_ind]
        return [ind for ind in dictionary if nodes[ind] == ind_node]
",Find other wildcards that may have already been matched.,9,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _matches_get_other_nodes(dictionary, nodes, node_ind):
        
        ind_node = nodes[node_ind]
        return [ind for ind in dictionary if nodes[ind] == ind_node]

        ```
    ###Assistant : Find other wildcards that may have already been matched.
    "
1390,"def save_pretrained(self, save_directory):
        
        for attribute_name in self.attributes:
            attribute = getattr(self, attribute_name)
            # Include the processor class in the attribute config so this processor can then be reloaded with the
            # `AutoProcessor` API.
            if hasattr(attribute, ""_set_processor_class""):
                attribute._set_processor_class(self.__class__.__name__)
            attribute.save_pretrained(save_directory)
","
        Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it
        can be reloaded using the [`~ProcessorMixin.from_pretrained`] method.

        <Tip>

        This class method is simply calling [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] and
        [`~tokenization_utils_base.PreTrainedTokenizer.save_pretrained`]. Please refer to the docstrings of the methods
        above for more information.

        </Tip>

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
                be created if it does not exist).
        ",74,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_pretrained(self, save_directory):
        
        for attribute_name in self.attributes:
            attribute = getattr(self, attribute_name)
            # Include the processor class in the attribute config so this processor can then be reloaded with the
            # `AutoProcessor` API.
            if hasattr(attribute, ""_set_processor_class""):
                attribute._set_processor_class(self.__class__.__name__)
            attribute.save_pretrained(save_directory)

        ```
    ###Assistant : 
        Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it
        can be reloaded using the [`~ProcessorMixin.from_pretrained`] method.

        <Tip>

        This class method is simply calling [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] and
        [`~tokenization_utils_base.PreTrainedTokenizer.save_pretrained`]. Please refer to the docstrings of the methods
        above for more information.

        </Tip>

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
                be created if it does not exist).
        
    "
1391,"def _user_has_module_perms(user, app_label):
    
    for backend in auth.get_backends():
        if not hasattr(backend, ""has_module_perms""):
            continue
        try:
            if backend.has_module_perms(user, app_label):
                return True
        except PermissionDenied:
            return False
    return False

",Backend can raise `PermissionDenied` to short-circuit permission checking.,8,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _user_has_module_perms(user, app_label):
    
    for backend in auth.get_backends():
        if not hasattr(backend, ""has_module_perms""):
            continue
        try:
            if backend.has_module_perms(user, app_label):
                return True
        except PermissionDenied:
            return False
    return False


        ```
    ###Assistant : Backend can raise `PermissionDenied` to short-circuit permission checking.
    "
1392,"def get_default_address(out, name):
	shipping_addresses = frappe.db.sql(
		,
		(name),
		as_dict=1,
	)

	if shipping_addresses:
		for out.shipping_address in shipping_addresses:
			if out.shipping_address.is_shipping_address:
				return out.shipping_address

		out.shipping_address = shipping_addresses[0]

		return out.shipping_address


@frappe.whitelist()","
			SELECT parent,
				(SELECT is_shipping_address FROM tabAddress a WHERE a.name=dl.parent) AS is_shipping_address
			FROM
				`tabDynamic Link` dl
			WHERE
				dl.link_doctype=""Customer""
				AND dl.link_name=%s
				AND dl.parenttype = ""Address""
		",23,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_default_address(out, name):
	shipping_addresses = frappe.db.sql(
		,
		(name),
		as_dict=1,
	)

	if shipping_addresses:
		for out.shipping_address in shipping_addresses:
			if out.shipping_address.is_shipping_address:
				return out.shipping_address

		out.shipping_address = shipping_addresses[0]

		return out.shipping_address


@frappe.whitelist()
        ```
    ###Assistant : 
			SELECT parent,
				(SELECT is_shipping_address FROM tabAddress a WHERE a.name=dl.parent) AS is_shipping_address
			FROM
				`tabDynamic Link` dl
			WHERE
				dl.link_doctype=""Customer""
				AND dl.link_name=%s
				AND dl.parenttype = ""Address""
		
    "
1393,"def force_list(elements=None, to_tuple=False):
    
    ctor = list
    if to_tuple is True:
        ctor = tuple
    return ctor() if elements is None else ctor(elements) \
        if type(elements) in [list, set, tuple] else ctor([elements])

","
    Makes sure `elements` is returned as a list, whether `elements` is a single
    item, already a list, or a tuple.

    Args:
        elements (Optional[any]): The inputs as single item, list, or tuple to
            be converted into a list/tuple. If None, returns empty list/tuple.
        to_tuple (bool): Whether to use tuple (instead of list).

    Returns:
        Union[list,tuple]: All given elements in a list/tuple depending on
            `to_tuple`'s value. If elements is None,
            returns an empty list/tuple.
    ",71,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def force_list(elements=None, to_tuple=False):
    
    ctor = list
    if to_tuple is True:
        ctor = tuple
    return ctor() if elements is None else ctor(elements) \
        if type(elements) in [list, set, tuple] else ctor([elements])


        ```
    ###Assistant : 
    Makes sure `elements` is returned as a list, whether `elements` is a single
    item, already a list, or a tuple.

    Args:
        elements (Optional[any]): The inputs as single item, list, or tuple to
            be converted into a list/tuple. If None, returns empty list/tuple.
        to_tuple (bool): Whether to use tuple (instead of list).

    Returns:
        Union[list,tuple]: All given elements in a list/tuple depending on
            `to_tuple`'s value. If elements is None,
            returns an empty list/tuple.
    
    "
1394,"def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
        
        if order == 3:
            K = steps // 3 + 1
            if steps % 3 == 0:
                orders = [3, ] * (K - 2) + [2, 1]
            elif steps % 3 == 1:
                orders = [3, ] * (K - 1) + [1]
            else:
                orders = [3, ] * (K - 1) + [2]
        elif order == 2:
            if steps % 2 == 0:
                K = steps // 2
                orders = [2, ] * K
            else:
                K = steps // 2 + 1
                orders = [2, ] * (K - 1) + [1]
        elif order == 1:
            K = 1
            orders = [1, ] * steps
        else:
            raise ValueError(""'order' must be '1' or '2' or '3'."")
        if skip_type == 'logSNR':
            # To reproduce the results in DPM-Solver paper
            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
        else:
            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
        return timesteps_outer, orders
","
        Get the order of each step for sampling by the singlestep DPM-Solver.
        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as ""DPM-Solver-fast"".
        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
            - If order == 1:
                We take `steps` of DPM-Solver-1 (i.e. DDIM).
            - If order == 2:
                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
            - If order == 3:
                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
        ============================================
        Args:
            order: A `int`. The max order for the solver (2 or 3).
            steps: A `int`. The total number of function evaluations (NFE).
            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
                - 'logSNR': uniform logSNR for the time steps.
                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
            t_T: A `float`. The starting time of the sampling (default is T).
            t_0: A `float`. The ending time of the sampling (default is epsilon).
            device: A torch device.
        Returns:
            orders: A list of the solver order of each step.
        ",309,159,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
        
        if order == 3:
            K = steps // 3 + 1
            if steps % 3 == 0:
                orders = [3, ] * (K - 2) + [2, 1]
            elif steps % 3 == 1:
                orders = [3, ] * (K - 1) + [1]
            else:
                orders = [3, ] * (K - 1) + [2]
        elif order == 2:
            if steps % 2 == 0:
                K = steps // 2
                orders = [2, ] * K
            else:
                K = steps // 2 + 1
                orders = [2, ] * (K - 1) + [1]
        elif order == 1:
            K = 1
            orders = [1, ] * steps
        else:
            raise ValueError(""'order' must be '1' or '2' or '3'."")
        if skip_type == 'logSNR':
            # To reproduce the results in DPM-Solver paper
            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
        else:
            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
        return timesteps_outer, orders

        ```
    ###Assistant : 
        Get the order of each step for sampling by the singlestep DPM-Solver.
        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as ""DPM-Solver-fast"".
        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
            - If order == 1:
                We take `steps` of DPM-Solver-1 (i.e. DDIM).
            - If order == 2:
                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
            - If order == 3:
                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
        ============================================
        Args:
            order: A `int`. The max order for the solver (2 or 3).
            steps: A `int`. The total number of function evaluations (NFE).
            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
                - 'logSNR': uniform logSNR for the time steps.
                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
            t_T: A `float`. The starting time of the sampling (default is T).
            t_0: A `float`. The ending time of the sampling (default is epsilon).
            device: A torch device.
        Returns:
            orders: A list of the solver order of each step.
        
    "
1395,"def _aligned_zeros(shape, dtype=float, order=""C"", align=None):
    
    dtype = np.dtype(dtype)
    if dtype == np.dtype(object):
        # Can't do this, fall back to standard allocation (which
        # should always be sufficiently aligned)
        if align is not None:
            raise ValueError(""object array alignment not supported"")
        return np.zeros(shape, dtype=dtype, order=order)
    if align is None:
        align = dtype.alignment
    if not hasattr(shape, '__len__'):
        shape = (shape,)
    size = functools.reduce(operator.mul, shape) * dtype.itemsize
    buf = np.empty(size + 2*align + 1, np.uint8)

    ptr = buf.__array_interface__['data'][0]
    offset = ptr % align
    if offset != 0:
        offset = align - offset
    if (ptr % (2*align)) == 0:
        offset += align

    # Note: slices producing 0-size arrays do not necessarily change
    # data pointer --- so we use and allocate size+1
    buf = buf[offset:offset+size+1][:-1]
    buf.fill(0)
    data = np.ndarray(shape, dtype, buf, order=order)
    return data

","
    Allocate a new ndarray with aligned memory.

    The ndarray is guaranteed *not* aligned to twice the requested alignment.
    Eg, if align=4, guarantees it is not aligned to 8. If align=None uses
    dtype.alignment.",32,129,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _aligned_zeros(shape, dtype=float, order=""C"", align=None):
    
    dtype = np.dtype(dtype)
    if dtype == np.dtype(object):
        # Can't do this, fall back to standard allocation (which
        # should always be sufficiently aligned)
        if align is not None:
            raise ValueError(""object array alignment not supported"")
        return np.zeros(shape, dtype=dtype, order=order)
    if align is None:
        align = dtype.alignment
    if not hasattr(shape, '__len__'):
        shape = (shape,)
    size = functools.reduce(operator.mul, shape) * dtype.itemsize
    buf = np.empty(size + 2*align + 1, np.uint8)

    ptr = buf.__array_interface__['data'][0]
    offset = ptr % align
    if offset != 0:
        offset = align - offset
    if (ptr % (2*align)) == 0:
        offset += align

    # Note: slices producing 0-size arrays do not necessarily change
    # data pointer --- so we use and allocate size+1
    buf = buf[offset:offset+size+1][:-1]
    buf.fill(0)
    data = np.ndarray(shape, dtype, buf, order=order)
    return data


        ```
    ###Assistant : 
    Allocate a new ndarray with aligned memory.

    The ndarray is guaranteed *not* aligned to twice the requested alignment.
    Eg, if align=4, guarantees it is not aligned to 8. If align=None uses
    dtype.alignment.
    "
1396,"def smart_resize(x, size, interpolation='bilinear'):
  
  if len(size) != 2:
    raise ValueError('Expected `size` to be a tuple of 2 integers, '
                     f'but got: {size}.')
  img = tf.convert_to_tensor(x)
  if img.shape.rank is not None:
    if img.shape.rank < 3 or img.shape.rank > 4:
      raise ValueError(
          'Expected an image array with shape `(height, width, channels)`, '
          'or `(batch_size, height, width, channels)`, but '
          f'got input with incorrect rank, of shape {img.shape}.')
  shape = tf.shape(img)
  height, width = shape[-3], shape[-2]
  target_height, target_width = size
  if img.shape.rank is not None:
    static_num_channels = img.shape[-1]
  else:
    static_num_channels = None

  crop_height = tf.cast(
      tf.cast(width * target_height, 'float32') / target_width, 'int32')
  crop_width = tf.cast(
      tf.cast(height * target_width, 'float32') / target_height, 'int32')

  # Set back to input height / width if crop_height / crop_width is not smaller.
  crop_height = tf.minimum(height, crop_height)
  crop_width = tf.minimum(width, crop_width)

  crop_box_hstart = tf.cast(
      tf.cast(height - crop_height, 'float32') / 2, 'int32')
  crop_box_wstart = tf.cast(tf.cast(width - crop_width, 'float32') / 2, 'int32')

  if img.shape.rank == 4:
    crop_box_start = tf.stack([0, crop_box_hstart, crop_box_wstart, 0])
    crop_box_size = tf.stack([-1, crop_height, crop_width, -1])
  else:
    crop_box_start = tf.stack([crop_box_hstart, crop_box_wstart, 0])
    crop_box_size = tf.stack([crop_height, crop_width, -1])

  img = tf.slice(img, crop_box_start, crop_box_size)
  img = tf.image.resize(images=img, size=size, method=interpolation)
  # Apparent bug in resize_images_v2 may cause shape to be lost
  if img.shape.rank is not None:
    if img.shape.rank == 4:
      img.set_shape((None, None, None, static_num_channels))
    if img.shape.rank == 3:
      img.set_shape((None, None, static_num_channels))
  if isinstance(x, np.ndarray):
    return img.numpy()
  return img


@keras_export('keras.utils.array_to_img',
              'keras.preprocessing.image.array_to_img')","Resize images to a target size without aspect ratio distortion.

  TensorFlow image datasets typically yield images that have each a different
  size. However, these images need to be batched before they can be
  processed by Keras layers. To be batched, images need to share the same height
  and width.

  You could simply do:

  ```python
  size = (200, 200)
  ds = ds.map(lambda img: tf.image.resize(img, size))
  ```

  However, if you do this, you distort the aspect ratio of your images, since
  in general they do not all have the same aspect ratio as `size`. This is
  fine in many cases, but not always (e.g. for GANs this can be a problem).

  Note that passing the argument `preserve_aspect_ratio=True` to `resize`
  will preserve the aspect ratio, but at the cost of no longer respecting the
  provided target size. Because `tf.image.resize` doesn't crop images,
  your output images will still have different sizes.

  This calls for:

  ```python
  size = (200, 200)
  ds = ds.map(lambda img: smart_resize(img, size))
  ```

  Your output images will actually be `(200, 200)`, and will not be distorted.
  Instead, the parts of the image that do not fit within the target size
  get cropped out.

  The resizing process is:

  1. Take the largest centered crop of the image that has the same aspect ratio
  as the target size. For instance, if `size=(200, 200)` and the input image has
  size `(340, 500)`, we take a crop of `(340, 340)` centered along the width.
  2. Resize the cropped image to the target size. In the example above,
  we resize the `(340, 340)` crop to `(200, 200)`.

  Args:
    x: Input image or batch of images (as a tensor or NumPy array). Must be in
      format `(height, width, channels)` or `(batch_size, height, width,
      channels)`.
    size: Tuple of `(height, width)` integer. Target size.
    interpolation: String, interpolation to use for resizing. Defaults to
      `'bilinear'`. Supports `bilinear`, `nearest`, `bicubic`, `area`,
      `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.

  Returns:
    Array with shape `(size[0], size[1], channels)`. If the input image was a
    NumPy array, the output is a NumPy array, and if it was a TF tensor,
    the output is a TF tensor.
  ",348,228,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def smart_resize(x, size, interpolation='bilinear'):
  
  if len(size) != 2:
    raise ValueError('Expected `size` to be a tuple of 2 integers, '
                     f'but got: {size}.')
  img = tf.convert_to_tensor(x)
  if img.shape.rank is not None:
    if img.shape.rank < 3 or img.shape.rank > 4:
      raise ValueError(
          'Expected an image array with shape `(height, width, channels)`, '
          'or `(batch_size, height, width, channels)`, but '
          f'got input with incorrect rank, of shape {img.shape}.')
  shape = tf.shape(img)
  height, width = shape[-3], shape[-2]
  target_height, target_width = size
  if img.shape.rank is not None:
    static_num_channels = img.shape[-1]
  else:
    static_num_channels = None

  crop_height = tf.cast(
      tf.cast(width * target_height, 'float32') / target_width, 'int32')
  crop_width = tf.cast(
      tf.cast(height * target_width, 'float32') / target_height, 'int32')

  # Set back to input height / width if crop_height / crop_width is not smaller.
  crop_height = tf.minimum(height, crop_height)
  crop_width = tf.minimum(width, crop_width)

  crop_box_hstart = tf.cast(
      tf.cast(height - crop_height, 'float32') / 2, 'int32')
  crop_box_wstart = tf.cast(tf.cast(width - crop_width, 'float32') / 2, 'int32')

  if img.shape.rank == 4:
    crop_box_start = tf.stack([0, crop_box_hstart, crop_box_wstart, 0])
    crop_box_size = tf.stack([-1, crop_height, crop_width, -1])
  else:
    crop_box_start = tf.stack([crop_box_hstart, crop_box_wstart, 0])
    crop_box_size = tf.stack([crop_height, crop_width, -1])

  img = tf.slice(img, crop_box_start, crop_box_size)
  img = tf.image.resize(images=img, size=size, method=interpolation)
  # Apparent bug in resize_images_v2 may cause shape to be lost
  if img.shape.rank is not None:
    if img.shape.rank == 4:
      img.set_shape((None, None, None, static_num_channels))
    if img.shape.rank == 3:
      img.set_shape((None, None, static_num_channels))
  if isinstance(x, np.ndarray):
    return img.numpy()
  return img


@keras_export('keras.utils.array_to_img',
              'keras.preprocessing.image.array_to_img')
        ```
    ###Assistant : Resize images to a target size without aspect ratio distortion.

  TensorFlow image datasets typically yield images that have each a different
  size. However, these images need to be batched before they can be
  processed by Keras layers. To be batched, images need to share the same height
  and width.

  You could simply do:

  ```python
  size = (200, 200)
  ds = ds.map(lambda img: tf.image.resize(img, size))
  ```

  However, if you do this, you distort the aspect ratio of your images, since
  in general they do not all have the same aspect ratio as `size`. This is
  fine in many cases, but not always (e.g. for GANs this can be a problem).

  Note that passing the argument `preserve_aspect_ratio=True` to `resize`
  will preserve the aspect ratio, but at the cost of no longer respecting the
  provided target size. Because `tf.image.resize` doesn't crop images,
  your output images will still have different sizes.

  This calls for:

  ```python
  size = (200, 200)
  ds = ds.map(lambda img: smart_resize(img, size))
  ```

  Your output images will actually be `(200, 200)`, and will not be distorted.
  Instead, the parts of the image that do not fit within the target size
  get cropped out.

  The resizing process is:

  1. Take the largest centered crop of the image that has the same aspect ratio
  as the target size. For instance, if `size=(200, 200)` and the input image has
  size `(340, 500)`, we take a crop of `(340, 340)` centered along the width.
  2. Resize the cropped image to the target size. In the example above,
  we resize the `(340, 340)` crop to `(200, 200)`.

  Args:
    x: Input image or batch of images (as a tensor or NumPy array). Must be in
      format `(height, width, channels)` or `(batch_size, height, width,
      channels)`.
    size: Tuple of `(height, width)` integer. Target size.
    interpolation: String, interpolation to use for resizing. Defaults to
      `'bilinear'`. Supports `bilinear`, `nearest`, `bicubic`, `area`,
      `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.

  Returns:
    Array with shape `(size[0], size[1], channels)`. If the input image was a
    NumPy array, the output is a NumPy array, and if it was a TF tensor,
    the output is a TF tensor.
  
    "
1397,"def gather(tensor):
    
    if AcceleratorState().distributed_type == DistributedType.TPU:
        return _tpu_gather(tensor, name=""accelerate.utils.gather"")
    elif AcceleratorState().distributed_type in [DistributedType.DEEPSPEED, DistributedType.MULTI_GPU]:
        return _gpu_gather(tensor)
    elif AcceleratorState().distributed_type == DistributedType.MULTI_CPU:
        return _cpu_gather(tensor)
    else:
        return tensor

","
    Recursively gather tensor in a nested list/tuple/dictionary of tensors from all devices.

    Args:
        tensor (nested list/tuple/dictionary of :obj:`torch.Tensor`):
            The data to gather.

    Returns:
        The same data structure as :obj:`tensor` with all tensors sent to the proper device.
    ",37,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def gather(tensor):
    
    if AcceleratorState().distributed_type == DistributedType.TPU:
        return _tpu_gather(tensor, name=""accelerate.utils.gather"")
    elif AcceleratorState().distributed_type in [DistributedType.DEEPSPEED, DistributedType.MULTI_GPU]:
        return _gpu_gather(tensor)
    elif AcceleratorState().distributed_type == DistributedType.MULTI_CPU:
        return _cpu_gather(tensor)
    else:
        return tensor


        ```
    ###Assistant : 
    Recursively gather tensor in a nested list/tuple/dictionary of tensors from all devices.

    Args:
        tensor (nested list/tuple/dictionary of :obj:`torch.Tensor`):
            The data to gather.

    Returns:
        The same data structure as :obj:`tensor` with all tensors sent to the proper device.
    
    "
1398,"def speed_metrics(split, start_time, num_samples=None, num_steps=None):
    
    runtime = time.time() - start_time
    result = {f""{split}_runtime"": round(runtime, 4)}
    if num_samples is not None:
        samples_per_second = num_samples / runtime
        result[f""{split}_samples_per_second""] = round(samples_per_second, 3)
    if num_steps is not None:
        steps_per_second = num_steps / runtime
        result[f""{split}_steps_per_second""] = round(steps_per_second, 3)
    return result

","
    Measure and return speed performance metrics.

    This function requires a time snapshot `start_time` before the operation to be measured starts and this function
    should be run immediately after the operation to be measured has completed.

    Args:
    - split: name to prefix metric (like train, eval, test...)
    - start_time: operation start time
    - num_samples: number of samples processed
    ",57,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def speed_metrics(split, start_time, num_samples=None, num_steps=None):
    
    runtime = time.time() - start_time
    result = {f""{split}_runtime"": round(runtime, 4)}
    if num_samples is not None:
        samples_per_second = num_samples / runtime
        result[f""{split}_samples_per_second""] = round(samples_per_second, 3)
    if num_steps is not None:
        steps_per_second = num_steps / runtime
        result[f""{split}_steps_per_second""] = round(steps_per_second, 3)
    return result


        ```
    ###Assistant : 
    Measure and return speed performance metrics.

    This function requires a time snapshot `start_time` before the operation to be measured starts and this function
    should be run immediately after the operation to be measured has completed.

    Args:
    - split: name to prefix metric (like train, eval, test...)
    - start_time: operation start time
    - num_samples: number of samples processed
    
    "
1399,"def parse_date_fields(year_col, month_col, day_col) -> npt.NDArray[np.object_]:
    
    warnings.warn(
        ,  # noqa: E501
        FutureWarning,
        stacklevel=find_stack_level(),
    )

    year_col = _maybe_cast(year_col)
    month_col = _maybe_cast(month_col)
    day_col = _maybe_cast(day_col)
    return parsing.try_parse_year_month_day(year_col, month_col, day_col)

","
    Parse columns with years, months and days into a single date column.

    .. deprecated:: 1.2
    
        Use pd.to_datetime({""year"": year_col, ""month"": month_col, ""day"": day_col}) instead to get a Pandas Series.
        Use ser = pd.to_datetime({""year"": year_col, ""month"": month_col, ""day"": day_col}) and
        np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.
",49,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_date_fields(year_col, month_col, day_col) -> npt.NDArray[np.object_]:
    
    warnings.warn(
        ,  # noqa: E501
        FutureWarning,
        stacklevel=find_stack_level(),
    )

    year_col = _maybe_cast(year_col)
    month_col = _maybe_cast(month_col)
    day_col = _maybe_cast(day_col)
    return parsing.try_parse_year_month_day(year_col, month_col, day_col)


        ```
    ###Assistant : 
    Parse columns with years, months and days into a single date column.

    .. deprecated:: 1.2
    
        Use pd.to_datetime({""year"": year_col, ""month"": month_col, ""day"": day_col}) instead to get a Pandas Series.
        Use ser = pd.to_datetime({""year"": year_col, ""month"": month_col, ""day"": day_col}) and
        np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.

    "
1400,"def test_poisson_vs_mse():
    
    rng = np.random.RandomState(42)
    n_train, n_test, n_features = 500, 500, 10
    X = datasets.make_low_rank_matrix(
        n_samples=n_train + n_test, n_features=n_features, random_state=rng
    )
    #  We create a log-linear Poisson model and downscale coef as it will get
    # exponentiated.
    coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
    y = rng.poisson(lam=np.exp(X @ coef))
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=n_test, random_state=rng
    )
    # We prevent some overfitting by setting min_samples_split=10.
    forest_poi = RandomForestRegressor(
        criterion=""poisson"", min_samples_leaf=10, max_features=""sqrt"", random_state=rng
    )
    forest_mse = RandomForestRegressor(
        criterion=""squared_error"",
        min_samples_leaf=10,
        max_features=""sqrt"",
        random_state=rng,
    )

    forest_poi.fit(X_train, y_train)
    forest_mse.fit(X_train, y_train)
    dummy = DummyRegressor(strategy=""mean"").fit(X_train, y_train)

    for X, y, val in [(X_train, y_train, ""train""), (X_test, y_test, ""test"")]:
        metric_poi = mean_poisson_deviance(y, forest_poi.predict(X))
        # squared_error forest might produce non-positive predictions => clip
        # If y = 0 for those, the poisson deviance gets too good.
        # If we drew more samples, we would eventually get y > 0 and the
        # poisson deviance would explode, i.e. be undefined. Therefore, we do
        # not clip to a tiny value like 1e-15, but to 1e-6. This acts like a
        # small penalty to the non-positive predictions.
        metric_mse = mean_poisson_deviance(
            y, np.clip(forest_mse.predict(X), 1e-6, None)
        )
        metric_dummy = mean_poisson_deviance(y, dummy.predict(X))
        # As squared_error might correctly predict 0 in train set, its train
        # score can be better than Poisson. This is no longer the case for the
        # test set. But keep the above comment for clipping in mind.
        if val == ""test"":
            assert metric_poi < metric_mse
        assert metric_poi < 0.5 * metric_dummy


@pytest.mark.parametrize(""criterion"", (""poisson"", ""squared_error""))","Test that random forest with poisson criterion performs better than
    mse for a poisson target.

    There is a similar test for DecisionTreeRegressor.
    ",22,247,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_poisson_vs_mse():
    
    rng = np.random.RandomState(42)
    n_train, n_test, n_features = 500, 500, 10
    X = datasets.make_low_rank_matrix(
        n_samples=n_train + n_test, n_features=n_features, random_state=rng
    )
    #  We create a log-linear Poisson model and downscale coef as it will get
    # exponentiated.
    coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
    y = rng.poisson(lam=np.exp(X @ coef))
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=n_test, random_state=rng
    )
    # We prevent some overfitting by setting min_samples_split=10.
    forest_poi = RandomForestRegressor(
        criterion=""poisson"", min_samples_leaf=10, max_features=""sqrt"", random_state=rng
    )
    forest_mse = RandomForestRegressor(
        criterion=""squared_error"",
        min_samples_leaf=10,
        max_features=""sqrt"",
        random_state=rng,
    )

    forest_poi.fit(X_train, y_train)
    forest_mse.fit(X_train, y_train)
    dummy = DummyRegressor(strategy=""mean"").fit(X_train, y_train)

    for X, y, val in [(X_train, y_train, ""train""), (X_test, y_test, ""test"")]:
        metric_poi = mean_poisson_deviance(y, forest_poi.predict(X))
        # squared_error forest might produce non-positive predictions => clip
        # If y = 0 for those, the poisson deviance gets too good.
        # If we drew more samples, we would eventually get y > 0 and the
        # poisson deviance would explode, i.e. be undefined. Therefore, we do
        # not clip to a tiny value like 1e-15, but to 1e-6. This acts like a
        # small penalty to the non-positive predictions.
        metric_mse = mean_poisson_deviance(
            y, np.clip(forest_mse.predict(X), 1e-6, None)
        )
        metric_dummy = mean_poisson_deviance(y, dummy.predict(X))
        # As squared_error might correctly predict 0 in train set, its train
        # score can be better than Poisson. This is no longer the case for the
        # test set. But keep the above comment for clipping in mind.
        if val == ""test"":
            assert metric_poi < metric_mse
        assert metric_poi < 0.5 * metric_dummy


@pytest.mark.parametrize(""criterion"", (""poisson"", ""squared_error""))
        ```
    ###Assistant : Test that random forest with poisson criterion performs better than
    mse for a poisson target.

    There is a similar test for DecisionTreeRegressor.
    
    "
1401,"def _external_caller_info():
    

    frame = inspect.currentframe()
    caller = frame
    levels = 0
    while caller.f_code.co_filename == __file__:
        caller = caller.f_back
        levels += 1
    return {
        ""lineno"": caller.f_lineno,
        ""filename"": os.path.basename(caller.f_code.co_filename),
    }

","Get the info from the caller frame.

    Used to override the logging function and line number with the correct
    ones. See the comment on _patched_makeRecord for more info.
    ",28,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _external_caller_info():
    

    frame = inspect.currentframe()
    caller = frame
    levels = 0
    while caller.f_code.co_filename == __file__:
        caller = caller.f_back
        levels += 1
    return {
        ""lineno"": caller.f_lineno,
        ""filename"": os.path.basename(caller.f_code.co_filename),
    }


        ```
    ###Assistant : Get the info from the caller frame.

    Used to override the logging function and line number with the correct
    ones. See the comment on _patched_makeRecord for more info.
    
    "
1402,"def _try_restart_fedora(self) -> None:
        

        try:
            util.run_script(['systemctl', 'restart', 'httpd'])
        except errors.SubprocessError as err:
            raise errors.MisconfigurationError(str(err))

        # Finish with actual config check to see if systemctl restart helped
        super().config_test()
","
        Tries to restart httpd using systemctl to generate the self signed key pair.
        ",13,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _try_restart_fedora(self) -> None:
        

        try:
            util.run_script(['systemctl', 'restart', 'httpd'])
        except errors.SubprocessError as err:
            raise errors.MisconfigurationError(str(err))

        # Finish with actual config check to see if systemctl restart helped
        super().config_test()

        ```
    ###Assistant : 
        Tries to restart httpd using systemctl to generate the self signed key pair.
        
    "
1403,"async def sock_accept(self, sock):
        
        base_events._check_ssl_socket(sock)
        if self._debug and sock.gettimeout() != 0:
            raise ValueError(""the socket must be non-blocking"")
        fut = self.create_future()
        self._sock_accept(fut, sock)
        return await fut
","Accept a connection.

        The socket must be bound to an address and listening for connections.
        The return value is a pair (conn, address) where conn is a new socket
        object usable to send and receive data on the connection, and address
        is the address bound to the socket on the other end of the connection.
        ",55,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def sock_accept(self, sock):
        
        base_events._check_ssl_socket(sock)
        if self._debug and sock.gettimeout() != 0:
            raise ValueError(""the socket must be non-blocking"")
        fut = self.create_future()
        self._sock_accept(fut, sock)
        return await fut

        ```
    ###Assistant : Accept a connection.

        The socket must be bound to an address and listening for connections.
        The return value is a pair (conn, address) where conn is a new socket
        object usable to send and receive data on the connection, and address
        is the address bound to the socket on the other end of the connection.
        
    "
1404,"def test_state(self, api, state):
        
        stream = AdsInsights(
            api=api,
            start_date=datetime(2010, 1, 1),
            end_date=datetime(2011, 1, 1),
        )

        assert stream.state == {}

        stream.state = state
        actual_state = stream.state
        actual_state[""slices""] = sorted(actual_state.get(""slices"", []))
        state[""slices""] = sorted(state.get(""slices"", []))

        assert actual_state == state
",State setter/getter should work with all combinations,7,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_state(self, api, state):
        
        stream = AdsInsights(
            api=api,
            start_date=datetime(2010, 1, 1),
            end_date=datetime(2011, 1, 1),
        )

        assert stream.state == {}

        stream.state = state
        actual_state = stream.state
        actual_state[""slices""] = sorted(actual_state.get(""slices"", []))
        state[""slices""] = sorted(state.get(""slices"", []))

        assert actual_state == state

        ```
    ###Assistant : State setter/getter should work with all combinations
    "
1405,"def get_snql_query(self) -> None:
        
        raise NotImplementedError(""get_snql_query cannot be implemented for MetricsQueryBuilder"")
","Because metrics table queries need to make multiple requests per metric type this function cannot be
        inmplemented see run_query",19,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_snql_query(self) -> None:
        
        raise NotImplementedError(""get_snql_query cannot be implemented for MetricsQueryBuilder"")

        ```
    ###Assistant : Because metrics table queries need to make multiple requests per metric type this function cannot be
        inmplemented see run_query
    "
1406,"def _validate(self, params):
        
        remote_worker_stats = [w.validate.remote(**params) for w in self.remote_workers]
        return remote_worker_stats
",Runs validation for each worker. Returns results as promises.,9,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate(self, params):
        
        remote_worker_stats = [w.validate.remote(**params) for w in self.remote_workers]
        return remote_worker_stats

        ```
    ###Assistant : Runs validation for each worker. Returns results as promises.
    "
1407,"def set_aspect(self, aspect, adjustable=None, anchor=None, share=False):
        
        _api.check_in_list(('auto', 'equal', 'equalxy', 'equalyz', 'equalxz'),
                           aspect=aspect)
        super().set_aspect(
            aspect='auto', adjustable=adjustable, anchor=anchor, share=share)

        if aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'):
            if aspect == 'equal':
                axis_indices = [0, 1, 2]
            elif aspect == 'equalxy':
                axis_indices = [0, 1]
            elif aspect == 'equalxz':
                axis_indices = [0, 2]
            elif aspect == 'equalyz':
                axis_indices = [1, 2]

            view_intervals = np.array([self.xaxis.get_view_interval(),
                                       self.yaxis.get_view_interval(),
                                       self.zaxis.get_view_interval()])
            mean = np.mean(view_intervals, axis=1)
            delta = np.max(np.ptp(view_intervals, axis=1))
            deltas = delta * self._box_aspect / min(self._box_aspect)

            for i, set_lim in enumerate((self.set_xlim3d,
                                         self.set_ylim3d,
                                         self.set_zlim3d)):
                if i in axis_indices:
                    set_lim(mean[i] - deltas[i]/2., mean[i] + deltas[i]/2.)
","
        Set the aspect ratios.

        Parameters
        ----------
        aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'}
            Possible values:

            =========   ==================================================
            value       description
            =========   ==================================================
            'auto'      automatic; fill the position rectangle with data.
            'equal'     adapt all the axes to have equal aspect ratios.
            'equalxy'   adapt the x and y axes to have equal aspect ratios.
            'equalxz'   adapt the x and z axes to have equal aspect ratios.
            'equalyz'   adapt the y and z axes to have equal aspect ratios.
            =========   ==================================================

        adjustable : None
            Currently ignored by Axes3D

            If not *None*, this defines which parameter will be adjusted to
            meet the required aspect. See `.set_adjustable` for further
            details.

        anchor : None or str or 2-tuple of float, optional
            If not *None*, this defines where the Axes will be drawn if there
            is extra space due to aspect constraints. The most common way to
            to specify the anchor are abbreviations of cardinal directions:

            =====   =====================
            value   description
            =====   =====================
            'C'     centered
            'SW'    lower left corner
            'S'     middle of bottom edge
            'SE'    lower right corner
            etc.
            =====   =====================

            See `~.Axes.set_anchor` for further details.

        share : bool, default: False
            If ``True``, apply the settings to all shared Axes.

        See Also
        --------
        mpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect
        ",195,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_aspect(self, aspect, adjustable=None, anchor=None, share=False):
        
        _api.check_in_list(('auto', 'equal', 'equalxy', 'equalyz', 'equalxz'),
                           aspect=aspect)
        super().set_aspect(
            aspect='auto', adjustable=adjustable, anchor=anchor, share=share)

        if aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'):
            if aspect == 'equal':
                axis_indices = [0, 1, 2]
            elif aspect == 'equalxy':
                axis_indices = [0, 1]
            elif aspect == 'equalxz':
                axis_indices = [0, 2]
            elif aspect == 'equalyz':
                axis_indices = [1, 2]

            view_intervals = np.array([self.xaxis.get_view_interval(),
                                       self.yaxis.get_view_interval(),
                                       self.zaxis.get_view_interval()])
            mean = np.mean(view_intervals, axis=1)
            delta = np.max(np.ptp(view_intervals, axis=1))
            deltas = delta * self._box_aspect / min(self._box_aspect)

            for i, set_lim in enumerate((self.set_xlim3d,
                                         self.set_ylim3d,
                                         self.set_zlim3d)):
                if i in axis_indices:
                    set_lim(mean[i] - deltas[i]/2., mean[i] + deltas[i]/2.)

        ```
    ###Assistant : 
        Set the aspect ratios.

        Parameters
        ----------
        aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'}
            Possible values:

            =========   ==================================================
            value       description
            =========   ==================================================
            'auto'      automatic; fill the position rectangle with data.
            'equal'     adapt all the axes to have equal aspect ratios.
            'equalxy'   adapt the x and y axes to have equal aspect ratios.
            'equalxz'   adapt the x and z axes to have equal aspect ratios.
            'equalyz'   adapt the y and z axes to have equal aspect ratios.
            =========   ==================================================

        adjustable : None
            Currently ignored by Axes3D

            If not *None*, this defines which parameter will be adjusted to
            meet the required aspect. See `.set_adjustable` for further
            details.

        anchor : None or str or 2-tuple of float, optional
            If not *None*, this defines where the Axes will be drawn if there
            is extra space due to aspect constraints. The most common way to
            to specify the anchor are abbreviations of cardinal directions:

            =====   =====================
            value   description
            =====   =====================
            'C'     centered
            'SW'    lower left corner
            'S'     middle of bottom edge
            'SE'    lower right corner
            etc.
            =====   =====================

            See `~.Axes.set_anchor` for further details.

        share : bool, default: False
            If ``True``, apply the settings to all shared Axes.

        See Also
        --------
        mpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect
        
    "
1408,"def __getattr__(cls, name):
        
        if _is_dunder(name):
            raise AttributeError(name)
        try:
            return cls._member_map_[name]
        except KeyError:
            raise AttributeError(name) from None
","
        Return the enum member matching `name`

        We use __getattr__ instead of descriptors or inserting into the enum
        class' __dict__ in order to support `name` and `value` being both
        properties for enum members (which live in the class' __dict__) and
        enum members themselves.
        ",42,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __getattr__(cls, name):
        
        if _is_dunder(name):
            raise AttributeError(name)
        try:
            return cls._member_map_[name]
        except KeyError:
            raise AttributeError(name) from None

        ```
    ###Assistant : 
        Return the enum member matching `name`

        We use __getattr__ instead of descriptors or inserting into the enum
        class' __dict__ in order to support `name` and `value` being both
        properties for enum members (which live in the class' __dict__) and
        enum members themselves.
        
    "
1409,"def test_n_clusters(n_clusters):
    

    rng = np.random.RandomState(0)
    X = rng.rand(10, 2)

    bisect_means = BisectingKMeans(n_clusters=n_clusters, random_state=0)
    bisect_means.fit(X)

    assert_array_equal(np.unique(bisect_means.labels_), np.arange(n_clusters))

","Test if resulting labels are in range [0, n_clusters - 1].",11,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_n_clusters(n_clusters):
    

    rng = np.random.RandomState(0)
    X = rng.rand(10, 2)

    bisect_means = BisectingKMeans(n_clusters=n_clusters, random_state=0)
    bisect_means.fit(X)

    assert_array_equal(np.unique(bisect_means.labels_), np.arange(n_clusters))


        ```
    ###Assistant : Test if resulting labels are in range [0, n_clusters - 1].
    "
1410,"def test_nested_ungrouped_nav(self):
        nav_cfg = [
            {'Home': 'index.md'},
            {'Contact': 'about/contact.md'},
            {'License Title': 'about/sub/license.md'},
        ]
        expected = dedent(
            
        )
        cfg = load_config(nav=nav_cfg, site_url='http://example.com/')
        fs = [
            File(list(item.values())[0], cfg['docs_dir'], cfg['site_dir'], cfg['use_directory_urls'])
            for item in nav_cfg
        ]
        files = Files(fs)
        site_navigation = get_navigation(files, cfg)
        self.assertEqual(str(site_navigation).strip(), expected)
        self.assertEqual(len(site_navigation.items), 3)
        self.assertEqual(len(site_navigation.pages), 3)
","
            Page(title='Home', url='/')
            Page(title='Contact', url='/about/contact/')
            Page(title='License Title', url='/about/sub/license/')
            ",7,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_nested_ungrouped_nav(self):
        nav_cfg = [
            {'Home': 'index.md'},
            {'Contact': 'about/contact.md'},
            {'License Title': 'about/sub/license.md'},
        ]
        expected = dedent(
            
        )
        cfg = load_config(nav=nav_cfg, site_url='http://example.com/')
        fs = [
            File(list(item.values())[0], cfg['docs_dir'], cfg['site_dir'], cfg['use_directory_urls'])
            for item in nav_cfg
        ]
        files = Files(fs)
        site_navigation = get_navigation(files, cfg)
        self.assertEqual(str(site_navigation).strip(), expected)
        self.assertEqual(len(site_navigation.items), 3)
        self.assertEqual(len(site_navigation.pages), 3)

        ```
    ###Assistant : 
            Page(title='Home', url='/')
            Page(title='Contact', url='/about/contact/')
            Page(title='License Title', url='/about/sub/license/')
            
    "
1411,"def _get_log_file_names(self, name, unique=False):
        

        if unique:
            log_stdout = self._make_inc_temp(
                suffix="".out"", prefix=name, directory_name=self._logs_dir
            )
            log_stderr = self._make_inc_temp(
                suffix="".err"", prefix=name, directory_name=self._logs_dir
            )
        else:
            log_stdout = os.path.join(self._logs_dir, f""{name}.out"")
            log_stderr = os.path.join(self._logs_dir, f""{name}.err"")
        return log_stdout, log_stderr
","Generate partially randomized filenames for log files.

        Args:
            name (str): descriptive string for this log file.
            unique (bool): if true, a counter will be attached to `name` to
                ensure the returned filename is not already used.

        Returns:
            A tuple of two file names for redirecting (stdout, stderr).
        ",47,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_log_file_names(self, name, unique=False):
        

        if unique:
            log_stdout = self._make_inc_temp(
                suffix="".out"", prefix=name, directory_name=self._logs_dir
            )
            log_stderr = self._make_inc_temp(
                suffix="".err"", prefix=name, directory_name=self._logs_dir
            )
        else:
            log_stdout = os.path.join(self._logs_dir, f""{name}.out"")
            log_stderr = os.path.join(self._logs_dir, f""{name}.err"")
        return log_stdout, log_stderr

        ```
    ###Assistant : Generate partially randomized filenames for log files.

        Args:
            name (str): descriptive string for this log file.
            unique (bool): if true, a counter will be attached to `name` to
                ensure the returned filename is not already used.

        Returns:
            A tuple of two file names for redirecting (stdout, stderr).
        
    "
1412,"def yeardayscalendar(self, year, width=3):
        
        months = [
            self.monthdayscalendar(year, i)
            for i in range(January, January+12)
        ]
        return [months[i:i+width] for i in range(0, len(months), width) ]

","
        Return the data for the specified year ready for formatting (similar to
        yeardatescalendar()). Entries in the week lists are day numbers.
        Day numbers outside this month are zero.
        ",28,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def yeardayscalendar(self, year, width=3):
        
        months = [
            self.monthdayscalendar(year, i)
            for i in range(January, January+12)
        ]
        return [months[i:i+width] for i in range(0, len(months), width) ]


        ```
    ###Assistant : 
        Return the data for the specified year ready for formatting (similar to
        yeardatescalendar()). Entries in the week lists are day numbers.
        Day numbers outside this month are zero.
        
    "
1413,"def test_simplelistfilter_without_parameter(self):
        
        modeladmin = DecadeFilterBookAdminWithoutParameter(Book, site)
        request = self.request_factory.get(""/"", {})
        request.user = self.alfred
        msg = ""The list filter 'DecadeListFilterWithoutParameter' does not specify a 'parameter_name'.""
        with self.assertRaisesMessage(ImproperlyConfigured, msg):
            modeladmin.get_changelist_instance(request)
","
        Any SimpleListFilter must define a parameter_name.
        ",6,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_simplelistfilter_without_parameter(self):
        
        modeladmin = DecadeFilterBookAdminWithoutParameter(Book, site)
        request = self.request_factory.get(""/"", {})
        request.user = self.alfred
        msg = ""The list filter 'DecadeListFilterWithoutParameter' does not specify a 'parameter_name'.""
        with self.assertRaisesMessage(ImproperlyConfigured, msg):
            modeladmin.get_changelist_instance(request)

        ```
    ###Assistant : 
        Any SimpleListFilter must define a parameter_name.
        
    "
1414,"def from_session_or_email(cls, request, organization, email, instance=None, logger=None):
        
        invite_token, invite_member_id = get_invite_details(request)

        try:
            if invite_token and invite_member_id:
                om = OrganizationMember.objects.get(token=invite_token, id=invite_member_id)
            else:
                om = OrganizationMember.objects.get(
                    email=email, organization=organization, user=None
                )
        except OrganizationMember.DoesNotExist:
            # Unable to locate the pending organization member. Cannot setup
            # the invite helper.
            return None

        return cls(
            request=request, member_id=om.id, token=om.token, instance=instance, logger=logger
        )
","
        Initializes the ApiInviteHelper by locating the pending organization
        member via the currently set pending invite details in the session, or
        via the passed email if no cookie is currently set.
        ",30,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_session_or_email(cls, request, organization, email, instance=None, logger=None):
        
        invite_token, invite_member_id = get_invite_details(request)

        try:
            if invite_token and invite_member_id:
                om = OrganizationMember.objects.get(token=invite_token, id=invite_member_id)
            else:
                om = OrganizationMember.objects.get(
                    email=email, organization=organization, user=None
                )
        except OrganizationMember.DoesNotExist:
            # Unable to locate the pending organization member. Cannot setup
            # the invite helper.
            return None

        return cls(
            request=request, member_id=om.id, token=om.token, instance=instance, logger=logger
        )

        ```
    ###Assistant : 
        Initializes the ApiInviteHelper by locating the pending organization
        member via the currently set pending invite details in the session, or
        via the passed email if no cookie is currently set.
        
    "
1415,"async def test_multiple_bleak_scanner_instances(hass):
    
    install_multiple_bleak_catcher()

    instance = bleak.BleakScanner()

    assert isinstance(instance, HaBleakScannerWrapper)

    uninstall_multiple_bleak_catcher()

    with patch(""bleak.get_platform_scanner_backend_type""):
        instance = bleak.BleakScanner()

    assert not isinstance(instance, HaBleakScannerWrapper)

",Test creating multiple BleakScanners without an integration.,7,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_multiple_bleak_scanner_instances(hass):
    
    install_multiple_bleak_catcher()

    instance = bleak.BleakScanner()

    assert isinstance(instance, HaBleakScannerWrapper)

    uninstall_multiple_bleak_catcher()

    with patch(""bleak.get_platform_scanner_backend_type""):
        instance = bleak.BleakScanner()

    assert not isinstance(instance, HaBleakScannerWrapper)


        ```
    ###Assistant : Test creating multiple BleakScanners without an integration.
    "
1416,"def highlight_string(value, highlight, trim_pre=None, trim_post=None, trim_placeholder='...'):
    
    # Split value on highlight string
    try:
        pre, match, post = re.split(fr'({highlight})', value, maxsplit=1, flags=re.IGNORECASE)
    except ValueError:
        # Match not found
        return escape(value)

    # Trim pre/post sections to length
    if trim_pre and len(pre) > trim_pre:
        pre = trim_placeholder + pre[-trim_pre:]
    if trim_post and len(post) > trim_post:
        post = post[:trim_post] + trim_placeholder

    return f'{escape(pre)}<mark>{escape(match)}</mark>{escape(post)}'
","
    Highlight a string within a string and optionally trim the pre/post portions of the original string.
    ",16,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def highlight_string(value, highlight, trim_pre=None, trim_post=None, trim_placeholder='...'):
    
    # Split value on highlight string
    try:
        pre, match, post = re.split(fr'({highlight})', value, maxsplit=1, flags=re.IGNORECASE)
    except ValueError:
        # Match not found
        return escape(value)

    # Trim pre/post sections to length
    if trim_pre and len(pre) > trim_pre:
        pre = trim_placeholder + pre[-trim_pre:]
    if trim_post and len(post) > trim_post:
        post = post[:trim_post] + trim_placeholder

    return f'{escape(pre)}<mark>{escape(match)}</mark>{escape(post)}'

        ```
    ###Assistant : 
    Highlight a string within a string and optionally trim the pre/post portions of the original string.
    
    "
1417,"def node_table(self):
        
        self._check_connected()

        node_table = self.global_state_accessor.get_node_table()

        results = []
        for node_info_item in node_table:
            item = gcs_utils.GcsNodeInfo.FromString(node_info_item)
            node_info = {
                ""NodeID"": ray._private.utils.binary_to_hex(item.node_id),
                ""Alive"": item.state
                == gcs_utils.GcsNodeInfo.GcsNodeState.Value(""ALIVE""),
                ""NodeManagerAddress"": item.node_manager_address,
                ""NodeManagerHostname"": item.node_manager_hostname,
                ""NodeManagerPort"": item.node_manager_port,
                ""ObjectManagerPort"": item.object_manager_port,
                ""ObjectStoreSocketName"": item.object_store_socket_name,
                ""RayletSocketName"": item.raylet_socket_name,
                ""MetricsExportPort"": item.metrics_export_port,
                ""NodeName"": item.node_name,
            }
            node_info[""alive""] = node_info[""Alive""]
            node_info[""Resources""] = (
                {key: value for key, value in item.resources_total.items()}
                if node_info[""Alive""]
                else {}
            )
            results.append(node_info)
        return results
","Fetch and parse the Gcs node info table.

        Returns:
            Information about the node in the cluster.
        ",16,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def node_table(self):
        
        self._check_connected()

        node_table = self.global_state_accessor.get_node_table()

        results = []
        for node_info_item in node_table:
            item = gcs_utils.GcsNodeInfo.FromString(node_info_item)
            node_info = {
                ""NodeID"": ray._private.utils.binary_to_hex(item.node_id),
                ""Alive"": item.state
                == gcs_utils.GcsNodeInfo.GcsNodeState.Value(""ALIVE""),
                ""NodeManagerAddress"": item.node_manager_address,
                ""NodeManagerHostname"": item.node_manager_hostname,
                ""NodeManagerPort"": item.node_manager_port,
                ""ObjectManagerPort"": item.object_manager_port,
                ""ObjectStoreSocketName"": item.object_store_socket_name,
                ""RayletSocketName"": item.raylet_socket_name,
                ""MetricsExportPort"": item.metrics_export_port,
                ""NodeName"": item.node_name,
            }
            node_info[""alive""] = node_info[""Alive""]
            node_info[""Resources""] = (
                {key: value for key, value in item.resources_total.items()}
                if node_info[""Alive""]
                else {}
            )
            results.append(node_info)
        return results

        ```
    ###Assistant : Fetch and parse the Gcs node info table.

        Returns:
            Information about the node in the cluster.
        
    "
1418,"def vocabulary_size(self):
        
        if tf.executing_eagerly():
            return (
                int(self.lookup_table.size().numpy())
                + self._token_start_index()
            )
        else:
            return self.lookup_table.size() + self._token_start_index()
","Gets the current size of the layer's vocabulary.

        Returns:
          The integer size of the vocabulary, including optional mask and oov indices.
        ",21,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def vocabulary_size(self):
        
        if tf.executing_eagerly():
            return (
                int(self.lookup_table.size().numpy())
                + self._token_start_index()
            )
        else:
            return self.lookup_table.size() + self._token_start_index()

        ```
    ###Assistant : Gets the current size of the layer's vocabulary.

        Returns:
          The integer size of the vocabulary, including optional mask and oov indices.
        
    "
1419,"def test_version_with_invalid_names():
    

    lslpp_mydog_out = 

    ver_chk = MagicMock(return_value={""retcode"": 1, ""stdout"": lslpp_mydog_out})
    with patch.dict(aixpkg.__grains__, {""osarch"": ""PowerPC_POWER8""}), patch.dict(
        aixpkg.__salt__,
        {""cmd.run_all"": ver_chk},
    ):
        versions_checked = aixpkg.version(
            ""mydog"", versions_as_list=True, use_context=False
        )
        assert ver_chk.call_count == 1
        ver_chk.assert_called_with(""lslpp -Lq mydog"", python_shell=False)
        assert versions_checked == """"

","
    test version of packages
    lslpp: Fileset mydog not installed.


State codes: 
 A -- Applied. 
 B -- Broken. 
 C -- Committed. 
 E -- EFIX Locked. 
 O -- Obsolete.  (partially migrated to newer version) 
 ? -- Inconsistent State...Run lppchk -v. 

Type codes: 
 F -- Installp Fileset 
 P -- Product 
 C -- Component 
 T -- Feature 
 R -- RPM Package 
 E -- Interim Fix 
",61,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_version_with_invalid_names():
    

    lslpp_mydog_out = 

    ver_chk = MagicMock(return_value={""retcode"": 1, ""stdout"": lslpp_mydog_out})
    with patch.dict(aixpkg.__grains__, {""osarch"": ""PowerPC_POWER8""}), patch.dict(
        aixpkg.__salt__,
        {""cmd.run_all"": ver_chk},
    ):
        versions_checked = aixpkg.version(
            ""mydog"", versions_as_list=True, use_context=False
        )
        assert ver_chk.call_count == 1
        ver_chk.assert_called_with(""lslpp -Lq mydog"", python_shell=False)
        assert versions_checked == """"


        ```
    ###Assistant : 
    test version of packages
    lslpp: Fileset mydog not installed.


State codes: 
 A -- Applied. 
 B -- Broken. 
 C -- Committed. 
 E -- EFIX Locked. 
 O -- Obsolete.  (partially migrated to newer version) 
 ? -- Inconsistent State...Run lppchk -v. 

Type codes: 
 F -- Installp Fileset 
 P -- Product 
 C -- Component 
 T -- Feature 
 R -- RPM Package 
 E -- Interim Fix 

    "
1420,"def test_mapped_literal_length_increase_adds_additional_ti(dag_maker, session):
    

    with dag_maker(session=session) as dag:
","Test that when the length of mapped literal increases, additional ti is added",13,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_mapped_literal_length_increase_adds_additional_ti(dag_maker, session):
    

    with dag_maker(session=session) as dag:

        ```
    ###Assistant : Test that when the length of mapped literal increases, additional ti is added
    "
1421,"def test_installed_without_username(self):
        

        # Remove username to simulate privacy mode
        del self.user_data_from_bitbucket[""principal""][""username""]

        response = self.client.post(self.path, data=self.user_data_from_bitbucket)
        assert response.status_code == 200
        integration = Integration.objects.get(provider=self.provider, external_id=self.client_key)
        assert integration.name == self.user_display_name
        assert integration.metadata == self.user_metadata
",Test a user (not team) installation where the user has hidden their username from public view,16,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_installed_without_username(self):
        

        # Remove username to simulate privacy mode
        del self.user_data_from_bitbucket[""principal""][""username""]

        response = self.client.post(self.path, data=self.user_data_from_bitbucket)
        assert response.status_code == 200
        integration = Integration.objects.get(provider=self.provider, external_id=self.client_key)
        assert integration.name == self.user_display_name
        assert integration.metadata == self.user_metadata

        ```
    ###Assistant : Test a user (not team) installation where the user has hidden their username from public view
    "
1422,"def _command_display(self, command):
        
        build_tabs = getattr(self, f""_{command}_tabs"")
        build_tabs()
"," Build the relevant command specific tabs based on the incoming Faceswap command.

        Parameters
        ----------
        command: str
            The Faceswap command that is being executed
        ",23,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _command_display(self, command):
        
        build_tabs = getattr(self, f""_{command}_tabs"")
        build_tabs()

        ```
    ###Assistant :  Build the relevant command specific tabs based on the incoming Faceswap command.

        Parameters
        ----------
        command: str
            The Faceswap command that is being executed
        
    "
1423,"def itermerged(self):
        
        for key in self:
            val = self._container[key.lower()]
            yield val[0], "", "".join(val[1:])
","Iterate over all headers, merging duplicate ones together.",8,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def itermerged(self):
        
        for key in self:
            val = self._container[key.lower()]
            yield val[0], "", "".join(val[1:])

        ```
    ###Assistant : Iterate over all headers, merging duplicate ones together.
    "
1424,"def get_file_from_path(self, path):
        
        return self.src_paths.get(os.path.normpath(path))
",Return a File instance with File.src_path equal to path.,9,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_file_from_path(self, path):
        
        return self.src_paths.get(os.path.normpath(path))

        ```
    ###Assistant : Return a File instance with File.src_path equal to path.
    "
1425,"def sleepdeprived(request):
    
    module = request.node.get_closest_marker(
        ""sleepdeprived_patched_module"").args[0]
    old_sleep, module.sleep = module.sleep, noop
    try:
        yield
    finally:
        module.sleep = old_sleep


# Taken from
# http://bitbucket.org/runeh/snippets/src/tip/missing_modules.py
@pytest.fixture","Mock sleep method in patched module to do nothing.

    Example:
        >>> import time
        >>> @pytest.mark.sleepdeprived_patched_module(time)
        >>> def test_foo(self, sleepdeprived):
        >>>     pass
    ",21,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sleepdeprived(request):
    
    module = request.node.get_closest_marker(
        ""sleepdeprived_patched_module"").args[0]
    old_sleep, module.sleep = module.sleep, noop
    try:
        yield
    finally:
        module.sleep = old_sleep


# Taken from
# http://bitbucket.org/runeh/snippets/src/tip/missing_modules.py
@pytest.fixture
        ```
    ###Assistant : Mock sleep method in patched module to do nothing.

    Example:
        >>> import time
        >>> @pytest.mark.sleepdeprived_patched_module(time)
        >>> def test_foo(self, sleepdeprived):
        >>>     pass
    
    "
1426,"def _set_mouse_bindings(self) -> None:
        
        logger.debug(""Binding mouse events"")
        if system() == ""Linux"":
            self._canvas.tag_bind(self._canvas.image_id, ""<Button-4>"", self._on_bound_zoom)
            self._canvas.tag_bind(self._canvas.image_id, ""<Button-5>"", self._on_bound_zoom)
        else:
            self._canvas.tag_bind(self._canvas.image_id, ""<MouseWheel>"", self._on_bound_zoom)

        self._canvas.tag_bind(self._canvas.image_id, ""<Button-1>"", self._on_mouse_click)
        self._canvas.tag_bind(self._canvas.image_id, ""<B1-Motion>"", self._on_mouse_drag)
        logger.debug(""Bound mouse events"")
"," Set the mouse bindings for interacting with the preview image

        Mousewheel: Zoom in and out
        Mouse click: Move image
        ",19,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_mouse_bindings(self) -> None:
        
        logger.debug(""Binding mouse events"")
        if system() == ""Linux"":
            self._canvas.tag_bind(self._canvas.image_id, ""<Button-4>"", self._on_bound_zoom)
            self._canvas.tag_bind(self._canvas.image_id, ""<Button-5>"", self._on_bound_zoom)
        else:
            self._canvas.tag_bind(self._canvas.image_id, ""<MouseWheel>"", self._on_bound_zoom)

        self._canvas.tag_bind(self._canvas.image_id, ""<Button-1>"", self._on_mouse_click)
        self._canvas.tag_bind(self._canvas.image_id, ""<B1-Motion>"", self._on_mouse_drag)
        logger.debug(""Bound mouse events"")

        ```
    ###Assistant :  Set the mouse bindings for interacting with the preview image

        Mousewheel: Zoom in and out
        Mouse click: Move image
        
    "
1427,"async def test_create_area_with_id_already_in_use(registry):
    
    area1 = registry.async_create(""mock"")

    updated_area1 = registry.async_update(area1.id, name=""New Name"")
    assert updated_area1.id == area1.id

    area2 = registry.async_create(""mock"")
    assert area2.id == ""mock_2""

",Make sure that we can't create an area with a name already in use.,14,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_create_area_with_id_already_in_use(registry):
    
    area1 = registry.async_create(""mock"")

    updated_area1 = registry.async_update(area1.id, name=""New Name"")
    assert updated_area1.id == area1.id

    area2 = registry.async_create(""mock"")
    assert area2.id == ""mock_2""


        ```
    ###Assistant : Make sure that we can't create an area with a name already in use.
    "
1428,"def forward(self, *args, **kwargs):
        
        labels = kwargs.pop('labels', None)
        pooled, encoded = super(ErnieModelForSequenceClassification, self).forward(*args, **kwargs)
        hidden = self.dropout(pooled)
        logits = self.classifier(hidden)

        if labels is not None:
            if len(labels.shape) != 1:
                labels = labels.squeeze()
            loss = F.cross_entropy(logits, labels)
        else:
            loss = None
        return loss, logits

","
        Args:
            labels (optional, `Variable` of shape [batch_size]):
                ground truth label id for each sentence
        Returns:
            loss (`Variable` of shape []):
                Cross entropy loss mean over batch
                if labels not set, returns None
            logits (`Variable` of shape [batch_size, hidden_size]):
                output logits of classifier
        ",42,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, *args, **kwargs):
        
        labels = kwargs.pop('labels', None)
        pooled, encoded = super(ErnieModelForSequenceClassification, self).forward(*args, **kwargs)
        hidden = self.dropout(pooled)
        logits = self.classifier(hidden)

        if labels is not None:
            if len(labels.shape) != 1:
                labels = labels.squeeze()
            loss = F.cross_entropy(logits, labels)
        else:
            loss = None
        return loss, logits


        ```
    ###Assistant : 
        Args:
            labels (optional, `Variable` of shape [batch_size]):
                ground truth label id for each sentence
        Returns:
            loss (`Variable` of shape []):
                Cross entropy loss mean over batch
                if labels not set, returns None
            logits (`Variable` of shape [batch_size, hidden_size]):
                output logits of classifier
        
    "
1429,"def _get_project(self, name):
        
        raise NotImplementedError('Please implement in the subclass')
","
        For a given project, get a dictionary mapping available versions to Distribution
        instances.

        This should be implemented in subclasses.

        If called from a locate() request, self.matcher will be set to a
        matcher for the requirement to satisfy, otherwise it will be None.
        ",42,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_project(self, name):
        
        raise NotImplementedError('Please implement in the subclass')

        ```
    ###Assistant : 
        For a given project, get a dictionary mapping available versions to Distribution
        instances.

        This should be implemented in subclasses.

        If called from a locate() request, self.matcher will be set to a
        matcher for the requirement to satisfy, otherwise it will be None.
        
    "
1430,"def test_pickle_empty(self):
        
        arr = np.array([]).reshape(999999, 0)
        pk_dmp = pickle.dumps(arr)
        pk_load = pickle.loads(pk_dmp)

        assert pk_load.size == 0
","Checking if an empty array pickled and un-pickled will not cause a
        segmentation fault",14,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pickle_empty(self):
        
        arr = np.array([]).reshape(999999, 0)
        pk_dmp = pickle.dumps(arr)
        pk_load = pickle.loads(pk_dmp)

        assert pk_load.size == 0

        ```
    ###Assistant : Checking if an empty array pickled and un-pickled will not cause a
        segmentation fault
    "
1431,"def get_semantics(cls, kwargs, semantics=None):
        
        # TODO this should be get_variables since we have included x and y
        if semantics is None:
            semantics = cls.semantics
        variables = {}
        for key, val in kwargs.items():
            if key in semantics and val is not None:
                variables[key] = val
        return variables
",Subset a dictionary arguments with known semantic variables.,8,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_semantics(cls, kwargs, semantics=None):
        
        # TODO this should be get_variables since we have included x and y
        if semantics is None:
            semantics = cls.semantics
        variables = {}
        for key, val in kwargs.items():
            if key in semantics and val is not None:
                variables[key] = val
        return variables

        ```
    ###Assistant : Subset a dictionary arguments with known semantic variables.
    "
1432,"def convert_deps_to_pip(deps, project=None, r=True, include_index=True):
    
    from pipenv.vendor.requirementslib.models.requirements import Requirement

    dependencies = []
    for dep_name, dep in deps.items():
        if project:
            project.clear_pipfile_cache()
        indexes = getattr(project, ""pipfile_sources"", []) if project is not None else []
        new_dep = Requirement.from_pipfile(dep_name, dep)
        if new_dep.index:
            include_index = True
        req = new_dep.as_line(sources=indexes if include_index else None).strip()
        dependencies.append(req)
    if not r:
        return dependencies

    # Write requirements.txt to tmp directory.
    from pipenv.vendor.vistir.path import create_tracked_tempfile
    f = create_tracked_tempfile(suffix=""-requirements.txt"", delete=False)
    f.write(""\n"".join(dependencies).encode(""utf-8""))
    f.close()
    return f.name

","""Converts a Pipfile-formatted dependency to a pip-formatted one.",8,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_deps_to_pip(deps, project=None, r=True, include_index=True):
    
    from pipenv.vendor.requirementslib.models.requirements import Requirement

    dependencies = []
    for dep_name, dep in deps.items():
        if project:
            project.clear_pipfile_cache()
        indexes = getattr(project, ""pipfile_sources"", []) if project is not None else []
        new_dep = Requirement.from_pipfile(dep_name, dep)
        if new_dep.index:
            include_index = True
        req = new_dep.as_line(sources=indexes if include_index else None).strip()
        dependencies.append(req)
    if not r:
        return dependencies

    # Write requirements.txt to tmp directory.
    from pipenv.vendor.vistir.path import create_tracked_tempfile
    f = create_tracked_tempfile(suffix=""-requirements.txt"", delete=False)
    f.write(""\n"".join(dependencies).encode(""utf-8""))
    f.close()
    return f.name


        ```
    ###Assistant : ""Converts a Pipfile-formatted dependency to a pip-formatted one.
    "
1433,"def handle_trial_end(self, data):
        
        hyper_params = nni.load(data['hyper_params'])
        if self.is_created_in_previous_exp(hyper_params['parameter_id']):
            # The end of the recovered trial is ignored
            return
        self._handle_trial_end(hyper_params['parameter_id'])
        if data['trial_job_id'] in self.job_id_para_id_map:
            del self.job_id_para_id_map[data['trial_job_id']]
","
        Parameters
        ----------
        data: dict()
            it has three keys: trial_job_id, event, hyper_params
            trial_job_id: the id generated by training service
            event: the job's state
            hyper_params: the hyperparameters (a string) generated and returned by tuner
        ",32,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def handle_trial_end(self, data):
        
        hyper_params = nni.load(data['hyper_params'])
        if self.is_created_in_previous_exp(hyper_params['parameter_id']):
            # The end of the recovered trial is ignored
            return
        self._handle_trial_end(hyper_params['parameter_id'])
        if data['trial_job_id'] in self.job_id_para_id_map:
            del self.job_id_para_id_map[data['trial_job_id']]

        ```
    ###Assistant : 
        Parameters
        ----------
        data: dict()
            it has three keys: trial_job_id, event, hyper_params
            trial_job_id: the id generated by training service
            event: the job's state
            hyper_params: the hyperparameters (a string) generated and returned by tuner
        
    "
1434,"def _copy_future_state(source, dest):
    
    assert source.done()
    if dest.cancelled():
        return
    assert not dest.done()
    if source.cancelled():
        dest.cancel()
    else:
        exception = source.exception()
        if exception is not None:
            dest.set_exception(_convert_future_exc(exception))
        else:
            result = source.result()
            dest.set_result(result)

","Internal helper to copy state from another Future.

    The other Future may be a concurrent.futures.Future.
    ",15,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _copy_future_state(source, dest):
    
    assert source.done()
    if dest.cancelled():
        return
    assert not dest.done()
    if source.cancelled():
        dest.cancel()
    else:
        exception = source.exception()
        if exception is not None:
            dest.set_exception(_convert_future_exc(exception))
        else:
            result = source.result()
            dest.set_result(result)


        ```
    ###Assistant : Internal helper to copy state from another Future.

    The other Future may be a concurrent.futures.Future.
    
    "
1435,"def test_get_feature_names_invalid_dtypes(names, dtypes):
    
    pd = pytest.importorskip(""pandas"")
    X = pd.DataFrame([[1, 2], [4, 5], [5, 6]], columns=names)

    msg = re.escape(
        ""Feature names only support names that are all strings. ""
        f""Got feature names with dtypes: {dtypes}.""
    )
    with pytest.raises(TypeError, match=msg):
        names = _get_feature_names(X)

",Get feature names errors when the feature names have mixed dtypes,11,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_feature_names_invalid_dtypes(names, dtypes):
    
    pd = pytest.importorskip(""pandas"")
    X = pd.DataFrame([[1, 2], [4, 5], [5, 6]], columns=names)

    msg = re.escape(
        ""Feature names only support names that are all strings. ""
        f""Got feature names with dtypes: {dtypes}.""
    )
    with pytest.raises(TypeError, match=msg):
        names = _get_feature_names(X)


        ```
    ###Assistant : Get feature names errors when the feature names have mixed dtypes
    "
1436,"def lookup(address, port, s):
    
    # We may get an ipv4-mapped ipv6 address here, e.g. ::ffff:127.0.0.1.
    # Those still appear as ""127.0.0.1"" in the table, so we need to strip the prefix.
    address = re.sub(r""^::ffff:(?=\d+.\d+.\d+.\d+$)"", """", address)
    s = s.decode()

    # ALL tcp 192.168.1.13:57474 -> 23.205.82.58:443       ESTABLISHED:ESTABLISHED
    specv4 = f""{address}:{port}""

    # ALL tcp 2a01:e35:8bae:50f0:9d9b:ef0d:2de3:b733[58505] -> 2606:4700:30::681f:4ad0[443]       ESTABLISHED:ESTABLISHED
    specv6 = f""{address}[{port}]""

    for i in s.split(""\n""):
        if ""ESTABLISHED:ESTABLISHED"" in i and specv4 in i:
            s = i.split()
            if len(s) > 4:
                if sys.platform.startswith(""freebsd""):
                    # strip parentheses for FreeBSD pfctl
                    s = s[3][1:-1].split("":"")
                else:
                    s = s[4].split("":"")

                if len(s) == 2:
                    return s[0], int(s[1])
        elif ""ESTABLISHED:ESTABLISHED"" in i and specv6 in i:
            s = i.split()
            if len(s) > 4:
                s = s[4].split(""["")
                port = s[1].split(""]"")
                port = port[0]
                return s[0], int(port)
    raise RuntimeError(""Could not resolve original destination."")
","
    Parse the pfctl state output s, to look up the destination host
    matching the client (address, port).

    Returns an (address, port) tuple, or None.
    ",24,133,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def lookup(address, port, s):
    
    # We may get an ipv4-mapped ipv6 address here, e.g. ::ffff:127.0.0.1.
    # Those still appear as ""127.0.0.1"" in the table, so we need to strip the prefix.
    address = re.sub(r""^::ffff:(?=\d+.\d+.\d+.\d+$)"", """", address)
    s = s.decode()

    # ALL tcp 192.168.1.13:57474 -> 23.205.82.58:443       ESTABLISHED:ESTABLISHED
    specv4 = f""{address}:{port}""

    # ALL tcp 2a01:e35:8bae:50f0:9d9b:ef0d:2de3:b733[58505] -> 2606:4700:30::681f:4ad0[443]       ESTABLISHED:ESTABLISHED
    specv6 = f""{address}[{port}]""

    for i in s.split(""\n""):
        if ""ESTABLISHED:ESTABLISHED"" in i and specv4 in i:
            s = i.split()
            if len(s) > 4:
                if sys.platform.startswith(""freebsd""):
                    # strip parentheses for FreeBSD pfctl
                    s = s[3][1:-1].split("":"")
                else:
                    s = s[4].split("":"")

                if len(s) == 2:
                    return s[0], int(s[1])
        elif ""ESTABLISHED:ESTABLISHED"" in i and specv6 in i:
            s = i.split()
            if len(s) > 4:
                s = s[4].split(""["")
                port = s[1].split(""]"")
                port = port[0]
                return s[0], int(port)
    raise RuntimeError(""Could not resolve original destination."")

        ```
    ###Assistant : 
    Parse the pfctl state output s, to look up the destination host
    matching the client (address, port).

    Returns an (address, port) tuple, or None.
    
    "
1437,"def _hydrate_rules(cls, project_id, rules, type=OwnerRuleType.OWNERSHIP_RULE.value):
        
        owners = [owner for rule in rules for owner in rule.owners]
        actors = {
            key: val
            for key, val in resolve_actors({owner for owner in owners}, project_id).items()
            if val
        }
        result = [
            (rule, ActorTuple.resolve_many([actors[owner] for owner in rule.owners]), type)
            for rule in rules
        ]
        return result
","
        Get the last matching rule to take the most precedence.
        ",10,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _hydrate_rules(cls, project_id, rules, type=OwnerRuleType.OWNERSHIP_RULE.value):
        
        owners = [owner for rule in rules for owner in rule.owners]
        actors = {
            key: val
            for key, val in resolve_actors({owner for owner in owners}, project_id).items()
            if val
        }
        result = [
            (rule, ActorTuple.resolve_many([actors[owner] for owner in rule.owners]), type)
            for rule in rules
        ]
        return result

        ```
    ###Assistant : 
        Get the last matching rule to take the most precedence.
        
    "
1438,"def activate(self) -> str:
        
        load_kube_config_from_dict(
            config_dict=self.config,
            context=self.context,
        )

        return self.current_context()
","
        Convenience method for activating the k8s config stored in an instance of this block

        Returns current_context for sanity check
        ",19,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def activate(self) -> str:
        
        load_kube_config_from_dict(
            config_dict=self.config,
            context=self.context,
        )

        return self.current_context()

        ```
    ###Assistant : 
        Convenience method for activating the k8s config stored in an instance of this block

        Returns current_context for sanity check
        
    "
1439,"def as_coeff_add(self, *deps) -> tuple[Expr, tuple[Expr, ...]]:
        
        if deps:
            if not self.has_free(*deps):
                return self, tuple()
        return S.Zero, (self,)
","Return the tuple (c, args) where self is written as an Add, ``a``.

        c should be a Rational added to any terms of the Add that are
        independent of deps.

        args should be a tuple of all other terms of ``a``; args is empty
        if self is a Number or if self is independent of deps (when given).

        This should be used when you do not know if self is an Add or not but
        you want to treat self as an Add or if you want to process the
        individual arguments of the tail of self as an Add.

        - if you know self is an Add and want only the head, use self.args[0];
        - if you do not want to process the arguments of the tail but need the
          tail then use self.as_two_terms() which gives the head and tail.
        - if you want to split self into an independent and dependent parts
          use ``self.as_independent(*deps)``

        >>> from sympy import S
        >>> from sympy.abc import x, y
        >>> (S(3)).as_coeff_add()
        (3, ())
        >>> (3 + x).as_coeff_add()
        (3, (x,))
        >>> (3 + x + y).as_coeff_add(x)
        (y + 3, (x,))
        >>> (3 + y).as_coeff_add(x)
        (y + 3, ())

        ",195,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def as_coeff_add(self, *deps) -> tuple[Expr, tuple[Expr, ...]]:
        
        if deps:
            if not self.has_free(*deps):
                return self, tuple()
        return S.Zero, (self,)

        ```
    ###Assistant : Return the tuple (c, args) where self is written as an Add, ``a``.

        c should be a Rational added to any terms of the Add that are
        independent of deps.

        args should be a tuple of all other terms of ``a``; args is empty
        if self is a Number or if self is independent of deps (when given).

        This should be used when you do not know if self is an Add or not but
        you want to treat self as an Add or if you want to process the
        individual arguments of the tail of self as an Add.

        - if you know self is an Add and want only the head, use self.args[0];
        - if you do not want to process the arguments of the tail but need the
          tail then use self.as_two_terms() which gives the head and tail.
        - if you want to split self into an independent and dependent parts
          use ``self.as_independent(*deps)``

        >>> from sympy import S
        >>> from sympy.abc import x, y
        >>> (S(3)).as_coeff_add()
        (3, ())
        >>> (3 + x).as_coeff_add()
        (3, (x,))
        >>> (3 + x + y).as_coeff_add(x)
        (y + 3, (x,))
        >>> (3 + y).as_coeff_add(x)
        (y + 3, ())

        
    "
1440,"def create_github_url(url):
    
    repo_only_url = re.compile(
        r""https:\/\/github\.com\/[a-z\d](?:[a-z\d]|-(?=[a-z\d])){0,38}\/[a-zA-Z0-9]+$""
    )
    re_branch = re.compile(""/(tree|blob)/(.+?)/"")

    # Check if the given url is a url to a GitHub repo. If it is, tell the
    # user to use 'git clone' to download it
    if re.match(repo_only_url, url):
        print(
            ""✘ The given url is a complete repository. Use 'git clone' to download the repository"",
            ""red"",
        )
        sys.exit()

    # extract the branch name from the given url (e.g master)
    branch = re_branch.search(url)
    if branch:
        download_dirs = url[branch.end() :]
        api_url = (
            url[: branch.start()].replace(""github.com"", ""api.github.com/repos"", 1)
            + ""/contents/""
            + download_dirs
            + ""?ref=""
            + branch.group(2)
        )
        return api_url, download_dirs

    raise ValueError(""The given url is not a valid GitHub url"")


# Modification of https://github.com/sdushantha/gitdir/blob/master/gitdir/gitdir.py","
    From the given url, produce a URL that is compatible with Github's REST API. Can handle blob or tree paths.
    ",20,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_github_url(url):
    
    repo_only_url = re.compile(
        r""https:\/\/github\.com\/[a-z\d](?:[a-z\d]|-(?=[a-z\d])){0,38}\/[a-zA-Z0-9]+$""
    )
    re_branch = re.compile(""/(tree|blob)/(.+?)/"")

    # Check if the given url is a url to a GitHub repo. If it is, tell the
    # user to use 'git clone' to download it
    if re.match(repo_only_url, url):
        print(
            ""✘ The given url is a complete repository. Use 'git clone' to download the repository"",
            ""red"",
        )
        sys.exit()

    # extract the branch name from the given url (e.g master)
    branch = re_branch.search(url)
    if branch:
        download_dirs = url[branch.end() :]
        api_url = (
            url[: branch.start()].replace(""github.com"", ""api.github.com/repos"", 1)
            + ""/contents/""
            + download_dirs
            + ""?ref=""
            + branch.group(2)
        )
        return api_url, download_dirs

    raise ValueError(""The given url is not a valid GitHub url"")


# Modification of https://github.com/sdushantha/gitdir/blob/master/gitdir/gitdir.py
        ```
    ###Assistant : 
    From the given url, produce a URL that is compatible with Github's REST API. Can handle blob or tree paths.
    
    "
1441,"def homogeneity_score(labels_true, labels_pred):
    
    return homogeneity_completeness_v_measure(labels_true, labels_pred)[0]

","Homogeneity metric of a cluster labeling given a ground truth.

    A clustering result satisfies homogeneity if all of its clusters
    contain only data points which are members of a single class.

    This metric is independent of the absolute values of the labels:
    a permutation of the class or cluster label values won't change the
    score value in any way.

    This metric is not symmetric: switching ``label_true`` with ``label_pred``
    will return the :func:`completeness_score` which will be different in
    general.

    Read more in the :ref:`User Guide <homogeneity_completeness>`.

    Parameters
    ----------
    labels_true : int array, shape = [n_samples]
        Ground truth class labels to be used as a reference.

    labels_pred : array-like of shape (n_samples,)
        Cluster labels to evaluate.

    Returns
    -------
    homogeneity : float
       Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling.

    See Also
    --------
    completeness_score : Completeness metric of cluster labeling.
    v_measure_score : V-Measure (NMI with arithmetic mean option).

    References
    ----------

    .. [1] `Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A
       conditional entropy-based external cluster evaluation measure
       <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_

    Examples
    --------

    Perfect labelings are homogeneous::

      >>> from sklearn.metrics.cluster import homogeneity_score
      >>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])
      1.0

    Non-perfect labelings that further split classes into more clusters can be
    perfectly homogeneous::

      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
      1.000000
      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
      1.000000

    Clusters that include samples from different classes do not make for an
    homogeneous labeling::

      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
      0.0...
      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
      0.0...
    ",263,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def homogeneity_score(labels_true, labels_pred):
    
    return homogeneity_completeness_v_measure(labels_true, labels_pred)[0]


        ```
    ###Assistant : Homogeneity metric of a cluster labeling given a ground truth.

    A clustering result satisfies homogeneity if all of its clusters
    contain only data points which are members of a single class.

    This metric is independent of the absolute values of the labels:
    a permutation of the class or cluster label values won't change the
    score value in any way.

    This metric is not symmetric: switching ``label_true`` with ``label_pred``
    will return the :func:`completeness_score` which will be different in
    general.

    Read more in the :ref:`User Guide <homogeneity_completeness>`.

    Parameters
    ----------
    labels_true : int array, shape = [n_samples]
        Ground truth class labels to be used as a reference.

    labels_pred : array-like of shape (n_samples,)
        Cluster labels to evaluate.

    Returns
    -------
    homogeneity : float
       Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling.

    See Also
    --------
    completeness_score : Completeness metric of cluster labeling.
    v_measure_score : V-Measure (NMI with arithmetic mean option).

    References
    ----------

    .. [1] `Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A
       conditional entropy-based external cluster evaluation measure
       <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_

    Examples
    --------

    Perfect labelings are homogeneous::

      >>> from sklearn.metrics.cluster import homogeneity_score
      >>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])
      1.0

    Non-perfect labelings that further split classes into more clusters can be
    perfectly homogeneous::

      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
      1.000000
      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
      1.000000

    Clusters that include samples from different classes do not make for an
    homogeneous labeling::

      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
      0.0...
      >>> print(""%.6f"" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
      0.0...
    
    "
1442,"def dup_chebyshevt(n, K):
    
    if n < 1:
        return [K.one]
    m2, m1 = [K.one], [K.one, K.zero]
    for i in range(2, n+1):
        m2, m1 = m1, dup_sub(dup_mul_ground(dup_lshift(m1, 1, K), K(2), K), m2, K)
    return m1
",Low-level implementation of Chebyshev polynomials of the first kind.,9,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dup_chebyshevt(n, K):
    
    if n < 1:
        return [K.one]
    m2, m1 = [K.one], [K.one, K.zero]
    for i in range(2, n+1):
        m2, m1 = m1, dup_sub(dup_mul_ground(dup_lshift(m1, 1, K), K(2), K), m2, K)
    return m1

        ```
    ###Assistant : Low-level implementation of Chebyshev polynomials of the first kind.
    "
1443,"def get_markdown_toc(markdown_source):
    
    md = markdown.Markdown(extensions=['toc'])
    md.convert(markdown_source)
    return md.toc_tokens

",Return TOC generated by Markdown parser from Markdown source text.,10,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_markdown_toc(markdown_source):
    
    md = markdown.Markdown(extensions=['toc'])
    md.convert(markdown_source)
    return md.toc_tokens


        ```
    ###Assistant : Return TOC generated by Markdown parser from Markdown source text.
    "
1444,"def get_value_data_from_instance(self, instance):
        
        return {
            ""id"": instance.pk,
            ""edit_url"": AdminURLFinder().get_edit_url(instance),
        }
","
        Given a model instance, return a value that we can pass to both the server-side template
        and the client-side rendering code (via telepath) that contains all the information needed
        for display. Typically this is a dict of id, title etc; it must be JSON-serialisable.
        ",44,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_value_data_from_instance(self, instance):
        
        return {
            ""id"": instance.pk,
            ""edit_url"": AdminURLFinder().get_edit_url(instance),
        }

        ```
    ###Assistant : 
        Given a model instance, return a value that we can pass to both the server-side template
        and the client-side rendering code (via telepath) that contains all the information needed
        for display. Typically this is a dict of id, title etc; it must be JSON-serialisable.
        
    "
1445,"def set_options(icon=None, button_color=None, element_size=(None, None), button_element_size=(None, None),
                margins=(None, None),
                element_padding=(None, None), auto_size_text=None, auto_size_buttons=None, font=None, border_width=None,
                slider_border_width=None, slider_relief=None, slider_orientation=None,
                autoclose_time=None, message_box_line_width=None,
                progress_meter_border_depth=None, progress_meter_style=None,
                progress_meter_relief=None, progress_meter_color=None, progress_meter_size=None,
                text_justification=None, background_color=None, element_background_color=None,
                text_element_background_color=None, input_elements_background_color=None, input_text_color=None,
                scrollbar_color=None, text_color=None, element_text_color=None, debug_win_size=(None, None),
                window_location=(None, None), error_button_color=(None, None), tooltip_time=None, tooltip_font=None, use_ttk_buttons=None, ttk_theme=None,
                suppress_error_popups=None, suppress_raise_key_errors=None, suppress_key_guessing=None,warn_button_key_duplicates=False, enable_treeview_869_patch=None,
                enable_mac_notitlebar_patch=None, use_custom_titlebar=None, titlebar_background_color=None, titlebar_text_color=None, titlebar_font=None,
                titlebar_icon=None, user_settings_path=None, pysimplegui_settings_path=None, pysimplegui_settings_filename=None, keep_on_top=None, dpi_awareness=None, scaling=None, disable_modal_windows=None, tooltip_offset=(None, None)):
    

    global DEFAULT_ELEMENT_SIZE
    global DEFAULT_BUTTON_ELEMENT_SIZE
    global DEFAULT_MARGINS  # Margins for each LEFT/RIGHT margin is first term
    global DEFAULT_ELEMENT_PADDING  # Padding between elements (row, col) in pixels
    global DEFAULT_AUTOSIZE_TEXT
    global DEFAULT_AUTOSIZE_BUTTONS
    global DEFAULT_FONT
    global DEFAULT_BORDER_WIDTH
    global DEFAULT_AUTOCLOSE_TIME
    global DEFAULT_BUTTON_COLOR
    global MESSAGE_BOX_LINE_WIDTH
    global DEFAULT_PROGRESS_BAR_BORDER_WIDTH
    global DEFAULT_PROGRESS_BAR_STYLE
    global DEFAULT_PROGRESS_BAR_RELIEF
    global DEFAULT_PROGRESS_BAR_COLOR
    global DEFAULT_PROGRESS_BAR_SIZE
    global DEFAULT_TEXT_JUSTIFICATION
    global DEFAULT_DEBUG_WINDOW_SIZE
    global DEFAULT_SLIDER_BORDER_WIDTH
    global DEFAULT_SLIDER_RELIEF
    global DEFAULT_SLIDER_ORIENTATION
    global DEFAULT_BACKGROUND_COLOR
    global DEFAULT_INPUT_ELEMENTS_COLOR
    global DEFAULT_ELEMENT_BACKGROUND_COLOR
    global DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR
    global DEFAULT_SCROLLBAR_COLOR
    global DEFAULT_TEXT_COLOR
    global DEFAULT_WINDOW_LOCATION
    global DEFAULT_ELEMENT_TEXT_COLOR
    global DEFAULT_INPUT_TEXT_COLOR
    global DEFAULT_TOOLTIP_TIME
    global DEFAULT_ERROR_BUTTON_COLOR
    global DEFAULT_TTK_THEME
    global USE_TTK_BUTTONS
    global TOOLTIP_FONT
    global SUPPRESS_ERROR_POPUPS
    global SUPPRESS_RAISE_KEY_ERRORS
    global SUPPRESS_KEY_GUESSING
    global WARN_DUPLICATE_BUTTON_KEY_ERRORS
    global ENABLE_TREEVIEW_869_PATCH
    global ENABLE_MAC_NOTITLEBAR_PATCH
    global USE_CUSTOM_TITLEBAR
    global CUSTOM_TITLEBAR_BACKGROUND_COLOR
    global CUSTOM_TITLEBAR_TEXT_COLOR
    global CUSTOM_TITLEBAR_ICON
    global CUSTOM_TITLEBAR_FONT
    global DEFAULT_USER_SETTINGS_PATH
    global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH
    global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME
    global DEFAULT_KEEP_ON_TOP
    global DEFAULT_SCALING
    global DEFAULT_MODAL_WINDOWS_ENABLED
    global DEFAULT_TOOLTIP_OFFSET
    global _pysimplegui_user_settings
    # global _my_windows

    if icon:
        Window._user_defined_icon = icon
        # _my_windows._user_defined_icon = icon

    if button_color != None:
        if button_color == COLOR_SYSTEM_DEFAULT:
            DEFAULT_BUTTON_COLOR = (COLOR_SYSTEM_DEFAULT, COLOR_SYSTEM_DEFAULT)
        else:
            DEFAULT_BUTTON_COLOR = button_color

    if element_size != (None, None):
        DEFAULT_ELEMENT_SIZE = element_size

    if button_element_size != (None, None):
        DEFAULT_BUTTON_ELEMENT_SIZE = button_element_size

    if margins != (None, None):
        DEFAULT_MARGINS = margins

    if element_padding != (None, None):
        DEFAULT_ELEMENT_PADDING = element_padding

    if auto_size_text != None:
        DEFAULT_AUTOSIZE_TEXT = auto_size_text

    if auto_size_buttons != None:
        DEFAULT_AUTOSIZE_BUTTONS = auto_size_buttons

    if font != None:
        DEFAULT_FONT = font

    if border_width != None:
        DEFAULT_BORDER_WIDTH = border_width

    if autoclose_time != None:
        DEFAULT_AUTOCLOSE_TIME = autoclose_time

    if message_box_line_width != None:
        MESSAGE_BOX_LINE_WIDTH = message_box_line_width

    if progress_meter_border_depth != None:
        DEFAULT_PROGRESS_BAR_BORDER_WIDTH = progress_meter_border_depth

    if progress_meter_style != None:
        warnings.warn('You can no longer set a progress bar style. All ttk styles must be the same for the window', UserWarning)
        # DEFAULT_PROGRESS_BAR_STYLE = progress_meter_style

    if progress_meter_relief != None:
        DEFAULT_PROGRESS_BAR_RELIEF = progress_meter_relief

    if progress_meter_color != None:
        DEFAULT_PROGRESS_BAR_COLOR = progress_meter_color

    if progress_meter_size != None:
        DEFAULT_PROGRESS_BAR_SIZE = progress_meter_size

    if slider_border_width != None:
        DEFAULT_SLIDER_BORDER_WIDTH = slider_border_width

    if slider_orientation != None:
        DEFAULT_SLIDER_ORIENTATION = slider_orientation

    if slider_relief != None:
        DEFAULT_SLIDER_RELIEF = slider_relief

    if text_justification != None:
        DEFAULT_TEXT_JUSTIFICATION = text_justification

    if background_color != None:
        DEFAULT_BACKGROUND_COLOR = background_color

    if text_element_background_color != None:
        DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR = text_element_background_color

    if input_elements_background_color != None:
        DEFAULT_INPUT_ELEMENTS_COLOR = input_elements_background_color

    if element_background_color != None:
        DEFAULT_ELEMENT_BACKGROUND_COLOR = element_background_color

    if window_location != (None, None):
        DEFAULT_WINDOW_LOCATION = window_location

    if debug_win_size != (None, None):
        DEFAULT_DEBUG_WINDOW_SIZE = debug_win_size

    if text_color != None:
        DEFAULT_TEXT_COLOR = text_color

    if scrollbar_color != None:
        DEFAULT_SCROLLBAR_COLOR = scrollbar_color

    if element_text_color != None:
        DEFAULT_ELEMENT_TEXT_COLOR = element_text_color

    if input_text_color is not None:
        DEFAULT_INPUT_TEXT_COLOR = input_text_color

    if tooltip_time is not None:
        DEFAULT_TOOLTIP_TIME = tooltip_time

    if error_button_color != (None, None):
        DEFAULT_ERROR_BUTTON_COLOR = error_button_color

    if ttk_theme is not None:
        DEFAULT_TTK_THEME = ttk_theme

    if use_ttk_buttons is not None:
        USE_TTK_BUTTONS = use_ttk_buttons

    if tooltip_font is not None:
        TOOLTIP_FONT = tooltip_font

    if suppress_error_popups is not None:
        SUPPRESS_ERROR_POPUPS = suppress_error_popups

    if suppress_raise_key_errors is not None:
        SUPPRESS_RAISE_KEY_ERRORS = suppress_raise_key_errors

    if suppress_key_guessing is not None:
        SUPPRESS_KEY_GUESSING = suppress_key_guessing

    if warn_button_key_duplicates is not None:
        WARN_DUPLICATE_BUTTON_KEY_ERRORS = warn_button_key_duplicates

    if enable_treeview_869_patch is not None:
        ENABLE_TREEVIEW_869_PATCH = enable_treeview_869_patch

    if enable_mac_notitlebar_patch is not None:
        ENABLE_MAC_NOTITLEBAR_PATCH = enable_mac_notitlebar_patch

    if use_custom_titlebar is not None:
        USE_CUSTOM_TITLEBAR = use_custom_titlebar

    if titlebar_background_color is not None:
        CUSTOM_TITLEBAR_BACKGROUND_COLOR = titlebar_background_color

    if titlebar_text_color is not None:
        CUSTOM_TITLEBAR_TEXT_COLOR = titlebar_text_color

    if titlebar_font is not None:
        CUSTOM_TITLEBAR_FONT = titlebar_font

    if titlebar_icon is not None:
        CUSTOM_TITLEBAR_ICON = titlebar_icon

    if user_settings_path is not None:
        DEFAULT_USER_SETTINGS_PATH = user_settings_path

    if pysimplegui_settings_path is not None:
        DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH = pysimplegui_settings_path

    if pysimplegui_settings_filename is not None:
        DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME = pysimplegui_settings_filename

    if pysimplegui_settings_filename is not None or pysimplegui_settings_filename is not None:
        _pysimplegui_user_settings = UserSettings(filename=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME,
                                                  path=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH)

    if keep_on_top is not None:
        DEFAULT_KEEP_ON_TOP = keep_on_top

    if dpi_awareness is True:
        if running_windows():
            if platform.release() == ""7"":
                ctypes.windll.user32.SetProcessDPIAware()
            elif platform.release() == ""8"" or platform.release() == ""10"":
                ctypes.windll.shcore.SetProcessDpiAwareness(1)

    if scaling is not None:
        DEFAULT_SCALING = scaling

    if disable_modal_windows is not None:
        DEFAULT_MODAL_WINDOWS_ENABLED = not disable_modal_windows

    if tooltip_offset != (None, None):
        DEFAULT_TOOLTIP_OFFSET = tooltip_offset

    return True


# ----------------------------------------------------------------- #

# .########.##.....##.########.##.....##.########..######.
# ....##....##.....##.##.......###...###.##.......##....##
# ....##....##.....##.##.......####.####.##.......##......
# ....##....#########.######...##.###.##.######....######.
# ....##....##.....##.##.......##.....##.##.............##
# ....##....##.....##.##.......##.....##.##.......##....##
# ....##....##.....##.########.##.....##.########..######.

# ----------------------------------------------------------------- #

# The official Theme code

#################### ChangeLookAndFeel #######################
# Predefined settings that will change the colors and styles #
# of the elements.                                           #
##############################################################
LOOK_AND_FEEL_TABLE = {
    ""SystemDefault"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                      ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1,
                      ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""SystemDefaultForReal"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT,
                             ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT, ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT,
                             ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""SystemDefault1"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                       ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                       ""PROGRESS_DEPTH"": 0, },
    ""Material1"": {""BACKGROUND"": ""#E3F2FD"", ""TEXT"": ""#000000"", ""INPUT"": ""#86A8FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#86A8FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#5079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""Material2"": {""BACKGROUND"": ""#FAFAFA"", ""TEXT"": ""#000000"", ""INPUT"": ""#004EA1"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#5EA7FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#0079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""Reddit"": {""BACKGROUND"": ""#ffffff"", ""TEXT"": ""#1a1a1b"", ""INPUT"": ""#dae0e6"", ""TEXT_INPUT"": ""#222222"", ""SCROLL"": ""#a5a4a4"", ""BUTTON"": (""#FFFFFF"", ""#0079d3""),
               ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, ""ACCENT1"": ""#ff5414"", ""ACCENT2"": ""#33a8ff"",
               ""ACCENT3"": ""#dbf0ff"", },
    ""Topanga"": {""BACKGROUND"": ""#282923"", ""TEXT"": ""#E7DB74"", ""INPUT"": ""#393a32"", ""TEXT_INPUT"": ""#E7C855"", ""SCROLL"": ""#E7C855"", ""BUTTON"": (""#E7C855"", ""#284B5A""),
                ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, ""ACCENT1"": ""#c15226"", ""ACCENT2"": ""#7a4d5f"",
                ""ACCENT3"": ""#889743"", },
    ""GreenTan"": {""BACKGROUND"": ""#9FB8AD"", ""TEXT"": '#000000', ""INPUT"": ""#F7F3EC"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#F7F3EC"", ""BUTTON"": (""#FFFFFF"", ""#475841""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Dark"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#FFFFFF"", ""#004F00""),
             ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen"": {""BACKGROUND"": ""#B7CECE"", ""TEXT"": ""#000000"", ""INPUT"": ""#FDFFF7"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#FDFFF7"",
                   ""BUTTON"": (""#FFFFFF"", ""#658268""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""ACCENT1"": ""#76506d"",
                   ""ACCENT2"": ""#5148f1"", ""ACCENT3"": ""#0a1c84"", ""PROGRESS_DEPTH"": 0, },
    ""Dark2"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#FFFFFF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#FFFFFF"", ""#004F00""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Black"": {""BACKGROUND"": ""#000000"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#000000"", ""#FFFFFF""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Tan"": {""BACKGROUND"": ""#fdf6e3"", ""TEXT"": ""#268bd1"", ""INPUT"": ""#eee8d5"", ""TEXT_INPUT"": ""#6c71c3"", ""SCROLL"": ""#eee8d5"", ""BUTTON"": (""#FFFFFF"", ""#063542""),
            ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""TanBlue"": {""BACKGROUND"": ""#e5dece"", ""TEXT"": ""#063289"", ""INPUT"": ""#f9f8f4"", ""TEXT_INPUT"": ""#242834"", ""SCROLL"": ""#eee8d5"", ""BUTTON"": (""#FFFFFF"", ""#063289""),
                ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkTanBlue"": {""BACKGROUND"": ""#242834"", ""TEXT"": ""#dfe6f8"", ""INPUT"": ""#97755c"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#a9afbb"",
                    ""BUTTON"": (""#FFFFFF"", ""#063289""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkAmber"": {""BACKGROUND"": ""#2c2825"", ""TEXT"": ""#fdcb52"", ""INPUT"": ""#705e52"", ""TEXT_INPUT"": ""#fdcb52"", ""SCROLL"": ""#705e52"",
                  ""BUTTON"": (""#000000"", ""#fdcb52""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue"": {""BACKGROUND"": ""#1a2835"", ""TEXT"": ""#d1ecff"", ""INPUT"": ""#335267"", ""TEXT_INPUT"": ""#acc2d0"", ""SCROLL"": ""#1b6497"", ""BUTTON"": (""#000000"", ""#fafaf8""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Reds"": {""BACKGROUND"": ""#280001"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#763e00"", ""BUTTON"": (""#000000"", ""#daad28""),
             ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Green"": {""BACKGROUND"": ""#82a459"", ""TEXT"": ""#000000"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e3ecf3"", ""BUTTON"": (""#FFFFFF"", ""#517239""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BluePurple"": {""BACKGROUND"": ""#A5CADD"", ""TEXT"": ""#6E266E"", ""INPUT"": ""#E0F5FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#E0F5FF"",
                   ""BUTTON"": (""#FFFFFF"", ""#303952""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Purple"": {""BACKGROUND"": ""#B0AAC2"", ""TEXT"": ""#000000"", ""INPUT"": ""#F2EFE8"", ""SCROLL"": ""#F2EFE8"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#000000"", ""#C2D4D8""),
               ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BlueMono"": {""BACKGROUND"": ""#AAB6D3"", ""TEXT"": ""#000000"", ""INPUT"": ""#F1F4FC"", ""SCROLL"": ""#F1F4FC"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#FFFFFF"", ""#7186C7""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""GreenMono"": {""BACKGROUND"": ""#A8C1B4"", ""TEXT"": ""#000000"", ""INPUT"": ""#DDE0DE"", ""SCROLL"": ""#E3E3E3"", ""TEXT_INPUT"": ""#000000"",
                  ""BUTTON"": (""#FFFFFF"", ""#6D9F85""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BrownBlue"": {""BACKGROUND"": ""#64778d"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#f0f3f7"", ""SCROLL"": ""#A6B2BE"", ""TEXT_INPUT"": ""#000000"",
                  ""BUTTON"": (""#FFFFFF"", ""#283b5b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BrightColors"": {""BACKGROUND"": ""#b4ffb4"", ""TEXT"": ""#000000"", ""INPUT"": ""#ffff64"", ""SCROLL"": ""#ffb482"", ""TEXT_INPUT"": ""#000000"",
                     ""BUTTON"": (""#000000"", ""#ffa0dc""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""NeutralBlue"": {""BACKGROUND"": ""#92aa9d"", ""TEXT"": ""#000000"", ""INPUT"": ""#fcfff6"", ""SCROLL"": ""#fcfff6"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#d0dbbd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Kayak"": {""BACKGROUND"": ""#a7ad7f"", ""TEXT"": ""#000000"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#FFFFFF"", ""#5d907d""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""SandyBeach"": {""BACKGROUND"": ""#efeccb"", ""TEXT"": ""#012f2f"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#012f2f"",
                   ""BUTTON"": (""#FFFFFF"", ""#046380""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""TealMono"": {""BACKGROUND"": ""#a8cfdd"", ""TEXT"": ""#000000"", ""INPUT"": ""#dfedf2"", ""SCROLL"": ""#dfedf2"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#FFFFFF"", ""#183440""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Default"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                ""PROGRESS_DEPTH"": 0, },
    ""Default1"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                 ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                 ""PROGRESS_DEPTH"": 0, },
    ""DefaultNoMoreNagging"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT,
                             ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT, ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR,
                             ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""GrayGrayGray"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                     ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                     ""PROGRESS_DEPTH"": 0, },
    ""LightBlue"": {""BACKGROUND"": ""#E3F2FD"", ""TEXT"": ""#000000"", ""INPUT"": ""#86A8FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#86A8FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#5079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""LightGrey"": {""BACKGROUND"": ""#FAFAFA"", ""TEXT"": ""#000000"", ""INPUT"": ""#004EA1"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#5EA7FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#0079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""LightGrey1"": {""BACKGROUND"": ""#ffffff"", ""TEXT"": ""#1a1a1b"", ""INPUT"": ""#dae0e6"", ""TEXT_INPUT"": ""#222222"", ""SCROLL"": ""#a5a4a4"",
                   ""BUTTON"": (""#FFFFFF"", ""#0079d3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""ACCENT1"": ""#ff5414"", ""ACCENT2"": ""#33a8ff"", ""ACCENT3"": ""#dbf0ff"", },
    ""DarkBrown"": {""BACKGROUND"": ""#282923"", ""TEXT"": ""#E7DB74"", ""INPUT"": ""#393a32"", ""TEXT_INPUT"": ""#E7C855"", ""SCROLL"": ""#E7C855"",
                  ""BUTTON"": (""#E7C855"", ""#284B5A""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#c15226"", ""ACCENT2"": ""#7a4d5f"", ""ACCENT3"": ""#889743"", },
    ""LightGreen1"": {""BACKGROUND"": ""#9FB8AD"", ""TEXT"": ""#000000"", ""INPUT"": ""#F7F3EC"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#F7F3EC"",
                    ""BUTTON"": (""#FFFFFF"", ""#475841""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#FFFFFF"", ""#004F00""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen2"": {""BACKGROUND"": ""#B7CECE"", ""TEXT"": ""#000000"", ""INPUT"": ""#FDFFF7"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#FDFFF7"",
                    ""BUTTON"": (""#FFFFFF"", ""#658268""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""ACCENT1"": ""#76506d"",
                    ""ACCENT2"": ""#5148f1"", ""ACCENT3"": ""#0a1c84"", ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey1"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#FFFFFF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#707070"",
                  ""BUTTON"": (""#FFFFFF"", ""#004F00""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlack"": {""BACKGROUND"": ""#000000"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"",
                  ""BUTTON"": (""#000000"", ""#FFFFFF""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown"": {""BACKGROUND"": ""#fdf6e3"", ""TEXT"": ""#268bd1"", ""INPUT"": ""#eee8d5"", ""TEXT_INPUT"": ""#6c71c3"", ""SCROLL"": ""#eee8d5"",
                   ""BUTTON"": (""#FFFFFF"", ""#063542""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown1"": {""BACKGROUND"": ""#e5dece"", ""TEXT"": ""#063289"", ""INPUT"": ""#f9f8f4"", ""TEXT_INPUT"": ""#242834"", ""SCROLL"": ""#eee8d5"",
                    ""BUTTON"": (""#FFFFFF"", ""#063289""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue1"": {""BACKGROUND"": ""#242834"", ""TEXT"": ""#dfe6f8"", ""INPUT"": ""#97755c"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#a9afbb"",
                  ""BUTTON"": (""#FFFFFF"", ""#063289""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBrown1"": {""BACKGROUND"": ""#2c2825"", ""TEXT"": ""#fdcb52"", ""INPUT"": ""#705e52"", ""TEXT_INPUT"": ""#fdcb52"", ""SCROLL"": ""#705e52"",
                   ""BUTTON"": (""#000000"", ""#fdcb52""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue2"": {""BACKGROUND"": ""#1a2835"", ""TEXT"": ""#d1ecff"", ""INPUT"": ""#335267"", ""TEXT_INPUT"": ""#acc2d0"", ""SCROLL"": ""#1b6497"",
                  ""BUTTON"": (""#000000"", ""#fafaf8""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBrown2"": {""BACKGROUND"": ""#280001"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#763e00"",
                   ""BUTTON"": (""#000000"", ""#daad28""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGreen"": {""BACKGROUND"": ""#82a459"", ""TEXT"": ""#000000"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e3ecf3"",
                  ""BUTTON"": (""#FFFFFF"", ""#517239""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBlue1"": {""BACKGROUND"": ""#A5CADD"", ""TEXT"": ""#6E266E"", ""INPUT"": ""#E0F5FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#E0F5FF"",
                   ""BUTTON"": (""#FFFFFF"", ""#303952""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightPurple"": {""BACKGROUND"": ""#B0AAC2"", ""TEXT"": ""#000000"", ""INPUT"": ""#F2EFE8"", ""SCROLL"": ""#F2EFE8"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#C2D4D8""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBlue2"": {""BACKGROUND"": ""#AAB6D3"", ""TEXT"": ""#000000"", ""INPUT"": ""#F1F4FC"", ""SCROLL"": ""#F1F4FC"", ""TEXT_INPUT"": ""#000000"",
                   ""BUTTON"": (""#FFFFFF"", ""#7186C7""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen3"": {""BACKGROUND"": ""#A8C1B4"", ""TEXT"": ""#000000"", ""INPUT"": ""#DDE0DE"", ""SCROLL"": ""#E3E3E3"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#FFFFFF"", ""#6D9F85""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue3"": {""BACKGROUND"": ""#64778d"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#f0f3f7"", ""SCROLL"": ""#A6B2BE"", ""TEXT_INPUT"": ""#000000"",
                  ""BUTTON"": (""#FFFFFF"", ""#283b5b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen4"": {""BACKGROUND"": ""#b4ffb4"", ""TEXT"": ""#000000"", ""INPUT"": ""#ffff64"", ""SCROLL"": ""#ffb482"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#ffa0dc""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen5"": {""BACKGROUND"": ""#92aa9d"", ""TEXT"": ""#000000"", ""INPUT"": ""#fcfff6"", ""SCROLL"": ""#fcfff6"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#d0dbbd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown2"": {""BACKGROUND"": ""#a7ad7f"", ""TEXT"": ""#000000"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#FFFFFF"", ""#5d907d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown3"": {""BACKGROUND"": ""#efeccb"", ""TEXT"": ""#012f2f"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#012f2f"",
                    ""BUTTON"": (""#FFFFFF"", ""#046380""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBlue3"": {""BACKGROUND"": ""#a8cfdd"", ""TEXT"": ""#000000"", ""INPUT"": ""#dfedf2"", ""SCROLL"": ""#dfedf2"", ""TEXT_INPUT"": ""#000000"",
                   ""BUTTON"": (""#FFFFFF"", ""#183440""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown4"": {""BACKGROUND"": ""#d7c79e"", ""TEXT"": ""#a35638"", ""INPUT"": ""#9dab86"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#a35638"",
                    ""BUTTON"": (""#FFFFFF"", ""#a35638""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#a35638"", ""#9dab86"", ""#e08f62"", ""#d7c79e""], },
    ""DarkTeal"": {""BACKGROUND"": ""#003f5c"", ""TEXT"": ""#fb5b5a"", ""INPUT"": ""#bc4873"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#bc4873"", ""BUTTON"": (""#FFFFFF"", ""#fb5b5a""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                 ""COLOR_LIST"": [""#003f5c"", ""#472b62"", ""#bc4873"", ""#fb5b5a""], },
    ""DarkPurple"": {""BACKGROUND"": ""#472b62"", ""TEXT"": ""#fb5b5a"", ""INPUT"": ""#bc4873"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#bc4873"",
                   ""BUTTON"": (""#FFFFFF"", ""#472b62""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#003f5c"", ""#472b62"", ""#bc4873"", ""#fb5b5a""], },
    ""LightGreen6"": {""BACKGROUND"": ""#eafbea"", ""TEXT"": ""#1f6650"", ""INPUT"": ""#6f9a8d"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#1f6650"",
                    ""BUTTON"": (""#FFFFFF"", ""#1f6650""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#1f6650"", ""#6f9a8d"", ""#ea5e5e"", ""#eafbea""], },
    ""DarkGrey2"": {""BACKGROUND"": ""#2b2b28"", ""TEXT"": ""#f8f8f8"", ""INPUT"": ""#f1d6ab"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f1d6ab"",
                  ""BUTTON"": (""#2b2b28"", ""#e3b04b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#2b2b28"", ""#e3b04b"", ""#f1d6ab"", ""#f8f8f8""], },
    ""LightBrown6"": {""BACKGROUND"": ""#f9b282"", ""TEXT"": ""#8f4426"", ""INPUT"": ""#de6b35"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#8f4426"",
                    ""BUTTON"": (""#FFFFFF"", ""#8f4426""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#8f4426"", ""#de6b35"", ""#64ccda"", ""#f9b282""], },
    ""DarkTeal1"": {""BACKGROUND"": ""#396362"", ""TEXT"": ""#ffe7d1"", ""INPUT"": ""#f6c89f"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f6c89f"",
                  ""BUTTON"": (""#ffe7d1"", ""#4b8e8d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#396362"", ""#4b8e8d"", ""#f6c89f"", ""#ffe7d1""], },
    ""LightBrown7"": {""BACKGROUND"": ""#f6c89f"", ""TEXT"": ""#396362"", ""INPUT"": ""#4b8e8d"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#396362"",
                    ""BUTTON"": (""#FFFFFF"", ""#396362""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#396362"", ""#4b8e8d"", ""#f6c89f"", ""#ffe7d1""], },
    ""DarkPurple1"": {""BACKGROUND"": ""#0c093c"", ""TEXT"": ""#fad6d6"", ""INPUT"": ""#eea5f6"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#eea5f6"",
                    ""BUTTON"": (""#FFFFFF"", ""#df42d1""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#0c093c"", ""#df42d1"", ""#eea5f6"", ""#fad6d6""], },
    ""DarkGrey3"": {""BACKGROUND"": ""#211717"", ""TEXT"": ""#dfddc7"", ""INPUT"": ""#f58b54"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f58b54"",
                  ""BUTTON"": (""#dfddc7"", ""#a34a28""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#211717"", ""#a34a28"", ""#f58b54"", ""#dfddc7""], },
    ""LightBrown8"": {""BACKGROUND"": ""#dfddc7"", ""TEXT"": ""#211717"", ""INPUT"": ""#a34a28"", ""TEXT_INPUT"": ""#dfddc7"", ""SCROLL"": ""#211717"",
                    ""BUTTON"": (""#dfddc7"", ""#a34a28""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#211717"", ""#a34a28"", ""#f58b54"", ""#dfddc7""], },
    ""DarkBlue4"": {""BACKGROUND"": ""#494ca2"", ""TEXT"": ""#e3e7f1"", ""INPUT"": ""#c6cbef"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#c6cbef"",
                  ""BUTTON"": (""#FFFFFF"", ""#8186d5""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#494ca2"", ""#8186d5"", ""#c6cbef"", ""#e3e7f1""], },
    ""LightBlue4"": {""BACKGROUND"": ""#5c94bd"", ""TEXT"": ""#470938"", ""INPUT"": ""#1a3e59"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#470938"",
                   ""BUTTON"": (""#FFFFFF"", ""#470938""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#470938"", ""#1a3e59"", ""#5c94bd"", ""#f2d6eb""], },
    ""DarkTeal2"": {""BACKGROUND"": ""#394a6d"", ""TEXT"": ""#c0ffb3"", ""INPUT"": ""#52de97"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#52de97"",
                  ""BUTTON"": (""#c0ffb3"", ""#394a6d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#394a6d"", ""#3c9d9b"", ""#52de97"", ""#c0ffb3""], },
    ""DarkTeal3"": {""BACKGROUND"": ""#3c9d9b"", ""TEXT"": ""#c0ffb3"", ""INPUT"": ""#52de97"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#52de97"",
                  ""BUTTON"": (""#c0ffb3"", ""#394a6d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#394a6d"", ""#3c9d9b"", ""#52de97"", ""#c0ffb3""], },
    ""DarkPurple5"": {""BACKGROUND"": ""#730068"", ""TEXT"": ""#f6f078"", ""INPUT"": ""#01d28e"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#01d28e"",
                    ""BUTTON"": (""#f6f078"", ""#730068""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#730068"", ""#434982"", ""#01d28e"", ""#f6f078""], },
    ""DarkPurple2"": {""BACKGROUND"": ""#202060"", ""TEXT"": ""#b030b0"", ""INPUT"": ""#602080"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#602080"",
                    ""BUTTON"": (""#FFFFFF"", ""#202040""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#202040"", ""#202060"", ""#602080"", ""#b030b0""], },
    ""DarkBlue5"": {""BACKGROUND"": ""#000272"", ""TEXT"": ""#ff6363"", ""INPUT"": ""#a32f80"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#a32f80"",
                  ""BUTTON"": (""#FFFFFF"", ""#341677""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#000272"", ""#341677"", ""#a32f80"", ""#ff6363""], },
    ""LightGrey2"": {""BACKGROUND"": ""#f6f6f6"", ""TEXT"": ""#420000"", ""INPUT"": ""#d4d7dd"", ""TEXT_INPUT"": ""#420000"", ""SCROLL"": ""#420000"",
                   ""BUTTON"": (""#420000"", ""#d4d7dd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#420000"", ""#d4d7dd"", ""#eae9e9"", ""#f6f6f6""], },
    ""LightGrey3"": {""BACKGROUND"": ""#eae9e9"", ""TEXT"": ""#420000"", ""INPUT"": ""#d4d7dd"", ""TEXT_INPUT"": ""#420000"", ""SCROLL"": ""#420000"",
                   ""BUTTON"": (""#420000"", ""#d4d7dd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#420000"", ""#d4d7dd"", ""#eae9e9"", ""#f6f6f6""], },
    ""DarkBlue6"": {""BACKGROUND"": ""#01024e"", ""TEXT"": ""#ff6464"", ""INPUT"": ""#8b4367"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#8b4367"",
                  ""BUTTON"": (""#FFFFFF"", ""#543864""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#01024e"", ""#543864"", ""#8b4367"", ""#ff6464""], },
    ""DarkBlue7"": {""BACKGROUND"": ""#241663"", ""TEXT"": ""#eae7af"", ""INPUT"": ""#a72693"", ""TEXT_INPUT"": ""#eae7af"", ""SCROLL"": ""#a72693"",
                  ""BUTTON"": (""#eae7af"", ""#160f30""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#160f30"", ""#241663"", ""#a72693"", ""#eae7af""], },
    ""LightBrown9"": {""BACKGROUND"": ""#f6d365"", ""TEXT"": ""#3a1f5d"", ""INPUT"": ""#c83660"", ""TEXT_INPUT"": ""#f6d365"", ""SCROLL"": ""#3a1f5d"",
                    ""BUTTON"": (""#f6d365"", ""#c83660""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#3a1f5d"", ""#c83660"", ""#e15249"", ""#f6d365""], },
    ""DarkPurple3"": {""BACKGROUND"": ""#6e2142"", ""TEXT"": ""#ffd692"", ""INPUT"": ""#e16363"", ""TEXT_INPUT"": ""#ffd692"", ""SCROLL"": ""#e16363"",
                    ""BUTTON"": (""#ffd692"", ""#943855""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#6e2142"", ""#943855"", ""#e16363"", ""#ffd692""], },
    ""LightBrown10"": {""BACKGROUND"": ""#ffd692"", ""TEXT"": ""#6e2142"", ""INPUT"": ""#943855"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#6e2142"",
                     ""BUTTON"": (""#FFFFFF"", ""#6e2142""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#6e2142"", ""#943855"", ""#e16363"", ""#ffd692""], },
    ""DarkPurple4"": {""BACKGROUND"": ""#200f21"", ""TEXT"": ""#f638dc"", ""INPUT"": ""#5a3d5c"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#5a3d5c"",
                    ""BUTTON"": (""#FFFFFF"", ""#382039""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#200f21"", ""#382039"", ""#5a3d5c"", ""#f638dc""], },
    ""LightBlue5"": {""BACKGROUND"": ""#b2fcff"", ""TEXT"": ""#3e64ff"", ""INPUT"": ""#5edfff"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#3e64ff"",
                   ""BUTTON"": (""#FFFFFF"", ""#3e64ff""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3e64ff"", ""#5edfff"", ""#b2fcff"", ""#ecfcff""], },
    ""DarkTeal4"": {""BACKGROUND"": ""#464159"", ""TEXT"": ""#c7f0db"", ""INPUT"": ""#8bbabb"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#8bbabb"",
                  ""BUTTON"": (""#FFFFFF"", ""#6c7b95""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#464159"", ""#6c7b95"", ""#8bbabb"", ""#c7f0db""], },
    ""LightTeal"": {""BACKGROUND"": ""#c7f0db"", ""TEXT"": ""#464159"", ""INPUT"": ""#6c7b95"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#464159"",
                  ""BUTTON"": (""#FFFFFF"", ""#464159""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#464159"", ""#6c7b95"", ""#8bbabb"", ""#c7f0db""], },
    ""DarkTeal5"": {""BACKGROUND"": ""#8bbabb"", ""TEXT"": ""#464159"", ""INPUT"": ""#6c7b95"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#464159"",
                  ""BUTTON"": (""#c7f0db"", ""#6c7b95""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#464159"", ""#6c7b95"", ""#8bbabb"", ""#c7f0db""], },
    ""LightGrey4"": {""BACKGROUND"": ""#faf5ef"", ""TEXT"": ""#672f2f"", ""INPUT"": ""#99b19c"", ""TEXT_INPUT"": ""#672f2f"", ""SCROLL"": ""#672f2f"",
                   ""BUTTON"": (""#672f2f"", ""#99b19c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#672f2f"", ""#99b19c"", ""#d7d1c9"", ""#faf5ef""], },
    ""LightGreen7"": {""BACKGROUND"": ""#99b19c"", ""TEXT"": ""#faf5ef"", ""INPUT"": ""#d7d1c9"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#d7d1c9"",
                    ""BUTTON"": (""#FFFFFF"", ""#99b19c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#672f2f"", ""#99b19c"", ""#d7d1c9"", ""#faf5ef""], },
    ""LightGrey5"": {""BACKGROUND"": ""#d7d1c9"", ""TEXT"": ""#672f2f"", ""INPUT"": ""#99b19c"", ""TEXT_INPUT"": ""#672f2f"", ""SCROLL"": ""#672f2f"",
                   ""BUTTON"": (""#FFFFFF"", ""#672f2f""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#672f2f"", ""#99b19c"", ""#d7d1c9"", ""#faf5ef""], },
    ""DarkBrown3"": {""BACKGROUND"": ""#a0855b"", ""TEXT"": ""#f9f6f2"", ""INPUT"": ""#f1d6ab"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f1d6ab"",
                   ""BUTTON"": (""#FFFFFF"", ""#38470b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#38470b"", ""#a0855b"", ""#f1d6ab"", ""#f9f6f2""], },
    ""LightBrown11"": {""BACKGROUND"": ""#f1d6ab"", ""TEXT"": ""#38470b"", ""INPUT"": ""#a0855b"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#38470b"",
                     ""BUTTON"": (""#f9f6f2"", ""#a0855b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#38470b"", ""#a0855b"", ""#f1d6ab"", ""#f9f6f2""], },
    ""DarkRed"": {""BACKGROUND"": ""#83142c"", ""TEXT"": ""#f9d276"", ""INPUT"": ""#ad1d45"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#ad1d45"", ""BUTTON"": (""#f9d276"", ""#ad1d45""),
                ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                ""COLOR_LIST"": [""#44000d"", ""#83142c"", ""#ad1d45"", ""#f9d276""], },
    ""DarkTeal6"": {""BACKGROUND"": ""#204969"", ""TEXT"": ""#fff7f7"", ""INPUT"": ""#dadada"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#dadada"",
                  ""BUTTON"": (""#000000"", ""#fff7f7""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#204969"", ""#08ffc8"", ""#dadada"", ""#fff7f7""], },
    ""DarkBrown4"": {""BACKGROUND"": ""#252525"", ""TEXT"": ""#ff0000"", ""INPUT"": ""#af0404"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#af0404"",
                   ""BUTTON"": (""#FFFFFF"", ""#252525""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#252525"", ""#414141"", ""#af0404"", ""#ff0000""], },
    ""LightYellow"": {""BACKGROUND"": ""#f4ff61"", ""TEXT"": ""#27aa80"", ""INPUT"": ""#32ff6a"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#27aa80"",
                    ""BUTTON"": (""#f4ff61"", ""#27aa80""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#27aa80"", ""#32ff6a"", ""#a8ff3e"", ""#f4ff61""], },
    ""DarkGreen1"": {""BACKGROUND"": ""#2b580c"", ""TEXT"": ""#fdef96"", ""INPUT"": ""#f7b71d"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f7b71d"",
                   ""BUTTON"": (""#fdef96"", ""#2b580c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#2b580c"", ""#afa939"", ""#f7b71d"", ""#fdef96""], },
    ""LightGreen8"": {""BACKGROUND"": ""#c8dad3"", ""TEXT"": ""#63707e"", ""INPUT"": ""#93b5b3"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#63707e"",
                    ""BUTTON"": (""#FFFFFF"", ""#63707e""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#63707e"", ""#93b5b3"", ""#c8dad3"", ""#f2f6f5""], },
    ""DarkTeal7"": {""BACKGROUND"": ""#248ea9"", ""TEXT"": ""#fafdcb"", ""INPUT"": ""#aee7e8"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#aee7e8"",
                  ""BUTTON"": (""#000000"", ""#fafdcb""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#248ea9"", ""#28c3d4"", ""#aee7e8"", ""#fafdcb""], },
    ""DarkBlue8"": {""BACKGROUND"": ""#454d66"", ""TEXT"": ""#d9d872"", ""INPUT"": ""#58b368"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#58b368"",
                  ""BUTTON"": (""#000000"", ""#009975""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#009975"", ""#454d66"", ""#58b368"", ""#d9d872""], },
    ""DarkBlue9"": {""BACKGROUND"": ""#263859"", ""TEXT"": ""#ff6768"", ""INPUT"": ""#6b778d"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#6b778d"",
                  ""BUTTON"": (""#ff6768"", ""#263859""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#17223b"", ""#263859"", ""#6b778d"", ""#ff6768""], },
    ""DarkBlue10"": {""BACKGROUND"": ""#0028ff"", ""TEXT"": ""#f1f4df"", ""INPUT"": ""#10eaf0"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#10eaf0"",
                   ""BUTTON"": (""#f1f4df"", ""#24009c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#24009c"", ""#0028ff"", ""#10eaf0"", ""#f1f4df""], },
    ""DarkBlue11"": {""BACKGROUND"": ""#6384b3"", ""TEXT"": ""#e6f0b6"", ""INPUT"": ""#b8e9c0"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#b8e9c0"",
                   ""BUTTON"": (""#e6f0b6"", ""#684949""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#684949"", ""#6384b3"", ""#b8e9c0"", ""#e6f0b6""], },
    ""DarkTeal8"": {""BACKGROUND"": ""#71a0a5"", ""TEXT"": ""#212121"", ""INPUT"": ""#665c84"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#212121"",
                  ""BUTTON"": (""#fab95b"", ""#665c84""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#212121"", ""#665c84"", ""#71a0a5"", ""#fab95b""], },
    ""DarkRed1"": {""BACKGROUND"": ""#c10000"", ""TEXT"": ""#eeeeee"", ""INPUT"": ""#dedede"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#dedede"", ""BUTTON"": (""#c10000"", ""#eeeeee""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                 ""COLOR_LIST"": [""#c10000"", ""#ff4949"", ""#dedede"", ""#eeeeee""], },
    ""LightBrown5"": {""BACKGROUND"": ""#fff591"", ""TEXT"": ""#e41749"", ""INPUT"": ""#f5587b"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e41749"",
                    ""BUTTON"": (""#fff591"", ""#e41749""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#e41749"", ""#f5587b"", ""#ff8a5c"", ""#fff591""], },
    ""LightGreen9"": {""BACKGROUND"": ""#f1edb3"", ""TEXT"": ""#3b503d"", ""INPUT"": ""#4a746e"", ""TEXT_INPUT"": ""#f1edb3"", ""SCROLL"": ""#3b503d"",
                    ""BUTTON"": (""#f1edb3"", ""#3b503d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#3b503d"", ""#4a746e"", ""#c8cf94"", ""#f1edb3""], ""DESCRIPTION"": [""Green"", ""Turquoise"", ""Yellow""], },
    ""DarkGreen2"": {""BACKGROUND"": ""#3b503d"", ""TEXT"": ""#f1edb3"", ""INPUT"": ""#c8cf94"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#c8cf94"",
                   ""BUTTON"": (""#f1edb3"", ""#3b503d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3b503d"", ""#4a746e"", ""#c8cf94"", ""#f1edb3""], ""DESCRIPTION"": [""Green"", ""Turquoise"", ""Yellow""], },
    ""LightGray1"": {""BACKGROUND"": ""#f2f2f2"", ""TEXT"": ""#222831"", ""INPUT"": ""#393e46"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#222831"",
                   ""BUTTON"": (""#f2f2f2"", ""#222831""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#222831"", ""#393e46"", ""#f96d00"", ""#f2f2f2""], ""DESCRIPTION"": [""#000000"", ""Grey"", ""Orange"", ""Grey"", ""Autumn""], },
    ""DarkGrey4"": {""BACKGROUND"": ""#52524e"", ""TEXT"": ""#e9e9e5"", ""INPUT"": ""#d4d6c8"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#d4d6c8"",
                  ""BUTTON"": (""#FFFFFF"", ""#9a9b94""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#52524e"", ""#9a9b94"", ""#d4d6c8"", ""#e9e9e5""], ""DESCRIPTION"": [""Grey"", ""Pastel"", ""Winter""], },
    ""DarkBlue12"": {""BACKGROUND"": ""#324e7b"", ""TEXT"": ""#f8f8f8"", ""INPUT"": ""#86a6df"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#86a6df"",
                   ""BUTTON"": (""#FFFFFF"", ""#5068a9""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#324e7b"", ""#5068a9"", ""#86a6df"", ""#f8f8f8""], ""DESCRIPTION"": [""Blue"", ""Grey"", ""Cold"", ""Winter""], },
    ""DarkPurple6"": {""BACKGROUND"": ""#070739"", ""TEXT"": ""#e1e099"", ""INPUT"": ""#c327ab"", ""TEXT_INPUT"": ""#e1e099"", ""SCROLL"": ""#c327ab"",
                    ""BUTTON"": (""#e1e099"", ""#521477""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#070739"", ""#521477"", ""#c327ab"", ""#e1e099""], ""DESCRIPTION"": [""#000000"", ""Purple"", ""Yellow"", ""Dark""], },
    ""DarkPurple7"": {""BACKGROUND"": ""#191930"", ""TEXT"": ""#B1B7C5"", ""INPUT"": ""#232B5C"", ""TEXT_INPUT"": ""#D0E3E7"", ""SCROLL"": ""#B1B7C5"",
                    ""BUTTON"": (""#272D38"", ""#B1B7C5""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue13"": {""BACKGROUND"": ""#203562"", ""TEXT"": ""#e3e8f8"", ""INPUT"": ""#c0c5cd"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#c0c5cd"",
                   ""BUTTON"": (""#FFFFFF"", ""#3e588f""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#203562"", ""#3e588f"", ""#c0c5cd"", ""#e3e8f8""], ""DESCRIPTION"": [""Blue"", ""Grey"", ""Wedding"", ""Cold""], },
    ""DarkBrown5"": {""BACKGROUND"": ""#3c1b1f"", ""TEXT"": ""#f6e1b5"", ""INPUT"": ""#e2bf81"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e2bf81"",
                   ""BUTTON"": (""#3c1b1f"", ""#f6e1b5""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3c1b1f"", ""#b21e4b"", ""#e2bf81"", ""#f6e1b5""], ""DESCRIPTION"": [""Brown"", ""Red"", ""Yellow"", ""Warm""], },
    ""DarkGreen3"": {""BACKGROUND"": ""#062121"", ""TEXT"": ""#eeeeee"", ""INPUT"": ""#e4dcad"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e4dcad"",
                   ""BUTTON"": (""#eeeeee"", ""#181810""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#062121"", ""#181810"", ""#e4dcad"", ""#eeeeee""], ""DESCRIPTION"": [""#000000"", ""#000000"", ""Brown"", ""Grey""], },
    ""DarkBlack1"": {""BACKGROUND"": ""#181810"", ""TEXT"": ""#eeeeee"", ""INPUT"": ""#e4dcad"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e4dcad"",
                   ""BUTTON"": (""#FFFFFF"", ""#062121""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#062121"", ""#181810"", ""#e4dcad"", ""#eeeeee""], ""DESCRIPTION"": [""#000000"", ""#000000"", ""Brown"", ""Grey""], },
    ""DarkGrey5"": {""BACKGROUND"": ""#343434"", ""TEXT"": ""#f3f3f3"", ""INPUT"": ""#e9dcbe"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e9dcbe"",
                  ""BUTTON"": (""#FFFFFF"", ""#8e8b82""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#343434"", ""#8e8b82"", ""#e9dcbe"", ""#f3f3f3""], ""DESCRIPTION"": [""Grey"", ""Brown""], },
    ""LightBrown12"": {""BACKGROUND"": ""#8e8b82"", ""TEXT"": ""#f3f3f3"", ""INPUT"": ""#e9dcbe"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e9dcbe"",
                     ""BUTTON"": (""#f3f3f3"", ""#8e8b82""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#343434"", ""#8e8b82"", ""#e9dcbe"", ""#f3f3f3""], ""DESCRIPTION"": [""Grey"", ""Brown""], },
    ""DarkTeal9"": {""BACKGROUND"": ""#13445a"", ""TEXT"": ""#fef4e8"", ""INPUT"": ""#446878"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#446878"",
                  ""BUTTON"": (""#fef4e8"", ""#446878""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#13445a"", ""#970747"", ""#446878"", ""#fef4e8""], ""DESCRIPTION"": [""Red"", ""Grey"", ""Blue"", ""Wedding"", ""Retro""], },
    ""DarkBlue14"": {""BACKGROUND"": ""#21273d"", ""TEXT"": ""#f1f6f8"", ""INPUT"": ""#b9d4f1"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#b9d4f1"",
                   ""BUTTON"": (""#FFFFFF"", ""#6a759b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#21273d"", ""#6a759b"", ""#b9d4f1"", ""#f1f6f8""], ""DESCRIPTION"": [""Blue"", ""#000000"", ""Grey"", ""Cold"", ""Winter""], },
    ""LightBlue6"": {""BACKGROUND"": ""#f1f6f8"", ""TEXT"": ""#21273d"", ""INPUT"": ""#6a759b"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#21273d"",
                   ""BUTTON"": (""#f1f6f8"", ""#6a759b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#21273d"", ""#6a759b"", ""#b9d4f1"", ""#f1f6f8""], ""DESCRIPTION"": [""Blue"", ""#000000"", ""Grey"", ""Cold"", ""Winter""], },
    ""DarkGreen4"": {""BACKGROUND"": ""#044343"", ""TEXT"": ""#e4e4e4"", ""INPUT"": ""#045757"", ""TEXT_INPUT"": ""#e4e4e4"", ""SCROLL"": ""#045757"",
                   ""BUTTON"": (""#e4e4e4"", ""#045757""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#222222"", ""#044343"", ""#045757"", ""#e4e4e4""], ""DESCRIPTION"": [""#000000"", ""Turquoise"", ""Grey"", ""Dark""], },
    ""DarkGreen5"": {""BACKGROUND"": ""#1b4b36"", ""TEXT"": ""#e0e7f1"", ""INPUT"": ""#aebd77"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#aebd77"",
                   ""BUTTON"": (""#FFFFFF"", ""#538f6a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#1b4b36"", ""#538f6a"", ""#aebd77"", ""#e0e7f1""], ""DESCRIPTION"": [""Green"", ""Grey""], },
    ""DarkTeal10"": {""BACKGROUND"": ""#0d3446"", ""TEXT"": ""#d8dfe2"", ""INPUT"": ""#71adb5"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#71adb5"",
                   ""BUTTON"": (""#FFFFFF"", ""#176d81""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#0d3446"", ""#176d81"", ""#71adb5"", ""#d8dfe2""], ""DESCRIPTION"": [""Grey"", ""Turquoise"", ""Winter"", ""Cold""], },
    ""DarkGrey6"": {""BACKGROUND"": ""#3e3e3e"", ""TEXT"": ""#ededed"", ""INPUT"": ""#68868c"", ""TEXT_INPUT"": ""#ededed"", ""SCROLL"": ""#68868c"",
                  ""BUTTON"": (""#FFFFFF"", ""#405559""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#3e3e3e"", ""#405559"", ""#68868c"", ""#ededed""], ""DESCRIPTION"": [""Grey"", ""Turquoise"", ""Winter""], },
    ""DarkTeal11"": {""BACKGROUND"": ""#405559"", ""TEXT"": ""#ededed"", ""INPUT"": ""#68868c"", ""TEXT_INPUT"": ""#ededed"", ""SCROLL"": ""#68868c"",
                   ""BUTTON"": (""#ededed"", ""#68868c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3e3e3e"", ""#405559"", ""#68868c"", ""#ededed""], ""DESCRIPTION"": [""Grey"", ""Turquoise"", ""Winter""], },
    ""LightBlue7"": {""BACKGROUND"": ""#9ed0e0"", ""TEXT"": ""#19483f"", ""INPUT"": ""#5c868e"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#19483f"",
                   ""BUTTON"": (""#FFFFFF"", ""#19483f""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#19483f"", ""#5c868e"", ""#ff6a38"", ""#9ed0e0""], ""DESCRIPTION"": [""Orange"", ""Blue"", ""Turquoise""], },
    ""LightGreen10"": {""BACKGROUND"": ""#d8ebb5"", ""TEXT"": ""#205d67"", ""INPUT"": ""#639a67"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#205d67"",
                     ""BUTTON"": (""#d8ebb5"", ""#205d67""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#205d67"", ""#639a67"", ""#d9bf77"", ""#d8ebb5""], ""DESCRIPTION"": [""Blue"", ""Green"", ""Brown"", ""Vintage""], },
    ""DarkBlue15"": {""BACKGROUND"": ""#151680"", ""TEXT"": ""#f1fea4"", ""INPUT"": ""#375fc0"", ""TEXT_INPUT"": ""#f1fea4"", ""SCROLL"": ""#375fc0"",
                   ""BUTTON"": (""#f1fea4"", ""#1c44ac""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#151680"", ""#1c44ac"", ""#375fc0"", ""#f1fea4""], ""DESCRIPTION"": [""Blue"", ""Yellow"", ""Cold""], },
    ""DarkBlue16"": {""BACKGROUND"": ""#1c44ac"", ""TEXT"": ""#f1fea4"", ""INPUT"": ""#375fc0"", ""TEXT_INPUT"": ""#f1fea4"", ""SCROLL"": ""#375fc0"",
                   ""BUTTON"": (""#f1fea4"", ""#151680""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#151680"", ""#1c44ac"", ""#375fc0"", ""#f1fea4""], ""DESCRIPTION"": [""Blue"", ""Yellow"", ""Cold""], },
    ""DarkTeal12"": {""BACKGROUND"": ""#004a7c"", ""TEXT"": ""#fafafa"", ""INPUT"": ""#e8f1f5"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e8f1f5"",
                   ""BUTTON"": (""#fafafa"", ""#005691""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#004a7c"", ""#005691"", ""#e8f1f5"", ""#fafafa""], ""DESCRIPTION"": [""Grey"", ""Blue"", ""Cold"", ""Winter""], },
    ""LightBrown13"": {""BACKGROUND"": ""#ebf5ee"", ""TEXT"": ""#921224"", ""INPUT"": ""#bdc6b8"", ""TEXT_INPUT"": ""#921224"", ""SCROLL"": ""#921224"",
                     ""BUTTON"": (""#FFFFFF"", ""#921224""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#921224"", ""#bdc6b8"", ""#bce0da"", ""#ebf5ee""], ""DESCRIPTION"": [""Red"", ""Blue"", ""Grey"", ""Vintage"", ""Wedding""], },
    ""DarkBlue17"": {""BACKGROUND"": ""#21294c"", ""TEXT"": ""#f9f2d7"", ""INPUT"": ""#f2dea8"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f2dea8"",
                   ""BUTTON"": (""#f9f2d7"", ""#141829""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#141829"", ""#21294c"", ""#f2dea8"", ""#f9f2d7""], ""DESCRIPTION"": [""#000000"", ""Blue"", ""Yellow""], },
    ""DarkBrown6"": {""BACKGROUND"": ""#785e4d"", ""TEXT"": ""#f2eee3"", ""INPUT"": ""#baaf92"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#baaf92"",
                   ""BUTTON"": (""#FFFFFF"", ""#785e4d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#785e4d"", ""#ff8426"", ""#baaf92"", ""#f2eee3""], ""DESCRIPTION"": [""Grey"", ""Brown"", ""Orange"", ""Autumn""], },
    ""DarkGreen6"": {""BACKGROUND"": ""#5c715e"", ""TEXT"": ""#f2f9f1"", ""INPUT"": ""#ddeedf"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#ddeedf"",
                   ""BUTTON"": (""#f2f9f1"", ""#5c715e""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#5c715e"", ""#b6cdbd"", ""#ddeedf"", ""#f2f9f1""], ""DESCRIPTION"": [""Grey"", ""Green"", ""Vintage""], },
    ""DarkGreen7"": {""BACKGROUND"": ""#0C231E"", ""TEXT"": ""#efbe1c"", ""INPUT"": ""#153C33"", ""TEXT_INPUT"": ""#efbe1c"", ""SCROLL"": ""#153C33"",
                   ""BUTTON"": (""#efbe1c"", ""#153C33""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey7"": {""BACKGROUND"": ""#4b586e"", ""TEXT"": ""#dddddd"", ""INPUT"": ""#574e6d"", ""TEXT_INPUT"": ""#dddddd"", ""SCROLL"": ""#574e6d"",
                  ""BUTTON"": (""#dddddd"", ""#43405d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#43405d"", ""#4b586e"", ""#574e6d"", ""#dddddd""], ""DESCRIPTION"": [""Grey"", ""Winter"", ""Cold""], },
    ""DarkRed2"": {""BACKGROUND"": ""#ab1212"", ""TEXT"": ""#f6e4b5"", ""INPUT"": ""#cd3131"", ""TEXT_INPUT"": ""#f6e4b5"", ""SCROLL"": ""#cd3131"", ""BUTTON"": (""#f6e4b5"", ""#ab1212""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                 ""COLOR_LIST"": [""#ab1212"", ""#1fad9f"", ""#cd3131"", ""#f6e4b5""], ""DESCRIPTION"": [""Turquoise"", ""Red"", ""Yellow""], },
    ""LightGrey6"": {""BACKGROUND"": ""#e3e3e3"", ""TEXT"": ""#233142"", ""INPUT"": ""#455d7a"", ""TEXT_INPUT"": ""#e3e3e3"", ""SCROLL"": ""#233142"",
                   ""BUTTON"": (""#e3e3e3"", ""#455d7a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#233142"", ""#455d7a"", ""#f95959"", ""#e3e3e3""], ""DESCRIPTION"": [""#000000"", ""Blue"", ""Red"", ""Grey""], },
    ""HotDogStand"": {""BACKGROUND"": ""red"", ""TEXT"": ""yellow"", ""INPUT"": ""yellow"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""yellow"", ""BUTTON"": (""red"", ""yellow""),
                    ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey8"": {""BACKGROUND"": ""#19232D"", ""TEXT"": ""#ffffff"", ""INPUT"": ""#32414B"", ""TEXT_INPUT"": ""#ffffff"", ""SCROLL"": ""#505F69"",
                  ""BUTTON"": (""#ffffff"", ""#32414B""), ""PROGRESS"": (""#505F69"", ""#32414B""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey9"": {""BACKGROUND"": ""#36393F"", ""TEXT"": ""#DCDDDE"", ""INPUT"": ""#40444B"", ""TEXT_INPUT"": ""#ffffff"", ""SCROLL"": ""#202225"",
                  ""BUTTON"": (""#202225"", ""#B9BBBE""), ""PROGRESS"": (""#202225"", ""#40444B""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey10"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#cccdcf"", ""INPUT"": ""#272a31"", ""TEXT_INPUT"": ""#8b9fde"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#f5f5f6"", ""#2e3d5a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey11"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#cccdcf"", ""INPUT"": ""#313641"", ""TEXT_INPUT"": ""#cccdcf"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#f5f5f6"", ""#313641""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey12"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#8b9fde"", ""INPUT"": ""#313641"", ""TEXT_INPUT"": ""#8b9fde"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#cccdcf"", ""#2e3d5a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey13"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#cccdcf"", ""INPUT"": ""#272a31"", ""TEXT_INPUT"": ""#cccdcf"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#8b9fde"", ""#313641""), ""PROGRESS"": (""#cccdcf"", ""#272a31""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey14"": {""BACKGROUND"": ""#24292e"", ""TEXT"": ""#fafbfc"", ""INPUT"": ""#1d2125"", ""TEXT_INPUT"": ""#fafbfc"", ""SCROLL"": ""#1d2125"",
                   ""BUTTON"": (""#fafbfc"", ""#155398""), ""PROGRESS"": (""#155398"", ""#1d2125""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBrown7"": {""BACKGROUND"": ""#2c2417"", ""TEXT"": ""#baa379"", ""INPUT"": ""#baa379"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#392e1c"",
                   ""BUTTON"": (""#000000"", ""#baa379""), ""PROGRESS"": (""#baa379"", ""#453923""), ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""Python"": {""BACKGROUND"": ""#3d7aab"", ""TEXT"": ""#ffde56"", ""INPUT"": ""#295273"", ""TEXT_INPUT"": ""#ffde56"", ""SCROLL"": ""#295273"", ""BUTTON"": (""#ffde56"", ""#295273""),
               ""PROGRESS"": (""#ffde56"", ""#295273""), ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
}

","
    :param icon:                            Can be either a filename or Base64 value. For Windows if filename, it MUST be ICO format. For Linux, must NOT be ICO. Most portable is to use a Base64 of a PNG file. This works universally across all OS's
    :type icon:                             bytes | str
    :param button_color:                    Color of the button (text, background)
    :type button_color:                     (str, str) or str
    :param element_size:                    element size (width, height) in characters
    :type element_size:                     (int, int)
    :param button_element_size:             Size of button
    :type button_element_size:              (int, int)
    :param margins:                         (left/right, top/bottom) tkinter margins around outsize. Amount of pixels to leave inside the window's frame around the edges before your elements are shown.
    :type margins:                          (int, int)
    :param element_padding:                 Default amount of padding to put around elements in window (left/right, top/bottom) or ((left, right), (top, bottom))
    :type element_padding:                  (int, int) or ((int, int),(int,int))
    :param auto_size_text:                  True if the Widget should be shrunk to exactly fit the number of chars to show
    :type auto_size_text:                   bool
    :param auto_size_buttons:               True if Buttons in this Window should be sized to exactly fit the text on this.
    :type auto_size_buttons:                (bool)
    :param font:                            specifies the  font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike
    :type font:                             (str or (str, int[, str]) or None)
    :param border_width:                    width of border around element
    :type border_width:                     (int)
    :param slider_border_width:             Width of the border around sliders
    :type slider_border_width:              (int)
    :param slider_relief:                   Type of relief to use for sliders
    :type slider_relief:                    (str)
    :param slider_orientation:              ???
    :type slider_orientation:               ???
    :param autoclose_time:                  ???
    :type autoclose_time:                   ???
    :param message_box_line_width:          ???
    :type message_box_line_width:           ???
    :param progress_meter_border_depth:     ???
    :type progress_meter_border_depth:      ???
    :param progress_meter_style:            You can no longer set a progress bar style. All ttk styles must be the same for the window
    :type progress_meter_style:             ???
    :param progress_meter_relief:
    :type progress_meter_relief:            ???
    :param progress_meter_color:            ???
    :type progress_meter_color:             ???
    :param progress_meter_size:             ???
    :type progress_meter_size:              ???
    :param text_justification:              Default text justification for all Text Elements in window
    :type text_justification:               'left' | 'right' | 'center'
    :param background_color:                color of background
    :type background_color:                 (str)
    :param element_background_color:        element background color
    :type element_background_color:         (str)
    :param text_element_background_color:   text element background color
    :type text_element_background_color:    (str)
    :param input_elements_background_color: Default color to use for the background of input elements
    :type input_elements_background_color:  (str)
    :param input_text_color:                Default color to use for the text for Input elements
    :type input_text_color:                 (str)
    :param scrollbar_color:                 Default color to use for the slider trough
    :type scrollbar_color:                  (str)
    :param text_color:                      color of the text
    :type text_color:                       (str)
    :param element_text_color:              Default color to use for Text elements
    :type element_text_color:               (str)
    :param debug_win_size:                  window size
    :type debug_win_size:                   (int, int)
    :param window_location:                 Default location to place windows. Not setting will center windows on the display
    :type window_location:                  (int, int) | None
    :param error_button_color:              (Default = (None))
    :type error_button_color:               ???
    :param tooltip_time:                    time in milliseconds to wait before showing a tooltip. Default is 400ms
    :type tooltip_time:                     (int)
    :param tooltip_font:                    font to use for all tooltips
    :type tooltip_font:                     str or Tuple[str, int] or Tuple[str, int, str]
    :param use_ttk_buttons:                 if True will cause all buttons to be ttk buttons
    :type use_ttk_buttons:                  (bool)
    :param ttk_theme:                       Theme to use with ttk widgets.  Choices (on Windows) include - 'default', 'winnative', 'clam', 'alt', 'classic', 'vista', 'xpnative'
    :type ttk_theme:                        (str)
    :param suppress_error_popups:           If True then error popups will not be shown if generated internally to PySimpleGUI
    :type suppress_error_popups:            (bool)
    :param suppress_raise_key_errors:       If True then key errors won't be raised (you'll still get popup error)
    :type suppress_raise_key_errors:        (bool)
    :param suppress_key_guessing:           If True then key errors won't try and find closest matches for you
    :type suppress_key_guessing:            (bool)
    :param warn_button_key_duplicates:      If True then duplicate Button Keys generate warnings (not recommended as they're expected)
    :type warn_button_key_duplicates:       (bool)    
    :param enable_treeview_869_patch:       If True, then will use the treeview color patch for tk 8.6.9
    :type enable_treeview_869_patch:        (bool)
    :param enable_mac_notitlebar_patch:     If True then Windows with no titlebar use an alternative technique when tkinter version < 8.6.10
    :type enable_mac_notitlebar_patch:      (bool)
    :param use_custom_titlebar:             If True then a custom titlebar is used instead of the normal system titlebar
    :type use_custom_titlebar:              (bool)
    :param titlebar_background_color:       If custom titlebar indicated by use_custom_titlebar, then use this as background color
    :type titlebar_background_color:        str | None
    :param titlebar_text_color:             If custom titlebar indicated by use_custom_titlebar, then use this as text color
    :type titlebar_text_color:              str | None
    :param titlebar_font:                   If custom titlebar indicated by use_custom_titlebar, then use this as title font
    :type titlebar_font:                    (str or (str, int[, str]) or None) | None
    :param titlebar_icon:                   If custom titlebar indicated by use_custom_titlebar, then use this as the icon (file or base64 bytes)
    :type titlebar_icon:                    bytes | str
    :param user_settings_path:              default path for user_settings API calls. Expanded with os.path.expanduser so can contain ~ to represent user
    :type user_settings_path:               (str)
    :param pysimplegui_settings_path:       default path for the global PySimpleGUI user_settings
    :type pysimplegui_settings_path:        (str)
    :param pysimplegui_settings_filename:   default filename for the global PySimpleGUI user_settings
    :type pysimplegui_settings_filename:    (str)
    :param keep_on_top:                     If True then all windows will automatically be set to keep_on_top=True
    :type keep_on_top:                      (bool)
    :param dpi_awareness:                   If True then will turn on DPI awareness (Windows only at the moment)
    :type dpi_awareness:                    (bool)
    :param scaling:                         Sets the default scaling for all windows including popups, etc.
    :type scaling:                          (float)
    :param disable_modal_windows:           If True then all windows, including popups, will not be modal windows
    :type disable_modal_windows:            (bool)
    :param tooltip_offset:                  Offset to use for tooltips as a tuple. These values will be added to the mouse location when the widget was entered.
    :type tooltip_offset:                   ((None, None) | (int, int))
    :return:                                None
    :rtype:                                 None
    ",889,4824,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_options(icon=None, button_color=None, element_size=(None, None), button_element_size=(None, None),
                margins=(None, None),
                element_padding=(None, None), auto_size_text=None, auto_size_buttons=None, font=None, border_width=None,
                slider_border_width=None, slider_relief=None, slider_orientation=None,
                autoclose_time=None, message_box_line_width=None,
                progress_meter_border_depth=None, progress_meter_style=None,
                progress_meter_relief=None, progress_meter_color=None, progress_meter_size=None,
                text_justification=None, background_color=None, element_background_color=None,
                text_element_background_color=None, input_elements_background_color=None, input_text_color=None,
                scrollbar_color=None, text_color=None, element_text_color=None, debug_win_size=(None, None),
                window_location=(None, None), error_button_color=(None, None), tooltip_time=None, tooltip_font=None, use_ttk_buttons=None, ttk_theme=None,
                suppress_error_popups=None, suppress_raise_key_errors=None, suppress_key_guessing=None,warn_button_key_duplicates=False, enable_treeview_869_patch=None,
                enable_mac_notitlebar_patch=None, use_custom_titlebar=None, titlebar_background_color=None, titlebar_text_color=None, titlebar_font=None,
                titlebar_icon=None, user_settings_path=None, pysimplegui_settings_path=None, pysimplegui_settings_filename=None, keep_on_top=None, dpi_awareness=None, scaling=None, disable_modal_windows=None, tooltip_offset=(None, None)):
    

    global DEFAULT_ELEMENT_SIZE
    global DEFAULT_BUTTON_ELEMENT_SIZE
    global DEFAULT_MARGINS  # Margins for each LEFT/RIGHT margin is first term
    global DEFAULT_ELEMENT_PADDING  # Padding between elements (row, col) in pixels
    global DEFAULT_AUTOSIZE_TEXT
    global DEFAULT_AUTOSIZE_BUTTONS
    global DEFAULT_FONT
    global DEFAULT_BORDER_WIDTH
    global DEFAULT_AUTOCLOSE_TIME
    global DEFAULT_BUTTON_COLOR
    global MESSAGE_BOX_LINE_WIDTH
    global DEFAULT_PROGRESS_BAR_BORDER_WIDTH
    global DEFAULT_PROGRESS_BAR_STYLE
    global DEFAULT_PROGRESS_BAR_RELIEF
    global DEFAULT_PROGRESS_BAR_COLOR
    global DEFAULT_PROGRESS_BAR_SIZE
    global DEFAULT_TEXT_JUSTIFICATION
    global DEFAULT_DEBUG_WINDOW_SIZE
    global DEFAULT_SLIDER_BORDER_WIDTH
    global DEFAULT_SLIDER_RELIEF
    global DEFAULT_SLIDER_ORIENTATION
    global DEFAULT_BACKGROUND_COLOR
    global DEFAULT_INPUT_ELEMENTS_COLOR
    global DEFAULT_ELEMENT_BACKGROUND_COLOR
    global DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR
    global DEFAULT_SCROLLBAR_COLOR
    global DEFAULT_TEXT_COLOR
    global DEFAULT_WINDOW_LOCATION
    global DEFAULT_ELEMENT_TEXT_COLOR
    global DEFAULT_INPUT_TEXT_COLOR
    global DEFAULT_TOOLTIP_TIME
    global DEFAULT_ERROR_BUTTON_COLOR
    global DEFAULT_TTK_THEME
    global USE_TTK_BUTTONS
    global TOOLTIP_FONT
    global SUPPRESS_ERROR_POPUPS
    global SUPPRESS_RAISE_KEY_ERRORS
    global SUPPRESS_KEY_GUESSING
    global WARN_DUPLICATE_BUTTON_KEY_ERRORS
    global ENABLE_TREEVIEW_869_PATCH
    global ENABLE_MAC_NOTITLEBAR_PATCH
    global USE_CUSTOM_TITLEBAR
    global CUSTOM_TITLEBAR_BACKGROUND_COLOR
    global CUSTOM_TITLEBAR_TEXT_COLOR
    global CUSTOM_TITLEBAR_ICON
    global CUSTOM_TITLEBAR_FONT
    global DEFAULT_USER_SETTINGS_PATH
    global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH
    global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME
    global DEFAULT_KEEP_ON_TOP
    global DEFAULT_SCALING
    global DEFAULT_MODAL_WINDOWS_ENABLED
    global DEFAULT_TOOLTIP_OFFSET
    global _pysimplegui_user_settings
    # global _my_windows

    if icon:
        Window._user_defined_icon = icon
        # _my_windows._user_defined_icon = icon

    if button_color != None:
        if button_color == COLOR_SYSTEM_DEFAULT:
            DEFAULT_BUTTON_COLOR = (COLOR_SYSTEM_DEFAULT, COLOR_SYSTEM_DEFAULT)
        else:
            DEFAULT_BUTTON_COLOR = button_color

    if element_size != (None, None):
        DEFAULT_ELEMENT_SIZE = element_size

    if button_element_size != (None, None):
        DEFAULT_BUTTON_ELEMENT_SIZE = button_element_size

    if margins != (None, None):
        DEFAULT_MARGINS = margins

    if element_padding != (None, None):
        DEFAULT_ELEMENT_PADDING = element_padding

    if auto_size_text != None:
        DEFAULT_AUTOSIZE_TEXT = auto_size_text

    if auto_size_buttons != None:
        DEFAULT_AUTOSIZE_BUTTONS = auto_size_buttons

    if font != None:
        DEFAULT_FONT = font

    if border_width != None:
        DEFAULT_BORDER_WIDTH = border_width

    if autoclose_time != None:
        DEFAULT_AUTOCLOSE_TIME = autoclose_time

    if message_box_line_width != None:
        MESSAGE_BOX_LINE_WIDTH = message_box_line_width

    if progress_meter_border_depth != None:
        DEFAULT_PROGRESS_BAR_BORDER_WIDTH = progress_meter_border_depth

    if progress_meter_style != None:
        warnings.warn('You can no longer set a progress bar style. All ttk styles must be the same for the window', UserWarning)
        # DEFAULT_PROGRESS_BAR_STYLE = progress_meter_style

    if progress_meter_relief != None:
        DEFAULT_PROGRESS_BAR_RELIEF = progress_meter_relief

    if progress_meter_color != None:
        DEFAULT_PROGRESS_BAR_COLOR = progress_meter_color

    if progress_meter_size != None:
        DEFAULT_PROGRESS_BAR_SIZE = progress_meter_size

    if slider_border_width != None:
        DEFAULT_SLIDER_BORDER_WIDTH = slider_border_width

    if slider_orientation != None:
        DEFAULT_SLIDER_ORIENTATION = slider_orientation

    if slider_relief != None:
        DEFAULT_SLIDER_RELIEF = slider_relief

    if text_justification != None:
        DEFAULT_TEXT_JUSTIFICATION = text_justification

    if background_color != None:
        DEFAULT_BACKGROUND_COLOR = background_color

    if text_element_background_color != None:
        DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR = text_element_background_color

    if input_elements_background_color != None:
        DEFAULT_INPUT_ELEMENTS_COLOR = input_elements_background_color

    if element_background_color != None:
        DEFAULT_ELEMENT_BACKGROUND_COLOR = element_background_color

    if window_location != (None, None):
        DEFAULT_WINDOW_LOCATION = window_location

    if debug_win_size != (None, None):
        DEFAULT_DEBUG_WINDOW_SIZE = debug_win_size

    if text_color != None:
        DEFAULT_TEXT_COLOR = text_color

    if scrollbar_color != None:
        DEFAULT_SCROLLBAR_COLOR = scrollbar_color

    if element_text_color != None:
        DEFAULT_ELEMENT_TEXT_COLOR = element_text_color

    if input_text_color is not None:
        DEFAULT_INPUT_TEXT_COLOR = input_text_color

    if tooltip_time is not None:
        DEFAULT_TOOLTIP_TIME = tooltip_time

    if error_button_color != (None, None):
        DEFAULT_ERROR_BUTTON_COLOR = error_button_color

    if ttk_theme is not None:
        DEFAULT_TTK_THEME = ttk_theme

    if use_ttk_buttons is not None:
        USE_TTK_BUTTONS = use_ttk_buttons

    if tooltip_font is not None:
        TOOLTIP_FONT = tooltip_font

    if suppress_error_popups is not None:
        SUPPRESS_ERROR_POPUPS = suppress_error_popups

    if suppress_raise_key_errors is not None:
        SUPPRESS_RAISE_KEY_ERRORS = suppress_raise_key_errors

    if suppress_key_guessing is not None:
        SUPPRESS_KEY_GUESSING = suppress_key_guessing

    if warn_button_key_duplicates is not None:
        WARN_DUPLICATE_BUTTON_KEY_ERRORS = warn_button_key_duplicates

    if enable_treeview_869_patch is not None:
        ENABLE_TREEVIEW_869_PATCH = enable_treeview_869_patch

    if enable_mac_notitlebar_patch is not None:
        ENABLE_MAC_NOTITLEBAR_PATCH = enable_mac_notitlebar_patch

    if use_custom_titlebar is not None:
        USE_CUSTOM_TITLEBAR = use_custom_titlebar

    if titlebar_background_color is not None:
        CUSTOM_TITLEBAR_BACKGROUND_COLOR = titlebar_background_color

    if titlebar_text_color is not None:
        CUSTOM_TITLEBAR_TEXT_COLOR = titlebar_text_color

    if titlebar_font is not None:
        CUSTOM_TITLEBAR_FONT = titlebar_font

    if titlebar_icon is not None:
        CUSTOM_TITLEBAR_ICON = titlebar_icon

    if user_settings_path is not None:
        DEFAULT_USER_SETTINGS_PATH = user_settings_path

    if pysimplegui_settings_path is not None:
        DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH = pysimplegui_settings_path

    if pysimplegui_settings_filename is not None:
        DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME = pysimplegui_settings_filename

    if pysimplegui_settings_filename is not None or pysimplegui_settings_filename is not None:
        _pysimplegui_user_settings = UserSettings(filename=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME,
                                                  path=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH)

    if keep_on_top is not None:
        DEFAULT_KEEP_ON_TOP = keep_on_top

    if dpi_awareness is True:
        if running_windows():
            if platform.release() == ""7"":
                ctypes.windll.user32.SetProcessDPIAware()
            elif platform.release() == ""8"" or platform.release() == ""10"":
                ctypes.windll.shcore.SetProcessDpiAwareness(1)

    if scaling is not None:
        DEFAULT_SCALING = scaling

    if disable_modal_windows is not None:
        DEFAULT_MODAL_WINDOWS_ENABLED = not disable_modal_windows

    if tooltip_offset != (None, None):
        DEFAULT_TOOLTIP_OFFSET = tooltip_offset

    return True


# ----------------------------------------------------------------- #

# .########.##.....##.########.##.....##.########..######.
# ....##....##.....##.##.......###...###.##.......##....##
# ....##....##.....##.##.......####.####.##.......##......
# ....##....#########.######...##.###.##.######....######.
# ....##....##.....##.##.......##.....##.##.............##
# ....##....##.....##.##.......##.....##.##.......##....##
# ....##....##.....##.########.##.....##.########..######.

# ----------------------------------------------------------------- #

# The official Theme code

#################### ChangeLookAndFeel #######################
# Predefined settings that will change the colors and styles #
# of the elements.                                           #
##############################################################
LOOK_AND_FEEL_TABLE = {
    ""SystemDefault"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                      ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1,
                      ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""SystemDefaultForReal"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT,
                             ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT, ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT,
                             ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""SystemDefault1"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                       ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                       ""PROGRESS_DEPTH"": 0, },
    ""Material1"": {""BACKGROUND"": ""#E3F2FD"", ""TEXT"": ""#000000"", ""INPUT"": ""#86A8FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#86A8FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#5079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""Material2"": {""BACKGROUND"": ""#FAFAFA"", ""TEXT"": ""#000000"", ""INPUT"": ""#004EA1"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#5EA7FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#0079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""Reddit"": {""BACKGROUND"": ""#ffffff"", ""TEXT"": ""#1a1a1b"", ""INPUT"": ""#dae0e6"", ""TEXT_INPUT"": ""#222222"", ""SCROLL"": ""#a5a4a4"", ""BUTTON"": (""#FFFFFF"", ""#0079d3""),
               ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, ""ACCENT1"": ""#ff5414"", ""ACCENT2"": ""#33a8ff"",
               ""ACCENT3"": ""#dbf0ff"", },
    ""Topanga"": {""BACKGROUND"": ""#282923"", ""TEXT"": ""#E7DB74"", ""INPUT"": ""#393a32"", ""TEXT_INPUT"": ""#E7C855"", ""SCROLL"": ""#E7C855"", ""BUTTON"": (""#E7C855"", ""#284B5A""),
                ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, ""ACCENT1"": ""#c15226"", ""ACCENT2"": ""#7a4d5f"",
                ""ACCENT3"": ""#889743"", },
    ""GreenTan"": {""BACKGROUND"": ""#9FB8AD"", ""TEXT"": '#000000', ""INPUT"": ""#F7F3EC"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#F7F3EC"", ""BUTTON"": (""#FFFFFF"", ""#475841""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Dark"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#FFFFFF"", ""#004F00""),
             ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen"": {""BACKGROUND"": ""#B7CECE"", ""TEXT"": ""#000000"", ""INPUT"": ""#FDFFF7"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#FDFFF7"",
                   ""BUTTON"": (""#FFFFFF"", ""#658268""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""ACCENT1"": ""#76506d"",
                   ""ACCENT2"": ""#5148f1"", ""ACCENT3"": ""#0a1c84"", ""PROGRESS_DEPTH"": 0, },
    ""Dark2"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#FFFFFF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#FFFFFF"", ""#004F00""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Black"": {""BACKGROUND"": ""#000000"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#000000"", ""#FFFFFF""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Tan"": {""BACKGROUND"": ""#fdf6e3"", ""TEXT"": ""#268bd1"", ""INPUT"": ""#eee8d5"", ""TEXT_INPUT"": ""#6c71c3"", ""SCROLL"": ""#eee8d5"", ""BUTTON"": (""#FFFFFF"", ""#063542""),
            ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""TanBlue"": {""BACKGROUND"": ""#e5dece"", ""TEXT"": ""#063289"", ""INPUT"": ""#f9f8f4"", ""TEXT_INPUT"": ""#242834"", ""SCROLL"": ""#eee8d5"", ""BUTTON"": (""#FFFFFF"", ""#063289""),
                ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkTanBlue"": {""BACKGROUND"": ""#242834"", ""TEXT"": ""#dfe6f8"", ""INPUT"": ""#97755c"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#a9afbb"",
                    ""BUTTON"": (""#FFFFFF"", ""#063289""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkAmber"": {""BACKGROUND"": ""#2c2825"", ""TEXT"": ""#fdcb52"", ""INPUT"": ""#705e52"", ""TEXT_INPUT"": ""#fdcb52"", ""SCROLL"": ""#705e52"",
                  ""BUTTON"": (""#000000"", ""#fdcb52""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue"": {""BACKGROUND"": ""#1a2835"", ""TEXT"": ""#d1ecff"", ""INPUT"": ""#335267"", ""TEXT_INPUT"": ""#acc2d0"", ""SCROLL"": ""#1b6497"", ""BUTTON"": (""#000000"", ""#fafaf8""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Reds"": {""BACKGROUND"": ""#280001"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#763e00"", ""BUTTON"": (""#000000"", ""#daad28""),
             ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Green"": {""BACKGROUND"": ""#82a459"", ""TEXT"": ""#000000"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e3ecf3"", ""BUTTON"": (""#FFFFFF"", ""#517239""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BluePurple"": {""BACKGROUND"": ""#A5CADD"", ""TEXT"": ""#6E266E"", ""INPUT"": ""#E0F5FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#E0F5FF"",
                   ""BUTTON"": (""#FFFFFF"", ""#303952""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Purple"": {""BACKGROUND"": ""#B0AAC2"", ""TEXT"": ""#000000"", ""INPUT"": ""#F2EFE8"", ""SCROLL"": ""#F2EFE8"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#000000"", ""#C2D4D8""),
               ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BlueMono"": {""BACKGROUND"": ""#AAB6D3"", ""TEXT"": ""#000000"", ""INPUT"": ""#F1F4FC"", ""SCROLL"": ""#F1F4FC"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#FFFFFF"", ""#7186C7""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""GreenMono"": {""BACKGROUND"": ""#A8C1B4"", ""TEXT"": ""#000000"", ""INPUT"": ""#DDE0DE"", ""SCROLL"": ""#E3E3E3"", ""TEXT_INPUT"": ""#000000"",
                  ""BUTTON"": (""#FFFFFF"", ""#6D9F85""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BrownBlue"": {""BACKGROUND"": ""#64778d"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#f0f3f7"", ""SCROLL"": ""#A6B2BE"", ""TEXT_INPUT"": ""#000000"",
                  ""BUTTON"": (""#FFFFFF"", ""#283b5b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""BrightColors"": {""BACKGROUND"": ""#b4ffb4"", ""TEXT"": ""#000000"", ""INPUT"": ""#ffff64"", ""SCROLL"": ""#ffb482"", ""TEXT_INPUT"": ""#000000"",
                     ""BUTTON"": (""#000000"", ""#ffa0dc""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""NeutralBlue"": {""BACKGROUND"": ""#92aa9d"", ""TEXT"": ""#000000"", ""INPUT"": ""#fcfff6"", ""SCROLL"": ""#fcfff6"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#d0dbbd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Kayak"": {""BACKGROUND"": ""#a7ad7f"", ""TEXT"": ""#000000"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#FFFFFF"", ""#5d907d""),
              ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""SandyBeach"": {""BACKGROUND"": ""#efeccb"", ""TEXT"": ""#012f2f"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#012f2f"",
                   ""BUTTON"": (""#FFFFFF"", ""#046380""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""TealMono"": {""BACKGROUND"": ""#a8cfdd"", ""TEXT"": ""#000000"", ""INPUT"": ""#dfedf2"", ""SCROLL"": ""#dfedf2"", ""TEXT_INPUT"": ""#000000"", ""BUTTON"": (""#FFFFFF"", ""#183440""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""Default"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                ""PROGRESS_DEPTH"": 0, },
    ""Default1"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                 ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                 ""PROGRESS_DEPTH"": 0, },
    ""DefaultNoMoreNagging"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT,
                             ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT, ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR,
                             ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""GrayGrayGray"": {""BACKGROUND"": COLOR_SYSTEM_DEFAULT, ""TEXT"": COLOR_SYSTEM_DEFAULT, ""INPUT"": COLOR_SYSTEM_DEFAULT, ""TEXT_INPUT"": COLOR_SYSTEM_DEFAULT,
                     ""SCROLL"": COLOR_SYSTEM_DEFAULT, ""BUTTON"": COLOR_SYSTEM_DEFAULT, ""PROGRESS"": COLOR_SYSTEM_DEFAULT, ""BORDER"": 1, ""SLIDER_DEPTH"": 1,
                     ""PROGRESS_DEPTH"": 0, },
    ""LightBlue"": {""BACKGROUND"": ""#E3F2FD"", ""TEXT"": ""#000000"", ""INPUT"": ""#86A8FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#86A8FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#5079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""LightGrey"": {""BACKGROUND"": ""#FAFAFA"", ""TEXT"": ""#000000"", ""INPUT"": ""#004EA1"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#5EA7FF"",
                  ""BUTTON"": (""#FFFFFF"", ""#0079D3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 0, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#FF0266"", ""ACCENT2"": ""#FF5C93"", ""ACCENT3"": ""#C5003C"", },
    ""LightGrey1"": {""BACKGROUND"": ""#ffffff"", ""TEXT"": ""#1a1a1b"", ""INPUT"": ""#dae0e6"", ""TEXT_INPUT"": ""#222222"", ""SCROLL"": ""#a5a4a4"",
                   ""BUTTON"": (""#FFFFFF"", ""#0079d3""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""ACCENT1"": ""#ff5414"", ""ACCENT2"": ""#33a8ff"", ""ACCENT3"": ""#dbf0ff"", },
    ""DarkBrown"": {""BACKGROUND"": ""#282923"", ""TEXT"": ""#E7DB74"", ""INPUT"": ""#393a32"", ""TEXT_INPUT"": ""#E7C855"", ""SCROLL"": ""#E7C855"",
                  ""BUTTON"": (""#E7C855"", ""#284B5A""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""ACCENT1"": ""#c15226"", ""ACCENT2"": ""#7a4d5f"", ""ACCENT3"": ""#889743"", },
    ""LightGreen1"": {""BACKGROUND"": ""#9FB8AD"", ""TEXT"": ""#000000"", ""INPUT"": ""#F7F3EC"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#F7F3EC"",
                    ""BUTTON"": (""#FFFFFF"", ""#475841""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"", ""BUTTON"": (""#FFFFFF"", ""#004F00""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen2"": {""BACKGROUND"": ""#B7CECE"", ""TEXT"": ""#000000"", ""INPUT"": ""#FDFFF7"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#FDFFF7"",
                    ""BUTTON"": (""#FFFFFF"", ""#658268""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""ACCENT1"": ""#76506d"",
                    ""ACCENT2"": ""#5148f1"", ""ACCENT3"": ""#0a1c84"", ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey1"": {""BACKGROUND"": ""#404040"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#FFFFFF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#707070"",
                  ""BUTTON"": (""#FFFFFF"", ""#004F00""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlack"": {""BACKGROUND"": ""#000000"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#4D4D4D"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#707070"",
                  ""BUTTON"": (""#000000"", ""#FFFFFF""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown"": {""BACKGROUND"": ""#fdf6e3"", ""TEXT"": ""#268bd1"", ""INPUT"": ""#eee8d5"", ""TEXT_INPUT"": ""#6c71c3"", ""SCROLL"": ""#eee8d5"",
                   ""BUTTON"": (""#FFFFFF"", ""#063542""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown1"": {""BACKGROUND"": ""#e5dece"", ""TEXT"": ""#063289"", ""INPUT"": ""#f9f8f4"", ""TEXT_INPUT"": ""#242834"", ""SCROLL"": ""#eee8d5"",
                    ""BUTTON"": (""#FFFFFF"", ""#063289""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue1"": {""BACKGROUND"": ""#242834"", ""TEXT"": ""#dfe6f8"", ""INPUT"": ""#97755c"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#a9afbb"",
                  ""BUTTON"": (""#FFFFFF"", ""#063289""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBrown1"": {""BACKGROUND"": ""#2c2825"", ""TEXT"": ""#fdcb52"", ""INPUT"": ""#705e52"", ""TEXT_INPUT"": ""#fdcb52"", ""SCROLL"": ""#705e52"",
                   ""BUTTON"": (""#000000"", ""#fdcb52""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue2"": {""BACKGROUND"": ""#1a2835"", ""TEXT"": ""#d1ecff"", ""INPUT"": ""#335267"", ""TEXT_INPUT"": ""#acc2d0"", ""SCROLL"": ""#1b6497"",
                  ""BUTTON"": (""#000000"", ""#fafaf8""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBrown2"": {""BACKGROUND"": ""#280001"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#763e00"",
                   ""BUTTON"": (""#000000"", ""#daad28""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGreen"": {""BACKGROUND"": ""#82a459"", ""TEXT"": ""#000000"", ""INPUT"": ""#d8d584"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e3ecf3"",
                  ""BUTTON"": (""#FFFFFF"", ""#517239""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBlue1"": {""BACKGROUND"": ""#A5CADD"", ""TEXT"": ""#6E266E"", ""INPUT"": ""#E0F5FF"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#E0F5FF"",
                   ""BUTTON"": (""#FFFFFF"", ""#303952""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightPurple"": {""BACKGROUND"": ""#B0AAC2"", ""TEXT"": ""#000000"", ""INPUT"": ""#F2EFE8"", ""SCROLL"": ""#F2EFE8"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#C2D4D8""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBlue2"": {""BACKGROUND"": ""#AAB6D3"", ""TEXT"": ""#000000"", ""INPUT"": ""#F1F4FC"", ""SCROLL"": ""#F1F4FC"", ""TEXT_INPUT"": ""#000000"",
                   ""BUTTON"": (""#FFFFFF"", ""#7186C7""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen3"": {""BACKGROUND"": ""#A8C1B4"", ""TEXT"": ""#000000"", ""INPUT"": ""#DDE0DE"", ""SCROLL"": ""#E3E3E3"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#FFFFFF"", ""#6D9F85""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue3"": {""BACKGROUND"": ""#64778d"", ""TEXT"": ""#FFFFFF"", ""INPUT"": ""#f0f3f7"", ""SCROLL"": ""#A6B2BE"", ""TEXT_INPUT"": ""#000000"",
                  ""BUTTON"": (""#FFFFFF"", ""#283b5b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen4"": {""BACKGROUND"": ""#b4ffb4"", ""TEXT"": ""#000000"", ""INPUT"": ""#ffff64"", ""SCROLL"": ""#ffb482"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#ffa0dc""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightGreen5"": {""BACKGROUND"": ""#92aa9d"", ""TEXT"": ""#000000"", ""INPUT"": ""#fcfff6"", ""SCROLL"": ""#fcfff6"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#000000"", ""#d0dbbd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown2"": {""BACKGROUND"": ""#a7ad7f"", ""TEXT"": ""#000000"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#000000"",
                    ""BUTTON"": (""#FFFFFF"", ""#5d907d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown3"": {""BACKGROUND"": ""#efeccb"", ""TEXT"": ""#012f2f"", ""INPUT"": ""#e6d3a8"", ""SCROLL"": ""#e6d3a8"", ""TEXT_INPUT"": ""#012f2f"",
                    ""BUTTON"": (""#FFFFFF"", ""#046380""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBlue3"": {""BACKGROUND"": ""#a8cfdd"", ""TEXT"": ""#000000"", ""INPUT"": ""#dfedf2"", ""SCROLL"": ""#dfedf2"", ""TEXT_INPUT"": ""#000000"",
                   ""BUTTON"": (""#FFFFFF"", ""#183440""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""LightBrown4"": {""BACKGROUND"": ""#d7c79e"", ""TEXT"": ""#a35638"", ""INPUT"": ""#9dab86"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#a35638"",
                    ""BUTTON"": (""#FFFFFF"", ""#a35638""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#a35638"", ""#9dab86"", ""#e08f62"", ""#d7c79e""], },
    ""DarkTeal"": {""BACKGROUND"": ""#003f5c"", ""TEXT"": ""#fb5b5a"", ""INPUT"": ""#bc4873"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#bc4873"", ""BUTTON"": (""#FFFFFF"", ""#fb5b5a""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                 ""COLOR_LIST"": [""#003f5c"", ""#472b62"", ""#bc4873"", ""#fb5b5a""], },
    ""DarkPurple"": {""BACKGROUND"": ""#472b62"", ""TEXT"": ""#fb5b5a"", ""INPUT"": ""#bc4873"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#bc4873"",
                   ""BUTTON"": (""#FFFFFF"", ""#472b62""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#003f5c"", ""#472b62"", ""#bc4873"", ""#fb5b5a""], },
    ""LightGreen6"": {""BACKGROUND"": ""#eafbea"", ""TEXT"": ""#1f6650"", ""INPUT"": ""#6f9a8d"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#1f6650"",
                    ""BUTTON"": (""#FFFFFF"", ""#1f6650""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#1f6650"", ""#6f9a8d"", ""#ea5e5e"", ""#eafbea""], },
    ""DarkGrey2"": {""BACKGROUND"": ""#2b2b28"", ""TEXT"": ""#f8f8f8"", ""INPUT"": ""#f1d6ab"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f1d6ab"",
                  ""BUTTON"": (""#2b2b28"", ""#e3b04b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#2b2b28"", ""#e3b04b"", ""#f1d6ab"", ""#f8f8f8""], },
    ""LightBrown6"": {""BACKGROUND"": ""#f9b282"", ""TEXT"": ""#8f4426"", ""INPUT"": ""#de6b35"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#8f4426"",
                    ""BUTTON"": (""#FFFFFF"", ""#8f4426""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#8f4426"", ""#de6b35"", ""#64ccda"", ""#f9b282""], },
    ""DarkTeal1"": {""BACKGROUND"": ""#396362"", ""TEXT"": ""#ffe7d1"", ""INPUT"": ""#f6c89f"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f6c89f"",
                  ""BUTTON"": (""#ffe7d1"", ""#4b8e8d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#396362"", ""#4b8e8d"", ""#f6c89f"", ""#ffe7d1""], },
    ""LightBrown7"": {""BACKGROUND"": ""#f6c89f"", ""TEXT"": ""#396362"", ""INPUT"": ""#4b8e8d"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#396362"",
                    ""BUTTON"": (""#FFFFFF"", ""#396362""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#396362"", ""#4b8e8d"", ""#f6c89f"", ""#ffe7d1""], },
    ""DarkPurple1"": {""BACKGROUND"": ""#0c093c"", ""TEXT"": ""#fad6d6"", ""INPUT"": ""#eea5f6"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#eea5f6"",
                    ""BUTTON"": (""#FFFFFF"", ""#df42d1""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#0c093c"", ""#df42d1"", ""#eea5f6"", ""#fad6d6""], },
    ""DarkGrey3"": {""BACKGROUND"": ""#211717"", ""TEXT"": ""#dfddc7"", ""INPUT"": ""#f58b54"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f58b54"",
                  ""BUTTON"": (""#dfddc7"", ""#a34a28""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#211717"", ""#a34a28"", ""#f58b54"", ""#dfddc7""], },
    ""LightBrown8"": {""BACKGROUND"": ""#dfddc7"", ""TEXT"": ""#211717"", ""INPUT"": ""#a34a28"", ""TEXT_INPUT"": ""#dfddc7"", ""SCROLL"": ""#211717"",
                    ""BUTTON"": (""#dfddc7"", ""#a34a28""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#211717"", ""#a34a28"", ""#f58b54"", ""#dfddc7""], },
    ""DarkBlue4"": {""BACKGROUND"": ""#494ca2"", ""TEXT"": ""#e3e7f1"", ""INPUT"": ""#c6cbef"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#c6cbef"",
                  ""BUTTON"": (""#FFFFFF"", ""#8186d5""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#494ca2"", ""#8186d5"", ""#c6cbef"", ""#e3e7f1""], },
    ""LightBlue4"": {""BACKGROUND"": ""#5c94bd"", ""TEXT"": ""#470938"", ""INPUT"": ""#1a3e59"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#470938"",
                   ""BUTTON"": (""#FFFFFF"", ""#470938""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#470938"", ""#1a3e59"", ""#5c94bd"", ""#f2d6eb""], },
    ""DarkTeal2"": {""BACKGROUND"": ""#394a6d"", ""TEXT"": ""#c0ffb3"", ""INPUT"": ""#52de97"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#52de97"",
                  ""BUTTON"": (""#c0ffb3"", ""#394a6d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#394a6d"", ""#3c9d9b"", ""#52de97"", ""#c0ffb3""], },
    ""DarkTeal3"": {""BACKGROUND"": ""#3c9d9b"", ""TEXT"": ""#c0ffb3"", ""INPUT"": ""#52de97"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#52de97"",
                  ""BUTTON"": (""#c0ffb3"", ""#394a6d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#394a6d"", ""#3c9d9b"", ""#52de97"", ""#c0ffb3""], },
    ""DarkPurple5"": {""BACKGROUND"": ""#730068"", ""TEXT"": ""#f6f078"", ""INPUT"": ""#01d28e"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#01d28e"",
                    ""BUTTON"": (""#f6f078"", ""#730068""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#730068"", ""#434982"", ""#01d28e"", ""#f6f078""], },
    ""DarkPurple2"": {""BACKGROUND"": ""#202060"", ""TEXT"": ""#b030b0"", ""INPUT"": ""#602080"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#602080"",
                    ""BUTTON"": (""#FFFFFF"", ""#202040""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#202040"", ""#202060"", ""#602080"", ""#b030b0""], },
    ""DarkBlue5"": {""BACKGROUND"": ""#000272"", ""TEXT"": ""#ff6363"", ""INPUT"": ""#a32f80"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#a32f80"",
                  ""BUTTON"": (""#FFFFFF"", ""#341677""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#000272"", ""#341677"", ""#a32f80"", ""#ff6363""], },
    ""LightGrey2"": {""BACKGROUND"": ""#f6f6f6"", ""TEXT"": ""#420000"", ""INPUT"": ""#d4d7dd"", ""TEXT_INPUT"": ""#420000"", ""SCROLL"": ""#420000"",
                   ""BUTTON"": (""#420000"", ""#d4d7dd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#420000"", ""#d4d7dd"", ""#eae9e9"", ""#f6f6f6""], },
    ""LightGrey3"": {""BACKGROUND"": ""#eae9e9"", ""TEXT"": ""#420000"", ""INPUT"": ""#d4d7dd"", ""TEXT_INPUT"": ""#420000"", ""SCROLL"": ""#420000"",
                   ""BUTTON"": (""#420000"", ""#d4d7dd""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#420000"", ""#d4d7dd"", ""#eae9e9"", ""#f6f6f6""], },
    ""DarkBlue6"": {""BACKGROUND"": ""#01024e"", ""TEXT"": ""#ff6464"", ""INPUT"": ""#8b4367"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#8b4367"",
                  ""BUTTON"": (""#FFFFFF"", ""#543864""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#01024e"", ""#543864"", ""#8b4367"", ""#ff6464""], },
    ""DarkBlue7"": {""BACKGROUND"": ""#241663"", ""TEXT"": ""#eae7af"", ""INPUT"": ""#a72693"", ""TEXT_INPUT"": ""#eae7af"", ""SCROLL"": ""#a72693"",
                  ""BUTTON"": (""#eae7af"", ""#160f30""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#160f30"", ""#241663"", ""#a72693"", ""#eae7af""], },
    ""LightBrown9"": {""BACKGROUND"": ""#f6d365"", ""TEXT"": ""#3a1f5d"", ""INPUT"": ""#c83660"", ""TEXT_INPUT"": ""#f6d365"", ""SCROLL"": ""#3a1f5d"",
                    ""BUTTON"": (""#f6d365"", ""#c83660""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#3a1f5d"", ""#c83660"", ""#e15249"", ""#f6d365""], },
    ""DarkPurple3"": {""BACKGROUND"": ""#6e2142"", ""TEXT"": ""#ffd692"", ""INPUT"": ""#e16363"", ""TEXT_INPUT"": ""#ffd692"", ""SCROLL"": ""#e16363"",
                    ""BUTTON"": (""#ffd692"", ""#943855""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#6e2142"", ""#943855"", ""#e16363"", ""#ffd692""], },
    ""LightBrown10"": {""BACKGROUND"": ""#ffd692"", ""TEXT"": ""#6e2142"", ""INPUT"": ""#943855"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#6e2142"",
                     ""BUTTON"": (""#FFFFFF"", ""#6e2142""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#6e2142"", ""#943855"", ""#e16363"", ""#ffd692""], },
    ""DarkPurple4"": {""BACKGROUND"": ""#200f21"", ""TEXT"": ""#f638dc"", ""INPUT"": ""#5a3d5c"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#5a3d5c"",
                    ""BUTTON"": (""#FFFFFF"", ""#382039""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#200f21"", ""#382039"", ""#5a3d5c"", ""#f638dc""], },
    ""LightBlue5"": {""BACKGROUND"": ""#b2fcff"", ""TEXT"": ""#3e64ff"", ""INPUT"": ""#5edfff"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#3e64ff"",
                   ""BUTTON"": (""#FFFFFF"", ""#3e64ff""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3e64ff"", ""#5edfff"", ""#b2fcff"", ""#ecfcff""], },
    ""DarkTeal4"": {""BACKGROUND"": ""#464159"", ""TEXT"": ""#c7f0db"", ""INPUT"": ""#8bbabb"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#8bbabb"",
                  ""BUTTON"": (""#FFFFFF"", ""#6c7b95""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#464159"", ""#6c7b95"", ""#8bbabb"", ""#c7f0db""], },
    ""LightTeal"": {""BACKGROUND"": ""#c7f0db"", ""TEXT"": ""#464159"", ""INPUT"": ""#6c7b95"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#464159"",
                  ""BUTTON"": (""#FFFFFF"", ""#464159""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#464159"", ""#6c7b95"", ""#8bbabb"", ""#c7f0db""], },
    ""DarkTeal5"": {""BACKGROUND"": ""#8bbabb"", ""TEXT"": ""#464159"", ""INPUT"": ""#6c7b95"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#464159"",
                  ""BUTTON"": (""#c7f0db"", ""#6c7b95""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#464159"", ""#6c7b95"", ""#8bbabb"", ""#c7f0db""], },
    ""LightGrey4"": {""BACKGROUND"": ""#faf5ef"", ""TEXT"": ""#672f2f"", ""INPUT"": ""#99b19c"", ""TEXT_INPUT"": ""#672f2f"", ""SCROLL"": ""#672f2f"",
                   ""BUTTON"": (""#672f2f"", ""#99b19c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#672f2f"", ""#99b19c"", ""#d7d1c9"", ""#faf5ef""], },
    ""LightGreen7"": {""BACKGROUND"": ""#99b19c"", ""TEXT"": ""#faf5ef"", ""INPUT"": ""#d7d1c9"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#d7d1c9"",
                    ""BUTTON"": (""#FFFFFF"", ""#99b19c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#672f2f"", ""#99b19c"", ""#d7d1c9"", ""#faf5ef""], },
    ""LightGrey5"": {""BACKGROUND"": ""#d7d1c9"", ""TEXT"": ""#672f2f"", ""INPUT"": ""#99b19c"", ""TEXT_INPUT"": ""#672f2f"", ""SCROLL"": ""#672f2f"",
                   ""BUTTON"": (""#FFFFFF"", ""#672f2f""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#672f2f"", ""#99b19c"", ""#d7d1c9"", ""#faf5ef""], },
    ""DarkBrown3"": {""BACKGROUND"": ""#a0855b"", ""TEXT"": ""#f9f6f2"", ""INPUT"": ""#f1d6ab"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f1d6ab"",
                   ""BUTTON"": (""#FFFFFF"", ""#38470b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#38470b"", ""#a0855b"", ""#f1d6ab"", ""#f9f6f2""], },
    ""LightBrown11"": {""BACKGROUND"": ""#f1d6ab"", ""TEXT"": ""#38470b"", ""INPUT"": ""#a0855b"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#38470b"",
                     ""BUTTON"": (""#f9f6f2"", ""#a0855b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#38470b"", ""#a0855b"", ""#f1d6ab"", ""#f9f6f2""], },
    ""DarkRed"": {""BACKGROUND"": ""#83142c"", ""TEXT"": ""#f9d276"", ""INPUT"": ""#ad1d45"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#ad1d45"", ""BUTTON"": (""#f9d276"", ""#ad1d45""),
                ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                ""COLOR_LIST"": [""#44000d"", ""#83142c"", ""#ad1d45"", ""#f9d276""], },
    ""DarkTeal6"": {""BACKGROUND"": ""#204969"", ""TEXT"": ""#fff7f7"", ""INPUT"": ""#dadada"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#dadada"",
                  ""BUTTON"": (""#000000"", ""#fff7f7""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#204969"", ""#08ffc8"", ""#dadada"", ""#fff7f7""], },
    ""DarkBrown4"": {""BACKGROUND"": ""#252525"", ""TEXT"": ""#ff0000"", ""INPUT"": ""#af0404"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#af0404"",
                   ""BUTTON"": (""#FFFFFF"", ""#252525""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#252525"", ""#414141"", ""#af0404"", ""#ff0000""], },
    ""LightYellow"": {""BACKGROUND"": ""#f4ff61"", ""TEXT"": ""#27aa80"", ""INPUT"": ""#32ff6a"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#27aa80"",
                    ""BUTTON"": (""#f4ff61"", ""#27aa80""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#27aa80"", ""#32ff6a"", ""#a8ff3e"", ""#f4ff61""], },
    ""DarkGreen1"": {""BACKGROUND"": ""#2b580c"", ""TEXT"": ""#fdef96"", ""INPUT"": ""#f7b71d"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f7b71d"",
                   ""BUTTON"": (""#fdef96"", ""#2b580c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#2b580c"", ""#afa939"", ""#f7b71d"", ""#fdef96""], },
    ""LightGreen8"": {""BACKGROUND"": ""#c8dad3"", ""TEXT"": ""#63707e"", ""INPUT"": ""#93b5b3"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#63707e"",
                    ""BUTTON"": (""#FFFFFF"", ""#63707e""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#63707e"", ""#93b5b3"", ""#c8dad3"", ""#f2f6f5""], },
    ""DarkTeal7"": {""BACKGROUND"": ""#248ea9"", ""TEXT"": ""#fafdcb"", ""INPUT"": ""#aee7e8"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#aee7e8"",
                  ""BUTTON"": (""#000000"", ""#fafdcb""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#248ea9"", ""#28c3d4"", ""#aee7e8"", ""#fafdcb""], },
    ""DarkBlue8"": {""BACKGROUND"": ""#454d66"", ""TEXT"": ""#d9d872"", ""INPUT"": ""#58b368"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#58b368"",
                  ""BUTTON"": (""#000000"", ""#009975""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#009975"", ""#454d66"", ""#58b368"", ""#d9d872""], },
    ""DarkBlue9"": {""BACKGROUND"": ""#263859"", ""TEXT"": ""#ff6768"", ""INPUT"": ""#6b778d"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#6b778d"",
                  ""BUTTON"": (""#ff6768"", ""#263859""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#17223b"", ""#263859"", ""#6b778d"", ""#ff6768""], },
    ""DarkBlue10"": {""BACKGROUND"": ""#0028ff"", ""TEXT"": ""#f1f4df"", ""INPUT"": ""#10eaf0"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#10eaf0"",
                   ""BUTTON"": (""#f1f4df"", ""#24009c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#24009c"", ""#0028ff"", ""#10eaf0"", ""#f1f4df""], },
    ""DarkBlue11"": {""BACKGROUND"": ""#6384b3"", ""TEXT"": ""#e6f0b6"", ""INPUT"": ""#b8e9c0"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#b8e9c0"",
                   ""BUTTON"": (""#e6f0b6"", ""#684949""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#684949"", ""#6384b3"", ""#b8e9c0"", ""#e6f0b6""], },
    ""DarkTeal8"": {""BACKGROUND"": ""#71a0a5"", ""TEXT"": ""#212121"", ""INPUT"": ""#665c84"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#212121"",
                  ""BUTTON"": (""#fab95b"", ""#665c84""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#212121"", ""#665c84"", ""#71a0a5"", ""#fab95b""], },
    ""DarkRed1"": {""BACKGROUND"": ""#c10000"", ""TEXT"": ""#eeeeee"", ""INPUT"": ""#dedede"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#dedede"", ""BUTTON"": (""#c10000"", ""#eeeeee""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                 ""COLOR_LIST"": [""#c10000"", ""#ff4949"", ""#dedede"", ""#eeeeee""], },
    ""LightBrown5"": {""BACKGROUND"": ""#fff591"", ""TEXT"": ""#e41749"", ""INPUT"": ""#f5587b"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e41749"",
                    ""BUTTON"": (""#fff591"", ""#e41749""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#e41749"", ""#f5587b"", ""#ff8a5c"", ""#fff591""], },
    ""LightGreen9"": {""BACKGROUND"": ""#f1edb3"", ""TEXT"": ""#3b503d"", ""INPUT"": ""#4a746e"", ""TEXT_INPUT"": ""#f1edb3"", ""SCROLL"": ""#3b503d"",
                    ""BUTTON"": (""#f1edb3"", ""#3b503d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#3b503d"", ""#4a746e"", ""#c8cf94"", ""#f1edb3""], ""DESCRIPTION"": [""Green"", ""Turquoise"", ""Yellow""], },
    ""DarkGreen2"": {""BACKGROUND"": ""#3b503d"", ""TEXT"": ""#f1edb3"", ""INPUT"": ""#c8cf94"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#c8cf94"",
                   ""BUTTON"": (""#f1edb3"", ""#3b503d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3b503d"", ""#4a746e"", ""#c8cf94"", ""#f1edb3""], ""DESCRIPTION"": [""Green"", ""Turquoise"", ""Yellow""], },
    ""LightGray1"": {""BACKGROUND"": ""#f2f2f2"", ""TEXT"": ""#222831"", ""INPUT"": ""#393e46"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#222831"",
                   ""BUTTON"": (""#f2f2f2"", ""#222831""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#222831"", ""#393e46"", ""#f96d00"", ""#f2f2f2""], ""DESCRIPTION"": [""#000000"", ""Grey"", ""Orange"", ""Grey"", ""Autumn""], },
    ""DarkGrey4"": {""BACKGROUND"": ""#52524e"", ""TEXT"": ""#e9e9e5"", ""INPUT"": ""#d4d6c8"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#d4d6c8"",
                  ""BUTTON"": (""#FFFFFF"", ""#9a9b94""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#52524e"", ""#9a9b94"", ""#d4d6c8"", ""#e9e9e5""], ""DESCRIPTION"": [""Grey"", ""Pastel"", ""Winter""], },
    ""DarkBlue12"": {""BACKGROUND"": ""#324e7b"", ""TEXT"": ""#f8f8f8"", ""INPUT"": ""#86a6df"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#86a6df"",
                   ""BUTTON"": (""#FFFFFF"", ""#5068a9""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#324e7b"", ""#5068a9"", ""#86a6df"", ""#f8f8f8""], ""DESCRIPTION"": [""Blue"", ""Grey"", ""Cold"", ""Winter""], },
    ""DarkPurple6"": {""BACKGROUND"": ""#070739"", ""TEXT"": ""#e1e099"", ""INPUT"": ""#c327ab"", ""TEXT_INPUT"": ""#e1e099"", ""SCROLL"": ""#c327ab"",
                    ""BUTTON"": (""#e1e099"", ""#521477""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                    ""COLOR_LIST"": [""#070739"", ""#521477"", ""#c327ab"", ""#e1e099""], ""DESCRIPTION"": [""#000000"", ""Purple"", ""Yellow"", ""Dark""], },
    ""DarkPurple7"": {""BACKGROUND"": ""#191930"", ""TEXT"": ""#B1B7C5"", ""INPUT"": ""#232B5C"", ""TEXT_INPUT"": ""#D0E3E7"", ""SCROLL"": ""#B1B7C5"",
                    ""BUTTON"": (""#272D38"", ""#B1B7C5""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBlue13"": {""BACKGROUND"": ""#203562"", ""TEXT"": ""#e3e8f8"", ""INPUT"": ""#c0c5cd"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#c0c5cd"",
                   ""BUTTON"": (""#FFFFFF"", ""#3e588f""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#203562"", ""#3e588f"", ""#c0c5cd"", ""#e3e8f8""], ""DESCRIPTION"": [""Blue"", ""Grey"", ""Wedding"", ""Cold""], },
    ""DarkBrown5"": {""BACKGROUND"": ""#3c1b1f"", ""TEXT"": ""#f6e1b5"", ""INPUT"": ""#e2bf81"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e2bf81"",
                   ""BUTTON"": (""#3c1b1f"", ""#f6e1b5""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3c1b1f"", ""#b21e4b"", ""#e2bf81"", ""#f6e1b5""], ""DESCRIPTION"": [""Brown"", ""Red"", ""Yellow"", ""Warm""], },
    ""DarkGreen3"": {""BACKGROUND"": ""#062121"", ""TEXT"": ""#eeeeee"", ""INPUT"": ""#e4dcad"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e4dcad"",
                   ""BUTTON"": (""#eeeeee"", ""#181810""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#062121"", ""#181810"", ""#e4dcad"", ""#eeeeee""], ""DESCRIPTION"": [""#000000"", ""#000000"", ""Brown"", ""Grey""], },
    ""DarkBlack1"": {""BACKGROUND"": ""#181810"", ""TEXT"": ""#eeeeee"", ""INPUT"": ""#e4dcad"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e4dcad"",
                   ""BUTTON"": (""#FFFFFF"", ""#062121""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#062121"", ""#181810"", ""#e4dcad"", ""#eeeeee""], ""DESCRIPTION"": [""#000000"", ""#000000"", ""Brown"", ""Grey""], },
    ""DarkGrey5"": {""BACKGROUND"": ""#343434"", ""TEXT"": ""#f3f3f3"", ""INPUT"": ""#e9dcbe"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e9dcbe"",
                  ""BUTTON"": (""#FFFFFF"", ""#8e8b82""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#343434"", ""#8e8b82"", ""#e9dcbe"", ""#f3f3f3""], ""DESCRIPTION"": [""Grey"", ""Brown""], },
    ""LightBrown12"": {""BACKGROUND"": ""#8e8b82"", ""TEXT"": ""#f3f3f3"", ""INPUT"": ""#e9dcbe"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e9dcbe"",
                     ""BUTTON"": (""#f3f3f3"", ""#8e8b82""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#343434"", ""#8e8b82"", ""#e9dcbe"", ""#f3f3f3""], ""DESCRIPTION"": [""Grey"", ""Brown""], },
    ""DarkTeal9"": {""BACKGROUND"": ""#13445a"", ""TEXT"": ""#fef4e8"", ""INPUT"": ""#446878"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#446878"",
                  ""BUTTON"": (""#fef4e8"", ""#446878""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#13445a"", ""#970747"", ""#446878"", ""#fef4e8""], ""DESCRIPTION"": [""Red"", ""Grey"", ""Blue"", ""Wedding"", ""Retro""], },
    ""DarkBlue14"": {""BACKGROUND"": ""#21273d"", ""TEXT"": ""#f1f6f8"", ""INPUT"": ""#b9d4f1"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#b9d4f1"",
                   ""BUTTON"": (""#FFFFFF"", ""#6a759b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#21273d"", ""#6a759b"", ""#b9d4f1"", ""#f1f6f8""], ""DESCRIPTION"": [""Blue"", ""#000000"", ""Grey"", ""Cold"", ""Winter""], },
    ""LightBlue6"": {""BACKGROUND"": ""#f1f6f8"", ""TEXT"": ""#21273d"", ""INPUT"": ""#6a759b"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#21273d"",
                   ""BUTTON"": (""#f1f6f8"", ""#6a759b""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#21273d"", ""#6a759b"", ""#b9d4f1"", ""#f1f6f8""], ""DESCRIPTION"": [""Blue"", ""#000000"", ""Grey"", ""Cold"", ""Winter""], },
    ""DarkGreen4"": {""BACKGROUND"": ""#044343"", ""TEXT"": ""#e4e4e4"", ""INPUT"": ""#045757"", ""TEXT_INPUT"": ""#e4e4e4"", ""SCROLL"": ""#045757"",
                   ""BUTTON"": (""#e4e4e4"", ""#045757""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#222222"", ""#044343"", ""#045757"", ""#e4e4e4""], ""DESCRIPTION"": [""#000000"", ""Turquoise"", ""Grey"", ""Dark""], },
    ""DarkGreen5"": {""BACKGROUND"": ""#1b4b36"", ""TEXT"": ""#e0e7f1"", ""INPUT"": ""#aebd77"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#aebd77"",
                   ""BUTTON"": (""#FFFFFF"", ""#538f6a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#1b4b36"", ""#538f6a"", ""#aebd77"", ""#e0e7f1""], ""DESCRIPTION"": [""Green"", ""Grey""], },
    ""DarkTeal10"": {""BACKGROUND"": ""#0d3446"", ""TEXT"": ""#d8dfe2"", ""INPUT"": ""#71adb5"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#71adb5"",
                   ""BUTTON"": (""#FFFFFF"", ""#176d81""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#0d3446"", ""#176d81"", ""#71adb5"", ""#d8dfe2""], ""DESCRIPTION"": [""Grey"", ""Turquoise"", ""Winter"", ""Cold""], },
    ""DarkGrey6"": {""BACKGROUND"": ""#3e3e3e"", ""TEXT"": ""#ededed"", ""INPUT"": ""#68868c"", ""TEXT_INPUT"": ""#ededed"", ""SCROLL"": ""#68868c"",
                  ""BUTTON"": (""#FFFFFF"", ""#405559""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#3e3e3e"", ""#405559"", ""#68868c"", ""#ededed""], ""DESCRIPTION"": [""Grey"", ""Turquoise"", ""Winter""], },
    ""DarkTeal11"": {""BACKGROUND"": ""#405559"", ""TEXT"": ""#ededed"", ""INPUT"": ""#68868c"", ""TEXT_INPUT"": ""#ededed"", ""SCROLL"": ""#68868c"",
                   ""BUTTON"": (""#ededed"", ""#68868c""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#3e3e3e"", ""#405559"", ""#68868c"", ""#ededed""], ""DESCRIPTION"": [""Grey"", ""Turquoise"", ""Winter""], },
    ""LightBlue7"": {""BACKGROUND"": ""#9ed0e0"", ""TEXT"": ""#19483f"", ""INPUT"": ""#5c868e"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#19483f"",
                   ""BUTTON"": (""#FFFFFF"", ""#19483f""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#19483f"", ""#5c868e"", ""#ff6a38"", ""#9ed0e0""], ""DESCRIPTION"": [""Orange"", ""Blue"", ""Turquoise""], },
    ""LightGreen10"": {""BACKGROUND"": ""#d8ebb5"", ""TEXT"": ""#205d67"", ""INPUT"": ""#639a67"", ""TEXT_INPUT"": ""#FFFFFF"", ""SCROLL"": ""#205d67"",
                     ""BUTTON"": (""#d8ebb5"", ""#205d67""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#205d67"", ""#639a67"", ""#d9bf77"", ""#d8ebb5""], ""DESCRIPTION"": [""Blue"", ""Green"", ""Brown"", ""Vintage""], },
    ""DarkBlue15"": {""BACKGROUND"": ""#151680"", ""TEXT"": ""#f1fea4"", ""INPUT"": ""#375fc0"", ""TEXT_INPUT"": ""#f1fea4"", ""SCROLL"": ""#375fc0"",
                   ""BUTTON"": (""#f1fea4"", ""#1c44ac""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#151680"", ""#1c44ac"", ""#375fc0"", ""#f1fea4""], ""DESCRIPTION"": [""Blue"", ""Yellow"", ""Cold""], },
    ""DarkBlue16"": {""BACKGROUND"": ""#1c44ac"", ""TEXT"": ""#f1fea4"", ""INPUT"": ""#375fc0"", ""TEXT_INPUT"": ""#f1fea4"", ""SCROLL"": ""#375fc0"",
                   ""BUTTON"": (""#f1fea4"", ""#151680""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#151680"", ""#1c44ac"", ""#375fc0"", ""#f1fea4""], ""DESCRIPTION"": [""Blue"", ""Yellow"", ""Cold""], },
    ""DarkTeal12"": {""BACKGROUND"": ""#004a7c"", ""TEXT"": ""#fafafa"", ""INPUT"": ""#e8f1f5"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#e8f1f5"",
                   ""BUTTON"": (""#fafafa"", ""#005691""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#004a7c"", ""#005691"", ""#e8f1f5"", ""#fafafa""], ""DESCRIPTION"": [""Grey"", ""Blue"", ""Cold"", ""Winter""], },
    ""LightBrown13"": {""BACKGROUND"": ""#ebf5ee"", ""TEXT"": ""#921224"", ""INPUT"": ""#bdc6b8"", ""TEXT_INPUT"": ""#921224"", ""SCROLL"": ""#921224"",
                     ""BUTTON"": (""#FFFFFF"", ""#921224""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                     ""COLOR_LIST"": [""#921224"", ""#bdc6b8"", ""#bce0da"", ""#ebf5ee""], ""DESCRIPTION"": [""Red"", ""Blue"", ""Grey"", ""Vintage"", ""Wedding""], },
    ""DarkBlue17"": {""BACKGROUND"": ""#21294c"", ""TEXT"": ""#f9f2d7"", ""INPUT"": ""#f2dea8"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#f2dea8"",
                   ""BUTTON"": (""#f9f2d7"", ""#141829""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#141829"", ""#21294c"", ""#f2dea8"", ""#f9f2d7""], ""DESCRIPTION"": [""#000000"", ""Blue"", ""Yellow""], },
    ""DarkBrown6"": {""BACKGROUND"": ""#785e4d"", ""TEXT"": ""#f2eee3"", ""INPUT"": ""#baaf92"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#baaf92"",
                   ""BUTTON"": (""#FFFFFF"", ""#785e4d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#785e4d"", ""#ff8426"", ""#baaf92"", ""#f2eee3""], ""DESCRIPTION"": [""Grey"", ""Brown"", ""Orange"", ""Autumn""], },
    ""DarkGreen6"": {""BACKGROUND"": ""#5c715e"", ""TEXT"": ""#f2f9f1"", ""INPUT"": ""#ddeedf"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#ddeedf"",
                   ""BUTTON"": (""#f2f9f1"", ""#5c715e""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#5c715e"", ""#b6cdbd"", ""#ddeedf"", ""#f2f9f1""], ""DESCRIPTION"": [""Grey"", ""Green"", ""Vintage""], },
    ""DarkGreen7"": {""BACKGROUND"": ""#0C231E"", ""TEXT"": ""#efbe1c"", ""INPUT"": ""#153C33"", ""TEXT_INPUT"": ""#efbe1c"", ""SCROLL"": ""#153C33"",
                   ""BUTTON"": (""#efbe1c"", ""#153C33""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey7"": {""BACKGROUND"": ""#4b586e"", ""TEXT"": ""#dddddd"", ""INPUT"": ""#574e6d"", ""TEXT_INPUT"": ""#dddddd"", ""SCROLL"": ""#574e6d"",
                  ""BUTTON"": (""#dddddd"", ""#43405d""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                  ""COLOR_LIST"": [""#43405d"", ""#4b586e"", ""#574e6d"", ""#dddddd""], ""DESCRIPTION"": [""Grey"", ""Winter"", ""Cold""], },
    ""DarkRed2"": {""BACKGROUND"": ""#ab1212"", ""TEXT"": ""#f6e4b5"", ""INPUT"": ""#cd3131"", ""TEXT_INPUT"": ""#f6e4b5"", ""SCROLL"": ""#cd3131"", ""BUTTON"": (""#f6e4b5"", ""#ab1212""),
                 ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                 ""COLOR_LIST"": [""#ab1212"", ""#1fad9f"", ""#cd3131"", ""#f6e4b5""], ""DESCRIPTION"": [""Turquoise"", ""Red"", ""Yellow""], },
    ""LightGrey6"": {""BACKGROUND"": ""#e3e3e3"", ""TEXT"": ""#233142"", ""INPUT"": ""#455d7a"", ""TEXT_INPUT"": ""#e3e3e3"", ""SCROLL"": ""#233142"",
                   ""BUTTON"": (""#e3e3e3"", ""#455d7a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0,
                   ""COLOR_LIST"": [""#233142"", ""#455d7a"", ""#f95959"", ""#e3e3e3""], ""DESCRIPTION"": [""#000000"", ""Blue"", ""Red"", ""Grey""], },
    ""HotDogStand"": {""BACKGROUND"": ""red"", ""TEXT"": ""yellow"", ""INPUT"": ""yellow"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""yellow"", ""BUTTON"": (""red"", ""yellow""),
                    ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey8"": {""BACKGROUND"": ""#19232D"", ""TEXT"": ""#ffffff"", ""INPUT"": ""#32414B"", ""TEXT_INPUT"": ""#ffffff"", ""SCROLL"": ""#505F69"",
                  ""BUTTON"": (""#ffffff"", ""#32414B""), ""PROGRESS"": (""#505F69"", ""#32414B""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey9"": {""BACKGROUND"": ""#36393F"", ""TEXT"": ""#DCDDDE"", ""INPUT"": ""#40444B"", ""TEXT_INPUT"": ""#ffffff"", ""SCROLL"": ""#202225"",
                  ""BUTTON"": (""#202225"", ""#B9BBBE""), ""PROGRESS"": (""#202225"", ""#40444B""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey10"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#cccdcf"", ""INPUT"": ""#272a31"", ""TEXT_INPUT"": ""#8b9fde"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#f5f5f6"", ""#2e3d5a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey11"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#cccdcf"", ""INPUT"": ""#313641"", ""TEXT_INPUT"": ""#cccdcf"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#f5f5f6"", ""#313641""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey12"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#8b9fde"", ""INPUT"": ""#313641"", ""TEXT_INPUT"": ""#8b9fde"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#cccdcf"", ""#2e3d5a""), ""PROGRESS"": DEFAULT_PROGRESS_BAR_COMPUTE, ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey13"": {""BACKGROUND"": ""#1c1e23"", ""TEXT"": ""#cccdcf"", ""INPUT"": ""#272a31"", ""TEXT_INPUT"": ""#cccdcf"", ""SCROLL"": ""#313641"",
                   ""BUTTON"": (""#8b9fde"", ""#313641""), ""PROGRESS"": (""#cccdcf"", ""#272a31""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkGrey14"": {""BACKGROUND"": ""#24292e"", ""TEXT"": ""#fafbfc"", ""INPUT"": ""#1d2125"", ""TEXT_INPUT"": ""#fafbfc"", ""SCROLL"": ""#1d2125"",
                   ""BUTTON"": (""#fafbfc"", ""#155398""), ""PROGRESS"": (""#155398"", ""#1d2125""), ""BORDER"": 1, ""SLIDER_DEPTH"": 0, ""PROGRESS_DEPTH"": 0, },
    ""DarkBrown7"": {""BACKGROUND"": ""#2c2417"", ""TEXT"": ""#baa379"", ""INPUT"": ""#baa379"", ""TEXT_INPUT"": ""#000000"", ""SCROLL"": ""#392e1c"",
                   ""BUTTON"": (""#000000"", ""#baa379""), ""PROGRESS"": (""#baa379"", ""#453923""), ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
    ""Python"": {""BACKGROUND"": ""#3d7aab"", ""TEXT"": ""#ffde56"", ""INPUT"": ""#295273"", ""TEXT_INPUT"": ""#ffde56"", ""SCROLL"": ""#295273"", ""BUTTON"": (""#ffde56"", ""#295273""),
               ""PROGRESS"": (""#ffde56"", ""#295273""), ""BORDER"": 1, ""SLIDER_DEPTH"": 1, ""PROGRESS_DEPTH"": 0, },
}


        ```
    ###Assistant : 
    :param icon:                            Can be either a filename or Base64 value. For Windows if filename, it MUST be ICO format. For Linux, must NOT be ICO. Most portable is to use a Base64 of a PNG file. This works universally across all OS's
    :type icon:                             bytes | str
    :param button_color:                    Color of the button (text, background)
    :type button_color:                     (str, str) or str
    :param element_size:                    element size (width, height) in characters
    :type element_size:                     (int, int)
    :param button_element_size:             Size of button
    :type button_element_size:              (int, int)
    :param margins:                         (left/right, top/bottom) tkinter margins around outsize. Amount of pixels to leave inside the window's frame around the edges before your elements are shown.
    :type margins:                          (int, int)
    :param element_padding:                 Default amount of padding to put around elements in window (left/right, top/bottom) or ((left, right), (top, bottom))
    :type element_padding:                  (int, int) or ((int, int),(int,int))
    :param auto_size_text:                  True if the Widget should be shrunk to exactly fit the number of chars to show
    :type auto_size_text:                   bool
    :param auto_size_buttons:               True if Buttons in this Window should be sized to exactly fit the text on this.
    :type auto_size_buttons:                (bool)
    :param font:                            specifies the  font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike
    :type font:                             (str or (str, int[, str]) or None)
    :param border_width:                    width of border around element
    :type border_width:                     (int)
    :param slider_border_width:             Width of the border around sliders
    :type slider_border_width:              (int)
    :param slider_relief:                   Type of relief to use for sliders
    :type slider_relief:                    (str)
    :param slider_orientation:              ???
    :type slider_orientation:               ???
    :param autoclose_time:                  ???
    :type autoclose_time:                   ???
    :param message_box_line_width:          ???
    :type message_box_line_width:           ???
    :param progress_meter_border_depth:     ???
    :type progress_meter_border_depth:      ???
    :param progress_meter_style:            You can no longer set a progress bar style. All ttk styles must be the same for the window
    :type progress_meter_style:             ???
    :param progress_meter_relief:
    :type progress_meter_relief:            ???
    :param progress_meter_color:            ???
    :type progress_meter_color:             ???
    :param progress_meter_size:             ???
    :type progress_meter_size:              ???
    :param text_justification:              Default text justification for all Text Elements in window
    :type text_justification:               'left' | 'right' | 'center'
    :param background_color:                color of background
    :type background_color:                 (str)
    :param element_background_color:        element background color
    :type element_background_color:         (str)
    :param text_element_background_color:   text element background color
    :type text_element_background_color:    (str)
    :param input_elements_background_color: Default color to use for the background of input elements
    :type input_elements_background_color:  (str)
    :param input_text_color:                Default color to use for the text for Input elements
    :type input_text_color:                 (str)
    :param scrollbar_color:                 Default color to use for the slider trough
    :type scrollbar_color:                  (str)
    :param text_color:                      color of the text
    :type text_color:                       (str)
    :param element_text_color:              Default color to use for Text elements
    :type element_text_color:               (str)
    :param debug_win_size:                  window size
    :type debug_win_size:                   (int, int)
    :param window_location:                 Default location to place windows. Not setting will center windows on the display
    :type window_location:                  (int, int) | None
    :param error_button_color:              (Default = (None))
    :type error_button_color:               ???
    :param tooltip_time:                    time in milliseconds to wait before showing a tooltip. Default is 400ms
    :type tooltip_time:                     (int)
    :param tooltip_font:                    font to use for all tooltips
    :type tooltip_font:                     str or Tuple[str, int] or Tuple[str, int, str]
    :param use_ttk_buttons:                 if True will cause all buttons to be ttk buttons
    :type use_ttk_buttons:                  (bool)
    :param ttk_theme:                       Theme to use with ttk widgets.  Choices (on Windows) include - 'default', 'winnative', 'clam', 'alt', 'classic', 'vista', 'xpnative'
    :type ttk_theme:                        (str)
    :param suppress_error_popups:           If True then error popups will not be shown if generated internally to PySimpleGUI
    :type suppress_error_popups:            (bool)
    :param suppress_raise_key_errors:       If True then key errors won't be raised (you'll still get popup error)
    :type suppress_raise_key_errors:        (bool)
    :param suppress_key_guessing:           If True then key errors won't try and find closest matches for you
    :type suppress_key_guessing:            (bool)
    :param warn_button_key_duplicates:      If True then duplicate Button Keys generate warnings (not recommended as they're expected)
    :type warn_button_key_duplicates:       (bool)    
    :param enable_treeview_869_patch:       If True, then will use the treeview color patch for tk 8.6.9
    :type enable_treeview_869_patch:        (bool)
    :param enable_mac_notitlebar_patch:     If True then Windows with no titlebar use an alternative technique when tkinter version < 8.6.10
    :type enable_mac_notitlebar_patch:      (bool)
    :param use_custom_titlebar:             If True then a custom titlebar is used instead of the normal system titlebar
    :type use_custom_titlebar:              (bool)
    :param titlebar_background_color:       If custom titlebar indicated by use_custom_titlebar, then use this as background color
    :type titlebar_background_color:        str | None
    :param titlebar_text_color:             If custom titlebar indicated by use_custom_titlebar, then use this as text color
    :type titlebar_text_color:              str | None
    :param titlebar_font:                   If custom titlebar indicated by use_custom_titlebar, then use this as title font
    :type titlebar_font:                    (str or (str, int[, str]) or None) | None
    :param titlebar_icon:                   If custom titlebar indicated by use_custom_titlebar, then use this as the icon (file or base64 bytes)
    :type titlebar_icon:                    bytes | str
    :param user_settings_path:              default path for user_settings API calls. Expanded with os.path.expanduser so can contain ~ to represent user
    :type user_settings_path:               (str)
    :param pysimplegui_settings_path:       default path for the global PySimpleGUI user_settings
    :type pysimplegui_settings_path:        (str)
    :param pysimplegui_settings_filename:   default filename for the global PySimpleGUI user_settings
    :type pysimplegui_settings_filename:    (str)
    :param keep_on_top:                     If True then all windows will automatically be set to keep_on_top=True
    :type keep_on_top:                      (bool)
    :param dpi_awareness:                   If True then will turn on DPI awareness (Windows only at the moment)
    :type dpi_awareness:                    (bool)
    :param scaling:                         Sets the default scaling for all windows including popups, etc.
    :type scaling:                          (float)
    :param disable_modal_windows:           If True then all windows, including popups, will not be modal windows
    :type disable_modal_windows:            (bool)
    :param tooltip_offset:                  Offset to use for tooltips as a tuple. These values will be added to the mouse location when the widget was entered.
    :type tooltip_offset:                   ((None, None) | (int, int))
    :return:                                None
    :rtype:                                 None
    
    "
1446,"def find_module(module, path=None, imp=None):
    
    if imp is None:
        imp = import_module
    with cwd_in_path():
        try:
            return imp(module)
        except ImportError:
            # Raise a more specific error if the problem is that one of the
            # dot-separated segments of the module name is not a package.
            if '.' in module:
                parts = module.split('.')
                for i, part in enumerate(parts[:-1]):
                    package = '.'.join(parts[:i + 1])
                    try:
                        mpart = imp(package)
                    except ImportError:
                        # Break out and re-raise the original ImportError
                        # instead.
                        break
                    try:
                        mpart.__path__
                    except AttributeError:
                        raise NotAPackage(package)
            raise

",Version of :func:`imp.find_module` supporting dots.,5,84,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_module(module, path=None, imp=None):
    
    if imp is None:
        imp = import_module
    with cwd_in_path():
        try:
            return imp(module)
        except ImportError:
            # Raise a more specific error if the problem is that one of the
            # dot-separated segments of the module name is not a package.
            if '.' in module:
                parts = module.split('.')
                for i, part in enumerate(parts[:-1]):
                    package = '.'.join(parts[:i + 1])
                    try:
                        mpart = imp(package)
                    except ImportError:
                        # Break out and re-raise the original ImportError
                        # instead.
                        break
                    try:
                        mpart.__path__
                    except AttributeError:
                        raise NotAPackage(package)
            raise


        ```
    ###Assistant : Version of :func:`imp.find_module` supporting dots.
    "
1447,"def get_ffmpeg_path() -> Optional[Path]:
    

    # Check if ffmpeg is installed
    global_ffmpeg = shutil.which(""ffmpeg"")
    if global_ffmpeg:
        return Path(global_ffmpeg)

    # Get local ffmpeg path
    return get_local_ffmpeg()

","
    Get path to global ffmpeg binary or a local ffmpeg binary.
    Or None if not found.
    ",16,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ffmpeg_path() -> Optional[Path]:
    

    # Check if ffmpeg is installed
    global_ffmpeg = shutil.which(""ffmpeg"")
    if global_ffmpeg:
        return Path(global_ffmpeg)

    # Get local ffmpeg path
    return get_local_ffmpeg()


        ```
    ###Assistant : 
    Get path to global ffmpeg binary or a local ffmpeg binary.
    Or None if not found.
    
    "
1448,"def __call__(self, inputs, state, scope=None):
        
        return self._call_wrapped_cell(
            inputs, state, cell_call_fn=self.cell.__call__, scope=scope
        )
","Runs the RNN cell step computation.

        We assume that the wrapped RNNCell is being built within its `__call__`
        method. We directly use the wrapped cell's `__call__` in the overridden
        wrapper `__call__` method.

        This allows to use the wrapped cell and the non-wrapped cell equivalently
        when using `__call__`.

        Args:
          inputs: A tensor with wrapped cell's input.
          state: A tensor or tuple of tensors with wrapped cell's state.
          scope: VariableScope for the subgraph created in the wrapped cells'
            `__call__`.

        Returns:
          A pair containing:

          - Output: A tensor with cell's output.
          - New state: A tensor or tuple of tensors with new wrapped cell's state.
        ",102,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self, inputs, state, scope=None):
        
        return self._call_wrapped_cell(
            inputs, state, cell_call_fn=self.cell.__call__, scope=scope
        )

        ```
    ###Assistant : Runs the RNN cell step computation.

        We assume that the wrapped RNNCell is being built within its `__call__`
        method. We directly use the wrapped cell's `__call__` in the overridden
        wrapper `__call__` method.

        This allows to use the wrapped cell and the non-wrapped cell equivalently
        when using `__call__`.

        Args:
          inputs: A tensor with wrapped cell's input.
          state: A tensor or tuple of tensors with wrapped cell's state.
          scope: VariableScope for the subgraph created in the wrapped cells'
            `__call__`.

        Returns:
          A pair containing:

          - Output: A tensor with cell's output.
          - New state: A tensor or tuple of tensors with new wrapped cell's state.
        
    "
1449,"def get_kurtosis(self) -> pd.DataFrame:
        
        vals = list()
        for period in portfolio_helper.PERIODS:
            vals.append(
                [
                    round(
                        scipy.stats.kurtosis(
                            portfolio_helper.filter_df_by_period(self.returns, period)
                        ),
                        3,
                    ),
                    round(
                        scipy.stats.skew(
                            portfolio_helper.filter_df_by_period(
                                self.benchmark_returns, period
                            )
                        ),
                        3,
                    ),
                ]
            )
        return pd.DataFrame(
            vals, index=portfolio_helper.PERIODS, columns=[""Portfolio"", ""Benchmark""]
        )
","Class method that retrieves kurtosis for portfolio and benchmark selected

        Returns
        -------
        pd.DataFrame
            DataFrame with kurtosis for portfolio and benchmark for different periods
        ",23,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_kurtosis(self) -> pd.DataFrame:
        
        vals = list()
        for period in portfolio_helper.PERIODS:
            vals.append(
                [
                    round(
                        scipy.stats.kurtosis(
                            portfolio_helper.filter_df_by_period(self.returns, period)
                        ),
                        3,
                    ),
                    round(
                        scipy.stats.skew(
                            portfolio_helper.filter_df_by_period(
                                self.benchmark_returns, period
                            )
                        ),
                        3,
                    ),
                ]
            )
        return pd.DataFrame(
            vals, index=portfolio_helper.PERIODS, columns=[""Portfolio"", ""Benchmark""]
        )

        ```
    ###Assistant : Class method that retrieves kurtosis for portfolio and benchmark selected

        Returns
        -------
        pd.DataFrame
            DataFrame with kurtosis for portfolio and benchmark for different periods
        
    "
1450,"def _save_model(self, epoch, batch, logs):
        
        logs = logs or {}

        if (
            isinstance(self.save_freq, int)
            or self.epochs_since_last_save >= self.period
        ):
            # Block only when saving interval is reached.
            logs = tf_utils.sync_to_numpy_or_python_type(logs)
            self.epochs_since_last_save = 0
            filepath = self._get_file_path(epoch, batch, logs)

            try:
                if self.save_best_only:
                    current = logs.get(self.monitor)
                    if current is None:
                        logging.warning(
                            ""Can save best model only with %s available, ""
                            ""skipping."",
                            self.monitor,
                        )
                    else:
                        if self.monitor_op(current, self.best):
                            if self.verbose > 0:
                                io_utils.print_msg(
                                    f""\nEpoch {epoch + 1}: {self.monitor} improved ""
                                    f""from {self.best:.5f} to {current:.5f}, ""
                                    f""saving model to {filepath}""
                                )
                            self.best = current
                            if self.save_weights_only:
                                self.model.save_weights(
                                    filepath,
                                    overwrite=True,
                                    options=self._options,
                                )
                            else:
                                self.model.save(
                                    filepath,
                                    overwrite=True,
                                    options=self._options,
                                )
                        else:
                            if self.verbose > 0:
                                io_utils.print_msg(
                                    f""\nEpoch {epoch + 1}: ""
                                    f""{self.monitor} did not improve from {self.best:.5f}""
                                )
                else:
                    if self.verbose > 0:
                        io_utils.print_msg(
                            f""\nEpoch {epoch + 1}: saving model to {filepath}""
                        )
                    if self.save_weights_only:
                        self.model.save_weights(
                            filepath, overwrite=True, options=self._options
                        )
                    else:
                        self.model.save(
                            filepath, overwrite=True, options=self._options
                        )

                self._maybe_remove_file()
            except IsADirectoryError as e:  # h5py 3.x
                raise IOError(
                    ""Please specify a non-directory filepath for ""
                    ""ModelCheckpoint. Filepath used is an existing ""
                    f""directory: {filepath}""
                )
            except IOError as e:  # h5py 2.x
                # `e.errno` appears to be `None` so checking the content of `e.args[0]`.
                if ""is a directory"" in str(e.args[0]).lower():
                    raise IOError(
                        ""Please specify a non-directory filepath for ""
                        ""ModelCheckpoint. Filepath used is an existing ""
                        f""directory: f{filepath}""
                    )
                # Re-throw the error for any other causes.
                raise e
","Saves the model.

        Args:
            epoch: the epoch this iteration is in.
            batch: the batch this iteration is in. `None` if the `save_freq`
              is set to `epoch`.
            logs: the `logs` dict passed in to `on_batch_end` or `on_epoch_end`.
        ",36,230,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _save_model(self, epoch, batch, logs):
        
        logs = logs or {}

        if (
            isinstance(self.save_freq, int)
            or self.epochs_since_last_save >= self.period
        ):
            # Block only when saving interval is reached.
            logs = tf_utils.sync_to_numpy_or_python_type(logs)
            self.epochs_since_last_save = 0
            filepath = self._get_file_path(epoch, batch, logs)

            try:
                if self.save_best_only:
                    current = logs.get(self.monitor)
                    if current is None:
                        logging.warning(
                            ""Can save best model only with %s available, ""
                            ""skipping."",
                            self.monitor,
                        )
                    else:
                        if self.monitor_op(current, self.best):
                            if self.verbose > 0:
                                io_utils.print_msg(
                                    f""\nEpoch {epoch + 1}: {self.monitor} improved ""
                                    f""from {self.best:.5f} to {current:.5f}, ""
                                    f""saving model to {filepath}""
                                )
                            self.best = current
                            if self.save_weights_only:
                                self.model.save_weights(
                                    filepath,
                                    overwrite=True,
                                    options=self._options,
                                )
                            else:
                                self.model.save(
                                    filepath,
                                    overwrite=True,
                                    options=self._options,
                                )
                        else:
                            if self.verbose > 0:
                                io_utils.print_msg(
                                    f""\nEpoch {epoch + 1}: ""
                                    f""{self.monitor} did not improve from {self.best:.5f}""
                                )
                else:
                    if self.verbose > 0:
                        io_utils.print_msg(
                            f""\nEpoch {epoch + 1}: saving model to {filepath}""
                        )
                    if self.save_weights_only:
                        self.model.save_weights(
                            filepath, overwrite=True, options=self._options
                        )
                    else:
                        self.model.save(
                            filepath, overwrite=True, options=self._options
                        )

                self._maybe_remove_file()
            except IsADirectoryError as e:  # h5py 3.x
                raise IOError(
                    ""Please specify a non-directory filepath for ""
                    ""ModelCheckpoint. Filepath used is an existing ""
                    f""directory: {filepath}""
                )
            except IOError as e:  # h5py 2.x
                # `e.errno` appears to be `None` so checking the content of `e.args[0]`.
                if ""is a directory"" in str(e.args[0]).lower():
                    raise IOError(
                        ""Please specify a non-directory filepath for ""
                        ""ModelCheckpoint. Filepath used is an existing ""
                        f""directory: f{filepath}""
                    )
                # Re-throw the error for any other causes.
                raise e

        ```
    ###Assistant : Saves the model.

        Args:
            epoch: the epoch this iteration is in.
            batch: the batch this iteration is in. `None` if the `save_freq`
              is set to `epoch`.
            logs: the `logs` dict passed in to `on_batch_end` or `on_epoch_end`.
        
    "
1451,"def steiner_tree(G, terminal_nodes, weight=""weight"", method=None):
    r
    if method is None:
        import warnings

        msg = (
            ""steiner_tree will change default method from 'kou' to 'mehlhorn'""
            ""in version 3.2.\nSet the `method` kwarg to remove this warning.""
        )
        warnings.warn(msg, FutureWarning, stacklevel=4)
        method = ""kou""

    try:
        algo = ALGORITHMS[method]
    except KeyError as e:
        msg = f""{method} is not a valid choice for an algorithm.""
        raise ValueError(msg) from e

    edges = algo(G, terminal_nodes, weight)
    # For multigraph we should add the minimal weight edge keys
    if G.is_multigraph():
        edges = (
            (u, v, min(G[u][v], key=lambda k: G[u][v][k][weight])) for u, v in edges
        )
    T = G.edge_subgraph(edges)
    return T
","Return an approximation to the minimum Steiner tree of a graph.

    The minimum Steiner tree of `G` w.r.t a set of `terminal_nodes` (also *S*)
    is a tree within `G` that spans those nodes and has minimum size (sum of
    edge weights) among all such trees.

    The approximation algorithm is specified with the `method` keyword
    argument. All three available algorithms produce a tree whose weight is
    within a (2 - (2 / l)) factor of the weight of the optimal Steiner tree,
    where *l* is the minimum number of leaf nodes across all possible Steiner
    trees.

    * `kou` [2]_ (runtime $O(|S| |V|^2)$) computes the minimum spanning tree of
    the subgraph of the metric closure of *G* induced by the terminal nodes,
    where the metric closure of *G* is the complete graph in which each edge is
    weighted by the shortest path distance between the nodes in *G*.
    * `mehlhorn` [3]_ (runtime $O(|E|+|V|\log|V|)$) modifies Kou et al.'s
    algorithm, beginning by finding the closest terminal node for each
    non-terminal. This data is used to create a complete graph containing only
    the terminal nodes, in which edge is weighted with the shortest path
    distance between them. The algorithm then proceeds in the same way as Kou
    et al..

    Parameters
    ----------
    G : NetworkX graph

    terminal_nodes : list
         A list of terminal nodes for which minimum steiner tree is
         to be found.

    weight : string (default = 'weight')
        Use the edge attribute specified by this string as the edge weight.
        Any edge attribute not present defaults to 1.

    method : string, optional (default = 'kou')
        The algorithm to use to approximate the Steiner tree.
        Supported options: 'kou', 'mehlhorn'.
        Other inputs produce a ValueError.

    Returns
    -------
    NetworkX graph
        Approximation to the minimum steiner tree of `G` induced by
        `terminal_nodes` .

    Notes
    -----
    For multigraphs, the edge between two nodes with minimum weight is the
    edge put into the Steiner tree.


    References
    ----------
    .. [1] Steiner_tree_problem on Wikipedia.
           https://en.wikipedia.org/wiki/Steiner_tree_problem
    .. [2] Kou, L., G. Markowsky, and L. Berman. 1981.
           ‘A Fast Algorithm for Steiner Trees’.
           Acta Informatica 15 (2): 141–45.
           https://doi.org/10.1007/BF00288961.
    .. [3] Mehlhorn, Kurt. 1988.
           ‘A Faster Approximation Algorithm for the Steiner Problem in Graphs’.
           Information Processing Letters 27 (3): 125–28.
           https://doi.org/10.1016/0020-0190(88)90066-X.
    ",366,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def steiner_tree(G, terminal_nodes, weight=""weight"", method=None):
    r
    if method is None:
        import warnings

        msg = (
            ""steiner_tree will change default method from 'kou' to 'mehlhorn'""
            ""in version 3.2.\nSet the `method` kwarg to remove this warning.""
        )
        warnings.warn(msg, FutureWarning, stacklevel=4)
        method = ""kou""

    try:
        algo = ALGORITHMS[method]
    except KeyError as e:
        msg = f""{method} is not a valid choice for an algorithm.""
        raise ValueError(msg) from e

    edges = algo(G, terminal_nodes, weight)
    # For multigraph we should add the minimal weight edge keys
    if G.is_multigraph():
        edges = (
            (u, v, min(G[u][v], key=lambda k: G[u][v][k][weight])) for u, v in edges
        )
    T = G.edge_subgraph(edges)
    return T

        ```
    ###Assistant : Return an approximation to the minimum Steiner tree of a graph.

    The minimum Steiner tree of `G` w.r.t a set of `terminal_nodes` (also *S*)
    is a tree within `G` that spans those nodes and has minimum size (sum of
    edge weights) among all such trees.

    The approximation algorithm is specified with the `method` keyword
    argument. All three available algorithms produce a tree whose weight is
    within a (2 - (2 / l)) factor of the weight of the optimal Steiner tree,
    where *l* is the minimum number of leaf nodes across all possible Steiner
    trees.

    * `kou` [2]_ (runtime $O(|S| |V|^2)$) computes the minimum spanning tree of
    the subgraph of the metric closure of *G* induced by the terminal nodes,
    where the metric closure of *G* is the complete graph in which each edge is
    weighted by the shortest path distance between the nodes in *G*.
    * `mehlhorn` [3]_ (runtime $O(|E|+|V|\log|V|)$) modifies Kou et al.'s
    algorithm, beginning by finding the closest terminal node for each
    non-terminal. This data is used to create a complete graph containing only
    the terminal nodes, in which edge is weighted with the shortest path
    distance between them. The algorithm then proceeds in the same way as Kou
    et al..

    Parameters
    ----------
    G : NetworkX graph

    terminal_nodes : list
         A list of terminal nodes for which minimum steiner tree is
         to be found.

    weight : string (default = 'weight')
        Use the edge attribute specified by this string as the edge weight.
        Any edge attribute not present defaults to 1.

    method : string, optional (default = 'kou')
        The algorithm to use to approximate the Steiner tree.
        Supported options: 'kou', 'mehlhorn'.
        Other inputs produce a ValueError.

    Returns
    -------
    NetworkX graph
        Approximation to the minimum steiner tree of `G` induced by
        `terminal_nodes` .

    Notes
    -----
    For multigraphs, the edge between two nodes with minimum weight is the
    edge put into the Steiner tree.


    References
    ----------
    .. [1] Steiner_tree_problem on Wikipedia.
           https://en.wikipedia.org/wiki/Steiner_tree_problem
    .. [2] Kou, L., G. Markowsky, and L. Berman. 1981.
           ‘A Fast Algorithm for Steiner Trees’.
           Acta Informatica 15 (2): 141–45.
           https://doi.org/10.1007/BF00288961.
    .. [3] Mehlhorn, Kurt. 1988.
           ‘A Faster Approximation Algorithm for the Steiner Problem in Graphs’.
           Information Processing Letters 27 (3): 125–28.
           https://doi.org/10.1016/0020-0190(88)90066-X.
    
    "
1452,"def find_object(self, queryset, request):
        
        if ""id"" in request.GET:
            return queryset.get(id=request.GET[""id""])
","
        Override this to implement more find methods.
        ",7,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_object(self, queryset, request):
        
        if ""id"" in request.GET:
            return queryset.get(id=request.GET[""id""])

        ```
    ###Assistant : 
        Override this to implement more find methods.
        
    "
1453,"def _maybe_infer_dtype_type(element):
    
    tipo = None
    if hasattr(element, ""dtype""):
        tipo = element.dtype
    elif is_list_like(element):
        element = np.asarray(element)
        tipo = element.dtype
    return tipo

","
    Try to infer an object's dtype, for use in arithmetic ops.

    Uses `element.dtype` if that's available.
    Objects implementing the iterator protocol are cast to a NumPy array,
    and from there the array's type is used.

    Parameters
    ----------
    element : object
        Possibly has a `.dtype` attribute, and possibly the iterator
        protocol.

    Returns
    -------
    tipo : type

    Examples
    --------
    >>> from collections import namedtuple
    >>> Foo = namedtuple(""Foo"", ""dtype"")
    >>> _maybe_infer_dtype_type(Foo(np.dtype(""i8"")))
    dtype('int64')
    ",70,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _maybe_infer_dtype_type(element):
    
    tipo = None
    if hasattr(element, ""dtype""):
        tipo = element.dtype
    elif is_list_like(element):
        element = np.asarray(element)
        tipo = element.dtype
    return tipo


        ```
    ###Assistant : 
    Try to infer an object's dtype, for use in arithmetic ops.

    Uses `element.dtype` if that's available.
    Objects implementing the iterator protocol are cast to a NumPy array,
    and from there the array's type is used.

    Parameters
    ----------
    element : object
        Possibly has a `.dtype` attribute, and possibly the iterator
        protocol.

    Returns
    -------
    tipo : type

    Examples
    --------
    >>> from collections import namedtuple
    >>> Foo = namedtuple(""Foo"", ""dtype"")
    >>> _maybe_infer_dtype_type(Foo(np.dtype(""i8"")))
    dtype('int64')
    
    "
1454,"def test_create_api_message_special():
    
    request = get_new_request(""Alexa.PowerController"", ""TurnOn"")
    directive_header = request[""directive""][""header""]
    directive_header.pop(""correlationToken"")
    directive = messages.AlexaDirective(request)

    msg = directive.response(""testName"", ""testNameSpace"")._response

    assert ""event"" in msg
    msg = msg[""event""]

    assert msg[""header""][""messageId""] is not None
    assert msg[""header""][""messageId""] != directive_header[""messageId""]
    assert ""correlationToken"" not in msg[""header""]
    assert msg[""header""][""name""] == ""testName""
    assert msg[""header""][""namespace""] == ""testNameSpace""
    assert msg[""header""][""payloadVersion""] == ""3""

    assert msg[""payload""] == {}
    assert ""endpoint"" not in msg

",Create an API message response of a request with non defaults.,11,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_create_api_message_special():
    
    request = get_new_request(""Alexa.PowerController"", ""TurnOn"")
    directive_header = request[""directive""][""header""]
    directive_header.pop(""correlationToken"")
    directive = messages.AlexaDirective(request)

    msg = directive.response(""testName"", ""testNameSpace"")._response

    assert ""event"" in msg
    msg = msg[""event""]

    assert msg[""header""][""messageId""] is not None
    assert msg[""header""][""messageId""] != directive_header[""messageId""]
    assert ""correlationToken"" not in msg[""header""]
    assert msg[""header""][""name""] == ""testName""
    assert msg[""header""][""namespace""] == ""testNameSpace""
    assert msg[""header""][""payloadVersion""] == ""3""

    assert msg[""payload""] == {}
    assert ""endpoint"" not in msg


        ```
    ###Assistant : Create an API message response of a request with non defaults.
    "
1455,"def tearDown(self):
        r
        del self.model_fp16
        del self.model_8bit

        gc.collect()
        torch.cuda.empty_cache()
","
        TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
        avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
        ",27,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tearDown(self):
        r
        del self.model_fp16
        del self.model_8bit

        gc.collect()
        torch.cuda.empty_cache()

        ```
    ###Assistant : 
        TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
        avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
        
    "
1456,"def test_pagination_from_sync_and_messages(self):
        
        channel = self._send_relation(RelationTypes.ANNOTATION, ""m.reaction"", ""A"")
        self.assertEquals(200, channel.code, channel.json_body)
        annotation_id = channel.json_body[""event_id""]
        # Send an event after the relation events.
        self.helper.send(self.room, body=""Latest event"", tok=self.user_token)

        # Request /sync, limiting it such that only the latest event is returned
        # (and not the relation).
        filter = urllib.parse.quote_plus(
            '{""room"": {""timeline"": {""limit"": 1}}}'.encode()
        )
        channel = self.make_request(
            ""GET"", f""/sync?filter={filter}"", access_token=self.user_token
        )
        self.assertEquals(200, channel.code, channel.json_body)
        room_timeline = channel.json_body[""rooms""][""join""][self.room][""timeline""]
        sync_prev_batch = room_timeline[""prev_batch""]
        self.assertIsNotNone(sync_prev_batch)
        # Ensure the relation event is not in the batch returned from /sync.
        self.assertNotIn(
            annotation_id, [ev[""event_id""] for ev in room_timeline[""events""]]
        )

        # Request /messages, limiting it such that only the latest event is
        # returned (and not the relation).
        channel = self.make_request(
            ""GET"",
            f""/rooms/{self.room}/messages?dir=b&limit=1"",
            access_token=self.user_token,
        )
        self.assertEquals(200, channel.code, channel.json_body)
        messages_end = channel.json_body[""end""]
        self.assertIsNotNone(messages_end)
        # Ensure the relation event is not in the chunk returned from /messages.
        self.assertNotIn(
            annotation_id, [ev[""event_id""] for ev in channel.json_body[""chunk""]]
        )

        # Request /relations with the pagination tokens received from both the
        # /sync and /messages responses above, in turn.
        #
        # This is a tiny bit silly since the client wouldn't know the parent ID
        # from the requests above; consider the parent ID to be known from a
        # previous /sync.
        for from_token in (sync_prev_batch, messages_end):
            channel = self.make_request(
                ""GET"",
                f""/_matrix/client/unstable/rooms/{self.room}/relations/{self.parent_id}?from={from_token}"",
                access_token=self.user_token,
            )
            self.assertEquals(200, channel.code, channel.json_body)

            # The relation should be in the returned chunk.
            self.assertIn(
                annotation_id, [ev[""event_id""] for ev in channel.json_body[""chunk""]]
            )
",Pagination tokens from /sync and /messages can be used to paginate /relations.,12,226,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pagination_from_sync_and_messages(self):
        
        channel = self._send_relation(RelationTypes.ANNOTATION, ""m.reaction"", ""A"")
        self.assertEquals(200, channel.code, channel.json_body)
        annotation_id = channel.json_body[""event_id""]
        # Send an event after the relation events.
        self.helper.send(self.room, body=""Latest event"", tok=self.user_token)

        # Request /sync, limiting it such that only the latest event is returned
        # (and not the relation).
        filter = urllib.parse.quote_plus(
            '{""room"": {""timeline"": {""limit"": 1}}}'.encode()
        )
        channel = self.make_request(
            ""GET"", f""/sync?filter={filter}"", access_token=self.user_token
        )
        self.assertEquals(200, channel.code, channel.json_body)
        room_timeline = channel.json_body[""rooms""][""join""][self.room][""timeline""]
        sync_prev_batch = room_timeline[""prev_batch""]
        self.assertIsNotNone(sync_prev_batch)
        # Ensure the relation event is not in the batch returned from /sync.
        self.assertNotIn(
            annotation_id, [ev[""event_id""] for ev in room_timeline[""events""]]
        )

        # Request /messages, limiting it such that only the latest event is
        # returned (and not the relation).
        channel = self.make_request(
            ""GET"",
            f""/rooms/{self.room}/messages?dir=b&limit=1"",
            access_token=self.user_token,
        )
        self.assertEquals(200, channel.code, channel.json_body)
        messages_end = channel.json_body[""end""]
        self.assertIsNotNone(messages_end)
        # Ensure the relation event is not in the chunk returned from /messages.
        self.assertNotIn(
            annotation_id, [ev[""event_id""] for ev in channel.json_body[""chunk""]]
        )

        # Request /relations with the pagination tokens received from both the
        # /sync and /messages responses above, in turn.
        #
        # This is a tiny bit silly since the client wouldn't know the parent ID
        # from the requests above; consider the parent ID to be known from a
        # previous /sync.
        for from_token in (sync_prev_batch, messages_end):
            channel = self.make_request(
                ""GET"",
                f""/_matrix/client/unstable/rooms/{self.room}/relations/{self.parent_id}?from={from_token}"",
                access_token=self.user_token,
            )
            self.assertEquals(200, channel.code, channel.json_body)

            # The relation should be in the returned chunk.
            self.assertIn(
                annotation_id, [ev[""event_id""] for ev in channel.json_body[""chunk""]]
            )

        ```
    ###Assistant : Pagination tokens from /sync and /messages can be used to paginate /relations.
    "
1457,"def download(self, url_or_urls):
        
        url_or_urls = map_nested(self._download, url_or_urls, map_tuple=True)
        return url_or_urls
","Download given url(s).

        Args:
            url_or_urls: url or `list`/`dict` of urls to download and extract. Each
                url is a `str`.

        Returns:
            downloaded_path(s): `str`, The downloaded paths matching the given input
                url_or_urls.

        Example:

        ```py
        >>> downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
        ```
        ",37,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def download(self, url_or_urls):
        
        url_or_urls = map_nested(self._download, url_or_urls, map_tuple=True)
        return url_or_urls

        ```
    ###Assistant : Download given url(s).

        Args:
            url_or_urls: url or `list`/`dict` of urls to download and extract. Each
                url is a `str`.

        Returns:
            downloaded_path(s): `str`, The downloaded paths matching the given input
                url_or_urls.

        Example:

        ```py
        >>> downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
        ```
        
    "
1458,"def draw_gaussian(image, point, sigma):
    
    # Check if the gaussian is inside
    point[0] = round(point[0], 2)
    point[1] = round(point[1], 2)

    ul = [math.floor(point[0] - 7.5 * sigma), math.floor(point[1] - 7.5 * sigma)]
    br = [math.floor(point[0] + 7.5 * sigma), math.floor(point[1] + 7.5 * sigma)]
    if (ul[0] > image.shape[1] or ul[1] >
            image.shape[0] or br[0] < 1 or br[1] < 1):
        return image
    size = 15 * sigma + 1
    g = _gaussian(size, sigma=0.1)
    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) -
           int(max(1, ul[0])) + int(max(1, -ul[0]))]
    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) -
           int(max(1, ul[1])) + int(max(1, -ul[1]))]
    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
    assert (g_x[0] > 0 and g_y[1] > 0)
    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] = \
        image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
    image[image > 1] = 1

    return image


# Adapted from: https://github.com/1adrianb/face-alignment/blob/master/face_alignment/api.py"," Draw gaussian circle at a point in an image.

    Args:
        image (np.array): An image of shape (H, W)
        point (np.array): The center point of the guassian circle
        sigma (float): Standard deviation of the gaussian kernel

    Returns:
        np.array: The image with the drawn gaussian.
    ",43,148,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def draw_gaussian(image, point, sigma):
    
    # Check if the gaussian is inside
    point[0] = round(point[0], 2)
    point[1] = round(point[1], 2)

    ul = [math.floor(point[0] - 7.5 * sigma), math.floor(point[1] - 7.5 * sigma)]
    br = [math.floor(point[0] + 7.5 * sigma), math.floor(point[1] + 7.5 * sigma)]
    if (ul[0] > image.shape[1] or ul[1] >
            image.shape[0] or br[0] < 1 or br[1] < 1):
        return image
    size = 15 * sigma + 1
    g = _gaussian(size, sigma=0.1)
    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) -
           int(max(1, ul[0])) + int(max(1, -ul[0]))]
    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) -
           int(max(1, ul[1])) + int(max(1, -ul[1]))]
    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
    assert (g_x[0] > 0 and g_y[1] > 0)
    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] = \
        image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
    image[image > 1] = 1

    return image


# Adapted from: https://github.com/1adrianb/face-alignment/blob/master/face_alignment/api.py
        ```
    ###Assistant :  Draw gaussian circle at a point in an image.

    Args:
        image (np.array): An image of shape (H, W)
        point (np.array): The center point of the guassian circle
        sigma (float): Standard deviation of the gaussian kernel

    Returns:
        np.array: The image with the drawn gaussian.
    
    "
1459,"def setdefault(self, key, default=None):
        
        if key in self:
            return self[key]
        self[key] = default
        return default
","Insert key with a value of default if key is not in the dictionary.

        Return the value for key if key is in the dictionary, else default.
        ",27,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setdefault(self, key, default=None):
        
        if key in self:
            return self[key]
        self[key] = default
        return default

        ```
    ###Assistant : Insert key with a value of default if key is not in the dictionary.

        Return the value for key if key is in the dictionary, else default.
        
    "
1460,"def current_year(context):
        
        context[""current_year""] = datetime.datetime.now().year
        return context
","
        Add the current year to the context, so it can be used for the copyright
        note, or other places where it is needed.
        ",23,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def current_year(context):
        
        context[""current_year""] = datetime.datetime.now().year
        return context

        ```
    ###Assistant : 
        Add the current year to the context, so it can be used for the copyright
        note, or other places where it is needed.
        
    "
1461,"def fileformatparser_map(self) -> Mapping[str, type]:
        
        return {
            ""csv"": CsvParser,
            ""parquet"": ParquetParser,
        }

    # TODO: make these user configurable in spec.json
    ab_additional_col = ""_ab_additional_properties""
    ab_last_mod_col = ""_ab_source_file_last_modified""
    ab_file_name_col = ""_ab_source_file_url""
    airbyte_columns = [ab_additional_col, ab_last_mod_col, ab_file_name_col]
    datetime_format_string = ""%Y-%m-%dT%H:%M:%S%z""
",Mapping where every key is equal  'filetype' and values are  corresponding  parser classes.,13,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fileformatparser_map(self) -> Mapping[str, type]:
        
        return {
            ""csv"": CsvParser,
            ""parquet"": ParquetParser,
        }

    # TODO: make these user configurable in spec.json
    ab_additional_col = ""_ab_additional_properties""
    ab_last_mod_col = ""_ab_source_file_last_modified""
    ab_file_name_col = ""_ab_source_file_url""
    airbyte_columns = [ab_additional_col, ab_last_mod_col, ab_file_name_col]
    datetime_format_string = ""%Y-%m-%dT%H:%M:%S%z""

        ```
    ###Assistant : Mapping where every key is equal  'filetype' and values are  corresponding  parser classes.
    "
1462,"def pivot_table(self, index=None, columns=None, values=None, aggfunc=""mean""):
        
        from dask.dataframe.reshape import pivot_table

        return pivot_table(
            self, index=index, columns=columns, values=values, aggfunc=aggfunc
        )
","
        Create a spreadsheet-style pivot table as a DataFrame. Target ``columns``
        must have category dtype to infer result's ``columns``.
        ``index``, ``columns``, ``values`` and ``aggfunc`` must be all scalar.

        Parameters
        ----------
        values : scalar
            column to aggregate
        index : scalar
            column to be index
        columns : scalar
            column to be columns
        aggfunc : {'mean', 'sum', 'count'}, default 'mean'

        Returns
        -------
        table : DataFrame
        ",61,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pivot_table(self, index=None, columns=None, values=None, aggfunc=""mean""):
        
        from dask.dataframe.reshape import pivot_table

        return pivot_table(
            self, index=index, columns=columns, values=values, aggfunc=aggfunc
        )

        ```
    ###Assistant : 
        Create a spreadsheet-style pivot table as a DataFrame. Target ``columns``
        must have category dtype to infer result's ``columns``.
        ``index``, ``columns``, ``values`` and ``aggfunc`` must be all scalar.

        Parameters
        ----------
        values : scalar
            column to aggregate
        index : scalar
            column to be index
        columns : scalar
            column to be columns
        aggfunc : {'mean', 'sum', 'count'}, default 'mean'

        Returns
        -------
        table : DataFrame
        
    "
1463,"def test_multi_trial_reuse_with_failing(ray_start_4_cpus_extra):
    
    os.environ[""TUNE_MAX_PENDING_TRIALS_PG""] = ""2""

    register_trainable(""foo2"", MyResettableClass)

    [trial1, trial2, trial3, trial4] = tune.run(
        ""foo2"",
        config={
            ""fail"": tune.grid_search([False, True, False, False]),
            ""id"": -1,
            ""sleep"": 2,
        },
        reuse_actors=True,
        resources_per_trial={""cpu"": 2},
        raise_on_failed_trial=False,
    ).trials

    assert trial1.last_result[""num_resets""] == 0
    assert trial3.last_result[""num_resets""] == 0
    assert trial4.last_result[""num_resets""] == 1

","Test that failing trial's actors are not reused.

    - 2 trials can run at the same time
    - Trial 1 succeeds, trial 2 fails
    - Trial 3 will be scheduled after trial 2 failed, so won't reuse actor
    - Trial 4 will be scheduled after trial 1 succeeded, so will reuse actor
    ",52,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_multi_trial_reuse_with_failing(ray_start_4_cpus_extra):
    
    os.environ[""TUNE_MAX_PENDING_TRIALS_PG""] = ""2""

    register_trainable(""foo2"", MyResettableClass)

    [trial1, trial2, trial3, trial4] = tune.run(
        ""foo2"",
        config={
            ""fail"": tune.grid_search([False, True, False, False]),
            ""id"": -1,
            ""sleep"": 2,
        },
        reuse_actors=True,
        resources_per_trial={""cpu"": 2},
        raise_on_failed_trial=False,
    ).trials

    assert trial1.last_result[""num_resets""] == 0
    assert trial3.last_result[""num_resets""] == 0
    assert trial4.last_result[""num_resets""] == 1


        ```
    ###Assistant : Test that failing trial's actors are not reused.

    - 2 trials can run at the same time
    - Trial 1 succeeds, trial 2 fails
    - Trial 3 will be scheduled after trial 2 failed, so won't reuse actor
    - Trial 4 will be scheduled after trial 1 succeeded, so will reuse actor
    
    "
1464,"def dispatch(self, request, *args, **kwargs):
        
        page_id = kwargs.get(""page_id"")

        if not get_forms_for_user(self.request.user).filter(id=page_id).exists():
            raise PermissionDenied

        self.page = get_object_or_404(Page, id=page_id).specific

        self.submissions = self.get_queryset()

        if self.request.method == ""POST"":
            self.handle_delete(self.submissions)
            return redirect(self.get_success_url(), page_id)

        return super().dispatch(request, *args, **kwargs)
","Check permissions, set the page and submissions, handle delete",9,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dispatch(self, request, *args, **kwargs):
        
        page_id = kwargs.get(""page_id"")

        if not get_forms_for_user(self.request.user).filter(id=page_id).exists():
            raise PermissionDenied

        self.page = get_object_or_404(Page, id=page_id).specific

        self.submissions = self.get_queryset()

        if self.request.method == ""POST"":
            self.handle_delete(self.submissions)
            return redirect(self.get_success_url(), page_id)

        return super().dispatch(request, *args, **kwargs)

        ```
    ###Assistant : Check permissions, set the page and submissions, handle delete
    "
1465,"def _prior_bpd(self, x_start):
        
        batch_size = x_start.shape[0]
        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
        return mean_flat(kl_prior) / np.log(2.0)
","
        Get the prior KL term for the variational lower-bound, measured in
        bits-per-dim.
        This term can't be optimized, as it only depends on the encoder.
        :param x_start: the [N x C x ...] tensor of inputs.
        :return: a batch of [N] KL values (in bits), one per batch element.
        ",48,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _prior_bpd(self, x_start):
        
        batch_size = x_start.shape[0]
        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
        return mean_flat(kl_prior) / np.log(2.0)

        ```
    ###Assistant : 
        Get the prior KL term for the variational lower-bound, measured in
        bits-per-dim.
        This term can't be optimized, as it only depends on the encoder.
        :param x_start: the [N x C x ...] tensor of inputs.
        :return: a batch of [N] KL values (in bits), one per batch element.
        
    "
1466,"def test_rolling_non_monotonic(method, expected):
    
    # Based on an example found in computation.rst
    use_expanding = [True, False, True, False, True, True, True, True]
    df = DataFrame({""values"": np.arange(len(use_expanding)) ** 2})
","
    Make sure the (rare) branch of non-monotonic indices is covered by a test.

    output from 1.1.3 is assumed to be the expected output. Output of sum/mean has
    manually been verified.

    GH 36933.
    ",32,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_rolling_non_monotonic(method, expected):
    
    # Based on an example found in computation.rst
    use_expanding = [True, False, True, False, True, True, True, True]
    df = DataFrame({""values"": np.arange(len(use_expanding)) ** 2})

        ```
    ###Assistant : 
    Make sure the (rare) branch of non-monotonic indices is covered by a test.

    output from 1.1.3 is assumed to be the expected output. Output of sum/mean has
    manually been verified.

    GH 36933.
    
    "
1467,"def clip(self, min=None, max=None):
        
        from dask.array.ufunc import clip

        return clip(self, min, max)
","Return an array whose values are limited to ``[min, max]``.
        One of max or min must be given.

        Refer to :func:`dask.array.clip` for full documentation.

        See Also
        --------
        dask.array.clip : equivalent function
        ",31,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clip(self, min=None, max=None):
        
        from dask.array.ufunc import clip

        return clip(self, min, max)

        ```
    ###Assistant : Return an array whose values are limited to ``[min, max]``.
        One of max or min must be given.

        Refer to :func:`dask.array.clip` for full documentation.

        See Also
        --------
        dask.array.clip : equivalent function
        
    "
1468,"def _floor_std(self, std):
        r
        original_tensor = std.clone().detach()
        std = torch.clamp(std, min=self.std_floor)
        if torch.any(original_tensor != std):
            print(
                ""[*] Standard deviation was floored! The model is preventing overfitting, nothing serious to worry about""
            )
        return std

","
        It clamps the standard deviation to not to go below some level
        This removes the problem when the model tries to cheat for higher likelihoods by converting
        one of the gaussians to a point mass.

        Args:
            std (float Tensor): tensor containing the standard deviation to be
        ",46,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _floor_std(self, std):
        r
        original_tensor = std.clone().detach()
        std = torch.clamp(std, min=self.std_floor)
        if torch.any(original_tensor != std):
            print(
                ""[*] Standard deviation was floored! The model is preventing overfitting, nothing serious to worry about""
            )
        return std


        ```
    ###Assistant : 
        It clamps the standard deviation to not to go below some level
        This removes the problem when the model tries to cheat for higher likelihoods by converting
        one of the gaussians to a point mass.

        Args:
            std (float Tensor): tensor containing the standard deviation to be
        
    "
1469,"def asXML(self, doctag=None, namedItemsOnly=False, indent="""", formatted=True):
        
        nl = ""\n""
        out = []
        namedItems = dict((v[1], k) for (k, vlist) in self.__tokdict.items()
                          for v in vlist)
        nextLevelIndent = indent + ""  ""

        # collapse out indents if formatting is not desired
        if not formatted:
            indent = """"
            nextLevelIndent = """"
            nl = """"

        selfTag = None
        if doctag is not None:
            selfTag = doctag
        else:
            if self.__name:
                selfTag = self.__name

        if not selfTag:
            if namedItemsOnly:
                return """"
            else:
                selfTag = ""ITEM""

        out += [nl, indent, ""<"", selfTag, "">""]

        for i, res in enumerate(self.__toklist):
            if isinstance(res, ParseResults):
                if i in namedItems:
                    out += [res.asXML(namedItems[i],
                                      namedItemsOnly and doctag is None,
                                      nextLevelIndent,
                                      formatted)]
                else:
                    out += [res.asXML(None,
                                      namedItemsOnly and doctag is None,
                                      nextLevelIndent,
                                      formatted)]
            else:
                # individual token, see if there is a name for it
                resTag = None
                if i in namedItems:
                    resTag = namedItems[i]
                if not resTag:
                    if namedItemsOnly:
                        continue
                    else:
                        resTag = ""ITEM""
                xmlBodyText = _xml_escape(_ustr(res))
                out += [nl, nextLevelIndent, ""<"", resTag, "">"",
                        xmlBodyText,
                                                ""</"", resTag, "">""]

        out += [nl, indent, ""</"", selfTag, "">""]
        return """".join(out)
","
        (Deprecated) Returns the parse results as XML. Tags are created for tokens and lists that have defined results names.
        ",19,175,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def asXML(self, doctag=None, namedItemsOnly=False, indent="""", formatted=True):
        
        nl = ""\n""
        out = []
        namedItems = dict((v[1], k) for (k, vlist) in self.__tokdict.items()
                          for v in vlist)
        nextLevelIndent = indent + ""  ""

        # collapse out indents if formatting is not desired
        if not formatted:
            indent = """"
            nextLevelIndent = """"
            nl = """"

        selfTag = None
        if doctag is not None:
            selfTag = doctag
        else:
            if self.__name:
                selfTag = self.__name

        if not selfTag:
            if namedItemsOnly:
                return """"
            else:
                selfTag = ""ITEM""

        out += [nl, indent, ""<"", selfTag, "">""]

        for i, res in enumerate(self.__toklist):
            if isinstance(res, ParseResults):
                if i in namedItems:
                    out += [res.asXML(namedItems[i],
                                      namedItemsOnly and doctag is None,
                                      nextLevelIndent,
                                      formatted)]
                else:
                    out += [res.asXML(None,
                                      namedItemsOnly and doctag is None,
                                      nextLevelIndent,
                                      formatted)]
            else:
                # individual token, see if there is a name for it
                resTag = None
                if i in namedItems:
                    resTag = namedItems[i]
                if not resTag:
                    if namedItemsOnly:
                        continue
                    else:
                        resTag = ""ITEM""
                xmlBodyText = _xml_escape(_ustr(res))
                out += [nl, nextLevelIndent, ""<"", resTag, "">"",
                        xmlBodyText,
                                                ""</"", resTag, "">""]

        out += [nl, indent, ""</"", selfTag, "">""]
        return """".join(out)

        ```
    ###Assistant : 
        (Deprecated) Returns the parse results as XML. Tags are created for tokens and lists that have defined results names.
        
    "
1470,"def times_seen_with_pending(self) -> int:
        
        return self.times_seen + self.times_seen_pending
","
        Returns `times_seen` with any additional pending updates from `buffers` added on. This value
        must be set first.
        ",17,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def times_seen_with_pending(self) -> int:
        
        return self.times_seen + self.times_seen_pending

        ```
    ###Assistant : 
        Returns `times_seen` with any additional pending updates from `buffers` added on. This value
        must be set first.
        
    "
1471,"def test_api_create_storage_path(self):
        
        response = self.client.post(
            self.ENDPOINT,
            json.dumps(
                {
                    ""name"": ""A storage path"",
                    ""path"": ""Somewhere/{asn}"",
                },
            ),
            content_type=""application/json"",
        )
        self.assertEqual(response.status_code, 201)
        self.assertEqual(StoragePath.objects.count(), 2)
","
        GIVEN:
            - API request to create a storage paths
        WHEN:
            - API is called
        THEN:
            - Correct HTTP response
            - New storage path is created
        ",25,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_api_create_storage_path(self):
        
        response = self.client.post(
            self.ENDPOINT,
            json.dumps(
                {
                    ""name"": ""A storage path"",
                    ""path"": ""Somewhere/{asn}"",
                },
            ),
            content_type=""application/json"",
        )
        self.assertEqual(response.status_code, 201)
        self.assertEqual(StoragePath.objects.count(), 2)

        ```
    ###Assistant : 
        GIVEN:
            - API request to create a storage paths
        WHEN:
            - API is called
        THEN:
            - Correct HTTP response
            - New storage path is created
        
    "
1472,"def test_set_all_ask_for_prompts_true_from_post(self, post, organization, inventory, org_admin):
        
        r = post(
            url=reverse('api:workflow_job_template_list'),
            data=dict(
                name='workflow that tests ask_for prompts',
                organization=organization.id,
                inventory=inventory.id,
                job_tags='',
                skip_tags='',
                ask_inventory_on_launch=True,
                ask_labels_on_launch=True,
                ask_limit_on_launch=True,
                ask_scm_branch_on_launch=True,
                ask_skip_tags_on_launch=True,
                ask_tags_on_launch=True,
                ask_variables_on_launch=True,
            ),
            user=org_admin,
            expect=201,
        )
        wfjt = WorkflowJobTemplate.objects.get(id=r.data['id'])

        assert wfjt.ask_inventory_on_launch is True
        assert wfjt.ask_labels_on_launch is True
        assert wfjt.ask_limit_on_launch is True
        assert wfjt.ask_scm_branch_on_launch is True
        assert wfjt.ask_skip_tags_on_launch is True
        assert wfjt.ask_tags_on_launch is True
        assert wfjt.ask_variables_on_launch is True


@pytest.mark.django_db","
        Tests behaviour and values of ask_for_* fields on WFJT via POST
        ",11,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_set_all_ask_for_prompts_true_from_post(self, post, organization, inventory, org_admin):
        
        r = post(
            url=reverse('api:workflow_job_template_list'),
            data=dict(
                name='workflow that tests ask_for prompts',
                organization=organization.id,
                inventory=inventory.id,
                job_tags='',
                skip_tags='',
                ask_inventory_on_launch=True,
                ask_labels_on_launch=True,
                ask_limit_on_launch=True,
                ask_scm_branch_on_launch=True,
                ask_skip_tags_on_launch=True,
                ask_tags_on_launch=True,
                ask_variables_on_launch=True,
            ),
            user=org_admin,
            expect=201,
        )
        wfjt = WorkflowJobTemplate.objects.get(id=r.data['id'])

        assert wfjt.ask_inventory_on_launch is True
        assert wfjt.ask_labels_on_launch is True
        assert wfjt.ask_limit_on_launch is True
        assert wfjt.ask_scm_branch_on_launch is True
        assert wfjt.ask_skip_tags_on_launch is True
        assert wfjt.ask_tags_on_launch is True
        assert wfjt.ask_variables_on_launch is True


@pytest.mark.django_db
        ```
    ###Assistant : 
        Tests behaviour and values of ask_for_* fields on WFJT via POST
        
    "
1473,"def get_used_memory():
    
    # Try to accurately figure out the memory usage if we are in a docker
    # container.
    docker_usage = None
    # For cgroups v1:
    memory_usage_filename = ""/sys/fs/cgroup/memory/memory.stat""
    # For cgroups v2:
    memory_usage_filename_v2 = ""/sys/fs/cgroup/memory.current""
    if os.path.exists(memory_usage_filename):
        docker_usage = get_cgroupv1_used_memory(memory_usage_filename)
    elif os.path.exists(memory_usage_filename_v2):
        with open(memory_usage_filename_v2, ""r"") as f:
            docker_usage = int(f.read())

    if docker_usage is not None:
        return docker_usage
    return psutil.virtual_memory().used

","Return the currently used system memory in bytes

    Returns:
        The total amount of used memory
    ",15,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_used_memory():
    
    # Try to accurately figure out the memory usage if we are in a docker
    # container.
    docker_usage = None
    # For cgroups v1:
    memory_usage_filename = ""/sys/fs/cgroup/memory/memory.stat""
    # For cgroups v2:
    memory_usage_filename_v2 = ""/sys/fs/cgroup/memory.current""
    if os.path.exists(memory_usage_filename):
        docker_usage = get_cgroupv1_used_memory(memory_usage_filename)
    elif os.path.exists(memory_usage_filename_v2):
        with open(memory_usage_filename_v2, ""r"") as f:
            docker_usage = int(f.read())

    if docker_usage is not None:
        return docker_usage
    return psutil.virtual_memory().used


        ```
    ###Assistant : Return the currently used system memory in bytes

    Returns:
        The total amount of used memory
    
    "
1474,"def pack_x_y_sample_weight(x, y=None, sample_weight=None):
    
    if y is None:
        # For single x-input, we do no tuple wrapping since in this case
        # there is no ambiguity. This also makes NumPy and Dataset
        # consistent in that the user does not have to wrap their Dataset
        # data in an unnecessary tuple
        if not tf.nest.is_nested(x):
            return x
        else:
            return (x,)
    elif sample_weight is None:
        return (x, y)
    else:
        return (x, y, sample_weight)

","Packs user-provided data into a tuple.

    This is a convenience utility for packing data into the tuple formats
    that `Model.fit` uses.

    Standalone usage:

    >>> x = tf.ones((10, 1))
    >>> data = tf.keras.utils.pack_x_y_sample_weight(x)
    >>> isinstance(data, tf.Tensor)
    True
    >>> y = tf.ones((10, 1))
    >>> data = tf.keras.utils.pack_x_y_sample_weight(x, y)
    >>> isinstance(data, tuple)
    True
    >>> x, y = data

    Args:
      x: Features to pass to `Model`.
      y: Ground-truth targets to pass to `Model`.
      sample_weight: Sample weight for each element.

    Returns:
      Tuple in the format used in `Model.fit`.
    ",83,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pack_x_y_sample_weight(x, y=None, sample_weight=None):
    
    if y is None:
        # For single x-input, we do no tuple wrapping since in this case
        # there is no ambiguity. This also makes NumPy and Dataset
        # consistent in that the user does not have to wrap their Dataset
        # data in an unnecessary tuple
        if not tf.nest.is_nested(x):
            return x
        else:
            return (x,)
    elif sample_weight is None:
        return (x, y)
    else:
        return (x, y, sample_weight)


        ```
    ###Assistant : Packs user-provided data into a tuple.

    This is a convenience utility for packing data into the tuple formats
    that `Model.fit` uses.

    Standalone usage:

    >>> x = tf.ones((10, 1))
    >>> data = tf.keras.utils.pack_x_y_sample_weight(x)
    >>> isinstance(data, tf.Tensor)
    True
    >>> y = tf.ones((10, 1))
    >>> data = tf.keras.utils.pack_x_y_sample_weight(x, y)
    >>> isinstance(data, tuple)
    True
    >>> x, y = data

    Args:
      x: Features to pass to `Model`.
      y: Ground-truth targets to pass to `Model`.
      sample_weight: Sample weight for each element.

    Returns:
      Tuple in the format used in `Model.fit`.
    
    "
1475,"def test_glm_regression_vstacked_X(solver, fit_intercept, glm_dataset):
    
    model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
    n_samples, n_features = X.shape
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    X = X[:, :-1]  # remove intercept
    X = np.concatenate((X, X), axis=0)
    assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)
    y = np.r_[y, y]
    if fit_intercept:
        coef = coef_with_intercept
        intercept = coef[-1]
        coef = coef[:-1]
    else:
        coef = coef_without_intercept
        intercept = 0
    model.fit(X, y)

    rtol = 3e-5
    assert model.intercept_ == pytest.approx(intercept, rel=rtol)
    assert_allclose(model.coef_, coef, rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])","Test that GLM converges for all solvers to correct solution on vstacked data.

    We work with a simple constructed data set with known solution.
    Fit on [X] with alpha is the same as fit on [X], [y]
                                                [X], [y] with 1 * alpha.
    It is the same alpha as the average loss stays the same.
    For wide X, [X', X'] is a singular matrix.
    ",64,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_glm_regression_vstacked_X(solver, fit_intercept, glm_dataset):
    
    model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
    n_samples, n_features = X.shape
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    X = X[:, :-1]  # remove intercept
    X = np.concatenate((X, X), axis=0)
    assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)
    y = np.r_[y, y]
    if fit_intercept:
        coef = coef_with_intercept
        intercept = coef[-1]
        coef = coef[:-1]
    else:
        coef = coef_without_intercept
        intercept = 0
    model.fit(X, y)

    rtol = 3e-5
    assert model.intercept_ == pytest.approx(intercept, rel=rtol)
    assert_allclose(model.coef_, coef, rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])
        ```
    ###Assistant : Test that GLM converges for all solvers to correct solution on vstacked data.

    We work with a simple constructed data set with known solution.
    Fit on [X] with alpha is the same as fit on [X], [y]
                                                [X], [y] with 1 * alpha.
    It is the same alpha as the average loss stays the same.
    For wide X, [X', X'] is a singular matrix.
    
    "
1476,"def deprecate_data():
    sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.4"",
        active_deprecations_target=""deprecated-tensorindextype-attrs"",
        stacklevel=4,
    )
","
        The data attribute of TensorIndexType is deprecated. Use The
        replace_with_arrays() method instead.
        ",12,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deprecate_data():
    sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.4"",
        active_deprecations_target=""deprecated-tensorindextype-attrs"",
        stacklevel=4,
    )

        ```
    ###Assistant : 
        The data attribute of TensorIndexType is deprecated. Use The
        replace_with_arrays() method instead.
        
    "
1477,"def fit(self, X, y=None):
        
        self._validate_params()
        X = self._check_input(X, reset=True)
        if self.check_inverse and not (self.func is None or self.inverse_func is None):
            self._check_inverse_transform(X)
        return self
","Fit transformer by checking X.

        If ``validate`` is ``True``, ``X`` will be checked.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Input array.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        self : object
            FunctionTransformer class instance.
        ",43,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y=None):
        
        self._validate_params()
        X = self._check_input(X, reset=True)
        if self.check_inverse and not (self.func is None or self.inverse_func is None):
            self._check_inverse_transform(X)
        return self

        ```
    ###Assistant : Fit transformer by checking X.

        If ``validate`` is ``True``, ``X`` will be checked.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Input array.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        self : object
            FunctionTransformer class instance.
        
    "
1478,"def styleof(expr, styles=default_styles):
    
    style = {}
    for typ, sty in styles:
        if isinstance(expr, typ):
            style.update(sty)
    return style

"," Merge style dictionaries in order

    Examples
    ========

    >>> from sympy import Symbol, Basic, Expr, S
    >>> from sympy.printing.dot import styleof
    >>> styles = [(Basic, {'color': 'blue', 'shape': 'ellipse'}),
    ...           (Expr,  {'color': 'black'})]

    >>> styleof(Basic(S(1)), styles)
    {'color': 'blue', 'shape': 'ellipse'}

    >>> x = Symbol('x')
    >>> styleof(x + 1, styles)  # this is an Expr
    {'color': 'black', 'shape': 'ellipse'}
    ",57,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def styleof(expr, styles=default_styles):
    
    style = {}
    for typ, sty in styles:
        if isinstance(expr, typ):
            style.update(sty)
    return style


        ```
    ###Assistant :  Merge style dictionaries in order

    Examples
    ========

    >>> from sympy import Symbol, Basic, Expr, S
    >>> from sympy.printing.dot import styleof
    >>> styles = [(Basic, {'color': 'blue', 'shape': 'ellipse'}),
    ...           (Expr,  {'color': 'black'})]

    >>> styleof(Basic(S(1)), styles)
    {'color': 'blue', 'shape': 'ellipse'}

    >>> x = Symbol('x')
    >>> styleof(x + 1, styles)  # this is an Expr
    {'color': 'black', 'shape': 'ellipse'}
    
    "
1479,"def execute(filters=None):
	columns = [
		{""fieldname"": ""creation_date"", ""label"": _(""Date""), ""fieldtype"": ""Date"", ""width"": 300},
		{
			""fieldname"": ""first_response_time"",
			""fieldtype"": ""Duration"",
			""label"": _(""First Response Time""),
			""width"": 300,
		},
	]

	data = frappe.db.sql(
		,
		(filters.from_date, filters.to_date),
	)

	return columns, data
","
		SELECT
			date(creation) as creation_date,
			avg(first_response_time) as avg_response_time
		FROM tabIssue
		WHERE
			date(creation) between %s and %s
			and first_response_time > 0
		GROUP BY creation_date
		ORDER BY creation_date desc
	",26,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute(filters=None):
	columns = [
		{""fieldname"": ""creation_date"", ""label"": _(""Date""), ""fieldtype"": ""Date"", ""width"": 300},
		{
			""fieldname"": ""first_response_time"",
			""fieldtype"": ""Duration"",
			""label"": _(""First Response Time""),
			""width"": 300,
		},
	]

	data = frappe.db.sql(
		,
		(filters.from_date, filters.to_date),
	)

	return columns, data

        ```
    ###Assistant : 
		SELECT
			date(creation) as creation_date,
			avg(first_response_time) as avg_response_time
		FROM tabIssue
		WHERE
			date(creation) between %s and %s
			and first_response_time > 0
		GROUP BY creation_date
		ORDER BY creation_date desc
	
    "
1480,"def choose_parent(self, new_node, near_inds):
        
        if not near_inds:
            return None

        # search nearest cost in near_inds
        costs = []
        for i in near_inds:
            near_node = self.node_list[i]
            t_node = self.steer(near_node, new_node)
            if t_node and self.check_collision(
                    t_node, self.obstacle_list, self.robot_radius):
                costs.append(self.calc_new_cost(near_node, new_node))
            else:
                costs.append(float(""inf""))  # the cost of collision node
        min_cost = min(costs)

        if min_cost == float(""inf""):
            print(""There is no good path.(min_cost is inf)"")
            return None

        min_ind = near_inds[costs.index(min_cost)]
        new_node = self.steer(self.node_list[min_ind], new_node)
        new_node.cost = min_cost

        return new_node
","
        Computes the cheapest point to new_node contained in the list
        near_inds and set such a node as the parent of new_node.
            Arguments:
            --------
                new_node, Node
                    randomly generated node with a path from its neared point
                    There are not coalitions between this node and th tree.
                near_inds: list
                    Indices of indices of the nodes what are near to new_node

            Returns.
            ------
                Node, a copy of new_node
        ",65,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def choose_parent(self, new_node, near_inds):
        
        if not near_inds:
            return None

        # search nearest cost in near_inds
        costs = []
        for i in near_inds:
            near_node = self.node_list[i]
            t_node = self.steer(near_node, new_node)
            if t_node and self.check_collision(
                    t_node, self.obstacle_list, self.robot_radius):
                costs.append(self.calc_new_cost(near_node, new_node))
            else:
                costs.append(float(""inf""))  # the cost of collision node
        min_cost = min(costs)

        if min_cost == float(""inf""):
            print(""There is no good path.(min_cost is inf)"")
            return None

        min_ind = near_inds[costs.index(min_cost)]
        new_node = self.steer(self.node_list[min_ind], new_node)
        new_node.cost = min_cost

        return new_node

        ```
    ###Assistant : 
        Computes the cheapest point to new_node contained in the list
        near_inds and set such a node as the parent of new_node.
            Arguments:
            --------
                new_node, Node
                    randomly generated node with a path from its neared point
                    There are not coalitions between this node and th tree.
                near_inds: list
                    Indices of indices of the nodes what are near to new_node

            Returns.
            ------
                Node, a copy of new_node
        
    "
1481,"def test_a3c_compilation(self):
        
        config = a3c.DEFAULT_CONFIG.copy()
        config[""num_workers""] = 2
        config[""num_envs_per_worker""] = 2

        num_iterations = 1

        # Test against all frameworks.
        for _ in framework_iterator(config, with_eager_tracing=True):
            for env in [""CartPole-v1"", ""Pendulum-v1"", ""PongDeterministic-v0""]:
                print(""env={}"".format(env))
                config[""model""][""use_lstm""] = env == ""CartPole-v1""
                trainer = a3c.A3CTrainer(config=config, env=env)
                for i in range(num_iterations):
                    results = trainer.train()
                    check_train_results(results)
                    print(results)
                check_compute_single_action(
                    trainer, include_state=config[""model""][""use_lstm""]
                )
                trainer.stop()
",Test whether an A3CTrainer can be built with both frameworks.,10,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_a3c_compilation(self):
        
        config = a3c.DEFAULT_CONFIG.copy()
        config[""num_workers""] = 2
        config[""num_envs_per_worker""] = 2

        num_iterations = 1

        # Test against all frameworks.
        for _ in framework_iterator(config, with_eager_tracing=True):
            for env in [""CartPole-v1"", ""Pendulum-v1"", ""PongDeterministic-v0""]:
                print(""env={}"".format(env))
                config[""model""][""use_lstm""] = env == ""CartPole-v1""
                trainer = a3c.A3CTrainer(config=config, env=env)
                for i in range(num_iterations):
                    results = trainer.train()
                    check_train_results(results)
                    print(results)
                check_compute_single_action(
                    trainer, include_state=config[""model""][""use_lstm""]
                )
                trainer.stop()

        ```
    ###Assistant : Test whether an A3CTrainer can be built with both frameworks.
    "
1482,"def extract(self, member, path="""", set_attrs=True):
        
        self._check(""r"")

        if isinstance(member, str):
            tarinfo = self.getmember(member)
        else:
            tarinfo = member

        # Prepare the link target for makelink().
        if tarinfo.islnk():
            tarinfo._link_target = os.path.join(path, tarinfo.linkname)

        try:
            self._extract_member(tarinfo, os.path.join(path, tarinfo.name),
                                 set_attrs=set_attrs)
        except EnvironmentError as e:
            if self.errorlevel > 0:
                raise
            else:
                if e.filename is None:
                    self._dbg(1, ""tarfile: %s"" % e.strerror)
                else:
                    self._dbg(1, ""tarfile: %s %r"" % (e.strerror, e.filename))
        except ExtractError as e:
            if self.errorlevel > 1:
                raise
            else:
                self._dbg(1, ""tarfile: %s"" % e)
","Extract a member from the archive to the current working directory,
           using its full name. Its file information is extracted as accurately
           as possible. `member' may be a filename or a TarInfo object. You can
           specify a different directory using `path'. File attributes (owner,
           mtime, mode) are set unless `set_attrs' is False.
        ",52,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extract(self, member, path="""", set_attrs=True):
        
        self._check(""r"")

        if isinstance(member, str):
            tarinfo = self.getmember(member)
        else:
            tarinfo = member

        # Prepare the link target for makelink().
        if tarinfo.islnk():
            tarinfo._link_target = os.path.join(path, tarinfo.linkname)

        try:
            self._extract_member(tarinfo, os.path.join(path, tarinfo.name),
                                 set_attrs=set_attrs)
        except EnvironmentError as e:
            if self.errorlevel > 0:
                raise
            else:
                if e.filename is None:
                    self._dbg(1, ""tarfile: %s"" % e.strerror)
                else:
                    self._dbg(1, ""tarfile: %s %r"" % (e.strerror, e.filename))
        except ExtractError as e:
            if self.errorlevel > 1:
                raise
            else:
                self._dbg(1, ""tarfile: %s"" % e)

        ```
    ###Assistant : Extract a member from the archive to the current working directory,
           using its full name. Its file information is extracted as accurately
           as possible. `member' may be a filename or a TarInfo object. You can
           specify a different directory using `path'. File attributes (owner,
           mtime, mode) are set unless `set_attrs' is False.
        
    "
1483,"def test_artist_from_string():
    

    artist = Artist.from_search_term(""artist:gorillaz"")

    assert artist.name == ""Gorillaz""
    assert artist.url == ""http://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQ""
    assert len(artist.urls) > 1
","
    Test if Artist class can be initialized from string.
    ",9,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_artist_from_string():
    

    artist = Artist.from_search_term(""artist:gorillaz"")

    assert artist.name == ""Gorillaz""
    assert artist.url == ""http://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQ""
    assert len(artist.urls) > 1

        ```
    ###Assistant : 
    Test if Artist class can be initialized from string.
    
    "
1484,"def reset(self):
        
        # custom_info is used for episodic reports and tensorboard logging
        self.custom_info[""Invalid""] = 0
        self.custom_info[""Hold""] = 0
        self.custom_info[""Unknown""] = 0
        self.custom_info[""pnl_factor""] = 0
        self.custom_info[""duration_factor""] = 0
        self.custom_info[""reward_exit""] = 0
        self.custom_info[""reward_hold""] = 0
        for action in self.actions:
            self.custom_info[f""{action.name}""] = 0

        self._done = False

        if self.starting_point is True:
            if self.rl_config.get('randomize_starting_position', False):
                length_of_data = int(self._end_tick / 4)
                start_tick = random.randint(self.window_size + 1, length_of_data)
                self._start_tick = start_tick
            self._position_history = (self._start_tick * [None]) + [self._position]
        else:
            self._position_history = (self.window_size * [None]) + [self._position]

        self._current_tick = self._start_tick
        self._last_trade_tick = None
        self._position = Positions.Neutral

        self.total_reward = 0.
        self._total_profit = 1.  # unit
        self.history = {}
        self.trade_history = []
        self.portfolio_log_returns = np.zeros(len(self.prices))

        self._profits = [(self._start_tick, 1)]
        self.close_trade_profit = []
        self._total_unrealized_profit = 1

        return self._get_observation()
","
        Reset is called at the beginning of every episode
        ",9,117,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reset(self):
        
        # custom_info is used for episodic reports and tensorboard logging
        self.custom_info[""Invalid""] = 0
        self.custom_info[""Hold""] = 0
        self.custom_info[""Unknown""] = 0
        self.custom_info[""pnl_factor""] = 0
        self.custom_info[""duration_factor""] = 0
        self.custom_info[""reward_exit""] = 0
        self.custom_info[""reward_hold""] = 0
        for action in self.actions:
            self.custom_info[f""{action.name}""] = 0

        self._done = False

        if self.starting_point is True:
            if self.rl_config.get('randomize_starting_position', False):
                length_of_data = int(self._end_tick / 4)
                start_tick = random.randint(self.window_size + 1, length_of_data)
                self._start_tick = start_tick
            self._position_history = (self._start_tick * [None]) + [self._position]
        else:
            self._position_history = (self.window_size * [None]) + [self._position]

        self._current_tick = self._start_tick
        self._last_trade_tick = None
        self._position = Positions.Neutral

        self.total_reward = 0.
        self._total_profit = 1.  # unit
        self.history = {}
        self.trade_history = []
        self.portfolio_log_returns = np.zeros(len(self.prices))

        self._profits = [(self._start_tick, 1)]
        self.close_trade_profit = []
        self._total_unrealized_profit = 1

        return self._get_observation()

        ```
    ###Assistant : 
        Reset is called at the beginning of every episode
        
    "
1485,"def get_ordered_to_be_billed_data(args):
	doctype, party = args.get(""doctype""), args.get(""party"")
	child_tab = doctype + "" Item""
	precision = (
		get_field_precision(
			frappe.get_meta(child_tab).get_field(""billed_amt""), currency=get_default_currency()
		)
		or 2
	)

	project_field = get_project_field(doctype, party)

	return frappe.db.sql(
		.format(
			parent_tab=""tab"" + doctype,
			child_tab=""tab"" + child_tab,
			precision=precision,
			party=party,
			date_field=args.get(""date""),
			project_field=project_field,
			order=args.get(""order""),
			order_by=args.get(""order_by""),
		)
	)

","
		Select
			`{parent_tab}`.name, `{parent_tab}`.{date_field},
			`{parent_tab}`.{party}, `{parent_tab}`.{party}_name,
			`{child_tab}`.item_code,
			`{child_tab}`.base_amount,
			(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)),
			(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0)),
			(`{child_tab}`.base_amount -
			(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)) -
			(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))),
			`{child_tab}`.item_name, `{child_tab}`.description,
			{project_field}, `{parent_tab}`.company
		from
			`{parent_tab}`, `{child_tab}`
		where
			`{parent_tab}`.name = `{child_tab}`.parent and `{parent_tab}`.docstatus = 1
			and `{parent_tab}`.status not in ('Closed', 'Completed')
			and `{child_tab}`.amount > 0
			and (`{child_tab}`.base_amount -
			round(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1), {precision}) -
			(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))) > 0
		order by
			`{parent_tab}`.{order} {order_by}
		",70,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ordered_to_be_billed_data(args):
	doctype, party = args.get(""doctype""), args.get(""party"")
	child_tab = doctype + "" Item""
	precision = (
		get_field_precision(
			frappe.get_meta(child_tab).get_field(""billed_amt""), currency=get_default_currency()
		)
		or 2
	)

	project_field = get_project_field(doctype, party)

	return frappe.db.sql(
		.format(
			parent_tab=""tab"" + doctype,
			child_tab=""tab"" + child_tab,
			precision=precision,
			party=party,
			date_field=args.get(""date""),
			project_field=project_field,
			order=args.get(""order""),
			order_by=args.get(""order_by""),
		)
	)


        ```
    ###Assistant : 
		Select
			`{parent_tab}`.name, `{parent_tab}`.{date_field},
			`{parent_tab}`.{party}, `{parent_tab}`.{party}_name,
			`{child_tab}`.item_code,
			`{child_tab}`.base_amount,
			(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)),
			(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0)),
			(`{child_tab}`.base_amount -
			(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)) -
			(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))),
			`{child_tab}`.item_name, `{child_tab}`.description,
			{project_field}, `{parent_tab}`.company
		from
			`{parent_tab}`, `{child_tab}`
		where
			`{parent_tab}`.name = `{child_tab}`.parent and `{parent_tab}`.docstatus = 1
			and `{parent_tab}`.status not in ('Closed', 'Completed')
			and `{child_tab}`.amount > 0
			and (`{child_tab}`.base_amount -
			round(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1), {precision}) -
			(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))) > 0
		order by
			`{parent_tab}`.{order} {order_by}
		
    "
1486,"def get_staged_trial(self):
        
        # TODO(xwjiang): This method should consider `self._cached_actor_pg`.
        for trial in self._staged_trials:
            if self._pg_manager.has_ready(trial):
                return trial

        return None
","Get a trial whose placement group was successfully staged.

        Can also return None if no trial is available.

        Returns:
            Trial object or None.

        ",23,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_staged_trial(self):
        
        # TODO(xwjiang): This method should consider `self._cached_actor_pg`.
        for trial in self._staged_trials:
            if self._pg_manager.has_ready(trial):
                return trial

        return None

        ```
    ###Assistant : Get a trial whose placement group was successfully staged.

        Can also return None if no trial is available.

        Returns:
            Trial object or None.

        
    "
1487,"def register(cls, func, squeeze_self=False, **kwargs):
        
        return super().register(
            Resampler.build_resample(func, squeeze_self),
            fn_name=func.__name__,
            **kwargs
        )
","
        Build function that do fallback to pandas and aggregate resampled data.

        Parameters
        ----------
        func : callable
            Aggregation function to execute under resampled frame.
        squeeze_self : bool, default: False
            Whether or not to squeeze frame before resampling.
        **kwargs : kwargs
            Additional arguments that will be passed to function builder.

        Returns
        -------
        callable
            Function that takes query compiler and does fallback to pandas to resample
            time-series data and apply aggregation on it.
        ",70,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def register(cls, func, squeeze_self=False, **kwargs):
        
        return super().register(
            Resampler.build_resample(func, squeeze_self),
            fn_name=func.__name__,
            **kwargs
        )

        ```
    ###Assistant : 
        Build function that do fallback to pandas and aggregate resampled data.

        Parameters
        ----------
        func : callable
            Aggregation function to execute under resampled frame.
        squeeze_self : bool, default: False
            Whether or not to squeeze frame before resampling.
        **kwargs : kwargs
            Additional arguments that will be passed to function builder.

        Returns
        -------
        callable
            Function that takes query compiler and does fallback to pandas to resample
            time-series data and apply aggregation on it.
        
    "
1488,"def aggregate(self, *args, **kwargs):
        
        if self.query.distinct_fields:
            raise NotImplementedError(""aggregate() + distinct(fields) not implemented."")
        self._validate_values_are_expressions(
            (*args, *kwargs.values()), method_name=""aggregate""
        )
        for arg in args:
            # The default_alias property raises TypeError if default_alias
            # can't be set automatically or AttributeError if it isn't an
            # attribute.
            try:
                arg.default_alias
            except (AttributeError, TypeError):
                raise TypeError(""Complex aggregates require an alias"")
            kwargs[arg.default_alias] = arg

        query = self.query.chain()
        for (alias, aggregate_expr) in kwargs.items():
            query.add_annotation(aggregate_expr, alias, is_summary=True)
            annotation = query.annotations[alias]
            if not annotation.contains_aggregate:
                raise TypeError(""%s is not an aggregate expression"" % alias)
            for expr in annotation.get_source_expressions():
                if (
                    expr.contains_aggregate
                    and isinstance(expr, Ref)
                    and expr.refs in kwargs
                ):
                    name = expr.refs
                    raise exceptions.FieldError(
                        ""Cannot compute %s('%s'): '%s' is an aggregate""
                        % (annotation.name, name, name)
                    )
        return query.get_aggregation(self.db, kwargs)
","
        Return a dictionary containing the calculations (aggregation)
        over the current queryset.

        If args is present the expression is passed as a kwarg using
        the Aggregate object's default alias.
        ",28,117,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def aggregate(self, *args, **kwargs):
        
        if self.query.distinct_fields:
            raise NotImplementedError(""aggregate() + distinct(fields) not implemented."")
        self._validate_values_are_expressions(
            (*args, *kwargs.values()), method_name=""aggregate""
        )
        for arg in args:
            # The default_alias property raises TypeError if default_alias
            # can't be set automatically or AttributeError if it isn't an
            # attribute.
            try:
                arg.default_alias
            except (AttributeError, TypeError):
                raise TypeError(""Complex aggregates require an alias"")
            kwargs[arg.default_alias] = arg

        query = self.query.chain()
        for (alias, aggregate_expr) in kwargs.items():
            query.add_annotation(aggregate_expr, alias, is_summary=True)
            annotation = query.annotations[alias]
            if not annotation.contains_aggregate:
                raise TypeError(""%s is not an aggregate expression"" % alias)
            for expr in annotation.get_source_expressions():
                if (
                    expr.contains_aggregate
                    and isinstance(expr, Ref)
                    and expr.refs in kwargs
                ):
                    name = expr.refs
                    raise exceptions.FieldError(
                        ""Cannot compute %s('%s'): '%s' is an aggregate""
                        % (annotation.name, name, name)
                    )
        return query.get_aggregation(self.db, kwargs)

        ```
    ###Assistant : 
        Return a dictionary containing the calculations (aggregation)
        over the current queryset.

        If args is present the expression is passed as a kwarg using
        the Aggregate object's default alias.
        
    "
1489,"def _detect_bytelen_from_str(s):
        # type: (str) -> int
        
        assert len(s) >= 2
        tmp_len = len(s)

        i = 1
        while orb(s[i]) & 0x80 > 0:
            i += 1
            assert i < tmp_len, 'EINVAL: s: out-of-bound read: unfinished AbstractUVarIntField detected'  # noqa: E501
        ret = i + 1

        assert ret >= 0
        return ret
"," _detect_bytelen_from_str returns the length of the machine
          representation of an AbstractUVarIntField starting at the beginning
          of s and which is assumed to expand over multiple bytes
          (value > _max_prefix_value).

        :param str s: the string to parse. It is assumed that it is a multibyte int.  # noqa: E501
        :return: The bytelength of the AbstractUVarIntField.
        :raises: AssertionError
        ",56,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _detect_bytelen_from_str(s):
        # type: (str) -> int
        
        assert len(s) >= 2
        tmp_len = len(s)

        i = 1
        while orb(s[i]) & 0x80 > 0:
            i += 1
            assert i < tmp_len, 'EINVAL: s: out-of-bound read: unfinished AbstractUVarIntField detected'  # noqa: E501
        ret = i + 1

        assert ret >= 0
        return ret

        ```
    ###Assistant :  _detect_bytelen_from_str returns the length of the machine
          representation of an AbstractUVarIntField starting at the beginning
          of s and which is assumed to expand over multiple bytes
          (value > _max_prefix_value).

        :param str s: the string to parse. It is assumed that it is a multibyte int.  # noqa: E501
        :return: The bytelength of the AbstractUVarIntField.
        :raises: AssertionError
        
    "
1490,"def get_gl_entries(voucher_type, voucher_no):
	return frappe.db.sql(
		,
		(voucher_type, voucher_no),
		as_dict=1,
	)

","select account, debit, credit, cost_center, is_cancelled
		from `tabGL Entry` where voucher_type=%s and voucher_no=%s
		order by account desc",17,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_gl_entries(voucher_type, voucher_no):
	return frappe.db.sql(
		,
		(voucher_type, voucher_no),
		as_dict=1,
	)


        ```
    ###Assistant : select account, debit, credit, cost_center, is_cancelled
		from `tabGL Entry` where voucher_type=%s and voucher_no=%s
		order by account desc
    "
1491,"def random_normal(mean=0.0, std=1.0, shape=None, dev=None, f=None):
    
    return _cur_framework(f=f).random_normal(mean, std, shape, dev)

","
    Draws samples from a normal distribution.

    :param mean: The mean of the normal distribution to sample from. Default is 0.
    :type mean: float
    :param std: The standard deviation of the normal distribution to sample from. Default is 1.
    :type std: float
    :param shape: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.
                    If size is None (default), a single value is returned.
    :param dev: device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
    :type dev: str
    :param f: Machine learning framework. Inferred from inputs if None.
    :type f: ml_framework, optional
    :return: Drawn samples from the parameterized uniform distribution.
    ",111,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def random_normal(mean=0.0, std=1.0, shape=None, dev=None, f=None):
    
    return _cur_framework(f=f).random_normal(mean, std, shape, dev)


        ```
    ###Assistant : 
    Draws samples from a normal distribution.

    :param mean: The mean of the normal distribution to sample from. Default is 0.
    :type mean: float
    :param std: The standard deviation of the normal distribution to sample from. Default is 1.
    :type std: float
    :param shape: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.
                    If size is None (default), a single value is returned.
    :param dev: device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
    :type dev: str
    :param f: Machine learning framework. Inferred from inputs if None.
    :type f: ml_framework, optional
    :return: Drawn samples from the parameterized uniform distribution.
    
    "
1492,"def get_font_preamble(cls):
        
        font_preamble, command = cls._get_font_preamble_and_command()
        return font_preamble
","
        Return a string containing font configuration for the tex preamble.
        ",10,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_font_preamble(cls):
        
        font_preamble, command = cls._get_font_preamble_and_command()
        return font_preamble

        ```
    ###Assistant : 
        Return a string containing font configuration for the tex preamble.
        
    "
1493,"def do_test_explorations(config, dummy_obs, prev_a=None, expected_mean_action=None):
    

    # Test all frameworks.
    for _ in framework_iterator(config):
        print(f""Algorithm={config.algo_class}"")

        # Test for both the default Agent's exploration AND the `Random`
        # exploration class.
        for exploration in [None, ""Random""]:
            local_config = config.copy()
            if exploration == ""Random"":
                local_config.exploration(exploration_config={""type"": ""Random""})
            print(""exploration={}"".format(exploration or ""default""))

            algo = local_config.build()

            # Make sure all actions drawn are the same, given same
            # observations.
            actions = []
            for _ in range(25):
                actions.append(
                    algo.compute_single_action(
                        observation=dummy_obs,
                        explore=False,
                        prev_action=prev_a,
                        prev_reward=1.0 if prev_a is not None else None,
                    )
                )
                check(actions[-1], actions[0])

            # Make sure actions drawn are different
            # (around some mean value), given constant observations.
            actions = []
            for _ in range(500):
                actions.append(
                    algo.compute_single_action(
                        observation=dummy_obs,
                        explore=True,
                        prev_action=prev_a,
                        prev_reward=1.0 if prev_a is not None else None,
                    )
                )
            check(
                np.mean(actions),
                expected_mean_action if expected_mean_action is not None else 0.5,
                atol=0.4,
            )
            # Check that the stddev is not 0.0 (values differ).
            check(np.std(actions), 0.0, false=True)

",Calls an Agent's `compute_actions` with different `explore` options.,8,147,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def do_test_explorations(config, dummy_obs, prev_a=None, expected_mean_action=None):
    

    # Test all frameworks.
    for _ in framework_iterator(config):
        print(f""Algorithm={config.algo_class}"")

        # Test for both the default Agent's exploration AND the `Random`
        # exploration class.
        for exploration in [None, ""Random""]:
            local_config = config.copy()
            if exploration == ""Random"":
                local_config.exploration(exploration_config={""type"": ""Random""})
            print(""exploration={}"".format(exploration or ""default""))

            algo = local_config.build()

            # Make sure all actions drawn are the same, given same
            # observations.
            actions = []
            for _ in range(25):
                actions.append(
                    algo.compute_single_action(
                        observation=dummy_obs,
                        explore=False,
                        prev_action=prev_a,
                        prev_reward=1.0 if prev_a is not None else None,
                    )
                )
                check(actions[-1], actions[0])

            # Make sure actions drawn are different
            # (around some mean value), given constant observations.
            actions = []
            for _ in range(500):
                actions.append(
                    algo.compute_single_action(
                        observation=dummy_obs,
                        explore=True,
                        prev_action=prev_a,
                        prev_reward=1.0 if prev_a is not None else None,
                    )
                )
            check(
                np.mean(actions),
                expected_mean_action if expected_mean_action is not None else 0.5,
                atol=0.4,
            )
            # Check that the stddev is not 0.0 (values differ).
            check(np.std(actions), 0.0, false=True)


        ```
    ###Assistant : Calls an Agent's `compute_actions` with different `explore` options.
    "
1494,"def exit_with_success(message, **kwargs):
    
    kwargs.setdefault(""style"", ""green"")
    app.console.print(message, **kwargs)
    raise typer.Exit(0)
","
    Utility to print a stylized success message and exit with a zero code
    ",13,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def exit_with_success(message, **kwargs):
    
    kwargs.setdefault(""style"", ""green"")
    app.console.print(message, **kwargs)
    raise typer.Exit(0)

        ```
    ###Assistant : 
    Utility to print a stylized success message and exit with a zero code
    
    "
1495,"def get_keywords() -> Dict[str, str]:
    
    # these strings will be replaced by git during git-archive.
    # setup.py/versioneer.py will grep for the variable names, so they must
    # each be defined on a line of their own. _version.py will just call
    # get_keywords().
    git_refnames = ""$Format:%d$""
    git_full = ""$Format:%H$""
    git_date = ""$Format:%ci$""
    keywords = {""refnames"": git_refnames, ""full"": git_full, ""date"": git_date}
    return keywords

",Get the keywords needed to look up the version information.,10,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_keywords() -> Dict[str, str]:
    
    # these strings will be replaced by git during git-archive.
    # setup.py/versioneer.py will grep for the variable names, so they must
    # each be defined on a line of their own. _version.py will just call
    # get_keywords().
    git_refnames = ""$Format:%d$""
    git_full = ""$Format:%H$""
    git_date = ""$Format:%ci$""
    keywords = {""refnames"": git_refnames, ""full"": git_full, ""date"": git_date}
    return keywords


        ```
    ###Assistant : Get the keywords needed to look up the version information.
    "
1496,"def take(outname, inname, chunks, index, itemsize, axis=0):
    
    from .core import PerformanceWarning

    plan = slicing_plan(chunks[axis], index)
    if len(plan) >= len(chunks[axis]) * 10:
        factor = math.ceil(len(plan) / len(chunks[axis]))

        warnings.warn(
            ""Slicing with an out-of-order index is generating %d ""
            ""times more chunks"" % factor,
            PerformanceWarning,
            stacklevel=6,
        )
    if not is_arraylike(index):
        index = np.asarray(index)

    # Check for chunks from the plan that would violate the user's
    # configured chunk size.
    nbytes = utils.parse_bytes(config.get(""array.chunk-size""))
    other_chunks = [chunks[i] for i in range(len(chunks)) if i != axis]
    other_numel = np.prod([sum(x) for x in other_chunks])

    if math.isnan(other_numel):
        warnsize = maxsize = math.inf
    else:
        maxsize = math.ceil(nbytes / (other_numel * itemsize))
        warnsize = maxsize * 5

    split = config.get(""array.slicing.split-large-chunks"", None)

    # Warn only when the default is not specified.
    warned = split is not None

    for _, index_list in plan:
        if not warned and len(index_list) > warnsize:
            msg = (
                ""Slicing is producing a large chunk. To accept the large\n""
                ""chunk and silence this warning, set the option\n""
                ""    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n""
                ""    ...     array[indexer]\n\n""
                ""To avoid creating the large chunks, set the option\n""
                ""    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n""
                ""    ...     array[indexer]""
            )
            warnings.warn(msg, PerformanceWarning, stacklevel=6)
            warned = True

    where_index = []
    index_lists = []
    for where_idx, index_list in plan:
        index_length = len(index_list)
        if split and index_length > maxsize:
            index_sublist = np.array_split(
                index_list, math.ceil(index_length / maxsize)
            )
            index_lists.extend(index_sublist)
            where_index.extend([where_idx] * len(index_sublist))
        else:
            if not is_arraylike(index_list):
                index_list = np.array(index_list)
            index_lists.append(index_list)
            where_index.append(where_idx)

    dims = [range(len(bd)) for bd in chunks]

    indims = list(dims)
    indims[axis] = list(range(len(where_index)))
    keys = list(product([outname], *indims))

    outdims = list(dims)
    outdims[axis] = where_index
    slices = [[colon] * len(bd) for bd in chunks]
    slices[axis] = index_lists
    slices = list(product(*slices))
    inkeys = list(product([inname], *outdims))
    values = [(getitem, inkey, slc) for inkey, slc in zip(inkeys, slices)]

    chunks2 = list(chunks)
    chunks2[axis] = tuple(map(len, index_lists))
    dsk = dict(zip(keys, values))

    return tuple(chunks2), dsk

","Index array with an iterable of index

    Handles a single index by a single list

    Mimics ``np.take``

    >>> from pprint import pprint
    >>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [5, 1, 47, 3], 8, axis=0)
    >>> chunks
    ((2, 1, 1),)
    >>> pprint(dsk)   # doctest: +ELLIPSIS
    {('y', 0): (<function getitem at ...>, ('x', 0), (array([5, 1]),)),
     ('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),)),
     ('y', 2): (<function getitem at ...>, ('x', 0), (array([3]),))}

    When list is sorted we retain original block structure

    >>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [1, 3, 5, 47], 8, axis=0)
    >>> chunks
    ((3, 1),)
    >>> pprint(dsk)     # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    {('y', 0): (<function getitem at ...>,
                ('x', 0),
                (array([1, 3, 5]),)),
     ('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),))}

    When any indexed blocks would otherwise grow larger than
    dask.config.array.chunk-size, we might split them,
    depending on the value of ``dask.config.slicing.split-large-chunks``.

    >>> import dask
    >>> with dask.config.set({""array.slicing.split-large-chunks"": True}):
    ...      chunks, dsk = take('y', 'x', [(1, 1, 1), (1000, 1000), (1000, 1000)],
    ...                        [0] + [1] * 6 + [2], axis=0, itemsize=8)
    >>> chunks
    ((1, 3, 3, 1), (1000, 1000), (1000, 1000))
    ",191,299,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def take(outname, inname, chunks, index, itemsize, axis=0):
    
    from .core import PerformanceWarning

    plan = slicing_plan(chunks[axis], index)
    if len(plan) >= len(chunks[axis]) * 10:
        factor = math.ceil(len(plan) / len(chunks[axis]))

        warnings.warn(
            ""Slicing with an out-of-order index is generating %d ""
            ""times more chunks"" % factor,
            PerformanceWarning,
            stacklevel=6,
        )
    if not is_arraylike(index):
        index = np.asarray(index)

    # Check for chunks from the plan that would violate the user's
    # configured chunk size.
    nbytes = utils.parse_bytes(config.get(""array.chunk-size""))
    other_chunks = [chunks[i] for i in range(len(chunks)) if i != axis]
    other_numel = np.prod([sum(x) for x in other_chunks])

    if math.isnan(other_numel):
        warnsize = maxsize = math.inf
    else:
        maxsize = math.ceil(nbytes / (other_numel * itemsize))
        warnsize = maxsize * 5

    split = config.get(""array.slicing.split-large-chunks"", None)

    # Warn only when the default is not specified.
    warned = split is not None

    for _, index_list in plan:
        if not warned and len(index_list) > warnsize:
            msg = (
                ""Slicing is producing a large chunk. To accept the large\n""
                ""chunk and silence this warning, set the option\n""
                ""    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n""
                ""    ...     array[indexer]\n\n""
                ""To avoid creating the large chunks, set the option\n""
                ""    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n""
                ""    ...     array[indexer]""
            )
            warnings.warn(msg, PerformanceWarning, stacklevel=6)
            warned = True

    where_index = []
    index_lists = []
    for where_idx, index_list in plan:
        index_length = len(index_list)
        if split and index_length > maxsize:
            index_sublist = np.array_split(
                index_list, math.ceil(index_length / maxsize)
            )
            index_lists.extend(index_sublist)
            where_index.extend([where_idx] * len(index_sublist))
        else:
            if not is_arraylike(index_list):
                index_list = np.array(index_list)
            index_lists.append(index_list)
            where_index.append(where_idx)

    dims = [range(len(bd)) for bd in chunks]

    indims = list(dims)
    indims[axis] = list(range(len(where_index)))
    keys = list(product([outname], *indims))

    outdims = list(dims)
    outdims[axis] = where_index
    slices = [[colon] * len(bd) for bd in chunks]
    slices[axis] = index_lists
    slices = list(product(*slices))
    inkeys = list(product([inname], *outdims))
    values = [(getitem, inkey, slc) for inkey, slc in zip(inkeys, slices)]

    chunks2 = list(chunks)
    chunks2[axis] = tuple(map(len, index_lists))
    dsk = dict(zip(keys, values))

    return tuple(chunks2), dsk


        ```
    ###Assistant : Index array with an iterable of index

    Handles a single index by a single list

    Mimics ``np.take``

    >>> from pprint import pprint
    >>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [5, 1, 47, 3], 8, axis=0)
    >>> chunks
    ((2, 1, 1),)
    >>> pprint(dsk)   # doctest: +ELLIPSIS
    {('y', 0): (<function getitem at ...>, ('x', 0), (array([5, 1]),)),
     ('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),)),
     ('y', 2): (<function getitem at ...>, ('x', 0), (array([3]),))}

    When list is sorted we retain original block structure

    >>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [1, 3, 5, 47], 8, axis=0)
    >>> chunks
    ((3, 1),)
    >>> pprint(dsk)     # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    {('y', 0): (<function getitem at ...>,
                ('x', 0),
                (array([1, 3, 5]),)),
     ('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),))}

    When any indexed blocks would otherwise grow larger than
    dask.config.array.chunk-size, we might split them,
    depending on the value of ``dask.config.slicing.split-large-chunks``.

    >>> import dask
    >>> with dask.config.set({""array.slicing.split-large-chunks"": True}):
    ...      chunks, dsk = take('y', 'x', [(1, 1, 1), (1000, 1000), (1000, 1000)],
    ...                        [0] + [1] * 6 + [2], axis=0, itemsize=8)
    >>> chunks
    ((1, 3, 3, 1), (1000, 1000), (1000, 1000))
    
    "
1497,"def test_multidb(self):
        
        ContentType.objects.clear_cache()
        with self.assertNumQueries(0, using=""default""), self.assertNumQueries(
            1, using=""other""
        ):
            ContentType.objects.get_for_model(Author)
","
        When using multiple databases, ContentType.objects.get_for_model() uses
        db_for_read().
        ",7,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_multidb(self):
        
        ContentType.objects.clear_cache()
        with self.assertNumQueries(0, using=""default""), self.assertNumQueries(
            1, using=""other""
        ):
            ContentType.objects.get_for_model(Author)

        ```
    ###Assistant : 
        When using multiple databases, ContentType.objects.get_for_model() uses
        db_for_read().
        
    "
1498,"def get_engle_granger_two_step_cointegration_test(y, x):
    
    warnings.simplefilter(action=""ignore"", category=FutureWarning)
    long_run_ols = sm.OLS(y, sm.add_constant(x))
    warnings.simplefilter(action=""default"", category=FutureWarning)

    long_run_ols_fit = long_run_ols.fit()

    c, gamma = long_run_ols_fit.params
    z = long_run_ols_fit.resid

    short_run_ols = sm.OLS(y.diff().iloc[1:], (z.shift().iloc[1:]))
    short_run_ols_fit = short_run_ols.fit()

    alpha = short_run_ols_fit.params[0]

    # NOTE: The p-value returned by the adfuller function assumes we do not estimate z
    # first, but test stationarity of an unestimated series directly. This assumption
    # should have limited effect for high N, however. Critical values taking this into
    # account more accurately are provided in e.g. McKinnon (1990) and Engle & Yoo (1987).

    adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None)

    return c, gamma, alpha, z, adfstat, pvalue
","Estimates long-run and short-run cointegration relationship for series y and x and apply
    the two-step Engle & Granger test for cointegration.

    Uses a 2-step process to first estimate coefficients for the long-run relationship
        y_t = c + gamma * x_t + z_t

    and then the short-term relationship,
        y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t,

    with z the found residuals of the first equation.

    Then tests cointegration by Dickey-Fuller phi=1 vs phi < 1 in
        z_t = phi * z_(t-1) + eta_t

    If this implies phi < 1, the z series is stationary is concluded to be
    stationary, and thus the series y and x are concluded to be cointegrated.

    Parameters
    ----------
    y : pd.Series
        The first time series of the pair to analyse.

    x : pd.Series
        The second time series of the pair to analyse.

    Returns
    -------
    c : float
        The constant term in the long-run relationship y_t = c + gamma * x_t + z_t. This
        describes the static shift of y with respect to gamma * x.

    gamma : float
        The gamma term in the long-run relationship y_t = c + gamma * x_t + z_t. This
        describes the ratio between the const-shifted y and x.

    alpha : float
        The alpha term in the short-run relationship y_t - y_(t-1) = alpha * z_(t-1) + epsilon. This
        gives an indication of the strength of the error correction toward the long-run mean.

    z : pd.Series
        Series of residuals z_t from the long-run relationship y_t = c + gamma * x_t + z_t, representing
        the value of the error correction term.

    dfstat : float
        The Dickey Fuller test-statistic for phi = 1 vs phi < 1 in the second equation. A more
        negative value implies the existence of stronger cointegration.

    pvalue : float
        The p-value corresponding to the Dickey Fuller test-statistic. A lower value implies
        stronger rejection of no-cointegration, thus stronger evidence of cointegration.

    ",315,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_engle_granger_two_step_cointegration_test(y, x):
    
    warnings.simplefilter(action=""ignore"", category=FutureWarning)
    long_run_ols = sm.OLS(y, sm.add_constant(x))
    warnings.simplefilter(action=""default"", category=FutureWarning)

    long_run_ols_fit = long_run_ols.fit()

    c, gamma = long_run_ols_fit.params
    z = long_run_ols_fit.resid

    short_run_ols = sm.OLS(y.diff().iloc[1:], (z.shift().iloc[1:]))
    short_run_ols_fit = short_run_ols.fit()

    alpha = short_run_ols_fit.params[0]

    # NOTE: The p-value returned by the adfuller function assumes we do not estimate z
    # first, but test stationarity of an unestimated series directly. This assumption
    # should have limited effect for high N, however. Critical values taking this into
    # account more accurately are provided in e.g. McKinnon (1990) and Engle & Yoo (1987).

    adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None)

    return c, gamma, alpha, z, adfstat, pvalue

        ```
    ###Assistant : Estimates long-run and short-run cointegration relationship for series y and x and apply
    the two-step Engle & Granger test for cointegration.

    Uses a 2-step process to first estimate coefficients for the long-run relationship
        y_t = c + gamma * x_t + z_t

    and then the short-term relationship,
        y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t,

    with z the found residuals of the first equation.

    Then tests cointegration by Dickey-Fuller phi=1 vs phi < 1 in
        z_t = phi * z_(t-1) + eta_t

    If this implies phi < 1, the z series is stationary is concluded to be
    stationary, and thus the series y and x are concluded to be cointegrated.

    Parameters
    ----------
    y : pd.Series
        The first time series of the pair to analyse.

    x : pd.Series
        The second time series of the pair to analyse.

    Returns
    -------
    c : float
        The constant term in the long-run relationship y_t = c + gamma * x_t + z_t. This
        describes the static shift of y with respect to gamma * x.

    gamma : float
        The gamma term in the long-run relationship y_t = c + gamma * x_t + z_t. This
        describes the ratio between the const-shifted y and x.

    alpha : float
        The alpha term in the short-run relationship y_t - y_(t-1) = alpha * z_(t-1) + epsilon. This
        gives an indication of the strength of the error correction toward the long-run mean.

    z : pd.Series
        Series of residuals z_t from the long-run relationship y_t = c + gamma * x_t + z_t, representing
        the value of the error correction term.

    dfstat : float
        The Dickey Fuller test-statistic for phi = 1 vs phi < 1 in the second equation. A more
        negative value implies the existence of stronger cointegration.

    pvalue : float
        The p-value corresponding to the Dickey Fuller test-statistic. A lower value implies
        stronger rejection of no-cointegration, thus stronger evidence of cointegration.

    
    "
1499,"def test_deterministic_order_for_unordered_model(self):
        
        superuser = self._create_superuser(""superuser"")

        for counter in range(1, 51):
            UnorderedObject.objects.create(id=counter, bool=True)
","
        The primary key is used in the ordering of the changelist's results to
        guarantee a deterministic order, even when the model doesn't have any
        default ordering defined (#17198).
        ",28,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_deterministic_order_for_unordered_model(self):
        
        superuser = self._create_superuser(""superuser"")

        for counter in range(1, 51):
            UnorderedObject.objects.create(id=counter, bool=True)

        ```
    ###Assistant : 
        The primary key is used in the ordering of the changelist's results to
        guarantee a deterministic order, even when the model doesn't have any
        default ordering defined (#17198).
        
    "
1500,"async def read_settings() -> prefect.settings.Settings:
    
    return prefect.settings.get_current_settings().with_obfuscated_secrets()


@router.get(""/version"")","
    Get the current Orion settings.

    Secret setting values will be obfuscated.
    ",11,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def read_settings() -> prefect.settings.Settings:
    
    return prefect.settings.get_current_settings().with_obfuscated_secrets()


@router.get(""/version"")
        ```
    ###Assistant : 
    Get the current Orion settings.

    Secret setting values will be obfuscated.
    
    "
1501,"def map_overlap(self, func, depth, boundary=None, trim=True, **kwargs):
        
        from dask.array.overlap import map_overlap

        return map_overlap(
            func, self, depth=depth, boundary=boundary, trim=trim, **kwargs
        )
","Map a function over blocks of the array with some overlap

        We share neighboring zones between blocks of the array, then map a
        function, then trim away the neighboring strips.

        Note that this function will attempt to automatically determine the output
        array type before computing it, please refer to the ``meta`` keyword argument
        in :func:`map_blocks <dask.array.core.map_blocks>` if you expect that the function will not succeed when
        operating on 0-d arrays.

        Parameters
        ----------
        func: function
            The function to apply to each extended block
        depth: int, tuple, or dict
            The number of elements that each block should share with its neighbors
            If a tuple or dict then this can be different per axis
        boundary: str, tuple, dict
            How to handle the boundaries.
            Values include 'reflect', 'periodic', 'nearest', 'none',
            or any constant value like 0 or np.nan
        trim: bool
            Whether or not to trim ``depth`` elements from each block after
            calling the map function.
            Set this to False if your mapping function already does this for you
        **kwargs:
            Other keyword arguments valid in :func:`map_blocks <dask.array.core.map_blocks>`.

        Examples
        --------
        >>> import dask.array as da
        >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1])
        >>> x = da.from_array(x, chunks=5)
        >>> def derivative(x):
        ...     return x - np.roll(x, 1)

        >>> y = x.map_overlap(derivative, depth=1, boundary=0)
        >>> y.compute()
        array([ 1,  0,  1,  1,  0,  0, -1, -1,  0])

        >>> import dask.array as da
        >>> x = np.arange(16).reshape((4, 4))
        >>> d = da.from_array(x, chunks=(2, 2))
        >>> y = d.map_overlap(lambda x: x + x.size, depth=1, boundary='reflect')
        >>> y.compute()
        array([[16, 17, 18, 19],
               [20, 21, 22, 23],
               [24, 25, 26, 27],
               [28, 29, 30, 31]])

        >>> func = lambda x: x + x.size
        >>> depth = {0: 1, 1: 1}
        >>> boundary = {0: 'reflect', 1: 'none'}
        >>> d.map_overlap(func, depth, boundary).compute()  # doctest: +NORMALIZE_WHITESPACE
        array([[12,  13,  14,  15],
               [16,  17,  18,  19],
               [20,  21,  22,  23],
               [24,  25,  26,  27]])

        >>> x = np.arange(16).reshape((4, 4))
        >>> d = da.from_array(x, chunks=(2, 2))
        >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=np.array(()))
        >>> y
        dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=numpy.ndarray>
        >>> y.compute()
        array([[ 4,  6,  8, 10],
               [ 8, 10, 12, 14],
               [20, 22, 24, 26],
               [24, 26, 28, 30]])

        >>> import cupy  # doctest: +SKIP
        >>> x = cupy.arange(16).reshape((4, 4))  # doctest: +SKIP
        >>> d = da.from_array(x, chunks=(2, 2))  # doctest: +SKIP
        >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=cupy.array(()))  # doctest: +SKIP
        >>> y  # doctest: +SKIP
        dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=cupy.ndarray>
        >>> y.compute()  # doctest: +SKIP
        array([[ 4,  6,  8, 10],
               [ 8, 10, 12, 14],
               [20, 22, 24, 26],
               [24, 26, 28, 30]])
        ",435,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def map_overlap(self, func, depth, boundary=None, trim=True, **kwargs):
        
        from dask.array.overlap import map_overlap

        return map_overlap(
            func, self, depth=depth, boundary=boundary, trim=trim, **kwargs
        )

        ```
    ###Assistant : Map a function over blocks of the array with some overlap

        We share neighboring zones between blocks of the array, then map a
        function, then trim away the neighboring strips.

        Note that this function will attempt to automatically determine the output
        array type before computing it, please refer to the ``meta`` keyword argument
        in :func:`map_blocks <dask.array.core.map_blocks>` if you expect that the function will not succeed when
        operating on 0-d arrays.

        Parameters
        ----------
        func: function
            The function to apply to each extended block
        depth: int, tuple, or dict
            The number of elements that each block should share with its neighbors
            If a tuple or dict then this can be different per axis
        boundary: str, tuple, dict
            How to handle the boundaries.
            Values include 'reflect', 'periodic', 'nearest', 'none',
            or any constant value like 0 or np.nan
        trim: bool
            Whether or not to trim ``depth`` elements from each block after
            calling the map function.
            Set this to False if your mapping function already does this for you
        **kwargs:
            Other keyword arguments valid in :func:`map_blocks <dask.array.core.map_blocks>`.

        Examples
        --------
        >>> import dask.array as da
        >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1])
        >>> x = da.from_array(x, chunks=5)
        >>> def derivative(x):
        ...     return x - np.roll(x, 1)

        >>> y = x.map_overlap(derivative, depth=1, boundary=0)
        >>> y.compute()
        array([ 1,  0,  1,  1,  0,  0, -1, -1,  0])

        >>> import dask.array as da
        >>> x = np.arange(16).reshape((4, 4))
        >>> d = da.from_array(x, chunks=(2, 2))
        >>> y = d.map_overlap(lambda x: x + x.size, depth=1, boundary='reflect')
        >>> y.compute()
        array([[16, 17, 18, 19],
               [20, 21, 22, 23],
               [24, 25, 26, 27],
               [28, 29, 30, 31]])

        >>> func = lambda x: x + x.size
        >>> depth = {0: 1, 1: 1}
        >>> boundary = {0: 'reflect', 1: 'none'}
        >>> d.map_overlap(func, depth, boundary).compute()  # doctest: +NORMALIZE_WHITESPACE
        array([[12,  13,  14,  15],
               [16,  17,  18,  19],
               [20,  21,  22,  23],
               [24,  25,  26,  27]])

        >>> x = np.arange(16).reshape((4, 4))
        >>> d = da.from_array(x, chunks=(2, 2))
        >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=np.array(()))
        >>> y
        dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=numpy.ndarray>
        >>> y.compute()
        array([[ 4,  6,  8, 10],
               [ 8, 10, 12, 14],
               [20, 22, 24, 26],
               [24, 26, 28, 30]])

        >>> import cupy  # doctest: +SKIP
        >>> x = cupy.arange(16).reshape((4, 4))  # doctest: +SKIP
        >>> d = da.from_array(x, chunks=(2, 2))  # doctest: +SKIP
        >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=cupy.array(()))  # doctest: +SKIP
        >>> y  # doctest: +SKIP
        dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=cupy.ndarray>
        >>> y.compute()  # doctest: +SKIP
        array([[ 4,  6,  8, 10],
               [ 8, 10, 12, 14],
               [20, 22, 24, 26],
               [24, 26, 28, 30]])
        
    "
1502,"def _flush(self):
        
        # Raise an Exception if the value is being changed in read mode.
        if not self._write:
            raise GDALException(
                ""Raster needs to be opened in write mode to change values.""
            )
        capi.flush_ds(self._ptr)
","
        Flush all data from memory into the source file if it exists.
        The data that needs flushing are geotransforms, coordinate systems,
        nodata_values and pixel values. This function will be called
        automatically wherever it is needed.
        ",35,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _flush(self):
        
        # Raise an Exception if the value is being changed in read mode.
        if not self._write:
            raise GDALException(
                ""Raster needs to be opened in write mode to change values.""
            )
        capi.flush_ds(self._ptr)

        ```
    ###Assistant : 
        Flush all data from memory into the source file if it exists.
        The data that needs flushing are geotransforms, coordinate systems,
        nodata_values and pixel values. This function will be called
        automatically wherever it is needed.
        
    "
1503,"def _round(self, places, rounding):
        
        if places <= 0:
            raise ValueError(""argument should be at least 1 in _round"")
        if self._is_special or not self:
            return Decimal(self)
        ans = self._rescale(self.adjusted()+1-places, rounding)
        # it can happen that the rescale alters the adjusted exponent;
        # for example when rounding 99.97 to 3 significant figures.
        # When this happens we end up with an extra 0 at the end of
        # the number; a second rescale fixes this.
        if ans.adjusted() != self.adjusted():
            ans = ans._rescale(ans.adjusted()+1-places, rounding)
        return ans
","Round a nonzero, nonspecial Decimal to a fixed number of
        significant figures, using the given rounding mode.

        Infinities, NaNs and zeros are returned unaltered.

        This operation is quiet: it raises no flags, and uses no
        information from the context.

        ",39,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _round(self, places, rounding):
        
        if places <= 0:
            raise ValueError(""argument should be at least 1 in _round"")
        if self._is_special or not self:
            return Decimal(self)
        ans = self._rescale(self.adjusted()+1-places, rounding)
        # it can happen that the rescale alters the adjusted exponent;
        # for example when rounding 99.97 to 3 significant figures.
        # When this happens we end up with an extra 0 at the end of
        # the number; a second rescale fixes this.
        if ans.adjusted() != self.adjusted():
            ans = ans._rescale(ans.adjusted()+1-places, rounding)
        return ans

        ```
    ###Assistant : Round a nonzero, nonspecial Decimal to a fixed number of
        significant figures, using the given rounding mode.

        Infinities, NaNs and zeros are returned unaltered.

        This operation is quiet: it raises no flags, and uses no
        information from the context.

        
    "
1504,"def is_redirect(self):
        
        return ""location"" in self.headers and self.status_code in REDIRECT_STATI
","True if this Response is a well-formed HTTP redirect that could have
        been processed automatically (by :meth:`Session.resolve_redirects`).
        ",17,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_redirect(self):
        
        return ""location"" in self.headers and self.status_code in REDIRECT_STATI

        ```
    ###Assistant : True if this Response is a well-formed HTTP redirect that could have
        been processed automatically (by :meth:`Session.resolve_redirects`).
        
    "
1505,"def write_eqapo_graphic_eq(self, file_path, normalize=True):
        
        file_path = os.path.abspath(file_path)
        s = self.eqapo_graphic_eq(normalize=normalize)
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(s)
        return s
",Writes equalization graph to a file as Equalizer APO config.,10,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def write_eqapo_graphic_eq(self, file_path, normalize=True):
        
        file_path = os.path.abspath(file_path)
        s = self.eqapo_graphic_eq(normalize=normalize)
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(s)
        return s

        ```
    ###Assistant : Writes equalization graph to a file as Equalizer APO config.
    "
1506,"def test_export_pipeline_6():
    

    pipeline_string = (
        'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'
        'DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8,'
        'DecisionTreeClassifier__min_samples_leaf=5, DecisionTreeClassifier__min_samples_split=5)'
    )
    pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
    expected_code = 
    exported_code = export_pipeline(pipeline, tpot_obj.operators,
                                    tpot_obj._pset, random_state=42,
                                    data_file_path='test_path')

    assert expected_code == exported_code

","Assert that exported_pipeline() generated a compile source file with random_state and data_file_path.import numpy as np
import pandas as pd
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeClassifier
from tpot.export_utils import set_param_recursive

# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('test_path', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \\
            train_test_split(features, tpot_data['target'], random_state=42)

exported_pipeline = make_pipeline(
    SelectPercentile(score_func=f_classif, percentile=20),
    DecisionTreeClassifier(criterion=""gini"", max_depth=8, min_samples_leaf=5, min_samples_split=5)
)
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 42)

exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
",102,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_export_pipeline_6():
    

    pipeline_string = (
        'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'
        'DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8,'
        'DecisionTreeClassifier__min_samples_leaf=5, DecisionTreeClassifier__min_samples_split=5)'
    )
    pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
    expected_code = 
    exported_code = export_pipeline(pipeline, tpot_obj.operators,
                                    tpot_obj._pset, random_state=42,
                                    data_file_path='test_path')

    assert expected_code == exported_code


        ```
    ###Assistant : Assert that exported_pipeline() generated a compile source file with random_state and data_file_path.import numpy as np
import pandas as pd
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeClassifier
from tpot.export_utils import set_param_recursive

# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('test_path', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \\
            train_test_split(features, tpot_data['target'], random_state=42)

exported_pipeline = make_pipeline(
    SelectPercentile(score_func=f_classif, percentile=20),
    DecisionTreeClassifier(criterion=""gini"", max_depth=8, min_samples_leaf=5, min_samples_split=5)
)
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 42)

exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)

    "
1507,"def _check_edge_connectivity(G):
    
    # Construct the auxiliary graph that can be used to make each k-cc or k-sub
    aux_graph = EdgeComponentAuxGraph.construct(G)

    # memoize the local connectivity in this graph
    memo = {}

    for k in it.count(1):
        # Test ""local"" k-edge-components and k-edge-subgraphs
        ccs_local = fset(aux_graph.k_edge_components(k))
        ccs_subgraph = fset(aux_graph.k_edge_subgraphs(k))

        # Check connectivity properties that should be guaranteed by the
        # algorithms.
        _assert_local_cc_edge_connectivity(G, ccs_local, k, memo)
        _assert_subgraph_edge_connectivity(G, ccs_subgraph, k)

        if k == 1 or k == 2 and not G.is_directed():
            assert (
                ccs_local == ccs_subgraph
            ), ""Subgraphs and components should be the same when k == 1 or (k == 2 and not G.directed())""

        if G.is_directed():
            # Test special case methods are the same as the aux graph
            if k == 1:
                alt_sccs = fset(nx.strongly_connected_components(G))
                assert alt_sccs == ccs_local, ""k=1 failed alt""
                assert alt_sccs == ccs_subgraph, ""k=1 failed alt""
        else:
            # Test special case methods are the same as the aux graph
            if k == 1:
                alt_ccs = fset(nx.connected_components(G))
                assert alt_ccs == ccs_local, ""k=1 failed alt""
                assert alt_ccs == ccs_subgraph, ""k=1 failed alt""
            elif k == 2:
                alt_bridge_ccs = fset(bridge_components(G))
                assert alt_bridge_ccs == ccs_local, ""k=2 failed alt""
                assert alt_bridge_ccs == ccs_subgraph, ""k=2 failed alt""
            # if new methods for k == 3 or k == 4 are implemented add them here

        # Check the general subgraph method works by itself
        alt_subgraph_ccs = fset(
            [set(C.nodes()) for C in general_k_edge_subgraphs(G, k=k)]
        )
        assert alt_subgraph_ccs == ccs_subgraph, ""alt subgraph method failed""

        # Stop once k is larger than all special case methods
        # and we cannot break down ccs any further.
        if k > 2 and all(len(cc) == 1 for cc in ccs_local):
            break


# ----------------
# Misc tests
# ----------------

","
    Helper - generates all k-edge-components using the aux graph.  Checks the
    both local and subgraph edge connectivity of each cc. Also checks that
    alternate methods of computing the k-edge-ccs generate the same result.
    ",33,275,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_edge_connectivity(G):
    
    # Construct the auxiliary graph that can be used to make each k-cc or k-sub
    aux_graph = EdgeComponentAuxGraph.construct(G)

    # memoize the local connectivity in this graph
    memo = {}

    for k in it.count(1):
        # Test ""local"" k-edge-components and k-edge-subgraphs
        ccs_local = fset(aux_graph.k_edge_components(k))
        ccs_subgraph = fset(aux_graph.k_edge_subgraphs(k))

        # Check connectivity properties that should be guaranteed by the
        # algorithms.
        _assert_local_cc_edge_connectivity(G, ccs_local, k, memo)
        _assert_subgraph_edge_connectivity(G, ccs_subgraph, k)

        if k == 1 or k == 2 and not G.is_directed():
            assert (
                ccs_local == ccs_subgraph
            ), ""Subgraphs and components should be the same when k == 1 or (k == 2 and not G.directed())""

        if G.is_directed():
            # Test special case methods are the same as the aux graph
            if k == 1:
                alt_sccs = fset(nx.strongly_connected_components(G))
                assert alt_sccs == ccs_local, ""k=1 failed alt""
                assert alt_sccs == ccs_subgraph, ""k=1 failed alt""
        else:
            # Test special case methods are the same as the aux graph
            if k == 1:
                alt_ccs = fset(nx.connected_components(G))
                assert alt_ccs == ccs_local, ""k=1 failed alt""
                assert alt_ccs == ccs_subgraph, ""k=1 failed alt""
            elif k == 2:
                alt_bridge_ccs = fset(bridge_components(G))
                assert alt_bridge_ccs == ccs_local, ""k=2 failed alt""
                assert alt_bridge_ccs == ccs_subgraph, ""k=2 failed alt""
            # if new methods for k == 3 or k == 4 are implemented add them here

        # Check the general subgraph method works by itself
        alt_subgraph_ccs = fset(
            [set(C.nodes()) for C in general_k_edge_subgraphs(G, k=k)]
        )
        assert alt_subgraph_ccs == ccs_subgraph, ""alt subgraph method failed""

        # Stop once k is larger than all special case methods
        # and we cannot break down ccs any further.
        if k > 2 and all(len(cc) == 1 for cc in ccs_local):
            break


# ----------------
# Misc tests
# ----------------


        ```
    ###Assistant : 
    Helper - generates all k-edge-components using the aux graph.  Checks the
    both local and subgraph edge connectivity of each cc. Also checks that
    alternate methods of computing the k-edge-ccs generate the same result.
    
    "
1508,"def test_sequence_name_length_limits_flush(self):
        
        # A full flush is expensive to the full test, so we dig into the
        # internals to generate the likely offending SQL and run it manually

        # Some convenience aliases
        VLM = VeryLongModelNameZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
        VLM_m2m = (
            VLM.m2m_also_quite_long_zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz.through
        )
        tables = [
            VLM._meta.db_table,
            VLM_m2m._meta.db_table,
        ]
        sql_list = connection.ops.sql_flush(no_style(), tables, reset_sequences=True)
        connection.ops.execute_sql_flush(sql_list)

","
        Sequence resetting as part of a flush with model with long name and
        long pk name doesn't error (#8901).
        ",19,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_sequence_name_length_limits_flush(self):
        
        # A full flush is expensive to the full test, so we dig into the
        # internals to generate the likely offending SQL and run it manually

        # Some convenience aliases
        VLM = VeryLongModelNameZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
        VLM_m2m = (
            VLM.m2m_also_quite_long_zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz.through
        )
        tables = [
            VLM._meta.db_table,
            VLM_m2m._meta.db_table,
        ]
        sql_list = connection.ops.sql_flush(no_style(), tables, reset_sequences=True)
        connection.ops.execute_sql_flush(sql_list)


        ```
    ###Assistant : 
        Sequence resetting as part of a flush with model with long name and
        long pk name doesn't error (#8901).
        
    "
1509,"def test_delete_media(self) -> None:
        

        download_resource = self.media_repo.children[b""download""]
        upload_resource = self.media_repo.children[b""upload""]

        # Upload some media into the room
        response = self.helper.upload_media(
            upload_resource,
            SMALL_PNG,
            tok=self.admin_user_tok,
            expect_code=200,
        )
        # Extract media ID from the response
        server_and_media_id = response[""content_uri""][6:]  # Cut off 'mxc://'
        server_name, media_id = server_and_media_id.split(""/"")

        self.assertEqual(server_name, self.server_name)

        # Attempt to access media
        channel = make_request(
            self.reactor,
            FakeSite(download_resource, self.reactor),
            ""GET"",
            server_and_media_id,
            shorthand=False,
            access_token=self.admin_user_tok,
        )

        # Should be successful
        self.assertEqual(
            200,
            channel.code,
            msg=(
                ""Expected to receive a 200 on accessing media: %s"" % server_and_media_id
            ),
        )

        # Test if the file exists
        local_path = self.filepaths.local_media_filepath(media_id)
        self.assertTrue(os.path.exists(local_path))

        url = ""/_synapse/admin/v1/media/%s/%s"" % (self.server_name, media_id)

        # Delete media
        channel = self.make_request(
            ""DELETE"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(1, channel.json_body[""total""])
        self.assertEqual(
            media_id,
            channel.json_body[""deleted_media""][0],
        )

        # Attempt to access media
        channel = make_request(
            self.reactor,
            FakeSite(download_resource, self.reactor),
            ""GET"",
            server_and_media_id,
            shorthand=False,
            access_token=self.admin_user_tok,
        )
        self.assertEqual(
            HTTPStatus.NOT_FOUND,
            channel.code,
            msg=(
                ""Expected to receive a HTTPStatus.NOT_FOUND on accessing deleted media: %s""
                % server_and_media_id
            ),
        )

        # Test if the file is deleted
        self.assertFalse(os.path.exists(local_path))

","
        Tests that delete a media is successfully
        ",7,159,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_media(self) -> None:
        

        download_resource = self.media_repo.children[b""download""]
        upload_resource = self.media_repo.children[b""upload""]

        # Upload some media into the room
        response = self.helper.upload_media(
            upload_resource,
            SMALL_PNG,
            tok=self.admin_user_tok,
            expect_code=200,
        )
        # Extract media ID from the response
        server_and_media_id = response[""content_uri""][6:]  # Cut off 'mxc://'
        server_name, media_id = server_and_media_id.split(""/"")

        self.assertEqual(server_name, self.server_name)

        # Attempt to access media
        channel = make_request(
            self.reactor,
            FakeSite(download_resource, self.reactor),
            ""GET"",
            server_and_media_id,
            shorthand=False,
            access_token=self.admin_user_tok,
        )

        # Should be successful
        self.assertEqual(
            200,
            channel.code,
            msg=(
                ""Expected to receive a 200 on accessing media: %s"" % server_and_media_id
            ),
        )

        # Test if the file exists
        local_path = self.filepaths.local_media_filepath(media_id)
        self.assertTrue(os.path.exists(local_path))

        url = ""/_synapse/admin/v1/media/%s/%s"" % (self.server_name, media_id)

        # Delete media
        channel = self.make_request(
            ""DELETE"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(1, channel.json_body[""total""])
        self.assertEqual(
            media_id,
            channel.json_body[""deleted_media""][0],
        )

        # Attempt to access media
        channel = make_request(
            self.reactor,
            FakeSite(download_resource, self.reactor),
            ""GET"",
            server_and_media_id,
            shorthand=False,
            access_token=self.admin_user_tok,
        )
        self.assertEqual(
            HTTPStatus.NOT_FOUND,
            channel.code,
            msg=(
                ""Expected to receive a HTTPStatus.NOT_FOUND on accessing deleted media: %s""
                % server_and_media_id
            ),
        )

        # Test if the file is deleted
        self.assertFalse(os.path.exists(local_path))


        ```
    ###Assistant : 
        Tests that delete a media is successfully
        
    "
1510,"def load_data_for_viz(load_type, model_file_statistics, **kwargs):
    
    supported_load_types = dict(
        load_json=load_json,
        load_from_file=partial(
            load_from_file, dtype=kwargs.get(""dtype"", int), ground_truth_split=kwargs.get(""ground_truth_split"", 2)
        ),
    )
    loader = supported_load_types[load_type]
    try:
        stats_per_model = [loader(stats_f) for stats_f in model_file_statistics]
    except (TypeError, AttributeError):
        logger.exception(f""Unable to open model statistics file {model_file_statistics}!"")
        raise
    return stats_per_model

","Load model file data in to list of .

    :param load_type: type of the data loader to be used.
    :param model_file_statistics: JSON file or list of json files containing any
           model experiment stats.
    :return List of training statistics loaded as json objects.
    ",42,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_data_for_viz(load_type, model_file_statistics, **kwargs):
    
    supported_load_types = dict(
        load_json=load_json,
        load_from_file=partial(
            load_from_file, dtype=kwargs.get(""dtype"", int), ground_truth_split=kwargs.get(""ground_truth_split"", 2)
        ),
    )
    loader = supported_load_types[load_type]
    try:
        stats_per_model = [loader(stats_f) for stats_f in model_file_statistics]
    except (TypeError, AttributeError):
        logger.exception(f""Unable to open model statistics file {model_file_statistics}!"")
        raise
    return stats_per_model


        ```
    ###Assistant : Load model file data in to list of .

    :param load_type: type of the data loader to be used.
    :param model_file_statistics: JSON file or list of json files containing any
           model experiment stats.
    :return List of training statistics loaded as json objects.
    
    "
1511,"def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=100000, trim_rule=None):
        
        logger.info(""collecting all words and their counts"")
        if corpus_file is not None:
            corpus_iterable = TaggedLineDocument(corpus_file)

        total_words, corpus_count = self._scan_vocab(corpus_iterable, progress_per, trim_rule)

        logger.info(
            ""collected %i word types and %i unique tags from a corpus of %i examples and %i words"",
            len(self.raw_vocab), len(self.dv), corpus_count, total_words,
        )

        return total_words, corpus_count
","Create the model's vocabulary: a mapping from unique words in the corpus to their frequency count.

        Parameters
        ----------
        documents : iterable of :class:`~gensim.models.doc2vec.TaggedDocument`, optional
            The tagged documents used to create the vocabulary. Their tags can be either str tokens or ints (faster).
        corpus_file : str, optional
            Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format.
            You may use this argument instead of `documents` to get performance boost. Only one of `documents` or
            `corpus_file` arguments need to be passed (not both of them).
        progress_per : int
            Progress will be logged every `progress_per` documents.
        trim_rule : function, optional
            Vocabulary trimming rule, specifies whether certain words should remain in the vocabulary,
            be trimmed away, or handled using the default (discard if word count < min_count).
            Can be None (min_count will be used, look to :func:`~gensim.utils.keep_vocab_item`),
            or a callable that accepts parameters (word, count, min_count) and returns either
            :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`.
            The rule, if given, is only used to prune vocabulary during
            :meth:`~gensim.models.doc2vec.Doc2Vec.build_vocab` and is not stored as part of the model.

            The input parameters are of the following types:
                * `word` (str) - the word we are examining
                * `count` (int) - the word's frequency count in the corpus
                * `min_count` (int) - the minimum count threshold.

        Returns
        -------
        (int, int)
            Tuple of `(total words in the corpus, number of documents)`.

        ",218,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=100000, trim_rule=None):
        
        logger.info(""collecting all words and their counts"")
        if corpus_file is not None:
            corpus_iterable = TaggedLineDocument(corpus_file)

        total_words, corpus_count = self._scan_vocab(corpus_iterable, progress_per, trim_rule)

        logger.info(
            ""collected %i word types and %i unique tags from a corpus of %i examples and %i words"",
            len(self.raw_vocab), len(self.dv), corpus_count, total_words,
        )

        return total_words, corpus_count

        ```
    ###Assistant : Create the model's vocabulary: a mapping from unique words in the corpus to their frequency count.

        Parameters
        ----------
        documents : iterable of :class:`~gensim.models.doc2vec.TaggedDocument`, optional
            The tagged documents used to create the vocabulary. Their tags can be either str tokens or ints (faster).
        corpus_file : str, optional
            Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format.
            You may use this argument instead of `documents` to get performance boost. Only one of `documents` or
            `corpus_file` arguments need to be passed (not both of them).
        progress_per : int
            Progress will be logged every `progress_per` documents.
        trim_rule : function, optional
            Vocabulary trimming rule, specifies whether certain words should remain in the vocabulary,
            be trimmed away, or handled using the default (discard if word count < min_count).
            Can be None (min_count will be used, look to :func:`~gensim.utils.keep_vocab_item`),
            or a callable that accepts parameters (word, count, min_count) and returns either
            :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`.
            The rule, if given, is only used to prune vocabulary during
            :meth:`~gensim.models.doc2vec.Doc2Vec.build_vocab` and is not stored as part of the model.

            The input parameters are of the following types:
                * `word` (str) - the word we are examining
                * `count` (int) - the word's frequency count in the corpus
                * `min_count` (int) - the minimum count threshold.

        Returns
        -------
        (int, int)
            Tuple of `(total words in the corpus, number of documents)`.

        
    "
1512,"def _parse_command_opts(self, parser, args):
        
        # late import because of mutual dependence between these modules
        from distutils.cmd import Command

        # Pull the current command from the head of the command line
        command = args[0]
        if not command_re.match(command):
            raise SystemExit(""invalid command name '%s'"" % command)
        self.commands.append(command)

        # Dig up the command class that implements this command, so we
        # 1) know that it's a valid command, and 2) know which options
        # it takes.
        try:
            cmd_class = self.get_command_class(command)
        except DistutilsModuleError as msg:
            raise DistutilsArgError(msg)

        # Require that the command class be derived from Command -- want
        # to be sure that the basic ""command"" interface is implemented.
        if not issubclass(cmd_class, Command):
            raise DistutilsClassError(
                ""command class %s must subclass Command"" % cmd_class)

        # Also make sure that the command object provides a list of its
        # known options.
        if not (hasattr(cmd_class, 'user_options') and
                isinstance(cmd_class.user_options, list)):
            msg = (""command class %s must provide ""
                ""'user_options' attribute (a list of tuples)"")
            raise DistutilsClassError(msg % cmd_class)

        # If the command class has a list of negative alias options,
        # merge it in with the global negative aliases.
        negative_opt = self.negative_opt
        if hasattr(cmd_class, 'negative_opt'):
            negative_opt = negative_opt.copy()
            negative_opt.update(cmd_class.negative_opt)

        # Check for help_options in command class.  They have a different
        # format (tuple of four) so we need to preprocess them here.
        if (hasattr(cmd_class, 'help_options') and
                isinstance(cmd_class.help_options, list)):
            help_options = fix_help_options(cmd_class.help_options)
        else:
            help_options = []

        # All commands support the global options too, just by adding
        # in 'global_options'.
        parser.set_option_table(self.global_options +
                                cmd_class.user_options +
                                help_options)
        parser.set_negative_aliases(negative_opt)
        (args, opts) = parser.getopt(args[1:])
        if hasattr(opts, 'help') and opts.help:
            self._show_help(parser, display_options=0, commands=[cmd_class])
            return

        if (hasattr(cmd_class, 'help_options') and
                isinstance(cmd_class.help_options, list)):
            help_option_found=0
            for (help_option, short, desc, func) in cmd_class.help_options:
                if hasattr(opts, parser.get_attr_name(help_option)):
                    help_option_found=1
                    if callable(func):
                        func()
                    else:
                        raise DistutilsClassError(
                            ""invalid help function %r for help option '%s': ""
                            ""must be a callable object (function, etc.)""
                            % (func, help_option))

            if help_option_found:
                return

        # Put the options from the command-line into their official
        # holding pen, the 'command_options' dictionary.
        opt_dict = self.get_option_dict(command)
        for (name, value) in vars(opts).items():
            opt_dict[name] = (""command line"", value)

        return args
","Parse the command-line options for a single command.
        'parser' must be a FancyGetopt instance; 'args' must be the list
        of arguments, starting with the current command (whose options
        we are about to parse).  Returns a new version of 'args' with
        the next command at the front of the list; will be the empty
        list if there are no more commands on the command line.  Returns
        None if the user asked for help on this command.
        ",75,337,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_command_opts(self, parser, args):
        
        # late import because of mutual dependence between these modules
        from distutils.cmd import Command

        # Pull the current command from the head of the command line
        command = args[0]
        if not command_re.match(command):
            raise SystemExit(""invalid command name '%s'"" % command)
        self.commands.append(command)

        # Dig up the command class that implements this command, so we
        # 1) know that it's a valid command, and 2) know which options
        # it takes.
        try:
            cmd_class = self.get_command_class(command)
        except DistutilsModuleError as msg:
            raise DistutilsArgError(msg)

        # Require that the command class be derived from Command -- want
        # to be sure that the basic ""command"" interface is implemented.
        if not issubclass(cmd_class, Command):
            raise DistutilsClassError(
                ""command class %s must subclass Command"" % cmd_class)

        # Also make sure that the command object provides a list of its
        # known options.
        if not (hasattr(cmd_class, 'user_options') and
                isinstance(cmd_class.user_options, list)):
            msg = (""command class %s must provide ""
                ""'user_options' attribute (a list of tuples)"")
            raise DistutilsClassError(msg % cmd_class)

        # If the command class has a list of negative alias options,
        # merge it in with the global negative aliases.
        negative_opt = self.negative_opt
        if hasattr(cmd_class, 'negative_opt'):
            negative_opt = negative_opt.copy()
            negative_opt.update(cmd_class.negative_opt)

        # Check for help_options in command class.  They have a different
        # format (tuple of four) so we need to preprocess them here.
        if (hasattr(cmd_class, 'help_options') and
                isinstance(cmd_class.help_options, list)):
            help_options = fix_help_options(cmd_class.help_options)
        else:
            help_options = []

        # All commands support the global options too, just by adding
        # in 'global_options'.
        parser.set_option_table(self.global_options +
                                cmd_class.user_options +
                                help_options)
        parser.set_negative_aliases(negative_opt)
        (args, opts) = parser.getopt(args[1:])
        if hasattr(opts, 'help') and opts.help:
            self._show_help(parser, display_options=0, commands=[cmd_class])
            return

        if (hasattr(cmd_class, 'help_options') and
                isinstance(cmd_class.help_options, list)):
            help_option_found=0
            for (help_option, short, desc, func) in cmd_class.help_options:
                if hasattr(opts, parser.get_attr_name(help_option)):
                    help_option_found=1
                    if callable(func):
                        func()
                    else:
                        raise DistutilsClassError(
                            ""invalid help function %r for help option '%s': ""
                            ""must be a callable object (function, etc.)""
                            % (func, help_option))

            if help_option_found:
                return

        # Put the options from the command-line into their official
        # holding pen, the 'command_options' dictionary.
        opt_dict = self.get_option_dict(command)
        for (name, value) in vars(opts).items():
            opt_dict[name] = (""command line"", value)

        return args

        ```
    ###Assistant : Parse the command-line options for a single command.
        'parser' must be a FancyGetopt instance; 'args' must be the list
        of arguments, starting with the current command (whose options
        we are about to parse).  Returns a new version of 'args' with
        the next command at the front of the list; will be the empty
        list if there are no more commands on the command line.  Returns
        None if the user asked for help on this command.
        
    "
1513,"def interval(self, confidence=None, *args, **kwds):
        
        # This function was originally written with parameter `alpha`, but
        # `alpha` is also the name of a shape parameter of two distributions.
        # This block allows the function to accept both `alpha` and its
        # replacement `confidence` during a deprecation period; it can be
        # removed in the second release after 1.9.0.
        # See description of logic in `moment` method.
        has_shape_alpha = (self.shapes is not None
                           and ""alpha"" in (self.shapes.split("", "")))
        got_confidence = confidence is not None
        got_keyword_alpha = kwds.get(""alpha"", None) is not None

        if not got_confidence and ((not got_keyword_alpha)
                                   or (got_keyword_alpha and has_shape_alpha)):
            message = (""interval() missing 1 required positional argument: ""
                       ""`confidence`"")
            raise TypeError(message)

        if got_keyword_alpha and not has_shape_alpha:
            if got_confidence:
                # this will change to ""interval got unexpected argument alpha""
                message = ""interval() got multiple values for first argument""
                raise TypeError(message)
            else:
                message = (""Use of keyword argument `alpha` for method ""
                           ""`interval` is deprecated. Use first positional ""
                           ""argument or keyword argument `confidence` ""
                           ""instead."")
                confidence = kwds.pop(""alpha"")
                warnings.warn(message, DeprecationWarning, stacklevel=2)
        alpha = confidence

        alpha = asarray(alpha)
        if np.any((alpha > 1) | (alpha < 0)):
            raise ValueError(""alpha must be between 0 and 1 inclusive"")
        q1 = (1.0-alpha)/2
        q2 = (1.0+alpha)/2
        a = self.ppf(q1, *args, **kwds)
        b = self.ppf(q2, *args, **kwds)
        return a, b
","Confidence interval with equal areas around the median.

        .. deprecated:: 1.9.0
           Parameter `alpha` is replaced by parameter `confidence` to avoid
           name collisions with the shape parameter `alpha` of some
           distributions. Parameter `alpha` will be removed in the second
           release after 1.9.0.

        Parameters
        ----------
        confidence : array_like of float
            Probability that an rv will be drawn from the returned range.
            Each value should be in the range [0, 1].
        arg1, arg2, ... : array_like
            The shape parameter(s) for the distribution (see docstring of the
            instance object for more information).
        loc : array_like, optional
            location parameter, Default is 0.
        scale : array_like, optional
            scale parameter, Default is 1.

        Returns
        -------
        a, b : ndarray of float
            end-points of range that contain ``100 * alpha %`` of the rv's
            possible values.

        ",128,213,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def interval(self, confidence=None, *args, **kwds):
        
        # This function was originally written with parameter `alpha`, but
        # `alpha` is also the name of a shape parameter of two distributions.
        # This block allows the function to accept both `alpha` and its
        # replacement `confidence` during a deprecation period; it can be
        # removed in the second release after 1.9.0.
        # See description of logic in `moment` method.
        has_shape_alpha = (self.shapes is not None
                           and ""alpha"" in (self.shapes.split("", "")))
        got_confidence = confidence is not None
        got_keyword_alpha = kwds.get(""alpha"", None) is not None

        if not got_confidence and ((not got_keyword_alpha)
                                   or (got_keyword_alpha and has_shape_alpha)):
            message = (""interval() missing 1 required positional argument: ""
                       ""`confidence`"")
            raise TypeError(message)

        if got_keyword_alpha and not has_shape_alpha:
            if got_confidence:
                # this will change to ""interval got unexpected argument alpha""
                message = ""interval() got multiple values for first argument""
                raise TypeError(message)
            else:
                message = (""Use of keyword argument `alpha` for method ""
                           ""`interval` is deprecated. Use first positional ""
                           ""argument or keyword argument `confidence` ""
                           ""instead."")
                confidence = kwds.pop(""alpha"")
                warnings.warn(message, DeprecationWarning, stacklevel=2)
        alpha = confidence

        alpha = asarray(alpha)
        if np.any((alpha > 1) | (alpha < 0)):
            raise ValueError(""alpha must be between 0 and 1 inclusive"")
        q1 = (1.0-alpha)/2
        q2 = (1.0+alpha)/2
        a = self.ppf(q1, *args, **kwds)
        b = self.ppf(q2, *args, **kwds)
        return a, b

        ```
    ###Assistant : Confidence interval with equal areas around the median.

        .. deprecated:: 1.9.0
           Parameter `alpha` is replaced by parameter `confidence` to avoid
           name collisions with the shape parameter `alpha` of some
           distributions. Parameter `alpha` will be removed in the second
           release after 1.9.0.

        Parameters
        ----------
        confidence : array_like of float
            Probability that an rv will be drawn from the returned range.
            Each value should be in the range [0, 1].
        arg1, arg2, ... : array_like
            The shape parameter(s) for the distribution (see docstring of the
            instance object for more information).
        loc : array_like, optional
            location parameter, Default is 0.
        scale : array_like, optional
            scale parameter, Default is 1.

        Returns
        -------
        a, b : ndarray of float
            end-points of range that contain ``100 * alpha %`` of the rv's
            possible values.

        
    "
1514,"def __call__(self) -> bool:
        
        for meta in tqdm(self._face_alignments,
                         desc=""Updating Alignments File from PNG Header"",
                         leave=False):
            src = meta[""source""]
            alignment = meta[""alignments""]
            if not any(alignment.get(key, {}) for key in self._updatable_keys):
                continue

            faces = self._alignments.get_faces_in_frame(src[""source_filename""])
            if len(faces) < src[""face_index""] + 1:  # list index out of range
                logger.debug(""Skipped face '%s'. Index does not exist in alignments file"",
                             src[""original_filename""])
                continue

            face = faces[src[""face_index""]]
            self._check_and_update(alignment, face)

        retval = False
        if self._counts:
            retval = True
            logger.info(""Updated alignments file from PNG Data: %s"", self._counts)
        return retval
"," Parse through the face data updating any entries in the alignments file.

        Returns
        -------
        bool
            ``True`` if any alignment information was updated otherwise ``False``
        ",24,80,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self) -> bool:
        
        for meta in tqdm(self._face_alignments,
                         desc=""Updating Alignments File from PNG Header"",
                         leave=False):
            src = meta[""source""]
            alignment = meta[""alignments""]
            if not any(alignment.get(key, {}) for key in self._updatable_keys):
                continue

            faces = self._alignments.get_faces_in_frame(src[""source_filename""])
            if len(faces) < src[""face_index""] + 1:  # list index out of range
                logger.debug(""Skipped face '%s'. Index does not exist in alignments file"",
                             src[""original_filename""])
                continue

            face = faces[src[""face_index""]]
            self._check_and_update(alignment, face)

        retval = False
        if self._counts:
            retval = True
            logger.info(""Updated alignments file from PNG Data: %s"", self._counts)
        return retval

        ```
    ###Assistant :  Parse through the face data updating any entries in the alignments file.

        Returns
        -------
        bool
            ``True`` if any alignment information was updated otherwise ``False``
        
    "
1515,"def test_dynamic_path(self):
        
        doc = Document.objects.create(
            title=""does not matter"",
            created=timezone.make_aware(datetime.datetime(2020, 6, 25, 7, 36, 51, 153)),
            mime_type=""application/pdf"",
            pk=2,
            checksum=""2"",
            storage_path=StoragePath.objects.create(path=""TestFolder/{created}""),
        )
        self.assertEqual(generate_filename(doc), ""TestFolder/2020-06-25.pdf"")
","
        GIVEN:
            - A document with a defined storage path
        WHEN:
            - the filename is generated for the document
        THEN:
            - the generated filename uses the defined storage path for the document
        ",31,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dynamic_path(self):
        
        doc = Document.objects.create(
            title=""does not matter"",
            created=timezone.make_aware(datetime.datetime(2020, 6, 25, 7, 36, 51, 153)),
            mime_type=""application/pdf"",
            pk=2,
            checksum=""2"",
            storage_path=StoragePath.objects.create(path=""TestFolder/{created}""),
        )
        self.assertEqual(generate_filename(doc), ""TestFolder/2020-06-25.pdf"")

        ```
    ###Assistant : 
        GIVEN:
            - A document with a defined storage path
        WHEN:
            - the filename is generated for the document
        THEN:
            - the generated filename uses the defined storage path for the document
        
    "
1516,"async def follower_loop(self):
        
        try:
            await self._connect_to_leaders()
        except Exception as e:
            logger.error(""Exception occurred in follower loop: "")
            logger.exception(e)
","
        Main follower coroutine

        This starts all of the leader connection coros
        ",11,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def follower_loop(self):
        
        try:
            await self._connect_to_leaders()
        except Exception as e:
            logger.error(""Exception occurred in follower loop: "")
            logger.exception(e)

        ```
    ###Assistant : 
        Main follower coroutine

        This starts all of the leader connection coros
        
    "
1517,"def test_launcher_ensures_stdio(self):
        from kitty.constants import kitty_exe
        import subprocess
        exe = kitty_exe()
        cp = subprocess.run([exe, '+runpy', ])
        self.assertEqual(cp.returncode, 0)

","\
import os, sys
if sys.stdin:
    os.close(sys.stdin.fileno())
if sys.stdout:
    os.close(sys.stdout.fileno())
if sys.stderr:
    os.close(sys.stderr.fileno())
os.execlp('kitty', 'kitty', '+runpy', 'import sys; raise SystemExit(1 if sys.stdout is None or sys.stdin is None or sys.stderr is None else 0)')
",34,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_launcher_ensures_stdio(self):
        from kitty.constants import kitty_exe
        import subprocess
        exe = kitty_exe()
        cp = subprocess.run([exe, '+runpy', ])
        self.assertEqual(cp.returncode, 0)


        ```
    ###Assistant : \
import os, sys
if sys.stdin:
    os.close(sys.stdin.fileno())
if sys.stdout:
    os.close(sys.stdout.fileno())
if sys.stderr:
    os.close(sys.stderr.fileno())
os.execlp('kitty', 'kitty', '+runpy', 'import sys; raise SystemExit(1 if sys.stdout is None or sys.stdin is None or sys.stderr is None else 0)')

    "
1518,"def test_interface_label_count_mismatch(self):
        
        bad_interface_data = {
            'device': self.device.pk,
            'name': 'eth[0-9]',
            'label': 'Interface[0-1]',
            'type': InterfaceTypeChoices.TYPE_1GE_GBIC,
        }
        form = InterfaceCreateForm(bad_interface_data)

        self.assertFalse(form.is_valid())
        self.assertIn('label', form.errors)
","
        Check that attempting to generate a differing number of names and labels results in a validation error.
        ",17,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_interface_label_count_mismatch(self):
        
        bad_interface_data = {
            'device': self.device.pk,
            'name': 'eth[0-9]',
            'label': 'Interface[0-1]',
            'type': InterfaceTypeChoices.TYPE_1GE_GBIC,
        }
        form = InterfaceCreateForm(bad_interface_data)

        self.assertFalse(form.is_valid())
        self.assertIn('label', form.errors)

        ```
    ###Assistant : 
        Check that attempting to generate a differing number of names and labels results in a validation error.
        
    "
1519,"def power_transform(X, method=""yeo-johnson"", *, standardize=True, copy=True):
    
    pt = PowerTransformer(method=method, standardize=standardize, copy=copy)
    return pt.fit_transform(X)
","Parametric, monotonic transformation to make data more Gaussian-like.

    Power transforms are a family of parametric, monotonic transformations
    that are applied to make data more Gaussian-like. This is useful for
    modeling issues related to heteroscedasticity (non-constant variance),
    or other situations where normality is desired.

    Currently, power_transform supports the Box-Cox transform and the
    Yeo-Johnson transform. The optimal parameter for stabilizing variance and
    minimizing skewness is estimated through maximum likelihood.

    Box-Cox requires input data to be strictly positive, while Yeo-Johnson
    supports both positive or negative data.

    By default, zero-mean, unit-variance normalization is applied to the
    transformed data.

    Read more in the :ref:`User Guide <preprocessing_transformer>`.

    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        The data to be transformed using a power transformation.

    method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson'
        The power transform method. Available methods are:

        - 'yeo-johnson' [1]_, works with positive and negative values
        - 'box-cox' [2]_, only works with strictly positive values

        .. versionchanged:: 0.23
            The default value of the `method` parameter changed from
            'box-cox' to 'yeo-johnson' in 0.23.

    standardize : bool, default=True
        Set to True to apply zero-mean, unit-variance normalization to the
        transformed output.

    copy : bool, default=True
        Set to False to perform inplace computation during transformation.

    Returns
    -------
    X_trans : ndarray of shape (n_samples, n_features)
        The transformed data.

    See Also
    --------
    PowerTransformer : Equivalent transformation with the
        Transformer API (e.g. as part of a preprocessing
        :class:`~sklearn.pipeline.Pipeline`).

    quantile_transform : Maps data to a standard normal distribution with
        the parameter `output_distribution='normal'`.

    Notes
    -----
    NaNs are treated as missing values: disregarded in ``fit``, and maintained
    in ``transform``.

    For a comparison of the different scalers, transformers, and normalizers,
    see :ref:`examples/preprocessing/plot_all_scaling.py
    <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.

    References
    ----------

    .. [1] I.K. Yeo and R.A. Johnson, ""A new family of power transformations to
           improve normality or symmetry."" Biometrika, 87(4), pp.954-959,
           (2000).

    .. [2] G.E.P. Box and D.R. Cox, ""An Analysis of Transformations"", Journal
           of the Royal Statistical Society B, 26, 211-252 (1964).

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.preprocessing import power_transform
    >>> data = [[1, 2], [3, 2], [4, 5]]
    >>> print(power_transform(data, method='box-cox'))
    [[-1.332... -0.707...]
     [ 0.256... -0.707...]
     [ 1.076...  1.414...]]

    .. warning:: Risk of data leak.
        Do not use :func:`~sklearn.preprocessing.power_transform` unless you
        know what you are doing. A common mistake is to apply it to the entire
        data *before* splitting into training and test sets. This will bias the
        model evaluation because information would have leaked from the test
        set to the training set.
        In general, we recommend using
        :class:`~sklearn.preprocessing.PowerTransformer` within a
        :ref:`Pipeline <pipeline>` in order to prevent most risks of data
        leaking, e.g.: `pipe = make_pipeline(PowerTransformer(),
        LogisticRegression())`.
    ",421,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def power_transform(X, method=""yeo-johnson"", *, standardize=True, copy=True):
    
    pt = PowerTransformer(method=method, standardize=standardize, copy=copy)
    return pt.fit_transform(X)

        ```
    ###Assistant : Parametric, monotonic transformation to make data more Gaussian-like.

    Power transforms are a family of parametric, monotonic transformations
    that are applied to make data more Gaussian-like. This is useful for
    modeling issues related to heteroscedasticity (non-constant variance),
    or other situations where normality is desired.

    Currently, power_transform supports the Box-Cox transform and the
    Yeo-Johnson transform. The optimal parameter for stabilizing variance and
    minimizing skewness is estimated through maximum likelihood.

    Box-Cox requires input data to be strictly positive, while Yeo-Johnson
    supports both positive or negative data.

    By default, zero-mean, unit-variance normalization is applied to the
    transformed data.

    Read more in the :ref:`User Guide <preprocessing_transformer>`.

    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        The data to be transformed using a power transformation.

    method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson'
        The power transform method. Available methods are:

        - 'yeo-johnson' [1]_, works with positive and negative values
        - 'box-cox' [2]_, only works with strictly positive values

        .. versionchanged:: 0.23
            The default value of the `method` parameter changed from
            'box-cox' to 'yeo-johnson' in 0.23.

    standardize : bool, default=True
        Set to True to apply zero-mean, unit-variance normalization to the
        transformed output.

    copy : bool, default=True
        Set to False to perform inplace computation during transformation.

    Returns
    -------
    X_trans : ndarray of shape (n_samples, n_features)
        The transformed data.

    See Also
    --------
    PowerTransformer : Equivalent transformation with the
        Transformer API (e.g. as part of a preprocessing
        :class:`~sklearn.pipeline.Pipeline`).

    quantile_transform : Maps data to a standard normal distribution with
        the parameter `output_distribution='normal'`.

    Notes
    -----
    NaNs are treated as missing values: disregarded in ``fit``, and maintained
    in ``transform``.

    For a comparison of the different scalers, transformers, and normalizers,
    see :ref:`examples/preprocessing/plot_all_scaling.py
    <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.

    References
    ----------

    .. [1] I.K. Yeo and R.A. Johnson, ""A new family of power transformations to
           improve normality or symmetry."" Biometrika, 87(4), pp.954-959,
           (2000).

    .. [2] G.E.P. Box and D.R. Cox, ""An Analysis of Transformations"", Journal
           of the Royal Statistical Society B, 26, 211-252 (1964).

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.preprocessing import power_transform
    >>> data = [[1, 2], [3, 2], [4, 5]]
    >>> print(power_transform(data, method='box-cox'))
    [[-1.332... -0.707...]
     [ 0.256... -0.707...]
     [ 1.076...  1.414...]]

    .. warning:: Risk of data leak.
        Do not use :func:`~sklearn.preprocessing.power_transform` unless you
        know what you are doing. A common mistake is to apply it to the entire
        data *before* splitting into training and test sets. This will bias the
        model evaluation because information would have leaked from the test
        set to the training set.
        In general, we recommend using
        :class:`~sklearn.preprocessing.PowerTransformer` within a
        :ref:`Pipeline <pipeline>` in order to prevent most risks of data
        leaking, e.g.: `pipe = make_pipeline(PowerTransformer(),
        LogisticRegression())`.
    
    "
1520,"def desargues_graph(create_using=None):
    
    G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
    G.name = ""Desargues Graph""
    return G

","
    Returns the Desargues Graph

    The Desargues Graph is a non-planar, distance-transitive cubic graph
    with 20 nodes and 30 edges [1]_.
    It is a symmetric graph. It can be represented in LCF notation
    as [5,-5,9,-9]^5 [2]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Desargues Graph with 20 nodes and 30 edges

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Desargues_graph
    .. [2] https://mathworld.wolfram.com/DesarguesGraph.html
    ",77,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def desargues_graph(create_using=None):
    
    G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
    G.name = ""Desargues Graph""
    return G


        ```
    ###Assistant : 
    Returns the Desargues Graph

    The Desargues Graph is a non-planar, distance-transitive cubic graph
    with 20 nodes and 30 edges [1]_.
    It is a symmetric graph. It can be represented in LCF notation
    as [5,-5,9,-9]^5 [2]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Desargues Graph with 20 nodes and 30 edges

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Desargues_graph
    .. [2] https://mathworld.wolfram.com/DesarguesGraph.html
    
    "
1521,"def get_output_feature_jsonschema():
    
    output_feature_types = sorted(list(output_type_registry.keys()))
    return {
        ""type"": ""array"",
        ""items"": {
            ""type"": ""object"",
            ""properties"": {
                ""name"": {""type"": ""string""},
                ""type"": {""type"": ""string"", ""enum"": output_feature_types},
                ""column"": {""type"": ""string""},
            },
            ""additionalProperties"": True,
            ""allOf"": get_output_feature_conds(),
            ""required"": [""name"", ""type""],
        },
    }

","This function returns a JSON schema structured to only requires a `type` key and then conditionally applies
    a corresponding output feature's field constraints.

    Returns: JSON Schema
    ",26,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_output_feature_jsonschema():
    
    output_feature_types = sorted(list(output_type_registry.keys()))
    return {
        ""type"": ""array"",
        ""items"": {
            ""type"": ""object"",
            ""properties"": {
                ""name"": {""type"": ""string""},
                ""type"": {""type"": ""string"", ""enum"": output_feature_types},
                ""column"": {""type"": ""string""},
            },
            ""additionalProperties"": True,
            ""allOf"": get_output_feature_conds(),
            ""required"": [""name"", ""type""],
        },
    }


        ```
    ###Assistant : This function returns a JSON schema structured to only requires a `type` key and then conditionally applies
    a corresponding output feature's field constraints.

    Returns: JSON Schema
    
    "
1522,"def greet(str):
    return str


with gr.Blocks() as demo:
    
    with gr.Row():
            text1 = gr.component(""textarea"")
            text2 = gr.TextArea()
            text3 = gr.templates.TextArea()
    text1.change(greet, text1, text2)
    text2.change(greet, text2, text3)
    text3.change(greet, text3, text1)
    demo.launch()
","
    You can make use of str shortcuts you use in Interface within Blocks as well.
    
    Interface shortcut example:
    Interface(greet, ""textarea"", ""textarea"")
    
    You can use 
    1. gr.component()
    2. gr.templates.Template()
    3. gr.Template()
    All the templates are listed in gradio/templates.py
    ",37,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def greet(str):
    return str


with gr.Blocks() as demo:
    
    with gr.Row():
            text1 = gr.component(""textarea"")
            text2 = gr.TextArea()
            text3 = gr.templates.TextArea()
    text1.change(greet, text1, text2)
    text2.change(greet, text2, text3)
    text3.change(greet, text3, text1)
    demo.launch()

        ```
    ###Assistant : 
    You can make use of str shortcuts you use in Interface within Blocks as well.
    
    Interface shortcut example:
    Interface(greet, ""textarea"", ""textarea"")
    
    You can use 
    1. gr.component()
    2. gr.templates.Template()
    3. gr.Template()
    All the templates are listed in gradio/templates.py
    
    "
1523,"def _impute_values(self, features):
        
        if self.verbosity > 1:
            print(""Imputing missing values in feature set"")

        if self._fitted_imputer is None:
            self._fitted_imputer = SimpleImputer(strategy=""median"")
            self._fitted_imputer.fit(features)

        return self._fitted_imputer.transform(features)
","Impute missing values in a feature set.

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            A feature matrix

        Returns
        -------
        array-like {n_samples, n_features}
        ",21,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _impute_values(self, features):
        
        if self.verbosity > 1:
            print(""Imputing missing values in feature set"")

        if self._fitted_imputer is None:
            self._fitted_imputer = SimpleImputer(strategy=""median"")
            self._fitted_imputer.fit(features)

        return self._fitted_imputer.transform(features)

        ```
    ###Assistant : Impute missing values in a feature set.

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            A feature matrix

        Returns
        -------
        array-like {n_samples, n_features}
        
    "
1524,"def get_current_site(request):
    
    # Import is inside the function because its point is to avoid importing the
    # Site models when django.contrib.sites isn't installed.
    if apps.is_installed(""django.contrib.sites""):
        from .models import Site

        return Site.objects.get_current(request)
    else:
        return RequestSite(request)
","
    Check if contrib.sites is installed and return either the current
    ``Site`` object or a ``RequestSite`` object based on the request.
    ",20,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_current_site(request):
    
    # Import is inside the function because its point is to avoid importing the
    # Site models when django.contrib.sites isn't installed.
    if apps.is_installed(""django.contrib.sites""):
        from .models import Site

        return Site.objects.get_current(request)
    else:
        return RequestSite(request)

        ```
    ###Assistant : 
    Check if contrib.sites is installed and return either the current
    ``Site`` object or a ``RequestSite`` object based on the request.
    
    "
1525,"def get_scripts(use_names=False):
    
    scripts = OrderedDict()
    # Iterate through all modules within the scripts path. These are the user-created files in which reports are
    # defined.
    for importer, module_name, _ in pkgutil.iter_modules([settings.SCRIPTS_ROOT]):
        # Remove cached module to ensure consistency with filesystem
        if module_name in sys.modules:
            del sys.modules[module_name]

        module = importer.find_module(module_name).load_module(module_name)
        if use_names and hasattr(module, 'name'):
            module_name = module.name
        module_scripts = OrderedDict()
        script_order = getattr(module, ""script_order"", ())
        ordered_scripts = [cls for cls in script_order if is_script(cls)]
        unordered_scripts = [cls for _, cls in inspect.getmembers(module, is_script) if cls not in script_order]
        for cls in [*ordered_scripts, *unordered_scripts]:
            module_scripts[cls.__name__] = cls
        if module_scripts:
            scripts[module_name] = module_scripts

    return scripts

","
    Return a dict of dicts mapping all scripts to their modules. Set use_names to True to use each module's human-
    defined name in place of the actual module name.
    ",29,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_scripts(use_names=False):
    
    scripts = OrderedDict()
    # Iterate through all modules within the scripts path. These are the user-created files in which reports are
    # defined.
    for importer, module_name, _ in pkgutil.iter_modules([settings.SCRIPTS_ROOT]):
        # Remove cached module to ensure consistency with filesystem
        if module_name in sys.modules:
            del sys.modules[module_name]

        module = importer.find_module(module_name).load_module(module_name)
        if use_names and hasattr(module, 'name'):
            module_name = module.name
        module_scripts = OrderedDict()
        script_order = getattr(module, ""script_order"", ())
        ordered_scripts = [cls for cls in script_order if is_script(cls)]
        unordered_scripts = [cls for _, cls in inspect.getmembers(module, is_script) if cls not in script_order]
        for cls in [*ordered_scripts, *unordered_scripts]:
            module_scripts[cls.__name__] = cls
        if module_scripts:
            scripts[module_name] = module_scripts

    return scripts


        ```
    ###Assistant : 
    Return a dict of dicts mapping all scripts to their modules. Set use_names to True to use each module's human-
    defined name in place of the actual module name.
    
    "
1526,"def render(self, template_name, extra_context=None):
        
        if extra_context is None:
            extra_context = {}
        elif not isinstance(extra_context, dict):
            raise TypeError(""extra_context must be a dictionary"")

        return get_template(template_name).render({**self.context, **extra_context})
","
        Convenience method for rendering the specified Django template using the default context data. An additional
        context dictionary may be passed as `extra_context`.
        ",22,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def render(self, template_name, extra_context=None):
        
        if extra_context is None:
            extra_context = {}
        elif not isinstance(extra_context, dict):
            raise TypeError(""extra_context must be a dictionary"")

        return get_template(template_name).render({**self.context, **extra_context})

        ```
    ###Assistant : 
        Convenience method for rendering the specified Django template using the default context data. An additional
        context dictionary may be passed as `extra_context`.
        
    "
1527,"def preprocess_input(x, data_format=None):
    
    return x


@keras_export(""keras.applications.mobilenet_v3.decode_predictions"")","A placeholder method for backward compatibility.

    The preprocessing logic has been included in the mobilenet_v3 model
    implementation. Users are no longer required to call this method to
    normalize the input data. This method does nothing and only kept as a
    placeholder to align the API surface between old and new version of model.

    Args:
      x: A floating point `numpy.array` or a `tf.Tensor`.
      data_format: Optional data format of the image tensor/array. Defaults to
        None, in which case the global setting
        `tf.keras.backend.image_data_format()` is used (unless you changed it,
        it defaults to ""channels_last"").{mode}

    Returns:
      Unchanged `numpy.array` or `tf.Tensor`.
    ",95,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def preprocess_input(x, data_format=None):
    
    return x


@keras_export(""keras.applications.mobilenet_v3.decode_predictions"")
        ```
    ###Assistant : A placeholder method for backward compatibility.

    The preprocessing logic has been included in the mobilenet_v3 model
    implementation. Users are no longer required to call this method to
    normalize the input data. This method does nothing and only kept as a
    placeholder to align the API surface between old and new version of model.

    Args:
      x: A floating point `numpy.array` or a `tf.Tensor`.
      data_format: Optional data format of the image tensor/array. Defaults to
        None, in which case the global setting
        `tf.keras.backend.image_data_format()` is used (unless you changed it,
        it defaults to ""channels_last"").{mode}

    Returns:
      Unchanged `numpy.array` or `tf.Tensor`.
    
    "
1528,"def message_level_tag(message):
    
    return MESSAGE_TAGS.get(message.level)


@register.simple_tag","
    Return the tag for this message's level as defined in
    django.contrib.messages.constants.DEFAULT_TAGS, ignoring the project-level
    MESSAGE_TAGS setting (which end-users might customise).
    ",20,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def message_level_tag(message):
    
    return MESSAGE_TAGS.get(message.level)


@register.simple_tag
        ```
    ###Assistant : 
    Return the tag for this message's level as defined in
    django.contrib.messages.constants.DEFAULT_TAGS, ignoring the project-level
    MESSAGE_TAGS setting (which end-users might customise).
    
    "
1529,"def _check_reg_targets(y_true, y_pred, multioutput, dtype=""numeric""):
    
    check_consistent_length(y_true, y_pred)
    y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
    y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)

    if y_true.ndim == 1:
        y_true = y_true.reshape((-1, 1))

    if y_pred.ndim == 1:
        y_pred = y_pred.reshape((-1, 1))

    if y_true.shape[1] != y_pred.shape[1]:
        raise ValueError(
            ""y_true and y_pred have different number of output ({0}!={1})"".format(
                y_true.shape[1], y_pred.shape[1]
            )
        )

    n_outputs = y_true.shape[1]
    allowed_multioutput_str = (""raw_values"", ""uniform_average"", ""variance_weighted"")
    if isinstance(multioutput, str):
        if multioutput not in allowed_multioutput_str:
            raise ValueError(
                ""Allowed 'multioutput' string values are {}. ""
                ""You provided multioutput={!r}"".format(
                    allowed_multioutput_str, multioutput
                )
            )
    elif multioutput is not None:
        multioutput = check_array(multioutput, ensure_2d=False)
        if n_outputs == 1:
            raise ValueError(""Custom weights are useful only in multi-output cases."")
        elif n_outputs != len(multioutput):
            raise ValueError(
                ""There must be equally many custom weights (%d) as outputs (%d).""
                % (len(multioutput), n_outputs)
            )
    y_type = ""continuous"" if n_outputs == 1 else ""continuous-multioutput""

    return y_type, y_true, y_pred, multioutput

","Check that y_true and y_pred belong to the same regression task.

    Parameters
    ----------
    y_true : array-like

    y_pred : array-like

    multioutput : array-like or string in ['raw_values', uniform_average',
        'variance_weighted'] or None
        None is accepted due to backward compatibility of r2_score().

    dtype : str or list, default=""numeric""
        the dtype argument passed to check_array.

    Returns
    -------
    type_true : one of {'continuous', continuous-multioutput'}
        The type of the true target data, as output by
        'utils.multiclass.type_of_target'.

    y_true : array-like of shape (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape (n_samples, n_outputs)
        Estimated target values.

    multioutput : array-like of shape (n_outputs) or string in ['raw_values',
        uniform_average', 'variance_weighted'] or None
        Custom output weights if ``multioutput`` is array-like or
        just the corresponding argument if ``multioutput`` is a
        correct keyword.
    ",124,141,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_reg_targets(y_true, y_pred, multioutput, dtype=""numeric""):
    
    check_consistent_length(y_true, y_pred)
    y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
    y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)

    if y_true.ndim == 1:
        y_true = y_true.reshape((-1, 1))

    if y_pred.ndim == 1:
        y_pred = y_pred.reshape((-1, 1))

    if y_true.shape[1] != y_pred.shape[1]:
        raise ValueError(
            ""y_true and y_pred have different number of output ({0}!={1})"".format(
                y_true.shape[1], y_pred.shape[1]
            )
        )

    n_outputs = y_true.shape[1]
    allowed_multioutput_str = (""raw_values"", ""uniform_average"", ""variance_weighted"")
    if isinstance(multioutput, str):
        if multioutput not in allowed_multioutput_str:
            raise ValueError(
                ""Allowed 'multioutput' string values are {}. ""
                ""You provided multioutput={!r}"".format(
                    allowed_multioutput_str, multioutput
                )
            )
    elif multioutput is not None:
        multioutput = check_array(multioutput, ensure_2d=False)
        if n_outputs == 1:
            raise ValueError(""Custom weights are useful only in multi-output cases."")
        elif n_outputs != len(multioutput):
            raise ValueError(
                ""There must be equally many custom weights (%d) as outputs (%d).""
                % (len(multioutput), n_outputs)
            )
    y_type = ""continuous"" if n_outputs == 1 else ""continuous-multioutput""

    return y_type, y_true, y_pred, multioutput


        ```
    ###Assistant : Check that y_true and y_pred belong to the same regression task.

    Parameters
    ----------
    y_true : array-like

    y_pred : array-like

    multioutput : array-like or string in ['raw_values', uniform_average',
        'variance_weighted'] or None
        None is accepted due to backward compatibility of r2_score().

    dtype : str or list, default=""numeric""
        the dtype argument passed to check_array.

    Returns
    -------
    type_true : one of {'continuous', continuous-multioutput'}
        The type of the true target data, as output by
        'utils.multiclass.type_of_target'.

    y_true : array-like of shape (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape (n_samples, n_outputs)
        Estimated target values.

    multioutput : array-like of shape (n_outputs) or string in ['raw_values',
        uniform_average', 'variance_weighted'] or None
        Custom output weights if ``multioutput`` is array-like or
        just the corresponding argument if ``multioutput`` is a
        correct keyword.
    
    "
1530,"def bind(self, *args, **kwargs) -> Union[ClassNode, FunctionNode]:
        

        copied_self = copy(self)
        copied_self._func_or_class = ""dummpy.module""
        schema_shell = deployment_to_schema(copied_self)

        if inspect.isfunction(self._func_or_class):
            return FunctionNode(
                self._func_or_class,
                args,  # Used to bind and resolve DAG only, can take user input
                kwargs,  # Used to bind and resolve DAG only, can take user input
                self._ray_actor_options or dict(),
                other_args_to_resolve={
                    ""deployment_schema"": schema_shell,
                    ""is_from_serve_deployment"": True,
                },
            )
        else:
            return ClassNode(
                self._func_or_class,
                args,
                kwargs,
                cls_options=self._ray_actor_options or dict(),
                other_args_to_resolve={
                    ""deployment_schema"": schema_shell,
                    ""is_from_serve_deployment"": True,
                },
            )
","Bind the provided arguments and return a class or function node.

        The returned bound deployment can be deployed or bound to other
        deployments to create a deployment graph.
        ",28,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bind(self, *args, **kwargs) -> Union[ClassNode, FunctionNode]:
        

        copied_self = copy(self)
        copied_self._func_or_class = ""dummpy.module""
        schema_shell = deployment_to_schema(copied_self)

        if inspect.isfunction(self._func_or_class):
            return FunctionNode(
                self._func_or_class,
                args,  # Used to bind and resolve DAG only, can take user input
                kwargs,  # Used to bind and resolve DAG only, can take user input
                self._ray_actor_options or dict(),
                other_args_to_resolve={
                    ""deployment_schema"": schema_shell,
                    ""is_from_serve_deployment"": True,
                },
            )
        else:
            return ClassNode(
                self._func_or_class,
                args,
                kwargs,
                cls_options=self._ray_actor_options or dict(),
                other_args_to_resolve={
                    ""deployment_schema"": schema_shell,
                    ""is_from_serve_deployment"": True,
                },
            )

        ```
    ###Assistant : Bind the provided arguments and return a class or function node.

        The returned bound deployment can be deployed or bound to other
        deployments to create a deployment graph.
        
    "
1531,"def _obtain_mask(cls, detected_face, mask_type):
        
        mask = detected_face.mask.get(mask_type)
        if not mask:
            return None
        if mask.stored_centering != ""face"":
            face = AlignedFace(detected_face.landmarks_xy)
            mask.set_sub_crop(face.pose.offset[mask.stored_centering],
                              face.pose.offset[""face""],
                              centering=""face"")
        return mask.mask.squeeze()
"," Obtain the mask for the correct ""face"" centering that is used in the thumbnail display.

        Parameters
        -----------
        detected_face: :class:`lib.align.DetectedFace`
            The Detected Face object to obtain the mask for
        mask_type: str
            The type of mask to obtain

        Returns
        -------
        :class:`numpy.ndarray` or ``None``
            The single channel mask of requested mask type, if it exists, otherwise ``None``
        ",54,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _obtain_mask(cls, detected_face, mask_type):
        
        mask = detected_face.mask.get(mask_type)
        if not mask:
            return None
        if mask.stored_centering != ""face"":
            face = AlignedFace(detected_face.landmarks_xy)
            mask.set_sub_crop(face.pose.offset[mask.stored_centering],
                              face.pose.offset[""face""],
                              centering=""face"")
        return mask.mask.squeeze()

        ```
    ###Assistant :  Obtain the mask for the correct ""face"" centering that is used in the thumbnail display.

        Parameters
        -----------
        detected_face: :class:`lib.align.DetectedFace`
            The Detected Face object to obtain the mask for
        mask_type: str
            The type of mask to obtain

        Returns
        -------
        :class:`numpy.ndarray` or ``None``
            The single channel mask of requested mask type, if it exists, otherwise ``None``
        
    "
1532,"def forward(self, input, mask=None):
        
        forward_input, backward_input = paddle.chunk(input, chunks=2, axis=2)
        # elementwise-sum forward_x and backward_x
        # Shape: (batch_size, max_seq_len, hidden_size)
        h = paddle.add_n([forward_input, backward_input])
        # Shape: (batch_size, hidden_size, 1)
        att_weight = self.att_weight.tile(
            repeat_times=(paddle.shape(h)[0], 1, 1))
        # Shape: (batch_size, max_seq_len, 1)
        att_score = paddle.bmm(paddle.tanh(h), att_weight)
        if mask is not None:
            # mask, remove the effect of 'PAD'
            mask = paddle.cast(mask, dtype='float32')
            mask = mask.unsqueeze(axis=-1)
            inf_tensor = paddle.full(
                shape=mask.shape, dtype='float32', fill_value=-INF)
            att_score = paddle.multiply(att_score, mask) + paddle.multiply(
                inf_tensor, (1 - mask))
        # Shape: (batch_size, max_seq_len, 1)
        att_weight = F.softmax(att_score, axis=1)
        # Shape: (batch_size, lstm_hidden_size)
        reps = paddle.bmm(h.transpose(perm=(0, 2, 1)),
                          att_weight).squeeze(axis=-1)
        reps = paddle.tanh(reps)
        return reps, att_weight

","
        Args:
            input (paddle.Tensor) of shape (batch, seq_len, input_size): Tensor containing the features of the input sequence.
            mask (paddle.Tensor) of shape (batch, seq_len) :
                Tensor is a bool tensor, whose each element identifies whether the input word id is pad token or not. 
                Defaults to `None`.
        ",45,104,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, input, mask=None):
        
        forward_input, backward_input = paddle.chunk(input, chunks=2, axis=2)
        # elementwise-sum forward_x and backward_x
        # Shape: (batch_size, max_seq_len, hidden_size)
        h = paddle.add_n([forward_input, backward_input])
        # Shape: (batch_size, hidden_size, 1)
        att_weight = self.att_weight.tile(
            repeat_times=(paddle.shape(h)[0], 1, 1))
        # Shape: (batch_size, max_seq_len, 1)
        att_score = paddle.bmm(paddle.tanh(h), att_weight)
        if mask is not None:
            # mask, remove the effect of 'PAD'
            mask = paddle.cast(mask, dtype='float32')
            mask = mask.unsqueeze(axis=-1)
            inf_tensor = paddle.full(
                shape=mask.shape, dtype='float32', fill_value=-INF)
            att_score = paddle.multiply(att_score, mask) + paddle.multiply(
                inf_tensor, (1 - mask))
        # Shape: (batch_size, max_seq_len, 1)
        att_weight = F.softmax(att_score, axis=1)
        # Shape: (batch_size, lstm_hidden_size)
        reps = paddle.bmm(h.transpose(perm=(0, 2, 1)),
                          att_weight).squeeze(axis=-1)
        reps = paddle.tanh(reps)
        return reps, att_weight


        ```
    ###Assistant : 
        Args:
            input (paddle.Tensor) of shape (batch, seq_len, input_size): Tensor containing the features of the input sequence.
            mask (paddle.Tensor) of shape (batch, seq_len) :
                Tensor is a bool tensor, whose each element identifies whether the input word id is pad token or not. 
                Defaults to `None`.
        
    "
1533,"def simple_test(self, feats, img_metas, **kwargs):
        
        all_cls_scores, all_mask_preds = self(feats, img_metas)
        mask_cls_results = all_cls_scores[-1]
        mask_pred_results = all_mask_preds[-1]

        # upsample masks
        img_shape = img_metas[0]['batch_input_shape']
        mask_pred_results = F.interpolate(
            mask_pred_results,
            size=(img_shape[0], img_shape[1]),
            mode='bilinear',
            align_corners=False)

        return mask_cls_results, mask_pred_results
","Test without augmentaton.

        Args:
            feats (list[Tensor]): Multi-level features from the
                upstream network, each is a 4D-tensor.
            img_metas (list[dict]): List of image information.

        Returns:
            tuple: A tuple contains two tensors.

            - mask_cls_results (Tensor): Mask classification logits,\
                shape (batch_size, num_queries, cls_out_channels).
                Note `cls_out_channels` should includes background.
            - mask_pred_results (Tensor): Mask logits, shape \
                (batch_size, num_queries, h, w).
        ",55,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def simple_test(self, feats, img_metas, **kwargs):
        
        all_cls_scores, all_mask_preds = self(feats, img_metas)
        mask_cls_results = all_cls_scores[-1]
        mask_pred_results = all_mask_preds[-1]

        # upsample masks
        img_shape = img_metas[0]['batch_input_shape']
        mask_pred_results = F.interpolate(
            mask_pred_results,
            size=(img_shape[0], img_shape[1]),
            mode='bilinear',
            align_corners=False)

        return mask_cls_results, mask_pred_results

        ```
    ###Assistant : Test without augmentaton.

        Args:
            feats (list[Tensor]): Multi-level features from the
                upstream network, each is a 4D-tensor.
            img_metas (list[dict]): List of image information.

        Returns:
            tuple: A tuple contains two tensors.

            - mask_cls_results (Tensor): Mask classification logits,\
                shape (batch_size, num_queries, cls_out_channels).
                Note `cls_out_channels` should includes background.
            - mask_pred_results (Tensor): Mask logits, shape \
                (batch_size, num_queries, h, w).
        
    "
1534,"def _format_list(self, extracted_list):
        

        Colors = self.Colors
        list = []
        for ind, (filename, lineno, name, line) in enumerate(extracted_list):
            normalCol, nameCol, fileCol, lineCol = (
                # Emphasize the last entry
                (Colors.normalEm, Colors.nameEm, Colors.filenameEm, Colors.line)
                if ind == len(extracted_list) - 1
                else (Colors.Normal, Colors.name, Colors.filename, """")
            )

            fns = _format_filename(filename, fileCol, normalCol, lineno=lineno)
            item = f""{normalCol}  {fns}""

            if name != ""<module>"":
                item += f"" in {nameCol}{name}{normalCol}\n""
            else:
                item += ""\n""
            if line:
                item += f""{lineCol}    {line.strip()}{normalCol}\n""
            list.append(item)

        return list
","Format a list of traceback entry tuples for printing.

        Given a list of tuples as returned by extract_tb() or
        extract_stack(), return a list of strings ready for printing.
        Each string in the resulting list corresponds to the item with the
        same index in the argument list.  Each string ends in a newline;
        the strings may contain internal newlines as well, for those items
        whose source text line is not None.

        Lifted almost verbatim from traceback.py
        ",75,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _format_list(self, extracted_list):
        

        Colors = self.Colors
        list = []
        for ind, (filename, lineno, name, line) in enumerate(extracted_list):
            normalCol, nameCol, fileCol, lineCol = (
                # Emphasize the last entry
                (Colors.normalEm, Colors.nameEm, Colors.filenameEm, Colors.line)
                if ind == len(extracted_list) - 1
                else (Colors.Normal, Colors.name, Colors.filename, """")
            )

            fns = _format_filename(filename, fileCol, normalCol, lineno=lineno)
            item = f""{normalCol}  {fns}""

            if name != ""<module>"":
                item += f"" in {nameCol}{name}{normalCol}\n""
            else:
                item += ""\n""
            if line:
                item += f""{lineCol}    {line.strip()}{normalCol}\n""
            list.append(item)

        return list

        ```
    ###Assistant : Format a list of traceback entry tuples for printing.

        Given a list of tuples as returned by extract_tb() or
        extract_stack(), return a list of strings ready for printing.
        Each string in the resulting list corresponds to the item with the
        same index in the argument list.  Each string ends in a newline;
        the strings may contain internal newlines as well, for those items
        whose source text line is not None.

        Lifted almost verbatim from traceback.py
        
    "
1535,"def euler_equations(L, funcs=(), vars=()):
    r

    funcs = tuple(funcs) if iterable(funcs) else (funcs,)

    if not funcs:
        funcs = tuple(L.atoms(Function))
    else:
        for f in funcs:
            if not isinstance(f, Function):
                raise TypeError('Function expected, got: %s' % f)

    vars = tuple(vars) if iterable(vars) else (vars,)

    if not vars:
        vars = funcs[0].args
    else:
        vars = tuple(sympify(var) for var in vars)

    if not all(isinstance(v, Symbol) for v in vars):
        raise TypeError('Variables are not symbols, got %s' % vars)

    for f in funcs:
        if not vars == f.args:
            raise ValueError(""Variables %s do not match args: %s"" % (vars, f))

    order = max([len(d.variables) for d in L.atoms(Derivative)
                        if d.expr in funcs] + [0])

    eqns = []
    for f in funcs:
        eq = diff(L, f)
        for i in range(1, order + 1):
            for p in combinations_with_replacement(vars, i):
                eq = eq + S.NegativeOne**i*diff(L, diff(f, *p), *p)
        new_eq = Eq(eq, 0)
        if isinstance(new_eq, Eq):
            eqns.append(new_eq)

    return eqns
","
    Find the Euler-Lagrange equations [1]_ for a given Lagrangian.

    Parameters
    ==========

    L : Expr
        The Lagrangian that should be a function of the functions listed
        in the second argument and their derivatives.

        For example, in the case of two functions `f(x,y)`, `g(x,y)` and
        two independent variables `x`, `y` the Lagrangian would have the form:

            .. math:: L\left(f(x,y),g(x,y),\frac{\partial f(x,y)}{\partial x},
                      \frac{\partial f(x,y)}{\partial y},
                      \frac{\partial g(x,y)}{\partial x},
                      \frac{\partial g(x,y)}{\partial y},x,y\right)

        In many cases it is not necessary to provide anything, except the
        Lagrangian, it will be auto-detected (and an error raised if this
        couldn't be done).

    funcs : Function or an iterable of Functions
        The functions that the Lagrangian depends on. The Euler equations
        are differential equations for each of these functions.

    vars : Symbol or an iterable of Symbols
        The Symbols that are the independent variables of the functions.

    Returns
    =======

    eqns : list of Eq
        The list of differential equations, one for each function.

    Examples
    ========

    >>> from sympy import euler_equations, Symbol, Function
    >>> x = Function('x')
    >>> t = Symbol('t')
    >>> L = (x(t).diff(t))**2/2 - x(t)**2/2
    >>> euler_equations(L, x(t), t)
    [Eq(-x(t) - Derivative(x(t), (t, 2)), 0)]
    >>> u = Function('u')
    >>> x = Symbol('x')
    >>> L = (u(t, x).diff(t))**2/2 - (u(t, x).diff(x))**2/2
    >>> euler_equations(L, u(t, x), [t, x])
    [Eq(-Derivative(u(t, x), (t, 2)) + Derivative(u(t, x), (x, 2)), 0)]

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Euler%E2%80%93Lagrange_equation

    ",224,146,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def euler_equations(L, funcs=(), vars=()):
    r

    funcs = tuple(funcs) if iterable(funcs) else (funcs,)

    if not funcs:
        funcs = tuple(L.atoms(Function))
    else:
        for f in funcs:
            if not isinstance(f, Function):
                raise TypeError('Function expected, got: %s' % f)

    vars = tuple(vars) if iterable(vars) else (vars,)

    if not vars:
        vars = funcs[0].args
    else:
        vars = tuple(sympify(var) for var in vars)

    if not all(isinstance(v, Symbol) for v in vars):
        raise TypeError('Variables are not symbols, got %s' % vars)

    for f in funcs:
        if not vars == f.args:
            raise ValueError(""Variables %s do not match args: %s"" % (vars, f))

    order = max([len(d.variables) for d in L.atoms(Derivative)
                        if d.expr in funcs] + [0])

    eqns = []
    for f in funcs:
        eq = diff(L, f)
        for i in range(1, order + 1):
            for p in combinations_with_replacement(vars, i):
                eq = eq + S.NegativeOne**i*diff(L, diff(f, *p), *p)
        new_eq = Eq(eq, 0)
        if isinstance(new_eq, Eq):
            eqns.append(new_eq)

    return eqns

        ```
    ###Assistant : 
    Find the Euler-Lagrange equations [1]_ for a given Lagrangian.

    Parameters
    ==========

    L : Expr
        The Lagrangian that should be a function of the functions listed
        in the second argument and their derivatives.

        For example, in the case of two functions `f(x,y)`, `g(x,y)` and
        two independent variables `x`, `y` the Lagrangian would have the form:

            .. math:: L\left(f(x,y),g(x,y),\frac{\partial f(x,y)}{\partial x},
                      \frac{\partial f(x,y)}{\partial y},
                      \frac{\partial g(x,y)}{\partial x},
                      \frac{\partial g(x,y)}{\partial y},x,y\right)

        In many cases it is not necessary to provide anything, except the
        Lagrangian, it will be auto-detected (and an error raised if this
        couldn't be done).

    funcs : Function or an iterable of Functions
        The functions that the Lagrangian depends on. The Euler equations
        are differential equations for each of these functions.

    vars : Symbol or an iterable of Symbols
        The Symbols that are the independent variables of the functions.

    Returns
    =======

    eqns : list of Eq
        The list of differential equations, one for each function.

    Examples
    ========

    >>> from sympy import euler_equations, Symbol, Function
    >>> x = Function('x')
    >>> t = Symbol('t')
    >>> L = (x(t).diff(t))**2/2 - x(t)**2/2
    >>> euler_equations(L, x(t), t)
    [Eq(-x(t) - Derivative(x(t), (t, 2)), 0)]
    >>> u = Function('u')
    >>> x = Symbol('x')
    >>> L = (u(t, x).diff(t))**2/2 - (u(t, x).diff(x))**2/2
    >>> euler_equations(L, u(t, x), [t, x])
    [Eq(-Derivative(u(t, x), (t, 2)) + Derivative(u(t, x), (x, 2)), 0)]

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Euler%E2%80%93Lagrange_equation

    
    "
1536,"def delay_update(self, skip_if_already_set=False, **kwargs):
        
        for key, value in kwargs.items():
            if key in self.extra_update_fields and skip_if_already_set:
                continue
            elif key in self.extra_update_fields and key in ('job_explanation', 'result_traceback'):
                if str(value) in self.extra_update_fields.get(key, ''):
                    continue  # if already set, avoid duplicating messages
                # In the case of these fields, we do not want to lose any prior information, so combine values
                self.extra_update_fields[key] = '\n'.join([str(self.extra_update_fields[key]), str(value)])
            else:
                self.extra_update_fields[key] = value
",Stash fields that should be saved along with the job status change,12,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delay_update(self, skip_if_already_set=False, **kwargs):
        
        for key, value in kwargs.items():
            if key in self.extra_update_fields and skip_if_already_set:
                continue
            elif key in self.extra_update_fields and key in ('job_explanation', 'result_traceback'):
                if str(value) in self.extra_update_fields.get(key, ''):
                    continue  # if already set, avoid duplicating messages
                # In the case of these fields, we do not want to lose any prior information, so combine values
                self.extra_update_fields[key] = '\n'.join([str(self.extra_update_fields[key]), str(value)])
            else:
                self.extra_update_fields[key] = value

        ```
    ###Assistant : Stash fields that should be saved along with the job status change
    "
1537,"def adjacent_tmp_file(path, **kwargs):
    # type: (str, **Any) -> Iterator[BinaryIO]
    
    with NamedTemporaryFile(
        delete=False,
        dir=os.path.dirname(path),
        prefix=os.path.basename(path),
        suffix="".tmp"",
        **kwargs,
    ) as f:
        result = cast(BinaryIO, f)
        try:
            yield result
        finally:
            result.flush()
            os.fsync(result.fileno())


# Tenacity raises RetryError by default, explictly raise the original exception
_replace_retry = retry(reraise=True, stop=stop_after_delay(1), wait=wait_fixed(0.25))

replace = _replace_retry(os.replace)


# test_writable_dir and _test_writable_dir_win are copied from Flit,
# with the author's agreement to also place them under pip's license.","Return a file-like object pointing to a tmp file next to path.

    The file is created securely and is ensured to be written to disk
    after the context reaches its end.

    kwargs will be passed to tempfile.NamedTemporaryFile to control
    the way the temporary file will be opened.
    ",47,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def adjacent_tmp_file(path, **kwargs):
    # type: (str, **Any) -> Iterator[BinaryIO]
    
    with NamedTemporaryFile(
        delete=False,
        dir=os.path.dirname(path),
        prefix=os.path.basename(path),
        suffix="".tmp"",
        **kwargs,
    ) as f:
        result = cast(BinaryIO, f)
        try:
            yield result
        finally:
            result.flush()
            os.fsync(result.fileno())


# Tenacity raises RetryError by default, explictly raise the original exception
_replace_retry = retry(reraise=True, stop=stop_after_delay(1), wait=wait_fixed(0.25))

replace = _replace_retry(os.replace)


# test_writable_dir and _test_writable_dir_win are copied from Flit,
# with the author's agreement to also place them under pip's license.
        ```
    ###Assistant : Return a file-like object pointing to a tmp file next to path.

    The file is created securely and is ensured to be written to disk
    after the context reaches its end.

    kwargs will be passed to tempfile.NamedTemporaryFile to control
    the way the temporary file will be opened.
    
    "
1538,"def _lsb_release_info(self):
        # type: () -> Dict[str, str]
        
        if not self.include_lsb:
            return {}
        with open(os.devnull, ""wb"") as devnull:
            try:
                cmd = (""lsb_release"", ""-a"")
                stdout = subprocess.check_output(cmd, stderr=devnull)
            # Command not found or lsb_release returned error
            except (OSError, subprocess.CalledProcessError):
                return {}
        content = self._to_str(stdout).splitlines()
        return self._parse_lsb_release_content(content)
","
        Get the information items from the lsb_release command output.

        Returns:
            A dictionary containing all information items.
        ",16,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _lsb_release_info(self):
        # type: () -> Dict[str, str]
        
        if not self.include_lsb:
            return {}
        with open(os.devnull, ""wb"") as devnull:
            try:
                cmd = (""lsb_release"", ""-a"")
                stdout = subprocess.check_output(cmd, stderr=devnull)
            # Command not found or lsb_release returned error
            except (OSError, subprocess.CalledProcessError):
                return {}
        content = self._to_str(stdout).splitlines()
        return self._parse_lsb_release_content(content)

        ```
    ###Assistant : 
        Get the information items from the lsb_release command output.

        Returns:
            A dictionary containing all information items.
        
    "
1539,"def as_dict(self) -> Dict[Text, Any]:
        
        serializable_graph_schema: Dict[Text, Dict[Text, Any]] = {""nodes"": {}}
        for node_name, node in self.nodes.items():
            serializable = dataclasses.asdict(node)

            # Classes are not JSON serializable (surprise)
            serializable[""uses""] = f""{node.uses.__module__}.{node.uses.__name__}""

            serializable_graph_schema[""nodes""][node_name] = serializable

        return serializable_graph_schema
","Returns graph schema in a serializable format.

        Returns:
            The graph schema in a format which can be dumped as JSON or other formats.
        ",23,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def as_dict(self) -> Dict[Text, Any]:
        
        serializable_graph_schema: Dict[Text, Dict[Text, Any]] = {""nodes"": {}}
        for node_name, node in self.nodes.items():
            serializable = dataclasses.asdict(node)

            # Classes are not JSON serializable (surprise)
            serializable[""uses""] = f""{node.uses.__module__}.{node.uses.__name__}""

            serializable_graph_schema[""nodes""][node_name] = serializable

        return serializable_graph_schema

        ```
    ###Assistant : Returns graph schema in a serializable format.

        Returns:
            The graph schema in a format which can be dumped as JSON or other formats.
        
    "
1540,"def _create_project_state(self, with_applied_migrations=False):
        
        state = ProjectState(real_apps=self.loader.unmigrated_apps)
        if with_applied_migrations:
            # Create the forwards plan Django would follow on an empty database
            full_plan = self.migration_plan(
                self.loader.graph.leaf_nodes(), clean_start=True
            )
            applied_migrations = {
                self.loader.graph.nodes[key]
                for key in self.loader.applied_migrations
                if key in self.loader.graph.nodes
            }
            for migration, _ in full_plan:
                if migration in applied_migrations:
                    migration.mutate_state(state, preserve=False)
        return state
","
        Create a project state including all the applications without
        migrations and applied migrations if with_applied_migrations=True.
        ",15,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_project_state(self, with_applied_migrations=False):
        
        state = ProjectState(real_apps=self.loader.unmigrated_apps)
        if with_applied_migrations:
            # Create the forwards plan Django would follow on an empty database
            full_plan = self.migration_plan(
                self.loader.graph.leaf_nodes(), clean_start=True
            )
            applied_migrations = {
                self.loader.graph.nodes[key]
                for key in self.loader.applied_migrations
                if key in self.loader.graph.nodes
            }
            for migration, _ in full_plan:
                if migration in applied_migrations:
                    migration.mutate_state(state, preserve=False)
        return state

        ```
    ###Assistant : 
        Create a project state including all the applications without
        migrations and applied migrations if with_applied_migrations=True.
        
    "
1541,"def revoke(state, task_id, terminate=False, signal=None, **kwargs):
    
    # pylint: disable=redefined-outer-name
    # XXX Note that this redefines `terminate`:
    #     Outside of this scope that is a function.
    # supports list argument since 3.1
    task_ids, task_id = set(maybe_list(task_id) or []), None
    task_ids = _revoke(state, task_ids, terminate, signal, **kwargs)
    return ok(f'tasks {task_ids} flagged as revoked')


@control_command(
    variadic='headers',
    signature='[key1=value1 [key2=value2 [... [keyN=valueN]]]]',
)","Revoke task by task id (or list of ids).

    Keyword Arguments:
        terminate (bool): Also terminate the process if the task is active.
        signal (str): Name of signal to use for terminate (e.g., ``KILL``).
    ",33,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def revoke(state, task_id, terminate=False, signal=None, **kwargs):
    
    # pylint: disable=redefined-outer-name
    # XXX Note that this redefines `terminate`:
    #     Outside of this scope that is a function.
    # supports list argument since 3.1
    task_ids, task_id = set(maybe_list(task_id) or []), None
    task_ids = _revoke(state, task_ids, terminate, signal, **kwargs)
    return ok(f'tasks {task_ids} flagged as revoked')


@control_command(
    variadic='headers',
    signature='[key1=value1 [key2=value2 [... [keyN=valueN]]]]',
)
        ```
    ###Assistant : Revoke task by task id (or list of ids).

    Keyword Arguments:
        terminate (bool): Also terminate the process if the task is active.
        signal (str): Name of signal to use for terminate (e.g., ``KILL``).
    
    "
1542,"def inplace_swap_row_csc(X, m, n):
    
    for t in [m, n]:
        if isinstance(t, np.ndarray):
            raise TypeError(""m and n should be valid integers"")

    if m < 0:
        m += X.shape[0]
    if n < 0:
        n += X.shape[0]

    m_mask = X.indices == m
    X.indices[X.indices == n] = m
    X.indices[m_mask] = n

","Swap two rows of a CSC matrix in-place.

    Parameters
    ----------
    X : sparse matrix of shape (n_samples, n_features)
        Matrix whose two rows are to be swapped. It should be of
        CSC format.

    m : int
        Index of the row of X to be swapped.

    n : int
        Index of the row of X to be swapped.
    ",56,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def inplace_swap_row_csc(X, m, n):
    
    for t in [m, n]:
        if isinstance(t, np.ndarray):
            raise TypeError(""m and n should be valid integers"")

    if m < 0:
        m += X.shape[0]
    if n < 0:
        n += X.shape[0]

    m_mask = X.indices == m
    X.indices[X.indices == n] = m
    X.indices[m_mask] = n


        ```
    ###Assistant : Swap two rows of a CSC matrix in-place.

    Parameters
    ----------
    X : sparse matrix of shape (n_samples, n_features)
        Matrix whose two rows are to be swapped. It should be of
        CSC format.

    m : int
        Index of the row of X to be swapped.

    n : int
        Index of the row of X to be swapped.
    
    "
1543,"def test_render_empty_table(self):
        
        block = TableBlock()
        result = block.render(
            {
                ""first_row_is_table_header"": False,
                ""first_col_is_header"": False,
                ""data"": [[None, None, None], [None, None, None], [None, None, None]],
            }
        )
        expected = 
        self.assertHTMLEqual(result, expected)
","
        An empty table should render okay.
        
            <table>
                <tbody>
                    <tr><td></td><td></td><td></td></tr>
                    <tr><td></td><td></td><td></td></tr>
                    <tr><td></td><td></td><td></td></tr>
                </tbody>
            </table>
        ",13,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_render_empty_table(self):
        
        block = TableBlock()
        result = block.render(
            {
                ""first_row_is_table_header"": False,
                ""first_col_is_header"": False,
                ""data"": [[None, None, None], [None, None, None], [None, None, None]],
            }
        )
        expected = 
        self.assertHTMLEqual(result, expected)

        ```
    ###Assistant : 
        An empty table should render okay.
        
            <table>
                <tbody>
                    <tr><td></td><td></td><td></td></tr>
                    <tr><td></td><td></td><td></td></tr>
                    <tr><td></td><td></td><td></td></tr>
                </tbody>
            </table>
        
    "
1544,"def rot_axis3(theta):
    
    ct = cos(theta)
    st = sin(theta)
    lil = ((ct, st, 0),
           (-st, ct, 0),
           (0, 0, 1))
    return Matrix(lil)

","Returns a rotation matrix for a rotation of theta (in radians) about
    the 3-axis.

    Examples
    ========

    >>> from sympy import pi, rot_axis3

    A rotation of pi/3 (60 degrees):

    >>> theta = pi/3
    >>> rot_axis3(theta)
    Matrix([
    [       1/2, sqrt(3)/2, 0],
    [-sqrt(3)/2,       1/2, 0],
    [         0,         0, 1]])

    If we rotate by pi/2 (90 degrees):

    >>> rot_axis3(pi/2)
    Matrix([
    [ 0, 1, 0],
    [-1, 0, 0],
    [ 0, 0, 1]])

    See Also
    ========

    rot_axis1: Returns a rotation matrix for a rotation of theta (in radians)
        about the 1-axis
    rot_axis2: Returns a rotation matrix for a rotation of theta (in radians)
        about the 2-axis
    ",100,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rot_axis3(theta):
    
    ct = cos(theta)
    st = sin(theta)
    lil = ((ct, st, 0),
           (-st, ct, 0),
           (0, 0, 1))
    return Matrix(lil)


        ```
    ###Assistant : Returns a rotation matrix for a rotation of theta (in radians) about
    the 3-axis.

    Examples
    ========

    >>> from sympy import pi, rot_axis3

    A rotation of pi/3 (60 degrees):

    >>> theta = pi/3
    >>> rot_axis3(theta)
    Matrix([
    [       1/2, sqrt(3)/2, 0],
    [-sqrt(3)/2,       1/2, 0],
    [         0,         0, 1]])

    If we rotate by pi/2 (90 degrees):

    >>> rot_axis3(pi/2)
    Matrix([
    [ 0, 1, 0],
    [-1, 0, 0],
    [ 0, 0, 1]])

    See Also
    ========

    rot_axis1: Returns a rotation matrix for a rotation of theta (in radians)
        about the 1-axis
    rot_axis2: Returns a rotation matrix for a rotation of theta (in radians)
        about the 2-axis
    
    "
1545,"def current(self):
        
        rv = self._current or '0'
        if not isinstance(rv, str):
            rv = bin(rv)[2:]
        return rv.rjust(self.n, '0')
","
        Returns the currently referenced Gray code as a bit string.

        Examples
        ========

        >>> from sympy.combinatorics import GrayCode
        >>> GrayCode(3, start='100').current
        '100'
        ",21,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def current(self):
        
        rv = self._current or '0'
        if not isinstance(rv, str):
            rv = bin(rv)[2:]
        return rv.rjust(self.n, '0')

        ```
    ###Assistant : 
        Returns the currently referenced Gray code as a bit string.

        Examples
        ========

        >>> from sympy.combinatorics import GrayCode
        >>> GrayCode(3, start='100').current
        '100'
        
    "
1546,"def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
    

    assert isinstance(factor, int) and factor >= 1

    # Check weight shape.
    assert len(w.shape) == 4
    convH = w.shape[2]
    convW = w.shape[3]
    inC = w.shape[1]

    assert convW == convH

    # Setup filter kernel.
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * (gain * (factor**2))
    p = (k.shape[0] - factor) - (convW - 1)

    stride = (factor, factor)

    # Determine data dimensions.
    stride = [1, 1, factor, factor]
    output_shape = ((_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW)
    output_padding = (
        output_shape[0] - (_shape(x, 2) - 1) * stride[0] - convH,
        output_shape[1] - (_shape(x, 3) - 1) * stride[1] - convW,
    )
    assert output_padding[0] >= 0 and output_padding[1] >= 0
    num_groups = _shape(x, 1) // inC

    # Transpose weights.
    w = torch.reshape(w, (num_groups, -1, inC, convH, convW))
    w = w[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
    w = torch.reshape(w, (num_groups * inC, -1, convH, convW))

    x = F.conv_transpose2d(x, w, stride=stride, output_padding=output_padding, padding=0)
    # Original TF code.
    # x = tf.nn.conv2d_transpose(
    #     x,
    #     w,
    #     output_shape=output_shape,
    #     strides=stride,
    #     padding='VALID',
    #     data_format=data_format)
    # JAX equivalent

    return upfirdn2d(x, torch.tensor(k, device=x.device), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))

","Fused `upsample_2d()` followed by `tf.nn.conv2d()`.

    Padding is performed only once at the beginning, not between the
    operations.
    The fused op is considerably more efficient than performing the same
    calculation
    using standard TensorFlow ops. It supports gradients of arbitrary order.
    Args:
      x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
        C]`.
      w:            Weight tensor of the shape `[filterH, filterW, inChannels,
        outChannels]`. Grouped convolution can be performed by `inChannels =
        x.shape[0] // numGroups`.
      k:            FIR filter of the shape `[firH, firW]` or `[firN]`
        (separable). The default is `[1] * factor`, which corresponds to
        nearest-neighbor upsampling.
      factor:       Integer upsampling factor (default: 2).
      gain:         Scaling factor for signal magnitude (default: 1.0).

    Returns:
      Tensor of the shape `[N, C, H * factor, W * factor]` or
      `[N, H * factor, W * factor, C]`, and same datatype as `x`.
    ",139,210,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
    

    assert isinstance(factor, int) and factor >= 1

    # Check weight shape.
    assert len(w.shape) == 4
    convH = w.shape[2]
    convW = w.shape[3]
    inC = w.shape[1]

    assert convW == convH

    # Setup filter kernel.
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * (gain * (factor**2))
    p = (k.shape[0] - factor) - (convW - 1)

    stride = (factor, factor)

    # Determine data dimensions.
    stride = [1, 1, factor, factor]
    output_shape = ((_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW)
    output_padding = (
        output_shape[0] - (_shape(x, 2) - 1) * stride[0] - convH,
        output_shape[1] - (_shape(x, 3) - 1) * stride[1] - convW,
    )
    assert output_padding[0] >= 0 and output_padding[1] >= 0
    num_groups = _shape(x, 1) // inC

    # Transpose weights.
    w = torch.reshape(w, (num_groups, -1, inC, convH, convW))
    w = w[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
    w = torch.reshape(w, (num_groups * inC, -1, convH, convW))

    x = F.conv_transpose2d(x, w, stride=stride, output_padding=output_padding, padding=0)
    # Original TF code.
    # x = tf.nn.conv2d_transpose(
    #     x,
    #     w,
    #     output_shape=output_shape,
    #     strides=stride,
    #     padding='VALID',
    #     data_format=data_format)
    # JAX equivalent

    return upfirdn2d(x, torch.tensor(k, device=x.device), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))


        ```
    ###Assistant : Fused `upsample_2d()` followed by `tf.nn.conv2d()`.

    Padding is performed only once at the beginning, not between the
    operations.
    The fused op is considerably more efficient than performing the same
    calculation
    using standard TensorFlow ops. It supports gradients of arbitrary order.
    Args:
      x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
        C]`.
      w:            Weight tensor of the shape `[filterH, filterW, inChannels,
        outChannels]`. Grouped convolution can be performed by `inChannels =
        x.shape[0] // numGroups`.
      k:            FIR filter of the shape `[firH, firW]` or `[firN]`
        (separable). The default is `[1] * factor`, which corresponds to
        nearest-neighbor upsampling.
      factor:       Integer upsampling factor (default: 2).
      gain:         Scaling factor for signal magnitude (default: 1.0).

    Returns:
      Tensor of the shape `[N, C, H * factor, W * factor]` or
      `[N, H * factor, W * factor, C]`, and same datatype as `x`.
    
    "
1547,"def read(self, size=None):
        
        if size is None:
            t = []
            while True:
                buf = self._read(self.bufsize)
                if not buf:
                    break
                t.append(buf)
            buf = """".join(t)
        else:
            buf = self._read(size)
        self.pos += len(buf)
        return buf
","Return the next size number of bytes from the stream.
           If size is not defined, return all bytes of the stream
           up to EOF.
        ",24,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read(self, size=None):
        
        if size is None:
            t = []
            while True:
                buf = self._read(self.bufsize)
                if not buf:
                    break
                t.append(buf)
            buf = """".join(t)
        else:
            buf = self._read(size)
        self.pos += len(buf)
        return buf

        ```
    ###Assistant : Return the next size number of bytes from the stream.
           If size is not defined, return all bytes of the stream
           up to EOF.
        
    "
1548,"def state(self) -> Mapping[str, Any]:
        
        if self._cursor_value:
            return {
                self.cursor_field: self._cursor_value,
                ""include_deleted"": self._include_deleted,
            }

        return {}
","State getter, get current state and serialize it to emmit Airbyte STATE message",13,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def state(self) -> Mapping[str, Any]:
        
        if self._cursor_value:
            return {
                self.cursor_field: self._cursor_value,
                ""include_deleted"": self._include_deleted,
            }

        return {}

        ```
    ###Assistant : State getter, get current state and serialize it to emmit Airbyte STATE message
    "
1549,"def set_task_factory(self, factory):
        
        if factory is not None and not callable(factory):
            raise TypeError('task factory must be a callable or None')
        self._task_factory = factory
","Set a task factory that will be used by loop.create_task().

        If factory is None the default task factory will be set.

        If factory is a callable, it should have a signature matching
        '(loop, coro)', where 'loop' will be a reference to the active
        event loop, 'coro' will be a coroutine object.  The callable
        must return a Future.
        ",57,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_task_factory(self, factory):
        
        if factory is not None and not callable(factory):
            raise TypeError('task factory must be a callable or None')
        self._task_factory = factory

        ```
    ###Assistant : Set a task factory that will be used by loop.create_task().

        If factory is None the default task factory will be set.

        If factory is a callable, it should have a signature matching
        '(loop, coro)', where 'loop' will be a reference to the active
        event loop, 'coro' will be a coroutine object.  The callable
        must return a Future.
        
    "
1550,"def date(self) -> npt.NDArray[np.object_]:
        
        # If the Timestamps have a timezone that is not UTC,
        # convert them into their i8 representation while
        # keeping their timezone and not using UTC
        timestamps = self._local_timestamps()

        return ints_to_pydatetime(timestamps, box=""date"")
","
        Returns numpy array of python :class:`datetime.date` objects.

        Namely, the date part of Timestamps without time and
        timezone information.
        ",18,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def date(self) -> npt.NDArray[np.object_]:
        
        # If the Timestamps have a timezone that is not UTC,
        # convert them into their i8 representation while
        # keeping their timezone and not using UTC
        timestamps = self._local_timestamps()

        return ints_to_pydatetime(timestamps, box=""date"")

        ```
    ###Assistant : 
        Returns numpy array of python :class:`datetime.date` objects.

        Namely, the date part of Timestamps without time and
        timezone information.
        
    "
1551,"def apply_transparency(self):
        
        if self.mode != ""P"" or ""transparency"" not in self.info:
            return

        from . import ImagePalette

        palette = self.getpalette(""RGBA"")
        transparency = self.info[""transparency""]
        if isinstance(transparency, bytes):
            for i, alpha in enumerate(transparency):
                palette[i * 4 + 3] = alpha
        else:
            palette[transparency * 4 + 3] = 0
        self.palette = ImagePalette.ImagePalette(""RGBA"", bytes(palette))
        self.palette.dirty = 1

        del self.info[""transparency""]
","
        If a P mode image has a ""transparency"" key in the info dictionary,
        remove the key and apply the transparency to the palette instead.
        ",24,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply_transparency(self):
        
        if self.mode != ""P"" or ""transparency"" not in self.info:
            return

        from . import ImagePalette

        palette = self.getpalette(""RGBA"")
        transparency = self.info[""transparency""]
        if isinstance(transparency, bytes):
            for i, alpha in enumerate(transparency):
                palette[i * 4 + 3] = alpha
        else:
            palette[transparency * 4 + 3] = 0
        self.palette = ImagePalette.ImagePalette(""RGBA"", bytes(palette))
        self.palette.dirty = 1

        del self.info[""transparency""]

        ```
    ###Assistant : 
        If a P mode image has a ""transparency"" key in the info dictionary,
        remove the key and apply the transparency to the palette instead.
        
    "
1552,"def get_frontend_app_asset_url(module, key):
    
    args = (settings.STATIC_FRONTEND_APP_URL.rstrip(""/""), module, key.lstrip(""/""))

    return ""{}/{}/{}"".format(*args)

","
    Returns an asset URL that is unversioned. These assets should have a
    `Cache-Control: max-age=0, must-revalidate` so that clients must validate with the origin
    server before using their locally cached asset.

    Example:
      {% frontend_app_asset_url 'sentry' 'sentry.css' %}
      =>  ""/_static/dist/sentry/sentry.css""
    ",38,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_frontend_app_asset_url(module, key):
    
    args = (settings.STATIC_FRONTEND_APP_URL.rstrip(""/""), module, key.lstrip(""/""))

    return ""{}/{}/{}"".format(*args)


        ```
    ###Assistant : 
    Returns an asset URL that is unversioned. These assets should have a
    `Cache-Control: max-age=0, must-revalidate` so that clients must validate with the origin
    server before using their locally cached asset.

    Example:
      {% frontend_app_asset_url 'sentry' 'sentry.css' %}
      =>  ""/_static/dist/sentry/sentry.css""
    
    "
1553,"def tridiagonal_solve(dl, d, du, b):
  r
  if dl.ndim != 1 or d.ndim != 1 or du.ndim != 1:
    raise ValueError('dl, d and du must be vectors')

  if dl.shape != d.shape or d.shape != du.shape:
    raise ValueError(
        f'dl={dl.shape}, d={d.shape} and du={du.shape} must all be `[m]`')

  if b.ndim != 2:
    raise ValueError(f'b={b.shape} must be a matrix')

  m, = dl.shape
  if m < 3:
    raise ValueError(f'm ({m}) must be >= 3')

  ldb, n = b.shape
  if ldb < max(1, m):
    raise ValueError(f'Leading dimension of b={ldb} must be ≥ max(1, {m})')

  if dl.dtype != d.dtype or d.dtype != du.dtype or du.dtype != b.dtype:
    raise ValueError(f'dl={dl.dtype}, d={d.dtype}, du={du.dtype} and '
                     f'b={b.dtype} must be the same dtype,')

  t = dl.dtype
  if t not in (np.float32, np.float64):
    raise ValueError(f'Only f32/f64 are supported, got {t}')

  return tridiagonal_solve_p.bind(dl, d, du, b, m=m, n=n, ldb=ldb, t=t)


# Schur Decomposition

","Computes the solution of a tridiagonal linear system.

  This function computes the solution of a tridiagonal linear system:

  .. math::
    A . X = B

  Args:
    dl: The lower diagonal of A: ``dl[i] := A[i, i-1]`` for i in ``[0,m)``.
      Note that ``dl[0] = 0``.
    d: The middle diagnoal of A: ``d[i]  := A[i, i]`` for i in ``[0,m)``.
    du: The upper diagonal of A: ``du[i] := A[i, i+1]`` for i in ``[0,m)``.
      Note that ``dl[m - 1] = 0``.
    b: Right hand side matrix.

  Returns:
    Solution ``X`` of tridiagonal system.
  ",91,139,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tridiagonal_solve(dl, d, du, b):
  r
  if dl.ndim != 1 or d.ndim != 1 or du.ndim != 1:
    raise ValueError('dl, d and du must be vectors')

  if dl.shape != d.shape or d.shape != du.shape:
    raise ValueError(
        f'dl={dl.shape}, d={d.shape} and du={du.shape} must all be `[m]`')

  if b.ndim != 2:
    raise ValueError(f'b={b.shape} must be a matrix')

  m, = dl.shape
  if m < 3:
    raise ValueError(f'm ({m}) must be >= 3')

  ldb, n = b.shape
  if ldb < max(1, m):
    raise ValueError(f'Leading dimension of b={ldb} must be ≥ max(1, {m})')

  if dl.dtype != d.dtype or d.dtype != du.dtype or du.dtype != b.dtype:
    raise ValueError(f'dl={dl.dtype}, d={d.dtype}, du={du.dtype} and '
                     f'b={b.dtype} must be the same dtype,')

  t = dl.dtype
  if t not in (np.float32, np.float64):
    raise ValueError(f'Only f32/f64 are supported, got {t}')

  return tridiagonal_solve_p.bind(dl, d, du, b, m=m, n=n, ldb=ldb, t=t)


# Schur Decomposition


        ```
    ###Assistant : Computes the solution of a tridiagonal linear system.

  This function computes the solution of a tridiagonal linear system:

  .. math::
    A . X = B

  Args:
    dl: The lower diagonal of A: ``dl[i] := A[i, i-1]`` for i in ``[0,m)``.
      Note that ``dl[0] = 0``.
    d: The middle diagnoal of A: ``d[i]  := A[i, i]`` for i in ``[0,m)``.
    du: The upper diagonal of A: ``du[i] := A[i, i+1]`` for i in ``[0,m)``.
      Note that ``dl[m - 1] = 0``.
    b: Right hand side matrix.

  Returns:
    Solution ``X`` of tridiagonal system.
  
    "
1554,"def _get_dependency_info() -> dict[str, JSONSerializable]:
    
    deps = [
        ""pandas"",
        # required
        ""numpy"",
        ""pytz"",
        ""dateutil"",
        # install / build,
        ""setuptools"",
        ""pip"",
        ""Cython"",
        # test
        ""pytest"",
        ""hypothesis"",
        # docs
        ""sphinx"",
        # Other, need a min version
        ""blosc"",
        ""feather"",
        ""xlsxwriter"",
        ""lxml.etree"",
        ""html5lib"",
        ""pymysql"",
        ""psycopg2"",
        ""jinja2"",
        # Other, not imported.
        ""IPython"",
        ""pandas_datareader"",
    ]
    deps.extend(list(VERSIONS))

    result: dict[str, JSONSerializable] = {}
    for modname in deps:
        mod = import_optional_dependency(modname, errors=""ignore"")
        result[modname] = get_version(mod) if mod else None
    return result

","
    Returns dependency information as a JSON serializable dictionary.
    ",8,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_dependency_info() -> dict[str, JSONSerializable]:
    
    deps = [
        ""pandas"",
        # required
        ""numpy"",
        ""pytz"",
        ""dateutil"",
        # install / build,
        ""setuptools"",
        ""pip"",
        ""Cython"",
        # test
        ""pytest"",
        ""hypothesis"",
        # docs
        ""sphinx"",
        # Other, need a min version
        ""blosc"",
        ""feather"",
        ""xlsxwriter"",
        ""lxml.etree"",
        ""html5lib"",
        ""pymysql"",
        ""psycopg2"",
        ""jinja2"",
        # Other, not imported.
        ""IPython"",
        ""pandas_datareader"",
    ]
    deps.extend(list(VERSIONS))

    result: dict[str, JSONSerializable] = {}
    for modname in deps:
        mod = import_optional_dependency(modname, errors=""ignore"")
        result[modname] = get_version(mod) if mod else None
    return result


        ```
    ###Assistant : 
    Returns dependency information as a JSON serializable dictionary.
    
    "
1555,"def execute():
	
	company_list = frappe.get_all(""Company"", filters={""country"": ""Germany""})

	for company in company_list:
		party_account_list = frappe.get_all(
			""Party Account"",
			filters={""company"": company.name},
			fields=[""name"", ""account"", ""debtor_creditor_number""],
		)
		for party_account in party_account_list:
			if (not party_account.account) or party_account.debtor_creditor_number:
				# account empty or debtor_creditor_number already filled
				continue

			account_number = frappe.db.get_value(""Account"", party_account.account, ""account_number"")
			if not account_number:
				continue

			frappe.db.set_value(
				""Party Account"", party_account.name, ""debtor_creditor_number"", account_number
			)
			frappe.db.set_value(""Party Account"", party_account.name, ""account"", """")
","Move account number into the new custom field debtor_creditor_number.

	German companies used to use a dedicated payable/receivable account for
	every party to mimick party accounts in the external accounting software
	""DATEV"". This is no longer necessary. The reference ID for DATEV will be
	stored in a new custom field ""debtor_creditor_number"".
	",50,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	
	company_list = frappe.get_all(""Company"", filters={""country"": ""Germany""})

	for company in company_list:
		party_account_list = frappe.get_all(
			""Party Account"",
			filters={""company"": company.name},
			fields=[""name"", ""account"", ""debtor_creditor_number""],
		)
		for party_account in party_account_list:
			if (not party_account.account) or party_account.debtor_creditor_number:
				# account empty or debtor_creditor_number already filled
				continue

			account_number = frappe.db.get_value(""Account"", party_account.account, ""account_number"")
			if not account_number:
				continue

			frappe.db.set_value(
				""Party Account"", party_account.name, ""debtor_creditor_number"", account_number
			)
			frappe.db.set_value(""Party Account"", party_account.name, ""account"", """")

        ```
    ###Assistant : Move account number into the new custom field debtor_creditor_number.

	German companies used to use a dedicated payable/receivable account for
	every party to mimick party accounts in the external accounting software
	""DATEV"". This is no longer necessary. The reference ID for DATEV will be
	stored in a new custom field ""debtor_creditor_number"".
	
    "
1556,"def compute_or_load(self, wav_file):
        
        pitch_file = self.create_pitch_file_path(wav_file, self.cache_path)
        if not os.path.exists(pitch_file):
            pitch = self._compute_and_save_pitch(self.ap, wav_file, pitch_file)
        else:
            pitch = np.load(pitch_file)
        return pitch.astype(np.float32)
","
        compute pitch and return a numpy array of pitch values
        ",10,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compute_or_load(self, wav_file):
        
        pitch_file = self.create_pitch_file_path(wav_file, self.cache_path)
        if not os.path.exists(pitch_file):
            pitch = self._compute_and_save_pitch(self.ap, wav_file, pitch_file)
        else:
            pitch = np.load(pitch_file)
        return pitch.astype(np.float32)

        ```
    ###Assistant : 
        compute pitch and return a numpy array of pitch values
        
    "
1557,"def check_output(self, want, got, optionflags):
        
        # Handle the common case first, for efficiency:
        # if they're string-identical, always return true.
        if got == want:
            return True

        # TODO parse integers as well ?
        # Parse floats and compare them. If some of the parsed floats contain
        # ellipses, skip the comparison.
        matches = self.num_got_rgx.finditer(got)
        numbers_got = [match.group(1) for match in matches] # list of strs
        matches = self.num_want_rgx.finditer(want)
        numbers_want = [match.group(1) for match in matches] # list of strs
        if len(numbers_got) != len(numbers_want):
            return False

        if len(numbers_got) > 0:
            nw_  = []
            for ng, nw in zip(numbers_got, numbers_want):
                if '...' in nw:
                    nw_.append(ng)
                    continue
                else:
                    nw_.append(nw)

                if abs(float(ng)-float(nw)) > 1e-5:
                    return False

            got = self.num_got_rgx.sub(r'%s', got)
            got = got % tuple(nw_)

        # <BLANKLINE> can be used as a special sequence to signify a
        # blank line, unless the DONT_ACCEPT_BLANKLINE flag is used.
        if not (optionflags & pdoctest.DONT_ACCEPT_BLANKLINE):
            # Replace <BLANKLINE> in want with a blank line.
            want = re.sub(r'(?m)^%s\s*?$' % re.escape(pdoctest.BLANKLINE_MARKER),
                          '', want)
            # If a line in got contains only spaces, then remove the
            # spaces.
            got = re.sub(r'(?m)^\s*?$', '', got)
            if got == want:
                return True

        # This flag causes doctest to ignore any differences in the
        # contents of whitespace strings.  Note that this can be used
        # in conjunction with the ELLIPSIS flag.
        if optionflags & pdoctest.NORMALIZE_WHITESPACE:
            got = ' '.join(got.split())
            want = ' '.join(want.split())
            if got == want:
                return True

        # The ELLIPSIS flag says to let the sequence ""..."" in `want`
        # match any substring in `got`.
        if optionflags & pdoctest.ELLIPSIS:
            if pdoctest._ellipsis_match(want, got):
                return True

        # We didn't find any match; return false.
        return False

","
        Return True iff the actual output from an example (`got`)
        matches the expected output (`want`).  These strings are
        always considered to match if they are identical; but
        depending on what option flags the test runner is using,
        several non-exact match types are also possible.  See the
        documentation for `TestRunner` for more information about
        option flags.
        ",55,272,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_output(self, want, got, optionflags):
        
        # Handle the common case first, for efficiency:
        # if they're string-identical, always return true.
        if got == want:
            return True

        # TODO parse integers as well ?
        # Parse floats and compare them. If some of the parsed floats contain
        # ellipses, skip the comparison.
        matches = self.num_got_rgx.finditer(got)
        numbers_got = [match.group(1) for match in matches] # list of strs
        matches = self.num_want_rgx.finditer(want)
        numbers_want = [match.group(1) for match in matches] # list of strs
        if len(numbers_got) != len(numbers_want):
            return False

        if len(numbers_got) > 0:
            nw_  = []
            for ng, nw in zip(numbers_got, numbers_want):
                if '...' in nw:
                    nw_.append(ng)
                    continue
                else:
                    nw_.append(nw)

                if abs(float(ng)-float(nw)) > 1e-5:
                    return False

            got = self.num_got_rgx.sub(r'%s', got)
            got = got % tuple(nw_)

        # <BLANKLINE> can be used as a special sequence to signify a
        # blank line, unless the DONT_ACCEPT_BLANKLINE flag is used.
        if not (optionflags & pdoctest.DONT_ACCEPT_BLANKLINE):
            # Replace <BLANKLINE> in want with a blank line.
            want = re.sub(r'(?m)^%s\s*?$' % re.escape(pdoctest.BLANKLINE_MARKER),
                          '', want)
            # If a line in got contains only spaces, then remove the
            # spaces.
            got = re.sub(r'(?m)^\s*?$', '', got)
            if got == want:
                return True

        # This flag causes doctest to ignore any differences in the
        # contents of whitespace strings.  Note that this can be used
        # in conjunction with the ELLIPSIS flag.
        if optionflags & pdoctest.NORMALIZE_WHITESPACE:
            got = ' '.join(got.split())
            want = ' '.join(want.split())
            if got == want:
                return True

        # The ELLIPSIS flag says to let the sequence ""..."" in `want`
        # match any substring in `got`.
        if optionflags & pdoctest.ELLIPSIS:
            if pdoctest._ellipsis_match(want, got):
                return True

        # We didn't find any match; return false.
        return False


        ```
    ###Assistant : 
        Return True iff the actual output from an example (`got`)
        matches the expected output (`want`).  These strings are
        always considered to match if they are identical; but
        depending on what option flags the test runner is using,
        several non-exact match types are also possible.  See the
        documentation for `TestRunner` for more information about
        option flags.
        
    "
1558,"def start(self, workflow_state, user=None):
        
        task_state = self.get_task_state_class()(workflow_state=workflow_state)
        task_state.status = TaskState.STATUS_IN_PROGRESS
        task_state.page_revision = workflow_state.page.get_latest_revision()
        task_state.task = self
        task_state.save()
        task_submitted.send(
            sender=task_state.specific.__class__,
            instance=task_state.specific,
            user=user,
        )
        return task_state
",Start this task on the provided workflow state by creating an instance of TaskState,14,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def start(self, workflow_state, user=None):
        
        task_state = self.get_task_state_class()(workflow_state=workflow_state)
        task_state.status = TaskState.STATUS_IN_PROGRESS
        task_state.page_revision = workflow_state.page.get_latest_revision()
        task_state.task = self
        task_state.save()
        task_submitted.send(
            sender=task_state.specific.__class__,
            instance=task_state.specific,
            user=user,
        )
        return task_state

        ```
    ###Assistant : Start this task on the provided workflow state by creating an instance of TaskState
    "
1559,"def get_datev_csv(data, filters, csv_class):
	
	empty_df = pd.DataFrame(columns=csv_class.COLUMNS)
	data_df = pd.DataFrame.from_records(data)
	result = empty_df.append(data_df, sort=True)

	if csv_class.DATA_CATEGORY == DataCategory.TRANSACTIONS:
		result[""Belegdatum""] = pd.to_datetime(result[""Belegdatum""])

		result[""Beleginfo - Inhalt 6""] = pd.to_datetime(result[""Beleginfo - Inhalt 6""])
		result[""Beleginfo - Inhalt 6""] = result[""Beleginfo - Inhalt 6""].dt.strftime(""%d%m%Y"")

		result[""Fälligkeit""] = pd.to_datetime(result[""Fälligkeit""])
		result[""Fälligkeit""] = result[""Fälligkeit""].dt.strftime(""%d%m%y"")

		result.sort_values(by=""Belegdatum"", inplace=True, kind=""stable"", ignore_index=True)

	if csv_class.DATA_CATEGORY == DataCategory.ACCOUNT_NAMES:
		result[""Sprach-ID""] = ""de-DE""

	data = result.to_csv(
		# Reason for str(';'): https://github.com/pandas-dev/pandas/issues/6035
		sep="";"",
		# European decimal seperator
		decimal="","",
		# Windows ""ANSI"" encoding
		encoding=""latin_1"",
		# format date as DDMM
		date_format=""%d%m"",
		# Windows line terminator
		line_terminator=""\r\n"",
		# Do not number rows
		index=False,
		# Use all columns defined above
		columns=csv_class.COLUMNS,
		# Quote most fields, even currency values with "","" separator
		quoting=QUOTE_NONNUMERIC,
	)

	data = data.encode(""latin_1"", errors=""replace"")

	header = get_header(filters, csv_class)
	header = "";"".join(header).encode(""latin_1"", errors=""replace"")

	# 1st Row: Header with meta data
	# 2nd Row: Data heading (Überschrift der Nutzdaten), included in `data` here.
	# 3rd - nth Row: Data (Nutzdaten)
	return header + b""\r\n"" + data

","
	Fill in missing columns and return a CSV in DATEV Format.

	For automatic processing, DATEV requires the first line of the CSV file to
	hold meta data such as the length of account numbers oder the category of
	the data.

	Arguments:
	data -- array of dictionaries
	filters -- dict
	csv_class -- defines DATA_CATEGORY, FORMAT_NAME and COLUMNS
	",56,155,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_datev_csv(data, filters, csv_class):
	
	empty_df = pd.DataFrame(columns=csv_class.COLUMNS)
	data_df = pd.DataFrame.from_records(data)
	result = empty_df.append(data_df, sort=True)

	if csv_class.DATA_CATEGORY == DataCategory.TRANSACTIONS:
		result[""Belegdatum""] = pd.to_datetime(result[""Belegdatum""])

		result[""Beleginfo - Inhalt 6""] = pd.to_datetime(result[""Beleginfo - Inhalt 6""])
		result[""Beleginfo - Inhalt 6""] = result[""Beleginfo - Inhalt 6""].dt.strftime(""%d%m%Y"")

		result[""Fälligkeit""] = pd.to_datetime(result[""Fälligkeit""])
		result[""Fälligkeit""] = result[""Fälligkeit""].dt.strftime(""%d%m%y"")

		result.sort_values(by=""Belegdatum"", inplace=True, kind=""stable"", ignore_index=True)

	if csv_class.DATA_CATEGORY == DataCategory.ACCOUNT_NAMES:
		result[""Sprach-ID""] = ""de-DE""

	data = result.to_csv(
		# Reason for str(';'): https://github.com/pandas-dev/pandas/issues/6035
		sep="";"",
		# European decimal seperator
		decimal="","",
		# Windows ""ANSI"" encoding
		encoding=""latin_1"",
		# format date as DDMM
		date_format=""%d%m"",
		# Windows line terminator
		line_terminator=""\r\n"",
		# Do not number rows
		index=False,
		# Use all columns defined above
		columns=csv_class.COLUMNS,
		# Quote most fields, even currency values with "","" separator
		quoting=QUOTE_NONNUMERIC,
	)

	data = data.encode(""latin_1"", errors=""replace"")

	header = get_header(filters, csv_class)
	header = "";"".join(header).encode(""latin_1"", errors=""replace"")

	# 1st Row: Header with meta data
	# 2nd Row: Data heading (Überschrift der Nutzdaten), included in `data` here.
	# 3rd - nth Row: Data (Nutzdaten)
	return header + b""\r\n"" + data


        ```
    ###Assistant : 
	Fill in missing columns and return a CSV in DATEV Format.

	For automatic processing, DATEV requires the first line of the CSV file to
	hold meta data such as the length of account numbers oder the category of
	the data.

	Arguments:
	data -- array of dictionaries
	filters -- dict
	csv_class -- defines DATA_CATEGORY, FORMAT_NAME and COLUMNS
	
    "
1560,"def CircularSymplecticEnsemble(sym, dim):
    
    sym, dim = _symbol_converter(sym), _sympify(dim)
    model = CircularSymplecticEnsembleModel(sym, dim)
    rmp = RandomMatrixPSpace(sym, model=model)
    return RandomMatrixSymbol(sym, dim, dim, pspace=rmp)
","
    Represents Circular Symplectic Ensembles.

    Examples
    ========

    >>> from sympy.stats import CircularSymplecticEnsemble as CSE
    >>> from sympy.stats import joint_eigen_distribution
    >>> C = CSE('S', 1)
    >>> joint_eigen_distribution(C)
    Lambda(t[1], Product(Abs(exp(I*t[_j]) - exp(I*t[_k]))**4, (_j, _k + 1, 1), (_k, 1, 0))/(2*pi))

    Note
    ====

    As can be seen above in the example, density of CiruclarSymplecticEnsemble
    is not evaluated because the exact definition is based on haar measure of
    unitary group which is not unique.
    ",69,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def CircularSymplecticEnsemble(sym, dim):
    
    sym, dim = _symbol_converter(sym), _sympify(dim)
    model = CircularSymplecticEnsembleModel(sym, dim)
    rmp = RandomMatrixPSpace(sym, model=model)
    return RandomMatrixSymbol(sym, dim, dim, pspace=rmp)

        ```
    ###Assistant : 
    Represents Circular Symplectic Ensembles.

    Examples
    ========

    >>> from sympy.stats import CircularSymplecticEnsemble as CSE
    >>> from sympy.stats import joint_eigen_distribution
    >>> C = CSE('S', 1)
    >>> joint_eigen_distribution(C)
    Lambda(t[1], Product(Abs(exp(I*t[_j]) - exp(I*t[_k]))**4, (_j, _k + 1, 1), (_k, 1, 0))/(2*pi))

    Note
    ====

    As can be seen above in the example, density of CiruclarSymplecticEnsemble
    is not evaluated because the exact definition is based on haar measure of
    unitary group which is not unique.
    
    "
1561,"def _executor_config_comparator(x, y):
    
    try:
        return x == y
    except AttributeError:
        return False

","
    The TaskInstance.executor_config attribute is a pickled object that may contain
    kubernetes objects.  If the installed library version has changed since the
    object was originally pickled, due to the underlying ``__eq__`` method on these
    objects (which converts them to JSON), we may encounter attribute errors. In this
    case we should replace the stored object.
    ",53,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _executor_config_comparator(x, y):
    
    try:
        return x == y
    except AttributeError:
        return False


        ```
    ###Assistant : 
    The TaskInstance.executor_config attribute is a pickled object that may contain
    kubernetes objects.  If the installed library version has changed since the
    object was originally pickled, due to the underlying ``__eq__`` method on these
    objects (which converts them to JSON), we may encounter attribute errors. In this
    case we should replace the stored object.
    
    "
1562,"def test_color_temperature_to_rgbww():
    
    # Coldest color temperature -> only cold channel enabled
    assert color_util.color_temperature_to_rgbww(6535, 255, 2000, 6535) == (
        0,
        0,
        0,
        255,
        0,
    )
    assert color_util.color_temperature_to_rgbww(6535, 128, 2000, 6535) == (
        0,
        0,
        0,
        128,
        0,
    )
    # Warmest color temperature -> only cold channel enabled
    assert color_util.color_temperature_to_rgbww(2000, 255, 2000, 6535) == (
        0,
        0,
        0,
        0,
        255,
    )
    assert color_util.color_temperature_to_rgbww(2000, 128, 2000, 6535) == (
        0,
        0,
        0,
        0,
        128,
    )
    # Warmer than mid point color temperature -> More warm than cold channel enabled
    assert color_util.color_temperature_to_rgbww(2881, 255, 2000, 6535) == (
        0,
        0,
        0,
        112,
        143,
    )
    assert color_util.color_temperature_to_rgbww(2881, 128, 2000, 6535) == (
        0,
        0,
        0,
        56,
        72,
    )

","Test color temp to warm, cold conversion.

    Temperature values must be in mireds
    Home Assistant uses rgbcw for rgbww
    ",19,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_color_temperature_to_rgbww():
    
    # Coldest color temperature -> only cold channel enabled
    assert color_util.color_temperature_to_rgbww(6535, 255, 2000, 6535) == (
        0,
        0,
        0,
        255,
        0,
    )
    assert color_util.color_temperature_to_rgbww(6535, 128, 2000, 6535) == (
        0,
        0,
        0,
        128,
        0,
    )
    # Warmest color temperature -> only cold channel enabled
    assert color_util.color_temperature_to_rgbww(2000, 255, 2000, 6535) == (
        0,
        0,
        0,
        0,
        255,
    )
    assert color_util.color_temperature_to_rgbww(2000, 128, 2000, 6535) == (
        0,
        0,
        0,
        0,
        128,
    )
    # Warmer than mid point color temperature -> More warm than cold channel enabled
    assert color_util.color_temperature_to_rgbww(2881, 255, 2000, 6535) == (
        0,
        0,
        0,
        112,
        143,
    )
    assert color_util.color_temperature_to_rgbww(2881, 128, 2000, 6535) == (
        0,
        0,
        0,
        56,
        72,
    )


        ```
    ###Assistant : Test color temp to warm, cold conversion.

    Temperature values must be in mireds
    Home Assistant uses rgbcw for rgbww
    
    "
1563,"def test_set_page_config_first(self):
        

        fake_enqueue = lambda msg: None
        ctx = ScriptRunContext(
            ""TestSessionID"",
            fake_enqueue,
            """",
            SessionState(),
            UploadedFileManager(),
        )

        ctx.on_script_start()

        markdown_msg = ForwardMsg()
        markdown_msg.delta.new_element.markdown.body = ""foo""

        msg = ForwardMsg()
        msg.page_config_changed.title = ""foo""

        ctx.enqueue(markdown_msg)
        with self.assertRaises(StreamlitAPIException):
            ctx.enqueue(msg)
","st.set_page_config must be called before other st commands
        when the script has been marked as started",16,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_set_page_config_first(self):
        

        fake_enqueue = lambda msg: None
        ctx = ScriptRunContext(
            ""TestSessionID"",
            fake_enqueue,
            """",
            SessionState(),
            UploadedFileManager(),
        )

        ctx.on_script_start()

        markdown_msg = ForwardMsg()
        markdown_msg.delta.new_element.markdown.body = ""foo""

        msg = ForwardMsg()
        msg.page_config_changed.title = ""foo""

        ctx.enqueue(markdown_msg)
        with self.assertRaises(StreamlitAPIException):
            ctx.enqueue(msg)

        ```
    ###Assistant : st.set_page_config must be called before other st commands
        when the script has been marked as started
    "
1564,"def set_vars(self) -> None:
        
        tk_vars = super().set_vars()

        smoothgraph = tk.DoubleVar()
        smoothgraph.set(0.900)
        tk_vars[""smoothgraph""] = smoothgraph

        raw_var = tk.BooleanVar()
        raw_var.set(True)
        tk_vars[""raw_data""] = raw_var

        smooth_var = tk.BooleanVar()
        smooth_var.set(True)
        tk_vars[""smooth_data""] = smooth_var

        iterations_var = tk.IntVar()
        iterations_var.set(10000)
        tk_vars[""display_iterations""] = iterations_var

        logger.debug(tk_vars)
        return tk_vars
"," Add graphing specific variables to the default variables.

        Overrides original method.

        Returns
        -------
        dict
            The variable names with their corresponding tkinter variable
        ",22,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_vars(self) -> None:
        
        tk_vars = super().set_vars()

        smoothgraph = tk.DoubleVar()
        smoothgraph.set(0.900)
        tk_vars[""smoothgraph""] = smoothgraph

        raw_var = tk.BooleanVar()
        raw_var.set(True)
        tk_vars[""raw_data""] = raw_var

        smooth_var = tk.BooleanVar()
        smooth_var.set(True)
        tk_vars[""smooth_data""] = smooth_var

        iterations_var = tk.IntVar()
        iterations_var.set(10000)
        tk_vars[""display_iterations""] = iterations_var

        logger.debug(tk_vars)
        return tk_vars

        ```
    ###Assistant :  Add graphing specific variables to the default variables.

        Overrides original method.

        Returns
        -------
        dict
            The variable names with their corresponding tkinter variable
        
    "
1565,"def _configure_kubernetes_library_client(self) -> None:
        
        # TODO: Investigate returning a configured client so calls on other threads
        #       will not invalidate the config needed here

        # if a k8s cluster block is provided to the flow runner, use that
        if self.cluster_config:
            self.cluster_config.configure_client()
        else:
            # If no block specified, try to load Kubernetes configuration within a cluster. If that doesn't
            # work, try to load the configuration from the local environment, allowing
            # any further ConfigExceptions to bubble up.
            try:
                kubernetes.config.load_incluster_config()
            except kubernetes.config.ConfigException:
                kubernetes.config.load_kube_config()
","
        Set the correct kubernetes client configuration.

        WARNING: This action is not threadsafe and may override the configuration
                  specified by another `KubernetesJob` instance.
        ",22,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _configure_kubernetes_library_client(self) -> None:
        
        # TODO: Investigate returning a configured client so calls on other threads
        #       will not invalidate the config needed here

        # if a k8s cluster block is provided to the flow runner, use that
        if self.cluster_config:
            self.cluster_config.configure_client()
        else:
            # If no block specified, try to load Kubernetes configuration within a cluster. If that doesn't
            # work, try to load the configuration from the local environment, allowing
            # any further ConfigExceptions to bubble up.
            try:
                kubernetes.config.load_incluster_config()
            except kubernetes.config.ConfigException:
                kubernetes.config.load_kube_config()

        ```
    ###Assistant : 
        Set the correct kubernetes client configuration.

        WARNING: This action is not threadsafe and may override the configuration
                  specified by another `KubernetesJob` instance.
        
    "
1566,"def test_runs_alert_rule_action_creator(self, mock_alert_rule_action_creator):
        
        self.login_as(user=self.user)

        project = self.create_project()

        self.create_sentry_app(
            name=""Pied Piper"",
            organization=project.organization,
            schema={""elements"": [self.create_alert_rule_action_schema()]},
        )
        install = self.create_sentry_app_installation(
            slug=""pied-piper"", organization=project.organization
        )

        actions = [
            {
                ""id"": ""sentry.rules.actions.notify_event_sentry_app.NotifyEventSentryAppAction"",
                ""settings"": [
                    {""name"": ""title"", ""value"": ""Team Rocket""},
                    {""name"": ""summary"", ""value"": ""We're blasting off again.""},
                ],
                ""sentryAppInstallationUuid"": install.uuid,
                ""hasSchemaFormConfig"": True,
            },
        ]

        url = reverse(
            ""sentry-api-0-project-rules"",
            kwargs={""organization_slug"": project.organization.slug, ""project_slug"": project.slug},
        )

        response = self.client.post(
            url,
            data={
                ""name"": ""my super cool rule"",
                ""owner"": f""user:{self.user.id}"",
                ""conditions"": [],
                ""filters"": [],
                ""actions"": actions,
                ""filterMatch"": ""any"",
                ""actionMatch"": ""any"",
                ""frequency"": 30,
            },
            format=""json"",
        )

        assert response.status_code == 200, response.content
        assert response.data[""id""]

        rule = Rule.objects.get(id=response.data[""id""])
        assert rule.data[""actions""] == actions

        kwargs = {
            ""install"": install,
            ""fields"": actions[0].get(""settings""),
        }

        call_kwargs = mock_alert_rule_action_creator.call_args[1]

        assert call_kwargs[""install""].id == kwargs[""install""].id
        assert call_kwargs[""fields""] == kwargs[""fields""]
","
        Ensures that Sentry Apps with schema forms (UI components)
        receive a payload when an alert rule is created with them.
        ",20,116,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_runs_alert_rule_action_creator(self, mock_alert_rule_action_creator):
        
        self.login_as(user=self.user)

        project = self.create_project()

        self.create_sentry_app(
            name=""Pied Piper"",
            organization=project.organization,
            schema={""elements"": [self.create_alert_rule_action_schema()]},
        )
        install = self.create_sentry_app_installation(
            slug=""pied-piper"", organization=project.organization
        )

        actions = [
            {
                ""id"": ""sentry.rules.actions.notify_event_sentry_app.NotifyEventSentryAppAction"",
                ""settings"": [
                    {""name"": ""title"", ""value"": ""Team Rocket""},
                    {""name"": ""summary"", ""value"": ""We're blasting off again.""},
                ],
                ""sentryAppInstallationUuid"": install.uuid,
                ""hasSchemaFormConfig"": True,
            },
        ]

        url = reverse(
            ""sentry-api-0-project-rules"",
            kwargs={""organization_slug"": project.organization.slug, ""project_slug"": project.slug},
        )

        response = self.client.post(
            url,
            data={
                ""name"": ""my super cool rule"",
                ""owner"": f""user:{self.user.id}"",
                ""conditions"": [],
                ""filters"": [],
                ""actions"": actions,
                ""filterMatch"": ""any"",
                ""actionMatch"": ""any"",
                ""frequency"": 30,
            },
            format=""json"",
        )

        assert response.status_code == 200, response.content
        assert response.data[""id""]

        rule = Rule.objects.get(id=response.data[""id""])
        assert rule.data[""actions""] == actions

        kwargs = {
            ""install"": install,
            ""fields"": actions[0].get(""settings""),
        }

        call_kwargs = mock_alert_rule_action_creator.call_args[1]

        assert call_kwargs[""install""].id == kwargs[""install""].id
        assert call_kwargs[""fields""] == kwargs[""fields""]

        ```
    ###Assistant : 
        Ensures that Sentry Apps with schema forms (UI components)
        receive a payload when an alert rule is created with them.
        
    "
1567,"def _determine_interval(self) -> int:
        
        intervals = {""default"": self._max_interval}
        for device in self._devices.values():
            # Max interval if no location
            if device.location is None:
                continue

            current_zone = run_callback_threadsafe(
                self.hass.loop,
                async_active_zone,
                self.hass,
                device.location[DEVICE_LOCATION_LATITUDE],
                device.location[DEVICE_LOCATION_LONGITUDE],
                device.location[DEVICE_LOCATION_HORIZONTAL_ACCURACY],
            ).result()

            # Max interval if in zone
            if current_zone is not None:
                continue

            zones = (
                self.hass.states.get(entity_id)
                for entity_id in sorted(self.hass.states.entity_ids(""zone""))
            )

            distances = []
            for zone_state in zones:
                if zone_state is None:
                    continue
                zone_state_lat = zone_state.attributes[DEVICE_LOCATION_LATITUDE]
                zone_state_long = zone_state.attributes[DEVICE_LOCATION_LONGITUDE]
                zone_distance = distance(
                    device.location[DEVICE_LOCATION_LATITUDE],
                    device.location[DEVICE_LOCATION_LONGITUDE],
                    zone_state_lat,
                    zone_state_long,
                )
                if zone_distance is not None:
                    distances.append(round(zone_distance / 1000, 1))

            # Max interval if no zone
            if not distances:
                continue
            mindistance = min(distances)

            # Calculate out how long it would take for the device to drive
            # to the nearest zone at 120 km/h:
            interval = round(mindistance / 2)

            # Never poll more than once per minute
            interval = max(interval, 1)

            if interval > 180:
                # Three hour drive?
                # This is far enough that they might be flying
                interval = self._max_interval

            if (
                device.battery_level is not None
                and device.battery_level <= 33
                and mindistance > 3
            ):
                # Low battery - let's check half as often
                interval = interval * 2

            intervals[device.name] = interval

        return max(
            int(min(intervals.items(), key=operator.itemgetter(1))[1]),
            self._max_interval,
        )
",Calculate new interval between two API fetch (in minutes).,9,199,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _determine_interval(self) -> int:
        
        intervals = {""default"": self._max_interval}
        for device in self._devices.values():
            # Max interval if no location
            if device.location is None:
                continue

            current_zone = run_callback_threadsafe(
                self.hass.loop,
                async_active_zone,
                self.hass,
                device.location[DEVICE_LOCATION_LATITUDE],
                device.location[DEVICE_LOCATION_LONGITUDE],
                device.location[DEVICE_LOCATION_HORIZONTAL_ACCURACY],
            ).result()

            # Max interval if in zone
            if current_zone is not None:
                continue

            zones = (
                self.hass.states.get(entity_id)
                for entity_id in sorted(self.hass.states.entity_ids(""zone""))
            )

            distances = []
            for zone_state in zones:
                if zone_state is None:
                    continue
                zone_state_lat = zone_state.attributes[DEVICE_LOCATION_LATITUDE]
                zone_state_long = zone_state.attributes[DEVICE_LOCATION_LONGITUDE]
                zone_distance = distance(
                    device.location[DEVICE_LOCATION_LATITUDE],
                    device.location[DEVICE_LOCATION_LONGITUDE],
                    zone_state_lat,
                    zone_state_long,
                )
                if zone_distance is not None:
                    distances.append(round(zone_distance / 1000, 1))

            # Max interval if no zone
            if not distances:
                continue
            mindistance = min(distances)

            # Calculate out how long it would take for the device to drive
            # to the nearest zone at 120 km/h:
            interval = round(mindistance / 2)

            # Never poll more than once per minute
            interval = max(interval, 1)

            if interval > 180:
                # Three hour drive?
                # This is far enough that they might be flying
                interval = self._max_interval

            if (
                device.battery_level is not None
                and device.battery_level <= 33
                and mindistance > 3
            ):
                # Low battery - let's check half as often
                interval = interval * 2

            intervals[device.name] = interval

        return max(
            int(min(intervals.items(), key=operator.itemgetter(1))[1]),
            self._max_interval,
        )

        ```
    ###Assistant : Calculate new interval between two API fetch (in minutes).
    "
1568,"def prepare_cookies(self, cookies):
        
        if isinstance(cookies, cookielib.CookieJar):
            self._cookies = cookies
        else:
            self._cookies = cookiejar_from_dict(cookies)

        cookie_header = get_cookie_header(self._cookies, self)
        if cookie_header is not None:
            self.headers[""Cookie""] = cookie_header
","Prepares the given HTTP cookie data.

        This function eventually generates a ``Cookie`` header from the
        given cookies using cookielib. Due to cookielib's design, the header
        will not be regenerated if it already exists, meaning this function
        can only be called once for the life of the
        :class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls
        to ``prepare_cookies`` will have no actual effect, unless the ""Cookie""
        header is removed beforehand.
        ",66,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def prepare_cookies(self, cookies):
        
        if isinstance(cookies, cookielib.CookieJar):
            self._cookies = cookies
        else:
            self._cookies = cookiejar_from_dict(cookies)

        cookie_header = get_cookie_header(self._cookies, self)
        if cookie_header is not None:
            self.headers[""Cookie""] = cookie_header

        ```
    ###Assistant : Prepares the given HTTP cookie data.

        This function eventually generates a ``Cookie`` header from the
        given cookies using cookielib. Due to cookielib's design, the header
        will not be regenerated if it already exists, meaning this function
        can only be called once for the life of the
        :class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls
        to ``prepare_cookies`` will have no actual effect, unless the ""Cookie""
        header is removed beforehand.
        
    "
1569,"def wait_for_contains_text(self, selector, text, timeout=None):
        
        return self._wait_for(
            method=contains_text,
            args=(selector, text),
            timeout=timeout,
            msg=f""text -> {text} not found inside element within {timeout or self._wait_timeout}s"",
        )
","Explicit wait until the element's text contains the expected `text`.

        timeout if not set, equals to the fixture's `wait_timeout`
        shortcut to `WebDriverWait` with customized `contains_text`
        condition.
        ",26,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wait_for_contains_text(self, selector, text, timeout=None):
        
        return self._wait_for(
            method=contains_text,
            args=(selector, text),
            timeout=timeout,
            msg=f""text -> {text} not found inside element within {timeout or self._wait_timeout}s"",
        )

        ```
    ###Assistant : Explicit wait until the element's text contains the expected `text`.

        timeout if not set, equals to the fixture's `wait_timeout`
        shortcut to `WebDriverWait` with customized `contains_text`
        condition.
        
    "
1570,"def remove_member(self, label):
        
        if label not in list(self._members):
            raise ValueError(""No such member exists in the Truss"")

        else:
            self._nodes_occupied.pop(tuple([self._members[label][0], self._members[label][1]]))
            self._nodes_occupied.pop(tuple([self._members[label][1], self._members[label][0]]))
            self._members.pop(label)
            self._internal_forces.pop(label)
","
        This method removes a member from the given truss.

        Parameters
        ==========
        label: String or Symbol
            The label for the member to be removed.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> t = Truss()
        >>> t.add_node('A', 0, 0)
        >>> t.add_node('B', 3, 0)
        >>> t.add_node('C', 2, 2)
        >>> t.add_member('AB', 'A', 'B')
        >>> t.add_member('AC', 'A', 'C')
        >>> t.add_member('BC', 'B', 'C')
        >>> t.members
        {'AB': ['A', 'B'], 'AC': ['A', 'C'], 'BC': ['B', 'C']}
        >>> t.remove_member('AC')
        >>> t.members
        {'AB': ['A', 'B'], 'BC': ['B', 'C']}
        ",79,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def remove_member(self, label):
        
        if label not in list(self._members):
            raise ValueError(""No such member exists in the Truss"")

        else:
            self._nodes_occupied.pop(tuple([self._members[label][0], self._members[label][1]]))
            self._nodes_occupied.pop(tuple([self._members[label][1], self._members[label][0]]))
            self._members.pop(label)
            self._internal_forces.pop(label)

        ```
    ###Assistant : 
        This method removes a member from the given truss.

        Parameters
        ==========
        label: String or Symbol
            The label for the member to be removed.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> t = Truss()
        >>> t.add_node('A', 0, 0)
        >>> t.add_node('B', 3, 0)
        >>> t.add_node('C', 2, 2)
        >>> t.add_member('AB', 'A', 'B')
        >>> t.add_member('AC', 'A', 'C')
        >>> t.add_member('BC', 'B', 'C')
        >>> t.members
        {'AB': ['A', 'B'], 'AC': ['A', 'C'], 'BC': ['B', 'C']}
        >>> t.remove_member('AC')
        >>> t.members
        {'AB': ['A', 'B'], 'BC': ['B', 'C']}
        
    "
1571,"def _create_mock_app_session(*args, **kwargs):
        
        mock_id = mock.PropertyMock(
            return_value=""mock_id:%s"" % ServerTestCase._next_session_id
        )
        ServerTestCase._next_session_id += 1

        mock_session = mock.MagicMock(AppSession, autospec=True, *args, **kwargs)
        type(mock_session).id = mock_id
        return mock_session
","Create a mock AppSession. Each mocked instance will have
        its own unique ID.",13,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_mock_app_session(*args, **kwargs):
        
        mock_id = mock.PropertyMock(
            return_value=""mock_id:%s"" % ServerTestCase._next_session_id
        )
        ServerTestCase._next_session_id += 1

        mock_session = mock.MagicMock(AppSession, autospec=True, *args, **kwargs)
        type(mock_session).id = mock_id
        return mock_session

        ```
    ###Assistant : Create a mock AppSession. Each mocked instance will have
        its own unique ID.
    "
1572,"def taggedsents_to_conll(sentences):
    
    for sentence in sentences:
        yield from taggedsent_to_conll(sentence)
        yield ""\n\n""


######################################################################
# { Test Suites
######################################################################

","
    A module to convert the a POS tagged document stream
    (i.e. list of list of tuples, a list of sentences) and yield lines
    in CONLL format. This module yields one line per word and two newlines
    for end of sentence.

    >>> from nltk import word_tokenize, sent_tokenize, pos_tag
    >>> text = ""This is a foobar sentence. Is that right?""
    >>> sentences = [pos_tag(word_tokenize(sent)) for sent in sent_tokenize(text)]
    >>> for line in taggedsents_to_conll(sentences): # doctest: +NORMALIZE_WHITESPACE
    ...     if line:
    ...         print(line, end="""")
    1	This	_	DT	DT	_	0	a	_	_
    2	is	_	VBZ	VBZ	_	0	a	_	_
    3	a	_	DT	DT	_	0	a	_	_
    4	foobar	_	JJ	JJ	_	0	a	_	_
    5	sentence	_	NN	NN	_	0	a	_	_
    6	.		_	.	.	_	0	a	_	_
    <BLANKLINE>
    <BLANKLINE>
    1	Is	_	VBZ	VBZ	_	0	a	_	_
    2	that	_	IN	IN	_	0	a	_	_
    3	right	_	NN	NN	_	0	a	_	_
    4	?	_	.	.	_	0	a	_	_
    <BLANKLINE>
    <BLANKLINE>

    :param sentences: Input sentences to parse
    :type sentence: list(list(tuple(str, str)))
    :rtype: iter(str)
    :return: a generator yielding sentences in CONLL format.
    ",204,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def taggedsents_to_conll(sentences):
    
    for sentence in sentences:
        yield from taggedsent_to_conll(sentence)
        yield ""\n\n""


######################################################################
# { Test Suites
######################################################################


        ```
    ###Assistant : 
    A module to convert the a POS tagged document stream
    (i.e. list of list of tuples, a list of sentences) and yield lines
    in CONLL format. This module yields one line per word and two newlines
    for end of sentence.

    >>> from nltk import word_tokenize, sent_tokenize, pos_tag
    >>> text = ""This is a foobar sentence. Is that right?""
    >>> sentences = [pos_tag(word_tokenize(sent)) for sent in sent_tokenize(text)]
    >>> for line in taggedsents_to_conll(sentences): # doctest: +NORMALIZE_WHITESPACE
    ...     if line:
    ...         print(line, end="""")
    1	This	_	DT	DT	_	0	a	_	_
    2	is	_	VBZ	VBZ	_	0	a	_	_
    3	a	_	DT	DT	_	0	a	_	_
    4	foobar	_	JJ	JJ	_	0	a	_	_
    5	sentence	_	NN	NN	_	0	a	_	_
    6	.		_	.	.	_	0	a	_	_
    <BLANKLINE>
    <BLANKLINE>
    1	Is	_	VBZ	VBZ	_	0	a	_	_
    2	that	_	IN	IN	_	0	a	_	_
    3	right	_	NN	NN	_	0	a	_	_
    4	?	_	.	.	_	0	a	_	_
    <BLANKLINE>
    <BLANKLINE>

    :param sentences: Input sentences to parse
    :type sentence: list(list(tuple(str, str)))
    :rtype: iter(str)
    :return: a generator yielding sentences in CONLL format.
    
    "
1573,"def _get_input_shape(self):
        
        arch = self.config[""enc_architecture""]
        enforce_size = _MODEL_MAPPING[arch].get(""enforce_for_weights"", False)
        default_size = _MODEL_MAPPING[arch][""default_size""]
        scaling = self.config[""enc_scaling""] / 100

        min_size = _MODEL_MAPPING[arch].get(""min_size"", 32)
        size = int(max(min_size, min(default_size, ((default_size * scaling) // 16) * 16)))

        if self.config[""enc_load_weights""] and enforce_size and scaling != 1.0:
            logger.warning(""%s requires input size to be %spx when loading imagenet weights. ""
                           ""Adjusting input size from %spx to %spx"",
                           arch, default_size, size, default_size)
            retval = (default_size, default_size, 3)
        else:
            retval = (size, size, 3)

        logger.debug(""Encoder input set to: %s"", retval)
        return retval
"," Obtain the input shape for the model.

        Input shape is calculated from the selected Encoder's input size, scaled to the user
        selected Input Scaling, rounded down to the nearest 16 pixels.

        Notes
        -----
        Some models (NasNet) require the input size to be of a certain dimension if loading
        imagenet weights. In these instances resize inputs and raise warning message

        Returns
        -------
        tuple
            The shape tuple for the input size to the Phaze-A model
        ",73,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_input_shape(self):
        
        arch = self.config[""enc_architecture""]
        enforce_size = _MODEL_MAPPING[arch].get(""enforce_for_weights"", False)
        default_size = _MODEL_MAPPING[arch][""default_size""]
        scaling = self.config[""enc_scaling""] / 100

        min_size = _MODEL_MAPPING[arch].get(""min_size"", 32)
        size = int(max(min_size, min(default_size, ((default_size * scaling) // 16) * 16)))

        if self.config[""enc_load_weights""] and enforce_size and scaling != 1.0:
            logger.warning(""%s requires input size to be %spx when loading imagenet weights. ""
                           ""Adjusting input size from %spx to %spx"",
                           arch, default_size, size, default_size)
            retval = (default_size, default_size, 3)
        else:
            retval = (size, size, 3)

        logger.debug(""Encoder input set to: %s"", retval)
        return retval

        ```
    ###Assistant :  Obtain the input shape for the model.

        Input shape is calculated from the selected Encoder's input size, scaled to the user
        selected Input Scaling, rounded down to the nearest 16 pixels.

        Notes
        -----
        Some models (NasNet) require the input size to be of a certain dimension if loading
        imagenet weights. In these instances resize inputs and raise warning message

        Returns
        -------
        tuple
            The shape tuple for the input size to the Phaze-A model
        
    "
1574,"def get_form_options(self):
        
        options = {}

        if not getattr(self.widget_overrides, ""is_original_method"", False):
            warn(
                ""The `widget_overrides` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`widgets` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""widgets""] = self.widget_overrides()

        if not getattr(self.required_fields, ""is_original_method"", False):
            warn(
                ""The `required_fields` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`fields` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""fields""] = self.required_fields()

        if not getattr(self.required_formsets, ""is_original_method"", False):
            warn(
                ""The `required_formsets` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`formsets` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""formsets""] = self.required_formsets()

        if not getattr(self.field_permissions, ""is_original_method"", False):
            warn(
                ""The `field_permissions` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`field_permissions` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""field_permissions""] = self.field_permissions()

        return options

    # RemovedInWagtail219Warning - edit handlers should override get_form_options instead","
        Return a dictionary of attributes such as 'fields', 'formsets' and 'widgets'
        which should be incorporated into the form class definition to generate a form
        that this EditHandler can use.
        This will only be called after binding to a model (i.e. self.model is available).
        ",43,148,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_form_options(self):
        
        options = {}

        if not getattr(self.widget_overrides, ""is_original_method"", False):
            warn(
                ""The `widget_overrides` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`widgets` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""widgets""] = self.widget_overrides()

        if not getattr(self.required_fields, ""is_original_method"", False):
            warn(
                ""The `required_fields` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`fields` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""fields""] = self.required_fields()

        if not getattr(self.required_formsets, ""is_original_method"", False):
            warn(
                ""The `required_formsets` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`formsets` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""formsets""] = self.required_formsets()

        if not getattr(self.field_permissions, ""is_original_method"", False):
            warn(
                ""The `field_permissions` method (on %r) is deprecated; ""
                ""these should be returned from `get_form_options` as a ""
                ""`field_permissions` item instead."" % type(self),
                category=RemovedInWagtail219Warning,
            )
            options[""field_permissions""] = self.field_permissions()

        return options

    # RemovedInWagtail219Warning - edit handlers should override get_form_options instead
        ```
    ###Assistant : 
        Return a dictionary of attributes such as 'fields', 'formsets' and 'widgets'
        which should be incorporated into the form class definition to generate a form
        that this EditHandler can use.
        This will only be called after binding to a model (i.e. self.model is available).
        
    "
1575,"def evaluate_links(self, link_evaluator, links):
        # type: (LinkEvaluator, Iterable[Link]) -> List[InstallationCandidate]
        
        candidates = []
        for link in self._sort_links(links):
            candidate = self.get_install_candidate(link_evaluator, link)
            if candidate is not None:
                candidates.append(candidate)

        return candidates
","
        Convert links that are candidates to InstallationCandidate objects.
        ",8,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def evaluate_links(self, link_evaluator, links):
        # type: (LinkEvaluator, Iterable[Link]) -> List[InstallationCandidate]
        
        candidates = []
        for link in self._sort_links(links):
            candidate = self.get_install_candidate(link_evaluator, link)
            if candidate is not None:
                candidates.append(candidate)

        return candidates

        ```
    ###Assistant : 
        Convert links that are candidates to InstallationCandidate objects.
        
    "
1576,"def columnarize(self):
        
        if len(self.columns) != 1 or (
            len(self.index) == 1 and self.index[0] == MODIN_UNNAMED_SERIES_LABEL
        ):
            return self.transpose()
        return self
","
        Transpose this QueryCompiler if it has a single row but multiple columns.

        This method should be called for QueryCompilers representing a Series object,
        i.e. ``self.is_series_like()`` should be True.

        Returns
        -------
        BaseQueryCompiler
            Transposed new QueryCompiler or self.
        ",36,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def columnarize(self):
        
        if len(self.columns) != 1 or (
            len(self.index) == 1 and self.index[0] == MODIN_UNNAMED_SERIES_LABEL
        ):
            return self.transpose()
        return self

        ```
    ###Assistant : 
        Transpose this QueryCompiler if it has a single row but multiple columns.

        This method should be called for QueryCompilers representing a Series object,
        i.e. ``self.is_series_like()`` should be True.

        Returns
        -------
        BaseQueryCompiler
            Transposed new QueryCompiler or self.
        
    "
1577,"def get_formatted_file_tags(self):
        # type: () -> List[str]
        
        return sorted(str(tag) for tag in self.file_tags)
",Return the wheel's tags as a sorted list of strings.,10,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_formatted_file_tags(self):
        # type: () -> List[str]
        
        return sorted(str(tag) for tag in self.file_tags)

        ```
    ###Assistant : Return the wheel's tags as a sorted list of strings.
    "
1578,"def test_python_render():
    syntax = Panel.fit(
        Syntax(
            CODE,
            lexer=""python"",
            line_numbers=True,
            line_range=(2, 10),
            theme=""monokai"",
            code_width=60,
            word_wrap=True,
        ),
        padding=0,
    )
    rendered_syntax = render(syntax)
    print(repr(rendered_syntax))
    expected = '╭─────────────────────────────────────────────────────────────────╮\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 2 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;230;219;116;48;2;39;40;34m\x1b[0m\x1b[48;2;39;40;34m                                          \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 3 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalues\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m                              \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 4 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mtry\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m                                                    \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 5 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m        \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mnext\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m                  \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 6 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mexcept\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;166;226;46;48;2;39;40;34mStopIteration\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m                                   \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 7 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m        \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mreturn\x1b[0m\x1b[48;2;39;40;34m                                              \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 8 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mTrue\x1b[0m\x1b[48;2;39;40;34m                                            \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 9 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mfor\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalue\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34min\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m                               \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m10 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m        \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34myield\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mFalse\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[48;2;39;40;34m                  \x1b[0m│\n╰─────────────────────────────────────────────────────────────────╯\n'
    assert rendered_syntax == expected

",Iterate and generate a tuple with a flag for first \x1b[0m\x1b[48;2;39;40;34m  \x1b[0m│\n│\x1b[48;2;39;40;34m     \x1b[0m\x1b[38;2;230;219;116;48;2;39;40;34mand last value.,15,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_python_render():
    syntax = Panel.fit(
        Syntax(
            CODE,
            lexer=""python"",
            line_numbers=True,
            line_range=(2, 10),
            theme=""monokai"",
            code_width=60,
            word_wrap=True,
        ),
        padding=0,
    )
    rendered_syntax = render(syntax)
    print(repr(rendered_syntax))
    expected = '╭─────────────────────────────────────────────────────────────────╮\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 2 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;230;219;116;48;2;39;40;34m\x1b[0m\x1b[48;2;39;40;34m                                          \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 3 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalues\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m                              \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 4 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mtry\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m                                                    \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 5 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m        \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mnext\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[48;2;39;40;34m                  \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 6 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mexcept\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;166;226;46;48;2;39;40;34mStopIteration\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m                                   \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 7 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m        \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mreturn\x1b[0m\x1b[48;2;39;40;34m                                              \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 8 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mTrue\x1b[0m\x1b[48;2;39;40;34m                                            \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 9 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m    \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mfor\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalue\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34min\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[48;2;39;40;34m                               \x1b[0m│\n│\x1b[1;38;2;227;227;221;48;2;39;40;34m  \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m10 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m        \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34myield\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mFalse\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[48;2;39;40;34m                  \x1b[0m│\n╰─────────────────────────────────────────────────────────────────╯\n'
    assert rendered_syntax == expected


        ```
    ###Assistant : Iterate and generate a tuple with a flag for first \x1b[0m\x1b[48;2;39;40;34m  \x1b[0m│\n│\x1b[48;2;39;40;34m     \x1b[0m\x1b[38;2;230;219;116;48;2;39;40;34mand last value.
    "
1579,"def test_state_changes_during_period_multiple_entities_single_test(hass_recorder):
    
    hass = hass_recorder()
    start = dt_util.utcnow()
    test_entites = {f""sensor.{i}"": str(i) for i in range(30)}
    for entity_id, value in test_entites.items():
        hass.states.set(entity_id, value)

    wait_recording_done(hass)
    end = dt_util.utcnow()

    hist = history.state_changes_during_period(hass, start, end, None)
    for entity_id, value in test_entites.items():
        hist[entity_id][0].state == value

    for entity_id, value in test_entites.items():
        hist = history.state_changes_during_period(hass, start, end, entity_id)
        assert len(hist) == 1
        hist[entity_id][0].state == value

    hist = history.state_changes_during_period(hass, start, end, None)
    for entity_id, value in test_entites.items():
        hist[entity_id][0].state == value
","Test state change during period with multiple entities in the same test.

    This test ensures the sqlalchemy query cache does not
    generate incorrect results.
    ",24,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_state_changes_during_period_multiple_entities_single_test(hass_recorder):
    
    hass = hass_recorder()
    start = dt_util.utcnow()
    test_entites = {f""sensor.{i}"": str(i) for i in range(30)}
    for entity_id, value in test_entites.items():
        hass.states.set(entity_id, value)

    wait_recording_done(hass)
    end = dt_util.utcnow()

    hist = history.state_changes_during_period(hass, start, end, None)
    for entity_id, value in test_entites.items():
        hist[entity_id][0].state == value

    for entity_id, value in test_entites.items():
        hist = history.state_changes_during_period(hass, start, end, entity_id)
        assert len(hist) == 1
        hist[entity_id][0].state == value

    hist = history.state_changes_during_period(hass, start, end, None)
    for entity_id, value in test_entites.items():
        hist[entity_id][0].state == value

        ```
    ###Assistant : Test state change during period with multiple entities in the same test.

    This test ensures the sqlalchemy query cache does not
    generate incorrect results.
    
    "
1580,"def O(self):  # NOQA: E743, E741
        
        if self._no_timezone_or_datetime_is_ambiguous_or_imaginary:
            return """"

        seconds = self.Z()
        sign = ""-"" if seconds < 0 else ""+""
        seconds = abs(seconds)
        return ""%s%02d%02d"" % (sign, seconds // 3600, (seconds // 60) % 60)
","
        Difference to Greenwich time in hours; e.g. '+0200', '-0430'.

        If timezone information is not available, return an empty string.
        ",19,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def O(self):  # NOQA: E743, E741
        
        if self._no_timezone_or_datetime_is_ambiguous_or_imaginary:
            return """"

        seconds = self.Z()
        sign = ""-"" if seconds < 0 else ""+""
        seconds = abs(seconds)
        return ""%s%02d%02d"" % (sign, seconds // 3600, (seconds // 60) % 60)

        ```
    ###Assistant : 
        Difference to Greenwich time in hours; e.g. '+0200', '-0430'.

        If timezone information is not available, return an empty string.
        
    "
1581,"def convert_mem_str_to_bytes(mem_str):
    
    # If there is no suffix, the memory sourced from the request is in bytes
    if mem_str.isdigit():
        return int(mem_str)

    conversions = {
        'Ei': lambda x: x * 2**60,
        'E': lambda x: x * 10**18,
        'Pi': lambda x: x * 2**50,
        'P': lambda x: x * 10**15,
        'Ti': lambda x: x * 2**40,
        'T': lambda x: x * 10**12,
        'Gi': lambda x: x * 2**30,
        'G': lambda x: x * 10**9,
        'Mi': lambda x: x * 2**20,
        'M': lambda x: x * 10**6,
        'Ki': lambda x: x * 2**10,
        'K': lambda x: x * 10**3,
    }
    mem = 0
    mem_unit = None
    for i, char in enumerate(mem_str):
        if not char.isdigit():
            mem_unit = mem_str[i:]
            mem = int(mem_str[:i])
            break
    if not mem_unit or mem_unit not in conversions.keys():
        error = f""Unsupported value for SYSTEM_TASK_ABS_MEM: {mem_str}, memory must be expressed in bytes or with known suffix: {conversions.keys()}. Falling back to 1 byte""
        logger.warning(error)
        return 1
    return max(1, conversions[mem_unit](mem))

","Convert string with suffix indicating units to memory in bytes (base 2)

    Useful for dealing with memory setting that may be expressed in units compatible with
    kubernetes.

    See https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory
    ",29,155,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_mem_str_to_bytes(mem_str):
    
    # If there is no suffix, the memory sourced from the request is in bytes
    if mem_str.isdigit():
        return int(mem_str)

    conversions = {
        'Ei': lambda x: x * 2**60,
        'E': lambda x: x * 10**18,
        'Pi': lambda x: x * 2**50,
        'P': lambda x: x * 10**15,
        'Ti': lambda x: x * 2**40,
        'T': lambda x: x * 10**12,
        'Gi': lambda x: x * 2**30,
        'G': lambda x: x * 10**9,
        'Mi': lambda x: x * 2**20,
        'M': lambda x: x * 10**6,
        'Ki': lambda x: x * 2**10,
        'K': lambda x: x * 10**3,
    }
    mem = 0
    mem_unit = None
    for i, char in enumerate(mem_str):
        if not char.isdigit():
            mem_unit = mem_str[i:]
            mem = int(mem_str[:i])
            break
    if not mem_unit or mem_unit not in conversions.keys():
        error = f""Unsupported value for SYSTEM_TASK_ABS_MEM: {mem_str}, memory must be expressed in bytes or with known suffix: {conversions.keys()}. Falling back to 1 byte""
        logger.warning(error)
        return 1
    return max(1, conversions[mem_unit](mem))


        ```
    ###Assistant : Convert string with suffix indicating units to memory in bytes (base 2)

    Useful for dealing with memory setting that may be expressed in units compatible with
    kubernetes.

    See https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory
    
    "
1582,"def sync_status_outbound(self, external_issue, is_resolved, project_id, **kwargs):
        
        client = self.get_client()
        jira_issue = client.get_issue(external_issue.key)
        jira_project = jira_issue[""fields""][""project""]

        try:
            external_project = IntegrationExternalProject.objects.get(
                external_id=jira_project[""id""],
                organization_integration_id__in=OrganizationIntegration.objects.filter(
                    organization_id=external_issue.organization_id,
                    integration_id=external_issue.integration_id,
                ),
            )
        except IntegrationExternalProject.DoesNotExist:
            return

        jira_status = (
            external_project.resolved_status if is_resolved else external_project.unresolved_status
        )

        # don't bother updating if it's already the status we'd change it to
        if jira_issue[""fields""][""status""][""id""] == jira_status:
            return
        try:
            transitions = client.get_transitions(external_issue.key)
        except ApiHostError:
            raise IntegrationError(""Could not reach host to get transitions."")

        try:
            transition = [t for t in transitions if t.get(""to"", {}).get(""id"") == jira_status][0]
        except IndexError:
            # TODO(jess): Email for failure
            logger.warning(
                ""jira.status-sync-fail"",
                extra={
                    ""organization_id"": external_issue.organization_id,
                    ""integration_id"": external_issue.integration_id,
                    ""issue_key"": external_issue.key,
                },
            )
            return

        client.transition_issue(external_issue.key, transition[""id""])
","
        Propagate a sentry issue's status to a linked issue's status.
        ",10,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sync_status_outbound(self, external_issue, is_resolved, project_id, **kwargs):
        
        client = self.get_client()
        jira_issue = client.get_issue(external_issue.key)
        jira_project = jira_issue[""fields""][""project""]

        try:
            external_project = IntegrationExternalProject.objects.get(
                external_id=jira_project[""id""],
                organization_integration_id__in=OrganizationIntegration.objects.filter(
                    organization_id=external_issue.organization_id,
                    integration_id=external_issue.integration_id,
                ),
            )
        except IntegrationExternalProject.DoesNotExist:
            return

        jira_status = (
            external_project.resolved_status if is_resolved else external_project.unresolved_status
        )

        # don't bother updating if it's already the status we'd change it to
        if jira_issue[""fields""][""status""][""id""] == jira_status:
            return
        try:
            transitions = client.get_transitions(external_issue.key)
        except ApiHostError:
            raise IntegrationError(""Could not reach host to get transitions."")

        try:
            transition = [t for t in transitions if t.get(""to"", {}).get(""id"") == jira_status][0]
        except IndexError:
            # TODO(jess): Email for failure
            logger.warning(
                ""jira.status-sync-fail"",
                extra={
                    ""organization_id"": external_issue.organization_id,
                    ""integration_id"": external_issue.integration_id,
                    ""issue_key"": external_issue.key,
                },
            )
            return

        client.transition_issue(external_issue.key, transition[""id""])

        ```
    ###Assistant : 
        Propagate a sentry issue's status to a linked issue's status.
        
    "
1583,"def _sci(self, im):
        
        _api.check_isinstance(
            (mpl.contour.ContourSet, mcoll.Collection, mimage.AxesImage),
            im=im)
        if isinstance(im, mpl.contour.ContourSet):
            if im.collections[0] not in self._children:
                raise ValueError(""ContourSet must be in current Axes"")
        elif im not in self._children:
            raise ValueError(""Argument must be an image, collection, or ""
                             ""ContourSet in this Axes"")
        self._current_image = im
","
        Set the current image.

        This image will be the target of colormap functions like
        ``pyplot.viridis``, and other functions such as `~.pyplot.clim`.  The
        current image is an attribute of the current Axes.
        ",31,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _sci(self, im):
        
        _api.check_isinstance(
            (mpl.contour.ContourSet, mcoll.Collection, mimage.AxesImage),
            im=im)
        if isinstance(im, mpl.contour.ContourSet):
            if im.collections[0] not in self._children:
                raise ValueError(""ContourSet must be in current Axes"")
        elif im not in self._children:
            raise ValueError(""Argument must be an image, collection, or ""
                             ""ContourSet in this Axes"")
        self._current_image = im

        ```
    ###Assistant : 
        Set the current image.

        This image will be the target of colormap functions like
        ``pyplot.viridis``, and other functions such as `~.pyplot.clim`.  The
        current image is an attribute of the current Axes.
        
    "
1584,"def __dask_postpersist__(self) -> tuple[PostPersistCallable, tuple]:
        
        raise NotImplementedError(""Inheriting class must implement this method."")
","Rebuilder function and optional arguments to contruct a persisted collection.

        Returns
        -------
        PostPersistCallable
            Callable that rebuilds the collection. The signature
            should be
            ``rebuild(dsk: Mapping, *args: Any, rename: Mapping[str, str] | None)``.
            The callable should return an equivalent Dask collection
            with the same keys as `self`, but with results that are
            computed through a different graph. In the case of
            :py:func:`dask.persist`, the new graph will have just the
            output keys and the values already computed.
        tuple[Any, ...]
            Optional arugments passed to the rebuild callable. If no
            additional arguments are to be passed then this must be an
            empty tuple.

        ",98,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __dask_postpersist__(self) -> tuple[PostPersistCallable, tuple]:
        
        raise NotImplementedError(""Inheriting class must implement this method."")

        ```
    ###Assistant : Rebuilder function and optional arguments to contruct a persisted collection.

        Returns
        -------
        PostPersistCallable
            Callable that rebuilds the collection. The signature
            should be
            ``rebuild(dsk: Mapping, *args: Any, rename: Mapping[str, str] | None)``.
            The callable should return an equivalent Dask collection
            with the same keys as `self`, but with results that are
            computed through a different graph. In the case of
            :py:func:`dask.persist`, the new graph will have just the
            output keys and the values already computed.
        tuple[Any, ...]
            Optional arugments passed to the rebuild callable. If no
            additional arguments are to be passed then this must be an
            empty tuple.

        
    "
1585,"def execute():
	for doctype in (""Sales Order Item"", ""Bin""):
		frappe.reload_doctype(doctype)

	repost_for = frappe.db.sql()

	for item_code, warehouse in repost_for:
		if not (item_code and warehouse):
			continue
		update_bin_qty(item_code, warehouse, {
			""reserved_qty"": get_reserved_qty(item_code, warehouse)
		})

	frappe.db.sql()
","
		select
			distinct item_code, warehouse
		from
			(
				(
					select distinct item_code, warehouse
								from `tabSales Order Item` where docstatus=1
				) UNION (
					select distinct item_code, warehouse
					from `tabPacked Item` where docstatus=1 and parenttype='Sales Order'
				)
			) so_item
		where
			exists(select name from tabItem where name=so_item.item_code and ifnull(is_stock_item, 0)=1)
	delete from tabBin
		where exists(
			select name from tabItem where name=tabBin.item_code and ifnull(is_stock_item, 0) = 0
		)
	",62,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	for doctype in (""Sales Order Item"", ""Bin""):
		frappe.reload_doctype(doctype)

	repost_for = frappe.db.sql()

	for item_code, warehouse in repost_for:
		if not (item_code and warehouse):
			continue
		update_bin_qty(item_code, warehouse, {
			""reserved_qty"": get_reserved_qty(item_code, warehouse)
		})

	frappe.db.sql()

        ```
    ###Assistant : 
		select
			distinct item_code, warehouse
		from
			(
				(
					select distinct item_code, warehouse
								from `tabSales Order Item` where docstatus=1
				) UNION (
					select distinct item_code, warehouse
					from `tabPacked Item` where docstatus=1 and parenttype='Sales Order'
				)
			) so_item
		where
			exists(select name from tabItem where name=so_item.item_code and ifnull(is_stock_item, 0)=1)
	delete from tabBin
		where exists(
			select name from tabItem where name=tabBin.item_code and ifnull(is_stock_item, 0) = 0
		)
	
    "
1586,"def rename_group_tables_reverse(apps, schema_editor):
    Group = apps.get_model(""auth"", ""Group"")
    schema_editor.alter_db_table(
        Group,
        ""account_group"",
        ""auth_group"",
    )
    PermissionGroup = Group.permissions.through
    schema_editor.alter_db_table(
        PermissionGroup,
        ""account_group_permissions"",
        ""auth_group_permissions"",
    )


RENAME_CONSTRAINTS_AND_INDEX_REVERSE = 

DROP_OLD_CONSTRAINTS_REVERSE_FROM_0072 = 

DROP_OLD_CONSTRAINTS_REVERSE_FROM_APP_0018 = 

","
ALTER TABLE account_group RENAME CONSTRAINT account_group_pkey
    TO auth_group_pkey;

ALTER TABLE account_group RENAME CONSTRAINT account_group_name_key
    TO auth_group_name_key;

ALTER INDEX IF EXISTS account_group_name_034e9f3f_like
    RENAME TO auth_group_name_a6ea08ec_like;

ALTER TABLE auth_group_permissions
    ADD CONSTRAINT auth_group_permissions_group_id_permission_id_0cd325b0_uniq
    UNIQUE (group_id, permission_id);

ALTER TABLE auth_group_permissions
    ADD CONSTRAINT auth_group_permissions_group_id_b120cbf9_fk_auth_group_id
    FOREIGN KEY (group_id) REFERENCES auth_group (id) DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE auth_group_permissions
    ADD CONSTRAINT auth_group_permissio_permission_id_84c5c92e_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE account_user_groups
    ADD CONSTRAINT userprofile_user_groups_group_id_c7eec74e_fk_auth_group_id
    FOREIGN KEY (group_id) REFERENCES auth_group (id) DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE account_user_user_permissions
    ADD CONSTRAINT userprofile_user_use_permission_id_1caa8a71_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE app_app_permissions
    ADD CONSTRAINT account_serviceaccou_permission_id_449791f0_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE app_appextension_permissions
    ADD CONSTRAINT app_appextension_per_permission_id_cb6c3ce0_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE app_appinstallation_permissions
    ADD CONSTRAINT app_appinstallation__permission_id_4ee9f6c8_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;
",138,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rename_group_tables_reverse(apps, schema_editor):
    Group = apps.get_model(""auth"", ""Group"")
    schema_editor.alter_db_table(
        Group,
        ""account_group"",
        ""auth_group"",
    )
    PermissionGroup = Group.permissions.through
    schema_editor.alter_db_table(
        PermissionGroup,
        ""account_group_permissions"",
        ""auth_group_permissions"",
    )


RENAME_CONSTRAINTS_AND_INDEX_REVERSE = 

DROP_OLD_CONSTRAINTS_REVERSE_FROM_0072 = 

DROP_OLD_CONSTRAINTS_REVERSE_FROM_APP_0018 = 


        ```
    ###Assistant : 
ALTER TABLE account_group RENAME CONSTRAINT account_group_pkey
    TO auth_group_pkey;

ALTER TABLE account_group RENAME CONSTRAINT account_group_name_key
    TO auth_group_name_key;

ALTER INDEX IF EXISTS account_group_name_034e9f3f_like
    RENAME TO auth_group_name_a6ea08ec_like;

ALTER TABLE auth_group_permissions
    ADD CONSTRAINT auth_group_permissions_group_id_permission_id_0cd325b0_uniq
    UNIQUE (group_id, permission_id);

ALTER TABLE auth_group_permissions
    ADD CONSTRAINT auth_group_permissions_group_id_b120cbf9_fk_auth_group_id
    FOREIGN KEY (group_id) REFERENCES auth_group (id) DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE auth_group_permissions
    ADD CONSTRAINT auth_group_permissio_permission_id_84c5c92e_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE account_user_groups
    ADD CONSTRAINT userprofile_user_groups_group_id_c7eec74e_fk_auth_group_id
    FOREIGN KEY (group_id) REFERENCES auth_group (id) DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE account_user_user_permissions
    ADD CONSTRAINT userprofile_user_use_permission_id_1caa8a71_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE app_app_permissions
    ADD CONSTRAINT account_serviceaccou_permission_id_449791f0_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE app_appextension_permissions
    ADD CONSTRAINT app_appextension_per_permission_id_cb6c3ce0_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

ALTER TABLE app_appinstallation_permissions
    ADD CONSTRAINT app_appinstallation__permission_id_4ee9f6c8_fk_auth_perm
    FOREIGN KEY (permission_id) REFERENCES auth_permission (id)
    DEFERRABLE INITIALLY DEFERRED;

    "
1587,"def test_session_is_accessed(self):
        
        response = self.client.get(""/auth_processor_attr_access/"")
        self.assertContains(response, ""Session accessed"")
","
        The session is accessed if the auth context processor
        is used and relevant attributes accessed.
        ",15,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_session_is_accessed(self):
        
        response = self.client.get(""/auth_processor_attr_access/"")
        self.assertContains(response, ""Session accessed"")

        ```
    ###Assistant : 
        The session is accessed if the auth context processor
        is used and relevant attributes accessed.
        
    "
1588,"def _find_vc2017():
    
    root = os.environ.get(""ProgramFiles(x86)"") or os.environ.get(""ProgramFiles"")
    if not root:
        return None, None

    try:
        path = subprocess.check_output([
            os.path.join(root, ""Microsoft Visual Studio"", ""Installer"", ""vswhere.exe""),
            ""-latest"",
            ""-prerelease"",
            ""-requires"", ""Microsoft.VisualStudio.Component.VC.Tools.x86.x64"",
            ""-property"", ""installationPath"",
            ""-products"", ""*"",
        ], encoding=""mbcs"", errors=""strict"").strip()
    except (subprocess.CalledProcessError, OSError, UnicodeDecodeError):
        return None, None

    path = os.path.join(path, ""VC"", ""Auxiliary"", ""Build"")
    if os.path.isdir(path):
        return 15, path

    return None, None

PLAT_SPEC_TO_RUNTIME = {
    'x86' : 'x86',
    'x86_amd64' : 'x64',
    'x86_arm' : 'arm',
    'x86_arm64' : 'arm64'
}
","Returns ""15, path"" based on the result of invoking vswhere.exe
    If no install is found, returns ""None, None""

    The version is returned to avoid unnecessarily changing the function
    result. It may be ignored when the path is not None.

    If vswhere.exe is not available, by definition, VS 2017 is not
    installed.
    ",51,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _find_vc2017():
    
    root = os.environ.get(""ProgramFiles(x86)"") or os.environ.get(""ProgramFiles"")
    if not root:
        return None, None

    try:
        path = subprocess.check_output([
            os.path.join(root, ""Microsoft Visual Studio"", ""Installer"", ""vswhere.exe""),
            ""-latest"",
            ""-prerelease"",
            ""-requires"", ""Microsoft.VisualStudio.Component.VC.Tools.x86.x64"",
            ""-property"", ""installationPath"",
            ""-products"", ""*"",
        ], encoding=""mbcs"", errors=""strict"").strip()
    except (subprocess.CalledProcessError, OSError, UnicodeDecodeError):
        return None, None

    path = os.path.join(path, ""VC"", ""Auxiliary"", ""Build"")
    if os.path.isdir(path):
        return 15, path

    return None, None

PLAT_SPEC_TO_RUNTIME = {
    'x86' : 'x86',
    'x86_amd64' : 'x64',
    'x86_arm' : 'arm',
    'x86_arm64' : 'arm64'
}

        ```
    ###Assistant : Returns ""15, path"" based on the result of invoking vswhere.exe
    If no install is found, returns ""None, None""

    The version is returned to avoid unnecessarily changing the function
    result. It may be ignored when the path is not None.

    If vswhere.exe is not available, by definition, VS 2017 is not
    installed.
    
    "
1589,"def save_attributes_to_hdf5_group(group, name, data):
    
    # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
    # because in that case even chunking the array would not make the saving
    # possible.
    bad_attributes = [x for x in data if len(x) > HDF5_OBJECT_HEADER_LIMIT]

    # Expecting this to never be true.
    if bad_attributes:
        raise RuntimeError(
            ""The following attributes cannot be saved to HDF5 file because they ""
            f""are larger than {HDF5_OBJECT_HEADER_LIMIT} bytes: {bad_attributes}""
        )

    data_npy = np.asarray(data)

    num_chunks = 1
    chunked_data = np.array_split(data_npy, num_chunks)

    # This will never loop forever thanks to the test above.
    while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
        num_chunks += 1
        chunked_data = np.array_split(data_npy, num_chunks)

    if num_chunks > 1:
        for chunk_id, chunk_data in enumerate(chunked_data):
            group.attrs[""%s%d"" % (name, chunk_id)] = chunk_data
    else:
        group.attrs[name] = data

","Saves attributes (data) of the specified name into the HDF5 group.

    This method deals with an inherent problem of HDF5 file which is not
    able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes.

    Args:
        group: A pointer to a HDF5 group.
        name: A name of the attributes to save.
        data: Attributes data to store.

    Raises:
      RuntimeError: If any single attribute is too large to be saved.
    ",65,127,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_attributes_to_hdf5_group(group, name, data):
    
    # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
    # because in that case even chunking the array would not make the saving
    # possible.
    bad_attributes = [x for x in data if len(x) > HDF5_OBJECT_HEADER_LIMIT]

    # Expecting this to never be true.
    if bad_attributes:
        raise RuntimeError(
            ""The following attributes cannot be saved to HDF5 file because they ""
            f""are larger than {HDF5_OBJECT_HEADER_LIMIT} bytes: {bad_attributes}""
        )

    data_npy = np.asarray(data)

    num_chunks = 1
    chunked_data = np.array_split(data_npy, num_chunks)

    # This will never loop forever thanks to the test above.
    while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
        num_chunks += 1
        chunked_data = np.array_split(data_npy, num_chunks)

    if num_chunks > 1:
        for chunk_id, chunk_data in enumerate(chunked_data):
            group.attrs[""%s%d"" % (name, chunk_id)] = chunk_data
    else:
        group.attrs[name] = data


        ```
    ###Assistant : Saves attributes (data) of the specified name into the HDF5 group.

    This method deals with an inherent problem of HDF5 file which is not
    able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes.

    Args:
        group: A pointer to a HDF5 group.
        name: A name of the attributes to save.
        data: Attributes data to store.

    Raises:
      RuntimeError: If any single attribute is too large to be saved.
    
    "
1590,"def dispatch_hook(cls, _pkt=None, *args, **kargs):
        
        if _pkt is not None:
            plen = len(_pkt)
            if plen >= 2:
                byte0, byte1 = struct.unpack(""BB"", _pkt[:2])
                s = kargs.get(""tls_session"", None)
                if byte0 not in _tls_type or byte1 != 3:  # Unknown type
                    # Check SSLv2: either the session is already SSLv2,
                    # either the packet looks like one. As said above, this
                    # isn't 100% reliable, but Wireshark does the same
                    if s and (s.tls_version == 0x0002 or
                              s.advertised_tls_version == 0x0002) or \
                             (_ssl_looks_like_sslv2(_pkt) and (not s or
                              s.tls_version is None)):
                        from scapy.layers.tls.record_sslv2 import SSLv2
                        return SSLv2
                    # Not SSLv2: continuation
                    return _TLSEncryptedContent
                # Check TLS 1.3
                if s and _tls_version_check(s.tls_version, 0x0304):
                    _has_cipher = lambda x: (
                        x and not isinstance(x.cipher, Cipher_NULL)
                    )
                    if (_has_cipher(s.rcs) or _has_cipher(s.prcs)) and \
                            byte0 == 0x17:
                        from scapy.layers.tls.record_tls13 import TLS13
                        return TLS13
            if plen < 5:
                # Layer detected as TLS but too small to be a
                # parsed. Scapy should not try to decode them
                return _TLSEncryptedContent
        return TLS

    # Parsing methods
","
        If the TLS class was called on raw SSLv2 data, we want to return an
        SSLv2 record instance. We acknowledge the risk of SSLv2 packets with a
        msglen of 0x1403, 0x1503, 0x1603 or 0x1703 which will never be casted
        as SSLv2 records but TLS ones instead, but hey, we can't be held
        responsible for low-minded extensibility choices.
        ",57,165,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dispatch_hook(cls, _pkt=None, *args, **kargs):
        
        if _pkt is not None:
            plen = len(_pkt)
            if plen >= 2:
                byte0, byte1 = struct.unpack(""BB"", _pkt[:2])
                s = kargs.get(""tls_session"", None)
                if byte0 not in _tls_type or byte1 != 3:  # Unknown type
                    # Check SSLv2: either the session is already SSLv2,
                    # either the packet looks like one. As said above, this
                    # isn't 100% reliable, but Wireshark does the same
                    if s and (s.tls_version == 0x0002 or
                              s.advertised_tls_version == 0x0002) or \
                             (_ssl_looks_like_sslv2(_pkt) and (not s or
                              s.tls_version is None)):
                        from scapy.layers.tls.record_sslv2 import SSLv2
                        return SSLv2
                    # Not SSLv2: continuation
                    return _TLSEncryptedContent
                # Check TLS 1.3
                if s and _tls_version_check(s.tls_version, 0x0304):
                    _has_cipher = lambda x: (
                        x and not isinstance(x.cipher, Cipher_NULL)
                    )
                    if (_has_cipher(s.rcs) or _has_cipher(s.prcs)) and \
                            byte0 == 0x17:
                        from scapy.layers.tls.record_tls13 import TLS13
                        return TLS13
            if plen < 5:
                # Layer detected as TLS but too small to be a
                # parsed. Scapy should not try to decode them
                return _TLSEncryptedContent
        return TLS

    # Parsing methods

        ```
    ###Assistant : 
        If the TLS class was called on raw SSLv2 data, we want to return an
        SSLv2 record instance. We acknowledge the risk of SSLv2 packets with a
        msglen of 0x1403, 0x1503, 0x1603 or 0x1703 which will never be casted
        as SSLv2 records but TLS ones instead, but hey, we can't be held
        responsible for low-minded extensibility choices.
        
    "
1591,"def get_leave_period(from_date, to_date, company):
	leave_period = frappe.db.sql(
		,
		{""from_date"": from_date, ""to_date"": to_date, ""company"": company},
		as_dict=1,
	)

	if leave_period:
		return leave_period

","
		select name, from_date, to_date
		from `tabLeave Period`
		where company=%(company)s and is_active=1
			and (from_date between %(from_date)s and %(to_date)s
				or to_date between %(from_date)s and %(to_date)s
				or (from_date < %(from_date)s and to_date > %(to_date)s))
	",31,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_leave_period(from_date, to_date, company):
	leave_period = frappe.db.sql(
		,
		{""from_date"": from_date, ""to_date"": to_date, ""company"": company},
		as_dict=1,
	)

	if leave_period:
		return leave_period


        ```
    ###Assistant : 
		select name, from_date, to_date
		from `tabLeave Period`
		where company=%(company)s and is_active=1
			and (from_date between %(from_date)s and %(to_date)s
				or to_date between %(from_date)s and %(to_date)s
				or (from_date < %(from_date)s and to_date > %(to_date)s))
	
    "
1592,"def rows(self) -> Iterator[Dict[str, TensorType]]:
        

        # Do we add seq_lens=[1] to each row?
        seq_lens = None if self.get(SampleBatch.SEQ_LENS) is None else np.array([1])

        self_as_dict = {k: v for k, v in self.items()}

        for i in range(self.count):
            yield tree.map_structure_with_path(
                lambda p, v: v[i] if p[0] != self.SEQ_LENS else seq_lens,
                self_as_dict,
            )
","Returns an iterator over data rows, i.e. dicts with column values.

        Note that if `seq_lens` is set in self, we set it to [1] in the rows.

        Yields:
            The column values of the row in this iteration.

        Examples:
            >>> batch = SampleBatch({
            ...    ""a"": [1, 2, 3],
            ...    ""b"": [4, 5, 6],
            ...    ""seq_lens"": [1, 2]
            ... })
            >>> for row in batch.rows():
                   print(row)
            {""a"": 1, ""b"": 4, ""seq_lens"": [1]}
            {""a"": 2, ""b"": 5, ""seq_lens"": [1]}
            {""a"": 3, ""b"": 6, ""seq_lens"": [1]}
        ",82,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rows(self) -> Iterator[Dict[str, TensorType]]:
        

        # Do we add seq_lens=[1] to each row?
        seq_lens = None if self.get(SampleBatch.SEQ_LENS) is None else np.array([1])

        self_as_dict = {k: v for k, v in self.items()}

        for i in range(self.count):
            yield tree.map_structure_with_path(
                lambda p, v: v[i] if p[0] != self.SEQ_LENS else seq_lens,
                self_as_dict,
            )

        ```
    ###Assistant : Returns an iterator over data rows, i.e. dicts with column values.

        Note that if `seq_lens` is set in self, we set it to [1] in the rows.

        Yields:
            The column values of the row in this iteration.

        Examples:
            >>> batch = SampleBatch({
            ...    ""a"": [1, 2, 3],
            ...    ""b"": [4, 5, 6],
            ...    ""seq_lens"": [1, 2]
            ... })
            >>> for row in batch.rows():
                   print(row)
            {""a"": 1, ""b"": 4, ""seq_lens"": [1]}
            {""a"": 2, ""b"": 5, ""seq_lens"": [1]}
            {""a"": 3, ""b"": 6, ""seq_lens"": [1]}
        
    "
1593,"def _dedup_weights(self, weights):
        
        output, seen_ids = [], set()
        for w in weights:
            if id(w) not in seen_ids:
                output.append(w)
                # Track the Variable's identity to avoid __eq__ issues.
                seen_ids.add(id(w))

        return output

    # SavedModel properties. Please see keras/saving/saved_model for details.
",Dedupe weights while maintaining order as much as possible.,9,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _dedup_weights(self, weights):
        
        output, seen_ids = [], set()
        for w in weights:
            if id(w) not in seen_ids:
                output.append(w)
                # Track the Variable's identity to avoid __eq__ issues.
                seen_ids.add(id(w))

        return output

    # SavedModel properties. Please see keras/saving/saved_model for details.

        ```
    ###Assistant : Dedupe weights while maintaining order as much as possible.
    "
1594,"def ancestors_with_self(self) -> list[DOMNode]:
        
        nodes: list[MessagePump | None] = []
        add_node = nodes.append
        node: MessagePump | None = self
        while node is not None:
            add_node(node)
            node = node._parent
        return cast(""list[DOMNode]"", nodes)
","list[DOMNode]: A list of Nodes by tracing a path all the way back to App.

        Note: This is inclusive of ``self``.
        ",21,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ancestors_with_self(self) -> list[DOMNode]:
        
        nodes: list[MessagePump | None] = []
        add_node = nodes.append
        node: MessagePump | None = self
        while node is not None:
            add_node(node)
            node = node._parent
        return cast(""list[DOMNode]"", nodes)

        ```
    ###Assistant : list[DOMNode]: A list of Nodes by tracing a path all the way back to App.

        Note: This is inclusive of ``self``.
        
    "
1595,"def function_converter(self) -> Mapping[str, fields.MetricsFunction]:
        
        resolve_metric_id = {
            ""name"": ""metric_id"",
            ""fn"": lambda args: self.resolve_metric(args[""column""]),
        }

        function_converter = {
            function.name: function
            for function in [
                # Note while the discover version of apdex, count_miserable, user_misery
                # accepts arguments, because this is precomputed with tags no parameters
                # are available
                fields.MetricsFunction(
                    ""apdex"",
                    optional_args=[fields.NullableNumberRange(""satisfaction"", 0, None)],
                    snql_distribution=self._resolve_apdex_function,
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""avg"",
                    required_args=[
                        fields.MetricArg(
                            ""column"",
                            allowed_columns=constants.METRIC_DURATION_COLUMNS,
                        )
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""avgIf"",
                        [
                            Column(""value""),
                            Function(
                                ""equals"",
                                [
                                    Column(""metric_id""),
                                    args[""metric_id""],
                                ],
                            ),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_miserable"",
                    required_args=[
                        fields.MetricArg(
                            ""column"", allowed_columns=[""user""], allow_custom_measurements=False
                        )
                    ],
                    optional_args=[fields.NullableNumberRange(""satisfaction"", 0, None)],
                    calculated_args=[resolve_metric_id],
                    snql_set=self._resolve_count_miserable_function,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_unparameterized_transactions"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""and"",
                                [
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                    Function(
                                        ""equals"",
                                        [
                                            self.builder.column(""transaction""),
                                            self.builder.resolve_tag_value(""<< unparameterized >>""),
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    # Not yet exposed, need to add far more validation around tag&value
                    private=True,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_null_transactions"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""and"",
                                [
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                    Function(
                                        ""equals"",
                                        [
                                            self.builder.column(""transaction""),
                                            """" if self.builder.tag_values_are_strings else 0,
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    private=True,
                ),
                fields.MetricsFunction(
                    ""count_has_transaction_name"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""and"",
                                [
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                    Function(
                                        ""and"",
                                        [
                                            Function(
                                                ""notEquals"",
                                                [
                                                    self.builder.column(""transaction""),
                                                    """"
                                                    if self.builder.tag_values_are_strings
                                                    else 0,
                                                ],
                                            ),
                                            Function(
                                                ""notEquals"",
                                                [
                                                    self.builder.column(""transaction""),
                                                    self.builder.resolve_tag_value(
                                                        ""<< unparameterized >>""
                                                    ),
                                                ],
                                            ),
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    private=True,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""user_misery"",
                    optional_args=[
                        fields.NullableNumberRange(""satisfaction"", 0, None),
                        fields.with_default(
                            constants.MISERY_ALPHA, fields.NumberRange(""alpha"", 0, None)
                        ),
                        fields.with_default(
                            constants.MISERY_BETA, fields.NumberRange(""beta"", 0, None)
                        ),
                    ],
                    calculated_args=[],
                    snql_set=self._resolve_user_misery_function,
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""p50"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.5
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p75"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.75
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p90"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.90
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p95"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.95
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p99"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.99
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p100"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(args, alias, 1),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""max"",
                    required_args=[
                        fields.MetricArg(""column""),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""maxIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                ),
                fields.MetricsFunction(
                    ""min"",
                    required_args=[
                        fields.MetricArg(""column""),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""minIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                ),
                fields.MetricsFunction(
                    ""sum"",
                    required_args=[
                        fields.MetricArg(""column""),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""sumIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                ),
                fields.MetricsFunction(
                    ""sumIf"",
                    required_args=[
                        fields.ColumnTagArg(""if_col""),
                        fields.FunctionArg(""if_val""),
                    ],
                    calculated_args=[
                        {
                            ""name"": ""resolved_val"",
                            ""fn"": lambda args: self.builder.resolve_tag_value(args[""if_val""]),
                        }
                    ],
                    snql_counter=lambda args, alias: Function(
                        ""sumIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [args[""if_col""], args[""resolved_val""]]),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""percentile"",
                    required_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                        fields.NumberRange(""percentile"", 0, 1),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=self._resolve_percentile,
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""count_unique"",
                    required_args=[
                        fields.MetricArg(
                            ""column"", allowed_columns=[""user""], allow_custom_measurements=False
                        )
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_set=lambda args, alias: Function(
                        ""uniqIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""uniq"",
                    snql_set=lambda args, alias: Function(
                        ""uniq"",
                        [Column(""value"")],
                        alias,
                    ),
                ),
                fields.MetricsFunction(
                    ""uniqIf"",
                    required_args=[
                        fields.ColumnTagArg(""if_col""),
                        fields.FunctionArg(""if_val""),
                    ],
                    calculated_args=[
                        {
                            ""name"": ""resolved_val"",
                            ""fn"": lambda args: self.builder.resolve_tag_value(args[""if_val""]),
                        }
                    ],
                    snql_set=lambda args, alias: Function(
                        ""uniqIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [args[""if_col""], args[""resolved_val""]]),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""equals"",
                                [
                                    Column(""metric_id""),
                                    self.resolve_metric(""transaction.duration""),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_web_vitals"",
                    required_args=[
                        fields.MetricArg(
                            ""column"",
                            allowed_columns=[
                                ""measurements.fp"",
                                ""measurements.fcp"",
                                ""measurements.lcp"",
                                ""measurements.fid"",
                                ""measurements.cls"",
                            ],
                            allow_custom_measurements=False,
                        ),
                        fields.SnQLStringArg(
                            ""quality"", allowed_strings=[""good"", ""meh"", ""poor"", ""any""]
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=self._resolve_web_vital_function,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""epm"",
                    snql_distribution=lambda args, alias: Function(
                        ""divide"",
                        [
                            Function(
                                ""countIf"",
                                [
                                    Column(""value""),
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                ],
                            ),
                            Function(""divide"", [args[""interval""], 60]),
                        ],
                        alias,
                    ),
                    optional_args=[fields.IntervalDefault(""interval"", 1, None)],
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""eps"",
                    snql_distribution=lambda args, alias: Function(
                        ""divide"",
                        [
                            Function(
                                ""countIf"",
                                [
                                    Column(""value""),
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                ],
                            ),
                            args[""interval""],
                        ],
                        alias,
                    ),
                    optional_args=[fields.IntervalDefault(""interval"", 1, None)],
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""failure_count"",
                    snql_distribution=self._resolve_failure_count,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""failure_rate"",
                    snql_distribution=lambda args, alias: Function(
                        ""divide"",
                        [
                            self._resolve_failure_count(args),
                            Function(
                                ""countIf"",
                                [
                                    Column(""value""),
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    default_result_type=""percentage"",
                ),
                fields.MetricsFunction(
                    ""histogram"",
                    required_args=[fields.MetricArg(""column"")],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=self._resolve_histogram_function,
                    default_result_type=""number"",
                    private=True,
                ),
            ]
        }

        for alias, name in constants.FUNCTION_ALIASES.items():
            if name in function_converter:
                function_converter[alias] = function_converter[name].alias_as(alias)

        return function_converter

    # Field Aliases","While the final functions in clickhouse must have their -Merge combinators in order to function, we don't
        need to add them here since snuba has a FunctionMapper that will add it for us. Basically it turns expressions
        like quantiles(0.9)(value) into quantilesMerge(0.9)(percentiles)
        Make sure to update METRIC_FUNCTION_LIST_BY_TYPE when adding functions here, can't be a dynamic list since the
        Metric Layer will actually handle which dataset each function goes to
        ",68,747,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def function_converter(self) -> Mapping[str, fields.MetricsFunction]:
        
        resolve_metric_id = {
            ""name"": ""metric_id"",
            ""fn"": lambda args: self.resolve_metric(args[""column""]),
        }

        function_converter = {
            function.name: function
            for function in [
                # Note while the discover version of apdex, count_miserable, user_misery
                # accepts arguments, because this is precomputed with tags no parameters
                # are available
                fields.MetricsFunction(
                    ""apdex"",
                    optional_args=[fields.NullableNumberRange(""satisfaction"", 0, None)],
                    snql_distribution=self._resolve_apdex_function,
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""avg"",
                    required_args=[
                        fields.MetricArg(
                            ""column"",
                            allowed_columns=constants.METRIC_DURATION_COLUMNS,
                        )
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""avgIf"",
                        [
                            Column(""value""),
                            Function(
                                ""equals"",
                                [
                                    Column(""metric_id""),
                                    args[""metric_id""],
                                ],
                            ),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_miserable"",
                    required_args=[
                        fields.MetricArg(
                            ""column"", allowed_columns=[""user""], allow_custom_measurements=False
                        )
                    ],
                    optional_args=[fields.NullableNumberRange(""satisfaction"", 0, None)],
                    calculated_args=[resolve_metric_id],
                    snql_set=self._resolve_count_miserable_function,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_unparameterized_transactions"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""and"",
                                [
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                    Function(
                                        ""equals"",
                                        [
                                            self.builder.column(""transaction""),
                                            self.builder.resolve_tag_value(""<< unparameterized >>""),
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    # Not yet exposed, need to add far more validation around tag&value
                    private=True,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_null_transactions"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""and"",
                                [
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                    Function(
                                        ""equals"",
                                        [
                                            self.builder.column(""transaction""),
                                            """" if self.builder.tag_values_are_strings else 0,
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    private=True,
                ),
                fields.MetricsFunction(
                    ""count_has_transaction_name"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""and"",
                                [
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                    Function(
                                        ""and"",
                                        [
                                            Function(
                                                ""notEquals"",
                                                [
                                                    self.builder.column(""transaction""),
                                                    """"
                                                    if self.builder.tag_values_are_strings
                                                    else 0,
                                                ],
                                            ),
                                            Function(
                                                ""notEquals"",
                                                [
                                                    self.builder.column(""transaction""),
                                                    self.builder.resolve_tag_value(
                                                        ""<< unparameterized >>""
                                                    ),
                                                ],
                                            ),
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    private=True,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""user_misery"",
                    optional_args=[
                        fields.NullableNumberRange(""satisfaction"", 0, None),
                        fields.with_default(
                            constants.MISERY_ALPHA, fields.NumberRange(""alpha"", 0, None)
                        ),
                        fields.with_default(
                            constants.MISERY_BETA, fields.NumberRange(""beta"", 0, None)
                        ),
                    ],
                    calculated_args=[],
                    snql_set=self._resolve_user_misery_function,
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""p50"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.5
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p75"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.75
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p90"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.90
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p95"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.95
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p99"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(
                        args, alias, 0.99
                    ),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""p100"",
                    optional_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: self._resolve_percentile(args, alias, 1),
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""max"",
                    required_args=[
                        fields.MetricArg(""column""),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""maxIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                ),
                fields.MetricsFunction(
                    ""min"",
                    required_args=[
                        fields.MetricArg(""column""),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""minIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                ),
                fields.MetricsFunction(
                    ""sum"",
                    required_args=[
                        fields.MetricArg(""column""),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=lambda args, alias: Function(
                        ""sumIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    result_type_fn=self.reflective_result_type(),
                ),
                fields.MetricsFunction(
                    ""sumIf"",
                    required_args=[
                        fields.ColumnTagArg(""if_col""),
                        fields.FunctionArg(""if_val""),
                    ],
                    calculated_args=[
                        {
                            ""name"": ""resolved_val"",
                            ""fn"": lambda args: self.builder.resolve_tag_value(args[""if_val""]),
                        }
                    ],
                    snql_counter=lambda args, alias: Function(
                        ""sumIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [args[""if_col""], args[""resolved_val""]]),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""percentile"",
                    required_args=[
                        fields.with_default(
                            ""transaction.duration"",
                            fields.MetricArg(
                                ""column"", allowed_columns=constants.METRIC_DURATION_COLUMNS
                            ),
                        ),
                        fields.NumberRange(""percentile"", 0, 1),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=self._resolve_percentile,
                    result_type_fn=self.reflective_result_type(),
                    default_result_type=""duration"",
                ),
                fields.MetricsFunction(
                    ""count_unique"",
                    required_args=[
                        fields.MetricArg(
                            ""column"", allowed_columns=[""user""], allow_custom_measurements=False
                        )
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_set=lambda args, alias: Function(
                        ""uniqIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [Column(""metric_id""), args[""metric_id""]]),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""uniq"",
                    snql_set=lambda args, alias: Function(
                        ""uniq"",
                        [Column(""value"")],
                        alias,
                    ),
                ),
                fields.MetricsFunction(
                    ""uniqIf"",
                    required_args=[
                        fields.ColumnTagArg(""if_col""),
                        fields.FunctionArg(""if_val""),
                    ],
                    calculated_args=[
                        {
                            ""name"": ""resolved_val"",
                            ""fn"": lambda args: self.builder.resolve_tag_value(args[""if_val""]),
                        }
                    ],
                    snql_set=lambda args, alias: Function(
                        ""uniqIf"",
                        [
                            Column(""value""),
                            Function(""equals"", [args[""if_col""], args[""resolved_val""]]),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count"",
                    snql_distribution=lambda args, alias: Function(
                        ""countIf"",
                        [
                            Column(""value""),
                            Function(
                                ""equals"",
                                [
                                    Column(""metric_id""),
                                    self.resolve_metric(""transaction.duration""),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""count_web_vitals"",
                    required_args=[
                        fields.MetricArg(
                            ""column"",
                            allowed_columns=[
                                ""measurements.fp"",
                                ""measurements.fcp"",
                                ""measurements.lcp"",
                                ""measurements.fid"",
                                ""measurements.cls"",
                            ],
                            allow_custom_measurements=False,
                        ),
                        fields.SnQLStringArg(
                            ""quality"", allowed_strings=[""good"", ""meh"", ""poor"", ""any""]
                        ),
                    ],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=self._resolve_web_vital_function,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""epm"",
                    snql_distribution=lambda args, alias: Function(
                        ""divide"",
                        [
                            Function(
                                ""countIf"",
                                [
                                    Column(""value""),
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                ],
                            ),
                            Function(""divide"", [args[""interval""], 60]),
                        ],
                        alias,
                    ),
                    optional_args=[fields.IntervalDefault(""interval"", 1, None)],
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""eps"",
                    snql_distribution=lambda args, alias: Function(
                        ""divide"",
                        [
                            Function(
                                ""countIf"",
                                [
                                    Column(""value""),
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                ],
                            ),
                            args[""interval""],
                        ],
                        alias,
                    ),
                    optional_args=[fields.IntervalDefault(""interval"", 1, None)],
                    default_result_type=""number"",
                ),
                fields.MetricsFunction(
                    ""failure_count"",
                    snql_distribution=self._resolve_failure_count,
                    default_result_type=""integer"",
                ),
                fields.MetricsFunction(
                    ""failure_rate"",
                    snql_distribution=lambda args, alias: Function(
                        ""divide"",
                        [
                            self._resolve_failure_count(args),
                            Function(
                                ""countIf"",
                                [
                                    Column(""value""),
                                    Function(
                                        ""equals"",
                                        [
                                            Column(""metric_id""),
                                            self.resolve_metric(""transaction.duration""),
                                        ],
                                    ),
                                ],
                            ),
                        ],
                        alias,
                    ),
                    default_result_type=""percentage"",
                ),
                fields.MetricsFunction(
                    ""histogram"",
                    required_args=[fields.MetricArg(""column"")],
                    calculated_args=[resolve_metric_id],
                    snql_distribution=self._resolve_histogram_function,
                    default_result_type=""number"",
                    private=True,
                ),
            ]
        }

        for alias, name in constants.FUNCTION_ALIASES.items():
            if name in function_converter:
                function_converter[alias] = function_converter[name].alias_as(alias)

        return function_converter

    # Field Aliases
        ```
    ###Assistant : While the final functions in clickhouse must have their -Merge combinators in order to function, we don't
        need to add them here since snuba has a FunctionMapper that will add it for us. Basically it turns expressions
        like quantiles(0.9)(value) into quantilesMerge(0.9)(percentiles)
        Make sure to update METRIC_FUNCTION_LIST_BY_TYPE when adding functions here, can't be a dynamic list since the
        Metric Layer will actually handle which dataset each function goes to
        
    "
1596,"def copyUsedDLLs(source_dir, dist_dir, standalone_entry_points):
    # This is terribly complex, because we check the list of used DLLs
    # trying to avoid duplicates, and detecting errors with them not
    # being binary identical, so we can report them. And then of course
    # we also need to handle OS specifics.
    # pylint: disable=too-many-branches,too-many-locals,too-many-statements

    used_dlls = detectUsedDLLs(
        source_dir=source_dir,
        standalone_entry_points=standalone_entry_points,
        use_cache=not Options.shallNotUseDependsExeCachedResults()
        and not Options.getWindowsDependencyTool() == ""depends.exe"",
        update_cache=not Options.shallNotStoreDependsExeCachedResults()
        and not Options.getWindowsDependencyTool() == ""depends.exe"",
    )

    removed_dlls = set()
    warned_about = set()

    # Fist make checks and remove some.
    for dll_filename1, sources1 in tuple(iterItems(used_dlls)):
        if dll_filename1 in removed_dlls:
            continue

        for dll_filename2, sources2 in tuple(iterItems(used_dlls)):
            if dll_filename1 == dll_filename2:
                continue

            if dll_filename2 in removed_dlls:
                continue

            # Colliding basenames are an issue to us.
            if os.path.basename(dll_filename1) != os.path.basename(dll_filename2):
                continue

            # May already have been removed earlier
            if dll_filename1 not in used_dlls:
                continue

            if dll_filename2 not in used_dlls:
                continue

            dll_name = os.path.basename(dll_filename1)

            if Options.isShowInclusion():
                inclusion_logger.info(
                    
                    % (dll_name, dll_filename1, dll_filename2)
                )

            # Check that if a DLL has the same name, if it's identical, then it's easy.
            if haveSameFileContents(dll_filename1, dll_filename2):
                del used_dlls[dll_filename2]
                removed_dlls.add(dll_filename2)

                continue

            # For Win32 we can check out file versions.
            if Utils.isWin32Windows():
                dll_version1 = getWindowsDLLVersion(dll_filename1)
                dll_version2 = getWindowsDLLVersion(dll_filename2)

                if dll_version2 < dll_version1:
                    del used_dlls[dll_filename2]
                    removed_dlls.add(dll_filename2)

                    solved = True
                elif dll_version1 < dll_version2:
                    del used_dlls[dll_filename1]
                    removed_dlls.add(dll_filename1)

                    solved = True
                else:
                    solved = False

                if solved:
                    if dll_name not in warned_about and dll_name not in ms_runtime_dlls:
                        warned_about.add(dll_name)

                        inclusion_logger.warning(
                            ""Conflicting DLLs for '%s' in your installation, newest file version used, hoping for the best.""
                            % dll_name
                        )

                    continue

            # So we have conflicting DLLs, in which case we do report the fact.
            inclusion_logger.warning(
                
                % (
                    dll_name,
                    dll_filename1,
                    ""\n   "".join(sources1),
                    dll_filename2,
                    ""\n   "".join(sources2),
                )
            )

            del used_dlls[dll_filename2]
            removed_dlls.add(dll_filename2)

    dll_map = []

    for dll_filename, sources in iterItems(used_dlls):
        dll_name = os.path.basename(dll_filename)

        target_path = os.path.join(dist_dir, dll_name)

        # Sometimes DLL dependencies were copied there already.
        if not os.path.exists(target_path):
            copyFile(dll_filename, target_path)

        dll_map.append((dll_filename, dll_name))

        if Options.isShowInclusion():
            inclusion_logger.info(
                ""Included used shared library '%s' (used by %s).""
                % (dll_filename, "", "".join(sources))
            )

    if Utils.isMacOS():
        # For macOS, the binary and the DLLs needs to be changed to reflect
        # the relative DLL location in the "".dist"" folder.
        for standalone_entry_point in standalone_entry_points:
            fixupBinaryDLLPathsMacOS(
                binary_filename=standalone_entry_point.dest_path,
                dll_map=dll_map,
                original_location=standalone_entry_point.source_path,
            )

        for original_path, dll_filename in dll_map:
            fixupBinaryDLLPathsMacOS(
                binary_filename=os.path.join(dist_dir, dll_filename),
                dll_map=dll_map,
                original_location=original_path,
            )

        # Remove code signature from CPython installed library
        candidate = os.path.join(
            dist_dir,
            ""Python"",
        )

        if os.path.exists(candidate):
            removeMacOSCodeSignature(candidate)

    # Remove or update rpath settings.
    if Utils.getOS() in (""Linux"", ""Darwin""):
        # For Linux, the ""rpath"" of libraries may be an issue and must be
        # removed.
        if Utils.isMacOS():
            start = 0
        else:
            start = 1

        for standalone_entry_point in standalone_entry_points[start:]:
            count = relpath(
                path=standalone_entry_point.dest_path, start=dist_dir
            ).count(os.path.sep)

            rpath = os.path.join(""$ORIGIN"", *([""..""] * count))
            setSharedLibraryRPATH(standalone_entry_point.dest_path, rpath)

        for _original_path, dll_filename in dll_map:
            setSharedLibraryRPATH(os.path.join(dist_dir, dll_filename), ""$ORIGIN"")

    if Utils.isWin32Windows():
        if python_version < 0x300:
            # For Win32, we might have to remove SXS paths
            for standalone_entry_point in standalone_entry_points[1:]:
                removeSxsFromDLL(standalone_entry_point.dest_path)

            for _original_path, dll_filename in dll_map:
                removeSxsFromDLL(os.path.join(dist_dir, dll_filename))

","Colliding DLL names for %s, checking identity of \
'%s' <-> '%s'.\
Ignoring non-identical DLLs for '%s'.
%s used by:
   %s
different from
%s used by
   %s",27,477,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copyUsedDLLs(source_dir, dist_dir, standalone_entry_points):
    # This is terribly complex, because we check the list of used DLLs
    # trying to avoid duplicates, and detecting errors with them not
    # being binary identical, so we can report them. And then of course
    # we also need to handle OS specifics.
    # pylint: disable=too-many-branches,too-many-locals,too-many-statements

    used_dlls = detectUsedDLLs(
        source_dir=source_dir,
        standalone_entry_points=standalone_entry_points,
        use_cache=not Options.shallNotUseDependsExeCachedResults()
        and not Options.getWindowsDependencyTool() == ""depends.exe"",
        update_cache=not Options.shallNotStoreDependsExeCachedResults()
        and not Options.getWindowsDependencyTool() == ""depends.exe"",
    )

    removed_dlls = set()
    warned_about = set()

    # Fist make checks and remove some.
    for dll_filename1, sources1 in tuple(iterItems(used_dlls)):
        if dll_filename1 in removed_dlls:
            continue

        for dll_filename2, sources2 in tuple(iterItems(used_dlls)):
            if dll_filename1 == dll_filename2:
                continue

            if dll_filename2 in removed_dlls:
                continue

            # Colliding basenames are an issue to us.
            if os.path.basename(dll_filename1) != os.path.basename(dll_filename2):
                continue

            # May already have been removed earlier
            if dll_filename1 not in used_dlls:
                continue

            if dll_filename2 not in used_dlls:
                continue

            dll_name = os.path.basename(dll_filename1)

            if Options.isShowInclusion():
                inclusion_logger.info(
                    
                    % (dll_name, dll_filename1, dll_filename2)
                )

            # Check that if a DLL has the same name, if it's identical, then it's easy.
            if haveSameFileContents(dll_filename1, dll_filename2):
                del used_dlls[dll_filename2]
                removed_dlls.add(dll_filename2)

                continue

            # For Win32 we can check out file versions.
            if Utils.isWin32Windows():
                dll_version1 = getWindowsDLLVersion(dll_filename1)
                dll_version2 = getWindowsDLLVersion(dll_filename2)

                if dll_version2 < dll_version1:
                    del used_dlls[dll_filename2]
                    removed_dlls.add(dll_filename2)

                    solved = True
                elif dll_version1 < dll_version2:
                    del used_dlls[dll_filename1]
                    removed_dlls.add(dll_filename1)

                    solved = True
                else:
                    solved = False

                if solved:
                    if dll_name not in warned_about and dll_name not in ms_runtime_dlls:
                        warned_about.add(dll_name)

                        inclusion_logger.warning(
                            ""Conflicting DLLs for '%s' in your installation, newest file version used, hoping for the best.""
                            % dll_name
                        )

                    continue

            # So we have conflicting DLLs, in which case we do report the fact.
            inclusion_logger.warning(
                
                % (
                    dll_name,
                    dll_filename1,
                    ""\n   "".join(sources1),
                    dll_filename2,
                    ""\n   "".join(sources2),
                )
            )

            del used_dlls[dll_filename2]
            removed_dlls.add(dll_filename2)

    dll_map = []

    for dll_filename, sources in iterItems(used_dlls):
        dll_name = os.path.basename(dll_filename)

        target_path = os.path.join(dist_dir, dll_name)

        # Sometimes DLL dependencies were copied there already.
        if not os.path.exists(target_path):
            copyFile(dll_filename, target_path)

        dll_map.append((dll_filename, dll_name))

        if Options.isShowInclusion():
            inclusion_logger.info(
                ""Included used shared library '%s' (used by %s).""
                % (dll_filename, "", "".join(sources))
            )

    if Utils.isMacOS():
        # For macOS, the binary and the DLLs needs to be changed to reflect
        # the relative DLL location in the "".dist"" folder.
        for standalone_entry_point in standalone_entry_points:
            fixupBinaryDLLPathsMacOS(
                binary_filename=standalone_entry_point.dest_path,
                dll_map=dll_map,
                original_location=standalone_entry_point.source_path,
            )

        for original_path, dll_filename in dll_map:
            fixupBinaryDLLPathsMacOS(
                binary_filename=os.path.join(dist_dir, dll_filename),
                dll_map=dll_map,
                original_location=original_path,
            )

        # Remove code signature from CPython installed library
        candidate = os.path.join(
            dist_dir,
            ""Python"",
        )

        if os.path.exists(candidate):
            removeMacOSCodeSignature(candidate)

    # Remove or update rpath settings.
    if Utils.getOS() in (""Linux"", ""Darwin""):
        # For Linux, the ""rpath"" of libraries may be an issue and must be
        # removed.
        if Utils.isMacOS():
            start = 0
        else:
            start = 1

        for standalone_entry_point in standalone_entry_points[start:]:
            count = relpath(
                path=standalone_entry_point.dest_path, start=dist_dir
            ).count(os.path.sep)

            rpath = os.path.join(""$ORIGIN"", *([""..""] * count))
            setSharedLibraryRPATH(standalone_entry_point.dest_path, rpath)

        for _original_path, dll_filename in dll_map:
            setSharedLibraryRPATH(os.path.join(dist_dir, dll_filename), ""$ORIGIN"")

    if Utils.isWin32Windows():
        if python_version < 0x300:
            # For Win32, we might have to remove SXS paths
            for standalone_entry_point in standalone_entry_points[1:]:
                removeSxsFromDLL(standalone_entry_point.dest_path)

            for _original_path, dll_filename in dll_map:
                removeSxsFromDLL(os.path.join(dist_dir, dll_filename))


        ```
    ###Assistant : Colliding DLL names for %s, checking identity of \
'%s' <-> '%s'.\
Ignoring non-identical DLLs for '%s'.
%s used by:
   %s
different from
%s used by
   %s
    "
1597,"def _laplace_rule_diff(f, t, s, doit=True, **hints):
    
    hints.pop('simplify', True)
    a = Wild('a', exclude=[t])
    y = Wild('y')
    n = Wild('n', exclude=[t])
    g = WildFunction('g', nargs=1)
    ma1 = f.match(a*Derivative(g, (t, n)))
    if ma1 and ma1[g].args[0] == t and ma1[n].is_integer:
        debug('_laplace_apply_rules match:')
        debug('      f:    %s'%(f,))
        debug('      rule: time derivative (1.11, 1.12)')
        d = []
        for k in range(ma1[n]):
            if k==0:
                y = ma1[g].func(t).subs(t, 0)
            else:
                y = Derivative(ma1[g].func(t), (t, k)).subs(t, 0)
            d.append(s**(ma1[n]-k-1)*y)
        r = s**ma1[n]*_laplace_apply_rules(ma1[g].func(t), t, s, doit=doit,
                                           **hints)
        return ma1[a]*(r - Add(*d))
    return None

","
    This internal helper function tries to transform an expression containing
    a derivative of an undefined function and returns `None` if it cannot
    do it.
    ",24,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _laplace_rule_diff(f, t, s, doit=True, **hints):
    
    hints.pop('simplify', True)
    a = Wild('a', exclude=[t])
    y = Wild('y')
    n = Wild('n', exclude=[t])
    g = WildFunction('g', nargs=1)
    ma1 = f.match(a*Derivative(g, (t, n)))
    if ma1 and ma1[g].args[0] == t and ma1[n].is_integer:
        debug('_laplace_apply_rules match:')
        debug('      f:    %s'%(f,))
        debug('      rule: time derivative (1.11, 1.12)')
        d = []
        for k in range(ma1[n]):
            if k==0:
                y = ma1[g].func(t).subs(t, 0)
            else:
                y = Derivative(ma1[g].func(t), (t, k)).subs(t, 0)
            d.append(s**(ma1[n]-k-1)*y)
        r = s**ma1[n]*_laplace_apply_rules(ma1[g].func(t), t, s, doit=doit,
                                           **hints)
        return ma1[a]*(r - Add(*d))
    return None


        ```
    ###Assistant : 
    This internal helper function tries to transform an expression containing
    a derivative of an undefined function and returns `None` if it cannot
    do it.
    
    "
1598,"def _check_flag(user, flag, attributes, user_flags_settings):
    
    new_flag = False
    is_role_key = ""is_%s_role"" % (flag)
    is_attr_key = ""is_%s_attr"" % (flag)
    is_value_key = ""is_%s_value"" % (flag)
    remove_setting = ""remove_%ss"" % (flag)

    # Check to see if we are respecting a role and, if so, does our user have that role?
    required_roles = user_flags_settings.get(is_role_key, None)
    if required_roles:
        matching_roles = _get_matches(required_roles, attributes.get('Role', []))

        # We do a 2 layer check here so that we don't spit out the else message if there is no role defined
        if matching_roles:
            logger.debug(""User %s has %s role(s) %s"" % (user.username, flag, ', '.join(matching_roles)))
            new_flag = True
        else:
            logger.debug(""User %s is missing the %s role(s) %s"" % (user.username, flag, ', '.join(required_roles)))

    # Next, check to see if we are respecting an attribute; this will take priority over the role if its defined
    attr_setting = user_flags_settings.get(is_attr_key, None)
    if attr_setting and attributes.get(attr_setting, None):
        # Do we have a required value for the attribute
        required_value = user_flags_settings.get(is_value_key, None)
        if required_value:
            # If so, check and see if the value of the attr matches the required value
            saml_user_attribute_value = attributes.get(attr_setting, None)
            matching_values = _get_matches(required_value, saml_user_attribute_value)

            if matching_values:
                logger.debug(""Giving %s %s from attribute %s with matching values %s"" % (user.username, flag, attr_setting, ', '.join(matching_values)))
                new_flag = True
            # if they don't match make sure that new_flag is false
            else:
                logger.debug(
                    ""Refusing %s for %s because attr %s (%s) did not match value(s) %s""
                    % (flag, user.username, attr_setting, "", "".join(saml_user_attribute_value), ', '.join(required_value))
                )
                new_flag = False
        # If there was no required value then we can just allow them in because of the attribute
        else:
            logger.debug(""Giving %s %s from attribute %s"" % (user.username, flag, attr_setting))
            new_flag = True

    # Get the users old flag
    old_value = getattr(user, ""is_%s"" % (flag))

    # If we are not removing the flag and they were a system admin and now we don't want them to be just return
    remove_flag = user_flags_settings.get(remove_setting, True)
    if not remove_flag and (old_value and not new_flag):
        logger.debug(""Remove flag %s preventing removal of %s for %s"" % (remove_flag, flag, user.username))
        return old_value, False

    # If the user was flagged and we are going to make them not flagged make sure there is a message
    if old_value and not new_flag:
        logger.debug(""Revoking %s from %s"" % (flag, user.username))

    return new_flag, old_value != new_flag

","
    Helper function to set the is_superuser is_system_auditor flags for the SAML adapter
    Returns the new flag and whether or not it changed the flag
    ",24,374,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_flag(user, flag, attributes, user_flags_settings):
    
    new_flag = False
    is_role_key = ""is_%s_role"" % (flag)
    is_attr_key = ""is_%s_attr"" % (flag)
    is_value_key = ""is_%s_value"" % (flag)
    remove_setting = ""remove_%ss"" % (flag)

    # Check to see if we are respecting a role and, if so, does our user have that role?
    required_roles = user_flags_settings.get(is_role_key, None)
    if required_roles:
        matching_roles = _get_matches(required_roles, attributes.get('Role', []))

        # We do a 2 layer check here so that we don't spit out the else message if there is no role defined
        if matching_roles:
            logger.debug(""User %s has %s role(s) %s"" % (user.username, flag, ', '.join(matching_roles)))
            new_flag = True
        else:
            logger.debug(""User %s is missing the %s role(s) %s"" % (user.username, flag, ', '.join(required_roles)))

    # Next, check to see if we are respecting an attribute; this will take priority over the role if its defined
    attr_setting = user_flags_settings.get(is_attr_key, None)
    if attr_setting and attributes.get(attr_setting, None):
        # Do we have a required value for the attribute
        required_value = user_flags_settings.get(is_value_key, None)
        if required_value:
            # If so, check and see if the value of the attr matches the required value
            saml_user_attribute_value = attributes.get(attr_setting, None)
            matching_values = _get_matches(required_value, saml_user_attribute_value)

            if matching_values:
                logger.debug(""Giving %s %s from attribute %s with matching values %s"" % (user.username, flag, attr_setting, ', '.join(matching_values)))
                new_flag = True
            # if they don't match make sure that new_flag is false
            else:
                logger.debug(
                    ""Refusing %s for %s because attr %s (%s) did not match value(s) %s""
                    % (flag, user.username, attr_setting, "", "".join(saml_user_attribute_value), ', '.join(required_value))
                )
                new_flag = False
        # If there was no required value then we can just allow them in because of the attribute
        else:
            logger.debug(""Giving %s %s from attribute %s"" % (user.username, flag, attr_setting))
            new_flag = True

    # Get the users old flag
    old_value = getattr(user, ""is_%s"" % (flag))

    # If we are not removing the flag and they were a system admin and now we don't want them to be just return
    remove_flag = user_flags_settings.get(remove_setting, True)
    if not remove_flag and (old_value and not new_flag):
        logger.debug(""Remove flag %s preventing removal of %s for %s"" % (remove_flag, flag, user.username))
        return old_value, False

    # If the user was flagged and we are going to make them not flagged make sure there is a message
    if old_value and not new_flag:
        logger.debug(""Revoking %s from %s"" % (flag, user.username))

    return new_flag, old_value != new_flag


        ```
    ###Assistant : 
    Helper function to set the is_superuser is_system_auditor flags for the SAML adapter
    Returns the new flag and whether or not it changed the flag
    
    "
1599,"def test_get_backfill_points_in_room(self):
        
        setup_info = self._setup_room_for_backfill_tests()
        room_id = setup_info.room_id

        backfill_points = self.get_success(
            self.store.get_backfill_points_in_room(room_id)
        )
        backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
        self.assertListEqual(
            backfill_event_ids, [""b6"", ""b5"", ""b4"", ""2"", ""b3"", ""b2"", ""b1""]
        )
","
        Test to make sure we get some backfill points
        ",9,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_backfill_points_in_room(self):
        
        setup_info = self._setup_room_for_backfill_tests()
        room_id = setup_info.room_id

        backfill_points = self.get_success(
            self.store.get_backfill_points_in_room(room_id)
        )
        backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
        self.assertListEqual(
            backfill_event_ids, [""b6"", ""b5"", ""b4"", ""2"", ""b3"", ""b2"", ""b1""]
        )

        ```
    ###Assistant : 
        Test to make sure we get some backfill points
        
    "
1600,"def find_asteroidal_triple(G):
    r
    V = set(G.nodes)

    if len(V) < 6:
        # An asteroidal triple cannot exist in a graph with 5 or less vertices.
        return None

    component_structure = create_component_structure(G)
    E_complement = set(nx.complement(G).edges)

    for e in E_complement:
        u = e[0]
        v = e[1]
        u_neighborhood = set(G[u]).union([u])
        v_neighborhood = set(G[v]).union([v])
        union_of_neighborhoods = u_neighborhood.union(v_neighborhood)
        for w in V - union_of_neighborhoods:
            # Check for each pair of vertices whether they belong to the
            # same connected component when the closed neighborhood of the
            # third is removed.
            if (
                component_structure[u][v] == component_structure[u][w]
                and component_structure[v][u] == component_structure[v][w]
                and component_structure[w][u] == component_structure[w][v]
            ):
                return [u, v, w]
    return None


@not_implemented_for(""directed"")
@not_implemented_for(""multigraph"")","Find an asteroidal triple in the given graph.

    An asteroidal triple is a triple of non-adjacent vertices such that
    there exists a path between any two of them which avoids the closed
    neighborhood of the third. It checks all independent triples of vertices
    and whether they are an asteroidal triple or not. This is done with the
    help of a data structure called a component structure.
    A component structure encodes information about which vertices belongs to
    the same connected component when the closed neighborhood of a given vertex
    is removed from the graph. The algorithm used to check is the trivial
    one, outlined in [1]_, which has a runtime of
    :math:`O(|V||\overline{E} + |V||E|)`, where the second term is the
    creation of the component structure.

    Parameters
    ----------
    G : NetworkX Graph
        The graph to check whether is AT-free or not

    Returns
    -------
    list or None
        An asteroidal triple is returned as a list of nodes. If no asteroidal
        triple exists, i.e. the graph is AT-free, then None is returned.
        The returned value depends on the certificate parameter. The default
        option is a bool which is True if the graph is AT-free, i.e. the
        given graph contains no asteroidal triples, and False otherwise, i.e.
        if the graph contains at least one asteroidal triple.

    Notes
    -----
    The component structure and the algorithm is described in [1]_. The current
    implementation implements the trivial algorithm for simple graphs.

    References
    ----------
    .. [1] Ekkehard Köhler,
       ""Recognizing Graphs without asteroidal triples"",
       Journal of Discrete Algorithms 2, pages 439-452, 2004.
       https://www.sciencedirect.com/science/article/pii/S157086670400019X
    ",253,105,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_asteroidal_triple(G):
    r
    V = set(G.nodes)

    if len(V) < 6:
        # An asteroidal triple cannot exist in a graph with 5 or less vertices.
        return None

    component_structure = create_component_structure(G)
    E_complement = set(nx.complement(G).edges)

    for e in E_complement:
        u = e[0]
        v = e[1]
        u_neighborhood = set(G[u]).union([u])
        v_neighborhood = set(G[v]).union([v])
        union_of_neighborhoods = u_neighborhood.union(v_neighborhood)
        for w in V - union_of_neighborhoods:
            # Check for each pair of vertices whether they belong to the
            # same connected component when the closed neighborhood of the
            # third is removed.
            if (
                component_structure[u][v] == component_structure[u][w]
                and component_structure[v][u] == component_structure[v][w]
                and component_structure[w][u] == component_structure[w][v]
            ):
                return [u, v, w]
    return None


@not_implemented_for(""directed"")
@not_implemented_for(""multigraph"")
        ```
    ###Assistant : Find an asteroidal triple in the given graph.

    An asteroidal triple is a triple of non-adjacent vertices such that
    there exists a path between any two of them which avoids the closed
    neighborhood of the third. It checks all independent triples of vertices
    and whether they are an asteroidal triple or not. This is done with the
    help of a data structure called a component structure.
    A component structure encodes information about which vertices belongs to
    the same connected component when the closed neighborhood of a given vertex
    is removed from the graph. The algorithm used to check is the trivial
    one, outlined in [1]_, which has a runtime of
    :math:`O(|V||\overline{E} + |V||E|)`, where the second term is the
    creation of the component structure.

    Parameters
    ----------
    G : NetworkX Graph
        The graph to check whether is AT-free or not

    Returns
    -------
    list or None
        An asteroidal triple is returned as a list of nodes. If no asteroidal
        triple exists, i.e. the graph is AT-free, then None is returned.
        The returned value depends on the certificate parameter. The default
        option is a bool which is True if the graph is AT-free, i.e. the
        given graph contains no asteroidal triples, and False otherwise, i.e.
        if the graph contains at least one asteroidal triple.

    Notes
    -----
    The component structure and the algorithm is described in [1]_. The current
    implementation implements the trivial algorithm for simple graphs.

    References
    ----------
    .. [1] Ekkehard Köhler,
       ""Recognizing Graphs without asteroidal triples"",
       Journal of Discrete Algorithms 2, pages 439-452, 2004.
       https://www.sciencedirect.com/science/article/pii/S157086670400019X
    
    "
1601,"def get_leave_entries(employee, leave_type, from_date, to_date):
	
	return frappe.db.sql(
		,
		{""from_date"": from_date, ""to_date"": to_date, ""employee"": employee, ""leave_type"": leave_type},
		as_dict=1,
	)


@frappe.whitelist()","Returns leave entries between from_date and to_date.
		SELECT
			employee, leave_type, from_date, to_date, leaves, transaction_name, transaction_type, holiday_list,
			is_carry_forward, is_expired
		FROM `tabLeave Ledger Entry`
		WHERE employee=%(employee)s AND leave_type=%(leave_type)s
			AND docstatus=1
			AND (leaves<0
				OR is_expired=1)
			AND (from_date between %(from_date)s AND %(to_date)s
				OR to_date between %(from_date)s AND %(to_date)s
				OR (from_date < %(from_date)s AND to_date > %(to_date)s))
	",52,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_leave_entries(employee, leave_type, from_date, to_date):
	
	return frappe.db.sql(
		,
		{""from_date"": from_date, ""to_date"": to_date, ""employee"": employee, ""leave_type"": leave_type},
		as_dict=1,
	)


@frappe.whitelist()
        ```
    ###Assistant : Returns leave entries between from_date and to_date.
		SELECT
			employee, leave_type, from_date, to_date, leaves, transaction_name, transaction_type, holiday_list,
			is_carry_forward, is_expired
		FROM `tabLeave Ledger Entry`
		WHERE employee=%(employee)s AND leave_type=%(leave_type)s
			AND docstatus=1
			AND (leaves<0
				OR is_expired=1)
			AND (from_date between %(from_date)s AND %(to_date)s
				OR to_date between %(from_date)s AND %(to_date)s
				OR (from_date < %(from_date)s AND to_date > %(to_date)s))
	
    "
1602,"def addslashes(value):
    
    return value.replace(""\\"", ""\\\\"").replace('""', '\\""').replace(""'"", ""\\'"")


@register.filter(is_safe=True)
@stringfilter","
    Add slashes before quotes. Useful for escaping strings in CSV, for
    example. Less useful for escaping JavaScript; use the ``escapejs``
    filter instead.
    ",22,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def addslashes(value):
    
    return value.replace(""\\"", ""\\\\"").replace('""', '\\""').replace(""'"", ""\\'"")


@register.filter(is_safe=True)
@stringfilter
        ```
    ###Assistant : 
    Add slashes before quotes. Useful for escaping strings in CSV, for
    example. Less useful for escaping JavaScript; use the ``escapejs``
    filter instead.
    
    "
1603,"def _get_raw_from_config(cls) -> str:
        
        if cls.varname is None:
            raise TypeError(""varname should not be None"")
        return os.environ[cls.varname]
","
        Read the value from environment variable.

        Returns
        -------
        str
            Config raw value.

        Raises
        ------
        TypeError
            If `varname` is None.
        KeyError
            If value is absent.
        ",24,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_raw_from_config(cls) -> str:
        
        if cls.varname is None:
            raise TypeError(""varname should not be None"")
        return os.environ[cls.varname]

        ```
    ###Assistant : 
        Read the value from environment variable.

        Returns
        -------
        str
            Config raw value.

        Raises
        ------
        TypeError
            If `varname` is None.
        KeyError
            If value is absent.
        
    "
1604,"def is_monotonic_decreasing(self):
        
        return SeriesDefault.register(pandas.Series.is_monotonic_decreasing)(self)
","
        Return boolean if values in the object are monotonically decreasing.

        Returns
        -------
        bool
        ",13,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_monotonic_decreasing(self):
        
        return SeriesDefault.register(pandas.Series.is_monotonic_decreasing)(self)

        ```
    ###Assistant : 
        Return boolean if values in the object are monotonically decreasing.

        Returns
        -------
        bool
        
    "
1605,"def get(self, model, columns, filters):
        
        key = self._make_key(model, filters)
        conn = self.cluster.get_local_client_for_key(key)
        pipe = conn.pipeline()

        for col in columns:
            pipe.hget(key, f""i+{col}"")
        results = pipe.execute()

        return {
            col: (int(results[i]) if results[i] is not None else 0) for i, col in enumerate(columns)
        }
","
        Fetches buffered values for a model/filter. Passed columns must be integer columns.
        ",12,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get(self, model, columns, filters):
        
        key = self._make_key(model, filters)
        conn = self.cluster.get_local_client_for_key(key)
        pipe = conn.pipeline()

        for col in columns:
            pipe.hget(key, f""i+{col}"")
        results = pipe.execute()

        return {
            col: (int(results[i]) if results[i] is not None else 0) for i, col in enumerate(columns)
        }

        ```
    ###Assistant : 
        Fetches buffered values for a model/filter. Passed columns must be integer columns.
        
    "
1606,"def test_non_ascii_subscription_for_principal(self) -> None:
        
        iago = self.example_user(""iago"")
        self.assert_adding_subscriptions_for_principal(
            iago.id, get_realm(""zulip""), [""hümbüǵ""], policy_name=""Public""
        )
","
        You can subscribe other people to streams even if they containing
        non-ASCII characters.
        ",13,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_non_ascii_subscription_for_principal(self) -> None:
        
        iago = self.example_user(""iago"")
        self.assert_adding_subscriptions_for_principal(
            iago.id, get_realm(""zulip""), [""hümbüǵ""], policy_name=""Public""
        )

        ```
    ###Assistant : 
        You can subscribe other people to streams even if they containing
        non-ASCII characters.
        
    "
1607,"def add_provs(self, reader):
        
        fileids = reader.fileids()
        for fileid in fileids:
            prov, langfile = os.path.split(fileid)
            file_name, file_extension = os.path.splitext(langfile)
            if file_extension == "".tab"":
                lang = file_name.split(""-"")[-1]
                if lang in self.provenances.keys():
                    # We already have another resource for this lang,
                    # so we need to further specify the lang id:
                    lang = f""{lang}_{prov}""
                self.provenances[lang] = prov
",Add languages from Multilingual Wordnet to the provenance dictionary,9,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_provs(self, reader):
        
        fileids = reader.fileids()
        for fileid in fileids:
            prov, langfile = os.path.split(fileid)
            file_name, file_extension = os.path.splitext(langfile)
            if file_extension == "".tab"":
                lang = file_name.split(""-"")[-1]
                if lang in self.provenances.keys():
                    # We already have another resource for this lang,
                    # so we need to further specify the lang id:
                    lang = f""{lang}_{prov}""
                self.provenances[lang] = prov

        ```
    ###Assistant : Add languages from Multilingual Wordnet to the provenance dictionary
    "
1608,"def test_thread_with_bundled_aggregations_for_latest(self) -> None:
        
        self._send_relation(RelationTypes.THREAD, ""m.room.test"")
        channel = self._send_relation(RelationTypes.THREAD, ""m.room.test"")
        thread_2 = channel.json_body[""event_id""]

        self._send_relation(
            RelationTypes.ANNOTATION, ""m.reaction"", ""a"", parent_id=thread_2
        )
","
        Bundled aggregations should get applied to the latest thread event.
        ",10,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_thread_with_bundled_aggregations_for_latest(self) -> None:
        
        self._send_relation(RelationTypes.THREAD, ""m.room.test"")
        channel = self._send_relation(RelationTypes.THREAD, ""m.room.test"")
        thread_2 = channel.json_body[""event_id""]

        self._send_relation(
            RelationTypes.ANNOTATION, ""m.reaction"", ""a"", parent_id=thread_2
        )

        ```
    ###Assistant : 
        Bundled aggregations should get applied to the latest thread event.
        
    "
1609,"def from_environment(cls):
        

        return cls.from_file(path=KUBE_CONFIG_DEFAULT_LOCATION)
","
        Factory method to produce an instance of this class using the default kube config location
        ",15,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_environment(cls):
        

        return cls.from_file(path=KUBE_CONFIG_DEFAULT_LOCATION)

        ```
    ###Assistant : 
        Factory method to produce an instance of this class using the default kube config location
        
    "
1610,"def test_thumbnail_repeated_thumbnail(self) -> None:
        
        self._test_thumbnail(
            ""scale"", self.test_image.expected_scaled, self.test_image.expected_found
        )

        if not self.test_image.expected_found:
            return

        # Fetching again should work, without re-requesting the image from the
        # remote.
        params = ""?width=32&height=32&method=scale""
        channel = make_request(
            self.reactor,
            FakeSite(self.thumbnail_resource, self.reactor),
            ""GET"",
            self.media_id + params,
            shorthand=False,
            await_result=False,
        )
        self.pump()

        self.assertEqual(channel.code, 200)
        if self.test_image.expected_scaled:
            self.assertEqual(
                channel.result[""body""],
                self.test_image.expected_scaled,
                channel.result[""body""],
            )

        # Deleting the thumbnail on disk then re-requesting it should work as
        # Synapse should regenerate missing thumbnails.
        origin, media_id = self.media_id.split(""/"")
        info = self.get_success(self.store.get_cached_remote_media(origin, media_id))
        file_id = info[""filesystem_id""]

        thumbnail_dir = self.media_repo.filepaths.remote_media_thumbnail_dir(
            origin, file_id
        )
        shutil.rmtree(thumbnail_dir, ignore_errors=True)

        channel = make_request(
            self.reactor,
            FakeSite(self.thumbnail_resource, self.reactor),
            ""GET"",
            self.media_id + params,
            shorthand=False,
            await_result=False,
        )
        self.pump()

        self.assertEqual(channel.code, 200)
        if self.test_image.expected_scaled:
            self.assertEqual(
                channel.result[""body""],
                self.test_image.expected_scaled,
                channel.result[""body""],
            )
","Test that fetching the same thumbnail works, and deleting the on disk
        thumbnail regenerates it.
        ",15,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_thumbnail_repeated_thumbnail(self) -> None:
        
        self._test_thumbnail(
            ""scale"", self.test_image.expected_scaled, self.test_image.expected_found
        )

        if not self.test_image.expected_found:
            return

        # Fetching again should work, without re-requesting the image from the
        # remote.
        params = ""?width=32&height=32&method=scale""
        channel = make_request(
            self.reactor,
            FakeSite(self.thumbnail_resource, self.reactor),
            ""GET"",
            self.media_id + params,
            shorthand=False,
            await_result=False,
        )
        self.pump()

        self.assertEqual(channel.code, 200)
        if self.test_image.expected_scaled:
            self.assertEqual(
                channel.result[""body""],
                self.test_image.expected_scaled,
                channel.result[""body""],
            )

        # Deleting the thumbnail on disk then re-requesting it should work as
        # Synapse should regenerate missing thumbnails.
        origin, media_id = self.media_id.split(""/"")
        info = self.get_success(self.store.get_cached_remote_media(origin, media_id))
        file_id = info[""filesystem_id""]

        thumbnail_dir = self.media_repo.filepaths.remote_media_thumbnail_dir(
            origin, file_id
        )
        shutil.rmtree(thumbnail_dir, ignore_errors=True)

        channel = make_request(
            self.reactor,
            FakeSite(self.thumbnail_resource, self.reactor),
            ""GET"",
            self.media_id + params,
            shorthand=False,
            await_result=False,
        )
        self.pump()

        self.assertEqual(channel.code, 200)
        if self.test_image.expected_scaled:
            self.assertEqual(
                channel.result[""body""],
                self.test_image.expected_scaled,
                channel.result[""body""],
            )

        ```
    ###Assistant : Test that fetching the same thumbnail works, and deleting the on disk
        thumbnail regenerates it.
        
    "
1611,"def update(self) -> None:
        
        ping_cmd = [
            ""ping"",
            ""-c"",
            ""1"",
            ""-W"",
            str(DEFAULT_PING_TIMEOUT),
            str(self._host),
        ]

        status = sp.call(ping_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
        self._state = not bool(status)
",Check if device is on and update the state. Only called if assumed state is false.,16,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self) -> None:
        
        ping_cmd = [
            ""ping"",
            ""-c"",
            ""1"",
            ""-W"",
            str(DEFAULT_PING_TIMEOUT),
            str(self._host),
        ]

        status = sp.call(ping_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
        self._state = not bool(status)

        ```
    ###Assistant : Check if device is on and update the state. Only called if assumed state is false.
    "
1612,"def get_transactions(self):
        

        df = self.__transactions[
            [
                ""Date"",
                ""Type"",
                ""Ticker"",
                ""Side"",
                ""Price"",
                ""Quantity"",
                ""Fees"",
                ""Investment"",
                ""Currency"",
                ""Sector"",
                ""Industry"",
                ""Country"",
                ""Region"",
            ]
        ]
        df = df.replace(np.nan, ""-"")
        df[""Date""] = df[""Date""].dt.strftime(""%Y-%m-%d"")
        df.sort_values(by=""Date"", ascending=False, inplace=True)
        return df
","Get formatted transactions

        Returns
        -------
            pd.DataFrame: formatted transactions
        ",8,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_transactions(self):
        

        df = self.__transactions[
            [
                ""Date"",
                ""Type"",
                ""Ticker"",
                ""Side"",
                ""Price"",
                ""Quantity"",
                ""Fees"",
                ""Investment"",
                ""Currency"",
                ""Sector"",
                ""Industry"",
                ""Country"",
                ""Region"",
            ]
        ]
        df = df.replace(np.nan, ""-"")
        df[""Date""] = df[""Date""].dt.strftime(""%Y-%m-%d"")
        df.sort_values(by=""Date"", ascending=False, inplace=True)
        return df

        ```
    ###Assistant : Get formatted transactions

        Returns
        -------
            pd.DataFrame: formatted transactions
        
    "
1613,"def test_pick_colors(self) -> None:
        used_colors: Set[str] = set()
        color_map: Dict[int, str] = {}
        recipient_ids = list(range(30))
        user_color_map = pick_colors(used_colors, color_map, recipient_ids)
        self.assertEqual(
            user_color_map,
            {
                0: ""#76ce90"",
                1: ""#fae589"",
                2: ""#a6c7e5"",
                3: ""#e79ab5"",
                4: ""#bfd56f"",
                5: ""#f4ae55"",
                6: ""#b0a5fd"",
                7: ""#addfe5"",
                8: ""#f5ce6e"",
                9: ""#c2726a"",
                10: ""#94c849"",
                11: ""#bd86e5"",
                12: ""#ee7e4a"",
                13: ""#a6dcbf"",
                14: ""#95a5fd"",
                15: ""#53a063"",
                16: ""#9987e1"",
                17: ""#e4523d"",
                18: ""#c2c2c2"",
                19: ""#4f8de4"",
                20: ""#c6a8ad"",
                21: ""#e7cc4d"",
                22: ""#c8bebf"",
                23: ""#a47462"",
                # start repeating
                24: ""#76ce90"",
                25: ""#fae589"",
                26: ""#a6c7e5"",
                27: ""#e79ab5"",
                28: ""#bfd56f"",
                29: ""#f4ae55"",
            },
        )

        color_map = {98: ""color98"", 99: ""color99""}
        used_colors = set(STREAM_ASSIGNMENT_COLORS) - {""#c6a8ad"", ""#9987e1""}
        recipient_ids = [99, 98, 1, 2, 3, 4]
        user_color_map = pick_colors(used_colors, color_map, recipient_ids)
        self.assertEqual(
            user_color_map,
            {98: ""color98"", 99: ""color99"", 1: ""#9987e1"", 2: ""#c6a8ad"", 3: ""#e79ab5"", 4: ""#bfd56f""},
        )

        
        used_colors = set(STREAM_ASSIGNMENT_COLORS)
        color_map = {}
        recipient_ids = [2, 26, 50, 74]
        user_color_map = pick_colors(used_colors, color_map, recipient_ids)
        self.assertEqual(
            user_color_map,
            {2: ""#a6c7e5"", 26: ""#a6c7e5"", 50: ""#a6c7e5"", 74: ""#a6c7e5""},
        )
","
        If we are assigning colors to a user with 24+ streams, we have to start
        re-using old colors.  Our algorithm basically uses recipient_id % 24, so
        the following code reflects the worse case scenario that our new
        streams have recipient ids spaced out by exact multiples of 24.  We
        don't try to work around this edge case, since users who really depend
        on the stream colors can always just assign themselves custom colors
        for the streams that they really want to stand out.

        Even if recipient_ids were completely random, the odds of collisions
        are low, but it's often the case that bulk-adds are done for streams
        that either were or are being created at roughly the same time, so the
        recipient_ids tend to have even fewer collisions.
        ",127,157,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pick_colors(self) -> None:
        used_colors: Set[str] = set()
        color_map: Dict[int, str] = {}
        recipient_ids = list(range(30))
        user_color_map = pick_colors(used_colors, color_map, recipient_ids)
        self.assertEqual(
            user_color_map,
            {
                0: ""#76ce90"",
                1: ""#fae589"",
                2: ""#a6c7e5"",
                3: ""#e79ab5"",
                4: ""#bfd56f"",
                5: ""#f4ae55"",
                6: ""#b0a5fd"",
                7: ""#addfe5"",
                8: ""#f5ce6e"",
                9: ""#c2726a"",
                10: ""#94c849"",
                11: ""#bd86e5"",
                12: ""#ee7e4a"",
                13: ""#a6dcbf"",
                14: ""#95a5fd"",
                15: ""#53a063"",
                16: ""#9987e1"",
                17: ""#e4523d"",
                18: ""#c2c2c2"",
                19: ""#4f8de4"",
                20: ""#c6a8ad"",
                21: ""#e7cc4d"",
                22: ""#c8bebf"",
                23: ""#a47462"",
                # start repeating
                24: ""#76ce90"",
                25: ""#fae589"",
                26: ""#a6c7e5"",
                27: ""#e79ab5"",
                28: ""#bfd56f"",
                29: ""#f4ae55"",
            },
        )

        color_map = {98: ""color98"", 99: ""color99""}
        used_colors = set(STREAM_ASSIGNMENT_COLORS) - {""#c6a8ad"", ""#9987e1""}
        recipient_ids = [99, 98, 1, 2, 3, 4]
        user_color_map = pick_colors(used_colors, color_map, recipient_ids)
        self.assertEqual(
            user_color_map,
            {98: ""color98"", 99: ""color99"", 1: ""#9987e1"", 2: ""#c6a8ad"", 3: ""#e79ab5"", 4: ""#bfd56f""},
        )

        
        used_colors = set(STREAM_ASSIGNMENT_COLORS)
        color_map = {}
        recipient_ids = [2, 26, 50, 74]
        user_color_map = pick_colors(used_colors, color_map, recipient_ids)
        self.assertEqual(
            user_color_map,
            {2: ""#a6c7e5"", 26: ""#a6c7e5"", 50: ""#a6c7e5"", 74: ""#a6c7e5""},
        )

        ```
    ###Assistant : 
        If we are assigning colors to a user with 24+ streams, we have to start
        re-using old colors.  Our algorithm basically uses recipient_id % 24, so
        the following code reflects the worse case scenario that our new
        streams have recipient ids spaced out by exact multiples of 24.  We
        don't try to work around this edge case, since users who really depend
        on the stream colors can always just assign themselves custom colors
        for the streams that they really want to stand out.

        Even if recipient_ids were completely random, the odds of collisions
        are low, but it's often the case that bulk-adds are done for streams
        that either were or are being created at roughly the same time, so the
        recipient_ids tend to have even fewer collisions.
        
    "
1614,"def test_import(self):
        
        data = (
            ('name', 'slug', 'status', 'cf_text', 'cf_longtext', 'cf_integer', 'cf_boolean', 'cf_date', 'cf_url', 'cf_json', 'cf_select', 'cf_multiselect'),
            ('Site 1', 'site-1', 'active', 'ABC', 'Foo', '123', 'True', '2020-01-01', 'http://example.com/1', '{""foo"": 123}', 'Choice A', '""Choice A,Choice B""'),
            ('Site 2', 'site-2', 'active', 'DEF', 'Bar', '456', 'False', '2020-01-02', 'http://example.com/2', '{""bar"": 456}', 'Choice B', '""Choice B,Choice C""'),
            ('Site 3', 'site-3', 'active', '', '', '', '', '', '', '', '', ''),
        )
        csv_data = '\n'.join(','.join(row) for row in data)

        response = self.client.post(reverse('dcim:site_import'), {'csv': csv_data})
        self.assertEqual(response.status_code, 200)
        self.assertEqual(Site.objects.count(), 3)

        # Validate data for site 1
        site1 = Site.objects.get(name='Site 1')
        self.assertEqual(len(site1.custom_field_data), 9)
        self.assertEqual(site1.custom_field_data['text'], 'ABC')
        self.assertEqual(site1.custom_field_data['longtext'], 'Foo')
        self.assertEqual(site1.custom_field_data['integer'], 123)
        self.assertEqual(site1.custom_field_data['boolean'], True)
        self.assertEqual(site1.custom_field_data['date'], '2020-01-01')
        self.assertEqual(site1.custom_field_data['url'], 'http://example.com/1')
        self.assertEqual(site1.custom_field_data['json'], {""foo"": 123})
        self.assertEqual(site1.custom_field_data['select'], 'Choice A')
        self.assertEqual(site1.custom_field_data['multiselect'], ['Choice A', 'Choice B'])

        # Validate data for site 2
        site2 = Site.objects.get(name='Site 2')
        self.assertEqual(len(site2.custom_field_data), 9)
        self.assertEqual(site2.custom_field_data['text'], 'DEF')
        self.assertEqual(site2.custom_field_data['longtext'], 'Bar')
        self.assertEqual(site2.custom_field_data['integer'], 456)
        self.assertEqual(site2.custom_field_data['boolean'], False)
        self.assertEqual(site2.custom_field_data['date'], '2020-01-02')
        self.assertEqual(site2.custom_field_data['url'], 'http://example.com/2')
        self.assertEqual(site2.custom_field_data['json'], {""bar"": 456})
        self.assertEqual(site2.custom_field_data['select'], 'Choice B')
        self.assertEqual(site2.custom_field_data['multiselect'], ['Choice B', 'Choice C'])

        # No custom field data should be set for site 3
        site3 = Site.objects.get(name='Site 3')
        self.assertFalse(any(site3.custom_field_data.values()))
","
        Import a Site in CSV format, including a value for each CustomField.
        ",12,167,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_import(self):
        
        data = (
            ('name', 'slug', 'status', 'cf_text', 'cf_longtext', 'cf_integer', 'cf_boolean', 'cf_date', 'cf_url', 'cf_json', 'cf_select', 'cf_multiselect'),
            ('Site 1', 'site-1', 'active', 'ABC', 'Foo', '123', 'True', '2020-01-01', 'http://example.com/1', '{""foo"": 123}', 'Choice A', '""Choice A,Choice B""'),
            ('Site 2', 'site-2', 'active', 'DEF', 'Bar', '456', 'False', '2020-01-02', 'http://example.com/2', '{""bar"": 456}', 'Choice B', '""Choice B,Choice C""'),
            ('Site 3', 'site-3', 'active', '', '', '', '', '', '', '', '', ''),
        )
        csv_data = '\n'.join(','.join(row) for row in data)

        response = self.client.post(reverse('dcim:site_import'), {'csv': csv_data})
        self.assertEqual(response.status_code, 200)
        self.assertEqual(Site.objects.count(), 3)

        # Validate data for site 1
        site1 = Site.objects.get(name='Site 1')
        self.assertEqual(len(site1.custom_field_data), 9)
        self.assertEqual(site1.custom_field_data['text'], 'ABC')
        self.assertEqual(site1.custom_field_data['longtext'], 'Foo')
        self.assertEqual(site1.custom_field_data['integer'], 123)
        self.assertEqual(site1.custom_field_data['boolean'], True)
        self.assertEqual(site1.custom_field_data['date'], '2020-01-01')
        self.assertEqual(site1.custom_field_data['url'], 'http://example.com/1')
        self.assertEqual(site1.custom_field_data['json'], {""foo"": 123})
        self.assertEqual(site1.custom_field_data['select'], 'Choice A')
        self.assertEqual(site1.custom_field_data['multiselect'], ['Choice A', 'Choice B'])

        # Validate data for site 2
        site2 = Site.objects.get(name='Site 2')
        self.assertEqual(len(site2.custom_field_data), 9)
        self.assertEqual(site2.custom_field_data['text'], 'DEF')
        self.assertEqual(site2.custom_field_data['longtext'], 'Bar')
        self.assertEqual(site2.custom_field_data['integer'], 456)
        self.assertEqual(site2.custom_field_data['boolean'], False)
        self.assertEqual(site2.custom_field_data['date'], '2020-01-02')
        self.assertEqual(site2.custom_field_data['url'], 'http://example.com/2')
        self.assertEqual(site2.custom_field_data['json'], {""bar"": 456})
        self.assertEqual(site2.custom_field_data['select'], 'Choice B')
        self.assertEqual(site2.custom_field_data['multiselect'], ['Choice B', 'Choice C'])

        # No custom field data should be set for site 3
        site3 = Site.objects.get(name='Site 3')
        self.assertFalse(any(site3.custom_field_data.values()))

        ```
    ###Assistant : 
        Import a Site in CSV format, including a value for each CustomField.
        
    "
1615,"def build_pattern():
    #bare = set()
    for module, replace in list(MAPPING.items()):
        for old_attr, new_attr in list(replace.items()):
            LOOKUP[(module, old_attr)] = new_attr
            #bare.add(module)
            #bare.add(old_attr)
            #yield  % (module, module)
            yield  % (module, old_attr, old_attr)
            yield  % (module, old_attr)
    #yield  % alternates(bare)

","
            #      import_name< 'import' (module=%r
            #          | dotted_as_names< any* module=%r any* >) >
            #      
                  import_from< 'from' module_name=%r 'import'
                      ( attr_name=%r | import_as_name< attr_name=%r 'as' any >) >
                  
                  power< module_name=%r trailer< '.' attr_name=%r > any* >
                  bare_name=%s",35,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def build_pattern():
    #bare = set()
    for module, replace in list(MAPPING.items()):
        for old_attr, new_attr in list(replace.items()):
            LOOKUP[(module, old_attr)] = new_attr
            #bare.add(module)
            #bare.add(old_attr)
            #yield  % (module, module)
            yield  % (module, old_attr, old_attr)
            yield  % (module, old_attr)
    #yield  % alternates(bare)


        ```
    ###Assistant : 
            #      import_name< 'import' (module=%r
            #          | dotted_as_names< any* module=%r any* >) >
            #      
                  import_from< 'from' module_name=%r 'import'
                      ( attr_name=%r | import_as_name< attr_name=%r 'as' any >) >
                  
                  power< module_name=%r trailer< '.' attr_name=%r > any* >
                  bare_name=%s
    "
1616,"async def async_refresh_sensor(self) -> None:
        
        _LOGGER.debug(""Refreshing library sensor for '%s'"", self.name)
        try:
            await self.hass.async_add_executor_job(self._update_state_and_attrs)
            self._attr_available = True
        except NotFound:
            self._attr_available = False
        except requests.exceptions.RequestException as err:
            _LOGGER.error(
                ""Could not update library sensor for '%s': %s"",
                self.library_section.title,
                err,
            )
            self._attr_available = False
        self.async_write_ha_state()
",Update state and attributes for the library sensor.,8,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_refresh_sensor(self) -> None:
        
        _LOGGER.debug(""Refreshing library sensor for '%s'"", self.name)
        try:
            await self.hass.async_add_executor_job(self._update_state_and_attrs)
            self._attr_available = True
        except NotFound:
            self._attr_available = False
        except requests.exceptions.RequestException as err:
            _LOGGER.error(
                ""Could not update library sensor for '%s': %s"",
                self.library_section.title,
                err,
            )
            self._attr_available = False
        self.async_write_ha_state()

        ```
    ###Assistant : Update state and attributes for the library sensor.
    "
1617,"def __hash__(self):
        
        return hash(self.name) + hash(self.version) + hash(self.source_url)

","
        Compute hash in a way which matches the equality test.
        ",10,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __hash__(self):
        
        return hash(self.name) + hash(self.version) + hash(self.source_url)


        ```
    ###Assistant : 
        Compute hash in a way which matches the equality test.
        
    "
1618,"def _min_nodes(self) -> int:
        
        if self._side == ""gblock"":
            return self._config[""fc_gblock_min_nodes""]
        retval = self._scale_filters(self._config[""fc_min_filters""])
        retval = int(retval * self._config[""fc_dimensions""] ** 2)
        return retval
"," int: The number of nodes for the first Dense. For non g-block layers this will be the
        given minimum filters multiplied by the dimensions squared. For g-block layers, this is the
        given value ",33,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _min_nodes(self) -> int:
        
        if self._side == ""gblock"":
            return self._config[""fc_gblock_min_nodes""]
        retval = self._scale_filters(self._config[""fc_min_filters""])
        retval = int(retval * self._config[""fc_dimensions""] ** 2)
        return retval

        ```
    ###Assistant :  int: The number of nodes for the first Dense. For non g-block layers this will be the
        given minimum filters multiplied by the dimensions squared. For g-block layers, this is the
        given value 
    "
1619,"def _get(cls) -> dict:
        
        custom_parameters = super().get()
        result = cls.default.copy()
        result.update(
            {key.replace(""-"", ""_""): value for key, value in custom_parameters.items()}
        )
        return result

","
        Get the resulted command-line options.

        Returns
        -------
        dict
            Decoded and verified config value.
        ",13,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get(cls) -> dict:
        
        custom_parameters = super().get()
        result = cls.default.copy()
        result.update(
            {key.replace(""-"", ""_""): value for key, value in custom_parameters.items()}
        )
        return result


        ```
    ###Assistant : 
        Get the resulted command-line options.

        Returns
        -------
        dict
            Decoded and verified config value.
        
    "
1620,"def add(self, node, *predecessors):
        
        if self._ready_nodes is not None:
            raise ValueError(""Nodes cannot be added after a call to prepare()"")

        # Create the node -> predecessor edges
        nodeinfo = self._get_nodeinfo(node)
        nodeinfo.npredecessors += len(predecessors)

        # Create the predecessor -> node edges
        for pred in predecessors:
            pred_info = self._get_nodeinfo(pred)
            pred_info.successors.append(node)
","Add a new node and its predecessors to the graph.

        Both the *node* and all elements in *predecessors* must be hashable.

        If called multiple times with the same node argument, the set of dependencies
        will be the union of all dependencies passed in.

        It is possible to add a node with no dependencies (*predecessors* is not provided)
        as well as provide a dependency twice. If a node that has not been provided before
        is included among *predecessors* it will be automatically added to the graph with
        no predecessors of its own.

        Raises ValueError if called after ""prepare"".
        ",97,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add(self, node, *predecessors):
        
        if self._ready_nodes is not None:
            raise ValueError(""Nodes cannot be added after a call to prepare()"")

        # Create the node -> predecessor edges
        nodeinfo = self._get_nodeinfo(node)
        nodeinfo.npredecessors += len(predecessors)

        # Create the predecessor -> node edges
        for pred in predecessors:
            pred_info = self._get_nodeinfo(pred)
            pred_info.successors.append(node)

        ```
    ###Assistant : Add a new node and its predecessors to the graph.

        Both the *node* and all elements in *predecessors* must be hashable.

        If called multiple times with the same node argument, the set of dependencies
        will be the union of all dependencies passed in.

        It is possible to add a node with no dependencies (*predecessors* is not provided)
        as well as provide a dependency twice. If a node that has not been provided before
        is included among *predecessors* it will be automatically added to the graph with
        no predecessors of its own.

        Raises ValueError if called after ""prepare"".
        
    "
1621,"def test_stream_slices_with_state_and_slices(self, api, async_manager_mock, start_date):
        
        end_date = start_date + duration(days=10)
        cursor_value = start_date + duration(days=5)
        state = {
            AdsInsights.cursor_field: cursor_value.date().isoformat(),
            ""slices"": [(cursor_value + duration(days=1)).date().isoformat(), (cursor_value + duration(days=3)).date().isoformat()],
        }
        stream = AdsInsights(api=api, start_date=start_date, end_date=end_date)
        async_manager_mock.completed_jobs.return_value = [1, 2, 3]

        slices = list(stream.stream_slices(stream_state=state, sync_mode=SyncMode.incremental))

        assert slices == [{""insight_job"": 1}, {""insight_job"": 2}, {""insight_job"": 3}]
        async_manager_mock.assert_called_once()
        args, kwargs = async_manager_mock.call_args
        generated_jobs = list(kwargs[""jobs""])
        assert len(generated_jobs) == (end_date - cursor_value).days - 2, ""should be 2 slices short because of state""
        assert generated_jobs[0].interval.start == cursor_value.date() + duration(days=2)
        assert generated_jobs[1].interval.start == cursor_value.date() + duration(days=4)
","Stream will use cursor_value from state, but will skip saved slices",11,87,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stream_slices_with_state_and_slices(self, api, async_manager_mock, start_date):
        
        end_date = start_date + duration(days=10)
        cursor_value = start_date + duration(days=5)
        state = {
            AdsInsights.cursor_field: cursor_value.date().isoformat(),
            ""slices"": [(cursor_value + duration(days=1)).date().isoformat(), (cursor_value + duration(days=3)).date().isoformat()],
        }
        stream = AdsInsights(api=api, start_date=start_date, end_date=end_date)
        async_manager_mock.completed_jobs.return_value = [1, 2, 3]

        slices = list(stream.stream_slices(stream_state=state, sync_mode=SyncMode.incremental))

        assert slices == [{""insight_job"": 1}, {""insight_job"": 2}, {""insight_job"": 3}]
        async_manager_mock.assert_called_once()
        args, kwargs = async_manager_mock.call_args
        generated_jobs = list(kwargs[""jobs""])
        assert len(generated_jobs) == (end_date - cursor_value).days - 2, ""should be 2 slices short because of state""
        assert generated_jobs[0].interval.start == cursor_value.date() + duration(days=2)
        assert generated_jobs[1].interval.start == cursor_value.date() + duration(days=4)

        ```
    ###Assistant : Stream will use cursor_value from state, but will skip saved slices
    "
1622,"def _test_readonly_foreignkey_links(self, admin_site):
        
        chapter = Chapter.objects.create(
            title=""Chapter 1"",
            content=""content"",
            book=Book.objects.create(name=""Book 1""),
        )
        language = Language.objects.create(iso=""_40"", name=""Test"")
        obj = ReadOnlyRelatedField.objects.create(
            chapter=chapter,
            language=language,
            user=self.superuser,
        )
        response = self.client.get(
            reverse(
                f""{admin_site}:admin_views_readonlyrelatedfield_change"", args=(obj.pk,)
            ),
        )
        # Related ForeignKey object registered in admin.
        user_url = reverse(f""{admin_site}:auth_user_change"", args=(self.superuser.pk,))
        self.assertContains(
            response,
            '<div class=""readonly""><a href=""%s"">super</a></div>' % user_url,
            html=True,
        )
        # Related ForeignKey with the string primary key registered in admin.
        language_url = reverse(
            f""{admin_site}:admin_views_language_change"",
            args=(quote(language.pk),),
        )
        self.assertContains(
            response,
            '<div class=""readonly""><a href=""%s"">_40</a></div>' % language_url,
            html=True,
        )
        # Related ForeignKey object not registered in admin.
        self.assertContains(
            response, '<div class=""readonly"">Chapter 1</div>', html=True
        )
","
        ForeignKey readonly fields render as links if the target model is
        registered in admin.
        ",14,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _test_readonly_foreignkey_links(self, admin_site):
        
        chapter = Chapter.objects.create(
            title=""Chapter 1"",
            content=""content"",
            book=Book.objects.create(name=""Book 1""),
        )
        language = Language.objects.create(iso=""_40"", name=""Test"")
        obj = ReadOnlyRelatedField.objects.create(
            chapter=chapter,
            language=language,
            user=self.superuser,
        )
        response = self.client.get(
            reverse(
                f""{admin_site}:admin_views_readonlyrelatedfield_change"", args=(obj.pk,)
            ),
        )
        # Related ForeignKey object registered in admin.
        user_url = reverse(f""{admin_site}:auth_user_change"", args=(self.superuser.pk,))
        self.assertContains(
            response,
            '<div class=""readonly""><a href=""%s"">super</a></div>' % user_url,
            html=True,
        )
        # Related ForeignKey with the string primary key registered in admin.
        language_url = reverse(
            f""{admin_site}:admin_views_language_change"",
            args=(quote(language.pk),),
        )
        self.assertContains(
            response,
            '<div class=""readonly""><a href=""%s"">_40</a></div>' % language_url,
            html=True,
        )
        # Related ForeignKey object not registered in admin.
        self.assertContains(
            response, '<div class=""readonly"">Chapter 1</div>', html=True
        )

        ```
    ###Assistant : 
        ForeignKey readonly fields render as links if the target model is
        registered in admin.
        
    "
1623,"def execute():
	for project in frappe.get_all(""Project"", fields=[""name"", ""percent_complete_method""]):
		total = frappe.db.count(""Task"", dict(project=project.name))
		if project.percent_complete_method == ""Task Completion"" and total > 0:
			completed = frappe.db.sql(
				,
				project.name,
			)[0][0]
			percent_complete = flt(flt(completed) / total * 100, 2)
			if project.percent_complete != percent_complete:
				frappe.db.set_value(""Project"", project.name, ""percent_complete"", percent_complete)
				if percent_complete == 100:
					frappe.db.set_value(""Project"", project.name, ""status"", ""Completed"")
","select count(name) from tabTask where
					project=%s and status in ('Cancelled', 'Completed')",11,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	for project in frappe.get_all(""Project"", fields=[""name"", ""percent_complete_method""]):
		total = frappe.db.count(""Task"", dict(project=project.name))
		if project.percent_complete_method == ""Task Completion"" and total > 0:
			completed = frappe.db.sql(
				,
				project.name,
			)[0][0]
			percent_complete = flt(flt(completed) / total * 100, 2)
			if project.percent_complete != percent_complete:
				frappe.db.set_value(""Project"", project.name, ""percent_complete"", percent_complete)
				if percent_complete == 100:
					frappe.db.set_value(""Project"", project.name, ""status"", ""Completed"")

        ```
    ###Assistant : select count(name) from tabTask where
					project=%s and status in ('Cancelled', 'Completed')
    "
1624,"def mathematica(s, additional_translations=None):
    
    parser = MathematicaParser(additional_translations)

    if additional_translations is not None:
        SymPyDeprecationWarning(
            feature=""additional_translations parameter for the Mathematica parser"",
            last_supported_version=""1.9"",
            useinstead=""Use SymPy's .replace( ) or .subs( ) methods on the output expression"",
            issue=""23042"",
        ).warn()
        return sympify(parser._parse_old(s))

    return parser.parse(s)

","
    Translate a string containing a Wolfram Mathematica expression to a SymPy
    expression.

    If the translator is unable to find a suitable SymPy expression, the
    ``FullForm`` of the Mathematica expression will be output, using SymPy
    ``Function`` objects as nodes of the syntax tree.

    Examples
    ========

    >>> from sympy.parsing.mathematica import mathematica
    >>> mathematica(""Sin[x]^2 Tan[y]"")
    sin(x)**2*tan(y)
    >>> e = mathematica(""F[7,5,3]"")
    >>> e
    F(7, 5, 3)
    >>> from sympy import Function, Max, Min
    >>> e.replace(Function(""F""), lambda *x: Max(*x)*Min(*x))
    21

    Both standard input form and Mathematica full form are supported:

    >>> mathematica(""x*(a + b)"")
    x*(a + b)
    >>> mathematica(""Times[x, Plus[a, b]]"")
    x*(a + b)

    To get a matrix from Wolfram's code:

    >>> m = mathematica(""{{a, b}, {c, d}}"")
    >>> m
    ((a, b), (c, d))
    >>> from sympy import Matrix
    >>> Matrix(m)
    Matrix([
    [a, b],
    [c, d]])

    If the translation into equivalent SymPy expressions fails, an SymPy
    expression equivalent to Wolfram Mathematica's ""FullForm"" will be created:

    >>> mathematica(""x_."")
    Optional(Pattern(x, Blank()))
    >>> mathematica(""Plus @@ {x, y, z}"")
    Apply(Plus, (x, y, z))
    >>> mathematica(""f[x_, 3] := x^3 /; x > 0"")
    SetDelayed(f(Pattern(x, Blank()), 3), Condition(x**3, x > 0))
    ",180,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mathematica(s, additional_translations=None):
    
    parser = MathematicaParser(additional_translations)

    if additional_translations is not None:
        SymPyDeprecationWarning(
            feature=""additional_translations parameter for the Mathematica parser"",
            last_supported_version=""1.9"",
            useinstead=""Use SymPy's .replace( ) or .subs( ) methods on the output expression"",
            issue=""23042"",
        ).warn()
        return sympify(parser._parse_old(s))

    return parser.parse(s)


        ```
    ###Assistant : 
    Translate a string containing a Wolfram Mathematica expression to a SymPy
    expression.

    If the translator is unable to find a suitable SymPy expression, the
    ``FullForm`` of the Mathematica expression will be output, using SymPy
    ``Function`` objects as nodes of the syntax tree.

    Examples
    ========

    >>> from sympy.parsing.mathematica import mathematica
    >>> mathematica(""Sin[x]^2 Tan[y]"")
    sin(x)**2*tan(y)
    >>> e = mathematica(""F[7,5,3]"")
    >>> e
    F(7, 5, 3)
    >>> from sympy import Function, Max, Min
    >>> e.replace(Function(""F""), lambda *x: Max(*x)*Min(*x))
    21

    Both standard input form and Mathematica full form are supported:

    >>> mathematica(""x*(a + b)"")
    x*(a + b)
    >>> mathematica(""Times[x, Plus[a, b]]"")
    x*(a + b)

    To get a matrix from Wolfram's code:

    >>> m = mathematica(""{{a, b}, {c, d}}"")
    >>> m
    ((a, b), (c, d))
    >>> from sympy import Matrix
    >>> Matrix(m)
    Matrix([
    [a, b],
    [c, d]])

    If the translation into equivalent SymPy expressions fails, an SymPy
    expression equivalent to Wolfram Mathematica's ""FullForm"" will be created:

    >>> mathematica(""x_."")
    Optional(Pattern(x, Blank()))
    >>> mathematica(""Plus @@ {x, y, z}"")
    Apply(Plus, (x, y, z))
    >>> mathematica(""f[x_, 3] := x^3 /; x > 0"")
    SetDelayed(f(Pattern(x, Blank()), 3), Condition(x**3, x > 0))
    
    "
1625,"def update_company_current_month_sales(company):
	current_month_year = formatdate(today(), ""MM-yyyy"")

	results = frappe.db.sql(
		.format(
			current_month_year=current_month_year, company=frappe.db.escape(company)
		),
		as_dict=True,
	)

	monthly_total = results[0][""total""] if len(results) > 0 else 0

	frappe.db.set_value(""Company"", company, ""total_monthly_sales"", monthly_total)

","
		SELECT
			SUM(base_grand_total) AS total,
			DATE_FORMAT(`posting_date`, '%m-%Y') AS month_year
		FROM
			`tabSales Invoice`
		WHERE
			DATE_FORMAT(`posting_date`, '%m-%Y') = '{current_month_year}'
			AND docstatus = 1
			AND company = {company}
		GROUP BY
			month_year
	",27,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_company_current_month_sales(company):
	current_month_year = formatdate(today(), ""MM-yyyy"")

	results = frappe.db.sql(
		.format(
			current_month_year=current_month_year, company=frappe.db.escape(company)
		),
		as_dict=True,
	)

	monthly_total = results[0][""total""] if len(results) > 0 else 0

	frappe.db.set_value(""Company"", company, ""total_monthly_sales"", monthly_total)


        ```
    ###Assistant : 
		SELECT
			SUM(base_grand_total) AS total,
			DATE_FORMAT(`posting_date`, '%m-%Y') AS month_year
		FROM
			`tabSales Invoice`
		WHERE
			DATE_FORMAT(`posting_date`, '%m-%Y') = '{current_month_year}'
			AND docstatus = 1
			AND company = {company}
		GROUP BY
			month_year
	
    "
1626,"def metadata_version(self):
        # type: () -> Optional[str]
        
        raise NotImplementedError()
","Value of ""Metadata-Version:"" in the distribution, if available.",8,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def metadata_version(self):
        # type: () -> Optional[str]
        
        raise NotImplementedError()

        ```
    ###Assistant : Value of ""Metadata-Version:"" in the distribution, if available.
    "
1627,"def call_ca(self, _):
        
        if self.screen_tickers:
            self.queue = ca_controller.ComparisonAnalysisController(
                self.screen_tickers, self.queue
            ).menu(custom_path_menu_above=""/stocks/"")
        else:
            print(""Some tickers must be screened first through one of the presets!\n"")
",Call the comparison analysis menu with selected tickers,8,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_ca(self, _):
        
        if self.screen_tickers:
            self.queue = ca_controller.ComparisonAnalysisController(
                self.screen_tickers, self.queue
            ).menu(custom_path_menu_above=""/stocks/"")
        else:
            print(""Some tickers must be screened first through one of the presets!\n"")

        ```
    ###Assistant : Call the comparison analysis menu with selected tickers
    "
1628,"def delete_tasks_predictions(project, queryset, **kwargs):
    
    task_ids = queryset.values_list('id', flat=True)
    predictions = Prediction.objects.filter(task__id__in=task_ids)
    count = predictions.count()
    predictions.delete()
    queryset.update(updated_at=datetime.now())
    return {'processed_items': count, 'detail': 'Deleted ' + str(count) + ' predictions'}


actions = [
    {
        'entry_point': retrieve_tasks_predictions,
        'permission': all_permissions.predictions_any,
        'title': 'Retrieve Predictions',
        'order': 90,
        'dialog': {
            'text': 'Send the selected tasks to all ML backends connected to the project.'
                    'This operation might be abruptly interrupted due to a timeout. '
                    'The recommended way to get predictions is to update tasks using the Label Studio API.'
                    '<a href=""https://labelstud.io/guide/ml.html>See more in the documentation</a>.'
                    'Please confirm your action.',
            'type': 'confirm'
        }
    },
    {
        'entry_point': delete_tasks,
        'permission': all_permissions.tasks_delete,
        'title': 'Delete Tasks',
        'order': 100,
        'reload': True,
        'dialog': {
            'text': 'You are going to delete the selected tasks. Please confirm your action.',
            'type': 'confirm'
        }
    },
    {
        'entry_point': delete_tasks_annotations,
        'permission': all_permissions.tasks_delete,
        'title': 'Delete Annotations',
        'order': 101,
        'dialog': {
            'text': 'You are going to delete all annotations from the selected tasks. Please confirm your action.',
            'type': 'confirm'
        }
    },
    {
        'entry_point': delete_tasks_predictions,
        'permission': all_permissions.predictions_any,
        'title': 'Delete Predictions',
        'order': 102,
        'dialog': {
            'text': 'You are going to delete all predictions from the selected tasks. Please confirm your action.',
            'type': 'confirm'
        }
    }
]
"," Delete all predictions by tasks ids

    :param project: project instance
    :param queryset: filtered tasks db queryset
    ",16,191,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_tasks_predictions(project, queryset, **kwargs):
    
    task_ids = queryset.values_list('id', flat=True)
    predictions = Prediction.objects.filter(task__id__in=task_ids)
    count = predictions.count()
    predictions.delete()
    queryset.update(updated_at=datetime.now())
    return {'processed_items': count, 'detail': 'Deleted ' + str(count) + ' predictions'}


actions = [
    {
        'entry_point': retrieve_tasks_predictions,
        'permission': all_permissions.predictions_any,
        'title': 'Retrieve Predictions',
        'order': 90,
        'dialog': {
            'text': 'Send the selected tasks to all ML backends connected to the project.'
                    'This operation might be abruptly interrupted due to a timeout. '
                    'The recommended way to get predictions is to update tasks using the Label Studio API.'
                    '<a href=""https://labelstud.io/guide/ml.html>See more in the documentation</a>.'
                    'Please confirm your action.',
            'type': 'confirm'
        }
    },
    {
        'entry_point': delete_tasks,
        'permission': all_permissions.tasks_delete,
        'title': 'Delete Tasks',
        'order': 100,
        'reload': True,
        'dialog': {
            'text': 'You are going to delete the selected tasks. Please confirm your action.',
            'type': 'confirm'
        }
    },
    {
        'entry_point': delete_tasks_annotations,
        'permission': all_permissions.tasks_delete,
        'title': 'Delete Annotations',
        'order': 101,
        'dialog': {
            'text': 'You are going to delete all annotations from the selected tasks. Please confirm your action.',
            'type': 'confirm'
        }
    },
    {
        'entry_point': delete_tasks_predictions,
        'permission': all_permissions.predictions_any,
        'title': 'Delete Predictions',
        'order': 102,
        'dialog': {
            'text': 'You are going to delete all predictions from the selected tasks. Please confirm your action.',
            'type': 'confirm'
        }
    }
]

        ```
    ###Assistant :  Delete all predictions by tasks ids

    :param project: project instance
    :param queryset: filtered tasks db queryset
    
    "
1629,"def clean_up(self):
        
        if not self._cleaned:
            for ref in self.referenced_paths():
                self._reference_counter[ref] -= 1
                if self._reference_counter[ref] <= 0:
                    os.remove(ref)
                    if self._reference_counter[ref] < 0:
                        _logger.warning('Referance counter error, the number of %s is %d',
                                        ref, self._reference_counter[ref])
            self._cleaned = True
        else:
            _logger.warning('Already clean up task %d', self.task_id)

","
        Counter of referenced file paths subtract 1. If the counter reach 0, then delete the file.
        ",16,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clean_up(self):
        
        if not self._cleaned:
            for ref in self.referenced_paths():
                self._reference_counter[ref] -= 1
                if self._reference_counter[ref] <= 0:
                    os.remove(ref)
                    if self._reference_counter[ref] < 0:
                        _logger.warning('Referance counter error, the number of %s is %d',
                                        ref, self._reference_counter[ref])
            self._cleaned = True
        else:
            _logger.warning('Already clean up task %d', self.task_id)


        ```
    ###Assistant : 
        Counter of referenced file paths subtract 1. If the counter reach 0, then delete the file.
        
    "
1630,"def load_backend(backend_name):
    
    # This backend was renamed in Django 1.9.
    if backend_name == ""django.db.backends.postgresql_psycopg2"":
        backend_name = ""django.db.backends.postgresql""

    try:
        return import_module(""%s.base"" % backend_name)
    except ImportError as e_user:
        # The database backend wasn't found. Display a helpful error message
        # listing all built-in database backends.
        import django.db.backends

        builtin_backends = [
            name
            for _, name, ispkg in pkgutil.iter_modules(django.db.backends.__path__)
            if ispkg and name not in {""base"", ""dummy""}
        ]
        if backend_name not in [""django.db.backends.%s"" % b for b in builtin_backends]:
            backend_reprs = map(repr, sorted(builtin_backends))
            raise ImproperlyConfigured(
                ""%r isn't an available database backend or couldn't be ""
                ""imported. Check the above exception. To use one of the ""
                ""built-in backends, use 'django.db.backends.XXX', where XXX ""
                ""is one of:\n""
                ""    %s"" % (backend_name, "", "".join(backend_reprs))
            ) from e_user
        else:
            # If there's some other error, this must be an error in Django
            raise

","
    Return a database backend's ""base"" module given a fully qualified database
    backend name, or raise an error if it doesn't exist.
    ",21,136,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_backend(backend_name):
    
    # This backend was renamed in Django 1.9.
    if backend_name == ""django.db.backends.postgresql_psycopg2"":
        backend_name = ""django.db.backends.postgresql""

    try:
        return import_module(""%s.base"" % backend_name)
    except ImportError as e_user:
        # The database backend wasn't found. Display a helpful error message
        # listing all built-in database backends.
        import django.db.backends

        builtin_backends = [
            name
            for _, name, ispkg in pkgutil.iter_modules(django.db.backends.__path__)
            if ispkg and name not in {""base"", ""dummy""}
        ]
        if backend_name not in [""django.db.backends.%s"" % b for b in builtin_backends]:
            backend_reprs = map(repr, sorted(builtin_backends))
            raise ImproperlyConfigured(
                ""%r isn't an available database backend or couldn't be ""
                ""imported. Check the above exception. To use one of the ""
                ""built-in backends, use 'django.db.backends.XXX', where XXX ""
                ""is one of:\n""
                ""    %s"" % (backend_name, "", "".join(backend_reprs))
            ) from e_user
        else:
            # If there's some other error, this must be an error in Django
            raise


        ```
    ###Assistant : 
    Return a database backend's ""base"" module given a fully qualified database
    backend name, or raise an error if it doesn't exist.
    
    "
1631,"def explode_superdims(sizes, dims):
  
  strides_to_sizes = {stride: size for size, stride in zip(sizes, strides_for_sizes(sizes))}
  dims = list(reversed(dims))
  final_dims = []
  for size, stride in dims:
    target_size = strides_to_sizes[stride]
    new_dims = []
    while size > target_size:
      assert target_size > 1  # Ensure progress
      assert size % target_size == 0
      new_dims.append((target_size, stride))
      size //= target_size
      stride *= target_size
      target_size = strides_to_sizes[stride]
    assert size == target_size
    new_dims.append((size, stride))
    final_dims += reversed(new_dims)
  return final_dims
","Explode superdims to fit a known shape.

  The unflattening process might mistakenly generate too few too large dimensions.
  For example, ``unflatten_superdims(np.arange(n))`` always returns ``[(n, 1)]``.
  This function takes a list of such contiguous super-dimensions and splits them
  into smaller dimensions such that::

    set(map(fst, explode_superdims(sizes, dims))) == set(sizes)
  ",47,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def explode_superdims(sizes, dims):
  
  strides_to_sizes = {stride: size for size, stride in zip(sizes, strides_for_sizes(sizes))}
  dims = list(reversed(dims))
  final_dims = []
  for size, stride in dims:
    target_size = strides_to_sizes[stride]
    new_dims = []
    while size > target_size:
      assert target_size > 1  # Ensure progress
      assert size % target_size == 0
      new_dims.append((target_size, stride))
      size //= target_size
      stride *= target_size
      target_size = strides_to_sizes[stride]
    assert size == target_size
    new_dims.append((size, stride))
    final_dims += reversed(new_dims)
  return final_dims

        ```
    ###Assistant : Explode superdims to fit a known shape.

  The unflattening process might mistakenly generate too few too large dimensions.
  For example, ``unflatten_superdims(np.arange(n))`` always returns ``[(n, 1)]``.
  This function takes a list of such contiguous super-dimensions and splits them
  into smaller dimensions such that::

    set(map(fst, explode_superdims(sizes, dims))) == set(sizes)
  
    "
1632,"def path_weight(G, path, weight):
    
    multigraph = G.is_multigraph()
    cost = 0

    if not nx.is_path(G, path):
        raise nx.NetworkXNoPath(""path does not exist"")
    for node, nbr in nx.utils.pairwise(path):
        if multigraph:
            cost += min(v[weight] for v in G[node][nbr].values())
        else:
            cost += G[node][nbr][weight]
    return cost
","Returns total cost associated with specified path and weight

    Parameters
    ----------
    G : graph
        A NetworkX graph.

    path: list
        A list of node labels which defines the path to traverse

    weight: string
        A string indicating which edge attribute to use for path cost

    Returns
    -------
    cost: int or float
        An integer or a float representing the total cost with respect to the
        specified weight of the specified path

    Raises
    ------
    NetworkXNoPath
        If the specified edge does not exist.
    ",78,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def path_weight(G, path, weight):
    
    multigraph = G.is_multigraph()
    cost = 0

    if not nx.is_path(G, path):
        raise nx.NetworkXNoPath(""path does not exist"")
    for node, nbr in nx.utils.pairwise(path):
        if multigraph:
            cost += min(v[weight] for v in G[node][nbr].values())
        else:
            cost += G[node][nbr][weight]
    return cost

        ```
    ###Assistant : Returns total cost associated with specified path and weight

    Parameters
    ----------
    G : graph
        A NetworkX graph.

    path: list
        A list of node labels which defines the path to traverse

    weight: string
        A string indicating which edge attribute to use for path cost

    Returns
    -------
    cost: int or float
        An integer or a float representing the total cost with respect to the
        specified weight of the specified path

    Raises
    ------
    NetworkXNoPath
        If the specified edge does not exist.
    
    "
1633,"def _run_sql(self, sql, params, raw=True, output=False, latest=False):
        
        toget = 'source_raw' if raw else 'source'
        sqlfrom = ""history""
        if output:
            sqlfrom = ""history LEFT JOIN output_history USING (session, line)""
            toget = ""history.%s, output_history.output"" % toget
        if latest:
            toget += "", MAX(session * 128 * 1024 + line)""
        this_querry = ""SELECT session, line, %s FROM %s "" % (toget, sqlfrom) + sql
        cur = self.db.execute(this_querry, params)
        if latest:
            cur = (row[:-1] for row in cur)
        if output:    # Regroup into 3-tuples, and parse JSON
            return ((ses, lin, (inp, out)) for ses, lin, inp, out in cur)
        return cur
","Prepares and runs an SQL query for the history database.

        Parameters
        ----------
        sql : str
            Any filtering expressions to go after SELECT ... FROM ...
        params : tuple
            Parameters passed to the SQL query (to replace ""?"")
        raw, output : bool
            See :meth:`get_range`
        latest : bool
            Select rows with max (session, line)

        Returns
        -------
        Tuples as :meth:`get_range`
        ",57,96,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _run_sql(self, sql, params, raw=True, output=False, latest=False):
        
        toget = 'source_raw' if raw else 'source'
        sqlfrom = ""history""
        if output:
            sqlfrom = ""history LEFT JOIN output_history USING (session, line)""
            toget = ""history.%s, output_history.output"" % toget
        if latest:
            toget += "", MAX(session * 128 * 1024 + line)""
        this_querry = ""SELECT session, line, %s FROM %s "" % (toget, sqlfrom) + sql
        cur = self.db.execute(this_querry, params)
        if latest:
            cur = (row[:-1] for row in cur)
        if output:    # Regroup into 3-tuples, and parse JSON
            return ((ses, lin, (inp, out)) for ses, lin, inp, out in cur)
        return cur

        ```
    ###Assistant : Prepares and runs an SQL query for the history database.

        Parameters
        ----------
        sql : str
            Any filtering expressions to go after SELECT ... FROM ...
        params : tuple
            Parameters passed to the SQL query (to replace ""?"")
        raw, output : bool
            See :meth:`get_range`
        latest : bool
            Select rows with max (session, line)

        Returns
        -------
        Tuples as :meth:`get_range`
        
    "
1634,"def __add__(self, other):
        
        if isinstance(other, PathSpec):
            return PathSpec(self.patterns + other.patterns)
        else:
            return NotImplemented
","
        Combines the :attr:`Pathspec.patterns` patterns from two
        :class:`PathSpec` instances.
        ",8,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __add__(self, other):
        
        if isinstance(other, PathSpec):
            return PathSpec(self.patterns + other.patterns)
        else:
            return NotImplemented

        ```
    ###Assistant : 
        Combines the :attr:`Pathspec.patterns` patterns from two
        :class:`PathSpec` instances.
        
    "
1635,"def address(self):
        
        if use_gcs_for_bootstrap():
            return self._gcs_address
        return self._redis_address
","Get the address for bootstrapping, e.g. the address to pass to
        `ray start` or `ray.int()` to start worker nodes, that has been
        converted to ip:port format.
        ",26,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def address(self):
        
        if use_gcs_for_bootstrap():
            return self._gcs_address
        return self._redis_address

        ```
    ###Assistant : Get the address for bootstrapping, e.g. the address to pass to
        `ray start` or `ray.int()` to start worker nodes, that has been
        converted to ip:port format.
        
    "
1636,"def similarity(self, texts=[], data={}, use_gpu=False, batch_size=1):
        
        if use_gpu:
            try:
                _places = os.environ[""CUDA_VISIBLE_DEVICES""]
                int(_places[0])
            except:
                raise RuntimeError(
                    ""Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id.""
                )

        data = self.check_data(texts, data)

        start_idx = 0
        iteration = int(math.ceil(len(data['text_1']) / batch_size))
        results = []
        for i in range(iteration):
            batch_data = {'text_1': [], 'text_2': []}
            if i < (iteration - 1):
                batch_data['text_1'] = data['text_1'][start_idx:(start_idx + batch_size)]
                batch_data['text_2'] = data['text_2'][start_idx:(start_idx + batch_size)]
            else:
                batch_data['text_1'] = data['text_1'][start_idx:(start_idx + batch_size)]
                batch_data['text_2'] = data['text_2'][start_idx:(start_idx + batch_size)]
            start_idx = start_idx + batch_size
            processed_results = preprocess(self.word_seg_module, self.vocab, batch_data, use_gpu, batch_size)

            data_1, lod_1, shape_1 = self._texts_process(processed_results[""text_1""])
            data_2, lod_2, shape_2 = self._texts_process(processed_results[""text_2""])

            predictor = self.gpu_predictor if use_gpu else self.cpu_predictor

            input_names = predictor.get_input_names()
            input_handle = predictor.get_input_handle(input_names[0])
            input_handle.copy_from_cpu(data_1)
            input_handle.set_lod(lod_1)
            input_handle.reshape(shape_1)

            input_handle = predictor.get_input_handle(input_names[1])
            input_handle.copy_from_cpu(data_2)
            input_handle.set_lod(lod_2)
            input_handle.reshape(shape_2)

            predictor.run()
            output_names = predictor.get_output_names()
            output_handle = predictor.get_output_handle(output_names[1])
            batch_out = output_handle.copy_to_cpu()

            batch_result = postprocess(batch_out, processed_results)
            results += batch_result
        return results
","
        Get the sentiment prediction results results with the texts as input
        Args:
             texts(list): the input texts to be predicted which the first element is text_1(list)
                          and the second element is text_2(list), such as [['这道题很难'], ['这道题不简单']]
                          if texts not data.
             data(dict): key must be 'text_1' and 'text_2', value is the texts(list) to be predicted
             use_gpu(bool): whether use gpu to predict or not
             batch_size(int): the program deals once with one batch
        Returns:
             results(list): the word segmentation results
        ",75,149,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def similarity(self, texts=[], data={}, use_gpu=False, batch_size=1):
        
        if use_gpu:
            try:
                _places = os.environ[""CUDA_VISIBLE_DEVICES""]
                int(_places[0])
            except:
                raise RuntimeError(
                    ""Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id.""
                )

        data = self.check_data(texts, data)

        start_idx = 0
        iteration = int(math.ceil(len(data['text_1']) / batch_size))
        results = []
        for i in range(iteration):
            batch_data = {'text_1': [], 'text_2': []}
            if i < (iteration - 1):
                batch_data['text_1'] = data['text_1'][start_idx:(start_idx + batch_size)]
                batch_data['text_2'] = data['text_2'][start_idx:(start_idx + batch_size)]
            else:
                batch_data['text_1'] = data['text_1'][start_idx:(start_idx + batch_size)]
                batch_data['text_2'] = data['text_2'][start_idx:(start_idx + batch_size)]
            start_idx = start_idx + batch_size
            processed_results = preprocess(self.word_seg_module, self.vocab, batch_data, use_gpu, batch_size)

            data_1, lod_1, shape_1 = self._texts_process(processed_results[""text_1""])
            data_2, lod_2, shape_2 = self._texts_process(processed_results[""text_2""])

            predictor = self.gpu_predictor if use_gpu else self.cpu_predictor

            input_names = predictor.get_input_names()
            input_handle = predictor.get_input_handle(input_names[0])
            input_handle.copy_from_cpu(data_1)
            input_handle.set_lod(lod_1)
            input_handle.reshape(shape_1)

            input_handle = predictor.get_input_handle(input_names[1])
            input_handle.copy_from_cpu(data_2)
            input_handle.set_lod(lod_2)
            input_handle.reshape(shape_2)

            predictor.run()
            output_names = predictor.get_output_names()
            output_handle = predictor.get_output_handle(output_names[1])
            batch_out = output_handle.copy_to_cpu()

            batch_result = postprocess(batch_out, processed_results)
            results += batch_result
        return results

        ```
    ###Assistant : 
        Get the sentiment prediction results results with the texts as input
        Args:
             texts(list): the input texts to be predicted which the first element is text_1(list)
                          and the second element is text_2(list), such as [['这道题很难'], ['这道题不简单']]
                          if texts not data.
             data(dict): key must be 'text_1' and 'text_2', value is the texts(list) to be predicted
             use_gpu(bool): whether use gpu to predict or not
             batch_size(int): the program deals once with one batch
        Returns:
             results(list): the word segmentation results
        
    "
1637,"def cli_collect_weights(sys_argv):
    
    parser = argparse.ArgumentParser(
        description=""This script loads a pretrained model "" ""and uses it collect weights."",
        prog=""ludwig collect_weights"",
        usage=""%(prog)s [options]"",
    )

    # ----------------
    # Model parameters
    # ----------------
    parser.add_argument(""-m"", ""--model_path"", help=""model to load"", required=True)
    parser.add_argument(""-t"", ""--tensors"", help=""tensors to collect"", nargs=""+"", required=True)

    # -------------------------
    # Output results parameters
    # -------------------------
    parser.add_argument(
        ""-od"", ""--output_directory"", type=str, default=""results"", help=""directory that contains the results""
    )

    # ------------------
    # Runtime parameters
    # ------------------
    parser.add_argument(
        ""-l"",
        ""--logging_level"",
        default=""info"",
        help=""the level of logging to use"",
        choices=[""critical"", ""error"", ""warning"", ""info"", ""debug"", ""notset""],
    )

    add_contrib_callback_args(parser)
    args = parser.parse_args(sys_argv)

    args.callbacks = args.callbacks or []
    for callback in args.callbacks:
        callback.on_cmdline(""collect_weights"", *sys_argv)

    args.logging_level = get_logging_level_registry()[args.logging_level]
    logging.getLogger(""ludwig"").setLevel(args.logging_level)
    global logger
    logger = logging.getLogger(""ludwig.collect"")

    print_ludwig(""Collect Weights"", LUDWIG_VERSION)

    collect_weights(**vars(args))

","Command Line Interface to collecting the weights for the model.

    --m: Input model that is necessary to collect to the tensors, this is a
         required *option*
    --t: Tensors to collect
    --od: Output directory of the model, defaults to results
    --v: Verbose: Defines the logging level that the user will be exposed to
    ",52,113,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cli_collect_weights(sys_argv):
    
    parser = argparse.ArgumentParser(
        description=""This script loads a pretrained model "" ""and uses it collect weights."",
        prog=""ludwig collect_weights"",
        usage=""%(prog)s [options]"",
    )

    # ----------------
    # Model parameters
    # ----------------
    parser.add_argument(""-m"", ""--model_path"", help=""model to load"", required=True)
    parser.add_argument(""-t"", ""--tensors"", help=""tensors to collect"", nargs=""+"", required=True)

    # -------------------------
    # Output results parameters
    # -------------------------
    parser.add_argument(
        ""-od"", ""--output_directory"", type=str, default=""results"", help=""directory that contains the results""
    )

    # ------------------
    # Runtime parameters
    # ------------------
    parser.add_argument(
        ""-l"",
        ""--logging_level"",
        default=""info"",
        help=""the level of logging to use"",
        choices=[""critical"", ""error"", ""warning"", ""info"", ""debug"", ""notset""],
    )

    add_contrib_callback_args(parser)
    args = parser.parse_args(sys_argv)

    args.callbacks = args.callbacks or []
    for callback in args.callbacks:
        callback.on_cmdline(""collect_weights"", *sys_argv)

    args.logging_level = get_logging_level_registry()[args.logging_level]
    logging.getLogger(""ludwig"").setLevel(args.logging_level)
    global logger
    logger = logging.getLogger(""ludwig.collect"")

    print_ludwig(""Collect Weights"", LUDWIG_VERSION)

    collect_weights(**vars(args))


        ```
    ###Assistant : Command Line Interface to collecting the weights for the model.

    --m: Input model that is necessary to collect to the tensors, this is a
         required *option*
    --t: Tensors to collect
    --od: Output directory of the model, defaults to results
    --v: Verbose: Defines the logging level that the user will be exposed to
    
    "
1638,"def get_user_emails_from_group(group):
	
	group_doc = group
	if isinstance(group_doc, str):
		group_doc = frappe.get_doc(""Daily Work Summary Group"", group)

	emails = get_users_email(group_doc)

	return emails

","Returns list of email of enabled users from the given group

	:param group: Daily Work Summary Group `name`",18,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_user_emails_from_group(group):
	
	group_doc = group
	if isinstance(group_doc, str):
		group_doc = frappe.get_doc(""Daily Work Summary Group"", group)

	emails = get_users_email(group_doc)

	return emails


        ```
    ###Assistant : Returns list of email of enabled users from the given group

	:param group: Daily Work Summary Group `name`
    "
1639,"def adapt_unknown_value(self, value):
        
        if isinstance(value, datetime.datetime):  # must be before date
            return self.adapt_datetimefield_value(value)
        elif isinstance(value, datetime.date):
            return self.adapt_datefield_value(value)
        elif isinstance(value, datetime.time):
            return self.adapt_timefield_value(value)
        elif isinstance(value, decimal.Decimal):
            return self.adapt_decimalfield_value(value)
        else:
            return value
","
        Transform a value to something compatible with the backend driver.

        This method only depends on the type of the value. It's designed for
        cases where the target type isn't known, such as .raw() SQL queries.
        As a consequence it may not work perfectly in all circumstances.
        ",46,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def adapt_unknown_value(self, value):
        
        if isinstance(value, datetime.datetime):  # must be before date
            return self.adapt_datetimefield_value(value)
        elif isinstance(value, datetime.date):
            return self.adapt_datefield_value(value)
        elif isinstance(value, datetime.time):
            return self.adapt_timefield_value(value)
        elif isinstance(value, decimal.Decimal):
            return self.adapt_decimalfield_value(value)
        else:
            return value

        ```
    ###Assistant : 
        Transform a value to something compatible with the backend driver.

        This method only depends on the type of the value. It's designed for
        cases where the target type isn't known, such as .raw() SQL queries.
        As a consequence it may not work perfectly in all circumstances.
        
    "
1640,"def test_get_settings_variable_assignment_request_context(self):
        
        request = self.get_request(site=self.other_site)
        context = Context({""request"": request})
        template = Template(
            ""{% load wagtailsettings_tags %}""
            ""{% get_settings as wagtail_settings %}""
            ""{{ wagtail_settings.tests.testsetting.title}}""
        )

        self.assertEqual(template.render(context), self.other_site_settings.title)
        # Also check that the default 'settings' variable hasn't been set
        template = Template(
            ""{% load wagtailsettings_tags %}""
            ""{% get_settings as wagtail_settings %}""
            ""{{ settings.tests.testsetting.title}}""
        )

        self.assertEqual(template.render(context), """")
","
        Check that assigning the setting to a context variable with
        {% get_settings as wagtail_settings %} works.
        ",16,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_settings_variable_assignment_request_context(self):
        
        request = self.get_request(site=self.other_site)
        context = Context({""request"": request})
        template = Template(
            ""{% load wagtailsettings_tags %}""
            ""{% get_settings as wagtail_settings %}""
            ""{{ wagtail_settings.tests.testsetting.title}}""
        )

        self.assertEqual(template.render(context), self.other_site_settings.title)
        # Also check that the default 'settings' variable hasn't been set
        template = Template(
            ""{% load wagtailsettings_tags %}""
            ""{% get_settings as wagtail_settings %}""
            ""{{ settings.tests.testsetting.title}}""
        )

        self.assertEqual(template.render(context), """")

        ```
    ###Assistant : 
        Check that assigning the setting to a context variable with
        {% get_settings as wagtail_settings %} works.
        
    "
1641,"def get_dataset(cls, path, engine, storage_options):
        
        if engine == ""auto"":
            # We follow in concordance with pandas
            engine_classes = [PyArrowDataset, FastParquetDataset]

            error_msgs = """"
            for engine_class in engine_classes:
                try:
                    return engine_class(path, storage_options)
                except ImportError as err:
                    error_msgs += ""\n - "" + str(err)

            raise ImportError(
                ""Unable to find a usable engine; ""
                + ""tried using: 'pyarrow', 'fastparquet'.\n""
                + ""A suitable version of ""
                + ""pyarrow or fastparquet is required for parquet ""
                + ""support.\n""
                + ""Trying to import the above resulted in these errors:""
                + f""{error_msgs}""
            )
        elif engine == ""pyarrow"":
            return PyArrowDataset(path, storage_options)
        elif engine == ""fastparquet"":
            return FastParquetDataset(path, storage_options)
        else:
            raise ValueError(""engine must be one of 'pyarrow', 'fastparquet'"")
","
        Retrieve Parquet engine specific Dataset implementation.

        Parameters
        ----------
        path : str, path object or file-like object
            The filepath of the parquet file in local filesystem or hdfs.
        engine : str
            Parquet library to use (only 'PyArrow' is supported for now).
        storage_options : dict
            Parameters for specific storage engine.

        Returns
        -------
        Dataset
            Either a PyArrowDataset or FastParquetDataset object.
        ",57,109,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_dataset(cls, path, engine, storage_options):
        
        if engine == ""auto"":
            # We follow in concordance with pandas
            engine_classes = [PyArrowDataset, FastParquetDataset]

            error_msgs = """"
            for engine_class in engine_classes:
                try:
                    return engine_class(path, storage_options)
                except ImportError as err:
                    error_msgs += ""\n - "" + str(err)

            raise ImportError(
                ""Unable to find a usable engine; ""
                + ""tried using: 'pyarrow', 'fastparquet'.\n""
                + ""A suitable version of ""
                + ""pyarrow or fastparquet is required for parquet ""
                + ""support.\n""
                + ""Trying to import the above resulted in these errors:""
                + f""{error_msgs}""
            )
        elif engine == ""pyarrow"":
            return PyArrowDataset(path, storage_options)
        elif engine == ""fastparquet"":
            return FastParquetDataset(path, storage_options)
        else:
            raise ValueError(""engine must be one of 'pyarrow', 'fastparquet'"")

        ```
    ###Assistant : 
        Retrieve Parquet engine specific Dataset implementation.

        Parameters
        ----------
        path : str, path object or file-like object
            The filepath of the parquet file in local filesystem or hdfs.
        engine : str
            Parquet library to use (only 'PyArrow' is supported for now).
        storage_options : dict
            Parameters for specific storage engine.

        Returns
        -------
        Dataset
            Either a PyArrowDataset or FastParquetDataset object.
        
    "
1642,"def test_causal_lm_model_past_with_attn_mask(self):
        
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
",Test the causal LM model with `past_key_values` and `attention_mask`,9,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_causal_lm_model_past_with_attn_mask(self):
        
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)

        ```
    ###Assistant : Test the causal LM model with `past_key_values` and `attention_mask`
    "
1643,"def _whatsnd(data):
    
    hdr = data[:512]
    fakefile = BytesIO(hdr)
    for testfn in sndhdr.tests:
        res = testfn(hdr, fakefile)
        if res is not None:
            return _sndhdr_MIMEmap.get(res[0])
    return None","Try to identify a sound file type.

    sndhdr.what() has a pretty cruddy interface, unfortunately.  This is why
    we re-do it here.  It would be easier to reverse engineer the Unix 'file'
    command and use the standard 'magic' file, as shipped with a modern Unix.
    ",44,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _whatsnd(data):
    
    hdr = data[:512]
    fakefile = BytesIO(hdr)
    for testfn in sndhdr.tests:
        res = testfn(hdr, fakefile)
        if res is not None:
            return _sndhdr_MIMEmap.get(res[0])
    return None
        ```
    ###Assistant : Try to identify a sound file type.

    sndhdr.what() has a pretty cruddy interface, unfortunately.  This is why
    we re-do it here.  It would be easier to reverse engineer the Unix 'file'
    command and use the standard 'magic' file, as shipped with a modern Unix.
    
    "
1644,"async def test_protect_loop_debugger_sleep(caplog):
    
    block_async_io.enable()

    with patch(
        ""homeassistant.util.async_.extract_stack"",
        return_value=[
            Mock(
                filename=""/home/paulus/homeassistant/.venv/blah/pydevd.py"",
                lineno=""23"",
                line=""do_something()"",
            ),
            Mock(
                filename=""/home/paulus/homeassistant/util/async.py"",
                lineno=""123"",
                line=""protected_loop_func"",
            ),
            Mock(
                filename=""/home/paulus/homeassistant/util/async.py"",
                lineno=""123"",
                line=""check_loop()"",
            ),
        ],
    ):
        time.sleep(0)
    assert ""Detected blocking call inside the event loop"" not in caplog.text

",Test time.sleep injected by the debugger is not reported.,9,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_protect_loop_debugger_sleep(caplog):
    
    block_async_io.enable()

    with patch(
        ""homeassistant.util.async_.extract_stack"",
        return_value=[
            Mock(
                filename=""/home/paulus/homeassistant/.venv/blah/pydevd.py"",
                lineno=""23"",
                line=""do_something()"",
            ),
            Mock(
                filename=""/home/paulus/homeassistant/util/async.py"",
                lineno=""123"",
                line=""protected_loop_func"",
            ),
            Mock(
                filename=""/home/paulus/homeassistant/util/async.py"",
                lineno=""123"",
                line=""check_loop()"",
            ),
        ],
    ):
        time.sleep(0)
    assert ""Detected blocking call inside the event loop"" not in caplog.text


        ```
    ###Assistant : Test time.sleep injected by the debugger is not reported.
    "
1645,"def update_worker_pea_args(self):
        
        self.peas_args['peas'] = self._set_peas_args(self.args)
", Update args of all its worker peas based on Pod args. Does not touch head and tail,17,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_worker_pea_args(self):
        
        self.peas_args['peas'] = self._set_peas_args(self.args)

        ```
    ###Assistant :  Update args of all its worker peas based on Pod args. Does not touch head and tail
    "
1646,"def prevprime(n):
    
    n = _as_int_ceiling(n)
    if n < 3:
        raise ValueError(""no preceding primes"")
    if n < 8:
        return {3: 2, 4: 3, 5: 3, 6: 5, 7: 5}[n]
    if n <= sieve._list[-1]:
        l, u = sieve.search(n)
        if l == u:
            return sieve[l-1]
        else:
            return sieve[l]
    nn = 6*(n//6)
    if n - nn <= 1:
        n = nn - 1
        if isprime(n):
            return n
        n -= 4
    else:
        n = nn + 1
    while 1:
        if isprime(n):
            return n
        n -= 2
        if isprime(n):
            return n
        n -= 4

"," Return the largest prime smaller than n.

        Notes
        =====

        Potential primes are located at 6*j +/- 1. This
        property is used during searching.

        >>> from sympy import prevprime
        >>> [(i, prevprime(i)) for i in range(10, 15)]
        [(10, 7), (11, 7), (12, 11), (13, 11), (14, 13)]

        See Also
        ========

        nextprime : Return the ith prime greater than n
        primerange : Generates all primes in a given range
    ",67,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def prevprime(n):
    
    n = _as_int_ceiling(n)
    if n < 3:
        raise ValueError(""no preceding primes"")
    if n < 8:
        return {3: 2, 4: 3, 5: 3, 6: 5, 7: 5}[n]
    if n <= sieve._list[-1]:
        l, u = sieve.search(n)
        if l == u:
            return sieve[l-1]
        else:
            return sieve[l]
    nn = 6*(n//6)
    if n - nn <= 1:
        n = nn - 1
        if isprime(n):
            return n
        n -= 4
    else:
        n = nn + 1
    while 1:
        if isprime(n):
            return n
        n -= 2
        if isprime(n):
            return n
        n -= 4


        ```
    ###Assistant :  Return the largest prime smaller than n.

        Notes
        =====

        Potential primes are located at 6*j +/- 1. This
        property is used during searching.

        >>> from sympy import prevprime
        >>> [(i, prevprime(i)) for i in range(10, 15)]
        [(10, 7), (11, 7), (12, 11), (13, 11), (14, 13)]

        See Also
        ========

        nextprime : Return the ith prime greater than n
        primerange : Generates all primes in a given range
    
    "
1647,"def test_duplicate_statistics_handle_integrity_error(hass_recorder, caplog):
    
    hass = hass_recorder()
    wait_recording_done(hass)

    period1 = dt_util.as_utc(dt_util.parse_datetime(""2021-09-01 00:00:00""))
    period2 = dt_util.as_utc(dt_util.parse_datetime(""2021-09-30 23:00:00""))

    external_energy_metadata_1 = {
        ""has_mean"": False,
        ""has_sum"": True,
        ""name"": ""Total imported energy"",
        ""source"": ""test"",
        ""state_unit_of_measurement"": ""kWh"",
        ""statistic_id"": ""test:total_energy_import_tariff_1"",
        ""unit_of_measurement"": ""kWh"",
    }
    external_energy_statistics_1 = [
        {
            ""start"": period1,
            ""last_reset"": None,
            ""state"": 3,
            ""sum"": 5,
        },
    ]
    external_energy_statistics_2 = [
        {
            ""start"": period2,
            ""last_reset"": None,
            ""state"": 3,
            ""sum"": 6,
        }
    ]

    with patch.object(
        statistics, ""_statistics_exists"", return_value=False
    ), patch.object(
        statistics, ""_insert_statistics"", wraps=statistics._insert_statistics
    ) as insert_statistics_mock:
        async_add_external_statistics(
            hass, external_energy_metadata_1, external_energy_statistics_1
        )
        async_add_external_statistics(
            hass, external_energy_metadata_1, external_energy_statistics_1
        )
        async_add_external_statistics(
            hass, external_energy_metadata_1, external_energy_statistics_2
        )
        wait_recording_done(hass)
        assert insert_statistics_mock.call_count == 3

    with session_scope(hass=hass) as session:
        tmp = session.query(recorder.db_schema.Statistics).all()
        assert len(tmp) == 2

    assert ""Blocked attempt to insert duplicated statistic rows"" in caplog.text

",Test the recorder does not blow up if statistics is duplicated.,11,117,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_duplicate_statistics_handle_integrity_error(hass_recorder, caplog):
    
    hass = hass_recorder()
    wait_recording_done(hass)

    period1 = dt_util.as_utc(dt_util.parse_datetime(""2021-09-01 00:00:00""))
    period2 = dt_util.as_utc(dt_util.parse_datetime(""2021-09-30 23:00:00""))

    external_energy_metadata_1 = {
        ""has_mean"": False,
        ""has_sum"": True,
        ""name"": ""Total imported energy"",
        ""source"": ""test"",
        ""state_unit_of_measurement"": ""kWh"",
        ""statistic_id"": ""test:total_energy_import_tariff_1"",
        ""unit_of_measurement"": ""kWh"",
    }
    external_energy_statistics_1 = [
        {
            ""start"": period1,
            ""last_reset"": None,
            ""state"": 3,
            ""sum"": 5,
        },
    ]
    external_energy_statistics_2 = [
        {
            ""start"": period2,
            ""last_reset"": None,
            ""state"": 3,
            ""sum"": 6,
        }
    ]

    with patch.object(
        statistics, ""_statistics_exists"", return_value=False
    ), patch.object(
        statistics, ""_insert_statistics"", wraps=statistics._insert_statistics
    ) as insert_statistics_mock:
        async_add_external_statistics(
            hass, external_energy_metadata_1, external_energy_statistics_1
        )
        async_add_external_statistics(
            hass, external_energy_metadata_1, external_energy_statistics_1
        )
        async_add_external_statistics(
            hass, external_energy_metadata_1, external_energy_statistics_2
        )
        wait_recording_done(hass)
        assert insert_statistics_mock.call_count == 3

    with session_scope(hass=hass) as session:
        tmp = session.query(recorder.db_schema.Statistics).all()
        assert len(tmp) == 2

    assert ""Blocked attempt to insert duplicated statistic rows"" in caplog.text


        ```
    ###Assistant : Test the recorder does not blow up if statistics is duplicated.
    "
1648,"def testRequestResourcesRaceConditionWithResourceDemands(self):
        
        config = copy.deepcopy(MULTI_WORKER_CLUSTER)
        config[""available_node_types""].update(
            {
                ""empty_node"": {
                    ""node_config"": {},
                    ""resources"": {""CPU"": 2, ""GPU"": 1},
                    ""max_workers"": 1,
                },
                ""def_worker"": {
                    ""node_config"": {},
                    ""resources"": {""CPU"": 2, ""GPU"": 1, ""WORKER"": 1},
                    ""max_workers"": 3,
                },
            }
        )
        config[""idle_timeout_minutes""] = 0

        config_path = self.write_config(config)
        self.provider = MockProvider()
        self.provider.create_node(
            {},
            {
                TAG_RAY_NODE_KIND: ""head"",
                TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE,
                TAG_RAY_USER_NODE_TYPE: ""empty_node"",
            },
            1,
        )

        runner = MockProcessRunner()
        runner.respond_to_call(""json .Config.Env"", [""[]"" for i in range(2)])
        lm = LoadMetrics()
        autoscaler = MockAutoscaler(
            config_path,
            lm,
            MockNodeInfoStub(),
            max_failures=0,
            process_runner=runner,
            update_interval_s=0,
        )
        lm.update(
            ""127.0.0.0"",
            mock_raylet_id(),
            {""CPU"": 2, ""GPU"": 1},
            {""CPU"": 2},
            {},
            waiting_bundles=[{""CPU"": 2}],
        )
        autoscaler.load_metrics.set_resource_requests([{""CPU"": 2, ""GPU"": 1}] * 2)
        autoscaler.update()
        # 1 head, 1 worker.
        self.waitForNodes(2)
        lm.update(
            ""127.0.0.0"",
            mock_raylet_id(),
            {""CPU"": 2, ""GPU"": 1},
            {""CPU"": 2},
            {},
            waiting_bundles=[{""CPU"": 2}],
        )
        # make sure it stays consistent.
        for _ in range(10):
            autoscaler.update()
        self.waitForNodes(2)

","Test request_resources() with resource_demands.

        Tests when request_resources() is called simultaneously with resource
        demands in multiple orders.
        ",16,130,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def testRequestResourcesRaceConditionWithResourceDemands(self):
        
        config = copy.deepcopy(MULTI_WORKER_CLUSTER)
        config[""available_node_types""].update(
            {
                ""empty_node"": {
                    ""node_config"": {},
                    ""resources"": {""CPU"": 2, ""GPU"": 1},
                    ""max_workers"": 1,
                },
                ""def_worker"": {
                    ""node_config"": {},
                    ""resources"": {""CPU"": 2, ""GPU"": 1, ""WORKER"": 1},
                    ""max_workers"": 3,
                },
            }
        )
        config[""idle_timeout_minutes""] = 0

        config_path = self.write_config(config)
        self.provider = MockProvider()
        self.provider.create_node(
            {},
            {
                TAG_RAY_NODE_KIND: ""head"",
                TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE,
                TAG_RAY_USER_NODE_TYPE: ""empty_node"",
            },
            1,
        )

        runner = MockProcessRunner()
        runner.respond_to_call(""json .Config.Env"", [""[]"" for i in range(2)])
        lm = LoadMetrics()
        autoscaler = MockAutoscaler(
            config_path,
            lm,
            MockNodeInfoStub(),
            max_failures=0,
            process_runner=runner,
            update_interval_s=0,
        )
        lm.update(
            ""127.0.0.0"",
            mock_raylet_id(),
            {""CPU"": 2, ""GPU"": 1},
            {""CPU"": 2},
            {},
            waiting_bundles=[{""CPU"": 2}],
        )
        autoscaler.load_metrics.set_resource_requests([{""CPU"": 2, ""GPU"": 1}] * 2)
        autoscaler.update()
        # 1 head, 1 worker.
        self.waitForNodes(2)
        lm.update(
            ""127.0.0.0"",
            mock_raylet_id(),
            {""CPU"": 2, ""GPU"": 1},
            {""CPU"": 2},
            {},
            waiting_bundles=[{""CPU"": 2}],
        )
        # make sure it stays consistent.
        for _ in range(10):
            autoscaler.update()
        self.waitForNodes(2)


        ```
    ###Assistant : Test request_resources() with resource_demands.

        Tests when request_resources() is called simultaneously with resource
        demands in multiple orders.
        
    "
1649,"def test_sitemap_unpublished_titles(self):
        
        sitemap = CMSSitemap()
        locations = []
        urlset = sitemap.get_urls()
        unpublished_titles = set()
        for item in urlset:
            locations.append(item['location'])
        for page in Page.objects.drafts():
            if page.get_public_object():
                set1 = set(page.get_public_object().title_set.values_list('path', flat=True))
                set2 = set(page.title_set.values_list('path', flat=True))
                unpublished_titles.update(set2.difference(set1))
            else:
                unpublished_titles.update(page.title_set.values_list('path', flat=True))

        for path in unpublished_titles:
            title = Title.objects.get(path=path)
            if title.path:
                url = f'http://example.com/{title.language}/{title.path}/'
            else:
                url = f'http://example.com/{title.language}/{title.path}'
            self.assertFalse(url in locations)
","
        Check that titles attached to unpublished pages are not in the urlset.
        As titles are 'published' depending on their attached page, we create a
        set of unpublished titles by checking titles attached to the draft and
        public version of each page
        ",41,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_sitemap_unpublished_titles(self):
        
        sitemap = CMSSitemap()
        locations = []
        urlset = sitemap.get_urls()
        unpublished_titles = set()
        for item in urlset:
            locations.append(item['location'])
        for page in Page.objects.drafts():
            if page.get_public_object():
                set1 = set(page.get_public_object().title_set.values_list('path', flat=True))
                set2 = set(page.title_set.values_list('path', flat=True))
                unpublished_titles.update(set2.difference(set1))
            else:
                unpublished_titles.update(page.title_set.values_list('path', flat=True))

        for path in unpublished_titles:
            title = Title.objects.get(path=path)
            if title.path:
                url = f'http://example.com/{title.language}/{title.path}/'
            else:
                url = f'http://example.com/{title.language}/{title.path}'
            self.assertFalse(url in locations)

        ```
    ###Assistant : 
        Check that titles attached to unpublished pages are not in the urlset.
        As titles are 'published' depending on their attached page, we create a
        set of unpublished titles by checking titles attached to the draft and
        public version of each page
        
    "
1650,"def mixin_gateway_parser(parser):
    
    gp = add_arg_group(parser, title='Gateway')
    _add_host(gp)
    _add_proxy(gp)

    gp.add_argument(
        '--uses',
        type=str,
        default=None,
        # TODO: add Jina Hub Gateway
        help=,
    )

    gp.add_argument(
        '--uses-with',
        action=KVAppendAction,
        metavar='KEY: VALUE',
        nargs='*',
        help=,
    )

    gp.add_argument(
        '--py-modules',
        type=str,
        nargs='*',
        metavar='PATH',
        help=,
    )

    mixin_base_runtime_parser(gp)

    gp.add_argument(
        '--port-expose',
        type=int,
        dest='port',
        default=helper.random_port(),
        help='The port that the gateway exposes for clients for GRPC connections.',
    )

    parser.add_argument(
        '--graph-description',
        type=str,
        help='Routing graph for the gateway',
        default='{}',
    )

    parser.add_argument(
        '--graph-conditions',
        type=str,
        help='Dictionary stating which filtering conditions each Executor in the graph requires to receive Documents.',
        default='{}',
    )

    parser.add_argument(
        '--deployments-addresses',
        type=str,
        help='dictionary JSON with the input addresses of each Deployment',
        default='{}',
    )

    parser.add_argument(
        '--deployments-disable-reduce',
        type=str,
        help='list JSON disabling the built-in merging mechanism for each Deployment listed',
        default='[]',
    )

    gp.add_argument(
        '--compression',
        choices=['NoCompression', 'Deflate', 'Gzip'],
        help='The compression mechanism used when sending requests from the Head to the WorkerRuntimes. For more details, '
        'check https://grpc.github.io/grpc/python/grpc.html#compression.',
    )

    gp.add_argument(
        '--timeout-send',
        type=int,
        default=None,
        help='The timeout in milliseconds used when sending data requests to Executors, -1 means no timeout, disabled by default',
    )

","Add the options for remote expose at the Gateway
    :param parser: the parser
    
        The config of the gateway, it could be one of the followings:
        * the string literal of an Gateway class name
        * a Gateway YAML file (.yml, .yaml, .jaml)
        * a docker image (must start with `docker://`)
        * the string literal of a YAML config (must start with `!` or `jtype: `)
        * the string literal of a JSON config

        When use it under Python, one can use the following values additionally:
        - a Python dict that represents the config
        - a text file stream has `.read()` interface
        
    Dictionary of keyword arguments that will override the `with` configuration in `uses`
    
The customized python modules need to be imported before loading the gateway

Note that the recommended way is to only import a single module - a simple python file, if your
gateway can be defined in a single file, or an ``__init__.py`` file if you have multiple files,
which should be structured as a python package.
",169,160,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mixin_gateway_parser(parser):
    
    gp = add_arg_group(parser, title='Gateway')
    _add_host(gp)
    _add_proxy(gp)

    gp.add_argument(
        '--uses',
        type=str,
        default=None,
        # TODO: add Jina Hub Gateway
        help=,
    )

    gp.add_argument(
        '--uses-with',
        action=KVAppendAction,
        metavar='KEY: VALUE',
        nargs='*',
        help=,
    )

    gp.add_argument(
        '--py-modules',
        type=str,
        nargs='*',
        metavar='PATH',
        help=,
    )

    mixin_base_runtime_parser(gp)

    gp.add_argument(
        '--port-expose',
        type=int,
        dest='port',
        default=helper.random_port(),
        help='The port that the gateway exposes for clients for GRPC connections.',
    )

    parser.add_argument(
        '--graph-description',
        type=str,
        help='Routing graph for the gateway',
        default='{}',
    )

    parser.add_argument(
        '--graph-conditions',
        type=str,
        help='Dictionary stating which filtering conditions each Executor in the graph requires to receive Documents.',
        default='{}',
    )

    parser.add_argument(
        '--deployments-addresses',
        type=str,
        help='dictionary JSON with the input addresses of each Deployment',
        default='{}',
    )

    parser.add_argument(
        '--deployments-disable-reduce',
        type=str,
        help='list JSON disabling the built-in merging mechanism for each Deployment listed',
        default='[]',
    )

    gp.add_argument(
        '--compression',
        choices=['NoCompression', 'Deflate', 'Gzip'],
        help='The compression mechanism used when sending requests from the Head to the WorkerRuntimes. For more details, '
        'check https://grpc.github.io/grpc/python/grpc.html#compression.',
    )

    gp.add_argument(
        '--timeout-send',
        type=int,
        default=None,
        help='The timeout in milliseconds used when sending data requests to Executors, -1 means no timeout, disabled by default',
    )


        ```
    ###Assistant : Add the options for remote expose at the Gateway
    :param parser: the parser
    
        The config of the gateway, it could be one of the followings:
        * the string literal of an Gateway class name
        * a Gateway YAML file (.yml, .yaml, .jaml)
        * a docker image (must start with `docker://`)
        * the string literal of a YAML config (must start with `!` or `jtype: `)
        * the string literal of a JSON config

        When use it under Python, one can use the following values additionally:
        - a Python dict that represents the config
        - a text file stream has `.read()` interface
        
    Dictionary of keyword arguments that will override the `with` configuration in `uses`
    
The customized python modules need to be imported before loading the gateway

Note that the recommended way is to only import a single module - a simple python file, if your
gateway can be defined in a single file, or an ``__init__.py`` file if you have multiple files,
which should be structured as a python package.

    "
1651,"def _format_changelog(self, changelog):
        
        if not changelog:
            return changelog
        new_changelog = []
        for line in changelog.strip().split('\n'):
            line = line.strip()
            if line[0] == '*':
                new_changelog.extend(['', line])
            elif line[0] == '-':
                new_changelog.append(line)
            else:
                new_changelog.append('  ' + line)

        # strip trailing newline inserted by first changelog entry
        if not new_changelog[0]:
            del new_changelog[0]

        return new_changelog
","Format the changelog correctly and convert it to a list of strings
        ",12,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _format_changelog(self, changelog):
        
        if not changelog:
            return changelog
        new_changelog = []
        for line in changelog.strip().split('\n'):
            line = line.strip()
            if line[0] == '*':
                new_changelog.extend(['', line])
            elif line[0] == '-':
                new_changelog.append(line)
            else:
                new_changelog.append('  ' + line)

        # strip trailing newline inserted by first changelog entry
        if not new_changelog[0]:
            del new_changelog[0]

        return new_changelog

        ```
    ###Assistant : Format the changelog correctly and convert it to a list of strings
        
    "
1652,"def _get_time(self) -> float:
        
        # N.B. We could remove this method and always call `self._timer.get_time()` internally,
        # but it's handy to have in mocking situations
        return self._timer.get_time()
","Get the current wall clock time, via the internal Timer.",10,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_time(self) -> float:
        
        # N.B. We could remove this method and always call `self._timer.get_time()` internally,
        # but it's handy to have in mocking situations
        return self._timer.get_time()

        ```
    ###Assistant : Get the current wall clock time, via the internal Timer.
    "
1653,"def _generate(self, pset, min_, max_, condition, type_=None):
        
        if type_ is None:
            type_ = pset.ret
        expr = []
        height = np.random.randint(min_, max_)
        stack = [(0, type_)]
        while len(stack) != 0:
            depth, type_ = stack.pop()

            # We've added a type_ parameter to the condition function
            if condition(height, depth, type_):
                try:
                    term = np.random.choice(pset.terminals[type_])
                except IndexError:
                    _, _, traceback = sys.exc_info()
                    raise IndexError(
                        ""The gp.generate function tried to add ""
                        ""a terminal of type {}, but there is""
                        ""none available. {}"".format(type_, traceback)
                    )
                if inspect.isclass(term):
                    term = term()
                expr.append(term)
            else:
                try:
                    prim = np.random.choice(pset.primitives[type_])
                except IndexError:
                    _, _, traceback = sys.exc_info()
                    raise IndexError(
                        ""The gp.generate function tried to add ""
                        ""a primitive of type {}, but there is""
                        ""none available. {}"".format(type_, traceback)
                    )
                expr.append(prim)
                for arg in reversed(prim.args):
                    stack.append((depth + 1, arg))
        return expr

","Generate a Tree as a list of lists.

        The tree is build from the root to the leaves, and it stop growing when
        the condition is fulfilled.

        Parameters
        ----------
        pset: PrimitiveSetTyped
            Primitive set from which primitives are selected.
        min_: int
            Minimum height of the produced trees.
        max_: int
            Maximum height of the produced trees.
        condition: function
            The condition is a function that takes two arguments,
            the height of the tree to build and the current
            depth in the tree.
        type_: class
            The type that should return the tree when called, when
            :obj:None (default) no return type is enforced.

        Returns
        -------
        individual: list
            A grown tree with leaves at possibly different depths
            depending on the condition function.
        ",116,131,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _generate(self, pset, min_, max_, condition, type_=None):
        
        if type_ is None:
            type_ = pset.ret
        expr = []
        height = np.random.randint(min_, max_)
        stack = [(0, type_)]
        while len(stack) != 0:
            depth, type_ = stack.pop()

            # We've added a type_ parameter to the condition function
            if condition(height, depth, type_):
                try:
                    term = np.random.choice(pset.terminals[type_])
                except IndexError:
                    _, _, traceback = sys.exc_info()
                    raise IndexError(
                        ""The gp.generate function tried to add ""
                        ""a terminal of type {}, but there is""
                        ""none available. {}"".format(type_, traceback)
                    )
                if inspect.isclass(term):
                    term = term()
                expr.append(term)
            else:
                try:
                    prim = np.random.choice(pset.primitives[type_])
                except IndexError:
                    _, _, traceback = sys.exc_info()
                    raise IndexError(
                        ""The gp.generate function tried to add ""
                        ""a primitive of type {}, but there is""
                        ""none available. {}"".format(type_, traceback)
                    )
                expr.append(prim)
                for arg in reversed(prim.args):
                    stack.append((depth + 1, arg))
        return expr


        ```
    ###Assistant : Generate a Tree as a list of lists.

        The tree is build from the root to the leaves, and it stop growing when
        the condition is fulfilled.

        Parameters
        ----------
        pset: PrimitiveSetTyped
            Primitive set from which primitives are selected.
        min_: int
            Minimum height of the produced trees.
        max_: int
            Maximum height of the produced trees.
        condition: function
            The condition is a function that takes two arguments,
            the height of the tree to build and the current
            depth in the tree.
        type_: class
            The type that should return the tree when called, when
            :obj:None (default) no return type is enforced.

        Returns
        -------
        individual: list
            A grown tree with leaves at possibly different depths
            depending on the condition function.
        
    "
1654,"def test_ohe_infrequent_multiple_categories_dtypes():
    

    pd = pytest.importorskip(""pandas"")
    X = pd.DataFrame(
        {
            ""str"": [""a"", ""f"", ""c"", ""f"", ""f"", ""a"", ""c"", ""b"", ""b""],
            ""int"": [5, 3, 0, 10, 10, 12, 0, 3, 5],
        },
        columns=[""str"", ""int""],
    )

    ohe = OneHotEncoder(
        categories=""auto"", max_categories=3, handle_unknown=""infrequent_if_exist""
    )
    # X[:, 0] 'a', 'b', 'c' have the same frequency. 'a' and 'b' will be
    # considered infrequent because they are greater

    # X[:, 1] 0, 3, 5, 10 has frequency 2 and 12 has frequency 1.
    # 0, 3, 12 will be considered infrequent

    X_trans = ohe.fit_transform(X).toarray()
    assert_array_equal(ohe.infrequent_categories_[0], [""a"", ""b""])
    assert_array_equal(ohe.infrequent_categories_[1], [0, 3, 12])

    expected = [
        [0, 0, 1, 1, 0, 0],
        [0, 1, 0, 0, 0, 1],
        [1, 0, 0, 0, 0, 1],
        [0, 1, 0, 0, 1, 0],
        [0, 1, 0, 0, 1, 0],
        [0, 0, 1, 0, 0, 1],
        [1, 0, 0, 0, 0, 1],
        [0, 0, 1, 0, 0, 1],
        [0, 0, 1, 1, 0, 0],
    ]

    assert_allclose(expected, X_trans)

    X_test = pd.DataFrame({""str"": [""b"", ""f""], ""int"": [14, 12]}, columns=[""str"", ""int""])

    expected = [[0, 0, 1, 0, 0, 1], [0, 1, 0, 0, 0, 1]]
    X_test_trans = ohe.transform(X_test)
    assert_allclose(expected, X_test_trans.toarray())

    X_inv = ohe.inverse_transform(X_test_trans)
    expected_inv = np.array(
        [[""infrequent_sklearn"", ""infrequent_sklearn""], [""f"", ""infrequent_sklearn""]],
        dtype=object,
    )
    assert_array_equal(expected_inv, X_inv)

    # only infrequent or known categories
    X_test = pd.DataFrame({""str"": [""c"", ""b""], ""int"": [12, 5]}, columns=[""str"", ""int""])
    X_test_trans = ohe.transform(X_test).toarray()
    expected = [[1, 0, 0, 0, 0, 1], [0, 0, 1, 1, 0, 0]]
    assert_allclose(expected, X_test_trans)

    X_inv = ohe.inverse_transform(X_test_trans)
    expected_inv = np.array(
        [[""c"", ""infrequent_sklearn""], [""infrequent_sklearn"", 5]], dtype=object
    )
    assert_array_equal(expected_inv, X_inv)


@pytest.mark.parametrize(""kwargs"", [{""min_frequency"": 21, ""max_categories"": 1}])",Test infrequent categories with a pandas dataframe with multiple dtypes.,10,252,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ohe_infrequent_multiple_categories_dtypes():
    

    pd = pytest.importorskip(""pandas"")
    X = pd.DataFrame(
        {
            ""str"": [""a"", ""f"", ""c"", ""f"", ""f"", ""a"", ""c"", ""b"", ""b""],
            ""int"": [5, 3, 0, 10, 10, 12, 0, 3, 5],
        },
        columns=[""str"", ""int""],
    )

    ohe = OneHotEncoder(
        categories=""auto"", max_categories=3, handle_unknown=""infrequent_if_exist""
    )
    # X[:, 0] 'a', 'b', 'c' have the same frequency. 'a' and 'b' will be
    # considered infrequent because they are greater

    # X[:, 1] 0, 3, 5, 10 has frequency 2 and 12 has frequency 1.
    # 0, 3, 12 will be considered infrequent

    X_trans = ohe.fit_transform(X).toarray()
    assert_array_equal(ohe.infrequent_categories_[0], [""a"", ""b""])
    assert_array_equal(ohe.infrequent_categories_[1], [0, 3, 12])

    expected = [
        [0, 0, 1, 1, 0, 0],
        [0, 1, 0, 0, 0, 1],
        [1, 0, 0, 0, 0, 1],
        [0, 1, 0, 0, 1, 0],
        [0, 1, 0, 0, 1, 0],
        [0, 0, 1, 0, 0, 1],
        [1, 0, 0, 0, 0, 1],
        [0, 0, 1, 0, 0, 1],
        [0, 0, 1, 1, 0, 0],
    ]

    assert_allclose(expected, X_trans)

    X_test = pd.DataFrame({""str"": [""b"", ""f""], ""int"": [14, 12]}, columns=[""str"", ""int""])

    expected = [[0, 0, 1, 0, 0, 1], [0, 1, 0, 0, 0, 1]]
    X_test_trans = ohe.transform(X_test)
    assert_allclose(expected, X_test_trans.toarray())

    X_inv = ohe.inverse_transform(X_test_trans)
    expected_inv = np.array(
        [[""infrequent_sklearn"", ""infrequent_sklearn""], [""f"", ""infrequent_sklearn""]],
        dtype=object,
    )
    assert_array_equal(expected_inv, X_inv)

    # only infrequent or known categories
    X_test = pd.DataFrame({""str"": [""c"", ""b""], ""int"": [12, 5]}, columns=[""str"", ""int""])
    X_test_trans = ohe.transform(X_test).toarray()
    expected = [[1, 0, 0, 0, 0, 1], [0, 0, 1, 1, 0, 0]]
    assert_allclose(expected, X_test_trans)

    X_inv = ohe.inverse_transform(X_test_trans)
    expected_inv = np.array(
        [[""c"", ""infrequent_sklearn""], [""infrequent_sklearn"", 5]], dtype=object
    )
    assert_array_equal(expected_inv, X_inv)


@pytest.mark.parametrize(""kwargs"", [{""min_frequency"": 21, ""max_categories"": 1}])
        ```
    ###Assistant : Test infrequent categories with a pandas dataframe with multiple dtypes.
    "
1655,"def _set_random_id(self):
        if getattr(self, ""persistence"", False):
            raise RuntimeError(
                
            )
        if ""dash_snapshots"" in sys.modules:
            raise RuntimeError(
                
            )

        if not hasattr(self, ""id""):
            v = str(uuid.UUID(int=rd.randint(0, 2 ** 128)))
            setattr(self, ""id"", v)
        return getattr(self, ""id"")
","
                Attempting to use an auto-generated ID with the `persistence` prop.
                This is prohibited because persistence is tied to component IDs and
                auto-generated IDs can easily change.

                Please assign an explicit ID to this component.
            
                Attempting to use an auto-generated ID in an app with `dash_snapshots`.
                This is prohibited because snapshots saves the whole app layout,
                including component IDs, and auto-generated IDs can easily change.
                Callbacks referencing the new IDs will not work old snapshots.

                Please assign an explicit ID to this component.
            ",82,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_random_id(self):
        if getattr(self, ""persistence"", False):
            raise RuntimeError(
                
            )
        if ""dash_snapshots"" in sys.modules:
            raise RuntimeError(
                
            )

        if not hasattr(self, ""id""):
            v = str(uuid.UUID(int=rd.randint(0, 2 ** 128)))
            setattr(self, ""id"", v)
        return getattr(self, ""id"")

        ```
    ###Assistant : 
                Attempting to use an auto-generated ID with the `persistence` prop.
                This is prohibited because persistence is tied to component IDs and
                auto-generated IDs can easily change.

                Please assign an explicit ID to this component.
            
                Attempting to use an auto-generated ID in an app with `dash_snapshots`.
                This is prohibited because snapshots saves the whole app layout,
                including component IDs, and auto-generated IDs can easily change.
                Callbacks referencing the new IDs will not work old snapshots.

                Please assign an explicit ID to this component.
            
    "
1656,"def final():
    
    head = []
    head.append((""layernorm.weight"", ""norm.weight""))
    head.append((""layernorm.bias"", ""norm.bias""))
    head.append((""classifier.weight"", ""head.weight""))
    head.append((""classifier.bias"", ""head.bias""))
    return head

","
    Function helps in renaming final classification layer
    ",7,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def final():
    
    head = []
    head.append((""layernorm.weight"", ""norm.weight""))
    head.append((""layernorm.bias"", ""norm.bias""))
    head.append((""classifier.weight"", ""head.weight""))
    head.append((""classifier.bias"", ""head.bias""))
    return head


        ```
    ###Assistant : 
    Function helps in renaming final classification layer
    
    "
1657,"def test_delete_index(self, ds, documents):
        
        ds.write_documents(documents, index=""custom_index"")
        assert ds.get_document_count(index=""custom_index"") == len(documents)
        ds.delete_index(index=""custom_index"")
        assert ds.get_document_count(index=""custom_index"") == 0
","Contrary to other Document Stores, SQLDocumentStore doesn't raise if the index is empty",13,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_index(self, ds, documents):
        
        ds.write_documents(documents, index=""custom_index"")
        assert ds.get_document_count(index=""custom_index"") == len(documents)
        ds.delete_index(index=""custom_index"")
        assert ds.get_document_count(index=""custom_index"") == 0

        ```
    ###Assistant : Contrary to other Document Stores, SQLDocumentStore doesn't raise if the index is empty
    "
1658,"def add_holidays(events, start, end, employee, company):
	applicable_holiday_list = get_holiday_list_for_employee(employee, company)
	if not applicable_holiday_list:
		return

	for holiday in frappe.db.sql(
		,
		(applicable_holiday_list, start, end),
		as_dict=True,
	):
		events.append(
			{
				""doctype"": ""Holiday"",
				""from_date"": holiday.holiday_date,
				""to_date"": holiday.holiday_date,
				""title"": _(""Holiday"") + "": "" + cstr(holiday.description),
				""name"": holiday.name,
			}
		)


@frappe.whitelist()","select name, holiday_date, description
		from `tabHoliday` where parent=%s and holiday_date between %s and %s",14,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_holidays(events, start, end, employee, company):
	applicable_holiday_list = get_holiday_list_for_employee(employee, company)
	if not applicable_holiday_list:
		return

	for holiday in frappe.db.sql(
		,
		(applicable_holiday_list, start, end),
		as_dict=True,
	):
		events.append(
			{
				""doctype"": ""Holiday"",
				""from_date"": holiday.holiday_date,
				""to_date"": holiday.holiday_date,
				""title"": _(""Holiday"") + "": "" + cstr(holiday.description),
				""name"": holiday.name,
			}
		)


@frappe.whitelist()
        ```
    ###Assistant : select name, holiday_date, description
		from `tabHoliday` where parent=%s and holiday_date between %s and %s
    "
1659,"def get_video_input_devices_names() -> List[str]:
    
    # based on https://docs.microsoft.com/ru-ru/windows/win32/directshow/selecting-a-capture-device

    names = []
    sys_dev_enum = strmif.ICreateDevEnum()
    if ole32.CoCreateInstance(uuids.CLSID_SystemDeviceEnum, None, ole32.CLSCTX.CLSCTX_INPROC_SERVER, strmif.ICreateDevEnum.IID, sys_dev_enum) == wintypes.ERROR.SUCCESS:
        pEnumCat = objidl.IEnumMoniker()

        if sys_dev_enum.CreateClassEnumerator(uuids.CLSID_VideoInputDeviceCategory, pEnumCat, 0) == wintypes.ERROR.SUCCESS:

            moniker = objidl.IMoniker()

            while pEnumCat.Next(1, moniker, None) == wintypes.ERROR.SUCCESS:

                prop_bag = oaidl.IPropertyBag()
                if moniker.BindToStorage(None, None, oaidl.IPropertyBag.IID, prop_bag) == wintypes.ERROR.SUCCESS:
                    var = wintypes.VARIANT()

                    hr = prop_bag.Read(wintypes.LPCOLESTR('Description'), var, None )
                    if hr != wintypes.ERROR.SUCCESS:
                        hr = prop_bag.Read(wintypes.LPCOLESTR('FriendlyName'), var, None )

                    names.append(var.value.bstrVal.value if hr == wintypes.ERROR.SUCCESS else 'unnamed')

                    prop_bag.Release()
                moniker.Release()
            pEnumCat.Release()
        sys_dev_enum.Release()

    return names","
    returns a list of available names of VideoInputDevice's

    ole32 should be initialized before use
    ",14,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_video_input_devices_names() -> List[str]:
    
    # based on https://docs.microsoft.com/ru-ru/windows/win32/directshow/selecting-a-capture-device

    names = []
    sys_dev_enum = strmif.ICreateDevEnum()
    if ole32.CoCreateInstance(uuids.CLSID_SystemDeviceEnum, None, ole32.CLSCTX.CLSCTX_INPROC_SERVER, strmif.ICreateDevEnum.IID, sys_dev_enum) == wintypes.ERROR.SUCCESS:
        pEnumCat = objidl.IEnumMoniker()

        if sys_dev_enum.CreateClassEnumerator(uuids.CLSID_VideoInputDeviceCategory, pEnumCat, 0) == wintypes.ERROR.SUCCESS:

            moniker = objidl.IMoniker()

            while pEnumCat.Next(1, moniker, None) == wintypes.ERROR.SUCCESS:

                prop_bag = oaidl.IPropertyBag()
                if moniker.BindToStorage(None, None, oaidl.IPropertyBag.IID, prop_bag) == wintypes.ERROR.SUCCESS:
                    var = wintypes.VARIANT()

                    hr = prop_bag.Read(wintypes.LPCOLESTR('Description'), var, None )
                    if hr != wintypes.ERROR.SUCCESS:
                        hr = prop_bag.Read(wintypes.LPCOLESTR('FriendlyName'), var, None )

                    names.append(var.value.bstrVal.value if hr == wintypes.ERROR.SUCCESS else 'unnamed')

                    prop_bag.Release()
                moniker.Release()
            pEnumCat.Release()
        sys_dev_enum.Release()

    return names
        ```
    ###Assistant : 
    returns a list of available names of VideoInputDevice's

    ole32 should be initialized before use
    
    "
1660,"def check_for_updates():
    

    version_message = get_update_status()

    print(version_message)

","
    Check for updates to the current version.
    ",7,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_for_updates():
    

    version_message = get_update_status()

    print(version_message)


        ```
    ###Assistant : 
    Check for updates to the current version.
    
    "
1661,"def _get_base_knot_positions(X, n_knots=10, knots=""uniform"", sample_weight=None):
        
        if knots == ""quantile"":
            percentiles = 100 * np.linspace(
                start=0, stop=1, num=n_knots, dtype=np.float64
            )

            if sample_weight is None:
                knots = np.percentile(X, percentiles, axis=0)
            else:
                knots = np.array(
                    [
                        _weighted_percentile(X, sample_weight, percentile)
                        for percentile in percentiles
                    ]
                )

        else:
            # knots == 'uniform':
            # Note that the variable `knots` has already been validated and
            # `else` is therefore safe.
            # Disregard observations with zero weight.
            mask = slice(None, None, 1) if sample_weight is None else sample_weight > 0
            x_min = np.amin(X[mask], axis=0)
            x_max = np.amax(X[mask], axis=0)

            knots = np.linspace(
                start=x_min,
                stop=x_max,
                num=n_knots,
                endpoint=True,
                dtype=np.float64,
            )

        return knots
","Calculate base knot positions.

        Base knots such that first knot <= feature <= last knot. For the
        B-spline construction with scipy.interpolate.BSpline, 2*degree knots
        beyond the base interval are added.

        Returns
        -------
        knots : ndarray of shape (n_knots, n_features), dtype=np.float64
            Knot positions (points) of base interval.
        ",45,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_base_knot_positions(X, n_knots=10, knots=""uniform"", sample_weight=None):
        
        if knots == ""quantile"":
            percentiles = 100 * np.linspace(
                start=0, stop=1, num=n_knots, dtype=np.float64
            )

            if sample_weight is None:
                knots = np.percentile(X, percentiles, axis=0)
            else:
                knots = np.array(
                    [
                        _weighted_percentile(X, sample_weight, percentile)
                        for percentile in percentiles
                    ]
                )

        else:
            # knots == 'uniform':
            # Note that the variable `knots` has already been validated and
            # `else` is therefore safe.
            # Disregard observations with zero weight.
            mask = slice(None, None, 1) if sample_weight is None else sample_weight > 0
            x_min = np.amin(X[mask], axis=0)
            x_max = np.amax(X[mask], axis=0)

            knots = np.linspace(
                start=x_min,
                stop=x_max,
                num=n_knots,
                endpoint=True,
                dtype=np.float64,
            )

        return knots

        ```
    ###Assistant : Calculate base knot positions.

        Base knots such that first knot <= feature <= last knot. For the
        B-spline construction with scipy.interpolate.BSpline, 2*degree knots
        beyond the base interval are added.

        Returns
        -------
        knots : ndarray of shape (n_knots, n_features), dtype=np.float64
            Knot positions (points) of base interval.
        
    "
1662,"def _num_tokens(self, data):
        
        if tf_utils.is_sparse(data):
            flat_values = data.values
        elif tf_utils.is_ragged(data):
            flat_values = data.flat_values
        else:
            flat_values = tf.reshape(data, [-1])
        tokens, _, counts = tf.unique_with_counts(flat_values, out_idx=tf.int64)
        return tokens, counts
","Count the number of tokens in a ragged, sparse or dense tensor.",12,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _num_tokens(self, data):
        
        if tf_utils.is_sparse(data):
            flat_values = data.values
        elif tf_utils.is_ragged(data):
            flat_values = data.flat_values
        else:
            flat_values = tf.reshape(data, [-1])
        tokens, _, counts = tf.unique_with_counts(flat_values, out_idx=tf.int64)
        return tokens, counts

        ```
    ###Assistant : Count the number of tokens in a ragged, sparse or dense tensor.
    "
1663,"def no_devicess_fixture():
    
    return json.loads(load_fixture(""awair/no_devices.json""))


@pytest.fixture(name=""awair_offline"", scope=""session"")",Fixture representing when no devices are found in Awair's cloud API.,11,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def no_devicess_fixture():
    
    return json.loads(load_fixture(""awair/no_devices.json""))


@pytest.fixture(name=""awair_offline"", scope=""session"")
        ```
    ###Assistant : Fixture representing when no devices are found in Awair's cloud API.
    "
1664,"def data():
    
    return pd.array(
        [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
        dtype=""boolean"",
    )


@pytest.mark.parametrize(
    ""values, exp_any, exp_all, exp_any_noskip, exp_all_noskip"",
    [
        ([True, pd.NA], True, True, True, pd.NA),
        ([False, pd.NA], False, False, pd.NA, False),
        ([pd.NA], False, True, pd.NA, pd.NA),
        ([], False, True, False, True),
        # GH-33253: all True / all False values buggy with skipna=False
        ([True, True], True, True, True, True),
        ([False, False], False, False, False, False),
    ],
)","Fixture returning boolean array, with valid and missing values.",9,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def data():
    
    return pd.array(
        [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
        dtype=""boolean"",
    )


@pytest.mark.parametrize(
    ""values, exp_any, exp_all, exp_any_noskip, exp_all_noskip"",
    [
        ([True, pd.NA], True, True, True, pd.NA),
        ([False, pd.NA], False, False, pd.NA, False),
        ([pd.NA], False, True, pd.NA, pd.NA),
        ([], False, True, False, True),
        # GH-33253: all True / all False values buggy with skipna=False
        ([True, True], True, True, True, True),
        ([False, False], False, False, False, False),
    ],
)
        ```
    ###Assistant : Fixture returning boolean array, with valid and missing values.
    "
1665,"def should_toggle_mask(self) -> bool:
        
        with self._lock:
            retval = self._toggle_mask
            if retval:
                logger.debug(""Sending toggle mask"")
                self._toggle_mask = False
        return retval
"," Check whether the mask should be toggled and return the value. If ``True`` is returned
        then resets :attr:`_toggle_mask` back to ``False``

        Returns
        -------
        bool
            ``True`` if the mask should be toggled otherwise ``False``. ",33,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def should_toggle_mask(self) -> bool:
        
        with self._lock:
            retval = self._toggle_mask
            if retval:
                logger.debug(""Sending toggle mask"")
                self._toggle_mask = False
        return retval

        ```
    ###Assistant :  Check whether the mask should be toggled and return the value. If ``True`` is returned
        then resets :attr:`_toggle_mask` back to ``False``

        Returns
        -------
        bool
            ``True`` if the mask should be toggled otherwise ``False``. 
    "
1666,"def load_mtpl2(n_samples=100000):
    
    # freMTPL2freq dataset from https://www.openml.org/d/41214
    df_freq = fetch_openml(data_id=41214, as_frame=True, parser=""pandas"").data
    df_freq[""IDpol""] = df_freq[""IDpol""].astype(int)
    df_freq.set_index(""IDpol"", inplace=True)

    # freMTPL2sev dataset from https://www.openml.org/d/41215
    df_sev = fetch_openml(data_id=41215, as_frame=True, parser=""pandas"").data

    # sum ClaimAmount over identical IDs
    df_sev = df_sev.groupby(""IDpol"").sum()

    df = df_freq.join(df_sev, how=""left"")
    df[""ClaimAmount""].fillna(0, inplace=True)

    # unquote string fields
    for column_name in df.columns[df.dtypes.values == object]:
        df[column_name] = df[column_name].str.strip(""'"")
    return df.iloc[:n_samples]

","Fetch the French Motor Third-Party Liability Claims dataset.

    Parameters
    ----------
    n_samples: int, default=100000
      number of samples to select (for faster run time). Full dataset has
      678013 samples.
    ",27,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_mtpl2(n_samples=100000):
    
    # freMTPL2freq dataset from https://www.openml.org/d/41214
    df_freq = fetch_openml(data_id=41214, as_frame=True, parser=""pandas"").data
    df_freq[""IDpol""] = df_freq[""IDpol""].astype(int)
    df_freq.set_index(""IDpol"", inplace=True)

    # freMTPL2sev dataset from https://www.openml.org/d/41215
    df_sev = fetch_openml(data_id=41215, as_frame=True, parser=""pandas"").data

    # sum ClaimAmount over identical IDs
    df_sev = df_sev.groupby(""IDpol"").sum()

    df = df_freq.join(df_sev, how=""left"")
    df[""ClaimAmount""].fillna(0, inplace=True)

    # unquote string fields
    for column_name in df.columns[df.dtypes.values == object]:
        df[column_name] = df[column_name].str.strip(""'"")
    return df.iloc[:n_samples]


        ```
    ###Assistant : Fetch the French Motor Third-Party Liability Claims dataset.

    Parameters
    ----------
    n_samples: int, default=100000
      number of samples to select (for faster run time). Full dataset has
      678013 samples.
    
    "
1667,"def _get_driver(self) -> str:
        
        try:
            driver = pynvml.nvmlSystemGetDriverVersion().decode(""utf-8"")
        except pynvml.NVMLError as err:
            self._log(""debug"", f""Unable to obtain driver. Original error: {str(err)}"")
            driver = ""No Nvidia driver found""
        self._log(""debug"", f""GPU Driver: {driver}"")
        return driver
"," Obtain the Nvidia driver version currently in use.

        Returns
        -------
        str
            The current GPU driver version
        ",16,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_driver(self) -> str:
        
        try:
            driver = pynvml.nvmlSystemGetDriverVersion().decode(""utf-8"")
        except pynvml.NVMLError as err:
            self._log(""debug"", f""Unable to obtain driver. Original error: {str(err)}"")
            driver = ""No Nvidia driver found""
        self._log(""debug"", f""GPU Driver: {driver}"")
        return driver

        ```
    ###Assistant :  Obtain the Nvidia driver version currently in use.

        Returns
        -------
        str
            The current GPU driver version
        
    "
1668,"def _load_serializers():
    
    global _serializers
    serializers = {}
    for format in BUILTIN_SERIALIZERS:
        register_serializer(format, BUILTIN_SERIALIZERS[format], serializers)
    if hasattr(settings, ""SERIALIZATION_MODULES""):
        for format in settings.SERIALIZATION_MODULES:
            register_serializer(
                format, settings.SERIALIZATION_MODULES[format], serializers
            )
    _serializers = serializers

","
    Register built-in and settings-defined serializers. This is done lazily so
    that user code has a chance to (e.g.) set up custom settings without
    needing to be careful of import order.
    ",30,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _load_serializers():
    
    global _serializers
    serializers = {}
    for format in BUILTIN_SERIALIZERS:
        register_serializer(format, BUILTIN_SERIALIZERS[format], serializers)
    if hasattr(settings, ""SERIALIZATION_MODULES""):
        for format in settings.SERIALIZATION_MODULES:
            register_serializer(
                format, settings.SERIALIZATION_MODULES[format], serializers
            )
    _serializers = serializers


        ```
    ###Assistant : 
    Register built-in and settings-defined serializers. This is done lazily so
    that user code has a chance to (e.g.) set up custom settings without
    needing to be careful of import order.
    
    "
1669,"def pop(self):
        
        if self.keyorder:
            value = self.keys()[0]
            self.remove(value)
            return value
        return None

","
        Pops the top element from the sorted keys if it exists. Returns None otherwise.
        ",14,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pop(self):
        
        if self.keyorder:
            value = self.keys()[0]
            self.remove(value)
            return value
        return None


        ```
    ###Assistant : 
        Pops the top element from the sorted keys if it exists. Returns None otherwise.
        
    "
1670,"def probs_to_pianoroll_viterbi(frame_probs, onset_probs, alpha=0.5):
  
  n, d = onset_probs.shape

  loss_matrix = np.zeros([n, d, 2], dtype=float)
  path_matrix = np.zeros([n, d, 2], dtype=bool)

  frame_losses = (1 - alpha) * -np.log(np.stack([1 - frame_probs,
                                                 frame_probs], axis=-1))
  onset_losses = alpha * -np.log(np.stack([1 - onset_probs,
                                           onset_probs], axis=-1))

  loss_matrix[0, :, :] = frame_losses[0, :, :] + onset_losses[0, :, :]

  for i in range(1, n):
    transition_loss = np.tile(loss_matrix[i - 1, :, :][:, :, np.newaxis],
                              [1, 1, 2])

    transition_loss[:, 0, 0] += onset_losses[i, :, 0]
    transition_loss[:, 0, 1] += onset_losses[i, :, 1]
    transition_loss[:, 1, 0] += onset_losses[i, :, 0]
    transition_loss[:, 1, 1] += onset_losses[i, :, 0]

    path_matrix[i, :, :] = np.argmin(transition_loss, axis=1)

    loss_matrix[i, :, 0] = transition_loss[
        np.arange(d), path_matrix[i, :, 0].astype(int), 0]
    loss_matrix[i, :, 1] = transition_loss[
        np.arange(d), path_matrix[i, :, 1].astype(int), 1]

    loss_matrix[i, :, :] += frame_losses[i, :, :]

  pianoroll = np.zeros([n, d], dtype=bool)
  pianoroll[n - 1, :] = np.argmin(loss_matrix[n - 1, :, :], axis=-1)
  for i in range(n - 2, -1, -1):
    pianoroll[i, :] = path_matrix[
        i + 1, np.arange(d), pianoroll[i + 1, :].astype(int)]

  return pianoroll

","Viterbi decoding of frame & onset probabilities to pianoroll.

  Args:
    frame_probs: A numpy array (num-frames-by-num-pitches) of frame
      probabilities.
    onset_probs: A numpy array (num-frames-by-num-pitches) of onset
      probabilities.
    alpha: Relative weight of onset and frame loss, a float between 0 and 1.
      With alpha = 0, onset probabilities will be ignored. With alpha = 1, frame
      probabilities will be ignored.

  Returns:
    A numpy array (num-frames-by-num-pitches) representing the boolean-valued
    pianoroll.
  ",67,167,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def probs_to_pianoroll_viterbi(frame_probs, onset_probs, alpha=0.5):
  
  n, d = onset_probs.shape

  loss_matrix = np.zeros([n, d, 2], dtype=float)
  path_matrix = np.zeros([n, d, 2], dtype=bool)

  frame_losses = (1 - alpha) * -np.log(np.stack([1 - frame_probs,
                                                 frame_probs], axis=-1))
  onset_losses = alpha * -np.log(np.stack([1 - onset_probs,
                                           onset_probs], axis=-1))

  loss_matrix[0, :, :] = frame_losses[0, :, :] + onset_losses[0, :, :]

  for i in range(1, n):
    transition_loss = np.tile(loss_matrix[i - 1, :, :][:, :, np.newaxis],
                              [1, 1, 2])

    transition_loss[:, 0, 0] += onset_losses[i, :, 0]
    transition_loss[:, 0, 1] += onset_losses[i, :, 1]
    transition_loss[:, 1, 0] += onset_losses[i, :, 0]
    transition_loss[:, 1, 1] += onset_losses[i, :, 0]

    path_matrix[i, :, :] = np.argmin(transition_loss, axis=1)

    loss_matrix[i, :, 0] = transition_loss[
        np.arange(d), path_matrix[i, :, 0].astype(int), 0]
    loss_matrix[i, :, 1] = transition_loss[
        np.arange(d), path_matrix[i, :, 1].astype(int), 1]

    loss_matrix[i, :, :] += frame_losses[i, :, :]

  pianoroll = np.zeros([n, d], dtype=bool)
  pianoroll[n - 1, :] = np.argmin(loss_matrix[n - 1, :, :], axis=-1)
  for i in range(n - 2, -1, -1):
    pianoroll[i, :] = path_matrix[
        i + 1, np.arange(d), pianoroll[i + 1, :].astype(int)]

  return pianoroll


        ```
    ###Assistant : Viterbi decoding of frame & onset probabilities to pianoroll.

  Args:
    frame_probs: A numpy array (num-frames-by-num-pitches) of frame
      probabilities.
    onset_probs: A numpy array (num-frames-by-num-pitches) of onset
      probabilities.
    alpha: Relative weight of onset and frame loss, a float between 0 and 1.
      With alpha = 0, onset probabilities will be ignored. With alpha = 1, frame
      probabilities will be ignored.

  Returns:
    A numpy array (num-frames-by-num-pitches) representing the boolean-valued
    pianoroll.
  
    "
1671,"def to_native_string(string, encoding=""ascii""):
    
    if isinstance(string, builtin_str):
        out = string
    else:
        out = string.decode(encoding)

    return out

","Given a string object, regardless of type, returns a representation of
    that string in the native string type, encoding and decoding where
    necessary. This assumes ASCII unless told otherwise.
    ",29,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_native_string(string, encoding=""ascii""):
    
    if isinstance(string, builtin_str):
        out = string
    else:
        out = string.decode(encoding)

    return out


        ```
    ###Assistant : Given a string object, regardless of type, returns a representation of
    that string in the native string type, encoding and decoding where
    necessary. This assumes ASCII unless told otherwise.
    
    "
1672,"def mathematica(s, additional_translations=None):
    
    parser = MathematicaParser(additional_translations)

    if additional_translations is not None:
        sympy_deprecation_warning(
            ,
            deprecated_since_version=""1.11"",
            active_deprecations_target=""mathematica-parser-additional-translations"",
        )
        return sympify(parser._parse_old(s))

    return parser.parse(s)

","
    Translate a string containing a Wolfram Mathematica expression to a SymPy
    expression.

    If the translator is unable to find a suitable SymPy expression, the
    ``FullForm`` of the Mathematica expression will be output, using SymPy
    ``Function`` objects as nodes of the syntax tree.

    Examples
    ========

    >>> from sympy.parsing.mathematica import mathematica
    >>> mathematica(""Sin[x]^2 Tan[y]"")
    sin(x)**2*tan(y)
    >>> e = mathematica(""F[7,5,3]"")
    >>> e
    F(7, 5, 3)
    >>> from sympy import Function, Max, Min
    >>> e.replace(Function(""F""), lambda *x: Max(*x)*Min(*x))
    21

    Both standard input form and Mathematica full form are supported:

    >>> mathematica(""x*(a + b)"")
    x*(a + b)
    >>> mathematica(""Times[x, Plus[a, b]]"")
    x*(a + b)

    To get a matrix from Wolfram's code:

    >>> m = mathematica(""{{a, b}, {c, d}}"")
    >>> m
    ((a, b), (c, d))
    >>> from sympy import Matrix
    >>> Matrix(m)
    Matrix([
    [a, b],
    [c, d]])

    If the translation into equivalent SymPy expressions fails, an SymPy
    expression equivalent to Wolfram Mathematica's ""FullForm"" will be created:

    >>> mathematica(""x_."")
    Optional(Pattern(x, Blank()))
    >>> mathematica(""Plus @@ {x, y, z}"")
    Apply(Plus, (x, y, z))
    >>> mathematica(""f[x_, 3] := x^3 /; x > 0"")
    SetDelayed(f(Pattern(x, Blank()), 3), Condition(x**3, x > 0))
    The ``additional_translations`` parameter for the Mathematica parser is now deprecated.
Use SymPy's .replace( ) or .subs( ) methods on the output expression instead.",203,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mathematica(s, additional_translations=None):
    
    parser = MathematicaParser(additional_translations)

    if additional_translations is not None:
        sympy_deprecation_warning(
            ,
            deprecated_since_version=""1.11"",
            active_deprecations_target=""mathematica-parser-additional-translations"",
        )
        return sympify(parser._parse_old(s))

    return parser.parse(s)


        ```
    ###Assistant : 
    Translate a string containing a Wolfram Mathematica expression to a SymPy
    expression.

    If the translator is unable to find a suitable SymPy expression, the
    ``FullForm`` of the Mathematica expression will be output, using SymPy
    ``Function`` objects as nodes of the syntax tree.

    Examples
    ========

    >>> from sympy.parsing.mathematica import mathematica
    >>> mathematica(""Sin[x]^2 Tan[y]"")
    sin(x)**2*tan(y)
    >>> e = mathematica(""F[7,5,3]"")
    >>> e
    F(7, 5, 3)
    >>> from sympy import Function, Max, Min
    >>> e.replace(Function(""F""), lambda *x: Max(*x)*Min(*x))
    21

    Both standard input form and Mathematica full form are supported:

    >>> mathematica(""x*(a + b)"")
    x*(a + b)
    >>> mathematica(""Times[x, Plus[a, b]]"")
    x*(a + b)

    To get a matrix from Wolfram's code:

    >>> m = mathematica(""{{a, b}, {c, d}}"")
    >>> m
    ((a, b), (c, d))
    >>> from sympy import Matrix
    >>> Matrix(m)
    Matrix([
    [a, b],
    [c, d]])

    If the translation into equivalent SymPy expressions fails, an SymPy
    expression equivalent to Wolfram Mathematica's ""FullForm"" will be created:

    >>> mathematica(""x_."")
    Optional(Pattern(x, Blank()))
    >>> mathematica(""Plus @@ {x, y, z}"")
    Apply(Plus, (x, y, z))
    >>> mathematica(""f[x_, 3] := x^3 /; x > 0"")
    SetDelayed(f(Pattern(x, Blank()), 3), Condition(x**3, x > 0))
    The ``additional_translations`` parameter for the Mathematica parser is now deprecated.
Use SymPy's .replace( ) or .subs( ) methods on the output expression instead.
    "
1673,"def evaluation(self):
        
        # adding info about the eval tasks
        if self.eval_tasks == self.train_tasks:
            msg = ""For evalution, we used the same training datasets; check the [Datasets Used](#datasets-used) section for more information""
            eval_list = ''
        else:
            msg = f""This model was evaluated on the datasets below (use the `parlai display_data` commands to show data). Visit the {make_link('task (dataset) list', task_site)} for more details about the datasets.\n""
            eval_list = get_dataset_info(self.eval_tasks)
            eval_list = '\n' + '\n'.join(eval_list)
        content = [msg + eval_list]

        # validation metric info: getting metric name and description
        splitted = re.sub(r'_+', ' ', self.valid_metric).split()
        key = splitted[-1]
        if extra_metric_info.get(key):
            mname, description = extra_metric_info[key]
        elif METRICS_DISPLAY_DATA.get(key):
            mname = METRICS_DISPLAY_DATA[key].title
            description = METRICS_DISPLAY_DATA[key].description
        else:
            description, mname = (None, None)

        # adding description for validation metric and re-wording it:
        msg = f""\n\nWe used the metric {metric_format(self.valid_metric)}""
        if len(splitted) == 3 and splitted[0] == 'class' and mname:
            msg += f"", the {mname.lower()} scores for the class {splitted[1]}""
        content.append(msg + ' as the validation metric. ')
        if description:
            description = description[0].lower() + description[1:]
            content[-1] += f""Recall that `{self.valid_metric}` is {description}.""

        # evaluation table
        # getting list of subtasks and making columns
        eval_tasks = self.eval_tasks
        if len(self.eval_tasks) > 1:
            eval_tasks.insert(0, 'All')
        columns = [' '] + [taskname(subtask) for subtask in eval_tasks]
        # only one row: validation
        row = [metric_format(self.valid_metric)]
        for subtask in eval_tasks:
            # creating the key to get metric and formatting
            pre = '' if subtask == 'All' or len(eval_tasks) == 1 else subtask + '/'
            key = pre + self.valid_metric
            fmt = '{:.4f}' if self.valid_metric in not_percent else '{:.2%}'
            row.append(fmt.format(self.eval_results[key]))
        return '\n'.join(content) + '\n\n' + '\n'.join(make_md_table([row], columns))
","
        returns a section with dataset info about the eval tasks if they exist,
        information about the validation metric if it exists, and create a table with
        the validation metric.
        ",29,262,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def evaluation(self):
        
        # adding info about the eval tasks
        if self.eval_tasks == self.train_tasks:
            msg = ""For evalution, we used the same training datasets; check the [Datasets Used](#datasets-used) section for more information""
            eval_list = ''
        else:
            msg = f""This model was evaluated on the datasets below (use the `parlai display_data` commands to show data). Visit the {make_link('task (dataset) list', task_site)} for more details about the datasets.\n""
            eval_list = get_dataset_info(self.eval_tasks)
            eval_list = '\n' + '\n'.join(eval_list)
        content = [msg + eval_list]

        # validation metric info: getting metric name and description
        splitted = re.sub(r'_+', ' ', self.valid_metric).split()
        key = splitted[-1]
        if extra_metric_info.get(key):
            mname, description = extra_metric_info[key]
        elif METRICS_DISPLAY_DATA.get(key):
            mname = METRICS_DISPLAY_DATA[key].title
            description = METRICS_DISPLAY_DATA[key].description
        else:
            description, mname = (None, None)

        # adding description for validation metric and re-wording it:
        msg = f""\n\nWe used the metric {metric_format(self.valid_metric)}""
        if len(splitted) == 3 and splitted[0] == 'class' and mname:
            msg += f"", the {mname.lower()} scores for the class {splitted[1]}""
        content.append(msg + ' as the validation metric. ')
        if description:
            description = description[0].lower() + description[1:]
            content[-1] += f""Recall that `{self.valid_metric}` is {description}.""

        # evaluation table
        # getting list of subtasks and making columns
        eval_tasks = self.eval_tasks
        if len(self.eval_tasks) > 1:
            eval_tasks.insert(0, 'All')
        columns = [' '] + [taskname(subtask) for subtask in eval_tasks]
        # only one row: validation
        row = [metric_format(self.valid_metric)]
        for subtask in eval_tasks:
            # creating the key to get metric and formatting
            pre = '' if subtask == 'All' or len(eval_tasks) == 1 else subtask + '/'
            key = pre + self.valid_metric
            fmt = '{:.4f}' if self.valid_metric in not_percent else '{:.2%}'
            row.append(fmt.format(self.eval_results[key]))
        return '\n'.join(content) + '\n\n' + '\n'.join(make_md_table([row], columns))

        ```
    ###Assistant : 
        returns a section with dataset info about the eval tasks if they exist,
        information about the validation metric if it exists, and create a table with
        the validation metric.
        
    "
1674,"def _train_with_recompute(n_steps):
    
    img_dim, n_channels, batch_size = 256, 1, 4
    x, y = _get_dummy_data(img_dim, n_channels, batch_size)
    # This model is the same model as _get_big_cnn_model but split into 3 parts.
    models = _get_split_cnn_model(
        img_dim, n_channels, num_partitions=3, blocks_per_partition=2
    )
    model1, model2, model3 = models
    # Apply gradient checkpointing to the submodels using tf.recompute_grad.
    model1_re = tf.recompute_grad(model1)
    model2_re = tf.recompute_grad(model2)
    model3_re = tf.recompute_grad(model3)
    optimizer = optimizers.SGD()
    tr_vars = (
        model1.trainable_variables
        + model2.trainable_variables
        + model3.trainable_variables
    )
    losses = []
    for _ in range(n_steps):
        with tf.GradientTape() as tape:
            logits1 = model1_re(x)
            logits2 = model2_re(logits1)
            logits3 = model3_re(logits2)
            loss = _compute_loss(logits3, y)
            losses.append(loss)
            grads = tape.gradient(loss, tr_vars)  # tr_vars
            optimizer.apply_gradients(zip(grads, tr_vars))
            del grads
    return losses


@tf_test_utils.with_eager_op_as_function",Trains a single large model with gradient checkpointing using tf.recompute_grad.,10,110,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _train_with_recompute(n_steps):
    
    img_dim, n_channels, batch_size = 256, 1, 4
    x, y = _get_dummy_data(img_dim, n_channels, batch_size)
    # This model is the same model as _get_big_cnn_model but split into 3 parts.
    models = _get_split_cnn_model(
        img_dim, n_channels, num_partitions=3, blocks_per_partition=2
    )
    model1, model2, model3 = models
    # Apply gradient checkpointing to the submodels using tf.recompute_grad.
    model1_re = tf.recompute_grad(model1)
    model2_re = tf.recompute_grad(model2)
    model3_re = tf.recompute_grad(model3)
    optimizer = optimizers.SGD()
    tr_vars = (
        model1.trainable_variables
        + model2.trainable_variables
        + model3.trainable_variables
    )
    losses = []
    for _ in range(n_steps):
        with tf.GradientTape() as tape:
            logits1 = model1_re(x)
            logits2 = model2_re(logits1)
            logits3 = model3_re(logits2)
            loss = _compute_loss(logits3, y)
            losses.append(loss)
            grads = tape.gradient(loss, tr_vars)  # tr_vars
            optimizer.apply_gradients(zip(grads, tr_vars))
            del grads
    return losses


@tf_test_utils.with_eager_op_as_function
        ```
    ###Assistant : Trains a single large model with gradient checkpointing using tf.recompute_grad.
    "
1675,"def test_syncer_callback_dead_node_log_error(caplog, ray_start_2_cpus, temp_data_dirs):
    
    caplog.set_level(logging.ERROR, logger=""ray.tune.syncer"")

    tmp_source, tmp_target = temp_data_dirs

    syncer_callback = TestSyncerCallback(
        sync_period=0,
        local_logdir_override=tmp_target,
    )

    trial1 = MockTrial(trial_id=""a"", logdir=tmp_source, on_dead_node=True)

    syncer_callback.on_trial_result(iteration=1, trials=[], trial=trial1, result={})

    assert (
        ""An error occurred when trying to get the node ip where this trial is running""
        in caplog.text
    )

",Check that we catch + log errors when trying syncing with a dead remote node.,15,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_syncer_callback_dead_node_log_error(caplog, ray_start_2_cpus, temp_data_dirs):
    
    caplog.set_level(logging.ERROR, logger=""ray.tune.syncer"")

    tmp_source, tmp_target = temp_data_dirs

    syncer_callback = TestSyncerCallback(
        sync_period=0,
        local_logdir_override=tmp_target,
    )

    trial1 = MockTrial(trial_id=""a"", logdir=tmp_source, on_dead_node=True)

    syncer_callback.on_trial_result(iteration=1, trials=[], trial=trial1, result={})

    assert (
        ""An error occurred when trying to get the node ip where this trial is running""
        in caplog.text
    )


        ```
    ###Assistant : Check that we catch + log errors when trying syncing with a dead remote node.
    "
1676,"def directed_modularity_matrix(G, nodelist=None, weight=None):
    
    import numpy as np

    if nodelist is None:
        nodelist = list(G)
    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format=""csr"")
    k_in = A.sum(axis=0)
    k_out = A.sum(axis=1)
    m = k_in.sum()
    # Expected adjacency matrix
    X = np.outer(k_out, k_in) / m

    return A - X
","Returns the directed modularity matrix of G.

    The modularity matrix is the matrix B = A - <A>, where A is the adjacency
    matrix and <A> is the expected adjacency matrix, assuming that the graph
    is described by the configuration model.

    More specifically, the element B_ij of B is defined as

    .. math::
        B_{ij} = A_{ij} - k_i^{out} k_j^{in} / m

    where :math:`k_i^{in}` is the in degree of node i, and :math:`k_j^{out}` is the out degree
    of node j, with m the number of edges in the graph. When weight is set
    to a name of an attribute edge, Aij, k_i, k_j and m are computed using
    its value.

    Parameters
    ----------
    G : DiGraph
       A NetworkX DiGraph

    nodelist : list, optional
       The rows and columns are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    weight : string or None, optional (default=None)
       The edge attribute that holds the numerical value used for
       the edge weight.  If None then all edge weights are 1.

    Returns
    -------
    B : Numpy array
      The modularity matrix of G.

    Examples
    --------
    >>> G = nx.DiGraph()
    >>> G.add_edges_from(
    ...     (
    ...         (1, 2),
    ...         (1, 3),
    ...         (3, 1),
    ...         (3, 2),
    ...         (3, 5),
    ...         (4, 5),
    ...         (4, 6),
    ...         (5, 4),
    ...         (5, 6),
    ...         (6, 4),
    ...     )
    ... )
    >>> B = nx.directed_modularity_matrix(G)


    Notes
    -----
    NetworkX defines the element A_ij of the adjacency matrix as 1 if there
    is a link going from node i to node j. Leicht and Newman use the opposite
    definition. This explains the different expression for B_ij.

    See Also
    --------
    to_numpy_array
    modularity_spectrum
    adjacency_matrix
    modularity_matrix

    References
    ----------
    .. [1] E. A. Leicht, M. E. J. Newman,
        ""Community structure in directed networks"",
        Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008.
    ",303,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def directed_modularity_matrix(G, nodelist=None, weight=None):
    
    import numpy as np

    if nodelist is None:
        nodelist = list(G)
    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format=""csr"")
    k_in = A.sum(axis=0)
    k_out = A.sum(axis=1)
    m = k_in.sum()
    # Expected adjacency matrix
    X = np.outer(k_out, k_in) / m

    return A - X

        ```
    ###Assistant : Returns the directed modularity matrix of G.

    The modularity matrix is the matrix B = A - <A>, where A is the adjacency
    matrix and <A> is the expected adjacency matrix, assuming that the graph
    is described by the configuration model.

    More specifically, the element B_ij of B is defined as

    .. math::
        B_{ij} = A_{ij} - k_i^{out} k_j^{in} / m

    where :math:`k_i^{in}` is the in degree of node i, and :math:`k_j^{out}` is the out degree
    of node j, with m the number of edges in the graph. When weight is set
    to a name of an attribute edge, Aij, k_i, k_j and m are computed using
    its value.

    Parameters
    ----------
    G : DiGraph
       A NetworkX DiGraph

    nodelist : list, optional
       The rows and columns are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    weight : string or None, optional (default=None)
       The edge attribute that holds the numerical value used for
       the edge weight.  If None then all edge weights are 1.

    Returns
    -------
    B : Numpy array
      The modularity matrix of G.

    Examples
    --------
    >>> G = nx.DiGraph()
    >>> G.add_edges_from(
    ...     (
    ...         (1, 2),
    ...         (1, 3),
    ...         (3, 1),
    ...         (3, 2),
    ...         (3, 5),
    ...         (4, 5),
    ...         (4, 6),
    ...         (5, 4),
    ...         (5, 6),
    ...         (6, 4),
    ...     )
    ... )
    >>> B = nx.directed_modularity_matrix(G)


    Notes
    -----
    NetworkX defines the element A_ij of the adjacency matrix as 1 if there
    is a link going from node i to node j. Leicht and Newman use the opposite
    definition. This explains the different expression for B_ij.

    See Also
    --------
    to_numpy_array
    modularity_spectrum
    adjacency_matrix
    modularity_matrix

    References
    ----------
    .. [1] E. A. Leicht, M. E. J. Newman,
        ""Community structure in directed networks"",
        Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008.
    
    "
1677,"def project_columns(self, columns):
        
        if columns == self.columns:
            return self
        return ParquetFunctionWrapper(
            self.engine,
            self.fs,
            self.meta,
            columns,
            self.index,
            None,  # Already merged into common_kwargs
            self.common_kwargs,
        )
","Return a new ParquetFunctionWrapper object
        with a sub-column projection.
        ",9,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def project_columns(self, columns):
        
        if columns == self.columns:
            return self
        return ParquetFunctionWrapper(
            self.engine,
            self.fs,
            self.meta,
            columns,
            self.index,
            None,  # Already merged into common_kwargs
            self.common_kwargs,
        )

        ```
    ###Assistant : Return a new ParquetFunctionWrapper object
        with a sub-column projection.
        
    "
1678,"def sort_args_by_name(self):
        
        expr = self.expr
        if not isinstance(expr, ArrayTensorProduct):
            return self
        args = expr.args
        sorted_data = sorted(enumerate(args), key=lambda x: default_sort_key(x[1]))
        pos_sorted, args_sorted = zip(*sorted_data)
        reordering_map = {i: pos_sorted.index(i) for i, arg in enumerate(args)}
        contraction_tuples = self._get_contraction_tuples()
        contraction_tuples = [[(reordering_map[j], k) for j, k in i] for i in contraction_tuples]
        c_tp = _array_tensor_product(*args_sorted)
        new_contr_indices = self._contraction_tuples_to_contraction_indices(
                c_tp,
                contraction_tuples
        )
        return _array_contraction(c_tp, *new_contr_indices)
","
        Sort arguments in the tensor product so that their order is lexicographical.

        Examples
        ========

        >>> from sympy.tensor.array.expressions.from_matrix_to_array import convert_matrix_to_array
        >>> from sympy import MatrixSymbol
        >>> from sympy.abc import N
        >>> A = MatrixSymbol(""A"", N, N)
        >>> B = MatrixSymbol(""B"", N, N)
        >>> C = MatrixSymbol(""C"", N, N)
        >>> D = MatrixSymbol(""D"", N, N)

        >>> cg = convert_matrix_to_array(C*D*A*B)
        >>> cg
        ArrayContraction(ArrayTensorProduct(A, D, C, B), (0, 3), (1, 6), (2, 5))
        >>> cg.sort_args_by_name()
        ArrayContraction(ArrayTensorProduct(A, D, B, C), (0, 3), (1, 4), (2, 7))
        ",81,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sort_args_by_name(self):
        
        expr = self.expr
        if not isinstance(expr, ArrayTensorProduct):
            return self
        args = expr.args
        sorted_data = sorted(enumerate(args), key=lambda x: default_sort_key(x[1]))
        pos_sorted, args_sorted = zip(*sorted_data)
        reordering_map = {i: pos_sorted.index(i) for i, arg in enumerate(args)}
        contraction_tuples = self._get_contraction_tuples()
        contraction_tuples = [[(reordering_map[j], k) for j, k in i] for i in contraction_tuples]
        c_tp = _array_tensor_product(*args_sorted)
        new_contr_indices = self._contraction_tuples_to_contraction_indices(
                c_tp,
                contraction_tuples
        )
        return _array_contraction(c_tp, *new_contr_indices)

        ```
    ###Assistant : 
        Sort arguments in the tensor product so that their order is lexicographical.

        Examples
        ========

        >>> from sympy.tensor.array.expressions.from_matrix_to_array import convert_matrix_to_array
        >>> from sympy import MatrixSymbol
        >>> from sympy.abc import N
        >>> A = MatrixSymbol(""A"", N, N)
        >>> B = MatrixSymbol(""B"", N, N)
        >>> C = MatrixSymbol(""C"", N, N)
        >>> D = MatrixSymbol(""D"", N, N)

        >>> cg = convert_matrix_to_array(C*D*A*B)
        >>> cg
        ArrayContraction(ArrayTensorProduct(A, D, C, B), (0, 3), (1, 6), (2, 5))
        >>> cg.sort_args_by_name()
        ArrayContraction(ArrayTensorProduct(A, D, B, C), (0, 3), (1, 4), (2, 7))
        
    "
1679,"def gather_async(self, batch_ms=0, num_async=1) -> ""LocalIterator[T]"":
        

        if num_async < 1:
            raise ValueError(""queue depth must be positive"")
        if batch_ms < 0:
            raise ValueError(""batch time must be positive"")

        # Forward reference to the returned iterator.
        local_iter = None
","Returns a local iterable for asynchronous iteration.

        New items will be fetched from the shards asynchronously as soon as
        the previous one is computed. Items arrive in non-deterministic order.

        Arguments:
            batch_ms (int): Batches items for batch_ms milliseconds
                on each shard before retrieving it.
                Increasing batch_ms increases latency but improves throughput.
                If this value is 0, then items are returned immediately.
            num_async (int): The max number of async requests in flight
                per actor. Increasing this improves the amount of pipeline
                parallelism in the iterator.

        Examples:
            >>> it = from_range(100, 1).gather_async()
            >>> next(it)
            ... 3
            >>> next(it)
            ... 0
            >>> next(it)
            ... 1
        ",101,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def gather_async(self, batch_ms=0, num_async=1) -> ""LocalIterator[T]"":
        

        if num_async < 1:
            raise ValueError(""queue depth must be positive"")
        if batch_ms < 0:
            raise ValueError(""batch time must be positive"")

        # Forward reference to the returned iterator.
        local_iter = None

        ```
    ###Assistant : Returns a local iterable for asynchronous iteration.

        New items will be fetched from the shards asynchronously as soon as
        the previous one is computed. Items arrive in non-deterministic order.

        Arguments:
            batch_ms (int): Batches items for batch_ms milliseconds
                on each shard before retrieving it.
                Increasing batch_ms increases latency but improves throughput.
                If this value is 0, then items are returned immediately.
            num_async (int): The max number of async requests in flight
                per actor. Increasing this improves the amount of pipeline
                parallelism in the iterator.

        Examples:
            >>> it = from_range(100, 1).gather_async()
            >>> next(it)
            ... 3
            >>> next(it)
            ... 0
            >>> next(it)
            ... 1
        
    "
1680,"def test_dict_checkpoint_dict(self):
        
        checkpoint = self._prepare_dict_checkpoint()

        # Convert into dict checkpoint
        data_dict = checkpoint.to_dict()
        self.assertIsInstance(data_dict, dict)

        # Create from dict
        checkpoint = Checkpoint.from_dict(data_dict)
        self.assertTrue(checkpoint._data_dict)

        self._assert_dict_checkpoint(checkpoint)
",Test conversion from dict to dict checkpoint and back.,9,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dict_checkpoint_dict(self):
        
        checkpoint = self._prepare_dict_checkpoint()

        # Convert into dict checkpoint
        data_dict = checkpoint.to_dict()
        self.assertIsInstance(data_dict, dict)

        # Create from dict
        checkpoint = Checkpoint.from_dict(data_dict)
        self.assertTrue(checkpoint._data_dict)

        self._assert_dict_checkpoint(checkpoint)

        ```
    ###Assistant : Test conversion from dict to dict checkpoint and back.
    "
1681,"def test_5_model(self):
        query = 

        predict_query = 

        for cid, char in [(CID_A, 'a'), (CID_B, 'b')]:
            self.sql_via_http(
                query.format(char, char),
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.OK
            )

            response = self.sql_via_http(
                predict_query.format(char),
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.TABLE
            )
            self.assertTrue(len(response['data']), 1)
","
            CREATE MODEL mindsdb.model_{}
            FROM test_integration_{} (
                select * from test_data.home_rentals limit 50
            ) PREDICT rental_price
            USING join_learn_process=true, time_aim=5
        
            select * from mindsdb.model_{} where sqft = 100
        ",26,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_5_model(self):
        query = 

        predict_query = 

        for cid, char in [(CID_A, 'a'), (CID_B, 'b')]:
            self.sql_via_http(
                query.format(char, char),
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.OK
            )

            response = self.sql_via_http(
                predict_query.format(char),
                company_id=cid,
                expected_resp_type=RESPONSE_TYPE.TABLE
            )
            self.assertTrue(len(response['data']), 1)

        ```
    ###Assistant : 
            CREATE MODEL mindsdb.model_{}
            FROM test_integration_{} (
                select * from test_data.home_rentals limit 50
            ) PREDICT rental_price
            USING join_learn_process=true, time_aim=5
        
            select * from mindsdb.model_{} where sqft = 100
        
    "
1682,"def putpalette(self, data, rawmode=""RGB""):
        
        from . import ImagePalette

        if self.mode not in (""L"", ""LA"", ""P"", ""PA""):
            raise ValueError(""illegal image mode"")
        if isinstance(data, ImagePalette.ImagePalette):
            palette = ImagePalette.raw(data.rawmode, data.palette)
        else:
            if not isinstance(data, bytes):
                data = bytes(data)
            palette = ImagePalette.raw(rawmode, data)
        self.mode = ""PA"" if ""A"" in self.mode else ""P""
        self.palette = palette
        self.palette.mode = ""RGB""
        self.load()  # install new palette
","
        Attaches a palette to this image.  The image must be a ""P"", ""PA"", ""L""
        or ""LA"" image.

        The palette sequence must contain at most 256 colors, made up of one
        integer value for each channel in the raw mode.
        For example, if the raw mode is ""RGB"", then it can contain at most 768
        values, made up of red, green and blue values for the corresponding pixel
        index in the 256 colors.
        If the raw mode is ""RGBA"", then it can contain at most 1024 values,
        containing red, green, blue and alpha values.

        Alternatively, an 8-bit string may be used instead of an integer sequence.

        :param data: A palette sequence (either a list or a string).
        :param rawmode: The raw mode of the palette. Either ""RGB"", ""RGBA"", or a mode
           that can be transformed to ""RGB"" or ""RGBA"" (e.g. ""R"", ""BGR;15"", ""RGBA;L"").
        ",142,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def putpalette(self, data, rawmode=""RGB""):
        
        from . import ImagePalette

        if self.mode not in (""L"", ""LA"", ""P"", ""PA""):
            raise ValueError(""illegal image mode"")
        if isinstance(data, ImagePalette.ImagePalette):
            palette = ImagePalette.raw(data.rawmode, data.palette)
        else:
            if not isinstance(data, bytes):
                data = bytes(data)
            palette = ImagePalette.raw(rawmode, data)
        self.mode = ""PA"" if ""A"" in self.mode else ""P""
        self.palette = palette
        self.palette.mode = ""RGB""
        self.load()  # install new palette

        ```
    ###Assistant : 
        Attaches a palette to this image.  The image must be a ""P"", ""PA"", ""L""
        or ""LA"" image.

        The palette sequence must contain at most 256 colors, made up of one
        integer value for each channel in the raw mode.
        For example, if the raw mode is ""RGB"", then it can contain at most 768
        values, made up of red, green and blue values for the corresponding pixel
        index in the 256 colors.
        If the raw mode is ""RGBA"", then it can contain at most 1024 values,
        containing red, green, blue and alpha values.

        Alternatively, an 8-bit string may be used instead of an integer sequence.

        :param data: A palette sequence (either a list or a string).
        :param rawmode: The raw mode of the palette. Either ""RGB"", ""RGBA"", or a mode
           that can be transformed to ""RGB"" or ""RGBA"" (e.g. ""R"", ""BGR;15"", ""RGBA;L"").
        
    "
1683,"def get_timeout() -> t.Optional[t.Dict[str, t.Any]]:
    
    if not os.path.exists(TIMEOUT_PATH):
        return None

    data = read_json_file(TIMEOUT_PATH)
    data['deadline'] = datetime.datetime.strptime(data['deadline'], '%Y-%m-%dT%H:%M:%SZ')

    return data

","Return details about the currently set timeout, if any, otherwise return None.",12,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_timeout() -> t.Optional[t.Dict[str, t.Any]]:
    
    if not os.path.exists(TIMEOUT_PATH):
        return None

    data = read_json_file(TIMEOUT_PATH)
    data['deadline'] = datetime.datetime.strptime(data['deadline'], '%Y-%m-%dT%H:%M:%SZ')

    return data


        ```
    ###Assistant : Return details about the currently set timeout, if any, otherwise return None.
    "
1684,"def my_mean_squared_error(y_true, y_pred):
    
    return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)


module_my_mean_squared_error = my_mean_squared_error


@test_utils.run_v2_only","Identical to built-in `mean_squared_error`, added here as a custom

    func.
    ",10,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def my_mean_squared_error(y_true, y_pred):
    
    return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)


module_my_mean_squared_error = my_mean_squared_error


@test_utils.run_v2_only
        ```
    ###Assistant : Identical to built-in `mean_squared_error`, added here as a custom

    func.
    
    "
1685,"def _amd_predict_with_optimized_batchsizes(self, feed, batch_size):
        
        if isinstance(feed, np.ndarray):
            feed = [feed]
        items = feed[0].shape[0]
        done_items = 0
        results = []
        while done_items < items:
            if batch_size < 4:  # Not much difference in BS < 4
                batch_size = 1
            batch_items = ((items - done_items) // batch_size) * batch_size
            if batch_items:
                pred_data = [x[done_items:done_items + batch_items] for x in feed]
                pred = self._model.predict(pred_data, batch_size=batch_size)
                done_items += batch_items
                results.append(pred)
            batch_size //= 2
        if isinstance(results[0], np.ndarray):
            return np.concatenate(results)
        return [np.concatenate(x) for x in zip(*results)]
"," Minimizes the amount of kernels to be compiled when using the ``amd`` backend with
        varying batch sizes while trying to keep the batchsize as high as possible.

        Parameters
        ----------
        feed: numpy.ndarray or list
            The feed to be provided to the model as input. This should be a ``numpy.ndarray``
            for single inputs or a ``list`` of ``numpy.ndarray`` objects for multiple inputs.
        batch_size: int
            The upper batchsize to use.
        ",67,80,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _amd_predict_with_optimized_batchsizes(self, feed, batch_size):
        
        if isinstance(feed, np.ndarray):
            feed = [feed]
        items = feed[0].shape[0]
        done_items = 0
        results = []
        while done_items < items:
            if batch_size < 4:  # Not much difference in BS < 4
                batch_size = 1
            batch_items = ((items - done_items) // batch_size) * batch_size
            if batch_items:
                pred_data = [x[done_items:done_items + batch_items] for x in feed]
                pred = self._model.predict(pred_data, batch_size=batch_size)
                done_items += batch_items
                results.append(pred)
            batch_size //= 2
        if isinstance(results[0], np.ndarray):
            return np.concatenate(results)
        return [np.concatenate(x) for x in zip(*results)]

        ```
    ###Assistant :  Minimizes the amount of kernels to be compiled when using the ``amd`` backend with
        varying batch sizes while trying to keep the batchsize as high as possible.

        Parameters
        ----------
        feed: numpy.ndarray or list
            The feed to be provided to the model as input. This should be a ``numpy.ndarray``
            for single inputs or a ``list`` of ``numpy.ndarray`` objects for multiple inputs.
        batch_size: int
            The upper batchsize to use.
        
    "
1686,"def set_positions(self, posA, posB):
        
        if posA is not None:
            self._posA_posB[0] = posA
        if posB is not None:
            self._posA_posB[1] = posB
        self.stale = True
","
        Set the start and end positions of the connecting path.

        Parameters
        ----------
        posA, posB : None, tuple
            (x, y) coordinates of arrow tail and arrow head respectively. If
            `None` use current value.
        ",32,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_positions(self, posA, posB):
        
        if posA is not None:
            self._posA_posB[0] = posA
        if posB is not None:
            self._posA_posB[1] = posB
        self.stale = True

        ```
    ###Assistant : 
        Set the start and end positions of the connecting path.

        Parameters
        ----------
        posA, posB : None, tuple
            (x, y) coordinates of arrow tail and arrow head respectively. If
            `None` use current value.
        
    "
1687,"def test_orderby_percentile_with_many_fields_multiple_entities_with_missing_data(self):
        
        for tag, value, numbers in (
            (""transaction"", ""/foo/"", [10, 11, 12]),
            (""transaction"", ""/bar/"", [4, 5, 6]),
        ):
            for subvalue in numbers:
                self.store_performance_metric(
                    name=TransactionMRI.MEASUREMENTS_LCP.value,
                    tags={tag: value},
                    value=subvalue,
                )

        response = self.get_success_response(
            self.organization.slug,
            field=[
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
                f""count_unique({TransactionMetricKey.USER.value})"",
            ],
            statsPeriod=""1h"",
            interval=""1h"",
            groupBy=[""project_id"", ""transaction""],
            orderBy=f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
            useCase=""performance"",
        )
        groups = response.data[""groups""]
        assert len(groups) == 2

        expected = [
            (""/bar/"", 5.0, 5),
            (""/foo/"", 11.0, 1),
        ]
        for (expected_tag_value, expected_lcp_count, users), group in zip(expected, groups):
            # With orderBy, you only get totals:
            assert group[""by""] == {""transaction"": expected_tag_value, ""project_id"": self.project.id}
            assert group[""totals""] == {
                f""count_unique({TransactionMetricKey.USER.value})"": 0,
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"": expected_lcp_count,
            }
            assert group[""series""] == {
                f""count_unique({TransactionMetricKey.USER.value})"": [0],
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"": [expected_lcp_count],
            }
","
        Test that ensures when transactions table has null values for some fields (i.e. fields
        with a different entity than the entity of the field in the order by), then the table gets
        populated accordingly
        ",34,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_orderby_percentile_with_many_fields_multiple_entities_with_missing_data(self):
        
        for tag, value, numbers in (
            (""transaction"", ""/foo/"", [10, 11, 12]),
            (""transaction"", ""/bar/"", [4, 5, 6]),
        ):
            for subvalue in numbers:
                self.store_performance_metric(
                    name=TransactionMRI.MEASUREMENTS_LCP.value,
                    tags={tag: value},
                    value=subvalue,
                )

        response = self.get_success_response(
            self.organization.slug,
            field=[
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
                f""count_unique({TransactionMetricKey.USER.value})"",
            ],
            statsPeriod=""1h"",
            interval=""1h"",
            groupBy=[""project_id"", ""transaction""],
            orderBy=f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"",
            useCase=""performance"",
        )
        groups = response.data[""groups""]
        assert len(groups) == 2

        expected = [
            (""/bar/"", 5.0, 5),
            (""/foo/"", 11.0, 1),
        ]
        for (expected_tag_value, expected_lcp_count, users), group in zip(expected, groups):
            # With orderBy, you only get totals:
            assert group[""by""] == {""transaction"": expected_tag_value, ""project_id"": self.project.id}
            assert group[""totals""] == {
                f""count_unique({TransactionMetricKey.USER.value})"": 0,
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"": expected_lcp_count,
            }
            assert group[""series""] == {
                f""count_unique({TransactionMetricKey.USER.value})"": [0],
                f""p50({TransactionMetricKey.MEASUREMENTS_LCP.value})"": [expected_lcp_count],
            }

        ```
    ###Assistant : 
        Test that ensures when transactions table has null values for some fields (i.e. fields
        with a different entity than the entity of the field in the order by), then the table gets
        populated accordingly
        
    "
1688,"def get_earning_components_max_benefits(employee, date, earning_component):
	salary_structure = get_assigned_salary_structure(employee, date)
	amount = frappe.db.sql(
		,
		salary_structure,
		earning_component,
	)

	return amount if amount else 0
","
			select amount
			from `tabSalary Detail`
			where parent = %s and is_flexible_benefit = 1
			and salary_component = %s
			order by name
		",20,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_earning_components_max_benefits(employee, date, earning_component):
	salary_structure = get_assigned_salary_structure(employee, date)
	amount = frappe.db.sql(
		,
		salary_structure,
		earning_component,
	)

	return amount if amount else 0

        ```
    ###Assistant : 
			select amount
			from `tabSalary Detail`
			where parent = %s and is_flexible_benefit = 1
			and salary_component = %s
			order by name
		
    "
1689,"def get_input_mask_at(self, node_index):
        
        inputs = self.get_input_at(node_index)
        if isinstance(inputs, list):
            return [getattr(x, ""_keras_mask"", None) for x in inputs]
        else:
            return getattr(inputs, ""_keras_mask"", None)
","Retrieves the input mask tensor(s) of a layer at a given node.

        Args:
            node_index: Integer, index of the node
                from which to retrieve the attribute.
                E.g. `node_index=0` will correspond to the
                first time the layer was called.

        Returns:
            A mask tensor
            (or list of tensors if the layer has multiple inputs).
        ",51,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_input_mask_at(self, node_index):
        
        inputs = self.get_input_at(node_index)
        if isinstance(inputs, list):
            return [getattr(x, ""_keras_mask"", None) for x in inputs]
        else:
            return getattr(inputs, ""_keras_mask"", None)

        ```
    ###Assistant : Retrieves the input mask tensor(s) of a layer at a given node.

        Args:
            node_index: Integer, index of the node
                from which to retrieve the attribute.
                E.g. `node_index=0` will correspond to the
                first time the layer was called.

        Returns:
            A mask tensor
            (or list of tensors if the layer has multiple inputs).
        
    "
1690,"def get_default_grpc_options():
        
        return [
            ('grpc.max_send_message_length', -1),
            ('grpc.max_receive_message_length', -1),
        ]
","
        Returns a list of default options used for creating grpc channels.
        Documentation is here https://github.com/grpc/grpc/blob/master/include/grpc/impl/codegen/grpc_types.h
        :returns: list of tuples defining grpc parameters
        ",22,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_default_grpc_options():
        
        return [
            ('grpc.max_send_message_length', -1),
            ('grpc.max_receive_message_length', -1),
        ]

        ```
    ###Assistant : 
        Returns a list of default options used for creating grpc channels.
        Documentation is here https://github.com/grpc/grpc/blob/master/include/grpc/impl/codegen/grpc_types.h
        :returns: list of tuples defining grpc parameters
        
    "
1691,"def _cancel_futures_kwargs(self):
        
        if sys.version_info[:2] < (3, 9):
            return {}
        return dict(cancel_futures=True)
","Shim older Pythons that do not have Executor.shutdown(...cancel_futures=).

        Remove this code when support for Python 3.8 is dropped.
        ",18,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _cancel_futures_kwargs(self):
        
        if sys.version_info[:2] < (3, 9):
            return {}
        return dict(cancel_futures=True)

        ```
    ###Assistant : Shim older Pythons that do not have Executor.shutdown(...cancel_futures=).

        Remove this code when support for Python 3.8 is dropped.
        
    "
1692,"def _estimate_available_parallelism() -> int:
    
    cur_pg = ray.util.get_current_placement_group()
    return _estimate_avail_cpus(cur_pg)
","Estimates the available CPU parallelism for this Dataset in the cluster.
    If we are currently in a placement group, take that into account.",23,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _estimate_available_parallelism() -> int:
    
    cur_pg = ray.util.get_current_placement_group()
    return _estimate_avail_cpus(cur_pg)

        ```
    ###Assistant : Estimates the available CPU parallelism for this Dataset in the cluster.
    If we are currently in a placement group, take that into account.
    "
1693,"def bin(num, max_bits=None):
    

    ceiling = 2 ** (num).bit_length()
    if num >= 0:
        s = bltns.bin(num + ceiling).replace('1', '0', 1)
    else:
        s = bltns.bin(~num ^ (ceiling - 1) + ceiling)
    sign = s[:3]
    digits = s[3:]
    if max_bits is not None:
        if len(digits) < max_bits:
            digits = (sign[-1] * max_bits + digits)[-max_bits:]
    return ""%s %s"" % (sign, digits)
","
    Like built-in bin(), except negative values are represented in
    twos-compliment, and the leading bit always indicates sign
    (0=positive, 1=negative).

    >>> bin(10)
    '0b0 1010'
    >>> bin(~10)   # ~10 is -11
    '0b1 0101'
    ",31,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bin(num, max_bits=None):
    

    ceiling = 2 ** (num).bit_length()
    if num >= 0:
        s = bltns.bin(num + ceiling).replace('1', '0', 1)
    else:
        s = bltns.bin(~num ^ (ceiling - 1) + ceiling)
    sign = s[:3]
    digits = s[3:]
    if max_bits is not None:
        if len(digits) < max_bits:
            digits = (sign[-1] * max_bits + digits)[-max_bits:]
    return ""%s %s"" % (sign, digits)

        ```
    ###Assistant : 
    Like built-in bin(), except negative values are represented in
    twos-compliment, and the leading bit always indicates sign
    (0=positive, 1=negative).

    >>> bin(10)
    '0b0 1010'
    >>> bin(~10)   # ~10 is -11
    '0b1 0101'
    
    "
1694,"def axis0_safe_slice(X, mask, len_mask):
    
    if len_mask != 0:
        return X[safe_mask(X, mask), :]
    return np.zeros(shape=(0, X.shape[1]))

","Return a mask which is safer to use on X than safe_mask.

    This mask is safer than safe_mask since it returns an
    empty array, when a sparse matrix is sliced with a boolean mask
    with all False, instead of raising an unhelpful error in older
    versions of SciPy.

    See: https://github.com/scipy/scipy/issues/5361

    Also note that we can avoid doing the dot product by checking if
    the len_mask is not zero in _huber_loss_and_gradient but this
    is not going to be the bottleneck, since the number of outliers
    and non_outliers are typically non-zero and it makes the code
    tougher to follow.

    Parameters
    ----------
    X : {array-like, sparse matrix}
        Data on which to apply mask.

    mask : ndarray
        Mask to be used on X.

    len_mask : int
        The length of the mask.

    Returns
    -------
    mask : ndarray
        Array that is safe to use on X.
    ",140,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def axis0_safe_slice(X, mask, len_mask):
    
    if len_mask != 0:
        return X[safe_mask(X, mask), :]
    return np.zeros(shape=(0, X.shape[1]))


        ```
    ###Assistant : Return a mask which is safer to use on X than safe_mask.

    This mask is safer than safe_mask since it returns an
    empty array, when a sparse matrix is sliced with a boolean mask
    with all False, instead of raising an unhelpful error in older
    versions of SciPy.

    See: https://github.com/scipy/scipy/issues/5361

    Also note that we can avoid doing the dot product by checking if
    the len_mask is not zero in _huber_loss_and_gradient but this
    is not going to be the bottleneck, since the number of outliers
    and non_outliers are typically non-zero and it makes the code
    tougher to follow.

    Parameters
    ----------
    X : {array-like, sparse matrix}
        Data on which to apply mask.

    mask : ndarray
        Mask to be used on X.

    len_mask : int
        The length of the mask.

    Returns
    -------
    mask : ndarray
        Array that is safe to use on X.
    
    "
1695,"def _most_frequent(array, extra_value, n_repeat):
    
    # Compute the most frequent value in array only
    if array.size > 0:
        if array.dtype == object:
            # scipy.stats.mode is slow with object dtype array.
            # Python Counter is more efficient
            counter = Counter(array)
            most_frequent_count = counter.most_common(1)[0][1]
            # tie breaking similarly to scipy.stats.mode
            most_frequent_value = min(
                value
                for value, count in counter.items()
                if count == most_frequent_count
            )
        else:
            mode = _mode(array)
            most_frequent_value = mode[0][0]
            most_frequent_count = mode[1][0]
    else:
        most_frequent_value = 0
        most_frequent_count = 0

    # Compare to array + [extra_value] * n_repeat
    if most_frequent_count == 0 and n_repeat == 0:
        return np.nan
    elif most_frequent_count < n_repeat:
        return extra_value
    elif most_frequent_count > n_repeat:
        return most_frequent_value
    elif most_frequent_count == n_repeat:
        # tie breaking similarly to scipy.stats.mode
        return min(most_frequent_value, extra_value)

","Compute the most frequent value in a 1d array extended with
    [extra_value] * n_repeat, where extra_value is assumed to be not part
    of the array.",25,121,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _most_frequent(array, extra_value, n_repeat):
    
    # Compute the most frequent value in array only
    if array.size > 0:
        if array.dtype == object:
            # scipy.stats.mode is slow with object dtype array.
            # Python Counter is more efficient
            counter = Counter(array)
            most_frequent_count = counter.most_common(1)[0][1]
            # tie breaking similarly to scipy.stats.mode
            most_frequent_value = min(
                value
                for value, count in counter.items()
                if count == most_frequent_count
            )
        else:
            mode = _mode(array)
            most_frequent_value = mode[0][0]
            most_frequent_count = mode[1][0]
    else:
        most_frequent_value = 0
        most_frequent_count = 0

    # Compare to array + [extra_value] * n_repeat
    if most_frequent_count == 0 and n_repeat == 0:
        return np.nan
    elif most_frequent_count < n_repeat:
        return extra_value
    elif most_frequent_count > n_repeat:
        return most_frequent_value
    elif most_frequent_count == n_repeat:
        # tie breaking similarly to scipy.stats.mode
        return min(most_frequent_value, extra_value)


        ```
    ###Assistant : Compute the most frequent value in a 1d array extended with
    [extra_value] * n_repeat, where extra_value is assumed to be not part
    of the array.
    "
1696,"def _cov(X, shrinkage=None, covariance_estimator=None):
    
    if covariance_estimator is None:
        shrinkage = ""empirical"" if shrinkage is None else shrinkage
        if isinstance(shrinkage, str):
            if shrinkage == ""auto"":
                sc = StandardScaler()  # standardize features
                X = sc.fit_transform(X)
                s = ledoit_wolf(X)[0]
                # rescale
                s = sc.scale_[:, np.newaxis] * s * sc.scale_[np.newaxis, :]
            elif shrinkage == ""empirical"":
                s = empirical_covariance(X)
            else:
                raise ValueError(""unknown shrinkage parameter"")
        elif isinstance(shrinkage, Real):
            if shrinkage < 0 or shrinkage > 1:
                raise ValueError(""shrinkage parameter must be between 0 and 1"")
            s = shrunk_covariance(empirical_covariance(X), shrinkage)
        else:
            raise TypeError(""shrinkage must be a float or a string"")
    else:
        if shrinkage is not None and shrinkage != 0:
            raise ValueError(
                ""covariance_estimator and shrinkage parameters ""
                ""are not None. Only one of the two can be set.""
            )
        covariance_estimator.fit(X)
        if not hasattr(covariance_estimator, ""covariance_""):
            raise ValueError(
                ""%s does not have a covariance_ attribute""
                % covariance_estimator.__class__.__name__
            )
        s = covariance_estimator.covariance_
    return s

","Estimate covariance matrix (using optional covariance_estimator).
    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        Input data.

    shrinkage : {'empirical', 'auto'} or float, default=None
        Shrinkage parameter, possible values:
          - None or 'empirical': no shrinkage (default).
          - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
          - float between 0 and 1: fixed shrinkage parameter.

        Shrinkage parameter is ignored if  `covariance_estimator`
        is not None.

    covariance_estimator : estimator, default=None
        If not None, `covariance_estimator` is used to estimate
        the covariance matrices instead of relying on the empirical
        covariance estimator (with potential shrinkage).
        The object should have a fit method and a ``covariance_`` attribute
        like the estimators in :mod:`sklearn.covariance``.
        if None the shrinkage parameter drives the estimate.

        .. versionadded:: 0.24

    Returns
    -------
    s : ndarray of shape (n_features, n_features)
        Estimated covariance matrix.
    ",126,144,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _cov(X, shrinkage=None, covariance_estimator=None):
    
    if covariance_estimator is None:
        shrinkage = ""empirical"" if shrinkage is None else shrinkage
        if isinstance(shrinkage, str):
            if shrinkage == ""auto"":
                sc = StandardScaler()  # standardize features
                X = sc.fit_transform(X)
                s = ledoit_wolf(X)[0]
                # rescale
                s = sc.scale_[:, np.newaxis] * s * sc.scale_[np.newaxis, :]
            elif shrinkage == ""empirical"":
                s = empirical_covariance(X)
            else:
                raise ValueError(""unknown shrinkage parameter"")
        elif isinstance(shrinkage, Real):
            if shrinkage < 0 or shrinkage > 1:
                raise ValueError(""shrinkage parameter must be between 0 and 1"")
            s = shrunk_covariance(empirical_covariance(X), shrinkage)
        else:
            raise TypeError(""shrinkage must be a float or a string"")
    else:
        if shrinkage is not None and shrinkage != 0:
            raise ValueError(
                ""covariance_estimator and shrinkage parameters ""
                ""are not None. Only one of the two can be set.""
            )
        covariance_estimator.fit(X)
        if not hasattr(covariance_estimator, ""covariance_""):
            raise ValueError(
                ""%s does not have a covariance_ attribute""
                % covariance_estimator.__class__.__name__
            )
        s = covariance_estimator.covariance_
    return s


        ```
    ###Assistant : Estimate covariance matrix (using optional covariance_estimator).
    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        Input data.

    shrinkage : {'empirical', 'auto'} or float, default=None
        Shrinkage parameter, possible values:
          - None or 'empirical': no shrinkage (default).
          - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
          - float between 0 and 1: fixed shrinkage parameter.

        Shrinkage parameter is ignored if  `covariance_estimator`
        is not None.

    covariance_estimator : estimator, default=None
        If not None, `covariance_estimator` is used to estimate
        the covariance matrices instead of relying on the empirical
        covariance estimator (with potential shrinkage).
        The object should have a fit method and a ``covariance_`` attribute
        like the estimators in :mod:`sklearn.covariance``.
        if None the shrinkage parameter drives the estimate.

        .. versionadded:: 0.24

    Returns
    -------
    s : ndarray of shape (n_features, n_features)
        Estimated covariance matrix.
    
    "
1697,"def from_key_val_list(value):
    
    if value is None:
        return None

    if isinstance(value, (str, bytes, bool, int)):
        raise ValueError(""cannot encode objects that are not 2-tuples"")

    return OrderedDict(value)

","Take an object and test to see if it can be represented as a
    dictionary. Unless it can not be represented as such, return an
    OrderedDict, e.g.,

    ::

        >>> from_key_val_list([('key', 'val')])
        OrderedDict([('key', 'val')])
        >>> from_key_val_list('string')
        Traceback (most recent call last):
        ...
        ValueError: cannot encode objects that are not 2-tuples
        >>> from_key_val_list({'key': 'val'})
        OrderedDict([('key', 'val')])

    :rtype: OrderedDict
    ",56,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_key_val_list(value):
    
    if value is None:
        return None

    if isinstance(value, (str, bytes, bool, int)):
        raise ValueError(""cannot encode objects that are not 2-tuples"")

    return OrderedDict(value)


        ```
    ###Assistant : Take an object and test to see if it can be represented as a
    dictionary. Unless it can not be represented as such, return an
    OrderedDict, e.g.,

    ::

        >>> from_key_val_list([('key', 'val')])
        OrderedDict([('key', 'val')])
        >>> from_key_val_list('string')
        Traceback (most recent call last):
        ...
        ValueError: cannot encode objects that are not 2-tuples
        >>> from_key_val_list({'key': 'val'})
        OrderedDict([('key', 'val')])

    :rtype: OrderedDict
    
    "
1698,"def load_lexer_from_file(filename, lexername=""CustomLexer"", **options):
    
    try:
        # This empty dict will contain the namespace for the exec'd file
        custom_namespace = {}
        with open(filename, 'rb') as f:
            exec(f.read(), custom_namespace)
        # Retrieve the class `lexername` from that namespace
        if lexername not in custom_namespace:
            raise ClassNotFound('no valid %s class found in %s' %
                                (lexername, filename))
        lexer_class = custom_namespace[lexername]
        # And finally instantiate it with the options
        return lexer_class(**options)
    except OSError as err:
        raise ClassNotFound('cannot read %s: %s' % (filename, err))
    except ClassNotFound:
        raise
    except Exception as err:
        raise ClassNotFound('error when loading custom lexer: %s' % err)

","Load a lexer from a file.

    This method expects a file located relative to the current working
    directory, which contains a Lexer class. By default, it expects the
    Lexer to be name CustomLexer; you can specify your own class name
    as the second argument to this function.

    Users should be very careful with the input, because this method
    is equivalent to running eval on the input file.

    Raises ClassNotFound if there are any problems importing the Lexer.

    .. versionadded:: 2.2
    ",80,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_lexer_from_file(filename, lexername=""CustomLexer"", **options):
    
    try:
        # This empty dict will contain the namespace for the exec'd file
        custom_namespace = {}
        with open(filename, 'rb') as f:
            exec(f.read(), custom_namespace)
        # Retrieve the class `lexername` from that namespace
        if lexername not in custom_namespace:
            raise ClassNotFound('no valid %s class found in %s' %
                                (lexername, filename))
        lexer_class = custom_namespace[lexername]
        # And finally instantiate it with the options
        return lexer_class(**options)
    except OSError as err:
        raise ClassNotFound('cannot read %s: %s' % (filename, err))
    except ClassNotFound:
        raise
    except Exception as err:
        raise ClassNotFound('error when loading custom lexer: %s' % err)


        ```
    ###Assistant : Load a lexer from a file.

    This method expects a file located relative to the current working
    directory, which contains a Lexer class. By default, it expects the
    Lexer to be name CustomLexer; you can specify your own class name
    as the second argument to this function.

    Users should be very careful with the input, because this method
    is equivalent to running eval on the input file.

    Raises ClassNotFound if there are any problems importing the Lexer.

    .. versionadded:: 2.2
    
    "
1699,"def get_price(item_code, price_list, customer_group, company, qty=1):
	from erpnext.e_commerce.shopping_cart.cart import get_party

	template_item_code = frappe.db.get_value(""Item"", item_code, ""variant_of"")

	if price_list:
		price = frappe.get_all(""Item Price"", fields=[""price_list_rate"", ""currency""],
			filters={""price_list"": price_list, ""item_code"": item_code})

		if template_item_code and not price:
			price = frappe.get_all(""Item Price"", fields=[""price_list_rate"", ""currency""],
				filters={""price_list"": price_list, ""item_code"": template_item_code})

		if price:
			party = get_party()
			pricing_rule_dict = frappe._dict({
				""item_code"": item_code,
				""qty"": qty,
				""stock_qty"": qty,
				""transaction_type"": ""selling"",
				""price_list"": price_list,
				""customer_group"": customer_group,
				""company"": company,
				""conversion_rate"": 1,
				""for_shopping_cart"": True,
				""currency"": frappe.db.get_value(""Price List"", price_list, ""currency"")
			})

			if party and party.doctype == ""Customer"":
				pricing_rule_dict.update({""customer"": party.name})

			pricing_rule = get_pricing_rule_for_item(pricing_rule_dict)
			price_obj = price[0]

			if pricing_rule:
				# price without any rules applied
				mrp = price_obj.price_list_rate or 0

				if pricing_rule.pricing_rule_for == ""Discount Percentage"":
					price_obj.discount_percent = pricing_rule.discount_percentage
					price_obj.formatted_discount_percent = str(flt(pricing_rule.discount_percentage, 0)) + ""%""
					price_obj.price_list_rate = flt(price_obj.price_list_rate * (1.0 - (flt(pricing_rule.discount_percentage) / 100.0)))

				if pricing_rule.pricing_rule_for == ""Rate"":
					rate_discount = flt(mrp) - flt(pricing_rule.price_list_rate)
					if rate_discount > 0:
						price_obj.formatted_discount_rate = fmt_money(rate_discount, currency=price_obj[""currency""])
					price_obj.price_list_rate = pricing_rule.price_list_rate or 0

			if price_obj:
				price_obj[""formatted_price""] = fmt_money(price_obj[""price_list_rate""], currency=price_obj[""currency""])
				if mrp != price_obj[""price_list_rate""]:
					price_obj[""formatted_mrp""] = fmt_money(mrp, currency=price_obj[""currency""])

				price_obj[""currency_symbol""] = not cint(frappe.db.get_default(""hide_currency_symbol"")) \
					and (frappe.db.get_value(""Currency"", price_obj.currency, ""symbol"", cache=True) or price_obj.currency) \
					or """"

				uom_conversion_factor = frappe.db.sql(, item_code)

				uom_conversion_factor = uom_conversion_factor[0][0] if uom_conversion_factor else 1
				price_obj[""formatted_price_sales_uom""] = fmt_money(price_obj[""price_list_rate""] * uom_conversion_factor, currency=price_obj[""currency""])

				if not price_obj[""price_list_rate""]:
					price_obj[""price_list_rate""] = 0

				if not price_obj[""currency""]:
					price_obj[""currency""] = """"

				if not price_obj[""formatted_price""]:
					price_obj[""formatted_price""], price_obj[""formatted_mrp""] = """", """"

			return price_obj
","select	C.conversion_factor
					from `tabUOM Conversion Detail` C
					inner join `tabItem` I on C.parent = I.name and C.uom = I.sales_uom
					where I.name = %s",23,214,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_price(item_code, price_list, customer_group, company, qty=1):
	from erpnext.e_commerce.shopping_cart.cart import get_party

	template_item_code = frappe.db.get_value(""Item"", item_code, ""variant_of"")

	if price_list:
		price = frappe.get_all(""Item Price"", fields=[""price_list_rate"", ""currency""],
			filters={""price_list"": price_list, ""item_code"": item_code})

		if template_item_code and not price:
			price = frappe.get_all(""Item Price"", fields=[""price_list_rate"", ""currency""],
				filters={""price_list"": price_list, ""item_code"": template_item_code})

		if price:
			party = get_party()
			pricing_rule_dict = frappe._dict({
				""item_code"": item_code,
				""qty"": qty,
				""stock_qty"": qty,
				""transaction_type"": ""selling"",
				""price_list"": price_list,
				""customer_group"": customer_group,
				""company"": company,
				""conversion_rate"": 1,
				""for_shopping_cart"": True,
				""currency"": frappe.db.get_value(""Price List"", price_list, ""currency"")
			})

			if party and party.doctype == ""Customer"":
				pricing_rule_dict.update({""customer"": party.name})

			pricing_rule = get_pricing_rule_for_item(pricing_rule_dict)
			price_obj = price[0]

			if pricing_rule:
				# price without any rules applied
				mrp = price_obj.price_list_rate or 0

				if pricing_rule.pricing_rule_for == ""Discount Percentage"":
					price_obj.discount_percent = pricing_rule.discount_percentage
					price_obj.formatted_discount_percent = str(flt(pricing_rule.discount_percentage, 0)) + ""%""
					price_obj.price_list_rate = flt(price_obj.price_list_rate * (1.0 - (flt(pricing_rule.discount_percentage) / 100.0)))

				if pricing_rule.pricing_rule_for == ""Rate"":
					rate_discount = flt(mrp) - flt(pricing_rule.price_list_rate)
					if rate_discount > 0:
						price_obj.formatted_discount_rate = fmt_money(rate_discount, currency=price_obj[""currency""])
					price_obj.price_list_rate = pricing_rule.price_list_rate or 0

			if price_obj:
				price_obj[""formatted_price""] = fmt_money(price_obj[""price_list_rate""], currency=price_obj[""currency""])
				if mrp != price_obj[""price_list_rate""]:
					price_obj[""formatted_mrp""] = fmt_money(mrp, currency=price_obj[""currency""])

				price_obj[""currency_symbol""] = not cint(frappe.db.get_default(""hide_currency_symbol"")) \
					and (frappe.db.get_value(""Currency"", price_obj.currency, ""symbol"", cache=True) or price_obj.currency) \
					or """"

				uom_conversion_factor = frappe.db.sql(, item_code)

				uom_conversion_factor = uom_conversion_factor[0][0] if uom_conversion_factor else 1
				price_obj[""formatted_price_sales_uom""] = fmt_money(price_obj[""price_list_rate""] * uom_conversion_factor, currency=price_obj[""currency""])

				if not price_obj[""price_list_rate""]:
					price_obj[""price_list_rate""] = 0

				if not price_obj[""currency""]:
					price_obj[""currency""] = """"

				if not price_obj[""formatted_price""]:
					price_obj[""formatted_price""], price_obj[""formatted_mrp""] = """", """"

			return price_obj

        ```
    ###Assistant : select	C.conversion_factor
					from `tabUOM Conversion Detail` C
					inner join `tabItem` I on C.parent = I.name and C.uom = I.sales_uom
					where I.name = %s
    "
1700,"def progress(self, msg):
        
        if self.paras.verbose:
            sys.stdout.write(""\033[K"")  # Clear line
            print('[{}] {}'.format(human_format(self.step), msg), end='\r')
", Verbose function for updating progress on stdout (do not include newline) ,11,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def progress(self, msg):
        
        if self.paras.verbose:
            sys.stdout.write(""\033[K"")  # Clear line
            print('[{}] {}'.format(human_format(self.step), msg), end='\r')

        ```
    ###Assistant :  Verbose function for updating progress on stdout (do not include newline) 
    "
1701,"def test_customize_compiler_before_get_config_vars(self):
        # Issue #21923: test that a Distribution compiler
        # instance can be called without an explicit call to
        # get_config_vars().
        with open(TESTFN, 'w') as f:
            f.writelines(textwrap.dedent())
        p = subprocess.Popen([str(sys.executable), TESTFN],
                stdout=subprocess.PIPE,
                stderr=subprocess.STDOUT,
                universal_newlines=True)
        outs, errs = p.communicate()
        self.assertEqual(0, p.returncode, ""Subprocess failed: "" + outs)

","\
                from distutils.core import Distribution
                config = Distribution().get_command_obj('config')
                # try_compile may pass or it may fail if no compiler
                # is found but it should not raise an exception.
                rc = config.try_compile('int x;')
                ",33,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_customize_compiler_before_get_config_vars(self):
        # Issue #21923: test that a Distribution compiler
        # instance can be called without an explicit call to
        # get_config_vars().
        with open(TESTFN, 'w') as f:
            f.writelines(textwrap.dedent())
        p = subprocess.Popen([str(sys.executable), TESTFN],
                stdout=subprocess.PIPE,
                stderr=subprocess.STDOUT,
                universal_newlines=True)
        outs, errs = p.communicate()
        self.assertEqual(0, p.returncode, ""Subprocess failed: "" + outs)


        ```
    ###Assistant : \
                from distutils.core import Distribution
                config = Distribution().get_command_obj('config')
                # try_compile may pass or it may fail if no compiler
                # is found but it should not raise an exception.
                rc = config.try_compile('int x;')
                
    "
1702,"def siren_platform_only():
    
    with patch(
        ""homeassistant.components.zha.PLATFORMS"",
        (
            Platform.DEVICE_TRACKER,
            Platform.NUMBER,
            Platform.SENSOR,
            Platform.SELECT,
            Platform.SIREN,
        ),
    ):
        yield


@pytest.fixture",Only setup the siren and required base platforms to speed up tests.,12,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def siren_platform_only():
    
    with patch(
        ""homeassistant.components.zha.PLATFORMS"",
        (
            Platform.DEVICE_TRACKER,
            Platform.NUMBER,
            Platform.SENSOR,
            Platform.SELECT,
            Platform.SIREN,
        ),
    ):
        yield


@pytest.fixture
        ```
    ###Assistant : Only setup the siren and required base platforms to speed up tests.
    "
1703,"def test_maybe_send_server_notice_when_alerting_suppressed_room_blocked(self):
        
        self._rlsn._auth.check_auth_blocking = Mock(
            return_value=make_awaitable(None),
            side_effect=ResourceLimitError(
                403, ""foo"", limit_type=LimitBlockingTypes.MONTHLY_ACTIVE_USER
            ),
        )

        self._rlsn._server_notices_manager.__is_room_currently_blocked = Mock(
            return_value=make_awaitable((True, []))
        )

        mock_event = Mock(
            type=EventTypes.Message, content={""msgtype"": ServerNoticeMsgType}
        )
        self._rlsn._store.get_events = Mock(
            return_value=make_awaitable({""123"": mock_event})
        )
        self.get_success(self._rlsn.maybe_send_server_notice_to_user(self.user_id))

        self._send_notice.assert_called_once()

","
        When the room is already in a blocked state, test that when alerting
        is suppressed that the room is returned to an unblocked state.
        ",24,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_maybe_send_server_notice_when_alerting_suppressed_room_blocked(self):
        
        self._rlsn._auth.check_auth_blocking = Mock(
            return_value=make_awaitable(None),
            side_effect=ResourceLimitError(
                403, ""foo"", limit_type=LimitBlockingTypes.MONTHLY_ACTIVE_USER
            ),
        )

        self._rlsn._server_notices_manager.__is_room_currently_blocked = Mock(
            return_value=make_awaitable((True, []))
        )

        mock_event = Mock(
            type=EventTypes.Message, content={""msgtype"": ServerNoticeMsgType}
        )
        self._rlsn._store.get_events = Mock(
            return_value=make_awaitable({""123"": mock_event})
        )
        self.get_success(self._rlsn.maybe_send_server_notice_to_user(self.user_id))

        self._send_notice.assert_called_once()


        ```
    ###Assistant : 
        When the room is already in a blocked state, test that when alerting
        is suppressed that the room is returned to an unblocked state.
        
    "
1704,"def test_send(event_listener, salt_master, salt_minion, salt_call_cli):
    
    event_tag = random_string(""salt/test/event/"")
    data = {""event.fire"": ""just test it!!!!""}
    start_time = time.time()
    ret = salt_call_cli.run(
        ""event.send"",
        event_tag,
        data=data,
        with_grains=True,
        with_pillar=True,
        preload={""foo"": ""bar""},
    )
    assert ret.exitcode == 0
    assert ret.json
    assert ret.json is True

    event_pattern = (salt_master.id, event_tag)
    matched_events = event_listener.wait_for_events(
        [event_pattern], after_time=start_time, timeout=30
    )
    assert matched_events.found_all_events
    for event in matched_events:
        assert event.data[""id""] == salt_minion.id
        assert event.data[""cmd""] == ""_minion_event""
        assert ""event.fire"" in event.data[""data""]
        assert event.data[""foo""] == ""bar""
        assert event.data[""data""][""grains""][""test_grain""] == ""cheese""
        assert event.data[""data""][""pillar""][""ext_spam""] == ""eggs""
","
    Test sending an event to the master event bus
    ",9,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_send(event_listener, salt_master, salt_minion, salt_call_cli):
    
    event_tag = random_string(""salt/test/event/"")
    data = {""event.fire"": ""just test it!!!!""}
    start_time = time.time()
    ret = salt_call_cli.run(
        ""event.send"",
        event_tag,
        data=data,
        with_grains=True,
        with_pillar=True,
        preload={""foo"": ""bar""},
    )
    assert ret.exitcode == 0
    assert ret.json
    assert ret.json is True

    event_pattern = (salt_master.id, event_tag)
    matched_events = event_listener.wait_for_events(
        [event_pattern], after_time=start_time, timeout=30
    )
    assert matched_events.found_all_events
    for event in matched_events:
        assert event.data[""id""] == salt_minion.id
        assert event.data[""cmd""] == ""_minion_event""
        assert ""event.fire"" in event.data[""data""]
        assert event.data[""foo""] == ""bar""
        assert event.data[""data""][""grains""][""test_grain""] == ""cheese""
        assert event.data[""data""][""pillar""][""ext_spam""] == ""eggs""

        ```
    ###Assistant : 
    Test sending an event to the master event bus
    
    "
1705,"def _check_tree_and_avals(what, tree1, avals1, tree2, avals2):
  
  if tree1 != tree2:
    raise TypeError(
        f""{what} must have same type structure, got {tree1} and {tree2}."")
  if not all(_map(core.typematch, avals1, avals2)):
    diff = tree_map(_show_diff, tree_unflatten(tree1, avals1),
                    tree_unflatten(tree2, avals2))
    raise TypeError(f""{what} must have identical types, got\n{diff}."")

","Raises TypeError if (tree1, avals1) does not match (tree2, avals2).

  Corresponding `tree` and `avals` must match in the sense that the number of
  leaves in `tree` must be equal to the length of `avals`. `what` will be
  prepended to details of the mismatch in TypeError.
  ",45,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_tree_and_avals(what, tree1, avals1, tree2, avals2):
  
  if tree1 != tree2:
    raise TypeError(
        f""{what} must have same type structure, got {tree1} and {tree2}."")
  if not all(_map(core.typematch, avals1, avals2)):
    diff = tree_map(_show_diff, tree_unflatten(tree1, avals1),
                    tree_unflatten(tree2, avals2))
    raise TypeError(f""{what} must have identical types, got\n{diff}."")


        ```
    ###Assistant : Raises TypeError if (tree1, avals1) does not match (tree2, avals2).

  Corresponding `tree` and `avals` must match in the sense that the number of
  leaves in `tree` must be equal to the length of `avals`. `what` will be
  prepended to details of the mismatch in TypeError.
  
    "
1706,"def test_load_global_local_flag_config(self):
        

        global_config = 

        local_config = 

        global_config_path = ""/mock/home/folder/.streamlit/config.toml""
        local_config_path = os.path.join(os.getcwd(), "".streamlit/config.toml"")

        global_open = mock_open(read_data=global_config)
        local_open = mock_open(read_data=local_config)
        open = mock_open()
        open.side_effect = [global_open.return_value, local_open.return_value]

        open_patch = patch(""streamlit.config.open"", open)
        # patch streamlit.*.os.* instead of os.* for py35 compat
        makedirs_patch = patch(""streamlit.config.os.makedirs"")
        makedirs_patch.return_value = True
        pathexists_patch = patch(""streamlit.config.os.path.exists"")
        pathexists_patch.side_effect = lambda path: path in [
            global_config_path,
            local_config_path,
        ]

        with open_patch, makedirs_patch, pathexists_patch:
            config.get_config_options(options_from_flags={""theme.font"": ""monospace""})

            self.assertEqual(""light"", config.get_option(""theme.base""))
            self.assertEqual(""#FFFFFF"", config.get_option(""theme.textColor""))
            self.assertEqual(""monospace"", config.get_option(""theme.font""))
","Test that CLI flags have higher priority than both
        ~/.streamlit/config.toml and $CWD/.streamlit/config.toml at parse time.
        
        [theme]
        base = ""dark""
        font = ""sans serif""
        textColor = ""#FFFFFF""
        
        [theme]
        base = ""light""
        font = ""serif""
        ",33,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_load_global_local_flag_config(self):
        

        global_config = 

        local_config = 

        global_config_path = ""/mock/home/folder/.streamlit/config.toml""
        local_config_path = os.path.join(os.getcwd(), "".streamlit/config.toml"")

        global_open = mock_open(read_data=global_config)
        local_open = mock_open(read_data=local_config)
        open = mock_open()
        open.side_effect = [global_open.return_value, local_open.return_value]

        open_patch = patch(""streamlit.config.open"", open)
        # patch streamlit.*.os.* instead of os.* for py35 compat
        makedirs_patch = patch(""streamlit.config.os.makedirs"")
        makedirs_patch.return_value = True
        pathexists_patch = patch(""streamlit.config.os.path.exists"")
        pathexists_patch.side_effect = lambda path: path in [
            global_config_path,
            local_config_path,
        ]

        with open_patch, makedirs_patch, pathexists_patch:
            config.get_config_options(options_from_flags={""theme.font"": ""monospace""})

            self.assertEqual(""light"", config.get_option(""theme.base""))
            self.assertEqual(""#FFFFFF"", config.get_option(""theme.textColor""))
            self.assertEqual(""monospace"", config.get_option(""theme.font""))

        ```
    ###Assistant : Test that CLI flags have higher priority than both
        ~/.streamlit/config.toml and $CWD/.streamlit/config.toml at parse time.
        
        [theme]
        base = ""dark""
        font = ""sans serif""
        textColor = ""#FFFFFF""
        
        [theme]
        base = ""light""
        font = ""serif""
        
    "
1707,"def get_valid_filename(name):
    
    s = str(name).strip().replace("" "", ""_"")
    s = re.sub(r""(?u)[^-\w.]"", """", s)
    if s in {"""", ""."", ""..""}:
        raise SuspiciousFileOperation(""Could not derive file name from '%s'"" % name)
    return s


@keep_lazy_text","
    Return the given string converted to a string that can be used for a clean
    filename. Remove leading and trailing spaces; convert other spaces to
    underscores; and remove anything that is not an alphanumeric, dash,
    underscore, or dot.
    >>> get_valid_filename(""john's portrait in 2004.jpg"")
    'johns_portrait_in_2004.jpg'
    ",44,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_valid_filename(name):
    
    s = str(name).strip().replace("" "", ""_"")
    s = re.sub(r""(?u)[^-\w.]"", """", s)
    if s in {"""", ""."", ""..""}:
        raise SuspiciousFileOperation(""Could not derive file name from '%s'"" % name)
    return s


@keep_lazy_text
        ```
    ###Assistant : 
    Return the given string converted to a string that can be used for a clean
    filename. Remove leading and trailing spaces; convert other spaces to
    underscores; and remove anything that is not an alphanumeric, dash,
    underscore, or dot.
    >>> get_valid_filename(""john's portrait in 2004.jpg"")
    'johns_portrait_in_2004.jpg'
    
    "
1708,"def test_no_duplicates_for_m2m_in_list_filter(self):
        
        blues = Genre.objects.create(name=""Blues"")
        band = Band.objects.create(name=""B.B. King Review"", nr_of_members=11)

        band.genres.add(blues)
        band.genres.add(blues)

        m = BandAdmin(Band, custom_site)
        request = self.factory.get(""/band/"", data={""genres"": blues.pk})
        request.user = self.superuser

        cl = m.get_changelist_instance(request)
        cl.get_results(request)

        # There's only one Group instance
        self.assertEqual(cl.result_count, 1)
        # Queryset must be deletable.
        self.assertIs(cl.queryset.query.distinct, False)
        cl.queryset.delete()
        self.assertEqual(cl.queryset.count(), 0)
","
        Regression test for #13902: When using a ManyToMany in list_filter,
        results shouldn't appear more than once. Basic ManyToMany.
        ",18,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_no_duplicates_for_m2m_in_list_filter(self):
        
        blues = Genre.objects.create(name=""Blues"")
        band = Band.objects.create(name=""B.B. King Review"", nr_of_members=11)

        band.genres.add(blues)
        band.genres.add(blues)

        m = BandAdmin(Band, custom_site)
        request = self.factory.get(""/band/"", data={""genres"": blues.pk})
        request.user = self.superuser

        cl = m.get_changelist_instance(request)
        cl.get_results(request)

        # There's only one Group instance
        self.assertEqual(cl.result_count, 1)
        # Queryset must be deletable.
        self.assertIs(cl.queryset.query.distinct, False)
        cl.queryset.delete()
        self.assertEqual(cl.queryset.count(), 0)

        ```
    ###Assistant : 
        Regression test for #13902: When using a ManyToMany in list_filter,
        results shouldn't appear more than once. Basic ManyToMany.
        
    "
1709,"def add_flex_arithmetic_methods(cls) -> None:
    
    flex_arith_method, flex_comp_method = _get_method_wrappers(cls)
    new_methods = _create_methods(cls, flex_arith_method, flex_comp_method)
    new_methods.update(
        {
            ""multiply"": new_methods[""mul""],
            ""subtract"": new_methods[""sub""],
            ""divide"": new_methods[""div""],
        }
    )
    # opt out of bool flex methods for now
    assert not any(kname in new_methods for kname in (""ror_"", ""rxor"", ""rand_""))

    _add_methods(cls, new_methods=new_methods)

","
    Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
    to the class.

    Parameters
    ----------
    cls : class
        flex methods will be defined and pinned to this class
    ",29,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_flex_arithmetic_methods(cls) -> None:
    
    flex_arith_method, flex_comp_method = _get_method_wrappers(cls)
    new_methods = _create_methods(cls, flex_arith_method, flex_comp_method)
    new_methods.update(
        {
            ""multiply"": new_methods[""mul""],
            ""subtract"": new_methods[""sub""],
            ""divide"": new_methods[""div""],
        }
    )
    # opt out of bool flex methods for now
    assert not any(kname in new_methods for kname in (""ror_"", ""rxor"", ""rand_""))

    _add_methods(cls, new_methods=new_methods)


        ```
    ###Assistant : 
    Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
    to the class.

    Parameters
    ----------
    cls : class
        flex methods will be defined and pinned to this class
    
    "
1710,"def get_collected_keypoint(self):
        
        output = []
        for tracker_id in self.id_to_pop:
            output.append([tracker_id, self.keypoint_saver[tracker_id]])
            del (self.keypoint_saver[tracker_id])
        self.flag_to_pop = False
        self.id_to_pop.clear()
        return output

","
            Output (List): List of keypoint results for Action Recognition task, where 
                           the format of each element is [tracker_id, KeyPointSequence of tracker_id]
        ",21,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_collected_keypoint(self):
        
        output = []
        for tracker_id in self.id_to_pop:
            output.append([tracker_id, self.keypoint_saver[tracker_id]])
            del (self.keypoint_saver[tracker_id])
        self.flag_to_pop = False
        self.id_to_pop.clear()
        return output


        ```
    ###Assistant : 
            Output (List): List of keypoint results for Action Recognition task, where 
                           the format of each element is [tracker_id, KeyPointSequence of tracker_id]
        
    "
1711,"def get_course_schedule_events(start, end, filters=None):
	
	from frappe.desk.calendar import get_event_conditions
	conditions = get_event_conditions(""Course Schedule"", filters)

	data = frappe.db.sql(.format(conditions=conditions), {
			""start"": start,
			""end"": end
			}, as_dict=True, update={""allDay"": 0})

	return data


@frappe.whitelist()","Returns events for Course Schedule Calendar view rendering.

	:param start: Start date-time.
	:param end: End date-time.
	:param filters: Filters (JSON).
	select name, course, color,
			timestamp(schedule_date, from_time) as from_time,
			timestamp(schedule_date, to_time) as to_time,
			room, student_group, 0 as 'allDay'
		from `tabCourse Schedule`
		where ( schedule_date between %(start)s and %(end)s )
		{conditions}",49,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_course_schedule_events(start, end, filters=None):
	
	from frappe.desk.calendar import get_event_conditions
	conditions = get_event_conditions(""Course Schedule"", filters)

	data = frappe.db.sql(.format(conditions=conditions), {
			""start"": start,
			""end"": end
			}, as_dict=True, update={""allDay"": 0})

	return data


@frappe.whitelist()
        ```
    ###Assistant : Returns events for Course Schedule Calendar view rendering.

	:param start: Start date-time.
	:param end: End date-time.
	:param filters: Filters (JSON).
	select name, course, color,
			timestamp(schedule_date, from_time) as from_time,
			timestamp(schedule_date, to_time) as to_time,
			room, student_group, 0 as 'allDay'
		from `tabCourse Schedule`
		where ( schedule_date between %(start)s and %(end)s )
		{conditions}
    "
1712,"def check_keys_split(self, decoded) -> None:
        
        bad_keys = set(decoded.keys()).difference(set(self._split_keys))
        if bad_keys:
            bad_keys_joined = "", "".join(bad_keys)
            raise ValueError(f""JSON data had unexpected key(s): {bad_keys_joined}"")
","
        Checks that dict has only the appropriate keys for orient='split'.
        ",10,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_keys_split(self, decoded) -> None:
        
        bad_keys = set(decoded.keys()).difference(set(self._split_keys))
        if bad_keys:
            bad_keys_joined = "", "".join(bad_keys)
            raise ValueError(f""JSON data had unexpected key(s): {bad_keys_joined}"")

        ```
    ###Assistant : 
        Checks that dict has only the appropriate keys for orient='split'.
        
    "
1713,"def adapt_datetimefield_value(self, value):
        

        if value is None:
            return None

        # Expression values are adapted by the database.
        if hasattr(value, ""resolve_expression""):
            return value

        # cx_Oracle doesn't support tz-aware datetimes
        if timezone.is_aware(value):
            if settings.USE_TZ:
                value = timezone.make_naive(value, self.connection.timezone)
            else:
                raise ValueError(
                    ""Oracle backend does not support timezone-aware datetimes when USE_TZ is False.""
                )

        return Oracle_datetime.from_datetime(value)
","
        Transform a datetime value to an object compatible with what is expected
        by the backend driver for datetime columns.

        If naive datetime is passed assumes that is in UTC. Normally Django
        models.DateTimeField makes sure that if USE_TZ is True passed datetime
        is timezone aware.
        ",44,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def adapt_datetimefield_value(self, value):
        

        if value is None:
            return None

        # Expression values are adapted by the database.
        if hasattr(value, ""resolve_expression""):
            return value

        # cx_Oracle doesn't support tz-aware datetimes
        if timezone.is_aware(value):
            if settings.USE_TZ:
                value = timezone.make_naive(value, self.connection.timezone)
            else:
                raise ValueError(
                    ""Oracle backend does not support timezone-aware datetimes when USE_TZ is False.""
                )

        return Oracle_datetime.from_datetime(value)

        ```
    ###Assistant : 
        Transform a datetime value to an object compatible with what is expected
        by the backend driver for datetime columns.

        If naive datetime is passed assumes that is in UTC. Normally Django
        models.DateTimeField makes sure that if USE_TZ is True passed datetime
        is timezone aware.
        
    "
1714,"def __pow__(a, b):
        
        if isinstance(b, numbers.Rational):
            if b.denominator == 1:
                power = b.numerator
                if power >= 0:
                    return Fraction(a._numerator ** power,
                                    a._denominator ** power,
                                    _normalize=False)
                elif a._numerator >= 0:
                    return Fraction(a._denominator ** -power,
                                    a._numerator ** -power,
                                    _normalize=False)
                else:
                    return Fraction((-a._denominator) ** -power,
                                    (-a._numerator) ** -power,
                                    _normalize=False)
            else:
                # A fractional power will generally produce an
                # irrational number.
                return float(a) ** float(b)
        else:
            return float(a) ** b
","a ** b

        If b is not an integer, the result will be a float or complex
        since roots are generally irrational. If b is an integer, the
        result will be rational.

        ",32,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __pow__(a, b):
        
        if isinstance(b, numbers.Rational):
            if b.denominator == 1:
                power = b.numerator
                if power >= 0:
                    return Fraction(a._numerator ** power,
                                    a._denominator ** power,
                                    _normalize=False)
                elif a._numerator >= 0:
                    return Fraction(a._denominator ** -power,
                                    a._numerator ** -power,
                                    _normalize=False)
                else:
                    return Fraction((-a._denominator) ** -power,
                                    (-a._numerator) ** -power,
                                    _normalize=False)
            else:
                # A fractional power will generally produce an
                # irrational number.
                return float(a) ** float(b)
        else:
            return float(a) ** b

        ```
    ###Assistant : a ** b

        If b is not an integer, the result will be a float or complex
        since roots are generally irrational. If b is an integer, the
        result will be rational.

        
    "
1715,"def fit(self, X, y, sample_weight=None):
        
        self._validate_params()

        super().fit(X, y, sample_weight=sample_weight)
        return self

","Fit Ridge regression model with cv.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_features)
            Training data. If using GCV, will be cast to float64
            if necessary.

        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
            Target values. Will be cast to X's dtype if necessary.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        Returns
        -------
        self : object
            Fitted estimator.

        Notes
        -----
        When sample_weight is provided, the selected hyperparameter may depend
        on whether we use leave-one-out cross-validation (cv=None or cv='auto')
        or another form of cross-validation, because only leave-one-out
        cross-validation takes the sample weights into account when computing
        the validation score.
        ",118,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y, sample_weight=None):
        
        self._validate_params()

        super().fit(X, y, sample_weight=sample_weight)
        return self


        ```
    ###Assistant : Fit Ridge regression model with cv.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_features)
            Training data. If using GCV, will be cast to float64
            if necessary.

        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
            Target values. Will be cast to X's dtype if necessary.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        Returns
        -------
        self : object
            Fitted estimator.

        Notes
        -----
        When sample_weight is provided, the selected hyperparameter may depend
        on whether we use leave-one-out cross-validation (cv=None or cv='auto')
        or another form of cross-validation, because only leave-one-out
        cross-validation takes the sample weights into account when computing
        the validation score.
        
    "
1716,"def calc_position(self, x):
        
        if x < self.x[0]:
            return None
        elif x > self.x[-1]:
            return None

        i = self.__search_index(x)
        dx = x - self.x[i]
        position = self.a[i] + self.b[i] * dx + \
            self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0

        return position
","
        Calc `y` position for given `x`.

        if `x` is outside the data point's `x` range, return None.

        Returns
        -------
        y : float
            y position for given x.
        ",27,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def calc_position(self, x):
        
        if x < self.x[0]:
            return None
        elif x > self.x[-1]:
            return None

        i = self.__search_index(x)
        dx = x - self.x[i]
        position = self.a[i] + self.b[i] * dx + \
            self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0

        return position

        ```
    ###Assistant : 
        Calc `y` position for given `x`.

        if `x` is outside the data point's `x` range, return None.

        Returns
        -------
        y : float
            y position for given x.
        
    "
1717,"def get_daily_sector_prices(start_date, end_date) -> dict:
    
    # sector ticker information
    sp500_tickers = {
        ""S&P 500 Materials (Sector)"": ""^SP500-15"",
        ""S&P 500 Industrials (Sector)"": ""^SP500-20"",
        ""S&P 500 Consumer Discretionary (Sector)"": ""^SP500-25"",
        ""S&P 500 Consumer Staples (Sector)"": ""^SP500-30"",
        ""S&P 500 Health Care (Sector)"": ""^SP500-35"",
        ""S&P 500 Financials (Sector)"": ""^SP500-40"",
        ""S&P 500 Information Technology (Sector)"": ""^SP500-45"",
        ""S&P 500 Telecommunication Services (Sector)"": ""^SP500-50"",
        ""S&P 500 Utilities (Sector)"": ""^SP500-55"",
        ""S&P 500 Real Estate (Sector)"": ""^SP500-60"",
        ""S&P 500 Energy (Sector)"": ""^GSPE"",
    }

    sp500_tickers_data = {}  # to store data

    for (
        sector,
        sector_ticker,
    ) in sp500_tickers.items():  # iterate thru the sectors
        # load the data required from yfinance
        sp500_tickers_data[
            sector
        ] = {  # builds a dictionary entry for the sector with adj close data
            ""sector_data"": yf.download(
                sector_ticker,
                start=start_date,
                end=end_date,
                progress=False,
            )[""Adj Close""]
        }  # stores the data here

    return sp500_tickers_data
","
    fetches daily sector prices for S&P500 for a fixed time period

    Parameters
    ----------
    start_date : str ('yyyy-mm-dd') or datetime.date
        start date for fetching data
    end_date : str ('yyyy-mm-dd') or datetime.date
        end date for fetching data

    Returns
    -------
    sp500_tickers_data : dict
        dictionary of dataframes with SPY daily sector prices
    ",48,133,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_daily_sector_prices(start_date, end_date) -> dict:
    
    # sector ticker information
    sp500_tickers = {
        ""S&P 500 Materials (Sector)"": ""^SP500-15"",
        ""S&P 500 Industrials (Sector)"": ""^SP500-20"",
        ""S&P 500 Consumer Discretionary (Sector)"": ""^SP500-25"",
        ""S&P 500 Consumer Staples (Sector)"": ""^SP500-30"",
        ""S&P 500 Health Care (Sector)"": ""^SP500-35"",
        ""S&P 500 Financials (Sector)"": ""^SP500-40"",
        ""S&P 500 Information Technology (Sector)"": ""^SP500-45"",
        ""S&P 500 Telecommunication Services (Sector)"": ""^SP500-50"",
        ""S&P 500 Utilities (Sector)"": ""^SP500-55"",
        ""S&P 500 Real Estate (Sector)"": ""^SP500-60"",
        ""S&P 500 Energy (Sector)"": ""^GSPE"",
    }

    sp500_tickers_data = {}  # to store data

    for (
        sector,
        sector_ticker,
    ) in sp500_tickers.items():  # iterate thru the sectors
        # load the data required from yfinance
        sp500_tickers_data[
            sector
        ] = {  # builds a dictionary entry for the sector with adj close data
            ""sector_data"": yf.download(
                sector_ticker,
                start=start_date,
                end=end_date,
                progress=False,
            )[""Adj Close""]
        }  # stores the data here

    return sp500_tickers_data

        ```
    ###Assistant : 
    fetches daily sector prices for S&P500 for a fixed time period

    Parameters
    ----------
    start_date : str ('yyyy-mm-dd') or datetime.date
        start date for fetching data
    end_date : str ('yyyy-mm-dd') or datetime.date
        end date for fetching data

    Returns
    -------
    sp500_tickers_data : dict
        dictionary of dataframes with SPY daily sector prices
    
    "
1718,"def get_unpositioned_tip(self, tip_shape=None, tip_length=None):
        
        from manim.mobject.geometry.tips import ArrowTriangleFilledTip

        if tip_shape is None:
            tip_shape = ArrowTriangleFilledTip
        if tip_length is None:
            tip_length = self.get_default_tip_length()
        color = self.get_color()
        style = {""fill_color"": color, ""stroke_color"": color}
        style.update(self.tip_style)
        tip = tip_shape(length=tip_length, **style)
        return tip
","
        Returns a tip that has been stylistically configured,
        but has not yet been given a position in space.
        ",18,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_unpositioned_tip(self, tip_shape=None, tip_length=None):
        
        from manim.mobject.geometry.tips import ArrowTriangleFilledTip

        if tip_shape is None:
            tip_shape = ArrowTriangleFilledTip
        if tip_length is None:
            tip_length = self.get_default_tip_length()
        color = self.get_color()
        style = {""fill_color"": color, ""stroke_color"": color}
        style.update(self.tip_style)
        tip = tip_shape(length=tip_length, **style)
        return tip

        ```
    ###Assistant : 
        Returns a tip that has been stylistically configured,
        but has not yet been given a position in space.
        
    "
1719,"def test_parse_due_date_without_timezone_uses_offset():
    
    data: DueDate = {
        ""date"": ""2022-02-02T14:00:00"",
        ""is_recurring"": False,
        ""lang"": ""en"",
        ""string"": ""Feb 2 2:00 PM"",
        ""timezone"": None,
    }
    actual = _parse_due_date(data, timezone_offset=-8)
    assert datetime(2022, 2, 2, 22, 0, 0, tzinfo=dt.UTC) == actual
",Test due date uses user local timezone offset when it has no timezone.,13,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_parse_due_date_without_timezone_uses_offset():
    
    data: DueDate = {
        ""date"": ""2022-02-02T14:00:00"",
        ""is_recurring"": False,
        ""lang"": ""en"",
        ""string"": ""Feb 2 2:00 PM"",
        ""timezone"": None,
    }
    actual = _parse_due_date(data, timezone_offset=-8)
    assert datetime(2022, 2, 2, 22, 0, 0, tzinfo=dt.UTC) == actual

        ```
    ###Assistant : Test due date uses user local timezone offset when it has no timezone.
    "
1720,"def serving_output(self, output):
        hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
        return TFBaseModelOutput(
            last_hidden_state=output.last_hidden_state, hidden_states=hidden_states, attentions=attentions
        )


@add_start_docstrings(
    ,
    HUBERT_START_DOCSTRING,
)",TFHubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).,14,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serving_output(self, output):
        hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
        return TFBaseModelOutput(
            last_hidden_state=output.last_hidden_state, hidden_states=hidden_states, attentions=attentions
        )


@add_start_docstrings(
    ,
    HUBERT_START_DOCSTRING,
)
        ```
    ###Assistant : TFHubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
    "
1721,"def _unique_np(values, return_inverse=False, return_counts=False):
    
    uniques = np.unique(
        values, return_inverse=return_inverse, return_counts=return_counts
    )

    inverse, counts = None, None

    if return_counts:
        *uniques, counts = uniques

    if return_inverse:
        *uniques, inverse = uniques

    if return_counts or return_inverse:
        uniques = uniques[0]

    # np.unique will have duplicate missing values at the end of `uniques`
    # here we clip the nans and remove it from uniques
    if uniques.size and is_scalar_nan(uniques[-1]):
        nan_idx = np.searchsorted(uniques, np.nan)
        uniques = uniques[: nan_idx + 1]
        if return_inverse:
            inverse[inverse > nan_idx] = nan_idx

        if return_counts:
            counts[nan_idx] = np.sum(counts[nan_idx:])
            counts = counts[: nan_idx + 1]

    ret = (uniques,)

    if return_inverse:
        ret += (inverse,)

    if return_counts:
        ret += (counts,)

    return ret[0] if len(ret) == 1 else ret

","Helper function to find unique values for numpy arrays that correctly
    accounts for nans. See `_unique` documentation for details.",19,111,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _unique_np(values, return_inverse=False, return_counts=False):
    
    uniques = np.unique(
        values, return_inverse=return_inverse, return_counts=return_counts
    )

    inverse, counts = None, None

    if return_counts:
        *uniques, counts = uniques

    if return_inverse:
        *uniques, inverse = uniques

    if return_counts or return_inverse:
        uniques = uniques[0]

    # np.unique will have duplicate missing values at the end of `uniques`
    # here we clip the nans and remove it from uniques
    if uniques.size and is_scalar_nan(uniques[-1]):
        nan_idx = np.searchsorted(uniques, np.nan)
        uniques = uniques[: nan_idx + 1]
        if return_inverse:
            inverse[inverse > nan_idx] = nan_idx

        if return_counts:
            counts[nan_idx] = np.sum(counts[nan_idx:])
            counts = counts[: nan_idx + 1]

    ret = (uniques,)

    if return_inverse:
        ret += (inverse,)

    if return_counts:
        ret += (counts,)

    return ret[0] if len(ret) == 1 else ret


        ```
    ###Assistant : Helper function to find unique values for numpy arrays that correctly
    accounts for nans. See `_unique` documentation for details.
    "
1722,"def switch_to_live(self) -> None:
        
        self.logbook_run.event_cache.clear()
        self.logbook_run.context_lookup.clear()
","Switch to live stream.

        Clear caches so we can reduce memory pressure.
        ",12,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def switch_to_live(self) -> None:
        
        self.logbook_run.event_cache.clear()
        self.logbook_run.context_lookup.clear()

        ```
    ###Assistant : Switch to live stream.

        Clear caches so we can reduce memory pressure.
        
    "
1723,"def require_comet_ml(test_case):
    
    return unittest.skipUnless(is_comet_ml_available(), ""test requires comet_ml"")(test_case)

","
    Decorator marking a test that requires comet_ml installed. These tests are skipped when comet_ml isn't installed
    ",16,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def require_comet_ml(test_case):
    
    return unittest.skipUnless(is_comet_ml_available(), ""test requires comet_ml"")(test_case)


        ```
    ###Assistant : 
    Decorator marking a test that requires comet_ml installed. These tests are skipped when comet_ml isn't installed
    
    "
1724,"def triggered_id(self):
        
        component_id = None
        if self.triggered:
            prop_id = self.triggered_prop_ids.first()
            component_id = self.triggered_prop_ids[prop_id]
        return component_id
","
        Returns the component id (str or dict) of the Input component that triggered the callback.

        Note - use `triggered_prop_ids` if you need both the component id and the prop that triggered the callback or if
        multiple Inputs triggered the callback.

        Example usage:
        `if ""btn-1"" == ctx.triggered_id:
            do_something()`

        ",47,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def triggered_id(self):
        
        component_id = None
        if self.triggered:
            prop_id = self.triggered_prop_ids.first()
            component_id = self.triggered_prop_ids[prop_id]
        return component_id

        ```
    ###Assistant : 
        Returns the component id (str or dict) of the Input component that triggered the callback.

        Note - use `triggered_prop_ids` if you need both the component id and the prop that triggered the callback or if
        multiple Inputs triggered the callback.

        Example usage:
        `if ""btn-1"" == ctx.triggered_id:
            do_something()`

        
    "
1725,"def _solve_W(self, X, H, max_iter):
        
        avg = np.sqrt(X.mean() / self._n_components)
        W = np.full((X.shape[0], self._n_components), avg, dtype=X.dtype)
        W_buffer = W.copy()

        # Get scaled regularization terms. Done for each minibatch to take into account
        # variable sizes of minibatches.
        l1_reg_W, _, l2_reg_W, _ = self._scale_regularization(X)

        for _ in range(max_iter):
            W, *_ = _multiplicative_update_w(
                X, W, H, self._beta_loss, l1_reg_W, l2_reg_W, self._gamma
            )

            W_diff = linalg.norm(W - W_buffer) / linalg.norm(W)
            if self.tol > 0 and W_diff <= self.tol:
                break

            W_buffer[:] = W

        return W
","Minimize the objective function w.r.t W.

        Update W with H being fixed, until convergence. This is the heart
        of `transform` but it's also used during `fit` when doing fresh restarts.
        ",30,80,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _solve_W(self, X, H, max_iter):
        
        avg = np.sqrt(X.mean() / self._n_components)
        W = np.full((X.shape[0], self._n_components), avg, dtype=X.dtype)
        W_buffer = W.copy()

        # Get scaled regularization terms. Done for each minibatch to take into account
        # variable sizes of minibatches.
        l1_reg_W, _, l2_reg_W, _ = self._scale_regularization(X)

        for _ in range(max_iter):
            W, *_ = _multiplicative_update_w(
                X, W, H, self._beta_loss, l1_reg_W, l2_reg_W, self._gamma
            )

            W_diff = linalg.norm(W - W_buffer) / linalg.norm(W)
            if self.tol > 0 and W_diff <= self.tol:
                break

            W_buffer[:] = W

        return W

        ```
    ###Assistant : Minimize the objective function w.r.t W.

        Update W with H being fixed, until convergence. This is the heart
        of `transform` but it's also used during `fit` when doing fresh restarts.
        
    "
1726,"def convert_bbox_to_z(bbox):
    
    w = bbox[2] - bbox[0]
    h = bbox[3] - bbox[1]
    x = bbox[0] + w / 2.
    y = bbox[1] + h / 2.
    s = w * h  # scale is just area
    r = w / float(h + 1e-6)
    return np.array([x, y, s, r]).reshape((4, 1))

","
    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
      [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
      the aspect ratio
    ",34,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_bbox_to_z(bbox):
    
    w = bbox[2] - bbox[0]
    h = bbox[3] - bbox[1]
    x = bbox[0] + w / 2.
    y = bbox[1] + h / 2.
    s = w * h  # scale is just area
    r = w / float(h + 1e-6)
    return np.array([x, y, s, r]).reshape((4, 1))


        ```
    ###Assistant : 
    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
      [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
      the aspect ratio
    
    "
1727,"def remove_whitespace(string, leading=False, trailing=False):
    
    # Remove any leading new line characters along with any surrounding white space
    if leading:
        string = re.sub(r'^\s*\n+\s*', '', string)

    # Remove any trailing new line characters along with any surrounding white space
    if trailing:
        string = re.sub(r'\s*\n+\s*$', '', string)

    # Replace new line characters and absorb any surrounding space.
    string = re.sub(r'\s*\n\s*', ' ', string)
    # TODO need some way to get rid of extra spaces in e.g. text <span>   </span>  text
    return re.sub(r'\s+', ' ', string)
","Remove white space from a string.
    Args:
        string(str): The string to remove white space from.
        leading(bool, optional): Remove leading new lines when True.
        trailing(bool, optional): Remove trailing new lines when False.
    Returns:
        str: The input string with new line characters removed and white space squashed.
    Examples:
        Single or multiple new line characters are replaced with space.
            >>> remove_whitespace(""abc\\ndef"")
            'abc def'
            >>> remove_whitespace(""abc\\n\\n\\ndef"")
            'abc def'
        New line characters surrounded by white space are replaced with a single space.
            >>> remove_whitespace(""abc \\n \\n \\n def"")
            'abc def'
            >>> remove_whitespace(""abc  \\n  \\n  \\n  def"")
            'abc def'
        Leading and trailing new lines are replaced with a single space.
            >>> remove_whitespace(""\\nabc"")
            ' abc'
            >>> remove_whitespace(""  \\n  abc"")
            ' abc'
            >>> remove_whitespace(""abc\\n"")
            'abc '
            >>> remove_whitespace(""abc  \\n  "")
            'abc '
        Use ``leading=True`` to remove leading new line characters, including any surrounding
        white space:
            >>> remove_whitespace(""\\nabc"", leading=True)
            'abc'
            >>> remove_whitespace(""  \\n  abc"", leading=True)
            'abc'
        Use ``trailing=True`` to remove trailing new line characters, including any surrounding
        white space:
            >>> remove_whitespace(""abc  \\n  "", trailing=True)
            'abc'
    ",166,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def remove_whitespace(string, leading=False, trailing=False):
    
    # Remove any leading new line characters along with any surrounding white space
    if leading:
        string = re.sub(r'^\s*\n+\s*', '', string)

    # Remove any trailing new line characters along with any surrounding white space
    if trailing:
        string = re.sub(r'\s*\n+\s*$', '', string)

    # Replace new line characters and absorb any surrounding space.
    string = re.sub(r'\s*\n\s*', ' ', string)
    # TODO need some way to get rid of extra spaces in e.g. text <span>   </span>  text
    return re.sub(r'\s+', ' ', string)

        ```
    ###Assistant : Remove white space from a string.
    Args:
        string(str): The string to remove white space from.
        leading(bool, optional): Remove leading new lines when True.
        trailing(bool, optional): Remove trailing new lines when False.
    Returns:
        str: The input string with new line characters removed and white space squashed.
    Examples:
        Single or multiple new line characters are replaced with space.
            >>> remove_whitespace(""abc\\ndef"")
            'abc def'
            >>> remove_whitespace(""abc\\n\\n\\ndef"")
            'abc def'
        New line characters surrounded by white space are replaced with a single space.
            >>> remove_whitespace(""abc \\n \\n \\n def"")
            'abc def'
            >>> remove_whitespace(""abc  \\n  \\n  \\n  def"")
            'abc def'
        Leading and trailing new lines are replaced with a single space.
            >>> remove_whitespace(""\\nabc"")
            ' abc'
            >>> remove_whitespace(""  \\n  abc"")
            ' abc'
            >>> remove_whitespace(""abc\\n"")
            'abc '
            >>> remove_whitespace(""abc  \\n  "")
            'abc '
        Use ``leading=True`` to remove leading new line characters, including any surrounding
        white space:
            >>> remove_whitespace(""\\nabc"", leading=True)
            'abc'
            >>> remove_whitespace(""  \\n  abc"", leading=True)
            'abc'
        Use ``trailing=True`` to remove trailing new line characters, including any surrounding
        white space:
            >>> remove_whitespace(""abc  \\n  "", trailing=True)
            'abc'
    
    "
1728,"def selectionChanged(self, selected, deselected):
        
        if not self._active:
            return
        super().selectionChanged(selected, deselected)
        indexes = selected.indexes()
        if not indexes:
            return
        data = str(self._model().data(indexes[0]))
        self.selection_changed.emit(data)
",Extend selectionChanged to call completers selection_changed.,6,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def selectionChanged(self, selected, deselected):
        
        if not self._active:
            return
        super().selectionChanged(selected, deselected)
        indexes = selected.indexes()
        if not indexes:
            return
        data = str(self._model().data(indexes[0]))
        self.selection_changed.emit(data)

        ```
    ###Assistant : Extend selectionChanged to call completers selection_changed.
    "
1729,"def __mul__(self, other):
        

        newlist = [v for v in self.args]
        other = sympify(other)
        for i, v in enumerate(newlist):
            newlist[i] = (other * newlist[i][0], newlist[i][1])
        return Vector(newlist)
","Multiplies the Vector by a sympifyable expression.

        Parameters
        ==========

        other : Sympifyable
            The scalar to multiply this Vector with

        Examples
        ========

        >>> from sympy.physics.vector import ReferenceFrame
        >>> from sympy import Symbol
        >>> N = ReferenceFrame('N')
        >>> b = Symbol('b')
        >>> V = 10 * b * N.x
        >>> print(V)
        10*b*N.x

        ",50,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __mul__(self, other):
        

        newlist = [v for v in self.args]
        other = sympify(other)
        for i, v in enumerate(newlist):
            newlist[i] = (other * newlist[i][0], newlist[i][1])
        return Vector(newlist)

        ```
    ###Assistant : Multiplies the Vector by a sympifyable expression.

        Parameters
        ==========

        other : Sympifyable
            The scalar to multiply this Vector with

        Examples
        ========

        >>> from sympy.physics.vector import ReferenceFrame
        >>> from sympy import Symbol
        >>> N = ReferenceFrame('N')
        >>> b = Symbol('b')
        >>> V = 10 * b * N.x
        >>> print(V)
        10*b*N.x

        
    "
1730,"def to_dict(self) -> Dict:
        

        return serve_application_to_schema(self._deployments.values()).dict()
","Returns this Application's deployments as a dictionary.

        This dictionary adheres to the Serve REST API schema. It can be deployed
        via the Serve REST API.

        Returns:
            Dict: The Application's deployments formatted in a dictionary.
        ",34,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_dict(self) -> Dict:
        

        return serve_application_to_schema(self._deployments.values()).dict()

        ```
    ###Assistant : Returns this Application's deployments as a dictionary.

        This dictionary adheres to the Serve REST API schema. It can be deployed
        via the Serve REST API.

        Returns:
            Dict: The Application's deployments formatted in a dictionary.
        
    "
1731,"def get_shift_details(shift_type_name, for_timestamp=None):
	
	if not shift_type_name:
		return None
	if not for_timestamp:
		for_timestamp = now_datetime()

	shift_type = frappe.get_doc('Shift Type', shift_type_name)
	shift_actual_start = shift_type.start_time - timedelta(minutes=shift_type.begin_check_in_before_shift_start_time)

	if shift_type.start_time > shift_type.end_time:
		# shift spans accross 2 different days
		if get_time(for_timestamp.time()) >= get_time(shift_actual_start):
			# if for_timestamp is greater than start time, its in the first day
			start_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.start_time
			for_timestamp = for_timestamp + timedelta(days=1)
			end_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.end_time
		elif get_time(for_timestamp.time()) < get_time(shift_actual_start):
			# if for_timestamp is less than start time, its in the second day
			end_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.end_time
			for_timestamp = for_timestamp + timedelta(days=-1)
			start_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.start_time
	else:
		# start and end times fall on the same day
		start_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.start_time
		end_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.end_time

	actual_start = start_datetime - timedelta(minutes=shift_type.begin_check_in_before_shift_start_time)
	actual_end = end_datetime + timedelta(minutes=shift_type.allow_check_out_after_shift_end_time)

	return frappe._dict({
		'shift_type': shift_type,
		'start_datetime': start_datetime,
		'end_datetime': end_datetime,
		'actual_start': actual_start,
		'actual_end': actual_end
	})
","Returns Shift Details which contain some additional information as described below.
	'shift_details' contains the following keys:
	        'shift_type' - Object of DocType Shift Type,
	        'start_datetime' - Date and Time of shift start on given date,
	        'end_datetime' - Date and Time of shift end on given date,
	        'actual_start' - datetime of shift start after adding 'begin_check_in_before_shift_start_time',
	        'actual_end' - datetime of shift end after adding 'allow_check_out_after_shift_end_time'(None is returned if this is zero)

	:param shift_type_name: shift type name for which shift_details is required.
	:param for_timestamp: DateTime value on which shift_details are required
	",88,149,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_shift_details(shift_type_name, for_timestamp=None):
	
	if not shift_type_name:
		return None
	if not for_timestamp:
		for_timestamp = now_datetime()

	shift_type = frappe.get_doc('Shift Type', shift_type_name)
	shift_actual_start = shift_type.start_time - timedelta(minutes=shift_type.begin_check_in_before_shift_start_time)

	if shift_type.start_time > shift_type.end_time:
		# shift spans accross 2 different days
		if get_time(for_timestamp.time()) >= get_time(shift_actual_start):
			# if for_timestamp is greater than start time, its in the first day
			start_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.start_time
			for_timestamp = for_timestamp + timedelta(days=1)
			end_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.end_time
		elif get_time(for_timestamp.time()) < get_time(shift_actual_start):
			# if for_timestamp is less than start time, its in the second day
			end_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.end_time
			for_timestamp = for_timestamp + timedelta(days=-1)
			start_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.start_time
	else:
		# start and end times fall on the same day
		start_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.start_time
		end_datetime = datetime.combine(for_timestamp, datetime.min.time()) + shift_type.end_time

	actual_start = start_datetime - timedelta(minutes=shift_type.begin_check_in_before_shift_start_time)
	actual_end = end_datetime + timedelta(minutes=shift_type.allow_check_out_after_shift_end_time)

	return frappe._dict({
		'shift_type': shift_type,
		'start_datetime': start_datetime,
		'end_datetime': end_datetime,
		'actual_start': actual_start,
		'actual_end': actual_end
	})

        ```
    ###Assistant : Returns Shift Details which contain some additional information as described below.
	'shift_details' contains the following keys:
	        'shift_type' - Object of DocType Shift Type,
	        'start_datetime' - Date and Time of shift start on given date,
	        'end_datetime' - Date and Time of shift end on given date,
	        'actual_start' - datetime of shift start after adding 'begin_check_in_before_shift_start_time',
	        'actual_end' - datetime of shift end after adding 'allow_check_out_after_shift_end_time'(None is returned if this is zero)

	:param shift_type_name: shift type name for which shift_details is required.
	:param for_timestamp: DateTime value on which shift_details are required
	
    "
1732,"def subprocess_run_helper(func, *args, timeout, extra_env=None):
    
    target = func.__name__
    module = func.__module__
    proc = subprocess.run(
        [sys.executable,
         ""-c"",
         f""from {module} import {target}; {target}()"",
         *args],
        env={**os.environ, ""SOURCE_DATE_EPOCH"": ""0"", **(extra_env or {})},
        timeout=timeout, check=True,
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE,
        universal_newlines=True)
    return proc

","
    Run a function in a sub-process.

    Parameters
    ----------
    func : function
        The function to be run.  It must be in a module that is importable.
    *args : str
        Any additional command line arguments to be passed in
        the first argument to ``subprocess.run``.
    extra_env : dict[str, str]
        Any additional environment variables to be set for the subprocess.
    ",56,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def subprocess_run_helper(func, *args, timeout, extra_env=None):
    
    target = func.__name__
    module = func.__module__
    proc = subprocess.run(
        [sys.executable,
         ""-c"",
         f""from {module} import {target}; {target}()"",
         *args],
        env={**os.environ, ""SOURCE_DATE_EPOCH"": ""0"", **(extra_env or {})},
        timeout=timeout, check=True,
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE,
        universal_newlines=True)
    return proc


        ```
    ###Assistant : 
    Run a function in a sub-process.

    Parameters
    ----------
    func : function
        The function to be run.  It must be in a module that is importable.
    *args : str
        Any additional command line arguments to be passed in
        the first argument to ``subprocess.run``.
    extra_env : dict[str, str]
        Any additional environment variables to be set for the subprocess.
    
    "
1733,"def add_preheated_app_session(self) -> None:
        
        session = self._create_or_reuse_app_session(ws=None)
        session.handle_rerun_script_request(is_preheat=True)
","Register a fake browser with the server and run the script.

        This is used to start running the user's script even before the first
        browser connects.
        ",26,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_preheated_app_session(self) -> None:
        
        session = self._create_or_reuse_app_session(ws=None)
        session.handle_rerun_script_request(is_preheat=True)

        ```
    ###Assistant : Register a fake browser with the server and run the script.

        This is used to start running the user's script even before the first
        browser connects.
        
    "
1734,"def score(self, testing_features, testing_target):
        
        if self.fitted_pipeline_ is None:
            raise RuntimeError(
                ""A pipeline has not yet been optimized. Please call fit() first.""
            )

        testing_features, testing_target = self._check_dataset(
            testing_features, testing_target, sample_weight=None
        )

        # If the scoring function is a string, we must adjust to use the sklearn
        # scoring interface
        if isinstance(self.scoring_function, str):
            scorer = SCORERS[self.scoring_function]
        elif callable(self.scoring_function):
            scorer = self.scoring_function
        else:
            raise RuntimeError(
                ""The scoring function should either be the name of a scikit-learn scorer or a scorer object""
            )
        score = scorer(
            self.fitted_pipeline_,
            testing_features.astype(np.float64),
            testing_target.astype(np.float64),
        )
        return score

","Return the score on the given testing data using the user-specified scoring function.

        Parameters
        ----------
        testing_features: array-like {n_samples, n_features}
            Feature matrix of the testing set
        testing_target: array-like {n_samples}
            List of class labels for prediction in the testing set

        Returns
        -------
        accuracy_score: float
            The estimated test set accuracy

        ",47,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def score(self, testing_features, testing_target):
        
        if self.fitted_pipeline_ is None:
            raise RuntimeError(
                ""A pipeline has not yet been optimized. Please call fit() first.""
            )

        testing_features, testing_target = self._check_dataset(
            testing_features, testing_target, sample_weight=None
        )

        # If the scoring function is a string, we must adjust to use the sklearn
        # scoring interface
        if isinstance(self.scoring_function, str):
            scorer = SCORERS[self.scoring_function]
        elif callable(self.scoring_function):
            scorer = self.scoring_function
        else:
            raise RuntimeError(
                ""The scoring function should either be the name of a scikit-learn scorer or a scorer object""
            )
        score = scorer(
            self.fitted_pipeline_,
            testing_features.astype(np.float64),
            testing_target.astype(np.float64),
        )
        return score


        ```
    ###Assistant : Return the score on the given testing data using the user-specified scoring function.

        Parameters
        ----------
        testing_features: array-like {n_samples, n_features}
            Feature matrix of the testing set
        testing_target: array-like {n_samples}
            List of class labels for prediction in the testing set

        Returns
        -------
        accuracy_score: float
            The estimated test set accuracy

        
    "
1735,"def test_as_dict():
    
    expected = {
        LENGTH: UnitOfLength.KILOMETERS,
        WIND_SPEED: UnitOfSpeed.METERS_PER_SECOND,
        TEMPERATURE: UnitOfTemperature.CELSIUS,
        VOLUME: UnitOfVolume.LITERS,
        MASS: UnitOfMass.GRAMS,
        PRESSURE: UnitOfPressure.PA,
        ACCUMULATED_PRECIPITATION: UnitOfLength.MILLIMETERS,
    }

    assert expected == METRIC_SYSTEM.as_dict()

",Test that the as_dict() method returns the expected dictionary.,9,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_as_dict():
    
    expected = {
        LENGTH: UnitOfLength.KILOMETERS,
        WIND_SPEED: UnitOfSpeed.METERS_PER_SECOND,
        TEMPERATURE: UnitOfTemperature.CELSIUS,
        VOLUME: UnitOfVolume.LITERS,
        MASS: UnitOfMass.GRAMS,
        PRESSURE: UnitOfPressure.PA,
        ACCUMULATED_PRECIPITATION: UnitOfLength.MILLIMETERS,
    }

    assert expected == METRIC_SYSTEM.as_dict()


        ```
    ###Assistant : Test that the as_dict() method returns the expected dictionary.
    "
1736,"def _get_inputs(self):
        
        logger.debug(""Getting inputs"")
        if len(self.input_shape) == 3:
            input_shapes = [self.input_shape, self.input_shape]
        else:
            input_shapes = self.input_shape
        inputs = [Input(shape=shape, name=f""face_in_{side}"")
                  for side, shape in zip((""a"", ""b""), input_shapes)]
        logger.debug(""inputs: %s"", inputs)
        return inputs
"," Obtain the standardized inputs for the model.

        The inputs will be returned for the ""A"" and ""B"" sides in the shape as defined by
        :attr:`input_shape`.

        Returns
        -------
        list
            A list of :class:`keras.layers.Input` tensors. This will be a list of 2 tensors (one
            for each side) each of shapes :attr:`input_shape`.
        ",49,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_inputs(self):
        
        logger.debug(""Getting inputs"")
        if len(self.input_shape) == 3:
            input_shapes = [self.input_shape, self.input_shape]
        else:
            input_shapes = self.input_shape
        inputs = [Input(shape=shape, name=f""face_in_{side}"")
                  for side, shape in zip((""a"", ""b""), input_shapes)]
        logger.debug(""inputs: %s"", inputs)
        return inputs

        ```
    ###Assistant :  Obtain the standardized inputs for the model.

        The inputs will be returned for the ""A"" and ""B"" sides in the shape as defined by
        :attr:`input_shape`.

        Returns
        -------
        list
            A list of :class:`keras.layers.Input` tensors. This will be a list of 2 tensors (one
            for each side) each of shapes :attr:`input_shape`.
        
    "
1737,"def collect_units_install() -> t.List[PipInstall]:
    
    requirements_paths = []  # type: t.List[t.Tuple[str, str]]
    constraints_paths = []  # type: t.List[t.Tuple[str, str]]

    path = os.path.join(data_context().content.unit_path, 'requirements.txt')
    requirements_paths.append((data_context().content.root, path))

    path = os.path.join(data_context().content.unit_path, 'constraints.txt')
    constraints_paths.append((data_context().content.root, path))

    return collect_install(requirements_paths, constraints_paths)

",Return details necessary for the specified units pip install(s).,9,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def collect_units_install() -> t.List[PipInstall]:
    
    requirements_paths = []  # type: t.List[t.Tuple[str, str]]
    constraints_paths = []  # type: t.List[t.Tuple[str, str]]

    path = os.path.join(data_context().content.unit_path, 'requirements.txt')
    requirements_paths.append((data_context().content.root, path))

    path = os.path.join(data_context().content.unit_path, 'constraints.txt')
    constraints_paths.append((data_context().content.root, path))

    return collect_install(requirements_paths, constraints_paths)


        ```
    ###Assistant : Return details necessary for the specified units pip install(s).
    "
1738,"def forward(self, features, **kwargs):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x)
        x = self.dense(x)
        x = ACT2FN[self.config.hidden_act](x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


@add_start_docstrings(
    ,
    YOSO_START_DOCSTRING,
)","YOSO Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks.",23,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, features, **kwargs):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x)
        x = self.dense(x)
        x = ACT2FN[self.config.hidden_act](x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


@add_start_docstrings(
    ,
    YOSO_START_DOCSTRING,
)
        ```
    ###Assistant : YOSO Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks.
    "
1739,"def delete_batch(self, pk_list, using):
        
        # number of objects deleted
        num_deleted = 0
        field = self.get_meta().pk
        for offset in range(0, len(pk_list), GET_ITERATOR_CHUNK_SIZE):
            self.clear_where()
            self.add_filter(
                f""{field.attname}__in"",
                pk_list[offset : offset + GET_ITERATOR_CHUNK_SIZE],
            )
            num_deleted += self.do_query(
                self.get_meta().db_table, self.where, using=using
            )
        return num_deleted

","
        Set up and execute delete queries for all the objects in pk_list.

        More than one physical query may be executed if there are a
        lot of values in pk_list.
        ",29,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_batch(self, pk_list, using):
        
        # number of objects deleted
        num_deleted = 0
        field = self.get_meta().pk
        for offset in range(0, len(pk_list), GET_ITERATOR_CHUNK_SIZE):
            self.clear_where()
            self.add_filter(
                f""{field.attname}__in"",
                pk_list[offset : offset + GET_ITERATOR_CHUNK_SIZE],
            )
            num_deleted += self.do_query(
                self.get_meta().db_table, self.where, using=using
            )
        return num_deleted


        ```
    ###Assistant : 
        Set up and execute delete queries for all the objects in pk_list.

        More than one physical query may be executed if there are a
        lot of values in pk_list.
        
    "
1740,"def generate_ansible_coverage_config() -> str:
    
    coverage_config = 

    return coverage_config

","Generate code coverage configuration for Ansible tests.
[run]
branch = True
concurrency = multiprocessing
parallel = True

omit =
    */python*/dist-packages/*
    */python*/site-packages/*
    */python*/distutils/*
    */pyshared/*
    */pytest
    */AnsiballZ_*.py
    */test/results/*
",26,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_ansible_coverage_config() -> str:
    
    coverage_config = 

    return coverage_config


        ```
    ###Assistant : Generate code coverage configuration for Ansible tests.
[run]
branch = True
concurrency = multiprocessing
parallel = True

omit =
    */python*/dist-packages/*
    */python*/site-packages/*
    */python*/distutils/*
    */pyshared/*
    */pytest
    */AnsiballZ_*.py
    */test/results/*

    "
1741,"def get_allowed_roles_to_invite(self):
        
        return [
            r
            for r in organization_roles.get_all()
            if r.priority <= organization_roles.get(self.role).priority
        ]
","
        Return a list of roles which that member could invite
        Must check if member member has member:admin first before checking
        ",20,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_allowed_roles_to_invite(self):
        
        return [
            r
            for r in organization_roles.get_all()
            if r.priority <= organization_roles.get(self.role).priority
        ]

        ```
    ###Assistant : 
        Return a list of roles which that member could invite
        Must check if member member has member:admin first before checking
        
    "
1742,"def search_space_spec(self) -> Dict[str, ParameterSpec]:
        
        raise NotImplementedError()
","
        Space specification (sample points).
        Mapping from spec name to ParameterSpec. The names in choices should be in the same format of export.

        For example: ::

            {""layer1"": ParameterSpec(values=[""conv"", ""pool""])}
        ",28,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def search_space_spec(self) -> Dict[str, ParameterSpec]:
        
        raise NotImplementedError()

        ```
    ###Assistant : 
        Space specification (sample points).
        Mapping from spec name to ParameterSpec. The names in choices should be in the same format of export.

        For example: ::

            {""layer1"": ParameterSpec(values=[""conv"", ""pool""])}
        
    "
1743,"def test_context_as_admin(self) -> None:
        

        # Create a room. We're not part of it.
        user_id = self.register_user(""test"", ""test"")
        user_tok = self.login(""test"", ""test"")
        room_id = self.helper.create_room_as(user_id, tok=user_tok)

        # Populate the room with events.
        events = []
        for i in range(30):
            events.append(
                self.helper.send_event(
                    room_id, ""com.example.test"", content={""index"": i}, tok=user_tok
                )
            )

        # Now let's fetch the context for this room.
        midway = (len(events) - 1) // 2
        channel = self.make_request(
            ""GET"",
            ""/_synapse/admin/v1/rooms/%s/context/%s""
            % (room_id, events[midway][""event_id""]),
            access_token=self.admin_user_tok,
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(
            channel.json_body[""event""][""event_id""], events[midway][""event_id""]
        )

        for found_event in channel.json_body[""events_before""]:
            for j, posted_event in enumerate(events):
                if found_event[""event_id""] == posted_event[""event_id""]:
                    self.assertTrue(j < midway)
                    break
            else:
                self.fail(""Event %s from events_before not found"" % j)

        for found_event in channel.json_body[""events_after""]:
            for j, posted_event in enumerate(events):
                if found_event[""event_id""] == posted_event[""event_id""]:
                    self.assertTrue(j > midway)
                    break
            else:
                self.fail(""Event %s from events_after not found"" % j)

","
        Test that, as admin, we can find the context of an event without having joined the room.
        ",17,132,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_context_as_admin(self) -> None:
        

        # Create a room. We're not part of it.
        user_id = self.register_user(""test"", ""test"")
        user_tok = self.login(""test"", ""test"")
        room_id = self.helper.create_room_as(user_id, tok=user_tok)

        # Populate the room with events.
        events = []
        for i in range(30):
            events.append(
                self.helper.send_event(
                    room_id, ""com.example.test"", content={""index"": i}, tok=user_tok
                )
            )

        # Now let's fetch the context for this room.
        midway = (len(events) - 1) // 2
        channel = self.make_request(
            ""GET"",
            ""/_synapse/admin/v1/rooms/%s/context/%s""
            % (room_id, events[midway][""event_id""]),
            access_token=self.admin_user_tok,
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(
            channel.json_body[""event""][""event_id""], events[midway][""event_id""]
        )

        for found_event in channel.json_body[""events_before""]:
            for j, posted_event in enumerate(events):
                if found_event[""event_id""] == posted_event[""event_id""]:
                    self.assertTrue(j < midway)
                    break
            else:
                self.fail(""Event %s from events_before not found"" % j)

        for found_event in channel.json_body[""events_after""]:
            for j, posted_event in enumerate(events):
                if found_event[""event_id""] == posted_event[""event_id""]:
                    self.assertTrue(j > midway)
                    break
            else:
                self.fail(""Event %s from events_after not found"" % j)


        ```
    ###Assistant : 
        Test that, as admin, we can find the context of an event without having joined the room.
        
    "
1744,"def marginal_std(self, t):
        
        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
","
        Compute sigma_t of a given continuous-time label t in [0, T].
        ",11,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def marginal_std(self, t):
        
        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))

        ```
    ###Assistant : 
        Compute sigma_t of a given continuous-time label t in [0, T].
        
    "
1745,"def for_request(cls, request):
        
        attr_name = cls.get_cache_attr_name()
        if hasattr(request, attr_name):
            return getattr(request, attr_name)
        site = Site.find_for_request(request)
        site_settings = cls.for_site(site)
        # to allow more efficient page url generation
        site_settings._request = request
        setattr(request, attr_name, site_settings)
        return site_settings
","
        Get or create an instance of this model for the request,
        and cache the result on the request for faster repeat access.
        ",22,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def for_request(cls, request):
        
        attr_name = cls.get_cache_attr_name()
        if hasattr(request, attr_name):
            return getattr(request, attr_name)
        site = Site.find_for_request(request)
        site_settings = cls.for_site(site)
        # to allow more efficient page url generation
        site_settings._request = request
        setattr(request, attr_name, site_settings)
        return site_settings

        ```
    ###Assistant : 
        Get or create an instance of this model for the request,
        and cache the result on the request for faster repeat access.
        
    "
1746,"def _enable_ocsp_stapling(self, ssl_vhost, unused_options):
        
        min_apache_ver = (2, 3, 3)
        if self.get_version() < min_apache_ver:
            raise errors.PluginError(
                ""Unable to set OCSP directives.\n""
                ""Apache version is below 2.3.3."")

        if ""socache_shmcb_module"" not in self.parser.modules:
            self.enable_mod(""socache_shmcb"")

        # Check if there's an existing SSLUseStapling directive on.
        use_stapling_aug_path = self.parser.find_dir(""SSLUseStapling"",
                                                     ""on"", start=ssl_vhost.path)
        if not use_stapling_aug_path:
            self.parser.add_dir(ssl_vhost.path, ""SSLUseStapling"", ""on"")

        ssl_vhost_aug_path = self._escape(parser.get_aug_path(ssl_vhost.filep))

        # Check if there's an existing SSLStaplingCache directive.
        stapling_cache_aug_path = self.parser.find_dir('SSLStaplingCache',
                                                       None, ssl_vhost_aug_path)

        # We'll simply delete the directive, so that we'll have a
        # consistent OCSP cache path.
        if stapling_cache_aug_path:
            self.parser.aug.remove(
                re.sub(r""/\w*$"", """", stapling_cache_aug_path[0]))

        self.parser.add_dir_to_ifmodssl(ssl_vhost_aug_path,
                                        ""SSLStaplingCache"",
                                        [""shmcb:/var/run/apache2/stapling_cache(128000)""])

        msg = ""OCSP Stapling was enabled on SSL Vhost: %s.\n""%(
            ssl_vhost.filep)
        self.save_notes += msg
        self.save()
        logger.info(msg)
","Enables OCSP Stapling

        In OCSP, each client (e.g. browser) would have to query the
        OCSP Responder to validate that the site certificate was not revoked.

        Enabling OCSP Stapling, would allow the web-server to query the OCSP
        Responder, and staple its response to the offered certificate during
        TLS. i.e. clients would not have to query the OCSP responder.

        OCSP Stapling enablement on Apache implicitly depends on
        SSLCertificateChainFile being set by other code.

        .. note:: This function saves the configuration

        :param ssl_vhost: Destination of traffic, an ssl enabled vhost
        :type ssl_vhost: :class:`~certbot_apache._internal.obj.VirtualHost`

        :param unused_options: Not currently used
        :type unused_options: Not Available

        :returns: Success, general_vhost (HTTP vhost)
        :rtype: (bool, :class:`~certbot_apache._internal.obj.VirtualHost`)

        ",107,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _enable_ocsp_stapling(self, ssl_vhost, unused_options):
        
        min_apache_ver = (2, 3, 3)
        if self.get_version() < min_apache_ver:
            raise errors.PluginError(
                ""Unable to set OCSP directives.\n""
                ""Apache version is below 2.3.3."")

        if ""socache_shmcb_module"" not in self.parser.modules:
            self.enable_mod(""socache_shmcb"")

        # Check if there's an existing SSLUseStapling directive on.
        use_stapling_aug_path = self.parser.find_dir(""SSLUseStapling"",
                                                     ""on"", start=ssl_vhost.path)
        if not use_stapling_aug_path:
            self.parser.add_dir(ssl_vhost.path, ""SSLUseStapling"", ""on"")

        ssl_vhost_aug_path = self._escape(parser.get_aug_path(ssl_vhost.filep))

        # Check if there's an existing SSLStaplingCache directive.
        stapling_cache_aug_path = self.parser.find_dir('SSLStaplingCache',
                                                       None, ssl_vhost_aug_path)

        # We'll simply delete the directive, so that we'll have a
        # consistent OCSP cache path.
        if stapling_cache_aug_path:
            self.parser.aug.remove(
                re.sub(r""/\w*$"", """", stapling_cache_aug_path[0]))

        self.parser.add_dir_to_ifmodssl(ssl_vhost_aug_path,
                                        ""SSLStaplingCache"",
                                        [""shmcb:/var/run/apache2/stapling_cache(128000)""])

        msg = ""OCSP Stapling was enabled on SSL Vhost: %s.\n""%(
            ssl_vhost.filep)
        self.save_notes += msg
        self.save()
        logger.info(msg)

        ```
    ###Assistant : Enables OCSP Stapling

        In OCSP, each client (e.g. browser) would have to query the
        OCSP Responder to validate that the site certificate was not revoked.

        Enabling OCSP Stapling, would allow the web-server to query the OCSP
        Responder, and staple its response to the offered certificate during
        TLS. i.e. clients would not have to query the OCSP responder.

        OCSP Stapling enablement on Apache implicitly depends on
        SSLCertificateChainFile being set by other code.

        .. note:: This function saves the configuration

        :param ssl_vhost: Destination of traffic, an ssl enabled vhost
        :type ssl_vhost: :class:`~certbot_apache._internal.obj.VirtualHost`

        :param unused_options: Not currently used
        :type unused_options: Not Available

        :returns: Success, general_vhost (HTTP vhost)
        :rtype: (bool, :class:`~certbot_apache._internal.obj.VirtualHost`)

        
    "
1747,"def make_regional_gl_entries(gl_entries, doc):
	
	country = frappe.get_cached_value(""Company"", doc.company, ""country"")

	if country != ""United Arab Emirates"":
		return gl_entries

	if doc.reverse_charge == ""Y"":
		tax_accounts = get_tax_accounts(doc.company)
		for tax in doc.get(""taxes""):
			if tax.category not in (""Total"", ""Valuation and Total""):
				continue
			gl_entries = make_gl_entry(tax, gl_entries, doc, tax_accounts)
	return gl_entries

",Hooked to make_regional_gl_entries in Purchase Invoice.It appends the region specific general ledger entries to the list of GL Entries.,19,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_regional_gl_entries(gl_entries, doc):
	
	country = frappe.get_cached_value(""Company"", doc.company, ""country"")

	if country != ""United Arab Emirates"":
		return gl_entries

	if doc.reverse_charge == ""Y"":
		tax_accounts = get_tax_accounts(doc.company)
		for tax in doc.get(""taxes""):
			if tax.category not in (""Total"", ""Valuation and Total""):
				continue
			gl_entries = make_gl_entry(tax, gl_entries, doc, tax_accounts)
	return gl_entries


        ```
    ###Assistant : Hooked to make_regional_gl_entries in Purchase Invoice.It appends the region specific general ledger entries to the list of GL Entries.
    "
1748,"def private_param(param):
    
    return pytest.param(
        *param,
        marks=pytest.mark.skipif(
            not _run_private_tests,
            reason=""Skipping: this test is marked private, set RUN_PRIVATE=1 in your environment to run"",
        ),
    )

","Wrap param to mark it as private, meaning it requires credentials to run.

    Private tests are skipped by default. Set the RUN_PRIVATE environment variable to a truth value to run them.
    ",31,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def private_param(param):
    
    return pytest.param(
        *param,
        marks=pytest.mark.skipif(
            not _run_private_tests,
            reason=""Skipping: this test is marked private, set RUN_PRIVATE=1 in your environment to run"",
        ),
    )


        ```
    ###Assistant : Wrap param to mark it as private, meaning it requires credentials to run.

    Private tests are skipped by default. Set the RUN_PRIVATE environment variable to a truth value to run them.
    
    "
1749,"def minimal_d_separator(G, u, v):
    
    if not nx.is_directed_acyclic_graph(G):
        raise nx.NetworkXError(""graph should be directed acyclic"")

    union_uv = {u, v}

    if any(n not in G.nodes for n in union_uv):
        raise nx.NodeNotFound(""one or more specified nodes not found in the graph"")

    # first construct the set of ancestors of X and Y
    x_anc = nx.ancestors(G, u)
    y_anc = nx.ancestors(G, v)
    D_anc_xy = x_anc.union(y_anc)
    D_anc_xy.update((u, v))

    # second, construct the moralization of the subgraph of Anc(X,Y)
    moral_G = nx.moral_graph(G.subgraph(D_anc_xy))

    # find a separating set Z' in moral_G
    Z_prime = set(G.predecessors(u)).union(set(G.predecessors(v)))

    # perform BFS on the graph from 'x' to mark
    Z_dprime = _bfs_with_marks(moral_G, u, Z_prime)
    Z = _bfs_with_marks(moral_G, v, Z_dprime)
    return Z


@not_implemented_for(""undirected"")","Compute a minimal d-separating set between 'u' and 'v'.

    A d-separating set in a DAG is a set of nodes that blocks all paths
    between the two nodes, 'u' and 'v'. This function
    constructs a d-separating set that is ""minimal"", meaning it is the smallest
    d-separating set for 'u' and 'v'. This is not necessarily
    unique. For more details, see Notes.

    Parameters
    ----------
    G : graph
        A networkx DAG.
    u : node
        A node in the graph, G.
    v : node
        A node in the graph, G.

    Raises
    ------
    NetworkXError
        Raises a :exc:`NetworkXError` if the input graph is not a DAG.

    NodeNotFound
        If any of the input nodes are not found in the graph,
        a :exc:`NodeNotFound` exception is raised.

    References
    ----------
    .. [1] Tian, J., & Paz, A. (1998). Finding Minimal D-separators.

    Notes
    -----
    This function only finds ``a`` minimal d-separator. It does not guarantee
    uniqueness, since in a DAG there may be more than one minimal d-separator
    between two nodes. Moreover, this only checks for minimal separators
    between two nodes, not two sets. Finding minimal d-separators between
    two sets of nodes is not supported.

    Uses the algorithm presented in [1]_. The complexity of the algorithm
    is :math:`O(|E_{An}^m|)`, where :math:`|E_{An}^m|` stands for the
    number of edges in the moralized graph of the sub-graph consisting
    of only the ancestors of 'u' and 'v'. For full details, see [1]_.

    The algorithm works by constructing the moral graph consisting of just
    the ancestors of `u` and `v`. Then it constructs a candidate for
    a separating set  ``Z'`` from the predecessors of `u` and `v`.
    Then BFS is run starting from `u` and marking nodes
    found from ``Z'`` and calling those nodes ``Z''``.
    Then BFS is run again starting from `v` and marking nodes if they are
    present in ``Z''``. Those marked nodes are the returned minimal
    d-separating set.

    https://en.wikipedia.org/wiki/Bayesian_network#d-separation
    ",306,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def minimal_d_separator(G, u, v):
    
    if not nx.is_directed_acyclic_graph(G):
        raise nx.NetworkXError(""graph should be directed acyclic"")

    union_uv = {u, v}

    if any(n not in G.nodes for n in union_uv):
        raise nx.NodeNotFound(""one or more specified nodes not found in the graph"")

    # first construct the set of ancestors of X and Y
    x_anc = nx.ancestors(G, u)
    y_anc = nx.ancestors(G, v)
    D_anc_xy = x_anc.union(y_anc)
    D_anc_xy.update((u, v))

    # second, construct the moralization of the subgraph of Anc(X,Y)
    moral_G = nx.moral_graph(G.subgraph(D_anc_xy))

    # find a separating set Z' in moral_G
    Z_prime = set(G.predecessors(u)).union(set(G.predecessors(v)))

    # perform BFS on the graph from 'x' to mark
    Z_dprime = _bfs_with_marks(moral_G, u, Z_prime)
    Z = _bfs_with_marks(moral_G, v, Z_dprime)
    return Z


@not_implemented_for(""undirected"")
        ```
    ###Assistant : Compute a minimal d-separating set between 'u' and 'v'.

    A d-separating set in a DAG is a set of nodes that blocks all paths
    between the two nodes, 'u' and 'v'. This function
    constructs a d-separating set that is ""minimal"", meaning it is the smallest
    d-separating set for 'u' and 'v'. This is not necessarily
    unique. For more details, see Notes.

    Parameters
    ----------
    G : graph
        A networkx DAG.
    u : node
        A node in the graph, G.
    v : node
        A node in the graph, G.

    Raises
    ------
    NetworkXError
        Raises a :exc:`NetworkXError` if the input graph is not a DAG.

    NodeNotFound
        If any of the input nodes are not found in the graph,
        a :exc:`NodeNotFound` exception is raised.

    References
    ----------
    .. [1] Tian, J., & Paz, A. (1998). Finding Minimal D-separators.

    Notes
    -----
    This function only finds ``a`` minimal d-separator. It does not guarantee
    uniqueness, since in a DAG there may be more than one minimal d-separator
    between two nodes. Moreover, this only checks for minimal separators
    between two nodes, not two sets. Finding minimal d-separators between
    two sets of nodes is not supported.

    Uses the algorithm presented in [1]_. The complexity of the algorithm
    is :math:`O(|E_{An}^m|)`, where :math:`|E_{An}^m|` stands for the
    number of edges in the moralized graph of the sub-graph consisting
    of only the ancestors of 'u' and 'v'. For full details, see [1]_.

    The algorithm works by constructing the moral graph consisting of just
    the ancestors of `u` and `v`. Then it constructs a candidate for
    a separating set  ``Z'`` from the predecessors of `u` and `v`.
    Then BFS is run starting from `u` and marking nodes
    found from ``Z'`` and calling those nodes ``Z''``.
    Then BFS is run again starting from `v` and marking nodes if they are
    present in ``Z''``. Those marked nodes are the returned minimal
    d-separating set.

    https://en.wikipedia.org/wiki/Bayesian_network#d-separation
    
    "
1750,"def _change_alignment_for_a_line(self, alignment, line_no):
        
        self.lines[1][line_no] = alignment
        if self.lines[1][line_no] == ""center"":
            self[line_no].move_to(
                np.array([self.get_center()[0], self[line_no].get_center()[1], 0]),
            )
        elif self.lines[1][line_no] == ""right"":
            self[line_no].move_to(
                np.array(
                    [
                        self.get_right()[0] - self[line_no].width / 2,
                        self[line_no].get_center()[1],
                        0,
                    ],
                ),
            )
        elif self.lines[1][line_no] == ""left"":
            self[line_no].move_to(
                np.array(
                    [
                        self.get_left()[0] + self[line_no].width / 2,
                        self[line_no].get_center()[1],
                        0,
                    ],
                ),
            )

","Function to change one line's alignment to a specific value.

        Parameters
        ----------
        alignment : :class:`str`
            Defines the alignment of paragraph. Possible values are ""left"", ""right"", ""center"".
        line_no : :class:`int`
            Defines the line number for which we want to set given alignment.
        ",41,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _change_alignment_for_a_line(self, alignment, line_no):
        
        self.lines[1][line_no] = alignment
        if self.lines[1][line_no] == ""center"":
            self[line_no].move_to(
                np.array([self.get_center()[0], self[line_no].get_center()[1], 0]),
            )
        elif self.lines[1][line_no] == ""right"":
            self[line_no].move_to(
                np.array(
                    [
                        self.get_right()[0] - self[line_no].width / 2,
                        self[line_no].get_center()[1],
                        0,
                    ],
                ),
            )
        elif self.lines[1][line_no] == ""left"":
            self[line_no].move_to(
                np.array(
                    [
                        self.get_left()[0] + self[line_no].width / 2,
                        self[line_no].get_center()[1],
                        0,
                    ],
                ),
            )


        ```
    ###Assistant : Function to change one line's alignment to a specific value.

        Parameters
        ----------
        alignment : :class:`str`
            Defines the alignment of paragraph. Possible values are ""left"", ""right"", ""center"".
        line_no : :class:`int`
            Defines the line number for which we want to set given alignment.
        
    "
1751,"def test_rejoin_forgotten_by_user(self) -> None:
        
        self.helper.join(self.room_id, user=self.bob, tok=self.bob_token)

        self.helper.leave(self.room_id, user=self.alice, tok=self.alice_token)
        self.get_success(self.handler.forget(self.alice_ID, self.room_id))
        self.assertTrue(
            self.get_success(self.store.did_forget(self.alice, self.room_id))
        )

        # the server has not forgotten the room
        self.assertFalse(
            self.get_success(self.store.is_locally_forgotten_room(self.room_id))
        )

        self.helper.join(self.room_id, user=self.alice, tok=self.alice_token)
        # TODO: A join to a room does not invalidate the forgotten cache
        # see https://github.com/matrix-org/synapse/issues/13262
        self.store.did_forget.invalidate_all()
        self.assertFalse(
            self.get_success(self.store.did_forget(self.alice, self.room_id))
        )
","Test that a user that has forgotten a room can do a re-join.
        The room was not forgotten from the local server.
        One local user is still member of the room.",31,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_rejoin_forgotten_by_user(self) -> None:
        
        self.helper.join(self.room_id, user=self.bob, tok=self.bob_token)

        self.helper.leave(self.room_id, user=self.alice, tok=self.alice_token)
        self.get_success(self.handler.forget(self.alice_ID, self.room_id))
        self.assertTrue(
            self.get_success(self.store.did_forget(self.alice, self.room_id))
        )

        # the server has not forgotten the room
        self.assertFalse(
            self.get_success(self.store.is_locally_forgotten_room(self.room_id))
        )

        self.helper.join(self.room_id, user=self.alice, tok=self.alice_token)
        # TODO: A join to a room does not invalidate the forgotten cache
        # see https://github.com/matrix-org/synapse/issues/13262
        self.store.did_forget.invalidate_all()
        self.assertFalse(
            self.get_success(self.store.did_forget(self.alice, self.room_id))
        )

        ```
    ###Assistant : Test that a user that has forgotten a room can do a re-join.
        The room was not forgotten from the local server.
        One local user is still member of the room.
    "
1752,"def std_call(func):
    
    if os.name == ""nt"":
        return lwingdal[func]
    else:
        return lgdal[func]


# #### Version-information functions. ####

# Return GDAL library version information with the given key.
_version_info = std_call(""GDALVersionInfo"")
_version_info.argtypes = [c_char_p]
_version_info.restype = c_char_p

","
    Return the correct STDCALL function for certain OSR routines on Win32
    platforms.
    ",12,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def std_call(func):
    
    if os.name == ""nt"":
        return lwingdal[func]
    else:
        return lgdal[func]


# #### Version-information functions. ####

# Return GDAL library version information with the given key.
_version_info = std_call(""GDALVersionInfo"")
_version_info.argtypes = [c_char_p]
_version_info.restype = c_char_p


        ```
    ###Assistant : 
    Return the correct STDCALL function for certain OSR routines on Win32
    platforms.
    
    "
1753,"def __getitem__(self, key):
        
        use_func = key.startswith(self.prefix)
        if use_func:
            key = key[len(self.prefix) :]
        value = super().__getitem__(key)
        if use_func:
            return self.func(value)
        return value

","
        Retrieve the real value after stripping the prefix string (if
        present). If the prefix is present, pass the value through self.func
        before returning, otherwise return the raw value.
        ",28,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __getitem__(self, key):
        
        use_func = key.startswith(self.prefix)
        if use_func:
            key = key[len(self.prefix) :]
        value = super().__getitem__(key)
        if use_func:
            return self.func(value)
        return value


        ```
    ###Assistant : 
        Retrieve the real value after stripping the prefix string (if
        present). If the prefix is present, pass the value through self.func
        before returning, otherwise return the raw value.
        
    "
1754,"def not_enough_open_files() -> bool:
    
    try:
        import resource
    except ImportError:
        # resource limits is not a concept on all systems, notably Windows
        return False

    soft_limit, hard_limit = resource.getrlimit(resource.RLIMIT_NOFILE)
    return soft_limit < 512 or hard_limit < 512

","
    The current process does not currently allow enough open files for this test.
    You can increase the number of open files with `ulimit -n 512`.
    ",25,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def not_enough_open_files() -> bool:
    
    try:
        import resource
    except ImportError:
        # resource limits is not a concept on all systems, notably Windows
        return False

    soft_limit, hard_limit = resource.getrlimit(resource.RLIMIT_NOFILE)
    return soft_limit < 512 or hard_limit < 512


        ```
    ###Assistant : 
    The current process does not currently allow enough open files for this test.
    You can increase the number of open files with `ulimit -n 512`.
    
    "
1755,"def configure(self, request):
        
        # Save ordering preference
        if request.user.is_authenticated:
            table_name = self.__class__.__name__
            if self.prefixed_order_by_field in request.GET:
                # If an ordering has been specified as a query parameter, save it as the
                # user's preferred ordering for this table.
                ordering = request.GET.getlist(self.prefixed_order_by_field)
                request.user.config.set(f'tables.{table_name}.ordering', ordering, commit=True)
            elif ordering := request.user.config.get(f'tables.{table_name}.ordering'):
                # If no ordering has been specified, set the preferred ordering (if any).
                self.order_by = ordering

        # Paginate the table results
        paginate = {
            'paginator_class': EnhancedPaginator,
            'per_page': get_paginate_count(request)
        }
        tables.RequestConfig(request, paginate).configure(self)
","
        Configure the table for a specific request context. This performs pagination and records
        the user's preferred ordering logic.
        ",18,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def configure(self, request):
        
        # Save ordering preference
        if request.user.is_authenticated:
            table_name = self.__class__.__name__
            if self.prefixed_order_by_field in request.GET:
                # If an ordering has been specified as a query parameter, save it as the
                # user's preferred ordering for this table.
                ordering = request.GET.getlist(self.prefixed_order_by_field)
                request.user.config.set(f'tables.{table_name}.ordering', ordering, commit=True)
            elif ordering := request.user.config.get(f'tables.{table_name}.ordering'):
                # If no ordering has been specified, set the preferred ordering (if any).
                self.order_by = ordering

        # Paginate the table results
        paginate = {
            'paginator_class': EnhancedPaginator,
            'per_page': get_paginate_count(request)
        }
        tables.RequestConfig(request, paginate).configure(self)

        ```
    ###Assistant : 
        Configure the table for a specific request context. This performs pagination and records
        the user's preferred ordering logic.
        
    "
1756,"def _shard_arg(arg, devices, arg_indices):
  
  if isinstance(arg, ShardedDeviceArray) and arg_indices == arg.indices:
    # The shard_arg_handlers allow an extensible set of types to be sharded, but
    # inline handling for ShardedDeviceArray as a special case for performance
    # NOTE: we compare indices instead of sharding_spec because
    # pmap_benchmark.pmap_shard_args_benchmark indicates this is faster.
    return [
        buf if buf.device() == d else buf.copy_to_device(d)
        for d, buf in zip(devices, arg.device_buffers)
    ]
  else:
    arg = xla.canonicalize_dtype(arg)
    return shard_arg_handlers[type(arg)](arg, devices, arg_indices)


@profiler.annotate_function","Returns a list of size len(devices) containing per-device buffers.

  For the C++ pmap path, we fallback to Python (this function) to shard
  arguments that are not supported by the C++ `ShardArg`.

  Arrgs:
    arg: The Python argument.
    devices: The list of devices to shard over.
    arg_indices: A list of `len(devices)` indices to use to shard the argument.
  ",56,75,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _shard_arg(arg, devices, arg_indices):
  
  if isinstance(arg, ShardedDeviceArray) and arg_indices == arg.indices:
    # The shard_arg_handlers allow an extensible set of types to be sharded, but
    # inline handling for ShardedDeviceArray as a special case for performance
    # NOTE: we compare indices instead of sharding_spec because
    # pmap_benchmark.pmap_shard_args_benchmark indicates this is faster.
    return [
        buf if buf.device() == d else buf.copy_to_device(d)
        for d, buf in zip(devices, arg.device_buffers)
    ]
  else:
    arg = xla.canonicalize_dtype(arg)
    return shard_arg_handlers[type(arg)](arg, devices, arg_indices)


@profiler.annotate_function
        ```
    ###Assistant : Returns a list of size len(devices) containing per-device buffers.

  For the C++ pmap path, we fallback to Python (this function) to shard
  arguments that are not supported by the C++ `ShardArg`.

  Arrgs:
    arg: The Python argument.
    devices: The list of devices to shard over.
    arg_indices: A list of `len(devices)` indices to use to shard the argument.
  
    "
1757,"def test_rect(self):
        
        n3x3 = coord_net_spec(ks=3, stride=1, pad=0)
        n5x5 = coord_net_spec(ks=5, stride=2, pad=10)
        n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10])
        ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data)
        ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data)
        ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data)
        self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5)
        self.assertEquals(a_3x3, a_3x5[0])
        self.assertEquals(b_3x3, b_3x5[0])
        self.assertEquals(a_5x5, a_3x5[1])
        self.assertEquals(b_5x5, b_3x5[1])
","
        Anisotropic mapping is equivalent to its isotropic parts.
        ",8,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_rect(self):
        
        n3x3 = coord_net_spec(ks=3, stride=1, pad=0)
        n5x5 = coord_net_spec(ks=5, stride=2, pad=10)
        n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10])
        ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data)
        ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data)
        ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data)
        self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5)
        self.assertEquals(a_3x3, a_3x5[0])
        self.assertEquals(b_3x3, b_3x5[0])
        self.assertEquals(a_5x5, a_3x5[1])
        self.assertEquals(b_5x5, b_3x5[1])

        ```
    ###Assistant : 
        Anisotropic mapping is equivalent to its isotropic parts.
        
    "
1758,"def test_get_page_url_when_for_settings_fetched_via_for_site(self):
        
        self._create_importantpages_object()

        settings = ImportantPages.for_site(self.default_site)

        # Force site root paths query beforehand
        self.default_site.root_page._get_site_root_paths()

        for page_fk_field, expected_result in (
            (""sign_up_page"", ""http://localhost/""),
            (""general_terms_page"", ""http://localhost/""),
            (""privacy_policy_page"", ""http://other/""),
        ):
            with self.subTest(page_fk_field=page_fk_field):

                # only the first request for each URL will trigger queries.
                # 2 are triggered instead of 1 here, because tests use the
                # database cache backed, and the cache is queried each time
                # to fetch site root paths (because there's no 'request' to
                # store them on)

                with self.assertNumQueries(2):

                    self.assertEqual(
                        settings.get_page_url(page_fk_field), expected_result
                    )

                    # when called directly
                    self.assertEqual(
                        settings.get_page_url(page_fk_field), expected_result
                    )

                    # when called indirectly via shortcut
                    self.assertEqual(
                        getattr(settings.page_url, page_fk_field), expected_result
                    )
","ImportantPages.for_site() cannot make the settings object
        request-aware, so things are a little less efficient, and the
        URLs returned will not be site-relative",22,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_page_url_when_for_settings_fetched_via_for_site(self):
        
        self._create_importantpages_object()

        settings = ImportantPages.for_site(self.default_site)

        # Force site root paths query beforehand
        self.default_site.root_page._get_site_root_paths()

        for page_fk_field, expected_result in (
            (""sign_up_page"", ""http://localhost/""),
            (""general_terms_page"", ""http://localhost/""),
            (""privacy_policy_page"", ""http://other/""),
        ):
            with self.subTest(page_fk_field=page_fk_field):

                # only the first request for each URL will trigger queries.
                # 2 are triggered instead of 1 here, because tests use the
                # database cache backed, and the cache is queried each time
                # to fetch site root paths (because there's no 'request' to
                # store them on)

                with self.assertNumQueries(2):

                    self.assertEqual(
                        settings.get_page_url(page_fk_field), expected_result
                    )

                    # when called directly
                    self.assertEqual(
                        settings.get_page_url(page_fk_field), expected_result
                    )

                    # when called indirectly via shortcut
                    self.assertEqual(
                        getattr(settings.page_url, page_fk_field), expected_result
                    )

        ```
    ###Assistant : ImportantPages.for_site() cannot make the settings object
        request-aware, so things are a little less efficient, and the
        URLs returned will not be site-relative
    "
1759,"def test_send_receipts_with_backoff(self):
        
        mock_send_transaction = (
            self.hs.get_federation_transport_client().send_transaction
        )
        mock_send_transaction.return_value = make_awaitable({})

        sender = self.hs.get_federation_sender()
        receipt = ReadReceipt(
            ""room_id"", ""m.read"", ""user_id"", [""event_id""], {""ts"": 1234}
        )
        self.successResultOf(defer.ensureDeferred(sender.send_read_receipt(receipt)))

        self.pump()

        # expect a call to send_transaction
        mock_send_transaction.assert_called_once()
        json_cb = mock_send_transaction.call_args[0][1]
        data = json_cb()
        self.assertEqual(
            data[""edus""],
            [
                {
                    ""edu_type"": EduTypes.RECEIPT,
                    ""content"": {
                        ""room_id"": {
                            ""m.read"": {
                                ""user_id"": {
                                    ""event_ids"": [""event_id""],
                                    ""data"": {""ts"": 1234},
                                }
                            }
                        }
                    },
                }
            ],
        )
        mock_send_transaction.reset_mock()

        # send the second RR
        receipt = ReadReceipt(
            ""room_id"", ""m.read"", ""user_id"", [""other_id""], {""ts"": 1234}
        )
        self.successResultOf(defer.ensureDeferred(sender.send_read_receipt(receipt)))
        self.pump()
        mock_send_transaction.assert_not_called()

        self.reactor.advance(19)
        mock_send_transaction.assert_not_called()

        self.reactor.advance(10)
        mock_send_transaction.assert_called_once()
        json_cb = mock_send_transaction.call_args[0][1]
        data = json_cb()
        self.assertEqual(
            data[""edus""],
            [
                {
                    ""edu_type"": EduTypes.RECEIPT,
                    ""content"": {
                        ""room_id"": {
                            ""m.read"": {
                                ""user_id"": {
                                    ""event_ids"": [""other_id""],
                                    ""data"": {""ts"": 1234},
                                }
                            }
                        }
                    },
                }
            ],
        )

","Send two receipts in quick succession; the second should be flushed, but
        only after 20ms",15,119,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_send_receipts_with_backoff(self):
        
        mock_send_transaction = (
            self.hs.get_federation_transport_client().send_transaction
        )
        mock_send_transaction.return_value = make_awaitable({})

        sender = self.hs.get_federation_sender()
        receipt = ReadReceipt(
            ""room_id"", ""m.read"", ""user_id"", [""event_id""], {""ts"": 1234}
        )
        self.successResultOf(defer.ensureDeferred(sender.send_read_receipt(receipt)))

        self.pump()

        # expect a call to send_transaction
        mock_send_transaction.assert_called_once()
        json_cb = mock_send_transaction.call_args[0][1]
        data = json_cb()
        self.assertEqual(
            data[""edus""],
            [
                {
                    ""edu_type"": EduTypes.RECEIPT,
                    ""content"": {
                        ""room_id"": {
                            ""m.read"": {
                                ""user_id"": {
                                    ""event_ids"": [""event_id""],
                                    ""data"": {""ts"": 1234},
                                }
                            }
                        }
                    },
                }
            ],
        )
        mock_send_transaction.reset_mock()

        # send the second RR
        receipt = ReadReceipt(
            ""room_id"", ""m.read"", ""user_id"", [""other_id""], {""ts"": 1234}
        )
        self.successResultOf(defer.ensureDeferred(sender.send_read_receipt(receipt)))
        self.pump()
        mock_send_transaction.assert_not_called()

        self.reactor.advance(19)
        mock_send_transaction.assert_not_called()

        self.reactor.advance(10)
        mock_send_transaction.assert_called_once()
        json_cb = mock_send_transaction.call_args[0][1]
        data = json_cb()
        self.assertEqual(
            data[""edus""],
            [
                {
                    ""edu_type"": EduTypes.RECEIPT,
                    ""content"": {
                        ""room_id"": {
                            ""m.read"": {
                                ""user_id"": {
                                    ""event_ids"": [""other_id""],
                                    ""data"": {""ts"": 1234},
                                }
                            }
                        }
                    },
                }
            ],
        )


        ```
    ###Assistant : Send two receipts in quick succession; the second should be flushed, but
        only after 20ms
    "
1760,"def process_frame(self, processable_frame, processing_task):
        
        frame = processable_frame.frame
        token = None

        cache = self.cache
        sourcemaps = self.sourcemaps
        all_errors = []
        sourcemap_applied = False

        # can't demangle if there's no filename or line number present
        if not frame.get(""abs_path"") or not frame.get(""lineno""):
            return

        # also can't demangle node's internal modules
        # therefore we only process user-land frames (starting with /)
        # or those created by bundle/webpack internals
        if self.data.get(""platform"") == ""node"" and not frame.get(""abs_path"").startswith(
            (""/"", ""app:"", ""webpack:"")
        ):
            return

        errors = cache.get_errors(frame[""abs_path""])
        if errors:
            all_errors.extend(errors)

        # This might fail but that's okay, we try with a different path a
        # bit later down the road.
        source = self.get_sourceview(frame[""abs_path""])

        in_app = None
        new_frame = dict(frame)
        raw_frame = dict(frame)

        sourcemap_url, sourcemap_view = sourcemaps.get_link(frame[""abs_path""])
        self.sourcemaps_touched.add(sourcemap_url)
        if sourcemap_view and frame.get(""colno"") is None:
            all_errors.append(
                {""type"": EventError.JS_NO_COLUMN, ""url"": http.expose_url(frame[""abs_path""])}
            )
        elif sourcemap_view:
            if is_data_uri(sourcemap_url):
                sourcemap_label = frame[""abs_path""]
            else:
                sourcemap_label = sourcemap_url

            sourcemap_label = http.expose_url(sourcemap_label)

            if frame.get(""function""):
                minified_function_name = frame[""function""]
                minified_source = self.get_sourceview(frame[""abs_path""])
            else:
                minified_function_name = minified_source = None

            try:
                # Errors are 1-indexed in the frames, so we need to -1 to get
                # zero-indexed value from tokens.
                assert frame[""lineno""] > 0, ""line numbers are 1-indexed""
                token = sourcemap_view.lookup(
                    frame[""lineno""] - 1, frame[""colno""] - 1, minified_function_name, minified_source
                )
            except Exception:
                token = None
                all_errors.append(
                    {
                        ""type"": EventError.JS_INVALID_SOURCEMAP_LOCATION,
                        ""column"": frame.get(""colno""),
                        ""row"": frame.get(""lineno""),
                        ""source"": frame[""abs_path""],
                        ""sourcemap"": sourcemap_label,
                    }
                )

            # persist the token so that we can find it later
            processable_frame.data[""token""] = token

            # Store original data in annotation
            new_frame[""data""] = dict(frame.get(""data"") or {}, sourcemap=sourcemap_label)

            sourcemap_applied = True

            if token is not None:
                abs_path = non_standard_url_join(sourcemap_url, token.src)

                logger.debug(
                    ""Mapping compressed source %r to mapping in %r"", frame[""abs_path""], abs_path
                )
                source = self.get_sourceview(abs_path)

                if source is None:
                    errors = cache.get_errors(abs_path)
                    if errors:
                        all_errors.extend(errors)
                    else:
                        all_errors.append(
                            {""type"": EventError.JS_MISSING_SOURCE, ""url"": http.expose_url(abs_path)}
                        )

                # the tokens are zero indexed, so offset correctly
                new_frame[""lineno""] = token.src_line + 1
                new_frame[""colno""] = token.src_col + 1

                # Try to use the function name we got from symbolic
                original_function_name = token.function_name

                # In the ideal case we can use the function name from the
                # frame and the location to resolve the original name
                # through the heuristics in our sourcemap library.
                if original_function_name is None:
                    last_token = None

                    # Find the previous token for function name handling as a
                    # fallback.
                    if (
                        processable_frame.previous_frame
                        and processable_frame.previous_frame.processor is self
                    ):
                        last_token = processable_frame.previous_frame.data.get(""token"")
                        if last_token:
                            original_function_name = last_token.name

                if original_function_name is not None:
                    new_frame[""function""] = original_function_name

                filename = token.src
                # special case webpack support
                # abs_path will always be the full path with webpack:/// prefix.
                # filename will be relative to that
                if abs_path.startswith(""webpack:""):
                    filename = abs_path
                    # webpack seems to use ~ to imply ""relative to resolver root""
                    # which is generally seen for third party deps
                    # (i.e. node_modules)
                    if ""/~/"" in filename:
                        filename = ""~/"" + abs_path.split(""/~/"", 1)[-1]
                    elif WEBPACK_NAMESPACE_RE.match(filename):
                        filename = re.sub(WEBPACK_NAMESPACE_RE, ""./"", abs_path)
                    else:
                        filename = filename.split(""webpack:///"", 1)[-1]

                    # As noted above:
                    # * [js/node] '~/' means they're coming from node_modules, so these are not app dependencies
                    # * [node] sames goes for `./node_modules/` and '../node_modules/', which is used when bundling node apps
                    # * [node] and webpack, which includes it's own code to bootstrap all modules and its internals
                    #   eg. webpack:///webpack/bootstrap, webpack:///external
                    if (
                        filename.startswith(""~/"")
                        or ""/node_modules/"" in filename
                        or not filename.startswith(""./"")
                    ):
                        in_app = False
                    # And conversely, local dependencies start with './'
                    elif filename.startswith(""./""):
                        in_app = True
                    # We want to explicitly generate a webpack module name
                    new_frame[""module""] = generate_module(filename)

                # while you could technically use a subpath of 'node_modules' for your libraries,
                # it would be an extremely complicated decision and we've not seen anyone do it
                # so instead we assume if node_modules is in the path its part of the vendored code
                elif ""/node_modules/"" in abs_path:
                    in_app = False

                if abs_path.startswith(""app:""):
                    if filename and NODE_MODULES_RE.search(filename):
                        in_app = False
                    else:
                        in_app = True

                new_frame[""abs_path""] = abs_path
                new_frame[""filename""] = filename
                if not frame.get(""module"") and abs_path.startswith(
                    (""http:"", ""https:"", ""webpack:"", ""app:"")
                ):
                    new_frame[""module""] = generate_module(abs_path)

        elif sourcemap_url:
            new_frame[""data""] = dict(
                new_frame.get(""data"") or {}, sourcemap=http.expose_url(sourcemap_url)
            )

        # TODO: theoretically a minified source could point to
        # another mapped, minified source
        changed_frame = self.expand_frame(new_frame, source=source)

        # If we did not manage to match but we do have a line or column
        # we want to report an error here.
        if not new_frame.get(""context_line"") and source and new_frame.get(""colno"") is not None:
            all_errors.append(
                {
                    ""type"": EventError.JS_INVALID_SOURCEMAP_LOCATION,
                    ""column"": new_frame[""colno""],
                    ""row"": new_frame[""lineno""],
                    ""source"": new_frame[""abs_path""],
                }
            )

        changed_raw = sourcemap_applied and self.expand_frame(raw_frame)

        if sourcemap_applied or all_errors or changed_frame or changed_raw:
            # In case we are done processing, we iterate over all errors that we got
            # and we filter out all `JS_MISSING_SOURCE` errors since we consider if we have
            # a `context_line` we have a symbolicated frame and we don't need to show the error
            has_context_line = bool(new_frame.get(""context_line""))
            if has_context_line:
                all_errors[:] = [
                    x for x in all_errors if x.get(""type"") is not EventError.JS_MISSING_SOURCE
                ]

            if in_app is not None:
                new_frame[""in_app""] = in_app
                raw_frame[""in_app""] = in_app

            # Run new processor only for frames that were actually modified in any way.
            if should_run_smcache(self) and new_frame != raw_frame:
                smcache_rv = self.smcache_processor.process_frame(processable_frame, None)
                set_path(new_frame, ""data"", ""smcache_frame"", value=smcache_rv[0][0])

            new_frames = [new_frame]
            raw_frames = [raw_frame] if changed_raw else None
            return new_frames, raw_frames, all_errors
","
        Attempt to demangle the given frame.
        ",6,857,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def process_frame(self, processable_frame, processing_task):
        
        frame = processable_frame.frame
        token = None

        cache = self.cache
        sourcemaps = self.sourcemaps
        all_errors = []
        sourcemap_applied = False

        # can't demangle if there's no filename or line number present
        if not frame.get(""abs_path"") or not frame.get(""lineno""):
            return

        # also can't demangle node's internal modules
        # therefore we only process user-land frames (starting with /)
        # or those created by bundle/webpack internals
        if self.data.get(""platform"") == ""node"" and not frame.get(""abs_path"").startswith(
            (""/"", ""app:"", ""webpack:"")
        ):
            return

        errors = cache.get_errors(frame[""abs_path""])
        if errors:
            all_errors.extend(errors)

        # This might fail but that's okay, we try with a different path a
        # bit later down the road.
        source = self.get_sourceview(frame[""abs_path""])

        in_app = None
        new_frame = dict(frame)
        raw_frame = dict(frame)

        sourcemap_url, sourcemap_view = sourcemaps.get_link(frame[""abs_path""])
        self.sourcemaps_touched.add(sourcemap_url)
        if sourcemap_view and frame.get(""colno"") is None:
            all_errors.append(
                {""type"": EventError.JS_NO_COLUMN, ""url"": http.expose_url(frame[""abs_path""])}
            )
        elif sourcemap_view:
            if is_data_uri(sourcemap_url):
                sourcemap_label = frame[""abs_path""]
            else:
                sourcemap_label = sourcemap_url

            sourcemap_label = http.expose_url(sourcemap_label)

            if frame.get(""function""):
                minified_function_name = frame[""function""]
                minified_source = self.get_sourceview(frame[""abs_path""])
            else:
                minified_function_name = minified_source = None

            try:
                # Errors are 1-indexed in the frames, so we need to -1 to get
                # zero-indexed value from tokens.
                assert frame[""lineno""] > 0, ""line numbers are 1-indexed""
                token = sourcemap_view.lookup(
                    frame[""lineno""] - 1, frame[""colno""] - 1, minified_function_name, minified_source
                )
            except Exception:
                token = None
                all_errors.append(
                    {
                        ""type"": EventError.JS_INVALID_SOURCEMAP_LOCATION,
                        ""column"": frame.get(""colno""),
                        ""row"": frame.get(""lineno""),
                        ""source"": frame[""abs_path""],
                        ""sourcemap"": sourcemap_label,
                    }
                )

            # persist the token so that we can find it later
            processable_frame.data[""token""] = token

            # Store original data in annotation
            new_frame[""data""] = dict(frame.get(""data"") or {}, sourcemap=sourcemap_label)

            sourcemap_applied = True

            if token is not None:
                abs_path = non_standard_url_join(sourcemap_url, token.src)

                logger.debug(
                    ""Mapping compressed source %r to mapping in %r"", frame[""abs_path""], abs_path
                )
                source = self.get_sourceview(abs_path)

                if source is None:
                    errors = cache.get_errors(abs_path)
                    if errors:
                        all_errors.extend(errors)
                    else:
                        all_errors.append(
                            {""type"": EventError.JS_MISSING_SOURCE, ""url"": http.expose_url(abs_path)}
                        )

                # the tokens are zero indexed, so offset correctly
                new_frame[""lineno""] = token.src_line + 1
                new_frame[""colno""] = token.src_col + 1

                # Try to use the function name we got from symbolic
                original_function_name = token.function_name

                # In the ideal case we can use the function name from the
                # frame and the location to resolve the original name
                # through the heuristics in our sourcemap library.
                if original_function_name is None:
                    last_token = None

                    # Find the previous token for function name handling as a
                    # fallback.
                    if (
                        processable_frame.previous_frame
                        and processable_frame.previous_frame.processor is self
                    ):
                        last_token = processable_frame.previous_frame.data.get(""token"")
                        if last_token:
                            original_function_name = last_token.name

                if original_function_name is not None:
                    new_frame[""function""] = original_function_name

                filename = token.src
                # special case webpack support
                # abs_path will always be the full path with webpack:/// prefix.
                # filename will be relative to that
                if abs_path.startswith(""webpack:""):
                    filename = abs_path
                    # webpack seems to use ~ to imply ""relative to resolver root""
                    # which is generally seen for third party deps
                    # (i.e. node_modules)
                    if ""/~/"" in filename:
                        filename = ""~/"" + abs_path.split(""/~/"", 1)[-1]
                    elif WEBPACK_NAMESPACE_RE.match(filename):
                        filename = re.sub(WEBPACK_NAMESPACE_RE, ""./"", abs_path)
                    else:
                        filename = filename.split(""webpack:///"", 1)[-1]

                    # As noted above:
                    # * [js/node] '~/' means they're coming from node_modules, so these are not app dependencies
                    # * [node] sames goes for `./node_modules/` and '../node_modules/', which is used when bundling node apps
                    # * [node] and webpack, which includes it's own code to bootstrap all modules and its internals
                    #   eg. webpack:///webpack/bootstrap, webpack:///external
                    if (
                        filename.startswith(""~/"")
                        or ""/node_modules/"" in filename
                        or not filename.startswith(""./"")
                    ):
                        in_app = False
                    # And conversely, local dependencies start with './'
                    elif filename.startswith(""./""):
                        in_app = True
                    # We want to explicitly generate a webpack module name
                    new_frame[""module""] = generate_module(filename)

                # while you could technically use a subpath of 'node_modules' for your libraries,
                # it would be an extremely complicated decision and we've not seen anyone do it
                # so instead we assume if node_modules is in the path its part of the vendored code
                elif ""/node_modules/"" in abs_path:
                    in_app = False

                if abs_path.startswith(""app:""):
                    if filename and NODE_MODULES_RE.search(filename):
                        in_app = False
                    else:
                        in_app = True

                new_frame[""abs_path""] = abs_path
                new_frame[""filename""] = filename
                if not frame.get(""module"") and abs_path.startswith(
                    (""http:"", ""https:"", ""webpack:"", ""app:"")
                ):
                    new_frame[""module""] = generate_module(abs_path)

        elif sourcemap_url:
            new_frame[""data""] = dict(
                new_frame.get(""data"") or {}, sourcemap=http.expose_url(sourcemap_url)
            )

        # TODO: theoretically a minified source could point to
        # another mapped, minified source
        changed_frame = self.expand_frame(new_frame, source=source)

        # If we did not manage to match but we do have a line or column
        # we want to report an error here.
        if not new_frame.get(""context_line"") and source and new_frame.get(""colno"") is not None:
            all_errors.append(
                {
                    ""type"": EventError.JS_INVALID_SOURCEMAP_LOCATION,
                    ""column"": new_frame[""colno""],
                    ""row"": new_frame[""lineno""],
                    ""source"": new_frame[""abs_path""],
                }
            )

        changed_raw = sourcemap_applied and self.expand_frame(raw_frame)

        if sourcemap_applied or all_errors or changed_frame or changed_raw:
            # In case we are done processing, we iterate over all errors that we got
            # and we filter out all `JS_MISSING_SOURCE` errors since we consider if we have
            # a `context_line` we have a symbolicated frame and we don't need to show the error
            has_context_line = bool(new_frame.get(""context_line""))
            if has_context_line:
                all_errors[:] = [
                    x for x in all_errors if x.get(""type"") is not EventError.JS_MISSING_SOURCE
                ]

            if in_app is not None:
                new_frame[""in_app""] = in_app
                raw_frame[""in_app""] = in_app

            # Run new processor only for frames that were actually modified in any way.
            if should_run_smcache(self) and new_frame != raw_frame:
                smcache_rv = self.smcache_processor.process_frame(processable_frame, None)
                set_path(new_frame, ""data"", ""smcache_frame"", value=smcache_rv[0][0])

            new_frames = [new_frame]
            raw_frames = [raw_frame] if changed_raw else None
            return new_frames, raw_frames, all_errors

        ```
    ###Assistant : 
        Attempt to demangle the given frame.
        
    "
1761,"def validate_csv(headers, fields, required_fields):
    
    # Validate provided column headers
    is_update = False
    for field, to_field in headers.items():
        if field == ""id"":
            is_update = True
            continue
        if field not in fields:
            raise forms.ValidationError(f'Unexpected column header ""{field}"" found.')
        if to_field and not hasattr(fields[field], 'to_field_name'):
            raise forms.ValidationError(f'Column ""{field}"" is not a related object; cannot use dots')
        if to_field and not hasattr(fields[field].queryset.model, to_field):
            raise forms.ValidationError(f'Invalid related object attribute for column ""{field}"": {to_field}')

    # Validate required fields (if not an update)
    if not is_update:
        for f in required_fields:
            if f not in headers:
                raise forms.ValidationError(f'Required column header ""{f}"" not found.')
","
    Validate that parsed csv data conforms to the object's available fields. Raise validation errors
    if parsed csv data contains invalid headers or does not contain required headers.
    ",27,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_csv(headers, fields, required_fields):
    
    # Validate provided column headers
    is_update = False
    for field, to_field in headers.items():
        if field == ""id"":
            is_update = True
            continue
        if field not in fields:
            raise forms.ValidationError(f'Unexpected column header ""{field}"" found.')
        if to_field and not hasattr(fields[field], 'to_field_name'):
            raise forms.ValidationError(f'Column ""{field}"" is not a related object; cannot use dots')
        if to_field and not hasattr(fields[field].queryset.model, to_field):
            raise forms.ValidationError(f'Invalid related object attribute for column ""{field}"": {to_field}')

    # Validate required fields (if not an update)
    if not is_update:
        for f in required_fields:
            if f not in headers:
                raise forms.ValidationError(f'Required column header ""{f}"" not found.')

        ```
    ###Assistant : 
    Validate that parsed csv data conforms to the object's available fields. Raise validation errors
    if parsed csv data contains invalid headers or does not contain required headers.
    
    "
1762,"def bernoulli_poly(n, x=None, polys=False):
    
    return appell_poly(n, [[1], [1, QQ(-1,2)]], QQ(1,2),
            lambda p, i: p * QQ(1<<(i-1), 1-(1<<i)), QQ, x, polys)


@public","Generates the Bernoulli polynomial of degree `n` in `x`.

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    ",35,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bernoulli_poly(n, x=None, polys=False):
    
    return appell_poly(n, [[1], [1, QQ(-1,2)]], QQ(1,2),
            lambda p, i: p * QQ(1<<(i-1), 1-(1<<i)), QQ, x, polys)


@public
        ```
    ###Assistant : Generates the Bernoulli polynomial of degree `n` in `x`.

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    
    "
1763,"def test_calculate_max_drawdown_abs(values, relative, result, result_rel):
    

    dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
    df = DataFrame(zip(values, dates), columns=['profit_abs', 'open_date'])
    # sort by profit and reset index
    df = df.sort_values('profit_abs').reset_index(drop=True)
    df1 = df.copy()
    drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
        df, date_col='open_date', starting_balance=1000, relative=relative)
    # Ensure df has not been altered.
    assert df.equals(df1)

    assert isinstance(drawdown, float)
    assert isinstance(drawdown_rel, float)
    # High must be before low
    assert hdate < ldate
    # High value must be higher than low value
    assert hval > lval
    assert drawdown == result
    assert pytest.approx(drawdown_rel) == result_rel
","
    Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
    [1000, 500,  1000, 11000, 10000] # absolute results
    [1000, 50%,  0%,   0%,       ~9%]   # Relative drawdowns
    ",21,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_calculate_max_drawdown_abs(values, relative, result, result_rel):
    

    dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
    df = DataFrame(zip(values, dates), columns=['profit_abs', 'open_date'])
    # sort by profit and reset index
    df = df.sort_values('profit_abs').reset_index(drop=True)
    df1 = df.copy()
    drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
        df, date_col='open_date', starting_balance=1000, relative=relative)
    # Ensure df has not been altered.
    assert df.equals(df1)

    assert isinstance(drawdown, float)
    assert isinstance(drawdown_rel, float)
    # High must be before low
    assert hdate < ldate
    # High value must be higher than low value
    assert hval > lval
    assert drawdown == result
    assert pytest.approx(drawdown_rel) == result_rel

        ```
    ###Assistant : 
    Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
    [1000, 500,  1000, 11000, 10000] # absolute results
    [1000, 50%,  0%,   0%,       ~9%]   # Relative drawdowns
    
    "
1764,"def testOnCheckpointUnavailableAttribute(self):
        
        checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num=1)

        no_attr_checkpoint = Checkpoint(Checkpoint.PERSISTENT, 0, {})
        with patch.object(logger, ""error"") as log_error_mock:
            checkpoint_manager.on_checkpoint(no_attr_checkpoint)
            log_error_mock.assert_called_once()
            # The newest checkpoint should still be set despite this error.
            self.assertEqual(
                checkpoint_manager.newest_persistent_checkpoint, no_attr_checkpoint
            )
","
        Tests that an error is logged when the associated result of the
        checkpoint has no checkpoint score attribute.
        ",18,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def testOnCheckpointUnavailableAttribute(self):
        
        checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num=1)

        no_attr_checkpoint = Checkpoint(Checkpoint.PERSISTENT, 0, {})
        with patch.object(logger, ""error"") as log_error_mock:
            checkpoint_manager.on_checkpoint(no_attr_checkpoint)
            log_error_mock.assert_called_once()
            # The newest checkpoint should still be set despite this error.
            self.assertEqual(
                checkpoint_manager.newest_persistent_checkpoint, no_attr_checkpoint
            )

        ```
    ###Assistant : 
        Tests that an error is logged when the associated result of the
        checkpoint has no checkpoint score attribute.
        
    "
1765,"def subscription_app_status_changed_webhook(subscription_webhook):
    return subscription_webhook(
        APP_STATUS_CHANGED_SUBSCRIPTION_QUERY,
        WebhookEventAsyncType.APP_STATUS_CHANGED,
    )


CATEGORY_CREATED_SUBSCRIPTION_QUERY = 


@pytest.fixture","
    subscription{
      event{
        ...on CategoryCreated{
          category{
            id
          }
        }
      }
    }
",10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def subscription_app_status_changed_webhook(subscription_webhook):
    return subscription_webhook(
        APP_STATUS_CHANGED_SUBSCRIPTION_QUERY,
        WebhookEventAsyncType.APP_STATUS_CHANGED,
    )


CATEGORY_CREATED_SUBSCRIPTION_QUERY = 


@pytest.fixture
        ```
    ###Assistant : 
    subscription{
      event{
        ...on CategoryCreated{
          category{
            id
          }
        }
      }
    }

    "
1766,"def binary_op(self, op, right_frame, join_type=""outer""):
        
        left_parts, right_parts, joined_index, row_lengths = self._copartition(
            0, right_frame, join_type, sort=True
        )
        # unwrap list returned by `copartition`.
        right_parts = right_parts[0]
        new_frame = self._partition_mgr_cls.binary_operation(
            1, left_parts, lambda l, r: op(l, r), right_parts
        )
        new_columns = self.columns.join(right_frame.columns, how=join_type)
        return self.__constructor__(
            new_frame,
            joined_index,
            new_columns,
            row_lengths,
            column_widths=self._column_widths_cache,
        )
","
        Perform an operation that requires joining with another Modin DataFrame.

        Parameters
        ----------
        op : callable
            Function to apply after the join.
        right_frame : PandasDataframe
            Modin DataFrame to join with.
        join_type : str, default: ""outer""
            Type of join to apply.

        Returns
        -------
        PandasDataframe
            New Modin DataFrame.
        ",45,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def binary_op(self, op, right_frame, join_type=""outer""):
        
        left_parts, right_parts, joined_index, row_lengths = self._copartition(
            0, right_frame, join_type, sort=True
        )
        # unwrap list returned by `copartition`.
        right_parts = right_parts[0]
        new_frame = self._partition_mgr_cls.binary_operation(
            1, left_parts, lambda l, r: op(l, r), right_parts
        )
        new_columns = self.columns.join(right_frame.columns, how=join_type)
        return self.__constructor__(
            new_frame,
            joined_index,
            new_columns,
            row_lengths,
            column_widths=self._column_widths_cache,
        )

        ```
    ###Assistant : 
        Perform an operation that requires joining with another Modin DataFrame.

        Parameters
        ----------
        op : callable
            Function to apply after the join.
        right_frame : PandasDataframe
            Modin DataFrame to join with.
        join_type : str, default: ""outer""
            Type of join to apply.

        Returns
        -------
        PandasDataframe
            New Modin DataFrame.
        
    "
1767,"def rc_file(fname, *, use_default_template=True):
    
    # Deprecation warnings were already handled in rc_params_from_file, no need
    # to reemit them here.
    with _api.suppress_matplotlib_deprecation_warning():
        from .style.core import STYLE_BLACKLIST
        rc_from_file = rc_params_from_file(
            fname, use_default_template=use_default_template)
        rcParams.update({k: rc_from_file[k] for k in rc_from_file
                         if k not in STYLE_BLACKLIST})


@contextlib.contextmanager","
    Update `.rcParams` from file.

    Style-blacklisted `.rcParams` (defined in
    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.

    Parameters
    ----------
    fname : str or path-like
        A file with Matplotlib rc settings.

    use_default_template : bool
        If True, initialize with default parameters before updating with those
        in the given file. If False, the current configuration persists
        and only the parameters specified in the file are updated.
    ",58,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rc_file(fname, *, use_default_template=True):
    
    # Deprecation warnings were already handled in rc_params_from_file, no need
    # to reemit them here.
    with _api.suppress_matplotlib_deprecation_warning():
        from .style.core import STYLE_BLACKLIST
        rc_from_file = rc_params_from_file(
            fname, use_default_template=use_default_template)
        rcParams.update({k: rc_from_file[k] for k in rc_from_file
                         if k not in STYLE_BLACKLIST})


@contextlib.contextmanager
        ```
    ###Assistant : 
    Update `.rcParams` from file.

    Style-blacklisted `.rcParams` (defined in
    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.

    Parameters
    ----------
    fname : str or path-like
        A file with Matplotlib rc settings.

    use_default_template : bool
        If True, initialize with default parameters before updating with those
        in the given file. If False, the current configuration persists
        and only the parameters specified in the file are updated.
    
    "
1768,"def create_gloo_context(rank, world_size):
    
    context = pygloo.rendezvous.Context(rank, world_size)
    return context

","Create a GLOO context using GLOO APIs.

    Args:
        rank: the rank of this process.
        world_size: the number of processes of this collective group.

    Returns:
        context (pygloo.Context): a GLOO context.
    ",29,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_gloo_context(rank, world_size):
    
    context = pygloo.rendezvous.Context(rank, world_size)
    return context


        ```
    ###Assistant : Create a GLOO context using GLOO APIs.

    Args:
        rank: the rank of this process.
        world_size: the number of processes of this collective group.

    Returns:
        context (pygloo.Context): a GLOO context.
    
    "
1769,"def require_bitsandbytes(test_case):
    
    if not is_bitsandbytes_available():
        return unittest.skip(""test requires bnb"")(test_case)
    else:
        return test_case

","
    Decorator for bits and bytes (bnb) dependency
    ",7,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def require_bitsandbytes(test_case):
    
    if not is_bitsandbytes_available():
        return unittest.skip(""test requires bnb"")(test_case)
    else:
        return test_case


        ```
    ###Assistant : 
    Decorator for bits and bytes (bnb) dependency
    
    "
1770,"def copy_func(f) -> Callable:
    
    g = types.FunctionType(
        f.__code__,
        f.__globals__,
        name=f.__name__,
        argdefs=f.__defaults__,
        closure=f.__closure__,
    )
    g = functools.update_wrapper(g, f)
    g.__kwdefaults__ = f.__kwdefaults__
    return g

","Copies the contents and attributes of the entered function. Based on https://stackoverflow.com/a/13503277
    Parameters
    ----------
    f: Callable
        Function to be copied
    Returns
    -------
    g: Callable
        New function
    ",26,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copy_func(f) -> Callable:
    
    g = types.FunctionType(
        f.__code__,
        f.__globals__,
        name=f.__name__,
        argdefs=f.__defaults__,
        closure=f.__closure__,
    )
    g = functools.update_wrapper(g, f)
    g.__kwdefaults__ = f.__kwdefaults__
    return g


        ```
    ###Assistant : Copies the contents and attributes of the entered function. Based on https://stackoverflow.com/a/13503277
    Parameters
    ----------
    f: Callable
        Function to be copied
    Returns
    -------
    g: Callable
        New function
    
    "
1771,"def evaluate(self, expr, context):
        
        if isinstance(expr, string_types):
            if expr[0] in '\'""':
                result = expr[1:-1]
            else:
                if expr not in context:
                    raise SyntaxError('unknown variable: %s' % expr)
                result = context[expr]
        else:
            assert isinstance(expr, dict)
            op = expr['op']
            if op not in self.operations:
                raise NotImplementedError('op not implemented: %s' % op)
            elhs = expr['lhs']
            erhs = expr['rhs']
            if _is_literal(expr['lhs']) and _is_literal(expr['rhs']):
                raise SyntaxError('invalid comparison: %s %s %s' % (elhs, op, erhs))

            lhs = self.evaluate(elhs, context)
            rhs = self.evaluate(erhs, context)
            if ((elhs == 'python_version' or erhs == 'python_version') and
                op in ('<', '<=', '>', '>=', '===', '==', '!=', '~=')):
                lhs = NV(lhs)
                rhs = NV(rhs)
            elif elhs == 'python_version' and op in ('in', 'not in'):
                lhs = NV(lhs)
                rhs = _get_versions(rhs)
            result = self.operations[op](lhs, rhs)
        return result
","
        Evaluate a marker expression returned by the :func:`parse_requirement`
        function in the specified context.
        ",13,123,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def evaluate(self, expr, context):
        
        if isinstance(expr, string_types):
            if expr[0] in '\'""':
                result = expr[1:-1]
            else:
                if expr not in context:
                    raise SyntaxError('unknown variable: %s' % expr)
                result = context[expr]
        else:
            assert isinstance(expr, dict)
            op = expr['op']
            if op not in self.operations:
                raise NotImplementedError('op not implemented: %s' % op)
            elhs = expr['lhs']
            erhs = expr['rhs']
            if _is_literal(expr['lhs']) and _is_literal(expr['rhs']):
                raise SyntaxError('invalid comparison: %s %s %s' % (elhs, op, erhs))

            lhs = self.evaluate(elhs, context)
            rhs = self.evaluate(erhs, context)
            if ((elhs == 'python_version' or erhs == 'python_version') and
                op in ('<', '<=', '>', '>=', '===', '==', '!=', '~=')):
                lhs = NV(lhs)
                rhs = NV(rhs)
            elif elhs == 'python_version' and op in ('in', 'not in'):
                lhs = NV(lhs)
                rhs = _get_versions(rhs)
            result = self.operations[op](lhs, rhs)
        return result

        ```
    ###Assistant : 
        Evaluate a marker expression returned by the :func:`parse_requirement`
        function in the specified context.
        
    "
1772,"def track_tf_optimizer(tf_optimizer):
    
    if tf.executing_eagerly():
        return
    optimizers = _GRAPH_TF_OPTIMIZERS[None]
    optimizers.add(tf_optimizer)


@keras_export(""keras.__internal__.backend.track_variable"", v1=[])",Tracks the given TF optimizer for initialization of its variables.,10,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def track_tf_optimizer(tf_optimizer):
    
    if tf.executing_eagerly():
        return
    optimizers = _GRAPH_TF_OPTIMIZERS[None]
    optimizers.add(tf_optimizer)


@keras_export(""keras.__internal__.backend.track_variable"", v1=[])
        ```
    ###Assistant : Tracks the given TF optimizer for initialization of its variables.
    "
1773,"def _galois_group_degree_5(T, max_tries=30, randomize=False):
    r
    from sympy.combinatorics.permutations import Permutation
    from sympy.combinatorics.named_groups import (
        CyclicGroup, DihedralGroup, AlternatingGroup, SymmetricGroup
    )

    # The ideas here are all the same as in the degree-4 method.
    # The specific resolvents we use, and how we interpret the results, are
    # adapted to the degree-5 case.

    X = symbols('X0 X1 X2 X3 X4')
    # For the first resolvent, we have G = S5,
    # and stabilizer H = M20 = < (01234), (1234) >.
    F1 = (X[0]**2*(X[1]*X[4] + X[2]*X[3])
        + X[1]**2*(X[2]*X[0] + X[3]*X[4])
        + X[2]**2*(X[3]*X[1] + X[4]*X[0])
        + X[3]**2*(X[4]*X[2] + X[0]*X[1])
        + X[4]**2*(X[0]*X[3] + X[1]*X[2]))
    s1 = [
        Permutation(4),
        Permutation(4)(0, 1),
        Permutation(4)(0, 2),
        Permutation(4)(0, 3),
        Permutation(4)(0, 4),
        Permutation(4)(1, 4)
    ]
    R1 = Resolvent(F1, X, s1)

    # For the second resolvent, we'll have G = D5, H = C5.
    F2_pre = X[0]*X[1]**2 + X[1]*X[2]**2 + X[2]*X[3]**2 + X[3]*X[4]**2 + X[4]*X[0]**2
    s2_pre = [
        Permutation(4),
        Permutation(4)(0, 1)(2, 4)
    ]

    history = set()
    for i in range(max_tries):
        if i > 0:
            _, T = tschirnhausen_transformation(T, max_tries=max_tries, history=history, fixed_order=not randomize)

        R_dup, _, i0 = R1.eval_for_poly(T, find_integer_root=True)
        if not dup_sqf_p(R_dup, ZZ):
            continue

        sq_disc = has_square_disc(T)

        if i0 is None:
            return (AlternatingGroup(5), True) if sq_disc else (SymmetricGroup(5), False)

        if not sq_disc:
            return (M20(), False)

        sigma = s1[i0]
        F2 = F2_pre.subs(zip(X, sigma(X)), simultaneous=True)
        s2 = [sigma*tau*sigma for tau in s2_pre]
        R2 = Resolvent(F2, X, s2)
        R_dup, _, _ = R2.eval_for_poly(T)
        d = dup_discriminant(R_dup, ZZ)
        if d == 0:
            continue
        if is_square(d):
            return (CyclicGroup(5), True)
        else:
            return (DihedralGroup(5), True)

    raise MaxTriesException


","
    Compute the Galois group of a polynomial of degree 5, following Alg 6.3.9
    of Cohen.

    References
    ==========

    .. [1] Cohen, H. *A Course in Computational Algebraic Number Theory*.

    ",28,247,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _galois_group_degree_5(T, max_tries=30, randomize=False):
    r
    from sympy.combinatorics.permutations import Permutation
    from sympy.combinatorics.named_groups import (
        CyclicGroup, DihedralGroup, AlternatingGroup, SymmetricGroup
    )

    # The ideas here are all the same as in the degree-4 method.
    # The specific resolvents we use, and how we interpret the results, are
    # adapted to the degree-5 case.

    X = symbols('X0 X1 X2 X3 X4')
    # For the first resolvent, we have G = S5,
    # and stabilizer H = M20 = < (01234), (1234) >.
    F1 = (X[0]**2*(X[1]*X[4] + X[2]*X[3])
        + X[1]**2*(X[2]*X[0] + X[3]*X[4])
        + X[2]**2*(X[3]*X[1] + X[4]*X[0])
        + X[3]**2*(X[4]*X[2] + X[0]*X[1])
        + X[4]**2*(X[0]*X[3] + X[1]*X[2]))
    s1 = [
        Permutation(4),
        Permutation(4)(0, 1),
        Permutation(4)(0, 2),
        Permutation(4)(0, 3),
        Permutation(4)(0, 4),
        Permutation(4)(1, 4)
    ]
    R1 = Resolvent(F1, X, s1)

    # For the second resolvent, we'll have G = D5, H = C5.
    F2_pre = X[0]*X[1]**2 + X[1]*X[2]**2 + X[2]*X[3]**2 + X[3]*X[4]**2 + X[4]*X[0]**2
    s2_pre = [
        Permutation(4),
        Permutation(4)(0, 1)(2, 4)
    ]

    history = set()
    for i in range(max_tries):
        if i > 0:
            _, T = tschirnhausen_transformation(T, max_tries=max_tries, history=history, fixed_order=not randomize)

        R_dup, _, i0 = R1.eval_for_poly(T, find_integer_root=True)
        if not dup_sqf_p(R_dup, ZZ):
            continue

        sq_disc = has_square_disc(T)

        if i0 is None:
            return (AlternatingGroup(5), True) if sq_disc else (SymmetricGroup(5), False)

        if not sq_disc:
            return (M20(), False)

        sigma = s1[i0]
        F2 = F2_pre.subs(zip(X, sigma(X)), simultaneous=True)
        s2 = [sigma*tau*sigma for tau in s2_pre]
        R2 = Resolvent(F2, X, s2)
        R_dup, _, _ = R2.eval_for_poly(T)
        d = dup_discriminant(R_dup, ZZ)
        if d == 0:
            continue
        if is_square(d):
            return (CyclicGroup(5), True)
        else:
            return (DihedralGroup(5), True)

    raise MaxTriesException



        ```
    ###Assistant : 
    Compute the Galois group of a polynomial of degree 5, following Alg 6.3.9
    of Cohen.

    References
    ==========

    .. [1] Cohen, H. *A Course in Computational Algebraic Number Theory*.

    
    "
1774,"def error(self, message):
        
        self.print_usage(_sys.stderr)
        args = {'prog': self.prog, 'message': message}
        self.exit(2, _('%(prog)s: error: %(message)s\n') % args)
","error(message: string)

        Prints a usage message incorporating the message to stderr and
        exits.

        If you override this in a subclass, it should not return -- it
        should either exit or raise an exception.
        ",33,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def error(self, message):
        
        self.print_usage(_sys.stderr)
        args = {'prog': self.prog, 'message': message}
        self.exit(2, _('%(prog)s: error: %(message)s\n') % args)

        ```
    ###Assistant : error(message: string)

        Prints a usage message incorporating the message to stderr and
        exits.

        If you override this in a subclass, it should not return -- it
        should either exit or raise an exception.
        
    "
1775,"def preprocess_input(x, data_format=None):  # pylint: disable=unused-argument
    
    return x


@keras_export(""keras.applications.regnet.decode_predictions"")","A placeholder method for backward compatibility.

    The preprocessing logic has been included in the regnet model
    implementation. Users are no longer required to call this method to normalize
    the input data. This method does nothing and only kept as a placeholder to
    align the API surface between old and new version of model.

    Args:
      x: A floating point `numpy.array` or a `tf.Tensor`.
      data_format: Optional data format of the image tensor/array. Defaults to
        None, in which case the global setting
        `tf.keras.backend.image_data_format()` is used (unless you changed it, it
        defaults to ""channels_last"").{mode}

    Returns:
      Unchanged `numpy.array` or `tf.Tensor`.
    ",95,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def preprocess_input(x, data_format=None):  # pylint: disable=unused-argument
    
    return x


@keras_export(""keras.applications.regnet.decode_predictions"")
        ```
    ###Assistant : A placeholder method for backward compatibility.

    The preprocessing logic has been included in the regnet model
    implementation. Users are no longer required to call this method to normalize
    the input data. This method does nothing and only kept as a placeholder to
    align the API surface between old and new version of model.

    Args:
      x: A floating point `numpy.array` or a `tf.Tensor`.
      data_format: Optional data format of the image tensor/array. Defaults to
        None, in which case the global setting
        `tf.keras.backend.image_data_format()` is used (unless you changed it, it
        defaults to ""channels_last"").{mode}

    Returns:
      Unchanged `numpy.array` or `tf.Tensor`.
    
    "
1776,"def test_edit_post(self):
        
        # Send request
        response = self.client.post(
            reverse(""wagtaildocs:edit_multiple"", args=(self.doc.id,)),
            {
                ""doc-%d-%s"" % (self.doc.id, field): data
                for field, data in self.edit_post_data.items()
            },
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertEqual(response[""Content-Type""], ""application/json"")

        # Check JSON
        response_json = json.loads(response.content.decode())
        self.assertIn(""doc_id"", response_json)
        self.assertNotIn(""form"", response_json)
        self.assertIn(""success"", response_json)
        self.assertEqual(response_json[""doc_id""], self.doc.id)
        self.assertTrue(response_json[""success""])

        self.check_doc_after_edit()
","
        This tests that a POST request to the edit view edits the document
        ",13,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_edit_post(self):
        
        # Send request
        response = self.client.post(
            reverse(""wagtaildocs:edit_multiple"", args=(self.doc.id,)),
            {
                ""doc-%d-%s"" % (self.doc.id, field): data
                for field, data in self.edit_post_data.items()
            },
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertEqual(response[""Content-Type""], ""application/json"")

        # Check JSON
        response_json = json.loads(response.content.decode())
        self.assertIn(""doc_id"", response_json)
        self.assertNotIn(""form"", response_json)
        self.assertIn(""success"", response_json)
        self.assertEqual(response_json[""doc_id""], self.doc.id)
        self.assertTrue(response_json[""success""])

        self.check_doc_after_edit()

        ```
    ###Assistant : 
        This tests that a POST request to the edit view edits the document
        
    "
1777,"def test_message_hiding(qtbot, view):
    
    with qtbot.wait_signal(view._clear_timer.timeout):
        view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    assert not view._messages

",Messages should be hidden after the timer times out.,9,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_message_hiding(qtbot, view):
    
    with qtbot.wait_signal(view._clear_timer.timeout):
        view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test'))
    assert not view._messages


        ```
    ###Assistant : Messages should be hidden after the timer times out.
    "
1778,"def test_avatar_allowed_mime_type_per_room(self):
        
        self._setup_local_files(
            {
                ""good"": {""mimetype"": ""image/png""},
                ""bad"": {""mimetype"": ""application/octet-stream""},
            }
        )

        room_id = self.helper.create_room_as(tok=self.owner_tok)

        channel = self.make_request(
            ""PUT"",
            f""/rooms/{room_id}/state/m.room.member/{self.owner}"",
            content={""membership"": ""join"", ""avatar_url"": ""mxc://test/bad""},
            access_token=self.owner_tok,
        )
        self.assertEqual(channel.code, 403, channel.result)
        self.assertEqual(
            channel.json_body[""errcode""], Codes.FORBIDDEN, channel.json_body
        )

        channel = self.make_request(
            ""PUT"",
            f""/rooms/{room_id}/state/m.room.member/{self.owner}"",
            content={""membership"": ""join"", ""avatar_url"": ""mxc://test/good""},
            access_token=self.owner_tok,
        )
        self.assertEqual(channel.code, 200, channel.result)
","Tests that the MIME type whitelist for avatars is enforced when updating a
        per-room profile.
        ",15,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_avatar_allowed_mime_type_per_room(self):
        
        self._setup_local_files(
            {
                ""good"": {""mimetype"": ""image/png""},
                ""bad"": {""mimetype"": ""application/octet-stream""},
            }
        )

        room_id = self.helper.create_room_as(tok=self.owner_tok)

        channel = self.make_request(
            ""PUT"",
            f""/rooms/{room_id}/state/m.room.member/{self.owner}"",
            content={""membership"": ""join"", ""avatar_url"": ""mxc://test/bad""},
            access_token=self.owner_tok,
        )
        self.assertEqual(channel.code, 403, channel.result)
        self.assertEqual(
            channel.json_body[""errcode""], Codes.FORBIDDEN, channel.json_body
        )

        channel = self.make_request(
            ""PUT"",
            f""/rooms/{room_id}/state/m.room.member/{self.owner}"",
            content={""membership"": ""join"", ""avatar_url"": ""mxc://test/good""},
            access_token=self.owner_tok,
        )
        self.assertEqual(channel.code, 200, channel.result)

        ```
    ###Assistant : Tests that the MIME type whitelist for avatars is enforced when updating a
        per-room profile.
        
    "
1779,"def container_name(self) -> t.Optional[str]:
        
        return self.state.get('container_name')
","Return the stored container name, if any, otherwise None.",9,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def container_name(self) -> t.Optional[str]:
        
        return self.state.get('container_name')

        ```
    ###Assistant : Return the stored container name, if any, otherwise None.
    "
1780,"def get_scorer(scoring):
    
    if isinstance(scoring, str):
        try:
            scorer = copy.deepcopy(_SCORERS[scoring])
        except KeyError:
            raise ValueError(
                ""%r is not a valid scoring value. ""
                ""Use sklearn.metrics.get_scorer_names() ""
                ""to get valid options."" % scoring
            )
    else:
        scorer = scoring
    return scorer

","Get a scorer from string.

    Read more in the :ref:`User Guide <scoring_parameter>`.
    :func:`~sklearn.metrics.get_scorer_names` can be used to retrieve the names
    of all available scorers.

    Parameters
    ----------
    scoring : str or callable
        Scoring method as string. If callable it is returned as is.

    Returns
    -------
    scorer : callable
        The scorer.

    Notes
    -----
    When passed a string, this function always returns a copy of the scorer
    object. Calling `get_scorer` twice for the same scorer results in two
    separate scorer objects.
    ",78,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_scorer(scoring):
    
    if isinstance(scoring, str):
        try:
            scorer = copy.deepcopy(_SCORERS[scoring])
        except KeyError:
            raise ValueError(
                ""%r is not a valid scoring value. ""
                ""Use sklearn.metrics.get_scorer_names() ""
                ""to get valid options."" % scoring
            )
    else:
        scorer = scoring
    return scorer


        ```
    ###Assistant : Get a scorer from string.

    Read more in the :ref:`User Guide <scoring_parameter>`.
    :func:`~sklearn.metrics.get_scorer_names` can be used to retrieve the names
    of all available scorers.

    Parameters
    ----------
    scoring : str or callable
        Scoring method as string. If callable it is returned as is.

    Returns
    -------
    scorer : callable
        The scorer.

    Notes
    -----
    When passed a string, this function always returns a copy of the scorer
    object. Calling `get_scorer` twice for the same scorer results in two
    separate scorer objects.
    
    "
1781,"def fit(self, X, y):
        
        X, y = self._validate_data(
            X, y, ensure_min_samples=2, dtype=[np.float64, np.float32]
        )
        self.classes_ = unique_labels(y)
        n_samples, _ = X.shape
        n_classes = len(self.classes_)

        if n_samples == n_classes:
            raise ValueError(
                ""The number of samples must be more than the number of classes.""
            )

        if self.priors is None:  # estimate priors from sample
            _, y_t = np.unique(y, return_inverse=True)  # non-negative ints
            self.priors_ = np.bincount(y_t) / float(len(y))
        else:
            self.priors_ = np.asarray(self.priors)

        if (self.priors_ < 0).any():
            raise ValueError(""priors must be non-negative"")
        if not np.isclose(self.priors_.sum(), 1.0):
            warnings.warn(""The priors do not sum to 1. Renormalizing"", UserWarning)
            self.priors_ = self.priors_ / self.priors_.sum()

        # Maximum number of components no matter what n_components is
        # specified:
        max_components = min(len(self.classes_) - 1, X.shape[1])

        if self.n_components is None:
            self._max_components = max_components
        else:
            if self.n_components > max_components:
                raise ValueError(
                    ""n_components cannot be larger than min(n_features, n_classes - 1).""
                )
            self._max_components = self.n_components

        if self.solver == ""svd"":
            if self.shrinkage is not None:
                raise NotImplementedError(""shrinkage not supported"")
            if self.covariance_estimator is not None:
                raise ValueError(
                    ""covariance estimator ""
                    ""is not supported ""
                    ""with svd solver. Try another solver""
                )
            self._solve_svd(X, y)
        elif self.solver == ""lsqr"":
            self._solve_lsqr(
                X,
                y,
                shrinkage=self.shrinkage,
                covariance_estimator=self.covariance_estimator,
            )
        elif self.solver == ""eigen"":
            self._solve_eigen(
                X,
                y,
                shrinkage=self.shrinkage,
                covariance_estimator=self.covariance_estimator,
            )
        else:
            raise ValueError(
                ""unknown solver {} (valid solvers are 'svd', ""
                ""'lsqr', and 'eigen')."".format(self.solver)
            )
        if self.classes_.size == 2:  # treat binary case as a special case
            self.coef_ = np.array(
                self.coef_[1, :] - self.coef_[0, :], ndmin=2, dtype=X.dtype
            )
            self.intercept_ = np.array(
                self.intercept_[1] - self.intercept_[0], ndmin=1, dtype=X.dtype
            )
        self._n_features_out = self._max_components
        return self
","Fit the Linear Discriminant Analysis model.

           .. versionchanged:: 0.19
              *store_covariance* has been moved to main constructor.

           .. versionchanged:: 0.19
              *tol* has been moved to main constructor.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data.

        y : array-like of shape (n_samples,)
            Target values.

        Returns
        -------
        self : object
            Fitted estimator.
        ",52,249,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y):
        
        X, y = self._validate_data(
            X, y, ensure_min_samples=2, dtype=[np.float64, np.float32]
        )
        self.classes_ = unique_labels(y)
        n_samples, _ = X.shape
        n_classes = len(self.classes_)

        if n_samples == n_classes:
            raise ValueError(
                ""The number of samples must be more than the number of classes.""
            )

        if self.priors is None:  # estimate priors from sample
            _, y_t = np.unique(y, return_inverse=True)  # non-negative ints
            self.priors_ = np.bincount(y_t) / float(len(y))
        else:
            self.priors_ = np.asarray(self.priors)

        if (self.priors_ < 0).any():
            raise ValueError(""priors must be non-negative"")
        if not np.isclose(self.priors_.sum(), 1.0):
            warnings.warn(""The priors do not sum to 1. Renormalizing"", UserWarning)
            self.priors_ = self.priors_ / self.priors_.sum()

        # Maximum number of components no matter what n_components is
        # specified:
        max_components = min(len(self.classes_) - 1, X.shape[1])

        if self.n_components is None:
            self._max_components = max_components
        else:
            if self.n_components > max_components:
                raise ValueError(
                    ""n_components cannot be larger than min(n_features, n_classes - 1).""
                )
            self._max_components = self.n_components

        if self.solver == ""svd"":
            if self.shrinkage is not None:
                raise NotImplementedError(""shrinkage not supported"")
            if self.covariance_estimator is not None:
                raise ValueError(
                    ""covariance estimator ""
                    ""is not supported ""
                    ""with svd solver. Try another solver""
                )
            self._solve_svd(X, y)
        elif self.solver == ""lsqr"":
            self._solve_lsqr(
                X,
                y,
                shrinkage=self.shrinkage,
                covariance_estimator=self.covariance_estimator,
            )
        elif self.solver == ""eigen"":
            self._solve_eigen(
                X,
                y,
                shrinkage=self.shrinkage,
                covariance_estimator=self.covariance_estimator,
            )
        else:
            raise ValueError(
                ""unknown solver {} (valid solvers are 'svd', ""
                ""'lsqr', and 'eigen')."".format(self.solver)
            )
        if self.classes_.size == 2:  # treat binary case as a special case
            self.coef_ = np.array(
                self.coef_[1, :] - self.coef_[0, :], ndmin=2, dtype=X.dtype
            )
            self.intercept_ = np.array(
                self.intercept_[1] - self.intercept_[0], ndmin=1, dtype=X.dtype
            )
        self._n_features_out = self._max_components
        return self

        ```
    ###Assistant : Fit the Linear Discriminant Analysis model.

           .. versionchanged:: 0.19
              *store_covariance* has been moved to main constructor.

           .. versionchanged:: 0.19
              *tol* has been moved to main constructor.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data.

        y : array-like of shape (n_samples,)
            Target values.

        Returns
        -------
        self : object
            Fitted estimator.
        
    "
1782,"def test_pr_opened_with_multiple_reviewers(self) -> None:
        expected_topic = ""sandbox / PR #6 sample_file: Add sample_file.txt.""
        expected_message = 
        self.check_webhook(
            ""pull_request_opened_with_multiple_reviewers"", expected_topic, expected_message
        )
","[hypro999](http://139.59.64.214:7990/users/hypro999) opened [PR #6](http://139.59.64.214:7990/projects/SBOX/repos/sandbox/pull-requests/6) from `master` to `master` (assigned to [sougo](http://139.59.64.214:7990/users/sougo), [zura](http://139.59.64.214:7990/users/zura) and [shimura](http://139.59.64.214:7990/users/shimura) for review):\n\n~~~ quote\nAdd a simple text file for further testing purposes.\n~~~",25,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pr_opened_with_multiple_reviewers(self) -> None:
        expected_topic = ""sandbox / PR #6 sample_file: Add sample_file.txt.""
        expected_message = 
        self.check_webhook(
            ""pull_request_opened_with_multiple_reviewers"", expected_topic, expected_message
        )

        ```
    ###Assistant : [hypro999](http://139.59.64.214:7990/users/hypro999) opened [PR #6](http://139.59.64.214:7990/projects/SBOX/repos/sandbox/pull-requests/6) from `master` to `master` (assigned to [sougo](http://139.59.64.214:7990/users/sougo), [zura](http://139.59.64.214:7990/users/zura) and [shimura](http://139.59.64.214:7990/users/shimura) for review):\n\n~~~ quote\nAdd a simple text file for further testing purposes.\n~~~
    "
1783,"def make_grouping_by_key(schema, source, default=None):
    
    return map_grouping(lambda s: source.get(s, default), schema)

","
    Create a grouping from a schema by using the schema's scalar values to look up
    items in the provided source object.

    :param schema: A grouping of potential keys in source
    :param source: Dict-like object to use to look up scalar grouping value using
        scalar grouping values as keys
    :param default: Default scalar value to use if grouping scalar key is not present
        in source
    :return: grouping
    ",66,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_grouping_by_key(schema, source, default=None):
    
    return map_grouping(lambda s: source.get(s, default), schema)


        ```
    ###Assistant : 
    Create a grouping from a schema by using the schema's scalar values to look up
    items in the provided source object.

    :param schema: A grouping of potential keys in source
    :param source: Dict-like object to use to look up scalar grouping value using
        scalar grouping values as keys
    :param default: Default scalar value to use if grouping scalar key is not present
        in source
    :return: grouping
    
    "
1784,"def taxicab_distance(self, p):
        
        s, p = Point._normalize_dimension(self, Point(p))
        return Add(*(abs(a - b) for a, b in zip(s, p)))
","The Taxicab Distance from self to point p.

        Returns the sum of the horizontal and vertical distances to point p.

        Parameters
        ==========

        p : Point

        Returns
        =======

        taxicab_distance : The sum of the horizontal
        and vertical distances to point p.

        See Also
        ========

        sympy.geometry.point.Point.distance

        Examples
        ========

        >>> from sympy import Point
        >>> p1, p2 = Point(1, 1), Point(4, 5)
        >>> p1.taxicab_distance(p2)
        7

        ",62,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def taxicab_distance(self, p):
        
        s, p = Point._normalize_dimension(self, Point(p))
        return Add(*(abs(a - b) for a, b in zip(s, p)))

        ```
    ###Assistant : The Taxicab Distance from self to point p.

        Returns the sum of the horizontal and vertical distances to point p.

        Parameters
        ==========

        p : Point

        Returns
        =======

        taxicab_distance : The sum of the horizontal
        and vertical distances to point p.

        See Also
        ========

        sympy.geometry.point.Point.distance

        Examples
        ========

        >>> from sympy import Point
        >>> p1, p2 = Point(1, 1), Point(4, 5)
        >>> p1.taxicab_distance(p2)
        7

        
    "
1785,"def cosine_similarity(y_true, y_pred, axis=-1):
  
  y_true = tf.linalg.l2_normalize(y_true, axis=axis)
  y_pred = tf.linalg.l2_normalize(y_pred, axis=axis)
  return tf.reduce_sum(y_true * y_pred, axis=axis)
","Computes the cosine similarity between labels and predictions.

  Args:
    y_true: The ground truth values.
    y_pred: The prediction values.
    axis: (Optional) Defaults to -1. The dimension along which the cosine
      similarity is computed.

  Returns:
    Cosine similarity value.
  ",36,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cosine_similarity(y_true, y_pred, axis=-1):
  
  y_true = tf.linalg.l2_normalize(y_true, axis=axis)
  y_pred = tf.linalg.l2_normalize(y_pred, axis=axis)
  return tf.reduce_sum(y_true * y_pred, axis=axis)

        ```
    ###Assistant : Computes the cosine similarity between labels and predictions.

  Args:
    y_true: The ground truth values.
    y_pred: The prediction values.
    axis: (Optional) Defaults to -1. The dimension along which the cosine
      similarity is computed.

  Returns:
    Cosine similarity value.
  
    "
1786,"def rotate(self, theta):
        
        a = math.cos(theta)
        b = math.sin(theta)
        mtx = self._mtx
        # Operating and assigning one scalar at a time is much faster.
        (xx, xy, x0), (yx, yy, y0), _ = mtx.tolist()
        # mtx = [[a -b 0], [b a 0], [0 0 1]] * mtx
        mtx[0, 0] = a * xx - b * yx
        mtx[0, 1] = a * xy - b * yy
        mtx[0, 2] = a * x0 - b * y0
        mtx[1, 0] = b * xx + a * yx
        mtx[1, 1] = b * xy + a * yy
        mtx[1, 2] = b * x0 + a * y0
        self.invalidate()
        return self
","
        Add a rotation (in radians) to this transform in place.

        Returns *self*, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
        and :meth:`scale`.
        ",28,110,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rotate(self, theta):
        
        a = math.cos(theta)
        b = math.sin(theta)
        mtx = self._mtx
        # Operating and assigning one scalar at a time is much faster.
        (xx, xy, x0), (yx, yy, y0), _ = mtx.tolist()
        # mtx = [[a -b 0], [b a 0], [0 0 1]] * mtx
        mtx[0, 0] = a * xx - b * yx
        mtx[0, 1] = a * xy - b * yy
        mtx[0, 2] = a * x0 - b * y0
        mtx[1, 0] = b * xx + a * yx
        mtx[1, 1] = b * xy + a * yy
        mtx[1, 2] = b * x0 + a * y0
        self.invalidate()
        return self

        ```
    ###Assistant : 
        Add a rotation (in radians) to this transform in place.

        Returns *self*, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
        and :meth:`scale`.
        
    "
1787,"def call_load(self, other_args):
        
        parser = argparse.ArgumentParser(
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            prog=""load"",
            description=,
        )
        parser.add_argument(
            ""-c"",
            ""--coin"",
            help=""Coin to get. Must be coin symbol (e.g., btc, eth)"",
            dest=""coin"",
            type=str,
            required=""-h"" not in other_args,
        )

        parser.add_argument(
            ""-s"",
            ""--start"",
            type=valid_date,
            default=(datetime.now() - timedelta(days=1100)).strftime(""%Y-%m-%d""),
            dest=""start"",
            help=""The starting date (format YYYY-MM-DD) of the crypto"",
        )

        parser.add_argument(
            ""--exchange"",
            help=""Exchange to search"",
            dest=""exchange"",
            type=str,
            default=""binance"",
            choices=self.exchanges,
        )

        parser.add_argument(
            ""-e"",
            ""--end"",
            type=valid_date,
            default=datetime.now().strftime(""%Y-%m-%d""),
            dest=""end"",
            help=""The ending date (format YYYY-MM-DD) of the crypto"",
        )
        parser.add_argument(
            ""-i"",
            ""--interval"",
            action=""store"",
            dest=""interval"",
            type=str,
            default=""1440"",
            choices=[""1"", ""5"", ""15"", ""30"", ""60"", ""240"", ""1440"", ""10080"", ""43200""],
            help=""The interval of the crypto"",
        )

        parser.add_argument(
            ""--vs"",
            help=""Quote currency (what to view coin vs). e.g., usdc, usdt, ... if source is ccxt, usd, eur, ... otherwise"",  # noqa
            dest=""vs"",
            default=""usdt"",
            type=str,
        )

        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-c"")

        ns_parser = self.parse_known_args_and_warn(
            parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
        )

        if ns_parser:
            if ns_parser.source in (""YahooFinance"", ""CoinGecko""):
                if ns_parser.vs == ""usdt"":
                    ns_parser.vs = ""usd""
            (self.current_df) = cryptocurrency_helpers.load(
                symbol=ns_parser.coin.lower(),
                vs_currency=ns_parser.vs,
                end_date=ns_parser.end.strftime(""%Y-%m-%d""),
                start_date=ns_parser.start.strftime(""%Y-%m-%d""),
                interval=ns_parser.interval,
                source=ns_parser.source,
                exchange=ns_parser.exchange,
            )
            if not self.current_df.empty:
                self.vs = ns_parser.vs
                self.exchange = ns_parser.exchange
                self.source = ns_parser.source
                self.current_interval = ns_parser.interval
                self.current_currency = ns_parser.vs
                self.symbol = ns_parser.coin.lower()
                cryptocurrency_helpers.show_quick_performance(
                    self.current_df,
                    self.symbol,
                    self.current_currency,
                    ns_parser.source,
                    ns_parser.exchange,
                    self.current_interval,
                )
                export_data(
                    ns_parser.export,
                    os.path.dirname(os.path.abspath(__file__)),
                    ""load"",
                    self.current_df.copy(),
                )
","Process load command.Load crypto currency to perform analysis on.
            Yahoo Finance is used as default source.
            Other sources can be used such as 'ccxt' or 'cg' with --source.
            If you select 'ccxt', you can then select any exchange with --exchange.
            You can also select a specific interval with --interval.",49,198,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_load(self, other_args):
        
        parser = argparse.ArgumentParser(
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            prog=""load"",
            description=,
        )
        parser.add_argument(
            ""-c"",
            ""--coin"",
            help=""Coin to get. Must be coin symbol (e.g., btc, eth)"",
            dest=""coin"",
            type=str,
            required=""-h"" not in other_args,
        )

        parser.add_argument(
            ""-s"",
            ""--start"",
            type=valid_date,
            default=(datetime.now() - timedelta(days=1100)).strftime(""%Y-%m-%d""),
            dest=""start"",
            help=""The starting date (format YYYY-MM-DD) of the crypto"",
        )

        parser.add_argument(
            ""--exchange"",
            help=""Exchange to search"",
            dest=""exchange"",
            type=str,
            default=""binance"",
            choices=self.exchanges,
        )

        parser.add_argument(
            ""-e"",
            ""--end"",
            type=valid_date,
            default=datetime.now().strftime(""%Y-%m-%d""),
            dest=""end"",
            help=""The ending date (format YYYY-MM-DD) of the crypto"",
        )
        parser.add_argument(
            ""-i"",
            ""--interval"",
            action=""store"",
            dest=""interval"",
            type=str,
            default=""1440"",
            choices=[""1"", ""5"", ""15"", ""30"", ""60"", ""240"", ""1440"", ""10080"", ""43200""],
            help=""The interval of the crypto"",
        )

        parser.add_argument(
            ""--vs"",
            help=""Quote currency (what to view coin vs). e.g., usdc, usdt, ... if source is ccxt, usd, eur, ... otherwise"",  # noqa
            dest=""vs"",
            default=""usdt"",
            type=str,
        )

        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-c"")

        ns_parser = self.parse_known_args_and_warn(
            parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
        )

        if ns_parser:
            if ns_parser.source in (""YahooFinance"", ""CoinGecko""):
                if ns_parser.vs == ""usdt"":
                    ns_parser.vs = ""usd""
            (self.current_df) = cryptocurrency_helpers.load(
                symbol=ns_parser.coin.lower(),
                vs_currency=ns_parser.vs,
                end_date=ns_parser.end.strftime(""%Y-%m-%d""),
                start_date=ns_parser.start.strftime(""%Y-%m-%d""),
                interval=ns_parser.interval,
                source=ns_parser.source,
                exchange=ns_parser.exchange,
            )
            if not self.current_df.empty:
                self.vs = ns_parser.vs
                self.exchange = ns_parser.exchange
                self.source = ns_parser.source
                self.current_interval = ns_parser.interval
                self.current_currency = ns_parser.vs
                self.symbol = ns_parser.coin.lower()
                cryptocurrency_helpers.show_quick_performance(
                    self.current_df,
                    self.symbol,
                    self.current_currency,
                    ns_parser.source,
                    ns_parser.exchange,
                    self.current_interval,
                )
                export_data(
                    ns_parser.export,
                    os.path.dirname(os.path.abspath(__file__)),
                    ""load"",
                    self.current_df.copy(),
                )

        ```
    ###Assistant : Process load command.Load crypto currency to perform analysis on.
            Yahoo Finance is used as default source.
            Other sources can be used such as 'ccxt' or 'cg' with --source.
            If you select 'ccxt', you can then select any exchange with --exchange.
            You can also select a specific interval with --interval.
    "
1788,"def render_markdown(value):
    
    schemes = '|'.join(get_config().ALLOWED_URL_SCHEMES)

    # Strip HTML tags
    value = strip_tags(value)

    # Sanitize Markdown links
    pattern = fr'\[([^\]]+)\]\((?!({schemes})).*:(.+)\)'
    value = re.sub(pattern, '[\\1](\\3)', value, flags=re.IGNORECASE)

    # Sanitize Markdown reference links
    pattern = fr'\[(.+)\]:\s*(?!({schemes}))\w*:(.+)'
    value = re.sub(pattern, '[\\1]: \\3', value, flags=re.IGNORECASE)

    # Render Markdown
    html = markdown(value, extensions=['fenced_code', 'tables', StrikethroughExtension()])

    # If the string is not empty wrap it in rendered-markdown to style tables
    if html:
        html = f'<div class=""rendered-markdown"">{html}</div>'

    return mark_safe(html)


@register.filter('json')","
    Render a string as Markdown. This filter is invoked as ""markdown"":

        {{ md_source_text|markdown }}
    ",14,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def render_markdown(value):
    
    schemes = '|'.join(get_config().ALLOWED_URL_SCHEMES)

    # Strip HTML tags
    value = strip_tags(value)

    # Sanitize Markdown links
    pattern = fr'\[([^\]]+)\]\((?!({schemes})).*:(.+)\)'
    value = re.sub(pattern, '[\\1](\\3)', value, flags=re.IGNORECASE)

    # Sanitize Markdown reference links
    pattern = fr'\[(.+)\]:\s*(?!({schemes}))\w*:(.+)'
    value = re.sub(pattern, '[\\1]: \\3', value, flags=re.IGNORECASE)

    # Render Markdown
    html = markdown(value, extensions=['fenced_code', 'tables', StrikethroughExtension()])

    # If the string is not empty wrap it in rendered-markdown to style tables
    if html:
        html = f'<div class=""rendered-markdown"">{html}</div>'

    return mark_safe(html)


@register.filter('json')
        ```
    ###Assistant : 
    Render a string as Markdown. This filter is invoked as ""markdown"":

        {{ md_source_text|markdown }}
    
    "
1789,"def _try_breadth_first(tasks, user):
    

    tasks = tasks.annotate(annotations_count=Count('annotations'))
    max_annotations_count = tasks.aggregate(Max('annotations_count'))['annotations_count__max']
    if max_annotations_count == 0:
        # there is no any labeled tasks found
        return

    # find any task with maximal amount of created annotations
    not_solved_tasks_labeling_started = tasks.annotate(
        reach_max_annotations_count=Case(
            When(annotations_count=max_annotations_count, then=Value(True)),
            default=Value(False),
            output_field=BooleanField(),
        )
    )
    not_solved_tasks_labeling_with_max_annotations = not_solved_tasks_labeling_started.filter(
        reach_max_annotations_count=True
    )
    if not_solved_tasks_labeling_with_max_annotations.exists():
        # try to complete tasks that are already in progress
        return _get_random_unlocked(not_solved_tasks_labeling_with_max_annotations, user)

","Try to find tasks with maximum amount of annotations, since we are trying to label tasks as fast as possible
    ",20,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _try_breadth_first(tasks, user):
    

    tasks = tasks.annotate(annotations_count=Count('annotations'))
    max_annotations_count = tasks.aggregate(Max('annotations_count'))['annotations_count__max']
    if max_annotations_count == 0:
        # there is no any labeled tasks found
        return

    # find any task with maximal amount of created annotations
    not_solved_tasks_labeling_started = tasks.annotate(
        reach_max_annotations_count=Case(
            When(annotations_count=max_annotations_count, then=Value(True)),
            default=Value(False),
            output_field=BooleanField(),
        )
    )
    not_solved_tasks_labeling_with_max_annotations = not_solved_tasks_labeling_started.filter(
        reach_max_annotations_count=True
    )
    if not_solved_tasks_labeling_with_max_annotations.exists():
        # try to complete tasks that are already in progress
        return _get_random_unlocked(not_solved_tasks_labeling_with_max_annotations, user)


        ```
    ###Assistant : Try to find tasks with maximum amount of annotations, since we are trying to label tasks as fast as possible
    
    "
1790,"def test_pad_batch_dynamic_max(self):
        
        view_requirements = {
            ""state_in_0"": ViewRequirement(
                ""state_out_0"",
                shift=[-1],
                used_for_training=False,
                used_for_compute_actions=True,
                batch_repeat_value=1,
            )
        }
        max_seq_len = 20
        num_seqs = np.random.randint(1, 20)
        seq_lens = np.random.randint(1, max_seq_len, size=(num_seqs))
        max_len = np.max(seq_lens)
        sum_seq_lens = np.sum(seq_lens)

        s1 = SampleBatch(
            {
                ""a"": np.arange(sum_seq_lens),
                ""b"": np.arange(sum_seq_lens),
                ""seq_lens"": seq_lens,
                ""state_in_0"": [[0]] * num_seqs,
            },
            _max_seq_len=max_seq_len,
        )

        pad_batch_to_sequences_of_same_size(
            s1,
            max_seq_len=max_seq_len,
            feature_keys=[""a"", ""b""],
            view_requirements=view_requirements,
        )
        check(s1.max_seq_len, max_len)
        check(s1[""a""].shape[0], max_len * num_seqs)
        check(s1[""b""].shape[0], max_len * num_seqs)
",Test pad_batch_to_sequences_of_same_size when dynamic_max = True,6,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pad_batch_dynamic_max(self):
        
        view_requirements = {
            ""state_in_0"": ViewRequirement(
                ""state_out_0"",
                shift=[-1],
                used_for_training=False,
                used_for_compute_actions=True,
                batch_repeat_value=1,
            )
        }
        max_seq_len = 20
        num_seqs = np.random.randint(1, 20)
        seq_lens = np.random.randint(1, max_seq_len, size=(num_seqs))
        max_len = np.max(seq_lens)
        sum_seq_lens = np.sum(seq_lens)

        s1 = SampleBatch(
            {
                ""a"": np.arange(sum_seq_lens),
                ""b"": np.arange(sum_seq_lens),
                ""seq_lens"": seq_lens,
                ""state_in_0"": [[0]] * num_seqs,
            },
            _max_seq_len=max_seq_len,
        )

        pad_batch_to_sequences_of_same_size(
            s1,
            max_seq_len=max_seq_len,
            feature_keys=[""a"", ""b""],
            view_requirements=view_requirements,
        )
        check(s1.max_seq_len, max_len)
        check(s1[""a""].shape[0], max_len * num_seqs)
        check(s1[""b""].shape[0], max_len * num_seqs)

        ```
    ###Assistant : Test pad_batch_to_sequences_of_same_size when dynamic_max = True
    "
1791,"def sensors_fans():
    
    ret = collections.defaultdict(list)
    basenames = glob.glob('/sys/class/hwmon/hwmon*/fan*_*')
    if not basenames:
        # CentOS has an intermediate /device directory:
        # https://github.com/giampaolo/psutil/issues/971
        basenames = glob.glob('/sys/class/hwmon/hwmon*/device/fan*_*')

    basenames = sorted(set([x.split('_')[0] for x in basenames]))
    for base in basenames:
        try:
            current = int(bcat(base + '_input'))
        except (IOError, OSError) as err:
            debug(err)
            continue
        unit_name = cat(os.path.join(os.path.dirname(base), 'name'))
        label = cat(base + '_label', fallback='')
        ret[unit_name].append(_common.sfan(label, current))

    return dict(ret)

","Return hardware fans info (for CPU and other peripherals) as a
    dict including hardware label and current speed.

    Implementation notes:
    - /sys/class/hwmon looks like the most recent interface to
      retrieve this info, and this implementation relies on it
      only (old distros will probably use something else)
    - lm-sensors on Ubuntu 16.04 relies on /sys/class/hwmon
    ",54,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sensors_fans():
    
    ret = collections.defaultdict(list)
    basenames = glob.glob('/sys/class/hwmon/hwmon*/fan*_*')
    if not basenames:
        # CentOS has an intermediate /device directory:
        # https://github.com/giampaolo/psutil/issues/971
        basenames = glob.glob('/sys/class/hwmon/hwmon*/device/fan*_*')

    basenames = sorted(set([x.split('_')[0] for x in basenames]))
    for base in basenames:
        try:
            current = int(bcat(base + '_input'))
        except (IOError, OSError) as err:
            debug(err)
            continue
        unit_name = cat(os.path.join(os.path.dirname(base), 'name'))
        label = cat(base + '_label', fallback='')
        ret[unit_name].append(_common.sfan(label, current))

    return dict(ret)


        ```
    ###Assistant : Return hardware fans info (for CPU and other peripherals) as a
    dict including hardware label and current speed.

    Implementation notes:
    - /sys/class/hwmon looks like the most recent interface to
      retrieve this info, and this implementation relies on it
      only (old distros will probably use something else)
    - lm-sensors on Ubuntu 16.04 relies on /sys/class/hwmon
    
    "
1792,"def factory(cls, loader):
        
        cls.__check_eager_loader(loader)
        return lambda *args, **kwargs: cls(loader(*args, **kwargs))
",Construct a callable which returns the eager loader made lazy.,10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def factory(cls, loader):
        
        cls.__check_eager_loader(loader)
        return lambda *args, **kwargs: cls(loader(*args, **kwargs))

        ```
    ###Assistant : Construct a callable which returns the eager loader made lazy.
    "
1793,"def test_table_block_caption_render(self):
        
        value = {
            ""table_caption"": ""caption"",
            ""first_row_is_table_header"": False,
            ""first_col_is_header"": False,
            ""data"": [
                [""Test 1"", ""Test 2"", ""Test 3""],
                [None, None, None],
                [None, None, None],
            ],
        }
        block = TableBlock()
        result = block.render(value)
        expected = 
        self.assertHTMLEqual(result, expected)
        self.assertIn(""Test 2"", result)
","
        Test a generic render with caption.
        
            <table>
                <caption>caption</caption>
                <tbody>
                    <tr><td>Test 1</td><td>Test 2</td><td>Test 3</td></tr>
                    <tr><td></td><td></td><td></td></tr>
                    <tr><td></td><td></td><td></td></tr>
                </tbody>
            </table>
        ",17,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_table_block_caption_render(self):
        
        value = {
            ""table_caption"": ""caption"",
            ""first_row_is_table_header"": False,
            ""first_col_is_header"": False,
            ""data"": [
                [""Test 1"", ""Test 2"", ""Test 3""],
                [None, None, None],
                [None, None, None],
            ],
        }
        block = TableBlock()
        result = block.render(value)
        expected = 
        self.assertHTMLEqual(result, expected)
        self.assertIn(""Test 2"", result)

        ```
    ###Assistant : 
        Test a generic render with caption.
        
            <table>
                <caption>caption</caption>
                <tbody>
                    <tr><td>Test 1</td><td>Test 2</td><td>Test 3</td></tr>
                    <tr><td></td><td></td><td></td></tr>
                    <tr><td></td><td></td><td></td></tr>
                </tbody>
            </table>
        
    "
1794,"def stream_config_without_start_date():
    
    return {
        ""client_id"": ""fake_client_id"",
        ""client_secret"": ""fake_client_secret"",
        ""refresh_token"": ""fake_refresh_token"",
        ""is_sandbox"": False,
        ""wait_timeout"": 15,
    }

",Generates streams settings for REST logic without start_date,8,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def stream_config_without_start_date():
    
    return {
        ""client_id"": ""fake_client_id"",
        ""client_secret"": ""fake_client_secret"",
        ""refresh_token"": ""fake_refresh_token"",
        ""is_sandbox"": False,
        ""wait_timeout"": 15,
    }


        ```
    ###Assistant : Generates streams settings for REST logic without start_date
    "
1795,"async def relay(self):
        
        while True:
            message = await self.queue.get()
            try:
                await self.send(message)
                self.queue.task_done()
            except RuntimeError:
                # The connection was closed, just exit the task
                return

","
        Relay messages from the channel's queue and send them out. This is started
        as a task.
        ",16,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def relay(self):
        
        while True:
            message = await self.queue.get()
            try:
                await self.send(message)
                self.queue.task_done()
            except RuntimeError:
                # The connection was closed, just exit the task
                return


        ```
    ###Assistant : 
        Relay messages from the channel's queue and send them out. This is started
        as a task.
        
    "
1796,"def q_sample(self, x_start, t, noise=None):
        
        if noise is None:
            # noise = th.randn_like(x_start)
            noise = paddle.randn(x_start.shape, x_start.dtype)
        assert noise.shape == x_start.shape
        return (_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
                _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
","
        Diffuse the data for a given number of diffusion steps.

        In other words, sample from q(x_t | x_0).

        :param x_start: the initial data batch.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :param noise: if specified, the split-out normal noise.
        :return: A noisy version of x_start.
        ",52,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def q_sample(self, x_start, t, noise=None):
        
        if noise is None:
            # noise = th.randn_like(x_start)
            noise = paddle.randn(x_start.shape, x_start.dtype)
        assert noise.shape == x_start.shape
        return (_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
                _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)

        ```
    ###Assistant : 
        Diffuse the data for a given number of diffusion steps.

        In other words, sample from q(x_t | x_0).

        :param x_start: the initial data batch.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :param noise: if specified, the split-out normal noise.
        :return: A noisy version of x_start.
        
    "
1797,"def get_views(self):
        
        query = f""SELECT * FROM information_schema.views WHERE table_schema NOT IN ('information_schema', 'pg_catalog')""
        result = self.run_native_query(query)
        return result
","
        List all views in PostgreSQL without the system views information_schema and pg_catalog
        ",12,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_views(self):
        
        query = f""SELECT * FROM information_schema.views WHERE table_schema NOT IN ('information_schema', 'pg_catalog')""
        result = self.run_native_query(query)
        return result

        ```
    ###Assistant : 
        List all views in PostgreSQL without the system views information_schema and pg_catalog
        
    "
1798,"def verify_ogr_field(self, ogr_field, model_field):
        
        if isinstance(ogr_field, OFTString) and isinstance(
            model_field, (models.CharField, models.TextField)
        ):
            if self.encoding and ogr_field.value is not None:
                # The encoding for OGR data sources may be specified here
                # (e.g., 'cp437' for Census Bureau boundary files).
                val = force_str(ogr_field.value, self.encoding)
            else:
                val = ogr_field.value
            if (
                model_field.max_length
                and val is not None
                and len(val) > model_field.max_length
            ):
                raise InvalidString(
                    ""%s model field maximum string length is %s, given %s characters.""
                    % (model_field.name, model_field.max_length, len(val))
                )
        elif isinstance(ogr_field, OFTReal) and isinstance(
            model_field, models.DecimalField
        ):
            try:
                # Creating an instance of the Decimal value to use.
                d = Decimal(str(ogr_field.value))
            except DecimalInvalidOperation:
                raise InvalidDecimal(
                    ""Could not construct decimal from: %s"" % ogr_field.value
                )

            # Getting the decimal value as a tuple.
            dtup = d.as_tuple()
            digits = dtup[1]
            d_idx = dtup[2]  # index where the decimal is

            # Maximum amount of precision, or digits to the left of the decimal.
            max_prec = model_field.max_digits - model_field.decimal_places

            # Getting the digits to the left of the decimal place for the
            # given decimal.
            if d_idx < 0:
                n_prec = len(digits[:d_idx])
            else:
                n_prec = len(digits) + d_idx

            # If we have more than the maximum digits allowed, then throw an
            # InvalidDecimal exception.
            if n_prec > max_prec:
                raise InvalidDecimal(
                    ""A DecimalField with max_digits %d, decimal_places %d must ""
                    ""round to an absolute value less than 10^%d.""
                    % (model_field.max_digits, model_field.decimal_places, max_prec)
                )
            val = d
        elif isinstance(ogr_field, (OFTReal, OFTString)) and isinstance(
            model_field, models.IntegerField
        ):
            # Attempt to convert any OFTReal and OFTString value to an OFTInteger.
            try:
                val = int(ogr_field.value)
            except ValueError:
                raise InvalidInteger(
                    ""Could not construct integer from: %s"" % ogr_field.value
                )
        else:
            val = ogr_field.value
        return val
","
        Verify if the OGR Field contents are acceptable to the model field. If
        they are, return the verified value, otherwise raise an exception.
        ",23,274,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def verify_ogr_field(self, ogr_field, model_field):
        
        if isinstance(ogr_field, OFTString) and isinstance(
            model_field, (models.CharField, models.TextField)
        ):
            if self.encoding and ogr_field.value is not None:
                # The encoding for OGR data sources may be specified here
                # (e.g., 'cp437' for Census Bureau boundary files).
                val = force_str(ogr_field.value, self.encoding)
            else:
                val = ogr_field.value
            if (
                model_field.max_length
                and val is not None
                and len(val) > model_field.max_length
            ):
                raise InvalidString(
                    ""%s model field maximum string length is %s, given %s characters.""
                    % (model_field.name, model_field.max_length, len(val))
                )
        elif isinstance(ogr_field, OFTReal) and isinstance(
            model_field, models.DecimalField
        ):
            try:
                # Creating an instance of the Decimal value to use.
                d = Decimal(str(ogr_field.value))
            except DecimalInvalidOperation:
                raise InvalidDecimal(
                    ""Could not construct decimal from: %s"" % ogr_field.value
                )

            # Getting the decimal value as a tuple.
            dtup = d.as_tuple()
            digits = dtup[1]
            d_idx = dtup[2]  # index where the decimal is

            # Maximum amount of precision, or digits to the left of the decimal.
            max_prec = model_field.max_digits - model_field.decimal_places

            # Getting the digits to the left of the decimal place for the
            # given decimal.
            if d_idx < 0:
                n_prec = len(digits[:d_idx])
            else:
                n_prec = len(digits) + d_idx

            # If we have more than the maximum digits allowed, then throw an
            # InvalidDecimal exception.
            if n_prec > max_prec:
                raise InvalidDecimal(
                    ""A DecimalField with max_digits %d, decimal_places %d must ""
                    ""round to an absolute value less than 10^%d.""
                    % (model_field.max_digits, model_field.decimal_places, max_prec)
                )
            val = d
        elif isinstance(ogr_field, (OFTReal, OFTString)) and isinstance(
            model_field, models.IntegerField
        ):
            # Attempt to convert any OFTReal and OFTString value to an OFTInteger.
            try:
                val = int(ogr_field.value)
            except ValueError:
                raise InvalidInteger(
                    ""Could not construct integer from: %s"" % ogr_field.value
                )
        else:
            val = ogr_field.value
        return val

        ```
    ###Assistant : 
        Verify if the OGR Field contents are acceptable to the model field. If
        they are, return the verified value, otherwise raise an exception.
        
    "
1799,"def get_streamer():
        
        if 'JINA_STREAMER_ARGS' in os.environ:
            args_dict = json.loads(os.environ['JINA_STREAMER_ARGS'])
            return GatewayStreamer(**args_dict)
        else:
            raise OSError('JINA_STREAMER_ARGS environment variable is not set')
","
        Return a streamer object based on the current environment context.
        The streamer object is contructed using runtime arguments stored in the `JINA_STREAMER_ARGS` environment variable.
        If this method is used outside a Jina context (process not controlled/orchestrated by jina), this method will
        raise an error.
        The streamer object does not have tracing/instrumentation capabilities.

        :return: Returns an instance of `GatewayStreamer`
        ",58,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_streamer():
        
        if 'JINA_STREAMER_ARGS' in os.environ:
            args_dict = json.loads(os.environ['JINA_STREAMER_ARGS'])
            return GatewayStreamer(**args_dict)
        else:
            raise OSError('JINA_STREAMER_ARGS environment variable is not set')

        ```
    ###Assistant : 
        Return a streamer object based on the current environment context.
        The streamer object is contructed using runtime arguments stored in the `JINA_STREAMER_ARGS` environment variable.
        If this method is used outside a Jina context (process not controlled/orchestrated by jina), this method will
        raise an error.
        The streamer object does not have tracing/instrumentation capabilities.

        :return: Returns an instance of `GatewayStreamer`
        
    "
1800,"def enter_positions(self) -> int:
        
        trades_created = 0

        whitelist = copy.deepcopy(self.active_pair_whitelist)
        if not whitelist:
            logger.info(""Active pair whitelist is empty."")
            return trades_created
        # Remove pairs for currently opened trades from the whitelist
        for trade in Trade.get_open_trades():
            if trade.pair in whitelist:
                whitelist.remove(trade.pair)
                logger.debug('Ignoring %s in pair whitelist', trade.pair)

        if not whitelist:
            logger.info(""No currency pair in active pair whitelist, ""
                        ""but checking to exit open trades."")
            return trades_created
        if PairLocks.is_global_lock(side='*'):
            # This only checks for total locks (both sides).
            # per-side locks will be evaluated by `is_pair_locked` within create_trade,
            # once the direction for the trade is clear.
            lock = PairLocks.get_pair_longest_lock('*')
            if lock:
                self.log_once(f""Global pairlock active until ""
                              f""{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}. ""
                              f""Not creating new trades, reason: {lock.reason}."", logger.info)
            else:
                self.log_once(""Global pairlock active. Not creating new trades."", logger.info)
            return trades_created
        # Create entity and execute trade for each pair from whitelist
        for pair in whitelist:
            try:
                trades_created += self.create_trade(pair)
            except DependencyException as exception:
                logger.warning('Unable to create trade for %s: %s', pair, exception)

        if not trades_created:
            logger.debug(""Found no enter signals for whitelisted currencies. Trying again..."")

        return trades_created
","
        Tries to execute entry orders for new trades (positions)
        ",9,170,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def enter_positions(self) -> int:
        
        trades_created = 0

        whitelist = copy.deepcopy(self.active_pair_whitelist)
        if not whitelist:
            logger.info(""Active pair whitelist is empty."")
            return trades_created
        # Remove pairs for currently opened trades from the whitelist
        for trade in Trade.get_open_trades():
            if trade.pair in whitelist:
                whitelist.remove(trade.pair)
                logger.debug('Ignoring %s in pair whitelist', trade.pair)

        if not whitelist:
            logger.info(""No currency pair in active pair whitelist, ""
                        ""but checking to exit open trades."")
            return trades_created
        if PairLocks.is_global_lock(side='*'):
            # This only checks for total locks (both sides).
            # per-side locks will be evaluated by `is_pair_locked` within create_trade,
            # once the direction for the trade is clear.
            lock = PairLocks.get_pair_longest_lock('*')
            if lock:
                self.log_once(f""Global pairlock active until ""
                              f""{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}. ""
                              f""Not creating new trades, reason: {lock.reason}."", logger.info)
            else:
                self.log_once(""Global pairlock active. Not creating new trades."", logger.info)
            return trades_created
        # Create entity and execute trade for each pair from whitelist
        for pair in whitelist:
            try:
                trades_created += self.create_trade(pair)
            except DependencyException as exception:
                logger.warning('Unable to create trade for %s: %s', pair, exception)

        if not trades_created:
            logger.debug(""Found no enter signals for whitelisted currencies. Trying again..."")

        return trades_created

        ```
    ###Assistant : 
        Tries to execute entry orders for new trades (positions)
        
    "
1801,"def validate_js_path(registered_paths, package_name, path_in_package_dist):
    if package_name not in registered_paths:
        raise exceptions.DependencyException(
            f
        )

    if path_in_package_dist not in registered_paths[package_name]:
        raise exceptions.DependencyException(
            f
        )

","
            Error loading dependency. ""{package_name}"" is not a registered library.
            Registered libraries are:
            {list(registered_paths.keys())}
            
            ""{package_name}"" is registered but the path requested is not valid.
            The path requested: ""{path_in_package_dist}""
            List of registered paths: {registered_paths}
            ",32,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_js_path(registered_paths, package_name, path_in_package_dist):
    if package_name not in registered_paths:
        raise exceptions.DependencyException(
            f
        )

    if path_in_package_dist not in registered_paths[package_name]:
        raise exceptions.DependencyException(
            f
        )


        ```
    ###Assistant : 
            Error loading dependency. ""{package_name}"" is not a registered library.
            Registered libraries are:
            {list(registered_paths.keys())}
            
            ""{package_name}"" is registered but the path requested is not valid.
            The path requested: ""{path_in_package_dist}""
            List of registered paths: {registered_paths}
            
    "
1802,"def get_all_node_ids() -> List[Tuple[str, str]]:
    
    node_ids = []
    # Sort on NodeID to ensure the ordering is deterministic across the cluster.
    for node in sorted(ray.nodes(), key=lambda entry: entry[""NodeID""]):
        # print(node)
        if node[""Alive""]:
            node_ids.append((node[""NodeID""], node[""NodeName""]))

    return node_ids

","Get IDs for all live nodes in the cluster.

    Returns a list of (node_id: str, ip_address: str). The node_id can be
    passed into the Ray SchedulingPolicy API.
    ",27,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all_node_ids() -> List[Tuple[str, str]]:
    
    node_ids = []
    # Sort on NodeID to ensure the ordering is deterministic across the cluster.
    for node in sorted(ray.nodes(), key=lambda entry: entry[""NodeID""]):
        # print(node)
        if node[""Alive""]:
            node_ids.append((node[""NodeID""], node[""NodeName""]))

    return node_ids


        ```
    ###Assistant : Get IDs for all live nodes in the cluster.

    Returns a list of (node_id: str, ip_address: str). The node_id can be
    passed into the Ray SchedulingPolicy API.
    
    "
1803,"def _ignore_comments(self, block):
        

        comment_spans = False
        while True:
            comment_start = block.find(b""#"")  # look for next comment
            if comment_start == -1:  # no comment found
                break
            comment_end = self._find_comment_end(block, comment_start)
            if comment_end != -1:  # comment ends in this block
                block = (
                    block[:comment_start] + block[comment_end + 1 :]
                )  # delete comment
            else:  # last comment continues to next block(s)
                block = block[:comment_start]
                comment_spans = True
                break
        return block, comment_spans
","
        Deletes comments from block. If comment does not end in this
        block, raises a flag.
        ",15,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _ignore_comments(self, block):
        

        comment_spans = False
        while True:
            comment_start = block.find(b""#"")  # look for next comment
            if comment_start == -1:  # no comment found
                break
            comment_end = self._find_comment_end(block, comment_start)
            if comment_end != -1:  # comment ends in this block
                block = (
                    block[:comment_start] + block[comment_end + 1 :]
                )  # delete comment
            else:  # last comment continues to next block(s)
                block = block[:comment_start]
                comment_spans = True
                break
        return block, comment_spans

        ```
    ###Assistant : 
        Deletes comments from block. If comment does not end in this
        block, raises a flag.
        
    "
1804,"def test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus):
    
    num_workers = 2
    assert os.environ[""CUDA_VISIBLE_DEVICES""] == """"
",Tests if GPU tensors are auto converted to CPU on driver.,11,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus):
    
    num_workers = 2
    assert os.environ[""CUDA_VISIBLE_DEVICES""] == """"

        ```
    ###Assistant : Tests if GPU tensors are auto converted to CPU on driver.
    "
1805,"def formatyear(self, theyear, width=3):
        
        v = []
        a = v.append
        width = max(width, 1)
        a('<table border=""0"" cellpadding=""0"" cellspacing=""0"" class=""%s"">' %
          self.cssclass_year)
        a('\n')
        a('<tr><th colspan=""%d"" class=""%s"">%s</th></tr>' % (
            width, self.cssclass_year_head, theyear))
        for i in range(January, January+12, width):
            # months in this row
            months = range(i, min(i+width, 13))
            a('<tr>')
            for m in months:
                a('<td>')
                a(self.formatmonth(theyear, m, withyear=False))
                a('</td>')
            a('</tr>')
        a('</table>')
        return ''.join(v)
","
        Return a formatted year as a table of tables.
        ",9,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def formatyear(self, theyear, width=3):
        
        v = []
        a = v.append
        width = max(width, 1)
        a('<table border=""0"" cellpadding=""0"" cellspacing=""0"" class=""%s"">' %
          self.cssclass_year)
        a('\n')
        a('<tr><th colspan=""%d"" class=""%s"">%s</th></tr>' % (
            width, self.cssclass_year_head, theyear))
        for i in range(January, January+12, width):
            # months in this row
            months = range(i, min(i+width, 13))
            a('<tr>')
            for m in months:
                a('<td>')
                a(self.formatmonth(theyear, m, withyear=False))
                a('</td>')
            a('</tr>')
        a('</table>')
        return ''.join(v)

        ```
    ###Assistant : 
        Return a formatted year as a table of tables.
        
    "
1806,"def predict(self, inputs):
        
        training = False
        user = inputs[""user""]
        input_seq = inputs[""input_seq""]
        candidate = inputs[""candidate""]

        mask = tf.expand_dims(tf.cast(tf.not_equal(input_seq, 0), tf.float32), -1)
        seq_embeddings, positional_embeddings = self.embedding(input_seq)  # (1, s, h)

        u0_latent = self.user_embedding_layer(user)
        u0_latent = u0_latent * (self.user_embedding_dim ** 0.5)  # (1, 1, h)
        u0_latent = tf.squeeze(u0_latent, axis=0)  # (1, h)
        test_user_emb = tf.tile(u0_latent, [1 + self.num_neg_test, 1])  # (101, h)

        u_latent = self.user_embedding_layer(user)
        u_latent = u_latent * (self.user_embedding_dim ** 0.5)  # (b, 1, h)
        u_latent = tf.tile(u_latent, [1, tf.shape(input_seq)[1], 1])  # (b, s, h)

        seq_embeddings = tf.reshape(
            tf.concat([seq_embeddings, u_latent], 2),
            [tf.shape(input_seq)[0], -1, self.hidden_units],
        )
        seq_embeddings += positional_embeddings  # (b, s, h1 + h2)

        seq_embeddings *= mask
        seq_attention = seq_embeddings
        seq_attention = self.encoder(seq_attention, training, mask)
        seq_attention = self.layer_normalization(seq_attention)  # (b, s, h1+h2)
        seq_emb = tf.reshape(
            seq_attention,
            [tf.shape(input_seq)[0] * self.seq_max_len, self.hidden_units],
        )  # (b*s1, h1+h2)

        candidate_emb = self.item_embedding_layer(candidate)  # (b, s2, h2)
        candidate_emb = tf.squeeze(candidate_emb, axis=0)  # (s2, h2)
        candidate_emb = tf.reshape(
            tf.concat([candidate_emb, test_user_emb], 1), [-1, self.hidden_units]
        )  # (b*s2, h1+h2)

        candidate_emb = tf.transpose(candidate_emb, perm=[1, 0])  # (h1+h2, b*s2)
        test_logits = tf.matmul(seq_emb, candidate_emb)  # (b*s1, b*s2)

        test_logits = tf.reshape(
            test_logits,
            [tf.shape(input_seq)[0], self.seq_max_len, 1 + self.num_neg_test],
        )  # (1, s, 101)
        test_logits = test_logits[:, -1, :]  # (1, 101)
        return test_logits
","
        Model prediction for candidate (negative) items

        ",6,198,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def predict(self, inputs):
        
        training = False
        user = inputs[""user""]
        input_seq = inputs[""input_seq""]
        candidate = inputs[""candidate""]

        mask = tf.expand_dims(tf.cast(tf.not_equal(input_seq, 0), tf.float32), -1)
        seq_embeddings, positional_embeddings = self.embedding(input_seq)  # (1, s, h)

        u0_latent = self.user_embedding_layer(user)
        u0_latent = u0_latent * (self.user_embedding_dim ** 0.5)  # (1, 1, h)
        u0_latent = tf.squeeze(u0_latent, axis=0)  # (1, h)
        test_user_emb = tf.tile(u0_latent, [1 + self.num_neg_test, 1])  # (101, h)

        u_latent = self.user_embedding_layer(user)
        u_latent = u_latent * (self.user_embedding_dim ** 0.5)  # (b, 1, h)
        u_latent = tf.tile(u_latent, [1, tf.shape(input_seq)[1], 1])  # (b, s, h)

        seq_embeddings = tf.reshape(
            tf.concat([seq_embeddings, u_latent], 2),
            [tf.shape(input_seq)[0], -1, self.hidden_units],
        )
        seq_embeddings += positional_embeddings  # (b, s, h1 + h2)

        seq_embeddings *= mask
        seq_attention = seq_embeddings
        seq_attention = self.encoder(seq_attention, training, mask)
        seq_attention = self.layer_normalization(seq_attention)  # (b, s, h1+h2)
        seq_emb = tf.reshape(
            seq_attention,
            [tf.shape(input_seq)[0] * self.seq_max_len, self.hidden_units],
        )  # (b*s1, h1+h2)

        candidate_emb = self.item_embedding_layer(candidate)  # (b, s2, h2)
        candidate_emb = tf.squeeze(candidate_emb, axis=0)  # (s2, h2)
        candidate_emb = tf.reshape(
            tf.concat([candidate_emb, test_user_emb], 1), [-1, self.hidden_units]
        )  # (b*s2, h1+h2)

        candidate_emb = tf.transpose(candidate_emb, perm=[1, 0])  # (h1+h2, b*s2)
        test_logits = tf.matmul(seq_emb, candidate_emb)  # (b*s1, b*s2)

        test_logits = tf.reshape(
            test_logits,
            [tf.shape(input_seq)[0], self.seq_max_len, 1 + self.num_neg_test],
        )  # (1, s, 101)
        test_logits = test_logits[:, -1, :]  # (1, 101)
        return test_logits

        ```
    ###Assistant : 
        Model prediction for candidate (negative) items

        
    "
1807,"def tobitmap(self, name=""image""):
        

        self.load()
        if self.mode != ""1"":
            msg = ""not a bitmap""
            raise ValueError(msg)
        data = self.tobytes(""xbm"")
        return b"""".join(
            [
                f""#define {name}_width {self.size[0]}\n"".encode(""ascii""),
                f""#define {name}_height {self.size[1]}\n"".encode(""ascii""),
                f""static char {name}_bits[] = {{\n"".encode(""ascii""),
                data,
                b""};"",
            ]
        )
","
        Returns the image converted to an X11 bitmap.

        .. note:: This method only works for mode ""1"" images.

        :param name: The name prefix to use for the bitmap variables.
        :returns: A string containing an X11 bitmap.
        :raises ValueError: If the mode is not ""1""
        ",44,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tobitmap(self, name=""image""):
        

        self.load()
        if self.mode != ""1"":
            msg = ""not a bitmap""
            raise ValueError(msg)
        data = self.tobytes(""xbm"")
        return b"""".join(
            [
                f""#define {name}_width {self.size[0]}\n"".encode(""ascii""),
                f""#define {name}_height {self.size[1]}\n"".encode(""ascii""),
                f""static char {name}_bits[] = {{\n"".encode(""ascii""),
                data,
                b""};"",
            ]
        )

        ```
    ###Assistant : 
        Returns the image converted to an X11 bitmap.

        .. note:: This method only works for mode ""1"" images.

        :param name: The name prefix to use for the bitmap variables.
        :returns: A string containing an X11 bitmap.
        :raises ValueError: If the mode is not ""1""
        
    "
1808,"def add_edges_from(self, ebunch_to_add, **attr):
        
        for e in ebunch_to_add:
            ne = len(e)
            if ne == 3:
                u, v, dd = e
            elif ne == 2:
                u, v = e
                dd = {}
            else:
                raise NetworkXError(f""Edge tuple {e} must be a 2-tuple or 3-tuple."")
            if u not in self._succ:
                if u is None:
                    raise ValueError(""None cannot be a node"")
                self._succ[u] = self.adjlist_inner_dict_factory()
                self._pred[u] = self.adjlist_inner_dict_factory()
                self._node[u] = self.node_attr_dict_factory()
            if v not in self._succ:
                if v is None:
                    raise ValueError(""None cannot be a node"")
                self._succ[v] = self.adjlist_inner_dict_factory()
                self._pred[v] = self.adjlist_inner_dict_factory()
                self._node[v] = self.node_attr_dict_factory()
            datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
            datadict.update(attr)
            datadict.update(dd)
            self._succ[u][v] = datadict
            self._pred[v][u] = datadict
","Add all the edges in ebunch_to_add.

        Parameters
        ----------
        ebunch_to_add : container of edges
            Each edge given in the container will be added to the
            graph. The edges must be given as 2-tuples (u, v) or
            3-tuples (u, v, d) where d is a dictionary containing edge data.
        attr : keyword arguments, optional
            Edge data (or labels or objects) can be assigned using
            keyword arguments.

        See Also
        --------
        add_edge : add a single edge
        add_weighted_edges_from : convenient way to add weighted edges

        Notes
        -----
        Adding the same edge twice has no effect but any edge data
        will be updated when each duplicate edge is added.

        Edge attributes specified in an ebunch take precedence over
        attributes specified via keyword arguments.

        When adding edges from an iterator over the graph you are changing,
        a `RuntimeError` can be raised with message:
        `RuntimeError: dictionary changed size during iteration`. This
        happens when the graph's underlying dictionary is modified during
        iteration. To avoid this error, evaluate the iterator into a separate
        object, e.g. by using `list(iterator_of_edges)`, and pass this
        object to `G.add_edges_from`.

        Examples
        --------
        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
        >>> e = zip(range(0, 3), range(1, 4))
        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3

        Associate data to edges

        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
        >>> G.add_edges_from([(3, 4), (1, 4)], label=""WN2898"")

        Evaluate an iterator over a graph if using it to modify the same graph

        >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
        >>> # Grow graph by one new node, adding edges to all existing nodes.
        >>> # wrong way - will raise RuntimeError
        >>> # G.add_edges_from(((5, n) for n in G.nodes))
        >>> # right way - note that there will be no self-edge for node 5
        >>> G.add_edges_from(list((5, n) for n in G.nodes))
        ",305,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_edges_from(self, ebunch_to_add, **attr):
        
        for e in ebunch_to_add:
            ne = len(e)
            if ne == 3:
                u, v, dd = e
            elif ne == 2:
                u, v = e
                dd = {}
            else:
                raise NetworkXError(f""Edge tuple {e} must be a 2-tuple or 3-tuple."")
            if u not in self._succ:
                if u is None:
                    raise ValueError(""None cannot be a node"")
                self._succ[u] = self.adjlist_inner_dict_factory()
                self._pred[u] = self.adjlist_inner_dict_factory()
                self._node[u] = self.node_attr_dict_factory()
            if v not in self._succ:
                if v is None:
                    raise ValueError(""None cannot be a node"")
                self._succ[v] = self.adjlist_inner_dict_factory()
                self._pred[v] = self.adjlist_inner_dict_factory()
                self._node[v] = self.node_attr_dict_factory()
            datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
            datadict.update(attr)
            datadict.update(dd)
            self._succ[u][v] = datadict
            self._pred[v][u] = datadict

        ```
    ###Assistant : Add all the edges in ebunch_to_add.

        Parameters
        ----------
        ebunch_to_add : container of edges
            Each edge given in the container will be added to the
            graph. The edges must be given as 2-tuples (u, v) or
            3-tuples (u, v, d) where d is a dictionary containing edge data.
        attr : keyword arguments, optional
            Edge data (or labels or objects) can be assigned using
            keyword arguments.

        See Also
        --------
        add_edge : add a single edge
        add_weighted_edges_from : convenient way to add weighted edges

        Notes
        -----
        Adding the same edge twice has no effect but any edge data
        will be updated when each duplicate edge is added.

        Edge attributes specified in an ebunch take precedence over
        attributes specified via keyword arguments.

        When adding edges from an iterator over the graph you are changing,
        a `RuntimeError` can be raised with message:
        `RuntimeError: dictionary changed size during iteration`. This
        happens when the graph's underlying dictionary is modified during
        iteration. To avoid this error, evaluate the iterator into a separate
        object, e.g. by using `list(iterator_of_edges)`, and pass this
        object to `G.add_edges_from`.

        Examples
        --------
        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
        >>> e = zip(range(0, 3), range(1, 4))
        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3

        Associate data to edges

        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
        >>> G.add_edges_from([(3, 4), (1, 4)], label=""WN2898"")

        Evaluate an iterator over a graph if using it to modify the same graph

        >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
        >>> # Grow graph by one new node, adding edges to all existing nodes.
        >>> # wrong way - will raise RuntimeError
        >>> # G.add_edges_from(((5, n) for n in G.nodes))
        >>> # right way - note that there will be no self-edge for node 5
        >>> G.add_edges_from(list((5, n) for n in G.nodes))
        
    "
1809,"def _select_device(self) -> None:
        
        if os.path.exists(plaidml.settings.user_settings):  # pylint:disable=no-member
            self._log(""debug"", ""Setting PlaidML devices from user_settings"")
        else:
            self._select_largest_gpu()
","
        If the plaidml user configuration settings exist, then set the default GPU from the
        settings file, Otherwise set the GPU to be the one with most VRAM. ",27,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _select_device(self) -> None:
        
        if os.path.exists(plaidml.settings.user_settings):  # pylint:disable=no-member
            self._log(""debug"", ""Setting PlaidML devices from user_settings"")
        else:
            self._select_largest_gpu()

        ```
    ###Assistant : 
        If the plaidml user configuration settings exist, then set the default GPU from the
        settings file, Otherwise set the GPU to be the one with most VRAM. 
    "
1810,"def test_readlink_non_canonical(file, source):
    
    intermediate = source.parent / ""intermediate.lnk""
    intermediate.symlink_to(source)
    target = source.parent / ""symlink.lnk""
    target.symlink_to(intermediate)
    try:
        result = file.readlink(path=target)
        assert result == str(intermediate)
    finally:
        intermediate.unlink()
        target.unlink()

","
    Test readlink where there are nested symlinks and canonicalize=False
    Should resolve to the first symlink
    ",15,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_readlink_non_canonical(file, source):
    
    intermediate = source.parent / ""intermediate.lnk""
    intermediate.symlink_to(source)
    target = source.parent / ""symlink.lnk""
    target.symlink_to(intermediate)
    try:
        result = file.readlink(path=target)
        assert result == str(intermediate)
    finally:
        intermediate.unlink()
        target.unlink()


        ```
    ###Assistant : 
    Test readlink where there are nested symlinks and canonicalize=False
    Should resolve to the first symlink
    
    "
1811,"def select_proxy(url, proxies):
    
    proxies = proxies or {}
    urlparts = urlparse(url)
    if urlparts.hostname is None:
        return proxies.get(urlparts.scheme, proxies.get(""all""))

    proxy_keys = [
        urlparts.scheme + ""://"" + urlparts.hostname,
        urlparts.scheme,
        ""all://"" + urlparts.hostname,
        ""all"",
    ]
    proxy = None
    for proxy_key in proxy_keys:
        if proxy_key in proxies:
            proxy = proxies[proxy_key]
            break

    return proxy

","Select a proxy for the url, if applicable.

    :param url: The url being for the request
    :param proxies: A dictionary of schemes or schemes and hosts to proxy URLs
    ",29,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def select_proxy(url, proxies):
    
    proxies = proxies or {}
    urlparts = urlparse(url)
    if urlparts.hostname is None:
        return proxies.get(urlparts.scheme, proxies.get(""all""))

    proxy_keys = [
        urlparts.scheme + ""://"" + urlparts.hostname,
        urlparts.scheme,
        ""all://"" + urlparts.hostname,
        ""all"",
    ]
    proxy = None
    for proxy_key in proxy_keys:
        if proxy_key in proxies:
            proxy = proxies[proxy_key]
            break

    return proxy


        ```
    ###Assistant : Select a proxy for the url, if applicable.

    :param url: The url being for the request
    :param proxies: A dictionary of schemes or schemes and hosts to proxy URLs
    
    "
1812,"def eof_received(self):
        
        try:
            if self._loop.get_debug():
                logger.debug(""%r received EOF"", self)

            self._wakeup_waiter(ConnectionResetError)

            if not self._in_handshake:
                keep_open = self._app_protocol.eof_received()
                if keep_open:
                    logger.warning('returning true from eof_received() '
                                   'has no effect when using ssl')
        finally:
            self._transport.close()
","Called when the other end of the low-level stream
        is half-closed.

        If this returns a false value (including None), the transport
        will close itself.  If it returns a true value, closing the
        transport is up to the protocol.
        ",38,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def eof_received(self):
        
        try:
            if self._loop.get_debug():
                logger.debug(""%r received EOF"", self)

            self._wakeup_waiter(ConnectionResetError)

            if not self._in_handshake:
                keep_open = self._app_protocol.eof_received()
                if keep_open:
                    logger.warning('returning true from eof_received() '
                                   'has no effect when using ssl')
        finally:
            self._transport.close()

        ```
    ###Assistant : Called when the other end of the low-level stream
        is half-closed.

        If this returns a false value (including None), the transport
        will close itself.  If it returns a true value, closing the
        transport is up to the protocol.
        
    "
1813,"def url_result(url, ie=None, video_id=None, video_title=None, *, url_transparent=False, **kwargs):
        
        if ie is not None:
            kwargs['ie_key'] = ie if isinstance(ie, str) else ie.ie_key()
        if video_id is not None:
            kwargs['id'] = video_id
        if video_title is not None:
            kwargs['title'] = video_title
        return {
            **kwargs,
            '_type': 'url_transparent' if url_transparent else 'url',
            'url': url,
        }
",Returns a URL that points to a page that should be processed,12,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def url_result(url, ie=None, video_id=None, video_title=None, *, url_transparent=False, **kwargs):
        
        if ie is not None:
            kwargs['ie_key'] = ie if isinstance(ie, str) else ie.ie_key()
        if video_id is not None:
            kwargs['id'] = video_id
        if video_title is not None:
            kwargs['title'] = video_title
        return {
            **kwargs,
            '_type': 'url_transparent' if url_transparent else 'url',
            'url': url,
        }

        ```
    ###Assistant : Returns a URL that points to a page that should be processed
    "
1814,"def _shade_colors(color, normals, lightsource=None):
    
    if lightsource is None:
        # chosen for backwards-compatibility
        lightsource = mcolors.LightSource(azdeg=225, altdeg=19.4712)

    with np.errstate(invalid=""ignore""):
        shade = ((normals / np.linalg.norm(normals, axis=1, keepdims=True))
                 @ lightsource.direction)
    mask = ~np.isnan(shade)

    if mask.any():
        # convert dot product to allowed shading fractions
        in_norm = mcolors.Normalize(-1, 1)
        out_norm = mcolors.Normalize(0.3, 1).inverse
","
    Shade *color* using normal vectors given by *normals*,
    assuming a *lightsource* (using default position if not given).
    *color* can also be an array of the same length as *normals*.
    ",29,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _shade_colors(color, normals, lightsource=None):
    
    if lightsource is None:
        # chosen for backwards-compatibility
        lightsource = mcolors.LightSource(azdeg=225, altdeg=19.4712)

    with np.errstate(invalid=""ignore""):
        shade = ((normals / np.linalg.norm(normals, axis=1, keepdims=True))
                 @ lightsource.direction)
    mask = ~np.isnan(shade)

    if mask.any():
        # convert dot product to allowed shading fractions
        in_norm = mcolors.Normalize(-1, 1)
        out_norm = mcolors.Normalize(0.3, 1).inverse

        ```
    ###Assistant : 
    Shade *color* using normal vectors given by *normals*,
    assuming a *lightsource* (using default position if not given).
    *color* can also be an array of the same length as *normals*.
    
    "
1815,"def score(self, X, y, **fit_params):
        
        check_is_fitted(self)
        return self.estimator_.score(self.transform(X), y, **fit_params)
","Reduce X to the selected features and return the score of the estimator.

        Parameters
        ----------
        X : array of shape [n_samples, n_features]
            The input samples.

        y : array of shape [n_samples]
            The target values.

        **fit_params : dict
            Parameters to pass to the `score` method of the underlying
            estimator.

            .. versionadded:: 1.0

        Returns
        -------
        score : float
            Score of the underlying base estimator computed with the selected
            features returned by `rfe.transform(X)` and `y`.
        ",72,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def score(self, X, y, **fit_params):
        
        check_is_fitted(self)
        return self.estimator_.score(self.transform(X), y, **fit_params)

        ```
    ###Assistant : Reduce X to the selected features and return the score of the estimator.

        Parameters
        ----------
        X : array of shape [n_samples, n_features]
            The input samples.

        y : array of shape [n_samples]
            The target values.

        **fit_params : dict
            Parameters to pass to the `score` method of the underlying
            estimator.

            .. versionadded:: 1.0

        Returns
        -------
        score : float
            Score of the underlying base estimator computed with the selected
            features returned by `rfe.transform(X)` and `y`.
        
    "
1816,"def reset(self, pos):
        
        self.value = pos
        self.velocity = 0
        if self.history:
            val = self.history[-1][1]
            self.history = [(time(), val)]
","(internal) Reset the value and the velocity to the `pos`.
        Mostly used when the bounds are checked.
        ",17,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reset(self, pos):
        
        self.value = pos
        self.velocity = 0
        if self.history:
            val = self.history[-1][1]
            self.history = [(time(), val)]

        ```
    ###Assistant : (internal) Reset the value and the velocity to the `pos`.
        Mostly used when the bounds are checked.
        
    "
1817,"def test_from_fan_speed(fan_speed, expected_result):
    
    assert _from_fan_speed(fan_speed) == expected_result


@pytest.mark.parametrize(
    ""percentage, expected_result"",
    [
        (1, 2),
        (100, 50),
        (50, 26),
    ],
)",Test that we can convert fan speed to percentage value.,10,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_from_fan_speed(fan_speed, expected_result):
    
    assert _from_fan_speed(fan_speed) == expected_result


@pytest.mark.parametrize(
    ""percentage, expected_result"",
    [
        (1, 2),
        (100, 50),
        (50, 26),
    ],
)
        ```
    ###Assistant : Test that we can convert fan speed to percentage value.
    "
1818,"def task_runner():
    
    from sentry.testutils.helpers.task_runner import TaskRunner

    return TaskRunner


@pytest.fixture","Context manager that ensures Celery tasks run directly inline where invoked.

    While this context manager is active any Celery tasks created will run immediately at
    the callsite rather than being sent to RabbitMQ and handled by a worker.
    ",38,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def task_runner():
    
    from sentry.testutils.helpers.task_runner import TaskRunner

    return TaskRunner


@pytest.fixture
        ```
    ###Assistant : Context manager that ensures Celery tasks run directly inline where invoked.

    While this context manager is active any Celery tasks created will run immediately at
    the callsite rather than being sent to RabbitMQ and handled by a worker.
    
    "
1819,"def get_random_string(length, allowed_chars=RANDOM_STRING_CHARS):
    
    return """".join(secrets.choice(allowed_chars) for i in range(length))

","
    Return a securely generated random string.

    The bit length of the returned value can be calculated with the formula:
        log_2(len(allowed_chars)^length)

    For example, with default `allowed_chars` (26+26+10), this gives:
      * length: 12, bit length =~ 71 bits
      * length: 22, bit length =~ 131 bits
    ",44,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_random_string(length, allowed_chars=RANDOM_STRING_CHARS):
    
    return """".join(secrets.choice(allowed_chars) for i in range(length))


        ```
    ###Assistant : 
    Return a securely generated random string.

    The bit length of the returned value can be calculated with the formula:
        log_2(len(allowed_chars)^length)

    For example, with default `allowed_chars` (26+26+10), this gives:
      * length: 12, bit length =~ 71 bits
      * length: 22, bit length =~ 131 bits
    
    "
1820,"def test_command_reply(tplaybook):
    
    tplaybook >> TEvent()
    tplaybook << TCommand()
    tplaybook >> tutils.reply()
    assert tplaybook
    assert tplaybook.actual[1] == tplaybook.actual[2].command

",CommandReplies can use relative offsets to point to the matching command.,11,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_command_reply(tplaybook):
    
    tplaybook >> TEvent()
    tplaybook << TCommand()
    tplaybook >> tutils.reply()
    assert tplaybook
    assert tplaybook.actual[1] == tplaybook.actual[2].command


        ```
    ###Assistant : CommandReplies can use relative offsets to point to the matching command.
    "
1821,"def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
    
    if all_reduce_alg is None:
        return None
    mirrored_all_reduce_options = {
        ""nccl"": tf.distribute.NcclAllReduce,
        ""hierarchical_copy"": tf.distribute.HierarchicalCopyAllReduce,
    }
    if all_reduce_alg not in mirrored_all_reduce_options:
        raise ValueError(
            ""When used with `mirrored`, valid values for all_reduce_alg are ""
            ""[`nccl`, `hierarchical_copy`].  Supplied value: {}"".format(
                all_reduce_alg
            )
        )
    cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
    return cross_device_ops_class(num_packs=num_packs)

","Return a CrossDeviceOps based on all_reduce_alg and num_packs.

    Args:
      all_reduce_alg: a string specifying which cross device op to pick, or None.
      num_packs: an integer specifying number of packs for the cross device op.

    Returns:
      tf.distribute.CrossDeviceOps object or None.

    Raises:
      ValueError: if `all_reduce_alg` not in [None, ""nccl"", ""hierarchical_copy""].
    ",47,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
    
    if all_reduce_alg is None:
        return None
    mirrored_all_reduce_options = {
        ""nccl"": tf.distribute.NcclAllReduce,
        ""hierarchical_copy"": tf.distribute.HierarchicalCopyAllReduce,
    }
    if all_reduce_alg not in mirrored_all_reduce_options:
        raise ValueError(
            ""When used with `mirrored`, valid values for all_reduce_alg are ""
            ""[`nccl`, `hierarchical_copy`].  Supplied value: {}"".format(
                all_reduce_alg
            )
        )
    cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
    return cross_device_ops_class(num_packs=num_packs)


        ```
    ###Assistant : Return a CrossDeviceOps based on all_reduce_alg and num_packs.

    Args:
      all_reduce_alg: a string specifying which cross device op to pick, or None.
      num_packs: an integer specifying number of packs for the cross device op.

    Returns:
      tf.distribute.CrossDeviceOps object or None.

    Raises:
      ValueError: if `all_reduce_alg` not in [None, ""nccl"", ""hierarchical_copy""].
    
    "
1822,"def test_delete_queue(self, mock_sb_admin_client):
        
        hook = AdminClientHook(azure_service_bus_conn_id=self.conn_id)
        hook.delete_queue(self.queue_name)
        expected_calls = [mock.call().__enter__().delete_queue(self.queue_name)]
        mock_sb_admin_client.assert_has_calls(expected_calls)
","
        Test Delete queue functionality by passing queue name, assert the function with values,
        mock the azure service bus function  `delete_queue`
        ",20,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_delete_queue(self, mock_sb_admin_client):
        
        hook = AdminClientHook(azure_service_bus_conn_id=self.conn_id)
        hook.delete_queue(self.queue_name)
        expected_calls = [mock.call().__enter__().delete_queue(self.queue_name)]
        mock_sb_admin_client.assert_has_calls(expected_calls)

        ```
    ###Assistant : 
        Test Delete queue functionality by passing queue name, assert the function with values,
        mock the azure service bus function  `delete_queue`
        
    "
1823,"def get_content_disposition(self):
        
        value = self.get('content-disposition')
        if value is None:
            return None
        c_d = _splitparam(value)[0].lower()
        return c_d

    # I.e. def walk(self): ...
    from email.iterators import walk

","Return the message's content-disposition if it exists, or None.

        The return values can be either 'inline', 'attachment' or None
        according to the rfc2183.
        ",23,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_content_disposition(self):
        
        value = self.get('content-disposition')
        if value is None:
            return None
        c_d = _splitparam(value)[0].lower()
        return c_d

    # I.e. def walk(self): ...
    from email.iterators import walk


        ```
    ###Assistant : Return the message's content-disposition if it exists, or None.

        The return values can be either 'inline', 'attachment' or None
        according to the rfc2183.
        
    "
1824,"def _get_or_create(self, s, name=None, dtype=None, broadcastable=None):
        

        # Defaults
        if name is None:
            name = s.name
        if dtype is None:
            dtype = 'floatX'
        if broadcastable is None:
            broadcastable = ()

        key = self._get_key(s, name, dtype=dtype, broadcastable=broadcastable)

        if key in self.cache:
            return self.cache[key]

        value = aet.tensor(name=name, dtype=dtype, shape=broadcastable)
        self.cache[key] = value
        return value
","
        Get the Aesara variable for a SymPy symbol from the cache, or create it
        if it does not exist.
        ",19,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_or_create(self, s, name=None, dtype=None, broadcastable=None):
        

        # Defaults
        if name is None:
            name = s.name
        if dtype is None:
            dtype = 'floatX'
        if broadcastable is None:
            broadcastable = ()

        key = self._get_key(s, name, dtype=dtype, broadcastable=broadcastable)

        if key in self.cache:
            return self.cache[key]

        value = aet.tensor(name=name, dtype=dtype, shape=broadcastable)
        self.cache[key] = value
        return value

        ```
    ###Assistant : 
        Get the Aesara variable for a SymPy symbol from the cache, or create it
        if it does not exist.
        
    "
1825,"def defer(self, *fields):
        
        self._not_support_combined_queries(""defer"")
        if self._fields is not None:
            raise TypeError(""Cannot call defer() after .values() or .values_list()"")
        clone = self._chain()
        if fields == (None,):
            clone.query.clear_deferred_loading()
        else:
            clone.query.add_deferred_loading(fields)
        return clone
","
        Defer the loading of data for certain fields until they are accessed.
        Add the set of deferred fields to any existing set of deferred fields.
        The only exception to this is if None is passed in as the only
        parameter, in which case removal all deferrals.
        ",46,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def defer(self, *fields):
        
        self._not_support_combined_queries(""defer"")
        if self._fields is not None:
            raise TypeError(""Cannot call defer() after .values() or .values_list()"")
        clone = self._chain()
        if fields == (None,):
            clone.query.clear_deferred_loading()
        else:
            clone.query.add_deferred_loading(fields)
        return clone

        ```
    ###Assistant : 
        Defer the loading of data for certain fields until they are accessed.
        Add the set of deferred fields to any existing set of deferred fields.
        The only exception to this is if None is passed in as the only
        parameter, in which case removal all deferrals.
        
    "
1826,"def match_seq(self, nodes, results=None):
        
        if len(nodes) != 1:
            return False
        return self.match(nodes[0], results)
","
        Does this pattern exactly match a sequence of nodes?

        Default implementation for non-wildcard patterns.
        ",14,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def match_seq(self, nodes, results=None):
        
        if len(nodes) != 1:
            return False
        return self.match(nodes[0], results)

        ```
    ###Assistant : 
        Does this pattern exactly match a sequence of nodes?

        Default implementation for non-wildcard patterns.
        
    "
1827,"def team_ids_with_membership(self) -> FrozenSet[int]:
        
        return frozenset(team.id for team in self._team_memberships.keys())
","Return the IDs of teams in which the user has actual membership.

        This represents the set of all teams for which `has_team_membership` returns
        true. Use that method where possible and use this property only when you need
        to iterate or query for all such teams.

        Compare to accessible_team_ids, which is equal to this property in the
        typical case but represents a superset of IDs in case of superuser access.
        ",69,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def team_ids_with_membership(self) -> FrozenSet[int]:
        
        return frozenset(team.id for team in self._team_memberships.keys())

        ```
    ###Assistant : Return the IDs of teams in which the user has actual membership.

        This represents the set of all teams for which `has_team_membership` returns
        true. Use that method where possible and use this property only when you need
        to iterate or query for all such teams.

        Compare to accessible_team_ids, which is equal to this property in the
        typical case but represents a superset of IDs in case of superuser access.
        
    "
1828,"def test_gumbel_softmax(self):
        
        for fw, sess in framework_iterator(frameworks=(""tf2"", ""tf""), session=True):
            batch_size = 1000
            num_categories = 5
            input_space = Box(-1.0, 1.0, shape=(batch_size, num_categories))
            input_space.seed(42)

            # Batch of size=n and deterministic.
            inputs = input_space.sample()
            gumbel_softmax = GumbelSoftmax(inputs, {}, temperature=1.0)

            expected = softmax(inputs)
            # Sample n times, expect always mean value (deterministic draw).
            out = gumbel_softmax.deterministic_sample()
            check(out, expected)

            # Batch of size=n and non-deterministic -> expect roughly that
            # the max-likelihood (argmax) ints are output (most of the time).
            inputs = input_space.sample()
            gumbel_softmax = GumbelSoftmax(inputs, {}, temperature=1.0)
            expected_mean = np.mean(np.argmax(inputs, -1)).astype(np.float32)
            outs = gumbel_softmax.sample()
            if sess:
                outs = sess.run(outs)
            check(np.mean(np.argmax(outs, -1)), expected_mean, rtol=0.08)
",Tests the GumbelSoftmax ActionDistribution (tf + eager only).,8,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_gumbel_softmax(self):
        
        for fw, sess in framework_iterator(frameworks=(""tf2"", ""tf""), session=True):
            batch_size = 1000
            num_categories = 5
            input_space = Box(-1.0, 1.0, shape=(batch_size, num_categories))
            input_space.seed(42)

            # Batch of size=n and deterministic.
            inputs = input_space.sample()
            gumbel_softmax = GumbelSoftmax(inputs, {}, temperature=1.0)

            expected = softmax(inputs)
            # Sample n times, expect always mean value (deterministic draw).
            out = gumbel_softmax.deterministic_sample()
            check(out, expected)

            # Batch of size=n and non-deterministic -> expect roughly that
            # the max-likelihood (argmax) ints are output (most of the time).
            inputs = input_space.sample()
            gumbel_softmax = GumbelSoftmax(inputs, {}, temperature=1.0)
            expected_mean = np.mean(np.argmax(inputs, -1)).astype(np.float32)
            outs = gumbel_softmax.sample()
            if sess:
                outs = sess.run(outs)
            check(np.mean(np.argmax(outs, -1)), expected_mean, rtol=0.08)

        ```
    ###Assistant : Tests the GumbelSoftmax ActionDistribution (tf + eager only).
    "
1829,"def get_confusion_matrix(gt_label, pred_label, num_classes):
    
    index = (gt_label * num_classes + pred_label).astype('int32')
    label_count = np.bincount(index)
    confusion_matrix = np.zeros((num_classes, num_classes))

    for i_label in range(num_classes):
        for i_pred_label in range(num_classes):
            cur_index = i_label * num_classes + i_pred_label
            if cur_index < len(label_count):
                confusion_matrix[i_label, i_pred_label] = label_count[cur_index]

    return confusion_matrix

","
    Calcute the confusion matrix by given label and pred
    :param gt_label: the ground truth label
    :param pred_label: the pred label
    :param num_classes: the nunber of class
    :return: the confusion matrix
    ",30,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_confusion_matrix(gt_label, pred_label, num_classes):
    
    index = (gt_label * num_classes + pred_label).astype('int32')
    label_count = np.bincount(index)
    confusion_matrix = np.zeros((num_classes, num_classes))

    for i_label in range(num_classes):
        for i_pred_label in range(num_classes):
            cur_index = i_label * num_classes + i_pred_label
            if cur_index < len(label_count):
                confusion_matrix[i_label, i_pred_label] = label_count[cur_index]

    return confusion_matrix


        ```
    ###Assistant : 
    Calcute the confusion matrix by given label and pred
    :param gt_label: the ground truth label
    :param pred_label: the pred label
    :param num_classes: the nunber of class
    :return: the confusion matrix
    
    "
1830,"def make_pad_mask(lengths, xs=None, length_dim=-1):
    
    if length_dim == 0:
        raise ValueError('length_dim cannot be 0: {}'.format(length_dim))

    if not isinstance(lengths, list):
        lengths = lengths.tolist()
    bs = int(len(lengths))
    if xs is None:
        maxlen = int(max(lengths))
    else:
        maxlen = xs.size(length_dim)

    seq_range = torch.arange(0, maxlen, dtype=torch.int64)
    seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
    seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
    mask = seq_range_expand >= seq_length_expand

    if xs is not None:
        assert xs.size(0) == bs, (xs.size(0), bs)

        if length_dim < 0:
            length_dim = xs.dim() + length_dim
        # ind = (:, None, ..., None, :, , None, ..., None)
        ind = tuple(slice(None) if i in (0, length_dim) else None
                    for i in range(xs.dim()))
        mask = mask[ind].expand_as(xs).to(xs.device)
    return mask

","Make mask tensor containing indices of padded part.

    Args:
        lengths (LongTensor or List): Batch of lengths (B,).
        xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor.
        length_dim (int, optional): Dimension indicator of the above tensor. See the example.

    Returns:
        Tensor: Mask tensor containing indices of padded part.
                dtype=torch.uint8 in PyTorch 1.2-
                dtype=torch.bool in PyTorch 1.2+ (including 1.2)

    Examples:
        With only lengths.

        >>> lengths = [5, 3, 2]
        >>> make_non_pad_mask(lengths)
        masks = [[0, 0, 0, 0 ,0],
                 [0, 0, 0, 1, 1],
                 [0, 0, 1, 1, 1]]

        With the reference tensor.

        >>> xs = torch.zeros((3, 2, 4))
        >>> make_pad_mask(lengths, xs)
        tensor([[[0, 0, 0, 0],
                 [0, 0, 0, 0]],
                [[0, 0, 0, 1],
                 [0, 0, 0, 1]],
                [[0, 0, 1, 1],
                 [0, 0, 1, 1]]], dtype=torch.uint8)
        >>> xs = torch.zeros((3, 2, 6))
        >>> make_pad_mask(lengths, xs)
        tensor([[[0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1]],
                [[0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1]],
                [[0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)

        With the reference tensor and dimension indicator.

        >>> xs = torch.zeros((3, 6, 6))
        >>> make_pad_mask(lengths, xs, 1)
        tensor([[[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1]],
                [[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1]],
                [[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)
        >>> make_pad_mask(lengths, xs, 2)
        tensor([[[0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1]],
                [[0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1]],
                [[0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)

    ",417,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_pad_mask(lengths, xs=None, length_dim=-1):
    
    if length_dim == 0:
        raise ValueError('length_dim cannot be 0: {}'.format(length_dim))

    if not isinstance(lengths, list):
        lengths = lengths.tolist()
    bs = int(len(lengths))
    if xs is None:
        maxlen = int(max(lengths))
    else:
        maxlen = xs.size(length_dim)

    seq_range = torch.arange(0, maxlen, dtype=torch.int64)
    seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
    seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
    mask = seq_range_expand >= seq_length_expand

    if xs is not None:
        assert xs.size(0) == bs, (xs.size(0), bs)

        if length_dim < 0:
            length_dim = xs.dim() + length_dim
        # ind = (:, None, ..., None, :, , None, ..., None)
        ind = tuple(slice(None) if i in (0, length_dim) else None
                    for i in range(xs.dim()))
        mask = mask[ind].expand_as(xs).to(xs.device)
    return mask


        ```
    ###Assistant : Make mask tensor containing indices of padded part.

    Args:
        lengths (LongTensor or List): Batch of lengths (B,).
        xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor.
        length_dim (int, optional): Dimension indicator of the above tensor. See the example.

    Returns:
        Tensor: Mask tensor containing indices of padded part.
                dtype=torch.uint8 in PyTorch 1.2-
                dtype=torch.bool in PyTorch 1.2+ (including 1.2)

    Examples:
        With only lengths.

        >>> lengths = [5, 3, 2]
        >>> make_non_pad_mask(lengths)
        masks = [[0, 0, 0, 0 ,0],
                 [0, 0, 0, 1, 1],
                 [0, 0, 1, 1, 1]]

        With the reference tensor.

        >>> xs = torch.zeros((3, 2, 4))
        >>> make_pad_mask(lengths, xs)
        tensor([[[0, 0, 0, 0],
                 [0, 0, 0, 0]],
                [[0, 0, 0, 1],
                 [0, 0, 0, 1]],
                [[0, 0, 1, 1],
                 [0, 0, 1, 1]]], dtype=torch.uint8)
        >>> xs = torch.zeros((3, 2, 6))
        >>> make_pad_mask(lengths, xs)
        tensor([[[0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1]],
                [[0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1]],
                [[0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)

        With the reference tensor and dimension indicator.

        >>> xs = torch.zeros((3, 6, 6))
        >>> make_pad_mask(lengths, xs, 1)
        tensor([[[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1]],
                [[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1]],
                [[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)
        >>> make_pad_mask(lengths, xs, 2)
        tensor([[[0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1],
                 [0, 0, 0, 0, 0, 1]],
                [[0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1],
                 [0, 0, 0, 1, 1, 1]],
                [[0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1],
                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)

    
    "
1831,"def test_guess_content_type_from_filename(self) -> None:
        
        data, content_type = encode_multipart_formdata({""file"": (""somefile"", b""zulip!"", None)})
        result = self.api_post(
            self.example_user(""hamlet""), ""/api/v1/user_uploads"", data, content_type=content_type
        )
        self.assert_json_success(result)

        data, content_type = encode_multipart_formdata({""file"": (""somefile.txt"", b""zulip!"", None)})
        result = self.api_post(
            self.example_user(""hamlet""), ""/api/v1/user_uploads"", data, content_type=content_type
        )
        self.assert_json_success(result)

    # This test will go through the code path for uploading files onto LOCAL storage
    # when Zulip is in DEVELOPMENT mode.","
        Test coverage for files without content-type in the metadata;
        in which case we try to guess the content-type from the filename.
        ",21,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_guess_content_type_from_filename(self) -> None:
        
        data, content_type = encode_multipart_formdata({""file"": (""somefile"", b""zulip!"", None)})
        result = self.api_post(
            self.example_user(""hamlet""), ""/api/v1/user_uploads"", data, content_type=content_type
        )
        self.assert_json_success(result)

        data, content_type = encode_multipart_formdata({""file"": (""somefile.txt"", b""zulip!"", None)})
        result = self.api_post(
            self.example_user(""hamlet""), ""/api/v1/user_uploads"", data, content_type=content_type
        )
        self.assert_json_success(result)

    # This test will go through the code path for uploading files onto LOCAL storage
    # when Zulip is in DEVELOPMENT mode.
        ```
    ###Assistant : 
        Test coverage for files without content-type in the metadata;
        in which case we try to guess the content-type from the filename.
        
    "
1832,"def get_member(name, members):
    
    # look first for a generic match - prepend lib and append .so
    expr = rf'lib{name}\.so'
    member = get_one_match(expr, members)
    if member:
        return member
    elif AIX_ABI == 64:
        expr = rf'lib{name}64\.so'
        member = get_one_match(expr, members)
    if member:
        return member
    # since an exact match with .so as suffix was not found
    # look for a versioned name
    # If a versioned name is not found, look for AIX legacy member name
    member = get_version(name, members)
    if member:
        return member
    else:
        return get_legacy(members)
","
    Return an archive member matching the request in name.
    Name is the library name without any prefix like lib, suffix like .so,
    or version number.
    Given a list of members find and return the most appropriate result
    Priority is given to generic libXXX.so, then a versioned libXXX.so.a.b.c
    and finally, legacy AIX naming scheme.
    ",53,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_member(name, members):
    
    # look first for a generic match - prepend lib and append .so
    expr = rf'lib{name}\.so'
    member = get_one_match(expr, members)
    if member:
        return member
    elif AIX_ABI == 64:
        expr = rf'lib{name}64\.so'
        member = get_one_match(expr, members)
    if member:
        return member
    # since an exact match with .so as suffix was not found
    # look for a versioned name
    # If a versioned name is not found, look for AIX legacy member name
    member = get_version(name, members)
    if member:
        return member
    else:
        return get_legacy(members)

        ```
    ###Assistant : 
    Return an archive member matching the request in name.
    Name is the library name without any prefix like lib, suffix like .so,
    or version number.
    Given a list of members find and return the most appropriate result
    Priority is given to generic libXXX.so, then a versioned libXXX.so.a.b.c
    and finally, legacy AIX naming scheme.
    
    "
1833,"def check_original_docker_image():
    if not os.path.isfile('/.dockerenv') or os.environ.get('PYTHON_BASE_IMAGE') is None:
        raise pytest.skip(
            
        )

","Adding/removing a user as part of a test is very bad for host os
(especially if the user already existed to begin with on the OS), therefore we check if we run inside a
the official docker container and only allow to run the test there. This is done by checking /.dockerenv
file (always present inside container) and checking for PYTHON_BASE_IMAGE variable.
",62,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_original_docker_image():
    if not os.path.isfile('/.dockerenv') or os.environ.get('PYTHON_BASE_IMAGE') is None:
        raise pytest.skip(
            
        )


        ```
    ###Assistant : Adding/removing a user as part of a test is very bad for host os
(especially if the user already existed to begin with on the OS), therefore we check if we run inside a
the official docker container and only allow to run the test there. This is done by checking /.dockerenv
file (always present inside container) and checking for PYTHON_BASE_IMAGE variable.

    "
1834,"def _useWizardInterface():
    

    if not conf.wizard:
        return

    logger.info(""starting wizard interface"")

    while not conf.url:
        message = ""Please enter full target URL (-u): ""
        conf.url = readInput(message, default=None)

    message = ""%s data (--data) [Enter for None]: "" % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
    conf.data = readInput(message, default=None)

    if not (any('=' in _ for _ in (conf.url, conf.data)) or '*' in conf.url):
        warnMsg = ""no GET and/or %s parameter(s) found for testing "" % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
        warnMsg += ""(e.g. GET parameter 'id' in 'http://www.site.com/vuln.php?id=1'). ""
        if not conf.crawlDepth and not conf.forms:
            warnMsg += ""Will search for forms""
            conf.forms = True
        logger.warning(warnMsg)

    choice = None

    while choice is None or choice not in ("""", ""1"", ""2"", ""3""):
        message = ""Injection difficulty (--level/--risk). Please choose:\n""
        message += ""[1] Normal (default)\n[2] Medium\n[3] Hard""
        choice = readInput(message, default='1')

        if choice == '2':
            conf.risk = 2
            conf.level = 3
        elif choice == '3':
            conf.risk = 3
            conf.level = 5
        else:
            conf.risk = 1
            conf.level = 1

    if not conf.getAll:
        choice = None

        while choice is None or choice not in ("""", ""1"", ""2"", ""3""):
            message = ""Enumeration (--banner/--current-user/etc). Please choose:\n""
            message += ""[1] Basic (default)\n[2] Intermediate\n[3] All""
            choice = readInput(message, default='1')

            if choice == '2':
                options = WIZARD.INTERMEDIATE
            elif choice == '3':
                options = WIZARD.ALL
            else:
                options = WIZARD.BASIC

            for _ in options:
                conf.__setitem__(_, True)

    logger.debug(""muting sqlmap.. it will do the magic for you"")
    conf.verbose = 0

    conf.batch = True
    conf.threads = 4

    dataToStdout(""\nsqlmap is running, please wait..\n\n"")

    kb.wizardMode = True
","
    Presents simple wizard interface for beginner users
    ",7,253,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _useWizardInterface():
    

    if not conf.wizard:
        return

    logger.info(""starting wizard interface"")

    while not conf.url:
        message = ""Please enter full target URL (-u): ""
        conf.url = readInput(message, default=None)

    message = ""%s data (--data) [Enter for None]: "" % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
    conf.data = readInput(message, default=None)

    if not (any('=' in _ for _ in (conf.url, conf.data)) or '*' in conf.url):
        warnMsg = ""no GET and/or %s parameter(s) found for testing "" % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
        warnMsg += ""(e.g. GET parameter 'id' in 'http://www.site.com/vuln.php?id=1'). ""
        if not conf.crawlDepth and not conf.forms:
            warnMsg += ""Will search for forms""
            conf.forms = True
        logger.warning(warnMsg)

    choice = None

    while choice is None or choice not in ("""", ""1"", ""2"", ""3""):
        message = ""Injection difficulty (--level/--risk). Please choose:\n""
        message += ""[1] Normal (default)\n[2] Medium\n[3] Hard""
        choice = readInput(message, default='1')

        if choice == '2':
            conf.risk = 2
            conf.level = 3
        elif choice == '3':
            conf.risk = 3
            conf.level = 5
        else:
            conf.risk = 1
            conf.level = 1

    if not conf.getAll:
        choice = None

        while choice is None or choice not in ("""", ""1"", ""2"", ""3""):
            message = ""Enumeration (--banner/--current-user/etc). Please choose:\n""
            message += ""[1] Basic (default)\n[2] Intermediate\n[3] All""
            choice = readInput(message, default='1')

            if choice == '2':
                options = WIZARD.INTERMEDIATE
            elif choice == '3':
                options = WIZARD.ALL
            else:
                options = WIZARD.BASIC

            for _ in options:
                conf.__setitem__(_, True)

    logger.debug(""muting sqlmap.. it will do the magic for you"")
    conf.verbose = 0

    conf.batch = True
    conf.threads = 4

    dataToStdout(""\nsqlmap is running, please wait..\n\n"")

    kb.wizardMode = True

        ```
    ###Assistant : 
    Presents simple wizard interface for beginner users
    
    "
1835,"async def run_migrations_online() -> None:
    

    engine = await db_interface.engine()

    versions_dir = context.get_x_argument(as_dictionary=True).get(""versions_dir"", None)

    if versions_dir is None:
        # if version dir is not explicitly provided determine versions location from dialect
        dialect = get_dialect(engine=engine)
        if dialect.name == ""postgresql"":
            versions_dir = Path(context.script.dir / ""postgresql"")
        elif dialect.name == ""sqlite"":
            versions_dir = Path(context.script.dir / ""sqlite"")
        else:
            raise ValueError(f""No versions dir exists for dialect: {dialect.name}"")

    context.script.version_locations = [versions_dir]
","
    Run migrations in 'online' mode.

    In this scenario we need to create an Engine
    and associate a connection with the context.
    ",21,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def run_migrations_online() -> None:
    

    engine = await db_interface.engine()

    versions_dir = context.get_x_argument(as_dictionary=True).get(""versions_dir"", None)

    if versions_dir is None:
        # if version dir is not explicitly provided determine versions location from dialect
        dialect = get_dialect(engine=engine)
        if dialect.name == ""postgresql"":
            versions_dir = Path(context.script.dir / ""postgresql"")
        elif dialect.name == ""sqlite"":
            versions_dir = Path(context.script.dir / ""sqlite"")
        else:
            raise ValueError(f""No versions dir exists for dialect: {dialect.name}"")

    context.script.version_locations = [versions_dir]

        ```
    ###Assistant : 
    Run migrations in 'online' mode.

    In this scenario we need to create an Engine
    and associate a connection with the context.
    
    "
1836,"def get_mop_query(doctype, txt, searchfield, start, page_len, filters):
	return frappe.db.sql(
		,
		{""parent"": filters.get(""parent""), ""start"": start, ""page_len"": page_len, ""txt"": ""%%%s%%"" % txt},
	)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs"," select mode_of_payment from `tabPayment Order Reference`
		where parent = %(parent)s and mode_of_payment like %(txt)s
		limit %(start)s, %(page_len)s",17,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_mop_query(doctype, txt, searchfield, start, page_len, filters):
	return frappe.db.sql(
		,
		{""parent"": filters.get(""parent""), ""start"": start, ""page_len"": page_len, ""txt"": ""%%%s%%"" % txt},
	)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs
        ```
    ###Assistant :  select mode_of_payment from `tabPayment Order Reference`
		where parent = %(parent)s and mode_of_payment like %(txt)s
		limit %(start)s, %(page_len)s
    "
1837,"def test_doc_pyplot_summary():
    
    pyplot_docs = Path(__file__).parent / '../../../doc/api/pyplot_summary.rst'
    if not pyplot_docs.exists():
        pytest.skip(""Documentation sources not available"")

    lines = pyplot_docs.read_text()
    m = re.search(r':nosignatures:\n\n(.*?)\n\n', lines, re.DOTALL)
    doc_functions = set(line.strip() for line in m.group(1).split('\n'))
    plot_commands = set(plt.get_plot_commands())
    missing = plot_commands.difference(doc_functions)
    if missing:
        raise AssertionError(
            f""The following pyplot functions are not listed in the ""
            f""documentation. Please add them to doc/api/pyplot_summary.rst: ""
            f""{missing!r}"")
    extra = doc_functions.difference(plot_commands)
    if extra:
        raise AssertionError(
            f""The following functions are listed in the pyplot documentation, ""
            f""but they do not exist in pyplot. ""
            f""Please remove them from doc/api/pyplot_summary.rst: {extra!r}"")
",Test that pyplot_summary lists all the plot functions.,8,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_doc_pyplot_summary():
    
    pyplot_docs = Path(__file__).parent / '../../../doc/api/pyplot_summary.rst'
    if not pyplot_docs.exists():
        pytest.skip(""Documentation sources not available"")

    lines = pyplot_docs.read_text()
    m = re.search(r':nosignatures:\n\n(.*?)\n\n', lines, re.DOTALL)
    doc_functions = set(line.strip() for line in m.group(1).split('\n'))
    plot_commands = set(plt.get_plot_commands())
    missing = plot_commands.difference(doc_functions)
    if missing:
        raise AssertionError(
            f""The following pyplot functions are not listed in the ""
            f""documentation. Please add them to doc/api/pyplot_summary.rst: ""
            f""{missing!r}"")
    extra = doc_functions.difference(plot_commands)
    if extra:
        raise AssertionError(
            f""The following functions are listed in the pyplot documentation, ""
            f""but they do not exist in pyplot. ""
            f""Please remove them from doc/api/pyplot_summary.rst: {extra!r}"")

        ```
    ###Assistant : Test that pyplot_summary lists all the plot functions.
    "
1838,"def validate_child_on_delete(row, parent):
	
	if parent.doctype == ""Sales Order"":
		if flt(row.delivered_qty):
			frappe.throw(
				_(""Row #{0}: Cannot delete item {1} which has already been delivered"").format(
					row.idx, row.item_code
				)
			)
		if flt(row.work_order_qty):
			frappe.throw(
				_(""Row #{0}: Cannot delete item {1} which has work order assigned to it."").format(
					row.idx, row.item_code
				)
			)
		if flt(row.ordered_qty):
			frappe.throw(
				_(""Row #{0}: Cannot delete item {1} which is assigned to customer's purchase order."").format(
					row.idx, row.item_code
				)
			)

	if parent.doctype == ""Purchase Order"" and flt(row.received_qty):
		frappe.throw(
			_(""Row #{0}: Cannot delete item {1} which has already been received"").format(
				row.idx, row.item_code
			)
		)

	if flt(row.billed_amt):
		frappe.throw(
			_(""Row #{0}: Cannot delete item {1} which has already been billed."").format(
				row.idx, row.item_code
			)
		)

",Check if partially transacted item (row) is being deleted.,9,107,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_child_on_delete(row, parent):
	
	if parent.doctype == ""Sales Order"":
		if flt(row.delivered_qty):
			frappe.throw(
				_(""Row #{0}: Cannot delete item {1} which has already been delivered"").format(
					row.idx, row.item_code
				)
			)
		if flt(row.work_order_qty):
			frappe.throw(
				_(""Row #{0}: Cannot delete item {1} which has work order assigned to it."").format(
					row.idx, row.item_code
				)
			)
		if flt(row.ordered_qty):
			frappe.throw(
				_(""Row #{0}: Cannot delete item {1} which is assigned to customer's purchase order."").format(
					row.idx, row.item_code
				)
			)

	if parent.doctype == ""Purchase Order"" and flt(row.received_qty):
		frappe.throw(
			_(""Row #{0}: Cannot delete item {1} which has already been received"").format(
				row.idx, row.item_code
			)
		)

	if flt(row.billed_amt):
		frappe.throw(
			_(""Row #{0}: Cannot delete item {1} which has already been billed."").format(
				row.idx, row.item_code
			)
		)


        ```
    ###Assistant : Check if partially transacted item (row) is being deleted.
    "
1839,"def _get_items(self):
        
        postprocess_items = {}
        # Debug Landmarks
        if (hasattr(self._args, 'debug_landmarks') and self._args.debug_landmarks):
            postprocess_items[""DebugLandmarks""] = None

        # Face Filter post processing
        if ((hasattr(self._args, ""filter"") and self._args.filter is not None) or
                (hasattr(self._args, ""nfilter"") and
                 self._args.nfilter is not None)):

            if hasattr(self._args, ""detector""):
                detector = self._args.detector.replace(""-"", ""_"").lower()
            else:
                detector = ""cv2_dnn""
            if hasattr(self._args, ""aligner""):
                aligner = self._args.aligner.replace(""-"", ""_"").lower()
            else:
                aligner = ""cv2_dnn""

            face_filter = dict(detector=detector,
                               aligner=aligner,
                               multiprocess=not self._args.singleprocess)
            filter_lists = {}
            if hasattr(self._args, ""ref_threshold""):
                face_filter[""ref_threshold""] = self._args.ref_threshold
            for filter_type in ('filter', 'nfilter'):
                filter_args = getattr(self._args, filter_type, None)
                filter_args = None if not filter_args else filter_args
                filter_lists[filter_type] = filter_args
            face_filter[""filter_lists""] = filter_lists
            postprocess_items[""FaceFilter""] = {""kwargs"": face_filter}

        logger.debug(""Postprocess Items: %s"", postprocess_items)
        return postprocess_items
"," Check the passed in command line arguments for requested actions,

        For any requested actions, add the item to the actions list along with
        any relevant arguments and keyword arguments.

        Returns
        -------
        dict
            The name of the action to be performed as the key. Any action specific
            arguments and keyword arguments as the value.
        ",53,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_items(self):
        
        postprocess_items = {}
        # Debug Landmarks
        if (hasattr(self._args, 'debug_landmarks') and self._args.debug_landmarks):
            postprocess_items[""DebugLandmarks""] = None

        # Face Filter post processing
        if ((hasattr(self._args, ""filter"") and self._args.filter is not None) or
                (hasattr(self._args, ""nfilter"") and
                 self._args.nfilter is not None)):

            if hasattr(self._args, ""detector""):
                detector = self._args.detector.replace(""-"", ""_"").lower()
            else:
                detector = ""cv2_dnn""
            if hasattr(self._args, ""aligner""):
                aligner = self._args.aligner.replace(""-"", ""_"").lower()
            else:
                aligner = ""cv2_dnn""

            face_filter = dict(detector=detector,
                               aligner=aligner,
                               multiprocess=not self._args.singleprocess)
            filter_lists = {}
            if hasattr(self._args, ""ref_threshold""):
                face_filter[""ref_threshold""] = self._args.ref_threshold
            for filter_type in ('filter', 'nfilter'):
                filter_args = getattr(self._args, filter_type, None)
                filter_args = None if not filter_args else filter_args
                filter_lists[filter_type] = filter_args
            face_filter[""filter_lists""] = filter_lists
            postprocess_items[""FaceFilter""] = {""kwargs"": face_filter}

        logger.debug(""Postprocess Items: %s"", postprocess_items)
        return postprocess_items

        ```
    ###Assistant :  Check the passed in command line arguments for requested actions,

        For any requested actions, add the item to the actions list along with
        any relevant arguments and keyword arguments.

        Returns
        -------
        dict
            The name of the action to be performed as the key. Any action specific
            arguments and keyword arguments as the value.
        
    "
1840,"def _collapse_addresses_internal(addresses):
    
    # First merge
    to_merge = list(addresses)
    subnets = {}
    while to_merge:
        net = to_merge.pop()
        supernet = net.supernet()
        existing = subnets.get(supernet)
        if existing is None:
            subnets[supernet] = net
        elif existing != net:
            # Merge consecutive subnets
            del subnets[supernet]
            to_merge.append(supernet)
    # Then iterate over resulting networks, skipping subsumed subnets
    last = None
    for net in sorted(subnets.values()):
        if last is not None:
            # Since they are sorted, last.network_address <= net.network_address
            # is a given.
            if last.broadcast_address >= net.broadcast_address:
                continue
        yield net
        last = net

","Loops through the addresses, collapsing concurrent netblocks.

    Example:

        ip1 = IPv4Network('192.0.2.0/26')
        ip2 = IPv4Network('192.0.2.64/26')
        ip3 = IPv4Network('192.0.2.128/26')
        ip4 = IPv4Network('192.0.2.192/26')

        _collapse_addresses_internal([ip1, ip2, ip3, ip4]) ->
          [IPv4Network('192.0.2.0/24')]

        This shouldn't be called directly; it is called via
          collapse_addresses([]).

    Args:
        addresses: A list of IPv4Network's or IPv6Network's

    Returns:
        A list of IPv4Network's or IPv6Network's depending on what we were
        passed.

    ",57,83,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _collapse_addresses_internal(addresses):
    
    # First merge
    to_merge = list(addresses)
    subnets = {}
    while to_merge:
        net = to_merge.pop()
        supernet = net.supernet()
        existing = subnets.get(supernet)
        if existing is None:
            subnets[supernet] = net
        elif existing != net:
            # Merge consecutive subnets
            del subnets[supernet]
            to_merge.append(supernet)
    # Then iterate over resulting networks, skipping subsumed subnets
    last = None
    for net in sorted(subnets.values()):
        if last is not None:
            # Since they are sorted, last.network_address <= net.network_address
            # is a given.
            if last.broadcast_address >= net.broadcast_address:
                continue
        yield net
        last = net


        ```
    ###Assistant : Loops through the addresses, collapsing concurrent netblocks.

    Example:

        ip1 = IPv4Network('192.0.2.0/26')
        ip2 = IPv4Network('192.0.2.64/26')
        ip3 = IPv4Network('192.0.2.128/26')
        ip4 = IPv4Network('192.0.2.192/26')

        _collapse_addresses_internal([ip1, ip2, ip3, ip4]) ->
          [IPv4Network('192.0.2.0/24')]

        This shouldn't be called directly; it is called via
          collapse_addresses([]).

    Args:
        addresses: A list of IPv4Network's or IPv6Network's

    Returns:
        A list of IPv4Network's or IPv6Network's depending on what we were
        passed.

    
    "
1841,"def inaxes(self, xy):
        
        axes_list = [a for a in self.figure.get_axes()
                     if a.patch.contains_point(xy) and a.get_visible()]
        if axes_list:
            axes = cbook._topmost_artist(axes_list)
        else:
            axes = None

        return axes
","
        Return the topmost visible `~.axes.Axes` containing the point *xy*.

        Parameters
        ----------
        xy : (float, float)
            (x, y) pixel positions from left/bottom of the canvas.

        Returns
        -------
        `~matplotlib.axes.Axes` or None
            The topmost visible Axes containing the point, or None if there
            is no Axes at the point.
        ",46,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def inaxes(self, xy):
        
        axes_list = [a for a in self.figure.get_axes()
                     if a.patch.contains_point(xy) and a.get_visible()]
        if axes_list:
            axes = cbook._topmost_artist(axes_list)
        else:
            axes = None

        return axes

        ```
    ###Assistant : 
        Return the topmost visible `~.axes.Axes` containing the point *xy*.

        Parameters
        ----------
        xy : (float, float)
            (x, y) pixel positions from left/bottom of the canvas.

        Returns
        -------
        `~matplotlib.axes.Axes` or None
            The topmost visible Axes containing the point, or None if there
            is no Axes at the point.
        
    "
1842,"def upgrade():
    
    conn = op.get_bind()
    is_sqlite = bool(conn.dialect.name == ""sqlite"")
    is_mssql = bool(conn.dialect.name == ""mssql"")

    if is_sqlite:
        op.execute(""PRAGMA foreign_keys=off"")

    with op.batch_alter_table('dag_run', schema=None) as batch_op:
        batch_op.add_column(sa.Column('last_scheduling_decision', TIMESTAMP, nullable=True))
        batch_op.create_index('idx_last_scheduling_decision', ['last_scheduling_decision'], unique=False)
        batch_op.add_column(sa.Column('dag_hash', sa.String(32), nullable=True))

    with op.batch_alter_table('dag', schema=None) as batch_op:
        batch_op.add_column(sa.Column('next_dagrun', TIMESTAMP, nullable=True))
        batch_op.add_column(sa.Column('next_dagrun_create_after', TIMESTAMP, nullable=True))
        # Create with nullable and no default, then ALTER to set values, to avoid table level lock
        batch_op.add_column(sa.Column('concurrency', sa.Integer(), nullable=True))
        batch_op.add_column(sa.Column('has_task_concurrency_limits', sa.Boolean(), nullable=True))

        batch_op.create_index('idx_next_dagrun_create_after', ['next_dagrun_create_after'], unique=False)

    try:
        from airflow.configuration import conf

        concurrency = conf.getint('core', 'dag_concurrency', fallback=16)
    except:  # noqa
        concurrency = 16

    # Set it to true here as it makes us take the slow/more complete path, and when it's next parsed by the
    # DagParser it will get set to correct value.

    op.execute(
        f
    )

    with op.batch_alter_table('dag', schema=None) as batch_op:
        batch_op.alter_column('concurrency', type_=sa.Integer(), nullable=False)
        batch_op.alter_column('has_task_concurrency_limits', type_=sa.Boolean(), nullable=False)

    if is_sqlite:
        op.execute(""PRAGMA foreign_keys=on"")

","Apply Add ``scheduling_decision`` to ``DagRun`` and ``DAG``
        UPDATE dag SET
            concurrency={concurrency},
            has_task_concurrency_limits={1 if is_sqlite or is_mssql else sa.true()}
        where concurrency IS NULL
        ",22,135,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def upgrade():
    
    conn = op.get_bind()
    is_sqlite = bool(conn.dialect.name == ""sqlite"")
    is_mssql = bool(conn.dialect.name == ""mssql"")

    if is_sqlite:
        op.execute(""PRAGMA foreign_keys=off"")

    with op.batch_alter_table('dag_run', schema=None) as batch_op:
        batch_op.add_column(sa.Column('last_scheduling_decision', TIMESTAMP, nullable=True))
        batch_op.create_index('idx_last_scheduling_decision', ['last_scheduling_decision'], unique=False)
        batch_op.add_column(sa.Column('dag_hash', sa.String(32), nullable=True))

    with op.batch_alter_table('dag', schema=None) as batch_op:
        batch_op.add_column(sa.Column('next_dagrun', TIMESTAMP, nullable=True))
        batch_op.add_column(sa.Column('next_dagrun_create_after', TIMESTAMP, nullable=True))
        # Create with nullable and no default, then ALTER to set values, to avoid table level lock
        batch_op.add_column(sa.Column('concurrency', sa.Integer(), nullable=True))
        batch_op.add_column(sa.Column('has_task_concurrency_limits', sa.Boolean(), nullable=True))

        batch_op.create_index('idx_next_dagrun_create_after', ['next_dagrun_create_after'], unique=False)

    try:
        from airflow.configuration import conf

        concurrency = conf.getint('core', 'dag_concurrency', fallback=16)
    except:  # noqa
        concurrency = 16

    # Set it to true here as it makes us take the slow/more complete path, and when it's next parsed by the
    # DagParser it will get set to correct value.

    op.execute(
        f
    )

    with op.batch_alter_table('dag', schema=None) as batch_op:
        batch_op.alter_column('concurrency', type_=sa.Integer(), nullable=False)
        batch_op.alter_column('has_task_concurrency_limits', type_=sa.Boolean(), nullable=False)

    if is_sqlite:
        op.execute(""PRAGMA foreign_keys=on"")


        ```
    ###Assistant : Apply Add ``scheduling_decision`` to ``DagRun`` and ``DAG``
        UPDATE dag SET
            concurrency={concurrency},
            has_task_concurrency_limits={1 if is_sqlite or is_mssql else sa.true()}
        where concurrency IS NULL
        
    "
1843,"def enrich_ledger_entries_with_event_data(self, ledger_entries):
        
        # Build up a list of the subset of ledger entries we are expected
        # to enrich with event metadata.
        event_id_to_ledger_entry = {}
        for entry in ledger_entries:
            maybe_event_id: Optional[str] = entry.get(""event_id"")
            if maybe_event_id:
                event_id_to_ledger_entry[maybe_event_id] = entry

        # Nothing to enrich; short-circuit
        if len(event_id_to_ledger_entry) == 0:
            return ledger_entries
","
        Enriches a list of ledger entries with event metadata (applies only to decrements that
        have an event_id property set, i.e. automated decrements to the ledger applied by Orb).
        ",28,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def enrich_ledger_entries_with_event_data(self, ledger_entries):
        
        # Build up a list of the subset of ledger entries we are expected
        # to enrich with event metadata.
        event_id_to_ledger_entry = {}
        for entry in ledger_entries:
            maybe_event_id: Optional[str] = entry.get(""event_id"")
            if maybe_event_id:
                event_id_to_ledger_entry[maybe_event_id] = entry

        # Nothing to enrich; short-circuit
        if len(event_id_to_ledger_entry) == 0:
            return ledger_entries

        ```
    ###Assistant : 
        Enriches a list of ledger entries with event metadata (applies only to decrements that
        have an event_id property set, i.e. automated decrements to the ledger applied by Orb).
        
    "
1844,"def test_cache() -> None:
    
    ledger_store = DictLedgerStore()
    user_key = b""1322""
    ledger = DataSubjectLedger.get_or_create(store=ledger_store, user_key=user_key)

    assert (
        ledger._cache_constant2epsilon[0] == 0.05372712063485988
    ), ""The first value in the cache is incorrect""
    assert (
        ledger._cache_constant2epsilon[1] == 0.07773597369831031
    ), ""Has the DP cache been changed?""

    rdp_700k = convert_constants_to_indices(np.array([700_000]))
    assert (
        ledger._cache_constant2epsilon.take(rdp_700k)[0] == 706213.1816144075
    ), ""Has the DP cache been changed?""
    rdp_50 = convert_constants_to_indices(np.array([50]))
    assert (
        ledger._cache_constant2epsilon.take(rdp_50)[0] == 100.68990516105825
    ), ""Has the DP cache been changed?""
    assert (
        len(ledger._cache_constant2epsilon) >= 1_200_000
    ), ""Has the cache been changed?""

",Ensure the most up to date RDP-to-epsilon cache is being used.,11,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_cache() -> None:
    
    ledger_store = DictLedgerStore()
    user_key = b""1322""
    ledger = DataSubjectLedger.get_or_create(store=ledger_store, user_key=user_key)

    assert (
        ledger._cache_constant2epsilon[0] == 0.05372712063485988
    ), ""The first value in the cache is incorrect""
    assert (
        ledger._cache_constant2epsilon[1] == 0.07773597369831031
    ), ""Has the DP cache been changed?""

    rdp_700k = convert_constants_to_indices(np.array([700_000]))
    assert (
        ledger._cache_constant2epsilon.take(rdp_700k)[0] == 706213.1816144075
    ), ""Has the DP cache been changed?""
    rdp_50 = convert_constants_to_indices(np.array([50]))
    assert (
        ledger._cache_constant2epsilon.take(rdp_50)[0] == 100.68990516105825
    ), ""Has the DP cache been changed?""
    assert (
        len(ledger._cache_constant2epsilon) >= 1_200_000
    ), ""Has the cache been changed?""


        ```
    ###Assistant : Ensure the most up to date RDP-to-epsilon cache is being used.
    "
1845,"def get_lexer_for_mimetype(_mime, **options):
    
    for modname, name, _, _, mimetypes in LEXERS.values():
        if _mime in mimetypes:
            if name not in _lexer_cache:
                _load_lexers(modname)
            return _lexer_cache[name](**options)
    for cls in find_plugin_lexers():
        if _mime in cls.mimetypes:
            return cls(**options)
    raise ClassNotFound('no lexer for mimetype %r found' % _mime)

","Get a lexer for a mimetype.

    Raises ClassNotFound if not found.
    ",11,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_lexer_for_mimetype(_mime, **options):
    
    for modname, name, _, _, mimetypes in LEXERS.values():
        if _mime in mimetypes:
            if name not in _lexer_cache:
                _load_lexers(modname)
            return _lexer_cache[name](**options)
    for cls in find_plugin_lexers():
        if _mime in cls.mimetypes:
            return cls(**options)
    raise ClassNotFound('no lexer for mimetype %r found' % _mime)


        ```
    ###Assistant : Get a lexer for a mimetype.

    Raises ClassNotFound if not found.
    
    "
1846,"def test_glm_regression(solver, fit_intercept, glm_dataset):
    
    model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # While _GeneralizedLinearRegressor exposes the solver parameter, public
        # estimators currently do not, and lbfgs is the only solver anyway.
        # TODO: Expose solver as soon as we have a second solver to choose from.
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    X = X[:, :-1]  # remove intercept
    if fit_intercept:
        coef = coef_with_intercept
        intercept = coef[-1]
        coef = coef[:-1]
    else:
        coef = coef_without_intercept
        intercept = 0

    model.fit(X, y)

    rtol = 5e-5
    assert model.intercept_ == pytest.approx(intercept, rel=rtol)
    assert_allclose(model.coef_, coef, rtol=rtol)

    # Same with sample_weight.
    model = (
        clone(model).set_params(**params).fit(X, y, sample_weight=np.ones(X.shape[0]))
    )
    assert model.intercept_ == pytest.approx(intercept, rel=rtol)
    assert_allclose(model.coef_, coef, rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])","Test that GLM converges for all solvers to correct solution.

    We work with a simple constructed data set with known solution.
    ",21,127,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_glm_regression(solver, fit_intercept, glm_dataset):
    
    model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # While _GeneralizedLinearRegressor exposes the solver parameter, public
        # estimators currently do not, and lbfgs is the only solver anyway.
        # TODO: Expose solver as soon as we have a second solver to choose from.
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    X = X[:, :-1]  # remove intercept
    if fit_intercept:
        coef = coef_with_intercept
        intercept = coef[-1]
        coef = coef[:-1]
    else:
        coef = coef_without_intercept
        intercept = 0

    model.fit(X, y)

    rtol = 5e-5
    assert model.intercept_ == pytest.approx(intercept, rel=rtol)
    assert_allclose(model.coef_, coef, rtol=rtol)

    # Same with sample_weight.
    model = (
        clone(model).set_params(**params).fit(X, y, sample_weight=np.ones(X.shape[0]))
    )
    assert model.intercept_ == pytest.approx(intercept, rel=rtol)
    assert_allclose(model.coef_, coef, rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])
        ```
    ###Assistant : Test that GLM converges for all solvers to correct solution.

    We work with a simple constructed data set with known solution.
    
    "
1847,"def _cuda_check(self):
        
        with Popen(""nvcc -V"", shell=True, stdout=PIPE, stderr=PIPE) as chk:
            stdout, stderr = chk.communicate()
        if not stderr:
            version = re.search(r"".*release (?P<cuda>\d+\.\d+)"",
                                stdout.decode(locale.getpreferredencoding()))
            self.cuda_version = version.groupdict().get(""cuda"", None)
            locate = ""where"" if self._os == ""windows"" else ""which""
            path = os.popen(f""{locate} nvcc"").read()
            if path:
                path = path.split(""\n"")[0]  # Split multiple entries and take first found
                while True:  # Get Cuda root folder
                    path, split = os.path.split(path)
                    if split == ""bin"":
                        break
                self.cuda_path = path
            return

        # Failed to load nvcc, manual check
        getattr(self, f""_cuda_check_{self._os}"")()
"," Obtain the location and version of Cuda and populate :attr:`cuda_version` and
        :attr:`cuda_path`

        Initially just calls `nvcc -V` to get the installed version of Cuda currently in use.
        If this fails, drills down to more OS specific checking methods.
        ",38,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _cuda_check(self):
        
        with Popen(""nvcc -V"", shell=True, stdout=PIPE, stderr=PIPE) as chk:
            stdout, stderr = chk.communicate()
        if not stderr:
            version = re.search(r"".*release (?P<cuda>\d+\.\d+)"",
                                stdout.decode(locale.getpreferredencoding()))
            self.cuda_version = version.groupdict().get(""cuda"", None)
            locate = ""where"" if self._os == ""windows"" else ""which""
            path = os.popen(f""{locate} nvcc"").read()
            if path:
                path = path.split(""\n"")[0]  # Split multiple entries and take first found
                while True:  # Get Cuda root folder
                    path, split = os.path.split(path)
                    if split == ""bin"":
                        break
                self.cuda_path = path
            return

        # Failed to load nvcc, manual check
        getattr(self, f""_cuda_check_{self._os}"")()

        ```
    ###Assistant :  Obtain the location and version of Cuda and populate :attr:`cuda_version` and
        :attr:`cuda_path`

        Initially just calls `nvcc -V` to get the installed version of Cuda currently in use.
        If this fails, drills down to more OS specific checking methods.
        
    "
1848,"def test_map_product_same(self, dag_maker, session):
        
        outputs = []

        with dag_maker(dag_id=""product_same"", session=session) as dag:
",Test a mapped task can refer to the same source multiple times.,12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_map_product_same(self, dag_maker, session):
        
        outputs = []

        with dag_maker(dag_id=""product_same"", session=session) as dag:

        ```
    ###Assistant : Test a mapped task can refer to the same source multiple times.
    "
1849,"def get_deepbooru_tags(pil_image, threshold=0.5):
    
    from modules import shared  # prevents circular reference
    create_deepbooru_process(threshold)
    shared.deepbooru_process_return[""value""] = -1
    shared.deepbooru_process_queue.put(pil_image)
    while shared.deepbooru_process_return[""value""] == -1:
        time.sleep(0.2)
    release_process()
    return ret

","
    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
    ",19,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_deepbooru_tags(pil_image, threshold=0.5):
    
    from modules import shared  # prevents circular reference
    create_deepbooru_process(threshold)
    shared.deepbooru_process_return[""value""] = -1
    shared.deepbooru_process_queue.put(pil_image)
    while shared.deepbooru_process_return[""value""] == -1:
        time.sleep(0.2)
    release_process()
    return ret


        ```
    ###Assistant : 
    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
    
    "
1850,"def test_update_from_select(self, mock_handler):
        self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})

        # --- use predictor ---
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical,
                'c': dtype.datetime
            },
            'predicted_value': 'ccc'
        }
        self.set_predictor(predictor)
        sql = 

        ret = self.command_executor.execute_command(
            parse_sql(sql, dialect='mindsdb'))
        assert ret.error_code is None

        # 1 select and 2 updates
        assert mock_handler().query.call_count == 3

        # second is update
        assert mock_handler().query.call_args_list[1][0][0].to_string() == ""update table2 set a1=1, c1='ccc' where (a1 = 1) AND (b1 = 'ccc')""


    # @patch('mindsdb.integrations.handlers.postgres_handler.Handler')
    # def test_union_type_mismatch(self, mock_handler):
    #     self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})
    #
    #     sql = 
    #     from mindsdb.api.mysql.mysql_proxy.utilities import ErSqlWrongArguments
    #     with pytest.raises(ErSqlWrongArguments):
    #         self.command_executor.execute_command(parse_sql(sql, dialect='mindsdb'))

","
            update 
                pg.table2                   
            set
                a1 = df.a,
                c1 = df.c
            from                            
                (
                    SELECT model.a as a, model.b as b, model.p as c
                      FROM pg.tasks as t
                     JOIN mindsdb.task_model as model
                     WHERE t.a=1 
                )
                as df
            where  
                table2.a1 = df.a 
                and table2.b1 = df.b     
        
    #          SELECT a, b  FROM pg.tasks
    #        UNION
    #          SELECT b, a  FROM pg.tasks
    #     ",57,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_update_from_select(self, mock_handler):
        self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})

        # --- use predictor ---
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical,
                'c': dtype.datetime
            },
            'predicted_value': 'ccc'
        }
        self.set_predictor(predictor)
        sql = 

        ret = self.command_executor.execute_command(
            parse_sql(sql, dialect='mindsdb'))
        assert ret.error_code is None

        # 1 select and 2 updates
        assert mock_handler().query.call_count == 3

        # second is update
        assert mock_handler().query.call_args_list[1][0][0].to_string() == ""update table2 set a1=1, c1='ccc' where (a1 = 1) AND (b1 = 'ccc')""


    # @patch('mindsdb.integrations.handlers.postgres_handler.Handler')
    # def test_union_type_mismatch(self, mock_handler):
    #     self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})
    #
    #     sql = 
    #     from mindsdb.api.mysql.mysql_proxy.utilities import ErSqlWrongArguments
    #     with pytest.raises(ErSqlWrongArguments):
    #         self.command_executor.execute_command(parse_sql(sql, dialect='mindsdb'))


        ```
    ###Assistant : 
            update 
                pg.table2                   
            set
                a1 = df.a,
                c1 = df.c
            from                            
                (
                    SELECT model.a as a, model.b as b, model.p as c
                      FROM pg.tasks as t
                     JOIN mindsdb.task_model as model
                     WHERE t.a=1 
                )
                as df
            where  
                table2.a1 = df.a 
                and table2.b1 = df.b     
        
    #          SELECT a, b  FROM pg.tasks
    #        UNION
    #          SELECT b, a  FROM pg.tasks
    #     
    "
1851,"def get_total_shipments(scorecard):
	
	supplier = frappe.get_doc(""Supplier"", scorecard.supplier)

	# Look up all PO Items with delivery dates between our dates
	data = frappe.db.sql(
		,
		{""supplier"": supplier.name, ""start_date"": scorecard.start_date, ""end_date"": scorecard.end_date},
		as_dict=0,
	)[0][0]

	if not data:
		data = 0
	return data

","Gets the total number of ordered shipments to arrive in the period (based on Purchase Receipts)
			SELECT
				COUNT(po_item.base_amount)
			FROM
				`tabPurchase Order Item` po_item,
				`tabPurchase Order` po
			WHERE
				po.supplier = %(supplier)s
				AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
				AND po_item.docstatus = 1
				AND po_item.parent = po.name",44,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_total_shipments(scorecard):
	
	supplier = frappe.get_doc(""Supplier"", scorecard.supplier)

	# Look up all PO Items with delivery dates between our dates
	data = frappe.db.sql(
		,
		{""supplier"": supplier.name, ""start_date"": scorecard.start_date, ""end_date"": scorecard.end_date},
		as_dict=0,
	)[0][0]

	if not data:
		data = 0
	return data


        ```
    ###Assistant : Gets the total number of ordered shipments to arrive in the period (based on Purchase Receipts)
			SELECT
				COUNT(po_item.base_amount)
			FROM
				`tabPurchase Order Item` po_item,
				`tabPurchase Order` po
			WHERE
				po.supplier = %(supplier)s
				AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
				AND po_item.docstatus = 1
				AND po_item.parent = po.name
    "
1852,"def source(object):
    
    print('In file: %s' % inspect.getsourcefile(object))
    print(inspect.getsource(object))

","
    Prints the source code of a given object.

    .. deprecated:: 1.3

       The ``source()`` function is deprecated. Use ``inspect.getsource()`` or
       ``??`` in IPython/Jupyter instead.

    ",23,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def source(object):
    
    print('In file: %s' % inspect.getsourcefile(object))
    print(inspect.getsource(object))


        ```
    ###Assistant : 
    Prints the source code of a given object.

    .. deprecated:: 1.3

       The ``source()`` function is deprecated. Use ``inspect.getsource()`` or
       ``??`` in IPython/Jupyter instead.

    
    "
1853,"def validate_axis(axis, input_shape):
    
    input_shape = tf.TensorShape(input_shape)
    rank = input_shape.rank
    if not rank:
        raise ValueError(
            f""Input has undefined rank. Received: input_shape={input_shape}""
        )

    # Convert axis to list and resolve negatives
    if isinstance(axis, int):
        axis = [axis]
    else:
        axis = list(axis)
    for idx, x in enumerate(axis):
        if x < 0:
            axis[idx] = rank + x

    # Validate axes
    for x in axis:
        if x < 0 or x >= rank:
            raise ValueError(
                ""Invalid value for `axis` argument. ""
                ""Expected 0 <= axis < inputs.rank (with ""
                f""inputs.rank={rank}). Received: axis={tuple(axis)}""
            )
    if len(axis) != len(set(axis)):
        raise ValueError(f""Duplicate axis: {tuple(axis)}"")
    return axis

","Validate an axis value and returns its standardized form.

    Args:
      axis: Value to validate. Can be an integer or a list/tuple of integers.
        Integers may be negative.
      input_shape: Reference input shape that the axis/axes refer to.

    Returns:
      Normalized form of `axis`, i.e. a list with all-positive values.
    ",47,98,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_axis(axis, input_shape):
    
    input_shape = tf.TensorShape(input_shape)
    rank = input_shape.rank
    if not rank:
        raise ValueError(
            f""Input has undefined rank. Received: input_shape={input_shape}""
        )

    # Convert axis to list and resolve negatives
    if isinstance(axis, int):
        axis = [axis]
    else:
        axis = list(axis)
    for idx, x in enumerate(axis):
        if x < 0:
            axis[idx] = rank + x

    # Validate axes
    for x in axis:
        if x < 0 or x >= rank:
            raise ValueError(
                ""Invalid value for `axis` argument. ""
                ""Expected 0 <= axis < inputs.rank (with ""
                f""inputs.rank={rank}). Received: axis={tuple(axis)}""
            )
    if len(axis) != len(set(axis)):
        raise ValueError(f""Duplicate axis: {tuple(axis)}"")
    return axis


        ```
    ###Assistant : Validate an axis value and returns its standardized form.

    Args:
      axis: Value to validate. Can be an integer or a list/tuple of integers.
        Integers may be negative.
      input_shape: Reference input shape that the axis/axes refer to.

    Returns:
      Normalized form of `axis`, i.e. a list with all-positive values.
    
    "
1854,"def _filter_to(self, it, pred):
        
        buf = ''
        idx = 0
        for i, t, v in it:
            if pred(t):
                if buf:
                    yield idx, None, buf
                    buf = ''
                yield i, t, v
            else:
                if not buf:
                    idx = i
                buf += v
        if buf:
            yield idx, None, buf
"," Keep only the tokens that match `pred`, merge the others together ",11,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _filter_to(self, it, pred):
        
        buf = ''
        idx = 0
        for i, t, v in it:
            if pred(t):
                if buf:
                    yield idx, None, buf
                    buf = ''
                yield i, t, v
            else:
                if not buf:
                    idx = i
                buf += v
        if buf:
            yield idx, None, buf

        ```
    ###Assistant :  Keep only the tokens that match `pred`, merge the others together 
    "
1855,"def entity_registry_enabled_default(self) -> bool:
        
        return bool(self._config[CONF_ENABLED_BY_DEFAULT])
",Return if the entity should be enabled when first added to the entity registry.,14,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def entity_registry_enabled_default(self) -> bool:
        
        return bool(self._config[CONF_ENABLED_BY_DEFAULT])

        ```
    ###Assistant : Return if the entity should be enabled when first added to the entity registry.
    "
1856,"def bisectors(self):
        
        # use lines containing sides so containment check during
        # intersection calculation can be avoided, thus reducing
        # the processing time for calculating the bisectors
        s = [Line(l) for l in self.sides]
        v = self.vertices
        c = self.incenter
        l1 = Segment(v[0], Line(v[0], c).intersection(s[1])[0])
        l2 = Segment(v[1], Line(v[1], c).intersection(s[2])[0])
        l3 = Segment(v[2], Line(v[2], c).intersection(s[0])[0])
        return {v[0]: l1, v[1]: l2, v[2]: l3}
","The angle bisectors of the triangle.

        An angle bisector of a triangle is a straight line through a vertex
        which cuts the corresponding angle in half.

        Returns
        =======

        bisectors : dict
            Each key is a vertex (Point) and each value is the corresponding
            bisector (Segment).

        See Also
        ========

        sympy.geometry.point.Point, sympy.geometry.line.Segment

        Examples
        ========

        >>> from sympy import Point, Triangle, Segment
        >>> p1, p2, p3 = Point(0, 0), Point(1, 0), Point(0, 1)
        >>> t = Triangle(p1, p2, p3)
        >>> from sympy import sqrt
        >>> t.bisectors()[p2] == Segment(Point(1, 0), Point(0, sqrt(2) - 1))
        True

        ",91,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bisectors(self):
        
        # use lines containing sides so containment check during
        # intersection calculation can be avoided, thus reducing
        # the processing time for calculating the bisectors
        s = [Line(l) for l in self.sides]
        v = self.vertices
        c = self.incenter
        l1 = Segment(v[0], Line(v[0], c).intersection(s[1])[0])
        l2 = Segment(v[1], Line(v[1], c).intersection(s[2])[0])
        l3 = Segment(v[2], Line(v[2], c).intersection(s[0])[0])
        return {v[0]: l1, v[1]: l2, v[2]: l3}

        ```
    ###Assistant : The angle bisectors of the triangle.

        An angle bisector of a triangle is a straight line through a vertex
        which cuts the corresponding angle in half.

        Returns
        =======

        bisectors : dict
            Each key is a vertex (Point) and each value is the corresponding
            bisector (Segment).

        See Also
        ========

        sympy.geometry.point.Point, sympy.geometry.line.Segment

        Examples
        ========

        >>> from sympy import Point, Triangle, Segment
        >>> p1, p2, p3 = Point(0, 0), Point(1, 0), Point(0, 1)
        >>> t = Triangle(p1, p2, p3)
        >>> from sympy import sqrt
        >>> t.bisectors()[p2] == Segment(Point(1, 0), Point(0, sqrt(2) - 1))
        True

        
    "
1857,"def query(query, filters={}, top_k_reader=5, top_k_retriever=5) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
    

    url = f""{API_ENDPOINT}/{DOC_REQUEST}""
    params = {""filters"": filters, ""Retriever"": {""top_k"": top_k_retriever}, ""Reader"": {""top_k"": top_k_reader}}
    req = {""query"": query, ""params"": params}
    response_raw = requests.post(url, json=req)

    if response_raw.status_code >= 400 and response_raw.status_code != 503:
        raise Exception(f""{vars(response_raw)}"")

    response = response_raw.json()
    if ""errors"" in response:
        raise Exception("", "".join(response[""errors""]))

    # Format response
    results = []
    answers = response[""answers""]
    for answer in answers:
        if answer.get(""answer"", None):
            results.append(
                {
                    ""context"": ""..."" + answer[""context""] + ""..."",
                    ""answer"": answer.get(""answer"", None),
                    ""source"": answer[""meta""][""name""],
                    ""relevance"": round(answer[""score""] * 100, 2),
                    ""document"": [doc for doc in response[""documents""] if doc[""id""] == answer[""document_id""]][0],
                    ""offset_start_in_doc"": answer[""offsets_in_document""][0][""start""],
                    ""_raw"": answer,
                }
            )
        else:
            results.append(
                {
                    ""context"": None,
                    ""answer"": None,
                    ""document"": None,
                    ""relevance"": round(answer[""score""] * 100, 2),
                    ""_raw"": answer,
                }
            )
    return results, response

","
    Send a query to the REST API and parse the answer.
    Returns both a ready-to-use representation of the results and the raw JSON.
    ",23,124,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def query(query, filters={}, top_k_reader=5, top_k_retriever=5) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
    

    url = f""{API_ENDPOINT}/{DOC_REQUEST}""
    params = {""filters"": filters, ""Retriever"": {""top_k"": top_k_retriever}, ""Reader"": {""top_k"": top_k_reader}}
    req = {""query"": query, ""params"": params}
    response_raw = requests.post(url, json=req)

    if response_raw.status_code >= 400 and response_raw.status_code != 503:
        raise Exception(f""{vars(response_raw)}"")

    response = response_raw.json()
    if ""errors"" in response:
        raise Exception("", "".join(response[""errors""]))

    # Format response
    results = []
    answers = response[""answers""]
    for answer in answers:
        if answer.get(""answer"", None):
            results.append(
                {
                    ""context"": ""..."" + answer[""context""] + ""..."",
                    ""answer"": answer.get(""answer"", None),
                    ""source"": answer[""meta""][""name""],
                    ""relevance"": round(answer[""score""] * 100, 2),
                    ""document"": [doc for doc in response[""documents""] if doc[""id""] == answer[""document_id""]][0],
                    ""offset_start_in_doc"": answer[""offsets_in_document""][0][""start""],
                    ""_raw"": answer,
                }
            )
        else:
            results.append(
                {
                    ""context"": None,
                    ""answer"": None,
                    ""document"": None,
                    ""relevance"": round(answer[""score""] * 100, 2),
                    ""_raw"": answer,
                }
            )
    return results, response


        ```
    ###Assistant : 
    Send a query to the REST API and parse the answer.
    Returns both a ready-to-use representation of the results and the raw JSON.
    
    "
1858,"def format_target_temperature(target_temperature):
    
    return str(round(float(target_temperature) * 2, 0) / 2).rstrip(""0"").rstrip(""."")

","Format target temperature to be sent to the Daikin unit, rounding to nearest half degree.",15,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def format_target_temperature(target_temperature):
    
    return str(round(float(target_temperature) * 2, 0) / 2).rstrip(""0"").rstrip(""."")


        ```
    ###Assistant : Format target temperature to be sent to the Daikin unit, rounding to nearest half degree.
    "
1859,"def get_data(filters=None):
	
	data = []
	emirates, amounts_by_emirate = append_vat_on_sales(data, filters)
	append_vat_on_expenses(data, filters)
	return data, emirates, amounts_by_emirate

",Returns the list of dictionaries. Each dictionary is a row in the datatable and chart data.,16,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_data(filters=None):
	
	data = []
	emirates, amounts_by_emirate = append_vat_on_sales(data, filters)
	append_vat_on_expenses(data, filters)
	return data, emirates, amounts_by_emirate


        ```
    ###Assistant : Returns the list of dictionaries. Each dictionary is a row in the datatable and chart data.
    "
1860,"def dict(self, *args, **kwargs):
        
        kwargs.setdefault(""exclude_none"", True)
        return super().dict(*args, **kwargs)

","Exclude `None` fields by default to comply with
        the OpenAPI spec.
        ",11,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dict(self, *args, **kwargs):
        
        kwargs.setdefault(""exclude_none"", True)
        return super().dict(*args, **kwargs)


        ```
    ###Assistant : Exclude `None` fields by default to comply with
        the OpenAPI spec.
        
    "
1861,"def extract_column_names(self) -> Dict[str, Tuple[str, str]]:
        
        fields = []
        for field in self.properties.keys():
            if not is_airbyte_column(field):
                fields.append(field)
        result = {}
        field_names = set()
        for field in fields:
            field_name = self.name_transformer.normalize_column_name(field, in_jinja=False)
            field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
            jinja_name = self.name_transformer.normalize_column_name(field, in_jinja=True)
            if field_name_lookup in field_names:
                # TODO handle column name duplicates or collisions deterministically in this stream
                for i in range(1, 1000):
                    field_name = self.name_transformer.normalize_column_name(f""{field}_{i}"", in_jinja=False)
                    field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
                    jinja_name = self.name_transformer.normalize_column_name(f""{field}_{i}"", in_jinja=True)
                    if field_name_lookup not in field_names:
                        break
            field_names.add(field_name_lookup)
            result[field] = (field_name, jinja_name)
        return result
","
        Generate a mapping of JSON properties to normalized SQL Column names, handling collisions and avoid duplicate names

        The mapped value to a field property is a tuple where:
         - the first value is the normalized ""raw"" column name
         - the second value is the normalized quoted column name to be used in jinja context
        ",54,83,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extract_column_names(self) -> Dict[str, Tuple[str, str]]:
        
        fields = []
        for field in self.properties.keys():
            if not is_airbyte_column(field):
                fields.append(field)
        result = {}
        field_names = set()
        for field in fields:
            field_name = self.name_transformer.normalize_column_name(field, in_jinja=False)
            field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
            jinja_name = self.name_transformer.normalize_column_name(field, in_jinja=True)
            if field_name_lookup in field_names:
                # TODO handle column name duplicates or collisions deterministically in this stream
                for i in range(1, 1000):
                    field_name = self.name_transformer.normalize_column_name(f""{field}_{i}"", in_jinja=False)
                    field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
                    jinja_name = self.name_transformer.normalize_column_name(f""{field}_{i}"", in_jinja=True)
                    if field_name_lookup not in field_names:
                        break
            field_names.add(field_name_lookup)
            result[field] = (field_name, jinja_name)
        return result

        ```
    ###Assistant : 
        Generate a mapping of JSON properties to normalized SQL Column names, handling collisions and avoid duplicate names

        The mapped value to a field property is a tuple where:
         - the first value is the normalized ""raw"" column name
         - the second value is the normalized quoted column name to be used in jinja context
        
    "
1862,"def _build_paths_from_predecessors(sources, target, pred):
    
    if target not in pred:
        raise nx.NetworkXNoPath(f""Target {target} cannot be reached from given sources"")

    seen = {target}
    stack = [[target, 0]]
    top = 0
    while top >= 0:
        node, i = stack[top]
        if node in sources:
            yield [p for p, n in reversed(stack[: top + 1])]
        if len(pred[node]) > i:
            stack[top][1] = i + 1
            next = pred[node][i]
            if next in seen:
                continue
            else:
                seen.add(next)
            top += 1
            if top == len(stack):
                stack.append([next, 0])
            else:
                stack[top][:] = [next, 0]
        else:
            seen.discard(node)
            top -= 1
","Compute all simple paths to target, given the predecessors found in
    pred, terminating when any source in sources is found.

    Parameters
    ----------
    sources : set
       Starting nodes for path.

    target : node
       Ending node for path.

    pred : dict
       A dictionary of predecessor lists, keyed by node

    Returns
    -------
    paths : generator of lists
        A generator of all paths between source and target.

    Raises
    ------
    NetworkXNoPath
        If `target` cannot be reached from `source`.

    Notes
    -----
    There may be many paths between the sources and target.  If there are
    cycles among the predecessors, this function will not produce all
    possible paths because doing so would produce infinitely many paths
    of unbounded length -- instead, we only produce simple paths.

    See Also
    --------
    shortest_path
    single_source_shortest_path
    all_pairs_shortest_path
    all_shortest_paths
    bellman_ford_path
    ",126,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _build_paths_from_predecessors(sources, target, pred):
    
    if target not in pred:
        raise nx.NetworkXNoPath(f""Target {target} cannot be reached from given sources"")

    seen = {target}
    stack = [[target, 0]]
    top = 0
    while top >= 0:
        node, i = stack[top]
        if node in sources:
            yield [p for p, n in reversed(stack[: top + 1])]
        if len(pred[node]) > i:
            stack[top][1] = i + 1
            next = pred[node][i]
            if next in seen:
                continue
            else:
                seen.add(next)
            top += 1
            if top == len(stack):
                stack.append([next, 0])
            else:
                stack[top][:] = [next, 0]
        else:
            seen.discard(node)
            top -= 1

        ```
    ###Assistant : Compute all simple paths to target, given the predecessors found in
    pred, terminating when any source in sources is found.

    Parameters
    ----------
    sources : set
       Starting nodes for path.

    target : node
       Ending node for path.

    pred : dict
       A dictionary of predecessor lists, keyed by node

    Returns
    -------
    paths : generator of lists
        A generator of all paths between source and target.

    Raises
    ------
    NetworkXNoPath
        If `target` cannot be reached from `source`.

    Notes
    -----
    There may be many paths between the sources and target.  If there are
    cycles among the predecessors, this function will not produce all
    possible paths because doing so would produce infinitely many paths
    of unbounded length -- instead, we only produce simple paths.

    See Also
    --------
    shortest_path
    single_source_shortest_path
    all_pairs_shortest_path
    all_shortest_paths
    bellman_ford_path
    
    "
1863,"def is_connected(self) -> bool:
        
        return self._backend is not None and self._backend.is_connected
",Return True if the client is connected to a device.,10,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_connected(self) -> bool:
        
        return self._backend is not None and self._backend.is_connected

        ```
    ###Assistant : Return True if the client is connected to a device.
    "
1864,"def _create_gnu_long_header(cls, name, type, encoding, errors):
        
        name = name.encode(encoding, errors) + NUL

        info = {}
        info[""name""] = ""././@LongLink""
        info[""type""] = type
        info[""size""] = len(name)
        info[""magic""] = GNU_MAGIC

        # create extended header + name blocks.
        return cls._create_header(info, USTAR_FORMAT, encoding, errors) + \
                cls._create_payload(name)
","Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
           for name.
        ",8,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_gnu_long_header(cls, name, type, encoding, errors):
        
        name = name.encode(encoding, errors) + NUL

        info = {}
        info[""name""] = ""././@LongLink""
        info[""type""] = type
        info[""size""] = len(name)
        info[""magic""] = GNU_MAGIC

        # create extended header + name blocks.
        return cls._create_header(info, USTAR_FORMAT, encoding, errors) + \
                cls._create_payload(name)

        ```
    ###Assistant : Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
           for name.
        
    "
1865,"def genocchi_poly(n, x=None, polys=False):
    
    if n < 0:
        raise ValueError(""Cannot generate Genocchi polynomial of degree %s"" % (n-1))
    poly = DMP(dup_genocchi(int(n), ZZ), ZZ)
    if x is not None:
        poly = Poly.new(poly, x)
    else:
        poly = PurePoly.new(poly, Dummy('x'))
    return poly if polys else poly.as_expr()

","Generates the Genocchi polynomial `\operatorname{G}_n(x)`.

    `\operatorname{G}_n(x)` is twice the difference between the plain and
    central Bernoulli polynomials, so has degree `n-1`:

    .. math :: \operatorname{G}_n(x) = 2 (\operatorname{B}_n(x) -
            \operatorname{B}_n^c(x))

    The factor of 2 in the definition endows `\operatorname{G}_n(x)` with
    integer coefficients.

    Parameters
    ==========

    n : int
        Degree of the polynomial plus one.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    ",70,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def genocchi_poly(n, x=None, polys=False):
    
    if n < 0:
        raise ValueError(""Cannot generate Genocchi polynomial of degree %s"" % (n-1))
    poly = DMP(dup_genocchi(int(n), ZZ), ZZ)
    if x is not None:
        poly = Poly.new(poly, x)
    else:
        poly = PurePoly.new(poly, Dummy('x'))
    return poly if polys else poly.as_expr()


        ```
    ###Assistant : Generates the Genocchi polynomial `\operatorname{G}_n(x)`.

    `\operatorname{G}_n(x)` is twice the difference between the plain and
    central Bernoulli polynomials, so has degree `n-1`:

    .. math :: \operatorname{G}_n(x) = 2 (\operatorname{B}_n(x) -
            \operatorname{B}_n^c(x))

    The factor of 2 in the definition endows `\operatorname{G}_n(x)` with
    integer coefficients.

    Parameters
    ==========

    n : int
        Degree of the polynomial plus one.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    
    "
1866,"def _triage_segments(window, nperseg, input_length):
  
  # parse window; if array like, then set nperseg = win.shape
  if isinstance(window, (str, tuple)):
    # if nperseg not specified
    if nperseg is None:
      nperseg = 256  # then change to default
    if nperseg > input_length:
      warnings.warn(f'nperseg = {nperseg} is greater than input length '
                    f' = {input_length}, using nperseg = {nperseg}')
      nperseg = input_length
    win = jnp.array(osp_signal.get_window(window, nperseg))
  else:
    win = jnp.asarray(window)
    if len(win.shape) != 1:
      raise ValueError('window must be 1-D')
    if input_length < win.shape[-1]:
      raise ValueError('window is longer than input signal')
    if nperseg is None:
      nperseg = win.shape[0]
    elif nperseg is not None:
      if nperseg != win.shape[0]:
        raise ValueError(""value specified for nperseg is different""
                          "" from length of window"")
  return win, nperseg

","
  Parses window and nperseg arguments for spectrogram and _spectral_helper.
  This is a helper function, not meant to be called externally.
  Parameters
  ----------
  window : string, tuple, or ndarray
      If window is specified by a string or tuple and nperseg is not
      specified, nperseg is set to the default of 256 and returns a window of
      that length.
      If instead the window is array_like and nperseg is not specified, then
      nperseg is set to the length of the window. A ValueError is raised if
      the user supplies both an array_like window and a value for nperseg but
      nperseg does not equal the length of the window.
  nperseg : int
      Length of each segment
  input_length: int
      Length of input signal, i.e. x.shape[-1]. Used to test for errors.
  Returns
  -------
  win : ndarray
      window. If function was called with string or tuple than this will hold
      the actual array used as a window.
  nperseg : int
      Length of each segment. If window is str or tuple, nperseg is set to
      256. If window is array_like, nperseg is set to the length of the
      6
      window.
  ",182,118,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _triage_segments(window, nperseg, input_length):
  
  # parse window; if array like, then set nperseg = win.shape
  if isinstance(window, (str, tuple)):
    # if nperseg not specified
    if nperseg is None:
      nperseg = 256  # then change to default
    if nperseg > input_length:
      warnings.warn(f'nperseg = {nperseg} is greater than input length '
                    f' = {input_length}, using nperseg = {nperseg}')
      nperseg = input_length
    win = jnp.array(osp_signal.get_window(window, nperseg))
  else:
    win = jnp.asarray(window)
    if len(win.shape) != 1:
      raise ValueError('window must be 1-D')
    if input_length < win.shape[-1]:
      raise ValueError('window is longer than input signal')
    if nperseg is None:
      nperseg = win.shape[0]
    elif nperseg is not None:
      if nperseg != win.shape[0]:
        raise ValueError(""value specified for nperseg is different""
                          "" from length of window"")
  return win, nperseg


        ```
    ###Assistant : 
  Parses window and nperseg arguments for spectrogram and _spectral_helper.
  This is a helper function, not meant to be called externally.
  Parameters
  ----------
  window : string, tuple, or ndarray
      If window is specified by a string or tuple and nperseg is not
      specified, nperseg is set to the default of 256 and returns a window of
      that length.
      If instead the window is array_like and nperseg is not specified, then
      nperseg is set to the length of the window. A ValueError is raised if
      the user supplies both an array_like window and a value for nperseg but
      nperseg does not equal the length of the window.
  nperseg : int
      Length of each segment
  input_length: int
      Length of input signal, i.e. x.shape[-1]. Used to test for errors.
  Returns
  -------
  win : ndarray
      window. If function was called with string or tuple than this will hold
      the actual array used as a window.
  nperseg : int
      Length of each segment. If window is str or tuple, nperseg is set to
      256. If window is array_like, nperseg is set to the length of the
      6
      window.
  
    "
1867,"def register_for_auto_class(cls, auto_class=""AutoModel""):
        
        if not isinstance(auto_class, str):
            auto_class = auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(auto_module, auto_class):
            raise ValueError(f""{auto_class} is not a valid auto class."")

        cls._auto_class = auto_class


# To update the docstring, we need to copy the method, otherwise we change the original docstring.
PreTrainedModel.push_to_hub = copy_func(PreTrainedModel.push_to_hub)
PreTrainedModel.push_to_hub.__doc__ = PreTrainedModel.push_to_hub.__doc__.format(
    object=""model"", object_class=""AutoModel"", object_files=""model checkpoint""
)

","
        Register this class with a given auto class. This should only be used for custom models as the ones in the
        library are already mapped with an auto class.

        Args:
            auto_class (`str` or `type`, *optional*, defaults to `""AutoModel""`):
                The auto class to register this new model with.
        ",47,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def register_for_auto_class(cls, auto_class=""AutoModel""):
        
        if not isinstance(auto_class, str):
            auto_class = auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(auto_module, auto_class):
            raise ValueError(f""{auto_class} is not a valid auto class."")

        cls._auto_class = auto_class


# To update the docstring, we need to copy the method, otherwise we change the original docstring.
PreTrainedModel.push_to_hub = copy_func(PreTrainedModel.push_to_hub)
PreTrainedModel.push_to_hub.__doc__ = PreTrainedModel.push_to_hub.__doc__.format(
    object=""model"", object_class=""AutoModel"", object_files=""model checkpoint""
)


        ```
    ###Assistant : 
        Register this class with a given auto class. This should only be used for custom models as the ones in the
        library are already mapped with an auto class.

        Args:
            auto_class (`str` or `type`, *optional*, defaults to `""AutoModel""`):
                The auto class to register this new model with.
        
    "
1868,"def is_accelerate_available():
    return _accelerate_available


# docstyle-ignore
FLAX_IMPORT_ERROR = 

# docstyle-ignore
INFLECT_IMPORT_ERROR = 

# docstyle-ignore
PYTORCH_IMPORT_ERROR = 

# docstyle-ignore
ONNX_IMPORT_ERROR = 

# docstyle-ignore
SCIPY_IMPORT_ERROR = 

# docstyle-ignore
TENSORFLOW_IMPORT_ERROR = 

# docstyle-ignore
TRANSFORMERS_IMPORT_ERROR = 

# docstyle-ignore
UNIDECODE_IMPORT_ERROR = 


BACKENDS_MAPPING = OrderedDict(
    [
        (""flax"", (is_flax_available, FLAX_IMPORT_ERROR)),
        (""inflect"", (is_inflect_available, INFLECT_IMPORT_ERROR)),
        (""onnx"", (is_onnx_available, ONNX_IMPORT_ERROR)),
        (""scipy"", (is_scipy_available, SCIPY_IMPORT_ERROR)),
        (""tf"", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
        (""torch"", (is_torch_available, PYTORCH_IMPORT_ERROR)),
        (""transformers"", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
        (""unidecode"", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
    ]
)

","
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.

{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
inflect`

{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.

{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
install onnxruntime`

{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
scipy`

{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.

{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
install transformers`

{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
Unidecode`
",197,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_accelerate_available():
    return _accelerate_available


# docstyle-ignore
FLAX_IMPORT_ERROR = 

# docstyle-ignore
INFLECT_IMPORT_ERROR = 

# docstyle-ignore
PYTORCH_IMPORT_ERROR = 

# docstyle-ignore
ONNX_IMPORT_ERROR = 

# docstyle-ignore
SCIPY_IMPORT_ERROR = 

# docstyle-ignore
TENSORFLOW_IMPORT_ERROR = 

# docstyle-ignore
TRANSFORMERS_IMPORT_ERROR = 

# docstyle-ignore
UNIDECODE_IMPORT_ERROR = 


BACKENDS_MAPPING = OrderedDict(
    [
        (""flax"", (is_flax_available, FLAX_IMPORT_ERROR)),
        (""inflect"", (is_inflect_available, INFLECT_IMPORT_ERROR)),
        (""onnx"", (is_onnx_available, ONNX_IMPORT_ERROR)),
        (""scipy"", (is_scipy_available, SCIPY_IMPORT_ERROR)),
        (""tf"", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
        (""torch"", (is_torch_available, PYTORCH_IMPORT_ERROR)),
        (""transformers"", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
        (""unidecode"", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
    ]
)


        ```
    ###Assistant : 
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.

{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
inflect`

{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.

{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
install onnxruntime`

{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
scipy`

{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.

{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
install transformers`

{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
Unidecode`

    "
1869,"def set_horizontalalignment(self, align):
        
        _api.check_in_list(['center', 'right', 'left'], align=align)
        self._horizontalalignment = align
        self.stale = True
","
        Set the horizontal alignment relative to the anchor point.

        See also :doc:`/gallery/text_labels_and_annotations/text_alignment`.

        Parameters
        ----------
        align : {'left', 'center', 'right'}
        ",19,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_horizontalalignment(self, align):
        
        _api.check_in_list(['center', 'right', 'left'], align=align)
        self._horizontalalignment = align
        self.stale = True

        ```
    ###Assistant : 
        Set the horizontal alignment relative to the anchor point.

        See also :doc:`/gallery/text_labels_and_annotations/text_alignment`.

        Parameters
        ----------
        align : {'left', 'center', 'right'}
        
    "
1870,"def _wrap_awaitable(awaitable):
    
    return (yield from awaitable.__await__())

_wrap_awaitable._is_coroutine = _is_coroutine

","Helper for asyncio.ensure_future().

    Wraps awaitable (an object with __await__) into a coroutine
    that will later be wrapped in a Task by ensure_future().
    ",22,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _wrap_awaitable(awaitable):
    
    return (yield from awaitable.__await__())

_wrap_awaitable._is_coroutine = _is_coroutine


        ```
    ###Assistant : Helper for asyncio.ensure_future().

    Wraps awaitable (an object with __await__) into a coroutine
    that will later be wrapped in a Task by ensure_future().
    
    "
1871,"def normalize_file(file, separators=None):
	
	# Normalize path separators.
	if separators is None:
		separators = NORMALIZE_PATH_SEPS

	# Convert path object to string.
	norm_file = str(file)

	for sep in separators:
		norm_file = norm_file.replace(sep, posixpath.sep)

	# Remove current directory prefix.
	if norm_file.startswith('./'):
		norm_file = norm_file[2:]

	return norm_file

","
	Normalizes the file path to use the POSIX path separator (i.e., ``'/'``).

	*file* (:class:`str` or :class:`pathlib.PurePath`) is the file path.

	*separators* (:class:`~collections.abc.Collection` of :class:`str`; or
	:data:`None`) optionally contains the path separators to normalize.
	This does not need to include the POSIX path separator (``'/'``), but
	including it will not affect the results. Default is :data:`None` for
	:data:`NORMALIZE_PATH_SEPS`. To prevent normalization, pass an empty
	container (e.g., an empty tuple ``()``).

	Returns the normalized file path (:class:`str`).
	",75,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def normalize_file(file, separators=None):
	
	# Normalize path separators.
	if separators is None:
		separators = NORMALIZE_PATH_SEPS

	# Convert path object to string.
	norm_file = str(file)

	for sep in separators:
		norm_file = norm_file.replace(sep, posixpath.sep)

	# Remove current directory prefix.
	if norm_file.startswith('./'):
		norm_file = norm_file[2:]

	return norm_file


        ```
    ###Assistant : 
	Normalizes the file path to use the POSIX path separator (i.e., ``'/'``).

	*file* (:class:`str` or :class:`pathlib.PurePath`) is the file path.

	*separators* (:class:`~collections.abc.Collection` of :class:`str`; or
	:data:`None`) optionally contains the path separators to normalize.
	This does not need to include the POSIX path separator (``'/'``), but
	including it will not affect the results. Default is :data:`None` for
	:data:`NORMALIZE_PATH_SEPS`. To prevent normalization, pass an empty
	container (e.g., an empty tuple ``()``).

	Returns the normalized file path (:class:`str`).
	
    "
1872,"def wildcard_types(self) -> List[str]:
        
        return [t for t, state_keys in self.types.items() if state_keys is None]
","Returns a list of event types which require us to fetch all state keys.
        This will be empty unless `has_wildcards` returns True.

        Returns:
            A list of event types.
        ",28,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wildcard_types(self) -> List[str]:
        
        return [t for t, state_keys in self.types.items() if state_keys is None]

        ```
    ###Assistant : Returns a list of event types which require us to fetch all state keys.
        This will be empty unless `has_wildcards` returns True.

        Returns:
            A list of event types.
        
    "
1873,"def get_columns(self, table_name) -> Response:
        
        q = f""SHOW COLUMNS IN TABLE {table_name};""
        result = self.native_query(q)
        return result
","
        List the columns in the tabels for which the user have access
        ",12,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_columns(self, table_name) -> Response:
        
        q = f""SHOW COLUMNS IN TABLE {table_name};""
        result = self.native_query(q)
        return result

        ```
    ###Assistant : 
        List the columns in the tabels for which the user have access
        
    "
1874,"def get_on_time_shipments(scorecard):
	

	supplier = frappe.get_doc(""Supplier"", scorecard.supplier)

	# Look up all PO Items with delivery dates between our dates
	total_items_delivered_on_time = frappe.db.sql(
		,
		{""supplier"": supplier.name, ""start_date"": scorecard.start_date, ""end_date"": scorecard.end_date},
		as_dict=0,
	)[0][0]

	if not total_items_delivered_on_time:
		total_items_delivered_on_time = 0
	return total_items_delivered_on_time

","Gets the number of late shipments (counting each item) in the period (based on Purchase Receipts vs POs)
			SELECT
				COUNT(pr_item.qty)
			FROM
				`tabPurchase Order Item` po_item,
				`tabPurchase Receipt Item` pr_item,
				`tabPurchase Order` po,
				`tabPurchase Receipt` pr
			WHERE
				po.supplier = %(supplier)s
				AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
				AND po_item.schedule_date <= pr.posting_date
				AND po_item.qty = pr_item.qty
				AND pr_item.docstatus = 1
				AND pr_item.purchase_order_item = po_item.name
				AND po_item.parent = po.name
				AND pr_item.parent = pr.name",69,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_on_time_shipments(scorecard):
	

	supplier = frappe.get_doc(""Supplier"", scorecard.supplier)

	# Look up all PO Items with delivery dates between our dates
	total_items_delivered_on_time = frappe.db.sql(
		,
		{""supplier"": supplier.name, ""start_date"": scorecard.start_date, ""end_date"": scorecard.end_date},
		as_dict=0,
	)[0][0]

	if not total_items_delivered_on_time:
		total_items_delivered_on_time = 0
	return total_items_delivered_on_time


        ```
    ###Assistant : Gets the number of late shipments (counting each item) in the period (based on Purchase Receipts vs POs)
			SELECT
				COUNT(pr_item.qty)
			FROM
				`tabPurchase Order Item` po_item,
				`tabPurchase Receipt Item` pr_item,
				`tabPurchase Order` po,
				`tabPurchase Receipt` pr
			WHERE
				po.supplier = %(supplier)s
				AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
				AND po_item.schedule_date <= pr.posting_date
				AND po_item.qty = pr_item.qty
				AND pr_item.docstatus = 1
				AND pr_item.purchase_order_item = po_item.name
				AND po_item.parent = po.name
				AND pr_item.parent = pr.name
    "
1875,"def test_commit_comment_deleted(self) -> None:
        expected_message = 
        self.check_webhook(""commit_comment_deleted"", TOPIC, expected_message)
",[hypro999](http://139.59.64.214:7990/users/hypro999) deleted their comment on [508d1b67f1f](http://139.59.64.214:7990/projects/SBOX/repos/sandbox/commits/508d1b67f1f8f3a25f543a030a7a178894aa9907):\n~~~ quote\n~~Just an arbitrary comment on a commit. Nothing to see here...~~\n~~~,17,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_commit_comment_deleted(self) -> None:
        expected_message = 
        self.check_webhook(""commit_comment_deleted"", TOPIC, expected_message)

        ```
    ###Assistant : [hypro999](http://139.59.64.214:7990/users/hypro999) deleted their comment on [508d1b67f1f](http://139.59.64.214:7990/projects/SBOX/repos/sandbox/commits/508d1b67f1f8f3a25f543a030a7a178894aa9907):\n~~~ quote\n~~Just an arbitrary comment on a commit. Nothing to see here...~~\n~~~
    "
1876,"async def get_work_queues(self) -> Optional[UUID]:
        
        work_queues = []
        for name in self.work_queues:
            try:
                # support IDs and names
                if isinstance(name, UUID):
                    work_queue = await self.client.read_work_queue(id=name)
                else:
                    work_queue = await self.client.read_work_queue_by_name(name)
            except ObjectNotFound:
                work_queue = await self.client.create_work_queue(
                    name=name, return_id=False
                )
            work_queues.append(work_queue)
        return work_queues
","
        Loads the work queue objects corresponding to the agent's target work queues. If any of them don't exist, they are created.
        ",21,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def get_work_queues(self) -> Optional[UUID]:
        
        work_queues = []
        for name in self.work_queues:
            try:
                # support IDs and names
                if isinstance(name, UUID):
                    work_queue = await self.client.read_work_queue(id=name)
                else:
                    work_queue = await self.client.read_work_queue_by_name(name)
            except ObjectNotFound:
                work_queue = await self.client.create_work_queue(
                    name=name, return_id=False
                )
            work_queues.append(work_queue)
        return work_queues

        ```
    ###Assistant : 
        Loads the work queue objects corresponding to the agent's target work queues. If any of them don't exist, they are created.
        
    "
1877,"def test_read_nonexistent_stream_raises_exception(mocker):
    
    s1 = MockStream(name=""s1"")
    s2 = MockStream(name=""this_stream_doesnt_exist_in_the_source"")

    mocker.patch.object(MockStream, ""get_json_schema"", return_value={})

    src = MockSource(streams=[s1])
    catalog = ConfiguredAirbyteCatalog(streams=[_configured_stream(s2, SyncMode.full_refresh)])
    with pytest.raises(KeyError):
        list(src.read(logger, {}, catalog))


GLOBAL_EMITTED_AT = 1

",Tests that attempting to sync a stream which the source does not return from the `streams` method raises an exception,20,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_read_nonexistent_stream_raises_exception(mocker):
    
    s1 = MockStream(name=""s1"")
    s2 = MockStream(name=""this_stream_doesnt_exist_in_the_source"")

    mocker.patch.object(MockStream, ""get_json_schema"", return_value={})

    src = MockSource(streams=[s1])
    catalog = ConfiguredAirbyteCatalog(streams=[_configured_stream(s2, SyncMode.full_refresh)])
    with pytest.raises(KeyError):
        list(src.read(logger, {}, catalog))


GLOBAL_EMITTED_AT = 1


        ```
    ###Assistant : Tests that attempting to sync a stream which the source does not return from the `streams` method raises an exception
    "
1878,"def _should_queue(self, link, referrer, rel):
        
        scheme, netloc, path, _, _, _ = urlparse(link)
        if path.endswith(self.source_extensions + self.binary_extensions +
                         self.excluded_extensions):
            result = False
        elif self.skip_externals and not link.startswith(self.base_url):
            result = False
        elif not referrer.startswith(self.base_url):
            result = False
        elif rel not in ('homepage', 'download'):
            result = False
        elif scheme not in ('http', 'https', 'ftp'):
            result = False
        elif self._is_platform_dependent(link):
            result = False
        else:
            host = netloc.split(':', 1)[0]
            if host.lower() == 'localhost':
                result = False
            else:
                result = True
        logger.debug('should_queue: %s (%s) from %s -> %s', link, rel,
                     referrer, result)
        return result
","
        Determine whether a link URL from a referring page and with a
        particular ""rel"" attribute should be queued for scraping.
        ",20,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _should_queue(self, link, referrer, rel):
        
        scheme, netloc, path, _, _, _ = urlparse(link)
        if path.endswith(self.source_extensions + self.binary_extensions +
                         self.excluded_extensions):
            result = False
        elif self.skip_externals and not link.startswith(self.base_url):
            result = False
        elif not referrer.startswith(self.base_url):
            result = False
        elif rel not in ('homepage', 'download'):
            result = False
        elif scheme not in ('http', 'https', 'ftp'):
            result = False
        elif self._is_platform_dependent(link):
            result = False
        else:
            host = netloc.split(':', 1)[0]
            if host.lower() == 'localhost':
                result = False
            else:
                result = True
        logger.debug('should_queue: %s (%s) from %s -> %s', link, rel,
                     referrer, result)
        return result

        ```
    ###Assistant : 
        Determine whether a link URL from a referring page and with a
        particular ""rel"" attribute should be queued for scraping.
        
    "
1879,"def receive():
    
    header = _in_file.read(16)
    _logger.debug('Received command, header: [%s]', header)
    if header is None or len(header) < 16:
        # Pipe EOF encountered
        _logger.debug('Pipe EOF encountered')
        return None, None
    length = int(header[2:])
    data = _in_file.read(length)
    command = CommandType(header[:2])
    data = data.decode('utf8')
    _logger.debug('Received command, data: [%s]', data)
    return command, data
","Receive a command from Training Service.
    Returns a tuple of command (CommandType) and payload (str)
    ",15,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def receive():
    
    header = _in_file.read(16)
    _logger.debug('Received command, header: [%s]', header)
    if header is None or len(header) < 16:
        # Pipe EOF encountered
        _logger.debug('Pipe EOF encountered')
        return None, None
    length = int(header[2:])
    data = _in_file.read(length)
    command = CommandType(header[:2])
    data = data.decode('utf8')
    _logger.debug('Received command, data: [%s]', data)
    return command, data

        ```
    ###Assistant : Receive a command from Training Service.
    Returns a tuple of command (CommandType) and payload (str)
    
    "
1880,"def bettertitle(value):
    
    return ' '.join([w[0].upper() + w[1:] for w in value.split()])


@register.filter()","
    Alternative to the builtin title(). Ensures that the first letter of each word is uppercase but retains the
    original case of all others.
    ",23,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bettertitle(value):
    
    return ' '.join([w[0].upper() + w[1:] for w in value.split()])


@register.filter()
        ```
    ###Assistant : 
    Alternative to the builtin title(). Ensures that the first letter of each word is uppercase but retains the
    original case of all others.
    
    "
1881,"def load_pascal_annotation(index, pascal_root):
    
    classes = ('__background__',  # always index 0
               'aeroplane', 'bicycle', 'bird', 'boat',
               'bottle', 'bus', 'car', 'cat', 'chair',
                         'cow', 'diningtable', 'dog', 'horse',
                         'motorbike', 'person', 'pottedplant',
                         'sheep', 'sofa', 'train', 'tvmonitor')
    class_to_ind = dict(zip(classes, xrange(21)))

    filename = osp.join(pascal_root, 'Annotations', index + '.xml')
    # print 'Loading: {}'.format(filename)
","
    This code is borrowed from Ross Girshick's FAST-RCNN code
    (https://github.com/rbgirshick/fast-rcnn).
    It parses the PASCAL .xml metadata files.
    See publication for further details: (http://arxiv.org/abs/1504.08083).

    Thanks Ross!

    ",25,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_pascal_annotation(index, pascal_root):
    
    classes = ('__background__',  # always index 0
               'aeroplane', 'bicycle', 'bird', 'boat',
               'bottle', 'bus', 'car', 'cat', 'chair',
                         'cow', 'diningtable', 'dog', 'horse',
                         'motorbike', 'person', 'pottedplant',
                         'sheep', 'sofa', 'train', 'tvmonitor')
    class_to_ind = dict(zip(classes, xrange(21)))

    filename = osp.join(pascal_root, 'Annotations', index + '.xml')
    # print 'Loading: {}'.format(filename)

        ```
    ###Assistant : 
    This code is borrowed from Ross Girshick's FAST-RCNN code
    (https://github.com/rbgirshick/fast-rcnn).
    It parses the PASCAL .xml metadata files.
    See publication for further details: (http://arxiv.org/abs/1504.08083).

    Thanks Ross!

    
    "
1882,"def get(self):
        
        logger = get_logger()
        logger.debug(f""ENTER::Partition.get::{self._identity}"")
        if len(self.call_queue):
            self.drain_call_queue()
        result = UnidistWrapper.materialize(self._data)
        logger.debug(f""EXIT::Partition.get::{self._identity}"")
        return result
","
        Get the object wrapped by this partition out of the object store.

        Returns
        -------
        pandas.DataFrame
            The object from the object store.
        ",21,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get(self):
        
        logger = get_logger()
        logger.debug(f""ENTER::Partition.get::{self._identity}"")
        if len(self.call_queue):
            self.drain_call_queue()
        result = UnidistWrapper.materialize(self._data)
        logger.debug(f""EXIT::Partition.get::{self._identity}"")
        return result

        ```
    ###Assistant : 
        Get the object wrapped by this partition out of the object store.

        Returns
        -------
        pandas.DataFrame
            The object from the object store.
        
    "
1883,"def test_custom_function_action_no_perm_response(self):
        
        action_data = {
            ACTION_CHECKBOX_NAME: [self.s1.pk],
            ""action"": ""no_perm"",
            ""index"": 0,
        }
        response = self.client.post(
            reverse(""admin:admin_views_externalsubscriber_changelist""), action_data
        )
        self.assertEqual(response.status_code, 403)
        self.assertEqual(response.content, b""No permission to perform this action"")
",A custom action may returns an HttpResponse with a 403 code.,11,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_custom_function_action_no_perm_response(self):
        
        action_data = {
            ACTION_CHECKBOX_NAME: [self.s1.pk],
            ""action"": ""no_perm"",
            ""index"": 0,
        }
        response = self.client.post(
            reverse(""admin:admin_views_externalsubscriber_changelist""), action_data
        )
        self.assertEqual(response.status_code, 403)
        self.assertEqual(response.content, b""No permission to perform this action"")

        ```
    ###Assistant : A custom action may returns an HttpResponse with a 403 code.
    "
1884,"def limit(self, *args):
        
        return self.applyfunc(lambda x: x.limit(*args))


# https://github.com/sympy/sympy/pull/12854","Calculate the limit of each element in the matrix.
        ``args`` will be passed to the ``limit`` function.

        Examples
        ========

        >>> from sympy import Matrix
        >>> from sympy.abc import x, y
        >>> M = Matrix([[x, y], [1, 0]])
        >>> M.limit(x, 2)
        Matrix([
        [2, y],
        [1, 0]])

        See Also
        ========

        integrate
        diff
        ",50,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def limit(self, *args):
        
        return self.applyfunc(lambda x: x.limit(*args))


# https://github.com/sympy/sympy/pull/12854
        ```
    ###Assistant : Calculate the limit of each element in the matrix.
        ``args`` will be passed to the ``limit`` function.

        Examples
        ========

        >>> from sympy import Matrix
        >>> from sympy.abc import x, y
        >>> M = Matrix([[x, y], [1, 0]])
        >>> M.limit(x, 2)
        Matrix([
        [2, y],
        [1, 0]])

        See Also
        ========

        integrate
        diff
        
    "
1885,"def test_device_classes_aligned():
    

    non_numeric_device_classes = {
        SensorDeviceClass.DATE,
        SensorDeviceClass.DURATION,
        SensorDeviceClass.TIMESTAMP,
    }

    for device_class in SensorDeviceClass:
        if device_class in non_numeric_device_classes:
            continue

        assert hasattr(NumberDeviceClass, device_class.name)
        assert getattr(NumberDeviceClass, device_class.name).value == device_class.value
",Make sure all sensor device classes are also available in NumberDeviceClass.,11,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_device_classes_aligned():
    

    non_numeric_device_classes = {
        SensorDeviceClass.DATE,
        SensorDeviceClass.DURATION,
        SensorDeviceClass.TIMESTAMP,
    }

    for device_class in SensorDeviceClass:
        if device_class in non_numeric_device_classes:
            continue

        assert hasattr(NumberDeviceClass, device_class.name)
        assert getattr(NumberDeviceClass, device_class.name).value == device_class.value

        ```
    ###Assistant : Make sure all sensor device classes are also available in NumberDeviceClass.
    "
1886,"def valid_tess_config(outdir):
    cfg_file = outdir / 'test.cfg'
    with cfg_file.open('w') as f:
        f.write(
            
        )
    yield cfg_file

","\
load_system_dawg 0
language_model_penalty_non_dict_word 0
language_model_penalty_non_freq_dict_word 0
",7,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def valid_tess_config(outdir):
    cfg_file = outdir / 'test.cfg'
    with cfg_file.open('w') as f:
        f.write(
            
        )
    yield cfg_file


        ```
    ###Assistant : \
load_system_dawg 0
language_model_penalty_non_dict_word 0
language_model_penalty_non_freq_dict_word 0

    "
1887,"def year_lookup_bounds_for_datetime_field(self, value, iso_year=False):
        
        if iso_year:
            first = datetime.datetime.fromisocalendar(value, 1, 1)
            second = datetime.datetime.fromisocalendar(
                value + 1, 1, 1
            ) - datetime.timedelta(microseconds=1)
        else:
            first = datetime.datetime(value, 1, 1)
            second = datetime.datetime(value, 12, 31, 23, 59, 59, 999999)
        if settings.USE_TZ:
            tz = timezone.get_current_timezone()
            first = timezone.make_aware(first, tz)
            second = timezone.make_aware(second, tz)
        first = self.adapt_datetimefield_value(first)
        second = self.adapt_datetimefield_value(second)
        return [first, second]
","
        Return a two-elements list with the lower and upper bound to be used
        with a BETWEEN operator to query a DateTimeField value using a year
        lookup.

        `value` is an int, containing the looked-up year.
        If `iso_year` is True, return bounds for ISO-8601 week-numbering years.
        ",44,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def year_lookup_bounds_for_datetime_field(self, value, iso_year=False):
        
        if iso_year:
            first = datetime.datetime.fromisocalendar(value, 1, 1)
            second = datetime.datetime.fromisocalendar(
                value + 1, 1, 1
            ) - datetime.timedelta(microseconds=1)
        else:
            first = datetime.datetime(value, 1, 1)
            second = datetime.datetime(value, 12, 31, 23, 59, 59, 999999)
        if settings.USE_TZ:
            tz = timezone.get_current_timezone()
            first = timezone.make_aware(first, tz)
            second = timezone.make_aware(second, tz)
        first = self.adapt_datetimefield_value(first)
        second = self.adapt_datetimefield_value(second)
        return [first, second]

        ```
    ###Assistant : 
        Return a two-elements list with the lower and upper bound to be used
        with a BETWEEN operator to query a DateTimeField value using a year
        lookup.

        `value` is an int, containing the looked-up year.
        If `iso_year` is True, return bounds for ISO-8601 week-numbering years.
        
    "
1888,"def gegenbauer_poly(n, a, x=None, polys=False):
    r
    return named_poly(n, dup_gegenbauer, None, ""Gegenbauer polynomial"", (x, a), polys)

","Generates the Gegenbauer polynomial `C_n^{(a)}(x)`.

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    a
        Decides minimal domain for the list of coefficients.
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    ",40,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def gegenbauer_poly(n, a, x=None, polys=False):
    r
    return named_poly(n, dup_gegenbauer, None, ""Gegenbauer polynomial"", (x, a), polys)


        ```
    ###Assistant : Generates the Gegenbauer polynomial `C_n^{(a)}(x)`.

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    a
        Decides minimal domain for the list of coefficients.
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    
    "
1889,"async def async_update(self, now=None):
        
        if not self.pollable_characteristics:
            self.async_update_available_state()
            _LOGGER.debug(
                ""HomeKit connection not polling any characteristics: %s"", self.unique_id
            )
            return

        if self._polling_lock.locked():
            if not self._polling_lock_warned:
                _LOGGER.warning(
                    (
                        ""HomeKit controller update skipped as previous poll still in""
                        "" flight: %s""
                    ),
                    self.unique_id,
                )
                self._polling_lock_warned = True
            return

        if self._polling_lock_warned:
            _LOGGER.info(
                (
                    ""HomeKit controller no longer detecting back pressure - not""
                    "" skipping poll: %s""
                ),
                self.unique_id,
            )
            self._polling_lock_warned = False
",Poll state of all entities attached to this bridge/accessory.,9,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_update(self, now=None):
        
        if not self.pollable_characteristics:
            self.async_update_available_state()
            _LOGGER.debug(
                ""HomeKit connection not polling any characteristics: %s"", self.unique_id
            )
            return

        if self._polling_lock.locked():
            if not self._polling_lock_warned:
                _LOGGER.warning(
                    (
                        ""HomeKit controller update skipped as previous poll still in""
                        "" flight: %s""
                    ),
                    self.unique_id,
                )
                self._polling_lock_warned = True
            return

        if self._polling_lock_warned:
            _LOGGER.info(
                (
                    ""HomeKit controller no longer detecting back pressure - not""
                    "" skipping poll: %s""
                ),
                self.unique_id,
            )
            self._polling_lock_warned = False

        ```
    ###Assistant : Poll state of all entities attached to this bridge/accessory.
    "
1890,"def _merge(self, start, end, left, right):
        # type: (int, int, int, int) -> Iterator[Tuple[int, int]]
        
        lslice, rslice = self._left[left:right], self._right[left:right]
        i = start = min([start]+lslice[:1])
        end = max([end]+rslice[-1:])
        for j, k in zip(lslice, rslice):
            if j > i:
                yield i, j-1
            i = k + 1
        if i <= end:
            yield i, end
        self._left[left:right], self._right[left:right] = [start], [end]
","Return an iterator of intervals to be fetched.

        Args:
            start (int): Start of needed interval
            end (int): End of needed interval
            left (int): Index of first overlapping downloaded data
            right (int): Index after last overlapping downloaded data
        ",37,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _merge(self, start, end, left, right):
        # type: (int, int, int, int) -> Iterator[Tuple[int, int]]
        
        lslice, rslice = self._left[left:right], self._right[left:right]
        i = start = min([start]+lslice[:1])
        end = max([end]+rslice[-1:])
        for j, k in zip(lslice, rslice):
            if j > i:
                yield i, j-1
            i = k + 1
        if i <= end:
            yield i, end
        self._left[left:right], self._right[left:right] = [start], [end]

        ```
    ###Assistant : Return an iterator of intervals to be fetched.

        Args:
            start (int): Start of needed interval
            end (int): End of needed interval
            left (int): Index of first overlapping downloaded data
            right (int): Index after last overlapping downloaded data
        
    "
1891,"def is_python_identifier(self):  # type: (str) -> bool
        
        # Ref: https://stackoverflow.com/a/55802320/595220
        return bool(re.match(_VALID_IDENTIFIER_STRING_REGEX, self))


PB_EXTENSIONS = ('.yml', '.yaml')

",Determine whether the given string is a Python identifier.,9,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_python_identifier(self):  # type: (str) -> bool
        
        # Ref: https://stackoverflow.com/a/55802320/595220
        return bool(re.match(_VALID_IDENTIFIER_STRING_REGEX, self))


PB_EXTENSIONS = ('.yml', '.yaml')


        ```
    ###Assistant : Determine whether the given string is a Python identifier.
    "
1892,"def _stream_response(self, start, end, base_headers=HEADERS):
        # type: (int, int, Dict[str, str]) -> Response
        
        headers = base_headers.copy()
        headers['Range'] = f'bytes={start}-{end}'
        # TODO: Get range requests to be correctly cached
        headers['Cache-Control'] = 'no-cache'
        return self._session.get(self._url, headers=headers, stream=True)
",Return HTTP response to a range request from start to end.,11,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _stream_response(self, start, end, base_headers=HEADERS):
        # type: (int, int, Dict[str, str]) -> Response
        
        headers = base_headers.copy()
        headers['Range'] = f'bytes={start}-{end}'
        # TODO: Get range requests to be correctly cached
        headers['Cache-Control'] = 'no-cache'
        return self._session.get(self._url, headers=headers, stream=True)

        ```
    ###Assistant : Return HTTP response to a range request from start to end.
    "
1893,"def _inject_greasemonkey_scripts(self, scripts):
        
        if sip.isdeleted(self._widget):
            return

        # Since we are inserting scripts into a per-tab collection,
        # rather than just injecting scripts on page load, we need to
        # make sure we replace existing scripts, not just add new ones.
        # While, taking care not to remove any other scripts that might
        # have been added elsewhere, like the one for stylesheets.
        page_scripts = self._widget.page().scripts()
        self._remove_all_greasemonkey_scripts()

        seen_names = set()
        for script in scripts:
            while script.full_name() in seen_names:
                script.dedup_suffix += 1
            seen_names.add(script.full_name())

            new_script = QWebEngineScript()

            try:
                world = int(script.jsworld)
                if not 0 <= world <= qtutils.MAX_WORLD_ID:
                    log.greasemonkey.error(
                        f""script {script.name} has invalid value for '@qute-js-world'""
                        f"": {script.jsworld}, should be between 0 and ""
                        f""{qtutils.MAX_WORLD_ID}"")
                    continue
            except ValueError:
                try:
                    world = _JS_WORLD_MAP[usertypes.JsWorld[script.jsworld.lower()]]
                except KeyError:
                    log.greasemonkey.error(
                        f""script {script.name} has invalid value for '@qute-js-world'""
                        f"": {script.jsworld}"")
                    continue
            new_script.setWorldId(world)

            # Corresponds to ""@run-at document-end"" which is the default according to
            # https://wiki.greasespot.net/Metadata_Block#.40run-at - however,
            # QtWebEngine uses QWebEngineScript.InjectionPoint.Deferred (@run-at document-idle) as
            # default.
            #
            # NOTE that this needs to be done before setSourceCode, so that
            # QtWebEngine's parsing of GreaseMonkey tags will override it if there is a
            # @run-at comment.
            new_script.setInjectionPoint(QWebEngineScript.InjectionPoint.DocumentReady)

            new_script.setSourceCode(script.code())
            new_script.setName(script.full_name())
            new_script.setRunsOnSubFrames(script.runs_on_sub_frames)

            if script.needs_document_end_workaround():
                log.greasemonkey.debug(
                    f""Forcing @run-at document-end for {script.name}"")
                new_script.setInjectionPoint(QWebEngineScript.InjectionPoint.DocumentReady)

            log.greasemonkey.debug(f'adding script: {new_script.name()}')
            page_scripts.insert(new_script)
","Register user JavaScript files with the current tab.

        Args:
            scripts: A list of GreasemonkeyScripts.
        ",14,203,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _inject_greasemonkey_scripts(self, scripts):
        
        if sip.isdeleted(self._widget):
            return

        # Since we are inserting scripts into a per-tab collection,
        # rather than just injecting scripts on page load, we need to
        # make sure we replace existing scripts, not just add new ones.
        # While, taking care not to remove any other scripts that might
        # have been added elsewhere, like the one for stylesheets.
        page_scripts = self._widget.page().scripts()
        self._remove_all_greasemonkey_scripts()

        seen_names = set()
        for script in scripts:
            while script.full_name() in seen_names:
                script.dedup_suffix += 1
            seen_names.add(script.full_name())

            new_script = QWebEngineScript()

            try:
                world = int(script.jsworld)
                if not 0 <= world <= qtutils.MAX_WORLD_ID:
                    log.greasemonkey.error(
                        f""script {script.name} has invalid value for '@qute-js-world'""
                        f"": {script.jsworld}, should be between 0 and ""
                        f""{qtutils.MAX_WORLD_ID}"")
                    continue
            except ValueError:
                try:
                    world = _JS_WORLD_MAP[usertypes.JsWorld[script.jsworld.lower()]]
                except KeyError:
                    log.greasemonkey.error(
                        f""script {script.name} has invalid value for '@qute-js-world'""
                        f"": {script.jsworld}"")
                    continue
            new_script.setWorldId(world)

            # Corresponds to ""@run-at document-end"" which is the default according to
            # https://wiki.greasespot.net/Metadata_Block#.40run-at - however,
            # QtWebEngine uses QWebEngineScript.InjectionPoint.Deferred (@run-at document-idle) as
            # default.
            #
            # NOTE that this needs to be done before setSourceCode, so that
            # QtWebEngine's parsing of GreaseMonkey tags will override it if there is a
            # @run-at comment.
            new_script.setInjectionPoint(QWebEngineScript.InjectionPoint.DocumentReady)

            new_script.setSourceCode(script.code())
            new_script.setName(script.full_name())
            new_script.setRunsOnSubFrames(script.runs_on_sub_frames)

            if script.needs_document_end_workaround():
                log.greasemonkey.debug(
                    f""Forcing @run-at document-end for {script.name}"")
                new_script.setInjectionPoint(QWebEngineScript.InjectionPoint.DocumentReady)

            log.greasemonkey.debug(f'adding script: {new_script.name()}')
            page_scripts.insert(new_script)

        ```
    ###Assistant : Register user JavaScript files with the current tab.

        Args:
            scripts: A list of GreasemonkeyScripts.
        
    "
1894,"def config(self) -> dict:
        
        global _CONFIG  # pylint: disable=global-statement
        if not _CONFIG:
            model_name = self._config_section
            logger.debug(""Loading config for: %s"", model_name)
            _CONFIG = Config(model_name, configfile=self._configfile).config_dict
        return _CONFIG
"," dict: The configuration dictionary for current plugin, as set by the user's
        configuration settings. ",14,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def config(self) -> dict:
        
        global _CONFIG  # pylint: disable=global-statement
        if not _CONFIG:
            model_name = self._config_section
            logger.debug(""Loading config for: %s"", model_name)
            _CONFIG = Config(model_name, configfile=self._configfile).config_dict
        return _CONFIG

        ```
    ###Assistant :  dict: The configuration dictionary for current plugin, as set by the user's
        configuration settings. 
    "
1895,"def execute():
	frappe.reload_doc(""stock"", ""doctype"", ""stock_entry"")
	if frappe.db.has_column(""Stock Entry"", ""add_to_transit""):
		frappe.db.sql(
			
		)

		frappe.db.sql(
			
		)

		frappe.reload_doc(""stock"", ""doctype"", ""warehouse_type"")
		if not frappe.db.exists(""Warehouse Type"", ""Transit""):
			doc = frappe.new_doc(""Warehouse Type"")
			doc.name = ""Transit""
			doc.insert()

		frappe.reload_doc(""stock"", ""doctype"", ""stock_entry_type"")
		frappe.delete_doc_if_exists(""Stock Entry Type"", ""Send to Warehouse"")
		frappe.delete_doc_if_exists(""Stock Entry Type"", ""Receive at Warehouse"")
","
            UPDATE `tabStock Entry` SET
            stock_entry_type = 'Material Transfer',
            purpose = 'Material Transfer',
            add_to_transit = 1 WHERE stock_entry_type = 'Send to Warehouse'
            UPDATE `tabStock Entry` SET
            stock_entry_type = 'Material Transfer',
            purpose = 'Material Transfer'
            WHERE stock_entry_type = 'Receive at Warehouse'
            ",39,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	frappe.reload_doc(""stock"", ""doctype"", ""stock_entry"")
	if frappe.db.has_column(""Stock Entry"", ""add_to_transit""):
		frappe.db.sql(
			
		)

		frappe.db.sql(
			
		)

		frappe.reload_doc(""stock"", ""doctype"", ""warehouse_type"")
		if not frappe.db.exists(""Warehouse Type"", ""Transit""):
			doc = frappe.new_doc(""Warehouse Type"")
			doc.name = ""Transit""
			doc.insert()

		frappe.reload_doc(""stock"", ""doctype"", ""stock_entry_type"")
		frappe.delete_doc_if_exists(""Stock Entry Type"", ""Send to Warehouse"")
		frappe.delete_doc_if_exists(""Stock Entry Type"", ""Receive at Warehouse"")

        ```
    ###Assistant : 
            UPDATE `tabStock Entry` SET
            stock_entry_type = 'Material Transfer',
            purpose = 'Material Transfer',
            add_to_transit = 1 WHERE stock_entry_type = 'Send to Warehouse'
            UPDATE `tabStock Entry` SET
            stock_entry_type = 'Material Transfer',
            purpose = 'Material Transfer'
            WHERE stock_entry_type = 'Receive at Warehouse'
            
    "
1896,"def query_task(doctype, txt, searchfield, start, page_len, filters):
	from frappe.desk.reportview import build_match_conditions

	search_string = ""%%%s%%"" % txt
	order_by_string = ""%s%%"" % txt
	match_conditions = build_match_conditions(""Task"")
	match_conditions = (""and"" + match_conditions) if match_conditions else """"

	return frappe.db.sql(
		
		% (searchfield, ""%s"", ""%s"", match_conditions, ""%s"", searchfield, ""%s"", searchfield, ""%s"", ""%s""),
		(search_string, search_string, order_by_string, order_by_string, start, page_len),
	)
","select name, subject from `tabTask`
		where (`%s` like %s or `subject` like %s) %s
		order by
			case when `subject` like %s then 0 else 1 end,
			case when `%s` like %s then 0 else 1 end,
			`%s`,
			subject
		limit %s, %s",41,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def query_task(doctype, txt, searchfield, start, page_len, filters):
	from frappe.desk.reportview import build_match_conditions

	search_string = ""%%%s%%"" % txt
	order_by_string = ""%s%%"" % txt
	match_conditions = build_match_conditions(""Task"")
	match_conditions = (""and"" + match_conditions) if match_conditions else """"

	return frappe.db.sql(
		
		% (searchfield, ""%s"", ""%s"", match_conditions, ""%s"", searchfield, ""%s"", searchfield, ""%s"", ""%s""),
		(search_string, search_string, order_by_string, order_by_string, start, page_len),
	)

        ```
    ###Assistant : select name, subject from `tabTask`
		where (`%s` like %s or `subject` like %s) %s
		order by
			case when `subject` like %s then 0 else 1 end,
			case when `%s` like %s then 0 else 1 end,
			`%s`,
			subject
		limit %s, %s
    "
1897,"def _parse_name(self, name):
        
        if name.endswith(""_float32_vars""):
            error_msg = (
                ""Policies ending in '_float32_vars' have been removed ""
                ""from TensorFlow.""
            )
            if name in (""infer_float32_vars"", ""infer_with_float32_vars""):
                error_msg += (
                    "" Please use the 'mixed_float16' or 'mixed_bfloat16' ""
                    ""policy instead.""
                )
            elif name == ""float16_with_float32_vars"":
                error_msg += "" Please use the 'mixed_float16' policy instead.""
            elif name == ""bfloat16_with_float32_vars"":
                error_msg += "" Please use the 'mixed_bfloat16' policy instead.""
            error_msg += "" Got policy name: '%s'"" % name
            raise ValueError(error_msg)

        if name == ""mixed_float16"":
            return ""float16"", ""float32""
        elif name == ""mixed_bfloat16"":
            return ""bfloat16"", ""float32""
        elif name == ""_infer"":
            # The ""_infer"" policy exists only for compatibility with TF 1, where
            # ""_infer"" is the default. The behavior matches the behavior of TF 1's
            # behavior before policies were introduced. With ""_infer"", the computation
            # and variable dtype are inferred from the first input the first time the
            # layer is called. Once the layer is called for the first time, the
            # layer's policy will change to the dtype of the first input, and it will
            # no longer have the ""_infer"" policy.
            #
            # The infer policy should be considered an implementation detail and may
            # be removed in the future.
            return None, None

        try:
            dtype = tf.as_dtype(name).name
        except TypeError:
            error = (
                ""Cannot convert value %s to a mixed precision Policy. ""
                ""Valid policies include 'mixed_float16', 'mixed_bfloat16', ""
                ""and the name of any dtype such as 'float32'."" % (name,)
            )
            raise ValueError(error)
        return dtype, dtype
","Parses a Policy name into a compute and variable dtype.

        Args:
          name: The name of the policy:

        Returns:
          The (compute_dtype, variable_dtype) pair.
        ",22,242,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_name(self, name):
        
        if name.endswith(""_float32_vars""):
            error_msg = (
                ""Policies ending in '_float32_vars' have been removed ""
                ""from TensorFlow.""
            )
            if name in (""infer_float32_vars"", ""infer_with_float32_vars""):
                error_msg += (
                    "" Please use the 'mixed_float16' or 'mixed_bfloat16' ""
                    ""policy instead.""
                )
            elif name == ""float16_with_float32_vars"":
                error_msg += "" Please use the 'mixed_float16' policy instead.""
            elif name == ""bfloat16_with_float32_vars"":
                error_msg += "" Please use the 'mixed_bfloat16' policy instead.""
            error_msg += "" Got policy name: '%s'"" % name
            raise ValueError(error_msg)

        if name == ""mixed_float16"":
            return ""float16"", ""float32""
        elif name == ""mixed_bfloat16"":
            return ""bfloat16"", ""float32""
        elif name == ""_infer"":
            # The ""_infer"" policy exists only for compatibility with TF 1, where
            # ""_infer"" is the default. The behavior matches the behavior of TF 1's
            # behavior before policies were introduced. With ""_infer"", the computation
            # and variable dtype are inferred from the first input the first time the
            # layer is called. Once the layer is called for the first time, the
            # layer's policy will change to the dtype of the first input, and it will
            # no longer have the ""_infer"" policy.
            #
            # The infer policy should be considered an implementation detail and may
            # be removed in the future.
            return None, None

        try:
            dtype = tf.as_dtype(name).name
        except TypeError:
            error = (
                ""Cannot convert value %s to a mixed precision Policy. ""
                ""Valid policies include 'mixed_float16', 'mixed_bfloat16', ""
                ""and the name of any dtype such as 'float32'."" % (name,)
            )
            raise ValueError(error)
        return dtype, dtype

        ```
    ###Assistant : Parses a Policy name into a compute and variable dtype.

        Args:
          name: The name of the policy:

        Returns:
          The (compute_dtype, variable_dtype) pair.
        
    "
1898,"def reraise_exceptions_as_crashes():
    
    try:
        yield
    except BaseException as exc:
        state = exception_to_crashed_state(exc)
        raise Crash(message=state.message, cause=exc, state=state) from exc

","
    Detect crashes during this context, wrapping unexpected exceptions into `Crash`
    signals.
    ",11,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reraise_exceptions_as_crashes():
    
    try:
        yield
    except BaseException as exc:
        state = exception_to_crashed_state(exc)
        raise Crash(message=state.message, cause=exc, state=state) from exc


        ```
    ###Assistant : 
    Detect crashes during this context, wrapping unexpected exceptions into `Crash`
    signals.
    
    "
1899,"def resoure_check():
    
    MINIMUM_ALLOWED_MEMORY = 4
    MINIMUM_ALLOWED_CPUS = 2
    MINIMUM_ALLOWED_DISK = 20
    print(""\nChecking resources.\n"")

    # Memory current available
    svmem = psutil.virtual_memory()
    mem_available = get_size(svmem.available)

    # Cpus current available
    cpus_available = psutil.cpu_count(logical=True)

    # Disk current available
    partitions = psutil.disk_partitions()
    partition_usage = psutil.disk_usage(partitions[0].mountpoint)
    disk_available = get_size(partition_usage.free)

    resources: Dict[str, Resource] = {
        'Memory': Resource(current=mem_available, minimumAllowed=MINIMUM_ALLOWED_MEMORY),
        'Cpus': Resource(current=cpus_available, minimumAllowed=MINIMUM_ALLOWED_CPUS),
        'Disk': Resource(current=disk_available, minimumAllowed=MINIMUM_ALLOWED_DISK),
    }
    return resources

","
    Use gsutil to get resources in bytes for memory and disk
    ",11,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resoure_check():
    
    MINIMUM_ALLOWED_MEMORY = 4
    MINIMUM_ALLOWED_CPUS = 2
    MINIMUM_ALLOWED_DISK = 20
    print(""\nChecking resources.\n"")

    # Memory current available
    svmem = psutil.virtual_memory()
    mem_available = get_size(svmem.available)

    # Cpus current available
    cpus_available = psutil.cpu_count(logical=True)

    # Disk current available
    partitions = psutil.disk_partitions()
    partition_usage = psutil.disk_usage(partitions[0].mountpoint)
    disk_available = get_size(partition_usage.free)

    resources: Dict[str, Resource] = {
        'Memory': Resource(current=mem_available, minimumAllowed=MINIMUM_ALLOWED_MEMORY),
        'Cpus': Resource(current=cpus_available, minimumAllowed=MINIMUM_ALLOWED_CPUS),
        'Disk': Resource(current=disk_available, minimumAllowed=MINIMUM_ALLOWED_DISK),
    }
    return resources


        ```
    ###Assistant : 
    Use gsutil to get resources in bytes for memory and disk
    
    "
1900,"def _pi_coeff(arg, cycles=1):
    r
    arg = sympify(arg)
    if arg is pi:
        return S.One
    elif not arg:
        return S.Zero
    elif arg.is_Mul:
        cx = arg.coeff(pi)
        if cx:
            c, x = cx.as_coeff_Mul()  # pi is not included as coeff
            if c.is_Float:
                # recast exact binary fractions to Rationals
                f = abs(c) % 1
                if f != 0:
                    p = -int(round(log(f, 2).evalf()))
                    m = 2**p
                    cm = c*m
                    i = int(cm)
                    if i == cm:
                        c = Rational(i, m)
                        cx = c*x
                else:
                    c = Rational(int(c))
                    cx = c*x
            if x.is_integer:
                c2 = c % 2
                if c2 == 1:
                    return x
                elif not c2:
                    if x.is_even is not None:  # known parity
                        return S.Zero
                    return Integer(2)
                else:
                    return c2*x
            return cx
    elif arg.is_zero:
        return S.Zero

","
    When arg is a Number times $\pi$ (e.g. $3\pi/2$) then return the Number
    normalized to be in the range $[0, 2]$, else `None`.

    When an even multiple of $\pi$ is encountered, if it is multiplying
    something with known parity then the multiple is returned as 0 otherwise
    as 2.

    Examples
    ========

    >>> from sympy.functions.elementary.trigonometric import _pi_coeff
    >>> from sympy import pi, Dummy
    >>> from sympy.abc import x
    >>> _pi_coeff(3*x*pi)
    3*x
    >>> _pi_coeff(11*pi/7)
    11/7
    >>> _pi_coeff(-11*pi/7)
    3/7
    >>> _pi_coeff(4*pi)
    0
    >>> _pi_coeff(5*pi)
    1
    >>> _pi_coeff(5.0*pi)
    1
    >>> _pi_coeff(5.5*pi)
    3/2
    >>> _pi_coeff(2 + pi)

    >>> _pi_coeff(2*Dummy(integer=True)*pi)
    2
    >>> _pi_coeff(2*Dummy(even=True)*pi)
    0

    ",98,122,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _pi_coeff(arg, cycles=1):
    r
    arg = sympify(arg)
    if arg is pi:
        return S.One
    elif not arg:
        return S.Zero
    elif arg.is_Mul:
        cx = arg.coeff(pi)
        if cx:
            c, x = cx.as_coeff_Mul()  # pi is not included as coeff
            if c.is_Float:
                # recast exact binary fractions to Rationals
                f = abs(c) % 1
                if f != 0:
                    p = -int(round(log(f, 2).evalf()))
                    m = 2**p
                    cm = c*m
                    i = int(cm)
                    if i == cm:
                        c = Rational(i, m)
                        cx = c*x
                else:
                    c = Rational(int(c))
                    cx = c*x
            if x.is_integer:
                c2 = c % 2
                if c2 == 1:
                    return x
                elif not c2:
                    if x.is_even is not None:  # known parity
                        return S.Zero
                    return Integer(2)
                else:
                    return c2*x
            return cx
    elif arg.is_zero:
        return S.Zero


        ```
    ###Assistant : 
    When arg is a Number times $\pi$ (e.g. $3\pi/2$) then return the Number
    normalized to be in the range $[0, 2]$, else `None`.

    When an even multiple of $\pi$ is encountered, if it is multiplying
    something with known parity then the multiple is returned as 0 otherwise
    as 2.

    Examples
    ========

    >>> from sympy.functions.elementary.trigonometric import _pi_coeff
    >>> from sympy import pi, Dummy
    >>> from sympy.abc import x
    >>> _pi_coeff(3*x*pi)
    3*x
    >>> _pi_coeff(11*pi/7)
    11/7
    >>> _pi_coeff(-11*pi/7)
    3/7
    >>> _pi_coeff(4*pi)
    0
    >>> _pi_coeff(5*pi)
    1
    >>> _pi_coeff(5.0*pi)
    1
    >>> _pi_coeff(5.5*pi)
    3/2
    >>> _pi_coeff(2 + pi)

    >>> _pi_coeff(2*Dummy(integer=True)*pi)
    2
    >>> _pi_coeff(2*Dummy(even=True)*pi)
    0

    
    "
1901,"async def async_test_still(hass, info) -> tuple[dict[str, str], str | None]:
    
    fmt = None
    if not (url := info.get(CONF_STILL_IMAGE_URL)):
        return {}, None
    if not isinstance(url, template_helper.Template) and url:
        url = cv.template(url)
        url.hass = hass
    try:
        url = url.async_render(parse_result=False)
    except TemplateError as err:
        _LOGGER.error(""Error parsing template %s: %s"", url, err)
        return {CONF_STILL_IMAGE_URL: ""template_error""}, None
    verify_ssl = info.get(CONF_VERIFY_SSL)
    auth = generate_auth(info)
    try:
        async_client = get_async_client(hass, verify_ssl=verify_ssl)",Verify that the still image is valid before we create an entity.,12,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_test_still(hass, info) -> tuple[dict[str, str], str | None]:
    
    fmt = None
    if not (url := info.get(CONF_STILL_IMAGE_URL)):
        return {}, None
    if not isinstance(url, template_helper.Template) and url:
        url = cv.template(url)
        url.hass = hass
    try:
        url = url.async_render(parse_result=False)
    except TemplateError as err:
        _LOGGER.error(""Error parsing template %s: %s"", url, err)
        return {CONF_STILL_IMAGE_URL: ""template_error""}, None
    verify_ssl = info.get(CONF_VERIFY_SSL)
    auth = generate_auth(info)
    try:
        async_client = get_async_client(hass, verify_ssl=verify_ssl)
        ```
    ###Assistant : Verify that the still image is valid before we create an entity.
    "
1902,"def normalize(self, a):
        
        a = _convert_other(a, raiseit=True)
        return a.normalize(context=self)
","normalize reduces an operand to its simplest form.

        Essentially a plus operation with all trailing zeros removed from the
        result.

        >>> ExtendedContext.normalize(Decimal('2.1'))
        Decimal('2.1')
        >>> ExtendedContext.normalize(Decimal('-2.0'))
        Decimal('-2')
        >>> ExtendedContext.normalize(Decimal('1.200'))
        Decimal('1.2')
        >>> ExtendedContext.normalize(Decimal('-120'))
        Decimal('-1.2E+2')
        >>> ExtendedContext.normalize(Decimal('120.00'))
        Decimal('1.2E+2')
        >>> ExtendedContext.normalize(Decimal('0.00'))
        Decimal('0')
        >>> ExtendedContext.normalize(6)
        Decimal('6')
        ",41,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def normalize(self, a):
        
        a = _convert_other(a, raiseit=True)
        return a.normalize(context=self)

        ```
    ###Assistant : normalize reduces an operand to its simplest form.

        Essentially a plus operation with all trailing zeros removed from the
        result.

        >>> ExtendedContext.normalize(Decimal('2.1'))
        Decimal('2.1')
        >>> ExtendedContext.normalize(Decimal('-2.0'))
        Decimal('-2')
        >>> ExtendedContext.normalize(Decimal('1.200'))
        Decimal('1.2')
        >>> ExtendedContext.normalize(Decimal('-120'))
        Decimal('-1.2E+2')
        >>> ExtendedContext.normalize(Decimal('120.00'))
        Decimal('1.2E+2')
        >>> ExtendedContext.normalize(Decimal('0.00'))
        Decimal('0')
        >>> ExtendedContext.normalize(6)
        Decimal('6')
        
    "
1903,"def get_tables(self) -> StatusResponse:
        

        query = 
        result = self.native_query(query)
        df = result.data_frame

        df = df[['TABLE_NAME' 'TABLE_TYPE']]
        result.data_frame = df.rename(columns={'TABLE_NAME': 'table_name', 'TABLE_TYPE': 'table_type'})

        return result
","
        Return list of entities that will be accessible as tables.
        Returns:
            HandlerResponse
        
            SELECT *
            FROM INFORMATION_SCHEMA.TABLES
        ",16,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_tables(self) -> StatusResponse:
        

        query = 
        result = self.native_query(query)
        df = result.data_frame

        df = df[['TABLE_NAME' 'TABLE_TYPE']]
        result.data_frame = df.rename(columns={'TABLE_NAME': 'table_name', 'TABLE_TYPE': 'table_type'})

        return result

        ```
    ###Assistant : 
        Return list of entities that will be accessible as tables.
        Returns:
            HandlerResponse
        
            SELECT *
            FROM INFORMATION_SCHEMA.TABLES
        
    "
1904,"def get_expected_values(self, image_inputs, batched=False):
        
        if not batched:
            image = image_inputs[0]
            if isinstance(image, Image.Image):
                w, h = image.size
            else:
                h, w = image.shape[1], image.shape[2]
            scale = self.size / min(w, h)
            if h < w:
                newh, neww = self.size, scale * w
            else:
                newh, neww = scale * h, self.size

            max_size = int((1333 / 800) * self.size)
            if max(newh, neww) > max_size:
                scale = max_size / max(newh, neww)
                newh = newh * scale
                neww = neww * scale

            newh, neww = int(newh + 0.5), int(neww + 0.5)
            expected_height, expected_width = (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            expected_values = []
            for image in image_inputs:
                expected_height, expected_width = self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            expected_height = max(expected_values, key=lambda item: item[0])[0]
            expected_width = max(expected_values, key=lambda item: item[1])[1]

        return expected_height, expected_width


@require_torch
@require_vision","
        This function computes the expected height and width when providing images to ViltFeatureExtractor,
        assuming do_resize is set to True with a scalar size and size_divisor.
        ",25,131,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_expected_values(self, image_inputs, batched=False):
        
        if not batched:
            image = image_inputs[0]
            if isinstance(image, Image.Image):
                w, h = image.size
            else:
                h, w = image.shape[1], image.shape[2]
            scale = self.size / min(w, h)
            if h < w:
                newh, neww = self.size, scale * w
            else:
                newh, neww = scale * h, self.size

            max_size = int((1333 / 800) * self.size)
            if max(newh, neww) > max_size:
                scale = max_size / max(newh, neww)
                newh = newh * scale
                neww = neww * scale

            newh, neww = int(newh + 0.5), int(neww + 0.5)
            expected_height, expected_width = (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            expected_values = []
            for image in image_inputs:
                expected_height, expected_width = self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            expected_height = max(expected_values, key=lambda item: item[0])[0]
            expected_width = max(expected_values, key=lambda item: item[1])[1]

        return expected_height, expected_width


@require_torch
@require_vision
        ```
    ###Assistant : 
        This function computes the expected height and width when providing images to ViltFeatureExtractor,
        assuming do_resize is set to True with a scalar size and size_divisor.
        
    "
1905,"def preprocss_testing_data(self, data):
        

        num_augs = len(data[0]['img'])
        batch_size = len(data)
        aug_batch_imgs = []
        aug_batch_data_samples = []

        # adjust `images` and `data_samples` to a list of list
        # outer list is test-time augmentation and inter list
        # is batch dimension
        for aug_index in range(num_augs):
            batch_imgs = []
            batch_data_samples = []
            for batch_index in range(batch_size):
                single_img = data[batch_index]['img'][aug_index]

                # to gpu and normalize
                single_img = single_img.to(self.device)
                if self.to_rgb and single_img[0].size(0) == 3:
                    single_img = single_img[[2, 1, 0], ...]
                single_img = (single_img - self.pixel_mean) / self.pixel_std

                batch_imgs.append(single_img)
                batch_data_samples.append(
                    data[batch_index]['data_sample'][aug_index])
            aug_batch_imgs.append(stack_batch(batch_imgs))
            aug_batch_data_samples.append(batch_data_samples)

        return aug_batch_imgs, aug_batch_data_samples
"," Process input data during training and testing phases.
        Args:
            data (list[dict]): The data to be processed, which
                comes from dataloader. The list indicate the batch dimension.
                Each dict contains these keys:

                - `img` (list[Tensor]): Image tensor with different test-time
                  augmentation.
                - `data_sample` (list[:obj:`GeneralData`]): Meta information
                  and annotations under different test-time augmentation.


        Returns:
            tuple:  It should contain 2 items.

                 - aug_batch_imgs (list[Tensor]):  List of batch image
                   tensor. The list indicate the test-time augmentations.
                   Note that the batch size always is 1
                   when do the augtest.
                 - aug_batch_data_samples
                   (list[list[:obj:`GeneralData`]], Optional):
                   The Data Samples. It usually includes information such as
                   `gt_instance`. Return None If the input datas does not
                   contain `data_sample`. The outer list indicate the
                   number of augmentations and inter list indicate the
                   batch dimension.
        ",123,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def preprocss_testing_data(self, data):
        

        num_augs = len(data[0]['img'])
        batch_size = len(data)
        aug_batch_imgs = []
        aug_batch_data_samples = []

        # adjust `images` and `data_samples` to a list of list
        # outer list is test-time augmentation and inter list
        # is batch dimension
        for aug_index in range(num_augs):
            batch_imgs = []
            batch_data_samples = []
            for batch_index in range(batch_size):
                single_img = data[batch_index]['img'][aug_index]

                # to gpu and normalize
                single_img = single_img.to(self.device)
                if self.to_rgb and single_img[0].size(0) == 3:
                    single_img = single_img[[2, 1, 0], ...]
                single_img = (single_img - self.pixel_mean) / self.pixel_std

                batch_imgs.append(single_img)
                batch_data_samples.append(
                    data[batch_index]['data_sample'][aug_index])
            aug_batch_imgs.append(stack_batch(batch_imgs))
            aug_batch_data_samples.append(batch_data_samples)

        return aug_batch_imgs, aug_batch_data_samples

        ```
    ###Assistant :  Process input data during training and testing phases.
        Args:
            data (list[dict]): The data to be processed, which
                comes from dataloader. The list indicate the batch dimension.
                Each dict contains these keys:

                - `img` (list[Tensor]): Image tensor with different test-time
                  augmentation.
                - `data_sample` (list[:obj:`GeneralData`]): Meta information
                  and annotations under different test-time augmentation.


        Returns:
            tuple:  It should contain 2 items.

                 - aug_batch_imgs (list[Tensor]):  List of batch image
                   tensor. The list indicate the test-time augmentations.
                   Note that the batch size always is 1
                   when do the augtest.
                 - aug_batch_data_samples
                   (list[list[:obj:`GeneralData`]], Optional):
                   The Data Samples. It usually includes information such as
                   `gt_instance`. Return None If the input datas does not
                   contain `data_sample`. The outer list indicate the
                   number of augmentations and inter list indicate the
                   batch dimension.
        
    "
1906,"def list_templates() -> List[pathlib.Path]:
        
        return (pathlib.Path(__file__).parent / ""templates"").glob(""*.html.j2"")
","List the available HTML templates.

        Returns:
            List[pathlib.Path]: A list of files with .html.j2 extensions inside
                ./templates/
        ",16,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list_templates() -> List[pathlib.Path]:
        
        return (pathlib.Path(__file__).parent / ""templates"").glob(""*.html.j2"")

        ```
    ###Assistant : List the available HTML templates.

        Returns:
            List[pathlib.Path]: A list of files with .html.j2 extensions inside
                ./templates/
        
    "
1907,"def usable_pip_file(path):  # type: (t.Optional[str]) -> bool
    
    return bool(path) and os.path.exists(path) and bool(os.path.getsize(path))


# Cryptography

","Return True if the specified pip file is usable, otherwise False.",11,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def usable_pip_file(path):  # type: (t.Optional[str]) -> bool
    
    return bool(path) and os.path.exists(path) and bool(os.path.getsize(path))


# Cryptography


        ```
    ###Assistant : Return True if the specified pip file is usable, otherwise False.
    "
1908,"def binary_accuracy(y_true, y_pred, threshold=0.5):
  
  y_pred = tf.convert_to_tensor(y_pred)
  threshold = tf.cast(threshold, y_pred.dtype)
  y_pred = tf.cast(y_pred > threshold, y_pred.dtype)
  return backend.mean(tf.equal(y_true, y_pred), axis=-1)


@keras_export('keras.metrics.categorical_accuracy')
@tf.__internal__.dispatch.add_dispatch_support","Calculates how often predictions match binary labels.

  Standalone usage:
  >>> y_true = [[1], [1], [0], [0]]
  >>> y_pred = [[1], [1], [0], [0]]
  >>> m = tf.keras.metrics.binary_accuracy(y_true, y_pred)
  >>> assert m.shape == (4,)
  >>> m.numpy()
  array([1., 1., 1., 1.], dtype=float32)

  Args:
    y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
    y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
    threshold: (Optional) Float representing the threshold for deciding whether
      prediction values are 1 or 0.

  Returns:
    Binary accuracy values. shape = `[batch_size, d0, .. dN-1]`
  ",86,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def binary_accuracy(y_true, y_pred, threshold=0.5):
  
  y_pred = tf.convert_to_tensor(y_pred)
  threshold = tf.cast(threshold, y_pred.dtype)
  y_pred = tf.cast(y_pred > threshold, y_pred.dtype)
  return backend.mean(tf.equal(y_true, y_pred), axis=-1)


@keras_export('keras.metrics.categorical_accuracy')
@tf.__internal__.dispatch.add_dispatch_support
        ```
    ###Assistant : Calculates how often predictions match binary labels.

  Standalone usage:
  >>> y_true = [[1], [1], [0], [0]]
  >>> y_pred = [[1], [1], [0], [0]]
  >>> m = tf.keras.metrics.binary_accuracy(y_true, y_pred)
  >>> assert m.shape == (4,)
  >>> m.numpy()
  array([1., 1., 1., 1.], dtype=float32)

  Args:
    y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
    y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
    threshold: (Optional) Float representing the threshold for deciding whether
      prediction values are 1 or 0.

  Returns:
    Binary accuracy values. shape = `[batch_size, d0, .. dN-1]`
  
    "
1909,"def _object2proto(self) -> SyftOblvClient_PB:
        
        return SyftOblvClient_PB(
            token=self.token,
            oblivious_user_id=self.oblivious_user_id,
            cookies=self.cookies,
            headers=self.headers,
            timeout=self.timeout,
            verify_ssl=self.verify_ssl,
        )
","Returns a protobuf serialization of self.
        As a requirement of all objects which inherit from Serializable,
        this method transforms the current object into the corresponding
        Protobuf object so that it can be further serialized.
        :return: returns a protobuf object
        :rtype: SyftOblvClient_PB
        .. note::
            This method is purely an internal method. Please use serialize(object) or one of
            the other public serialization methods if you wish to serialize an
            object.
        ",68,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _object2proto(self) -> SyftOblvClient_PB:
        
        return SyftOblvClient_PB(
            token=self.token,
            oblivious_user_id=self.oblivious_user_id,
            cookies=self.cookies,
            headers=self.headers,
            timeout=self.timeout,
            verify_ssl=self.verify_ssl,
        )

        ```
    ###Assistant : Returns a protobuf serialization of self.
        As a requirement of all objects which inherit from Serializable,
        this method transforms the current object into the corresponding
        Protobuf object so that it can be further serialized.
        :return: returns a protobuf object
        :rtype: SyftOblvClient_PB
        .. note::
            This method is purely an internal method. Please use serialize(object) or one of
            the other public serialization methods if you wish to serialize an
            object.
        
    "
1910,"def test_bulk_handle_digest_email_skips_deactivated_users(self) -> None:
        
        realm = get_realm(""zulip"")
        hamlet = self.example_user(""hamlet"")
        user_ids = list(
            UserProfile.objects.filter(is_bot=False, realm=realm).values_list(""id"", flat=True)
        )

        do_deactivate_user(hamlet, acting_user=None)

        with mock.patch(""zerver.lib.digest.enough_traffic"", return_value=True), mock.patch(
            ""zerver.lib.digest.send_future_email""
        ) as mock_send_email:
            bulk_handle_digest_email(user_ids, 1)

        emailed_user_ids = [
            call_args[1][""to_user_ids""][0] for call_args in mock_send_email.call_args_list
        ]

        self.assertEqual(
            set(emailed_user_ids), set(user_id for user_id in user_ids if user_id != hamlet.id)
        )
","
        A user id may be added to the queue before the user is deactivated. In such a case,
        the function responsible for sending the email should correctly skip them.
        ",29,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_bulk_handle_digest_email_skips_deactivated_users(self) -> None:
        
        realm = get_realm(""zulip"")
        hamlet = self.example_user(""hamlet"")
        user_ids = list(
            UserProfile.objects.filter(is_bot=False, realm=realm).values_list(""id"", flat=True)
        )

        do_deactivate_user(hamlet, acting_user=None)

        with mock.patch(""zerver.lib.digest.enough_traffic"", return_value=True), mock.patch(
            ""zerver.lib.digest.send_future_email""
        ) as mock_send_email:
            bulk_handle_digest_email(user_ids, 1)

        emailed_user_ids = [
            call_args[1][""to_user_ids""][0] for call_args in mock_send_email.call_args_list
        ]

        self.assertEqual(
            set(emailed_user_ids), set(user_id for user_id in user_ids if user_id != hamlet.id)
        )

        ```
    ###Assistant : 
        A user id may be added to the queue before the user is deactivated. In such a case,
        the function responsible for sending the email should correctly skip them.
        
    "
1911,"def test_driver_4():
    
    args_list = [
                'tests/tests.csv',
                '-is', ',',
                '-target', 'class',
                '-g', '1',
                '-p', '2',
                '-cv', '3',
                '-s', '42',
                '-config', 'TPOT light',
                '-v', '3'
                ]
    args = _get_arg_parser().parse_args(args_list)
    with captured_output() as (out, err):
        tpot_driver(args)
    ret_stdout = out.getvalue()

    assert ""TPOT settings"" in ret_stdout
    assert ""Final Pareto front testing scores"" in ret_stdout
    try:
        ret_val = float(ret_stdout.split('\n')[-2].split('\t')[1])
    except Exception:
        ret_val = -float('inf')
    assert ret_val > 0.0

",Assert that the tpot_driver() in TPOT driver outputs normal result with verbosity = 3.,14,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_driver_4():
    
    args_list = [
                'tests/tests.csv',
                '-is', ',',
                '-target', 'class',
                '-g', '1',
                '-p', '2',
                '-cv', '3',
                '-s', '42',
                '-config', 'TPOT light',
                '-v', '3'
                ]
    args = _get_arg_parser().parse_args(args_list)
    with captured_output() as (out, err):
        tpot_driver(args)
    ret_stdout = out.getvalue()

    assert ""TPOT settings"" in ret_stdout
    assert ""Final Pareto front testing scores"" in ret_stdout
    try:
        ret_val = float(ret_stdout.split('\n')[-2].split('\t')[1])
    except Exception:
        ret_val = -float('inf')
    assert ret_val > 0.0


        ```
    ###Assistant : Assert that the tpot_driver() in TPOT driver outputs normal result with verbosity = 3.
    "
1912,"async def test_set_avatar(self) -> None:
        
        handler = self.hs.get_sso_handler()

        # Create a new user to set avatar for
        reg_handler = self.hs.get_registration_handler()
        user_id = self.get_success(reg_handler.register_user(approved=True))

        self.assertTrue(
            self.get_success(handler.set_avatar(user_id, ""http://my.server/me.png""))
        )

        # Ensure avatar is set on this newly created user,
        # so no need to compare for the exact image
        profile_handler = self.hs.get_profile_handler()
        profile = self.get_success(profile_handler.get_profile(user_id))
        self.assertIsNot(profile[""avatar_url""], None)
",Tests successfully setting the avatar of a newly created user,10,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_set_avatar(self) -> None:
        
        handler = self.hs.get_sso_handler()

        # Create a new user to set avatar for
        reg_handler = self.hs.get_registration_handler()
        user_id = self.get_success(reg_handler.register_user(approved=True))

        self.assertTrue(
            self.get_success(handler.set_avatar(user_id, ""http://my.server/me.png""))
        )

        # Ensure avatar is set on this newly created user,
        # so no need to compare for the exact image
        profile_handler = self.hs.get_profile_handler()
        profile = self.get_success(profile_handler.get_profile(user_id))
        self.assertIsNot(profile[""avatar_url""], None)

        ```
    ###Assistant : Tests successfully setting the avatar of a newly created user
    "
1913,"def tune_decorated(api_key_file):
    
    tuner = tune.Tuner(
        decorated_train_function,
        tune_config=tune.TuneConfig(
            metric=""loss"",
            mode=""min"",
        ),
        param_space={
            ""mean"": tune.grid_search([1, 2, 3, 4, 5]),
            ""sd"": tune.uniform(0.2, 0.8),
            ""wandb"": {""api_key_file"": api_key_file, ""project"": ""Wandb_example""},
        },
    )
    tuner.fit()

",Example for using the @wandb_mixin decorator with the function API,10,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tune_decorated(api_key_file):
    
    tuner = tune.Tuner(
        decorated_train_function,
        tune_config=tune.TuneConfig(
            metric=""loss"",
            mode=""min"",
        ),
        param_space={
            ""mean"": tune.grid_search([1, 2, 3, 4, 5]),
            ""sd"": tune.uniform(0.2, 0.8),
            ""wandb"": {""api_key_file"": api_key_file, ""project"": ""Wandb_example""},
        },
    )
    tuner.fit()


        ```
    ###Assistant : Example for using the @wandb_mixin decorator with the function API
    "
1914,"def format_usage(self, usage):
        # type: (str) -> str
        
        msg = ""\nUsage: {}\n"".format(self.indent_lines(textwrap.dedent(usage), ""  ""))
        return msg
","
        Ensure there is only one newline between usage and the first heading
        if there is no description.
        ",17,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def format_usage(self, usage):
        # type: (str) -> str
        
        msg = ""\nUsage: {}\n"".format(self.indent_lines(textwrap.dedent(usage), ""  ""))
        return msg

        ```
    ###Assistant : 
        Ensure there is only one newline between usage and the first heading
        if there is no description.
        
    "
1915,"def from_index_summation(expr, first_index=None, last_index=None, dimensions=None):
        r
        from sympy.tensor.array.expressions.from_indexed_to_array import convert_indexed_to_array
        from sympy.tensor.array.expressions.from_array_to_matrix import convert_array_to_matrix
        first_indices = []
        if first_index is not None:
            first_indices.append(first_index)
        if last_index is not None:
            first_indices.append(last_index)
        arr = convert_indexed_to_array(expr, first_indices=first_indices)
        return convert_array_to_matrix(arr)
","
        Parse expression of matrices with explicitly summed indices into a
        matrix expression without indices, if possible.

        This transformation expressed in mathematical notation:

        `\sum_{j=0}^{N-1} A_{i,j} B_{j,k} \Longrightarrow \mathbf{A}\cdot \mathbf{B}`

        Optional parameter ``first_index``: specify which free index to use as
        the index starting the expression.

        Examples
        ========

        >>> from sympy import MatrixSymbol, MatrixExpr, Sum
        >>> from sympy.abc import i, j, k, l, N
        >>> A = MatrixSymbol(""A"", N, N)
        >>> B = MatrixSymbol(""B"", N, N)
        >>> expr = Sum(A[i, j]*B[j, k], (j, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A*B

        Transposition is detected:

        >>> expr = Sum(A[j, i]*B[j, k], (j, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A.T*B

        Detect the trace:

        >>> expr = Sum(A[i, i], (i, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        Trace(A)

        More complicated expressions:

        >>> expr = Sum(A[i, j]*B[k, j]*A[l, k], (j, 0, N-1), (k, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A*B.T*A.T
        ",133,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_index_summation(expr, first_index=None, last_index=None, dimensions=None):
        r
        from sympy.tensor.array.expressions.from_indexed_to_array import convert_indexed_to_array
        from sympy.tensor.array.expressions.from_array_to_matrix import convert_array_to_matrix
        first_indices = []
        if first_index is not None:
            first_indices.append(first_index)
        if last_index is not None:
            first_indices.append(last_index)
        arr = convert_indexed_to_array(expr, first_indices=first_indices)
        return convert_array_to_matrix(arr)

        ```
    ###Assistant : 
        Parse expression of matrices with explicitly summed indices into a
        matrix expression without indices, if possible.

        This transformation expressed in mathematical notation:

        `\sum_{j=0}^{N-1} A_{i,j} B_{j,k} \Longrightarrow \mathbf{A}\cdot \mathbf{B}`

        Optional parameter ``first_index``: specify which free index to use as
        the index starting the expression.

        Examples
        ========

        >>> from sympy import MatrixSymbol, MatrixExpr, Sum
        >>> from sympy.abc import i, j, k, l, N
        >>> A = MatrixSymbol(""A"", N, N)
        >>> B = MatrixSymbol(""B"", N, N)
        >>> expr = Sum(A[i, j]*B[j, k], (j, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A*B

        Transposition is detected:

        >>> expr = Sum(A[j, i]*B[j, k], (j, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A.T*B

        Detect the trace:

        >>> expr = Sum(A[i, i], (i, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        Trace(A)

        More complicated expressions:

        >>> expr = Sum(A[i, j]*B[k, j]*A[l, k], (j, 0, N-1), (k, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A*B.T*A.T
        
    "
1916,"def get_views(self):
        
        q = f""SHOW FULL TABLES IN {self.database} WHERE TABLE_TYPE LIKE 'VIEW';""
        result = self.native_query(q)
        return result
","
        Get more information about specific database views
        ",7,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_views(self):
        
        q = f""SHOW FULL TABLES IN {self.database} WHERE TABLE_TYPE LIKE 'VIEW';""
        result = self.native_query(q)
        return result

        ```
    ###Assistant : 
        Get more information about specific database views
        
    "
1917,"def _mac(model, obs, h):
    
    B, n_agents = obs.size(0), obs.size(1)
    if not isinstance(obs, dict):
        obs = {""obs"": obs}
    obs_agents_as_batches = {k: _drop_agent_dim(v) for k, v in obs.items()}
    h_flat = [s.reshape([B * n_agents, -1]) for s in h]
    q_flat, h_flat = model(obs_agents_as_batches, h_flat, None)
    return q_flat.reshape([B, n_agents, -1]), [
        s.reshape([B, n_agents, -1]) for s in h_flat
    ]

","Forward pass of the multi-agent controller.

    Args:
        model: TorchModelV2 class
        obs: Tensor of shape [B, n_agents, obs_size]
        h: List of tensors of shape [B, n_agents, h_size]

    Returns:
        q_vals: Tensor of shape [B, n_agents, n_actions]
        h: Tensor of shape [B, n_agents, h_size]
    ",41,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _mac(model, obs, h):
    
    B, n_agents = obs.size(0), obs.size(1)
    if not isinstance(obs, dict):
        obs = {""obs"": obs}
    obs_agents_as_batches = {k: _drop_agent_dim(v) for k, v in obs.items()}
    h_flat = [s.reshape([B * n_agents, -1]) for s in h]
    q_flat, h_flat = model(obs_agents_as_batches, h_flat, None)
    return q_flat.reshape([B, n_agents, -1]), [
        s.reshape([B, n_agents, -1]) for s in h_flat
    ]


        ```
    ###Assistant : Forward pass of the multi-agent controller.

    Args:
        model: TorchModelV2 class
        obs: Tensor of shape [B, n_agents, obs_size]
        h: List of tensors of shape [B, n_agents, h_size]

    Returns:
        q_vals: Tensor of shape [B, n_agents, n_actions]
        h: Tensor of shape [B, n_agents, h_size]
    
    "
1918,"def _get_count(self):
        
        has_meta = all(val is not None for val in self._alignments.video_meta_data.values())
        retval = len(self._alignments.video_meta_data[""pts_time""]) if has_meta else None
        logger.debug(""Frame count from alignments file: (has_meta: %s, %s"", has_meta, retval)
        return retval
"," If the alignments file has been run through the manual tool, then it will hold video
        meta information, meaning that the count of frames in the alignment file can be relied
        on to be accurate.

        Returns
        -------
        int or ``None``
        For video input which contain video meta-data in the alignments file then the count of
        frames is returned. In all other cases ``None`` is returned
        ",65,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_count(self):
        
        has_meta = all(val is not None for val in self._alignments.video_meta_data.values())
        retval = len(self._alignments.video_meta_data[""pts_time""]) if has_meta else None
        logger.debug(""Frame count from alignments file: (has_meta: %s, %s"", has_meta, retval)
        return retval

        ```
    ###Assistant :  If the alignments file has been run through the manual tool, then it will hold video
        meta information, meaning that the count of frames in the alignment file can be relied
        on to be accurate.

        Returns
        -------
        int or ``None``
        For video input which contain video meta-data in the alignments file then the count of
        frames is returned. In all other cases ``None`` is returned
        
    "
1919,"def test_sparse1_with_non_sparse_components():
    
    fit_then_transform(
        sparse1_paratial_1h.todense(),
        sparse1,
        categorical_features=[True, False]
    )

",Test fit_transform a sparse matrix with specifying categorical_features.,8,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_sparse1_with_non_sparse_components():
    
    fit_then_transform(
        sparse1_paratial_1h.todense(),
        sparse1,
        categorical_features=[True, False]
    )


        ```
    ###Assistant : Test fit_transform a sparse matrix with specifying categorical_features.
    "
1920,"def test_mod_gen_f77(capfd, hello_world_f90, monkeypatch):
    
    MNAME = ""hi""
    foutl = get_io_paths(hello_world_f90, mname=MNAME)
    ipath = foutl.f90inp
    monkeypatch.setattr(sys, ""argv"", f'f2py {ipath} -m {MNAME}'.split())
    with util.switchdir(ipath.parent):
        f2pycli()

    # Always generate C module
    assert Path.exists(foutl.cmodf)
    # File contains a function, check for F77 wrappers
    assert Path.exists(foutl.wrap77)

","Checks the generation of files based on a module name
    CLI :: -m
    ",13,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_mod_gen_f77(capfd, hello_world_f90, monkeypatch):
    
    MNAME = ""hi""
    foutl = get_io_paths(hello_world_f90, mname=MNAME)
    ipath = foutl.f90inp
    monkeypatch.setattr(sys, ""argv"", f'f2py {ipath} -m {MNAME}'.split())
    with util.switchdir(ipath.parent):
        f2pycli()

    # Always generate C module
    assert Path.exists(foutl.cmodf)
    # File contains a function, check for F77 wrappers
    assert Path.exists(foutl.wrap77)


        ```
    ###Assistant : Checks the generation of files based on a module name
    CLI :: -m
    
    "
1921,"def delegate(args, host_state, exclude, require):  # type: (CommonConfig, HostState, t.List[str], t.List[str]) -> None
    
    assert isinstance(args, EnvironmentConfig)

    with delegation_context(args, host_state):
        if isinstance(args, TestConfig):
            args.metadata.ci_provider = get_ci_provider().code

            make_dirs(ResultType.TMP.path)

            with tempfile.NamedTemporaryFile(prefix='metadata-', suffix='.json', dir=ResultType.TMP.path) as metadata_fd:
                args.metadata_path = os.path.join(ResultType.TMP.relative_path, os.path.basename(metadata_fd.name))
                args.metadata.to_file(args.metadata_path)

                try:
                    delegate_command(args, host_state, exclude, require)
                finally:
                    args.metadata_path = None
        else:
            delegate_command(args, host_state, exclude, require)

",Delegate execution of ansible-test to another environment.,7,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delegate(args, host_state, exclude, require):  # type: (CommonConfig, HostState, t.List[str], t.List[str]) -> None
    
    assert isinstance(args, EnvironmentConfig)

    with delegation_context(args, host_state):
        if isinstance(args, TestConfig):
            args.metadata.ci_provider = get_ci_provider().code

            make_dirs(ResultType.TMP.path)

            with tempfile.NamedTemporaryFile(prefix='metadata-', suffix='.json', dir=ResultType.TMP.path) as metadata_fd:
                args.metadata_path = os.path.join(ResultType.TMP.relative_path, os.path.basename(metadata_fd.name))
                args.metadata.to_file(args.metadata_path)

                try:
                    delegate_command(args, host_state, exclude, require)
                finally:
                    args.metadata_path = None
        else:
            delegate_command(args, host_state, exclude, require)


        ```
    ###Assistant : Delegate execution of ansible-test to another environment.
    "
1922,"def require_torch_non_multi_gpu(test_case):
    
    if not is_torch_available():
        return unittest.skip(""test requires PyTorch"")(test_case)

    import torch

    return unittest.skipUnless(torch.cuda.device_count() < 2, ""test requires 0 or 1 GPU"")(test_case)

","
    Decorator marking a test that requires 0 or 1 GPU setup (in PyTorch).
    ",13,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def require_torch_non_multi_gpu(test_case):
    
    if not is_torch_available():
        return unittest.skip(""test requires PyTorch"")(test_case)

    import torch

    return unittest.skipUnless(torch.cuda.device_count() < 2, ""test requires 0 or 1 GPU"")(test_case)


        ```
    ###Assistant : 
    Decorator marking a test that requires 0 or 1 GPU setup (in PyTorch).
    
    "
1923,"def _ReturnKeyHandler(self, event):
        
        # if the element is disabled, ignore the event
        if self.Disabled:
            return

        MyForm = self.ParentForm
        button_element = self._FindReturnKeyBoundButton(MyForm)
        if button_element is not None:
            button_element.ButtonCallBack()
","
        Internal callback for the ENTER / RETURN key. Results in calling the ButtonCallBack for element that has the return key bound to it, just as if button was clicked.

        :param event:
        :type event:

        ",33,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _ReturnKeyHandler(self, event):
        
        # if the element is disabled, ignore the event
        if self.Disabled:
            return

        MyForm = self.ParentForm
        button_element = self._FindReturnKeyBoundButton(MyForm)
        if button_element is not None:
            button_element.ButtonCallBack()

        ```
    ###Assistant : 
        Internal callback for the ENTER / RETURN key. Results in calling the ButtonCallBack for element that has the return key bound to it, just as if button was clicked.

        :param event:
        :type event:

        
    "
1924,"def uint64_frame():
    
    return DataFrame(
        {""A"": np.arange(3), ""B"": [2**63, 2**63 + 5, 2**63 + 10]}, dtype=np.uint64
    )


@pytest.fixture","
    Fixture for DataFrame with uint64 values

    Columns are ['A', 'B']
    ",10,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def uint64_frame():
    
    return DataFrame(
        {""A"": np.arange(3), ""B"": [2**63, 2**63 + 5, 2**63 + 10]}, dtype=np.uint64
    )


@pytest.fixture
        ```
    ###Assistant : 
    Fixture for DataFrame with uint64 values

    Columns are ['A', 'B']
    
    "
1925,"def test_null_annotation(self):
        
        book = Book.objects.annotate(
            no_value=Value(None, output_field=IntegerField())
        ).first()
        self.assertIsNone(book.no_value)
","
        Annotating None onto a model round-trips
        ",6,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_null_annotation(self):
        
        book = Book.objects.annotate(
            no_value=Value(None, output_field=IntegerField())
        ).first()
        self.assertIsNone(book.no_value)

        ```
    ###Assistant : 
        Annotating None onto a model round-trips
        
    "
1926,"def icosahedral_graph(create_using=None):
    
    description = [
        ""adjacencylist"",
        ""Platonic Icosahedral Graph"",
        12,
        [
            [2, 6, 8, 9, 12],
            [3, 6, 7, 9],
            [4, 7, 9, 10],
            [5, 7, 10, 11],
            [6, 7, 11, 12],
            [7, 12],
            [],
            [9, 10, 11, 12],
            [10],
            [11],
            [12],
            [],
        ],
    ]
    G = make_small_undirected_graph(description, create_using)
    return G

","
    Returns the Platonic Icosahedral graph.

    The icosahedral graph has 12 nodes and 30 edges. It is a Platonic graph
    whose nodes have the connectivity of the icosahedron. It is undirected,
    regular and Hamiltonian [1]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Icosahedral graph with 12 nodes and 30 edges.

    References
    ----------
    .. [1] https://mathworld.wolfram.com/IcosahedralGraph.html
    ",73,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def icosahedral_graph(create_using=None):
    
    description = [
        ""adjacencylist"",
        ""Platonic Icosahedral Graph"",
        12,
        [
            [2, 6, 8, 9, 12],
            [3, 6, 7, 9],
            [4, 7, 9, 10],
            [5, 7, 10, 11],
            [6, 7, 11, 12],
            [7, 12],
            [],
            [9, 10, 11, 12],
            [10],
            [11],
            [12],
            [],
        ],
    ]
    G = make_small_undirected_graph(description, create_using)
    return G


        ```
    ###Assistant : 
    Returns the Platonic Icosahedral graph.

    The icosahedral graph has 12 nodes and 30 edges. It is a Platonic graph
    whose nodes have the connectivity of the icosahedron. It is undirected,
    regular and Hamiltonian [1]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        Icosahedral graph with 12 nodes and 30 edges.

    References
    ----------
    .. [1] https://mathworld.wolfram.com/IcosahedralGraph.html
    
    "
1927,"def evaluate(model, criterion, metric, data_loader):
    
    model.eval()
    metric.reset()
    losses = []
    for batch in tqdm(data_loader):
        input_ids, token_type_ids, position_ids, masks, ent_label, spo_label = batch
        max_batch_len = input_ids.shape[-1]
        ent_mask = paddle.unsqueeze(masks, axis=2)
        spo_mask = paddle.matmul(ent_mask, ent_mask, transpose_y=True)
        spo_mask = paddle.unsqueeze(spo_mask, axis=1)

        logits = model(input_ids, token_type_ids, position_ids)

        ent_loss = criterion(
            logits[0], ent_label[0], weight=ent_mask, reduction='sum')
        spo_loss = criterion(
            logits[1], spo_label[0], weight=spo_mask, reduction='sum')
        loss = ent_loss + spo_loss
        losses.append(loss.numpy())
        lengths = paddle.sum(masks, axis=-1)
        correct = metric.compute(lengths, logits[0], logits[1], ent_label[1],
                                 spo_label[1])
        metric.update(correct)
    results = metric.accumulate()
    print('eval loss: %.5f, entity f1: %.5f, spo f1: %.5f' %
          (np.mean(losses), results['entity'][2], results['spo'][2]))
    model.train()
    metric.reset()

","
    Given a dataset, it evals model and compute the metric.
    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        dataloader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
        criterion(`paddle.nn.functional`): It can compute the loss.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
    ",34,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def evaluate(model, criterion, metric, data_loader):
    
    model.eval()
    metric.reset()
    losses = []
    for batch in tqdm(data_loader):
        input_ids, token_type_ids, position_ids, masks, ent_label, spo_label = batch
        max_batch_len = input_ids.shape[-1]
        ent_mask = paddle.unsqueeze(masks, axis=2)
        spo_mask = paddle.matmul(ent_mask, ent_mask, transpose_y=True)
        spo_mask = paddle.unsqueeze(spo_mask, axis=1)

        logits = model(input_ids, token_type_ids, position_ids)

        ent_loss = criterion(
            logits[0], ent_label[0], weight=ent_mask, reduction='sum')
        spo_loss = criterion(
            logits[1], spo_label[0], weight=spo_mask, reduction='sum')
        loss = ent_loss + spo_loss
        losses.append(loss.numpy())
        lengths = paddle.sum(masks, axis=-1)
        correct = metric.compute(lengths, logits[0], logits[1], ent_label[1],
                                 spo_label[1])
        metric.update(correct)
    results = metric.accumulate()
    print('eval loss: %.5f, entity f1: %.5f, spo f1: %.5f' %
          (np.mean(losses), results['entity'][2], results['spo'][2]))
    model.train()
    metric.reset()


        ```
    ###Assistant : 
    Given a dataset, it evals model and compute the metric.
    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        dataloader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
        criterion(`paddle.nn.functional`): It can compute the loss.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
    
    "
1928,"def get_fields(self, include_parents=True, include_hidden=False):
        
        if include_parents is False:
            include_parents = PROXY_PARENTS
        return self._get_fields(
            include_parents=include_parents, include_hidden=include_hidden
        )
","
        Return a list of fields associated to the model. By default, include
        forward and reverse fields, fields derived from inheritance, but not
        hidden fields. The returned fields can be changed using the parameters:

        - include_parents: include fields derived from inheritance
        - include_hidden:  include fields that have a related_name that
                           starts with a ""+""
        ",53,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_fields(self, include_parents=True, include_hidden=False):
        
        if include_parents is False:
            include_parents = PROXY_PARENTS
        return self._get_fields(
            include_parents=include_parents, include_hidden=include_hidden
        )

        ```
    ###Assistant : 
        Return a list of fields associated to the model. By default, include
        forward and reverse fields, fields derived from inheritance, but not
        hidden fields. The returned fields can be changed using the parameters:

        - include_parents: include fields derived from inheritance
        - include_hidden:  include fields that have a related_name that
                           starts with a ""+""
        
    "
1929,"def _key_to_file(self, session_key=None):
        
        if session_key is None:
            session_key = self._get_or_create_session_key()

        # Make sure we're not vulnerable to directory traversal. Session keys
        # should always be md5s, so they should never contain directory
        # components.
        if not set(session_key).issubset(VALID_KEY_CHARS):
            raise InvalidSessionKey(""Invalid characters in session key"")

        return os.path.join(self.storage_path, self.file_prefix + session_key)
","
        Get the file associated with this session key.
        ",8,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _key_to_file(self, session_key=None):
        
        if session_key is None:
            session_key = self._get_or_create_session_key()

        # Make sure we're not vulnerable to directory traversal. Session keys
        # should always be md5s, so they should never contain directory
        # components.
        if not set(session_key).issubset(VALID_KEY_CHARS):
            raise InvalidSessionKey(""Invalid characters in session key"")

        return os.path.join(self.storage_path, self.file_prefix + session_key)

        ```
    ###Assistant : 
        Get the file associated with this session key.
        
    "
1930,"def check_against_chunks(self, chunks):
        # type: (Iterator[bytes]) -> None
        
        gots = {}
        for hash_name in self._allowed.keys():
            try:
                gots[hash_name] = hashlib.new(hash_name)
            except (ValueError, TypeError):
                raise InstallationError(f""Unknown hash name: {hash_name}"")

        for chunk in chunks:
            for hash in gots.values():
                hash.update(chunk)

        for hash_name, got in gots.items():
            if got.hexdigest() in self._allowed[hash_name]:
                return
        self._raise(gots)
","Check good hashes against ones built from iterable of chunks of
        data.

        Raise HashMismatch if none match.

        ",17,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_against_chunks(self, chunks):
        # type: (Iterator[bytes]) -> None
        
        gots = {}
        for hash_name in self._allowed.keys():
            try:
                gots[hash_name] = hashlib.new(hash_name)
            except (ValueError, TypeError):
                raise InstallationError(f""Unknown hash name: {hash_name}"")

        for chunk in chunks:
            for hash in gots.values():
                hash.update(chunk)

        for hash_name, got in gots.items():
            if got.hexdigest() in self._allowed[hash_name]:
                return
        self._raise(gots)

        ```
    ###Assistant : Check good hashes against ones built from iterable of chunks of
        data.

        Raise HashMismatch if none match.

        
    "
1931,"def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
    
    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
    valid_mask = (labels >= 0) & (labels != ignore_index)
    inds = torch.nonzero(
        valid_mask & (labels < label_channels), as_tuple=False)

    if inds.numel() > 0:
        bin_labels[inds, labels[inds]] = 1

    valid_mask = valid_mask.view(-1, 1).expand(labels.size(0),
                                               label_channels).float()
    if label_weights is None:
        bin_label_weights = valid_mask
    else:
        bin_label_weights = label_weights.view(-1, 1).repeat(1, label_channels)
        bin_label_weights *= valid_mask

    return bin_labels, bin_label_weights, valid_mask

",Expand onehot labels to match the size of prediction.,9,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
    
    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
    valid_mask = (labels >= 0) & (labels != ignore_index)
    inds = torch.nonzero(
        valid_mask & (labels < label_channels), as_tuple=False)

    if inds.numel() > 0:
        bin_labels[inds, labels[inds]] = 1

    valid_mask = valid_mask.view(-1, 1).expand(labels.size(0),
                                               label_channels).float()
    if label_weights is None:
        bin_label_weights = valid_mask
    else:
        bin_label_weights = label_weights.view(-1, 1).repeat(1, label_channels)
        bin_label_weights *= valid_mask

    return bin_labels, bin_label_weights, valid_mask


        ```
    ###Assistant : Expand onehot labels to match the size of prediction.
    "
1932,"def test_sequence_input_types(self, input_type):
        
        if not tf.executing_eagerly():
            self.skipTest(""Improved checking is only present in data_adapter."")

        xy_function, x_function = self._make_sequence_input_functions(
            input_type
        )
        fit_kwargs, evaluate_kwargs, predict_kwargs = {}, {}, {}
        if input_type == ""generator"":
            fit_kwargs[""steps_per_epoch""] = 4
            evaluate_kwargs[""steps""] = 4
            predict_kwargs[""steps""] = 4

        model = test_utils.get_small_mlp(1, 1, 1)
        model.compile(
            loss=""mse"",
            optimizer=""sgd"",
            run_eagerly=test_utils.should_run_eagerly(),
        )

        model.fit(xy_function(use_namedtuple=False), **fit_kwargs)
        model.evaluate(xy_function(use_namedtuple=False), **evaluate_kwargs)
        model.predict(x_function(use_namedtuple=False), **predict_kwargs)
",Ensure that namedtuples and tuples are plumbed identically.,8,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_sequence_input_types(self, input_type):
        
        if not tf.executing_eagerly():
            self.skipTest(""Improved checking is only present in data_adapter."")

        xy_function, x_function = self._make_sequence_input_functions(
            input_type
        )
        fit_kwargs, evaluate_kwargs, predict_kwargs = {}, {}, {}
        if input_type == ""generator"":
            fit_kwargs[""steps_per_epoch""] = 4
            evaluate_kwargs[""steps""] = 4
            predict_kwargs[""steps""] = 4

        model = test_utils.get_small_mlp(1, 1, 1)
        model.compile(
            loss=""mse"",
            optimizer=""sgd"",
            run_eagerly=test_utils.should_run_eagerly(),
        )

        model.fit(xy_function(use_namedtuple=False), **fit_kwargs)
        model.evaluate(xy_function(use_namedtuple=False), **evaluate_kwargs)
        model.predict(x_function(use_namedtuple=False), **predict_kwargs)

        ```
    ###Assistant : Ensure that namedtuples and tuples are plumbed identically.
    "
1933,"def get_stock_value_on(warehouse=None, posting_date=None, item_code=None):
	if not posting_date:
		posting_date = nowdate()

	values, condition = [posting_date], """"

	if warehouse:

		lft, rgt, is_group = frappe.db.get_value(""Warehouse"", warehouse, [""lft"", ""rgt"", ""is_group""])

		if is_group:
			values.extend([lft, rgt])
			condition += ""and exists (\
				select name from `tabWarehouse` wh where wh.name = sle.warehouse\
				and wh.lft >= %s and wh.rgt <= %s)""

		else:
			values.append(warehouse)
			condition += "" AND warehouse = %s""

	if item_code:
		values.append(item_code)
		condition += "" AND item_code = %s""

	stock_ledger_entries = frappe.db.sql(
		.format(
			condition
		),
		values,
		as_dict=1,
	)

	sle_map = {}
	for sle in stock_ledger_entries:
		if not (sle.item_code, sle.warehouse) in sle_map:
			sle_map[(sle.item_code, sle.warehouse)] = flt(sle.stock_value)

	return sum(sle_map.values())


@frappe.whitelist()","
		SELECT item_code, stock_value, name, warehouse
		FROM `tabStock Ledger Entry` sle
		WHERE posting_date <= %s {0}
			and is_cancelled = 0
		ORDER BY timestamp(posting_date, posting_time) DESC, creation DESC
	",26,100,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_stock_value_on(warehouse=None, posting_date=None, item_code=None):
	if not posting_date:
		posting_date = nowdate()

	values, condition = [posting_date], """"

	if warehouse:

		lft, rgt, is_group = frappe.db.get_value(""Warehouse"", warehouse, [""lft"", ""rgt"", ""is_group""])

		if is_group:
			values.extend([lft, rgt])
			condition += ""and exists (\
				select name from `tabWarehouse` wh where wh.name = sle.warehouse\
				and wh.lft >= %s and wh.rgt <= %s)""

		else:
			values.append(warehouse)
			condition += "" AND warehouse = %s""

	if item_code:
		values.append(item_code)
		condition += "" AND item_code = %s""

	stock_ledger_entries = frappe.db.sql(
		.format(
			condition
		),
		values,
		as_dict=1,
	)

	sle_map = {}
	for sle in stock_ledger_entries:
		if not (sle.item_code, sle.warehouse) in sle_map:
			sle_map[(sle.item_code, sle.warehouse)] = flt(sle.stock_value)

	return sum(sle_map.values())


@frappe.whitelist()
        ```
    ###Assistant : 
		SELECT item_code, stock_value, name, warehouse
		FROM `tabStock Ledger Entry` sle
		WHERE posting_date <= %s {0}
			and is_cancelled = 0
		ORDER BY timestamp(posting_date, posting_time) DESC, creation DESC
	
    "
1934,"def _is_installed_rpm(name):
    
    log.debug(f""_is_installed_rpm '{name}'"")
    cmd = [""/usr/bin/rpm"", ""-q"", name]
    return __salt__[""cmd.retcode""](cmd) == 0

","
    Returns True if the rpm package is installed. Otherwise returns False.
    ",11,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _is_installed_rpm(name):
    
    log.debug(f""_is_installed_rpm '{name}'"")
    cmd = [""/usr/bin/rpm"", ""-q"", name]
    return __salt__[""cmd.retcode""](cmd) == 0


        ```
    ###Assistant : 
    Returns True if the rpm package is installed. Otherwise returns False.
    
    "
1935,"def iter_tree_files(root, on_error=None, follow_links=None):
	
	if on_error is not None and not callable(on_error):
		raise TypeError(""on_error:{!r} is not callable."".format(on_error))

	if follow_links is None:
		follow_links = True

	for entry in _iter_tree_entries_next(os.path.abspath(root), '', {}, on_error, follow_links):
		if not entry.is_dir(follow_links):
			yield entry.path


# Alias `iter_tree_files()` as `iter_tree()`.
iter_tree = iter_tree_files

","
	Walks the specified directory for all files.

	*root* (:class:`str`) is the root directory to search for files.

	*on_error* (:class:`~collections.abc.Callable` or :data:`None`)
	optionally is the error handler for file-system exceptions. It will be
	called with the exception (:exc:`OSError`). Reraise the exception to
	abort the walk. Default is :data:`None` to ignore file-system
	exceptions.

	*follow_links* (:class:`bool` or :data:`None`) optionally is whether
	to walk symbolic links that resolve to directories. Default is
	:data:`None` for :data:`True`.

	Raises :exc:`RecursionError` if recursion is detected.

	Returns an :class:`~collections.abc.Iterable` yielding the path to
	each file (:class:`str`) relative to *root*.
	",90,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def iter_tree_files(root, on_error=None, follow_links=None):
	
	if on_error is not None and not callable(on_error):
		raise TypeError(""on_error:{!r} is not callable."".format(on_error))

	if follow_links is None:
		follow_links = True

	for entry in _iter_tree_entries_next(os.path.abspath(root), '', {}, on_error, follow_links):
		if not entry.is_dir(follow_links):
			yield entry.path


# Alias `iter_tree_files()` as `iter_tree()`.
iter_tree = iter_tree_files


        ```
    ###Assistant : 
	Walks the specified directory for all files.

	*root* (:class:`str`) is the root directory to search for files.

	*on_error* (:class:`~collections.abc.Callable` or :data:`None`)
	optionally is the error handler for file-system exceptions. It will be
	called with the exception (:exc:`OSError`). Reraise the exception to
	abort the walk. Default is :data:`None` to ignore file-system
	exceptions.

	*follow_links* (:class:`bool` or :data:`None`) optionally is whether
	to walk symbolic links that resolve to directories. Default is
	:data:`None` for :data:`True`.

	Raises :exc:`RecursionError` if recursion is detected.

	Returns an :class:`~collections.abc.Iterable` yielding the path to
	each file (:class:`str`) relative to *root*.
	
    "
1936,"def _get_url_from_path(path, name):
    # type: (str, str) -> Optional[str]
    
    if _looks_like_path(name) and os.path.isdir(path):
        if is_installable_dir(path):
            return path_to_url(path)
        raise InstallationError(
            f""Directory {name!r} is not installable. Neither 'setup.py' ""
            ""nor 'pyproject.toml' found.""
        )
    if not is_archive_file(path):
        return None
    if os.path.isfile(path):
        return path_to_url(path)
    urlreq_parts = name.split('@', 1)
    if len(urlreq_parts) >= 2 and not _looks_like_path(urlreq_parts[0]):
        # If the path contains '@' and the part before it does not look
        # like a path, try to treat it as a PEP 440 URL req instead.
        return None
    logger.warning(
        'Requirement %r looks like a filename, but the '
        'file does not exist',
        name
    )
    return path_to_url(path)

","
    First, it checks whether a provided path is an installable directory
    (e.g. it has a setup.py). If it is, returns the path.

    If false, check if the path is an archive file (such as a .whl).
    The function checks if the path is a file. If false, if the path has
    an @, it will treat it as a PEP 440 URL requirement and return the path.
    ",67,100,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_url_from_path(path, name):
    # type: (str, str) -> Optional[str]
    
    if _looks_like_path(name) and os.path.isdir(path):
        if is_installable_dir(path):
            return path_to_url(path)
        raise InstallationError(
            f""Directory {name!r} is not installable. Neither 'setup.py' ""
            ""nor 'pyproject.toml' found.""
        )
    if not is_archive_file(path):
        return None
    if os.path.isfile(path):
        return path_to_url(path)
    urlreq_parts = name.split('@', 1)
    if len(urlreq_parts) >= 2 and not _looks_like_path(urlreq_parts[0]):
        # If the path contains '@' and the part before it does not look
        # like a path, try to treat it as a PEP 440 URL req instead.
        return None
    logger.warning(
        'Requirement %r looks like a filename, but the '
        'file does not exist',
        name
    )
    return path_to_url(path)


        ```
    ###Assistant : 
    First, it checks whether a provided path is an installable directory
    (e.g. it has a setup.py). If it is, returns the path.

    If false, check if the path is an archive file (such as a .whl).
    The function checks if the path is a file. If false, if the path has
    an @, it will treat it as a PEP 440 URL requirement and return the path.
    
    "
1937,"def test_install_fileset_with_bff_extension():
    
    installp_call = MagicMock(return_value={""retcode"": 0, ""stdout"": """"})
    fileset_pkg_name = (
        ""/cecc/repos/aix72/TL3/BASE/installp/ppc/bos.rte.printers_7.2.2.0.bff""
    )
    list_pkgs_mock = MagicMock(
        side_effect=[{""bos.rte.printers"": ""7.1.6.0""}, {""bos.rte.printers"": ""7.2.4.0""}]
    )
    with patch(""pathlib.Path.is_file"", return_value=True):
        with patch.dict(
            aixpkg.__salt__,
            {""cmd.run_all"": installp_call, ""config.get"": MagicMock(return_value=False)},
        ), patch.object(aixpkg, ""list_pkgs"", list_pkgs_mock):
            result = aixpkg.install(fileset_pkg_name)
            assert installp_call.call_count == 1
            installp_call.assert_any_call(
                ""/usr/sbin/installp -acYXg -d /cecc/repos/aix72/TL3/BASE/installp/ppc bos.rte.printers_7.2.2.0.bff"",
                python_shell=False,
            )
            expected = {""bos.rte.printers"": {""old"": ""7.1.6.0"", ""new"": ""7.2.4.0""}}
            assert result == expected

","
    Test install of fileset with bff extension
    ",7,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_install_fileset_with_bff_extension():
    
    installp_call = MagicMock(return_value={""retcode"": 0, ""stdout"": """"})
    fileset_pkg_name = (
        ""/cecc/repos/aix72/TL3/BASE/installp/ppc/bos.rte.printers_7.2.2.0.bff""
    )
    list_pkgs_mock = MagicMock(
        side_effect=[{""bos.rte.printers"": ""7.1.6.0""}, {""bos.rte.printers"": ""7.2.4.0""}]
    )
    with patch(""pathlib.Path.is_file"", return_value=True):
        with patch.dict(
            aixpkg.__salt__,
            {""cmd.run_all"": installp_call, ""config.get"": MagicMock(return_value=False)},
        ), patch.object(aixpkg, ""list_pkgs"", list_pkgs_mock):
            result = aixpkg.install(fileset_pkg_name)
            assert installp_call.call_count == 1
            installp_call.assert_any_call(
                ""/usr/sbin/installp -acYXg -d /cecc/repos/aix72/TL3/BASE/installp/ppc bos.rte.printers_7.2.2.0.bff"",
                python_shell=False,
            )
            expected = {""bos.rte.printers"": {""old"": ""7.1.6.0"", ""new"": ""7.2.4.0""}}
            assert result == expected


        ```
    ###Assistant : 
    Test install of fileset with bff extension
    
    "
1938,"def is_user_in_allow_list(block_list):
	return frappe.session.user in frappe.db.sql_list(
		,
		block_list,
	)
","select allow_user
		from `tabLeave Block List Allow` where parent=%s",9,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_user_in_allow_list(block_list):
	return frappe.session.user in frappe.db.sql_list(
		,
		block_list,
	)

        ```
    ###Assistant : select allow_user
		from `tabLeave Block List Allow` where parent=%s
    "
1939,"def test_normalization(push_channel):
    
    types = {
        ""list"": list,
    }
    msgs = [
        {""list"": tuple([1, 2, 3])},
    ]
    for msg in msgs:
        ret = push_channel.send(msg, timeout=5, tries=1)
        for key, value in ret[""load""].items():
            assert types[key] == type(value)

","
    Since we use msgpack, we need to test that list types are converted to lists
    ",15,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_normalization(push_channel):
    
    types = {
        ""list"": list,
    }
    msgs = [
        {""list"": tuple([1, 2, 3])},
    ]
    for msg in msgs:
        ret = push_channel.send(msg, timeout=5, tries=1)
        for key, value in ret[""load""].items():
            assert types[key] == type(value)


        ```
    ###Assistant : 
    Since we use msgpack, we need to test that list types are converted to lists
    
    "
1940,"def to_perioddelta(self, freq) -> TimedeltaArray:
        
        # Deprecaation GH#34853
        warnings.warn(
            ""to_perioddelta is deprecated and will be removed in a ""
            ""future version. ""
            ""Use `dtindex - dtindex.to_period(freq).to_timestamp()` instead."",
            FutureWarning,
            # stacklevel chosen to be correct for when called from DatetimeIndex
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        from pandas.core.arrays.timedeltas import TimedeltaArray

        if self._ndarray.dtype != ""M8[ns]"":
            raise NotImplementedError(""Only supported for nanosecond resolution."")

        i8delta = self.asi8 - self.to_period(freq).to_timestamp().asi8
        m8delta = i8delta.view(""m8[ns]"")
        return TimedeltaArray(m8delta)

    # -----------------------------------------------------------------
    # Properties - Vectorized Timestamp Properties/Methods
","
        Calculate deltas between self values and self converted to Periods at a freq.

        Used for vectorized offsets.

        Parameters
        ----------
        freq : Period frequency

        Returns
        -------
        TimedeltaArray/Index
        ",26,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_perioddelta(self, freq) -> TimedeltaArray:
        
        # Deprecaation GH#34853
        warnings.warn(
            ""to_perioddelta is deprecated and will be removed in a ""
            ""future version. ""
            ""Use `dtindex - dtindex.to_period(freq).to_timestamp()` instead."",
            FutureWarning,
            # stacklevel chosen to be correct for when called from DatetimeIndex
            stacklevel=find_stack_level(inspect.currentframe()),
        )
        from pandas.core.arrays.timedeltas import TimedeltaArray

        if self._ndarray.dtype != ""M8[ns]"":
            raise NotImplementedError(""Only supported for nanosecond resolution."")

        i8delta = self.asi8 - self.to_period(freq).to_timestamp().asi8
        m8delta = i8delta.view(""m8[ns]"")
        return TimedeltaArray(m8delta)

    # -----------------------------------------------------------------
    # Properties - Vectorized Timestamp Properties/Methods

        ```
    ###Assistant : 
        Calculate deltas between self values and self converted to Periods at a freq.

        Used for vectorized offsets.

        Parameters
        ----------
        freq : Period frequency

        Returns
        -------
        TimedeltaArray/Index
        
    "
1941,"def set(self, components):
        
        if len(components) > 0:
            self.__components = components
        else:
            raise Exception(""please give any vector"")
","
        input: new components
        changes the components of the vector.
        replace the components with newer one.
        ",15,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set(self, components):
        
        if len(components) > 0:
            self.__components = components
        else:
            raise Exception(""please give any vector"")

        ```
    ###Assistant : 
        input: new components
        changes the components of the vector.
        replace the components with newer one.
        
    "
1942,"def get_best_result(self) -> Optional[Tuple[Union[int, str], Module, Dict[str, Dict[str, Tensor]], Optional[float], List[Dict]]]:
        
        if self._best_task_id is not None:
            compact_model = torch.load(Path(self._log_dir_root, 'best_result', 'model.pth'))
            compact_model_masks = torch.load(Path(self._log_dir_root, 'best_result', 'masks.pth'))
            with Path(self._log_dir_root, 'best_result', 'config_list.json').open('r') as f:
                config_list = json_tricks.load(f)
            return self._best_task_id, compact_model, compact_model_masks, self._best_score, config_list
        return None
","
        Returns
        -------
        Optional[Tuple[int, Module, Dict[str, Dict[str, Tensor]], float, List[Dict]]]
            If self._best_task_id is not None,
            return best task id, best compact model, masks on the compact model, score, config list used in this task.
        ",33,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_best_result(self) -> Optional[Tuple[Union[int, str], Module, Dict[str, Dict[str, Tensor]], Optional[float], List[Dict]]]:
        
        if self._best_task_id is not None:
            compact_model = torch.load(Path(self._log_dir_root, 'best_result', 'model.pth'))
            compact_model_masks = torch.load(Path(self._log_dir_root, 'best_result', 'masks.pth'))
            with Path(self._log_dir_root, 'best_result', 'config_list.json').open('r') as f:
                config_list = json_tricks.load(f)
            return self._best_task_id, compact_model, compact_model_masks, self._best_score, config_list
        return None

        ```
    ###Assistant : 
        Returns
        -------
        Optional[Tuple[int, Module, Dict[str, Dict[str, Tensor]], float, List[Dict]]]
            If self._best_task_id is not None,
            return best task id, best compact model, masks on the compact model, score, config list used in this task.
        
    "
1943,"def set_variation_by_axes(self, axes):
        
        try:
            self.font.setvaraxes(axes)
        except AttributeError as e:
            msg = ""FreeType 2.9.1 or greater is required""
            raise NotImplementedError(msg) from e

","
        :param axes: A list of values for each axis.
        :exception OSError: If the font is not a variation font.
        ",19,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_variation_by_axes(self, axes):
        
        try:
            self.font.setvaraxes(axes)
        except AttributeError as e:
            msg = ""FreeType 2.9.1 or greater is required""
            raise NotImplementedError(msg) from e


        ```
    ###Assistant : 
        :param axes: A list of values for each axis.
        :exception OSError: If the font is not a variation font.
        
    "
1944,"def dispatch_line(self, frame):
        
        if self.stop_here(frame) or self.break_here(frame):
            self.user_line(frame)
            if self.quitting: raise BdbQuit
        return self.trace_dispatch
","Invoke user function and return trace function for line event.

        If the debugger stops on the current line, invoke
        self.user_line(). Raise BdbQuit if self.quitting is set.
        Return self.trace_dispatch to continue tracing in this scope.
        ",34,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dispatch_line(self, frame):
        
        if self.stop_here(frame) or self.break_here(frame):
            self.user_line(frame)
            if self.quitting: raise BdbQuit
        return self.trace_dispatch

        ```
    ###Assistant : Invoke user function and return trace function for line event.

        If the debugger stops on the current line, invoke
        self.user_line(). Raise BdbQuit if self.quitting is set.
        Return self.trace_dispatch to continue tracing in this scope.
        
    "
1945,"def clear(self) -> None:
        
        self._in_blocks.clear()
        self._snapshot_blocks = None
        self._snapshot_stats = None
        # We're erasing the snapshot, so put all stages into the ""after snapshot""
        # bucket.
        self._stages_after_snapshot = (
            self._stages_before_snapshot + self._stages_after_snapshot
        )
        self._stages_before_snapshot = []
","Clear all cached block references of this plan, including input blocks.

        This will render the plan un-executable unless the root is a LazyBlockList.",23,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clear(self) -> None:
        
        self._in_blocks.clear()
        self._snapshot_blocks = None
        self._snapshot_stats = None
        # We're erasing the snapshot, so put all stages into the ""after snapshot""
        # bucket.
        self._stages_after_snapshot = (
            self._stages_before_snapshot + self._stages_after_snapshot
        )
        self._stages_before_snapshot = []

        ```
    ###Assistant : Clear all cached block references of this plan, including input blocks.

        This will render the plan un-executable unless the root is a LazyBlockList.
    "
1946,"def _global_clustering(self, X=None):
        
        clusterer = self.n_clusters
        centroids = self.subcluster_centers_
        compute_labels = (X is not None) and self.compute_labels

        # Preprocessing for the global clustering.
        not_enough_centroids = False
        if isinstance(clusterer, Integral):
            clusterer = AgglomerativeClustering(n_clusters=self.n_clusters)
            # There is no need to perform the global clustering step.
            if len(centroids) < self.n_clusters:
                not_enough_centroids = True

        # To use in predict to avoid recalculation.
        self._subcluster_norms = row_norms(self.subcluster_centers_, squared=True)

        if clusterer is None or not_enough_centroids:
            self.subcluster_labels_ = np.arange(len(centroids))
            if not_enough_centroids:
                warnings.warn(
                    ""Number of subclusters found (%d) by BIRCH is less ""
                    ""than (%d). Decrease the threshold.""
                    % (len(centroids), self.n_clusters),
                    ConvergenceWarning,
                )
        else:
            # The global clustering step that clusters the subclusters of
            # the leaves. It assumes the centroids of the subclusters as
            # samples and finds the final centroids.
            self.subcluster_labels_ = clusterer.fit_predict(self.subcluster_centers_)

        if compute_labels:
            self.labels_ = self._predict(X)
","
        Global clustering for the subclusters obtained after fitting
        ",8,131,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _global_clustering(self, X=None):
        
        clusterer = self.n_clusters
        centroids = self.subcluster_centers_
        compute_labels = (X is not None) and self.compute_labels

        # Preprocessing for the global clustering.
        not_enough_centroids = False
        if isinstance(clusterer, Integral):
            clusterer = AgglomerativeClustering(n_clusters=self.n_clusters)
            # There is no need to perform the global clustering step.
            if len(centroids) < self.n_clusters:
                not_enough_centroids = True

        # To use in predict to avoid recalculation.
        self._subcluster_norms = row_norms(self.subcluster_centers_, squared=True)

        if clusterer is None or not_enough_centroids:
            self.subcluster_labels_ = np.arange(len(centroids))
            if not_enough_centroids:
                warnings.warn(
                    ""Number of subclusters found (%d) by BIRCH is less ""
                    ""than (%d). Decrease the threshold.""
                    % (len(centroids), self.n_clusters),
                    ConvergenceWarning,
                )
        else:
            # The global clustering step that clusters the subclusters of
            # the leaves. It assumes the centroids of the subclusters as
            # samples and finds the final centroids.
            self.subcluster_labels_ = clusterer.fit_predict(self.subcluster_centers_)

        if compute_labels:
            self.labels_ = self._predict(X)

        ```
    ###Assistant : 
        Global clustering for the subclusters obtained after fitting
        
    "
1947,"def test_prefill_form_backcompat(extras, expected):
    
    mock_form = mock.Mock()
    mock_form.data = {""conn_id"": ""test"", ""extra"": json.dumps(extras), ""conn_type"": ""test""}
    cmv = ConnectionModelView()
    cmv.extra_fields = ['extra__test__my_param']

    # this is set by `lazy_add_provider_discovered_options_to_connection_form`
    cmv.extra_field_name_mapping['extra__test__my_param'] = 'my_param'

    cmv.prefill_form(form=mock_form, pk=1)
    assert mock_form.extra__test__my_param.data == expected


@pytest.mark.parametrize('field_name', ['extra__test__custom_field', 'custom_field'])
@mock.patch('airflow.utils.module_loading.import_string')
@mock.patch('airflow.providers_manager.ProvidersManager.hooks', new_callable=PropertyMock)","
    When populating custom fields in the connection form we should first check for the non-prefixed
    value (since prefixes in extra are deprecated) and then fallback to the prefixed value.

    Either way, the field is known internally to the model view as the prefixed value.
    ",44,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_prefill_form_backcompat(extras, expected):
    
    mock_form = mock.Mock()
    mock_form.data = {""conn_id"": ""test"", ""extra"": json.dumps(extras), ""conn_type"": ""test""}
    cmv = ConnectionModelView()
    cmv.extra_fields = ['extra__test__my_param']

    # this is set by `lazy_add_provider_discovered_options_to_connection_form`
    cmv.extra_field_name_mapping['extra__test__my_param'] = 'my_param'

    cmv.prefill_form(form=mock_form, pk=1)
    assert mock_form.extra__test__my_param.data == expected


@pytest.mark.parametrize('field_name', ['extra__test__custom_field', 'custom_field'])
@mock.patch('airflow.utils.module_loading.import_string')
@mock.patch('airflow.providers_manager.ProvidersManager.hooks', new_callable=PropertyMock)
        ```
    ###Assistant : 
    When populating custom fields in the connection form we should first check for the non-prefixed
    value (since prefixes in extra are deprecated) and then fallback to the prefixed value.

    Either way, the field is known internally to the model view as the prefixed value.
    
    "
1948,"def tokenize_query(query):
    
    result = defaultdict(list)
    query_params = defaultdict(list)
    tokens = split_query_into_tokens(query)
    for token in tokens:
        if token.upper() in [""OR"", ""AND""] or token.strip(""()"") == """":
            continue

        state = ""query""
        for idx, char in enumerate(token):
            next_char = token[idx + 1] if idx < len(token) - 1 else None
            if idx == 0 and char in ('""', ""'"", "":""):
                break
            if char == "":"":
                if next_char in ("":"", "" ""):
                    state = ""query""
                else:
                    state = ""tags""
                break
        query_params[state].append(token)

    if ""query"" in query_params:
        result[""query""] = [format_query(query) for query in query_params[""query""]]
    for tag in query_params[""tags""]:
        key, value = format_tag(tag)
        result[key].append(value)
    return dict(result)

","
    Tokenizes a standard Sentry search query.

    Example:
    >>> query = 'is:resolved foo bar tag:value'
    >>> tokenize_query(query)
    {
        'is': ['resolved'],
        'query': ['foo', 'bar'],
        'tag': ['value'],
    }

    Has a companion implementation in static/app/utils/tokenizeSearch.tsx
    ",31,98,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tokenize_query(query):
    
    result = defaultdict(list)
    query_params = defaultdict(list)
    tokens = split_query_into_tokens(query)
    for token in tokens:
        if token.upper() in [""OR"", ""AND""] or token.strip(""()"") == """":
            continue

        state = ""query""
        for idx, char in enumerate(token):
            next_char = token[idx + 1] if idx < len(token) - 1 else None
            if idx == 0 and char in ('""', ""'"", "":""):
                break
            if char == "":"":
                if next_char in ("":"", "" ""):
                    state = ""query""
                else:
                    state = ""tags""
                break
        query_params[state].append(token)

    if ""query"" in query_params:
        result[""query""] = [format_query(query) for query in query_params[""query""]]
    for tag in query_params[""tags""]:
        key, value = format_tag(tag)
        result[key].append(value)
    return dict(result)


        ```
    ###Assistant : 
    Tokenizes a standard Sentry search query.

    Example:
    >>> query = 'is:resolved foo bar tag:value'
    >>> tokenize_query(query)
    {
        'is': ['resolved'],
        'query': ['foo', 'bar'],
        'tag': ['value'],
    }

    Has a companion implementation in static/app/utils/tokenizeSearch.tsx
    
    "
1949,"def save_model(model, filepath, overwrite=True, save_format=None, **kwargs):
    
    save_format = get_save_format(filepath, save_format)
    if save_format not in (""keras"", ""tf"", ""h5"", ""keras_v3""):
        raise ValueError(
            ""Unknown `save_format` argument. Expected one of ""
            ""'keras', 'tf', or 'h5'. ""
            f""Received: save_format{save_format}""
        )
    if save_format == ""keras_v3"" or (
        saving_lib.saving_v3_enabled() and save_format == ""keras""
    ):
        # If file exists and should not be overwritten.
        try:
            exists = os.path.exists(filepath)
        except TypeError:
            exists = False
        if exists and not overwrite:
            proceed = io_utils.ask_to_proceed_with_overwrite(filepath)
            if not proceed:
                return
        if kwargs:
            raise ValueError(
                ""The following argument(s) are not supported ""
                f""with the native Keras format: {list(kwargs.keys())}""
            )
        saving_lib.save_model(model, filepath)
    else:
        # Legacy case
        return legacy_sm_saving_lib.save_model(
            model,
            filepath,
            overwrite=overwrite,
            save_format=save_format,
            **kwargs,
        )


@keras_export(""keras.models.load_model"")","Saves a model as a TensorFlow SavedModel or HDF5 file.

    See the [Serialization and Saving guide](
        https://keras.io/guides/serialization_and_saving/) for details.

    Args:
        model: Keras model instance to be saved.
        filepath: `str` or `pathlib.Path` object. Path where to save the model.
        overwrite: Whether we should overwrite any existing model at the target
            location, or instead ask the user via an interactive prompt.
        save_format: Either `""keras""`, `""tf""`, `""h5""`,
            indicating whether to save the model
            in the native Keras format (`.keras`),
            in the TensorFlow SavedModel format (referred to as ""SavedModel""
            below), or in the legacy HDF5 format (`.h5`).
            Defaults to `""tf""` in TF 2.X, and `""h5""` in TF 1.X.

    SavedModel format arguments:
        include_optimizer: Only applied to SavedModel and legacy HDF5 formats.
            If False, do not save the optimizer state. Defaults to True.
        signatures: Only applies to SavedModel format. Signatures to save
            with the SavedModel. See the `signatures` argument in
            `tf.saved_model.save` for details.
        options: Only applies to SavedModel format.
            `tf.saved_model.SaveOptions` object that specifies SavedModel
            saving options.
        save_traces: Only applies to SavedModel format. When enabled, the
            SavedModel will store the function traces for each layer. This
            can be disabled, so that only the configs of each layer are stored.
            Defaults to `True`. Disabling this will decrease serialization time
            and reduce file size, but it requires that all custom layers/models
            implement a `get_config()` method.

    Example:

    ```python
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(5, input_shape=(3,)),
        tf.keras.layers.Softmax()])
    model.save(""model.keras"")
    loaded_model = tf.keras.models.load_model(""model.keras"")
    x = tf.random.uniform((10, 3))
    assert np.allclose(model.predict(x), loaded_model.predict(x))
    ```

    Note that `model.save()` is an alias for `tf.keras.models.save_model()`.

    The SavedModel or HDF5 file contains:

    - The model's configuration (architecture)
    - The model's weights
    - The model's optimizer's state (if any)

    Thus models can be reinstantiated in the exact same state, without any of
    the code used for model definition or training.

    Note that the model weights may have different scoped names after being
    loaded. Scoped names include the model/layer names, such as
    `""dense_1/kernel:0""`. It is recommended that you use the layer properties to
    access specific variables, e.g. `model.get_layer(""dense_1"").kernel`.

    __SavedModel serialization format__

    With `save_format=""tf""`, the model and all trackable objects attached
    to the it (e.g. layers and variables) are saved as a TensorFlow SavedModel.
    The model config, weights, and optimizer are included in the SavedModel.
    Additionally, for every Keras layer attached to the model, the SavedModel
    stores:

    * The config and metadata -- e.g. name, dtype, trainable status
    * Traced call and loss functions, which are stored as TensorFlow
      subgraphs.

    The traced functions allow the SavedModel format to save and load custom
    layers without the original class definition.

    You can choose to not save the traced functions by disabling the
    `save_traces` option. This will decrease the time it takes to save the model
    and the amount of disk space occupied by the output SavedModel. If you
    enable this option, then you _must_ provide all custom class definitions
    when loading the model. See the `custom_objects` argument in
    `tf.keras.models.load_model`.
    ",472,110,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_model(model, filepath, overwrite=True, save_format=None, **kwargs):
    
    save_format = get_save_format(filepath, save_format)
    if save_format not in (""keras"", ""tf"", ""h5"", ""keras_v3""):
        raise ValueError(
            ""Unknown `save_format` argument. Expected one of ""
            ""'keras', 'tf', or 'h5'. ""
            f""Received: save_format{save_format}""
        )
    if save_format == ""keras_v3"" or (
        saving_lib.saving_v3_enabled() and save_format == ""keras""
    ):
        # If file exists and should not be overwritten.
        try:
            exists = os.path.exists(filepath)
        except TypeError:
            exists = False
        if exists and not overwrite:
            proceed = io_utils.ask_to_proceed_with_overwrite(filepath)
            if not proceed:
                return
        if kwargs:
            raise ValueError(
                ""The following argument(s) are not supported ""
                f""with the native Keras format: {list(kwargs.keys())}""
            )
        saving_lib.save_model(model, filepath)
    else:
        # Legacy case
        return legacy_sm_saving_lib.save_model(
            model,
            filepath,
            overwrite=overwrite,
            save_format=save_format,
            **kwargs,
        )


@keras_export(""keras.models.load_model"")
        ```
    ###Assistant : Saves a model as a TensorFlow SavedModel or HDF5 file.

    See the [Serialization and Saving guide](
        https://keras.io/guides/serialization_and_saving/) for details.

    Args:
        model: Keras model instance to be saved.
        filepath: `str` or `pathlib.Path` object. Path where to save the model.
        overwrite: Whether we should overwrite any existing model at the target
            location, or instead ask the user via an interactive prompt.
        save_format: Either `""keras""`, `""tf""`, `""h5""`,
            indicating whether to save the model
            in the native Keras format (`.keras`),
            in the TensorFlow SavedModel format (referred to as ""SavedModel""
            below), or in the legacy HDF5 format (`.h5`).
            Defaults to `""tf""` in TF 2.X, and `""h5""` in TF 1.X.

    SavedModel format arguments:
        include_optimizer: Only applied to SavedModel and legacy HDF5 formats.
            If False, do not save the optimizer state. Defaults to True.
        signatures: Only applies to SavedModel format. Signatures to save
            with the SavedModel. See the `signatures` argument in
            `tf.saved_model.save` for details.
        options: Only applies to SavedModel format.
            `tf.saved_model.SaveOptions` object that specifies SavedModel
            saving options.
        save_traces: Only applies to SavedModel format. When enabled, the
            SavedModel will store the function traces for each layer. This
            can be disabled, so that only the configs of each layer are stored.
            Defaults to `True`. Disabling this will decrease serialization time
            and reduce file size, but it requires that all custom layers/models
            implement a `get_config()` method.

    Example:

    ```python
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(5, input_shape=(3,)),
        tf.keras.layers.Softmax()])
    model.save(""model.keras"")
    loaded_model = tf.keras.models.load_model(""model.keras"")
    x = tf.random.uniform((10, 3))
    assert np.allclose(model.predict(x), loaded_model.predict(x))
    ```

    Note that `model.save()` is an alias for `tf.keras.models.save_model()`.

    The SavedModel or HDF5 file contains:

    - The model's configuration (architecture)
    - The model's weights
    - The model's optimizer's state (if any)

    Thus models can be reinstantiated in the exact same state, without any of
    the code used for model definition or training.

    Note that the model weights may have different scoped names after being
    loaded. Scoped names include the model/layer names, such as
    `""dense_1/kernel:0""`. It is recommended that you use the layer properties to
    access specific variables, e.g. `model.get_layer(""dense_1"").kernel`.

    __SavedModel serialization format__

    With `save_format=""tf""`, the model and all trackable objects attached
    to the it (e.g. layers and variables) are saved as a TensorFlow SavedModel.
    The model config, weights, and optimizer are included in the SavedModel.
    Additionally, for every Keras layer attached to the model, the SavedModel
    stores:

    * The config and metadata -- e.g. name, dtype, trainable status
    * Traced call and loss functions, which are stored as TensorFlow
      subgraphs.

    The traced functions allow the SavedModel format to save and load custom
    layers without the original class definition.

    You can choose to not save the traced functions by disabling the
    `save_traces` option. This will decrease the time it takes to save the model
    and the amount of disk space occupied by the output SavedModel. If you
    enable this option, then you _must_ provide all custom class definitions
    when loading the model. See the `custom_objects` argument in
    `tf.keras.models.load_model`.
    
    "
1950,"def test_random_spanning_tree_additive_small():
    
    pytest.importorskip(""numpy"")

    edges = {
        (0, 1): 1,
        (0, 2): 1,
        (0, 5): 3,
        (1, 2): 2,
        (1, 4): 3,
        (2, 3): 3,
        (5, 3): 4,
        (5, 4): 5,
        (4, 3): 4,
    }

    # Build the graph
    G = nx.Graph()
    for u, v in edges:
        G.add_edge(u, v, weight=edges[(u, v)])

    solution_edges = [(0, 2), (1, 2), (2, 3), (3, 4), (3, 5)]
    solution = nx.Graph()
    solution.add_edges_from(solution_edges)

    sampled_tree = nx.random_spanning_tree(
        G, weight=""weight"", multiplicative=False, seed=37
    )

    assert nx.utils.edges_equal(solution.edges, sampled_tree.edges)


@pytest.mark.slow","
    Sample a single spanning tree from the additive method.
    ",9,78,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_random_spanning_tree_additive_small():
    
    pytest.importorskip(""numpy"")

    edges = {
        (0, 1): 1,
        (0, 2): 1,
        (0, 5): 3,
        (1, 2): 2,
        (1, 4): 3,
        (2, 3): 3,
        (5, 3): 4,
        (5, 4): 5,
        (4, 3): 4,
    }

    # Build the graph
    G = nx.Graph()
    for u, v in edges:
        G.add_edge(u, v, weight=edges[(u, v)])

    solution_edges = [(0, 2), (1, 2), (2, 3), (3, 4), (3, 5)]
    solution = nx.Graph()
    solution.add_edges_from(solution_edges)

    sampled_tree = nx.random_spanning_tree(
        G, weight=""weight"", multiplicative=False, seed=37
    )

    assert nx.utils.edges_equal(solution.edges, sampled_tree.edges)


@pytest.mark.slow
        ```
    ###Assistant : 
    Sample a single spanning tree from the additive method.
    
    "
1951,"def sequence_loss(flow_preds, flow_gt, valid_flow_mask, gamma=0.8, max_flow=400):
    

    if gamma > 1:
        raise ValueError(f""Gamma should be < 1, got {gamma}."")

    # exlude invalid pixels and extremely large diplacements
    flow_norm = torch.sum(flow_gt**2, dim=1).sqrt()
    valid_flow_mask = valid_flow_mask & (flow_norm < max_flow)

    valid_flow_mask = valid_flow_mask[:, None, :, :]

    flow_preds = torch.stack(flow_preds)  # shape = (num_flow_updates, batch_size, 2, H, W)

    abs_diff = (flow_preds - flow_gt).abs()
    abs_diff = (abs_diff * valid_flow_mask).mean(axis=(1, 2, 3, 4))

    num_predictions = flow_preds.shape[0]
    weights = gamma ** torch.arange(num_predictions - 1, -1, -1).to(flow_gt.device)
    flow_loss = (abs_diff * weights).sum()

    return flow_loss

",Loss function defined over sequence of flow predictions,8,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sequence_loss(flow_preds, flow_gt, valid_flow_mask, gamma=0.8, max_flow=400):
    

    if gamma > 1:
        raise ValueError(f""Gamma should be < 1, got {gamma}."")

    # exlude invalid pixels and extremely large diplacements
    flow_norm = torch.sum(flow_gt**2, dim=1).sqrt()
    valid_flow_mask = valid_flow_mask & (flow_norm < max_flow)

    valid_flow_mask = valid_flow_mask[:, None, :, :]

    flow_preds = torch.stack(flow_preds)  # shape = (num_flow_updates, batch_size, 2, H, W)

    abs_diff = (flow_preds - flow_gt).abs()
    abs_diff = (abs_diff * valid_flow_mask).mean(axis=(1, 2, 3, 4))

    num_predictions = flow_preds.shape[0]
    weights = gamma ** torch.arange(num_predictions - 1, -1, -1).to(flow_gt.device)
    flow_loss = (abs_diff * weights).sum()

    return flow_loss


        ```
    ###Assistant : Loss function defined over sequence of flow predictions
    "
1952,"def booleans_processing(config, **kwargs):
    
    final_booleans = {}

    if tf.executing_eagerly():
        final_booleans[""output_attentions""] = (
            kwargs[""output_attentions""] if kwargs[""output_attentions""] is not None else config.output_attentions
        )
        final_booleans[""output_hidden_states""] = (
            kwargs[""output_hidden_states""]
            if kwargs[""output_hidden_states""] is not None
            else config.output_hidden_states
        )
        final_booleans[""return_dict""] = (
            kwargs[""return_dict""] if kwargs[""return_dict""] is not None else config.return_dict
        )

        if ""use_cache"" in kwargs:
            final_booleans[""use_cache""] = (
                kwargs[""use_cache""] if kwargs[""use_cache""] is not None else getattr(config, ""use_cache"", None)
            )
    else:
        final_booleans[""output_attentions""] = config.output_attentions
        final_booleans[""output_hidden_states""] = config.output_hidden_states

        if kwargs.get(""return_dict"", None) not in (None, True):
            tf_logger.warning(
                ""The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.""
            )
        final_booleans[""return_dict""] = True

        if ""use_cache"" in kwargs:
            final_booleans[""use_cache""] = getattr(config, ""use_cache"", None)

    return final_booleans

","
    Process the input booleans of each model in order to be sure they are compliant with the execution mode (eager or
    graph)

    Args:
        config ([`PretrainedConfig`]):
            The config of the running model.
        **kwargs:
            The boolean parameters

    Returns:
        A dictionary with the proper values for each boolean
    ",45,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def booleans_processing(config, **kwargs):
    
    final_booleans = {}

    if tf.executing_eagerly():
        final_booleans[""output_attentions""] = (
            kwargs[""output_attentions""] if kwargs[""output_attentions""] is not None else config.output_attentions
        )
        final_booleans[""output_hidden_states""] = (
            kwargs[""output_hidden_states""]
            if kwargs[""output_hidden_states""] is not None
            else config.output_hidden_states
        )
        final_booleans[""return_dict""] = (
            kwargs[""return_dict""] if kwargs[""return_dict""] is not None else config.return_dict
        )

        if ""use_cache"" in kwargs:
            final_booleans[""use_cache""] = (
                kwargs[""use_cache""] if kwargs[""use_cache""] is not None else getattr(config, ""use_cache"", None)
            )
    else:
        final_booleans[""output_attentions""] = config.output_attentions
        final_booleans[""output_hidden_states""] = config.output_hidden_states

        if kwargs.get(""return_dict"", None) not in (None, True):
            tf_logger.warning(
                ""The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.""
            )
        final_booleans[""return_dict""] = True

        if ""use_cache"" in kwargs:
            final_booleans[""use_cache""] = getattr(config, ""use_cache"", None)

    return final_booleans


        ```
    ###Assistant : 
    Process the input booleans of each model in order to be sure they are compliant with the execution mode (eager or
    graph)

    Args:
        config ([`PretrainedConfig`]):
            The config of the running model.
        **kwargs:
            The boolean parameters

    Returns:
        A dictionary with the proper values for each boolean
    
    "
1953,"def test_dagrun_root_fail_unfinished(self):
        
        # TODO: this should live in test_dagrun.py
        # Run both the failed and successful tasks
        dag_id = 'test_dagrun_states_root_fail_unfinished'
        dag = self.dagbag.get_dag(dag_id)
        dr = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE,
            state=None,
        )
        self.null_exec.mock_task_fail(dag_id, 'test_dagrun_fail', dr.run_id)

        with pytest.raises(AirflowException):
            dag.run(start_date=dr.execution_date, end_date=dr.execution_date, executor=self.null_exec)

        # Mark the successful task as never having run since we want to see if the
        # dagrun will be in a running state despite having an unfinished task.
        with create_session() as session:
            ti = dr.get_task_instance('test_dagrun_unfinished', session=session)
            ti.state = State.NONE
            session.commit()
        dr.update_state()
        assert dr.state == State.RUNNING
","
        DagRuns with one unfinished and one failed root task -> RUNNING
        ",11,84,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dagrun_root_fail_unfinished(self):
        
        # TODO: this should live in test_dagrun.py
        # Run both the failed and successful tasks
        dag_id = 'test_dagrun_states_root_fail_unfinished'
        dag = self.dagbag.get_dag(dag_id)
        dr = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE,
            state=None,
        )
        self.null_exec.mock_task_fail(dag_id, 'test_dagrun_fail', dr.run_id)

        with pytest.raises(AirflowException):
            dag.run(start_date=dr.execution_date, end_date=dr.execution_date, executor=self.null_exec)

        # Mark the successful task as never having run since we want to see if the
        # dagrun will be in a running state despite having an unfinished task.
        with create_session() as session:
            ti = dr.get_task_instance('test_dagrun_unfinished', session=session)
            ti.state = State.NONE
            session.commit()
        dr.update_state()
        assert dr.state == State.RUNNING

        ```
    ###Assistant : 
        DagRuns with one unfinished and one failed root task -> RUNNING
        
    "
1954,"def _get_call_args(backend_name, data, args, kwargs):
        
        if isinstance(data, ABCSeries):
            arg_def = [
                (""kind"", ""line""),
                (""ax"", None),
                (""figsize"", None),
                (""use_index"", True),
                (""title"", None),
                (""grid"", None),
                (""legend"", False),
                (""style"", None),
                (""logx"", False),
                (""logy"", False),
                (""loglog"", False),
                (""xticks"", None),
                (""yticks"", None),
                (""xlim"", None),
                (""ylim"", None),
                (""rot"", None),
                (""fontsize"", None),
                (""colormap"", None),
                (""table"", False),
                (""yerr"", None),
                (""xerr"", None),
                (""label"", None),
                (""secondary_y"", False),
                (""xlabel"", None),
                (""ylabel"", None),
            ]
        elif isinstance(data, ABCDataFrame):
            arg_def = [
                (""x"", None),
                (""y"", None),
                (""kind"", ""line""),
                (""ax"", None),
                (""subplots"", False),
                (""sharex"", None),
                (""sharey"", False),
                (""layout"", None),
                (""figsize"", None),
                (""use_index"", True),
                (""title"", None),
                (""grid"", None),
                (""legend"", True),
                (""style"", None),
                (""logx"", False),
                (""logy"", False),
                (""loglog"", False),
                (""xticks"", None),
                (""yticks"", None),
                (""xlim"", None),
                (""ylim"", None),
                (""rot"", None),
                (""fontsize"", None),
                (""colormap"", None),
                (""table"", False),
                (""yerr"", None),
                (""xerr"", None),
                (""secondary_y"", False),
                (""sort_columns"", False),
                (""xlabel"", None),
                (""ylabel"", None),
            ]
        else:
            raise TypeError(
                f""Called plot accessor for type {type(data).__name__}, ""
                ""expected Series or DataFrame""
            )

        if ""sort_columns"" in itertools.chain(args, kwargs.keys()):
            warnings.warn(
                ""`sort_columns` is deprecated and will be removed in a future ""
                ""version."",
                FutureWarning,
                stacklevel=find_stack_level(inspect.currentframe()),
            )

        if args and isinstance(data, ABCSeries):
            positional_args = str(args)[1:-1]
            keyword_args = "", "".join(
                [f""{name}={repr(value)}"" for (name, _), value in zip(arg_def, args)]
            )
            msg = (
                ""`Series.plot()` should not be called with positional ""
                ""arguments, only keyword arguments. The order of ""
                ""positional arguments will change in the future. ""
                f""Use `Series.plot({keyword_args})` instead of ""
                f""`Series.plot({positional_args})`.""
            )
            raise TypeError(msg)

        pos_args = {name: value for (name, _), value in zip(arg_def, args)}
        if backend_name == ""pandas.plotting._matplotlib"":
            kwargs = dict(arg_def, **pos_args, **kwargs)
        else:
            kwargs = dict(pos_args, **kwargs)

        x = kwargs.pop(""x"", None)
        y = kwargs.pop(""y"", None)
        kind = kwargs.pop(""kind"", ""line"")
        return x, y, kind, kwargs
","
        This function makes calls to this accessor `__call__` method compatible
        with the previous `SeriesPlotMethods.__call__` and
        `DataFramePlotMethods.__call__`. Those had slightly different
        signatures, since `DataFramePlotMethods` accepted `x` and `y`
        parameters.
        ",28,266,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_call_args(backend_name, data, args, kwargs):
        
        if isinstance(data, ABCSeries):
            arg_def = [
                (""kind"", ""line""),
                (""ax"", None),
                (""figsize"", None),
                (""use_index"", True),
                (""title"", None),
                (""grid"", None),
                (""legend"", False),
                (""style"", None),
                (""logx"", False),
                (""logy"", False),
                (""loglog"", False),
                (""xticks"", None),
                (""yticks"", None),
                (""xlim"", None),
                (""ylim"", None),
                (""rot"", None),
                (""fontsize"", None),
                (""colormap"", None),
                (""table"", False),
                (""yerr"", None),
                (""xerr"", None),
                (""label"", None),
                (""secondary_y"", False),
                (""xlabel"", None),
                (""ylabel"", None),
            ]
        elif isinstance(data, ABCDataFrame):
            arg_def = [
                (""x"", None),
                (""y"", None),
                (""kind"", ""line""),
                (""ax"", None),
                (""subplots"", False),
                (""sharex"", None),
                (""sharey"", False),
                (""layout"", None),
                (""figsize"", None),
                (""use_index"", True),
                (""title"", None),
                (""grid"", None),
                (""legend"", True),
                (""style"", None),
                (""logx"", False),
                (""logy"", False),
                (""loglog"", False),
                (""xticks"", None),
                (""yticks"", None),
                (""xlim"", None),
                (""ylim"", None),
                (""rot"", None),
                (""fontsize"", None),
                (""colormap"", None),
                (""table"", False),
                (""yerr"", None),
                (""xerr"", None),
                (""secondary_y"", False),
                (""sort_columns"", False),
                (""xlabel"", None),
                (""ylabel"", None),
            ]
        else:
            raise TypeError(
                f""Called plot accessor for type {type(data).__name__}, ""
                ""expected Series or DataFrame""
            )

        if ""sort_columns"" in itertools.chain(args, kwargs.keys()):
            warnings.warn(
                ""`sort_columns` is deprecated and will be removed in a future ""
                ""version."",
                FutureWarning,
                stacklevel=find_stack_level(inspect.currentframe()),
            )

        if args and isinstance(data, ABCSeries):
            positional_args = str(args)[1:-1]
            keyword_args = "", "".join(
                [f""{name}={repr(value)}"" for (name, _), value in zip(arg_def, args)]
            )
            msg = (
                ""`Series.plot()` should not be called with positional ""
                ""arguments, only keyword arguments. The order of ""
                ""positional arguments will change in the future. ""
                f""Use `Series.plot({keyword_args})` instead of ""
                f""`Series.plot({positional_args})`.""
            )
            raise TypeError(msg)

        pos_args = {name: value for (name, _), value in zip(arg_def, args)}
        if backend_name == ""pandas.plotting._matplotlib"":
            kwargs = dict(arg_def, **pos_args, **kwargs)
        else:
            kwargs = dict(pos_args, **kwargs)

        x = kwargs.pop(""x"", None)
        y = kwargs.pop(""y"", None)
        kind = kwargs.pop(""kind"", ""line"")
        return x, y, kind, kwargs

        ```
    ###Assistant : 
        This function makes calls to this accessor `__call__` method compatible
        with the previous `SeriesPlotMethods.__call__` and
        `DataFramePlotMethods.__call__`. Those had slightly different
        signatures, since `DataFramePlotMethods` accepted `x` and `y`
        parameters.
        
    "
1955,"async def test_ahas_key(self):
        
        await cache.aset(""hello1"", ""goodbye1"")
        self.assertIs(await cache.ahas_key(""hello1""), False)
        self.assertIs(await cache.ahas_key(""goodbye1""), False)
",ahas_key() doesn't ever return True for the dummy cache backend.,10,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_ahas_key(self):
        
        await cache.aset(""hello1"", ""goodbye1"")
        self.assertIs(await cache.ahas_key(""hello1""), False)
        self.assertIs(await cache.ahas_key(""goodbye1""), False)

        ```
    ###Assistant : ahas_key() doesn't ever return True for the dummy cache backend.
    "
1956,"def min_temp(self) -> float:
        
        if self.temperature_unit == UnitOfTemperature.CELSIUS:
            return TEMP_MIN
        return TEMP_MIN_F
",Return the minimum temperature supported by the device.,8,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def min_temp(self) -> float:
        
        if self.temperature_unit == UnitOfTemperature.CELSIUS:
            return TEMP_MIN
        return TEMP_MIN_F

        ```
    ###Assistant : Return the minimum temperature supported by the device.
    "
1957,"def nseries(self, x=None, x0=0, n=6, dir='+', logx=None, cdir=0):
        
        if x and x not in self.free_symbols:
            return self
        if x is None or x0 or dir != '+':  # {see XPOS above} or (x.is_positive == x.is_negative == None):
            return self.series(x, x0, n, dir, cdir=cdir)
        else:
            return self._eval_nseries(x, n=n, logx=logx, cdir=cdir)
","
        Wrapper to _eval_nseries if assumptions allow, else to series.

        If x is given, x0 is 0, dir='+', and self has x, then _eval_nseries is
        called. This calculates ""n"" terms in the innermost expressions and
        then builds up the final series just by ""cross-multiplying"" everything
        out.

        The optional ``logx`` parameter can be used to replace any log(x) in the
        returned series with a symbolic value to avoid evaluating log(x) at 0. A
        symbol to use in place of log(x) should be provided.

        Advantage -- it's fast, because we do not have to determine how many
        terms we need to calculate in advance.

        Disadvantage -- you may end up with less terms than you may have
        expected, but the O(x**n) term appended will always be correct and
        so the result, though perhaps shorter, will also be correct.

        If any of those assumptions is not met, this is treated like a
        wrapper to series which will try harder to return the correct
        number of terms.

        See also lseries().

        Examples
        ========

        >>> from sympy import sin, log, Symbol
        >>> from sympy.abc import x, y
        >>> sin(x).nseries(x, 0, 6)
        x - x**3/6 + x**5/120 + O(x**6)
        >>> log(x+1).nseries(x, 0, 5)
        x - x**2/2 + x**3/3 - x**4/4 + O(x**5)

        Handling of the ``logx`` parameter --- in the following example the
        expansion fails since ``sin`` does not have an asymptotic expansion
        at -oo (the limit of log(x) as x approaches 0):

        >>> e = sin(log(x))
        >>> e.nseries(x, 0, 6)
        Traceback (most recent call last):
        ...
        PoleError: ...
        ...
        >>> logx = Symbol('logx')
        >>> e.nseries(x, 0, 6, logx=logx)
        sin(logx)

        In the following example, the expansion works but only returns self
        unless the ``logx`` parameter is used:

        >>> e = x**y
        >>> e.nseries(x, 0, 2)
        x**y
        >>> e.nseries(x, 0, 2, logx=logx)
        exp(logx*y)

        ",294,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def nseries(self, x=None, x0=0, n=6, dir='+', logx=None, cdir=0):
        
        if x and x not in self.free_symbols:
            return self
        if x is None or x0 or dir != '+':  # {see XPOS above} or (x.is_positive == x.is_negative == None):
            return self.series(x, x0, n, dir, cdir=cdir)
        else:
            return self._eval_nseries(x, n=n, logx=logx, cdir=cdir)

        ```
    ###Assistant : 
        Wrapper to _eval_nseries if assumptions allow, else to series.

        If x is given, x0 is 0, dir='+', and self has x, then _eval_nseries is
        called. This calculates ""n"" terms in the innermost expressions and
        then builds up the final series just by ""cross-multiplying"" everything
        out.

        The optional ``logx`` parameter can be used to replace any log(x) in the
        returned series with a symbolic value to avoid evaluating log(x) at 0. A
        symbol to use in place of log(x) should be provided.

        Advantage -- it's fast, because we do not have to determine how many
        terms we need to calculate in advance.

        Disadvantage -- you may end up with less terms than you may have
        expected, but the O(x**n) term appended will always be correct and
        so the result, though perhaps shorter, will also be correct.

        If any of those assumptions is not met, this is treated like a
        wrapper to series which will try harder to return the correct
        number of terms.

        See also lseries().

        Examples
        ========

        >>> from sympy import sin, log, Symbol
        >>> from sympy.abc import x, y
        >>> sin(x).nseries(x, 0, 6)
        x - x**3/6 + x**5/120 + O(x**6)
        >>> log(x+1).nseries(x, 0, 5)
        x - x**2/2 + x**3/3 - x**4/4 + O(x**5)

        Handling of the ``logx`` parameter --- in the following example the
        expansion fails since ``sin`` does not have an asymptotic expansion
        at -oo (the limit of log(x) as x approaches 0):

        >>> e = sin(log(x))
        >>> e.nseries(x, 0, 6)
        Traceback (most recent call last):
        ...
        PoleError: ...
        ...
        >>> logx = Symbol('logx')
        >>> e.nseries(x, 0, 6, logx=logx)
        sin(logx)

        In the following example, the expansion works but only returns self
        unless the ``logx`` parameter is used:

        >>> e = x**y
        >>> e.nseries(x, 0, 2)
        x**y
        >>> e.nseries(x, 0, 2, logx=logx)
        exp(logx*y)

        
    "
1958,"def test_getitem_error(self, exception):
        container = self.Container(exception(""failure""))
        with pytest.raises(validate.ValidationError) as cm:
            validate.validate(validate.get(""foo"", default=""default""), container)
        assert_validationerror(cm.value, )
","
            ValidationError(GetItemSchema):
              Could not get key 'foo' from object Container
              Context:
                failure
        ",11,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_getitem_error(self, exception):
        container = self.Container(exception(""failure""))
        with pytest.raises(validate.ValidationError) as cm:
            validate.validate(validate.get(""foo"", default=""default""), container)
        assert_validationerror(cm.value, )

        ```
    ###Assistant : 
            ValidationError(GetItemSchema):
              Could not get key 'foo' from object Container
              Context:
                failure
        
    "
1959,"def num_base_priors(self) -> List[int]:
        
        return [1 for _ in range(len(self.strides))]
","list[int]: The number of priors (points) at a point
        on the feature grid",13,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def num_base_priors(self) -> List[int]:
        
        return [1 for _ in range(len(self.strides))]

        ```
    ###Assistant : list[int]: The number of priors (points) at a point
        on the feature grid
    "
1960,"def test_logentry_change_message_localized_datetime_input(self):
        
        post_data = {
            ""site"": self.site.pk,
            ""title"": ""Changed"",
            ""hist"": ""Some content"",
            ""created_0"": ""12/03/2008"",
            ""created_1"": ""11:54"",
        }
        with translation.override(""fr""):
            change_url = reverse(
                ""admin:admin_utils_article_change"", args=[quote(self.a1.pk)]
            )
            response = self.client.post(change_url, post_data)
            self.assertRedirects(
                response, reverse(""admin:admin_utils_article_changelist"")
            )
        logentry = LogEntry.objects.filter(
            content_type__model__iexact=""article""
        ).latest(""id"")
        self.assertEqual(logentry.get_change_message(), ""Changed Title and History."")
","
        Localized date/time inputs shouldn't affect changed form data detection.
        ",9,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_logentry_change_message_localized_datetime_input(self):
        
        post_data = {
            ""site"": self.site.pk,
            ""title"": ""Changed"",
            ""hist"": ""Some content"",
            ""created_0"": ""12/03/2008"",
            ""created_1"": ""11:54"",
        }
        with translation.override(""fr""):
            change_url = reverse(
                ""admin:admin_utils_article_change"", args=[quote(self.a1.pk)]
            )
            response = self.client.post(change_url, post_data)
            self.assertRedirects(
                response, reverse(""admin:admin_utils_article_changelist"")
            )
        logentry = LogEntry.objects.filter(
            content_type__model__iexact=""article""
        ).latest(""id"")
        self.assertEqual(logentry.get_change_message(), ""Changed Title and History."")

        ```
    ###Assistant : 
        Localized date/time inputs shouldn't affect changed form data detection.
        
    "
1961,"def _cleanup_code(code):
        return code  # Nothing to do here

    # language=PythonVerboseRegExp
    _call_function_bytecode = bytecode_regex(
        rb
    )
else:
    # Starting with python 3.11, the bytecode is peppered with CACHE instructions (which dis module conveniently hides
    # unless show_caches=True is used). Dealing with these CACHE instructions in regex rules is going to render them
    # unreadable, so instead we pre-process the bytecode and filter the offending opcodes out.","
        # Matches `global_function('some', 'constant', 'arguments')`.

        # Load the global function. In code with >256 of names, this may require extended name references.
        ((?:`EXTENDED_ARG`.)*
         (?:`LOAD_NAME`|`LOAD_GLOBAL`|`LOAD_FAST`).)

        # For foo.bar.whizz(), the above is the 'foo', below is the 'bar.whizz'.
        ((?:(?:`EXTENDED_ARG`.)*
         (?:`LOAD_METHOD`|`LOAD_ATTR`).)*)

        # Load however many arguments it takes. These (for now) must all be constants.
        # Again, code with >256 constants may need extended enumeration.
        ((?:(?:`EXTENDED_ARG`.)*
         `LOAD_CONST`.)*)

        # Call the function. The parameter is the argument count (which may also be >256) if CALL_FUNCTION or
        # CALL_METHOD are used. For CALL_FUNCTION_EX, the parameter are flags.
        ((?:`EXTENDED_ARG`.)*
         (?:`CALL_FUNCTION`|`CALL_METHOD`|`CALL_FUNCTION_EX`).)
    ",94,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _cleanup_code(code):
        return code  # Nothing to do here

    # language=PythonVerboseRegExp
    _call_function_bytecode = bytecode_regex(
        rb
    )
else:
    # Starting with python 3.11, the bytecode is peppered with CACHE instructions (which dis module conveniently hides
    # unless show_caches=True is used). Dealing with these CACHE instructions in regex rules is going to render them
    # unreadable, so instead we pre-process the bytecode and filter the offending opcodes out.
        ```
    ###Assistant : 
        # Matches `global_function('some', 'constant', 'arguments')`.

        # Load the global function. In code with >256 of names, this may require extended name references.
        ((?:`EXTENDED_ARG`.)*
         (?:`LOAD_NAME`|`LOAD_GLOBAL`|`LOAD_FAST`).)

        # For foo.bar.whizz(), the above is the 'foo', below is the 'bar.whizz'.
        ((?:(?:`EXTENDED_ARG`.)*
         (?:`LOAD_METHOD`|`LOAD_ATTR`).)*)

        # Load however many arguments it takes. These (for now) must all be constants.
        # Again, code with >256 constants may need extended enumeration.
        ((?:(?:`EXTENDED_ARG`.)*
         `LOAD_CONST`.)*)

        # Call the function. The parameter is the argument count (which may also be >256) if CALL_FUNCTION or
        # CALL_METHOD are used. For CALL_FUNCTION_EX, the parameter are flags.
        ((?:`EXTENDED_ARG`.)*
         (?:`CALL_FUNCTION`|`CALL_METHOD`|`CALL_FUNCTION_EX`).)
    
    "
1962,"def filemode(mode):
    
    perm = []
    for table in filemode_table:
        for bit, char in table:
            if mode & bit == bit:
                perm.append(char)
                break
        else:
            perm.append(""-"")
    return """".join(perm)
","Convert a file's mode to a string of the form
       -rwxrwxrwx.
       Used by TarFile.list()
    ",14,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def filemode(mode):
    
    perm = []
    for table in filemode_table:
        for bit, char in table:
            if mode & bit == bit:
                perm.append(char)
                break
        else:
            perm.append(""-"")
    return """".join(perm)

        ```
    ###Assistant : Convert a file's mode to a string of the form
       -rwxrwxrwx.
       Used by TarFile.list()
    
    "
1963,"def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'):
    r

    k = _setup_kernel(k) * gain
    p = k.shape[0] - 1
    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)

#----------------------------------------------------------------------------
","Filter a batch of 2D images with the given FIR filter.

    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
    and filters each image with the given filter. The filter is normalized so that
    if the input pixels are constant, they will be scaled by the specified `gain`.
    Pixels outside the image are assumed to be zero.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `""ref""` or `""cuda""` (default).

    Returns:
        Tensor of the same shape and datatype as `x`.
    ",130,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'):
    r

    k = _setup_kernel(k) * gain
    p = k.shape[0] - 1
    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)

#----------------------------------------------------------------------------

        ```
    ###Assistant : Filter a batch of 2D images with the given FIR filter.

    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
    and filters each image with the given filter. The filter is normalized so that
    if the input pixels are constant, they will be scaled by the specified `gain`.
    Pixels outside the image are assumed to be zero.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `""ref""` or `""cuda""` (default).

    Returns:
        Tensor of the same shape and datatype as `x`.
    
    "
1964,"def execute():
	frappe.reload_doctype(""Pricing Rule"")

	currency = frappe.db.get_default(""currency"")
	for doc in frappe.get_all(""Pricing Rule"", fields=[""company"", ""name""]):
		if doc.company:
			currency = frappe.get_cached_value(""Company"", doc.company, ""default_currency"")

		frappe.db.sql(
			, (currency, doc.name)
		)
",update `tabPricing Rule` set currency = %s where name = %s,11,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
	frappe.reload_doctype(""Pricing Rule"")

	currency = frappe.db.get_default(""currency"")
	for doc in frappe.get_all(""Pricing Rule"", fields=[""company"", ""name""]):
		if doc.company:
			currency = frappe.get_cached_value(""Company"", doc.company, ""default_currency"")

		frappe.db.sql(
			, (currency, doc.name)
		)

        ```
    ###Assistant : update `tabPricing Rule` set currency = %s where name = %s
    "
1965,"def forward(self, x, mask=None):
        
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape((B_, N, 3, self.num_heads, C // self.num_heads)).transpose((2, 0, 3, 1, 4))
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose((0, 1, 3, 2)))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.reshape(
            (-1, ))].reshape((self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1],
                              -1))  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.transpose((2, 0, 1))  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.reshape((B_ // nW, nW, self.num_heads, N, N)) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.reshape((-1, self.num_heads, N, N))
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose((0, 2, 1, 3)).reshape((B_, N, C))
        x = self.proj(x)
        x = self.proj_drop(x)
        return x
","
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        ",21,131,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, x, mask=None):
        
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape((B_, N, 3, self.num_heads, C // self.num_heads)).transpose((2, 0, 3, 1, 4))
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose((0, 1, 3, 2)))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.reshape(
            (-1, ))].reshape((self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1],
                              -1))  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.transpose((2, 0, 1))  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.reshape((B_ // nW, nW, self.num_heads, N, N)) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.reshape((-1, self.num_heads, N, N))
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose((0, 2, 1, 3)).reshape((B_, N, C))
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

        ```
    ###Assistant : 
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        
    "
1966,"def test_join_rules_invite(self):
        
        creator = ""@creator:example.com""
        pleb = ""@joiner:example.com""

        auth_events = {
            (""m.room.create"", """"): _create_event(RoomVersions.V6, creator),
            (""m.room.member"", creator): _join_event(RoomVersions.V6, creator),
            (""m.room.join_rules"", """"): _join_rules_event(
                RoomVersions.V6, creator, ""invite""
            ),
        }

        # A join without an invite is rejected.
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _join_event(RoomVersions.V6, pleb),
                auth_events.values(),
            )

        # A user cannot be force-joined to a room.
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _member_event(RoomVersions.V6, pleb, ""join"", sender=creator),
                auth_events.values(),
            )

        # Banned should be rejected.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""ban""
        )
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _join_event(RoomVersions.V6, pleb),
                auth_events.values(),
            )

        # A user who left cannot re-join.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""leave""
        )
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _join_event(RoomVersions.V6, pleb),
                auth_events.values(),
            )

        # A user can send a join if they're in the room.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""join""
        )
        event_auth.check_auth_rules_for_event(
            RoomVersions.V6,
            _join_event(RoomVersions.V6, pleb),
            auth_events.values(),
        )

        # A user can accept an invite.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""invite"", sender=creator
        )
        event_auth.check_auth_rules_for_event(
            RoomVersions.V6,
            _join_event(RoomVersions.V6, pleb),
            auth_events.values(),
        )
","
        Test joining an invite only room.
        ",6,154,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_join_rules_invite(self):
        
        creator = ""@creator:example.com""
        pleb = ""@joiner:example.com""

        auth_events = {
            (""m.room.create"", """"): _create_event(RoomVersions.V6, creator),
            (""m.room.member"", creator): _join_event(RoomVersions.V6, creator),
            (""m.room.join_rules"", """"): _join_rules_event(
                RoomVersions.V6, creator, ""invite""
            ),
        }

        # A join without an invite is rejected.
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _join_event(RoomVersions.V6, pleb),
                auth_events.values(),
            )

        # A user cannot be force-joined to a room.
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _member_event(RoomVersions.V6, pleb, ""join"", sender=creator),
                auth_events.values(),
            )

        # Banned should be rejected.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""ban""
        )
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _join_event(RoomVersions.V6, pleb),
                auth_events.values(),
            )

        # A user who left cannot re-join.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""leave""
        )
        with self.assertRaises(AuthError):
            event_auth.check_auth_rules_for_event(
                RoomVersions.V6,
                _join_event(RoomVersions.V6, pleb),
                auth_events.values(),
            )

        # A user can send a join if they're in the room.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""join""
        )
        event_auth.check_auth_rules_for_event(
            RoomVersions.V6,
            _join_event(RoomVersions.V6, pleb),
            auth_events.values(),
        )

        # A user can accept an invite.
        auth_events[(""m.room.member"", pleb)] = _member_event(
            RoomVersions.V6, pleb, ""invite"", sender=creator
        )
        event_auth.check_auth_rules_for_event(
            RoomVersions.V6,
            _join_event(RoomVersions.V6, pleb),
            auth_events.values(),
        )

        ```
    ###Assistant : 
        Test joining an invite only room.
        
    "
1967,"def validate_onboarding(data):
    
    logging.info(f""Validating onboarding data {data}"")
    messages = data['outputs']['messages']
    if len(messages) == 0:
        return False
    status_message = messages[-2]
    if status_message is None:
        return False
    submitted_data = status_message.get('data')
    if submitted_data is None:
        return False
    final_status = submitted_data.get('final_status')
    return final_status == ONBOARD_SUCCESS


# TODO: find a better way to avoid duplicating this from model_chat world.py","
    Check the contents of the data to ensure they are valid.
    ",11,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def validate_onboarding(data):
    
    logging.info(f""Validating onboarding data {data}"")
    messages = data['outputs']['messages']
    if len(messages) == 0:
        return False
    status_message = messages[-2]
    if status_message is None:
        return False
    submitted_data = status_message.get('data')
    if submitted_data is None:
        return False
    final_status = submitted_data.get('final_status')
    return final_status == ONBOARD_SUCCESS


# TODO: find a better way to avoid duplicating this from model_chat world.py
        ```
    ###Assistant : 
    Check the contents of the data to ensure they are valid.
    
    "
1968,"async def test_async_track_entity_registry_updated_event_with_empty_list(hass):
    
    unsub_single = async_track_entity_registry_updated_event(
        hass, [], ha.callback(lambda event: None)
    )
    unsub_single2 = async_track_entity_registry_updated_event(
        hass, [], ha.callback(lambda event: None)
    )

    unsub_single2()
    unsub_single()
",Test async_track_entity_registry_updated_event passing an empty list of entities.,8,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_async_track_entity_registry_updated_event_with_empty_list(hass):
    
    unsub_single = async_track_entity_registry_updated_event(
        hass, [], ha.callback(lambda event: None)
    )
    unsub_single2 = async_track_entity_registry_updated_event(
        hass, [], ha.callback(lambda event: None)
    )

    unsub_single2()
    unsub_single()

        ```
    ###Assistant : Test async_track_entity_registry_updated_event passing an empty list of entities.
    "
1969,"def generate_self_signed_tls_certs():
    
    try:
        from cryptography import x509
        from cryptography.hazmat.backends import default_backend
        from cryptography.hazmat.primitives import hashes, serialization
        from cryptography.hazmat.primitives.asymmetric import rsa
        from cryptography.x509.oid import NameOID
    except ImportError:
        raise ImportError(
            ""Using `Security.temporary` requires `cryptography`, please ""
            ""install it using either pip or conda""
        )
    key = rsa.generate_private_key(
        public_exponent=65537, key_size=2048, backend=default_backend()
    )
    key_contents = key.private_bytes(
        encoding=serialization.Encoding.PEM,
        format=serialization.PrivateFormat.PKCS8,
        encryption_algorithm=serialization.NoEncryption(),
    ).decode()

    ray_interal = x509.Name([x509.NameAttribute(NameOID.COMMON_NAME, ""ray-internal"")])
    # This is the same logic used by the GCS server to acquire a
    # private/interal IP address to listen on. If we just use localhost +
    # 127.0.0.1 then we won't be able to connect to the GCS and will get
    # an error like ""No match found for server name: 192.168.X.Y""
    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
    s.connect((""8.8.8.8"", 80))
    private_ip_address = s.getsockname()[0]
    s.close()
    altnames = x509.SubjectAlternativeName(
        [
            x509.DNSName(
                socket.gethostbyname(socket.gethostname())
            ),  # Probably 127.0.0.1
            x509.DNSName(""127.0.0.1""),
            x509.DNSName(private_ip_address),  # 192.168.*.*
            x509.DNSName(""localhost""),
        ]
    )
    now = datetime.datetime.utcnow()
    cert = (
        x509.CertificateBuilder()
        .subject_name(ray_interal)
        .issuer_name(ray_interal)
        .add_extension(altnames, critical=False)
        .public_key(key.public_key())
        .serial_number(x509.random_serial_number())
        .not_valid_before(now)
        .not_valid_after(now + datetime.timedelta(days=365))
        .sign(key, hashes.SHA256(), default_backend())
    )

    cert_contents = cert.public_bytes(serialization.Encoding.PEM).decode()

    return cert_contents, key_contents

","Create self-signed key/cert pair for testing.

    This method requires the library ``cryptography`` be installed.
    ",14,167,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_self_signed_tls_certs():
    
    try:
        from cryptography import x509
        from cryptography.hazmat.backends import default_backend
        from cryptography.hazmat.primitives import hashes, serialization
        from cryptography.hazmat.primitives.asymmetric import rsa
        from cryptography.x509.oid import NameOID
    except ImportError:
        raise ImportError(
            ""Using `Security.temporary` requires `cryptography`, please ""
            ""install it using either pip or conda""
        )
    key = rsa.generate_private_key(
        public_exponent=65537, key_size=2048, backend=default_backend()
    )
    key_contents = key.private_bytes(
        encoding=serialization.Encoding.PEM,
        format=serialization.PrivateFormat.PKCS8,
        encryption_algorithm=serialization.NoEncryption(),
    ).decode()

    ray_interal = x509.Name([x509.NameAttribute(NameOID.COMMON_NAME, ""ray-internal"")])
    # This is the same logic used by the GCS server to acquire a
    # private/interal IP address to listen on. If we just use localhost +
    # 127.0.0.1 then we won't be able to connect to the GCS and will get
    # an error like ""No match found for server name: 192.168.X.Y""
    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
    s.connect((""8.8.8.8"", 80))
    private_ip_address = s.getsockname()[0]
    s.close()
    altnames = x509.SubjectAlternativeName(
        [
            x509.DNSName(
                socket.gethostbyname(socket.gethostname())
            ),  # Probably 127.0.0.1
            x509.DNSName(""127.0.0.1""),
            x509.DNSName(private_ip_address),  # 192.168.*.*
            x509.DNSName(""localhost""),
        ]
    )
    now = datetime.datetime.utcnow()
    cert = (
        x509.CertificateBuilder()
        .subject_name(ray_interal)
        .issuer_name(ray_interal)
        .add_extension(altnames, critical=False)
        .public_key(key.public_key())
        .serial_number(x509.random_serial_number())
        .not_valid_before(now)
        .not_valid_after(now + datetime.timedelta(days=365))
        .sign(key, hashes.SHA256(), default_backend())
    )

    cert_contents = cert.public_bytes(serialization.Encoding.PEM).decode()

    return cert_contents, key_contents


        ```
    ###Assistant : Create self-signed key/cert pair for testing.

    This method requires the library ``cryptography`` be installed.
    
    "
1970,"def print_help(self):
        
        has_ticker_start = ""[unvl]"" if not self.ticker else """"
        has_ticker_end = ""[/unvl]"" if not self.ticker else """"
        help_text = f
        console.print(text=help_text, menu=""Stocks - Government"")
","Print help
[src][QuiverQuant][/src]

[info]Explore:[/info][cmds]
    lasttrades       last trades
    topbuys          show most purchased stocks
    topsells         show most sold stocks
    lastcontracts    show last government contracts given out
    qtrcontracts     quarterly government contracts analysis
    toplobbying      top corporate lobbying tickers

    load             load a specific ticker for analysis[/cmds]

[param]Ticker: [/param]{self.ticker or None}{has_ticker_start}[cmds]

    gtrades          show government trades for ticker
    contracts        show government contracts for ticker
    histcont         show historical quarterly government contracts for ticker
    lobbying         corporate lobbying details for ticker[/cmds]{has_ticker_end}
            ",71,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        
        has_ticker_start = ""[unvl]"" if not self.ticker else """"
        has_ticker_end = ""[/unvl]"" if not self.ticker else """"
        help_text = f
        console.print(text=help_text, menu=""Stocks - Government"")

        ```
    ###Assistant : Print help
[src][QuiverQuant][/src]

[info]Explore:[/info][cmds]
    lasttrades       last trades
    topbuys          show most purchased stocks
    topsells         show most sold stocks
    lastcontracts    show last government contracts given out
    qtrcontracts     quarterly government contracts analysis
    toplobbying      top corporate lobbying tickers

    load             load a specific ticker for analysis[/cmds]

[param]Ticker: [/param]{self.ticker or None}{has_ticker_start}[cmds]

    gtrades          show government trades for ticker
    contracts        show government contracts for ticker
    histcont         show historical quarterly government contracts for ticker
    lobbying         corporate lobbying details for ticker[/cmds]{has_ticker_end}
            
    "
1971,"def get_item_warehouse_projected_qty(items_to_consider):
	item_warehouse_projected_qty = {}

	for item_code, warehouse, projected_qty in frappe.db.sql(
		.format(
			"", "".join([""%s""] * len(items_to_consider))
		),
		items_to_consider,
	):

		if item_code not in item_warehouse_projected_qty:
			item_warehouse_projected_qty.setdefault(item_code, {})

		if warehouse not in item_warehouse_projected_qty.get(item_code):
			item_warehouse_projected_qty[item_code][warehouse] = flt(projected_qty)

		warehouse_doc = frappe.get_doc(""Warehouse"", warehouse)

		while warehouse_doc.parent_warehouse:
			if not item_warehouse_projected_qty.get(item_code, {}).get(warehouse_doc.parent_warehouse):
				item_warehouse_projected_qty.setdefault(item_code, {})[warehouse_doc.parent_warehouse] = flt(
					projected_qty
				)
			else:
				item_warehouse_projected_qty[item_code][warehouse_doc.parent_warehouse] += flt(projected_qty)
			warehouse_doc = frappe.get_doc(""Warehouse"", warehouse_doc.parent_warehouse)

	return item_warehouse_projected_qty

","select item_code, warehouse, projected_qty
		from tabBin where item_code in ({0})
			and (warehouse != """" and warehouse is not null)",19,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_item_warehouse_projected_qty(items_to_consider):
	item_warehouse_projected_qty = {}

	for item_code, warehouse, projected_qty in frappe.db.sql(
		.format(
			"", "".join([""%s""] * len(items_to_consider))
		),
		items_to_consider,
	):

		if item_code not in item_warehouse_projected_qty:
			item_warehouse_projected_qty.setdefault(item_code, {})

		if warehouse not in item_warehouse_projected_qty.get(item_code):
			item_warehouse_projected_qty[item_code][warehouse] = flt(projected_qty)

		warehouse_doc = frappe.get_doc(""Warehouse"", warehouse)

		while warehouse_doc.parent_warehouse:
			if not item_warehouse_projected_qty.get(item_code, {}).get(warehouse_doc.parent_warehouse):
				item_warehouse_projected_qty.setdefault(item_code, {})[warehouse_doc.parent_warehouse] = flt(
					projected_qty
				)
			else:
				item_warehouse_projected_qty[item_code][warehouse_doc.parent_warehouse] += flt(projected_qty)
			warehouse_doc = frappe.get_doc(""Warehouse"", warehouse_doc.parent_warehouse)

	return item_warehouse_projected_qty


        ```
    ###Assistant : select item_code, warehouse, projected_qty
		from tabBin where item_code in ({0})
			and (warehouse != """" and warehouse is not null)
    "
1972,"def _collect_type_vars(types, typevar_types=None):
    
    if typevar_types is None:
        typevar_types = typing.TypeVar
    tvars = []
    for t in types:
        if (
            isinstance(t, typevar_types) and
            t not in tvars and
            not _is_unpack(t)
        ):
            tvars.append(t)
        if _should_collect_from_parameters(t):
            tvars.extend([t for t in t.__parameters__ if t not in tvars])
    return tuple(tvars)


NoReturn = typing.NoReturn

# Some unconstrained type variables.  These are used by the container types.
# (These are not for export.)
T = typing.TypeVar('T')  # Any type.
KT = typing.TypeVar('KT')  # Key type.
VT = typing.TypeVar('VT')  # Value type.
T_co = typing.TypeVar('T_co', covariant=True)  # Any type covariant containers.
T_contra = typing.TypeVar('T_contra', contravariant=True)  # Ditto contravariant.

ClassVar = typing.ClassVar

# On older versions of typing there is an internal class named ""Final"".
# 3.8+
if hasattr(typing, 'Final') and sys.version_info[:2] >= (3, 7):
    Final = typing.Final
# 3.7
else:","Collect all type variable contained in types in order of
    first appearance (lexicographic order). For example::

        _collect_type_vars((T, List[S, T])) == (T, S)
    ",22,132,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _collect_type_vars(types, typevar_types=None):
    
    if typevar_types is None:
        typevar_types = typing.TypeVar
    tvars = []
    for t in types:
        if (
            isinstance(t, typevar_types) and
            t not in tvars and
            not _is_unpack(t)
        ):
            tvars.append(t)
        if _should_collect_from_parameters(t):
            tvars.extend([t for t in t.__parameters__ if t not in tvars])
    return tuple(tvars)


NoReturn = typing.NoReturn

# Some unconstrained type variables.  These are used by the container types.
# (These are not for export.)
T = typing.TypeVar('T')  # Any type.
KT = typing.TypeVar('KT')  # Key type.
VT = typing.TypeVar('VT')  # Value type.
T_co = typing.TypeVar('T_co', covariant=True)  # Any type covariant containers.
T_contra = typing.TypeVar('T_contra', contravariant=True)  # Ditto contravariant.

ClassVar = typing.ClassVar

# On older versions of typing there is an internal class named ""Final"".
# 3.8+
if hasattr(typing, 'Final') and sys.version_info[:2] >= (3, 7):
    Final = typing.Final
# 3.7
else:
        ```
    ###Assistant : Collect all type variable contained in types in order of
    first appearance (lexicographic order). For example::

        _collect_type_vars((T, List[S, T])) == (T, S)
    
    "
1973,"def from_dataframe(df, allow_copy=True):
    
    if isinstance(df, pd.DataFrame):
        return df

    if not hasattr(df, ""__dataframe__""):
        raise ValueError(""`df` does not support __dataframe__"")

    return _from_dataframe(df.__dataframe__(allow_copy=allow_copy))

","
    Build a ``pd.DataFrame`` from any DataFrame supporting the interchange protocol.

    Parameters
    ----------
    df : DataFrameXchg
        Object supporting the exchange protocol, i.e. `__dataframe__` method.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pd.DataFrame
    ",48,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_dataframe(df, allow_copy=True):
    
    if isinstance(df, pd.DataFrame):
        return df

    if not hasattr(df, ""__dataframe__""):
        raise ValueError(""`df` does not support __dataframe__"")

    return _from_dataframe(df.__dataframe__(allow_copy=allow_copy))


        ```
    ###Assistant : 
    Build a ``pd.DataFrame`` from any DataFrame supporting the interchange protocol.

    Parameters
    ----------
    df : DataFrameXchg
        Object supporting the exchange protocol, i.e. `__dataframe__` method.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pd.DataFrame
    
    "
1974,"def parse_wheel(wheel_zip, name):
    # type: (ZipFile, str) -> Tuple[str, Message]
    
    try:
        info_dir = wheel_dist_info_dir(wheel_zip, name)
        metadata = wheel_metadata(wheel_zip, info_dir)
        version = wheel_version(metadata)
    except UnsupportedWheel as e:
        raise UnsupportedWheel(""{} has an invalid wheel, {}"".format(name, str(e)))

    check_compatibility(version, name)

    return info_dir, metadata

","Extract information from the provided wheel, ensuring it meets basic
    standards.

    Returns the name of the .dist-info directory and the parsed WHEEL metadata.
    ",23,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_wheel(wheel_zip, name):
    # type: (ZipFile, str) -> Tuple[str, Message]
    
    try:
        info_dir = wheel_dist_info_dir(wheel_zip, name)
        metadata = wheel_metadata(wheel_zip, info_dir)
        version = wheel_version(metadata)
    except UnsupportedWheel as e:
        raise UnsupportedWheel(""{} has an invalid wheel, {}"".format(name, str(e)))

    check_compatibility(version, name)

    return info_dir, metadata


        ```
    ###Assistant : Extract information from the provided wheel, ensuring it meets basic
    standards.

    Returns the name of the .dist-info directory and the parsed WHEEL metadata.
    
    "
1975,"def style_docstrings_in_code(code, max_len=119):
    
    # fmt: off
    splits = code.split('\""\""\""')
    splits = [
        (s if i % 2 == 0 or _re_doc_ignore.search(splits[i - 1]) is not None else style_docstring(s, max_len=max_len))
        for i, s in enumerate(splits)
    ]
    black_errors = ""\n\n"".join([s[1] for s in splits if isinstance(s, tuple) and len(s[1]) > 0])
    splits = [s[0] if isinstance(s, tuple) else s for s in splits]
    clean_code = '\""\""\""'.join(splits)
    # fmt: on

    return clean_code, black_errors

","
    Style all docstrings in some code.

    Args:
        code (`str`): The code in which we want to style the docstrings.
        max_len (`int`): The maximum number of characters per line.

    Returns:
        `Tuple[str, str]`: A tuple with the clean code and the black errors (if any)
    ",43,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def style_docstrings_in_code(code, max_len=119):
    
    # fmt: off
    splits = code.split('\""\""\""')
    splits = [
        (s if i % 2 == 0 or _re_doc_ignore.search(splits[i - 1]) is not None else style_docstring(s, max_len=max_len))
        for i, s in enumerate(splits)
    ]
    black_errors = ""\n\n"".join([s[1] for s in splits if isinstance(s, tuple) and len(s[1]) > 0])
    splits = [s[0] if isinstance(s, tuple) else s for s in splits]
    clean_code = '\""\""\""'.join(splits)
    # fmt: on

    return clean_code, black_errors


        ```
    ###Assistant : 
    Style all docstrings in some code.

    Args:
        code (`str`): The code in which we want to style the docstrings.
        max_len (`int`): The maximum number of characters per line.

    Returns:
        `Tuple[str, str]`: A tuple with the clean code and the black errors (if any)
    
    "
1976,"def check_version_info(cluster_metadata):
    
    cluster_version_info = (
        cluster_metadata[""ray_version""],
        cluster_metadata[""python_version""],
    )
    version_info = compute_version_info()
    if version_info != cluster_version_info:
        node_ip_address = ray._private.services.get_node_ip_address()
        error_message = (
            ""Version mismatch: The cluster was started with:\n""
            ""    Ray: "" + cluster_version_info[0] + ""\n""
            ""    Python: "" + cluster_version_info[1] + ""\n""
            ""This process on node "" + node_ip_address + "" was started with:"" + ""\n""
            ""    Ray: "" + version_info[0] + ""\n""
            ""    Python: "" + version_info[1] + ""\n""
        )
        raise RuntimeError(error_message)
","Check if the Python and Ray versions stored in GCS matches this process.
    Args:
        cluster_metadata: Ray cluster metadata from GCS.

    Raises:
        Exception: An exception is raised if there is a version mismatch.
    ",32,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_version_info(cluster_metadata):
    
    cluster_version_info = (
        cluster_metadata[""ray_version""],
        cluster_metadata[""python_version""],
    )
    version_info = compute_version_info()
    if version_info != cluster_version_info:
        node_ip_address = ray._private.services.get_node_ip_address()
        error_message = (
            ""Version mismatch: The cluster was started with:\n""
            ""    Ray: "" + cluster_version_info[0] + ""\n""
            ""    Python: "" + cluster_version_info[1] + ""\n""
            ""This process on node "" + node_ip_address + "" was started with:"" + ""\n""
            ""    Ray: "" + version_info[0] + ""\n""
            ""    Python: "" + version_info[1] + ""\n""
        )
        raise RuntimeError(error_message)

        ```
    ###Assistant : Check if the Python and Ray versions stored in GCS matches this process.
    Args:
        cluster_metadata: Ray cluster metadata from GCS.

    Raises:
        Exception: An exception is raised if there is a version mismatch.
    
    "
1977,"def get_console() -> ""Console"":
    
    global _console
    if _console is None:
        from .console import Console

        _console = Console()

    return _console

","Get a global :class:`~rich.console.Console` instance. This function is used when Rich requires a Console,
    and hasn't been explicitly given one.

    Returns:
        Console: A console instance.
    ",25,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_console() -> ""Console"":
    
    global _console
    if _console is None:
        from .console import Console

        _console = Console()

    return _console


        ```
    ###Assistant : Get a global :class:`~rich.console.Console` instance. This function is used when Rich requires a Console,
    and hasn't been explicitly given one.

    Returns:
        Console: A console instance.
    
    "
1978,"def is_hash_allowed(self, hashes):
        # type: (Optional[Hashes]) -> bool
        
        if hashes is None or not self.has_hash:
            return False
        # Assert non-None so mypy knows self.hash_name and self.hash are str.
        assert self.hash_name is not None
        assert self.hash is not None

        return hashes.is_hash_allowed(self.hash_name, hex_digest=self.hash)


# TODO: Relax this comparison logic to ignore, for example, fragments.","
        Return True if the link has a hash and it is allowed.
        ",12,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_hash_allowed(self, hashes):
        # type: (Optional[Hashes]) -> bool
        
        if hashes is None or not self.has_hash:
            return False
        # Assert non-None so mypy knows self.hash_name and self.hash are str.
        assert self.hash_name is not None
        assert self.hash is not None

        return hashes.is_hash_allowed(self.hash_name, hex_digest=self.hash)


# TODO: Relax this comparison logic to ignore, for example, fragments.
        ```
    ###Assistant : 
        Return True if the link has a hash and it is allowed.
        
    "
1979,"def coverage_error(y_true, y_score, *, sample_weight=None):
    
    y_true = check_array(y_true, ensure_2d=True)
    y_score = check_array(y_score, ensure_2d=True)
    check_consistent_length(y_true, y_score, sample_weight)

    y_type = type_of_target(y_true, input_name=""y_true"")
    if y_type != ""multilabel-indicator"":
        raise ValueError(""{0} format is not supported"".format(y_type))

    if y_true.shape != y_score.shape:
        raise ValueError(""y_true and y_score have different shape"")

    y_score_mask = np.ma.masked_array(y_score, mask=np.logical_not(y_true))
    y_min_relevant = y_score_mask.min(axis=1).reshape((-1, 1))
    coverage = (y_score >= y_min_relevant).sum(axis=1)
    coverage = coverage.filled(0)

    return np.average(coverage, weights=sample_weight)

","Coverage error measure.

    Compute how far we need to go through the ranked scores to cover all
    true labels. The best value is equal to the average number
    of labels in ``y_true`` per sample.

    Ties in ``y_scores`` are broken by giving maximal rank that would have
    been assigned to all tied values.

    Note: Our implementation's score is 1 greater than the one given in
    Tsoumakas et al., 2010. This extends it to handle the degenerate case
    in which an instance has 0 true labels.

    Read more in the :ref:`User Guide <coverage_error>`.

    Parameters
    ----------
    y_true : ndarray of shape (n_samples, n_labels)
        True binary labels in binary indicator format.

    y_score : ndarray of shape (n_samples, n_labels)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by ""decision_function"" on some classifiers).

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights.

    Returns
    -------
    coverage_error : float

    References
    ----------
    .. [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010).
           Mining multi-label data. In Data mining and knowledge discovery
           handbook (pp. 667-685). Springer US.

    ",179,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def coverage_error(y_true, y_score, *, sample_weight=None):
    
    y_true = check_array(y_true, ensure_2d=True)
    y_score = check_array(y_score, ensure_2d=True)
    check_consistent_length(y_true, y_score, sample_weight)

    y_type = type_of_target(y_true, input_name=""y_true"")
    if y_type != ""multilabel-indicator"":
        raise ValueError(""{0} format is not supported"".format(y_type))

    if y_true.shape != y_score.shape:
        raise ValueError(""y_true and y_score have different shape"")

    y_score_mask = np.ma.masked_array(y_score, mask=np.logical_not(y_true))
    y_min_relevant = y_score_mask.min(axis=1).reshape((-1, 1))
    coverage = (y_score >= y_min_relevant).sum(axis=1)
    coverage = coverage.filled(0)

    return np.average(coverage, weights=sample_weight)


        ```
    ###Assistant : Coverage error measure.

    Compute how far we need to go through the ranked scores to cover all
    true labels. The best value is equal to the average number
    of labels in ``y_true`` per sample.

    Ties in ``y_scores`` are broken by giving maximal rank that would have
    been assigned to all tied values.

    Note: Our implementation's score is 1 greater than the one given in
    Tsoumakas et al., 2010. This extends it to handle the degenerate case
    in which an instance has 0 true labels.

    Read more in the :ref:`User Guide <coverage_error>`.

    Parameters
    ----------
    y_true : ndarray of shape (n_samples, n_labels)
        True binary labels in binary indicator format.

    y_score : ndarray of shape (n_samples, n_labels)
        Target scores, can either be probability estimates of the positive
        class, confidence values, or non-thresholded measure of decisions
        (as returned by ""decision_function"" on some classifiers).

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights.

    Returns
    -------
    coverage_error : float

    References
    ----------
    .. [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010).
           Mining multi-label data. In Data mining and knowledge discovery
           handbook (pp. 667-685). Springer US.

    
    "
1980,"def total_predict_batches(self) -> int:
        
        return sum(self.trainer.num_predict_batches)
","The total number of prediction batches, which may change from epoch to epoch.

        Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the predict dataloader
        is of infinite size.
        ",37,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def total_predict_batches(self) -> int:
        
        return sum(self.trainer.num_predict_batches)

        ```
    ###Assistant : The total number of prediction batches, which may change from epoch to epoch.

        Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the predict dataloader
        is of infinite size.
        
    "
1981,"def shape(self) -> Dict[str, Tuple[int]]:
        
        self._check_values_type()
        return {k: dataset.shape for k, dataset in self.items()}
","Shape of each split of the dataset (number of columns, number of rows).

        Example:

        ```py
        >>> from datasets import load_dataset
        >>> ds = load_dataset(""rotten_tomatoes"")
        >>> ds.shape
        {'test': (1066, 2), 'train': (8530, 2), 'validation': (1066, 2)}
        ```
        ",36,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def shape(self) -> Dict[str, Tuple[int]]:
        
        self._check_values_type()
        return {k: dataset.shape for k, dataset in self.items()}

        ```
    ###Assistant : Shape of each split of the dataset (number of columns, number of rows).

        Example:

        ```py
        >>> from datasets import load_dataset
        >>> ds = load_dataset(""rotten_tomatoes"")
        >>> ds.shape
        {'test': (1066, 2), 'train': (8530, 2), 'validation': (1066, 2)}
        ```
        
    "
1982,"def is_torch_support_available(self) -> bool:
        
        if is_torch_available():
            from transformers.file_utils import torch_version

            return torch_version >= self.torch_onnx_minimum_version
        else:
            return False
","
        The minimum PyTorch version required to export the model.

        Returns:
            `bool`: Whether the installed version of PyTorch is compatible with the model.
        ",22,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_torch_support_available(self) -> bool:
        
        if is_torch_available():
            from transformers.file_utils import torch_version

            return torch_version >= self.torch_onnx_minimum_version
        else:
            return False

        ```
    ###Assistant : 
        The minimum PyTorch version required to export the model.

        Returns:
            `bool`: Whether the installed version of PyTorch is compatible with the model.
        
    "
1983,"def check_connection(self, logger, config) -> Tuple[bool, any]:
        
        auth_header = TokenAuthenticator(token=config[""api_key""]).get_auth_header()
        ping_url = ORB_API_BASE_URL + ""ping""
        ping_response = requests.get(ping_url, headers=auth_header)
        try:
            ping_response.raise_for_status()
            return True, None
        except Exception as e:
            return False, e
","
        Makes a request to the /ping endpoint, which validates that the authentication credentials are appropriate.
        API Docs: https://docs.withorb.com/reference/ping
        ",18,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_connection(self, logger, config) -> Tuple[bool, any]:
        
        auth_header = TokenAuthenticator(token=config[""api_key""]).get_auth_header()
        ping_url = ORB_API_BASE_URL + ""ping""
        ping_response = requests.get(ping_url, headers=auth_header)
        try:
            ping_response.raise_for_status()
            return True, None
        except Exception as e:
            return False, e

        ```
    ###Assistant : 
        Makes a request to the /ping endpoint, which validates that the authentication credentials are appropriate.
        API Docs: https://docs.withorb.com/reference/ping
        
    "
1984,"def add_items_upsert(self, content_type_pk, indexers):
        compiler = InsertQuery(IndexEntry).get_compiler(connection=self.connection)
        title_sql = []
        autocomplete_sql = []
        body_sql = []
        data_params = []

        for indexer in indexers:
            data_params.extend((content_type_pk, indexer.id))

            # Compile title value
            value = compiler.prepare_value(
                IndexEntry._meta.get_field(""title""), indexer.title
            )
            sql, params = value.as_sql(compiler, self.connection)
            title_sql.append(sql)
            data_params.extend(params)

            # Compile autocomplete value
            value = compiler.prepare_value(
                IndexEntry._meta.get_field(""autocomplete""), indexer.autocomplete
            )
            sql, params = value.as_sql(compiler, self.connection)
            autocomplete_sql.append(sql)
            data_params.extend(params)

            # Compile body value
            value = compiler.prepare_value(
                IndexEntry._meta.get_field(""body""), indexer.body
            )
            sql, params = value.as_sql(compiler, self.connection)
            body_sql.append(sql)
            data_params.extend(params)

        data_sql = "", "".join(
            [
                ""(%%s, %%s, %s, %s, %s, 1.0)"" % (a, b, c)
                for a, b, c in zip(title_sql, autocomplete_sql, body_sql)
            ]
        )

        with self.connection.cursor() as cursor:
            cursor.execute(
                
                % (IndexEntry._meta.db_table, data_sql),
                data_params,
            )

        self._refresh_title_norms()
","
                INSERT INTO %s (content_type_id, object_id, title, autocomplete, body, title_norm)
                (VALUES %s)
                ON CONFLICT (content_type_id, object_id)
                DO UPDATE SET title = EXCLUDED.title,
                              title_norm = 1.0,
                              autocomplete = EXCLUDED.autocomplete,
                              body = EXCLUDED.body
                ",30,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_items_upsert(self, content_type_pk, indexers):
        compiler = InsertQuery(IndexEntry).get_compiler(connection=self.connection)
        title_sql = []
        autocomplete_sql = []
        body_sql = []
        data_params = []

        for indexer in indexers:
            data_params.extend((content_type_pk, indexer.id))

            # Compile title value
            value = compiler.prepare_value(
                IndexEntry._meta.get_field(""title""), indexer.title
            )
            sql, params = value.as_sql(compiler, self.connection)
            title_sql.append(sql)
            data_params.extend(params)

            # Compile autocomplete value
            value = compiler.prepare_value(
                IndexEntry._meta.get_field(""autocomplete""), indexer.autocomplete
            )
            sql, params = value.as_sql(compiler, self.connection)
            autocomplete_sql.append(sql)
            data_params.extend(params)

            # Compile body value
            value = compiler.prepare_value(
                IndexEntry._meta.get_field(""body""), indexer.body
            )
            sql, params = value.as_sql(compiler, self.connection)
            body_sql.append(sql)
            data_params.extend(params)

        data_sql = "", "".join(
            [
                ""(%%s, %%s, %s, %s, %s, 1.0)"" % (a, b, c)
                for a, b, c in zip(title_sql, autocomplete_sql, body_sql)
            ]
        )

        with self.connection.cursor() as cursor:
            cursor.execute(
                
                % (IndexEntry._meta.db_table, data_sql),
                data_params,
            )

        self._refresh_title_norms()

        ```
    ###Assistant : 
                INSERT INTO %s (content_type_id, object_id, title, autocomplete, body, title_norm)
                (VALUES %s)
                ON CONFLICT (content_type_id, object_id)
                DO UPDATE SET title = EXCLUDED.title,
                              title_norm = 1.0,
                              autocomplete = EXCLUDED.autocomplete,
                              body = EXCLUDED.body
                
    "
1985,"def test_already_created_plus_written_results(indexer, indexer_cache) -> None:
    
    org_id = 1234

    raw_indexer = indexer
    indexer = CachingIndexer(indexer_cache, indexer)

    v0 = raw_indexer.record(use_case_id, org_id, ""v1.2.0"")
    v1 = raw_indexer.record(use_case_id, org_id, ""v1.2.1"")
    v2 = raw_indexer.record(use_case_id, org_id, ""v1.2.2"")

    expected_mapping = {""v1.2.0"": v0, ""v1.2.1"": v1, ""v1.2.2"": v2}

    results = indexer.bulk_record(
        use_case_id=use_case_id, org_strings={org_id: {""v1.2.0"", ""v1.2.1"", ""v1.2.2""}}
    )
    assert len(results[org_id]) == len(expected_mapping) == 3

    for string, id in results[org_id].items():
        assert expected_mapping[string] == id

    results = indexer.bulk_record(
        use_case_id=use_case_id,
        org_strings={org_id: {""v1.2.0"", ""v1.2.1"", ""v1.2.2"", ""v1.2.3""}},
    )
    v3 = raw_indexer.resolve(use_case_id, org_id, ""v1.2.3"")
    expected_mapping[""v1.2.3""] = v3

    assert len(results[org_id]) == len(expected_mapping) == 4

    for string, id in results[org_id].items():
        assert expected_mapping[string] == id

    fetch_meta = results.get_fetch_metadata()
    assert_fetch_type_for_tag_string_set(
        fetch_meta[org_id], FetchType.CACHE_HIT, {""v1.2.0"", ""v1.2.1"", ""v1.2.2""}
    )
    assert_fetch_type_for_tag_string_set(fetch_meta[org_id], FetchType.FIRST_SEEN, {""v1.2.3""})

","
    Test that we correctly combine db read results with db write results
    for the same organization.
    ",16,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_already_created_plus_written_results(indexer, indexer_cache) -> None:
    
    org_id = 1234

    raw_indexer = indexer
    indexer = CachingIndexer(indexer_cache, indexer)

    v0 = raw_indexer.record(use_case_id, org_id, ""v1.2.0"")
    v1 = raw_indexer.record(use_case_id, org_id, ""v1.2.1"")
    v2 = raw_indexer.record(use_case_id, org_id, ""v1.2.2"")

    expected_mapping = {""v1.2.0"": v0, ""v1.2.1"": v1, ""v1.2.2"": v2}

    results = indexer.bulk_record(
        use_case_id=use_case_id, org_strings={org_id: {""v1.2.0"", ""v1.2.1"", ""v1.2.2""}}
    )
    assert len(results[org_id]) == len(expected_mapping) == 3

    for string, id in results[org_id].items():
        assert expected_mapping[string] == id

    results = indexer.bulk_record(
        use_case_id=use_case_id,
        org_strings={org_id: {""v1.2.0"", ""v1.2.1"", ""v1.2.2"", ""v1.2.3""}},
    )
    v3 = raw_indexer.resolve(use_case_id, org_id, ""v1.2.3"")
    expected_mapping[""v1.2.3""] = v3

    assert len(results[org_id]) == len(expected_mapping) == 4

    for string, id in results[org_id].items():
        assert expected_mapping[string] == id

    fetch_meta = results.get_fetch_metadata()
    assert_fetch_type_for_tag_string_set(
        fetch_meta[org_id], FetchType.CACHE_HIT, {""v1.2.0"", ""v1.2.1"", ""v1.2.2""}
    )
    assert_fetch_type_for_tag_string_set(fetch_meta[org_id], FetchType.FIRST_SEEN, {""v1.2.3""})


        ```
    ###Assistant : 
    Test that we correctly combine db read results with db write results
    for the same organization.
    
    "
1986,"def compiler_fixup(compiler_so, cc_args):
    
    stripArch = stripSysroot = False

    compiler_so = list(compiler_so)

    if not _supports_universal_builds():
        # OSX before 10.4.0, these don't support -arch and -isysroot at
        # all.
        stripArch = stripSysroot = True
    else:
        stripArch = '-arch' in cc_args
        stripSysroot = any(arg for arg in cc_args if arg.startswith('-isysroot'))

    if stripArch or 'ARCHFLAGS' in os.environ:
        while True:
            try:
                index = compiler_so.index('-arch')
                # Strip this argument and the next one:
                del compiler_so[index:index+2]
            except ValueError:
                break

    elif not _supports_arm64_builds():
        # Look for ""-arch arm64"" and drop that
        for idx in reversed(range(len(compiler_so))):
            if compiler_so[idx] == '-arch' and compiler_so[idx+1] == ""arm64"":
                del compiler_so[idx:idx+2]

    if 'ARCHFLAGS' in os.environ and not stripArch:
        # User specified different -arch flags in the environ,
        # see also distutils.sysconfig
        compiler_so = compiler_so + os.environ['ARCHFLAGS'].split()

    if stripSysroot:
        while True:
            indices = [i for i,x in enumerate(compiler_so) if x.startswith('-isysroot')]
            if not indices:
                break
            index = indices[0]
            if compiler_so[index] == '-isysroot':
                # Strip this argument and the next one:
                del compiler_so[index:index+2]
            else:
                # It's '-isysroot/some/path' in one arg
                del compiler_so[index:index+1]

    # Check if the SDK that is used during compilation actually exists,
    # the universal build requires the usage of a universal SDK and not all
    # users have that installed by default.
    sysroot = None
    argvar = cc_args
    indices = [i for i,x in enumerate(cc_args) if x.startswith('-isysroot')]
    if not indices:
        argvar = compiler_so
        indices = [i for i,x in enumerate(compiler_so) if x.startswith('-isysroot')]

    for idx in indices:
        if argvar[idx] == '-isysroot':
            sysroot = argvar[idx+1]
            break
        else:
            sysroot = argvar[idx][len('-isysroot'):]
            break

    if sysroot and not os.path.isdir(sysroot):
        sys.stderr.write(f""Compiling with an SDK that doesn't seem to exist: {sysroot}\n"")
        sys.stderr.write(""Please check your Xcode installation\n"")
        sys.stderr.flush()

    return compiler_so

","
    This function will strip '-isysroot PATH' and '-arch ARCH' from the
    compile flags if the user has specified one them in extra_compile_flags.

    This is needed because '-arch ARCH' adds another architecture to the
    build, without a way to remove an architecture. Furthermore GCC will
    barf if multiple '-isysroot' arguments are present.
    ",51,268,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compiler_fixup(compiler_so, cc_args):
    
    stripArch = stripSysroot = False

    compiler_so = list(compiler_so)

    if not _supports_universal_builds():
        # OSX before 10.4.0, these don't support -arch and -isysroot at
        # all.
        stripArch = stripSysroot = True
    else:
        stripArch = '-arch' in cc_args
        stripSysroot = any(arg for arg in cc_args if arg.startswith('-isysroot'))

    if stripArch or 'ARCHFLAGS' in os.environ:
        while True:
            try:
                index = compiler_so.index('-arch')
                # Strip this argument and the next one:
                del compiler_so[index:index+2]
            except ValueError:
                break

    elif not _supports_arm64_builds():
        # Look for ""-arch arm64"" and drop that
        for idx in reversed(range(len(compiler_so))):
            if compiler_so[idx] == '-arch' and compiler_so[idx+1] == ""arm64"":
                del compiler_so[idx:idx+2]

    if 'ARCHFLAGS' in os.environ and not stripArch:
        # User specified different -arch flags in the environ,
        # see also distutils.sysconfig
        compiler_so = compiler_so + os.environ['ARCHFLAGS'].split()

    if stripSysroot:
        while True:
            indices = [i for i,x in enumerate(compiler_so) if x.startswith('-isysroot')]
            if not indices:
                break
            index = indices[0]
            if compiler_so[index] == '-isysroot':
                # Strip this argument and the next one:
                del compiler_so[index:index+2]
            else:
                # It's '-isysroot/some/path' in one arg
                del compiler_so[index:index+1]

    # Check if the SDK that is used during compilation actually exists,
    # the universal build requires the usage of a universal SDK and not all
    # users have that installed by default.
    sysroot = None
    argvar = cc_args
    indices = [i for i,x in enumerate(cc_args) if x.startswith('-isysroot')]
    if not indices:
        argvar = compiler_so
        indices = [i for i,x in enumerate(compiler_so) if x.startswith('-isysroot')]

    for idx in indices:
        if argvar[idx] == '-isysroot':
            sysroot = argvar[idx+1]
            break
        else:
            sysroot = argvar[idx][len('-isysroot'):]
            break

    if sysroot and not os.path.isdir(sysroot):
        sys.stderr.write(f""Compiling with an SDK that doesn't seem to exist: {sysroot}\n"")
        sys.stderr.write(""Please check your Xcode installation\n"")
        sys.stderr.flush()

    return compiler_so


        ```
    ###Assistant : 
    This function will strip '-isysroot PATH' and '-arch ARCH' from the
    compile flags if the user has specified one them in extra_compile_flags.

    This is needed because '-arch ARCH' adds another architecture to the
    build, without a way to remove an architecture. Furthermore GCC will
    barf if multiple '-isysroot' arguments are present.
    
    "
1987,"def test_copy_page_with_excluded_parental_and_child_relations(self):
        

        try:
            # modify excluded fields for this test
            EventPage.exclude_fields_in_copy = [
                ""advert_placements"",
                ""categories"",
                ""signup_link"",
            ]

            # set up data
            christmas_event = EventPage.objects.get(url_path=""/home/events/christmas/"")
            summer_category = EventCategory.objects.create(name=""Summer"")
            holiday_category = EventCategory.objects.create(name=""Holidays"")

            # add URL (to test excluding a basic field)
            christmas_event.signup_link = ""https://christmas-is-awesome.com/rsvp""

            # add parental many to many relations
            christmas_event.categories = (summer_category, holiday_category)
            christmas_event.save()

            # Copy it
            new_christmas_event = christmas_event.copy(
                update_attrs={
                    ""title"": ""New christmas event"",
                    ""slug"": ""new-christmas-event"",
                }
            )

            # check that the signup_link was NOT copied
            self.assertEqual(
                christmas_event.signup_link, ""https://christmas-is-awesome.com/rsvp""
            )
            self.assertEqual(new_christmas_event.signup_link, """")

            # check that original event is untouched
            self.assertEqual(
                christmas_event.categories.count(),
                2,
                ""Child objects (parental many to many) defined on the superclass were removed from the original page"",
            )

            # check that parental many to many are NOT copied
            self.assertEqual(
                new_christmas_event.categories.count(),
                0,
                ""Child objects (parental many to many) were copied but should be excluded"",
            )

            # check that child objects on original event were left untouched
            self.assertEqual(
                christmas_event.advert_placements.count(),
                1,
                ""Child objects defined on the original superclass were edited when copied"",
            )

            # check that child objects were NOT copied
            self.assertEqual(
                new_christmas_event.advert_placements.count(),
                0,
                ""Child objects defined on the superclass were copied and should not be"",
            )

        finally:
            # reset excluded fields for future tests
            EventPage.exclude_fields_in_copy = []
",Test that a page will be copied with parental and child relations removed if excluded.,15,197,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_copy_page_with_excluded_parental_and_child_relations(self):
        

        try:
            # modify excluded fields for this test
            EventPage.exclude_fields_in_copy = [
                ""advert_placements"",
                ""categories"",
                ""signup_link"",
            ]

            # set up data
            christmas_event = EventPage.objects.get(url_path=""/home/events/christmas/"")
            summer_category = EventCategory.objects.create(name=""Summer"")
            holiday_category = EventCategory.objects.create(name=""Holidays"")

            # add URL (to test excluding a basic field)
            christmas_event.signup_link = ""https://christmas-is-awesome.com/rsvp""

            # add parental many to many relations
            christmas_event.categories = (summer_category, holiday_category)
            christmas_event.save()

            # Copy it
            new_christmas_event = christmas_event.copy(
                update_attrs={
                    ""title"": ""New christmas event"",
                    ""slug"": ""new-christmas-event"",
                }
            )

            # check that the signup_link was NOT copied
            self.assertEqual(
                christmas_event.signup_link, ""https://christmas-is-awesome.com/rsvp""
            )
            self.assertEqual(new_christmas_event.signup_link, """")

            # check that original event is untouched
            self.assertEqual(
                christmas_event.categories.count(),
                2,
                ""Child objects (parental many to many) defined on the superclass were removed from the original page"",
            )

            # check that parental many to many are NOT copied
            self.assertEqual(
                new_christmas_event.categories.count(),
                0,
                ""Child objects (parental many to many) were copied but should be excluded"",
            )

            # check that child objects on original event were left untouched
            self.assertEqual(
                christmas_event.advert_placements.count(),
                1,
                ""Child objects defined on the original superclass were edited when copied"",
            )

            # check that child objects were NOT copied
            self.assertEqual(
                new_christmas_event.advert_placements.count(),
                0,
                ""Child objects defined on the superclass were copied and should not be"",
            )

        finally:
            # reset excluded fields for future tests
            EventPage.exclude_fields_in_copy = []

        ```
    ###Assistant : Test that a page will be copied with parental and child relations removed if excluded.
    "
1988,"def test_app_model_in_list_body_class(self):
        
        response = self.client.get(reverse(""admin:admin_views_section_changelist""))
        self.assertContains(response, '<body class="" app-admin_views model-section ')
","
        Ensure app and model tag are correctly read by change_list template
        ",11,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_app_model_in_list_body_class(self):
        
        response = self.client.get(reverse(""admin:admin_views_section_changelist""))
        self.assertContains(response, '<body class="" app-admin_views model-section ')

        ```
    ###Assistant : 
        Ensure app and model tag are correctly read by change_list template
        
    "
1989,"def version_parts(best=False):
    # type: (bool) -> Tuple[str, str, str]
    
    return _distro.version_parts(best)

","
    Return the version of the current OS distribution as a tuple
    ``(major, minor, build_number)`` with items as follows:

    * ``major``:  The result of :func:`distro.major_version`.

    * ``minor``:  The result of :func:`distro.minor_version`.

    * ``build_number``:  The result of :func:`distro.build_number`.

    For a description of the *best* parameter, see the :func:`distro.version`
    method.
    ",47,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def version_parts(best=False):
    # type: (bool) -> Tuple[str, str, str]
    
    return _distro.version_parts(best)


        ```
    ###Assistant : 
    Return the version of the current OS distribution as a tuple
    ``(major, minor, build_number)`` with items as follows:

    * ``major``:  The result of :func:`distro.major_version`.

    * ``minor``:  The result of :func:`distro.minor_version`.

    * ``build_number``:  The result of :func:`distro.build_number`.

    For a description of the *best* parameter, see the :func:`distro.version`
    method.
    
    "
1990,"def check_status(self):
        
        status = {
            'success': False
        }
        try:
            con = self.__connect()
            with closing(con) as con:
                #TODO: best way to check con.connected ?             
                status['success'] = True
        except Exception as e:
            log.error(f'Error connecting to SQL Server {self.database}, {e}!')
            status['error'] = e
        return status
","
        Check the connection of the SQL Server database
        :return: success status and error message if error occurs
        ",17,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_status(self):
        
        status = {
            'success': False
        }
        try:
            con = self.__connect()
            with closing(con) as con:
                #TODO: best way to check con.connected ?             
                status['success'] = True
        except Exception as e:
            log.error(f'Error connecting to SQL Server {self.database}, {e}!')
            status['error'] = e
        return status

        ```
    ###Assistant : 
        Check the connection of the SQL Server database
        :return: success status and error message if error occurs
        
    "
1991,"def model_from_config(config, custom_objects=None):
    
    if isinstance(config, list):
        raise TypeError(
            ""`model_from_config` expects a dictionary, not a list. ""
            f""Received: config={config}. Did you meant to use ""
            ""`Sequential.from_config(config)`?""
        )
    from keras.layers import deserialize  # pylint: disable=g-import-not-at-top

    return deserialize(config, custom_objects=custom_objects)


@keras_export(""keras.models.model_from_yaml"")","Instantiates a Keras model from its config.

    Usage:
    ```
    # for a Functional API model
    tf.keras.Model().from_config(model.get_config())

    # for a Sequential model
    tf.keras.Sequential().from_config(model.get_config())
    ```

    Args:
        config: Configuration dictionary.
        custom_objects: Optional dictionary mapping names
            (strings) to custom classes or functions to be
            considered during deserialization.

    Returns:
        A Keras model instance (uncompiled).

    Raises:
        TypeError: if `config` is not a dictionary.
    ",57,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def model_from_config(config, custom_objects=None):
    
    if isinstance(config, list):
        raise TypeError(
            ""`model_from_config` expects a dictionary, not a list. ""
            f""Received: config={config}. Did you meant to use ""
            ""`Sequential.from_config(config)`?""
        )
    from keras.layers import deserialize  # pylint: disable=g-import-not-at-top

    return deserialize(config, custom_objects=custom_objects)


@keras_export(""keras.models.model_from_yaml"")
        ```
    ###Assistant : Instantiates a Keras model from its config.

    Usage:
    ```
    # for a Functional API model
    tf.keras.Model().from_config(model.get_config())

    # for a Sequential model
    tf.keras.Sequential().from_config(model.get_config())
    ```

    Args:
        config: Configuration dictionary.
        custom_objects: Optional dictionary mapping names
            (strings) to custom classes or functions to be
            considered during deserialization.

    Returns:
        A Keras model instance (uncompiled).

    Raises:
        TypeError: if `config` is not a dictionary.
    
    "
1992,"def test_golden_path(self):
        
        with self.assertNumQueries(0):
            result = self.page.cached_content_type
        self.assertEqual(result, ContentType.objects.get(id=self.page.content_type_id))

","
        The return value should match the value you'd get
        if fetching the ContentType from the database,
        and shouldn't trigger any database queries when
        the ContentType is already in memory.
        ",29,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_golden_path(self):
        
        with self.assertNumQueries(0):
            result = self.page.cached_content_type
        self.assertEqual(result, ContentType.objects.get(id=self.page.content_type_id))


        ```
    ###Assistant : 
        The return value should match the value you'd get
        if fetching the ContentType from the database,
        and shouldn't trigger any database queries when
        the ContentType is already in memory.
        
    "
1993,"def fold_function_name(function_name):
    

    parts = function_name.split(""."")

    if len(parts) == 1:
        return function_name

    tail = parts.pop()
    grouped = [list(g) for _, g in groupby(parts)]
","
    Fold multiple consecutive occurences of the same property name into a single group, excluding the last component.

    foo | foo
    foo.foo | foo.foo
    foo.foo.foo | {foo#2}.foo
    bar.foo.foo | bar.foo.foo
    bar.foo.foo.foo | bar.{foo#2}.foo
    bar.foo.foo.onError | bar.{foo#2}.onError
    bar.bar.bar.foo.foo.onError | {bar#3}.{foo#2}.onError
    bar.foo.foo.bar.bar.onError | bar.{foo#2}.{bar#2}.onError
    ",41,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fold_function_name(function_name):
    

    parts = function_name.split(""."")

    if len(parts) == 1:
        return function_name

    tail = parts.pop()
    grouped = [list(g) for _, g in groupby(parts)]

        ```
    ###Assistant : 
    Fold multiple consecutive occurences of the same property name into a single group, excluding the last component.

    foo | foo
    foo.foo | foo.foo
    foo.foo.foo | {foo#2}.foo
    bar.foo.foo | bar.foo.foo
    bar.foo.foo.foo | bar.{foo#2}.foo
    bar.foo.foo.onError | bar.{foo#2}.onError
    bar.bar.bar.foo.foo.onError | {bar#3}.{foo#2}.onError
    bar.foo.foo.bar.bar.onError | bar.{foo#2}.{bar#2}.onError
    
    "
1994,"def _laplace_rule_exp(f, t, s, doit=True, **hints):
    
    hints.pop('simplify', True)
    a = Wild('a', exclude=[t])

    y = Wild('y')
    z = Wild('z')
    k, func = f.as_independent(t, as_Add=False)
    ma1 = func.match(exp(y)*z)
    if ma1:
        ma2 = ma1[y].collect(t).match(a*t)
        if ma2:
            debug('_laplace_apply_rules match:')
            debug('      f:    %s ( %s, %s )'%(f, ma1, ma2))
            debug('      rule: multiply with exp (1.5)')
            L = _laplace_apply_rules(ma1[z], t, s-ma2[a], doit=doit, **hints)
            try:
                r, p, c = L
                return (k*r, p+ma2[a], c)
            except TypeError:
                return k*L
    return None
","
    This internal helper function tries to transform a product containing the
    `exp` function and returns `None` if it cannot do it.
    ",21,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _laplace_rule_exp(f, t, s, doit=True, **hints):
    
    hints.pop('simplify', True)
    a = Wild('a', exclude=[t])

    y = Wild('y')
    z = Wild('z')
    k, func = f.as_independent(t, as_Add=False)
    ma1 = func.match(exp(y)*z)
    if ma1:
        ma2 = ma1[y].collect(t).match(a*t)
        if ma2:
            debug('_laplace_apply_rules match:')
            debug('      f:    %s ( %s, %s )'%(f, ma1, ma2))
            debug('      rule: multiply with exp (1.5)')
            L = _laplace_apply_rules(ma1[z], t, s-ma2[a], doit=doit, **hints)
            try:
                r, p, c = L
                return (k*r, p+ma2[a], c)
            except TypeError:
                return k*L
    return None

        ```
    ###Assistant : 
    This internal helper function tries to transform a product containing the
    `exp` function and returns `None` if it cannot do it.
    
    "
1995,"def FindEndOfExpressionInLine(line, startpos, depth, startchar, endchar):
  
  for i in xrange(startpos, len(line)):
    if line[i] == startchar:
      depth += 1
    elif line[i] == endchar:
      depth -= 1
      if depth == 0:
        return (i + 1, 0)
  return (-1, depth)

","Find the position just after the matching endchar.

  Args:
    line: a CleansedLines line.
    startpos: start searching at this position.
    depth: nesting level at startpos.
    startchar: expression opening character.
    endchar: expression closing character.

  Returns:
    On finding matching endchar: (index just after matching endchar, 0)
    Otherwise: (-1, new depth at end of this line)
  ",52,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def FindEndOfExpressionInLine(line, startpos, depth, startchar, endchar):
  
  for i in xrange(startpos, len(line)):
    if line[i] == startchar:
      depth += 1
    elif line[i] == endchar:
      depth -= 1
      if depth == 0:
        return (i + 1, 0)
  return (-1, depth)


        ```
    ###Assistant : Find the position just after the matching endchar.

  Args:
    line: a CleansedLines line.
    startpos: start searching at this position.
    depth: nesting level at startpos.
    startchar: expression opening character.
    endchar: expression closing character.

  Returns:
    On finding matching endchar: (index just after matching endchar, 0)
    Otherwise: (-1, new depth at end of this line)
  
    "
1996,"def test_toy_example_collapse_points():
    
    rng = np.random.RandomState(42)
    input_dim = 5
    two_points = rng.randn(2, input_dim)
    X = np.vstack([two_points, two_points.mean(axis=0)[np.newaxis, :]])
    y = [0, 0, 1]
","Test on a toy example of three points that should collapse

    We build a simple example: two points from the same class and a point from
    a different class in the middle of them. On this simple example, the new
    (transformed) points should all collapse into one single point. Indeed, the
    objective is 2/(1 + exp(d/2)), with d the euclidean distance between the
    two samples from the same class. This is maximized for d=0 (because d>=0),
    with an objective equal to 1 (loss=-1.).

    ",83,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_toy_example_collapse_points():
    
    rng = np.random.RandomState(42)
    input_dim = 5
    two_points = rng.randn(2, input_dim)
    X = np.vstack([two_points, two_points.mean(axis=0)[np.newaxis, :]])
    y = [0, 0, 1]

        ```
    ###Assistant : Test on a toy example of three points that should collapse

    We build a simple example: two points from the same class and a point from
    a different class in the middle of them. On this simple example, the new
    (transformed) points should all collapse into one single point. Indeed, the
    objective is 2/(1 + exp(d/2)), with d the euclidean distance between the
    two samples from the same class. This is maximized for d=0 (because d>=0),
    with an objective equal to 1 (loss=-1.).

    
    "
1997,"def _get_string_indexer_log_records(caplog):
    
    return [
        (
            rec.message,
            {
                k: v
                for k, v in rec.__dict__.items()
                if k
                in (
                    ""string_type"",
                    ""is_global_quota"",
                    ""num_global_quotas"",
                    ""num_global_quotas"",
                    ""org_batch_size"",
                )
            },
        )
        for rec in caplog.records
    ]

","
    Get all log records and relevant extra arguments for easy snapshotting.
    ",11,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_string_indexer_log_records(caplog):
    
    return [
        (
            rec.message,
            {
                k: v
                for k, v in rec.__dict__.items()
                if k
                in (
                    ""string_type"",
                    ""is_global_quota"",
                    ""num_global_quotas"",
                    ""num_global_quotas"",
                    ""org_batch_size"",
                )
            },
        )
        for rec in caplog.records
    ]


        ```
    ###Assistant : 
    Get all log records and relevant extra arguments for easy snapshotting.
    
    "
1998,"async def test_registered_pin_required(hass, user_form):
    
    with patch(MOCK_API_CONNECT, return_value=True), patch(
        MOCK_API_DEVICE_REGISTERED, new_callable=PropertyMock
    ) as mock_device_registered, patch(MOCK_API_IS_PIN_REQUIRED, return_value=True):
        mock_device_registered.return_value = True
        await hass.config_entries.flow.async_configure(
            user_form[""flow_id""], user_input=TEST_CREDS
        )

",Test if the device is already registered and PIN required.,10,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_registered_pin_required(hass, user_form):
    
    with patch(MOCK_API_CONNECT, return_value=True), patch(
        MOCK_API_DEVICE_REGISTERED, new_callable=PropertyMock
    ) as mock_device_registered, patch(MOCK_API_IS_PIN_REQUIRED, return_value=True):
        mock_device_registered.return_value = True
        await hass.config_entries.flow.async_configure(
            user_form[""flow_id""], user_input=TEST_CREDS
        )


        ```
    ###Assistant : Test if the device is already registered and PIN required.
    "
1999,"def test_send_push_multiple_workers(self):
        
        http_client_mock1 = Mock(spec_set=[""post_json_get_json""])
        http_client_mock1.post_json_get_json.side_effect = (
            lambda *_, **__: defer.succeed({})
        )

        self.make_worker_hs(
            ""synapse.app.generic_worker"",
            {
                ""worker_name"": ""pusher1"",
                ""pusher_instances"": [""pusher1"", ""pusher2""],
            },
            proxied_blacklisted_http_client=http_client_mock1,
        )

        http_client_mock2 = Mock(spec_set=[""post_json_get_json""])
        http_client_mock2.post_json_get_json.side_effect = (
            lambda *_, **__: defer.succeed({})
        )

        self.make_worker_hs(
            ""synapse.app.generic_worker"",
            {
                ""worker_name"": ""pusher2"",
                ""pusher_instances"": [""pusher1"", ""pusher2""],
            },
            proxied_blacklisted_http_client=http_client_mock2,
        )

        # We choose a user name that we know should go to pusher1.
        event_id = self._create_pusher_and_send_msg(""user2"")

        # Advance time a bit, so the pusher will register something has happened
        self.pump()

        http_client_mock1.post_json_get_json.assert_called_once()
        http_client_mock2.post_json_get_json.assert_not_called()
        self.assertEqual(
            http_client_mock1.post_json_get_json.call_args[0][0],
            ""https://push.example.com/_matrix/push/v1/notify"",
        )
        self.assertEqual(
            event_id,
            http_client_mock1.post_json_get_json.call_args[0][1][""notification""][
                ""event_id""
            ],
        )

        http_client_mock1.post_json_get_json.reset_mock()
        http_client_mock2.post_json_get_json.reset_mock()

        # Now we choose a user name that we know should go to pusher2.
        event_id = self._create_pusher_and_send_msg(""user4"")

        # Advance time a bit, so the pusher will register something has happened
        self.pump()

        http_client_mock1.post_json_get_json.assert_not_called()
        http_client_mock2.post_json_get_json.assert_called_once()
        self.assertEqual(
            http_client_mock2.post_json_get_json.call_args[0][0],
            ""https://push.example.com/_matrix/push/v1/notify"",
        )
        self.assertEqual(
            event_id,
            http_client_mock2.post_json_get_json.call_args[0][1][""notification""][
                ""event_id""
            ],
        )
",Test that registration works when using sharded pusher workers.,9,133,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_send_push_multiple_workers(self):
        
        http_client_mock1 = Mock(spec_set=[""post_json_get_json""])
        http_client_mock1.post_json_get_json.side_effect = (
            lambda *_, **__: defer.succeed({})
        )

        self.make_worker_hs(
            ""synapse.app.generic_worker"",
            {
                ""worker_name"": ""pusher1"",
                ""pusher_instances"": [""pusher1"", ""pusher2""],
            },
            proxied_blacklisted_http_client=http_client_mock1,
        )

        http_client_mock2 = Mock(spec_set=[""post_json_get_json""])
        http_client_mock2.post_json_get_json.side_effect = (
            lambda *_, **__: defer.succeed({})
        )

        self.make_worker_hs(
            ""synapse.app.generic_worker"",
            {
                ""worker_name"": ""pusher2"",
                ""pusher_instances"": [""pusher1"", ""pusher2""],
            },
            proxied_blacklisted_http_client=http_client_mock2,
        )

        # We choose a user name that we know should go to pusher1.
        event_id = self._create_pusher_and_send_msg(""user2"")

        # Advance time a bit, so the pusher will register something has happened
        self.pump()

        http_client_mock1.post_json_get_json.assert_called_once()
        http_client_mock2.post_json_get_json.assert_not_called()
        self.assertEqual(
            http_client_mock1.post_json_get_json.call_args[0][0],
            ""https://push.example.com/_matrix/push/v1/notify"",
        )
        self.assertEqual(
            event_id,
            http_client_mock1.post_json_get_json.call_args[0][1][""notification""][
                ""event_id""
            ],
        )

        http_client_mock1.post_json_get_json.reset_mock()
        http_client_mock2.post_json_get_json.reset_mock()

        # Now we choose a user name that we know should go to pusher2.
        event_id = self._create_pusher_and_send_msg(""user4"")

        # Advance time a bit, so the pusher will register something has happened
        self.pump()

        http_client_mock1.post_json_get_json.assert_not_called()
        http_client_mock2.post_json_get_json.assert_called_once()
        self.assertEqual(
            http_client_mock2.post_json_get_json.call_args[0][0],
            ""https://push.example.com/_matrix/push/v1/notify"",
        )
        self.assertEqual(
            event_id,
            http_client_mock2.post_json_get_json.call_args[0][1][""notification""][
                ""event_id""
            ],
        )

        ```
    ###Assistant : Test that registration works when using sharded pusher workers.
    "
2000,"def _fix_unknown_dimension(self, input_shape, output_shape):
        
        output_shape = list(output_shape)
        msg = (
            ""total size of new array must be unchanged, ""
            ""input_shape = {}, output_shape = {}"".format(
                input_shape, output_shape
            )
        )

        known, unknown = 1, None
        for index, dim in enumerate(output_shape):
            if dim < 0:
                if unknown is None:
                    unknown = index
                else:
                    raise ValueError(
                        f""There must be at most one unknown dimension in ""
                        f""output_shape. Received: output_shape={output_shape}.""
                    )
            else:
                known *= dim

        original = np.prod(input_shape, dtype=int)
        if unknown is not None:
            if known == 0 or original % known != 0:
                raise ValueError(msg)
            output_shape[unknown] = original // known
        elif original != known:
            raise ValueError(msg)
        return output_shape
","Find and replace a missing dimension in an output shape.

        This is a near direct port of the internal Numpy function
        `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`

        Args:
          input_shape: Shape of array being reshaped
          output_shape: Desired shape of the array with at most a single -1
            which indicates a dimension that should be derived from the input
            shape.

        Returns:
          The new output shape with a -1 replaced with its computed value.

        Raises:
          ValueError: If the total array size of the output_shape is
          different than the input_shape, or more than one unknown dimension
          is specified.
        ",91,105,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _fix_unknown_dimension(self, input_shape, output_shape):
        
        output_shape = list(output_shape)
        msg = (
            ""total size of new array must be unchanged, ""
            ""input_shape = {}, output_shape = {}"".format(
                input_shape, output_shape
            )
        )

        known, unknown = 1, None
        for index, dim in enumerate(output_shape):
            if dim < 0:
                if unknown is None:
                    unknown = index
                else:
                    raise ValueError(
                        f""There must be at most one unknown dimension in ""
                        f""output_shape. Received: output_shape={output_shape}.""
                    )
            else:
                known *= dim

        original = np.prod(input_shape, dtype=int)
        if unknown is not None:
            if known == 0 or original % known != 0:
                raise ValueError(msg)
            output_shape[unknown] = original // known
        elif original != known:
            raise ValueError(msg)
        return output_shape

        ```
    ###Assistant : Find and replace a missing dimension in an output shape.

        This is a near direct port of the internal Numpy function
        `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`

        Args:
          input_shape: Shape of array being reshaped
          output_shape: Desired shape of the array with at most a single -1
            which indicates a dimension that should be derived from the input
            shape.

        Returns:
          The new output shape with a -1 replaced with its computed value.

        Raises:
          ValueError: If the total array size of the output_shape is
          different than the input_shape, or more than one unknown dimension
          is specified.
        
    "
2001,"def test_converter_with_unicode_dtype():
    
    txt = StringIO('abc,def\nrst,xyz')
    conv = bytes.upper
    res = np.loadtxt(
            txt, dtype=np.dtype(""U3""), converters=conv, delimiter="","")
    expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']])
    assert_equal(res, expected)

","
    With the default 'bytes' encoding, tokens are encoded prior to being
    passed to the converter. This means that the output of the converter may
    be bytes instead of unicode as expected by `read_rows`.

    This test checks that outputs from the above scenario are properly decoded
    prior to parsing by `read_rows`.
    ",50,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_converter_with_unicode_dtype():
    
    txt = StringIO('abc,def\nrst,xyz')
    conv = bytes.upper
    res = np.loadtxt(
            txt, dtype=np.dtype(""U3""), converters=conv, delimiter="","")
    expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']])
    assert_equal(res, expected)


        ```
    ###Assistant : 
    With the default 'bytes' encoding, tokens are encoded prior to being
    passed to the converter. This means that the output of the converter may
    be bytes instead of unicode as expected by `read_rows`.

    This test checks that outputs from the above scenario are properly decoded
    prior to parsing by `read_rows`.
    
    "
2002,"def alembic_stamp(revision):
    
    # lazy import for performance
    import alembic.command

    alembic.command.stamp(alembic_config(), revision=revision)
","
    Stamp the revision table with the given revision; don’t run any migrations

    Args:
        revision: The revision passed to `alembic stamp`.
    ",20,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def alembic_stamp(revision):
    
    # lazy import for performance
    import alembic.command

    alembic.command.stamp(alembic_config(), revision=revision)

        ```
    ###Assistant : 
    Stamp the revision table with the given revision; don’t run any migrations

    Args:
        revision: The revision passed to `alembic stamp`.
    
    "
2003,"def unflatten_superdims(assignment):
  
  def check(cond):
    if cond: return
    raise NotImplementedError(""Failed to convert OpSharding into a ShardingSpec. ""
                              ""Please open a bug report!"")
  flat_assignment = np.asarray(assignment, dtype=np.int64)
  check(flat_assignment[0] == 0)
  dims = []
  while flat_assignment.size > 1:
    stride = flat_assignment[1]
    for i in range(len(flat_assignment)):
      if flat_assignment[i] != i * stride: break
    else:
      # After this loop i should point to an ""element after the sequence"", so
      # we have to increment it if the whole array is a strided sequence.
      i += 1
    size = i
    dims.append((size, stride))
    assert size > 1  # Ensure progress
    flat_assignment = flat_assignment[::size]
  return dims
","Unflatten a list of dimension sizes and their strides that generates assignment.

  If this function succeeds for a given ``assignment``, then the following property
  should be satisfied::

    dims_with_strides = unflatten_superdims(assignment)
    base_array = np.arange(map(fst, sorted(dims_with_strides, key=snd, reverse=True)))
    assignment == base_array.transpose(argsort(dims_with_strides, key=snd, reverse=True)).flatten()

  That is, the returned dimensions list all sizes of the base array (with strides
  indicating their initial order). The order of dimensions in the list corresponds
  to the permutation that applied to the base array generates the assignment.
  ",79,98,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unflatten_superdims(assignment):
  
  def check(cond):
    if cond: return
    raise NotImplementedError(""Failed to convert OpSharding into a ShardingSpec. ""
                              ""Please open a bug report!"")
  flat_assignment = np.asarray(assignment, dtype=np.int64)
  check(flat_assignment[0] == 0)
  dims = []
  while flat_assignment.size > 1:
    stride = flat_assignment[1]
    for i in range(len(flat_assignment)):
      if flat_assignment[i] != i * stride: break
    else:
      # After this loop i should point to an ""element after the sequence"", so
      # we have to increment it if the whole array is a strided sequence.
      i += 1
    size = i
    dims.append((size, stride))
    assert size > 1  # Ensure progress
    flat_assignment = flat_assignment[::size]
  return dims

        ```
    ###Assistant : Unflatten a list of dimension sizes and their strides that generates assignment.

  If this function succeeds for a given ``assignment``, then the following property
  should be satisfied::

    dims_with_strides = unflatten_superdims(assignment)
    base_array = np.arange(map(fst, sorted(dims_with_strides, key=snd, reverse=True)))
    assignment == base_array.transpose(argsort(dims_with_strides, key=snd, reverse=True)).flatten()

  That is, the returned dimensions list all sizes of the base array (with strides
  indicating their initial order). The order of dimensions in the list corresponds
  to the permutation that applied to the base array generates the assignment.
  
    "
2004,"def test_perf_issue_no_associate_error_event(self):
        
        self.project.update_option(""sentry:performance_issue_creation_rate"", 1.0)

        with mock.patch(""sentry_sdk.tracing.Span.containing_transaction""), self.feature(
            {
                ""projects:performance-suspect-spans-ingestion"": True,
                ""organizations:performance-issues-ingest"": True,
            }
        ):
            manager = EventManager(make_event())
            manager.normalize()
            event = manager.save(self.project.id)
            assert len(event.groups) == 1

            # sneakily make the group type wrong
            group = event.groups[0]
            group.type = GroupType.PERFORMANCE_N_PLUS_ONE.value
            group.save()
            manager = EventManager(make_event())
            manager.normalize()
            event = manager.save(self.project.id)

            assert len(event.groups) == 0

",Test that you can't associate an error event with a performance issue,12,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_perf_issue_no_associate_error_event(self):
        
        self.project.update_option(""sentry:performance_issue_creation_rate"", 1.0)

        with mock.patch(""sentry_sdk.tracing.Span.containing_transaction""), self.feature(
            {
                ""projects:performance-suspect-spans-ingestion"": True,
                ""organizations:performance-issues-ingest"": True,
            }
        ):
            manager = EventManager(make_event())
            manager.normalize()
            event = manager.save(self.project.id)
            assert len(event.groups) == 1

            # sneakily make the group type wrong
            group = event.groups[0]
            group.type = GroupType.PERFORMANCE_N_PLUS_ONE.value
            group.save()
            manager = EventManager(make_event())
            manager.normalize()
            event = manager.save(self.project.id)

            assert len(event.groups) == 0


        ```
    ###Assistant : Test that you can't associate an error event with a performance issue
    "
2005,"def testNodeTerminatedDuringUpdate(self):
        
        cluster_config = copy.deepcopy(MOCK_DEFAULT_CONFIG)
        cluster_config[""available_node_types""][""ray.worker.default""][""min_workers""] = 2
        cluster_config[""worker_start_ray_commands""] = [""ray_start_cmd""]

        # Don't need the extra node type or a docker config.
        cluster_config[""head_node_type""] = [""ray.worker.default""]
        del cluster_config[""available_node_types""][""ray.head.default""]
        del cluster_config[""docker""]

        config_path = self.write_config(cluster_config)

        self.provider = MockProvider()
        runner = MockProcessRunner()
        lm = LoadMetrics()
        mock_metrics = Mock(spec=AutoscalerPrometheusMetrics())
        autoscaler = MockAutoscaler(
            config_path,
            lm,
            MockNodeInfoStub(),
            max_failures=0,
            process_runner=runner,
            update_interval_s=0,
            prom_metrics=mock_metrics,
        )

        # Scale up to two up-to-date workers
        autoscaler.update()
        self.waitForNodes(2)
        self.provider.finish_starting_nodes()
        autoscaler.update()
        self.waitForNodes(2, tag_filters={TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE})

        # Mark both nodes as unhealthy
        for _ in range(5):
            if autoscaler.updaters:
                time.sleep(0.05)
                autoscaler.update()

        lm.last_heartbeat_time_by_ip[""172.0.0.0""] = 0
        lm.last_heartbeat_time_by_ip[""172.0.0.1""] = 0

        # Expect both updates to be successful, no nodes in updating state
        assert mock_metrics.successful_updates.inc.call_count == 2
        assert mock_metrics.worker_update_time.observe.call_count == 2
        mock_metrics.updating_nodes.set.assert_called_with(0)
        assert not autoscaler.updaters

        # Set up process runner to terminate worker 0 during missed heartbeat
        # recovery and also cause the updater to fail.","
        Tests autoscaler handling a node getting terminated during an update
        triggered by the node missing a heartbeat.

        Extension of testRecoverUnhealthyWorkers.

        In this test, two nodes miss a heartbeat.
        One of them (node 0) is terminated during its recovery update.
        The other (node 1) just fails its update.

        When processing completed updates, the autoscaler terminates node 1
        but does not try to terminate node 0 again.
        ",65,134,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def testNodeTerminatedDuringUpdate(self):
        
        cluster_config = copy.deepcopy(MOCK_DEFAULT_CONFIG)
        cluster_config[""available_node_types""][""ray.worker.default""][""min_workers""] = 2
        cluster_config[""worker_start_ray_commands""] = [""ray_start_cmd""]

        # Don't need the extra node type or a docker config.
        cluster_config[""head_node_type""] = [""ray.worker.default""]
        del cluster_config[""available_node_types""][""ray.head.default""]
        del cluster_config[""docker""]

        config_path = self.write_config(cluster_config)

        self.provider = MockProvider()
        runner = MockProcessRunner()
        lm = LoadMetrics()
        mock_metrics = Mock(spec=AutoscalerPrometheusMetrics())
        autoscaler = MockAutoscaler(
            config_path,
            lm,
            MockNodeInfoStub(),
            max_failures=0,
            process_runner=runner,
            update_interval_s=0,
            prom_metrics=mock_metrics,
        )

        # Scale up to two up-to-date workers
        autoscaler.update()
        self.waitForNodes(2)
        self.provider.finish_starting_nodes()
        autoscaler.update()
        self.waitForNodes(2, tag_filters={TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE})

        # Mark both nodes as unhealthy
        for _ in range(5):
            if autoscaler.updaters:
                time.sleep(0.05)
                autoscaler.update()

        lm.last_heartbeat_time_by_ip[""172.0.0.0""] = 0
        lm.last_heartbeat_time_by_ip[""172.0.0.1""] = 0

        # Expect both updates to be successful, no nodes in updating state
        assert mock_metrics.successful_updates.inc.call_count == 2
        assert mock_metrics.worker_update_time.observe.call_count == 2
        mock_metrics.updating_nodes.set.assert_called_with(0)
        assert not autoscaler.updaters

        # Set up process runner to terminate worker 0 during missed heartbeat
        # recovery and also cause the updater to fail.
        ```
    ###Assistant : 
        Tests autoscaler handling a node getting terminated during an update
        triggered by the node missing a heartbeat.

        Extension of testRecoverUnhealthyWorkers.

        In this test, two nodes miss a heartbeat.
        One of them (node 0) is terminated during its recovery update.
        The other (node 1) just fails its update.

        When processing completed updates, the autoscaler terminates node 1
        but does not try to terminate node 0 again.
        
    "
2006,"def call_cr(self, other_args):
        
        parser = argparse.ArgumentParser(
            prog=""cr"",
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            description=,
        )

        parser.add_argument(
            ""-t"",
            ""--type"",
            dest=""type"",
            type=str,
            help=""Select interest rate type"",
            default=""supply"",
            choices=[""borrow"", ""supply""],
        )
        parser.add_argument(
            ""-c"",
            ""--cryptocurrrencies"",
            dest=""cryptos"",
            type=loanscan_model.check_valid_coin,
            help=f,
            default=""BTC,ETH,USDT,USDC"",
        )

        parser.add_argument(
            ""-p"",
            ""--platforms"",
            dest=""platforms"",
            type=loanscan_model.check_valid_platform,
            help=f,
            default=""BlockFi,Ledn,SwissBorg,Youhodler"",
        )

        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-t"")

        ns_parser = parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED, limit=10
        )
        if ns_parser:
            loanscan_view.display_crypto_rates(
                rate_type=ns_parser.type,
                cryptos=ns_parser.cryptos,
                platforms=ns_parser.platforms,
                limit=ns_parser.limit,
                export=ns_parser.export,
            )
","Process cr commandDisplays crypto {borrow,supply} interest rates for cryptocurrencies across several platforms.
                You can select rate type with --type {borrow,supply}
                You can display only N number of platforms with --limit parameter.Cryptocurrencies to search interest rates for separated by comma.
            Default: BTC,ETH,USDT,USDC. Options: {"","".join(loanscan_model.CRYPTOS)}Platforms to search interest rates in separated by comma.
            Default: BlockFi,Ledn,SwissBorg,Youhodler. Options: {"","".join(loanscan_model.PLATFORMS)}",55,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call_cr(self, other_args):
        
        parser = argparse.ArgumentParser(
            prog=""cr"",
            add_help=False,
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
            description=,
        )

        parser.add_argument(
            ""-t"",
            ""--type"",
            dest=""type"",
            type=str,
            help=""Select interest rate type"",
            default=""supply"",
            choices=[""borrow"", ""supply""],
        )
        parser.add_argument(
            ""-c"",
            ""--cryptocurrrencies"",
            dest=""cryptos"",
            type=loanscan_model.check_valid_coin,
            help=f,
            default=""BTC,ETH,USDT,USDC"",
        )

        parser.add_argument(
            ""-p"",
            ""--platforms"",
            dest=""platforms"",
            type=loanscan_model.check_valid_platform,
            help=f,
            default=""BlockFi,Ledn,SwissBorg,Youhodler"",
        )

        if other_args and ""-"" not in other_args[0][0]:
            other_args.insert(0, ""-t"")

        ns_parser = parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED, limit=10
        )
        if ns_parser:
            loanscan_view.display_crypto_rates(
                rate_type=ns_parser.type,
                cryptos=ns_parser.cryptos,
                platforms=ns_parser.platforms,
                limit=ns_parser.limit,
                export=ns_parser.export,
            )

        ```
    ###Assistant : Process cr commandDisplays crypto {borrow,supply} interest rates for cryptocurrencies across several platforms.
                You can select rate type with --type {borrow,supply}
                You can display only N number of platforms with --limit parameter.Cryptocurrencies to search interest rates for separated by comma.
            Default: BTC,ETH,USDT,USDC. Options: {"","".join(loanscan_model.CRYPTOS)}Platforms to search interest rates in separated by comma.
            Default: BlockFi,Ledn,SwissBorg,Youhodler. Options: {"","".join(loanscan_model.PLATFORMS)}
    "
2007,"def export(self):
        
        result = {}
        for name, module in self.nas_modules:
            if name not in result:
                result[name] = module.export()
        return result
","
        Export the NAS result, ideally the best choice of each nas_modules.
        You may implement an ``export`` method for your customized nas_module.

        Returns
        --------
        result : Dict[str, int]
            Keys are names of nas_modules, and values are the choice indices of them.
        ",40,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def export(self):
        
        result = {}
        for name, module in self.nas_modules:
            if name not in result:
                result[name] = module.export()
        return result

        ```
    ###Assistant : 
        Export the NAS result, ideally the best choice of each nas_modules.
        You may implement an ``export`` method for your customized nas_module.

        Returns
        --------
        result : Dict[str, int]
            Keys are names of nas_modules, and values are the choice indices of them.
        
    "
2008,"def clean_pipeline_string(self, individual):
        
        dirty_string = str(individual)
        # There are many parameter prefixes in the pipeline strings, used solely for
        # making the terminal name unique, eg. LinearSVC__.
        parameter_prefixes = [
            (m.start(), m.end()) for m in re.finditer("", [\w]+__"", dirty_string)
        ]
        # We handle them in reverse so we do not mess up indices
        pretty = dirty_string
        for (start, end) in reversed(parameter_prefixes):
            pretty = pretty[: start + 2] + pretty[end:]

        return pretty
","Provide a string of the individual without the parameter prefixes.

        Parameters
        ----------
        individual: individual
            Individual which should be represented by a pretty string

        Returns
        -------
        A string like str(individual), but with parameter prefixes removed.

        ",34,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clean_pipeline_string(self, individual):
        
        dirty_string = str(individual)
        # There are many parameter prefixes in the pipeline strings, used solely for
        # making the terminal name unique, eg. LinearSVC__.
        parameter_prefixes = [
            (m.start(), m.end()) for m in re.finditer("", [\w]+__"", dirty_string)
        ]
        # We handle them in reverse so we do not mess up indices
        pretty = dirty_string
        for (start, end) in reversed(parameter_prefixes):
            pretty = pretty[: start + 2] + pretty[end:]

        return pretty

        ```
    ###Assistant : Provide a string of the individual without the parameter prefixes.

        Parameters
        ----------
        individual: individual
            Individual which should be represented by a pretty string

        Returns
        -------
        A string like str(individual), but with parameter prefixes removed.

        
    "
2009,"def _lin_eq2dict(a, symset):
    
    if a in symset:
        return S.Zero, {a: S.One}
    elif a.is_Add:
        terms_list = defaultdict(list)
        coeff_list = []
        for ai in a.args:
            ci, ti = _lin_eq2dict(ai, symset)
            coeff_list.append(ci)
            for mij, cij in ti.items():
                terms_list[mij].append(cij)
        coeff = Add(*coeff_list)
        terms = {sym: Add(*coeffs) for sym, coeffs in terms_list.items()}
        return coeff, terms
    elif a.is_Mul:
        terms = terms_coeff = None
        coeff_list = []
        for ai in a.args:
            ci, ti = _lin_eq2dict(ai, symset)
            if not ti:
                coeff_list.append(ci)
            elif terms is None:
                terms = ti
                terms_coeff = ci
            else:
                raise PolyNonlinearError
        coeff = Mul(*coeff_list)
        if terms is None:
            return coeff, {}
        else:
            terms = {sym: coeff * c for sym, c in terms.items()}
            return  coeff * terms_coeff, terms
    elif a.is_Equality:
        return _lin_eq2dict(a.lhs - a.rhs, symset)
    elif not a.has_free(*symset):
        return a, {}
    else:
        raise PolyNonlinearError
",Efficiently convert a linear equation to a dict of coefficients,10,129,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _lin_eq2dict(a, symset):
    
    if a in symset:
        return S.Zero, {a: S.One}
    elif a.is_Add:
        terms_list = defaultdict(list)
        coeff_list = []
        for ai in a.args:
            ci, ti = _lin_eq2dict(ai, symset)
            coeff_list.append(ci)
            for mij, cij in ti.items():
                terms_list[mij].append(cij)
        coeff = Add(*coeff_list)
        terms = {sym: Add(*coeffs) for sym, coeffs in terms_list.items()}
        return coeff, terms
    elif a.is_Mul:
        terms = terms_coeff = None
        coeff_list = []
        for ai in a.args:
            ci, ti = _lin_eq2dict(ai, symset)
            if not ti:
                coeff_list.append(ci)
            elif terms is None:
                terms = ti
                terms_coeff = ci
            else:
                raise PolyNonlinearError
        coeff = Mul(*coeff_list)
        if terms is None:
            return coeff, {}
        else:
            terms = {sym: coeff * c for sym, c in terms.items()}
            return  coeff * terms_coeff, terms
    elif a.is_Equality:
        return _lin_eq2dict(a.lhs - a.rhs, symset)
    elif not a.has_free(*symset):
        return a, {}
    else:
        raise PolyNonlinearError

        ```
    ###Assistant : Efficiently convert a linear equation to a dict of coefficients
    "
2010,"def internal_ip(self, node_id):
        
        ip = (
            self._get_cached_node(node_id=node_id)[""internal_ip""]
            or self._get_node(node_id=node_id)[""internal_ip""]
        )
        return ip
",Returns the internal ip (Ray ip) of the given node.,10,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def internal_ip(self, node_id):
        
        ip = (
            self._get_cached_node(node_id=node_id)[""internal_ip""]
            or self._get_node(node_id=node_id)[""internal_ip""]
        )
        return ip

        ```
    ###Assistant : Returns the internal ip (Ray ip) of the given node.
    "
2011,"def _write_file(self, source, dest, type, compress=False):
        
        start = self.lib.tell()
        length = os.stat(source).st_size
        with open(source, 'rb') as f:
            if compress:
                buffer = bytearray(16 * 1024)
                compressor = zlib.compressobj(self.LEVEL)
                while 1:
                    read = f.readinto(buffer)
                    if not read:
                        break
                    self.lib.write(compressor.compress(buffer[:read]))
                self.lib.write(compressor.flush())

            else:
                shutil.copyfileobj(f, self.lib)
        self.toc.add(start, self.lib.tell() - start, length, int(compress), type, dest)
","
        Stream copy a large file into the archive and update the table of contents.
        ",14,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _write_file(self, source, dest, type, compress=False):
        
        start = self.lib.tell()
        length = os.stat(source).st_size
        with open(source, 'rb') as f:
            if compress:
                buffer = bytearray(16 * 1024)
                compressor = zlib.compressobj(self.LEVEL)
                while 1:
                    read = f.readinto(buffer)
                    if not read:
                        break
                    self.lib.write(compressor.compress(buffer[:read]))
                self.lib.write(compressor.flush())

            else:
                shutil.copyfileobj(f, self.lib)
        self.toc.add(start, self.lib.tell() - start, length, int(compress), type, dest)

        ```
    ###Assistant : 
        Stream copy a large file into the archive and update the table of contents.
        
    "
2012,"def incidence_matrix(G, nodelist=None, edgelist=None, oriented=False, weight=None):
    
    import scipy as sp
    import scipy.sparse  # call as sp.sparse

    if nodelist is None:
        nodelist = list(G)
    if edgelist is None:
        if G.is_multigraph():
            edgelist = list(G.edges(keys=True))
        else:
            edgelist = list(G.edges())
    A = sp.sparse.lil_array((len(nodelist), len(edgelist)))
    node_index = {node: i for i, node in enumerate(nodelist)}
    for ei, e in enumerate(edgelist):
        (u, v) = e[:2]
        if u == v:
            continue  # self loops give zero column
        try:
            ui = node_index[u]
            vi = node_index[v]
        except KeyError as err:
            raise nx.NetworkXError(
                f""node {u} or {v} in edgelist but not in nodelist""
            ) from err
        if weight is None:
            wt = 1
        else:
            if G.is_multigraph():
                ekey = e[2]
                wt = G[u][v][ekey].get(weight, 1)
            else:
                wt = G[u][v].get(weight, 1)
        if oriented:
            A[ui, ei] = -wt
            A[vi, ei] = wt
        else:
            A[ui, ei] = wt
            A[vi, ei] = wt
    import warnings

    warnings.warn(
        ""incidence_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0."",
        FutureWarning,
        stacklevel=2,
    )
    # TODO: Rm sp.sparse.csc_matrix in Networkx 3.0
    return A.asformat(""csc"")

","Returns incidence matrix of G.

    The incidence matrix assigns each row to a node and each column to an edge.
    For a standard incidence matrix a 1 appears wherever a row's node is
    incident on the column's edge.  For an oriented incidence matrix each
    edge is assigned an orientation (arbitrarily for undirected and aligning to
    direction for directed).  A -1 appears for the source (tail) of an edge and
    1 for the destination (head) of the edge.  The elements are zero otherwise.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list, optional   (default= all nodes in G)
       The rows are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    edgelist : list, optional (default= all edges in G)
       The columns are ordered according to the edges in edgelist.
       If edgelist is None, then the ordering is produced by G.edges().

    oriented: bool, optional (default=False)
       If True, matrix elements are +1 or -1 for the head or tail node
       respectively of each edge.  If False, +1 occurs at both nodes.

    weight : string or None, optional (default=None)
       The edge data key used to provide each value in the matrix.
       If None, then each edge has weight 1.  Edge weights, if used,
       should be positive so that the orientation can provide the sign.

    Returns
    -------
    A : SciPy sparse matrix
      The incidence matrix of G.

    Notes
    -----
    For MultiGraph/MultiDiGraph, the edges in edgelist should be
    (u,v,key) 3-tuples.

    ""Networks are the best discrete model for so many problems in
    applied mathematics"" [1]_.

    References
    ----------
    .. [1] Gil Strang, Network applications: A = incidence matrix,
       http://videolectures.net/mit18085f07_strang_lec03/
    ",272,164,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def incidence_matrix(G, nodelist=None, edgelist=None, oriented=False, weight=None):
    
    import scipy as sp
    import scipy.sparse  # call as sp.sparse

    if nodelist is None:
        nodelist = list(G)
    if edgelist is None:
        if G.is_multigraph():
            edgelist = list(G.edges(keys=True))
        else:
            edgelist = list(G.edges())
    A = sp.sparse.lil_array((len(nodelist), len(edgelist)))
    node_index = {node: i for i, node in enumerate(nodelist)}
    for ei, e in enumerate(edgelist):
        (u, v) = e[:2]
        if u == v:
            continue  # self loops give zero column
        try:
            ui = node_index[u]
            vi = node_index[v]
        except KeyError as err:
            raise nx.NetworkXError(
                f""node {u} or {v} in edgelist but not in nodelist""
            ) from err
        if weight is None:
            wt = 1
        else:
            if G.is_multigraph():
                ekey = e[2]
                wt = G[u][v][ekey].get(weight, 1)
            else:
                wt = G[u][v].get(weight, 1)
        if oriented:
            A[ui, ei] = -wt
            A[vi, ei] = wt
        else:
            A[ui, ei] = wt
            A[vi, ei] = wt
    import warnings

    warnings.warn(
        ""incidence_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0."",
        FutureWarning,
        stacklevel=2,
    )
    # TODO: Rm sp.sparse.csc_matrix in Networkx 3.0
    return A.asformat(""csc"")


        ```
    ###Assistant : Returns incidence matrix of G.

    The incidence matrix assigns each row to a node and each column to an edge.
    For a standard incidence matrix a 1 appears wherever a row's node is
    incident on the column's edge.  For an oriented incidence matrix each
    edge is assigned an orientation (arbitrarily for undirected and aligning to
    direction for directed).  A -1 appears for the source (tail) of an edge and
    1 for the destination (head) of the edge.  The elements are zero otherwise.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list, optional   (default= all nodes in G)
       The rows are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    edgelist : list, optional (default= all edges in G)
       The columns are ordered according to the edges in edgelist.
       If edgelist is None, then the ordering is produced by G.edges().

    oriented: bool, optional (default=False)
       If True, matrix elements are +1 or -1 for the head or tail node
       respectively of each edge.  If False, +1 occurs at both nodes.

    weight : string or None, optional (default=None)
       The edge data key used to provide each value in the matrix.
       If None, then each edge has weight 1.  Edge weights, if used,
       should be positive so that the orientation can provide the sign.

    Returns
    -------
    A : SciPy sparse matrix
      The incidence matrix of G.

    Notes
    -----
    For MultiGraph/MultiDiGraph, the edges in edgelist should be
    (u,v,key) 3-tuples.

    ""Networks are the best discrete model for so many problems in
    applied mathematics"" [1]_.

    References
    ----------
    .. [1] Gil Strang, Network applications: A = incidence matrix,
       http://videolectures.net/mit18085f07_strang_lec03/
    
    "
2013,"def call(self, features, training=None):
        
        if not isinstance(features, dict):
            raise ValueError(
                ""We expected a dictionary here. Instead we got: "", features
            )
        if training is None:
            training = backend.learning_phase()
        transformation_cache = (
            tf.__internal__.feature_column.FeatureTransformationCache(features)
        )
        output_tensors = []
        sequence_lengths = []

        for column in self._feature_columns:
            with backend.name_scope(column.name):
                try:
                    (
                        dense_tensor,
                        sequence_length,
                    ) = column.get_sequence_dense_tensor(
                        transformation_cache,
                        self._state_manager,
                        training=training,
                    )
                except TypeError:
                    (
                        dense_tensor,
                        sequence_length,
                    ) = column.get_sequence_dense_tensor(
                        transformation_cache, self._state_manager
                    )
                # Flattens the final dimension to produce a 3D Tensor.
                output_tensors.append(
                    self._process_dense_tensor(column, dense_tensor)
                )
                sequence_lengths.append(sequence_length)

        # Check and process sequence lengths.
        kfc._verify_static_batch_size_equality(
            sequence_lengths, self._feature_columns
        )
        sequence_length = _assert_all_equal_and_return(sequence_lengths)

        return self._verify_and_concat_tensors(output_tensors), sequence_length

","Returns sequence input corresponding to the `feature_columns`.

        Args:
          features: A dict mapping keys to tensors.
          training: Python boolean or None, indicating whether to the layer is
            being run in training mode. This argument is passed to the call
            method of any `FeatureColumn` that takes a `training` argument. For
            example, if a `FeatureColumn` performed dropout, the column could
            expose a `training` argument to control whether the dropout should
            be applied. If `None`, defaults to
            `tf.keras.backend.learning_phase()`.


        Returns:
          An `(input_layer, sequence_length)` tuple where:
          - input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
              `T` is the maximum sequence length for this batch, which could
              differ from batch to batch. `D` is the sum of `num_elements` for
              all `feature_columns`.
          - sequence_length: An int `Tensor` of shape `[batch_size]`. The
            sequence length for each example.

        Raises:
          ValueError: If features are not a dictionary.
        ",137,98,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call(self, features, training=None):
        
        if not isinstance(features, dict):
            raise ValueError(
                ""We expected a dictionary here. Instead we got: "", features
            )
        if training is None:
            training = backend.learning_phase()
        transformation_cache = (
            tf.__internal__.feature_column.FeatureTransformationCache(features)
        )
        output_tensors = []
        sequence_lengths = []

        for column in self._feature_columns:
            with backend.name_scope(column.name):
                try:
                    (
                        dense_tensor,
                        sequence_length,
                    ) = column.get_sequence_dense_tensor(
                        transformation_cache,
                        self._state_manager,
                        training=training,
                    )
                except TypeError:
                    (
                        dense_tensor,
                        sequence_length,
                    ) = column.get_sequence_dense_tensor(
                        transformation_cache, self._state_manager
                    )
                # Flattens the final dimension to produce a 3D Tensor.
                output_tensors.append(
                    self._process_dense_tensor(column, dense_tensor)
                )
                sequence_lengths.append(sequence_length)

        # Check and process sequence lengths.
        kfc._verify_static_batch_size_equality(
            sequence_lengths, self._feature_columns
        )
        sequence_length = _assert_all_equal_and_return(sequence_lengths)

        return self._verify_and_concat_tensors(output_tensors), sequence_length


        ```
    ###Assistant : Returns sequence input corresponding to the `feature_columns`.

        Args:
          features: A dict mapping keys to tensors.
          training: Python boolean or None, indicating whether to the layer is
            being run in training mode. This argument is passed to the call
            method of any `FeatureColumn` that takes a `training` argument. For
            example, if a `FeatureColumn` performed dropout, the column could
            expose a `training` argument to control whether the dropout should
            be applied. If `None`, defaults to
            `tf.keras.backend.learning_phase()`.


        Returns:
          An `(input_layer, sequence_length)` tuple where:
          - input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
              `T` is the maximum sequence length for this batch, which could
              differ from batch to batch. `D` is the sum of `num_elements` for
              all `feature_columns`.
          - sequence_length: An int `Tensor` of shape `[batch_size]`. The
            sequence length for each example.

        Raises:
          ValueError: If features are not a dictionary.
        
    "
2014,"def feed_eof(self):
        
        self._incoming.write_eof()
        ssldata, appdata = self.feed_ssldata(b'')
        assert appdata == [] or appdata == [b'']
","Send a potentially ""ragged"" EOF.

        This method will raise an SSL_ERROR_EOF exception if the EOF is
        unexpected.
        ",17,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def feed_eof(self):
        
        self._incoming.write_eof()
        ssldata, appdata = self.feed_ssldata(b'')
        assert appdata == [] or appdata == [b'']

        ```
    ###Assistant : Send a potentially ""ragged"" EOF.

        This method will raise an SSL_ERROR_EOF exception if the EOF is
        unexpected.
        
    "
2015,"def screen(self) -> Screen:
        
        try:
            return self._screen_stack[-1]
        except IndexError:
            raise ScreenStackError(""No screens on stack"") from None
","Get the current screen.

        Raises:
            ScreenStackError: If there are no screens on the stack.

        Returns:
            Screen: The currently active screen.
        ",20,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def screen(self) -> Screen:
        
        try:
            return self._screen_stack[-1]
        except IndexError:
            raise ScreenStackError(""No screens on stack"") from None

        ```
    ###Assistant : Get the current screen.

        Raises:
            ScreenStackError: If there are no screens on the stack.

        Returns:
            Screen: The currently active screen.
        
    "
2016,"def test_empty_dunder_path_no_dunder_file(self):
        
        with self.assertRaises(ImproperlyConfigured):
            AppConfig(""label"", Stub(__path__=[]))
","If the __path__ attr is empty and there is no __file__, raise.",12,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_empty_dunder_path_no_dunder_file(self):
        
        with self.assertRaises(ImproperlyConfigured):
            AppConfig(""label"", Stub(__path__=[]))

        ```
    ###Assistant : If the __path__ attr is empty and there is no __file__, raise.
    "
2017,"def train_epoch_ch3(net, train_iter, loss, updater):
    
    # Sum of training loss, sum of training accuracy, no. of examples
    metric = Accumulator(3)
    for X, y in train_iter:
        # Compute gradients and update parameters
        with tf.GradientTape() as tape:
            y_hat = net(X)
            # Keras implementations for loss takes (labels, predictions)
            # instead of (predictions, labels) that users might implement
            # in this book, e.g. `cross_entropy` that we implemented above
            if isinstance(loss, tf.keras.losses.Loss):
                l = loss(y, y_hat)
            else:
                l = loss(y_hat, y)
        if isinstance(updater, tf.keras.optimizers.Optimizer):
            params = net.trainable_variables
            grads = tape.gradient(l, params)
            updater.apply_gradients(zip(grads, params))
        else:
            updater(X.shape[0], tape.gradient(l, updater.params))
        # Keras loss by default returns the average loss in a batch
        l_sum = l * float(tf.size(y)) if isinstance(
            loss, tf.keras.losses.Loss) else tf.reduce_sum(l)
        metric.add(l_sum, accuracy(y_hat, y), tf.size(y))
    # Return training loss and training accuracy
    return metric[0] / metric[2], metric[1] / metric[2]
","The training loop defined in Chapter 3.

    Defined in :numref:`sec_softmax_scratch`",10,134,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def train_epoch_ch3(net, train_iter, loss, updater):
    
    # Sum of training loss, sum of training accuracy, no. of examples
    metric = Accumulator(3)
    for X, y in train_iter:
        # Compute gradients and update parameters
        with tf.GradientTape() as tape:
            y_hat = net(X)
            # Keras implementations for loss takes (labels, predictions)
            # instead of (predictions, labels) that users might implement
            # in this book, e.g. `cross_entropy` that we implemented above
            if isinstance(loss, tf.keras.losses.Loss):
                l = loss(y, y_hat)
            else:
                l = loss(y_hat, y)
        if isinstance(updater, tf.keras.optimizers.Optimizer):
            params = net.trainable_variables
            grads = tape.gradient(l, params)
            updater.apply_gradients(zip(grads, params))
        else:
            updater(X.shape[0], tape.gradient(l, updater.params))
        # Keras loss by default returns the average loss in a batch
        l_sum = l * float(tf.size(y)) if isinstance(
            loss, tf.keras.losses.Loss) else tf.reduce_sum(l)
        metric.add(l_sum, accuracy(y_hat, y), tf.size(y))
    # Return training loss and training accuracy
    return metric[0] / metric[2], metric[1] / metric[2]

        ```
    ###Assistant : The training loop defined in Chapter 3.

    Defined in :numref:`sec_softmax_scratch`
    "
2018,"def all_estimators(type_filter=None):
    
    # lazy import to avoid circular imports from sklearn.base
    from . import IS_PYPY
    from ._testing import ignore_warnings
    from ..base import (
        BaseEstimator,
        ClassifierMixin,
        RegressorMixin,
        TransformerMixin,
        ClusterMixin,
    )
","Get a list of all estimators from `sklearn`.

    This function crawls the module and gets all classes that inherit
    from BaseEstimator. Classes that are defined in test-modules are not
    included.

    Parameters
    ----------
    type_filter : {""classifier"", ""regressor"", ""cluster"", ""transformer""} \
            or list of such str, default=None
        Which kind of estimators should be returned. If None, no filter is
        applied and all estimators are returned.  Possible values are
        'classifier', 'regressor', 'cluster' and 'transformer' to get
        estimators only of these specific types, or a list of these to
        get the estimators that fit at least one of the types.

    Returns
    -------
    estimators : list of tuples
        List of (name, class), where ``name`` is the class name as string
        and ``class`` is the actual type of the class.
    ",124,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def all_estimators(type_filter=None):
    
    # lazy import to avoid circular imports from sklearn.base
    from . import IS_PYPY
    from ._testing import ignore_warnings
    from ..base import (
        BaseEstimator,
        ClassifierMixin,
        RegressorMixin,
        TransformerMixin,
        ClusterMixin,
    )

        ```
    ###Assistant : Get a list of all estimators from `sklearn`.

    This function crawls the module and gets all classes that inherit
    from BaseEstimator. Classes that are defined in test-modules are not
    included.

    Parameters
    ----------
    type_filter : {""classifier"", ""regressor"", ""cluster"", ""transformer""} \
            or list of such str, default=None
        Which kind of estimators should be returned. If None, no filter is
        applied and all estimators are returned.  Possible values are
        'classifier', 'regressor', 'cluster' and 'transformer' to get
        estimators only of these specific types, or a list of these to
        get the estimators that fit at least one of the types.

    Returns
    -------
    estimators : list of tuples
        List of (name, class), where ``name`` is the class name as string
        and ``class`` is the actual type of the class.
    
    "
2019,"def test_legend_auto5():
    
    fig, axs = plt.subplots(ncols=2, figsize=(9.6, 4.8))

    leg_bboxes = []
    for ax, loc in zip(axs.flat, (""center"", ""best"")):
        # An Ellipse patch at the top, a U-shaped Polygon patch at the
        # bottom and a ring-like Wedge patch: the correct placement of
        # the legend should be in the center.
        for _patch in [
                mpatches.Ellipse(
                    xy=(0.5, 0.9), width=0.8, height=0.2, fc=""C1""),
                mpatches.Polygon(np.array([
                    [0, 1], [0, 0], [1, 0], [1, 1], [0.9, 1.0], [0.9, 0.1],
                    [0.1, 0.1], [0.1, 1.0], [0.1, 1.0]]), fc=""C1""),
                mpatches.Wedge((0.5, 0.5), 0.5, 0, 360, width=0.05, fc=""C0"")
                ]:
            ax.add_patch(_patch)

        ax.plot([0.1, 0.9], [0.9, 0.9], label=""A segment"")  # sthg to label

        leg = ax.legend(loc=loc)
        fig.canvas.draw()
        leg_bboxes.append(
            leg.get_window_extent().transformed(ax.transAxes.inverted()))

    assert_allclose(leg_bboxes[1].bounds, leg_bboxes[0].bounds)


@image_comparison(['legend_various_labels'], remove_text=True)","
    Check that the automatic placement handle a rather complex
    case with non rectangular patch. Related to issue #9580.
    ",18,109,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_legend_auto5():
    
    fig, axs = plt.subplots(ncols=2, figsize=(9.6, 4.8))

    leg_bboxes = []
    for ax, loc in zip(axs.flat, (""center"", ""best"")):
        # An Ellipse patch at the top, a U-shaped Polygon patch at the
        # bottom and a ring-like Wedge patch: the correct placement of
        # the legend should be in the center.
        for _patch in [
                mpatches.Ellipse(
                    xy=(0.5, 0.9), width=0.8, height=0.2, fc=""C1""),
                mpatches.Polygon(np.array([
                    [0, 1], [0, 0], [1, 0], [1, 1], [0.9, 1.0], [0.9, 0.1],
                    [0.1, 0.1], [0.1, 1.0], [0.1, 1.0]]), fc=""C1""),
                mpatches.Wedge((0.5, 0.5), 0.5, 0, 360, width=0.05, fc=""C0"")
                ]:
            ax.add_patch(_patch)

        ax.plot([0.1, 0.9], [0.9, 0.9], label=""A segment"")  # sthg to label

        leg = ax.legend(loc=loc)
        fig.canvas.draw()
        leg_bboxes.append(
            leg.get_window_extent().transformed(ax.transAxes.inverted()))

    assert_allclose(leg_bboxes[1].bounds, leg_bboxes[0].bounds)


@image_comparison(['legend_various_labels'], remove_text=True)
        ```
    ###Assistant : 
    Check that the automatic placement handle a rather complex
    case with non rectangular patch. Related to issue #9580.
    
    "
2020,"def _toggle_cursor_visible(self):
        
        if time.monotonic() - self._last_keypress_time > self.cursor_blink_period:
            self._cursor_blink_visible = not self._cursor_blink_visible
            self.refresh()
","Manages the blinking of the cursor - ensuring blinking only starts when the
        user hasn't pressed a key in some time",21,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _toggle_cursor_visible(self):
        
        if time.monotonic() - self._last_keypress_time > self.cursor_blink_period:
            self._cursor_blink_visible = not self._cursor_blink_visible
            self.refresh()

        ```
    ###Assistant : Manages the blinking of the cursor - ensuring blinking only starts when the
        user hasn't pressed a key in some time
    "
2021,"def parse_semver(version, operator) -> Optional[SemverFilter]:
    
    (operator, negated) = handle_operator_negation(operator)
    try:
        operator = OPERATOR_TO_DJANGO[operator]
    except KeyError:
        raise InvalidSearchQuery(""Invalid operation 'IN' for semantic version filter."")

    version = version if ""@"" in version else f""{SEMVER_FAKE_PACKAGE}@{version}""
    parsed = parse_release_relay(version)
    parsed_version = parsed.get(""version_parsed"")
    if parsed_version:
        # Convert `pre` to always be a string
        prerelease = parsed_version[""pre""] if parsed_version[""pre""] else """"
        semver_filter = SemverFilter(
            operator,
            [
                parsed_version[""major""],
                parsed_version[""minor""],
                parsed_version[""patch""],
                parsed_version[""revision""],
                0 if prerelease else 1,
                prerelease,
            ],
            negated=negated,
        )
        if parsed[""package""] and parsed[""package""] != SEMVER_FAKE_PACKAGE:
            semver_filter.package = parsed[""package""]
        return semver_filter
    else:
        # Try to parse as a wildcard match
        package, version = version.split(""@"", 1)
        version_parts = []
        if version:
            for part in version.split(""."", 3):
                if part in SEMVER_WILDCARDS:
                    break
                try:
                    # We assume all ints for a wildcard match - not handling prerelease as
                    # part of these
                    version_parts.append(int(part))
                except ValueError:
                    raise InvalidSearchQuery(INVALID_SEMVER_MESSAGE)

        package = package if package and package != SEMVER_FAKE_PACKAGE else None
        return SemverFilter(""exact"", version_parts, package, negated)


key_conversion_map: Mapping[
    str,
    Callable[[SearchFilter, str, Mapping[str, Union[int, str, datetime]]], Optional[Sequence[any]]],
] = {
    ""environment"": _environment_filter_converter,
    ""message"": _message_filter_converter,
    TRANSACTION_STATUS_ALIAS: _transaction_status_filter_converter,
    ""issue.id"": _issue_id_filter_converter,
    USER_DISPLAY_ALIAS: _user_display_filter_converter,
    ERROR_UNHANDLED_ALIAS: _error_unhandled_filter_converter,
    ""error.handled"": _error_handled_filter_converter,
    TEAM_KEY_TRANSACTION_ALIAS: _team_key_transaction_filter_converter,
    RELEASE_STAGE_ALIAS: _release_stage_filter_converter,
    SEMVER_ALIAS: _semver_filter_converter,
    SEMVER_PACKAGE_ALIAS: _semver_package_filter_converter,
    SEMVER_BUILD_ALIAS: _semver_build_filter_converter,
}

","
    Attempts to parse a release version using our semver syntax. version should be in
    format `<package_name>@<version>` or `<version>`, where package_name is a string and
    version is a version string matching semver format (https://semver.org/). We've
    slightly extended this format to allow up to 4 integers. EG
     - sentry@1.2.3.4
     - sentry@1.2.3.4-alpha
     - 1.2.3.4
     - 1.2.3.4-alpha
     - 1.*
    ",55,191,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_semver(version, operator) -> Optional[SemverFilter]:
    
    (operator, negated) = handle_operator_negation(operator)
    try:
        operator = OPERATOR_TO_DJANGO[operator]
    except KeyError:
        raise InvalidSearchQuery(""Invalid operation 'IN' for semantic version filter."")

    version = version if ""@"" in version else f""{SEMVER_FAKE_PACKAGE}@{version}""
    parsed = parse_release_relay(version)
    parsed_version = parsed.get(""version_parsed"")
    if parsed_version:
        # Convert `pre` to always be a string
        prerelease = parsed_version[""pre""] if parsed_version[""pre""] else """"
        semver_filter = SemverFilter(
            operator,
            [
                parsed_version[""major""],
                parsed_version[""minor""],
                parsed_version[""patch""],
                parsed_version[""revision""],
                0 if prerelease else 1,
                prerelease,
            ],
            negated=negated,
        )
        if parsed[""package""] and parsed[""package""] != SEMVER_FAKE_PACKAGE:
            semver_filter.package = parsed[""package""]
        return semver_filter
    else:
        # Try to parse as a wildcard match
        package, version = version.split(""@"", 1)
        version_parts = []
        if version:
            for part in version.split(""."", 3):
                if part in SEMVER_WILDCARDS:
                    break
                try:
                    # We assume all ints for a wildcard match - not handling prerelease as
                    # part of these
                    version_parts.append(int(part))
                except ValueError:
                    raise InvalidSearchQuery(INVALID_SEMVER_MESSAGE)

        package = package if package and package != SEMVER_FAKE_PACKAGE else None
        return SemverFilter(""exact"", version_parts, package, negated)


key_conversion_map: Mapping[
    str,
    Callable[[SearchFilter, str, Mapping[str, Union[int, str, datetime]]], Optional[Sequence[any]]],
] = {
    ""environment"": _environment_filter_converter,
    ""message"": _message_filter_converter,
    TRANSACTION_STATUS_ALIAS: _transaction_status_filter_converter,
    ""issue.id"": _issue_id_filter_converter,
    USER_DISPLAY_ALIAS: _user_display_filter_converter,
    ERROR_UNHANDLED_ALIAS: _error_unhandled_filter_converter,
    ""error.handled"": _error_handled_filter_converter,
    TEAM_KEY_TRANSACTION_ALIAS: _team_key_transaction_filter_converter,
    RELEASE_STAGE_ALIAS: _release_stage_filter_converter,
    SEMVER_ALIAS: _semver_filter_converter,
    SEMVER_PACKAGE_ALIAS: _semver_package_filter_converter,
    SEMVER_BUILD_ALIAS: _semver_build_filter_converter,
}


        ```
    ###Assistant : 
    Attempts to parse a release version using our semver syntax. version should be in
    format `<package_name>@<version>` or `<version>`, where package_name is a string and
    version is a version string matching semver format (https://semver.org/). We've
    slightly extended this format to allow up to 4 integers. EG
     - sentry@1.2.3.4
     - sentry@1.2.3.4-alpha
     - 1.2.3.4
     - 1.2.3.4-alpha
     - 1.*
    
    "
2022,"def test_not_logged_in_gives_403_to_ajax_requests(self):
        
        # Get dashboard
        response = self.client.get(
            reverse(""wagtailadmin_home""), HTTP_X_REQUESTED_WITH=""XMLHttpRequest""
        )

        # AJAX requests should be given a 403 error instead of being redirected
        self.assertEqual(response.status_code, 403)
","
        This tests that a not logged in user is given a 403 error on AJAX requests
        ",16,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_not_logged_in_gives_403_to_ajax_requests(self):
        
        # Get dashboard
        response = self.client.get(
            reverse(""wagtailadmin_home""), HTTP_X_REQUESTED_WITH=""XMLHttpRequest""
        )

        # AJAX requests should be given a 403 error instead of being redirected
        self.assertEqual(response.status_code, 403)

        ```
    ###Assistant : 
        This tests that a not logged in user is given a 403 error on AJAX requests
        
    "
2023,"def bytes_to_unicode(self) -> Dict[int, str]:
        
        bs: List[int] = (
            list(range(ord(""!""), ord(""~"") + 1))
            + list(range(ord(""¡""), ord(""¬"") + 1))
            + list(range(ord(""®""), ord(""ÿ"") + 1))
        )
        cs: List[int] = bs[:]
        n = 0
        for b in range(2 ** 8):
            if b not in bs:
                bs.append(b)
                cs.append(2 ** 8 + n)
                n += 1
        str_cs: List[str] = [chr(n) for n in cs]
        return dict(zip(bs, str_cs))
","
        Returns list of utf-8 byte and a corresponding list of unicode strings.

        The reversible bpe codes work on unicode strings. This means you need a large #
        of unicode characters in your vocab if you want to avoid UNKs. When you're at
        something like a 10B token dataset you end up needing around 5K for decent
        coverage. This is a significant percentage of your normal, say, 32K bpe vocab.
        To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
        And avoids mapping to whitespace/control characters the bpe code barfs on.
        ",93,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bytes_to_unicode(self) -> Dict[int, str]:
        
        bs: List[int] = (
            list(range(ord(""!""), ord(""~"") + 1))
            + list(range(ord(""¡""), ord(""¬"") + 1))
            + list(range(ord(""®""), ord(""ÿ"") + 1))
        )
        cs: List[int] = bs[:]
        n = 0
        for b in range(2 ** 8):
            if b not in bs:
                bs.append(b)
                cs.append(2 ** 8 + n)
                n += 1
        str_cs: List[str] = [chr(n) for n in cs]
        return dict(zip(bs, str_cs))

        ```
    ###Assistant : 
        Returns list of utf-8 byte and a corresponding list of unicode strings.

        The reversible bpe codes work on unicode strings. This means you need a large #
        of unicode characters in your vocab if you want to avoid UNKs. When you're at
        something like a 10B token dataset you end up needing around 5K for decent
        coverage. This is a significant percentage of your normal, say, 32K bpe vocab.
        To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
        And avoids mapping to whitespace/control characters the bpe code barfs on.
        
    "
2024,"async def test_get_events_custom_calendars(hass, calendar, get_api_events):
    
    config = dict(CALDAV_CONFIG)
    config[""custom_calendars""] = [
        {""name"": ""Private"", ""calendar"": ""Private"", ""search"": ""This is a normal event""}
    ]

    assert await async_setup_component(hass, ""calendar"", {""calendar"": config})
    await hass.async_block_till_done()

    events = await get_api_events(""calendar.private_private"")
    assert events == [
        {
            ""end"": {""dateTime"": ""2017-11-27T10:00:00-08:00""},
            ""start"": {""dateTime"": ""2017-11-27T09:00:00-08:00""},
            ""summary"": ""This is a normal event"",
            ""location"": ""Hamburg"",
            ""description"": ""Surprisingly rainy"",
            ""uid"": None,
            ""recurrence_id"": None,
            ""rrule"": None,
        }
    ]
",Test that only searched events are returned on API.,9,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_get_events_custom_calendars(hass, calendar, get_api_events):
    
    config = dict(CALDAV_CONFIG)
    config[""custom_calendars""] = [
        {""name"": ""Private"", ""calendar"": ""Private"", ""search"": ""This is a normal event""}
    ]

    assert await async_setup_component(hass, ""calendar"", {""calendar"": config})
    await hass.async_block_till_done()

    events = await get_api_events(""calendar.private_private"")
    assert events == [
        {
            ""end"": {""dateTime"": ""2017-11-27T10:00:00-08:00""},
            ""start"": {""dateTime"": ""2017-11-27T09:00:00-08:00""},
            ""summary"": ""This is a normal event"",
            ""location"": ""Hamburg"",
            ""description"": ""Surprisingly rainy"",
            ""uid"": None,
            ""recurrence_id"": None,
            ""rrule"": None,
        }
    ]

        ```
    ###Assistant : Test that only searched events are returned on API.
    "
2025,"def leaf_symbols(self) -> Iterable[Symbol]:
        
        for arg in self.arguments:
            if isinstance(arg, SymbolicExpression):
                yield from arg.leaf_symbols()
","
        Return a generator of all leaf symbols.

        Useful for when you want to inspect when the symbols come from.
        No deduplication even if the symbols has duplicates.
        ",27,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def leaf_symbols(self) -> Iterable[Symbol]:
        
        for arg in self.arguments:
            if isinstance(arg, SymbolicExpression):
                yield from arg.leaf_symbols()

        ```
    ###Assistant : 
        Return a generator of all leaf symbols.

        Useful for when you want to inspect when the symbols come from.
        No deduplication even if the symbols has duplicates.
        
    "
2026,"def append_step(self, obs, action, next_obs, reward, terminated, truncated, info):
        
        if self._outfile:
            if self._save_info:
                self._current_rollout.append(
                    [obs, action, next_obs, reward, terminated, truncated, info]
                )
            else:
                self._current_rollout.append(
                    [obs, action, next_obs, reward, terminated, truncated]
                )
        self._total_steps += 1


@eval_app.command()","Add a step to the current rollout, if we are saving them",12,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def append_step(self, obs, action, next_obs, reward, terminated, truncated, info):
        
        if self._outfile:
            if self._save_info:
                self._current_rollout.append(
                    [obs, action, next_obs, reward, terminated, truncated, info]
                )
            else:
                self._current_rollout.append(
                    [obs, action, next_obs, reward, terminated, truncated]
                )
        self._total_steps += 1


@eval_app.command()
        ```
    ###Assistant : Add a step to the current rollout, if we are saving them
    "
2027,"def test_nested_prefetch_is_not_overwritten_by_related_object(self):
        
        queryset = House.objects.only('name').prefetch_related(
            Prefetch('rooms', queryset=Room.objects.prefetch_related(
                Prefetch('house', queryset=House.objects.only('address')),
            )),
        )
        with self.assertNumQueries(3):
            house = queryset.first()

        self.assertIs(Room.house.is_cached(self.room), True)
        with self.assertNumQueries(0):
            house.rooms.first().house.address
","
        The prefetched relationship is used rather than populating the reverse
        relationship from the parent, when prefetching a set of child objects
        related to a set of parent objects and the child queryset itself
        specifies a prefetch back to the parent.
        ",40,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_nested_prefetch_is_not_overwritten_by_related_object(self):
        
        queryset = House.objects.only('name').prefetch_related(
            Prefetch('rooms', queryset=Room.objects.prefetch_related(
                Prefetch('house', queryset=House.objects.only('address')),
            )),
        )
        with self.assertNumQueries(3):
            house = queryset.first()

        self.assertIs(Room.house.is_cached(self.room), True)
        with self.assertNumQueries(0):
            house.rooms.first().house.address

        ```
    ###Assistant : 
        The prefetched relationship is used rather than populating the reverse
        relationship from the parent, when prefetching a set of child objects
        related to a set of parent objects and the child queryset itself
        specifies a prefetch back to the parent.
        
    "
2028,"def apply(self, i):
        r
        i = _sympify(i)
        if i.is_integer is False:
            raise NotImplementedError(""{} should be an integer."".format(i))

        n = self.size
        if (i < 0) == True or (i >= n) == True:
            raise NotImplementedError(
                ""{} should be an integer between 0 and {}"".format(i, n-1))

        if i.is_Integer:
            return Integer(self._array_form[i])
        return AppliedPermutation(self, i)
","Apply the permutation to an expression.

        Parameters
        ==========

        i : Expr
            It should be an integer between $0$ and $n-1$ where $n$
            is the size of the permutation.

            If it is a symbol or a symbolic expression that can
            have integer values, an ``AppliedPermutation`` object
            will be returned which can represent an unevaluated
            function.

        Notes
        =====

        Any permutation can be defined as a bijective function
        $\sigma : \{ 0, 1, \dots, n-1 \} \rightarrow \{ 0, 1, \dots, n-1 \}$
        where $n$ denotes the size of the permutation.

        The definition may even be extended for any set with distinctive
        elements, such that the permutation can even be applied for
        real numbers or such, however, it is not implemented for now for
        computational reasons and the integrity with the group theory
        module.

        This function is similar to the ``__call__`` magic, however,
        ``__call__`` magic already has some other applications like
        permuting an array or attaching new cycles, which would
        not always be mathematically consistent.

        This also guarantees that the return type is a SymPy integer,
        which guarantees the safety to use assumptions.
        ",180,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply(self, i):
        r
        i = _sympify(i)
        if i.is_integer is False:
            raise NotImplementedError(""{} should be an integer."".format(i))

        n = self.size
        if (i < 0) == True or (i >= n) == True:
            raise NotImplementedError(
                ""{} should be an integer between 0 and {}"".format(i, n-1))

        if i.is_Integer:
            return Integer(self._array_form[i])
        return AppliedPermutation(self, i)

        ```
    ###Assistant : Apply the permutation to an expression.

        Parameters
        ==========

        i : Expr
            It should be an integer between $0$ and $n-1$ where $n$
            is the size of the permutation.

            If it is a symbol or a symbolic expression that can
            have integer values, an ``AppliedPermutation`` object
            will be returned which can represent an unevaluated
            function.

        Notes
        =====

        Any permutation can be defined as a bijective function
        $\sigma : \{ 0, 1, \dots, n-1 \} \rightarrow \{ 0, 1, \dots, n-1 \}$
        where $n$ denotes the size of the permutation.

        The definition may even be extended for any set with distinctive
        elements, such that the permutation can even be applied for
        real numbers or such, however, it is not implemented for now for
        computational reasons and the integrity with the group theory
        module.

        This function is similar to the ``__call__`` magic, however,
        ``__call__`` magic already has some other applications like
        permuting an array or attaching new cycles, which would
        not always be mathematically consistent.

        This also guarantees that the return type is a SymPy integer,
        which guarantees the safety to use assumptions.
        
    "
2029,"def _convert_mesh_to_triangles(self, coordinates):
        
        if isinstance(coordinates, np.ma.MaskedArray):
            p = coordinates.data
        else:
            p = coordinates

        p_a = p[:-1, :-1]
        p_b = p[:-1, 1:]
        p_c = p[1:, 1:]
        p_d = p[1:, :-1]
        p_center = (p_a + p_b + p_c + p_d) / 4.0
        triangles = np.concatenate([
            p_a, p_b, p_center,
            p_b, p_c, p_center,
            p_c, p_d, p_center,
            p_d, p_a, p_center,
        ], axis=2).reshape((-1, 3, 2))

        c = self.get_facecolor().reshape((*coordinates.shape[:2], 4))
        c_a = c[:-1, :-1]
        c_b = c[:-1, 1:]
        c_c = c[1:, 1:]
        c_d = c[1:, :-1]
        c_center = (c_a + c_b + c_c + c_d) / 4.0
        colors = np.concatenate([
            c_a, c_b, c_center,
            c_b, c_c, c_center,
            c_c, c_d, c_center,
            c_d, c_a, c_center,
        ], axis=2).reshape((-1, 3, 4))

        return triangles, colors
","
        Convert a given mesh into a sequence of triangles, each point
        with its own color.  The result can be used to construct a call to
        `~.RendererBase.draw_gouraud_triangles`.
        ",26,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _convert_mesh_to_triangles(self, coordinates):
        
        if isinstance(coordinates, np.ma.MaskedArray):
            p = coordinates.data
        else:
            p = coordinates

        p_a = p[:-1, :-1]
        p_b = p[:-1, 1:]
        p_c = p[1:, 1:]
        p_d = p[1:, :-1]
        p_center = (p_a + p_b + p_c + p_d) / 4.0
        triangles = np.concatenate([
            p_a, p_b, p_center,
            p_b, p_c, p_center,
            p_c, p_d, p_center,
            p_d, p_a, p_center,
        ], axis=2).reshape((-1, 3, 2))

        c = self.get_facecolor().reshape((*coordinates.shape[:2], 4))
        c_a = c[:-1, :-1]
        c_b = c[:-1, 1:]
        c_c = c[1:, 1:]
        c_d = c[1:, :-1]
        c_center = (c_a + c_b + c_c + c_d) / 4.0
        colors = np.concatenate([
            c_a, c_b, c_center,
            c_b, c_c, c_center,
            c_c, c_d, c_center,
            c_d, c_a, c_center,
        ], axis=2).reshape((-1, 3, 4))

        return triangles, colors

        ```
    ###Assistant : 
        Convert a given mesh into a sequence of triangles, each point
        with its own color.  The result can be used to construct a call to
        `~.RendererBase.draw_gouraud_triangles`.
        
    "
2030,"def count(self, level=None):
        
        if level is None:
            return notna(self._values).sum().astype(""int64"")
        else:
            warnings.warn(
                ""Using the level keyword in DataFrame and Series aggregations is ""
                ""deprecated and will be removed in a future version. Use groupby ""
                ""instead. ser.count(level=1) should use ser.groupby(level=1).count()."",
                FutureWarning,
                stacklevel=find_stack_level(),
            )
            if not isinstance(self.index, MultiIndex):
                raise ValueError(""Series.count level is only valid with a MultiIndex"")

        index = self.index
        assert isinstance(index, MultiIndex)  # for mypy

        if isinstance(level, str):
            level = index._get_level_number(level)

        lev = index.levels[level]
        level_codes = np.array(index.codes[level], subok=False, copy=True)

        mask = level_codes == -1
        if mask.any():
            level_codes[mask] = cnt = len(lev)
            lev = lev.insert(cnt, lev._na_value)

        obs = level_codes[notna(self._values)]
        # Argument ""minlength"" to ""bincount"" has incompatible type ""Optional[int]"";
        # expected ""SupportsIndex""  [arg-type]
        out = np.bincount(obs, minlength=len(lev) or None)  # type: ignore[arg-type]
        return self._constructor(out, index=lev, dtype=""int64"").__finalize__(
            self, method=""count""
        )
","
        Return number of non-NA/null observations in the Series.

        Parameters
        ----------
        level : int or level name, default None
            If the axis is a MultiIndex (hierarchical), count along a
            particular level, collapsing into a smaller Series.

        Returns
        -------
        int or Series (if level specified)
            Number of non-null values in the Series.

        See Also
        --------
        DataFrame.count : Count non-NA cells for each column or row.

        Examples
        --------
        >>> s = pd.Series([0.0, 1.0, np.nan])
        >>> s.count()
        2
        ",74,126,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def count(self, level=None):
        
        if level is None:
            return notna(self._values).sum().astype(""int64"")
        else:
            warnings.warn(
                ""Using the level keyword in DataFrame and Series aggregations is ""
                ""deprecated and will be removed in a future version. Use groupby ""
                ""instead. ser.count(level=1) should use ser.groupby(level=1).count()."",
                FutureWarning,
                stacklevel=find_stack_level(),
            )
            if not isinstance(self.index, MultiIndex):
                raise ValueError(""Series.count level is only valid with a MultiIndex"")

        index = self.index
        assert isinstance(index, MultiIndex)  # for mypy

        if isinstance(level, str):
            level = index._get_level_number(level)

        lev = index.levels[level]
        level_codes = np.array(index.codes[level], subok=False, copy=True)

        mask = level_codes == -1
        if mask.any():
            level_codes[mask] = cnt = len(lev)
            lev = lev.insert(cnt, lev._na_value)

        obs = level_codes[notna(self._values)]
        # Argument ""minlength"" to ""bincount"" has incompatible type ""Optional[int]"";
        # expected ""SupportsIndex""  [arg-type]
        out = np.bincount(obs, minlength=len(lev) or None)  # type: ignore[arg-type]
        return self._constructor(out, index=lev, dtype=""int64"").__finalize__(
            self, method=""count""
        )

        ```
    ###Assistant : 
        Return number of non-NA/null observations in the Series.

        Parameters
        ----------
        level : int or level name, default None
            If the axis is a MultiIndex (hierarchical), count along a
            particular level, collapsing into a smaller Series.

        Returns
        -------
        int or Series (if level specified)
            Number of non-null values in the Series.

        See Also
        --------
        DataFrame.count : Count non-NA cells for each column or row.

        Examples
        --------
        >>> s = pd.Series([0.0, 1.0, np.nan])
        >>> s.count()
        2
        
    "
2031,"def __format__(self, specifier, context=None, _localeconv=None):
        

        # Note: PEP 3101 says that if the type is not present then
        # there should be at least one digit after the decimal point.
        # We take the liberty of ignoring this requirement for
        # Decimal---it's presumably there to make sure that
        # format(float, '') behaves similarly to str(float).
        if context is None:
            context = getcontext()

        spec = _parse_format_specifier(specifier, _localeconv=_localeconv)

        # special values don't care about the type or precision
        if self._is_special:
            sign = _format_sign(self._sign, spec)
            body = str(self.copy_abs())
            if spec['type'] == '%':
                body += '%'
            return _format_align(sign, body, spec)

        # a type of None defaults to 'g' or 'G', depending on context
        if spec['type'] is None:
            spec['type'] = ['g', 'G'][context.capitals]

        # if type is '%', adjust exponent of self accordingly
        if spec['type'] == '%':
            self = _dec_from_triple(self._sign, self._int, self._exp+2)

        # round if necessary, taking rounding mode from the context
        rounding = context.rounding
        precision = spec['precision']
        if precision is not None:
            if spec['type'] in 'eE':
                self = self._round(precision+1, rounding)
            elif spec['type'] in 'fF%':
                self = self._rescale(-precision, rounding)
            elif spec['type'] in 'gG' and len(self._int) > precision:
                self = self._round(precision, rounding)
        # special case: zeros with a positive exponent can't be
        # represented in fixed point; rescale them to 0e0.
        if not self and self._exp > 0 and spec['type'] in 'fF%':
            self = self._rescale(0, rounding)

        # figure out placement of the decimal point
        leftdigits = self._exp + len(self._int)
        if spec['type'] in 'eE':
            if not self and precision is not None:
                dotplace = 1 - precision
            else:
                dotplace = 1
        elif spec['type'] in 'fF%':
            dotplace = leftdigits
        elif spec['type'] in 'gG':
            if self._exp <= 0 and leftdigits > -6:
                dotplace = leftdigits
            else:
                dotplace = 1

        # find digits before and after decimal point, and get exponent
        if dotplace < 0:
            intpart = '0'
            fracpart = '0'*(-dotplace) + self._int
        elif dotplace > len(self._int):
            intpart = self._int + '0'*(dotplace-len(self._int))
            fracpart = ''
        else:
            intpart = self._int[:dotplace] or '0'
            fracpart = self._int[dotplace:]
        exp = leftdigits-dotplace

        # done with the decimal-specific stuff;  hand over the rest
        # of the formatting to the _format_number function
        return _format_number(self._sign, intpart, fracpart, exp, spec)
","Format a Decimal instance according to the given specifier.

        The specifier should be a standard format specifier, with the
        form described in PEP 3101.  Formatting types 'e', 'E', 'f',
        'F', 'g', 'G', 'n' and '%' are supported.  If the formatting
        type is omitted it defaults to 'g' or 'G', depending on the
        value of context.capitals.
        ",55,350,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __format__(self, specifier, context=None, _localeconv=None):
        

        # Note: PEP 3101 says that if the type is not present then
        # there should be at least one digit after the decimal point.
        # We take the liberty of ignoring this requirement for
        # Decimal---it's presumably there to make sure that
        # format(float, '') behaves similarly to str(float).
        if context is None:
            context = getcontext()

        spec = _parse_format_specifier(specifier, _localeconv=_localeconv)

        # special values don't care about the type or precision
        if self._is_special:
            sign = _format_sign(self._sign, spec)
            body = str(self.copy_abs())
            if spec['type'] == '%':
                body += '%'
            return _format_align(sign, body, spec)

        # a type of None defaults to 'g' or 'G', depending on context
        if spec['type'] is None:
            spec['type'] = ['g', 'G'][context.capitals]

        # if type is '%', adjust exponent of self accordingly
        if spec['type'] == '%':
            self = _dec_from_triple(self._sign, self._int, self._exp+2)

        # round if necessary, taking rounding mode from the context
        rounding = context.rounding
        precision = spec['precision']
        if precision is not None:
            if spec['type'] in 'eE':
                self = self._round(precision+1, rounding)
            elif spec['type'] in 'fF%':
                self = self._rescale(-precision, rounding)
            elif spec['type'] in 'gG' and len(self._int) > precision:
                self = self._round(precision, rounding)
        # special case: zeros with a positive exponent can't be
        # represented in fixed point; rescale them to 0e0.
        if not self and self._exp > 0 and spec['type'] in 'fF%':
            self = self._rescale(0, rounding)

        # figure out placement of the decimal point
        leftdigits = self._exp + len(self._int)
        if spec['type'] in 'eE':
            if not self and precision is not None:
                dotplace = 1 - precision
            else:
                dotplace = 1
        elif spec['type'] in 'fF%':
            dotplace = leftdigits
        elif spec['type'] in 'gG':
            if self._exp <= 0 and leftdigits > -6:
                dotplace = leftdigits
            else:
                dotplace = 1

        # find digits before and after decimal point, and get exponent
        if dotplace < 0:
            intpart = '0'
            fracpart = '0'*(-dotplace) + self._int
        elif dotplace > len(self._int):
            intpart = self._int + '0'*(dotplace-len(self._int))
            fracpart = ''
        else:
            intpart = self._int[:dotplace] or '0'
            fracpart = self._int[dotplace:]
        exp = leftdigits-dotplace

        # done with the decimal-specific stuff;  hand over the rest
        # of the formatting to the _format_number function
        return _format_number(self._sign, intpart, fracpart, exp, spec)

        ```
    ###Assistant : Format a Decimal instance according to the given specifier.

        The specifier should be a standard format specifier, with the
        form described in PEP 3101.  Formatting types 'e', 'E', 'f',
        'F', 'g', 'G', 'n' and '%' are supported.  If the formatting
        type is omitted it defaults to 'g' or 'G', depending on the
        value of context.capitals.
        
    "
2032,"def test_feature_names_in():
    
    pd = pytest.importorskip(""pandas"")
    iris = datasets.load_iris()
    X_np = iris.data
    df = pd.DataFrame(X_np, columns=iris.feature_names)
",Check that feature_name_in are recorded by `_validate_data`,7,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_feature_names_in():
    
    pd = pytest.importorskip(""pandas"")
    iris = datasets.load_iris()
    X_np = iris.data
    df = pd.DataFrame(X_np, columns=iris.feature_names)

        ```
    ###Assistant : Check that feature_name_in are recorded by `_validate_data`
    "
2033,"def check_interactive_compatibility(self):
        
        from pytorch_lightning.utilities import _IS_INTERACTIVE

        if _IS_INTERACTIVE and self._strategy_type is not None and not self._strategy_type.is_interactive_compatible():
            raise MisconfigurationException(
                f""`Trainer(strategy={self._strategy_type.value!r})` or""
                f"" `Trainer(accelerator={self._strategy_type.value!r})` is not compatible with an interactive""
                "" environment. Run your code as a script, or choose one of the compatible backends:""
                f"" {', '.join(_StrategyType.interactive_compatible_types())}.""
                "" In case you are spawning processes yourself, make sure to include the Trainer""
                "" creation inside the worker function.""
            )
","Raises a `MisconfigurationException` if the accelerator and/or plugin is not compatible with an
        interactive environment.",15,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_interactive_compatibility(self):
        
        from pytorch_lightning.utilities import _IS_INTERACTIVE

        if _IS_INTERACTIVE and self._strategy_type is not None and not self._strategy_type.is_interactive_compatible():
            raise MisconfigurationException(
                f""`Trainer(strategy={self._strategy_type.value!r})` or""
                f"" `Trainer(accelerator={self._strategy_type.value!r})` is not compatible with an interactive""
                "" environment. Run your code as a script, or choose one of the compatible backends:""
                f"" {', '.join(_StrategyType.interactive_compatible_types())}.""
                "" In case you are spawning processes yourself, make sure to include the Trainer""
                "" creation inside the worker function.""
            )

        ```
    ###Assistant : Raises a `MisconfigurationException` if the accelerator and/or plugin is not compatible with an
        interactive environment.
    "
2034,"def theme_global(new_theme=None):
    
    if new_theme is not None:
        if new_theme not in theme_list():
            popup_error_with_traceback('Cannot use custom themes with theme_global call',
                                       'Your request to use theme {} cannot be performed.'.format(new_theme),
                                       'The PySimpleGUI Global User Settings are meant for PySimpleGUI standard items, not user config items',
                                       'You can use any of the many built-in themes instead or use your own UserSettings file to store your custom theme')
            return pysimplegui_user_settings.get('-theme-', CURRENT_LOOK_AND_FEEL)
        pysimplegui_user_settings.set('-theme-', new_theme)
        theme(new_theme)
        return new_theme
    else:
        return pysimplegui_user_settings.get('-theme-', CURRENT_LOOK_AND_FEEL)

","
    Sets / Gets the global PySimpleGUI Theme.  If none is specified then returns the global theme from user settings.
    Note the theme must be a standard, built-in PySimpleGUI theme... not a user-created theme.

    :param new_theme: the new theme name to use
    :type new_theme:  (str)
    :return:          the currently selected theme
    :rtype:           (str)
    ",51,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def theme_global(new_theme=None):
    
    if new_theme is not None:
        if new_theme not in theme_list():
            popup_error_with_traceback('Cannot use custom themes with theme_global call',
                                       'Your request to use theme {} cannot be performed.'.format(new_theme),
                                       'The PySimpleGUI Global User Settings are meant for PySimpleGUI standard items, not user config items',
                                       'You can use any of the many built-in themes instead or use your own UserSettings file to store your custom theme')
            return pysimplegui_user_settings.get('-theme-', CURRENT_LOOK_AND_FEEL)
        pysimplegui_user_settings.set('-theme-', new_theme)
        theme(new_theme)
        return new_theme
    else:
        return pysimplegui_user_settings.get('-theme-', CURRENT_LOOK_AND_FEEL)


        ```
    ###Assistant : 
    Sets / Gets the global PySimpleGUI Theme.  If none is specified then returns the global theme from user settings.
    Note the theme must be a standard, built-in PySimpleGUI theme... not a user-created theme.

    :param new_theme: the new theme name to use
    :type new_theme:  (str)
    :return:          the currently selected theme
    :rtype:           (str)
    
    "
2035,"def close(self):
        
        self._reset_retries()
        self._closed = True

        # Chunked-encoded posts are terminated with '0\r\n\r\n'
        # For some reason, either Python or node.js seems to
        # require an extra \r\n.
        try:
            self._conn.send(""\r\n0\r\n\r\n"".encode(""utf-8""))
        except http_client.socket.error:
            # In case the socket has already been closed
            return """"

        return self._getresponse()
","Close the connection to server.

        If available, return a http_client.HTTPResponse object.

        Closing the connection involves sending the
        Transfer-Encoding terminating bytes.
        ",20,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def close(self):
        
        self._reset_retries()
        self._closed = True

        # Chunked-encoded posts are terminated with '0\r\n\r\n'
        # For some reason, either Python or node.js seems to
        # require an extra \r\n.
        try:
            self._conn.send(""\r\n0\r\n\r\n"".encode(""utf-8""))
        except http_client.socket.error:
            # In case the socket has already been closed
            return """"

        return self._getresponse()

        ```
    ###Assistant : Close the connection to server.

        If available, return a http_client.HTTPResponse object.

        Closing the connection involves sending the
        Transfer-Encoding terminating bytes.
        
    "
2036,"def _get_svc_path(name=""*"", status=None):
    

    # This is the core routine to work with services, called by many
    # other functions of this module.
    #
    # The name of a service is the ""apparent"" folder's name that contains its
    # ""run"" script. If its ""folder"" is a symlink, the service is an ""alias"" of
    # the targeted service.

    if not SERVICE_DIR:
        raise CommandExecutionError(""Could not find service directory."")

    # path list of enabled services as /AVAIL_SVR_DIRS/$service,
    # taking care of any service aliases (do not use os.path.realpath()).
    ena = set()
    for el in glob.glob(os.path.join(SERVICE_DIR, name)):
        if _is_svc(el):
            if os.path.islink(el):
                ena.add(os.readlink(el))
            else:
                ena.add(el)
            log.trace(""found enabled service path: %s"", el)

    if status == ""ENABLED"":
        return sorted(ena)

    # path list of available services as /AVAIL_SVR_DIRS/$service
    ava = set()
    for d in AVAIL_SVR_DIRS:
        for el in glob.glob(os.path.join(d, name)):
            if _is_svc(el):
                ava.add(el)
                log.trace(""found available service path: %s"", el)

    if status == ""DISABLED"":
        # service available but not enabled
        ret = ava.difference(ena)
    else:
        # default: return available services
        ret = ava.union(ena)

    return sorted(ret)

","
    Return a list of paths to services with ``name`` that have the specified ``status``

    name
        a glob for service name. default is '*'

    status
        None       : all services (no filter, default choice)
        'DISABLED' : available service(s) that is not enabled
        'ENABLED'  : enabled service (whether started on boot or not)
    ",50,164,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_svc_path(name=""*"", status=None):
    

    # This is the core routine to work with services, called by many
    # other functions of this module.
    #
    # The name of a service is the ""apparent"" folder's name that contains its
    # ""run"" script. If its ""folder"" is a symlink, the service is an ""alias"" of
    # the targeted service.

    if not SERVICE_DIR:
        raise CommandExecutionError(""Could not find service directory."")

    # path list of enabled services as /AVAIL_SVR_DIRS/$service,
    # taking care of any service aliases (do not use os.path.realpath()).
    ena = set()
    for el in glob.glob(os.path.join(SERVICE_DIR, name)):
        if _is_svc(el):
            if os.path.islink(el):
                ena.add(os.readlink(el))
            else:
                ena.add(el)
            log.trace(""found enabled service path: %s"", el)

    if status == ""ENABLED"":
        return sorted(ena)

    # path list of available services as /AVAIL_SVR_DIRS/$service
    ava = set()
    for d in AVAIL_SVR_DIRS:
        for el in glob.glob(os.path.join(d, name)):
            if _is_svc(el):
                ava.add(el)
                log.trace(""found available service path: %s"", el)

    if status == ""DISABLED"":
        # service available but not enabled
        ret = ava.difference(ena)
    else:
        # default: return available services
        ret = ava.union(ena)

    return sorted(ret)


        ```
    ###Assistant : 
    Return a list of paths to services with ``name`` that have the specified ``status``

    name
        a glob for service name. default is '*'

    status
        None       : all services (no filter, default choice)
        'DISABLED' : available service(s) that is not enabled
        'ENABLED'  : enabled service (whether started on boot or not)
    
    "
2037,"def test_model_torch_save_ddp_cpu(tmpdir):
    
    model = BoringModel()
    num_epochs = 1
    trainer = Trainer(
        default_root_dir=tmpdir, max_epochs=num_epochs, strategy=""ddp_spawn"", accelerator=""cpu"", devices=2, logger=False
    )
    temp_path = os.path.join(tmpdir, ""temp.pt"")
    trainer.fit(model)

    # Ensure these do not fail
    torch.save(trainer.model, temp_path)
    torch.save(trainer, temp_path)


@RunIf(min_gpus=2)",Test to ensure torch save does not fail for model and trainer using cpu ddp.,15,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_model_torch_save_ddp_cpu(tmpdir):
    
    model = BoringModel()
    num_epochs = 1
    trainer = Trainer(
        default_root_dir=tmpdir, max_epochs=num_epochs, strategy=""ddp_spawn"", accelerator=""cpu"", devices=2, logger=False
    )
    temp_path = os.path.join(tmpdir, ""temp.pt"")
    trainer.fit(model)

    # Ensure these do not fail
    torch.save(trainer.model, temp_path)
    torch.save(trainer, temp_path)


@RunIf(min_gpus=2)
        ```
    ###Assistant : Test to ensure torch save does not fail for model and trainer using cpu ddp.
    "
2038,"def default_batch_size(self) -> int:
        
        # Using 2 avoid ONNX making assumption about single sample batch
        return OnnxConfig.default_fixed_batch
","
        The default batch size to use if no other indication

        Returns:
            Integer > 0
        ",14,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def default_batch_size(self) -> int:
        
        # Using 2 avoid ONNX making assumption about single sample batch
        return OnnxConfig.default_fixed_batch

        ```
    ###Assistant : 
        The default batch size to use if no other indication

        Returns:
            Integer > 0
        
    "
2039,"def addMacOSCodeSignature(filenames):
    

    # Weak signing.
    identity = getMacOSSigningIdentity()

    command = [
        ""codesign"",
        ""-s"",
        identity,
        ""--force"",
        ""--deep"",
        ""--preserve-metadata=entitlements"",
    ]

    assert type(filenames) is not str
    command.extend(filenames)

    with withMadeWritableFileMode(filenames):
        executeToolChecked(
            logger=postprocessing_logger,
            command=command,
            absence_message=macos_codesign_usage,
            stderr_filter=_filterSigntoolErrorOutput,
        )
","Remove the code signature from a filename.

    Args:
        filenames - The files to be signed.

    Returns:
        None

    Notes:
        This is macOS specific.
    ",22,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def addMacOSCodeSignature(filenames):
    

    # Weak signing.
    identity = getMacOSSigningIdentity()

    command = [
        ""codesign"",
        ""-s"",
        identity,
        ""--force"",
        ""--deep"",
        ""--preserve-metadata=entitlements"",
    ]

    assert type(filenames) is not str
    command.extend(filenames)

    with withMadeWritableFileMode(filenames):
        executeToolChecked(
            logger=postprocessing_logger,
            command=command,
            absence_message=macos_codesign_usage,
            stderr_filter=_filterSigntoolErrorOutput,
        )

        ```
    ###Assistant : Remove the code signature from a filename.

    Args:
        filenames - The files to be signed.

    Returns:
        None

    Notes:
        This is macOS specific.
    
    "
2040,"def exception_handler(exc, context):
    
    if isinstance(exc, Http404):
        exc = exceptions.NotFound(*(exc.args))
    elif isinstance(exc, PermissionDenied):
        exc = exceptions.PermissionDenied(*(exc.args))

    if isinstance(exc, exceptions.APIException):
        headers = {}
        if getattr(exc, 'auth_header', None):
            headers['WWW-Authenticate'] = exc.auth_header
        if getattr(exc, 'wait', None):
            headers['Retry-After'] = '%d' % exc.wait

        if isinstance(exc.detail, (list, dict)):
            data = exc.detail
        else:
            data = {'detail': exc.detail}

        set_rollback()
        return Response(data, status=exc.status_code, headers=headers)

    return None

","
    Returns the response that should be used for any given exception.

    By default we handle the REST framework `APIException`, and also
    Django's built-in `Http404` and `PermissionDenied` exceptions.

    Any unhandled exceptions may return `None`, which will cause a 500 error
    to be raised.
    ",42,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def exception_handler(exc, context):
    
    if isinstance(exc, Http404):
        exc = exceptions.NotFound(*(exc.args))
    elif isinstance(exc, PermissionDenied):
        exc = exceptions.PermissionDenied(*(exc.args))

    if isinstance(exc, exceptions.APIException):
        headers = {}
        if getattr(exc, 'auth_header', None):
            headers['WWW-Authenticate'] = exc.auth_header
        if getattr(exc, 'wait', None):
            headers['Retry-After'] = '%d' % exc.wait

        if isinstance(exc.detail, (list, dict)):
            data = exc.detail
        else:
            data = {'detail': exc.detail}

        set_rollback()
        return Response(data, status=exc.status_code, headers=headers)

    return None


        ```
    ###Assistant : 
    Returns the response that should be used for any given exception.

    By default we handle the REST framework `APIException`, and also
    Django's built-in `Http404` and `PermissionDenied` exceptions.

    Any unhandled exceptions may return `None`, which will cause a 500 error
    to be raised.
    
    "
2041,"def compute_cooccurrence_matrix(self, df):
        

        user_item_hits = sparse.coo_matrix(
            (np.repeat(1, df.shape[0]), (df[self.col_user_id], df[self.col_item_id])),
            shape=(self.n_users, self.n_items),
        ).tocsr()

        item_cooccurrence = user_item_hits.transpose().dot(user_item_hits)
        item_cooccurrence = item_cooccurrence.multiply(
            item_cooccurrence >= self.threshold
        )

        return item_cooccurrence.astype(df[self.col_rating].dtype)
","Co-occurrence matrix.

        The co-occurrence matrix is defined as :math:`C = U^T * U`

        where U is the user_affinity matrix with 1's as values (instead of ratings).

        Args:
            df (pandas.DataFrame): DataFrame of users and items

        Returns:
            numpy.ndarray: Co-occurrence matrix
        ",38,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compute_cooccurrence_matrix(self, df):
        

        user_item_hits = sparse.coo_matrix(
            (np.repeat(1, df.shape[0]), (df[self.col_user_id], df[self.col_item_id])),
            shape=(self.n_users, self.n_items),
        ).tocsr()

        item_cooccurrence = user_item_hits.transpose().dot(user_item_hits)
        item_cooccurrence = item_cooccurrence.multiply(
            item_cooccurrence >= self.threshold
        )

        return item_cooccurrence.astype(df[self.col_rating].dtype)

        ```
    ###Assistant : Co-occurrence matrix.

        The co-occurrence matrix is defined as :math:`C = U^T * U`

        where U is the user_affinity matrix with 1's as values (instead of ratings).

        Args:
            df (pandas.DataFrame): DataFrame of users and items

        Returns:
            numpy.ndarray: Co-occurrence matrix
        
    "
2042,"def _makeTags(tagStr, xml, suppress_LT=Suppress(""<""), suppress_GT=Suppress("">"")):
    
    if isinstance(tagStr, str_type):
        resname = tagStr
        tagStr = Keyword(tagStr, caseless=not xml)
    else:
        resname = tagStr.name

    tagAttrName = Word(alphas, alphanums + ""_-:"")
    if xml:
        tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes)
        openTag = (
            suppress_LT
            + tagStr(""tag"")
            + Dict(ZeroOrMore(Group(tagAttrName + Suppress(""="") + tagAttrValue)))
            + Opt(""/"", default=[False])(""empty"").set_parse_action(
                lambda s, l, t: t[0] == ""/""
            )
            + suppress_GT
        )
    else:
        tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word(
            printables, exclude_chars="">""
        )
        openTag = (
            suppress_LT
            + tagStr(""tag"")
            + Dict(
                ZeroOrMore(
                    Group(
                        tagAttrName.set_parse_action(lambda t: t[0].lower())
                        + Opt(Suppress(""="") + tagAttrValue)
                    )
                )
            )
            + Opt(""/"", default=[False])(""empty"").set_parse_action(
                lambda s, l, t: t[0] == ""/""
            )
            + suppress_GT
        )
    closeTag = Combine(Literal(""</"") + tagStr + "">"", adjacent=False)

    openTag.set_name(""<%s>"" % resname)
    # add start<tagname> results name in parse action now that ungrouped names are not reported at two levels
    openTag.add_parse_action(
        lambda t: t.__setitem__(
            ""start"" + """".join(resname.replace("":"", "" "").title().split()), t.copy()
        )
    )
    closeTag = closeTag(
        ""end"" + """".join(resname.replace("":"", "" "").title().split())
    ).set_name(""</%s>"" % resname)
    openTag.tag = resname
    closeTag.tag = resname
    openTag.tag_body = SkipTo(closeTag())
    return openTag, closeTag

","Internal helper to construct opening and closing tag expressions, given a tag name",13,164,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _makeTags(tagStr, xml, suppress_LT=Suppress(""<""), suppress_GT=Suppress("">"")):
    
    if isinstance(tagStr, str_type):
        resname = tagStr
        tagStr = Keyword(tagStr, caseless=not xml)
    else:
        resname = tagStr.name

    tagAttrName = Word(alphas, alphanums + ""_-:"")
    if xml:
        tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes)
        openTag = (
            suppress_LT
            + tagStr(""tag"")
            + Dict(ZeroOrMore(Group(tagAttrName + Suppress(""="") + tagAttrValue)))
            + Opt(""/"", default=[False])(""empty"").set_parse_action(
                lambda s, l, t: t[0] == ""/""
            )
            + suppress_GT
        )
    else:
        tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word(
            printables, exclude_chars="">""
        )
        openTag = (
            suppress_LT
            + tagStr(""tag"")
            + Dict(
                ZeroOrMore(
                    Group(
                        tagAttrName.set_parse_action(lambda t: t[0].lower())
                        + Opt(Suppress(""="") + tagAttrValue)
                    )
                )
            )
            + Opt(""/"", default=[False])(""empty"").set_parse_action(
                lambda s, l, t: t[0] == ""/""
            )
            + suppress_GT
        )
    closeTag = Combine(Literal(""</"") + tagStr + "">"", adjacent=False)

    openTag.set_name(""<%s>"" % resname)
    # add start<tagname> results name in parse action now that ungrouped names are not reported at two levels
    openTag.add_parse_action(
        lambda t: t.__setitem__(
            ""start"" + """".join(resname.replace("":"", "" "").title().split()), t.copy()
        )
    )
    closeTag = closeTag(
        ""end"" + """".join(resname.replace("":"", "" "").title().split())
    ).set_name(""</%s>"" % resname)
    openTag.tag = resname
    closeTag.tag = resname
    openTag.tag_body = SkipTo(closeTag())
    return openTag, closeTag


        ```
    ###Assistant : Internal helper to construct opening and closing tag expressions, given a tag name
    "
2043,"def load_data(label_mode=""fine""):
    
    if label_mode not in [""fine"", ""coarse""]:
        raise ValueError(
            '`label_mode` must be one of `""fine""`, `""coarse""`. '
            f""Received: label_mode={label_mode}.""
        )

    dirname = ""cifar-100-python""
    origin = ""https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz""
    path = get_file(
        dirname,
        origin=origin,
        untar=True,
        file_hash=""85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7"",
    )

    fpath = os.path.join(path, ""train"")
    x_train, y_train = load_batch(fpath, label_key=label_mode + ""_labels"")

    fpath = os.path.join(path, ""test"")
    x_test, y_test = load_batch(fpath, label_key=label_mode + ""_labels"")

    y_train = np.reshape(y_train, (len(y_train), 1))
    y_test = np.reshape(y_test, (len(y_test), 1))

    if backend.image_data_format() == ""channels_last"":
        x_train = x_train.transpose(0, 2, 3, 1)
        x_test = x_test.transpose(0, 2, 3, 1)

    return (x_train, y_train), (x_test, y_test)
","Loads the CIFAR100 dataset.

    This is a dataset of 50,000 32x32 color training images and
    10,000 test images, labeled over 100 fine-grained classes that are
    grouped into 20 coarse-grained classes. See more info at the
    [CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html).

    Args:
      label_mode: one of ""fine"", ""coarse"". If it is ""fine"" the category labels
        are the fine-grained labels, if it is ""coarse"" the output labels are the
        coarse-grained superclasses.

    Returns:
      Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.

    **x_train**: uint8 NumPy array of grayscale image data with shapes
      `(50000, 32, 32, 3)`, containing the training data. Pixel values range
      from 0 to 255.

    **y_train**: uint8 NumPy array of labels (integers in range 0-99)
      with shape `(50000, 1)` for the training data.

    **x_test**: uint8 NumPy array of grayscale image data with shapes
      `(10000, 32, 32, 3)`, containing the test data. Pixel values range
      from 0 to 255.

    **y_test**: uint8 NumPy array of labels (integers in range 0-99)
      with shape `(10000, 1)` for the test data.

    Example:

    ```python
    (x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()
    assert x_train.shape == (50000, 32, 32, 3)
    assert x_test.shape == (10000, 32, 32, 3)
    assert y_train.shape == (50000, 1)
    assert y_test.shape == (10000, 1)
    ```
    ",193,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_data(label_mode=""fine""):
    
    if label_mode not in [""fine"", ""coarse""]:
        raise ValueError(
            '`label_mode` must be one of `""fine""`, `""coarse""`. '
            f""Received: label_mode={label_mode}.""
        )

    dirname = ""cifar-100-python""
    origin = ""https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz""
    path = get_file(
        dirname,
        origin=origin,
        untar=True,
        file_hash=""85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7"",
    )

    fpath = os.path.join(path, ""train"")
    x_train, y_train = load_batch(fpath, label_key=label_mode + ""_labels"")

    fpath = os.path.join(path, ""test"")
    x_test, y_test = load_batch(fpath, label_key=label_mode + ""_labels"")

    y_train = np.reshape(y_train, (len(y_train), 1))
    y_test = np.reshape(y_test, (len(y_test), 1))

    if backend.image_data_format() == ""channels_last"":
        x_train = x_train.transpose(0, 2, 3, 1)
        x_test = x_test.transpose(0, 2, 3, 1)

    return (x_train, y_train), (x_test, y_test)

        ```
    ###Assistant : Loads the CIFAR100 dataset.

    This is a dataset of 50,000 32x32 color training images and
    10,000 test images, labeled over 100 fine-grained classes that are
    grouped into 20 coarse-grained classes. See more info at the
    [CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html).

    Args:
      label_mode: one of ""fine"", ""coarse"". If it is ""fine"" the category labels
        are the fine-grained labels, if it is ""coarse"" the output labels are the
        coarse-grained superclasses.

    Returns:
      Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.

    **x_train**: uint8 NumPy array of grayscale image data with shapes
      `(50000, 32, 32, 3)`, containing the training data. Pixel values range
      from 0 to 255.

    **y_train**: uint8 NumPy array of labels (integers in range 0-99)
      with shape `(50000, 1)` for the training data.

    **x_test**: uint8 NumPy array of grayscale image data with shapes
      `(10000, 32, 32, 3)`, containing the test data. Pixel values range
      from 0 to 255.

    **y_test**: uint8 NumPy array of labels (integers in range 0-99)
      with shape `(10000, 1)` for the test data.

    Example:

    ```python
    (x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()
    assert x_train.shape == (50000, 32, 32, 3)
    assert x_test.shape == (10000, 32, 32, 3)
    assert y_train.shape == (50000, 1)
    assert y_test.shape == (10000, 1)
    ```
    
    "
2044,"def find_airflow_sources_root() -> Path:
    
    default_airflow_sources_root = Path.cwd()
    # Try to find airflow sources in current working dir
    airflow_sources_root = search_upwards_for_airflow_sources_root(Path.cwd())
    if not airflow_sources_root:
        # Or if it fails, find it in parents of the directory where the ./breeze.py is.
        airflow_sources_root = search_upwards_for_airflow_sources_root(Path(__file__).resolve().parent)
    if airflow_sources_root:
        os.chdir(airflow_sources_root)
        return Path(airflow_sources_root)
    else:
        console.print(
            f""\n[bright_yellow]Could not find Airflow sources location. ""
            f""Assuming {default_airflow_sources_root}""
        )
    os.chdir(default_airflow_sources_root)
    return Path(default_airflow_sources_root)


AIRFLOW_SOURCES_ROOT = find_airflow_sources_root()

BUILD_CACHE_DIR = AIRFLOW_SOURCES_ROOT / '.build'
FILES_DIR = AIRFLOW_SOURCES_ROOT / 'files'
MSSQL_DATA_VOLUME = AIRFLOW_SOURCES_ROOT / 'tmp_mssql_volume'
MYPY_CACHE_DIR = AIRFLOW_SOURCES_ROOT / '.mypy_cache'
LOGS_DIR = AIRFLOW_SOURCES_ROOT / 'logs'
DIST_DIR = AIRFLOW_SOURCES_ROOT / 'dist'
SCRIPTS_CI_DIR = AIRFLOW_SOURCES_ROOT / 'scripts' / 'ci'
DOCKER_CONTEXT_DIR = AIRFLOW_SOURCES_ROOT / 'docker-context-files'
CACHE_TMP_FILE_DIR = tempfile.TemporaryDirectory()
OUTPUT_LOG = Path(CACHE_TMP_FILE_DIR.name, 'out.log')

","
    Find the root of airflow sources. When Breeze is run from sources, it is easy, but this one also
    has to handle the case when Breeze is installed via `pipx` so it searches upwards of the current
    directory to find the right root of airflow directory.

    If not found, current directory is returned (this handles the case when Breeze is run from the local
    directory.

    :return: Path for the found sources.

    ",71,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_airflow_sources_root() -> Path:
    
    default_airflow_sources_root = Path.cwd()
    # Try to find airflow sources in current working dir
    airflow_sources_root = search_upwards_for_airflow_sources_root(Path.cwd())
    if not airflow_sources_root:
        # Or if it fails, find it in parents of the directory where the ./breeze.py is.
        airflow_sources_root = search_upwards_for_airflow_sources_root(Path(__file__).resolve().parent)
    if airflow_sources_root:
        os.chdir(airflow_sources_root)
        return Path(airflow_sources_root)
    else:
        console.print(
            f""\n[bright_yellow]Could not find Airflow sources location. ""
            f""Assuming {default_airflow_sources_root}""
        )
    os.chdir(default_airflow_sources_root)
    return Path(default_airflow_sources_root)


AIRFLOW_SOURCES_ROOT = find_airflow_sources_root()

BUILD_CACHE_DIR = AIRFLOW_SOURCES_ROOT / '.build'
FILES_DIR = AIRFLOW_SOURCES_ROOT / 'files'
MSSQL_DATA_VOLUME = AIRFLOW_SOURCES_ROOT / 'tmp_mssql_volume'
MYPY_CACHE_DIR = AIRFLOW_SOURCES_ROOT / '.mypy_cache'
LOGS_DIR = AIRFLOW_SOURCES_ROOT / 'logs'
DIST_DIR = AIRFLOW_SOURCES_ROOT / 'dist'
SCRIPTS_CI_DIR = AIRFLOW_SOURCES_ROOT / 'scripts' / 'ci'
DOCKER_CONTEXT_DIR = AIRFLOW_SOURCES_ROOT / 'docker-context-files'
CACHE_TMP_FILE_DIR = tempfile.TemporaryDirectory()
OUTPUT_LOG = Path(CACHE_TMP_FILE_DIR.name, 'out.log')


        ```
    ###Assistant : 
    Find the root of airflow sources. When Breeze is run from sources, it is easy, but this one also
    has to handle the case when Breeze is installed via `pipx` so it searches upwards of the current
    directory to find the right root of airflow directory.

    If not found, current directory is returned (this handles the case when Breeze is run from the local
    directory.

    :return: Path for the found sources.

    
    "
2045,"def _parse_proxy_entry(proxy_str):
        
        config = [c.strip() for c in proxy_str.split(' ') if c]
        if not config:
            raise ParseProxyError(""Empty proxy entry"")

        if config[0] == ""DIRECT"":
            if len(config) != 1:
                raise ParseProxyError(""Invalid number of parameters for "" +
                                      ""DIRECT"")
            return QNetworkProxy(QNetworkProxy.ProxyType.NoProxy)
        elif config[0] == ""PROXY"":
            if len(config) != 2:
                raise ParseProxyError(""Invalid number of parameters for PROXY"")
            host, port = PACResolver._parse_proxy_host(config[1])
            return QNetworkProxy(QNetworkProxy.ProxyType.HttpProxy, host, port)
        elif config[0] in [""SOCKS"", ""SOCKS5""]:
            if len(config) != 2:
                raise ParseProxyError(""Invalid number of parameters for SOCKS"")
            host, port = PACResolver._parse_proxy_host(config[1])
            return QNetworkProxy(QNetworkProxy.ProxyType.Socks5Proxy, host, port)
        else:
            err = ""Unknown proxy type: {}""
            raise ParseProxyError(err.format(config[0]))
","Parse one proxy string entry, as described in PAC specification.",10,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_proxy_entry(proxy_str):
        
        config = [c.strip() for c in proxy_str.split(' ') if c]
        if not config:
            raise ParseProxyError(""Empty proxy entry"")

        if config[0] == ""DIRECT"":
            if len(config) != 1:
                raise ParseProxyError(""Invalid number of parameters for "" +
                                      ""DIRECT"")
            return QNetworkProxy(QNetworkProxy.ProxyType.NoProxy)
        elif config[0] == ""PROXY"":
            if len(config) != 2:
                raise ParseProxyError(""Invalid number of parameters for PROXY"")
            host, port = PACResolver._parse_proxy_host(config[1])
            return QNetworkProxy(QNetworkProxy.ProxyType.HttpProxy, host, port)
        elif config[0] in [""SOCKS"", ""SOCKS5""]:
            if len(config) != 2:
                raise ParseProxyError(""Invalid number of parameters for SOCKS"")
            host, port = PACResolver._parse_proxy_host(config[1])
            return QNetworkProxy(QNetworkProxy.ProxyType.Socks5Proxy, host, port)
        else:
            err = ""Unknown proxy type: {}""
            raise ParseProxyError(err.format(config[0]))

        ```
    ###Assistant : Parse one proxy string entry, as described in PAC specification.
    "
2046,"def test_gen_pyf(capfd, hello_world_f90, monkeypatch):
    
    ipath = Path(hello_world_f90)
    opath = Path(hello_world_f90).stem + "".pyf""
    monkeypatch.setattr(sys, ""argv"", f'f2py -h {opath} {ipath}'.split())

    with util.switchdir(ipath.parent):
        f2pycli()  # Generate wrappers
        out, _ = capfd.readouterr()
        assert ""Saving signatures to file"" in out
        assert Path(f'{opath}').exists()

","Ensures that a signature file is generated via the CLI
    CLI :: -h
    ",13,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_gen_pyf(capfd, hello_world_f90, monkeypatch):
    
    ipath = Path(hello_world_f90)
    opath = Path(hello_world_f90).stem + "".pyf""
    monkeypatch.setattr(sys, ""argv"", f'f2py -h {opath} {ipath}'.split())

    with util.switchdir(ipath.parent):
        f2pycli()  # Generate wrappers
        out, _ = capfd.readouterr()
        assert ""Saving signatures to file"" in out
        assert Path(f'{opath}').exists()


        ```
    ###Assistant : Ensures that a signature file is generated via the CLI
    CLI :: -h
    
    "
2047,"def _dictionary(self):
        # type: () -> Dict[str, Any]
        
        # NOTE: Dictionaries are not populated if not loaded. So, conditionals
        #       are not needed here.
        retval = {}

        for variant in OVERRIDE_ORDER:
            retval.update(self._config[variant])

        return retval
","A dictionary representing the loaded configuration.
        ",6,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _dictionary(self):
        # type: () -> Dict[str, Any]
        
        # NOTE: Dictionaries are not populated if not loaded. So, conditionals
        #       are not needed here.
        retval = {}

        for variant in OVERRIDE_ORDER:
            retval.update(self._config[variant])

        return retval

        ```
    ###Assistant : A dictionary representing the loaded configuration.
        
    "
2048,"def using(self, alias):
        
        return RawQuerySet(
            self.raw_query,
            model=self.model,
            query=self.query.chain(using=alias),
            params=self.params,
            translations=self.translations,
            using=alias,
        )
",Select the database this RawQuerySet should execute against.,8,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def using(self, alias):
        
        return RawQuerySet(
            self.raw_query,
            model=self.model,
            query=self.query.chain(using=alias),
            params=self.params,
            translations=self.translations,
            using=alias,
        )

        ```
    ###Assistant : Select the database this RawQuerySet should execute against.
    "
2049,"def get_weights(self):
        
        params = self.weights
        return backend.batch_get_value(params)

    # TODO(tanzheny): Maybe share this logic with base_layer.","Returns the current weights of the optimizer.

        The weights of an optimizer are its state (ie, variables).
        This function returns the weight values associated with this
        optimizer as a list of Numpy arrays. The first value is always the
        iterations count of the optimizer, followed by the optimizer's state
        variables in the order they were created. The returned list can in turn
        be used to load state into similarly parameterized optimizers.

        For example, the RMSprop optimizer for this simple model returns a list of
        three values-- the iteration count, followed by the root-mean-square value
        of the kernel and bias of the single Dense layer:

        >>> opt = tf.keras.optimizers.RMSprop()
        >>> m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
        >>> m.compile(opt, loss='mse')
        >>> data = np.arange(100).reshape(5, 20)
        >>> labels = np.zeros(5)
        >>> results = m.fit(data, labels)  # Training.
        >>> len(opt.get_weights())
        3

        Returns:
            Weights values as a list of numpy arrays.
        ",143,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_weights(self):
        
        params = self.weights
        return backend.batch_get_value(params)

    # TODO(tanzheny): Maybe share this logic with base_layer.
        ```
    ###Assistant : Returns the current weights of the optimizer.

        The weights of an optimizer are its state (ie, variables).
        This function returns the weight values associated with this
        optimizer as a list of Numpy arrays. The first value is always the
        iterations count of the optimizer, followed by the optimizer's state
        variables in the order they were created. The returned list can in turn
        be used to load state into similarly parameterized optimizers.

        For example, the RMSprop optimizer for this simple model returns a list of
        three values-- the iteration count, followed by the root-mean-square value
        of the kernel and bias of the single Dense layer:

        >>> opt = tf.keras.optimizers.RMSprop()
        >>> m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
        >>> m.compile(opt, loss='mse')
        >>> data = np.arange(100).reshape(5, 20)
        >>> labels = np.zeros(5)
        >>> results = m.fit(data, labels)  # Training.
        >>> len(opt.get_weights())
        3

        Returns:
            Weights values as a list of numpy arrays.
        
    "
2050,"def _focal_loss_cost(self, cls_pred, gt_labels):
        
        cls_pred = cls_pred.sigmoid()
        neg_cost = -(1 - cls_pred + self.eps).log() * (
            1 - self.alpha) * cls_pred.pow(self.gamma)
        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
            1 - cls_pred).pow(self.gamma)

        cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels]
        return cls_cost * self.weight
","
        Args:
            cls_pred (Tensor): Predicted classification logits, shape
                (num_query, num_class).
            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).

        Returns:
            torch.Tensor: cls_cost value with weight
        ",22,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _focal_loss_cost(self, cls_pred, gt_labels):
        
        cls_pred = cls_pred.sigmoid()
        neg_cost = -(1 - cls_pred + self.eps).log() * (
            1 - self.alpha) * cls_pred.pow(self.gamma)
        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
            1 - cls_pred).pow(self.gamma)

        cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels]
        return cls_cost * self.weight

        ```
    ###Assistant : 
        Args:
            cls_pred (Tensor): Predicted classification logits, shape
                (num_query, num_class).
            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).

        Returns:
            torch.Tensor: cls_cost value with weight
        
    "
2051,"def date_extract_sql(self, lookup_type, field_name):
        
        raise NotImplementedError(
            ""subclasses of BaseDatabaseOperations may require a date_extract_sql() method""
        )
","
        Given a lookup_type of 'year', 'month', or 'day', return the SQL that
        extracts a value from the given date field field_name.
        ",21,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def date_extract_sql(self, lookup_type, field_name):
        
        raise NotImplementedError(
            ""subclasses of BaseDatabaseOperations may require a date_extract_sql() method""
        )

        ```
    ###Assistant : 
        Given a lookup_type of 'year', 'month', or 'day', return the SQL that
        extracts a value from the given date field field_name.
        
    "
2052,"def test_threepid_invite_spamcheck(self) -> None:
        
        # Mock a few functions to prevent the test from failing due to failing to talk to
        # a remote IS. We keep the mock for make_and_store_3pid_invite around so we
        # can check its call_count later on during the test.
        make_invite_mock = Mock(return_value=make_awaitable(0))
        self.hs.get_room_member_handler()._make_and_store_3pid_invite = make_invite_mock
        self.hs.get_identity_handler().lookup_3pid = Mock(
            return_value=make_awaitable(None),
        )

        # Add a mock to the spamchecker callbacks for user_may_send_3pid_invite. Make it
        # allow everything for now.
        # `spec` argument is needed for this function mock to have `__qualname__`, which
        # is needed for `Measure` metrics buried in SpamChecker.
        mock = Mock(
            return_value=make_awaitable(synapse.module_api.NOT_SPAM),
            spec=lambda *x: None,
        )
        self.hs.get_spam_checker()._user_may_send_3pid_invite_callbacks.append(mock)

        # Send a 3PID invite into the room and check that it succeeded.
        email_to_invite = ""teresa@example.com""
        channel = self.make_request(
            method=""POST"",
            path=""/rooms/"" + self.room_id + ""/invite"",
            content={
                ""id_server"": ""example.com"",
                ""id_access_token"": ""sometoken"",
                ""medium"": ""email"",
                ""address"": email_to_invite,
            },
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 200)

        # Check that the callback was called with the right params.
        mock.assert_called_with(self.user_id, ""email"", email_to_invite, self.room_id)

        # Check that the call to send the invite was made.
        make_invite_mock.assert_called_once()

        # Now change the return value of the callback to deny any invite and test that
        # we can't send the invite.
        mock.return_value = make_awaitable(Codes.CONSENT_NOT_GIVEN)
        channel = self.make_request(
            method=""POST"",
            path=""/rooms/"" + self.room_id + ""/invite"",
            content={
                ""id_server"": ""example.com"",
                ""id_access_token"": ""sometoken"",
                ""medium"": ""email"",
                ""address"": email_to_invite,
            },
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 403)

        # Also check that it stopped before calling _make_and_store_3pid_invite.
        make_invite_mock.assert_called_once()
","
        Test allowing/blocking threepid invites with a spam-check module.

        In this test, we use the more recent API in which callbacks return a `Union[Codes, Literal[""NOT_SPAM""]]`.",24,227,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_threepid_invite_spamcheck(self) -> None:
        
        # Mock a few functions to prevent the test from failing due to failing to talk to
        # a remote IS. We keep the mock for make_and_store_3pid_invite around so we
        # can check its call_count later on during the test.
        make_invite_mock = Mock(return_value=make_awaitable(0))
        self.hs.get_room_member_handler()._make_and_store_3pid_invite = make_invite_mock
        self.hs.get_identity_handler().lookup_3pid = Mock(
            return_value=make_awaitable(None),
        )

        # Add a mock to the spamchecker callbacks for user_may_send_3pid_invite. Make it
        # allow everything for now.
        # `spec` argument is needed for this function mock to have `__qualname__`, which
        # is needed for `Measure` metrics buried in SpamChecker.
        mock = Mock(
            return_value=make_awaitable(synapse.module_api.NOT_SPAM),
            spec=lambda *x: None,
        )
        self.hs.get_spam_checker()._user_may_send_3pid_invite_callbacks.append(mock)

        # Send a 3PID invite into the room and check that it succeeded.
        email_to_invite = ""teresa@example.com""
        channel = self.make_request(
            method=""POST"",
            path=""/rooms/"" + self.room_id + ""/invite"",
            content={
                ""id_server"": ""example.com"",
                ""id_access_token"": ""sometoken"",
                ""medium"": ""email"",
                ""address"": email_to_invite,
            },
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 200)

        # Check that the callback was called with the right params.
        mock.assert_called_with(self.user_id, ""email"", email_to_invite, self.room_id)

        # Check that the call to send the invite was made.
        make_invite_mock.assert_called_once()

        # Now change the return value of the callback to deny any invite and test that
        # we can't send the invite.
        mock.return_value = make_awaitable(Codes.CONSENT_NOT_GIVEN)
        channel = self.make_request(
            method=""POST"",
            path=""/rooms/"" + self.room_id + ""/invite"",
            content={
                ""id_server"": ""example.com"",
                ""id_access_token"": ""sometoken"",
                ""medium"": ""email"",
                ""address"": email_to_invite,
            },
            access_token=self.tok,
        )
        self.assertEqual(channel.code, 403)

        # Also check that it stopped before calling _make_and_store_3pid_invite.
        make_invite_mock.assert_called_once()

        ```
    ###Assistant : 
        Test allowing/blocking threepid invites with a spam-check module.

        In this test, we use the more recent API in which callbacks return a `Union[Codes, Literal[""NOT_SPAM""]]`.
    "
2053,"def test_generate_invalid_param_val_all_valid(constraints):
    
    with pytest.raises(NotImplementedError):
        generate_invalid_param_val(constraints[0], constraints=constraints)


@pytest.mark.parametrize(
    ""constraint"",
    [
        _ArrayLikes,
        _Callables,
        _InstancesOf,
        _NoneConstraint,
        _RandomStates,
        _SparseMatrices,
    ],
)","Check that the function raises NotImplementedError when there's no invalid value
    for the constraint.
    ",14,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_generate_invalid_param_val_all_valid(constraints):
    
    with pytest.raises(NotImplementedError):
        generate_invalid_param_val(constraints[0], constraints=constraints)


@pytest.mark.parametrize(
    ""constraint"",
    [
        _ArrayLikes,
        _Callables,
        _InstancesOf,
        _NoneConstraint,
        _RandomStates,
        _SparseMatrices,
    ],
)
        ```
    ###Assistant : Check that the function raises NotImplementedError when there's no invalid value
    for the constraint.
    
    "
2054,"def load_drawer_from_disk(self):
        
        exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists()
        if exists:
            with open(self.full_path / str('pair_dictionary.json'), ""r"") as fp:
                self.pair_dict = json.load(fp)
        elif not self.follow_mode:
            logger.info(""Could not find existing datadrawer, starting from scratch"")
        else:
            logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
                           'sending null values back to strategy')

        return exists
","
        Locate and load a previously saved data drawer full of all pair model metadata in
        present model folder.
        :returns:
        exists: bool = whether or not the drawer was located
        ",29,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_drawer_from_disk(self):
        
        exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists()
        if exists:
            with open(self.full_path / str('pair_dictionary.json'), ""r"") as fp:
                self.pair_dict = json.load(fp)
        elif not self.follow_mode:
            logger.info(""Could not find existing datadrawer, starting from scratch"")
        else:
            logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
                           'sending null values back to strategy')

        return exists

        ```
    ###Assistant : 
        Locate and load a previously saved data drawer full of all pair model metadata in
        present model folder.
        :returns:
        exists: bool = whether or not the drawer was located
        
    "
2055,"def address_exclude(self, other):
        
        if not self._version == other._version:
            raise TypeError(""%s and %s are not of the same version"" % (
                             self, other))

        if not isinstance(other, _BaseNetwork):
            raise TypeError(""%s is not a network object"" % other)

        if not other.subnet_of(self):
            raise ValueError('%s not contained in %s' % (other, self))
        if other == self:
            return

        # Make sure we're comparing the network of other.
        other = other.__class__('%s/%s' % (other.network_address,
                                           other.prefixlen))

        s1, s2 = self.subnets()
        while s1 != other and s2 != other:
            if other.subnet_of(s1):
                yield s2
                s1, s2 = s1.subnets()
            elif other.subnet_of(s2):
                yield s1
                s1, s2 = s2.subnets()
            else:
                # If we got here, there's a bug somewhere.
                raise AssertionError('Error performing exclusion: '
                                     's1: %s s2: %s other: %s' %
                                     (s1, s2, other))
        if s1 == other:
            yield s2
        elif s2 == other:
            yield s1
        else:
            # If we got here, there's a bug somewhere.
            raise AssertionError('Error performing exclusion: '
                                 's1: %s s2: %s other: %s' %
                                 (s1, s2, other))
","Remove an address from a larger block.

        For example:

            addr1 = ip_network('192.0.2.0/28')
            addr2 = ip_network('192.0.2.1/32')
            list(addr1.address_exclude(addr2)) =
                [IPv4Network('192.0.2.0/32'), IPv4Network('192.0.2.2/31'),
                 IPv4Network('192.0.2.4/30'), IPv4Network('192.0.2.8/29')]

        or IPv6:

            addr1 = ip_network('2001:db8::1/32')
            addr2 = ip_network('2001:db8::1/128')
            list(addr1.address_exclude(addr2)) =
                [ip_network('2001:db8::1/128'),
                 ip_network('2001:db8::2/127'),
                 ip_network('2001:db8::4/126'),
                 ip_network('2001:db8::8/125'),
                 ...
                 ip_network('2001:db8:8000::/33')]

        Args:
            other: An IPv4Network or IPv6Network object of the same type.

        Returns:
            An iterator of the IPv(4|6)Network objects which is self
            minus other.

        Raises:
            TypeError: If self and other are of differing address
              versions, or if other is not a network object.
            ValueError: If other is not completely contained by self.

        ",88,157,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def address_exclude(self, other):
        
        if not self._version == other._version:
            raise TypeError(""%s and %s are not of the same version"" % (
                             self, other))

        if not isinstance(other, _BaseNetwork):
            raise TypeError(""%s is not a network object"" % other)

        if not other.subnet_of(self):
            raise ValueError('%s not contained in %s' % (other, self))
        if other == self:
            return

        # Make sure we're comparing the network of other.
        other = other.__class__('%s/%s' % (other.network_address,
                                           other.prefixlen))

        s1, s2 = self.subnets()
        while s1 != other and s2 != other:
            if other.subnet_of(s1):
                yield s2
                s1, s2 = s1.subnets()
            elif other.subnet_of(s2):
                yield s1
                s1, s2 = s2.subnets()
            else:
                # If we got here, there's a bug somewhere.
                raise AssertionError('Error performing exclusion: '
                                     's1: %s s2: %s other: %s' %
                                     (s1, s2, other))
        if s1 == other:
            yield s2
        elif s2 == other:
            yield s1
        else:
            # If we got here, there's a bug somewhere.
            raise AssertionError('Error performing exclusion: '
                                 's1: %s s2: %s other: %s' %
                                 (s1, s2, other))

        ```
    ###Assistant : Remove an address from a larger block.

        For example:

            addr1 = ip_network('192.0.2.0/28')
            addr2 = ip_network('192.0.2.1/32')
            list(addr1.address_exclude(addr2)) =
                [IPv4Network('192.0.2.0/32'), IPv4Network('192.0.2.2/31'),
                 IPv4Network('192.0.2.4/30'), IPv4Network('192.0.2.8/29')]

        or IPv6:

            addr1 = ip_network('2001:db8::1/32')
            addr2 = ip_network('2001:db8::1/128')
            list(addr1.address_exclude(addr2)) =
                [ip_network('2001:db8::1/128'),
                 ip_network('2001:db8::2/127'),
                 ip_network('2001:db8::4/126'),
                 ip_network('2001:db8::8/125'),
                 ...
                 ip_network('2001:db8:8000::/33')]

        Args:
            other: An IPv4Network or IPv6Network object of the same type.

        Returns:
            An iterator of the IPv(4|6)Network objects which is self
            minus other.

        Raises:
            TypeError: If self and other are of differing address
              versions, or if other is not a network object.
            ValueError: If other is not completely contained by self.

        
    "
2056,"def mutual_information(cooccurrence):
    

    with np.errstate(invalid=""ignore"", divide=""ignore""):
        result = np.log2(cooccurrence.shape[0] * lift(cooccurrence))

    return np.array(result)

","Helper method to calculate the Mutual Information of a matrix of
    co-occurrences.

    Mutual information is a measurement of the amount of information
    explained by the i-th j-th item column vector.

    Args:
        cooccurrence (numpy.ndarray): The symmetric matrix of co-occurrences of items.

    Returns:
        numpy.ndarray: The matrix of mutual information between any two items.

    ",51,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mutual_information(cooccurrence):
    

    with np.errstate(invalid=""ignore"", divide=""ignore""):
        result = np.log2(cooccurrence.shape[0] * lift(cooccurrence))

    return np.array(result)


        ```
    ###Assistant : Helper method to calculate the Mutual Information of a matrix of
    co-occurrences.

    Mutual information is a measurement of the amount of information
    explained by the i-th j-th item column vector.

    Args:
        cooccurrence (numpy.ndarray): The symmetric matrix of co-occurrences of items.

    Returns:
        numpy.ndarray: The matrix of mutual information between any two items.

    
    "
2057,"def _is_dunder(name):
        
        return len(name) > 4 and name.startswith('__') and name.endswith('__')

    # Prior to Python 3.7 types did not have `copy_with`. A lot of the equality
    # checks, argument expansion etc. are done on the _subs_tre. As a result we
    # can't provide a get_type_hints function that strips out annotations.
",Returns True if name is a __dunder_variable_name__.,7,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _is_dunder(name):
        
        return len(name) > 4 and name.startswith('__') and name.endswith('__')

    # Prior to Python 3.7 types did not have `copy_with`. A lot of the equality
    # checks, argument expansion etc. are done on the _subs_tre. As a result we
    # can't provide a get_type_hints function that strips out annotations.

        ```
    ###Assistant : Returns True if name is a __dunder_variable_name__.
    "
2058,"def fit(self, X, y=None, sample_weight=None):
        

        algorithm = self._choose_algorithm(self.algorithm, self.metric)

        if isinstance(self.bandwidth, str):
            methods_supported = (""scott"", ""silvermann"")
            if self.bandwidth not in methods_supported:
                raise ValueError(
                    ""When `bandwidth` is a string, it should be one of: ""
                    f""{', '.join(methods_supported)}. Got {self.bandwidth!r} instead.""
                )
            if self.bandwidth == ""scott"":
                self.bandwidth_ = X.shape[0] ** (-1 / (X.shape[1] + 4))
            elif self.bandwidth == ""silvermann"":
                self.bandwidth_ = (X.shape[0] * (X.shape[1] + 2) / 4) ** (
                    -1 / (X.shape[1] + 4)
                )
        else:
            check_scalar(
                self.bandwidth,
                ""bandwidth"",
                target_type=numbers.Real,
                min_val=0,
                include_boundaries=""neither"",
            )
            self.bandwidth_ = self.bandwidth
        if self.kernel not in VALID_KERNELS:
            raise ValueError(""invalid kernel: '{0}'"".format(self.kernel))

        X = self._validate_data(X, order=""C"", dtype=DTYPE)

        if sample_weight is not None:
            sample_weight = _check_sample_weight(
                sample_weight, X, DTYPE, only_non_negative=True
            )

        kwargs = self.metric_params
        if kwargs is None:
            kwargs = {}
        self.tree_ = TREE_DICT[algorithm](
            X,
            metric=self.metric,
            leaf_size=self.leaf_size,
            sample_weight=sample_weight,
            **kwargs,
        )
        return self
","Fit the Kernel Density model on the data.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            List of n_features-dimensional data points.  Each row
            corresponds to a single data point.

        y : None
            Ignored. This parameter exists only for compatibility with
            :class:`~sklearn.pipeline.Pipeline`.

        sample_weight : array-like of shape (n_samples,), default=None
            List of sample weights attached to the data X.

            .. versionadded:: 0.20

        Returns
        -------
        self : object
            Returns the instance itself.
        ",70,133,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y=None, sample_weight=None):
        

        algorithm = self._choose_algorithm(self.algorithm, self.metric)

        if isinstance(self.bandwidth, str):
            methods_supported = (""scott"", ""silvermann"")
            if self.bandwidth not in methods_supported:
                raise ValueError(
                    ""When `bandwidth` is a string, it should be one of: ""
                    f""{', '.join(methods_supported)}. Got {self.bandwidth!r} instead.""
                )
            if self.bandwidth == ""scott"":
                self.bandwidth_ = X.shape[0] ** (-1 / (X.shape[1] + 4))
            elif self.bandwidth == ""silvermann"":
                self.bandwidth_ = (X.shape[0] * (X.shape[1] + 2) / 4) ** (
                    -1 / (X.shape[1] + 4)
                )
        else:
            check_scalar(
                self.bandwidth,
                ""bandwidth"",
                target_type=numbers.Real,
                min_val=0,
                include_boundaries=""neither"",
            )
            self.bandwidth_ = self.bandwidth
        if self.kernel not in VALID_KERNELS:
            raise ValueError(""invalid kernel: '{0}'"".format(self.kernel))

        X = self._validate_data(X, order=""C"", dtype=DTYPE)

        if sample_weight is not None:
            sample_weight = _check_sample_weight(
                sample_weight, X, DTYPE, only_non_negative=True
            )

        kwargs = self.metric_params
        if kwargs is None:
            kwargs = {}
        self.tree_ = TREE_DICT[algorithm](
            X,
            metric=self.metric,
            leaf_size=self.leaf_size,
            sample_weight=sample_weight,
            **kwargs,
        )
        return self

        ```
    ###Assistant : Fit the Kernel Density model on the data.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            List of n_features-dimensional data points.  Each row
            corresponds to a single data point.

        y : None
            Ignored. This parameter exists only for compatibility with
            :class:`~sklearn.pipeline.Pipeline`.

        sample_weight : array-like of shape (n_samples,), default=None
            List of sample weights attached to the data X.

            .. versionadded:: 0.20

        Returns
        -------
        self : object
            Returns the instance itself.
        
    "
2059,"def get_on_pixels(self, image):
        

        if image.mode != ""L"":
            msg = ""Image mode must be L""
            raise ValueError(msg)
        return _imagingmorph.get_on_pixels(image.im.id)
","Get a list of all turned on pixels in a binary image

        Returns a list of tuples of (x,y) coordinates
        of all matching pixels. See :ref:`coordinate-system`.",26,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_on_pixels(self, image):
        

        if image.mode != ""L"":
            msg = ""Image mode must be L""
            raise ValueError(msg)
        return _imagingmorph.get_on_pixels(image.im.id)

        ```
    ###Assistant : Get a list of all turned on pixels in a binary image

        Returns a list of tuples of (x,y) coordinates
        of all matching pixels. See :ref:`coordinate-system`.
    "
2060,"def visit_Num(self, node):
        
        if isinstance(node.n, int):
            return fix_missing_locations(Call(func=Name('Integer', Load()),
                    args=[node], keywords=[]))
        elif isinstance(node.n, float):
            return fix_missing_locations(Call(func=Name('Float', Load()),
                    args=[node], keywords=[]))
        return node
","This function exists for backwards compatibility with Python 3.7.
           It should be removed when SymPy removes support for Python 3.7.",20,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def visit_Num(self, node):
        
        if isinstance(node.n, int):
            return fix_missing_locations(Call(func=Name('Integer', Load()),
                    args=[node], keywords=[]))
        elif isinstance(node.n, float):
            return fix_missing_locations(Call(func=Name('Float', Load()),
                    args=[node], keywords=[]))
        return node

        ```
    ###Assistant : This function exists for backwards compatibility with Python 3.7.
           It should be removed when SymPy removes support for Python 3.7.
    "
2061,"def enable(display=1, logdir=None, context=5, format=""html""):
    
    sys.excepthook = Hook(display=display, logdir=logdir,
                          context=context, format=format)
","Install an exception handler that formats tracebacks as HTML.

    The optional argument 'display' can be set to 0 to suppress sending the
    traceback to the browser, and 'logdir' can be set to a directory to cause
    tracebacks to be written to files there.",43,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def enable(display=1, logdir=None, context=5, format=""html""):
    
    sys.excepthook = Hook(display=display, logdir=logdir,
                          context=context, format=format)

        ```
    ###Assistant : Install an exception handler that formats tracebacks as HTML.

    The optional argument 'display' can be set to 0 to suppress sending the
    traceback to the browser, and 'logdir' can be set to a directory to cause
    tracebacks to be written to files there.
    "
2062,"def parse_list_header(value):
    
    result = []
    for item in _parse_list_header(value):
        if item[:1] == item[-1:] == '""':
            item = unquote_header_value(item[1:-1])
        result.append(item)
    return result


# From mitsuhiko/werkzeug (used with permission).","Parse lists as described by RFC 2068 Section 2.

    In particular, parse comma-separated lists where the elements of
    the list may include quoted-strings.  A quoted-string could
    contain a comma.  A non-quoted string could have quotes in the
    middle.  Quotes are removed automatically after parsing.

    It basically works like :func:`parse_set_header` just that items
    may appear multiple times and case sensitivity is preserved.

    The return value is a standard :class:`list`:

    >>> parse_list_header('token, ""quoted value""')
    ['token', 'quoted value']

    To create a header from the :class:`list` again, use the
    :func:`dump_header` function.

    :param value: a string with a list header.
    :return: :class:`list`
    :rtype: list
    ",99,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_list_header(value):
    
    result = []
    for item in _parse_list_header(value):
        if item[:1] == item[-1:] == '""':
            item = unquote_header_value(item[1:-1])
        result.append(item)
    return result


# From mitsuhiko/werkzeug (used with permission).
        ```
    ###Assistant : Parse lists as described by RFC 2068 Section 2.

    In particular, parse comma-separated lists where the elements of
    the list may include quoted-strings.  A quoted-string could
    contain a comma.  A non-quoted string could have quotes in the
    middle.  Quotes are removed automatically after parsing.

    It basically works like :func:`parse_set_header` just that items
    may appear multiple times and case sensitivity is preserved.

    The return value is a standard :class:`list`:

    >>> parse_list_header('token, ""quoted value""')
    ['token', 'quoted value']

    To create a header from the :class:`list` again, use the
    :func:`dump_header` function.

    :param value: a string with a list header.
    :return: :class:`list`
    :rtype: list
    
    "
2063,"def media_series_title(self) -> str | None:
        
        if self._playing and self._is_feature_available(FeatureName.SeriesName):
            return self._playing.series_name
        return None
","Title of series of current playing media, TV show only.",10,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def media_series_title(self) -> str | None:
        
        if self._playing and self._is_feature_available(FeatureName.SeriesName):
            return self._playing.series_name
        return None

        ```
    ###Assistant : Title of series of current playing media, TV show only.
    "
2064,"def _build_template(name, template, files, config, nav):
    

    # Run `pre_template` plugin events.
    template = config['plugins'].run_event(
        'pre_template', template, template_name=name, config=config
    )

    if utils.is_error_template(name):
        # Force absolute URLs in the nav of error pages and account for the
        # possibility that the docs root might be different than the server root.
        # See https://github.com/mkdocs/mkdocs/issues/77.
        # However, if site_url is not set, assume the docs root and server root
        # are the same. See https://github.com/mkdocs/mkdocs/issues/1598.
        base_url = urlsplit(config['site_url'] or '/').path
    else:
        base_url = utils.get_relative_url('.', name)

    context = get_context(nav, files, config, base_url=base_url)

    # Run `template_context` plugin events.
    context = config['plugins'].run_event(
        'template_context', context, template_name=name, config=config
    )

    output = template.render(context)

    # Run `post_template` plugin events.
    output = config['plugins'].run_event('post_template', output, template_name=name, config=config)

    return output

","
    Return rendered output for given template as a string.
    ",9,116,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _build_template(name, template, files, config, nav):
    

    # Run `pre_template` plugin events.
    template = config['plugins'].run_event(
        'pre_template', template, template_name=name, config=config
    )

    if utils.is_error_template(name):
        # Force absolute URLs in the nav of error pages and account for the
        # possibility that the docs root might be different than the server root.
        # See https://github.com/mkdocs/mkdocs/issues/77.
        # However, if site_url is not set, assume the docs root and server root
        # are the same. See https://github.com/mkdocs/mkdocs/issues/1598.
        base_url = urlsplit(config['site_url'] or '/').path
    else:
        base_url = utils.get_relative_url('.', name)

    context = get_context(nav, files, config, base_url=base_url)

    # Run `template_context` plugin events.
    context = config['plugins'].run_event(
        'template_context', context, template_name=name, config=config
    )

    output = template.render(context)

    # Run `post_template` plugin events.
    output = config['plugins'].run_event('post_template', output, template_name=name, config=config)

    return output


        ```
    ###Assistant : 
    Return rendered output for given template as a string.
    
    "
2065,"def matches(self, expr, repl_dict=None, old=False):
        
        expr = sympify(expr)
        if not isinstance(expr, self.__class__):
            return None

        if repl_dict is None:
            repl_dict = {}
        else:
            repl_dict = repl_dict.copy()

        if self == expr:
            return repl_dict

        if len(self.args) != len(expr.args):
            return None

        d = repl_dict  # already a copy
        for arg, other_arg in zip(self.args, expr.args):
            if arg == other_arg:
                continue
            if arg.is_Relational:
                try:
                    d = arg.xreplace(d).matches(other_arg, d, old=old)
                except TypeError: # Should be InvalidComparisonError when introduced
                    d = None
            else:
                    d = arg.xreplace(d).matches(other_arg, d, old=old)
            if d is None:
                return None
        return d
","
        Helper method for match() that looks for a match between Wild symbols
        in self and expressions in expr.

        Examples
        ========

        >>> from sympy import symbols, Wild, Basic
        >>> a, b, c = symbols('a b c')
        >>> x = Wild('x')
        >>> Basic(a + x, x).matches(Basic(a + b, c)) is None
        True
        >>> Basic(a + x, x).matches(Basic(a + b + c, b + c))
        {x_: b + c}
        ",66,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def matches(self, expr, repl_dict=None, old=False):
        
        expr = sympify(expr)
        if not isinstance(expr, self.__class__):
            return None

        if repl_dict is None:
            repl_dict = {}
        else:
            repl_dict = repl_dict.copy()

        if self == expr:
            return repl_dict

        if len(self.args) != len(expr.args):
            return None

        d = repl_dict  # already a copy
        for arg, other_arg in zip(self.args, expr.args):
            if arg == other_arg:
                continue
            if arg.is_Relational:
                try:
                    d = arg.xreplace(d).matches(other_arg, d, old=old)
                except TypeError: # Should be InvalidComparisonError when introduced
                    d = None
            else:
                    d = arg.xreplace(d).matches(other_arg, d, old=old)
            if d is None:
                return None
        return d

        ```
    ###Assistant : 
        Helper method for match() that looks for a match between Wild symbols
        in self and expressions in expr.

        Examples
        ========

        >>> from sympy import symbols, Wild, Basic
        >>> a, b, c = symbols('a b c')
        >>> x = Wild('x')
        >>> Basic(a + x, x).matches(Basic(a + b, c)) is None
        True
        >>> Basic(a + x, x).matches(Basic(a + b + c, b + c))
        {x_: b + c}
        
    "
2066,"def _create_placement_group(self, num_workers):
        
        pg = get_current_placement_group()
        if pg is None:
            bundle = {""CPU"": self._num_cpus_per_worker, ""GPU"": int(self._use_gpu)}
            bundles = [bundle] * num_workers
            pg = ray.util.placement_group(bundles, strategy=""SPREAD"")
            logger.debug(""Waiting for placement group to start."")
            ready, _ = ray.wait([pg.ready()], timeout=SGD_PLACEMENT_GROUP_TIMEOUT_S)
            if ready:
                logger.debug(""Placement group has started."")
            else:
                raise TimeoutError(
                    ""Placement group creation timed out. Make sure ""
                    ""your cluster either has enough resources or use ""
                    ""an autoscaling cluster. Current resources ""
                    ""available: {}, resources requested by the ""
                    ""placement group: {}"".format(
                        ray.available_resources(), pg.bundle_specs
                    )
                )
            self._worker_placement_group = pg
","Creates a placement group for the workers.

        If this worker is already in a placement group then a new one will
        not be created. This is primarily for when Tune is the upstream and
        will allocate resources for SGD workers.

        If this worker is not in a placement group, a new one will be created
        and set. The placement group will have a single bundle for each worker
        and use the SPREAD strategy for an even distribution.
        ",77,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_placement_group(self, num_workers):
        
        pg = get_current_placement_group()
        if pg is None:
            bundle = {""CPU"": self._num_cpus_per_worker, ""GPU"": int(self._use_gpu)}
            bundles = [bundle] * num_workers
            pg = ray.util.placement_group(bundles, strategy=""SPREAD"")
            logger.debug(""Waiting for placement group to start."")
            ready, _ = ray.wait([pg.ready()], timeout=SGD_PLACEMENT_GROUP_TIMEOUT_S)
            if ready:
                logger.debug(""Placement group has started."")
            else:
                raise TimeoutError(
                    ""Placement group creation timed out. Make sure ""
                    ""your cluster either has enough resources or use ""
                    ""an autoscaling cluster. Current resources ""
                    ""available: {}, resources requested by the ""
                    ""placement group: {}"".format(
                        ray.available_resources(), pg.bundle_specs
                    )
                )
            self._worker_placement_group = pg

        ```
    ###Assistant : Creates a placement group for the workers.

        If this worker is already in a placement group then a new one will
        not be created. This is primarily for when Tune is the upstream and
        will allocate resources for SGD workers.

        If this worker is not in a placement group, a new one will be created
        and set. The placement group will have a single bundle for each worker
        and use the SPREAD strategy for an even distribution.
        
    "
2067,"def get_warehouse_list(filters):
	from frappe.core.doctype.user_permission.user_permission import get_permitted_documents

	condition = """"
	user_permitted_warehouse = get_permitted_documents(""Warehouse"")
	value = ()
	if user_permitted_warehouse:
		condition = ""and name in %s""
		value = set(user_permitted_warehouse)
	elif not user_permitted_warehouse and filters.get(""warehouse""):
		condition = ""and name = %s""
		value = filters.get(""warehouse"")

	return frappe.db.sql(
		.format(
			condition=condition
		),
		value,
		as_dict=1,
	)

","select name
		from `tabWarehouse` where is_group = 0
		{condition}",9,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_warehouse_list(filters):
	from frappe.core.doctype.user_permission.user_permission import get_permitted_documents

	condition = """"
	user_permitted_warehouse = get_permitted_documents(""Warehouse"")
	value = ()
	if user_permitted_warehouse:
		condition = ""and name in %s""
		value = set(user_permitted_warehouse)
	elif not user_permitted_warehouse and filters.get(""warehouse""):
		condition = ""and name = %s""
		value = filters.get(""warehouse"")

	return frappe.db.sql(
		.format(
			condition=condition
		),
		value,
		as_dict=1,
	)


        ```
    ###Assistant : select name
		from `tabWarehouse` where is_group = 0
		{condition}
    "
2068,"def unmap(self) -> ""BaseOperator"":
        
        dag = self.dag
        if not dag:
            raise RuntimeError(""Cannot unmap a task without a DAG"")
        dag._remove_task(self.task_id)
        if isinstance(self.operator_class, str):
            raise RuntimeError(""Cannot unmap a deserialized operator"")
        return self.operator_class(**self._get_unmap_kwargs())
","Get the ""normal"" Operator after applying the current mapping.",9,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unmap(self) -> ""BaseOperator"":
        
        dag = self.dag
        if not dag:
            raise RuntimeError(""Cannot unmap a task without a DAG"")
        dag._remove_task(self.task_id)
        if isinstance(self.operator_class, str):
            raise RuntimeError(""Cannot unmap a deserialized operator"")
        return self.operator_class(**self._get_unmap_kwargs())

        ```
    ###Assistant : Get the ""normal"" Operator after applying the current mapping.
    "
2069,"def require_tensorflow(test_case):
    
    if not is_tensorflow_available():
        return unittest.skip(""test requires TensorFlow"")(test_case)
    else:
        return test_case

","
    Decorator marking a test that requires TensorFlow installed. These tests are skipped when TensorFlow isn't
    installed
    ",16,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def require_tensorflow(test_case):
    
    if not is_tensorflow_available():
        return unittest.skip(""test requires TensorFlow"")(test_case)
    else:
        return test_case


        ```
    ###Assistant : 
    Decorator marking a test that requires TensorFlow installed. These tests are skipped when TensorFlow isn't
    installed
    
    "
2070,"def as_completed(fs, timeout=None):
    
    if timeout is not None:
        end_time = timeout + time.monotonic()

    fs = set(fs)
    total_futures = len(fs)
    with _AcquireFutures(fs):
        finished = set(
                f for f in fs
                if f._state in [CANCELLED_AND_NOTIFIED, FINISHED])
        pending = fs - finished
        waiter = _create_and_install_waiters(fs, _AS_COMPLETED)
    finished = list(finished)
    try:
        yield from _yield_finished_futures(finished, waiter,
                                           ref_collect=(fs,))

        while pending:
            if timeout is None:
                wait_timeout = None
            else:
                wait_timeout = end_time - time.monotonic()
                if wait_timeout < 0:
                    raise TimeoutError(
                            '%d (of %d) futures unfinished' % (
                            len(pending), total_futures))

            waiter.event.wait(wait_timeout)

            with waiter.lock:
                finished = waiter.finished_futures
                waiter.finished_futures = []
                waiter.event.clear()

            # reverse to keep finishing order
            finished.reverse()
            yield from _yield_finished_futures(finished, waiter,
                                               ref_collect=(fs, pending))

    finally:
        # Remove waiter from unfinished futures
        for f in fs:
            with f._condition:
                f._waiters.remove(waiter)

DoneAndNotDoneFutures = collections.namedtuple(
        'DoneAndNotDoneFutures', 'done not_done')","An iterator over the given futures that yields each as it completes.

    Args:
        fs: The sequence of Futures (possibly created by different Executors) to
            iterate over.
        timeout: The maximum number of seconds to wait. If None, then there
            is no limit on the wait time.

    Returns:
        An iterator that yields the given Futures as they complete (finished or
        cancelled). If any given Futures are duplicated, they will be returned
        once.

    Raises:
        TimeoutError: If the entire result iterator could not be generated
            before the given timeout.
    ",85,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def as_completed(fs, timeout=None):
    
    if timeout is not None:
        end_time = timeout + time.monotonic()

    fs = set(fs)
    total_futures = len(fs)
    with _AcquireFutures(fs):
        finished = set(
                f for f in fs
                if f._state in [CANCELLED_AND_NOTIFIED, FINISHED])
        pending = fs - finished
        waiter = _create_and_install_waiters(fs, _AS_COMPLETED)
    finished = list(finished)
    try:
        yield from _yield_finished_futures(finished, waiter,
                                           ref_collect=(fs,))

        while pending:
            if timeout is None:
                wait_timeout = None
            else:
                wait_timeout = end_time - time.monotonic()
                if wait_timeout < 0:
                    raise TimeoutError(
                            '%d (of %d) futures unfinished' % (
                            len(pending), total_futures))

            waiter.event.wait(wait_timeout)

            with waiter.lock:
                finished = waiter.finished_futures
                waiter.finished_futures = []
                waiter.event.clear()

            # reverse to keep finishing order
            finished.reverse()
            yield from _yield_finished_futures(finished, waiter,
                                               ref_collect=(fs, pending))

    finally:
        # Remove waiter from unfinished futures
        for f in fs:
            with f._condition:
                f._waiters.remove(waiter)

DoneAndNotDoneFutures = collections.namedtuple(
        'DoneAndNotDoneFutures', 'done not_done')
        ```
    ###Assistant : An iterator over the given futures that yields each as it completes.

    Args:
        fs: The sequence of Futures (possibly created by different Executors) to
            iterate over.
        timeout: The maximum number of seconds to wait. If None, then there
            is no limit on the wait time.

    Returns:
        An iterator that yields the given Futures as they complete (finished or
        cancelled). If any given Futures are duplicated, they will be returned
        once.

    Raises:
        TimeoutError: If the entire result iterator could not be generated
            before the given timeout.
    
    "
2071,"def pack_env_dict(self) -> Dict[str, Any]:
        
        env_info = {""window_size"": self.CONV_WIDTH,
                    ""reward_kwargs"": self.reward_params,
                    ""config"": self.config,
                    ""live"": self.live}
        if self.data_provider:
            env_info[""fee""] = self.data_provider._exchange \
                .get_fee(symbol=self.data_provider.current_whitelist()[0])  # type: ignore

        return env_info
","
        Create dictionary of environment arguments
        ",5,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pack_env_dict(self) -> Dict[str, Any]:
        
        env_info = {""window_size"": self.CONV_WIDTH,
                    ""reward_kwargs"": self.reward_params,
                    ""config"": self.config,
                    ""live"": self.live}
        if self.data_provider:
            env_info[""fee""] = self.data_provider._exchange \
                .get_fee(symbol=self.data_provider.current_whitelist()[0])  # type: ignore

        return env_info

        ```
    ###Assistant : 
        Create dictionary of environment arguments
        
    "
2072,"def sample(self) -> SampleBatchType:
        

        if self.fake_sampler and self.last_batch is not None:
            return self.last_batch
        elif self.input_reader is None:
            raise ValueError(
                ""RolloutWorker has no `input_reader` object! ""
                ""Cannot call `sample()`. You can try setting ""
                ""`create_env_on_driver` to True.""
            )

        if log_once(""sample_start""):
            logger.info(
                ""Generating sample batch of size {}"".format(
                    self.rollout_fragment_length
                )
            )

        batches = [self.input_reader.next()]
        steps_so_far = (
            batches[0].count
            if self.count_steps_by == ""env_steps""
            else batches[0].agent_steps()
        )

        # In truncate_episodes mode, never pull more than 1 batch per env.
        # This avoids over-running the target batch size.
        if self.batch_mode == ""truncate_episodes"":
            max_batches = self.num_envs
        else:
            max_batches = float(""inf"")

        while (
            steps_so_far < self.rollout_fragment_length and len(batches) < max_batches
        ):
            batch = self.input_reader.next()
            steps_so_far += (
                batch.count
                if self.count_steps_by == ""env_steps""
                else batch.agent_steps()
            )
            batches.append(batch)
        batch = batches[0].concat_samples(batches) if len(batches) > 1 else batches[0]

        self.callbacks.on_sample_end(worker=self, samples=batch)

        # Always do writes prior to compression for consistency and to allow
        # for better compression inside the writer.
        self.output_writer.write(batch)

        # Do off-policy estimation, if needed.
        if self.reward_estimators:
            for sub_batch in batch.split_by_episode():
                for estimator in self.reward_estimators:
                    estimator.process(sub_batch)

        if log_once(""sample_end""):
            logger.info(""Completed sample batch:\n\n{}\n"".format(summarize(batch)))

        if self.compress_observations:
            batch.compress(bulk=self.compress_observations == ""bulk"")

        if self.fake_sampler:
            self.last_batch = batch
        return batch
","Returns a batch of experience sampled from this worker.

        This method must be implemented by subclasses.

        Returns:
            A columnar batch of experiences (e.g., tensors).

        Examples:
            >>> import gym
            >>> from ray.rllib.evaluation.rollout_worker import RolloutWorker
            >>> from ray.rllib.algorithms.pg.pg_tf_policy import PGTFPolicy
            >>> worker = RolloutWorker( # doctest: +SKIP
            ...   env_creator=lambda _: gym.make(""CartPole-v0""), # doctest: +SKIP
            ...   policy_spec=PGTFPolicy) # doctest: +SKIP
            >>> print(worker.sample()) # doctest: +SKIP
            SampleBatch({""obs"": [...], ""action"": [...], ...})
        ",67,184,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sample(self) -> SampleBatchType:
        

        if self.fake_sampler and self.last_batch is not None:
            return self.last_batch
        elif self.input_reader is None:
            raise ValueError(
                ""RolloutWorker has no `input_reader` object! ""
                ""Cannot call `sample()`. You can try setting ""
                ""`create_env_on_driver` to True.""
            )

        if log_once(""sample_start""):
            logger.info(
                ""Generating sample batch of size {}"".format(
                    self.rollout_fragment_length
                )
            )

        batches = [self.input_reader.next()]
        steps_so_far = (
            batches[0].count
            if self.count_steps_by == ""env_steps""
            else batches[0].agent_steps()
        )

        # In truncate_episodes mode, never pull more than 1 batch per env.
        # This avoids over-running the target batch size.
        if self.batch_mode == ""truncate_episodes"":
            max_batches = self.num_envs
        else:
            max_batches = float(""inf"")

        while (
            steps_so_far < self.rollout_fragment_length and len(batches) < max_batches
        ):
            batch = self.input_reader.next()
            steps_so_far += (
                batch.count
                if self.count_steps_by == ""env_steps""
                else batch.agent_steps()
            )
            batches.append(batch)
        batch = batches[0].concat_samples(batches) if len(batches) > 1 else batches[0]

        self.callbacks.on_sample_end(worker=self, samples=batch)

        # Always do writes prior to compression for consistency and to allow
        # for better compression inside the writer.
        self.output_writer.write(batch)

        # Do off-policy estimation, if needed.
        if self.reward_estimators:
            for sub_batch in batch.split_by_episode():
                for estimator in self.reward_estimators:
                    estimator.process(sub_batch)

        if log_once(""sample_end""):
            logger.info(""Completed sample batch:\n\n{}\n"".format(summarize(batch)))

        if self.compress_observations:
            batch.compress(bulk=self.compress_observations == ""bulk"")

        if self.fake_sampler:
            self.last_batch = batch
        return batch

        ```
    ###Assistant : Returns a batch of experience sampled from this worker.

        This method must be implemented by subclasses.

        Returns:
            A columnar batch of experiences (e.g., tensors).

        Examples:
            >>> import gym
            >>> from ray.rllib.evaluation.rollout_worker import RolloutWorker
            >>> from ray.rllib.algorithms.pg.pg_tf_policy import PGTFPolicy
            >>> worker = RolloutWorker( # doctest: +SKIP
            ...   env_creator=lambda _: gym.make(""CartPole-v0""), # doctest: +SKIP
            ...   policy_spec=PGTFPolicy) # doctest: +SKIP
            >>> print(worker.sample()) # doctest: +SKIP
            SampleBatch({""obs"": [...], ""action"": [...], ...})
        
    "
2073,"def test_no_duplicates_for_non_unique_related_object_in_list_filter(self):
        
        parent = Parent.objects.create(name=""Mary"")
        # Two children with the same name
        Child.objects.create(parent=parent, name=""Daniel"")
        Child.objects.create(parent=parent, name=""Daniel"")

        m = ParentAdmin(Parent, custom_site)
        request = self.factory.get(""/parent/"", data={""child__name"": ""Daniel""})
        request.user = self.superuser

        cl = m.get_changelist_instance(request)
        # Exists() is applied.
        self.assertEqual(cl.queryset.count(), 1)
        # Queryset must be deletable.
        self.assertIs(cl.queryset.query.distinct, False)
        cl.queryset.delete()
        self.assertEqual(cl.queryset.count(), 0)
","
        Regressions tests for #15819: If a field listed in list_filters is a
        non-unique related object, results shouldn't appear more than once.
        ",21,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_no_duplicates_for_non_unique_related_object_in_list_filter(self):
        
        parent = Parent.objects.create(name=""Mary"")
        # Two children with the same name
        Child.objects.create(parent=parent, name=""Daniel"")
        Child.objects.create(parent=parent, name=""Daniel"")

        m = ParentAdmin(Parent, custom_site)
        request = self.factory.get(""/parent/"", data={""child__name"": ""Daniel""})
        request.user = self.superuser

        cl = m.get_changelist_instance(request)
        # Exists() is applied.
        self.assertEqual(cl.queryset.count(), 1)
        # Queryset must be deletable.
        self.assertIs(cl.queryset.query.distinct, False)
        cl.queryset.delete()
        self.assertEqual(cl.queryset.count(), 0)

        ```
    ###Assistant : 
        Regressions tests for #15819: If a field listed in list_filters is a
        non-unique related object, results shouldn't appear more than once.
        
    "
2074,"def get_current_tax_app() -> Optional[App]:
    
    return (
        App.objects.order_by(""pk"")
        .for_event_type(WebhookEventSyncType.CHECKOUT_CALCULATE_TAXES)
        .for_event_type(WebhookEventSyncType.ORDER_CALCULATE_TAXES)
        .last()
    )

","Return currently used tax app or None, if there aren't any.",11,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_current_tax_app() -> Optional[App]:
    
    return (
        App.objects.order_by(""pk"")
        .for_event_type(WebhookEventSyncType.CHECKOUT_CALCULATE_TAXES)
        .for_event_type(WebhookEventSyncType.ORDER_CALCULATE_TAXES)
        .last()
    )


        ```
    ###Assistant : Return currently used tax app or None, if there aren't any.
    "
2075,"def test_callbacks(self) -> None:
        
        cache: DeferredCache[str, int] = DeferredCache(""test"")
        callbacks = set()

        # start with an entry, with a callback
        cache.prefill(""k1"", 10, callback=lambda: callbacks.add(""prefill""))

        # now replace that entry with a pending result
        origin_d: ""defer.Deferred[int]"" = defer.Deferred()
        set_d = cache.set(""k1"", origin_d, callback=lambda: callbacks.add(""set""))

        # ... and also make a get request
        get_d = cache.get(""k1"", callback=lambda: callbacks.add(""get""))

        # we don't expect the invalidation callback for the original value to have
        # been called yet, even though get() will now return a different result.
        # I'm not sure if that is by design or not.
        self.assertEqual(callbacks, set())

        # now fire off all the deferreds
        origin_d.callback(20)
        self.assertEqual(self.successResultOf(set_d), 20)
        self.assertEqual(self.successResultOf(get_d), 20)

        # now the original invalidation callback should have been called, but none of
        # the others
        self.assertEqual(callbacks, {""prefill""})
        callbacks.clear()

        # another update should invalidate both the previous results
        cache.prefill(""k1"", 30)
        self.assertEqual(callbacks, {""set"", ""get""})
",Invalidation callbacks are called at the right time,8,140,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_callbacks(self) -> None:
        
        cache: DeferredCache[str, int] = DeferredCache(""test"")
        callbacks = set()

        # start with an entry, with a callback
        cache.prefill(""k1"", 10, callback=lambda: callbacks.add(""prefill""))

        # now replace that entry with a pending result
        origin_d: ""defer.Deferred[int]"" = defer.Deferred()
        set_d = cache.set(""k1"", origin_d, callback=lambda: callbacks.add(""set""))

        # ... and also make a get request
        get_d = cache.get(""k1"", callback=lambda: callbacks.add(""get""))

        # we don't expect the invalidation callback for the original value to have
        # been called yet, even though get() will now return a different result.
        # I'm not sure if that is by design or not.
        self.assertEqual(callbacks, set())

        # now fire off all the deferreds
        origin_d.callback(20)
        self.assertEqual(self.successResultOf(set_d), 20)
        self.assertEqual(self.successResultOf(get_d), 20)

        # now the original invalidation callback should have been called, but none of
        # the others
        self.assertEqual(callbacks, {""prefill""})
        callbacks.clear()

        # another update should invalidate both the previous results
        cache.prefill(""k1"", 30)
        self.assertEqual(callbacks, {""set"", ""get""})

        ```
    ###Assistant : Invalidation callbacks are called at the right time
    "
2076,"def sql_flush(style, connection, reset_sequences=True, allow_cascade=False):
    
    tables = connection.introspection.django_table_names(
        only_existing=True, include_views=False
    )
    return connection.ops.sql_flush(
        style,
        tables,
        reset_sequences=reset_sequences,
        allow_cascade=allow_cascade,
    )

","
    Return a list of the SQL statements used to flush the database.
    ",12,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sql_flush(style, connection, reset_sequences=True, allow_cascade=False):
    
    tables = connection.introspection.django_table_names(
        only_existing=True, include_views=False
    )
    return connection.ops.sql_flush(
        style,
        tables,
        reset_sequences=reset_sequences,
        allow_cascade=allow_cascade,
    )


        ```
    ###Assistant : 
    Return a list of the SQL statements used to flush the database.
    
    "
2077,"def generate(cls, size, callback, channels=3, target_mode=None):
        
        size_1d, size_2d, size_3d = cls._check_size(size)
        if channels not in (3, 4):
            raise ValueError(""Only 3 or 4 output channels are supported"")

        table = [0] * (size_1d * size_2d * size_3d * channels)
        idx_out = 0
        for b in range(size_3d):
            for g in range(size_2d):
                for r in range(size_1d):
                    table[idx_out : idx_out + channels] = callback(
                        r / (size_1d - 1), g / (size_2d - 1), b / (size_3d - 1)
                    )
                    idx_out += channels

        return cls(
            (size_1d, size_2d, size_3d),
            table,
            channels=channels,
            target_mode=target_mode,
            _copy_table=False,
        )
","Generates new LUT using provided callback.

        :param size: Size of the table. Passed to the constructor.
        :param callback: Function with three parameters which correspond
                         three color channels. Will be called ``size**3``
                         times with values from 0.0 to 1.0 and should return
                         a tuple with ``channels`` elements.
        :param channels: The number of channels which should return callback.
        :param target_mode: Passed to the constructor of the resulting
                            lookup table.
        ",67,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate(cls, size, callback, channels=3, target_mode=None):
        
        size_1d, size_2d, size_3d = cls._check_size(size)
        if channels not in (3, 4):
            raise ValueError(""Only 3 or 4 output channels are supported"")

        table = [0] * (size_1d * size_2d * size_3d * channels)
        idx_out = 0
        for b in range(size_3d):
            for g in range(size_2d):
                for r in range(size_1d):
                    table[idx_out : idx_out + channels] = callback(
                        r / (size_1d - 1), g / (size_2d - 1), b / (size_3d - 1)
                    )
                    idx_out += channels

        return cls(
            (size_1d, size_2d, size_3d),
            table,
            channels=channels,
            target_mode=target_mode,
            _copy_table=False,
        )

        ```
    ###Assistant : Generates new LUT using provided callback.

        :param size: Size of the table. Passed to the constructor.
        :param callback: Function with three parameters which correspond
                         three color channels. Will be called ``size**3``
                         times with values from 0.0 to 1.0 and should return
                         a tuple with ``channels`` elements.
        :param channels: The number of channels which should return callback.
        :param target_mode: Passed to the constructor of the resulting
                            lookup table.
        
    "
2078,"def regex_lookup(self, lookup_type):
        
        raise NotImplementedError(
            ""subclasses of BaseDatabaseOperations may require a regex_lookup() method""
        )
","
        Return the string to use in a query when performing regular expression
        lookups (using ""regex"" or ""iregex""). It should contain a '%s'
        placeholder for the column being searched against.

        If the feature is not supported (or part of it is not supported), raise
        NotImplementedError.
        ",44,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def regex_lookup(self, lookup_type):
        
        raise NotImplementedError(
            ""subclasses of BaseDatabaseOperations may require a regex_lookup() method""
        )

        ```
    ###Assistant : 
        Return the string to use in a query when performing regular expression
        lookups (using ""regex"" or ""iregex""). It should contain a '%s'
        placeholder for the column being searched against.

        If the feature is not supported (or part of it is not supported), raise
        NotImplementedError.
        
    "
2079,"def link_existing_conversations(doc, state):
	
	if doc.doctype != ""Contact"":
		return
	try:
		numbers = [d.phone for d in doc.phone_nos]

		for number in numbers:
			number = strip_number(number)
			if not number:
				continue
			logs = frappe.db.sql_list(
				,
				dict(phone_number=""%{}"".format(number), docname=doc.name, doctype=doc.doctype),
			)

			for log in logs:
				call_log = frappe.get_doc(""Call Log"", log)
				call_log.add_link(link_type=doc.doctype, link_name=doc.name)
				call_log.save(ignore_permissions=True)
			frappe.db.commit()
	except Exception:
		frappe.log_error(title=_(""Error during caller information update""))

","
	Called from hooks on creation of Contact or Lead to link all the existing conversations.
	
				SELECT cl.name FROM `tabCall Log` cl
				LEFT JOIN `tabDynamic Link` dl
				ON cl.name = dl.parent
				WHERE (cl.`from` like %(phone_number)s or cl.`to` like %(phone_number)s)
				GROUP BY cl.name
				HAVING SUM(
					CASE
						WHEN dl.link_doctype = %(doctype)s AND dl.link_name = %(docname)s
						THEN 1
						ELSE 0
					END
				)=0
			",58,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def link_existing_conversations(doc, state):
	
	if doc.doctype != ""Contact"":
		return
	try:
		numbers = [d.phone for d in doc.phone_nos]

		for number in numbers:
			number = strip_number(number)
			if not number:
				continue
			logs = frappe.db.sql_list(
				,
				dict(phone_number=""%{}"".format(number), docname=doc.name, doctype=doc.doctype),
			)

			for log in logs:
				call_log = frappe.get_doc(""Call Log"", log)
				call_log.add_link(link_type=doc.doctype, link_name=doc.name)
				call_log.save(ignore_permissions=True)
			frappe.db.commit()
	except Exception:
		frappe.log_error(title=_(""Error during caller information update""))


        ```
    ###Assistant : 
	Called from hooks on creation of Contact or Lead to link all the existing conversations.
	
				SELECT cl.name FROM `tabCall Log` cl
				LEFT JOIN `tabDynamic Link` dl
				ON cl.name = dl.parent
				WHERE (cl.`from` like %(phone_number)s or cl.`to` like %(phone_number)s)
				GROUP BY cl.name
				HAVING SUM(
					CASE
						WHEN dl.link_doctype = %(doctype)s AND dl.link_name = %(docname)s
						THEN 1
						ELSE 0
					END
				)=0
			
    "
2080,"async def async_volume_up(self) -> None:
        
        if hasattr(self, ""volume_up""):
            await self.hass.async_add_executor_job(self.volume_up)
            return

        if (
            self.volume_level is not None
            and self.volume_level < 1
            and self.supported_features & MediaPlayerEntityFeature.VOLUME_SET
        ):
            await self.async_set_volume_level(min(1, self.volume_level + 0.1))
","Turn volume up for media player.

        This method is a coroutine.
        ",11,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_volume_up(self) -> None:
        
        if hasattr(self, ""volume_up""):
            await self.hass.async_add_executor_job(self.volume_up)
            return

        if (
            self.volume_level is not None
            and self.volume_level < 1
            and self.supported_features & MediaPlayerEntityFeature.VOLUME_SET
        ):
            await self.async_set_volume_level(min(1, self.volume_level + 0.1))

        ```
    ###Assistant : Turn volume up for media player.

        This method is a coroutine.
        
    "
2081,"def infer_axes(self) -> bool:
        
        s = self.storable
        if s is None:
            return False
        self.get_attrs()
        return True
","
        infer the axes of my storer
        return a boolean indicating if we have a valid storer or not
        ",18,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def infer_axes(self) -> bool:
        
        s = self.storable
        if s is None:
            return False
        self.get_attrs()
        return True

        ```
    ###Assistant : 
        infer the axes of my storer
        return a boolean indicating if we have a valid storer or not
        
    "
2082,"def _validate_set_axis(self, new_labels, old_labels):
        
        new_labels = ensure_index(new_labels)
        old_len = len(old_labels)
        new_len = len(new_labels)
        if old_len != new_len:
            raise ValueError(
                f""Length mismatch: Expected axis has {old_len} elements, ""
                + ""new values have {new_len} elements""
            )
        return new_labels
","
        Validate the possibility of replacement of old labels with the new labels.

        Parameters
        ----------
        new_labels : list-like
            The labels to replace with.
        old_labels : list-like
            The labels to replace.

        Returns
        -------
        list-like
            The validated labels.
        ",35,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate_set_axis(self, new_labels, old_labels):
        
        new_labels = ensure_index(new_labels)
        old_len = len(old_labels)
        new_len = len(new_labels)
        if old_len != new_len:
            raise ValueError(
                f""Length mismatch: Expected axis has {old_len} elements, ""
                + ""new values have {new_len} elements""
            )
        return new_labels

        ```
    ###Assistant : 
        Validate the possibility of replacement of old labels with the new labels.

        Parameters
        ----------
        new_labels : list-like
            The labels to replace with.
        old_labels : list-like
            The labels to replace.

        Returns
        -------
        list-like
            The validated labels.
        
    "
2083,"def transform_data(result, translated_columns, query_builder) -> EventsResponse:
    
    final_result: EventsResponse = {""data"": result[""data""], ""meta"": result[""meta""]}
    for col in final_result[""meta""]:
        # Translate back column names that were converted to snuba format
        col[""name""] = translated_columns.get(col[""name""], col[""name""])
","
    Transform internal names back to the public schema ones.

    When getting timeseries results via rollup, this function will
    zerofill the output results.
    ",22,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transform_data(result, translated_columns, query_builder) -> EventsResponse:
    
    final_result: EventsResponse = {""data"": result[""data""], ""meta"": result[""meta""]}
    for col in final_result[""meta""]:
        # Translate back column names that were converted to snuba format
        col[""name""] = translated_columns.get(col[""name""], col[""name""])

        ```
    ###Assistant : 
    Transform internal names back to the public schema ones.

    When getting timeseries results via rollup, this function will
    zerofill the output results.
    
    "
2084,"def plot_contour(*args, show=True, **kwargs):
    

    args = list(map(sympify, args))
    plot_expr = check_arguments(args, 1, 2)
    series = [ContourSeries(*arg) for arg in plot_expr]
    plot_contours = Plot(*series, **kwargs)
    if len(plot_expr[0].free_symbols) > 2:
        raise ValueError('Contour Plot cannot Plot for more than two variables.')
    if show:
        plot_contours.show()
    return plot_contours
","
    Draws contour plot of a function

    Usage
    =====

    Single plot

    ``plot_contour(expr, range_x, range_y, **kwargs)``

    If the ranges are not specified, then a default range of (-10, 10) is used.

    Multiple plot with the same range.

    ``plot_contour(expr1, expr2, range_x, range_y, **kwargs)``

    If the ranges are not specified, then a default range of (-10, 10) is used.

    Multiple plots with different ranges.

    ``plot_contour((expr1, range_x, range_y), (expr2, range_x, range_y), ..., **kwargs)``

    Ranges have to be specified for every expression.

    Default range may change in the future if a more advanced default range
    detection algorithm is implemented.

    Arguments
    =========

    expr : Expression representing the function along x.

    range_x : (:class:`Symbol`, float, float)
        A 3-tuple denoting the range of the x variable, e.g. (x, 0, 5).

    range_y : (:class:`Symbol`, float, float)
        A 3-tuple denoting the range of the y variable, e.g. (y, 0, 5).

    Keyword Arguments
    =================

    Arguments for ``ContourSeries`` class:

    nb_of_points_x : int
        The x range is sampled uniformly at ``nb_of_points_x`` of points.

    nb_of_points_y : int
        The y range is sampled uniformly at ``nb_of_points_y`` of points.

    Aesthetics:

    surface_color : Function which returns a float
        Specifies the color for the surface of the plot. See
        :class:`sympy.plotting.Plot` for more details.

    If there are multiple plots, then the same series arguments are applied to
    all the plots. If you want to set these options separately, you can index
    the returned ``Plot`` object and set it.

    Arguments for ``Plot`` class:

    title : str
        Title of the plot.

    size : (float, float), optional
        A tuple in the form (width, height) in inches to specify the size of
        the overall figure. The default value is set to ``None``, meaning
        the size will be set by the default backend.

    See Also
    ========

    Plot, ContourSeries

    ",283,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def plot_contour(*args, show=True, **kwargs):
    

    args = list(map(sympify, args))
    plot_expr = check_arguments(args, 1, 2)
    series = [ContourSeries(*arg) for arg in plot_expr]
    plot_contours = Plot(*series, **kwargs)
    if len(plot_expr[0].free_symbols) > 2:
        raise ValueError('Contour Plot cannot Plot for more than two variables.')
    if show:
        plot_contours.show()
    return plot_contours

        ```
    ###Assistant : 
    Draws contour plot of a function

    Usage
    =====

    Single plot

    ``plot_contour(expr, range_x, range_y, **kwargs)``

    If the ranges are not specified, then a default range of (-10, 10) is used.

    Multiple plot with the same range.

    ``plot_contour(expr1, expr2, range_x, range_y, **kwargs)``

    If the ranges are not specified, then a default range of (-10, 10) is used.

    Multiple plots with different ranges.

    ``plot_contour((expr1, range_x, range_y), (expr2, range_x, range_y), ..., **kwargs)``

    Ranges have to be specified for every expression.

    Default range may change in the future if a more advanced default range
    detection algorithm is implemented.

    Arguments
    =========

    expr : Expression representing the function along x.

    range_x : (:class:`Symbol`, float, float)
        A 3-tuple denoting the range of the x variable, e.g. (x, 0, 5).

    range_y : (:class:`Symbol`, float, float)
        A 3-tuple denoting the range of the y variable, e.g. (y, 0, 5).

    Keyword Arguments
    =================

    Arguments for ``ContourSeries`` class:

    nb_of_points_x : int
        The x range is sampled uniformly at ``nb_of_points_x`` of points.

    nb_of_points_y : int
        The y range is sampled uniformly at ``nb_of_points_y`` of points.

    Aesthetics:

    surface_color : Function which returns a float
        Specifies the color for the surface of the plot. See
        :class:`sympy.plotting.Plot` for more details.

    If there are multiple plots, then the same series arguments are applied to
    all the plots. If you want to set these options separately, you can index
    the returned ``Plot`` object and set it.

    Arguments for ``Plot`` class:

    title : str
        Title of the plot.

    size : (float, float), optional
        A tuple in the form (width, height) in inches to specify the size of
        the overall figure. The default value is set to ``None``, meaning
        the size will be set by the default backend.

    See Also
    ========

    Plot, ContourSeries

    
    "
2085,"def peek(self, n=0):
        
        self._check_can_read()
        # Relies on the undocumented fact that BufferedReader.peek()
        # always returns at least one byte (except at EOF), independent
        # of the value of n
        return self._buffer.peek(n)
","Return buffered data without advancing the file position.

        Always returns at least one byte of data, unless at EOF.
        The exact number of bytes returned is unspecified.
        ",27,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def peek(self, n=0):
        
        self._check_can_read()
        # Relies on the undocumented fact that BufferedReader.peek()
        # always returns at least one byte (except at EOF), independent
        # of the value of n
        return self._buffer.peek(n)

        ```
    ###Assistant : Return buffered data without advancing the file position.

        Always returns at least one byte of data, unless at EOF.
        The exact number of bytes returned is unspecified.
        
    "
2086,"def cache_full(self) -> bool:
        
        if self._cache_info[""cache_full""]:
            return self._cache_info[""cache_full""]
        with self._lock:
            return self._cache_info[""cache_full""]
","bool: ``True`` if the cache has been fully populated. ``False`` if there are items still
        to be cached. ",18,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cache_full(self) -> bool:
        
        if self._cache_info[""cache_full""]:
            return self._cache_info[""cache_full""]
        with self._lock:
            return self._cache_info[""cache_full""]

        ```
    ###Assistant : bool: ``True`` if the cache has been fully populated. ``False`` if there are items still
        to be cached. 
    "
2087,"def get_queryset(self, request):
        
        if self.queryset is None:
            raise ImproperlyConfigured(
                f""{self.__class__.__name__} does not define a queryset. Set queryset on the class or ""
                f""override its get_queryset() method.""
            )
        return self.queryset.all()

","
        Return the base queryset for the view. By default, this returns self.queryset.all().

        Args:
            request: The current request
        ",17,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_queryset(self, request):
        
        if self.queryset is None:
            raise ImproperlyConfigured(
                f""{self.__class__.__name__} does not define a queryset. Set queryset on the class or ""
                f""override its get_queryset() method.""
            )
        return self.queryset.all()


        ```
    ###Assistant : 
        Return the base queryset for the view. By default, this returns self.queryset.all().

        Args:
            request: The current request
        
    "
2088,"def _preprocess(self, inputs):
        
        inputs = self._check_input_text(inputs)
        self._max_cls_len = 5
        num_workers = self.kwargs[
            'num_workers'] if 'num_workers' in self.kwargs else 0
        lazy_load = self.kwargs[
            'lazy_load'] if 'lazy_load' in self.kwargs else False

        # Prompt template: input_text + ""是"" + ""[MASK]"" * cls_seq_length
        prompt_template = [""是""] + [""[MASK]""] * self._max_cls_len
","
        Create the dataset and dataloader for the predict.
        ",8,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _preprocess(self, inputs):
        
        inputs = self._check_input_text(inputs)
        self._max_cls_len = 5
        num_workers = self.kwargs[
            'num_workers'] if 'num_workers' in self.kwargs else 0
        lazy_load = self.kwargs[
            'lazy_load'] if 'lazy_load' in self.kwargs else False

        # Prompt template: input_text + ""是"" + ""[MASK]"" * cls_seq_length
        prompt_template = [""是""] + [""[MASK]""] * self._max_cls_len

        ```
    ###Assistant : 
        Create the dataset and dataloader for the predict.
        
    "
2089,"def session_destroy(consul_url=None, token=None, session=None, **kwargs):
    
    ret = {}
    if not consul_url:
        consul_url = _get_config()
        if not consul_url:
            log.error(""No Consul URL found."")
            ret[""message""] = ""No Consul URL found.""
            ret[""res""] = False
            return ret

    if not session:
        raise SaltInvocationError('Required argument ""session"" is missing.')

    query_params = {}

    if ""dc"" in kwargs:
        query_params[""dc""] = kwargs[""dc""]

    function = ""session/destroy/{}"".format(session)
    res = _query(
        consul_url=consul_url,
        function=function,
        token=token,
        method=""PUT"",
        query_params=query_params,
    )
    if res[""res""]:
        ret[""res""] = True
        ret[""message""] = ""Destroyed Session {}."".format(session)
    else:
        ret[""res""] = False
        ret[""message""] = ""Unable to destroy session {}."".format(session)
    return ret

","
    Destroy session

    :param consul_url: The Consul server URL.
    :param session: The ID of the session to destroy.
    :param dc: By default, the datacenter of the agent is queried;
               however, the dc can be provided using the ""dc"" parameter.
    :return: Boolean & message of success or failure.

    CLI Example:

    .. code-block:: bash

        salt '*' consul.session_destroy session='c1c4d223-91cb-3d1f-1ee8-f2af9e7b6716'

    ",55,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def session_destroy(consul_url=None, token=None, session=None, **kwargs):
    
    ret = {}
    if not consul_url:
        consul_url = _get_config()
        if not consul_url:
            log.error(""No Consul URL found."")
            ret[""message""] = ""No Consul URL found.""
            ret[""res""] = False
            return ret

    if not session:
        raise SaltInvocationError('Required argument ""session"" is missing.')

    query_params = {}

    if ""dc"" in kwargs:
        query_params[""dc""] = kwargs[""dc""]

    function = ""session/destroy/{}"".format(session)
    res = _query(
        consul_url=consul_url,
        function=function,
        token=token,
        method=""PUT"",
        query_params=query_params,
    )
    if res[""res""]:
        ret[""res""] = True
        ret[""message""] = ""Destroyed Session {}."".format(session)
    else:
        ret[""res""] = False
        ret[""message""] = ""Unable to destroy session {}."".format(session)
    return ret


        ```
    ###Assistant : 
    Destroy session

    :param consul_url: The Consul server URL.
    :param session: The ID of the session to destroy.
    :param dc: By default, the datacenter of the agent is queried;
               however, the dc can be provided using the ""dc"" parameter.
    :return: Boolean & message of success or failure.

    CLI Example:

    .. code-block:: bash

        salt '*' consul.session_destroy session='c1c4d223-91cb-3d1f-1ee8-f2af9e7b6716'

    
    "
2090,"def post_save_action(cls, info, instance, cleaned_input):
        
        manager = load_plugin_manager(info.context)
        cls.call_event(manager.collection_updated, instance)
",Override this method with `pass` to avoid triggering product webhook.,10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def post_save_action(cls, info, instance, cleaned_input):
        
        manager = load_plugin_manager(info.context)
        cls.call_event(manager.collection_updated, instance)

        ```
    ###Assistant : Override this method with `pass` to avoid triggering product webhook.
    "
2091,"def _deserialize_metric(metric_config):
    
    from keras import (
        metrics as metrics_module,
    )  # pylint:disable=g-import-not-at-top

    if metric_config in [""accuracy"", ""acc"", ""crossentropy"", ""ce""]:
        # Do not deserialize accuracy and cross-entropy strings as we have special
        # case handling for these in compile, based on model output shape.
        return metric_config
    return metrics_module.deserialize(metric_config)

","Deserialize metrics, leaving special strings untouched.",6,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _deserialize_metric(metric_config):
    
    from keras import (
        metrics as metrics_module,
    )  # pylint:disable=g-import-not-at-top

    if metric_config in [""accuracy"", ""acc"", ""crossentropy"", ""ce""]:
        # Do not deserialize accuracy and cross-entropy strings as we have special
        # case handling for these in compile, based on model output shape.
        return metric_config
    return metrics_module.deserialize(metric_config)


        ```
    ###Assistant : Deserialize metrics, leaving special strings untouched.
    "
2092,"def softsign(x):
    
    return tf.math.softsign(x)


@keras_export(""keras.activations.swish"")
@tf.__internal__.dispatch.add_dispatch_support","Softsign activation function, `softsign(x) = x / (abs(x) + 1)`.

    Example Usage:

    >>> a = tf.constant([-1.0, 0.0, 1.0], dtype = tf.float32)
    >>> b = tf.keras.activations.softsign(a)
    >>> b.numpy()
    array([-0.5,  0. ,  0.5], dtype=float32)

    Args:
        x: Input tensor.

    Returns:
        The softsign activation: `x / (abs(x) + 1)`.
    ",45,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def softsign(x):
    
    return tf.math.softsign(x)


@keras_export(""keras.activations.swish"")
@tf.__internal__.dispatch.add_dispatch_support
        ```
    ###Assistant : Softsign activation function, `softsign(x) = x / (abs(x) + 1)`.

    Example Usage:

    >>> a = tf.constant([-1.0, 0.0, 1.0], dtype = tf.float32)
    >>> b = tf.keras.activations.softsign(a)
    >>> b.numpy()
    array([-0.5,  0. ,  0.5], dtype=float32)

    Args:
        x: Input tensor.

    Returns:
        The softsign activation: `x / (abs(x) + 1)`.
    
    "
2093,"def compute_inlier_metric(self) -> None:
        

        import scipy.stats as ss
    
        nmb_previous_points = self.data['InlierMetric_nmb_points']
        weibull_percentile = self.data['InlierMetric_weib_perc']

        train_ft_df = self.data_dictionary['train_features']
        train_ft_df_reindexed = train_ft_df.reindex(
            index=np.flip(train_ft_df.index) 
        )

        pairwise = pd.DataFrame(
            np.triu(
                pairwise_distances(train_ft_df_reindexed, n_jobs=self.thread_count)
            ),
            columns=train_ft_df_reindexed.index,
            index=train_ft_df_reindexed.index
        )
        pairwise = pairwise.round(5)

        column_labels = [
            '{}{}'.format('d', i) for i in range(1, nmb_previous_points+1)
        ]
        distances = pd.DataFrame(
            columns=column_labels, index=train_ft_df.index
        )
        for index in train_ft_df.index[nmb_previous_points]:
            current_row = pairwise.loc[[index]]
            current_row_no_zeros = current_row.loc[
                :, (current_row!=0).any(axis=0)
            ]
            distances.loc[[index]] = current_row_no_zeros.iloc[
                :, :nmb_previous_points
            ]
        distances = distances.replace([np.inf, -np.inf], np.nan)
        drop_index = pd.isnull(distances).any(1)
        distances = distances[drop_index==0]

        inliers = pd.DataFrame(index=distances.index)
        for key in distances.keys():
            current_distances = distances[key].dropna()
            fit_params = ss.weibull_min.fit(current_distances)
            cutoff = ss.weibull_min.ppf(weibull_percentile, *fit_params)
            is_inlier = np.where(
                current_distances<=cutoff, 1, 0
            )
            df_inlier = pd.DataFrame(
                {key+'_IsInlier':is_inlier}, index=distances.index
            )
            inliers = pd.concat(
                [inliers, df_inlier], axis=1
            )

        self.data_dictionary['train_features'] = pd.DataFrame(
            data=inliers.sum(axis=1)/nmb_previous_points,
            columns=['inlier_metric'],
            index = train_ft_df.index
        )

        percent_outliers = np.round(
            100*(1-self.data_dictionary['iniler_metric'].sum()/
            len(train_ft_df.index)), 2
        )
        logger.info('{percent_outliers}%% of data points were identified as outliers')

        return None
","
        
        Compute inlier metric from backwards distance distributions. 
        This metric defines how well features from a timepoint fit 
        into previous timepoints.
        ",20,145,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compute_inlier_metric(self) -> None:
        

        import scipy.stats as ss
    
        nmb_previous_points = self.data['InlierMetric_nmb_points']
        weibull_percentile = self.data['InlierMetric_weib_perc']

        train_ft_df = self.data_dictionary['train_features']
        train_ft_df_reindexed = train_ft_df.reindex(
            index=np.flip(train_ft_df.index) 
        )

        pairwise = pd.DataFrame(
            np.triu(
                pairwise_distances(train_ft_df_reindexed, n_jobs=self.thread_count)
            ),
            columns=train_ft_df_reindexed.index,
            index=train_ft_df_reindexed.index
        )
        pairwise = pairwise.round(5)

        column_labels = [
            '{}{}'.format('d', i) for i in range(1, nmb_previous_points+1)
        ]
        distances = pd.DataFrame(
            columns=column_labels, index=train_ft_df.index
        )
        for index in train_ft_df.index[nmb_previous_points]:
            current_row = pairwise.loc[[index]]
            current_row_no_zeros = current_row.loc[
                :, (current_row!=0).any(axis=0)
            ]
            distances.loc[[index]] = current_row_no_zeros.iloc[
                :, :nmb_previous_points
            ]
        distances = distances.replace([np.inf, -np.inf], np.nan)
        drop_index = pd.isnull(distances).any(1)
        distances = distances[drop_index==0]

        inliers = pd.DataFrame(index=distances.index)
        for key in distances.keys():
            current_distances = distances[key].dropna()
            fit_params = ss.weibull_min.fit(current_distances)
            cutoff = ss.weibull_min.ppf(weibull_percentile, *fit_params)
            is_inlier = np.where(
                current_distances<=cutoff, 1, 0
            )
            df_inlier = pd.DataFrame(
                {key+'_IsInlier':is_inlier}, index=distances.index
            )
            inliers = pd.concat(
                [inliers, df_inlier], axis=1
            )

        self.data_dictionary['train_features'] = pd.DataFrame(
            data=inliers.sum(axis=1)/nmb_previous_points,
            columns=['inlier_metric'],
            index = train_ft_df.index
        )

        percent_outliers = np.round(
            100*(1-self.data_dictionary['iniler_metric'].sum()/
            len(train_ft_df.index)), 2
        )
        logger.info('{percent_outliers}%% of data points were identified as outliers')

        return None

        ```
    ###Assistant : 
        
        Compute inlier metric from backwards distance distributions. 
        This metric defines how well features from a timepoint fit 
        into previous timepoints.
        
    "
2094,"def items_view(self, traverser, items):
        
        if len(items) == 1:
            traverser(items[0])
            self.write("","")
        else:
            self.interleave(lambda: self.write("", ""), traverser, items)
","Traverse and separate the given *items* with a comma and append it to
        the buffer. If *items* is a single item sequence, a trailing comma
        will be added.",28,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def items_view(self, traverser, items):
        
        if len(items) == 1:
            traverser(items[0])
            self.write("","")
        else:
            self.interleave(lambda: self.write("", ""), traverser, items)

        ```
    ###Assistant : Traverse and separate the given *items* with a comma and append it to
        the buffer. If *items* is a single item sequence, a trailing comma
        will be added.
    "
2095,"def refresh_stats(self) -> None:
        
        try:
            self._mallctl(""epoch"", read=False, write=1)
        except Exception as e:
            logger.warning(""Failed to reload jemalloc stats: %s"", e)
","Request that jemalloc updates its internal statistics. This needs to
        be called before querying for stats, otherwise it will return stale
        values.
        ",22,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def refresh_stats(self) -> None:
        
        try:
            self._mallctl(""epoch"", read=False, write=1)
        except Exception as e:
            logger.warning(""Failed to reload jemalloc stats: %s"", e)

        ```
    ###Assistant : Request that jemalloc updates its internal statistics. This needs to
        be called before querying for stats, otherwise it will return stale
        values.
        
    "
2096,"def test_to_numpy_array_multiweight_reduction(func, expected):
    
    G = nx.MultiDiGraph()
    weights = [-1, 2, 10.0]
    for w in weights:
        G.add_edge(0, 1, weight=w)
    A = nx.to_numpy_array(G, multigraph_weight=func, dtype=float)
    assert np.allclose(A, [[0, expected], [0, 0]])

    # Undirected case
    A = nx.to_numpy_array(G.to_undirected(), multigraph_weight=func, dtype=float)
    assert np.allclose(A, [[0, expected], [expected, 0]])
",Test various functions for reducing multiedge weights.,7,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_to_numpy_array_multiweight_reduction(func, expected):
    
    G = nx.MultiDiGraph()
    weights = [-1, 2, 10.0]
    for w in weights:
        G.add_edge(0, 1, weight=w)
    A = nx.to_numpy_array(G, multigraph_weight=func, dtype=float)
    assert np.allclose(A, [[0, expected], [0, 0]])

    # Undirected case
    A = nx.to_numpy_array(G.to_undirected(), multigraph_weight=func, dtype=float)
    assert np.allclose(A, [[0, expected], [expected, 0]])

        ```
    ###Assistant : Test various functions for reducing multiedge weights.
    "
2097,"def test_vr_connector_causal_slice(self):
        
        view_rq_dict = {
            ""state"": ViewRequirement(""obs""),
            # shift array should be [-2, -1, 0]
            ""prev_states"": ViewRequirement(""obs"", shift=""-2:0""),
            # shift array should be [-4, -2, 0]
            ""prev_strided_states_even"": ViewRequirement(""obs"", shift=""-4:0:2""),
            # shift array should be [-3, -1]
            ""prev_strided_states_odd"": ViewRequirement(""obs"", shift=""-3:0:2""),
        }

        obs_arrs = np.arange(10)[:, None] + 1
        config = PPOConfig().to_dict()
        ctx = ConnectorContext(
            view_requirements=view_rq_dict, config=config, is_policy_recurrent=True
        )
        c = ViewRequirementAgentConnector(ctx)

        # keep a queue of observations
        obs_list = []
        for t, obs in enumerate(obs_arrs):
            # t=0 is the next state of t=-1
            data = AgentConnectorDataType(
                0, 1, {SampleBatch.NEXT_OBS: obs, SampleBatch.T: t - 1}
            )
            processed = c([data])
            for_action = processed[0].data.for_action

            if t == 0:
                obs_list.extend([obs for _ in range(5)])
            else:
                # remove the first obs and add the current obs to the end
                obs_list.pop(0)
                obs_list.append(obs)

            # check state
            check(for_action[""state""], obs[None])

            # check prev_states
            check(
                for_action[""prev_states""],
                np.stack(obs_list)[np.array([-3, -2, -1])][None],
            )

            # check prev_strided_states_even
            check(
                for_action[""prev_strided_states_even""],
                np.stack(obs_list)[np.array([-5, -3, -1])][None],
            )

            check(
                for_action[""prev_strided_states_odd""],
                np.stack(obs_list)[np.array([-4, -2])][None],
            )
",Test that the ViewRequirementConnector can handle slice shifts correctly.,9,152,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_vr_connector_causal_slice(self):
        
        view_rq_dict = {
            ""state"": ViewRequirement(""obs""),
            # shift array should be [-2, -1, 0]
            ""prev_states"": ViewRequirement(""obs"", shift=""-2:0""),
            # shift array should be [-4, -2, 0]
            ""prev_strided_states_even"": ViewRequirement(""obs"", shift=""-4:0:2""),
            # shift array should be [-3, -1]
            ""prev_strided_states_odd"": ViewRequirement(""obs"", shift=""-3:0:2""),
        }

        obs_arrs = np.arange(10)[:, None] + 1
        config = PPOConfig().to_dict()
        ctx = ConnectorContext(
            view_requirements=view_rq_dict, config=config, is_policy_recurrent=True
        )
        c = ViewRequirementAgentConnector(ctx)

        # keep a queue of observations
        obs_list = []
        for t, obs in enumerate(obs_arrs):
            # t=0 is the next state of t=-1
            data = AgentConnectorDataType(
                0, 1, {SampleBatch.NEXT_OBS: obs, SampleBatch.T: t - 1}
            )
            processed = c([data])
            for_action = processed[0].data.for_action

            if t == 0:
                obs_list.extend([obs for _ in range(5)])
            else:
                # remove the first obs and add the current obs to the end
                obs_list.pop(0)
                obs_list.append(obs)

            # check state
            check(for_action[""state""], obs[None])

            # check prev_states
            check(
                for_action[""prev_states""],
                np.stack(obs_list)[np.array([-3, -2, -1])][None],
            )

            # check prev_strided_states_even
            check(
                for_action[""prev_strided_states_even""],
                np.stack(obs_list)[np.array([-5, -3, -1])][None],
            )

            check(
                for_action[""prev_strided_states_odd""],
                np.stack(obs_list)[np.array([-4, -2])][None],
            )

        ```
    ###Assistant : Test that the ViewRequirementConnector can handle slice shifts correctly.
    "
2098,"def render(self) -> RenderableType:
        
        return Padding(
            Align.right(FigletText(self.value), vertical=""middle""),
            (0, 1),
            style=""white on rgb(51,51,51)"",
        )

",Build a Rich renderable to render the calculator display.,9,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def render(self) -> RenderableType:
        
        return Padding(
            Align.right(FigletText(self.value), vertical=""middle""),
            (0, 1),
            style=""white on rgb(51,51,51)"",
        )


        ```
    ###Assistant : Build a Rich renderable to render the calculator display.
    "
2099,"def in_place_subclassed_model_state_restoration(model):
    
    assert not model._is_graph_network
    # Restore layers and build attributes
    if (
        hasattr(model, ""_original_attributes_cache"")
        and model._original_attributes_cache is not None
    ):
        # Models have sticky attribute assignment, so we want to be careful to
        # add back the previous attributes and track Layers by their original
        # names without adding dependencies on ""utility"" attributes which Models
        # exempt when they're constructed.
        setattr_tracking = model._setattr_tracking
        model._setattr_tracking = False
        model._self_tracked_trackables = []
        for name, value in model._original_attributes_cache.items():
            setattr(model, name, value)
            if isinstance(value, Layer):
                model._self_tracked_trackables.append(value)
        model._original_attributes_cache = None
        model._setattr_tracking = setattr_tracking
    else:
        # Restore to the state of a never-called model.
        _reset_build_compile_trackers(model)


@keras_export(""keras.__internal__.models.clone_and_build_model"", v1=[])","Restores the original state of a model after it was ""reset"".

    This undoes this action of `_in_place_subclassed_model_reset`, which is
    called in `clone_and_build_model` if `in_place_reset` is set to True.

    Args:
      model: Instance of a Keras model created via subclassing, on which
        `_in_place_subclassed_model_reset` was previously called.
    ",44,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def in_place_subclassed_model_state_restoration(model):
    
    assert not model._is_graph_network
    # Restore layers and build attributes
    if (
        hasattr(model, ""_original_attributes_cache"")
        and model._original_attributes_cache is not None
    ):
        # Models have sticky attribute assignment, so we want to be careful to
        # add back the previous attributes and track Layers by their original
        # names without adding dependencies on ""utility"" attributes which Models
        # exempt when they're constructed.
        setattr_tracking = model._setattr_tracking
        model._setattr_tracking = False
        model._self_tracked_trackables = []
        for name, value in model._original_attributes_cache.items():
            setattr(model, name, value)
            if isinstance(value, Layer):
                model._self_tracked_trackables.append(value)
        model._original_attributes_cache = None
        model._setattr_tracking = setattr_tracking
    else:
        # Restore to the state of a never-called model.
        _reset_build_compile_trackers(model)


@keras_export(""keras.__internal__.models.clone_and_build_model"", v1=[])
        ```
    ###Assistant : Restores the original state of a model after it was ""reset"".

    This undoes this action of `_in_place_subclassed_model_reset`, which is
    called in `clone_and_build_model` if `in_place_reset` is set to True.

    Args:
      model: Instance of a Keras model created via subclassing, on which
        `_in_place_subclassed_model_reset` was previously called.
    
    "
2100,"def _imaginary_unit_as_coefficient(arg):
    
    if getattr(arg, 'is_real', True):
        return None
    else:
        return arg.as_coefficient(S.ImaginaryUnit)


@sympify_method_args", Helper to extract symbolic coefficient for imaginary unit ,8,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _imaginary_unit_as_coefficient(arg):
    
    if getattr(arg, 'is_real', True):
        return None
    else:
        return arg.as_coefficient(S.ImaginaryUnit)


@sympify_method_args
        ```
    ###Assistant :  Helper to extract symbolic coefficient for imaginary unit 
    "
2101,"def from_config(cls, config, custom_objects=None):
        
        if ""learning_rate"" in config:
            if isinstance(config[""learning_rate""], dict):
                config[""learning_rate""] = learning_rate_schedule.deserialize(
                    config[""learning_rate""], custom_objects=custom_objects
                )
        return cls(**config)
","Creates an optimizer from its config.

        This method is the reverse of `get_config`, capable of instantiating the
        same optimizer from the config dictionary.

        Args:
            config: A Python dictionary, typically the output of get_config.
            custom_objects: A Python dictionary mapping names to additional
              user-defined Python objects needed to recreate this optimizer.

        Returns:
            An optimizer instance.
        ",53,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def from_config(cls, config, custom_objects=None):
        
        if ""learning_rate"" in config:
            if isinstance(config[""learning_rate""], dict):
                config[""learning_rate""] = learning_rate_schedule.deserialize(
                    config[""learning_rate""], custom_objects=custom_objects
                )
        return cls(**config)

        ```
    ###Assistant : Creates an optimizer from its config.

        This method is the reverse of `get_config`, capable of instantiating the
        same optimizer from the config dictionary.

        Args:
            config: A Python dictionary, typically the output of get_config.
            custom_objects: A Python dictionary mapping names to additional
              user-defined Python objects needed to recreate this optimizer.

        Returns:
            An optimizer instance.
        
    "
2102,"def test_generate_pdf_from_mail(self):
        
        mail = self.parser.get_parsed(os.path.join(self.SAMPLE_FILES, ""html.eml""))

        pdf_path = os.path.join(self.parser.tempdir, ""html.eml.pdf"")

        with open(pdf_path, ""wb"") as file:
            file.write(self.parser.generate_pdf_from_mail(mail))

        converted = os.path.join(
            self.parser.tempdir,
            ""html.eml.pdf.webp"",
        )
        run_convert(
            density=300,
            scale=""500x5000>"",
            alpha=""remove"",
            strip=True,
            trim=False,
            auto_orient=True,
            input_file=f""{pdf_path}"",  # Do net define an index to convert all pages.
            output_file=converted,
            logging_group=None,
        )
        self.assertTrue(os.path.isfile(converted))
        thumb_hash = self.imagehash(converted)

        # The created pdf is not reproducible. But the converted image should always look the same.
        expected_hash = self.imagehash(
            os.path.join(self.SAMPLE_FILES, ""html.eml.pdf.webp""),
        )
        self.assertEqual(
            thumb_hash,
            expected_hash,
            f""PDF looks different. Check if {converted} looks weird."",
        )
","
        GIVEN:
            - Fresh start
        WHEN:
            - pdf generation from simple eml file is requested
        THEN:
            - gotenberg is called and the resulting file is returned and look as expected.
        ",29,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_generate_pdf_from_mail(self):
        
        mail = self.parser.get_parsed(os.path.join(self.SAMPLE_FILES, ""html.eml""))

        pdf_path = os.path.join(self.parser.tempdir, ""html.eml.pdf"")

        with open(pdf_path, ""wb"") as file:
            file.write(self.parser.generate_pdf_from_mail(mail))

        converted = os.path.join(
            self.parser.tempdir,
            ""html.eml.pdf.webp"",
        )
        run_convert(
            density=300,
            scale=""500x5000>"",
            alpha=""remove"",
            strip=True,
            trim=False,
            auto_orient=True,
            input_file=f""{pdf_path}"",  # Do net define an index to convert all pages.
            output_file=converted,
            logging_group=None,
        )
        self.assertTrue(os.path.isfile(converted))
        thumb_hash = self.imagehash(converted)

        # The created pdf is not reproducible. But the converted image should always look the same.
        expected_hash = self.imagehash(
            os.path.join(self.SAMPLE_FILES, ""html.eml.pdf.webp""),
        )
        self.assertEqual(
            thumb_hash,
            expected_hash,
            f""PDF looks different. Check if {converted} looks weird."",
        )

        ```
    ###Assistant : 
        GIVEN:
            - Fresh start
        WHEN:
            - pdf generation from simple eml file is requested
        THEN:
            - gotenberg is called and the resulting file is returned and look as expected.
        
    "
2103,"def new(self, degree=0, color=None):
            
            (center_x, center_y, angle, inner_radius, outer_radius,
             outer_color, pointer_color, origin_color, line_width) = self.all
            pointer_color = color or pointer_color
            if self.figure != []:
                for figure in self.figure:
                    self.graph_elem.DeleteFigure(figure)
                self.figure = []
            d = degree - 90
            self.all[2] = degree
            dx1 = int(2 * inner_radius * math.sin(d / 180 * math.pi))
            dy1 = int(2 * inner_radius * math.cos(d / 180 * math.pi))
            dx2 = int(outer_radius * math.sin(d / 180 * math.pi))
            dy2 = int(outer_radius * math.cos(d / 180 * math.pi))
            self.figure.append(self.graph_elem.DrawLine((center_x - dx1, center_y - dy1),
                                                        (center_x + dx2, center_y + dy2),
                                                        color=pointer_color, width=line_width))
            self.figure.append(self.graph_elem.DrawCircle((center_x, center_y), inner_radius,
                                                          fill_color=origin_color, line_color=outer_color, line_width=line_width))
","
            Draw new pointer by angle, erase old pointer if exist
            degree defined as clockwise from negative x-axis.
            ",17,100,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def new(self, degree=0, color=None):
            
            (center_x, center_y, angle, inner_radius, outer_radius,
             outer_color, pointer_color, origin_color, line_width) = self.all
            pointer_color = color or pointer_color
            if self.figure != []:
                for figure in self.figure:
                    self.graph_elem.DeleteFigure(figure)
                self.figure = []
            d = degree - 90
            self.all[2] = degree
            dx1 = int(2 * inner_radius * math.sin(d / 180 * math.pi))
            dy1 = int(2 * inner_radius * math.cos(d / 180 * math.pi))
            dx2 = int(outer_radius * math.sin(d / 180 * math.pi))
            dy2 = int(outer_radius * math.cos(d / 180 * math.pi))
            self.figure.append(self.graph_elem.DrawLine((center_x - dx1, center_y - dy1),
                                                        (center_x + dx2, center_y + dy2),
                                                        color=pointer_color, width=line_width))
            self.figure.append(self.graph_elem.DrawCircle((center_x, center_y), inner_radius,
                                                          fill_color=origin_color, line_color=outer_color, line_width=line_width))

        ```
    ###Assistant : 
            Draw new pointer by angle, erase old pointer if exist
            degree defined as clockwise from negative x-axis.
            
    "
2104,"def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization=True):
    
    results = data_out.copy_to_cpu()
    lod = data_out.lod()[0]

    check_dir(output_dir)

    if paths:
        assert type(paths) is list, ""type(paths) is not list.""
        if handle_id < len(paths):
            unhandled_paths = paths[handle_id:]
            unhandled_paths_num = len(unhandled_paths)
        else:
            unhandled_paths_num = 0
    if images is not None:
        if handle_id < len(images):
            unhandled_paths = None
            unhandled_paths_num = len(images) - handle_id
        else:
            unhandled_paths_num = 0

    output = list()
    for index in range(len(lod) - 1):
        output_i = {'data': []}
        if unhandled_paths and index < unhandled_paths_num:
            org_img_path = unhandled_paths[index]
            org_img = Image.open(org_img_path)
        else:
            org_img = images[index - unhandled_paths_num]
            org_img = org_img.astype(np.uint8)
            org_img = Image.fromarray(org_img[:, :, ::-1])
            if visualization:
                org_img_path = get_save_image_name(org_img, output_dir, 'image_numpy_{}'.format((handle_id + index)))
                org_img.save(org_img_path)
        org_img_height = org_img.height
        org_img_width = org_img.width
        result_i = results[lod[index]:lod[index + 1]]
        for row in result_i:
            if len(row) != 6:
                continue
            if row[1] < score_thresh:
                continue
            category_id = int(row[0])
            confidence = row[1]
            bbox = row[2:]
            dt = {}
            dt['label'] = label_names[category_id]
            dt['confidence'] = float(confidence)
            dt['left'], dt['top'], dt['right'], dt['bottom'] = clip_bbox(bbox, org_img_width, org_img_height)
            output_i['data'].append(dt)

        output.append(output_i)
        if visualization:
            output_i['save_path'] = draw_bounding_box_on_image(org_img_path, output_i['data'], output_dir)

    return output
","
    postprocess the lod_tensor produced by Executor.run

    Args:
        paths (list[str]): The paths of images.
        images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
        data_out (lod_tensor): data output of predictor.
        output_dir (str): The path to store output images.
        visualization (bool): Whether to save image or not.
        score_thresh (float): the low limit of bounding box.
        label_names (list[str]): label names.
        handle_id (int): The number of images that have been handled.

    Returns:
        res (list[dict]): The result of vehicles detecion. keys include 'data', 'save_path', the corresponding value is:
            data (dict): the result of object detection, keys include 'left', 'top', 'right', 'bottom', 'label', 'confidence', the corresponding value is:
                left (float): The X coordinate of the upper left corner of the bounding box;
                top (float): The Y coordinate of the upper left corner of the bounding box;
                right (float): The X coordinate of the lower right corner of the bounding box;
                bottom (float): The Y coordinate of the lower right corner of the bounding box;
                label (str): The label of detection result;
                confidence (float): The confidence of detection result.
            save_path (str): The path to save output images.
    ",181,172,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization=True):
    
    results = data_out.copy_to_cpu()
    lod = data_out.lod()[0]

    check_dir(output_dir)

    if paths:
        assert type(paths) is list, ""type(paths) is not list.""
        if handle_id < len(paths):
            unhandled_paths = paths[handle_id:]
            unhandled_paths_num = len(unhandled_paths)
        else:
            unhandled_paths_num = 0
    if images is not None:
        if handle_id < len(images):
            unhandled_paths = None
            unhandled_paths_num = len(images) - handle_id
        else:
            unhandled_paths_num = 0

    output = list()
    for index in range(len(lod) - 1):
        output_i = {'data': []}
        if unhandled_paths and index < unhandled_paths_num:
            org_img_path = unhandled_paths[index]
            org_img = Image.open(org_img_path)
        else:
            org_img = images[index - unhandled_paths_num]
            org_img = org_img.astype(np.uint8)
            org_img = Image.fromarray(org_img[:, :, ::-1])
            if visualization:
                org_img_path = get_save_image_name(org_img, output_dir, 'image_numpy_{}'.format((handle_id + index)))
                org_img.save(org_img_path)
        org_img_height = org_img.height
        org_img_width = org_img.width
        result_i = results[lod[index]:lod[index + 1]]
        for row in result_i:
            if len(row) != 6:
                continue
            if row[1] < score_thresh:
                continue
            category_id = int(row[0])
            confidence = row[1]
            bbox = row[2:]
            dt = {}
            dt['label'] = label_names[category_id]
            dt['confidence'] = float(confidence)
            dt['left'], dt['top'], dt['right'], dt['bottom'] = clip_bbox(bbox, org_img_width, org_img_height)
            output_i['data'].append(dt)

        output.append(output_i)
        if visualization:
            output_i['save_path'] = draw_bounding_box_on_image(org_img_path, output_i['data'], output_dir)

    return output

        ```
    ###Assistant : 
    postprocess the lod_tensor produced by Executor.run

    Args:
        paths (list[str]): The paths of images.
        images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
        data_out (lod_tensor): data output of predictor.
        output_dir (str): The path to store output images.
        visualization (bool): Whether to save image or not.
        score_thresh (float): the low limit of bounding box.
        label_names (list[str]): label names.
        handle_id (int): The number of images that have been handled.

    Returns:
        res (list[dict]): The result of vehicles detecion. keys include 'data', 'save_path', the corresponding value is:
            data (dict): the result of object detection, keys include 'left', 'top', 'right', 'bottom', 'label', 'confidence', the corresponding value is:
                left (float): The X coordinate of the upper left corner of the bounding box;
                top (float): The Y coordinate of the upper left corner of the bounding box;
                right (float): The X coordinate of the lower right corner of the bounding box;
                bottom (float): The Y coordinate of the lower right corner of the bounding box;
                label (str): The label of detection result;
                confidence (float): The confidence of detection result.
            save_path (str): The path to save output images.
    
    "
2105,"def series_with_multilevel_index() -> Series:
    
    arrays = [
        [""bar"", ""bar"", ""baz"", ""baz"", ""qux"", ""qux"", ""foo"", ""foo""],
        [""one"", ""two"", ""one"", ""two"", ""one"", ""two"", ""one"", ""two""],
    ]
    tuples = zip(*arrays)
    index = MultiIndex.from_tuples(tuples)
    data = np.random.randn(8)
    ser = Series(data, index=index)
    ser[3] = np.NaN
    return ser


_narrow_series = {
    f""{dtype.__name__}-series"": tm.make_rand_series(name=""a"", dtype=dtype)
    for dtype in tm.NARROW_NP_DTYPES
}


_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}


@pytest.fixture(params=_index_or_series_objs.keys())","
    Fixture with a Series with a 2-level MultiIndex.
    ",8,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def series_with_multilevel_index() -> Series:
    
    arrays = [
        [""bar"", ""bar"", ""baz"", ""baz"", ""qux"", ""qux"", ""foo"", ""foo""],
        [""one"", ""two"", ""one"", ""two"", ""one"", ""two"", ""one"", ""two""],
    ]
    tuples = zip(*arrays)
    index = MultiIndex.from_tuples(tuples)
    data = np.random.randn(8)
    ser = Series(data, index=index)
    ser[3] = np.NaN
    return ser


_narrow_series = {
    f""{dtype.__name__}-series"": tm.make_rand_series(name=""a"", dtype=dtype)
    for dtype in tm.NARROW_NP_DTYPES
}


_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}


@pytest.fixture(params=_index_or_series_objs.keys())
        ```
    ###Assistant : 
    Fixture with a Series with a 2-level MultiIndex.
    
    "
2106,"def _async_stop(self) -> None:
        
        if self._async_unsub_state_changed:
            self._async_unsub_state_changed()
            self._async_unsub_state_changed = None
","Unregister the group from Home Assistant.

        This method must be run in the event loop.
        ",15,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _async_stop(self) -> None:
        
        if self._async_unsub_state_changed:
            self._async_unsub_state_changed()
            self._async_unsub_state_changed = None

        ```
    ###Assistant : Unregister the group from Home Assistant.

        This method must be run in the event loop.
        
    "
2107,"def test_database_connection_url(generate_test_database_connection_url):
    
    url = generate_test_database_connection_url
    if url is None:
        yield None
    else:
        # TODO: https://github.com/PrefectHQ/orion/issues/2045
        # Also temporarily override the environment variable, so that child
        # subprocesses that we spin off are correctly configured as well
        original_envvar = os.environ.get(""PREFECT_ORION_DATABASE_CONNECTION_URL"")
        os.environ[""PREFECT_ORION_DATABASE_CONNECTION_URL""] = url

        with temporary_settings({PREFECT_ORION_DATABASE_CONNECTION_URL: url}):
            yield url

        os.environ[""PREFECT_ORION_DATABASE_CONNECTION_URL""] = original_envvar


@pytest.fixture(scope=""session"")","
    Update the setting for the database connection url to the generated value from
    `generate_test_database_connection_url`

    This _must_ be separate from the generation of the test url because async fixtures
    are run in a separate context from the test suite.
    ",38,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_database_connection_url(generate_test_database_connection_url):
    
    url = generate_test_database_connection_url
    if url is None:
        yield None
    else:
        # TODO: https://github.com/PrefectHQ/orion/issues/2045
        # Also temporarily override the environment variable, so that child
        # subprocesses that we spin off are correctly configured as well
        original_envvar = os.environ.get(""PREFECT_ORION_DATABASE_CONNECTION_URL"")
        os.environ[""PREFECT_ORION_DATABASE_CONNECTION_URL""] = url

        with temporary_settings({PREFECT_ORION_DATABASE_CONNECTION_URL: url}):
            yield url

        os.environ[""PREFECT_ORION_DATABASE_CONNECTION_URL""] = original_envvar


@pytest.fixture(scope=""session"")
        ```
    ###Assistant : 
    Update the setting for the database connection url to the generated value from
    `generate_test_database_connection_url`

    This _must_ be separate from the generation of the test url because async fixtures
    are run in a separate context from the test suite.
    
    "
2108,"def define_by_run_func(trial) -> Optional[Dict[str, Any]]:
    
    # This param is not used in the objective function.
    activation = trial.suggest_categorical(""activation"", [""relu"", ""tanh""])
    trial.suggest_float(""width"", 0, 20)
    trial.suggest_float(""height"", -100, 100)

    # Define-by-run allows for conditional search spaces.
    if activation == ""relu"":
        trial.suggest_float(""mult"", 1, 2)

    # Return all constants in a dictionary.
    return {""steps"": 100}

","Define-by-run function to create the search space.

    Ensure no actual computation takes place here. That should go into
    the trainable passed to ``Tuner`` (in this example, that's
    ``easy_objective``).

    For more information, see https://optuna.readthedocs.io/en/stable\
/tutorial/10_key_features/002_configurations.html

    This function should either return None or a dict with constant values.
    ",46,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def define_by_run_func(trial) -> Optional[Dict[str, Any]]:
    
    # This param is not used in the objective function.
    activation = trial.suggest_categorical(""activation"", [""relu"", ""tanh""])
    trial.suggest_float(""width"", 0, 20)
    trial.suggest_float(""height"", -100, 100)

    # Define-by-run allows for conditional search spaces.
    if activation == ""relu"":
        trial.suggest_float(""mult"", 1, 2)

    # Return all constants in a dictionary.
    return {""steps"": 100}


        ```
    ###Assistant : Define-by-run function to create the search space.

    Ensure no actual computation takes place here. That should go into
    the trainable passed to ``Tuner`` (in this example, that's
    ``easy_objective``).

    For more information, see https://optuna.readthedocs.io/en/stable\
/tutorial/10_key_features/002_configurations.html

    This function should either return None or a dict with constant values.
    
    "
2109,"def _compile_to_sklearn(self, expr):
        
        sklearn_pipeline_str = generate_pipeline_code(
            expr_to_tree(expr, self._pset), self.operators
        )
        sklearn_pipeline = eval(sklearn_pipeline_str, self.operators_context)
        sklearn_pipeline.memory = self._memory
        if self.random_state:
            # Fix random state when the operator allows
            set_param_recursive(
                sklearn_pipeline.steps, ""random_state"", self.random_state
            )
        return sklearn_pipeline
","Compile a DEAP pipeline into a sklearn pipeline.

        Parameters
        ----------
        expr: DEAP individual
            The DEAP pipeline to be compiled

        Returns
        -------
        sklearn_pipeline: sklearn.pipeline.Pipeline
        ",23,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _compile_to_sklearn(self, expr):
        
        sklearn_pipeline_str = generate_pipeline_code(
            expr_to_tree(expr, self._pset), self.operators
        )
        sklearn_pipeline = eval(sklearn_pipeline_str, self.operators_context)
        sklearn_pipeline.memory = self._memory
        if self.random_state:
            # Fix random state when the operator allows
            set_param_recursive(
                sklearn_pipeline.steps, ""random_state"", self.random_state
            )
        return sklearn_pipeline

        ```
    ###Assistant : Compile a DEAP pipeline into a sklearn pipeline.

        Parameters
        ----------
        expr: DEAP individual
            The DEAP pipeline to be compiled

        Returns
        -------
        sklearn_pipeline: sklearn.pipeline.Pipeline
        
    "
2110,"def equals(self, other, failing_expression=False):
        
        if self.shape != getattr(other, 'shape', None):
            return False

        rv = True
        for i in range(self.rows):
            for j in range(self.cols):
                ans = self[i, j].equals(other[i, j], failing_expression)
                if ans is False:
                    return False
                elif ans is not True and rv is True:
                    rv = ans
        return rv

","Applies ``equals`` to corresponding elements of the matrices,
        trying to prove that the elements are equivalent, returning True
        if they are, False if any pair is not, and None (or the first
        failing expression if failing_expression is True) if it cannot
        be decided if the expressions are equivalent or not. This is, in
        general, an expensive operation.

        Examples
        ========

        >>> from sympy import Matrix
        >>> from sympy.abc import x
        >>> A = Matrix([x*(x - 1), 0])
        >>> B = Matrix([x**2 - x, 0])
        >>> A == B
        False
        >>> A.simplify() == B.simplify()
        True
        >>> A.equals(B)
        True
        >>> A.equals(2)
        False

        See Also
        ========
        sympy.core.expr.Expr.equals
        ",103,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def equals(self, other, failing_expression=False):
        
        if self.shape != getattr(other, 'shape', None):
            return False

        rv = True
        for i in range(self.rows):
            for j in range(self.cols):
                ans = self[i, j].equals(other[i, j], failing_expression)
                if ans is False:
                    return False
                elif ans is not True and rv is True:
                    rv = ans
        return rv


        ```
    ###Assistant : Applies ``equals`` to corresponding elements of the matrices,
        trying to prove that the elements are equivalent, returning True
        if they are, False if any pair is not, and None (or the first
        failing expression if failing_expression is True) if it cannot
        be decided if the expressions are equivalent or not. This is, in
        general, an expensive operation.

        Examples
        ========

        >>> from sympy import Matrix
        >>> from sympy.abc import x
        >>> A = Matrix([x*(x - 1), 0])
        >>> B = Matrix([x**2 - x, 0])
        >>> A == B
        False
        >>> A.simplify() == B.simplify()
        True
        >>> A.equals(B)
        True
        >>> A.equals(2)
        False

        See Also
        ========
        sympy.core.expr.Expr.equals
        
    "
2111,"def load(self, loader):
        loader.add_option(
            ""block_list"", Sequence[str], [],
            
        )
","
            Block matching requests and return an empty response with the specified HTTP status.
            Option syntax is ""/flow-filter/status-code"", where flow-filter describes
            which requests this rule should be applied to and status-code is the HTTP status code to return for
            blocked requests. The separator (""/"" in the example) can be any character.
            Setting a non-standard status code of 444 will close the connection without sending a response.
            ",65,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load(self, loader):
        loader.add_option(
            ""block_list"", Sequence[str], [],
            
        )

        ```
    ###Assistant : 
            Block matching requests and return an empty response with the specified HTTP status.
            Option syntax is ""/flow-filter/status-code"", where flow-filter describes
            which requests this rule should be applied to and status-code is the HTTP status code to return for
            blocked requests. The separator (""/"" in the example) can be any character.
            Setting a non-standard status code of 444 will close the connection without sending a response.
            
    "
2112,"def train(self, tagged_docs):
        
        m1 = [self.source_lang_vec.dv[item.tags].flatten() for item in tagged_docs]
        m2 = [self.target_lang_vec.dv[item.tags].flatten() for item in tagged_docs]

        self.translation_matrix = np.linalg.lstsq(m2, m1, -1)[0]
        return self.translation_matrix
","Build the translation matrix to map from the source model's vectors to target model's vectors

        Parameters
        ----------
        tagged_docs : list of :class:`~gensim.models.doc2vec.TaggedDocument`, Documents
            that will be used for training, both the source language document vector and
            target language document vector trained on those tagged documents.

        Returns
        -------
        numpy.ndarray
            Translation matrix that maps from the source model's vectors to target model's vectors.

        ",61,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def train(self, tagged_docs):
        
        m1 = [self.source_lang_vec.dv[item.tags].flatten() for item in tagged_docs]
        m2 = [self.target_lang_vec.dv[item.tags].flatten() for item in tagged_docs]

        self.translation_matrix = np.linalg.lstsq(m2, m1, -1)[0]
        return self.translation_matrix

        ```
    ###Assistant : Build the translation matrix to map from the source model's vectors to target model's vectors

        Parameters
        ----------
        tagged_docs : list of :class:`~gensim.models.doc2vec.TaggedDocument`, Documents
            that will be used for training, both the source language document vector and
            target language document vector trained on those tagged documents.

        Returns
        -------
        numpy.ndarray
            Translation matrix that maps from the source model's vectors to target model's vectors.

        
    "
2113,"def serialize(input, tree=""etree"", encoding=None, **serializer_opts):
    
    # XXX: Should we cache this?
    walker = treewalkers.getTreeWalker(tree)
    s = HTMLSerializer(**serializer_opts)
    return s.render(walker(input), encoding)

","Serializes the input token stream using the specified treewalker

    :arg input: the token stream to serialize

    :arg tree: the treewalker to use

    :arg encoding: the encoding to use

    :arg serializer_opts: any options to pass to the
        :py:class:`html5lib.serializer.HTMLSerializer` that gets created

    :returns: the tree serialized as a string

    Example:

    >>> from html5lib.html5parser import parse
    >>> from html5lib.serializer import serialize
    >>> token_stream = parse('<html><body><p>Hi!</p></body></html>')
    >>> serialize(token_stream, omit_optional_tags=False)
    '<html><head></head><body><p>Hi!</p></body></html>'

    ",66,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serialize(input, tree=""etree"", encoding=None, **serializer_opts):
    
    # XXX: Should we cache this?
    walker = treewalkers.getTreeWalker(tree)
    s = HTMLSerializer(**serializer_opts)
    return s.render(walker(input), encoding)


        ```
    ###Assistant : Serializes the input token stream using the specified treewalker

    :arg input: the token stream to serialize

    :arg tree: the treewalker to use

    :arg encoding: the encoding to use

    :arg serializer_opts: any options to pass to the
        :py:class:`html5lib.serializer.HTMLSerializer` that gets created

    :returns: the tree serialized as a string

    Example:

    >>> from html5lib.html5parser import parse
    >>> from html5lib.serializer import serialize
    >>> token_stream = parse('<html><body><p>Hi!</p></body></html>')
    >>> serialize(token_stream, omit_optional_tags=False)
    '<html><head></head><body><p>Hi!</p></body></html>'

    
    "
2114,"def set_client_cli_parser(parser=None):
    
    if not parser:
        from jina.parsers.base import set_base_parser

        parser = set_base_parser()

    from jina.parsers.peapods.runtimes.remote import mixin_client_gateway_parser
    from jina.parsers.client import (
        mixin_client_features_parser,
        mixin_comm_protocol_parser,
    )

    mixin_client_gateway_parser(parser)
    mixin_client_features_parser(parser)
    mixin_comm_protocol_parser(parser)

    return parser

","Set the parser for the cli client

    :param parser: an optional existing parser to build upon
    :return: the parser
    ",19,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_client_cli_parser(parser=None):
    
    if not parser:
        from jina.parsers.base import set_base_parser

        parser = set_base_parser()

    from jina.parsers.peapods.runtimes.remote import mixin_client_gateway_parser
    from jina.parsers.client import (
        mixin_client_features_parser,
        mixin_comm_protocol_parser,
    )

    mixin_client_gateway_parser(parser)
    mixin_client_features_parser(parser)
    mixin_comm_protocol_parser(parser)

    return parser


        ```
    ###Assistant : Set the parser for the cli client

    :param parser: an optional existing parser to build upon
    :return: the parser
    
    "
2115,"def send_sale_toggle_notification(info, instance, catalogue):
        
        manager = info.context.plugins
        now = datetime.now(pytz.utc)

        start_date = instance.start_date
        end_date = instance.end_date

        if (start_date and start_date <= now) and (not end_date or not end_date <= now):
            manager.sale_toggle(instance, catalogue)
            instance.notification_sent_datetime = now
            instance.save(update_fields=[""notification_sent_datetime""])
","Send a notification about starting or ending sale if it hasn't been sent yet.

        Send the notification when the start date is before the current date and the
        sale is not already finished.
        ",33,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def send_sale_toggle_notification(info, instance, catalogue):
        
        manager = info.context.plugins
        now = datetime.now(pytz.utc)

        start_date = instance.start_date
        end_date = instance.end_date

        if (start_date and start_date <= now) and (not end_date or not end_date <= now):
            manager.sale_toggle(instance, catalogue)
            instance.notification_sent_datetime = now
            instance.save(update_fields=[""notification_sent_datetime""])

        ```
    ###Assistant : Send a notification about starting or ending sale if it hasn't been sent yet.

        Send the notification when the start date is before the current date and the
        sale is not already finished.
        
    "
2116,"def bcoo_todense(data, indices, *, spinfo):
  
  return bcoo_todense_p.bind(jnp.asarray(data), jnp.asarray(indices), spinfo=spinfo)

@bcoo_todense_p.def_impl","Convert batched sparse matrix to a dense matrix.

  Args:
    data : array of shape ``batch_dims + (nse,) + block_dims``.
    indices : array of shape ``batch_dims + (n_sparse, nse)``
    spinfo : BCOOInfo. In particular, this includes the shape
      of the matrix, which is equal to ``batch_dims + sparse_dims + block_dims``
      where ``len(sparse_dims) == n_sparse``

  Returns:
    mat : array with specified shape and dtype matching ``data``
  ",64,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def bcoo_todense(data, indices, *, spinfo):
  
  return bcoo_todense_p.bind(jnp.asarray(data), jnp.asarray(indices), spinfo=spinfo)

@bcoo_todense_p.def_impl
        ```
    ###Assistant : Convert batched sparse matrix to a dense matrix.

  Args:
    data : array of shape ``batch_dims + (nse,) + block_dims``.
    indices : array of shape ``batch_dims + (n_sparse, nse)``
    spinfo : BCOOInfo. In particular, this includes the shape
      of the matrix, which is equal to ``batch_dims + sparse_dims + block_dims``
      where ``len(sparse_dims) == n_sparse``

  Returns:
    mat : array with specified shape and dtype matching ``data``
  
    "
2117,"def ignore_ray_errors(self) -> Iterator[ResultOrError]:
        
        return self._Iterator(
            [r for r in self.result_or_errors if not isinstance(r.get(), RayError)]
        )

","Return an iterator over the results, skipping only Ray errors.

        Similar to ignore_errors, but only skips Errors raised from the
        Ray framework. This is useful for application that wants to handle
        errors from user code differently.
        ",36,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ignore_ray_errors(self) -> Iterator[ResultOrError]:
        
        return self._Iterator(
            [r for r in self.result_or_errors if not isinstance(r.get(), RayError)]
        )


        ```
    ###Assistant : Return an iterator over the results, skipping only Ray errors.

        Similar to ignore_errors, but only skips Errors raised from the
        Ray framework. This is useful for application that wants to handle
        errors from user code differently.
        
    "
2118,"def certificate_was_accepted(self) -> None:
        
        if not self.is_overridable():
            return False
        if self._certificate_accepted is None:
            raise ValueError(""No decision taken yet"")
        return self._certificate_accepted


@dataclasses.dataclass",Check whether the certificate was accepted by the user.,9,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def certificate_was_accepted(self) -> None:
        
        if not self.is_overridable():
            return False
        if self._certificate_accepted is None:
            raise ValueError(""No decision taken yet"")
        return self._certificate_accepted


@dataclasses.dataclass
        ```
    ###Assistant : Check whether the certificate was accepted by the user.
    "
2119,"def mask(self, row_indices, col_indices):
        
        return (
            self.force_materialization()
            .list_of_partitions_to_combine[0]
            .mask(row_indices, col_indices)
        )
","
        Create (synchronously) a mask that extracts the indices provided.

        Parameters
        ----------
        row_indices : list-like, slice or label
            The row labels for the rows to extract.
        col_indices : list-like, slice or label
            The column labels for the columns to extract.

        Returns
        -------
        PandasOnRayDataframeVirtualPartition
            A new ``PandasOnRayDataframeVirtualPartition`` object,
            materialized.
        ",47,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mask(self, row_indices, col_indices):
        
        return (
            self.force_materialization()
            .list_of_partitions_to_combine[0]
            .mask(row_indices, col_indices)
        )

        ```
    ###Assistant : 
        Create (synchronously) a mask that extracts the indices provided.

        Parameters
        ----------
        row_indices : list-like, slice or label
            The row labels for the rows to extract.
        col_indices : list-like, slice or label
            The column labels for the columns to extract.

        Returns
        -------
        PandasOnRayDataframeVirtualPartition
            A new ``PandasOnRayDataframeVirtualPartition`` object,
            materialized.
        
    "
2120,"def left_integral3D(facets, index, expr, vertices, hp_param, degree):
    
    value = S.Zero
    facet = facets[index]
    x0 = vertices[facet[0]]
    facet_len = len(facet)
    for i, fac in enumerate(facet):
        side = (vertices[fac], vertices[facet[(i + 1) % facet_len]])
        value += distance_to_side(x0, side, hp_param[0]) * \
            lineseg_integrate(facet, i, side, expr, degree)
    return value

","Computes the left integral of Eq 10 in Chin et al.

    Explanation
    ===========

    For the 3D case, this is the sum of the integral values over constituting
    line segments of the face (which is accessed by facets[index]) multiplied
    by the distance between the first point of facet and that line segment.

    Parameters
    ==========

    facets :
        List of faces of the 3-Polytope.
    index :
        Index of face over which integral is to be calculated.
    expr :
        Input polynomial.
    vertices :
        List of vertices that constitute the 3-Polytope.
    hp_param :
        The hyperplane parameters of the face.
    degree :
        Degree of the ``expr``.

    Examples
    ========

    >>> from sympy.integrals.intpoly import left_integral3D
    >>> cube = [[(0, 0, 0), (0, 0, 5), (0, 5, 0), (0, 5, 5), (5, 0, 0),\
                 (5, 0, 5), (5, 5, 0), (5, 5, 5)],\
                 [2, 6, 7, 3], [3, 7, 5, 1], [7, 6, 4, 5], [1, 5, 4, 0],\
                 [3, 1, 0, 2], [0, 4, 6, 2]]
    >>> facets = cube[1:]
    >>> vertices = cube[0]
    >>> left_integral3D(facets, 3, 1, vertices, ([0, -1, 0], -5), 0)
    -50
    ",177,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def left_integral3D(facets, index, expr, vertices, hp_param, degree):
    
    value = S.Zero
    facet = facets[index]
    x0 = vertices[facet[0]]
    facet_len = len(facet)
    for i, fac in enumerate(facet):
        side = (vertices[fac], vertices[facet[(i + 1) % facet_len]])
        value += distance_to_side(x0, side, hp_param[0]) * \
            lineseg_integrate(facet, i, side, expr, degree)
    return value


        ```
    ###Assistant : Computes the left integral of Eq 10 in Chin et al.

    Explanation
    ===========

    For the 3D case, this is the sum of the integral values over constituting
    line segments of the face (which is accessed by facets[index]) multiplied
    by the distance between the first point of facet and that line segment.

    Parameters
    ==========

    facets :
        List of faces of the 3-Polytope.
    index :
        Index of face over which integral is to be calculated.
    expr :
        Input polynomial.
    vertices :
        List of vertices that constitute the 3-Polytope.
    hp_param :
        The hyperplane parameters of the face.
    degree :
        Degree of the ``expr``.

    Examples
    ========

    >>> from sympy.integrals.intpoly import left_integral3D
    >>> cube = [[(0, 0, 0), (0, 0, 5), (0, 5, 0), (0, 5, 5), (5, 0, 0),\
                 (5, 0, 5), (5, 5, 0), (5, 5, 5)],\
                 [2, 6, 7, 3], [3, 7, 5, 1], [7, 6, 4, 5], [1, 5, 4, 0],\
                 [3, 1, 0, 2], [0, 4, 6, 2]]
    >>> facets = cube[1:]
    >>> vertices = cube[0]
    >>> left_integral3D(facets, 3, 1, vertices, ([0, -1, 0], -5), 0)
    -50
    
    "
2121,"def xdropout(self, inputs):
        
        mask = tf.cast(
            1
            - tf.compat.v1.distributions.Bernoulli(probs=1.0 - self.drop_prob).sample(sample_shape=shape_list(inputs)),
            tf.bool,
        )
        scale = tf.convert_to_tensor(1.0 / (1 - self.drop_prob), dtype=tf.float32)
        if self.drop_prob > 0:
            inputs = tf.where(mask, 0.0, inputs) * scale
","
        Applies dropout to the inputs, as vanilla dropout, but also scales the remaining elements up by 1/drop_prob.
        ",17,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def xdropout(self, inputs):
        
        mask = tf.cast(
            1
            - tf.compat.v1.distributions.Bernoulli(probs=1.0 - self.drop_prob).sample(sample_shape=shape_list(inputs)),
            tf.bool,
        )
        scale = tf.convert_to_tensor(1.0 / (1 - self.drop_prob), dtype=tf.float32)
        if self.drop_prob > 0:
            inputs = tf.where(mask, 0.0, inputs) * scale

        ```
    ###Assistant : 
        Applies dropout to the inputs, as vanilla dropout, but also scales the remaining elements up by 1/drop_prob.
        
    "
2122,"def test_deltas_that_melt_dataframes(self):
        
        deltas = self._get_deltas_that_melt_dataframes()

        for delta in deltas:
            el = delta(DATAFRAME)
            el._legacy_add_rows(NEW_ROWS)

            df_proto = _get_data_frame(self.get_delta_from_queue())

            # Test that the add_rows delta is properly melted
            rows = df_proto.data.cols[0].int64s.data
            self.assertEqual([2, 3, 4, 2, 3, 4], rows)
","Some element types require that their dataframes are
        'melted' (https://pandas.pydata.org/docs/reference/api/pandas.melt.html)
         before being sent to the frontend. Test that the melting occurs.
        ",21,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_deltas_that_melt_dataframes(self):
        
        deltas = self._get_deltas_that_melt_dataframes()

        for delta in deltas:
            el = delta(DATAFRAME)
            el._legacy_add_rows(NEW_ROWS)

            df_proto = _get_data_frame(self.get_delta_from_queue())

            # Test that the add_rows delta is properly melted
            rows = df_proto.data.cols[0].int64s.data
            self.assertEqual([2, 3, 4, 2, 3, 4], rows)

        ```
    ###Assistant : Some element types require that their dataframes are
        'melted' (https://pandas.pydata.org/docs/reference/api/pandas.melt.html)
         before being sent to the frontend. Test that the melting occurs.
        
    "
2123,"def sensors_temperatures():
    
    ret = collections.defaultdict(list)
    basenames = glob.glob('/sys/class/hwmon/hwmon*/temp*_*')
    # CentOS has an intermediate /device directory:
    # https://github.com/giampaolo/psutil/issues/971
    # https://github.com/nicolargo/glances/issues/1060
    basenames.extend(glob.glob('/sys/class/hwmon/hwmon*/device/temp*_*'))
    basenames = sorted(set([x.split('_')[0] for x in basenames]))

    # Only add the coretemp hwmon entries if they're not already in
    # /sys/class/hwmon/
    # https://github.com/giampaolo/psutil/issues/1708
    # https://github.com/giampaolo/psutil/pull/1648
    basenames2 = glob.glob(
        '/sys/devices/platform/coretemp.*/hwmon/hwmon*/temp*_*')
    repl = re.compile('/sys/devices/platform/coretemp.*/hwmon/')
    for name in basenames2:
        altname = repl.sub('/sys/class/hwmon/', name)
        if altname not in basenames:
            basenames.append(name)

    for base in basenames:
        try:
            path = base + '_input'
            current = float(bcat(path)) / 1000.0
            path = os.path.join(os.path.dirname(base), 'name')
            unit_name = cat(path).strip()
        except (IOError, OSError, ValueError):
            # A lot of things can go wrong here, so let's just skip the
            # whole entry. Sure thing is Linux's /sys/class/hwmon really
            # is a stinky broken mess.
            # https://github.com/giampaolo/psutil/issues/1009
            # https://github.com/giampaolo/psutil/issues/1101
            # https://github.com/giampaolo/psutil/issues/1129
            # https://github.com/giampaolo/psutil/issues/1245
            # https://github.com/giampaolo/psutil/issues/1323
            continue

        high = bcat(base + '_max', fallback=None)
        critical = bcat(base + '_crit', fallback=None)
        label = cat(base + '_label', fallback='').strip()

        if high is not None:
            try:
                high = float(high) / 1000.0
            except ValueError:
                high = None
        if critical is not None:
            try:
                critical = float(critical) / 1000.0
            except ValueError:
                critical = None

        ret[unit_name].append((label, current, high, critical))

    # Indication that no sensors were detected in /sys/class/hwmon/
    if not basenames:
        basenames = glob.glob('/sys/class/thermal/thermal_zone*')
        basenames = sorted(set(basenames))

        for base in basenames:
            try:
                path = os.path.join(base, 'temp')
                current = float(bcat(path)) / 1000.0
                path = os.path.join(base, 'type')
                unit_name = cat(path).strip()
            except (IOError, OSError, ValueError) as err:
                debug(err)
                continue

            trip_paths = glob.glob(base + '/trip_point*')
            trip_points = set(['_'.join(
                os.path.basename(p).split('_')[0:3]) for p in trip_paths])
            critical = None
            high = None
            for trip_point in trip_points:
                path = os.path.join(base, trip_point + ""_type"")
                trip_type = cat(path, fallback='').strip()
                if trip_type == 'critical':
                    critical = bcat(os.path.join(base, trip_point + ""_temp""),
                                    fallback=None)
                elif trip_type == 'high':
                    high = bcat(os.path.join(base, trip_point + ""_temp""),
                                fallback=None)

                if high is not None:
                    try:
                        high = float(high) / 1000.0
                    except ValueError:
                        high = None
                if critical is not None:
                    try:
                        critical = float(critical) / 1000.0
                    except ValueError:
                        critical = None

            ret[unit_name].append(('', current, high, critical))

    return dict(ret)

","Return hardware (CPU and others) temperatures as a dict
    including hardware name, label, current, max and critical
    temperatures.

    Implementation notes:
    - /sys/class/hwmon looks like the most recent interface to
      retrieve this info, and this implementation relies on it
      only (old distros will probably use something else)
    - lm-sensors on Ubuntu 16.04 relies on /sys/class/hwmon
    - /sys/class/thermal/thermal_zone* is another one but it's more
      difficult to parse
    ",65,326,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sensors_temperatures():
    
    ret = collections.defaultdict(list)
    basenames = glob.glob('/sys/class/hwmon/hwmon*/temp*_*')
    # CentOS has an intermediate /device directory:
    # https://github.com/giampaolo/psutil/issues/971
    # https://github.com/nicolargo/glances/issues/1060
    basenames.extend(glob.glob('/sys/class/hwmon/hwmon*/device/temp*_*'))
    basenames = sorted(set([x.split('_')[0] for x in basenames]))

    # Only add the coretemp hwmon entries if they're not already in
    # /sys/class/hwmon/
    # https://github.com/giampaolo/psutil/issues/1708
    # https://github.com/giampaolo/psutil/pull/1648
    basenames2 = glob.glob(
        '/sys/devices/platform/coretemp.*/hwmon/hwmon*/temp*_*')
    repl = re.compile('/sys/devices/platform/coretemp.*/hwmon/')
    for name in basenames2:
        altname = repl.sub('/sys/class/hwmon/', name)
        if altname not in basenames:
            basenames.append(name)

    for base in basenames:
        try:
            path = base + '_input'
            current = float(bcat(path)) / 1000.0
            path = os.path.join(os.path.dirname(base), 'name')
            unit_name = cat(path).strip()
        except (IOError, OSError, ValueError):
            # A lot of things can go wrong here, so let's just skip the
            # whole entry. Sure thing is Linux's /sys/class/hwmon really
            # is a stinky broken mess.
            # https://github.com/giampaolo/psutil/issues/1009
            # https://github.com/giampaolo/psutil/issues/1101
            # https://github.com/giampaolo/psutil/issues/1129
            # https://github.com/giampaolo/psutil/issues/1245
            # https://github.com/giampaolo/psutil/issues/1323
            continue

        high = bcat(base + '_max', fallback=None)
        critical = bcat(base + '_crit', fallback=None)
        label = cat(base + '_label', fallback='').strip()

        if high is not None:
            try:
                high = float(high) / 1000.0
            except ValueError:
                high = None
        if critical is not None:
            try:
                critical = float(critical) / 1000.0
            except ValueError:
                critical = None

        ret[unit_name].append((label, current, high, critical))

    # Indication that no sensors were detected in /sys/class/hwmon/
    if not basenames:
        basenames = glob.glob('/sys/class/thermal/thermal_zone*')
        basenames = sorted(set(basenames))

        for base in basenames:
            try:
                path = os.path.join(base, 'temp')
                current = float(bcat(path)) / 1000.0
                path = os.path.join(base, 'type')
                unit_name = cat(path).strip()
            except (IOError, OSError, ValueError) as err:
                debug(err)
                continue

            trip_paths = glob.glob(base + '/trip_point*')
            trip_points = set(['_'.join(
                os.path.basename(p).split('_')[0:3]) for p in trip_paths])
            critical = None
            high = None
            for trip_point in trip_points:
                path = os.path.join(base, trip_point + ""_type"")
                trip_type = cat(path, fallback='').strip()
                if trip_type == 'critical':
                    critical = bcat(os.path.join(base, trip_point + ""_temp""),
                                    fallback=None)
                elif trip_type == 'high':
                    high = bcat(os.path.join(base, trip_point + ""_temp""),
                                fallback=None)

                if high is not None:
                    try:
                        high = float(high) / 1000.0
                    except ValueError:
                        high = None
                if critical is not None:
                    try:
                        critical = float(critical) / 1000.0
                    except ValueError:
                        critical = None

            ret[unit_name].append(('', current, high, critical))

    return dict(ret)


        ```
    ###Assistant : Return hardware (CPU and others) temperatures as a dict
    including hardware name, label, current, max and critical
    temperatures.

    Implementation notes:
    - /sys/class/hwmon looks like the most recent interface to
      retrieve this info, and this implementation relies on it
      only (old distros will probably use something else)
    - lm-sensors on Ubuntu 16.04 relies on /sys/class/hwmon
    - /sys/class/thermal/thermal_zone* is another one but it's more
      difficult to parse
    
    "
2124,"def preview_revision_for_task(request, page_id, task_id):
    

    page = get_object_or_404(Page, id=page_id)
    task = get_object_or_404(Task, id=task_id).specific
    try:
        task_state = TaskState.objects.get(
            page_revision__page=page, task=task, status=TaskState.STATUS_IN_PROGRESS
        )
    except TaskState.DoesNotExist:
        messages.error(
            request,
            _(
                ""The page '{0}' is not currently awaiting moderation in task '{1}'.""
            ).format(page.get_admin_display_title(), task.name),
        )
        return redirect(""wagtailadmin_home"")

    revision = task_state.page_revision

    if not task.get_actions(page, request.user):
        raise PermissionDenied

    page_to_view = revision.as_page_object()

    # TODO: provide workflow actions within this view

    return page_to_view.make_preview_request(
        request,
        page.default_preview_mode,
        extra_request_attrs={""revision_id"": revision.id},
    )
","Preview the revision linked to the in-progress TaskState of a specified Task. This enables pages in moderation
    to be edited and new TaskStates linked to the new revisions created, with preview links remaining valid",34,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def preview_revision_for_task(request, page_id, task_id):
    

    page = get_object_or_404(Page, id=page_id)
    task = get_object_or_404(Task, id=task_id).specific
    try:
        task_state = TaskState.objects.get(
            page_revision__page=page, task=task, status=TaskState.STATUS_IN_PROGRESS
        )
    except TaskState.DoesNotExist:
        messages.error(
            request,
            _(
                ""The page '{0}' is not currently awaiting moderation in task '{1}'.""
            ).format(page.get_admin_display_title(), task.name),
        )
        return redirect(""wagtailadmin_home"")

    revision = task_state.page_revision

    if not task.get_actions(page, request.user):
        raise PermissionDenied

    page_to_view = revision.as_page_object()

    # TODO: provide workflow actions within this view

    return page_to_view.make_preview_request(
        request,
        page.default_preview_mode,
        extra_request_attrs={""revision_id"": revision.id},
    )

        ```
    ###Assistant : Preview the revision linked to the in-progress TaskState of a specified Task. This enables pages in moderation
    to be edited and new TaskStates linked to the new revisions created, with preview links remaining valid
    "
2125,"def push_async_callback(self, callback, /, *args, **kwds):
        
        _exit_wrapper = self._create_async_cb_wrapper(callback, *args, **kwds)

        # We changed the signature, so using @wraps is not appropriate, but
        # setting __wrapped__ may still help with introspection.
        _exit_wrapper.__wrapped__ = callback
        self._push_exit_callback(_exit_wrapper, False)
        return callback  # Allow use as a decorator
","Registers an arbitrary coroutine function and arguments.

        Cannot suppress exceptions.
        ",10,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def push_async_callback(self, callback, /, *args, **kwds):
        
        _exit_wrapper = self._create_async_cb_wrapper(callback, *args, **kwds)

        # We changed the signature, so using @wraps is not appropriate, but
        # setting __wrapped__ may still help with introspection.
        _exit_wrapper.__wrapped__ = callback
        self._push_exit_callback(_exit_wrapper, False)
        return callback  # Allow use as a decorator

        ```
    ###Assistant : Registers an arbitrary coroutine function and arguments.

        Cannot suppress exceptions.
        
    "
2126,"def scanString(self, instring, maxMatches=_MAX_INT, overlap=False):
        
        if not self.streamlined:
            self.streamline()
        for e in self.ignoreExprs:
            e.streamline()

        if not self.keepTabs:
            instring = _ustr(instring).expandtabs()
        instrlen = len(instring)
        loc = 0
        preparseFn = self.preParse
        parseFn = self._parse
        ParserElement.resetCache()
        matches = 0
        try:
            while loc <= instrlen and matches < maxMatches:
                try:
                    preloc = preparseFn(instring, loc)
                    nextLoc, tokens = parseFn(instring, preloc, callPreParse=False)
                except ParseException:
                    loc = preloc + 1
                else:
                    if nextLoc > loc:
                        matches += 1
                        yield tokens, preloc, nextLoc
                        if overlap:
                            nextloc = preparseFn(instring, loc)
                            if nextloc > loc:
                                loc = nextLoc
                            else:
                                loc += 1
                        else:
                            loc = nextLoc
                    else:
                        loc = preloc + 1
        except ParseBaseException as exc:
            if ParserElement.verbose_stacktrace:
                raise
            else:
                # catch and re-raise exception from here, clearing out pyparsing internal stack trace
                if getattr(exc, '__traceback__', None) is not None:
                    exc.__traceback__ = self._trim_traceback(exc.__traceback__)
                raise exc
","
        Scan the input string for expression matches.  Each match will return the
        matching tokens, start location, and end location.  May be called with optional
        ``maxMatches`` argument, to clip scanning after 'n' matches are found.  If
        ``overlap`` is specified, then overlapping matches will be reported.

        Note that the start and end locations are reported relative to the string
        being parsed.  See :class:`parseString` for more information on parsing
        strings with embedded tabs.

        Example::

            source = ""sldjf123lsdjjkf345sldkjf879lkjsfd987""
            print(source)
            for tokens, start, end in Word(alphas).scanString(source):
                print(' '*start + '^'*(end-start))
                print(' '*start + tokens[0])

        prints::

            sldjf123lsdjjkf345sldkjf879lkjsfd987
            ^^^^^
            sldjf
                    ^^^^^^^
                    lsdjjkf
                              ^^^^^^
                              sldkjf
                                       ^^^^^^
                                       lkjsfd
        ",99,135,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def scanString(self, instring, maxMatches=_MAX_INT, overlap=False):
        
        if not self.streamlined:
            self.streamline()
        for e in self.ignoreExprs:
            e.streamline()

        if not self.keepTabs:
            instring = _ustr(instring).expandtabs()
        instrlen = len(instring)
        loc = 0
        preparseFn = self.preParse
        parseFn = self._parse
        ParserElement.resetCache()
        matches = 0
        try:
            while loc <= instrlen and matches < maxMatches:
                try:
                    preloc = preparseFn(instring, loc)
                    nextLoc, tokens = parseFn(instring, preloc, callPreParse=False)
                except ParseException:
                    loc = preloc + 1
                else:
                    if nextLoc > loc:
                        matches += 1
                        yield tokens, preloc, nextLoc
                        if overlap:
                            nextloc = preparseFn(instring, loc)
                            if nextloc > loc:
                                loc = nextLoc
                            else:
                                loc += 1
                        else:
                            loc = nextLoc
                    else:
                        loc = preloc + 1
        except ParseBaseException as exc:
            if ParserElement.verbose_stacktrace:
                raise
            else:
                # catch and re-raise exception from here, clearing out pyparsing internal stack trace
                if getattr(exc, '__traceback__', None) is not None:
                    exc.__traceback__ = self._trim_traceback(exc.__traceback__)
                raise exc

        ```
    ###Assistant : 
        Scan the input string for expression matches.  Each match will return the
        matching tokens, start location, and end location.  May be called with optional
        ``maxMatches`` argument, to clip scanning after 'n' matches are found.  If
        ``overlap`` is specified, then overlapping matches will be reported.

        Note that the start and end locations are reported relative to the string
        being parsed.  See :class:`parseString` for more information on parsing
        strings with embedded tabs.

        Example::

            source = ""sldjf123lsdjjkf345sldkjf879lkjsfd987""
            print(source)
            for tokens, start, end in Word(alphas).scanString(source):
                print(' '*start + '^'*(end-start))
                print(' '*start + tokens[0])

        prints::

            sldjf123lsdjjkf345sldkjf879lkjsfd987
            ^^^^^
            sldjf
                    ^^^^^^^
                    lsdjjkf
                              ^^^^^^
                              sldkjf
                                       ^^^^^^
                                       lkjsfd
        
    "
2127,"def add_to_apply_calls(self, func, *args, length=None, width=None, **kwargs):
        
        return PandasOnDaskDataframePartition(
            self._data,
            call_queue=self.call_queue + [[func, args, kwargs]],
            length=length,
            width=width,
        )
","
        Add a function to the call queue.

        Parameters
        ----------
        func : callable
            Function to be added to the call queue.
        *args : iterable
            Additional positional arguments to be passed in `func`.
        length : distributed.Future or int, optional
            Length, or reference to length, of wrapped ``pandas.DataFrame``.
        width : distributed.Future or int, optional
            Width, or reference to width, of wrapped ``pandas.DataFrame``.
        **kwargs : dict
            Additional keyword arguments to be passed in `func`.

        Returns
        -------
        PandasOnDaskDataframePartition
            A new ``PandasOnDaskDataframePartition`` object.

        Notes
        -----
        The keyword arguments are sent as a dictionary.
        ",87,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_to_apply_calls(self, func, *args, length=None, width=None, **kwargs):
        
        return PandasOnDaskDataframePartition(
            self._data,
            call_queue=self.call_queue + [[func, args, kwargs]],
            length=length,
            width=width,
        )

        ```
    ###Assistant : 
        Add a function to the call queue.

        Parameters
        ----------
        func : callable
            Function to be added to the call queue.
        *args : iterable
            Additional positional arguments to be passed in `func`.
        length : distributed.Future or int, optional
            Length, or reference to length, of wrapped ``pandas.DataFrame``.
        width : distributed.Future or int, optional
            Width, or reference to width, of wrapped ``pandas.DataFrame``.
        **kwargs : dict
            Additional keyword arguments to be passed in `func`.

        Returns
        -------
        PandasOnDaskDataframePartition
            A new ``PandasOnDaskDataframePartition`` object.

        Notes
        -----
        The keyword arguments are sent as a dictionary.
        
    "
2128,"def to_json_string(self) -> str:
        
        dictionary = self.to_dict()

        for key, value in dictionary.items():
            if isinstance(value, np.ndarray):
                dictionary[key] = value.tolist()

        # make sure private name ""_processor_class"" is correctly
        # saved as ""processor_class""
        _processor_class = dictionary.pop(""_processor_class"", None)
        if _processor_class is not None:
            dictionary[""processor_class""] = _processor_class

        return json.dumps(dictionary, indent=2, sort_keys=True) + ""\n""
","
        Serializes this instance to a JSON string.

        Returns:
            `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
        ",23,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to_json_string(self) -> str:
        
        dictionary = self.to_dict()

        for key, value in dictionary.items():
            if isinstance(value, np.ndarray):
                dictionary[key] = value.tolist()

        # make sure private name ""_processor_class"" is correctly
        # saved as ""processor_class""
        _processor_class = dictionary.pop(""_processor_class"", None)
        if _processor_class is not None:
            dictionary[""processor_class""] = _processor_class

        return json.dumps(dictionary, indent=2, sort_keys=True) + ""\n""

        ```
    ###Assistant : 
        Serializes this instance to a JSON string.

        Returns:
            `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
        
    "
2129,"def one_of_permissions_or_auth_filter_required(context, permissions):
    
    if not permissions:
        return True

    authorization_filters = [
        p for p in permissions if isinstance(p, AuthorizationFilters)
    ]
    permissions = [p for p in permissions if not isinstance(p, AuthorizationFilters)]

    granted_by_permissions = False
    granted_by_authorization_filters = False

    # TODO: move this function from graphql to core
    from saleor.graphql.utils import get_user_or_app_from_context

    is_app = bool(getattr(context, ""app"", None))
    requestor = get_user_or_app_from_context(context)

    if permissions:
        perm_checks_results = []
        for permission in permissions:
            if is_app and permission == AccountPermissions.MANAGE_STAFF:
                # `MANAGE_STAFF` permission for apps is not supported, as apps using it
                # could create a staff user with full access.
                perm_checks_results.append(False)
            else:
                perm_checks_results.append(requestor.has_perm(permission))
        granted_by_permissions = any(perm_checks_results)

    if authorization_filters:
        auth_filters_results = []
        for p in authorization_filters:
            perm_fn = resolve_authorization_filter_fn(p)
            if perm_fn:
                res = perm_fn(context)
                auth_filters_results.append(bool(res))
        granted_by_authorization_filters = any(auth_filters_results)

    return granted_by_permissions or granted_by_authorization_filters

","Determine whether user or app has rights to perform an action.

    The `context` parameter is the Context instance associated with the request.
    ",22,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def one_of_permissions_or_auth_filter_required(context, permissions):
    
    if not permissions:
        return True

    authorization_filters = [
        p for p in permissions if isinstance(p, AuthorizationFilters)
    ]
    permissions = [p for p in permissions if not isinstance(p, AuthorizationFilters)]

    granted_by_permissions = False
    granted_by_authorization_filters = False

    # TODO: move this function from graphql to core
    from saleor.graphql.utils import get_user_or_app_from_context

    is_app = bool(getattr(context, ""app"", None))
    requestor = get_user_or_app_from_context(context)

    if permissions:
        perm_checks_results = []
        for permission in permissions:
            if is_app and permission == AccountPermissions.MANAGE_STAFF:
                # `MANAGE_STAFF` permission for apps is not supported, as apps using it
                # could create a staff user with full access.
                perm_checks_results.append(False)
            else:
                perm_checks_results.append(requestor.has_perm(permission))
        granted_by_permissions = any(perm_checks_results)

    if authorization_filters:
        auth_filters_results = []
        for p in authorization_filters:
            perm_fn = resolve_authorization_filter_fn(p)
            if perm_fn:
                res = perm_fn(context)
                auth_filters_results.append(bool(res))
        granted_by_authorization_filters = any(auth_filters_results)

    return granted_by_permissions or granted_by_authorization_filters


        ```
    ###Assistant : Determine whether user or app has rights to perform an action.

    The `context` parameter is the Context instance associated with the request.
    
    "
2130,"def _dirmatch(path, matchwith):
    
    matchlen = len(matchwith)
    if (path.startswith(matchwith)
        and path[matchlen:matchlen + 1] in [os.sep, '']):
        return True
    return False

","Check if path is within matchwith's tree.
    >>> _dirmatch('/home/foo/bar', '/home/foo/bar')
    True
    >>> _dirmatch('/home/foo/bar/', '/home/foo/bar')
    True
    >>> _dirmatch('/home/foo/bar/etc', '/home/foo/bar')
    True
    >>> _dirmatch('/home/foo/bar2', '/home/foo/bar')
    False
    >>> _dirmatch('/home/foo/bar2/etc', '/home/foo/bar')
    False
    ",27,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _dirmatch(path, matchwith):
    
    matchlen = len(matchwith)
    if (path.startswith(matchwith)
        and path[matchlen:matchlen + 1] in [os.sep, '']):
        return True
    return False


        ```
    ###Assistant : Check if path is within matchwith's tree.
    >>> _dirmatch('/home/foo/bar', '/home/foo/bar')
    True
    >>> _dirmatch('/home/foo/bar/', '/home/foo/bar')
    True
    >>> _dirmatch('/home/foo/bar/etc', '/home/foo/bar')
    True
    >>> _dirmatch('/home/foo/bar2', '/home/foo/bar')
    False
    >>> _dirmatch('/home/foo/bar2/etc', '/home/foo/bar')
    False
    
    "
2131,"def deform_sampling(self, feat, offset):
        
        # it is an equivalent implementation of bilinear interpolation
        b, c, h, w = feat.shape
        weight = feat.new_ones(c, 1, 1, 1)
        y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
        return y
","Sampling the feature x according to offset.

        Args:
            feat (Tensor): Feature
            offset (Tensor): Spatial offset for feature sampling
        ",18,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deform_sampling(self, feat, offset):
        
        # it is an equivalent implementation of bilinear interpolation
        b, c, h, w = feat.shape
        weight = feat.new_ones(c, 1, 1, 1)
        y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
        return y

        ```
    ###Assistant : Sampling the feature x according to offset.

        Args:
            feat (Tensor): Feature
            offset (Tensor): Spatial offset for feature sampling
        
    "
2132,"def page_identity(self, response, request_json=None):
        
        request_path = response.request.path_url
        if request_path == '/migrations_notran/':
            raise exc.IsMigrating('You have been redirected to the migration-in-progress page.')
        request_method = response.request.method.lower()

        self.last_elapsed = response.elapsed

        if isinstance(request_json, dict) and 'ds' in request_json:
            ds = request_json.ds
        else:
            ds = None

        data = self.extract_data(response)
        exc_str = ""%s (%s) received"" % (http.responses[response.status_code], response.status_code)

        exception = exception_from_status_code(response.status_code)
        if exception:
            raise exception(exc_str, data)

        if response.status_code in (http.OK, http.CREATED, http.ACCEPTED):

            # Not all JSON responses include a URL.  Grab it from the request
            # object, if needed.
            if 'url' in data:
                endpoint = data['url']
            else:
                endpoint = request_path

            data = objectify_response_json(response)

            if request_method in ('get', 'patch', 'put'):
                #  Update existing resource and return it
                if are_same_endpoint(self.endpoint, request_path):
                    self.json = data
                    self.r = response
                    return self

            registered_type = get_registered_page(request_path, request_method)
            return registered_type(self.connection, endpoint=endpoint, json=data, last_elapsed=response.elapsed, r=response, ds=ds)

        elif response.status_code == http.FORBIDDEN:
            if is_license_invalid(response):
                raise exc.LicenseInvalid(exc_str, data)
            elif is_license_exceeded(response):
                raise exc.LicenseExceeded(exc_str, data)
            else:
                raise exc.Forbidden(exc_str, data)

        elif response.status_code == http.BAD_REQUEST:
            if is_license_invalid(response):
                raise exc.LicenseInvalid(exc_str, data)
            if is_duplicate_error(response):
                raise exc.Duplicate(exc_str, data)
            else:
                raise exc.BadRequest(exc_str, data)
        else:
            raise exc.Unknown(exc_str, data)
","Takes a `requests.Response` and
        returns a new __item_class__ instance if the request method is not a get, or returns
           a __class__ instance if the request path is different than the caller's `endpoint`.
        ",32,171,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def page_identity(self, response, request_json=None):
        
        request_path = response.request.path_url
        if request_path == '/migrations_notran/':
            raise exc.IsMigrating('You have been redirected to the migration-in-progress page.')
        request_method = response.request.method.lower()

        self.last_elapsed = response.elapsed

        if isinstance(request_json, dict) and 'ds' in request_json:
            ds = request_json.ds
        else:
            ds = None

        data = self.extract_data(response)
        exc_str = ""%s (%s) received"" % (http.responses[response.status_code], response.status_code)

        exception = exception_from_status_code(response.status_code)
        if exception:
            raise exception(exc_str, data)

        if response.status_code in (http.OK, http.CREATED, http.ACCEPTED):

            # Not all JSON responses include a URL.  Grab it from the request
            # object, if needed.
            if 'url' in data:
                endpoint = data['url']
            else:
                endpoint = request_path

            data = objectify_response_json(response)

            if request_method in ('get', 'patch', 'put'):
                #  Update existing resource and return it
                if are_same_endpoint(self.endpoint, request_path):
                    self.json = data
                    self.r = response
                    return self

            registered_type = get_registered_page(request_path, request_method)
            return registered_type(self.connection, endpoint=endpoint, json=data, last_elapsed=response.elapsed, r=response, ds=ds)

        elif response.status_code == http.FORBIDDEN:
            if is_license_invalid(response):
                raise exc.LicenseInvalid(exc_str, data)
            elif is_license_exceeded(response):
                raise exc.LicenseExceeded(exc_str, data)
            else:
                raise exc.Forbidden(exc_str, data)

        elif response.status_code == http.BAD_REQUEST:
            if is_license_invalid(response):
                raise exc.LicenseInvalid(exc_str, data)
            if is_duplicate_error(response):
                raise exc.Duplicate(exc_str, data)
            else:
                raise exc.BadRequest(exc_str, data)
        else:
            raise exc.Unknown(exc_str, data)

        ```
    ###Assistant : Takes a `requests.Response` and
        returns a new __item_class__ instance if the request method is not a get, or returns
           a __class__ instance if the request path is different than the caller's `endpoint`.
        
    "
2133,"def _installed_conda(self):
        
        if not self._is_conda:
            return None
        with Popen(""conda list"", shell=True, stdout=PIPE, stderr=PIPE) as conda:
            stdout, stderr = conda.communicate()
        if stderr:
            return ""Could not get package list""
        installed = stdout.decode(self._encoding, errors=""replace"").splitlines()
        return ""\n"".join(installed)
", str: The list of installed Conda packages within Faceswap's scope. ,10,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _installed_conda(self):
        
        if not self._is_conda:
            return None
        with Popen(""conda list"", shell=True, stdout=PIPE, stderr=PIPE) as conda:
            stdout, stderr = conda.communicate()
        if stderr:
            return ""Could not get package list""
        installed = stdout.decode(self._encoding, errors=""replace"").splitlines()
        return ""\n"".join(installed)

        ```
    ###Assistant :  str: The list of installed Conda packages within Faceswap's scope. 
    "
2134,"def embedding(self, input_seq):
        

        seq_embeddings = self.item_embedding_layer(input_seq)
        seq_embeddings = seq_embeddings * (self.embedding_dim ** 0.5)

        # FIXME
        positional_seq = tf.expand_dims(tf.range(tf.shape(input_seq)[1]), 0)
        positional_seq = tf.tile(positional_seq, [tf.shape(input_seq)[0], 1])
        positional_embeddings = self.positional_embedding_layer(positional_seq)

        return seq_embeddings, positional_embeddings
","Compute the sequence and positional embeddings.

        Args:
            input_seq (tf.Tensor): Input sequence
        
        Returns:
            tf.Tensor, tf.Tensor:
            - Sequence embeddings.
            - Positional embeddings.
        
        ",20,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def embedding(self, input_seq):
        

        seq_embeddings = self.item_embedding_layer(input_seq)
        seq_embeddings = seq_embeddings * (self.embedding_dim ** 0.5)

        # FIXME
        positional_seq = tf.expand_dims(tf.range(tf.shape(input_seq)[1]), 0)
        positional_seq = tf.tile(positional_seq, [tf.shape(input_seq)[0], 1])
        positional_embeddings = self.positional_embedding_layer(positional_seq)

        return seq_embeddings, positional_embeddings

        ```
    ###Assistant : Compute the sequence and positional embeddings.

        Args:
            input_seq (tf.Tensor): Input sequence
        
        Returns:
            tf.Tensor, tf.Tensor:
            - Sequence embeddings.
            - Positional embeddings.
        
        
    "
2135,"def display(self, msg, color=None, stderr=False, screen_only=False, log_only=False, newline=True):
        

        nocolor = msg

        if not log_only:

            has_newline = msg.endswith(u'\n')
            if has_newline:
                msg2 = msg[:-1]
            else:
                msg2 = msg

            if color:
                msg2 = stringc(msg2, color)

            if has_newline or newline:
                msg2 = msg2 + u'\n'

            msg2 = to_bytes(msg2, encoding=self._output_encoding(stderr=stderr))
            # Convert back to text string
            # We first convert to a byte string so that we get rid of
            # characters that are invalid in the user's locale
            msg2 = to_text(msg2, self._output_encoding(stderr=stderr), errors='replace')

            # Note: After Display() class is refactored need to update the log capture
            # code in 'bin/ansible-connection' (and other relevant places).
            if not stderr:
                fileobj = sys.stdout
            else:
                fileobj = sys.stderr

            fileobj.write(msg2)

            try:
                fileobj.flush()
            except IOError as e:
                # Ignore EPIPE in case fileobj has been prematurely closed, eg.
                # when piping to ""head -n1""
                if e.errno != errno.EPIPE:
                    raise

        if logger and not screen_only:
            # We first convert to a byte string so that we get rid of
            # color and characters that are invalid in the user's locale
            msg2 = to_bytes(nocolor.lstrip(u'\n'))

            # Convert back to text string
            msg2 = to_text(msg2, self._output_encoding(stderr=stderr))

            lvl = logging.INFO
            if color:
                # set logger level based on color (not great)
                try:
                    lvl = color_to_log_level[color]
                except KeyError:
                    # this should not happen, but JIC
                    raise AnsibleAssertionError('Invalid color supplied to display: %s' % color)
            # actually log
            logger.log(lvl, msg2)
"," Display a message to the user

        Note: msg *must* be a unicode string to prevent UnicodeError tracebacks.
        ",17,223,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def display(self, msg, color=None, stderr=False, screen_only=False, log_only=False, newline=True):
        

        nocolor = msg

        if not log_only:

            has_newline = msg.endswith(u'\n')
            if has_newline:
                msg2 = msg[:-1]
            else:
                msg2 = msg

            if color:
                msg2 = stringc(msg2, color)

            if has_newline or newline:
                msg2 = msg2 + u'\n'

            msg2 = to_bytes(msg2, encoding=self._output_encoding(stderr=stderr))
            # Convert back to text string
            # We first convert to a byte string so that we get rid of
            # characters that are invalid in the user's locale
            msg2 = to_text(msg2, self._output_encoding(stderr=stderr), errors='replace')

            # Note: After Display() class is refactored need to update the log capture
            # code in 'bin/ansible-connection' (and other relevant places).
            if not stderr:
                fileobj = sys.stdout
            else:
                fileobj = sys.stderr

            fileobj.write(msg2)

            try:
                fileobj.flush()
            except IOError as e:
                # Ignore EPIPE in case fileobj has been prematurely closed, eg.
                # when piping to ""head -n1""
                if e.errno != errno.EPIPE:
                    raise

        if logger and not screen_only:
            # We first convert to a byte string so that we get rid of
            # color and characters that are invalid in the user's locale
            msg2 = to_bytes(nocolor.lstrip(u'\n'))

            # Convert back to text string
            msg2 = to_text(msg2, self._output_encoding(stderr=stderr))

            lvl = logging.INFO
            if color:
                # set logger level based on color (not great)
                try:
                    lvl = color_to_log_level[color]
                except KeyError:
                    # this should not happen, but JIC
                    raise AnsibleAssertionError('Invalid color supplied to display: %s' % color)
            # actually log
            logger.log(lvl, msg2)

        ```
    ###Assistant :  Display a message to the user

        Note: msg *must* be a unicode string to prevent UnicodeError tracebacks.
        
    "
2136,"def get_queryset(self, request):
        
        queryset = SavedFilter.objects.all()
        user = request.user
        if user.is_superuser:
            return queryset
        if user.is_anonymous:
            return queryset.filter(shared=True)
        return queryset.filter(
            Q(shared=True) | Q(user=user)
        )

","
        Return only shared SavedFilters, or those owned by the current user, unless
        this is a superuser.
        ",16,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_queryset(self, request):
        
        queryset = SavedFilter.objects.all()
        user = request.user
        if user.is_superuser:
            return queryset
        if user.is_anonymous:
            return queryset.filter(shared=True)
        return queryset.filter(
            Q(shared=True) | Q(user=user)
        )


        ```
    ###Assistant : 
        Return only shared SavedFilters, or those owned by the current user, unless
        this is a superuser.
        
    "
2137,"def list(self, request):
        
        report_list = []
        report_content_type = ContentType.objects.get(app_label='extras', model='report')
        results = {
            r.name: r
            for r in JobResult.objects.filter(
                obj_type=report_content_type,
                status__in=JobResultStatusChoices.TERMINAL_STATE_CHOICES
            ).order_by('name', '-created').distinct('name').defer('data')
        }

        # Iterate through all available Reports.
        for module_name, reports in get_reports():
            for report in reports:

                # Attach the relevant JobResult (if any) to each Report.
                report.result = results.get(report.full_name, None)
                report_list.append(report)

        serializer = serializers.ReportSerializer(report_list, many=True, context={
            'request': request,
        })

        return Response(serializer.data)
","
        Compile all reports and their related results (if any). Result data is deferred in the list view.
        ",17,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list(self, request):
        
        report_list = []
        report_content_type = ContentType.objects.get(app_label='extras', model='report')
        results = {
            r.name: r
            for r in JobResult.objects.filter(
                obj_type=report_content_type,
                status__in=JobResultStatusChoices.TERMINAL_STATE_CHOICES
            ).order_by('name', '-created').distinct('name').defer('data')
        }

        # Iterate through all available Reports.
        for module_name, reports in get_reports():
            for report in reports:

                # Attach the relevant JobResult (if any) to each Report.
                report.result = results.get(report.full_name, None)
                report_list.append(report)

        serializer = serializers.ReportSerializer(report_list, many=True, context={
            'request': request,
        })

        return Response(serializer.data)

        ```
    ###Assistant : 
        Compile all reports and their related results (if any). Result data is deferred in the list view.
        
    "
2138,"def _usable(self, queryset, name, value):
        
        user = self.request.user if self.request else None
        if not user or user.is_anonymous:
            if value:
                return queryset.filter(enabled=True, shared=True)
            return queryset.filter(Q(enabled=False) | Q(shared=False))
        if value:
            return queryset.filter(enabled=True).filter(Q(shared=True) | Q(user=user))
        return queryset.filter(Q(enabled=False) | Q(Q(shared=False) & ~Q(user=user)))

","
        Return only SavedFilters that are both enabled and are shared (or belong to the current user).
        ",16,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _usable(self, queryset, name, value):
        
        user = self.request.user if self.request else None
        if not user or user.is_anonymous:
            if value:
                return queryset.filter(enabled=True, shared=True)
            return queryset.filter(Q(enabled=False) | Q(shared=False))
        if value:
            return queryset.filter(enabled=True).filter(Q(shared=True) | Q(user=user))
        return queryset.filter(Q(enabled=False) | Q(Q(shared=False) & ~Q(user=user)))


        ```
    ###Assistant : 
        Return only SavedFilters that are both enabled and are shared (or belong to the current user).
        
    "
2139,"def get_current_enrollment(student, academic_year=None):
	current_academic_year = academic_year or frappe.defaults.get_defaults().academic_year
	program_enrollment_list = frappe.db.sql(
		,
		(student, current_academic_year),
		as_dict=1,
	)

	if program_enrollment_list:
		return program_enrollment_list[0]
	else:
		return None
","
		select
			name as program_enrollment, student_name, program, student_batch_name as student_batch,
			student_category, academic_term, academic_year
		from
			`tabProgram Enrollment`
		where
			student = %s and academic_year = %s
		order by creation",26,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_current_enrollment(student, academic_year=None):
	current_academic_year = academic_year or frappe.defaults.get_defaults().academic_year
	program_enrollment_list = frappe.db.sql(
		,
		(student, current_academic_year),
		as_dict=1,
	)

	if program_enrollment_list:
		return program_enrollment_list[0]
	else:
		return None

        ```
    ###Assistant : 
		select
			name as program_enrollment, student_name, program, student_batch_name as student_batch,
			student_category, academic_term, academic_year
		from
			`tabProgram Enrollment`
		where
			student = %s and academic_year = %s
		order by creation
    "
2140,"def inception_resnet_block(x, scale, block_type, block_idx, activation=""relu""):
    
    if block_type == ""block35"":
        branch_0 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(branch_1, 32, 3)
        branch_2 = conv2d_bn(x, 32, 1)
        branch_2 = conv2d_bn(branch_2, 48, 3)
        branch_2 = conv2d_bn(branch_2, 64, 3)
        branches = [branch_0, branch_1, branch_2]
    elif block_type == ""block17"":
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 128, 1)
        branch_1 = conv2d_bn(branch_1, 160, [1, 7])
        branch_1 = conv2d_bn(branch_1, 192, [7, 1])
        branches = [branch_0, branch_1]
    elif block_type == ""block8"":
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(branch_1, 224, [1, 3])
        branch_1 = conv2d_bn(branch_1, 256, [3, 1])
        branches = [branch_0, branch_1]
    else:
        raise ValueError(
            ""Unknown Inception-ResNet block type. ""
            'Expects ""block35"", ""block17"" or ""block8"", '
            ""but got: "" + str(block_type)
        )

    block_name = block_type + ""_"" + str(block_idx)
    channel_axis = 1 if backend.image_data_format() == ""channels_first"" else 3
    mixed = layers.Concatenate(axis=channel_axis, name=block_name + ""_mixed"")(
        branches
    )
    up = conv2d_bn(
        mixed,
        backend.int_shape(x)[channel_axis],
        1,
        activation=None,
        use_bias=True,
        name=block_name + ""_conv"",
    )

    x = CustomScaleLayer()(x, up, scale)
    if activation is not None:
        x = layers.Activation(activation, name=block_name + ""_ac"")(x)
    return x


@keras_export(""keras.applications.inception_resnet_v2.preprocess_input"")","Adds an Inception-ResNet block.

    This function builds 3 types of Inception-ResNet blocks mentioned
    in the paper, controlled by the `block_type` argument (which is the
    block name used in the official TF-slim implementation):
    - Inception-ResNet-A: `block_type='block35'`
    - Inception-ResNet-B: `block_type='block17'`
    - Inception-ResNet-C: `block_type='block8'`

    Args:
      x: input tensor.
      scale: scaling factor to scale the residuals (i.e., the output of passing
        `x` through an inception module) before adding them to the shortcut
        branch. Let `r` be the output from the residual branch, the output of
        this block will be `x + scale * r`.
      block_type: `'block35'`, `'block17'` or `'block8'`, determines the network
        structure in the residual branch.
      block_idx: an `int` used for generating layer names. The Inception-ResNet
        blocks are repeated many times in this network. We use `block_idx` to
        identify each of the repetitions. For example, the first
        Inception-ResNet-A block will have `block_type='block35', block_idx=0`,
        and the layer names will have a common prefix `'block35_0'`.
      activation: activation function to use at the end of the block (see
        [activations](../activations.md)). When `activation=None`, no activation
        is applied
        (i.e., ""linear"" activation: `a(x) = x`).

    Returns:
        Output tensor for the block.

    Raises:
      ValueError: if `block_type` is not one of `'block35'`,
        `'block17'` or `'block8'`.
    ",193,180,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def inception_resnet_block(x, scale, block_type, block_idx, activation=""relu""):
    
    if block_type == ""block35"":
        branch_0 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(branch_1, 32, 3)
        branch_2 = conv2d_bn(x, 32, 1)
        branch_2 = conv2d_bn(branch_2, 48, 3)
        branch_2 = conv2d_bn(branch_2, 64, 3)
        branches = [branch_0, branch_1, branch_2]
    elif block_type == ""block17"":
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 128, 1)
        branch_1 = conv2d_bn(branch_1, 160, [1, 7])
        branch_1 = conv2d_bn(branch_1, 192, [7, 1])
        branches = [branch_0, branch_1]
    elif block_type == ""block8"":
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(branch_1, 224, [1, 3])
        branch_1 = conv2d_bn(branch_1, 256, [3, 1])
        branches = [branch_0, branch_1]
    else:
        raise ValueError(
            ""Unknown Inception-ResNet block type. ""
            'Expects ""block35"", ""block17"" or ""block8"", '
            ""but got: "" + str(block_type)
        )

    block_name = block_type + ""_"" + str(block_idx)
    channel_axis = 1 if backend.image_data_format() == ""channels_first"" else 3
    mixed = layers.Concatenate(axis=channel_axis, name=block_name + ""_mixed"")(
        branches
    )
    up = conv2d_bn(
        mixed,
        backend.int_shape(x)[channel_axis],
        1,
        activation=None,
        use_bias=True,
        name=block_name + ""_conv"",
    )

    x = CustomScaleLayer()(x, up, scale)
    if activation is not None:
        x = layers.Activation(activation, name=block_name + ""_ac"")(x)
    return x


@keras_export(""keras.applications.inception_resnet_v2.preprocess_input"")
        ```
    ###Assistant : Adds an Inception-ResNet block.

    This function builds 3 types of Inception-ResNet blocks mentioned
    in the paper, controlled by the `block_type` argument (which is the
    block name used in the official TF-slim implementation):
    - Inception-ResNet-A: `block_type='block35'`
    - Inception-ResNet-B: `block_type='block17'`
    - Inception-ResNet-C: `block_type='block8'`

    Args:
      x: input tensor.
      scale: scaling factor to scale the residuals (i.e., the output of passing
        `x` through an inception module) before adding them to the shortcut
        branch. Let `r` be the output from the residual branch, the output of
        this block will be `x + scale * r`.
      block_type: `'block35'`, `'block17'` or `'block8'`, determines the network
        structure in the residual branch.
      block_idx: an `int` used for generating layer names. The Inception-ResNet
        blocks are repeated many times in this network. We use `block_idx` to
        identify each of the repetitions. For example, the first
        Inception-ResNet-A block will have `block_type='block35', block_idx=0`,
        and the layer names will have a common prefix `'block35_0'`.
      activation: activation function to use at the end of the block (see
        [activations](../activations.md)). When `activation=None`, no activation
        is applied
        (i.e., ""linear"" activation: `a(x) = x`).

    Returns:
        Output tensor for the block.

    Raises:
      ValueError: if `block_type` is not one of `'block35'`,
        `'block17'` or `'block8'`.
    
    "
2141,"def _get_calculations(self):
        
        for selection in self._selections:
            if selection == ""raw"":
                continue
            logger.debug(""Calculating: %s"", selection)
            method = getattr(self, f""_calc_{selection}"")
            raw_keys = [key for key in self._stats if key.startswith(""raw_"")]
            for key in raw_keys:
                selected_key = f""{selection}_{key.replace('raw_', '')}""
                self._stats[selected_key] = method(self._stats[key])
", Perform the required calculations and populate :attr:`stats`. ,7,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_calculations(self):
        
        for selection in self._selections:
            if selection == ""raw"":
                continue
            logger.debug(""Calculating: %s"", selection)
            method = getattr(self, f""_calc_{selection}"")
            raw_keys = [key for key in self._stats if key.startswith(""raw_"")]
            for key in raw_keys:
                selected_key = f""{selection}_{key.replace('raw_', '')}""
                self._stats[selected_key] = method(self._stats[key])

        ```
    ###Assistant :  Perform the required calculations and populate :attr:`stats`. 
    "
2142,"def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type=""dpm_solver""):
        
        if solver_type not in ['dpm_solver', 'taylor']:
            raise ValueError(""'solver_type' must be either 'dpm_solver' or 'taylor', got {}"".format(solver_type))
        ns = self.noise_schedule
        dims = x.dim()
        model_prev_1, model_prev_0 = model_prev_list
        t_prev_1, t_prev_0 = t_prev_list
        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
            t_prev_0), ns.marginal_lambda(t)
        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
        alpha_t = torch.exp(log_alpha_t)

        h_0 = lambda_prev_0 - lambda_prev_1
        h = lambda_t - lambda_prev_0
        r0 = h_0 / h
        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
        if self.predict_x0:
            if solver_type == 'dpm_solver':
                x_t = (
                        expand_dims(sigma_t / sigma_prev_0, dims) * x
                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
                )
            elif solver_type == 'taylor':
                x_t = (
                        expand_dims(sigma_t / sigma_prev_0, dims) * x
                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
                )
        else:
            if solver_type == 'dpm_solver':
                x_t = (
                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
                )
            elif solver_type == 'taylor':
                x_t = (
                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
                )
        return x_t
","
        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            model_prev_list: A list of pytorch tensor. The previous computed model values.
            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        ",91,228,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type=""dpm_solver""):
        
        if solver_type not in ['dpm_solver', 'taylor']:
            raise ValueError(""'solver_type' must be either 'dpm_solver' or 'taylor', got {}"".format(solver_type))
        ns = self.noise_schedule
        dims = x.dim()
        model_prev_1, model_prev_0 = model_prev_list
        t_prev_1, t_prev_0 = t_prev_list
        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
            t_prev_0), ns.marginal_lambda(t)
        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
        alpha_t = torch.exp(log_alpha_t)

        h_0 = lambda_prev_0 - lambda_prev_1
        h = lambda_t - lambda_prev_0
        r0 = h_0 / h
        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
        if self.predict_x0:
            if solver_type == 'dpm_solver':
                x_t = (
                        expand_dims(sigma_t / sigma_prev_0, dims) * x
                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
                )
            elif solver_type == 'taylor':
                x_t = (
                        expand_dims(sigma_t / sigma_prev_0, dims) * x
                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
                )
        else:
            if solver_type == 'dpm_solver':
                x_t = (
                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
                )
            elif solver_type == 'taylor':
                x_t = (
                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
                )
        return x_t

        ```
    ###Assistant : 
        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            model_prev_list: A list of pytorch tensor. The previous computed model values.
            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        
    "
2143,"def global_enum(cls, update_str=False):
    
    if issubclass(cls, Flag):
        cls.__repr__ = global_flag_repr
    else:
        cls.__repr__ = global_enum_repr
    if not issubclass(cls, ReprEnum) or update_str:
        cls.__str__ = global_str
    sys.modules[cls.__module__].__dict__.update(cls.__members__)
    return cls
","
    decorator that makes the repr() of an enum member reference its module
    instead of its class; also exports all members to the enum's module's
    global namespace
    ",26,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def global_enum(cls, update_str=False):
    
    if issubclass(cls, Flag):
        cls.__repr__ = global_flag_repr
    else:
        cls.__repr__ = global_enum_repr
    if not issubclass(cls, ReprEnum) or update_str:
        cls.__str__ = global_str
    sys.modules[cls.__module__].__dict__.update(cls.__members__)
    return cls

        ```
    ###Assistant : 
    decorator that makes the repr() of an enum member reference its module
    instead of its class; also exports all members to the enum's module's
    global namespace
    
    "
2144,"def create_training_target(self, target, run_eagerly=False):
        
        if self.has_training_target():
            raise ValueError(
                ""The training_target field for the _TrainingEndpoint ""
                ""instance has already been populated""
            )
        if run_eagerly:
            # When run_eagerly, the target tensor is ignored, and the None placeholder
            # is created instead.
            self.training_target = _TrainingTarget(
                None, feedable=True, skip_target_weights=False
            )
            return

        if self.should_skip_target():
            self.training_target = _TrainingTarget(None)
        else:
            if target is not None and not backend.is_placeholder(target):
                feedable = False
                skip_target_weights = True
            else:
                feedable = True
                skip_target_weights = False

            if target is None:
                target_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get(
                    self.loss_fn, backend.dtype(self.output)
                )

                target = backend.placeholder(
                    ndim=len(self.shape),
                    name=self.output_name + ""_target"",
                    sparse=backend.is_sparse(self.output),
                    dtype=target_dtype,
                )

            self.training_target = _TrainingTarget(
                target,
                feedable=feedable,
                skip_target_weights=skip_target_weights,
            )
","Create training_target instance and update the self.training_target.

        Note that the input target should just be a tensor or None, and
        corresponding training target will be created based on the output and
        loss_fn.

        Args:
          target: the target tensor for the current output. Could be None.
          run_eagerly: boolean, whether the model is in run_eagerly mode.

        Raises:
          ValueError if the training_target field for the current instance has
          already been populated.
        ",67,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def create_training_target(self, target, run_eagerly=False):
        
        if self.has_training_target():
            raise ValueError(
                ""The training_target field for the _TrainingEndpoint ""
                ""instance has already been populated""
            )
        if run_eagerly:
            # When run_eagerly, the target tensor is ignored, and the None placeholder
            # is created instead.
            self.training_target = _TrainingTarget(
                None, feedable=True, skip_target_weights=False
            )
            return

        if self.should_skip_target():
            self.training_target = _TrainingTarget(None)
        else:
            if target is not None and not backend.is_placeholder(target):
                feedable = False
                skip_target_weights = True
            else:
                feedable = True
                skip_target_weights = False

            if target is None:
                target_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get(
                    self.loss_fn, backend.dtype(self.output)
                )

                target = backend.placeholder(
                    ndim=len(self.shape),
                    name=self.output_name + ""_target"",
                    sparse=backend.is_sparse(self.output),
                    dtype=target_dtype,
                )

            self.training_target = _TrainingTarget(
                target,
                feedable=feedable,
                skip_target_weights=skip_target_weights,
            )

        ```
    ###Assistant : Create training_target instance and update the self.training_target.

        Note that the input target should just be a tensor or None, and
        corresponding training target will be created based on the output and
        loss_fn.

        Args:
          target: the target tensor for the current output. Could be None.
          run_eagerly: boolean, whether the model is in run_eagerly mode.

        Raises:
          ValueError if the training_target field for the current instance has
          already been populated.
        
    "
2145,"def test_set_task_instance_state(run_id, execution_date, session, dag_maker):
    

    start_date = datetime_tz(2020, 1, 1)
    with dag_maker(""test_set_task_instance_state"", start_date=start_date, session=session) as dag:
        task_1 = DummyOperator(task_id=""task_1"")
        task_2 = DummyOperator(task_id=""task_2"")
        task_3 = DummyOperator(task_id=""task_3"")
        task_4 = DummyOperator(task_id=""task_4"")
        task_5 = DummyOperator(task_id=""task_5"")

        task_1 >> [task_2, task_3, task_4, task_5]

    dagrun = dag_maker.create_dagrun(
        run_id=run_id,
        execution_date=execution_date,
        state=State.FAILED,
        run_type=DagRunType.SCHEDULED,
    )
",Test that set_task_instance_state updates the TaskInstance state and clear downstream failed,11,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_set_task_instance_state(run_id, execution_date, session, dag_maker):
    

    start_date = datetime_tz(2020, 1, 1)
    with dag_maker(""test_set_task_instance_state"", start_date=start_date, session=session) as dag:
        task_1 = DummyOperator(task_id=""task_1"")
        task_2 = DummyOperator(task_id=""task_2"")
        task_3 = DummyOperator(task_id=""task_3"")
        task_4 = DummyOperator(task_id=""task_4"")
        task_5 = DummyOperator(task_id=""task_5"")

        task_1 >> [task_2, task_3, task_4, task_5]

    dagrun = dag_maker.create_dagrun(
        run_id=run_id,
        execution_date=execution_date,
        state=State.FAILED,
        run_type=DagRunType.SCHEDULED,
    )

        ```
    ###Assistant : Test that set_task_instance_state updates the TaskInstance state and clear downstream failed
    "
2146,"async def async_step_link(self, user_input=None):
        
        errors = {}
        if user_input is not None:
            # Do not authenticate if the host is already configured
            self._async_abort_entries_match({CONF_HOST: self._host})

            try:
                info = await authenticate(
                    self.hass, self._host, self._port, self._servers
                )

            except InvalidAuth:
                errors[""base""] = ""invalid_auth""
            except Exception:  # pylint: disable=broad-except
                _LOGGER.exception(""Unexpected exception"")
                errors[""base""] = ""unknown""
            else:
                return self.async_create_entry(title=DEFAULT_NAME, data=info)

        return self.async_show_form(step_id=""link"", errors=errors)

",Handle linking and authenticting with the roon server.,8,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_step_link(self, user_input=None):
        
        errors = {}
        if user_input is not None:
            # Do not authenticate if the host is already configured
            self._async_abort_entries_match({CONF_HOST: self._host})

            try:
                info = await authenticate(
                    self.hass, self._host, self._port, self._servers
                )

            except InvalidAuth:
                errors[""base""] = ""invalid_auth""
            except Exception:  # pylint: disable=broad-except
                _LOGGER.exception(""Unexpected exception"")
                errors[""base""] = ""unknown""
            else:
                return self.async_create_entry(title=DEFAULT_NAME, data=info)

        return self.async_show_form(step_id=""link"", errors=errors)


        ```
    ###Assistant : Handle linking and authenticting with the roon server.
    "
2147,"def test_get_sid_string_none():
    
    sid_obj = salt.utils.win_dacl.get_sid(None)
    assert isinstance(sid_obj, pywintypes.SIDType)
    assert salt.utils.win_dacl.get_sid_string(sid_obj) == ""S-1-0-0""
","
    Validate getting a null sid (S-1-0-0) when a null sid is passed
    ",12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_sid_string_none():
    
    sid_obj = salt.utils.win_dacl.get_sid(None)
    assert isinstance(sid_obj, pywintypes.SIDType)
    assert salt.utils.win_dacl.get_sid_string(sid_obj) == ""S-1-0-0""

        ```
    ###Assistant : 
    Validate getting a null sid (S-1-0-0) when a null sid is passed
    
    "
2148,"def read(self, filename, split='train'):
        

        label_list = self.get_labels()
        vocab_info = self.get_vocab()
","
        Returns a dataset containing all the examples that can be read from the file path.

        If `self.lazy` is False, this eagerly reads all instances from `self._read()`
        and returns a `MapDataset`.

        If `self.lazy` is True, this returns an `IterDataset`, which internally
        relies on the generator created from `self._read()` to lazily produce examples.
        In this case your implementation of `_read()` must also be lazy
        (that is, not load all examples into memory at once).

        Args:
            filename (str): Path of data file to read, usually provided by `_get_data` 
                function.
            split (str, optional): The split name of selected dataset. This only makes
                a different when data files of different splits have different structures.
        
        Returns:
            A `MapDataset|IterDataset`.
        ",112,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read(self, filename, split='train'):
        

        label_list = self.get_labels()
        vocab_info = self.get_vocab()

        ```
    ###Assistant : 
        Returns a dataset containing all the examples that can be read from the file path.

        If `self.lazy` is False, this eagerly reads all instances from `self._read()`
        and returns a `MapDataset`.

        If `self.lazy` is True, this returns an `IterDataset`, which internally
        relies on the generator created from `self._read()` to lazily produce examples.
        In this case your implementation of `_read()` must also be lazy
        (that is, not load all examples into memory at once).

        Args:
            filename (str): Path of data file to read, usually provided by `_get_data` 
                function.
            split (str, optional): The split name of selected dataset. This only makes
                a different when data files of different splits have different structures.
        
        Returns:
            A `MapDataset|IterDataset`.
        
    "
2149,"def test_no_rule_for_attr(self):
        
        tag = self.soup.b
        fn = attribute_rule({""snowman"": ""barbecue""})
        fn(tag)
        self.assertEqual(str(tag), ""<b>baz</b>"")
","
        Test that attribute_rule() drops attributes for
        which no rule has been defined.
        ",12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_no_rule_for_attr(self):
        
        tag = self.soup.b
        fn = attribute_rule({""snowman"": ""barbecue""})
        fn(tag)
        self.assertEqual(str(tag), ""<b>baz</b>"")

        ```
    ###Assistant : 
        Test that attribute_rule() drops attributes for
        which no rule has been defined.
        
    "
2150,"def _inspect_generic_serialization(base_obj, depth, parent, failure_set):
    
    assert not inspect.isfunction(base_obj)
    functions = inspect.getmembers(base_obj, predicate=inspect.isfunction)
    found = False
    with _printer.indent():
        for name, obj in functions:
            serializable, _ = inspect_serializability(
                obj,
                name=name,
                depth=depth - 1,
                _parent=parent,
                _failure_set=failure_set,
            )
            found = found or not serializable
            if found:
                break

    with _printer.indent():
        members = inspect.getmembers(base_obj)
        for name, obj in members:
            if name.startswith(""__"") and name.endswith(""__"") or inspect.isbuiltin(obj):
                continue
            serializable, _ = inspect_serializability(
                obj,
                name=name,
                depth=depth - 1,
                _parent=parent,
                _failure_set=failure_set,
            )
            found = found or not serializable
            if found:
                break
    if not found:
        _printer.print(
            f""WARNING: Did not find non-serializable object in {base_obj}. ""
            ""This may be an oversight.""
        )
    return found


@DeveloperAPI",Adds the first-found non-serializable element to the failure_set.,8,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _inspect_generic_serialization(base_obj, depth, parent, failure_set):
    
    assert not inspect.isfunction(base_obj)
    functions = inspect.getmembers(base_obj, predicate=inspect.isfunction)
    found = False
    with _printer.indent():
        for name, obj in functions:
            serializable, _ = inspect_serializability(
                obj,
                name=name,
                depth=depth - 1,
                _parent=parent,
                _failure_set=failure_set,
            )
            found = found or not serializable
            if found:
                break

    with _printer.indent():
        members = inspect.getmembers(base_obj)
        for name, obj in members:
            if name.startswith(""__"") and name.endswith(""__"") or inspect.isbuiltin(obj):
                continue
            serializable, _ = inspect_serializability(
                obj,
                name=name,
                depth=depth - 1,
                _parent=parent,
                _failure_set=failure_set,
            )
            found = found or not serializable
            if found:
                break
    if not found:
        _printer.print(
            f""WARNING: Did not find non-serializable object in {base_obj}. ""
            ""This may be an oversight.""
        )
    return found


@DeveloperAPI
        ```
    ###Assistant : Adds the first-found non-serializable element to the failure_set.
    "
2151,"def is_dihedral(self):
        r
        if self._is_dihedral is not None:
            return self._is_dihedral

        order = self.order()

        if order % 2 == 1:
            self._is_dihedral = False
            return False
        if order == 2:
            self._is_dihedral = True
            return True
        if order == 4:
            # The dihedral group of order 4 is the Klein 4-group.
            self._is_dihedral = not self.is_cyclic
            return self._is_dihedral
        if self.is_abelian:
            # The only abelian dihedral groups are the ones of orders 2 and 4.
            self._is_dihedral = False
            return False

        # Now we know the group is of even order >= 6, and nonabelian.
        n = order // 2

        # Handle special cases where there are exactly two generators.
        gens = self.generators
        if len(gens) == 2:
            x, y = gens
            a, b = x.order(), y.order()
            # Make a >= b
            if a < b:
                x, y, a, b = y, x, b, a
            # Using Theorem 2.1 of [3]:
            if {a, b} == {2}:
                self._is_dihedral = True
                return True
            # Using Theorem 1.1 of [3]:
            if (a, b) == (n, 2) and y*x*y == ~x:
                self._is_dihedral = True
                return True

        # Procede with algorithm of [1]
        # Find elements of orders 2 and n
        order_2, order_n = [], []
        for p in self.elements:
            k = p.order()
            if k == 2:
                order_2.append(p)
            elif k == n:
                order_n.append(p)

        if len(order_2) != n + 1 - (n % 2):
            self._is_dihedral = False
            return False

        if not order_n:
            self._is_dihedral = False
            return False

        x = order_n[0]
        # Want an element y of order 2 that is not a power of x
        # (i.e. that is not the 180-deg rotation, when n is even).
        y = order_2[0]
        if n % 2 == 0 and y == x**(n//2):
            y = order_2[1]

        self._is_dihedral = (y*x*y == ~x)
        return self._is_dihedral
","
        Return ``True`` if the group is dihedral.

        Examples
        ========

        >>> from sympy.combinatorics.perm_groups import PermutationGroup
        >>> from sympy.combinatorics.permutations import Permutation
        >>> from sympy.combinatorics.named_groups import SymmetricGroup, CyclicGroup
        >>> G = PermutationGroup(Permutation(1, 6)(2, 5)(3, 4), Permutation(0, 1, 2, 3, 4, 5, 6))
        >>> G.is_dihedral
        True
        >>> G = SymmetricGroup(3)
        >>> G.is_dihedral
        True
        >>> G = CyclicGroup(6)
        >>> G.is_dihedral
        False

        References
        ==========

        .. [1] https://math.stackexchange.com/a/827273
        .. [2] https://kconrad.math.uconn.edu/blurbs/grouptheory/dihedral.pdf
        .. [3] https://kconrad.math.uconn.edu/blurbs/grouptheory/dihedral2.pdf
        .. [4] https://en.wikipedia.org/wiki/Dihedral_group
        ",70,287,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_dihedral(self):
        r
        if self._is_dihedral is not None:
            return self._is_dihedral

        order = self.order()

        if order % 2 == 1:
            self._is_dihedral = False
            return False
        if order == 2:
            self._is_dihedral = True
            return True
        if order == 4:
            # The dihedral group of order 4 is the Klein 4-group.
            self._is_dihedral = not self.is_cyclic
            return self._is_dihedral
        if self.is_abelian:
            # The only abelian dihedral groups are the ones of orders 2 and 4.
            self._is_dihedral = False
            return False

        # Now we know the group is of even order >= 6, and nonabelian.
        n = order // 2

        # Handle special cases where there are exactly two generators.
        gens = self.generators
        if len(gens) == 2:
            x, y = gens
            a, b = x.order(), y.order()
            # Make a >= b
            if a < b:
                x, y, a, b = y, x, b, a
            # Using Theorem 2.1 of [3]:
            if {a, b} == {2}:
                self._is_dihedral = True
                return True
            # Using Theorem 1.1 of [3]:
            if (a, b) == (n, 2) and y*x*y == ~x:
                self._is_dihedral = True
                return True

        # Procede with algorithm of [1]
        # Find elements of orders 2 and n
        order_2, order_n = [], []
        for p in self.elements:
            k = p.order()
            if k == 2:
                order_2.append(p)
            elif k == n:
                order_n.append(p)

        if len(order_2) != n + 1 - (n % 2):
            self._is_dihedral = False
            return False

        if not order_n:
            self._is_dihedral = False
            return False

        x = order_n[0]
        # Want an element y of order 2 that is not a power of x
        # (i.e. that is not the 180-deg rotation, when n is even).
        y = order_2[0]
        if n % 2 == 0 and y == x**(n//2):
            y = order_2[1]

        self._is_dihedral = (y*x*y == ~x)
        return self._is_dihedral

        ```
    ###Assistant : 
        Return ``True`` if the group is dihedral.

        Examples
        ========

        >>> from sympy.combinatorics.perm_groups import PermutationGroup
        >>> from sympy.combinatorics.permutations import Permutation
        >>> from sympy.combinatorics.named_groups import SymmetricGroup, CyclicGroup
        >>> G = PermutationGroup(Permutation(1, 6)(2, 5)(3, 4), Permutation(0, 1, 2, 3, 4, 5, 6))
        >>> G.is_dihedral
        True
        >>> G = SymmetricGroup(3)
        >>> G.is_dihedral
        True
        >>> G = CyclicGroup(6)
        >>> G.is_dihedral
        False

        References
        ==========

        .. [1] https://math.stackexchange.com/a/827273
        .. [2] https://kconrad.math.uconn.edu/blurbs/grouptheory/dihedral.pdf
        .. [3] https://kconrad.math.uconn.edu/blurbs/grouptheory/dihedral2.pdf
        .. [4] https://en.wikipedia.org/wiki/Dihedral_group
        
    "
2152,"def extend_rearport_cable_paths(instance, created, **kwargs):
    
    if created:
        rearport = instance.rear_port
        for cablepath in CablePath.objects.filter(_nodes__contains=rearport):
            cablepath.retrace()
","
    When a new FrontPort is created, add it to any CablePaths which end at its corresponding RearPort.
    ",17,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extend_rearport_cable_paths(instance, created, **kwargs):
    
    if created:
        rearport = instance.rear_port
        for cablepath in CablePath.objects.filter(_nodes__contains=rearport):
            cablepath.retrace()

        ```
    ###Assistant : 
    When a new FrontPort is created, add it to any CablePaths which end at its corresponding RearPort.
    
    "
2153,"def _load_images_to_cache(self, image_files, frame_dims, thumbnail_size):
        
        logger.debug(""Number image_files: %s, frame_dims: %s, thumbnail_size: %s"",
                     len(image_files), frame_dims, thumbnail_size)
        num_images = (frame_dims[0] // thumbnail_size) * (frame_dims[1] // thumbnail_size)
        logger.debug(""num_images: %s"", num_images)
        if num_images == 0:
            return False
        samples = []
        start_idx = len(image_files) - num_images if len(image_files) > num_images else 0
        show_files = sorted(image_files, key=os.path.getctime)[start_idx:]
        dropped_files = []
        for fname in show_files:
            try:
                img = Image.open(fname)
            except PermissionError as err:
                logger.debug(""Permission error opening preview file: '%s'. Original error: %s"",
                             fname, str(err))
                dropped_files.append(fname)
                continue
            except Exception as err:  # pylint:disable=broad-except
                # Swallow any issues with opening an image rather than spamming console
                # Can happen when trying to read partially saved images
                logger.debug(""Error opening preview file: '%s'. Original error: %s"",
                             fname, str(err))
                dropped_files.append(fname)
                continue

            width, height = img.size
            scaling = thumbnail_size / max(width, height)
            logger.debug(""image width: %s, height: %s, scaling: %s"", width, height, scaling)

            try:
                img = img.resize((int(width * scaling), int(height * scaling)))
            except OSError as err:
                # Image only gets loaded when we call a method, so may error on partial loads
                logger.debug(""OS Error resizing preview image: '%s'. Original error: %s"",
                             fname, err)
                dropped_files.append(fname)
                continue

            if img.size[0] != img.size[1]:
                # Pad to square
                new_img = Image.new(""RGB"", (thumbnail_size, thumbnail_size))
                new_img.paste(img, ((thumbnail_size - img.size[0])//2,
                                    (thumbnail_size - img.size[1])//2))
                img = new_img
            draw = ImageDraw.Draw(img)
            draw.rectangle(((0, 0), (thumbnail_size, thumbnail_size)), outline=""#E5E5E5"", width=1)
            samples.append(np.array(img))

        samples = np.array(samples)
        if not np.any(samples):
            logger.debug(""No preview images collected."")
            return False

        if dropped_files:
            logger.debug(""Removing dropped files: %s"", dropped_files)
            show_files = [fname for fname in show_files if fname not in dropped_files]

        self._previewcache[""filenames""] = (self._previewcache[""filenames""] +
                                           show_files)[-num_images:]
        cache = self._previewcache[""images""]
        if cache is None:
            logger.debug(""Creating new cache"")
            cache = samples[-num_images:]
        else:
            logger.debug(""Appending to existing cache"")
            cache = np.concatenate((cache, samples))[-num_images:]
        self._previewcache[""images""] = cache
        logger.debug(""Cache shape: %s"", self._previewcache[""images""].shape)
        return True
"," Load preview images to the image cache.

        Load new images and append to cache, filtering the cache the number of thumbnails that will
        fit  inside the display panel.

        Parameters
        ----------
        image_files: list
            A list of new image files that have been modified since the last check
        frame_dims: tuple
            The (width (`int`), height (`int`)) of the display panel that will display the preview
        thumbnail_size: int
            The size of each thumbnail that should be created

        Returns
        -------
        bool
            ``True`` if images were successfully loaded to cache otherwise ``False``
        ",86,281,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _load_images_to_cache(self, image_files, frame_dims, thumbnail_size):
        
        logger.debug(""Number image_files: %s, frame_dims: %s, thumbnail_size: %s"",
                     len(image_files), frame_dims, thumbnail_size)
        num_images = (frame_dims[0] // thumbnail_size) * (frame_dims[1] // thumbnail_size)
        logger.debug(""num_images: %s"", num_images)
        if num_images == 0:
            return False
        samples = []
        start_idx = len(image_files) - num_images if len(image_files) > num_images else 0
        show_files = sorted(image_files, key=os.path.getctime)[start_idx:]
        dropped_files = []
        for fname in show_files:
            try:
                img = Image.open(fname)
            except PermissionError as err:
                logger.debug(""Permission error opening preview file: '%s'. Original error: %s"",
                             fname, str(err))
                dropped_files.append(fname)
                continue
            except Exception as err:  # pylint:disable=broad-except
                # Swallow any issues with opening an image rather than spamming console
                # Can happen when trying to read partially saved images
                logger.debug(""Error opening preview file: '%s'. Original error: %s"",
                             fname, str(err))
                dropped_files.append(fname)
                continue

            width, height = img.size
            scaling = thumbnail_size / max(width, height)
            logger.debug(""image width: %s, height: %s, scaling: %s"", width, height, scaling)

            try:
                img = img.resize((int(width * scaling), int(height * scaling)))
            except OSError as err:
                # Image only gets loaded when we call a method, so may error on partial loads
                logger.debug(""OS Error resizing preview image: '%s'. Original error: %s"",
                             fname, err)
                dropped_files.append(fname)
                continue

            if img.size[0] != img.size[1]:
                # Pad to square
                new_img = Image.new(""RGB"", (thumbnail_size, thumbnail_size))
                new_img.paste(img, ((thumbnail_size - img.size[0])//2,
                                    (thumbnail_size - img.size[1])//2))
                img = new_img
            draw = ImageDraw.Draw(img)
            draw.rectangle(((0, 0), (thumbnail_size, thumbnail_size)), outline=""#E5E5E5"", width=1)
            samples.append(np.array(img))

        samples = np.array(samples)
        if not np.any(samples):
            logger.debug(""No preview images collected."")
            return False

        if dropped_files:
            logger.debug(""Removing dropped files: %s"", dropped_files)
            show_files = [fname for fname in show_files if fname not in dropped_files]

        self._previewcache[""filenames""] = (self._previewcache[""filenames""] +
                                           show_files)[-num_images:]
        cache = self._previewcache[""images""]
        if cache is None:
            logger.debug(""Creating new cache"")
            cache = samples[-num_images:]
        else:
            logger.debug(""Appending to existing cache"")
            cache = np.concatenate((cache, samples))[-num_images:]
        self._previewcache[""images""] = cache
        logger.debug(""Cache shape: %s"", self._previewcache[""images""].shape)
        return True

        ```
    ###Assistant :  Load preview images to the image cache.

        Load new images and append to cache, filtering the cache the number of thumbnails that will
        fit  inside the display panel.

        Parameters
        ----------
        image_files: list
            A list of new image files that have been modified since the last check
        frame_dims: tuple
            The (width (`int`), height (`int`)) of the display panel that will display the preview
        thumbnail_size: int
            The size of each thumbnail that should be created

        Returns
        -------
        bool
            ``True`` if images were successfully loaded to cache otherwise ``False``
        
    "
2154,"def target_profile(self) -> t.Optional[PosixProfile]:
        
        return t.cast(PosixProfile, self.profiles[0]) if self.profiles else None
","The POSIX target profile, if it uses a different Python interpreter than the controller, otherwise None.",16,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def target_profile(self) -> t.Optional[PosixProfile]:
        
        return t.cast(PosixProfile, self.profiles[0]) if self.profiles else None

        ```
    ###Assistant : The POSIX target profile, if it uses a different Python interpreter than the controller, otherwise None.
    "
2155,"def preprocess_transactions(self):
        

        p_bar = tqdm(range(14), desc=""Preprocessing transactions"")

        try:

            # 0. If optional fields not in the transactions add missing
            optional_fields = [
                ""Sector"",
                ""Industry"",
                ""Country"",
                ""Region"",
                ""Fees"",
                ""Premium"",
                ""ISIN"",
            ]
            if not set(optional_fields).issubset(set(self.__transactions.columns)):
                for field in optional_fields:
                    if field not in self.__transactions.columns:
                        self.__transactions[field] = np.nan

            p_bar.n += 1
            p_bar.refresh()

            # 1. Convert Date to datetime
            self.__transactions[""Date""] = pd.to_datetime(self.__transactions[""Date""])

            p_bar.n += 1
            p_bar.refresh()

            # 2. Sort transactions by date
            self.__transactions = self.__transactions.sort_values(by=""Date"")

            p_bar.n += 1
            p_bar.refresh()

            # 3. Capitalize Ticker and Type [of instrument...]
            self.__transactions[""Ticker""] = self.__transactions[""Ticker""].map(
                lambda x: x.upper()
            )
            self.__transactions[""Type""] = self.__transactions[""Type""].map(
                lambda x: x.upper()
            )

            p_bar.n += 1
            p_bar.refresh()

            # 4. Translate side: [""deposit"", ""buy""] -> 1 and [""withdrawal"", ""sell""] -> -1
            self.__transactions[""Signal""] = self.__transactions[""Side""].map(
                lambda x: 1
                if x.lower() in [""deposit"", ""buy""]
                else (-1 if x.lower() in [""withdrawal"", ""sell""] else 0)
            )

            p_bar.n += 1
            p_bar.refresh()

            # 5. Convert quantity to signed integer
            self.__transactions[""Quantity""] = (
                abs(self.__transactions[""Quantity""]) * self.__transactions[""Signal""]
            )

            p_bar.n += 1
            p_bar.refresh()

            # 6. Determining the investment/divestment value
            self.__transactions[""Investment""] = (
                self.__transactions[""Quantity""] * self.__transactions[""Price""]
                + self.__transactions[""Fees""]
            )

            p_bar.n += 1
            p_bar.refresh()

            # 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)
            crypto_trades = self.__transactions[self.__transactions.Type == ""CRYPTO""]
            self.__transactions.loc[
                (self.__transactions.Type == ""CRYPTO""), ""Ticker""
            ] = [
                f""{crypto}-{currency}""
                for crypto, currency in zip(
                    crypto_trades.Ticker, crypto_trades.Currency
                )
            ]

            p_bar.n += 1
            p_bar.refresh()

            # 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided.

            # If isin not valid ticker is empty
            self.__transactions[""yf_Ticker""] = self.__transactions[""ISIN""].apply(
                lambda x: yf.utils.get_ticker_by_isin(x) if not pd.isna(x) else np.nan
            )

            empty_tickers = list(
                self.__transactions[
                    (self.__transactions[""yf_Ticker""] == """")
                    | (self.__transactions[""yf_Ticker""].isna())
                ][""Ticker""].unique()
            )

            # If ticker from isin is empty it is not valid in yfinance, so check if user provided ticker is supported
            removed_tickers = []
            for item in empty_tickers:
                with contextlib.redirect_stdout(None):
                    # Suppress yfinance failed download message if occurs
                    valid_ticker = not (
                        yf.download(
                            item,
                            start=datetime.datetime.now() + datetime.timedelta(days=-5),
                            progress=False,
                        ).empty
                    )
                    if valid_ticker:
                        # Invalid ISIN but valid ticker
                        self.__transactions.loc[
                            self.__transactions[""Ticker""] == item, ""yf_Ticker""
                        ] = np.nan
                    else:
                        self.__transactions.loc[
                            self.__transactions[""Ticker""] == item, ""yf_Ticker""
                        ] = """"
                        removed_tickers.append(item)

            # Merge reformated tickers into Ticker
            self.__transactions[""Ticker""] = self.__transactions[""yf_Ticker""].fillna(
                self.__transactions[""Ticker""]
            )

            p_bar.n += 1
            p_bar.refresh()

            # 9. Remove unsupported ISINs that came out empty
            self.__transactions.drop(
                self.__transactions[self.__transactions[""Ticker""] == """"].index,
                inplace=True,
            )

            p_bar.n += 1
            p_bar.refresh()

            # 10. Create tickers dictionary with structure {'Type': [Ticker]}
            for ticker_type in set(self.__transactions[""Type""]):
                self.tickers[ticker_type] = list(
                    set(
                        self.__transactions[
                            self.__transactions[""Type""].isin([ticker_type])
                        ][""Ticker""]
                    )
                )

            p_bar.n += 1
            p_bar.refresh()

            # 11. Create list with tickers except cash
            self.tickers_list = list(set(self.__transactions[""Ticker""]))

            p_bar.n += 1
            p_bar.refresh()

            # 12. Save transactions inception date
            self.inception_date = self.__transactions[""Date""][0]

            p_bar.n += 1
            p_bar.refresh()

            # 13. Populate fields Sector, Industry and Country
            if (
                self.__transactions.loc[
                    self.__transactions[""Type""] == ""STOCK"",
                    optional_fields,
                ]
                .isnull()
                .values.any()
            ):
                # If any fields is empty for stocks (overwrites any info there)
                self.load_company_data()

            p_bar.n += 1
            p_bar.refresh()

            # Warn user of removed ISINs
            if removed_tickers:
                console.print(
                    f""\n\n[red]The following tickers are not supported and were removed: {removed_tickers}.""
                    f""\nManually edit the 'Ticker' field with the proper Yahoo Finance suffix or provide a valid ISIN.""
                    f""\nSuffix info on 'Yahoo Finance market coverage':""
                    "" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html""
                    f""\nE.g. IWDA -> IWDA.AS[/red]""
                )
        except Exception:
            console.print(""\nCould not preprocess transactions."")
","Method to preprocess, format and compute auxiliary fields.

        Preprocessing steps:
            0. If optional fields not in the transactions add missing
            1. Convert Date to datetime
            2. Sort transactions by date
            3. Capitalize Ticker and Type [of instrument...]
            4. Translate side: [""deposit"", ""buy""] -> 1 and [""withdrawal"", ""sell""] -> -1
            5. Convert quantity to signed integer
            6. Determining the investment/divestment value
            7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)
            8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided
            9. Remove unsupported ISINs that came out empty
            10. Create tickers dictionary with structure {'Type': [Ticker]}
            11. Create list with tickers except cash
            12. Save transactions inception date
            13. Populate fields Sector, Industry and Country
        ",116,512,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def preprocess_transactions(self):
        

        p_bar = tqdm(range(14), desc=""Preprocessing transactions"")

        try:

            # 0. If optional fields not in the transactions add missing
            optional_fields = [
                ""Sector"",
                ""Industry"",
                ""Country"",
                ""Region"",
                ""Fees"",
                ""Premium"",
                ""ISIN"",
            ]
            if not set(optional_fields).issubset(set(self.__transactions.columns)):
                for field in optional_fields:
                    if field not in self.__transactions.columns:
                        self.__transactions[field] = np.nan

            p_bar.n += 1
            p_bar.refresh()

            # 1. Convert Date to datetime
            self.__transactions[""Date""] = pd.to_datetime(self.__transactions[""Date""])

            p_bar.n += 1
            p_bar.refresh()

            # 2. Sort transactions by date
            self.__transactions = self.__transactions.sort_values(by=""Date"")

            p_bar.n += 1
            p_bar.refresh()

            # 3. Capitalize Ticker and Type [of instrument...]
            self.__transactions[""Ticker""] = self.__transactions[""Ticker""].map(
                lambda x: x.upper()
            )
            self.__transactions[""Type""] = self.__transactions[""Type""].map(
                lambda x: x.upper()
            )

            p_bar.n += 1
            p_bar.refresh()

            # 4. Translate side: [""deposit"", ""buy""] -> 1 and [""withdrawal"", ""sell""] -> -1
            self.__transactions[""Signal""] = self.__transactions[""Side""].map(
                lambda x: 1
                if x.lower() in [""deposit"", ""buy""]
                else (-1 if x.lower() in [""withdrawal"", ""sell""] else 0)
            )

            p_bar.n += 1
            p_bar.refresh()

            # 5. Convert quantity to signed integer
            self.__transactions[""Quantity""] = (
                abs(self.__transactions[""Quantity""]) * self.__transactions[""Signal""]
            )

            p_bar.n += 1
            p_bar.refresh()

            # 6. Determining the investment/divestment value
            self.__transactions[""Investment""] = (
                self.__transactions[""Quantity""] * self.__transactions[""Price""]
                + self.__transactions[""Fees""]
            )

            p_bar.n += 1
            p_bar.refresh()

            # 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)
            crypto_trades = self.__transactions[self.__transactions.Type == ""CRYPTO""]
            self.__transactions.loc[
                (self.__transactions.Type == ""CRYPTO""), ""Ticker""
            ] = [
                f""{crypto}-{currency}""
                for crypto, currency in zip(
                    crypto_trades.Ticker, crypto_trades.Currency
                )
            ]

            p_bar.n += 1
            p_bar.refresh()

            # 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided.

            # If isin not valid ticker is empty
            self.__transactions[""yf_Ticker""] = self.__transactions[""ISIN""].apply(
                lambda x: yf.utils.get_ticker_by_isin(x) if not pd.isna(x) else np.nan
            )

            empty_tickers = list(
                self.__transactions[
                    (self.__transactions[""yf_Ticker""] == """")
                    | (self.__transactions[""yf_Ticker""].isna())
                ][""Ticker""].unique()
            )

            # If ticker from isin is empty it is not valid in yfinance, so check if user provided ticker is supported
            removed_tickers = []
            for item in empty_tickers:
                with contextlib.redirect_stdout(None):
                    # Suppress yfinance failed download message if occurs
                    valid_ticker = not (
                        yf.download(
                            item,
                            start=datetime.datetime.now() + datetime.timedelta(days=-5),
                            progress=False,
                        ).empty
                    )
                    if valid_ticker:
                        # Invalid ISIN but valid ticker
                        self.__transactions.loc[
                            self.__transactions[""Ticker""] == item, ""yf_Ticker""
                        ] = np.nan
                    else:
                        self.__transactions.loc[
                            self.__transactions[""Ticker""] == item, ""yf_Ticker""
                        ] = """"
                        removed_tickers.append(item)

            # Merge reformated tickers into Ticker
            self.__transactions[""Ticker""] = self.__transactions[""yf_Ticker""].fillna(
                self.__transactions[""Ticker""]
            )

            p_bar.n += 1
            p_bar.refresh()

            # 9. Remove unsupported ISINs that came out empty
            self.__transactions.drop(
                self.__transactions[self.__transactions[""Ticker""] == """"].index,
                inplace=True,
            )

            p_bar.n += 1
            p_bar.refresh()

            # 10. Create tickers dictionary with structure {'Type': [Ticker]}
            for ticker_type in set(self.__transactions[""Type""]):
                self.tickers[ticker_type] = list(
                    set(
                        self.__transactions[
                            self.__transactions[""Type""].isin([ticker_type])
                        ][""Ticker""]
                    )
                )

            p_bar.n += 1
            p_bar.refresh()

            # 11. Create list with tickers except cash
            self.tickers_list = list(set(self.__transactions[""Ticker""]))

            p_bar.n += 1
            p_bar.refresh()

            # 12. Save transactions inception date
            self.inception_date = self.__transactions[""Date""][0]

            p_bar.n += 1
            p_bar.refresh()

            # 13. Populate fields Sector, Industry and Country
            if (
                self.__transactions.loc[
                    self.__transactions[""Type""] == ""STOCK"",
                    optional_fields,
                ]
                .isnull()
                .values.any()
            ):
                # If any fields is empty for stocks (overwrites any info there)
                self.load_company_data()

            p_bar.n += 1
            p_bar.refresh()

            # Warn user of removed ISINs
            if removed_tickers:
                console.print(
                    f""\n\n[red]The following tickers are not supported and were removed: {removed_tickers}.""
                    f""\nManually edit the 'Ticker' field with the proper Yahoo Finance suffix or provide a valid ISIN.""
                    f""\nSuffix info on 'Yahoo Finance market coverage':""
                    "" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html""
                    f""\nE.g. IWDA -> IWDA.AS[/red]""
                )
        except Exception:
            console.print(""\nCould not preprocess transactions."")

        ```
    ###Assistant : Method to preprocess, format and compute auxiliary fields.

        Preprocessing steps:
            0. If optional fields not in the transactions add missing
            1. Convert Date to datetime
            2. Sort transactions by date
            3. Capitalize Ticker and Type [of instrument...]
            4. Translate side: [""deposit"", ""buy""] -> 1 and [""withdrawal"", ""sell""] -> -1
            5. Convert quantity to signed integer
            6. Determining the investment/divestment value
            7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)
            8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided
            9. Remove unsupported ISINs that came out empty
            10. Create tickers dictionary with structure {'Type': [Ticker]}
            11. Create list with tickers except cash
            12. Save transactions inception date
            13. Populate fields Sector, Industry and Country
        
    "
2156,"def _var_key(var):
  

  # pylint: disable=protected-access
  # Get the distributed variable if it exists.
  if hasattr(var, ""_distributed_container""):
    var = var._distributed_container()
  if getattr(var, ""_in_graph_mode"", False):
    return var._shared_name
  return var._unique_id

","Key for representing a primary variable, for looking up slots.

  In graph mode the name is derived from the var shared name.
  In eager mode the name is derived from the var unique id.
  If distribution strategy exists, get the primary variable first.

  Args:
    var: the variable.

  Returns:
    the unique name of the variable.
  ",54,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _var_key(var):
  

  # pylint: disable=protected-access
  # Get the distributed variable if it exists.
  if hasattr(var, ""_distributed_container""):
    var = var._distributed_container()
  if getattr(var, ""_in_graph_mode"", False):
    return var._shared_name
  return var._unique_id


        ```
    ###Assistant : Key for representing a primary variable, for looking up slots.

  In graph mode the name is derived from the var shared name.
  In eager mode the name is derived from the var unique id.
  If distribution strategy exists, get the primary variable first.

  Args:
    var: the variable.

  Returns:
    the unique name of the variable.
  
    "
2157,"def test_stacking_classifier_base_regressor():
    
    X_train, X_test, y_train, y_test = train_test_split(
        scale(X_iris), y_iris, stratify=y_iris, random_state=42
    )
    clf = StackingClassifier(estimators=[(""ridge"", Ridge())])
    clf.fit(X_train, y_train)
    clf.predict(X_test)
    clf.predict_proba(X_test)
    assert clf.score(X_test, y_test) > 0.8
",Check that a regressor can be used as the first layer in `StackingClassifier`.,13,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stacking_classifier_base_regressor():
    
    X_train, X_test, y_train, y_test = train_test_split(
        scale(X_iris), y_iris, stratify=y_iris, random_state=42
    )
    clf = StackingClassifier(estimators=[(""ridge"", Ridge())])
    clf.fit(X_train, y_train)
    clf.predict(X_test)
    clf.predict_proba(X_test)
    assert clf.score(X_test, y_test) > 0.8

        ```
    ###Assistant : Check that a regressor can be used as the first layer in `StackingClassifier`.
    "
2158,"def _get_style_dict(self, gc, rgbFace):
        
        attrib = {}

        forced_alpha = gc.get_forced_alpha()

        if gc.get_hatch() is not None:
            attrib['fill'] = ""url(#%s)"" % self._get_hatch(gc, rgbFace)
            if (rgbFace is not None and len(rgbFace) == 4 and rgbFace[3] != 1.0
                    and not forced_alpha):
                attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])
        else:
            if rgbFace is None:
                attrib['fill'] = 'none'
            else:
                if tuple(rgbFace[:3]) != (0, 0, 0):
                    attrib['fill'] = rgb2hex(rgbFace)
                if (len(rgbFace) == 4 and rgbFace[3] != 1.0
                        and not forced_alpha):
                    attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])

        if forced_alpha and gc.get_alpha() != 1.0:
            attrib['opacity'] = _short_float_fmt(gc.get_alpha())

        offset, seq = gc.get_dashes()
        if seq is not None:
            attrib['stroke-dasharray'] = ','.join(
                _short_float_fmt(val) for val in seq)
            attrib['stroke-dashoffset'] = _short_float_fmt(float(offset))

        linewidth = gc.get_linewidth()
        if linewidth:
            rgb = gc.get_rgb()
            attrib['stroke'] = rgb2hex(rgb)
            if not forced_alpha and rgb[3] != 1.0:
                attrib['stroke-opacity'] = _short_float_fmt(rgb[3])
            if linewidth != 1.0:
                attrib['stroke-width'] = _short_float_fmt(linewidth)
            if gc.get_joinstyle() != 'round':
                attrib['stroke-linejoin'] = gc.get_joinstyle()
            if gc.get_capstyle() != 'butt':
                attrib['stroke-linecap'] = _capstyle_d[gc.get_capstyle()]

        return attrib
",Generate a style string from the GraphicsContext and rgbFace.,9,145,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_style_dict(self, gc, rgbFace):
        
        attrib = {}

        forced_alpha = gc.get_forced_alpha()

        if gc.get_hatch() is not None:
            attrib['fill'] = ""url(#%s)"" % self._get_hatch(gc, rgbFace)
            if (rgbFace is not None and len(rgbFace) == 4 and rgbFace[3] != 1.0
                    and not forced_alpha):
                attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])
        else:
            if rgbFace is None:
                attrib['fill'] = 'none'
            else:
                if tuple(rgbFace[:3]) != (0, 0, 0):
                    attrib['fill'] = rgb2hex(rgbFace)
                if (len(rgbFace) == 4 and rgbFace[3] != 1.0
                        and not forced_alpha):
                    attrib['fill-opacity'] = _short_float_fmt(rgbFace[3])

        if forced_alpha and gc.get_alpha() != 1.0:
            attrib['opacity'] = _short_float_fmt(gc.get_alpha())

        offset, seq = gc.get_dashes()
        if seq is not None:
            attrib['stroke-dasharray'] = ','.join(
                _short_float_fmt(val) for val in seq)
            attrib['stroke-dashoffset'] = _short_float_fmt(float(offset))

        linewidth = gc.get_linewidth()
        if linewidth:
            rgb = gc.get_rgb()
            attrib['stroke'] = rgb2hex(rgb)
            if not forced_alpha and rgb[3] != 1.0:
                attrib['stroke-opacity'] = _short_float_fmt(rgb[3])
            if linewidth != 1.0:
                attrib['stroke-width'] = _short_float_fmt(linewidth)
            if gc.get_joinstyle() != 'round':
                attrib['stroke-linejoin'] = gc.get_joinstyle()
            if gc.get_capstyle() != 'butt':
                attrib['stroke-linecap'] = _capstyle_d[gc.get_capstyle()]

        return attrib

        ```
    ###Assistant : Generate a style string from the GraphicsContext and rgbFace.
    "
2159,"def test_union(self, mock_handler):

        self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})

        # --- use predictor ---
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical,
                'c': dtype.datetime
            },
            'predicted_value': 'ccc'
        }
        self.set_predictor(predictor)
        sql = 
        # union all
        ret = self.command_executor.execute_command(
            parse_sql(sql.format(union='ALL'), dialect='mindsdb'))
        assert ret.error_code is None

        ret_df = self.ret_to_df(ret)
        assert list(ret_df.columns) == ['a1', 'target']
        assert ret_df.shape[0] == 3 + 2

        # union
        ret = self.command_executor.execute_command(
            parse_sql(sql.format(union=''), dialect='mindsdb'))
        assert ret.error_code is None

        ret_df = self.ret_to_df(ret)
        assert list(ret_df.columns) == ['a1', 'target']
        assert ret_df.shape[0] == 3

","
             SELECT a as a1, b as target
              FROM pg.tasks
           UNION {union}
             SELECT model.a as a2, model.p as target2
              FROM pg.tasks as t
             JOIN mindsdb.task_model as model
             WHERE t.a=1           
        ",28,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_union(self, mock_handler):

        self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})

        # --- use predictor ---
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical,
                'c': dtype.datetime
            },
            'predicted_value': 'ccc'
        }
        self.set_predictor(predictor)
        sql = 
        # union all
        ret = self.command_executor.execute_command(
            parse_sql(sql.format(union='ALL'), dialect='mindsdb'))
        assert ret.error_code is None

        ret_df = self.ret_to_df(ret)
        assert list(ret_df.columns) == ['a1', 'target']
        assert ret_df.shape[0] == 3 + 2

        # union
        ret = self.command_executor.execute_command(
            parse_sql(sql.format(union=''), dialect='mindsdb'))
        assert ret.error_code is None

        ret_df = self.ret_to_df(ret)
        assert list(ret_df.columns) == ['a1', 'target']
        assert ret_df.shape[0] == 3


        ```
    ###Assistant : 
             SELECT a as a1, b as target
              FROM pg.tasks
           UNION {union}
             SELECT model.a as a2, model.p as target2
              FROM pg.tasks as t
             JOIN mindsdb.task_model as model
             WHERE t.a=1           
        
    "
2160,"def test_run_cleanup_tables(self, clean_table_mock, table_names):
        
        base_kwargs = dict(
            clean_before_timestamp=None,
            dry_run=None,
            verbose=None,
        )
        run_cleanup(**base_kwargs, table_names=table_names)
        assert clean_table_mock.call_count == len(table_names) if table_names else len(config_dict)
","
        ``_cleanup_table`` should be called for each table in subset if one
        is provided else should be called for all tables.
        ",20,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_run_cleanup_tables(self, clean_table_mock, table_names):
        
        base_kwargs = dict(
            clean_before_timestamp=None,
            dry_run=None,
            verbose=None,
        )
        run_cleanup(**base_kwargs, table_names=table_names)
        assert clean_table_mock.call_count == len(table_names) if table_names else len(config_dict)

        ```
    ###Assistant : 
        ``_cleanup_table`` should be called for each table in subset if one
        is provided else should be called for all tables.
        
    "
2161,"def test_set_serialize_call_old_signature(self, get_import, session):
        
        serialize_watcher = MagicMock()
","
        When XCom.serialize_value takes only param ``value``, other kwargs should be ignored.
        ",11,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_set_serialize_call_old_signature(self, get_import, session):
        
        serialize_watcher = MagicMock()

        ```
    ###Assistant : 
        When XCom.serialize_value takes only param ``value``, other kwargs should be ignored.
        
    "
2162,"def gmean(a, axis=0, dtype=None, weights=None):
    
    if not isinstance(a, np.ndarray):
        # if not an ndarray object attempt to convert it
        log_a = np.log(np.array(a, dtype=dtype))
    elif dtype:
        # Must change the default dtype allowing array type
        if isinstance(a, np.ma.MaskedArray):
            log_a = np.log(np.ma.asarray(a, dtype=dtype))
        else:
            log_a = np.log(np.asarray(a, dtype=dtype))
    else:
        log_a = np.log(a)

    if weights is not None:
        weights = np.asanyarray(weights, dtype=dtype)

    return np.exp(np.average(log_a, axis=axis, weights=weights))

","Compute the geometric mean along the specified axis.

    Return the geometric average of the array elements.
    That is:  n-th root of (x1 * x2 * ... * xn)

    Parameters
    ----------
    a : array_like
        Input array or object that can be converted to an array.
    axis : int or None, optional
        Axis along which the geometric mean is computed. Default is 0.
        If None, compute over the whole array `a`.
    dtype : dtype, optional
        Type of the returned array and of the accumulator in which the
        elements are summed. If dtype is not specified, it defaults to the
        dtype of a, unless a has an integer dtype with a precision less than
        that of the default platform integer. In that case, the default
        platform integer is used.
    weights : array_like, optional
        The `weights` array must be broadcastable to the same shape as `a`.
        Default is None, which gives each value a weight of 1.0.

    Returns
    -------
    gmean : ndarray
        See `dtype` parameter above.

    See Also
    --------
    numpy.mean : Arithmetic average
    numpy.average : Weighted average
    hmean : Harmonic mean

    Notes
    -----
    The geometric average is computed over a single dimension of the input
    array, axis=0 by default, or all values in the array if axis=None.
    float64 intermediate and return values are used for integer inputs.
    Beginning in SciPy 1.9, ``np.matrix`` inputs are converted to
    ``np.ndarray``s before the calculation is performed. In this case, the
    output will be a scalar or ``np.ndarray`` of appropriate shape rather than
    a 2D ``np.matrix``. Similarly, while masked elements of masked arrays
    are still ignored, the output will be a scalar or ``np.ndarray`` rather
    than a masked array with ``mask=False``.

    References
    ----------
    .. [1] ""Weighted Geometric Mean"", *Wikipedia*, https://en.wikipedia.org/wiki/Weighted_geometric_mean.

    Examples
    --------
    >>> from scipy.stats import gmean
    >>> gmean([1, 4])
    2.0
    >>> gmean([1, 2, 3, 4, 5, 6, 7])
    3.3800151591412964

    ",301,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def gmean(a, axis=0, dtype=None, weights=None):
    
    if not isinstance(a, np.ndarray):
        # if not an ndarray object attempt to convert it
        log_a = np.log(np.array(a, dtype=dtype))
    elif dtype:
        # Must change the default dtype allowing array type
        if isinstance(a, np.ma.MaskedArray):
            log_a = np.log(np.ma.asarray(a, dtype=dtype))
        else:
            log_a = np.log(np.asarray(a, dtype=dtype))
    else:
        log_a = np.log(a)

    if weights is not None:
        weights = np.asanyarray(weights, dtype=dtype)

    return np.exp(np.average(log_a, axis=axis, weights=weights))


        ```
    ###Assistant : Compute the geometric mean along the specified axis.

    Return the geometric average of the array elements.
    That is:  n-th root of (x1 * x2 * ... * xn)

    Parameters
    ----------
    a : array_like
        Input array or object that can be converted to an array.
    axis : int or None, optional
        Axis along which the geometric mean is computed. Default is 0.
        If None, compute over the whole array `a`.
    dtype : dtype, optional
        Type of the returned array and of the accumulator in which the
        elements are summed. If dtype is not specified, it defaults to the
        dtype of a, unless a has an integer dtype with a precision less than
        that of the default platform integer. In that case, the default
        platform integer is used.
    weights : array_like, optional
        The `weights` array must be broadcastable to the same shape as `a`.
        Default is None, which gives each value a weight of 1.0.

    Returns
    -------
    gmean : ndarray
        See `dtype` parameter above.

    See Also
    --------
    numpy.mean : Arithmetic average
    numpy.average : Weighted average
    hmean : Harmonic mean

    Notes
    -----
    The geometric average is computed over a single dimension of the input
    array, axis=0 by default, or all values in the array if axis=None.
    float64 intermediate and return values are used for integer inputs.
    Beginning in SciPy 1.9, ``np.matrix`` inputs are converted to
    ``np.ndarray``s before the calculation is performed. In this case, the
    output will be a scalar or ``np.ndarray`` of appropriate shape rather than
    a 2D ``np.matrix``. Similarly, while masked elements of masked arrays
    are still ignored, the output will be a scalar or ``np.ndarray`` rather
    than a masked array with ``mask=False``.

    References
    ----------
    .. [1] ""Weighted Geometric Mean"", *Wikipedia*, https://en.wikipedia.org/wiki/Weighted_geometric_mean.

    Examples
    --------
    >>> from scipy.stats import gmean
    >>> gmean([1, 4])
    2.0
    >>> gmean([1, 2, 3, 4, 5, 6, 7])
    3.3800151591412964

    
    "
2163,"def handle_m2m_field(self, obj, field):
        
        raise NotImplementedError(
            ""subclasses of Serializer must provide a handle_m2m_field() method""
        )
","
        Called to handle a ManyToManyField.
        ",5,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def handle_m2m_field(self, obj, field):
        
        raise NotImplementedError(
            ""subclasses of Serializer must provide a handle_m2m_field() method""
        )

        ```
    ###Assistant : 
        Called to handle a ManyToManyField.
        
    "
2164,"def check_connection(self) -> StatusResponse:
        

        response = StatusResponse(False)
        need_to_close = self.is_connected is False

        try:
            connection = self.connect()
            with connection.cursor() as cur:
                cur.execute('SELECT * FROM SYS.M_DATABASE')
            response.success = True
        except dbapi.Error as e:
            log.error(f'Error connecting to SAP HANA {self.address}, {e}!')
            response.error_message = e

        if response.success is True and need_to_close:
            self.disconnect()
        if response.success is False and self.is_connected is True:
            self.is_connected = False

        return response
","
        Check the connection of the SAP HANA database
        :return: success status and error message if error occurs
        ",17,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_connection(self) -> StatusResponse:
        

        response = StatusResponse(False)
        need_to_close = self.is_connected is False

        try:
            connection = self.connect()
            with connection.cursor() as cur:
                cur.execute('SELECT * FROM SYS.M_DATABASE')
            response.success = True
        except dbapi.Error as e:
            log.error(f'Error connecting to SAP HANA {self.address}, {e}!')
            response.error_message = e

        if response.success is True and need_to_close:
            self.disconnect()
        if response.success is False and self.is_connected is True:
            self.is_connected = False

        return response

        ```
    ###Assistant : 
        Check the connection of the SAP HANA database
        :return: success status and error message if error occurs
        
    "
2165,"def extract(self, rowsList, colsList):
        r

        if not is_sequence(rowsList) or not is_sequence(colsList):
            raise TypeError(""rowsList and colsList must be iterable"")
        # ensure rowsList and colsList are lists of integers
        if rowsList and all(isinstance(i, bool) for i in rowsList):
            rowsList = [index for index, item in enumerate(rowsList) if item]
        if colsList and all(isinstance(i, bool) for i in colsList):
            colsList = [index for index, item in enumerate(colsList) if item]

        # ensure everything is in range
        rowsList = [a2idx(k, self.rows) for k in rowsList]
        colsList = [a2idx(k, self.cols) for k in colsList]

        return self._eval_extract(rowsList, colsList)
","Return a submatrix by specifying a list of rows and columns.
        Negative indices can be given. All indices must be in the range
        $-n \le i < n$ where $n$ is the number of rows or columns.

        Examples
        ========

        >>> from sympy import Matrix
        >>> m = Matrix(4, 3, range(12))
        >>> m
        Matrix([
        [0,  1,  2],
        [3,  4,  5],
        [6,  7,  8],
        [9, 10, 11]])
        >>> m.extract([0, 1, 3], [0, 1])
        Matrix([
        [0,  1],
        [3,  4],
        [9, 10]])

        Rows or columns can be repeated:

        >>> m.extract([0, 0, 1], [-1])
        Matrix([
        [2],
        [2],
        [5]])

        Every other row can be taken by using range to provide the indices:

        >>> m.extract(range(0, m.rows, 2), [-1])
        Matrix([
        [2],
        [8]])

        RowsList or colsList can also be a list of booleans, in which case
        the rows or columns corresponding to the True values will be selected:

        >>> m.extract([0, 1, 2, 3], [True, False, True])
        Matrix([
        [0,  2],
        [3,  5],
        [6,  8],
        [9, 11]])
        ",156,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extract(self, rowsList, colsList):
        r

        if not is_sequence(rowsList) or not is_sequence(colsList):
            raise TypeError(""rowsList and colsList must be iterable"")
        # ensure rowsList and colsList are lists of integers
        if rowsList and all(isinstance(i, bool) for i in rowsList):
            rowsList = [index for index, item in enumerate(rowsList) if item]
        if colsList and all(isinstance(i, bool) for i in colsList):
            colsList = [index for index, item in enumerate(colsList) if item]

        # ensure everything is in range
        rowsList = [a2idx(k, self.rows) for k in rowsList]
        colsList = [a2idx(k, self.cols) for k in colsList]

        return self._eval_extract(rowsList, colsList)

        ```
    ###Assistant : Return a submatrix by specifying a list of rows and columns.
        Negative indices can be given. All indices must be in the range
        $-n \le i < n$ where $n$ is the number of rows or columns.

        Examples
        ========

        >>> from sympy import Matrix
        >>> m = Matrix(4, 3, range(12))
        >>> m
        Matrix([
        [0,  1,  2],
        [3,  4,  5],
        [6,  7,  8],
        [9, 10, 11]])
        >>> m.extract([0, 1, 3], [0, 1])
        Matrix([
        [0,  1],
        [3,  4],
        [9, 10]])

        Rows or columns can be repeated:

        >>> m.extract([0, 0, 1], [-1])
        Matrix([
        [2],
        [2],
        [5]])

        Every other row can be taken by using range to provide the indices:

        >>> m.extract(range(0, m.rows, 2), [-1])
        Matrix([
        [2],
        [8]])

        RowsList or colsList can also be a list of booleans, in which case
        the rows or columns corresponding to the True values will be selected:

        >>> m.extract([0, 1, 2, 3], [True, False, True])
        Matrix([
        [0,  2],
        [3,  5],
        [6,  8],
        [9, 11]])
        
    "
2166,"def integrate(*args, meijerg=None, conds='piecewise', risch=None, heurisch=None, manual=None, **kwargs):
    
    doit_flags = {
        'deep': False,
        'meijerg': meijerg,
        'conds': conds,
        'risch': risch,
        'heurisch': heurisch,
        'manual': manual
        }
    integral = Integral(*args, **kwargs)

    if isinstance(integral, Integral):
        return integral.doit(**doit_flags)
    else:
        new_args = [a.doit(**doit_flags) if isinstance(a, Integral) else a
            for a in integral.args]
        return integral.func(*new_args)

","integrate(f, var, ...)

    Explanation
    ===========

    Compute definite or indefinite integral of one or more variables
    using Risch-Norman algorithm and table lookup. This procedure is
    able to handle elementary algebraic and transcendental functions
    and also a huge class of special functions, including Airy,
    Bessel, Whittaker and Lambert.

    var can be:

    - a symbol                   -- indefinite integration
    - a tuple (symbol, a)        -- indefinite integration with result
                                    given with ``a`` replacing ``symbol``
    - a tuple (symbol, a, b)     -- definite integration

    Several variables can be specified, in which case the result is
    multiple integration. (If var is omitted and the integrand is
    univariate, the indefinite integral in that variable will be performed.)

    Indefinite integrals are returned without terms that are independent
    of the integration variables. (see examples)

    Definite improper integrals often entail delicate convergence
    conditions. Pass conds='piecewise', 'separate' or 'none' to have
    these returned, respectively, as a Piecewise function, as a separate
    result (i.e. result will be a tuple), or not at all (default is
    'piecewise').

    **Strategy**

    SymPy uses various approaches to definite integration. One method is to
    find an antiderivative for the integrand, and then use the fundamental
    theorem of calculus. Various functions are implemented to integrate
    polynomial, rational and trigonometric functions, and integrands
    containing DiracDelta terms.

    SymPy also implements the part of the Risch algorithm, which is a decision
    procedure for integrating elementary functions, i.e., the algorithm can
    either find an elementary antiderivative, or prove that one does not
    exist.  There is also a (very successful, albeit somewhat slow) general
    implementation of the heuristic Risch algorithm.  This algorithm will
    eventually be phased out as more of the full Risch algorithm is
    implemented. See the docstring of Integral._eval_integral() for more
    details on computing the antiderivative using algebraic methods.

    The option risch=True can be used to use only the (full) Risch algorithm.
    This is useful if you want to know if an elementary function has an
    elementary antiderivative.  If the indefinite Integral returned by this
    function is an instance of NonElementaryIntegral, that means that the
    Risch algorithm has proven that integral to be non-elementary.  Note that
    by default, additional methods (such as the Meijer G method outlined
    below) are tried on these integrals, as they may be expressible in terms
    of special functions, so if you only care about elementary answers, use
    risch=True.  Also note that an unevaluated Integral returned by this
    function is not necessarily a NonElementaryIntegral, even with risch=True,
    as it may just be an indication that the particular part of the Risch
    algorithm needed to integrate that function is not yet implemented.

    Another family of strategies comes from re-writing the integrand in
    terms of so-called Meijer G-functions. Indefinite integrals of a
    single G-function can always be computed, and the definite integral
    of a product of two G-functions can be computed from zero to
    infinity. Various strategies are implemented to rewrite integrands
    as G-functions, and use this information to compute integrals (see
    the ``meijerint`` module).

    The option manual=True can be used to use only an algorithm that tries
    to mimic integration by hand. This algorithm does not handle as many
    integrands as the other algorithms implemented but may return results in
    a more familiar form. The ``manualintegrate`` module has functions that
    return the steps used (see the module docstring for more information).

    In general, the algebraic methods work best for computing
    antiderivatives of (possibly complicated) combinations of elementary
    functions. The G-function methods work best for computing definite
    integrals from zero to infinity of moderately complicated
    combinations of special functions, or indefinite integrals of very
    simple combinations of special functions.

    The strategy employed by the integration code is as follows:

    - If computing a definite integral, and both limits are real,
      and at least one limit is +- oo, try the G-function method of
      definite integration first.

    - Try to find an antiderivative, using all available methods, ordered
      by performance (that is try fastest method first, slowest last; in
      particular polynomial integration is tried first, Meijer
      G-functions second to last, and heuristic Risch last).

    - If still not successful, try G-functions irrespective of the
      limits.

    The option meijerg=True, False, None can be used to, respectively:
    always use G-function methods and no others, never use G-function
    methods, or use all available methods (in order as described above).
    It defaults to None.

    Examples
    ========

    >>> from sympy import integrate, log, exp, oo
    >>> from sympy.abc import a, x, y

    >>> integrate(x*y, x)
    x**2*y/2

    >>> integrate(log(x), x)
    x*log(x) - x

    >>> integrate(log(x), (x, 1, a))
    a*log(a) - a + 1

    >>> integrate(x)
    x**2/2

    Terms that are independent of x are dropped by indefinite integration:

    >>> from sympy import sqrt
    >>> integrate(sqrt(1 + x), (x, 0, x))
    2*(x + 1)**(3/2)/3 - 2/3
    >>> integrate(sqrt(1 + x), x)
    2*(x + 1)**(3/2)/3

    >>> integrate(x*y)
    Traceback (most recent call last):
    ...
    ValueError: specify integration variables to integrate x*y

    Note that ``integrate(x)`` syntax is meant only for convenience
    in interactive sessions and should be avoided in library code.

    >>> integrate(x**a*exp(-x), (x, 0, oo)) # same as conds='piecewise'
    Piecewise((gamma(a + 1), re(a) > -1),
        (Integral(x**a*exp(-x), (x, 0, oo)), True))

    >>> integrate(x**a*exp(-x), (x, 0, oo), conds='none')
    gamma(a + 1)

    >>> integrate(x**a*exp(-x), (x, 0, oo), conds='separate')
    (gamma(a + 1), re(a) > -1)

    See Also
    ========

    Integral, Integral.doit

    ",865,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def integrate(*args, meijerg=None, conds='piecewise', risch=None, heurisch=None, manual=None, **kwargs):
    
    doit_flags = {
        'deep': False,
        'meijerg': meijerg,
        'conds': conds,
        'risch': risch,
        'heurisch': heurisch,
        'manual': manual
        }
    integral = Integral(*args, **kwargs)

    if isinstance(integral, Integral):
        return integral.doit(**doit_flags)
    else:
        new_args = [a.doit(**doit_flags) if isinstance(a, Integral) else a
            for a in integral.args]
        return integral.func(*new_args)


        ```
    ###Assistant : integrate(f, var, ...)

    Explanation
    ===========

    Compute definite or indefinite integral of one or more variables
    using Risch-Norman algorithm and table lookup. This procedure is
    able to handle elementary algebraic and transcendental functions
    and also a huge class of special functions, including Airy,
    Bessel, Whittaker and Lambert.

    var can be:

    - a symbol                   -- indefinite integration
    - a tuple (symbol, a)        -- indefinite integration with result
                                    given with ``a`` replacing ``symbol``
    - a tuple (symbol, a, b)     -- definite integration

    Several variables can be specified, in which case the result is
    multiple integration. (If var is omitted and the integrand is
    univariate, the indefinite integral in that variable will be performed.)

    Indefinite integrals are returned without terms that are independent
    of the integration variables. (see examples)

    Definite improper integrals often entail delicate convergence
    conditions. Pass conds='piecewise', 'separate' or 'none' to have
    these returned, respectively, as a Piecewise function, as a separate
    result (i.e. result will be a tuple), or not at all (default is
    'piecewise').

    **Strategy**

    SymPy uses various approaches to definite integration. One method is to
    find an antiderivative for the integrand, and then use the fundamental
    theorem of calculus. Various functions are implemented to integrate
    polynomial, rational and trigonometric functions, and integrands
    containing DiracDelta terms.

    SymPy also implements the part of the Risch algorithm, which is a decision
    procedure for integrating elementary functions, i.e., the algorithm can
    either find an elementary antiderivative, or prove that one does not
    exist.  There is also a (very successful, albeit somewhat slow) general
    implementation of the heuristic Risch algorithm.  This algorithm will
    eventually be phased out as more of the full Risch algorithm is
    implemented. See the docstring of Integral._eval_integral() for more
    details on computing the antiderivative using algebraic methods.

    The option risch=True can be used to use only the (full) Risch algorithm.
    This is useful if you want to know if an elementary function has an
    elementary antiderivative.  If the indefinite Integral returned by this
    function is an instance of NonElementaryIntegral, that means that the
    Risch algorithm has proven that integral to be non-elementary.  Note that
    by default, additional methods (such as the Meijer G method outlined
    below) are tried on these integrals, as they may be expressible in terms
    of special functions, so if you only care about elementary answers, use
    risch=True.  Also note that an unevaluated Integral returned by this
    function is not necessarily a NonElementaryIntegral, even with risch=True,
    as it may just be an indication that the particular part of the Risch
    algorithm needed to integrate that function is not yet implemented.

    Another family of strategies comes from re-writing the integrand in
    terms of so-called Meijer G-functions. Indefinite integrals of a
    single G-function can always be computed, and the definite integral
    of a product of two G-functions can be computed from zero to
    infinity. Various strategies are implemented to rewrite integrands
    as G-functions, and use this information to compute integrals (see
    the ``meijerint`` module).

    The option manual=True can be used to use only an algorithm that tries
    to mimic integration by hand. This algorithm does not handle as many
    integrands as the other algorithms implemented but may return results in
    a more familiar form. The ``manualintegrate`` module has functions that
    return the steps used (see the module docstring for more information).

    In general, the algebraic methods work best for computing
    antiderivatives of (possibly complicated) combinations of elementary
    functions. The G-function methods work best for computing definite
    integrals from zero to infinity of moderately complicated
    combinations of special functions, or indefinite integrals of very
    simple combinations of special functions.

    The strategy employed by the integration code is as follows:

    - If computing a definite integral, and both limits are real,
      and at least one limit is +- oo, try the G-function method of
      definite integration first.

    - Try to find an antiderivative, using all available methods, ordered
      by performance (that is try fastest method first, slowest last; in
      particular polynomial integration is tried first, Meijer
      G-functions second to last, and heuristic Risch last).

    - If still not successful, try G-functions irrespective of the
      limits.

    The option meijerg=True, False, None can be used to, respectively:
    always use G-function methods and no others, never use G-function
    methods, or use all available methods (in order as described above).
    It defaults to None.

    Examples
    ========

    >>> from sympy import integrate, log, exp, oo
    >>> from sympy.abc import a, x, y

    >>> integrate(x*y, x)
    x**2*y/2

    >>> integrate(log(x), x)
    x*log(x) - x

    >>> integrate(log(x), (x, 1, a))
    a*log(a) - a + 1

    >>> integrate(x)
    x**2/2

    Terms that are independent of x are dropped by indefinite integration:

    >>> from sympy import sqrt
    >>> integrate(sqrt(1 + x), (x, 0, x))
    2*(x + 1)**(3/2)/3 - 2/3
    >>> integrate(sqrt(1 + x), x)
    2*(x + 1)**(3/2)/3

    >>> integrate(x*y)
    Traceback (most recent call last):
    ...
    ValueError: specify integration variables to integrate x*y

    Note that ``integrate(x)`` syntax is meant only for convenience
    in interactive sessions and should be avoided in library code.

    >>> integrate(x**a*exp(-x), (x, 0, oo)) # same as conds='piecewise'
    Piecewise((gamma(a + 1), re(a) > -1),
        (Integral(x**a*exp(-x), (x, 0, oo)), True))

    >>> integrate(x**a*exp(-x), (x, 0, oo), conds='none')
    gamma(a + 1)

    >>> integrate(x**a*exp(-x), (x, 0, oo), conds='separate')
    (gamma(a + 1), re(a) > -1)

    See Also
    ========

    Integral, Integral.doit

    
    "
2167,"def parsed_paths(self) -> List[str]:
        

        res_paths: List[str] = []

        paths = self.parser.existing_paths
        for directory in paths:
            for filename in paths[directory]:
                res_paths.append(os.path.join(directory, filename))

        return res_paths
","
        Returns a list of file paths that have currently been parsed into the parser
        tree. The returned list may include paths with wildcard characters, for
        example: ['/etc/apache2/conf.d/*.load']

        This is typically called on the root node of the ParserNode tree.

        :returns: list of file paths of files that have been parsed
        ",50,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parsed_paths(self) -> List[str]:
        

        res_paths: List[str] = []

        paths = self.parser.existing_paths
        for directory in paths:
            for filename in paths[directory]:
                res_paths.append(os.path.join(directory, filename))

        return res_paths

        ```
    ###Assistant : 
        Returns a list of file paths that have currently been parsed into the parser
        tree. The returned list may include paths with wildcard characters, for
        example: ['/etc/apache2/conf.d/*.load']

        This is typically called on the root node of the ParserNode tree.

        :returns: list of file paths of files that have been parsed
        
    "
2168,"def _check_guts_toc_mtime(attr_name, old_toc, new_toc, last_build, pyc=False):
    
    for dest_name, src_name, typecode in old_toc:
        if misc.mtime(src_name) > last_build:
            logger.info(""Building because %s changed"", src_name)
            return True
        elif pyc and typecode == 'PYMODULE':
            py_filename = src_name[:-1]
            if misc.mtime(py_filename) > last_build:
                logger.info(""Building because %s changed"", py_filename)
                return True
    return False

","
    Rebuild is required if mtimes of files listed in old TOC are newer than last_build.

    If pyc=True, check for .py files as well.

    Use this for calculated/analysed values read from cache.
    ",31,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_guts_toc_mtime(attr_name, old_toc, new_toc, last_build, pyc=False):
    
    for dest_name, src_name, typecode in old_toc:
        if misc.mtime(src_name) > last_build:
            logger.info(""Building because %s changed"", src_name)
            return True
        elif pyc and typecode == 'PYMODULE':
            py_filename = src_name[:-1]
            if misc.mtime(py_filename) > last_build:
                logger.info(""Building because %s changed"", py_filename)
                return True
    return False


        ```
    ###Assistant : 
    Rebuild is required if mtimes of files listed in old TOC are newer than last_build.

    If pyc=True, check for .py files as well.

    Use this for calculated/analysed values read from cache.
    
    "
2169,"def check_connection(self) -> StatusResponse:
        

        response = StatusResponse(False)
        need_to_close = self.is_connected is False

        try:
            connection = self.connect()
            with connection.cursor() as cur:
                cur.execute('SELECT 1 FROM (SELECT 1 AS ""dual"") AS ""dual""')
            response.success = True
        except teradatasql.Error as e:
            log.error(f'Error connecting to Teradata {self.host}, {e}!')
            response.error_message = e

        if response.success is True and need_to_close:
            self.disconnect()
        if response.success is False and self.is_connected is True:
            self.is_connected = False

        return response
","
        Check the connection of the Teradata database
        :return: success status and error message if error occurs
        ",16,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_connection(self) -> StatusResponse:
        

        response = StatusResponse(False)
        need_to_close = self.is_connected is False

        try:
            connection = self.connect()
            with connection.cursor() as cur:
                cur.execute('SELECT 1 FROM (SELECT 1 AS ""dual"") AS ""dual""')
            response.success = True
        except teradatasql.Error as e:
            log.error(f'Error connecting to Teradata {self.host}, {e}!')
            response.error_message = e

        if response.success is True and need_to_close:
            self.disconnect()
        if response.success is False and self.is_connected is True:
            self.is_connected = False

        return response

        ```
    ###Assistant : 
        Check the connection of the Teradata database
        :return: success status and error message if error occurs
        
    "
2170,"def process_doc_file(code_file, add_new_line=True):
    
    with open(code_file, ""r"", encoding=""utf-8"", newline=""\n"") as f:
        code = f.read()

    # fmt: off
    splits = code.split(""```"")
    if len(splits) % 2 != 1:
        raise ValueError(""The number of occurrences of ``` should be an even number."")

    splits = [s if i % 2 == 0 else process_code_block(s, add_new_line=add_new_line) for i, s in enumerate(splits)]
    clean_code = ""```"".join(splits)
    # fmt: on

    diff = clean_code != code
    if diff:
        print(f""Overwriting content of {code_file}."")
        with open(code_file, ""w"", encoding=""utf-8"", newline=""\n"") as f:
            f.write(clean_code)

","
    Process given file.

    Args:
        code_file (`str` or `os.PathLike`): The file in which we want to style the docstring.
    ",18,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def process_doc_file(code_file, add_new_line=True):
    
    with open(code_file, ""r"", encoding=""utf-8"", newline=""\n"") as f:
        code = f.read()

    # fmt: off
    splits = code.split(""```"")
    if len(splits) % 2 != 1:
        raise ValueError(""The number of occurrences of ``` should be an even number."")

    splits = [s if i % 2 == 0 else process_code_block(s, add_new_line=add_new_line) for i, s in enumerate(splits)]
    clean_code = ""```"".join(splits)
    # fmt: on

    diff = clean_code != code
    if diff:
        print(f""Overwriting content of {code_file}."")
        with open(code_file, ""w"", encoding=""utf-8"", newline=""\n"") as f:
            f.write(clean_code)


        ```
    ###Assistant : 
    Process given file.

    Args:
        code_file (`str` or `os.PathLike`): The file in which we want to style the docstring.
    
    "
2171,"def finalize(self, batch):
        

        for face, landmarks in zip(batch[""detected_faces""], batch[""landmarks""]):
            if not isinstance(landmarks, np.ndarray):
                landmarks = np.array(landmarks)
            face._landmarks_xy = landmarks

        logger.trace(""Item out: %s"", {key: val.shape if isinstance(val, np.ndarray) else val
                                      for key, val in batch.items()})

        for filename, face in zip(batch[""filename""], batch[""detected_faces""]):
            self._output_faces.append(face)
            if len(self._output_faces) != self._faces_per_filename[filename]:
                continue

            output = self._extract_media.pop(filename)
            output.add_detected_faces(self._output_faces)
            self._output_faces = []

            logger.trace(""Final Output: (filename: '%s', image shape: %s, detected_faces: %s, ""
                         ""item: %s)"",
                         output.filename, output.image_shape, output.detected_faces, output)
            yield output

    # <<< PROTECTED METHODS >>> #

    # << PROCESS_INPUT WRAPPER >>"," Finalize the output from Aligner

        This should be called as the final task of each `plugin`.

        Pairs the detected faces back up with their original frame before yielding each frame.

        Parameters
        ----------
        batch : dict
            The final ``dict`` from the `plugin` process. It must contain the `keys`:
            ``detected_faces``, ``landmarks``, ``filename``

        Yields
        ------
        :class:`~plugins.extract.pipeline.ExtractMedia`
            The :attr:`DetectedFaces` list will be populated for this class with the bounding boxes
            and landmarks for the detected faces found in the frame.
        ",76,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def finalize(self, batch):
        

        for face, landmarks in zip(batch[""detected_faces""], batch[""landmarks""]):
            if not isinstance(landmarks, np.ndarray):
                landmarks = np.array(landmarks)
            face._landmarks_xy = landmarks

        logger.trace(""Item out: %s"", {key: val.shape if isinstance(val, np.ndarray) else val
                                      for key, val in batch.items()})

        for filename, face in zip(batch[""filename""], batch[""detected_faces""]):
            self._output_faces.append(face)
            if len(self._output_faces) != self._faces_per_filename[filename]:
                continue

            output = self._extract_media.pop(filename)
            output.add_detected_faces(self._output_faces)
            self._output_faces = []

            logger.trace(""Final Output: (filename: '%s', image shape: %s, detected_faces: %s, ""
                         ""item: %s)"",
                         output.filename, output.image_shape, output.detected_faces, output)
            yield output

    # <<< PROTECTED METHODS >>> #

    # << PROCESS_INPUT WRAPPER >>
        ```
    ###Assistant :  Finalize the output from Aligner

        This should be called as the final task of each `plugin`.

        Pairs the detected faces back up with their original frame before yielding each frame.

        Parameters
        ----------
        batch : dict
            The final ``dict`` from the `plugin` process. It must contain the `keys`:
            ``detected_faces``, ``landmarks``, ``filename``

        Yields
        ------
        :class:`~plugins.extract.pipeline.ExtractMedia`
            The :attr:`DetectedFaces` list will be populated for this class with the bounding boxes
            and landmarks for the detected faces found in the frame.
        
    "
2172,"def find_dcerpc_interface(name):
    
    try:
        return next(x for x in DCE_RPC_INTERFACES.values() if x.name == name)
    except StopIteration:
        raise AttributeError(""Unknown interface !"")


# --- NDR fields - [C706] chap 14
","
    Find an interface object through the name in the IDL
    ",10,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_dcerpc_interface(name):
    
    try:
        return next(x for x in DCE_RPC_INTERFACES.values() if x.name == name)
    except StopIteration:
        raise AttributeError(""Unknown interface !"")


# --- NDR fields - [C706] chap 14

        ```
    ###Assistant : 
    Find an interface object through the name in the IDL
    
    "
2173,"def test_animatable():
    

    animatable = AnimateTest()

    # Fake wall-clock time
    time = 100.0

    # Object that does the animation
    animation = SimpleAnimation(
        animatable,
        ""bar"",
        time,
        3.0,
        start_value=Animatable(20.0),
        end_value=Animatable(50.0),
        final_value=Animatable(50.0),
        easing=lambda x: x,
    )

    assert animation(time) is False
    assert animatable.bar.value == 20.0

    assert animation(time + 1.0) is False
    assert animatable.bar.value == 30.0

    assert animation(time + 2.0) is False
    assert animatable.bar.value == 40.0

    assert animation(time + 2.9) is False
    assert pytest.approx(animatable.bar.value, 49.0)

    assert animation(time + 3.0) is True  # True to indicate animation is complete
    assert animatable.bar.value == 50.0
",Test SimpleAnimation works with the Animatable protocol,7,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_animatable():
    

    animatable = AnimateTest()

    # Fake wall-clock time
    time = 100.0

    # Object that does the animation
    animation = SimpleAnimation(
        animatable,
        ""bar"",
        time,
        3.0,
        start_value=Animatable(20.0),
        end_value=Animatable(50.0),
        final_value=Animatable(50.0),
        easing=lambda x: x,
    )

    assert animation(time) is False
    assert animatable.bar.value == 20.0

    assert animation(time + 1.0) is False
    assert animatable.bar.value == 30.0

    assert animation(time + 2.0) is False
    assert animatable.bar.value == 40.0

    assert animation(time + 2.9) is False
    assert pytest.approx(animatable.bar.value, 49.0)

    assert animation(time + 3.0) is True  # True to indicate animation is complete
    assert animatable.bar.value == 50.0

        ```
    ###Assistant : Test SimpleAnimation works with the Animatable protocol
    "
2174,"def update(self, bbox, score, label, gt_box, gt_label, difficult=None):
        
        if difficult is None:
            difficult = np.zeros_like(gt_label)

        # record class gt count
        for gtl, diff in zip(gt_label, difficult):
            if self.evaluate_difficult or int(diff) == 0:
                self.class_gt_counts[int(np.array(gtl))] += 1

        # record class score positive
        visited = [False] * len(gt_label)
        for b, s, l in zip(bbox, score, label):
            pred = b.tolist() if isinstance(b, np.ndarray) else b
            max_idx = -1
            max_overlap = -1.0
            for i, gl in enumerate(gt_label):
                if int(gl) == int(l):
                    if len(gt_box[i]) == 8:
                        overlap = calc_rbox_iou(pred, gt_box[i])
                    else:
                        overlap = jaccard_overlap(pred, gt_box[i],
                                                  self.is_bbox_normalized)
                    if overlap > max_overlap:
                        max_overlap = overlap
                        max_idx = i

            if max_overlap > self.overlap_thresh:
                if self.evaluate_difficult or \
                        int(np.array(difficult[max_idx])) == 0:
                    if not visited[max_idx]:
                        self.class_score_poss[int(l)].append([s, 1.0])
                        visited[max_idx] = True
                    else:
                        self.class_score_poss[int(l)].append([s, 0.0])
            else:
                self.class_score_poss[int(l)].append([s, 0.0])
","
        Update metric statics from given prediction and ground
        truth infomations.
        ",10,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self, bbox, score, label, gt_box, gt_label, difficult=None):
        
        if difficult is None:
            difficult = np.zeros_like(gt_label)

        # record class gt count
        for gtl, diff in zip(gt_label, difficult):
            if self.evaluate_difficult or int(diff) == 0:
                self.class_gt_counts[int(np.array(gtl))] += 1

        # record class score positive
        visited = [False] * len(gt_label)
        for b, s, l in zip(bbox, score, label):
            pred = b.tolist() if isinstance(b, np.ndarray) else b
            max_idx = -1
            max_overlap = -1.0
            for i, gl in enumerate(gt_label):
                if int(gl) == int(l):
                    if len(gt_box[i]) == 8:
                        overlap = calc_rbox_iou(pred, gt_box[i])
                    else:
                        overlap = jaccard_overlap(pred, gt_box[i],
                                                  self.is_bbox_normalized)
                    if overlap > max_overlap:
                        max_overlap = overlap
                        max_idx = i

            if max_overlap > self.overlap_thresh:
                if self.evaluate_difficult or \
                        int(np.array(difficult[max_idx])) == 0:
                    if not visited[max_idx]:
                        self.class_score_poss[int(l)].append([s, 1.0])
                        visited[max_idx] = True
                    else:
                        self.class_score_poss[int(l)].append([s, 0.0])
            else:
                self.class_score_poss[int(l)].append([s, 0.0])

        ```
    ###Assistant : 
        Update metric statics from given prediction and ground
        truth infomations.
        
    "
2175,"def _get_permission_objects_for_actions(self, actions):
        
        permission_codenames = [
            ""%s_%s"" % (action, self.model_name) for action in actions
        ]
        return Permission.objects.filter(
            content_type=self._content_type, codename__in=permission_codenames
        )
","
        Get a queryset of the Permission objects for the given actions
        ",11,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_permission_objects_for_actions(self, actions):
        
        permission_codenames = [
            ""%s_%s"" % (action, self.model_name) for action in actions
        ]
        return Permission.objects.filter(
            content_type=self._content_type, codename__in=permission_codenames
        )

        ```
    ###Assistant : 
        Get a queryset of the Permission objects for the given actions
        
    "
2176,"def createWindow(self, wintype):
        
        debug_type = debug.qenum_key(QWebEnginePage, wintype)
        background = config.val.tabs.background

        log.webview.debug(""createWindow with type {}, background {}"".format(
            debug_type, background))

        if wintype == QWebEnginePage.WebWindowType.WebBrowserWindow:
            # Shift-Alt-Click
            target = usertypes.ClickTarget.window
        elif wintype == QWebEnginePage.WebWindowType.WebDialog:
            log.webview.warning(""{} requested, but we don't support ""
                                ""that!"".format(debug_type))
            target = usertypes.ClickTarget.tab
        elif wintype == QWebEnginePage.WebWindowType.WebBrowserTab:
            # Middle-click / Ctrl-Click with Shift
            # FIXME:qtwebengine this also affects target=_blank links...
            if background:
                target = usertypes.ClickTarget.tab
            else:
                target = usertypes.ClickTarget.tab_bg
        elif wintype == QWebEnginePage.WebWindowType.WebBrowserBackgroundTab:
            # Middle-click / Ctrl-Click
            if background:
                target = usertypes.ClickTarget.tab_bg
            else:
                target = usertypes.ClickTarget.tab
        else:
            raise ValueError(""Invalid wintype {}"".format(debug_type))

        tab = shared.get_tab(self._win_id, target)
        return tab._widget  # pylint: disable=protected-access
","Called by Qt when a page wants to create a new window.

        This function is called from the createWindow() method of the
        associated QWebEnginePage, each time the page wants to create a new
        window of the given type. This might be the result, for example, of a
        JavaScript request to open a document in a new window.

        Args:
            wintype: This enum describes the types of window that can be
                     created by the createWindow() function.

                     QWebEnginePage::WebBrowserWindow:
                         A complete web browser window.
                     QWebEnginePage::WebBrowserTab:
                         A web browser tab.
                     QWebEnginePage::WebDialog:
                         A window without decoration.
                     QWebEnginePage::WebBrowserBackgroundTab:
                         A web browser tab without hiding the current visible
                         WebEngineView.

        Return:
            The new QWebEngineView object.
        ",106,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def createWindow(self, wintype):
        
        debug_type = debug.qenum_key(QWebEnginePage, wintype)
        background = config.val.tabs.background

        log.webview.debug(""createWindow with type {}, background {}"".format(
            debug_type, background))

        if wintype == QWebEnginePage.WebWindowType.WebBrowserWindow:
            # Shift-Alt-Click
            target = usertypes.ClickTarget.window
        elif wintype == QWebEnginePage.WebWindowType.WebDialog:
            log.webview.warning(""{} requested, but we don't support ""
                                ""that!"".format(debug_type))
            target = usertypes.ClickTarget.tab
        elif wintype == QWebEnginePage.WebWindowType.WebBrowserTab:
            # Middle-click / Ctrl-Click with Shift
            # FIXME:qtwebengine this also affects target=_blank links...
            if background:
                target = usertypes.ClickTarget.tab
            else:
                target = usertypes.ClickTarget.tab_bg
        elif wintype == QWebEnginePage.WebWindowType.WebBrowserBackgroundTab:
            # Middle-click / Ctrl-Click
            if background:
                target = usertypes.ClickTarget.tab_bg
            else:
                target = usertypes.ClickTarget.tab
        else:
            raise ValueError(""Invalid wintype {}"".format(debug_type))

        tab = shared.get_tab(self._win_id, target)
        return tab._widget  # pylint: disable=protected-access

        ```
    ###Assistant : Called by Qt when a page wants to create a new window.

        This function is called from the createWindow() method of the
        associated QWebEnginePage, each time the page wants to create a new
        window of the given type. This might be the result, for example, of a
        JavaScript request to open a document in a new window.

        Args:
            wintype: This enum describes the types of window that can be
                     created by the createWindow() function.

                     QWebEnginePage::WebBrowserWindow:
                         A complete web browser window.
                     QWebEnginePage::WebBrowserTab:
                         A web browser tab.
                     QWebEnginePage::WebDialog:
                         A window without decoration.
                     QWebEnginePage::WebBrowserBackgroundTab:
                         A web browser tab without hiding the current visible
                         WebEngineView.

        Return:
            The new QWebEngineView object.
        
    "
2177,"def predict(self, X):
        
        raw_predictions = self.decision_function(X)
        encoded_labels = self._loss._raw_prediction_to_decision(raw_predictions)
        return self.classes_.take(encoded_labels, axis=0)
","Predict class for X.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        y : ndarray of shape (n_samples,)
            The predicted values.
        ",47,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def predict(self, X):
        
        raw_predictions = self.decision_function(X)
        encoded_labels = self._loss._raw_prediction_to_decision(raw_predictions)
        return self.classes_.take(encoded_labels, axis=0)

        ```
    ###Assistant : Predict class for X.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        y : ndarray of shape (n_samples,)
            The predicted values.
        
    "
2178,"def union_all(graphs, rename=()):
    
    R = None
    seen_nodes = set()

    # rename graph to obtain disjoint node labels","Returns the union of all graphs.

    The graphs must be disjoint, otherwise an exception is raised.

    Parameters
    ----------
    graphs : iterable
       Iterable of NetworkX graphs

    rename : iterable , optional
       Node names of graphs can be changed by specifying the tuple
       rename=('G-','H-') (for example).  Node ""u"" in G is then renamed
       ""G-u"" and ""v"" in H is renamed ""H-v"". Infinite generators (like itertools.count)
       are also supported.

    Returns
    -------
    U : a graph with the same type as the first graph in list

    Raises
    ------
    ValueError
       If `graphs` is an empty list.

    Notes
    -----
    To force a disjoint union with node relabeling, use
    disjoint_union_all(G,H) or convert_node_labels_to integers().

    Graph, edge, and node attributes are propagated to the union graph.
    If a graph attribute is present in multiple graphs, then the value
    from the last graph in the list with that attribute is used.

    See Also
    --------
    union
    disjoint_union_all
    ",146,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def union_all(graphs, rename=()):
    
    R = None
    seen_nodes = set()

    # rename graph to obtain disjoint node labels
        ```
    ###Assistant : Returns the union of all graphs.

    The graphs must be disjoint, otherwise an exception is raised.

    Parameters
    ----------
    graphs : iterable
       Iterable of NetworkX graphs

    rename : iterable , optional
       Node names of graphs can be changed by specifying the tuple
       rename=('G-','H-') (for example).  Node ""u"" in G is then renamed
       ""G-u"" and ""v"" in H is renamed ""H-v"". Infinite generators (like itertools.count)
       are also supported.

    Returns
    -------
    U : a graph with the same type as the first graph in list

    Raises
    ------
    ValueError
       If `graphs` is an empty list.

    Notes
    -----
    To force a disjoint union with node relabeling, use
    disjoint_union_all(G,H) or convert_node_labels_to integers().

    Graph, edge, and node attributes are propagated to the union graph.
    If a graph attribute is present in multiple graphs, then the value
    from the last graph in the list with that attribute is used.

    See Also
    --------
    union
    disjoint_union_all
    
    "
2179,"def test_unknown_category_that_are_negative():
    
    rng = np.random.RandomState(42)
    n_samples = 1000
    X = np.c_[rng.rand(n_samples), rng.randint(4, size=n_samples)]
    y = np.zeros(shape=n_samples)
    y[X[:, 1] % 2 == 0] = 1

    hist = HistGradientBoostingRegressor(
        random_state=0,
        categorical_features=[False, True],
        max_iter=10,
    ).fit(X, y)

    # Check that negative values from the second column are treated like a
    # missing category
    X_test_neg = np.asarray([[1, -2], [3, -4]])
    X_test_nan = np.asarray([[1, np.nan], [3, np.nan]])

    assert_allclose(hist.predict(X_test_neg), hist.predict(X_test_nan))
","Check that unknown categories that are negative does not error.

    Non-regression test for #24274.
    ",14,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unknown_category_that_are_negative():
    
    rng = np.random.RandomState(42)
    n_samples = 1000
    X = np.c_[rng.rand(n_samples), rng.randint(4, size=n_samples)]
    y = np.zeros(shape=n_samples)
    y[X[:, 1] % 2 == 0] = 1

    hist = HistGradientBoostingRegressor(
        random_state=0,
        categorical_features=[False, True],
        max_iter=10,
    ).fit(X, y)

    # Check that negative values from the second column are treated like a
    # missing category
    X_test_neg = np.asarray([[1, -2], [3, -4]])
    X_test_nan = np.asarray([[1, np.nan], [3, np.nan]])

    assert_allclose(hist.predict(X_test_neg), hist.predict(X_test_nan))

        ```
    ###Assistant : Check that unknown categories that are negative does not error.

    Non-regression test for #24274.
    
    "
2180,"def forward(self, x_input, mask, cache=None):
        
        if isinstance(x_input, tuple):
            x, pos_emb = x_input[0], x_input[1]
        else:
            x, pos_emb = x_input, None

        # whether to use macaron style
        if self.feed_forward_macaron is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_ff_macaron(x)
            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
            if not self.normalize_before:
                x = self.norm_ff_macaron(x)

        # multi-headed self-attention module
        residual = x
        if self.normalize_before:
            x = self.norm_mha(x)

        if cache is None:
            x_q = x
        else:
            assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
            x_q = x[:, -1:, :]
            residual = residual[:, -1:, :]
            mask = None if mask is None else mask[:, -1:, :]

        if pos_emb is not None:
            x_att = self.self_attn(x_q, x, x, pos_emb, mask)
        else:
            x_att = self.self_attn(x_q, x, x, mask)

        if self.concat_after:
            x_concat = torch.cat((x, x_att), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(x_att)
        if not self.normalize_before:
            x = self.norm_mha(x)

        # convolution module
        if self.conv_module is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_conv(x)
            x = residual + self.dropout(self.conv_module(x))
            if not self.normalize_before:
                x = self.norm_conv(x)

        # feed forward module
        residual = x
        if self.normalize_before:
            x = self.norm_ff(x)
        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm_ff(x)

        if self.conv_module is not None:
            x = self.norm_final(x)

        if cache is not None:
            x = torch.cat([cache, x], dim=1)

        if pos_emb is not None:
            return (x, pos_emb), mask

        return x, mask
","Compute encoded features.

        :param torch.Tensor x_input: encoded source features, w/o pos_emb
        tuple((batch, max_time_in, size), (1, max_time_in, size))
        or (batch, max_time_in, size)
        :param torch.Tensor mask: mask for x (batch, max_time_in)
        :param torch.Tensor cache: cache for x (batch, max_time_in - 1, size)
        :rtype: Tuple[torch.Tensor, torch.Tensor]
        ",43,225,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, x_input, mask, cache=None):
        
        if isinstance(x_input, tuple):
            x, pos_emb = x_input[0], x_input[1]
        else:
            x, pos_emb = x_input, None

        # whether to use macaron style
        if self.feed_forward_macaron is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_ff_macaron(x)
            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
            if not self.normalize_before:
                x = self.norm_ff_macaron(x)

        # multi-headed self-attention module
        residual = x
        if self.normalize_before:
            x = self.norm_mha(x)

        if cache is None:
            x_q = x
        else:
            assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
            x_q = x[:, -1:, :]
            residual = residual[:, -1:, :]
            mask = None if mask is None else mask[:, -1:, :]

        if pos_emb is not None:
            x_att = self.self_attn(x_q, x, x, pos_emb, mask)
        else:
            x_att = self.self_attn(x_q, x, x, mask)

        if self.concat_after:
            x_concat = torch.cat((x, x_att), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(x_att)
        if not self.normalize_before:
            x = self.norm_mha(x)

        # convolution module
        if self.conv_module is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_conv(x)
            x = residual + self.dropout(self.conv_module(x))
            if not self.normalize_before:
                x = self.norm_conv(x)

        # feed forward module
        residual = x
        if self.normalize_before:
            x = self.norm_ff(x)
        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm_ff(x)

        if self.conv_module is not None:
            x = self.norm_final(x)

        if cache is not None:
            x = torch.cat([cache, x], dim=1)

        if pos_emb is not None:
            return (x, pos_emb), mask

        return x, mask

        ```
    ###Assistant : Compute encoded features.

        :param torch.Tensor x_input: encoded source features, w/o pos_emb
        tuple((batch, max_time_in, size), (1, max_time_in, size))
        or (batch, max_time_in, size)
        :param torch.Tensor mask: mask for x (batch, max_time_in)
        :param torch.Tensor cache: cache for x (batch, max_time_in - 1, size)
        :rtype: Tuple[torch.Tensor, torch.Tensor]
        
    "
2181,"def test_prompt_invalid_template_format() -> None:
    
    template = ""This is a {foo} test.""
    input_variables = [""foo""]
    with pytest.raises(ValueError):
        Prompt(
            input_variables=input_variables, template=template, template_format=""bar""
        )
",Test initializing a prompt with invalid template format.,8,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_prompt_invalid_template_format() -> None:
    
    template = ""This is a {foo} test.""
    input_variables = [""foo""]
    with pytest.raises(ValueError):
        Prompt(
            input_variables=input_variables, template=template, template_format=""bar""
        )

        ```
    ###Assistant : Test initializing a prompt with invalid template format.
    "
2182,"def test_medium_does_not_exist(self) -> None:
        
        # test for unknown medium
        url = ""/_synapse/admin/v1/threepid/publickey/users/unknown-key""

        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])

        # test for unknown user with a known medium
        url = ""/_synapse/admin/v1/threepid/email/users/unknown""

        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])
","Tests that both a lookup for a medium that does not exist and a user that
        doesn't exist with that third party ID returns a 404",26,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_medium_does_not_exist(self) -> None:
        
        # test for unknown medium
        url = ""/_synapse/admin/v1/threepid/publickey/users/unknown-key""

        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])

        # test for unknown user with a known medium
        url = ""/_synapse/admin/v1/threepid/email/users/unknown""

        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])

        ```
    ###Assistant : Tests that both a lookup for a medium that does not exist and a user that
        doesn't exist with that third party ID returns a 404
    "
2183,"def deepspeed_config_process(self, prefix="""", mismatches=None, config=None, must_match=True, **kwargs):
        
        mismatches = [] if mismatches is None else mismatches
        if config is None:
            config = self.deepspeed_config
        for key, value in config.items():
            if isinstance(value, dict):
                self.deepspeed_config_process(
                    prefix=prefix + key + ""."", mismatches=mismatches, config=value, must_match=must_match, **kwargs
                )
            else:
                self.fill_match(prefix + key, mismatches, must_match=must_match, **kwargs)
        if len(mismatches) > 0 and prefix == """":
            mismatches_msg = ""\n"".join(mismatches)
            raise ValueError(
                ""Please correct the following DeepSpeed config values that mismatch kwargs ""
                f"" values:\n{mismatches_msg}\nThe easiest method is to set these DeepSpeed config values to 'auto'.""
            )


@dataclass",Process the DeepSpeed config with the values from the kwargs.,10,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deepspeed_config_process(self, prefix="""", mismatches=None, config=None, must_match=True, **kwargs):
        
        mismatches = [] if mismatches is None else mismatches
        if config is None:
            config = self.deepspeed_config
        for key, value in config.items():
            if isinstance(value, dict):
                self.deepspeed_config_process(
                    prefix=prefix + key + ""."", mismatches=mismatches, config=value, must_match=must_match, **kwargs
                )
            else:
                self.fill_match(prefix + key, mismatches, must_match=must_match, **kwargs)
        if len(mismatches) > 0 and prefix == """":
            mismatches_msg = ""\n"".join(mismatches)
            raise ValueError(
                ""Please correct the following DeepSpeed config values that mismatch kwargs ""
                f"" values:\n{mismatches_msg}\nThe easiest method is to set these DeepSpeed config values to 'auto'.""
            )


@dataclass
        ```
    ###Assistant : Process the DeepSpeed config with the values from the kwargs.
    "
2184,"def get_metrics_result(self):
        
        # Collect metrics to return
        return_metrics = {}
        for metric in self.metrics:
            result = metric.result()
            if isinstance(result, dict):
                return_metrics.update(result)
            else:
                return_metrics[metric.name] = result
        return return_metrics
","Returns the model's metrics values as a dict.

        If any of the metric result is a dict (containing multiple metrics),
        each of them gets added to the top level returned dict of this method.

        Returns:
          A `dict` containing values of the metrics listed in `self.metrics`.
          Example:
          `{'loss': 0.2, 'accuracy': 0.7}`.
        ",50,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_metrics_result(self):
        
        # Collect metrics to return
        return_metrics = {}
        for metric in self.metrics:
            result = metric.result()
            if isinstance(result, dict):
                return_metrics.update(result)
            else:
                return_metrics[metric.name] = result
        return return_metrics

        ```
    ###Assistant : Returns the model's metrics values as a dict.

        If any of the metric result is a dict (containing multiple metrics),
        each of them gets added to the top level returned dict of this method.

        Returns:
          A `dict` containing values of the metrics listed in `self.metrics`.
          Example:
          `{'loss': 0.2, 'accuracy': 0.7}`.
        
    "
2185,"def refactor_docstring(self, input, filename):
        
        result = []
        block = None
        block_lineno = None
        indent = None
        lineno = 0
        for line in input.splitlines(keepends=True):
            lineno += 1
            if line.lstrip().startswith(self.PS1):
                if block is not None:
                    result.extend(self.refactor_doctest(block, block_lineno,
                                                        indent, filename))
                block_lineno = lineno
                block = [line]
                i = line.find(self.PS1)
                indent = line[:i]
            elif (indent is not None and
                  (line.startswith(indent + self.PS2) or
                   line == indent + self.PS2.rstrip() + ""\n"")):
                block.append(line)
            else:
                if block is not None:
                    result.extend(self.refactor_doctest(block, block_lineno,
                                                        indent, filename))
                block = None
                indent = None
                result.append(line)
        if block is not None:
            result.extend(self.refactor_doctest(block, block_lineno,
                                                indent, filename))
        return """".join(result)
","Refactors a docstring, looking for doctests.

        This returns a modified version of the input string.  It looks
        for doctests, which start with a "">>>"" prompt, and may be
        continued with ""..."" prompts, as long as the ""..."" is indented
        the same as the "">>>"".

        (Unfortunately we can't use the doctest module's parser,
        since, like most parsers, it is not geared towards preserving
        the original source.)
        ",65,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def refactor_docstring(self, input, filename):
        
        result = []
        block = None
        block_lineno = None
        indent = None
        lineno = 0
        for line in input.splitlines(keepends=True):
            lineno += 1
            if line.lstrip().startswith(self.PS1):
                if block is not None:
                    result.extend(self.refactor_doctest(block, block_lineno,
                                                        indent, filename))
                block_lineno = lineno
                block = [line]
                i = line.find(self.PS1)
                indent = line[:i]
            elif (indent is not None and
                  (line.startswith(indent + self.PS2) or
                   line == indent + self.PS2.rstrip() + ""\n"")):
                block.append(line)
            else:
                if block is not None:
                    result.extend(self.refactor_doctest(block, block_lineno,
                                                        indent, filename))
                block = None
                indent = None
                result.append(line)
        if block is not None:
            result.extend(self.refactor_doctest(block, block_lineno,
                                                indent, filename))
        return """".join(result)

        ```
    ###Assistant : Refactors a docstring, looking for doctests.

        This returns a modified version of the input string.  It looks
        for doctests, which start with a "">>>"" prompt, and may be
        continued with ""..."" prompts, as long as the ""..."" is indented
        the same as the "">>>"".

        (Unfortunately we can't use the doctest module's parser,
        since, like most parsers, it is not geared towards preserving
        the original source.)
        
    "
2186,"def test_complex_reversed_dag(self, test_complex_taskgroup_dag, complex_dag_expected_edges):
        
        (
            dag,
            group,
            (
                group_dm1,
                group_dm2,
                group_dm3,
                dm_in1,
                dm_in2,
                dm_in3,
                dm_in4,
                dm_out1,
                dm_out2,
                dm_out3,
                dm_out4,
                op_in1,
                op_out1,
            ),
        ) = test_complex_taskgroup_dag

        group_dm1 << [group_dm2, group_dm3]

        group << dm_in1
        group << Label('label dm_in2 <=> group') << dm_in2
        group << Label('label dm_in3/dm_in4 <=> group') << [dm_in3, dm_in4]
        group << Label('label op_in1 <=> group') << XComArg(op_in1, 'test_key')

        dm_out1 << group
        dm_out2 << Label('label group <=> dm_out2') << group
        [dm_out3, dm_out4] << Label('label group <=> dm_out3/dm_out4') << group
        XComArg(op_out1, 'test_key') << Label('label group <=> op_out1') << group

        compare_dag_edges(dag_edges(dag), complex_dag_expected_edges)
",Tests the complex reversed dag with a TaskGroup and a Label,11,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_complex_reversed_dag(self, test_complex_taskgroup_dag, complex_dag_expected_edges):
        
        (
            dag,
            group,
            (
                group_dm1,
                group_dm2,
                group_dm3,
                dm_in1,
                dm_in2,
                dm_in3,
                dm_in4,
                dm_out1,
                dm_out2,
                dm_out3,
                dm_out4,
                op_in1,
                op_out1,
            ),
        ) = test_complex_taskgroup_dag

        group_dm1 << [group_dm2, group_dm3]

        group << dm_in1
        group << Label('label dm_in2 <=> group') << dm_in2
        group << Label('label dm_in3/dm_in4 <=> group') << [dm_in3, dm_in4]
        group << Label('label op_in1 <=> group') << XComArg(op_in1, 'test_key')

        dm_out1 << group
        dm_out2 << Label('label group <=> dm_out2') << group
        [dm_out3, dm_out4] << Label('label group <=> dm_out3/dm_out4') << group
        XComArg(op_out1, 'test_key') << Label('label group <=> op_out1') << group

        compare_dag_edges(dag_edges(dag), complex_dag_expected_edges)

        ```
    ###Assistant : Tests the complex reversed dag with a TaskGroup and a Label
    "
2187,"def postprocess_response(token_ids, tokenizer):
    
    eos_pos = len(token_ids)
    for i, tok_id in enumerate(token_ids):
        if tok_id == tokenizer.sep_token_id:
            eos_pos = i
            break
    token_ids = token_ids[:eos_pos]
    tokens = tokenizer.convert_ids_to_tokens(token_ids)
    tokens = tokenizer.merge_subword(tokens)
    return tokens

",Post-process the decoded sequence. Truncate from the first <eos>.,9,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def postprocess_response(token_ids, tokenizer):
    
    eos_pos = len(token_ids)
    for i, tok_id in enumerate(token_ids):
        if tok_id == tokenizer.sep_token_id:
            eos_pos = i
            break
    token_ids = token_ids[:eos_pos]
    tokens = tokenizer.convert_ids_to_tokens(token_ids)
    tokens = tokenizer.merge_subword(tokens)
    return tokens


        ```
    ###Assistant : Post-process the decoded sequence. Truncate from the first <eos>.
    "
2188,"def clip_grad_norm_(self, parameters, max_norm, norm_type=2):
        
        if self.distributed_type == DistributedType.FSDP:
            self.unscale_gradients()
            parameters = [p for p in parameters]
            for model in self._models:
                if parameters == [p for p in model.parameters()]:
                    return model.clip_grad_norm_(max_norm, norm_type)
        elif self.distributed_type == DistributedType.DEEPSPEED:
            # `accelerator.backward(loss)` is doing that automatically. Therefore, it's implementation is not needed
            # We cannot return the gradient norm because DeepSpeed does it.
            return None
        self.unscale_gradients()
        return torch.nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=norm_type)
","
        Should be used in place of `torch.nn.utils.clip_grad_norm_`.

        Returns:
            `torch.Tensor`: Total norm of the parameter gradients (viewed as a single vector).

        Example:

        ```python
        >>> from accelerate import Accelerator

        >>> accelerator = Accelerator(gradient_accumulation_steps=2)
        >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)

        >>> for (input, target) in dataloader:
        ...     optimizer.zero_grad()
        ...     output = model(input)
        ...     loss = loss_func(output, target)
        ...     accelerator.backward(loss)
        ...     if accelerator.sync_gradients:
        ...         accelerator.clip_grad_norm_(model.parameters(), max_grad_norm)
        ...     optimizer.step()
        ```
        ",69,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clip_grad_norm_(self, parameters, max_norm, norm_type=2):
        
        if self.distributed_type == DistributedType.FSDP:
            self.unscale_gradients()
            parameters = [p for p in parameters]
            for model in self._models:
                if parameters == [p for p in model.parameters()]:
                    return model.clip_grad_norm_(max_norm, norm_type)
        elif self.distributed_type == DistributedType.DEEPSPEED:
            # `accelerator.backward(loss)` is doing that automatically. Therefore, it's implementation is not needed
            # We cannot return the gradient norm because DeepSpeed does it.
            return None
        self.unscale_gradients()
        return torch.nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=norm_type)

        ```
    ###Assistant : 
        Should be used in place of `torch.nn.utils.clip_grad_norm_`.

        Returns:
            `torch.Tensor`: Total norm of the parameter gradients (viewed as a single vector).

        Example:

        ```python
        >>> from accelerate import Accelerator

        >>> accelerator = Accelerator(gradient_accumulation_steps=2)
        >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)

        >>> for (input, target) in dataloader:
        ...     optimizer.zero_grad()
        ...     output = model(input)
        ...     loss = loss_func(output, target)
        ...     accelerator.backward(loss)
        ...     if accelerator.sync_gradients:
        ...         accelerator.clip_grad_norm_(model.parameters(), max_grad_norm)
        ...     optimizer.step()
        ```
        
    "
2189,"def check_library_list(self, libraries):
        
        if not isinstance(libraries, list):
            raise DistutilsSetupError(
                  ""'libraries' option must be a list of tuples"")

        for lib in libraries:
            if not isinstance(lib, tuple) and len(lib) != 2:
                raise DistutilsSetupError(
                      ""each element of 'libraries' must a 2-tuple"")

            name, build_info = lib

            if not isinstance(name, str):
                raise DistutilsSetupError(
                      ""first element of each tuple in 'libraries' ""
                      ""must be a string (the library name)"")

            if '/' in name or (os.sep != '/' and os.sep in name):
                raise DistutilsSetupError(""bad library name '%s': ""
                       ""may not contain directory separators"" % lib[0])

            if not isinstance(build_info, dict):
                raise DistutilsSetupError(
                      ""second element of each tuple in 'libraries' ""
                      ""must be a dictionary (build info)"")

","Ensure that the list of libraries is valid.

        `library` is presumably provided as a command option 'libraries'.
        This method checks that it is a list of 2-tuples, where the tuples
        are (library_name, build_info_dict).

        Raise DistutilsSetupError if the structure is invalid anywhere;
        just returns otherwise.
        ",44,108,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_library_list(self, libraries):
        
        if not isinstance(libraries, list):
            raise DistutilsSetupError(
                  ""'libraries' option must be a list of tuples"")

        for lib in libraries:
            if not isinstance(lib, tuple) and len(lib) != 2:
                raise DistutilsSetupError(
                      ""each element of 'libraries' must a 2-tuple"")

            name, build_info = lib

            if not isinstance(name, str):
                raise DistutilsSetupError(
                      ""first element of each tuple in 'libraries' ""
                      ""must be a string (the library name)"")

            if '/' in name or (os.sep != '/' and os.sep in name):
                raise DistutilsSetupError(""bad library name '%s': ""
                       ""may not contain directory separators"" % lib[0])

            if not isinstance(build_info, dict):
                raise DistutilsSetupError(
                      ""second element of each tuple in 'libraries' ""
                      ""must be a dictionary (build info)"")


        ```
    ###Assistant : Ensure that the list of libraries is valid.

        `library` is presumably provided as a command option 'libraries'.
        This method checks that it is a list of 2-tuples, where the tuples
        are (library_name, build_info_dict).

        Raise DistutilsSetupError if the structure is invalid anywhere;
        just returns otherwise.
        
    "
2190,"def _process_new_state(cls, new_state, unprocessed, processed):
        
        if isinstance(new_state, str):
            # an existing state
            if new_state == '#pop':
                return -1
            elif new_state in unprocessed:
                return (new_state,)
            elif new_state == '#push':
                return new_state
            elif new_state[:5] == '#pop:':
                return -int(new_state[5:])
            else:
                assert False, 'unknown new state %r' % new_state
        elif isinstance(new_state, combined):
            # combine a new state from existing ones
            tmp_state = '_tmp_%d' % cls._tmpname
            cls._tmpname += 1
            itokens = []
            for istate in new_state:
                assert istate != new_state, 'circular state ref %r' % istate
                itokens.extend(cls._process_state(unprocessed,
                                                  processed, istate))
            processed[tmp_state] = itokens
            return (tmp_state,)
        elif isinstance(new_state, tuple):
            # push more than one state
            for istate in new_state:
                assert (istate in unprocessed or
                        istate in ('#pop', '#push')), \
                    'unknown new state ' + istate
            return new_state
        else:
            assert False, 'unknown new state def %r' % new_state
",Preprocess the state transition action of a token definition.,9,130,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _process_new_state(cls, new_state, unprocessed, processed):
        
        if isinstance(new_state, str):
            # an existing state
            if new_state == '#pop':
                return -1
            elif new_state in unprocessed:
                return (new_state,)
            elif new_state == '#push':
                return new_state
            elif new_state[:5] == '#pop:':
                return -int(new_state[5:])
            else:
                assert False, 'unknown new state %r' % new_state
        elif isinstance(new_state, combined):
            # combine a new state from existing ones
            tmp_state = '_tmp_%d' % cls._tmpname
            cls._tmpname += 1
            itokens = []
            for istate in new_state:
                assert istate != new_state, 'circular state ref %r' % istate
                itokens.extend(cls._process_state(unprocessed,
                                                  processed, istate))
            processed[tmp_state] = itokens
            return (tmp_state,)
        elif isinstance(new_state, tuple):
            # push more than one state
            for istate in new_state:
                assert (istate in unprocessed or
                        istate in ('#pop', '#push')), \
                    'unknown new state ' + istate
            return new_state
        else:
            assert False, 'unknown new state def %r' % new_state

        ```
    ###Assistant : Preprocess the state transition action of a token definition.
    "
2191,"def test_ohe_infrequent_two_levels_user_cats():
    
    X_train = np.array(
        [[""a""] * 5 + [""b""] * 20 + [""c""] * 10 + [""d""] * 3], dtype=object
    ).T
    ohe = OneHotEncoder(
        categories=[[""c"", ""d"", ""a"", ""b""]],
        sparse=False,
        handle_unknown=""infrequent_if_exist"",
        max_categories=2,
    ).fit(X_train)

    assert_array_equal(ohe.infrequent_categories_, [[""c"", ""d"", ""a""]])

    X_test = [[""b""], [""a""], [""c""], [""d""], [""e""]]
    expected = np.array([[1, 0], [0, 1], [0, 1], [0, 1], [0, 1]])

    X_trans = ohe.transform(X_test)
    assert_allclose(expected, X_trans)

    # 'infrequent' is used to denote the infrequent categories for
    # `inverse_transform`
    expected_inv = [[col] for col in [""b""] + [""infrequent_sklearn""] * 4]
    X_inv = ohe.inverse_transform(X_trans)
    assert_array_equal(expected_inv, X_inv)

",Test that the order of the categories provided by a user is respected.,13,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ohe_infrequent_two_levels_user_cats():
    
    X_train = np.array(
        [[""a""] * 5 + [""b""] * 20 + [""c""] * 10 + [""d""] * 3], dtype=object
    ).T
    ohe = OneHotEncoder(
        categories=[[""c"", ""d"", ""a"", ""b""]],
        sparse=False,
        handle_unknown=""infrequent_if_exist"",
        max_categories=2,
    ).fit(X_train)

    assert_array_equal(ohe.infrequent_categories_, [[""c"", ""d"", ""a""]])

    X_test = [[""b""], [""a""], [""c""], [""d""], [""e""]]
    expected = np.array([[1, 0], [0, 1], [0, 1], [0, 1], [0, 1]])

    X_trans = ohe.transform(X_test)
    assert_allclose(expected, X_trans)

    # 'infrequent' is used to denote the infrequent categories for
    # `inverse_transform`
    expected_inv = [[col] for col in [""b""] + [""infrequent_sklearn""] * 4]
    X_inv = ohe.inverse_transform(X_trans)
    assert_array_equal(expected_inv, X_inv)


        ```
    ###Assistant : Test that the order of the categories provided by a user is respected.
    "
2192,"def fit(self, X, y=None):
        
        self._validate_params()
        random_state = check_random_state(self.random_state)
        X = self._validate_data(X)

        self.mean_ = X.mean(axis=0)
        X = X - self.mean_

        if self.n_components is None:
            n_components = X.shape[1]
        else:
            n_components = self.n_components

        return self._fit(X, n_components, random_state)
","Fit the model from data in X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training vector, where `n_samples` is the number of samples
            and `n_features` is the number of features.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        self : object
            Returns the instance itself.
        ",53,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y=None):
        
        self._validate_params()
        random_state = check_random_state(self.random_state)
        X = self._validate_data(X)

        self.mean_ = X.mean(axis=0)
        X = X - self.mean_

        if self.n_components is None:
            n_components = X.shape[1]
        else:
            n_components = self.n_components

        return self._fit(X, n_components, random_state)

        ```
    ###Assistant : Fit the model from data in X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training vector, where `n_samples` is the number of samples
            and `n_features` is the number of features.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        self : object
            Returns the instance itself.
        
    "
2193,"def test_create_profile():
    invoke_and_assert(
        [""profile"", ""create"", ""foo""],
        expected_output=(
            f
        ),
    )

    profiles = load_profiles()
    assert profiles[""foo""] == Profile(
        name=""foo"", settings={}, source=PREFECT_PROFILES_PATH.value()
    )

","
            Created profile 'foo'.

            Switch to your new profile with:

                prefect profile use 'foo'

            Or, to use it for a single command, include the `-p` option:

                prefect -p 'foo' config view
            ",30,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_create_profile():
    invoke_and_assert(
        [""profile"", ""create"", ""foo""],
        expected_output=(
            f
        ),
    )

    profiles = load_profiles()
    assert profiles[""foo""] == Profile(
        name=""foo"", settings={}, source=PREFECT_PROFILES_PATH.value()
    )


        ```
    ###Assistant : 
            Created profile 'foo'.

            Switch to your new profile with:

                prefect profile use 'foo'

            Or, to use it for a single command, include the `-p` option:

                prefect -p 'foo' config view
            
    "
2194,"def test_mixed_errorbar_polar_caps():
    
    fig = plt.figure()
    ax = plt.subplot(111, projection='polar')

    # symmetric errorbars
    th_sym = [1, 2, 3]
    r_sym = [0.9]*3
    ax.errorbar(th_sym, r_sym, xerr=0.35, yerr=0.2, fmt=""o"")

    # long errorbars
    th_long = [np.pi/2 + .1, np.pi + .1]
    r_long = [1.8, 2.2]
    ax.errorbar(th_long, r_long, xerr=0.8 * np.pi, yerr=0.15, fmt=""o"")

    # asymmetric errorbars
    th_asym = [4*np.pi/3 + .1, 5*np.pi/3 + .1, 2*np.pi-0.1]
    r_asym = [1.1]*3
    xerr = [[.3, .3, .2], [.2, .3, .3]]
    yerr = [[.35, .5, .5], [.5, .35, .5]]
    ax.errorbar(th_asym, r_asym, xerr=xerr, yerr=yerr, fmt=""o"")

    # overlapping errorbar
    th_over = [2.1]
    r_over = [3.1]
    ax.errorbar(th_over, r_over, xerr=10, yerr=.2, fmt=""o"")

","
    Mix several polar errorbar use cases in a single test figure.

    It is advisable to position individual points off the grid. If there are
    problems with reproducibility of this test, consider removing grid.
    ",33,97,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_mixed_errorbar_polar_caps():
    
    fig = plt.figure()
    ax = plt.subplot(111, projection='polar')

    # symmetric errorbars
    th_sym = [1, 2, 3]
    r_sym = [0.9]*3
    ax.errorbar(th_sym, r_sym, xerr=0.35, yerr=0.2, fmt=""o"")

    # long errorbars
    th_long = [np.pi/2 + .1, np.pi + .1]
    r_long = [1.8, 2.2]
    ax.errorbar(th_long, r_long, xerr=0.8 * np.pi, yerr=0.15, fmt=""o"")

    # asymmetric errorbars
    th_asym = [4*np.pi/3 + .1, 5*np.pi/3 + .1, 2*np.pi-0.1]
    r_asym = [1.1]*3
    xerr = [[.3, .3, .2], [.2, .3, .3]]
    yerr = [[.35, .5, .5], [.5, .35, .5]]
    ax.errorbar(th_asym, r_asym, xerr=xerr, yerr=yerr, fmt=""o"")

    # overlapping errorbar
    th_over = [2.1]
    r_over = [3.1]
    ax.errorbar(th_over, r_over, xerr=10, yerr=.2, fmt=""o"")


        ```
    ###Assistant : 
    Mix several polar errorbar use cases in a single test figure.

    It is advisable to position individual points off the grid. If there are
    problems with reproducibility of this test, consider removing grid.
    
    "
2195,"def test_edgeql_for_01(self):
        self.assert_test_query(
            r,
            {(1, 1), (2, 2), (3, 3)},
        )
","
            FOR X IN {1,2,3} UNION ((SELECT X), (SELECT X));
            ",9,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_edgeql_for_01(self):
        self.assert_test_query(
            r,
            {(1, 1), (2, 2), (3, 3)},
        )

        ```
    ###Assistant : 
            FOR X IN {1,2,3} UNION ((SELECT X), (SELECT X));
            
    "
2196,"def on_clicked(self, index):
        
        if not index.isValid():
            return
        item = self._model().data(index, downloads.ModelRole.item)
        if item.done and item.successful:
            item.open_file()
            item.remove()
","Handle clicking of an item.

        Args:
            index: The QModelIndex of the clicked item.
        ",13,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def on_clicked(self, index):
        
        if not index.isValid():
            return
        item = self._model().data(index, downloads.ModelRole.item)
        if item.done and item.successful:
            item.open_file()
            item.remove()

        ```
    ###Assistant : Handle clicking of an item.

        Args:
            index: The QModelIndex of the clicked item.
        
    "
2197,"def _tie_weights(self):
        # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
        self.bias = self.decoder.bias


@add_start_docstrings(
    ,
    XLM_ROBERTA_XL_START_DOCSTRING,
)","
    XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top
    of the pooled output) e.g. for GLUE tasks.
    ",23,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _tie_weights(self):
        # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
        self.bias = self.decoder.bias


@add_start_docstrings(
    ,
    XLM_ROBERTA_XL_START_DOCSTRING,
)
        ```
    ###Assistant : 
    XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top
    of the pooled output) e.g. for GLUE tasks.
    
    "
2198,"def matches_minor(self, other):
        
        return (self.major, self.minor) == (other.major, other.minor)

","Check whether this version matches the other in (major, minor).",10,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def matches_minor(self, other):
        
        return (self.major, self.minor) == (other.major, other.minor)


        ```
    ###Assistant : Check whether this version matches the other in (major, minor).
    "
2199,"def test_04_query_predictor_single_where_condition(self):
        time.sleep(120) # TODO 
        query = f
        response = self.handler.native_query(query)
        self.assertTrue(response.type == RESPONSE_TYPE.TABLE)
        self.assertTrue(len(response.data_frame) == 1)
        self.assertTrue(response.data_frame['sqft'][0] == 100)
        self.assertTrue(response.data_frame['rental_price'][0] is not None)
","
            SELECT target
            from {self.test_model_1}
            WHERE sqft=100
        ",6,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_04_query_predictor_single_where_condition(self):
        time.sleep(120) # TODO 
        query = f
        response = self.handler.native_query(query)
        self.assertTrue(response.type == RESPONSE_TYPE.TABLE)
        self.assertTrue(len(response.data_frame) == 1)
        self.assertTrue(response.data_frame['sqft'][0] == 100)
        self.assertTrue(response.data_frame['rental_price'][0] is not None)

        ```
    ###Assistant : 
            SELECT target
            from {self.test_model_1}
            WHERE sqft=100
        
    "
2200,"def _setup_room_for_insertion_backfill_tests(self) -> _BackfillSetupInfo:
        
        room_id = ""!backfill-room-test:some-host""

        depth_map: Dict[str, int] = {
            ""1"": 1,
            ""2"": 2,
            ""insertion_eventA"": 3,
            ""3"": 4,
            ""insertion_eventB"": 5,
            ""4"": 6,
            ""5"": 7,
        }
","
        Sets up a room with various insertion event backward extremities to test
        backfill functions against.

        Returns:
            _BackfillSetupInfo including the `room_id` to test against and
            `depth_map` of events in the room
        ",30,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _setup_room_for_insertion_backfill_tests(self) -> _BackfillSetupInfo:
        
        room_id = ""!backfill-room-test:some-host""

        depth_map: Dict[str, int] = {
            ""1"": 1,
            ""2"": 2,
            ""insertion_eventA"": 3,
            ""3"": 4,
            ""insertion_eventB"": 5,
            ""4"": 6,
            ""5"": 7,
        }

        ```
    ###Assistant : 
        Sets up a room with various insertion event backward extremities to test
        backfill functions against.

        Returns:
            _BackfillSetupInfo including the `room_id` to test against and
            `depth_map` of events in the room
        
    "
2201,"def postprocessing(data):
    

    if type_to_string(type(data)) == ""torch.Tensor"":
        try:
            import torch
            from torchvision import transforms

            # By default Torch tensors are displayed as images. To display them as JSON,
            # the user can simply convert them to numpy arrays.
            transformer = transforms.ToPILImage()
            return transformer(torch.squeeze(data))
        except ModuleNotFoundError:
            logger.warning(
                ""Module `torchvision` isn't installed, unable to process torch tensor.""
            )
            return data

    return data

","Add support for types that are not supported by Gradio.

    Some data types like PyTorch tensors, cannot be processed and displayed through
    Gradio. Thus we extend support to these data types by transforming them into a form
    that Gradio can process and display.
    ",43,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def postprocessing(data):
    

    if type_to_string(type(data)) == ""torch.Tensor"":
        try:
            import torch
            from torchvision import transforms

            # By default Torch tensors are displayed as images. To display them as JSON,
            # the user can simply convert them to numpy arrays.
            transformer = transforms.ToPILImage()
            return transformer(torch.squeeze(data))
        except ModuleNotFoundError:
            logger.warning(
                ""Module `torchvision` isn't installed, unable to process torch tensor.""
            )
            return data

    return data


        ```
    ###Assistant : Add support for types that are not supported by Gradio.

    Some data types like PyTorch tensors, cannot be processed and displayed through
    Gradio. Thus we extend support to these data types by transforming them into a form
    that Gradio can process and display.
    
    "
2202,"def get_scrap_item_details(bom_no):
	scrap_items = {}
	for item in frappe.db.sql(
		,
		bom_no,
		as_dict=1,
	):
		scrap_items[item.item_code] = item.stock_qty

	return scrap_items

","select item_code, stock_qty from `tabBOM Scrap Item`
		where parent = %s",11,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_scrap_item_details(bom_no):
	scrap_items = {}
	for item in frappe.db.sql(
		,
		bom_no,
		as_dict=1,
	):
		scrap_items[item.item_code] = item.stock_qty

	return scrap_items


        ```
    ###Assistant : select item_code, stock_qty from `tabBOM Scrap Item`
		where parent = %s
    "
2203,"def get_is_active(self, session=NEW_SESSION) -> Optional[None]:
        
        return session.query(DagModel.is_active).filter(DagModel.dag_id == self.dag_id).scalar()
",Returns a boolean indicating whether this DAG is active,9,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_is_active(self, session=NEW_SESSION) -> Optional[None]:
        
        return session.query(DagModel.is_active).filter(DagModel.dag_id == self.dag_id).scalar()

        ```
    ###Assistant : Returns a boolean indicating whether this DAG is active
    "
2204,"def is_homepage(self) -> bool:
        
        return self.is_top_level and self.is_index and self.file.url in ('.', './', 'index.html')

    previous_page: Optional[Page]
    

    next_page: Optional[Page]
    

    parent: Optional[Section]
    

    children: None = None
    

    is_section: bool = False
    

    is_page: bool = True
    

    is_link: bool = False
    
","Evaluates to `True` for the homepage of the site and `False` for all other pages.The [page][mkdocs.structure.pages.Page] object for the previous page or `None`.
    The value will be `None` if the current page is the first item in the site navigation
    or if the current page is not included in the navigation at all.The [page][mkdocs.structure.pages.Page] object for the next page or `None`.
    The value will be `None` if the current page is the last item in the site navigation
    or if the current page is not included in the navigation at all.The immediate parent of the page in the site navigation. `None` if the
    page is at the top level.Pages do not contain children and the attribute is always `None`.Indicates that the navigation object is a ""section"" object. Always `False` for page objects.Indicates that the navigation object is a ""page"" object. Always `True` for page objects.Indicates that the navigation object is a ""link"" object. Always `False` for page objects.",158,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_homepage(self) -> bool:
        
        return self.is_top_level and self.is_index and self.file.url in ('.', './', 'index.html')

    previous_page: Optional[Page]
    

    next_page: Optional[Page]
    

    parent: Optional[Section]
    

    children: None = None
    

    is_section: bool = False
    

    is_page: bool = True
    

    is_link: bool = False
    

        ```
    ###Assistant : Evaluates to `True` for the homepage of the site and `False` for all other pages.The [page][mkdocs.structure.pages.Page] object for the previous page or `None`.
    The value will be `None` if the current page is the first item in the site navigation
    or if the current page is not included in the navigation at all.The [page][mkdocs.structure.pages.Page] object for the next page or `None`.
    The value will be `None` if the current page is the last item in the site navigation
    or if the current page is not included in the navigation at all.The immediate parent of the page in the site navigation. `None` if the
    page is at the top level.Pages do not contain children and the attribute is always `None`.Indicates that the navigation object is a ""section"" object. Always `False` for page objects.Indicates that the navigation object is a ""page"" object. Always `True` for page objects.Indicates that the navigation object is a ""link"" object. Always `False` for page objects.
    "
2205,"def _filetypes(self):
        
        all_files = (""All files"", ""*.*"")
        filetypes = dict(
            default=(all_files,),
            alignments=[(""Faceswap Alignments"", ""*.fsa""), all_files],
            config_project=[(""Faceswap Project files"", ""*.fsw""), all_files],
            config_task=[(""Faceswap Task files"", ""*.fst""), all_files],
            config_all=[(""Faceswap Project and Task files"", ""*.fst *.fsw""), all_files],
            csv=[(""Comma separated values"", ""*.csv""), all_files],
            image=[(""Bitmap"", ""*.bmp""),
                   (""JPG"", ""*.jpeg *.jpg""),
                   (""PNG"", ""*.png""),
                   (""TIFF"", ""*.tif *.tiff""),
                   all_files],
            ini=[(""Faceswap config files"", ""*.ini""), all_files],
            json=[(""JSON file"", ""*.json""), all_files],
            model=[(""Keras model files"", ""*.h5""), all_files],
            state=[(""State files"", ""*.json""), all_files],
            log=[(""Log files"", ""*.log""), all_files],
            video=[(""Audio Video Interleave"", ""*.avi""),
                   (""Flash Video"", ""*.flv""),
                   (""Matroska"", ""*.mkv""),
                   (""MOV"", ""*.mov""),
                   (""MP4"", ""*.mp4""),
                   (""MPEG"", ""*.mpeg *.mpg *.ts *.vob""),
                   (""WebM"", ""*.webm""),
                   (""Windows Media Video"", ""*.wmv""),
                   all_files])

        # Add in multi-select options and upper case extensions for Linux
        for key in filetypes:
            if platform.system() == ""Linux"":
                filetypes[key] = [item
                                  if item[0] == ""All files""
                                  else (item[0], f""{item[1]} {item[1].upper()}"")
                                  for item in filetypes[key]]
            if len(filetypes[key]) > 2:
                multi = [f""{key.title()} Files""]
                multi.append("" "".join([ftype[1]
                                       for ftype in filetypes[key] if ftype[0] != ""All files""]))
                filetypes[key].insert(0, tuple(multi))
        return filetypes
", dict: The accepted extensions for each file type for opening/saving ,10,154,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _filetypes(self):
        
        all_files = (""All files"", ""*.*"")
        filetypes = dict(
            default=(all_files,),
            alignments=[(""Faceswap Alignments"", ""*.fsa""), all_files],
            config_project=[(""Faceswap Project files"", ""*.fsw""), all_files],
            config_task=[(""Faceswap Task files"", ""*.fst""), all_files],
            config_all=[(""Faceswap Project and Task files"", ""*.fst *.fsw""), all_files],
            csv=[(""Comma separated values"", ""*.csv""), all_files],
            image=[(""Bitmap"", ""*.bmp""),
                   (""JPG"", ""*.jpeg *.jpg""),
                   (""PNG"", ""*.png""),
                   (""TIFF"", ""*.tif *.tiff""),
                   all_files],
            ini=[(""Faceswap config files"", ""*.ini""), all_files],
            json=[(""JSON file"", ""*.json""), all_files],
            model=[(""Keras model files"", ""*.h5""), all_files],
            state=[(""State files"", ""*.json""), all_files],
            log=[(""Log files"", ""*.log""), all_files],
            video=[(""Audio Video Interleave"", ""*.avi""),
                   (""Flash Video"", ""*.flv""),
                   (""Matroska"", ""*.mkv""),
                   (""MOV"", ""*.mov""),
                   (""MP4"", ""*.mp4""),
                   (""MPEG"", ""*.mpeg *.mpg *.ts *.vob""),
                   (""WebM"", ""*.webm""),
                   (""Windows Media Video"", ""*.wmv""),
                   all_files])

        # Add in multi-select options and upper case extensions for Linux
        for key in filetypes:
            if platform.system() == ""Linux"":
                filetypes[key] = [item
                                  if item[0] == ""All files""
                                  else (item[0], f""{item[1]} {item[1].upper()}"")
                                  for item in filetypes[key]]
            if len(filetypes[key]) > 2:
                multi = [f""{key.title()} Files""]
                multi.append("" "".join([ftype[1]
                                       for ftype in filetypes[key] if ftype[0] != ""All files""]))
                filetypes[key].insert(0, tuple(multi))
        return filetypes

        ```
    ###Assistant :  dict: The accepted extensions for each file type for opening/saving 
    "
2206,"def result(self):
        
        if self._state == _CANCELLED:
            exc = self._make_cancelled_error()
            raise exc
        if self._state != _FINISHED:
            raise exceptions.InvalidStateError('Result is not ready.')
        self.__log_traceback = False
        if self._exception is not None:
            raise self._exception
        return self._result
","Return the result this future represents.

        If the future has been cancelled, raises CancelledError.  If the
        future's result isn't yet available, raises InvalidStateError.  If
        the future is done and has an exception set, this exception is raised.
        ",37,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def result(self):
        
        if self._state == _CANCELLED:
            exc = self._make_cancelled_error()
            raise exc
        if self._state != _FINISHED:
            raise exceptions.InvalidStateError('Result is not ready.')
        self.__log_traceback = False
        if self._exception is not None:
            raise self._exception
        return self._result

        ```
    ###Assistant : Return the result this future represents.

        If the future has been cancelled, raises CancelledError.  If the
        future's result isn't yet available, raises InvalidStateError.  If
        the future is done and has an exception set, this exception is raised.
        
    "
2207,"def _set_fsspec_for_multiprocess() -> None:
    
    fsspec.asyn.iothread[0] = None
    fsspec.asyn.loop[0] = None

","
    Clear reference to the loop and thread.
    This is necessary otherwise HTTPFileSystem hangs in the ML training loop.
    Only required for fsspec >= 0.9.0
    See https://github.com/fsspec/gcsfs/issues/379
    ",26,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_fsspec_for_multiprocess() -> None:
    
    fsspec.asyn.iothread[0] = None
    fsspec.asyn.loop[0] = None


        ```
    ###Assistant : 
    Clear reference to the loop and thread.
    This is necessary otherwise HTTPFileSystem hangs in the ML training loop.
    Only required for fsspec >= 0.9.0
    See https://github.com/fsspec/gcsfs/issues/379
    
    "
2208,"def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
    
    out = np.cumsum(arr, axis=axis, dtype=np.float64)
    expected = np.sum(arr, axis=axis, dtype=np.float64)
    if not np.all(
        np.isclose(
            out.take(-1, axis=axis), expected, rtol=rtol, atol=atol, equal_nan=True
        )
    ):
        warnings.warn(
            ""cumsum was found to be unstable: ""
            ""its last element does not correspond to sum"",
            RuntimeWarning,
        )
    return out
","Use high precision for cumsum and check that final value matches sum.

    Warns if the final cumulative sum does not match the sum (up to the chosen
    tolerance).

    Parameters
    ----------
    arr : array-like
        To be cumulatively summed as flat.
    axis : int, default=None
        Axis along which the cumulative sum is computed.
        The default (None) is to compute the cumsum over the flattened array.
    rtol : float, default=1e-05
        Relative tolerance, see ``np.allclose``.
    atol : float, default=1e-08
        Absolute tolerance, see ``np.allclose``.

    Returns
    -------
    out : ndarray
        Array with the cumulative sums along the chosen axis.
    ",93,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
    
    out = np.cumsum(arr, axis=axis, dtype=np.float64)
    expected = np.sum(arr, axis=axis, dtype=np.float64)
    if not np.all(
        np.isclose(
            out.take(-1, axis=axis), expected, rtol=rtol, atol=atol, equal_nan=True
        )
    ):
        warnings.warn(
            ""cumsum was found to be unstable: ""
            ""its last element does not correspond to sum"",
            RuntimeWarning,
        )
    return out

        ```
    ###Assistant : Use high precision for cumsum and check that final value matches sum.

    Warns if the final cumulative sum does not match the sum (up to the chosen
    tolerance).

    Parameters
    ----------
    arr : array-like
        To be cumulatively summed as flat.
    axis : int, default=None
        Axis along which the cumulative sum is computed.
        The default (None) is to compute the cumsum over the flattened array.
    rtol : float, default=1e-05
        Relative tolerance, see ``np.allclose``.
    atol : float, default=1e-08
        Absolute tolerance, see ``np.allclose``.

    Returns
    -------
    out : ndarray
        Array with the cumulative sums along the chosen axis.
    
    "
2209,"def confirm(self):
        
        args = request.args
        dag_id = args.get('dag_id')
        task_id = args.get('task_id')
        dag_run_id = args.get('dag_run_id')
        state = args.get('state')
        origin = args.get('origin')

        if 'map_index' not in args:
            map_indexes: Optional[List[int]] = None
        else:
            map_indexes = args.getlist('map_index', type=int)

        upstream = to_boolean(args.get('upstream'))
        downstream = to_boolean(args.get('downstream'))
        future = to_boolean(args.get('future'))
        past = to_boolean(args.get('past'))
        origin = origin or url_for('Airflow.index')

        dag = get_airflow_app().dag_bag.get_dag(dag_id)
        if not dag:
            msg = f'DAG {dag_id} not found'
            return redirect_or_json(origin, msg, status='error', status_code=404)

        try:
            task = dag.get_task(task_id)
        except airflow.exceptions.TaskNotFound:
            msg = f""Task {task_id} not found""
            return redirect_or_json(origin, msg, status='error', status_code=404)

        task.dag = dag

        if state not in (
            'success',
            'failed',
        ):
            msg = f""Invalid state {state}, must be either 'success' or 'failed'""
            return redirect_or_json(origin, msg, status='error', status_code=400)

        latest_execution_date = dag.get_latest_execution_date()
        if not latest_execution_date:
            msg = f""Cannot mark tasks as {state}, seem that dag {dag_id} has never run""
            return redirect_or_json(origin, msg, status='error', status_code=400)

        if map_indexes is None:
            tasks: Union[List[Operator], List[Tuple[Operator, int]]] = [task]
        else:
            tasks = [(task, map_index) for map_index in map_indexes]

        to_be_altered = set_state(
            tasks=tasks,
            run_id=dag_run_id,
            upstream=upstream,
            downstream=downstream,
            future=future,
            past=past,
            state=state,
            commit=False,
        )

        if request.headers.get('Accept') == 'application/json':
            details = [str(t) for t in to_be_altered]
            return htmlsafe_json_dumps(details, separators=(',', ':'))

        details = ""\n"".join(str(t) for t in to_be_altered)

        response = self.render_template(
            ""airflow/confirm.html"",
            endpoint=url_for(f'Airflow.{state}'),
            message=f""Task instances you are about to mark as {state}:"",
            details=details,
        )

        return response
",Show confirmation page for marking tasks as success or failed.,10,208,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def confirm(self):
        
        args = request.args
        dag_id = args.get('dag_id')
        task_id = args.get('task_id')
        dag_run_id = args.get('dag_run_id')
        state = args.get('state')
        origin = args.get('origin')

        if 'map_index' not in args:
            map_indexes: Optional[List[int]] = None
        else:
            map_indexes = args.getlist('map_index', type=int)

        upstream = to_boolean(args.get('upstream'))
        downstream = to_boolean(args.get('downstream'))
        future = to_boolean(args.get('future'))
        past = to_boolean(args.get('past'))
        origin = origin or url_for('Airflow.index')

        dag = get_airflow_app().dag_bag.get_dag(dag_id)
        if not dag:
            msg = f'DAG {dag_id} not found'
            return redirect_or_json(origin, msg, status='error', status_code=404)

        try:
            task = dag.get_task(task_id)
        except airflow.exceptions.TaskNotFound:
            msg = f""Task {task_id} not found""
            return redirect_or_json(origin, msg, status='error', status_code=404)

        task.dag = dag

        if state not in (
            'success',
            'failed',
        ):
            msg = f""Invalid state {state}, must be either 'success' or 'failed'""
            return redirect_or_json(origin, msg, status='error', status_code=400)

        latest_execution_date = dag.get_latest_execution_date()
        if not latest_execution_date:
            msg = f""Cannot mark tasks as {state}, seem that dag {dag_id} has never run""
            return redirect_or_json(origin, msg, status='error', status_code=400)

        if map_indexes is None:
            tasks: Union[List[Operator], List[Tuple[Operator, int]]] = [task]
        else:
            tasks = [(task, map_index) for map_index in map_indexes]

        to_be_altered = set_state(
            tasks=tasks,
            run_id=dag_run_id,
            upstream=upstream,
            downstream=downstream,
            future=future,
            past=past,
            state=state,
            commit=False,
        )

        if request.headers.get('Accept') == 'application/json':
            details = [str(t) for t in to_be_altered]
            return htmlsafe_json_dumps(details, separators=(',', ':'))

        details = ""\n"".join(str(t) for t in to_be_altered)

        response = self.render_template(
            ""airflow/confirm.html"",
            endpoint=url_for(f'Airflow.{state}'),
            message=f""Task instances you are about to mark as {state}:"",
            details=details,
        )

        return response

        ```
    ###Assistant : Show confirmation page for marking tasks as success or failed.
    "
2210,"def resample(self) -> Dict[str, Any]:
        
        result = {}
        for module in self.nas_modules:
            result.update(module.resample(memo=result))
        return result
","Trigger the resample for each ``nas_module``.
        Sometimes (e.g., in differentiable cases), it does nothing.

        Returns
        -------
        dict
            Sampled architecture.
        ",19,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resample(self) -> Dict[str, Any]:
        
        result = {}
        for module in self.nas_modules:
            result.update(module.resample(memo=result))
        return result

        ```
    ###Assistant : Trigger the resample for each ``nas_module``.
        Sometimes (e.g., in differentiable cases), it does nothing.

        Returns
        -------
        dict
            Sampled architecture.
        
    "
2211,"def test_json_to_doc_attribute_consistency(doc):
    
    doc_json = doc.to_json()
    doc_json[""tokens""][1].pop(""morph"")
    with pytest.raises(ValueError):
        Doc(doc.vocab).from_json(doc_json)

",Test that Doc.from_json() raises an exception if tokens don't all have the same set of properties.,16,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_json_to_doc_attribute_consistency(doc):
    
    doc_json = doc.to_json()
    doc_json[""tokens""][1].pop(""morph"")
    with pytest.raises(ValueError):
        Doc(doc.vocab).from_json(doc_json)


        ```
    ###Assistant : Test that Doc.from_json() raises an exception if tokens don't all have the same set of properties.
    "
2212,"def test_index_css_classes(self):
        
        # General index page
        response = self.client.get(reverse(""admin:index""))
        self.assertContains(response, '<div class=""app-admin_views module')
        self.assertContains(response, '<tr class=""model-actor"">')
        self.assertContains(response, '<tr class=""model-album"">')

        # App index page
        response = self.client.get(reverse(""admin:app_list"", args=(""admin_views"",)))
        self.assertContains(response, '<div class=""app-admin_views module')
        self.assertContains(response, '<tr class=""model-actor"">')
        self.assertContains(response, '<tr class=""model-album"">')
","
        CSS class names are used for each app and model on the admin index
        pages (#17050).
        ",16,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_index_css_classes(self):
        
        # General index page
        response = self.client.get(reverse(""admin:index""))
        self.assertContains(response, '<div class=""app-admin_views module')
        self.assertContains(response, '<tr class=""model-actor"">')
        self.assertContains(response, '<tr class=""model-album"">')

        # App index page
        response = self.client.get(reverse(""admin:app_list"", args=(""admin_views"",)))
        self.assertContains(response, '<div class=""app-admin_views module')
        self.assertContains(response, '<tr class=""model-actor"">')
        self.assertContains(response, '<tr class=""model-album"">')

        ```
    ###Assistant : 
        CSS class names are used for each app and model on the admin index
        pages (#17050).
        
    "
2213,"def test_action_column_class(self):
        
        response = self.client.get(reverse(""admin:admin_views_subscriber_changelist""))
        self.assertIsNotNone(response.context[""action_form""])
        self.assertContains(response, ""action-checkbox-column"")
",The checkbox column class is present in the response.,9,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_action_column_class(self):
        
        response = self.client.get(reverse(""admin:admin_views_subscriber_changelist""))
        self.assertIsNotNone(response.context[""action_form""])
        self.assertContains(response, ""action-checkbox-column"")

        ```
    ###Assistant : The checkbox column class is present in the response.
    "
2214,"def hashkey(cls, *args, **kwargs):
        
        return cachetools.keys.hashkey(f""<{cls.__name__}>"", *args, **kwargs)

","
        Usage of @cachetools.cached has changed to @cachetools.cachedmethod
        The previous cachetools decorator called the hash function and passed in (self, key).
        The new cachtools decorator calls the hash function with just (key).
        Ideally, we would continue to pass self, however, the cachetools decorator interface
        does not allow us to.

        This hashkey function is to maintain that the key generated looks like
        ('<SettingsWrapper>', key). The thought is that maybe it is important to namespace
        our cache to the SettingsWrapper scope in case some other usage of this cache exists.
        I can not think of how any other system could and would use our private cache, but
        for safety sake we are ensuring the key schema does not change.
        ",116,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def hashkey(cls, *args, **kwargs):
        
        return cachetools.keys.hashkey(f""<{cls.__name__}>"", *args, **kwargs)


        ```
    ###Assistant : 
        Usage of @cachetools.cached has changed to @cachetools.cachedmethod
        The previous cachetools decorator called the hash function and passed in (self, key).
        The new cachtools decorator calls the hash function with just (key).
        Ideally, we would continue to pass self, however, the cachetools decorator interface
        does not allow us to.

        This hashkey function is to maintain that the key generated looks like
        ('<SettingsWrapper>', key). The thought is that maybe it is important to namespace
        our cache to the SettingsWrapper scope in case some other usage of this cache exists.
        I can not think of how any other system could and would use our private cache, but
        for safety sake we are ensuring the key schema does not change.
        
    "
2215,"def load(cls, path):
        
        with open(path) as yaml_file:
            data = yaml.safe_load(yaml_file)
        if not isinstance(data, dict):
            raise TypeError(f'Conent of config file {path} is not a dict/object')
        utils.set_base_path(Path(path).parent)
        config = cls(**data)
        utils.unset_base_path()
        return config
","
        Load a YAML config file from file system.

        Since YAML is a superset of JSON, it can also load JSON files.

        This method raises exception if:

        - The file is not available
        - The file content is not valid YAML
        - Top level value of the YAML is not object
        - The YAML contains not supported fields

        It does not raise exception when the YAML misses fields or contains bad fields.

        Parameters
        ----------
        path : PathLike
            Path of the config file.

        Returns
        -------
        cls
            An object of ConfigBase subclass.
        ",89,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load(cls, path):
        
        with open(path) as yaml_file:
            data = yaml.safe_load(yaml_file)
        if not isinstance(data, dict):
            raise TypeError(f'Conent of config file {path} is not a dict/object')
        utils.set_base_path(Path(path).parent)
        config = cls(**data)
        utils.unset_base_path()
        return config

        ```
    ###Assistant : 
        Load a YAML config file from file system.

        Since YAML is a superset of JSON, it can also load JSON files.

        This method raises exception if:

        - The file is not available
        - The file content is not valid YAML
        - Top level value of the YAML is not object
        - The YAML contains not supported fields

        It does not raise exception when the YAML misses fields or contains bad fields.

        Parameters
        ----------
        path : PathLike
            Path of the config file.

        Returns
        -------
        cls
            An object of ConfigBase subclass.
        
    "
2216,"def get_leave_allocation_for_period(employee, leave_type, from_date, to_date):
	leave_allocated = 0
	leave_allocations = frappe.db.sql(
		,
		{""from_date"": from_date, ""to_date"": to_date, ""employee"": employee, ""leave_type"": leave_type},
		as_dict=1,
	)

	if leave_allocations:
		for leave_alloc in leave_allocations:
			leave_allocated += leave_alloc.total_leaves_allocated

	return leave_allocated


@frappe.whitelist()","
		select employee, leave_type, from_date, to_date, total_leaves_allocated
		from `tabLeave Allocation`
		where employee=%(employee)s and leave_type=%(leave_type)s
			and docstatus=1
			and (from_date between %(from_date)s and %(to_date)s
				or to_date between %(from_date)s and %(to_date)s
				or (from_date < %(from_date)s and to_date > %(to_date)s))
	",35,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_leave_allocation_for_period(employee, leave_type, from_date, to_date):
	leave_allocated = 0
	leave_allocations = frappe.db.sql(
		,
		{""from_date"": from_date, ""to_date"": to_date, ""employee"": employee, ""leave_type"": leave_type},
		as_dict=1,
	)

	if leave_allocations:
		for leave_alloc in leave_allocations:
			leave_allocated += leave_alloc.total_leaves_allocated

	return leave_allocated


@frappe.whitelist()
        ```
    ###Assistant : 
		select employee, leave_type, from_date, to_date, total_leaves_allocated
		from `tabLeave Allocation`
		where employee=%(employee)s and leave_type=%(leave_type)s
			and docstatus=1
			and (from_date between %(from_date)s and %(to_date)s
				or to_date between %(from_date)s and %(to_date)s
				or (from_date < %(from_date)s and to_date > %(to_date)s))
	
    "
2217,"def test_proxy_model_content_type_is_used_for_log_entries(self):
        
        proxy_content_type = ContentType.objects.get_for_model(
            ArticleProxy, for_concrete_model=False
        )
        post_data = {
            ""site"": self.site.pk,
            ""title"": ""Foo"",
            ""hist"": ""Bar"",
            ""created_0"": ""2015-12-25"",
            ""created_1"": ""00:00"",
        }
        changelist_url = reverse(""admin:admin_utils_articleproxy_changelist"")

        # add
        proxy_add_url = reverse(""admin:admin_utils_articleproxy_add"")
        response = self.client.post(proxy_add_url, post_data)
        self.assertRedirects(response, changelist_url)
        proxy_addition_log = LogEntry.objects.latest(""id"")
        self.assertEqual(proxy_addition_log.action_flag, ADDITION)
        self.assertEqual(proxy_addition_log.content_type, proxy_content_type)

        # change
        article_id = proxy_addition_log.object_id
        proxy_change_url = reverse(
            ""admin:admin_utils_articleproxy_change"", args=(article_id,)
        )
        post_data[""title""] = ""New""
        response = self.client.post(proxy_change_url, post_data)
        self.assertRedirects(response, changelist_url)
        proxy_change_log = LogEntry.objects.latest(""id"")
        self.assertEqual(proxy_change_log.action_flag, CHANGE)
        self.assertEqual(proxy_change_log.content_type, proxy_content_type)

        # delete
        proxy_delete_url = reverse(
            ""admin:admin_utils_articleproxy_delete"", args=(article_id,)
        )
        response = self.client.post(proxy_delete_url, {""post"": ""yes""})
        self.assertRedirects(response, changelist_url)
        proxy_delete_log = LogEntry.objects.latest(""id"")
        self.assertEqual(proxy_delete_log.action_flag, DELETION)
        self.assertEqual(proxy_delete_log.content_type, proxy_content_type)
","
        Log entries for proxy models should have the proxy model's contenttype
        (#21084).
        ",12,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_proxy_model_content_type_is_used_for_log_entries(self):
        
        proxy_content_type = ContentType.objects.get_for_model(
            ArticleProxy, for_concrete_model=False
        )
        post_data = {
            ""site"": self.site.pk,
            ""title"": ""Foo"",
            ""hist"": ""Bar"",
            ""created_0"": ""2015-12-25"",
            ""created_1"": ""00:00"",
        }
        changelist_url = reverse(""admin:admin_utils_articleproxy_changelist"")

        # add
        proxy_add_url = reverse(""admin:admin_utils_articleproxy_add"")
        response = self.client.post(proxy_add_url, post_data)
        self.assertRedirects(response, changelist_url)
        proxy_addition_log = LogEntry.objects.latest(""id"")
        self.assertEqual(proxy_addition_log.action_flag, ADDITION)
        self.assertEqual(proxy_addition_log.content_type, proxy_content_type)

        # change
        article_id = proxy_addition_log.object_id
        proxy_change_url = reverse(
            ""admin:admin_utils_articleproxy_change"", args=(article_id,)
        )
        post_data[""title""] = ""New""
        response = self.client.post(proxy_change_url, post_data)
        self.assertRedirects(response, changelist_url)
        proxy_change_log = LogEntry.objects.latest(""id"")
        self.assertEqual(proxy_change_log.action_flag, CHANGE)
        self.assertEqual(proxy_change_log.content_type, proxy_content_type)

        # delete
        proxy_delete_url = reverse(
            ""admin:admin_utils_articleproxy_delete"", args=(article_id,)
        )
        response = self.client.post(proxy_delete_url, {""post"": ""yes""})
        self.assertRedirects(response, changelist_url)
        proxy_delete_log = LogEntry.objects.latest(""id"")
        self.assertEqual(proxy_delete_log.action_flag, DELETION)
        self.assertEqual(proxy_delete_log.content_type, proxy_content_type)

        ```
    ###Assistant : 
        Log entries for proxy models should have the proxy model's contenttype
        (#21084).
        
    "
2218,"def typename(typ, short=False) -> str:
    
    if not isinstance(typ, type):
        return typename(type(typ))
    try:
        if not typ.__module__ or typ.__module__ == ""builtins"":
            return typ.__name__
        else:
            if short:
                module, *_ = typ.__module__.split(""."")
            else:
                module = typ.__module__
            return module + ""."" + typ.__name__
    except AttributeError:
        return str(typ)

","
    Return the name of a type

    Examples
    --------
    >>> typename(int)
    'int'

    >>> from dask.core import literal
    >>> typename(literal)
    'dask.core.literal'
    >>> typename(literal, short=True)
    'dask.literal'
    ",23,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def typename(typ, short=False) -> str:
    
    if not isinstance(typ, type):
        return typename(type(typ))
    try:
        if not typ.__module__ or typ.__module__ == ""builtins"":
            return typ.__name__
        else:
            if short:
                module, *_ = typ.__module__.split(""."")
            else:
                module = typ.__module__
            return module + ""."" + typ.__name__
    except AttributeError:
        return str(typ)


        ```
    ###Assistant : 
    Return the name of a type

    Examples
    --------
    >>> typename(int)
    'int'

    >>> from dask.core import literal
    >>> typename(literal)
    'dask.core.literal'
    >>> typename(literal, short=True)
    'dask.literal'
    
    "
2219,"def fetch_command(self, subcommand):
        
        # Get commands outside of try block to prevent swallowing exceptions
        commands = get_commands()
        try:
            app_name = commands[subcommand]
        except KeyError:
            if os.environ.get(""DJANGO_SETTINGS_MODULE""):
                # If `subcommand` is missing due to misconfigured settings, the
                # following line will retrigger an ImproperlyConfigured exception
                # (get_commands() swallows the original one) so the user is
                # informed about it.
                settings.INSTALLED_APPS
            elif not settings.configured:
                sys.stderr.write(""No Django settings specified.\n"")
            possible_matches = get_close_matches(subcommand, commands)
            sys.stderr.write(""Unknown command: %r"" % subcommand)
            if possible_matches:
                sys.stderr.write("". Did you mean %s?"" % possible_matches[0])
            sys.stderr.write(""\nType '%s help' for usage.\n"" % self.prog_name)
            sys.exit(1)
        if isinstance(app_name, BaseCommand):
            # If the command is already loaded, use it directly.
            klass = app_name
        else:
            klass = load_command_class(app_name, subcommand)
        return klass
","
        Try to fetch the given subcommand, printing a message with the
        appropriate command called from the command line (usually
        ""django-admin"" or ""manage.py"") if it can't be found.
        ",27,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fetch_command(self, subcommand):
        
        # Get commands outside of try block to prevent swallowing exceptions
        commands = get_commands()
        try:
            app_name = commands[subcommand]
        except KeyError:
            if os.environ.get(""DJANGO_SETTINGS_MODULE""):
                # If `subcommand` is missing due to misconfigured settings, the
                # following line will retrigger an ImproperlyConfigured exception
                # (get_commands() swallows the original one) so the user is
                # informed about it.
                settings.INSTALLED_APPS
            elif not settings.configured:
                sys.stderr.write(""No Django settings specified.\n"")
            possible_matches = get_close_matches(subcommand, commands)
            sys.stderr.write(""Unknown command: %r"" % subcommand)
            if possible_matches:
                sys.stderr.write("". Did you mean %s?"" % possible_matches[0])
            sys.stderr.write(""\nType '%s help' for usage.\n"" % self.prog_name)
            sys.exit(1)
        if isinstance(app_name, BaseCommand):
            # If the command is already loaded, use it directly.
            klass = app_name
        else:
            klass = load_command_class(app_name, subcommand)
        return klass

        ```
    ###Assistant : 
        Try to fetch the given subcommand, printing a message with the
        appropriate command called from the command line (usually
        ""django-admin"" or ""manage.py"") if it can't be found.
        
    "
2220,"def url(self, name):
        
        name = self._normalize_name(clean_name(name))
        blob = self.bucket.blob(name)
        blob_params = self.get_object_parameters(name)
        no_signed_url = (
            blob_params.get('acl', self.default_acl) == 'publicRead' or not self.querystring_auth)

        if not self.custom_endpoint and no_signed_url:
            return blob.public_url
        elif no_signed_url:
            out = '{storage_base_url}/{quoted_name}'.format(
                storage_base_url=self.custom_endpoint,
                quoted_name=_quote(name, safe=b""/~""),
            )
            return out
        elif not self.custom_endpoint:
            out2 = blob.generate_signed_url(
                expiration=self.expiration,
                version=""v4"",
                **self._get_signing_kwargs()
            )
            return out2
        else:
            out3 = blob.generate_signed_url(
                bucket_bound_hostname=self.custom_endpoint,
                expiration=self.expiration,
                version=""v4"",
                **self._get_signing_kwargs()
            )
            return out3
","
        Return public url or a signed url for the Blob.
        This DOES NOT check for existance of Blob - that makes codes too slow
        for many use cases.
        Overridden to force the use of the IAM signBlob API.
        See https://github.com/googleapis/python-storage/blob/519074112775c19742522158f612b467cf590219/google/cloud/storage/_signing.py#L628  # NOQA
        ",42,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def url(self, name):
        
        name = self._normalize_name(clean_name(name))
        blob = self.bucket.blob(name)
        blob_params = self.get_object_parameters(name)
        no_signed_url = (
            blob_params.get('acl', self.default_acl) == 'publicRead' or not self.querystring_auth)

        if not self.custom_endpoint and no_signed_url:
            return blob.public_url
        elif no_signed_url:
            out = '{storage_base_url}/{quoted_name}'.format(
                storage_base_url=self.custom_endpoint,
                quoted_name=_quote(name, safe=b""/~""),
            )
            return out
        elif not self.custom_endpoint:
            out2 = blob.generate_signed_url(
                expiration=self.expiration,
                version=""v4"",
                **self._get_signing_kwargs()
            )
            return out2
        else:
            out3 = blob.generate_signed_url(
                bucket_bound_hostname=self.custom_endpoint,
                expiration=self.expiration,
                version=""v4"",
                **self._get_signing_kwargs()
            )
            return out3

        ```
    ###Assistant : 
        Return public url or a signed url for the Blob.
        This DOES NOT check for existance of Blob - that makes codes too slow
        for many use cases.
        Overridden to force the use of the IAM signBlob API.
        See https://github.com/googleapis/python-storage/blob/519074112775c19742522158f612b467cf590219/google/cloud/storage/_signing.py#L628  # NOQA
        
    "
2221,"def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all):
    
    if isclass(estimator):
        raise TypeError(""{} is a class, not an instance."".format(estimator))
    if msg is None:
        msg = (
            ""This %(name)s instance is not fitted yet. Call 'fit' with ""
            ""appropriate arguments before using this estimator.""
        )

    if not hasattr(estimator, ""fit""):
        raise TypeError(""%s is not an estimator instance."" % (estimator))

    if attributes is not None:
        if not isinstance(attributes, (list, tuple)):
            attributes = [attributes]
        fitted = all_or_any([hasattr(estimator, attr) for attr in attributes])
    elif hasattr(estimator, ""__sklearn_is_fitted__""):
        fitted = estimator.__sklearn_is_fitted__()
    else:
        fitted = [
            v for v in vars(estimator) if v.endswith(""_"") and not v.startswith(""__"")
        ]

    if not fitted:
        raise NotFittedError(msg % {""name"": type(estimator).__name__})

","Perform is_fitted validation for estimator.

    Checks if the estimator is fitted by verifying the presence of
    fitted attributes (ending with a trailing underscore) and otherwise
    raises a NotFittedError with the given message.

    If an estimator does not set any attributes with a trailing underscore, it
    can define a ``__sklearn_is_fitted__`` method returning a boolean to specify if the
    estimator is fitted or not.

    Parameters
    ----------
    estimator : estimator instance
        Estimator instance for which the check is performed.

    attributes : str, list or tuple of str, default=None
        Attribute name(s) given as string or a list/tuple of strings
        Eg.: ``[""coef_"", ""estimator_"", ...], ""coef_""``

        If `None`, `estimator` is considered fitted if there exist an
        attribute that ends with a underscore and does not start with double
        underscore.

    msg : str, default=None
        The default error message is, ""This %(name)s instance is not fitted
        yet. Call 'fit' with appropriate arguments before using this
        estimator.""

        For custom messages if ""%(name)s"" is present in the message string,
        it is substituted for the estimator name.

        Eg. : ""Estimator, %(name)s, must be fitted before sparsifying"".

    all_or_any : callable, {all, any}, default=all
        Specify whether all or any of the given attributes must exist.

    Raises
    ------
    TypeError
        If the estimator is a class or not an estimator instance

    NotFittedError
        If the attributes are not found.
    ",213,104,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all):
    
    if isclass(estimator):
        raise TypeError(""{} is a class, not an instance."".format(estimator))
    if msg is None:
        msg = (
            ""This %(name)s instance is not fitted yet. Call 'fit' with ""
            ""appropriate arguments before using this estimator.""
        )

    if not hasattr(estimator, ""fit""):
        raise TypeError(""%s is not an estimator instance."" % (estimator))

    if attributes is not None:
        if not isinstance(attributes, (list, tuple)):
            attributes = [attributes]
        fitted = all_or_any([hasattr(estimator, attr) for attr in attributes])
    elif hasattr(estimator, ""__sklearn_is_fitted__""):
        fitted = estimator.__sklearn_is_fitted__()
    else:
        fitted = [
            v for v in vars(estimator) if v.endswith(""_"") and not v.startswith(""__"")
        ]

    if not fitted:
        raise NotFittedError(msg % {""name"": type(estimator).__name__})


        ```
    ###Assistant : Perform is_fitted validation for estimator.

    Checks if the estimator is fitted by verifying the presence of
    fitted attributes (ending with a trailing underscore) and otherwise
    raises a NotFittedError with the given message.

    If an estimator does not set any attributes with a trailing underscore, it
    can define a ``__sklearn_is_fitted__`` method returning a boolean to specify if the
    estimator is fitted or not.

    Parameters
    ----------
    estimator : estimator instance
        Estimator instance for which the check is performed.

    attributes : str, list or tuple of str, default=None
        Attribute name(s) given as string or a list/tuple of strings
        Eg.: ``[""coef_"", ""estimator_"", ...], ""coef_""``

        If `None`, `estimator` is considered fitted if there exist an
        attribute that ends with a underscore and does not start with double
        underscore.

    msg : str, default=None
        The default error message is, ""This %(name)s instance is not fitted
        yet. Call 'fit' with appropriate arguments before using this
        estimator.""

        For custom messages if ""%(name)s"" is present in the message string,
        it is substituted for the estimator name.

        Eg. : ""Estimator, %(name)s, must be fitted before sparsifying"".

    all_or_any : callable, {all, any}, default=all
        Specify whether all or any of the given attributes must exist.

    Raises
    ------
    TypeError
        If the estimator is a class or not an estimator instance

    NotFittedError
        If the attributes are not found.
    
    "
2222,"def p_mean_variance(self, model, x, t, transformer_out, clip_denoised=True, model_kwargs=None):
        
        if model_kwargs is None:
            model_kwargs = {}

        B, C = x.shape[:2]
        assert t.shape == (B,)
        model_output = model(x, t, transformer_out)

        assert model_output.shape == (B, C * 2, *x.shape[2:])
        model_output, model_var_values = torch.split(model_output, C, dim=1)
        min_log = _extract_into_tensor(self.noise_scheduler.posterior_log_variance_clipped, t, x.shape)
        max_log = _extract_into_tensor(np.log(self.noise_scheduler.betas), t, x.shape)
        # The model_var_values is [-1, 1] for [min_var, max_var].
        frac = (model_var_values + 1) / 2
        model_log_variance = frac * max_log + (1 - frac) * min_log
        model_variance = torch.exp(model_log_variance)

        pred_xstart = self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
        if clip_denoised:
            pred_xstart = pred_xstart.clamp(-1, 1)
        model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)

        assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
        return model_mean, model_variance, model_log_variance, pred_xstart
","
        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
        the initial x, x_0.

        :param model: the model, which takes a signal and a batch of timesteps
                      as input.
        :param x: the [N x C x ...] tensor at time t.
        :param t: a 1-D Tensor of timesteps.
        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict with the following keys:
                 - 'mean': the model mean output.
                 - 'variance': the model variance output.
                 - 'log_variance': the log of 'variance'.
                 - 'pred_xstart': the prediction for x_0.
        ",116,113,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def p_mean_variance(self, model, x, t, transformer_out, clip_denoised=True, model_kwargs=None):
        
        if model_kwargs is None:
            model_kwargs = {}

        B, C = x.shape[:2]
        assert t.shape == (B,)
        model_output = model(x, t, transformer_out)

        assert model_output.shape == (B, C * 2, *x.shape[2:])
        model_output, model_var_values = torch.split(model_output, C, dim=1)
        min_log = _extract_into_tensor(self.noise_scheduler.posterior_log_variance_clipped, t, x.shape)
        max_log = _extract_into_tensor(np.log(self.noise_scheduler.betas), t, x.shape)
        # The model_var_values is [-1, 1] for [min_var, max_var].
        frac = (model_var_values + 1) / 2
        model_log_variance = frac * max_log + (1 - frac) * min_log
        model_variance = torch.exp(model_log_variance)

        pred_xstart = self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
        if clip_denoised:
            pred_xstart = pred_xstart.clamp(-1, 1)
        model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)

        assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
        return model_mean, model_variance, model_log_variance, pred_xstart

        ```
    ###Assistant : 
        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
        the initial x, x_0.

        :param model: the model, which takes a signal and a batch of timesteps
                      as input.
        :param x: the [N x C x ...] tensor at time t.
        :param t: a 1-D Tensor of timesteps.
        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict with the following keys:
                 - 'mean': the model mean output.
                 - 'variance': the model variance output.
                 - 'log_variance': the log of 'variance'.
                 - 'pred_xstart': the prediction for x_0.
        
    "
2223,"def test_invalid_parameters_in_stacking():
    
    stacker = StackingClassifier(estimators=[])

    html_output = estimator_html_repr(stacker)
    assert html.escape(str(stacker)) in html_output
","Invalidate stacking configuration uses default repr.

    Non-regression test for #24009.
    ",10,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_invalid_parameters_in_stacking():
    
    stacker = StackingClassifier(estimators=[])

    html_output = estimator_html_repr(stacker)
    assert html.escape(str(stacker)) in html_output

        ```
    ###Assistant : Invalidate stacking configuration uses default repr.

    Non-regression test for #24009.
    
    "
2224,"def add_permissions():
	
	for doctype in (""South Africa VAT Settings"", ""South Africa VAT Account""):
		add_permission(doctype, ""All"", 0)
		for role in (""Accounts Manager"", ""Accounts User"", ""System Manager""):
			add_permission(doctype, role, 0)
			update_permission_property(doctype, role, 0, ""write"", 1)
			update_permission_property(doctype, role, 0, ""create"", 1)

	if not frappe.db.get_value(""Custom Role"", dict(report=""VAT Audit Report"")):
		frappe.get_doc(
			dict(
				doctype=""Custom Role"",
				report=""VAT Audit Report"",
				roles=[dict(role=""Accounts User""), dict(role=""Accounts Manager""), dict(role=""Auditor"")],
			)
		).insert()
","Add Permissions for South Africa VAT Settings and South Africa VAT Account
	and VAT Audit Report",16,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_permissions():
	
	for doctype in (""South Africa VAT Settings"", ""South Africa VAT Account""):
		add_permission(doctype, ""All"", 0)
		for role in (""Accounts Manager"", ""Accounts User"", ""System Manager""):
			add_permission(doctype, role, 0)
			update_permission_property(doctype, role, 0, ""write"", 1)
			update_permission_property(doctype, role, 0, ""create"", 1)

	if not frappe.db.get_value(""Custom Role"", dict(report=""VAT Audit Report"")):
		frappe.get_doc(
			dict(
				doctype=""Custom Role"",
				report=""VAT Audit Report"",
				roles=[dict(role=""Accounts User""), dict(role=""Accounts Manager""), dict(role=""Auditor"")],
			)
		).insert()

        ```
    ###Assistant : Add Permissions for South Africa VAT Settings and South Africa VAT Account
	and VAT Audit Report
    "
2225,"def has_delete_permission(self, request, obj=None):
        
        opts = self.opts
        codename = get_permission_codename(""delete"", opts)
        return request.user.has_perm(""%s.%s"" % (opts.app_label, codename))
","
        Return True if the given request has permission to change the given
        Django model instance, the default implementation doesn't examine the
        `obj` parameter.

        Can be overridden by the user in subclasses. In such case it should
        return True if the given request has permission to delete the `obj`
        model instance. If `obj` is None, this should return True if the given
        request has permission to delete *any* object of the given type.
        ",72,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def has_delete_permission(self, request, obj=None):
        
        opts = self.opts
        codename = get_permission_codename(""delete"", opts)
        return request.user.has_perm(""%s.%s"" % (opts.app_label, codename))

        ```
    ###Assistant : 
        Return True if the given request has permission to change the given
        Django model instance, the default implementation doesn't examine the
        `obj` parameter.

        Can be overridden by the user in subclasses. In such case it should
        return True if the given request has permission to delete the `obj`
        model instance. If `obj` is None, this should return True if the given
        request has permission to delete *any* object of the given type.
        
    "
2226,"def set_3d_properties(self, zs=0, zdir='z'):
        
        xs = self.get_xdata()
        ys = self.get_ydata()
        zs = cbook._to_unmasked_float_array(zs).ravel()
        zs = np.broadcast_to(zs, len(xs))
        self._verts3d = juggle_axes(xs, ys, zs, zdir)
        self.stale = True
","
        Set the *z* position and direction of the line.

        Parameters
        ----------
        zs : float or array of floats
            The location along the *zdir* axis in 3D space to position the
            line.
        zdir : {'x', 'y', 'z'}
            Plane to plot line orthogonal to. Default: 'z'.
            See `.get_dir_vector` for a description of the values.
        ",52,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_3d_properties(self, zs=0, zdir='z'):
        
        xs = self.get_xdata()
        ys = self.get_ydata()
        zs = cbook._to_unmasked_float_array(zs).ravel()
        zs = np.broadcast_to(zs, len(xs))
        self._verts3d = juggle_axes(xs, ys, zs, zdir)
        self.stale = True

        ```
    ###Assistant : 
        Set the *z* position and direction of the line.

        Parameters
        ----------
        zs : float or array of floats
            The location along the *zdir* axis in 3D space to position the
            line.
        zdir : {'x', 'y', 'z'}
            Plane to plot line orthogonal to. Default: 'z'.
            See `.get_dir_vector` for a description of the values.
        
    "
2227,"def clear_tasks(self):
        
        logger.debug(""Clearing stored tasks"")
        self._tasks = {}
"," Clears all of the stored tasks.

        This is required when loading a task stored in a legacy project file, and is only to be
        called by :class:`Project` when a project has been loaded which is in fact a task.
        ",39,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def clear_tasks(self):
        
        logger.debug(""Clearing stored tasks"")
        self._tasks = {}

        ```
    ###Assistant :  Clears all of the stored tasks.

        This is required when loading a task stored in a legacy project file, and is only to be
        called by :class:`Project` when a project has been loaded which is in fact a task.
        
    "
2228,"def add_department_leaves(events, start, end, employee, company):
	department = frappe.db.get_value(""Employee"", employee, ""department"")

	if not department:
		return

	# department leaves
	department_employees = frappe.db.sql_list(
		,
		(department, company),
	)

	filter_conditions = ' and employee in (""%s"")' % '"", ""'.join(department_employees)
	add_leaves(events, start, end, filter_conditions=filter_conditions)

","select name from tabEmployee where department=%s
		and company=%s",8,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_department_leaves(events, start, end, employee, company):
	department = frappe.db.get_value(""Employee"", employee, ""department"")

	if not department:
		return

	# department leaves
	department_employees = frappe.db.sql_list(
		,
		(department, company),
	)

	filter_conditions = ' and employee in (""%s"")' % '"", ""'.join(department_employees)
	add_leaves(events, start, end, filter_conditions=filter_conditions)


        ```
    ###Assistant : select name from tabEmployee where department=%s
		and company=%s
    "
2229,"def is_file(self, follow_links=None):
        
        if follow_links is None:
            follow_links = True

        node_stat = self._stat if follow_links else self._lstat
        return stat.S_ISREG(node_stat.st_mode)
","
        Get whether the entry is a regular file.

        *follow_links* (:class:`bool` or :data:`None`) is whether to follow
        symbolic links. If this is :data:`True`, a symlink to a regular file
        will result in :data:`True`. Default is :data:`None` for :data:`True`.

        Returns whether the entry is a regular file (:class:`bool`).
        ",46,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_file(self, follow_links=None):
        
        if follow_links is None:
            follow_links = True

        node_stat = self._stat if follow_links else self._lstat
        return stat.S_ISREG(node_stat.st_mode)

        ```
    ###Assistant : 
        Get whether the entry is a regular file.

        *follow_links* (:class:`bool` or :data:`None`) is whether to follow
        symbolic links. If this is :data:`True`, a symlink to a regular file
        will result in :data:`True`. Default is :data:`None` for :data:`True`.

        Returns whether the entry is a regular file (:class:`bool`).
        
    "
2230,"def test_tabular_model_form_meta_readonly_field(self):
        
        response = self.client.get(reverse(""admin:admin_inlines_someparentmodel_add""))
        self.assertContains(
            response,
            '<img src=""/static/admin/img/icon-unknown.svg"" '
            'class=""help help-tooltip"" width=""10"" height=""10"" '
            'alt=""(Help text from ModelForm.Meta)"" '
            'title=""Help text from ModelForm.Meta"">',
        )
        self.assertContains(response, ""Label from ModelForm.Meta"")
","
        Tabular inlines use ModelForm.Meta.help_texts and labels for read-only
        fields.
        ",9,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_tabular_model_form_meta_readonly_field(self):
        
        response = self.client.get(reverse(""admin:admin_inlines_someparentmodel_add""))
        self.assertContains(
            response,
            '<img src=""/static/admin/img/icon-unknown.svg"" '
            'class=""help help-tooltip"" width=""10"" height=""10"" '
            'alt=""(Help text from ModelForm.Meta)"" '
            'title=""Help text from ModelForm.Meta"">',
        )
        self.assertContains(response, ""Label from ModelForm.Meta"")

        ```
    ###Assistant : 
        Tabular inlines use ModelForm.Meta.help_texts and labels for read-only
        fields.
        
    "
2231,"def test_edit_get_unlocked_no_lock_permission(self):
        
        # Use edit permission only
        self.set_permissions([""change""])

        # Get the edit page
        response = self.client.get(self.get_url(""edit""))
        html = response.content.decode()
        lock_url = self.get_url(""lock"")

        # Should not show lock message
        self.assertNotContains(
            response,
            ""<b>'I&#x27;m a lockable snippet!' was locked</b>"",
        )

        # Should show unlocked information in the side panel
        self.assertContains(
            response,
            f""Anyone can edit this {self.model_name}."",
        )

        # Should not show info to lock the object in the side panel
        self.assertNotContains(
            response,
            ""Lock it to prevent others from editing."",
        )

        # Should show Save action menu item
        self.assertContains(
            response,
            f""<em>{self.save_button_label}</em>"",
            html=True,
        )

        # Should not show Locked action menu item
        self.assertTagInHTML(
            '<button type=""submit"" disabled>Locked</button>',
            html,
            count=0,
            allow_extra_attrs=True,
        )

        # Should not show the lock button
        self.assertTagInHTML(
            f'<button type=""button"" data-url=""{lock_url}"" data-action-lock-unlock>Lock</button>',
            html,
            count=0,
            allow_extra_attrs=True,
        )
",A user cannot lock an object without the lock permission.,10,121,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_edit_get_unlocked_no_lock_permission(self):
        
        # Use edit permission only
        self.set_permissions([""change""])

        # Get the edit page
        response = self.client.get(self.get_url(""edit""))
        html = response.content.decode()
        lock_url = self.get_url(""lock"")

        # Should not show lock message
        self.assertNotContains(
            response,
            ""<b>'I&#x27;m a lockable snippet!' was locked</b>"",
        )

        # Should show unlocked information in the side panel
        self.assertContains(
            response,
            f""Anyone can edit this {self.model_name}."",
        )

        # Should not show info to lock the object in the side panel
        self.assertNotContains(
            response,
            ""Lock it to prevent others from editing."",
        )

        # Should show Save action menu item
        self.assertContains(
            response,
            f""<em>{self.save_button_label}</em>"",
            html=True,
        )

        # Should not show Locked action menu item
        self.assertTagInHTML(
            '<button type=""submit"" disabled>Locked</button>',
            html,
            count=0,
            allow_extra_attrs=True,
        )

        # Should not show the lock button
        self.assertTagInHTML(
            f'<button type=""button"" data-url=""{lock_url}"" data-action-lock-unlock>Lock</button>',
            html,
            count=0,
            allow_extra_attrs=True,
        )

        ```
    ###Assistant : A user cannot lock an object without the lock permission.
    "
2232,"def increment_project_counter(project, delta=1, using=""default""):
    
    if delta <= 0:
        raise ValueError(""There is only one way, and that's up."")

    sample_rate = options.get(""store.projectcounter-modern-upsert-sample-rate"")

    modern_upsert = sample_rate and random.random() <= sample_rate

    # To prevent the statement_timeout leaking into the session we need to use
    # set local which can be used only within a transaction
    with transaction.atomic(using=using):
        cur = connections[using].cursor()
        try:
            statement_timeout = None
            if settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT:
                # WARNING: This is not a proper fix and should be removed once
                #          we have better way of generating next_short_id.
                cur.execute(""show statement_timeout"")
                statement_timeout = cur.fetchone()[0]
                cur.execute(
                    ""set local statement_timeout = %s"",
                    [settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT],
                )

            if modern_upsert:
                # Our postgres wrapper thing does not allow for named arguments
                cur.execute(
                    ""insert into sentry_projectcounter (project_id, value) ""
                    ""values (%s, %s) ""
                    ""on conflict (project_id) do update ""
                    ""set value = sentry_projectcounter.value + %s ""
                    ""returning value"",
                    [project.id, delta, delta],
                )
            else:
                cur.execute(
                    ""select sentry_increment_project_counter(%s, %s)"",
                    [project.id, delta],
                )

            project_counter = cur.fetchone()[0]

            if statement_timeout is not None:
                cur.execute(
                    ""set local statement_timeout = %s"",
                    [statement_timeout],
                )

            return project_counter

        finally:
            cur.close()


# this must be idempotent because it seems to execute twice
# (at least during test runs)",This method primarily exists so that south code can use it.,11,184,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def increment_project_counter(project, delta=1, using=""default""):
    
    if delta <= 0:
        raise ValueError(""There is only one way, and that's up."")

    sample_rate = options.get(""store.projectcounter-modern-upsert-sample-rate"")

    modern_upsert = sample_rate and random.random() <= sample_rate

    # To prevent the statement_timeout leaking into the session we need to use
    # set local which can be used only within a transaction
    with transaction.atomic(using=using):
        cur = connections[using].cursor()
        try:
            statement_timeout = None
            if settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT:
                # WARNING: This is not a proper fix and should be removed once
                #          we have better way of generating next_short_id.
                cur.execute(""show statement_timeout"")
                statement_timeout = cur.fetchone()[0]
                cur.execute(
                    ""set local statement_timeout = %s"",
                    [settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT],
                )

            if modern_upsert:
                # Our postgres wrapper thing does not allow for named arguments
                cur.execute(
                    ""insert into sentry_projectcounter (project_id, value) ""
                    ""values (%s, %s) ""
                    ""on conflict (project_id) do update ""
                    ""set value = sentry_projectcounter.value + %s ""
                    ""returning value"",
                    [project.id, delta, delta],
                )
            else:
                cur.execute(
                    ""select sentry_increment_project_counter(%s, %s)"",
                    [project.id, delta],
                )

            project_counter = cur.fetchone()[0]

            if statement_timeout is not None:
                cur.execute(
                    ""set local statement_timeout = %s"",
                    [statement_timeout],
                )

            return project_counter

        finally:
            cur.close()


# this must be idempotent because it seems to execute twice
# (at least during test runs)
        ```
    ###Assistant : This method primarily exists so that south code can use it.
    "
2233,"def test_multiple_gen_nexts_closed_in_different_order(self) -> None:
        
        id_gen = self._create_id_generator()
","Check that we handle overlapping calls to gen_next, even when their IDs
        created and persisted in different orders.",18,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_multiple_gen_nexts_closed_in_different_order(self) -> None:
        
        id_gen = self._create_id_generator()

        ```
    ###Assistant : Check that we handle overlapping calls to gen_next, even when their IDs
        created and persisted in different orders.
    "
2234,"def str_presenter(dumper, data):
    
    if len(data.splitlines()) > 1:  # check for multiline string
        return dumper.represent_scalar(""tag:yaml.org,2002:str"", data, style=""|"")
    return dumper.represent_scalar(""tag:yaml.org,2002:str"", data)


yaml.add_representer(str, str_presenter)
yaml.representer.SafeRepresenter.add_representer(str, str_presenter)

deployment_app = PrefectTyper(
    name=""deployment"", help=""Commands for working with deployments.""
)
app.add_typer(deployment_app)

","
    configures yaml for dumping multiline strings
    Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data
    ",8,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def str_presenter(dumper, data):
    
    if len(data.splitlines()) > 1:  # check for multiline string
        return dumper.represent_scalar(""tag:yaml.org,2002:str"", data, style=""|"")
    return dumper.represent_scalar(""tag:yaml.org,2002:str"", data)


yaml.add_representer(str, str_presenter)
yaml.representer.SafeRepresenter.add_representer(str, str_presenter)

deployment_app = PrefectTyper(
    name=""deployment"", help=""Commands for working with deployments.""
)
app.add_typer(deployment_app)


        ```
    ###Assistant : 
    configures yaml for dumping multiline strings
    Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data
    
    "
2235,"def _get_permissions(self, user_obj, obj, from_name):
        
        if not user_obj.is_active or user_obj.is_anonymous or obj is not None:
            return set()

        perm_cache_name = ""_effective_permissions_cache""
        if not getattr(user_obj, perm_cache_name, None):
            perms = getattr(self, f""_get_{from_name}_permissions"")(user_obj)
            perms = perms.values_list(""content_type__app_label"", ""codename"").order_by()
            setattr(user_obj, perm_cache_name, {f""{ct}.{name}"" for ct, name in perms})
        return getattr(user_obj, perm_cache_name)

","Return the permissions of `user_obj` from `from_name`.

        `from_name` can be either ""group"" or ""user"" to return permissions from
        `_get_group_permissions` or `_get_user_permissions` respectively.
        ",22,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_permissions(self, user_obj, obj, from_name):
        
        if not user_obj.is_active or user_obj.is_anonymous or obj is not None:
            return set()

        perm_cache_name = ""_effective_permissions_cache""
        if not getattr(user_obj, perm_cache_name, None):
            perms = getattr(self, f""_get_{from_name}_permissions"")(user_obj)
            perms = perms.values_list(""content_type__app_label"", ""codename"").order_by()
            setattr(user_obj, perm_cache_name, {f""{ct}.{name}"" for ct, name in perms})
        return getattr(user_obj, perm_cache_name)


        ```
    ###Assistant : Return the permissions of `user_obj` from `from_name`.

        `from_name` can be either ""group"" or ""user"" to return permissions from
        `_get_group_permissions` or `_get_user_permissions` respectively.
        
    "
2236,"def test_trainable_layers(self):
        
        model = model = self._get_model()
        # Set the last layer to *not* be trainable.
        model.layers[-1].trainable = False
        self._train_model(model, use_dataset=True)
        loaded = self._save_and_load(model)

        self._test_evaluation(model, loaded)
        self.assertFalse(model.layers[-1].trainable)
        self.assertFalse(loaded.layers[-1].trainable)
",Tests that trainable status of individual layers is preserved.,9,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_trainable_layers(self):
        
        model = model = self._get_model()
        # Set the last layer to *not* be trainable.
        model.layers[-1].trainable = False
        self._train_model(model, use_dataset=True)
        loaded = self._save_and_load(model)

        self._test_evaluation(model, loaded)
        self.assertFalse(model.layers[-1].trainable)
        self.assertFalse(loaded.layers[-1].trainable)

        ```
    ###Assistant : Tests that trainable status of individual layers is preserved.
    "
2237,"def _reorder_labels(self, row_positions=None, col_positions=None):
        
        if row_positions is not None:
            ordered_rows = self._partition_mgr_cls.map_axis_partitions(
                0, self._partitions, lambda df: df.iloc[row_positions]
            )
            row_idx = self.index[row_positions]
        else:
            ordered_rows = self._partitions
            row_idx = self.index
        if col_positions is not None:
            ordered_cols = self._partition_mgr_cls.map_axis_partitions(
                1, ordered_rows, lambda df: df.iloc[:, col_positions]
            )
            col_idx = self.columns[col_positions]
        else:
            ordered_cols = ordered_rows
            col_idx = self.columns
        return self.__constructor__(ordered_cols, row_idx, col_idx)
","
        Reorder the column and or rows in this DataFrame.

        Parameters
        ----------
        row_positions : list of int, optional
            The ordered list of new row orders such that each position within the list
            indicates the new position.
        col_positions : list of int, optional
            The ordered list of new column orders such that each position within the
            list indicates the new position.

        Returns
        -------
        PandasDataframe
            A new PandasDataframe with reordered columns and/or rows.
        ",70,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _reorder_labels(self, row_positions=None, col_positions=None):
        
        if row_positions is not None:
            ordered_rows = self._partition_mgr_cls.map_axis_partitions(
                0, self._partitions, lambda df: df.iloc[row_positions]
            )
            row_idx = self.index[row_positions]
        else:
            ordered_rows = self._partitions
            row_idx = self.index
        if col_positions is not None:
            ordered_cols = self._partition_mgr_cls.map_axis_partitions(
                1, ordered_rows, lambda df: df.iloc[:, col_positions]
            )
            col_idx = self.columns[col_positions]
        else:
            ordered_cols = ordered_rows
            col_idx = self.columns
        return self.__constructor__(ordered_cols, row_idx, col_idx)

        ```
    ###Assistant : 
        Reorder the column and or rows in this DataFrame.

        Parameters
        ----------
        row_positions : list of int, optional
            The ordered list of new row orders such that each position within the list
            indicates the new position.
        col_positions : list of int, optional
            The ordered list of new column orders such that each position within the
            list indicates the new position.

        Returns
        -------
        PandasDataframe
            A new PandasDataframe with reordered columns and/or rows.
        
    "
2238,"def get_actual_sle_dict(name):
	sles = frappe.db.sql(
		,
		name,
		as_dict=1,
	)

	sle_dict = {}
	for d in sles:
		sle_dict[(d.item_code, d.warehouse)] = {
			""actual_qty"": d.actual_qty,
			""stock_value_difference"": d.stock_value_difference,
		}

	return sle_dict
","
		select
			item_code, warehouse,
			sum(actual_qty) as actual_qty,
			sum(stock_value_difference) as stock_value_difference
		from `tabStock Ledger Entry`
		where voucher_type = 'Asset Capitalization' and voucher_no = %s
		group by item_code, warehouse
		having actual_qty != 0
	",30,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_actual_sle_dict(name):
	sles = frappe.db.sql(
		,
		name,
		as_dict=1,
	)

	sle_dict = {}
	for d in sles:
		sle_dict[(d.item_code, d.warehouse)] = {
			""actual_qty"": d.actual_qty,
			""stock_value_difference"": d.stock_value_difference,
		}

	return sle_dict

        ```
    ###Assistant : 
		select
			item_code, warehouse,
			sum(actual_qty) as actual_qty,
			sum(stock_value_difference) as stock_value_difference
		from `tabStock Ledger Entry`
		where voucher_type = 'Asset Capitalization' and voucher_no = %s
		group by item_code, warehouse
		having actual_qty != 0
	
    "
2239,"async def _async_process_on_unload(self) -> None:
        
        if self._on_unload is not None:
            while self._on_unload:
                self._on_unload.pop()()

        while self._pending_tasks:
            pending = [task for task in self._pending_tasks if not task.done()]
            self._pending_tasks.clear()
            if pending:
                await asyncio.gather(*pending)
",Process the on_unload callbacks and wait for pending tasks.,9,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _async_process_on_unload(self) -> None:
        
        if self._on_unload is not None:
            while self._on_unload:
                self._on_unload.pop()()

        while self._pending_tasks:
            pending = [task for task in self._pending_tasks if not task.done()]
            self._pending_tasks.clear()
            if pending:
                await asyncio.gather(*pending)

        ```
    ###Assistant : Process the on_unload callbacks and wait for pending tasks.
    "
2240,"def apply_func(partition, func, *args, **kwargs):
    
    result = func(partition, *args, **kwargs)
    return result, get_ip()

","
    Execute a function on the partition in a worker process.

    Parameters
    ----------
    partition : pandas.DataFrame
        A pandas DataFrame the function needs to be executed on.
    func : callable
        The function to perform.
    *args : list
        Positional arguments to pass to ``func``.
    **kwargs : dict
        Keyword arguments to pass to ``func``.

    Returns
    -------
    pandas.DataFrame
        The resulting pandas DataFrame.
    str
        The node IP address of the worker process.

    Notes
    -----
    Directly passing a call queue entry (i.e. a list of [func, args, kwargs]) instead of
    destructuring it causes a performance penalty.
    ",89,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply_func(partition, func, *args, **kwargs):
    
    result = func(partition, *args, **kwargs)
    return result, get_ip()


        ```
    ###Assistant : 
    Execute a function on the partition in a worker process.

    Parameters
    ----------
    partition : pandas.DataFrame
        A pandas DataFrame the function needs to be executed on.
    func : callable
        The function to perform.
    *args : list
        Positional arguments to pass to ``func``.
    **kwargs : dict
        Keyword arguments to pass to ``func``.

    Returns
    -------
    pandas.DataFrame
        The resulting pandas DataFrame.
    str
        The node IP address of the worker process.

    Notes
    -----
    Directly passing a call queue entry (i.e. a list of [func, args, kwargs]) instead of
    destructuring it causes a performance penalty.
    
    "
2241,"def _proc_function_remote(self, *, fun, low, user, tag, jid, daemonize=True):
        
        if daemonize and not salt.utils.platform.is_windows():
            # Shutdown the multiprocessing before daemonizing
            salt.log.setup.shutdown_multiprocessing_logging()

            salt.utils.process.daemonize()

            # Reconfigure multiprocessing logging after daemonizing
            salt.log.setup.setup_multiprocessing_logging()

        # pack a few things into low
        low[""__jid__""] = jid
        low[""__user__""] = user
        low[""__tag__""] = tag

        try:
            return self.cmd_sync(low)
        except salt.exceptions.EauthAuthenticationError as exc:
            log.error(exc)
","
        Run this method in a multiprocess target to execute the function on the
        master and fire the return data on the event bus
        ",23,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _proc_function_remote(self, *, fun, low, user, tag, jid, daemonize=True):
        
        if daemonize and not salt.utils.platform.is_windows():
            # Shutdown the multiprocessing before daemonizing
            salt.log.setup.shutdown_multiprocessing_logging()

            salt.utils.process.daemonize()

            # Reconfigure multiprocessing logging after daemonizing
            salt.log.setup.setup_multiprocessing_logging()

        # pack a few things into low
        low[""__jid__""] = jid
        low[""__user__""] = user
        low[""__tag__""] = tag

        try:
            return self.cmd_sync(low)
        except salt.exceptions.EauthAuthenticationError as exc:
            log.error(exc)

        ```
    ###Assistant : 
        Run this method in a multiprocess target to execute the function on the
        master and fire the return data on the event bus
        
    "
2242,"def array_safe(a, like, **kwargs):
    
    from dask.array.routines import array

    return _array_like_safe(np.array, array, a, like, **kwargs)

","
    If `a` is `dask.array`, return `dask.array.asarray(a, **kwargs)`,
    otherwise return `np.asarray(a, like=like, **kwargs)`, dispatching
    the call to the library that implements the like array. Note that
    when `a` is a `dask.Array` backed by `cupy.ndarray` but `like`
    isn't, this function will call `a.compute(scheduler=""sync"")`
    before `np.array`, as downstream libraries are unlikely to know how
    to convert a `dask.Array` and CuPy doesn't implement `__array__` to
    prevent implicit copies to host.
    ",66,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def array_safe(a, like, **kwargs):
    
    from dask.array.routines import array

    return _array_like_safe(np.array, array, a, like, **kwargs)


        ```
    ###Assistant : 
    If `a` is `dask.array`, return `dask.array.asarray(a, **kwargs)`,
    otherwise return `np.asarray(a, like=like, **kwargs)`, dispatching
    the call to the library that implements the like array. Note that
    when `a` is a `dask.Array` backed by `cupy.ndarray` but `like`
    isn't, this function will call `a.compute(scheduler=""sync"")`
    before `np.array`, as downstream libraries are unlikely to know how
    to convert a `dask.Array` and CuPy doesn't implement `__array__` to
    prevent implicit copies to host.
    
    "
2243,"def get_tables(self) -> StatusResponse:
        

        query = 
        result = self.native_query(query)
        df = result.data_frame
        df = df.drop(['type', 'type'], axis=1)
        result.data_frame = df.rename(columns={'name': 'table_name'})

        return result
","
        Return list of entities that will be accessible as tables.
        Returns:
            HandlerResponse
        
            SHOW TABLES;
        ",14,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_tables(self) -> StatusResponse:
        

        query = 
        result = self.native_query(query)
        df = result.data_frame
        df = df.drop(['type', 'type'], axis=1)
        result.data_frame = df.rename(columns={'name': 'table_name'})

        return result

        ```
    ###Assistant : 
        Return list of entities that will be accessible as tables.
        Returns:
            HandlerResponse
        
            SHOW TABLES;
        
    "
2244,"def accuracy(self, params, X, Y, averaged=True):
        
        Y_hat = self.apply(params, X)
        Y_hat = d2l.reshape(Y_hat, (-1, Y_hat.shape[-1]))
        preds = d2l.astype(d2l.argmax(Y_hat, axis=1), Y.dtype)
        compare = d2l.astype(preds == d2l.reshape(Y, -1), d2l.float32)
        return d2l.reduce_mean(compare) if averaged else compare
","Compute the number of correct predictions.
    
        Defined in :numref:`sec_classification`",9,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def accuracy(self, params, X, Y, averaged=True):
        
        Y_hat = self.apply(params, X)
        Y_hat = d2l.reshape(Y_hat, (-1, Y_hat.shape[-1]))
        preds = d2l.astype(d2l.argmax(Y_hat, axis=1), Y.dtype)
        compare = d2l.astype(preds == d2l.reshape(Y, -1), d2l.float32)
        return d2l.reduce_mean(compare) if averaged else compare

        ```
    ###Assistant : Compute the number of correct predictions.
    
        Defined in :numref:`sec_classification`
    "
2245,"def open_metadata(self, book, custom_columns):
        if config.config_use_google_drive:
            if not gdriveutils.is_gdrive_ready():
                raise Exception('Google Drive is configured but not ready')

            web_content_link = gdriveutils.get_metadata_backup_via_gdrive(book.path)
            if not web_content_link:
                raise Exception('Google Drive cover url not found')

            stream = None
            try:
                stream = urlopen(web_content_link)
            except Exception as ex:
                # Bubble exception to calling function
                self.log.debug('Error reading metadata.opf: ' + str(ex))       # ToDo Check whats going on
                raise ex
            finally:
                if stream is not None:
                    stream.close()
        else:
            # ToDo: Handle book folder not found or not readable
            book_metadata_filepath = os.path.join(config.config_calibre_dir, book.path, 'metadata.opf')
            #if not os.path.isfile(book_metadata_filepath):
            self.create_new_metadata_backup(book,  custom_columns, book_metadata_filepath)
            # else:
            
","namespaces = {'dc': PURL_NAMESPACE, 'opf': OPF_NAMESPACE}
            test = etree.parse(book_metadata_filepath)
            root = test.getroot()
            for i in root.iter():
                self.log.info(i)
            title = root.find(""dc:metadata"", namespaces)
            pass
            with open(book_metadata_filepath, ""rb"") as f:
                xml = f.read()

            root = objectify.fromstring(xml)
            # root.metadata['{http://purl.org/dc/elements/1.1/}title']
            # root.metadata[PURL + 'title']
            # getattr(root.metadata, PURL +'title')
            # test = objectify.parse()
            pass
            # backup not found has to be created
            #raise Exception('Book cover file not found')",62,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def open_metadata(self, book, custom_columns):
        if config.config_use_google_drive:
            if not gdriveutils.is_gdrive_ready():
                raise Exception('Google Drive is configured but not ready')

            web_content_link = gdriveutils.get_metadata_backup_via_gdrive(book.path)
            if not web_content_link:
                raise Exception('Google Drive cover url not found')

            stream = None
            try:
                stream = urlopen(web_content_link)
            except Exception as ex:
                # Bubble exception to calling function
                self.log.debug('Error reading metadata.opf: ' + str(ex))       # ToDo Check whats going on
                raise ex
            finally:
                if stream is not None:
                    stream.close()
        else:
            # ToDo: Handle book folder not found or not readable
            book_metadata_filepath = os.path.join(config.config_calibre_dir, book.path, 'metadata.opf')
            #if not os.path.isfile(book_metadata_filepath):
            self.create_new_metadata_backup(book,  custom_columns, book_metadata_filepath)
            # else:
            

        ```
    ###Assistant : namespaces = {'dc': PURL_NAMESPACE, 'opf': OPF_NAMESPACE}
            test = etree.parse(book_metadata_filepath)
            root = test.getroot()
            for i in root.iter():
                self.log.info(i)
            title = root.find(""dc:metadata"", namespaces)
            pass
            with open(book_metadata_filepath, ""rb"") as f:
                xml = f.read()

            root = objectify.fromstring(xml)
            # root.metadata['{http://purl.org/dc/elements/1.1/}title']
            # root.metadata[PURL + 'title']
            # getattr(root.metadata, PURL +'title')
            # test = objectify.parse()
            pass
            # backup not found has to be created
            #raise Exception('Book cover file not found')
    "
2246,"def test_gevent_monkey(pyi_builder):
    pyi_builder.test_source()


# The tkinter module may be available for import, but not actually importable due to missing shared libraries.
# Therefore, we need to use `can_import_module`-based skip decorator instead of `@importorskip`.
@pytest.mark.skipif(not can_import_module(""tkinter""), reason=""tkinter cannot be imported."")","
        from gevent.monkey import patch_all
        patch_all()
        ",5,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_gevent_monkey(pyi_builder):
    pyi_builder.test_source()


# The tkinter module may be available for import, but not actually importable due to missing shared libraries.
# Therefore, we need to use `can_import_module`-based skip decorator instead of `@importorskip`.
@pytest.mark.skipif(not can_import_module(""tkinter""), reason=""tkinter cannot be imported."")
        ```
    ###Assistant : 
        from gevent.monkey import patch_all
        patch_all()
        
    "
2247,"def split_auth_netloc_from_url(url):
    # type: (str) -> Tuple[str, str, Tuple[str, str]]
    
    url_without_auth, (netloc, auth) = _transform_url(url, _get_netloc)
    return url_without_auth, netloc, auth

","
    Parse a url into separate netloc, auth, and url with no auth.

    Returns: (url_without_auth, netloc, (username, password))
    ",17,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def split_auth_netloc_from_url(url):
    # type: (str) -> Tuple[str, str, Tuple[str, str]]
    
    url_without_auth, (netloc, auth) = _transform_url(url, _get_netloc)
    return url_without_auth, netloc, auth


        ```
    ###Assistant : 
    Parse a url into separate netloc, auth, and url with no auth.

    Returns: (url_without_auth, netloc, (username, password))
    
    "
2248,"def test_non_str_color():
    

    text = Text(""test_color_inheritance"", color=Color(""blue""))
    markup_text = MarkupText(""test_color_inheritance"", color=Color(""blue""))
","Test that the Text and MarkupText can accept non_str color values
    i.e. colour.Color(red).",13,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_non_str_color():
    

    text = Text(""test_color_inheritance"", color=Color(""blue""))
    markup_text = MarkupText(""test_color_inheritance"", color=Color(""blue""))

        ```
    ###Assistant : Test that the Text and MarkupText can accept non_str color values
    i.e. colour.Color(red).
    "
2249,"async def test_strategy_no_network_settings(pick_radio, mock_app, hass):
    
    mock_app.load_network_info = MagicMock(side_effect=NetworkNotFormed())

    result, port = await pick_radio(RadioType.ezsp)
    assert (
        config_flow.FORMATION_REUSE_SETTINGS
        not in result[""data_schema""].schema[""next_step_id""].container
    )

",Test formation strategy when no network settings are present.,9,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_strategy_no_network_settings(pick_radio, mock_app, hass):
    
    mock_app.load_network_info = MagicMock(side_effect=NetworkNotFormed())

    result, port = await pick_radio(RadioType.ezsp)
    assert (
        config_flow.FORMATION_REUSE_SETTINGS
        not in result[""data_schema""].schema[""next_step_id""].container
    )


        ```
    ###Assistant : Test formation strategy when no network settings are present.
    "
2250,"def detrend(x, key=None, axis=None):
    
    if key is None or key in ['constant', 'mean', 'default']:
        return detrend(x, key=detrend_mean, axis=axis)
    elif key == 'linear':
        return detrend(x, key=detrend_linear, axis=axis)
    elif key == 'none':
        return detrend(x, key=detrend_none, axis=axis)
    elif callable(key):
        x = np.asarray(x)
        if axis is not None and axis + 1 > x.ndim:
            raise ValueError(f'axis(={axis}) out of bounds')
        if (axis is None and x.ndim == 0) or (not axis and x.ndim == 1):
            return key(x)
        # try to use the 'axis' argument if the function supports it,
        # otherwise use apply_along_axis to do it
        try:
            return key(x, axis=axis)
        except TypeError:
            return np.apply_along_axis(key, axis=axis, arr=x)
    else:
        raise ValueError(
            f""Unknown value for key: {key!r}, must be one of: 'default', ""
            f""'constant', 'mean', 'linear', or a function"")

","
    Return *x* with its trend removed.

    Parameters
    ----------
    x : array or sequence
        Array or sequence containing the data.

    key : {'default', 'constant', 'mean', 'linear', 'none'} or function
        The detrending algorithm to use. 'default', 'mean', and 'constant' are
        the same as `detrend_mean`. 'linear' is the same as `detrend_linear`.
        'none' is the same as `detrend_none`. The default is 'mean'. See the
        corresponding functions for more details regarding the algorithms. Can
        also be a function that carries out the detrend operation.

    axis : int
        The axis along which to do the detrending.

    See Also
    --------
    detrend_mean : Implementation of the 'mean' algorithm.
    detrend_linear : Implementation of the 'linear' algorithm.
    detrend_none : Implementation of the 'none' algorithm.
    ",114,121,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def detrend(x, key=None, axis=None):
    
    if key is None or key in ['constant', 'mean', 'default']:
        return detrend(x, key=detrend_mean, axis=axis)
    elif key == 'linear':
        return detrend(x, key=detrend_linear, axis=axis)
    elif key == 'none':
        return detrend(x, key=detrend_none, axis=axis)
    elif callable(key):
        x = np.asarray(x)
        if axis is not None and axis + 1 > x.ndim:
            raise ValueError(f'axis(={axis}) out of bounds')
        if (axis is None and x.ndim == 0) or (not axis and x.ndim == 1):
            return key(x)
        # try to use the 'axis' argument if the function supports it,
        # otherwise use apply_along_axis to do it
        try:
            return key(x, axis=axis)
        except TypeError:
            return np.apply_along_axis(key, axis=axis, arr=x)
    else:
        raise ValueError(
            f""Unknown value for key: {key!r}, must be one of: 'default', ""
            f""'constant', 'mean', 'linear', or a function"")


        ```
    ###Assistant : 
    Return *x* with its trend removed.

    Parameters
    ----------
    x : array or sequence
        Array or sequence containing the data.

    key : {'default', 'constant', 'mean', 'linear', 'none'} or function
        The detrending algorithm to use. 'default', 'mean', and 'constant' are
        the same as `detrend_mean`. 'linear' is the same as `detrend_linear`.
        'none' is the same as `detrend_none`. The default is 'mean'. See the
        corresponding functions for more details regarding the algorithms. Can
        also be a function that carries out the detrend operation.

    axis : int
        The axis along which to do the detrending.

    See Also
    --------
    detrend_mean : Implementation of the 'mean' algorithm.
    detrend_linear : Implementation of the 'linear' algorithm.
    detrend_none : Implementation of the 'none' algorithm.
    
    "
2251,"def has_unrendered_errors(bound_field):
    
    return bound_field.errors and not hasattr(
        bound_field.field.widget, ""render_with_errors""
    )


@register.filter(is_safe=True)
@stringfilter","
    Return true if this field has errors that were not accounted for by render_with_errors, because
    the widget does not support the render_with_errors method
    ",23,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def has_unrendered_errors(bound_field):
    
    return bound_field.errors and not hasattr(
        bound_field.field.widget, ""render_with_errors""
    )


@register.filter(is_safe=True)
@stringfilter
        ```
    ###Assistant : 
    Return true if this field has errors that were not accounted for by render_with_errors, because
    the widget does not support the render_with_errors method
    
    "
2252,"def test_raw_id_threshold_page_permission_inline_admin(self):
        
        with self.settings(CMS_RAW_ID_USERS=1):
            with self.assertNumQueries(1):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, [])

        # Create users to check if threshold is honored
        self._get_guys()

        with self.settings(CMS_RAW_ID_USERS=False):
            with self.assertNumQueries(0):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, [])

        with self.settings(CMS_RAW_ID_USERS=True):
            with self.assertNumQueries(0):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, ['user'])

        with self.settings(CMS_RAW_ID_USERS=1):
            with self.assertNumQueries(1):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, ['user'])
","
        Only count users when using an integer value as threshold for
        CMS_RAW_ID_USERS.
        ",12,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_raw_id_threshold_page_permission_inline_admin(self):
        
        with self.settings(CMS_RAW_ID_USERS=1):
            with self.assertNumQueries(1):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, [])

        # Create users to check if threshold is honored
        self._get_guys()

        with self.settings(CMS_RAW_ID_USERS=False):
            with self.assertNumQueries(0):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, [])

        with self.settings(CMS_RAW_ID_USERS=True):
            with self.assertNumQueries(0):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, ['user'])

        with self.settings(CMS_RAW_ID_USERS=1):
            with self.assertNumQueries(1):
                self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, ['user'])

        ```
    ###Assistant : 
        Only count users when using an integer value as threshold for
        CMS_RAW_ID_USERS.
        
    "
2253,"def get_breaks(self, filename, lineno):
        
        filename = self.canonic(filename)
        return filename in self.breaks and \
            lineno in self.breaks[filename] and \
            Breakpoint.bplist[filename, lineno] or []
","Return all breakpoints for filename:lineno.

        If no breakpoints are set, return an empty list.
        ",14,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_breaks(self, filename, lineno):
        
        filename = self.canonic(filename)
        return filename in self.breaks and \
            lineno in self.breaks[filename] and \
            Breakpoint.bplist[filename, lineno] or []

        ```
    ###Assistant : Return all breakpoints for filename:lineno.

        If no breakpoints are set, return an empty list.
        
    "
2254,"def test_override(self) -> None:
        
        self.get_success(
            self.store.register_user(
                self.user_id,
                self.pwhash,
                approved=True,
            )
        )

        user = self.get_success(self.store.get_user_by_id(self.user_id))
        self.assertIsNotNone(user)
        assert user is not None
        self.assertEqual(user[""approved""], 1)

        approved = self.get_success(self.store.is_user_approved(self.user_id))
        self.assertTrue(approved)
","Tests that if we require approval for new accounts, but we explicitly say the
        new user should be considered approved, they're marked as approved.
        ",24,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_override(self) -> None:
        
        self.get_success(
            self.store.register_user(
                self.user_id,
                self.pwhash,
                approved=True,
            )
        )

        user = self.get_success(self.store.get_user_by_id(self.user_id))
        self.assertIsNotNone(user)
        assert user is not None
        self.assertEqual(user[""approved""], 1)

        approved = self.get_success(self.store.is_user_approved(self.user_id))
        self.assertTrue(approved)

        ```
    ###Assistant : Tests that if we require approval for new accounts, but we explicitly say the
        new user should be considered approved, they're marked as approved.
        
    "
2255,"def tokenize(lines, token='word'):
    
    if token == 'word':
        return [line.split() for line in lines]
    elif token == 'char':
        return [list(line) for line in lines]
    else:
        print('ERROR: unknown token type: ' + token)
","Split text lines into word or character tokens.

    Defined in :numref:`sec_text_preprocessing`",11,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tokenize(lines, token='word'):
    
    if token == 'word':
        return [line.split() for line in lines]
    elif token == 'char':
        return [list(line) for line in lines]
    else:
        print('ERROR: unknown token type: ' + token)

        ```
    ###Assistant : Split text lines into word or character tokens.

    Defined in :numref:`sec_text_preprocessing`
    "
2256,"def make_purchase_order_for_default_supplier(source_name, selected_items=None, target_doc=None):
	
	if not selected_items:
		return

	if isinstance(selected_items, str):
		selected_items = json.loads(selected_items)

	def set_missing_values(source, target):
		target.supplier = supplier
		target.apply_discount_on = """"
		target.additional_discount_percentage = 0.0
		target.discount_amount = 0.0
		target.inter_company_order_reference = """"

		default_price_list = frappe.get_value(""Supplier"", supplier, ""default_price_list"")
		if default_price_list:
			target.buying_price_list = default_price_list

		if any(item.delivered_by_supplier == 1 for item in source.items):
			if source.shipping_address_name:
				target.shipping_address = source.shipping_address_name
				target.shipping_address_display = source.shipping_address
			else:
				target.shipping_address = source.customer_address
				target.shipping_address_display = source.address_display

			target.customer_contact_person = source.contact_person
			target.customer_contact_display = source.contact_display
			target.customer_contact_mobile = source.contact_mobile
			target.customer_contact_email = source.contact_email

		else:
			target.customer = """"
			target.customer_name = """"

		target.run_method(""set_missing_values"")
		target.run_method(""calculate_taxes_and_totals"")

	def update_item(source, target, source_parent):
		target.schedule_date = source.delivery_date
		target.qty = flt(source.qty) - (flt(source.ordered_qty) / flt(source.conversion_factor))
		target.stock_qty = flt(source.stock_qty) - flt(source.ordered_qty)
		target.project = source_parent.project

	suppliers = [item.get(""supplier"") for item in selected_items if item.get(""supplier"")]
	suppliers = list(dict.fromkeys(suppliers))  # remove duplicates while preserving order

	items_to_map = [item.get(""item_code"") for item in selected_items if item.get(""item_code"")]
	items_to_map = list(set(items_to_map))

	if not suppliers:
		frappe.throw(
			_(""Please set a Supplier against the Items to be considered in the Purchase Order."")
		)

	purchase_orders = []
	for supplier in suppliers:
		doc = get_mapped_doc(
			""Sales Order"",
			source_name,
			{
				""Sales Order"": {
					""doctype"": ""Purchase Order"",
					""field_no_map"": [
						""address_display"",
						""contact_display"",
						""contact_mobile"",
						""contact_email"",
						""contact_person"",
						""taxes_and_charges"",
						""shipping_address"",
						""terms"",
					],
					""validation"": {""docstatus"": [""="", 1]},
				},
				""Sales Order Item"": {
					""doctype"": ""Purchase Order Item"",
					""field_map"": [
						[""name"", ""sales_order_item""],
						[""parent"", ""sales_order""],
						[""stock_uom"", ""stock_uom""],
						[""uom"", ""uom""],
						[""conversion_factor"", ""conversion_factor""],
						[""delivery_date"", ""schedule_date""],
					],
					""field_no_map"": [
						""rate"",
						""price_list_rate"",
						""item_tax_template"",
						""discount_percentage"",
						""discount_amount"",
						""pricing_rules"",
					],
					""postprocess"": update_item,
					""condition"": lambda doc: doc.ordered_qty < doc.stock_qty
					and doc.supplier == supplier
					and doc.item_code in items_to_map,
				},
			},
			target_doc,
			set_missing_values,
		)

		doc.insert()
		frappe.db.commit()
		purchase_orders.append(doc)

	return purchase_orders


@frappe.whitelist()",Creates Purchase Order for each Supplier. Returns a list of doc objects.,12,252,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_purchase_order_for_default_supplier(source_name, selected_items=None, target_doc=None):
	
	if not selected_items:
		return

	if isinstance(selected_items, str):
		selected_items = json.loads(selected_items)

	def set_missing_values(source, target):
		target.supplier = supplier
		target.apply_discount_on = """"
		target.additional_discount_percentage = 0.0
		target.discount_amount = 0.0
		target.inter_company_order_reference = """"

		default_price_list = frappe.get_value(""Supplier"", supplier, ""default_price_list"")
		if default_price_list:
			target.buying_price_list = default_price_list

		if any(item.delivered_by_supplier == 1 for item in source.items):
			if source.shipping_address_name:
				target.shipping_address = source.shipping_address_name
				target.shipping_address_display = source.shipping_address
			else:
				target.shipping_address = source.customer_address
				target.shipping_address_display = source.address_display

			target.customer_contact_person = source.contact_person
			target.customer_contact_display = source.contact_display
			target.customer_contact_mobile = source.contact_mobile
			target.customer_contact_email = source.contact_email

		else:
			target.customer = """"
			target.customer_name = """"

		target.run_method(""set_missing_values"")
		target.run_method(""calculate_taxes_and_totals"")

	def update_item(source, target, source_parent):
		target.schedule_date = source.delivery_date
		target.qty = flt(source.qty) - (flt(source.ordered_qty) / flt(source.conversion_factor))
		target.stock_qty = flt(source.stock_qty) - flt(source.ordered_qty)
		target.project = source_parent.project

	suppliers = [item.get(""supplier"") for item in selected_items if item.get(""supplier"")]
	suppliers = list(dict.fromkeys(suppliers))  # remove duplicates while preserving order

	items_to_map = [item.get(""item_code"") for item in selected_items if item.get(""item_code"")]
	items_to_map = list(set(items_to_map))

	if not suppliers:
		frappe.throw(
			_(""Please set a Supplier against the Items to be considered in the Purchase Order."")
		)

	purchase_orders = []
	for supplier in suppliers:
		doc = get_mapped_doc(
			""Sales Order"",
			source_name,
			{
				""Sales Order"": {
					""doctype"": ""Purchase Order"",
					""field_no_map"": [
						""address_display"",
						""contact_display"",
						""contact_mobile"",
						""contact_email"",
						""contact_person"",
						""taxes_and_charges"",
						""shipping_address"",
						""terms"",
					],
					""validation"": {""docstatus"": [""="", 1]},
				},
				""Sales Order Item"": {
					""doctype"": ""Purchase Order Item"",
					""field_map"": [
						[""name"", ""sales_order_item""],
						[""parent"", ""sales_order""],
						[""stock_uom"", ""stock_uom""],
						[""uom"", ""uom""],
						[""conversion_factor"", ""conversion_factor""],
						[""delivery_date"", ""schedule_date""],
					],
					""field_no_map"": [
						""rate"",
						""price_list_rate"",
						""item_tax_template"",
						""discount_percentage"",
						""discount_amount"",
						""pricing_rules"",
					],
					""postprocess"": update_item,
					""condition"": lambda doc: doc.ordered_qty < doc.stock_qty
					and doc.supplier == supplier
					and doc.item_code in items_to_map,
				},
			},
			target_doc,
			set_missing_values,
		)

		doc.insert()
		frappe.db.commit()
		purchase_orders.append(doc)

	return purchase_orders


@frappe.whitelist()
        ```
    ###Assistant : Creates Purchase Order for each Supplier. Returns a list of doc objects.
    "
2257,"def resolve_relation(model, app_label=None, model_name=None):
    
    if isinstance(model, str):
        if model == RECURSIVE_RELATIONSHIP_CONSTANT:
            if app_label is None or model_name is None:
                raise TypeError(
                    'app_label and model_name must be provided to resolve '
                    'recursive relationships.'
                )
            return app_label, model_name
        if '.' in model:
            app_label, model_name = model.split('.', 1)
            return app_label, model_name.lower()
        if app_label is None:
            raise TypeError(
                'app_label must be provided to resolve unscoped model relationships.'
            )
        return app_label, model.lower()
    return model._meta.app_label, model._meta.model_name

","
    Turn a model class or model reference string and return a model tuple.

    app_label and model_name are used to resolve the scope of recursive and
    unscoped model relationship.
    ",28,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resolve_relation(model, app_label=None, model_name=None):
    
    if isinstance(model, str):
        if model == RECURSIVE_RELATIONSHIP_CONSTANT:
            if app_label is None or model_name is None:
                raise TypeError(
                    'app_label and model_name must be provided to resolve '
                    'recursive relationships.'
                )
            return app_label, model_name
        if '.' in model:
            app_label, model_name = model.split('.', 1)
            return app_label, model_name.lower()
        if app_label is None:
            raise TypeError(
                'app_label must be provided to resolve unscoped model relationships.'
            )
        return app_label, model.lower()
    return model._meta.app_label, model._meta.model_name


        ```
    ###Assistant : 
    Turn a model class or model reference string and return a model tuple.

    app_label and model_name are used to resolve the scope of recursive and
    unscoped model relationship.
    
    "
2258,"def load_tf_weights(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None):
    
    missing_layers = []
    unexpected_layers = []
    mismatched_layers = []

    # Read the H5 file
    with h5py.File(resolved_archive_file, ""r"") as sharded_checkpoint_file:
        # Retrieve the name of each layer from the H5 file
        saved_h5_model_layers_name = set(
            hdf5_format.load_attributes_from_hdf5_group(sharded_checkpoint_file, ""layer_names"")
        )

        # Find the missing layers from the high level list of layers
        missing_layers = list(set([layer.name for layer in model.layers]) - saved_h5_model_layers_name)

        # Find the unexpected layers from the high level list of layers
        unexpected_layers = list(saved_h5_model_layers_name - set([layer.name for layer in model.layers]))
        saved_weight_names_set = set()
        symbolic_weights_names = set()
        weight_value_tuples = []

        # Compute missing and unexpected sub layers
        # Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...]
        for layer in model.layers:
            # if layer_name from the H5 file belongs to the layers from the instantiated model
            if layer.name in saved_h5_model_layers_name:
                # Get the H5 layer object from its name
                h5_layer_object = sharded_checkpoint_file[layer.name]
                # Get all the weights as a list from the layer object
                symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
                saved_weights = {}

                # Create a dict from the H5 saved model that looks like {""weight_name"": weight_value}
                # And a set with only the names
                for weight_name in hdf5_format.load_attributes_from_hdf5_group(h5_layer_object, ""weight_names""):
                    # TF names always start with the model name so we ignore it
                    name = ""/"".join(weight_name.split(""/"")[1:])

                    if _prefix is not None:
                        name = _prefix + ""/"" + name

                    saved_weights[name] = np.asarray(h5_layer_object[weight_name])

                    # Add the updated name to the final list for computing missing/unexpected values
                    saved_weight_names_set.add(name)

                # Loop over each weights from the instantiated model and compare with the weights from the H5 file
                for symbolic_weight in symbolic_weights:
                    # TF names always start with the model name so we ignore it
                    if _prefix is not None:
                        delimeter = len(_prefix.split(""/""))
                        symbolic_weight_name = ""/"".join(
                            symbolic_weight.name.split(""/"")[:delimeter]
                            + symbolic_weight.name.split(""/"")[delimeter + 1 :]
                        )
                    else:
                        symbolic_weight_name = ""/"".join(symbolic_weight.name.split(""/"")[1:])

                    # here we check if the current weight is among the weights from the H5 file
                    # If yes, get the weight_value of the corresponding weight from the H5 file
                    # If not, make the value to None
                    saved_weight_value = saved_weights.get(symbolic_weight_name, None)

                    # Add the updated name to the final list for computing missing/unexpected values
                    symbolic_weights_names.add(symbolic_weight_name)

                    # If the current weight is found
                    if saved_weight_value is not None:
                        # Check if the shape of the current weight and the one from the H5 file are different
                        if K.int_shape(symbolic_weight) != saved_weight_value.shape:
                            # If yes we reshape the weight from the H5 file accordingly to the current weight
                            # If the two shapes are not compatible we raise an issue
                            try:
                                array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight))
                            except ValueError as e:
                                if ignore_mismatched_sizes:
                                    mismatched_layers.append(
                                        (symbolic_weight_name, saved_weight_value.shape, K.int_shape(symbolic_weight))
                                    )
                                    continue
                                else:
                                    raise e
                        else:
                            array = saved_weight_value

                        # We create the tuple that will be loaded and add it to the final list
                        weight_value_tuples.append((symbolic_weight, array))

    # Load all the weights
    K.batch_set_value(weight_value_tuples)

    # Compute the missing and unexpected layers
    missing_layers.extend(list(symbolic_weights_names - saved_weight_names_set))
    unexpected_layers.extend(list(saved_weight_names_set - symbolic_weights_names))

    return missing_layers, unexpected_layers, mismatched_layers

","
    Detect missing and unexpected layers and load the TF weights from the shard file accordingly to their names and
    shapes.

    Args:
        model (`tf.keras.models.Model`):
            The model to load the weights into.
        resolved_archive_file (`str`):
            The location of the H5 file.
        ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
            Whether or not to ignore weights with shapes that don't match between the checkpoint of the model.

    Returns:
        Three lists, one for the missing layers, another one for the unexpected layers, and a last one for the
        mismatched layers.
    ",83,479,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_tf_weights(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None):
    
    missing_layers = []
    unexpected_layers = []
    mismatched_layers = []

    # Read the H5 file
    with h5py.File(resolved_archive_file, ""r"") as sharded_checkpoint_file:
        # Retrieve the name of each layer from the H5 file
        saved_h5_model_layers_name = set(
            hdf5_format.load_attributes_from_hdf5_group(sharded_checkpoint_file, ""layer_names"")
        )

        # Find the missing layers from the high level list of layers
        missing_layers = list(set([layer.name for layer in model.layers]) - saved_h5_model_layers_name)

        # Find the unexpected layers from the high level list of layers
        unexpected_layers = list(saved_h5_model_layers_name - set([layer.name for layer in model.layers]))
        saved_weight_names_set = set()
        symbolic_weights_names = set()
        weight_value_tuples = []

        # Compute missing and unexpected sub layers
        # Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...]
        for layer in model.layers:
            # if layer_name from the H5 file belongs to the layers from the instantiated model
            if layer.name in saved_h5_model_layers_name:
                # Get the H5 layer object from its name
                h5_layer_object = sharded_checkpoint_file[layer.name]
                # Get all the weights as a list from the layer object
                symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
                saved_weights = {}

                # Create a dict from the H5 saved model that looks like {""weight_name"": weight_value}
                # And a set with only the names
                for weight_name in hdf5_format.load_attributes_from_hdf5_group(h5_layer_object, ""weight_names""):
                    # TF names always start with the model name so we ignore it
                    name = ""/"".join(weight_name.split(""/"")[1:])

                    if _prefix is not None:
                        name = _prefix + ""/"" + name

                    saved_weights[name] = np.asarray(h5_layer_object[weight_name])

                    # Add the updated name to the final list for computing missing/unexpected values
                    saved_weight_names_set.add(name)

                # Loop over each weights from the instantiated model and compare with the weights from the H5 file
                for symbolic_weight in symbolic_weights:
                    # TF names always start with the model name so we ignore it
                    if _prefix is not None:
                        delimeter = len(_prefix.split(""/""))
                        symbolic_weight_name = ""/"".join(
                            symbolic_weight.name.split(""/"")[:delimeter]
                            + symbolic_weight.name.split(""/"")[delimeter + 1 :]
                        )
                    else:
                        symbolic_weight_name = ""/"".join(symbolic_weight.name.split(""/"")[1:])

                    # here we check if the current weight is among the weights from the H5 file
                    # If yes, get the weight_value of the corresponding weight from the H5 file
                    # If not, make the value to None
                    saved_weight_value = saved_weights.get(symbolic_weight_name, None)

                    # Add the updated name to the final list for computing missing/unexpected values
                    symbolic_weights_names.add(symbolic_weight_name)

                    # If the current weight is found
                    if saved_weight_value is not None:
                        # Check if the shape of the current weight and the one from the H5 file are different
                        if K.int_shape(symbolic_weight) != saved_weight_value.shape:
                            # If yes we reshape the weight from the H5 file accordingly to the current weight
                            # If the two shapes are not compatible we raise an issue
                            try:
                                array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight))
                            except ValueError as e:
                                if ignore_mismatched_sizes:
                                    mismatched_layers.append(
                                        (symbolic_weight_name, saved_weight_value.shape, K.int_shape(symbolic_weight))
                                    )
                                    continue
                                else:
                                    raise e
                        else:
                            array = saved_weight_value

                        # We create the tuple that will be loaded and add it to the final list
                        weight_value_tuples.append((symbolic_weight, array))

    # Load all the weights
    K.batch_set_value(weight_value_tuples)

    # Compute the missing and unexpected layers
    missing_layers.extend(list(symbolic_weights_names - saved_weight_names_set))
    unexpected_layers.extend(list(saved_weight_names_set - symbolic_weights_names))

    return missing_layers, unexpected_layers, mismatched_layers


        ```
    ###Assistant : 
    Detect missing and unexpected layers and load the TF weights from the shard file accordingly to their names and
    shapes.

    Args:
        model (`tf.keras.models.Model`):
            The model to load the weights into.
        resolved_archive_file (`str`):
            The location of the H5 file.
        ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
            Whether or not to ignore weights with shapes that don't match between the checkpoint of the model.

    Returns:
        Three lists, one for the missing layers, another one for the unexpected layers, and a last one for the
        mismatched layers.
    
    "
2259,"def css_classes(self, extra_classes=None):
        
        if hasattr(extra_classes, ""split""):
            extra_classes = extra_classes.split()
        extra_classes = set(extra_classes or [])
        if self.errors and hasattr(self.form, ""error_css_class""):
            extra_classes.add(self.form.error_css_class)
        if self.field.required and hasattr(self.form, ""required_css_class""):
            extra_classes.add(self.form.required_css_class)
        return "" "".join(extra_classes)
","
        Return a string of space-separated CSS classes for this field.
        ",10,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def css_classes(self, extra_classes=None):
        
        if hasattr(extra_classes, ""split""):
            extra_classes = extra_classes.split()
        extra_classes = set(extra_classes or [])
        if self.errors and hasattr(self.form, ""error_css_class""):
            extra_classes.add(self.form.error_css_class)
        if self.field.required and hasattr(self.form, ""required_css_class""):
            extra_classes.add(self.form.required_css_class)
        return "" "".join(extra_classes)

        ```
    ###Assistant : 
        Return a string of space-separated CSS classes for this field.
        
    "
2260,"def _get_free_vram(self) -> List[float]:
        
        vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024)
                for handle in self._handles]
        self._log(""debug"", f""GPU VRAM free: {vram}"")
        return vram
"," Obtain the amount of VRAM that is available, in Megabytes, for each connected Nvidia
        GPU.

        Returns
        -------
        list
             List of `float`s containing the amount of VRAM available, in Megabytes, for each
             connected GPU as corresponding to the values in :attr:`_handles
        ",40,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_free_vram(self) -> List[float]:
        
        vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024)
                for handle in self._handles]
        self._log(""debug"", f""GPU VRAM free: {vram}"")
        return vram

        ```
    ###Assistant :  Obtain the amount of VRAM that is available, in Megabytes, for each connected Nvidia
        GPU.

        Returns
        -------
        list
             List of `float`s containing the amount of VRAM available, in Megabytes, for each
             connected GPU as corresponding to the values in :attr:`_handles
        
    "
2261,"def get_project(doctype, txt, searchfield, start, page_len, filters):
	from erpnext.controllers.queries import get_match_cond

	meta = frappe.get_meta(doctype)
	searchfields = meta.get_search_fields()
	search_columns = "", "" + "", "".join(searchfields) if searchfields else """"
	search_cond = "" or "" + "" or "".join(field + "" like %(txt)s"" for field in searchfields)

	return frappe.db.sql(
		.format(
			search_columns=search_columns, search_condition=search_cond
		),
		{
			""key"": searchfield,
			""txt"": ""%"" + txt + ""%"",
			""mcond"": get_match_cond(doctype),
			""start"": start,
			""page_len"": page_len,
		},
	)


@frappe.whitelist()"," select name {search_columns} from `tabProject`
		where %(key)s like %(txt)s
			%(mcond)s
			{search_condition}
		order by name
		limit %(start)s, %(page_len)s",17,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_project(doctype, txt, searchfield, start, page_len, filters):
	from erpnext.controllers.queries import get_match_cond

	meta = frappe.get_meta(doctype)
	searchfields = meta.get_search_fields()
	search_columns = "", "" + "", "".join(searchfields) if searchfields else """"
	search_cond = "" or "" + "" or "".join(field + "" like %(txt)s"" for field in searchfields)

	return frappe.db.sql(
		.format(
			search_columns=search_columns, search_condition=search_cond
		),
		{
			""key"": searchfield,
			""txt"": ""%"" + txt + ""%"",
			""mcond"": get_match_cond(doctype),
			""start"": start,
			""page_len"": page_len,
		},
	)


@frappe.whitelist()
        ```
    ###Assistant :  select name {search_columns} from `tabProject`
		where %(key)s like %(txt)s
			%(mcond)s
			{search_condition}
		order by name
		limit %(start)s, %(page_len)s
    "
2262,"def build(self, var_list):
        
        super().build(var_list)
        if getattr(self, ""_built"", False):
            return
        self._built = True
        self._momentums = []
        self._velocities = []
        self._u_product = tf.Variable(1.0, dtype=var_list[0].dtype)
        # Keep a counter on how many times of _u_product has been computed to
        # avoid duplicated computations.
        self._u_product_counter = 1

        for var in var_list:
            self._momentums.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name=""m""
                )
            )
            self._velocities.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name=""v""
                )
            )
","Initialize optimizer variables.

        Nadam optimizer has 2 types of variables: momentums and velocities.

        Args:
          var_list: list of model variables to build Nadam variables on.
        ",24,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def build(self, var_list):
        
        super().build(var_list)
        if getattr(self, ""_built"", False):
            return
        self._built = True
        self._momentums = []
        self._velocities = []
        self._u_product = tf.Variable(1.0, dtype=var_list[0].dtype)
        # Keep a counter on how many times of _u_product has been computed to
        # avoid duplicated computations.
        self._u_product_counter = 1

        for var in var_list:
            self._momentums.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name=""m""
                )
            )
            self._velocities.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name=""v""
                )
            )

        ```
    ###Assistant : Initialize optimizer variables.

        Nadam optimizer has 2 types of variables: momentums and velocities.

        Args:
          var_list: list of model variables to build Nadam variables on.
        
    "
2263,"def list_distinfo_files(self, absolute=False):
        
        record_path = os.path.join(self.path, 'installed-files.txt')
        if os.path.exists(record_path):
            skip = True
            with codecs.open(record_path, 'r', encoding='utf-8') as f:
                for line in f:
                    line = line.strip()
                    if line == './':
                        skip = False
                        continue
                    if not skip:
                        p = os.path.normpath(os.path.join(self.path, line))
                        if p.startswith(self.path):
                            if absolute:
                                yield p
                            else:
                                yield line
","
        Iterates over the ``installed-files.txt`` entries and returns paths for
        each line if the path is pointing to a file located in the
        ``.egg-info`` directory or one of its subdirectories.

        :parameter absolute: If *absolute* is ``True``, each returned path is
                          transformed into a local absolute path. Otherwise the
                          raw value from ``installed-files.txt`` is returned.
        :type absolute: boolean
        :returns: iterator of paths
        ",60,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list_distinfo_files(self, absolute=False):
        
        record_path = os.path.join(self.path, 'installed-files.txt')
        if os.path.exists(record_path):
            skip = True
            with codecs.open(record_path, 'r', encoding='utf-8') as f:
                for line in f:
                    line = line.strip()
                    if line == './':
                        skip = False
                        continue
                    if not skip:
                        p = os.path.normpath(os.path.join(self.path, line))
                        if p.startswith(self.path):
                            if absolute:
                                yield p
                            else:
                                yield line

        ```
    ###Assistant : 
        Iterates over the ``installed-files.txt`` entries and returns paths for
        each line if the path is pointing to a file located in the
        ``.egg-info`` directory or one of its subdirectories.

        :parameter absolute: If *absolute* is ``True``, each returned path is
                          transformed into a local absolute path. Otherwise the
                          raw value from ``installed-files.txt`` is returned.
        :type absolute: boolean
        :returns: iterator of paths
        
    "
2264,"def mixin_scalable_deployment_parser(parser):
    
    gp = mixin_base_deployment_parser(parser, title='Scalable Deployment')

    gp.add_argument(
        '--polling',
        type=str,
        default=PollingType.ANY.name,
        help=,
    )

    gp.add_argument(
        '--shards',
        type=int,
        default=1,
        help='The number of shards in the deployment running at the same time. For more details check '
        'https://docs.jina.ai/fundamentals/flow/create-flow/#complex-flow-topologies',
    )

    gp.add_argument(
        '--replicas',
        type=int,
        default=1,
        help='The number of replicas in the deployment',
    )

    gp.add_argument(
        '--native',
        action='store_true',
        default=False,
        help='If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.',
    )
","Mixing in arguments required by a scalable deployment into the given parser.
    The deployment is scalable and can have shards, replicas and polling
    :param parser: the parser instance to which we add arguments
    
    The polling strategy of the Deployment and its endpoints (when `shards>1`).
    Can be defined for all endpoints of a Deployment or by endpoint.
    Define per Deployment:
    - ANY: only one (whoever is idle) Pod polls the message
    - ALL: all Pods poll the message (like a broadcast)
    Define per Endpoint:
    JSON dict, {endpoint: PollingType}
    {'/custom': 'ALL', '/search': 'ANY', '*': 'ANY'}
    
    ",93,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mixin_scalable_deployment_parser(parser):
    
    gp = mixin_base_deployment_parser(parser, title='Scalable Deployment')

    gp.add_argument(
        '--polling',
        type=str,
        default=PollingType.ANY.name,
        help=,
    )

    gp.add_argument(
        '--shards',
        type=int,
        default=1,
        help='The number of shards in the deployment running at the same time. For more details check '
        'https://docs.jina.ai/fundamentals/flow/create-flow/#complex-flow-topologies',
    )

    gp.add_argument(
        '--replicas',
        type=int,
        default=1,
        help='The number of replicas in the deployment',
    )

    gp.add_argument(
        '--native',
        action='store_true',
        default=False,
        help='If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.',
    )

        ```
    ###Assistant : Mixing in arguments required by a scalable deployment into the given parser.
    The deployment is scalable and can have shards, replicas and polling
    :param parser: the parser instance to which we add arguments
    
    The polling strategy of the Deployment and its endpoints (when `shards>1`).
    Can be defined for all endpoints of a Deployment or by endpoint.
    Define per Deployment:
    - ANY: only one (whoever is idle) Pod polls the message
    - ALL: all Pods poll the message (like a broadcast)
    Define per Endpoint:
    JSON dict, {endpoint: PollingType}
    {'/custom': 'ALL', '/search': 'ANY', '*': 'ANY'}
    
    
    "
2265,"def test_explorer_list_private(self):
        
        response = self.client.get(
            reverse(""wagtailadmin_explore"", args=(self.private_page.id,))
        )

        # Check the response
        self.assertEqual(response.status_code, 200)

        # Must have one privacy icon (next to the private child page)
        self.assertContains(
            response,
            '<span class=""indicator privacy-indicator icon icon-no-view""',
            count=1,
        )
","
        This tests that there is a padlock displayed
        next to the private child page in the private pages explorer listing
        ",20,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_explorer_list_private(self):
        
        response = self.client.get(
            reverse(""wagtailadmin_explore"", args=(self.private_page.id,))
        )

        # Check the response
        self.assertEqual(response.status_code, 200)

        # Must have one privacy icon (next to the private child page)
        self.assertContains(
            response,
            '<span class=""indicator privacy-indicator icon icon-no-view""',
            count=1,
        )

        ```
    ###Assistant : 
        This tests that there is a padlock displayed
        next to the private child page in the private pages explorer listing
        
    "
2266,"def next(self):
        
        self._check(""ra"")
        if self.firstmember is not None:
            m = self.firstmember
            self.firstmember = None
            return m

        # Read the next block.
        self.fileobj.seek(self.offset)
        tarinfo = None
        while True:
            try:
                tarinfo = self.tarinfo.fromtarfile(self)
            except EOFHeaderError as e:
                if self.ignore_zeros:
                    self._dbg(2, ""0x%X: %s"" % (self.offset, e))
                    self.offset += BLOCKSIZE
                    continue
            except InvalidHeaderError as e:
                if self.ignore_zeros:
                    self._dbg(2, ""0x%X: %s"" % (self.offset, e))
                    self.offset += BLOCKSIZE
                    continue
                elif self.offset == 0:
                    raise ReadError(str(e))
            except EmptyHeaderError:
                if self.offset == 0:
                    raise ReadError(""empty file"")
            except TruncatedHeaderError as e:
                if self.offset == 0:
                    raise ReadError(str(e))
            except SubsequentHeaderError as e:
                raise ReadError(str(e))
            break

        if tarinfo is not None:
            self.members.append(tarinfo)
        else:
            self._loaded = True

        return tarinfo

    #--------------------------------------------------------------------------
    # Little helper methods:
","Return the next member of the archive as a TarInfo object, when
           TarFile is opened for reading. Return None if there is no more
           available.
        ",25,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def next(self):
        
        self._check(""ra"")
        if self.firstmember is not None:
            m = self.firstmember
            self.firstmember = None
            return m

        # Read the next block.
        self.fileobj.seek(self.offset)
        tarinfo = None
        while True:
            try:
                tarinfo = self.tarinfo.fromtarfile(self)
            except EOFHeaderError as e:
                if self.ignore_zeros:
                    self._dbg(2, ""0x%X: %s"" % (self.offset, e))
                    self.offset += BLOCKSIZE
                    continue
            except InvalidHeaderError as e:
                if self.ignore_zeros:
                    self._dbg(2, ""0x%X: %s"" % (self.offset, e))
                    self.offset += BLOCKSIZE
                    continue
                elif self.offset == 0:
                    raise ReadError(str(e))
            except EmptyHeaderError:
                if self.offset == 0:
                    raise ReadError(""empty file"")
            except TruncatedHeaderError as e:
                if self.offset == 0:
                    raise ReadError(str(e))
            except SubsequentHeaderError as e:
                raise ReadError(str(e))
            break

        if tarinfo is not None:
            self.members.append(tarinfo)
        else:
            self._loaded = True

        return tarinfo

    #--------------------------------------------------------------------------
    # Little helper methods:

        ```
    ###Assistant : Return the next member of the archive as a TarInfo object, when
           TarFile is opened for reading. Return None if there is no more
           available.
        
    "
2267,"def predict_proba(self, X):
        
        check_is_fitted(self)

        ovr = self.multi_class in [""ovr"", ""warn""] or (
            self.multi_class == ""auto""
            and (
                self.classes_.size <= 2
                or self.solver in (""liblinear"", ""newton-cholesky"")
            )
        )
        if ovr:
            return super()._predict_proba_lr(X)
        else:
            decision = self.decision_function(X)
            if decision.ndim == 1:
                # Workaround for multi_class=""multinomial"" and binary outcomes
                # which requires softmax prediction with only a 1D decision.
                decision_2d = np.c_[-decision, decision]
            else:
                decision_2d = decision
            return softmax(decision_2d, copy=False)
","
        Probability estimates.

        The returned estimates for all classes are ordered by the
        label of classes.

        For a multi_class problem, if multi_class is set to be ""multinomial""
        the softmax function is used to find the predicted probability of
        each class.
        Else use a one-vs-rest approach, i.e calculate the probability
        of each class assuming it to be positive using the logistic function.
        and normalize these values across all the classes.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Vector to be scored, where `n_samples` is the number of samples and
            `n_features` is the number of features.

        Returns
        -------
        T : array-like of shape (n_samples, n_classes)
            Returns the probability of the sample for each class in the model,
            where classes are ordered as they are in ``self.classes_``.
        ",125,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def predict_proba(self, X):
        
        check_is_fitted(self)

        ovr = self.multi_class in [""ovr"", ""warn""] or (
            self.multi_class == ""auto""
            and (
                self.classes_.size <= 2
                or self.solver in (""liblinear"", ""newton-cholesky"")
            )
        )
        if ovr:
            return super()._predict_proba_lr(X)
        else:
            decision = self.decision_function(X)
            if decision.ndim == 1:
                # Workaround for multi_class=""multinomial"" and binary outcomes
                # which requires softmax prediction with only a 1D decision.
                decision_2d = np.c_[-decision, decision]
            else:
                decision_2d = decision
            return softmax(decision_2d, copy=False)

        ```
    ###Assistant : 
        Probability estimates.

        The returned estimates for all classes are ordered by the
        label of classes.

        For a multi_class problem, if multi_class is set to be ""multinomial""
        the softmax function is used to find the predicted probability of
        each class.
        Else use a one-vs-rest approach, i.e calculate the probability
        of each class assuming it to be positive using the logistic function.
        and normalize these values across all the classes.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Vector to be scored, where `n_samples` is the number of samples and
            `n_features` is the number of features.

        Returns
        -------
        T : array-like of shape (n_samples, n_classes)
            Returns the probability of the sample for each class in the model,
            where classes are ordered as they are in ``self.classes_``.
        
    "
2268,"def selu(x):
    
    return tf.nn.selu(x)


@keras_export(""keras.activations.softplus"")
@tf.__internal__.dispatch.add_dispatch_support","Scaled Exponential Linear Unit (SELU).

    The Scaled Exponential Linear Unit (SELU) activation function is defined as:

    - `if x > 0: return scale * x`
    - `if x < 0: return scale * alpha * (exp(x) - 1)`

    where `alpha` and `scale` are pre-defined constants
    (`alpha=1.67326324` and `scale=1.05070098`).

    Basically, the SELU activation function multiplies `scale` (> 1) with the
    output of the `tf.keras.activations.elu` function to ensure a slope larger
    than one for positive inputs.

    The values of `alpha` and `scale` are
    chosen so that the mean and variance of the inputs are preserved
    between two consecutive layers as long as the weights are initialized
    correctly (see `tf.keras.initializers.LecunNormal` initializer)
    and the number of input units is ""large enough""
    (see reference paper for more information).

    Example Usage:

    >>> num_classes = 10  # 10-class problem
    >>> model = tf.keras.Sequential()
    >>> model.add(tf.keras.layers.Dense(64, kernel_initializer='lecun_normal',
    ...                                 activation='selu'))
    >>> model.add(tf.keras.layers.Dense(32, kernel_initializer='lecun_normal',
    ...                                 activation='selu'))
    >>> model.add(tf.keras.layers.Dense(16, kernel_initializer='lecun_normal',
    ...                                 activation='selu'))
    >>> model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))

    Args:
        x: A tensor or variable to compute the activation function for.

    Returns:
        The scaled exponential unit activation: `scale * elu(x, alpha)`.

    Notes:
        - To be used together with the
          `tf.keras.initializers.LecunNormal` initializer.
        - To be used together with the dropout variant
          `tf.keras.layers.AlphaDropout` (not regular dropout).

    References:
        - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)
    ",205,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def selu(x):
    
    return tf.nn.selu(x)


@keras_export(""keras.activations.softplus"")
@tf.__internal__.dispatch.add_dispatch_support
        ```
    ###Assistant : Scaled Exponential Linear Unit (SELU).

    The Scaled Exponential Linear Unit (SELU) activation function is defined as:

    - `if x > 0: return scale * x`
    - `if x < 0: return scale * alpha * (exp(x) - 1)`

    where `alpha` and `scale` are pre-defined constants
    (`alpha=1.67326324` and `scale=1.05070098`).

    Basically, the SELU activation function multiplies `scale` (> 1) with the
    output of the `tf.keras.activations.elu` function to ensure a slope larger
    than one for positive inputs.

    The values of `alpha` and `scale` are
    chosen so that the mean and variance of the inputs are preserved
    between two consecutive layers as long as the weights are initialized
    correctly (see `tf.keras.initializers.LecunNormal` initializer)
    and the number of input units is ""large enough""
    (see reference paper for more information).

    Example Usage:

    >>> num_classes = 10  # 10-class problem
    >>> model = tf.keras.Sequential()
    >>> model.add(tf.keras.layers.Dense(64, kernel_initializer='lecun_normal',
    ...                                 activation='selu'))
    >>> model.add(tf.keras.layers.Dense(32, kernel_initializer='lecun_normal',
    ...                                 activation='selu'))
    >>> model.add(tf.keras.layers.Dense(16, kernel_initializer='lecun_normal',
    ...                                 activation='selu'))
    >>> model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))

    Args:
        x: A tensor or variable to compute the activation function for.

    Returns:
        The scaled exponential unit activation: `scale * elu(x, alpha)`.

    Notes:
        - To be used together with the
          `tf.keras.initializers.LecunNormal` initializer.
        - To be used together with the dropout variant
          `tf.keras.layers.AlphaDropout` (not regular dropout).

    References:
        - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)
    
    "
2269,"def _find_safe_escape_tokens(self, text):
        
        for i, t, v in self._filter_to(
            self.lang.get_tokens_unprocessed(text),
            lambda t: t in Token.Comment or t in Token.String
        ):
            if t is None:
                for i2, t2, v2 in self._find_escape_tokens(v):
                    yield i + i2, t2, v2
            else:
                yield i, None, v
", find escape tokens that are not in strings or comments ,10,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _find_safe_escape_tokens(self, text):
        
        for i, t, v in self._filter_to(
            self.lang.get_tokens_unprocessed(text),
            lambda t: t in Token.Comment or t in Token.String
        ):
            if t is None:
                for i2, t2, v2 in self._find_escape_tokens(v):
                    yield i + i2, t2, v2
            else:
                yield i, None, v

        ```
    ###Assistant :  find escape tokens that are not in strings or comments 
    "
2270,"def set_up_fileselector(quteproc, py_proc, kind, files, output_type):
    
    cmd, args = py_proc(r)
    args += files.split(' ')
    if output_type == ""a temporary file"":
        args += ['--file={}']
    fileselect_cmd = json.dumps([cmd, *args])
    quteproc.set_setting('fileselect.handler', 'external')
    quteproc.set_setting(f'fileselect.{kind}.command', fileselect_cmd)
","Set up fileselect.xxx.command to select the file(s).
        import os
        import sys
        tmp_file = None
        for i, arg in enumerate(sys.argv):
            if arg.startswith('--file='):
                tmp_file = arg[len('--file='):]
                sys.argv.pop(i)
                break
        selected_files = sys.argv[1:]
        if tmp_file is None:
            for selected_file in selected_files:
                print(os.path.abspath(selected_file))
        else:
            with open(tmp_file, 'w') as f:
                for selected_file in selected_files:
                    f.write(os.path.abspath(selected_file) + '\n')
    ",51,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_up_fileselector(quteproc, py_proc, kind, files, output_type):
    
    cmd, args = py_proc(r)
    args += files.split(' ')
    if output_type == ""a temporary file"":
        args += ['--file={}']
    fileselect_cmd = json.dumps([cmd, *args])
    quteproc.set_setting('fileselect.handler', 'external')
    quteproc.set_setting(f'fileselect.{kind}.command', fileselect_cmd)

        ```
    ###Assistant : Set up fileselect.xxx.command to select the file(s).
        import os
        import sys
        tmp_file = None
        for i, arg in enumerate(sys.argv):
            if arg.startswith('--file='):
                tmp_file = arg[len('--file='):]
                sys.argv.pop(i)
                break
        selected_files = sys.argv[1:]
        if tmp_file is None:
            for selected_file in selected_files:
                print(os.path.abspath(selected_file))
        else:
            with open(tmp_file, 'w') as f:
                for selected_file in selected_files:
                    f.write(os.path.abspath(selected_file) + '\n')
    
    "
2271,"def task_runner(request):
    

    if not hasattr(request.param, ""_pytestfixturefunction""):
        raise TypeError(""Received invalid `task_runner` parameter. Expected fixture."")

    yield request.getfixturevalue(request.param.__name__)

","
    An indirect fixture that expects to receive a pytest fixture that yields a task
    runner.
    ",15,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def task_runner(request):
    

    if not hasattr(request.param, ""_pytestfixturefunction""):
        raise TypeError(""Received invalid `task_runner` parameter. Expected fixture."")

    yield request.getfixturevalue(request.param.__name__)


        ```
    ###Assistant : 
    An indirect fixture that expects to receive a pytest fixture that yields a task
    runner.
    
    "
2272,"def test_get_apns_context(self) -> None:
        
        import zerver.lib.push_notifications

        zerver.lib.push_notifications.get_apns_context.cache_clear()
        try:
            with self.settings(APNS_CERT_FILE=""/foo.pem""), mock.patch(""aioapns.APNs"") as mock_apns:
                apns_context = get_apns_context()
                assert apns_context is not None
                try:
                    self.assertEqual(mock_apns.return_value, apns_context.apns)
                finally:
                    apns_context.loop.close()
        finally:
            # Reset the cache for `get_apns_context` so that we don't
            # leak changes to the rest of the world.
            zerver.lib.push_notifications.get_apns_context.cache_clear()
","This test is pretty hacky, and needs to carefully reset the state
        it modifies in order to avoid leaking state that can lead to
        nondeterministic results for other tests.
        ",29,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_apns_context(self) -> None:
        
        import zerver.lib.push_notifications

        zerver.lib.push_notifications.get_apns_context.cache_clear()
        try:
            with self.settings(APNS_CERT_FILE=""/foo.pem""), mock.patch(""aioapns.APNs"") as mock_apns:
                apns_context = get_apns_context()
                assert apns_context is not None
                try:
                    self.assertEqual(mock_apns.return_value, apns_context.apns)
                finally:
                    apns_context.loop.close()
        finally:
            # Reset the cache for `get_apns_context` so that we don't
            # leak changes to the rest of the world.
            zerver.lib.push_notifications.get_apns_context.cache_clear()

        ```
    ###Assistant : This test is pretty hacky, and needs to carefully reset the state
        it modifies in order to avoid leaking state that can lead to
        nondeterministic results for other tests.
        
    "
2273,"def _validate(self) -> None:
        
        if (self._args.writer == ""ffmpeg"" and
                not self._images.is_video and
                self._args.reference_video is None):
            raise FaceswapError(""Output as video selected, but using frames as input. You must ""
                                ""provide a reference video ('-ref', '--reference-video')."")

        if (self._args.on_the_fly and
                self._args.mask_type not in (""none"", ""extended"", ""components"")):
            logger.warning(""You have selected an incompatible mask type ('%s') for On-The-Fly ""
                           ""conversion. Switching to 'extended'"", self._args.mask_type)
            self._args.mask_type = ""extended""

        if (not self._args.on_the_fly and
                self._args.mask_type not in (""none"", ""predicted"") and
                not self._alignments.mask_is_valid(self._args.mask_type)):
            msg = (f""You have selected the Mask Type `{self._args.mask_type}` but at least one ""
                   ""face does not have this mask stored in the Alignments File.\nYou should ""
                   ""generate the required masks with the Mask Tool or set the Mask Type option to ""
                   ""an existing Mask Type.\nA summary of existing masks is as follows:\nTotal ""
                   f""faces: {self._alignments.faces_count}, ""
                   f""Masks: {self._alignments.mask_summary}"")
            raise FaceswapError(msg)

        if self._args.mask_type == ""predicted"" and not self._predictor.has_predicted_mask:
            available_masks = [k for k, v in self._alignments.mask_summary.items()
                               if k != ""none"" and v == self._alignments.faces_count]
            if not available_masks:
                msg = (""Predicted Mask selected, but the model was not trained with a mask and no ""
                       ""masks are stored in the Alignments File.\nYou should generate the ""
                       ""required masks with the Mask Tool or set the Mask Type to `none`."")
                raise FaceswapError(msg)
            mask_type = available_masks[0]
            logger.warning(""Predicted Mask selected, but the model was not trained with a ""
                           ""mask. Selecting first available mask: '%s'"", mask_type)
            self._args.mask_type = mask_type
"," Validate the Command Line Options.

        Ensure that certain cli selections are valid and won't result in an error. Checks:
            * If frames have been passed in with video output, ensure user supplies reference
            video.
            * If ""on-the-fly"" and a Neural Network mask is selected, warn and switch to 'extended'
            * If a mask-type is selected, ensure it exists in the alignments file.
            * If a predicted mask-type is selected, ensure model has been trained with a mask
            otherwise attempt to select first available masks, otherwise raise error.

        Raises
        ------
        FaceswapError
            If an invalid selection has been found.

        ",97,230,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _validate(self) -> None:
        
        if (self._args.writer == ""ffmpeg"" and
                not self._images.is_video and
                self._args.reference_video is None):
            raise FaceswapError(""Output as video selected, but using frames as input. You must ""
                                ""provide a reference video ('-ref', '--reference-video')."")

        if (self._args.on_the_fly and
                self._args.mask_type not in (""none"", ""extended"", ""components"")):
            logger.warning(""You have selected an incompatible mask type ('%s') for On-The-Fly ""
                           ""conversion. Switching to 'extended'"", self._args.mask_type)
            self._args.mask_type = ""extended""

        if (not self._args.on_the_fly and
                self._args.mask_type not in (""none"", ""predicted"") and
                not self._alignments.mask_is_valid(self._args.mask_type)):
            msg = (f""You have selected the Mask Type `{self._args.mask_type}` but at least one ""
                   ""face does not have this mask stored in the Alignments File.\nYou should ""
                   ""generate the required masks with the Mask Tool or set the Mask Type option to ""
                   ""an existing Mask Type.\nA summary of existing masks is as follows:\nTotal ""
                   f""faces: {self._alignments.faces_count}, ""
                   f""Masks: {self._alignments.mask_summary}"")
            raise FaceswapError(msg)

        if self._args.mask_type == ""predicted"" and not self._predictor.has_predicted_mask:
            available_masks = [k for k, v in self._alignments.mask_summary.items()
                               if k != ""none"" and v == self._alignments.faces_count]
            if not available_masks:
                msg = (""Predicted Mask selected, but the model was not trained with a mask and no ""
                       ""masks are stored in the Alignments File.\nYou should generate the ""
                       ""required masks with the Mask Tool or set the Mask Type to `none`."")
                raise FaceswapError(msg)
            mask_type = available_masks[0]
            logger.warning(""Predicted Mask selected, but the model was not trained with a ""
                           ""mask. Selecting first available mask: '%s'"", mask_type)
            self._args.mask_type = mask_type

        ```
    ###Assistant :  Validate the Command Line Options.

        Ensure that certain cli selections are valid and won't result in an error. Checks:
            * If frames have been passed in with video output, ensure user supplies reference
            video.
            * If ""on-the-fly"" and a Neural Network mask is selected, warn and switch to 'extended'
            * If a mask-type is selected, ensure it exists in the alignments file.
            * If a predicted mask-type is selected, ensure model has been trained with a mask
            otherwise attempt to select first available masks, otherwise raise error.

        Raises
        ------
        FaceswapError
            If an invalid selection has been found.

        
    "
2274,"def get_authenticators(self) -> List[BaseAuthentication]:
        

        # TODO: Increase test coverage and get this working for monolith mode.
        if SiloMode.get_current_mode() == SiloMode.MONOLITH:
            return super().get_authenticators()

        last_api_authenticator = ApiAuthentication([])
        result: List[BaseAuthentication] = []
        for authenticator_cls in self.authentication_classes:
            auth_type = ApiAuthenticatorType.from_authenticator(authenticator_cls)
            if auth_type:
                last_api_authenticator.types.append(auth_type)
            else:
                if last_api_authenticator.types:
                    result.append(last_api_authenticator)
                    last_api_authenticator = ApiAuthentication([])
                result.append(authenticator_cls())

        if last_api_authenticator.types:
            result.append(last_api_authenticator)
        return result
","
        Instantiates and returns the list of authenticators that this view can use.
        Aggregates together authenticators that can be supported using HybridCloud.
        ",21,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_authenticators(self) -> List[BaseAuthentication]:
        

        # TODO: Increase test coverage and get this working for monolith mode.
        if SiloMode.get_current_mode() == SiloMode.MONOLITH:
            return super().get_authenticators()

        last_api_authenticator = ApiAuthentication([])
        result: List[BaseAuthentication] = []
        for authenticator_cls in self.authentication_classes:
            auth_type = ApiAuthenticatorType.from_authenticator(authenticator_cls)
            if auth_type:
                last_api_authenticator.types.append(auth_type)
            else:
                if last_api_authenticator.types:
                    result.append(last_api_authenticator)
                    last_api_authenticator = ApiAuthentication([])
                result.append(authenticator_cls())

        if last_api_authenticator.types:
            result.append(last_api_authenticator)
        return result

        ```
    ###Assistant : 
        Instantiates and returns the list of authenticators that this view can use.
        Aggregates together authenticators that can be supported using HybridCloud.
        
    "
2275,"def test_naive_lowest_common_ancestor2(self):
        
        G = nx.DiGraph()
        G.add_edge(0, 1)
        G.add_edge(2, 0)
        G.add_edge(2, 3)
        G.add_edge(4, 0)
        G.add_edge(5, 2)

        assert naive_lca(G, 1, 3) == 2
",Test that the one-pair function works for issue #4942.,9,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_naive_lowest_common_ancestor2(self):
        
        G = nx.DiGraph()
        G.add_edge(0, 1)
        G.add_edge(2, 0)
        G.add_edge(2, 3)
        G.add_edge(4, 0)
        G.add_edge(5, 2)

        assert naive_lca(G, 1, 3) == 2

        ```
    ###Assistant : Test that the one-pair function works for issue #4942.
    "
2276,"def get_references(state, model_tuple, field_tuple=()):
    
    for state_model_tuple, model_state in state.models.items():
        for name, field in model_state.fields.items():
            reference = field_references(
                state_model_tuple, field, model_tuple, *field_tuple
            )
            if reference:
                yield model_state, name, field, reference

","
    Generator of (model_state, name, field, reference) referencing
    provided context.

    If field_tuple is provided only references to this particular field of
    model_tuple will be generated.
    ",24,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_references(state, model_tuple, field_tuple=()):
    
    for state_model_tuple, model_state in state.models.items():
        for name, field in model_state.fields.items():
            reference = field_references(
                state_model_tuple, field, model_tuple, *field_tuple
            )
            if reference:
                yield model_state, name, field, reference


        ```
    ###Assistant : 
    Generator of (model_state, name, field, reference) referencing
    provided context.

    If field_tuple is provided only references to this particular field of
    model_tuple will be generated.
    
    "
2277,"async def test_media_player_eq_bands_not_supported(hass):
    
    device = (
        ""media_player.test_bands"",
        ""on"",
        {
            ""friendly_name"": ""Test media player"",
            ""supported_features"": SUPPORT_SELECT_SOUND_MODE,
            ""sound_mode"": ""tv"",
            ""sound_mode_list"": [""movie"", ""music"", ""night"", ""sport"", ""tv"", ""rocknroll""],
        },
    )
    await discovery_test(device, hass)

    context = Context()

    # Test for SetBands Error
    request = get_new_request(
        ""Alexa.EqualizerController"", ""SetBands"", ""media_player#test_bands""
    )
    request[""directive""][""payload""] = {""bands"": [{""name"": ""BASS"", ""value"": -2}]}
    msg = await smart_home.async_handle_message(
        hass, get_default_config(hass), request, context
    )

    assert ""event"" in msg
    msg = msg[""event""]
    assert msg[""header""][""name""] == ""ErrorResponse""
    assert msg[""header""][""namespace""] == ""Alexa""
    assert msg[""payload""][""type""] == ""INVALID_DIRECTIVE""

    # Test for AdjustBands Error
    request = get_new_request(
        ""Alexa.EqualizerController"", ""AdjustBands"", ""media_player#test_bands""
    )
    request[""directive""][""payload""] = {
        ""bands"": [{""name"": ""BASS"", ""levelDelta"": 3, ""levelDirection"": ""UP""}]
    }
    msg = await smart_home.async_handle_message(
        hass, get_default_config(hass), request, context
    )

    assert ""event"" in msg
    msg = msg[""event""]
    assert msg[""header""][""name""] == ""ErrorResponse""
    assert msg[""header""][""namespace""] == ""Alexa""
    assert msg[""payload""][""type""] == ""INVALID_DIRECTIVE""

    # Test for ResetBands Error
    request = get_new_request(
        ""Alexa.EqualizerController"", ""ResetBands"", ""media_player#test_bands""
    )
    request[""directive""][""payload""] = {
        ""bands"": [{""name"": ""BASS"", ""levelDelta"": 3, ""levelDirection"": ""UP""}]
    }
    msg = await smart_home.async_handle_message(
        hass, get_default_config(hass), request, context
    )

    assert ""event"" in msg
    msg = msg[""event""]
    assert msg[""header""][""name""] == ""ErrorResponse""
    assert msg[""header""][""namespace""] == ""Alexa""
    assert msg[""payload""][""type""] == ""INVALID_DIRECTIVE""

",Test EqualizerController bands directive not supported.,6,181,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_media_player_eq_bands_not_supported(hass):
    
    device = (
        ""media_player.test_bands"",
        ""on"",
        {
            ""friendly_name"": ""Test media player"",
            ""supported_features"": SUPPORT_SELECT_SOUND_MODE,
            ""sound_mode"": ""tv"",
            ""sound_mode_list"": [""movie"", ""music"", ""night"", ""sport"", ""tv"", ""rocknroll""],
        },
    )
    await discovery_test(device, hass)

    context = Context()

    # Test for SetBands Error
    request = get_new_request(
        ""Alexa.EqualizerController"", ""SetBands"", ""media_player#test_bands""
    )
    request[""directive""][""payload""] = {""bands"": [{""name"": ""BASS"", ""value"": -2}]}
    msg = await smart_home.async_handle_message(
        hass, get_default_config(hass), request, context
    )

    assert ""event"" in msg
    msg = msg[""event""]
    assert msg[""header""][""name""] == ""ErrorResponse""
    assert msg[""header""][""namespace""] == ""Alexa""
    assert msg[""payload""][""type""] == ""INVALID_DIRECTIVE""

    # Test for AdjustBands Error
    request = get_new_request(
        ""Alexa.EqualizerController"", ""AdjustBands"", ""media_player#test_bands""
    )
    request[""directive""][""payload""] = {
        ""bands"": [{""name"": ""BASS"", ""levelDelta"": 3, ""levelDirection"": ""UP""}]
    }
    msg = await smart_home.async_handle_message(
        hass, get_default_config(hass), request, context
    )

    assert ""event"" in msg
    msg = msg[""event""]
    assert msg[""header""][""name""] == ""ErrorResponse""
    assert msg[""header""][""namespace""] == ""Alexa""
    assert msg[""payload""][""type""] == ""INVALID_DIRECTIVE""

    # Test for ResetBands Error
    request = get_new_request(
        ""Alexa.EqualizerController"", ""ResetBands"", ""media_player#test_bands""
    )
    request[""directive""][""payload""] = {
        ""bands"": [{""name"": ""BASS"", ""levelDelta"": 3, ""levelDirection"": ""UP""}]
    }
    msg = await smart_home.async_handle_message(
        hass, get_default_config(hass), request, context
    )

    assert ""event"" in msg
    msg = msg[""event""]
    assert msg[""header""][""name""] == ""ErrorResponse""
    assert msg[""header""][""namespace""] == ""Alexa""
    assert msg[""payload""][""type""] == ""INVALID_DIRECTIVE""


        ```
    ###Assistant : Test EqualizerController bands directive not supported.
    "
2278,"def lstsq(a, b):
    
    q, r = qr(a)
    x = solve_triangular(r, q.T.conj().dot(b))
    residuals = b - a.dot(x)
    residuals = abs(residuals**2).sum(axis=0, keepdims=b.ndim == 1)

    token = tokenize(a, b)

    # r must be a triangular with single block

    # rank
    rname = ""lstsq-rank-"" + token
    rdsk = {(rname,): (np.linalg.matrix_rank, (r.name, 0, 0))}
    graph = HighLevelGraph.from_collections(rname, rdsk, dependencies=[r])
    # rank must be an integer
    rank = Array(graph, rname, shape=(), chunks=(), dtype=int)

    # singular
    sname = ""lstsq-singular-"" + token
    rt = r.T.conj()
    sdsk = {
        (sname, 0): (
            _reverse,
            (np.sqrt, (np.linalg.eigvalsh, (np.dot, (rt.name, 0, 0), (r.name, 0, 0)))),
        )
    }
    graph = HighLevelGraph.from_collections(sname, sdsk, dependencies=[rt, r])
    meta = meta_from_array(residuals, 1)
    s = Array(graph, sname, shape=(r.shape[0],), chunks=r.shape[0], meta=meta)

    return x, residuals, rank, s


@derived_from(np.linalg)","
    Return the least-squares solution to a linear matrix equation using
    QR decomposition.

    Solves the equation `a x = b` by computing a vector `x` that
    minimizes the Euclidean 2-norm `|| b - a x ||^2`.  The equation may
    be under-, well-, or over- determined (i.e., the number of
    linearly independent rows of `a` can be less than, equal to, or
    greater than its number of linearly independent columns).  If `a`
    is square and of full rank, then `x` (but for round-off error) is
    the ""exact"" solution of the equation.

    Parameters
    ----------
    a : (M, N) array_like
        ""Coefficient"" matrix.
    b : {(M,), (M, K)} array_like
        Ordinate or ""dependent variable"" values. If `b` is two-dimensional,
        the least-squares solution is calculated for each of the `K` columns
        of `b`.

    Returns
    -------
    x : {(N,), (N, K)} Array
        Least-squares solution. If `b` is two-dimensional,
        the solutions are in the `K` columns of `x`.
    residuals : {(1,), (K,)} Array
        Sums of residuals; squared Euclidean 2-norm for each column in
        ``b - a*x``.
        If `b` is 1-dimensional, this is a (1,) shape array.
        Otherwise the shape is (K,).
    rank : Array
        Rank of matrix `a`.
    s : (min(M, N),) Array
        Singular values of `a`.
    ",198,118,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def lstsq(a, b):
    
    q, r = qr(a)
    x = solve_triangular(r, q.T.conj().dot(b))
    residuals = b - a.dot(x)
    residuals = abs(residuals**2).sum(axis=0, keepdims=b.ndim == 1)

    token = tokenize(a, b)

    # r must be a triangular with single block

    # rank
    rname = ""lstsq-rank-"" + token
    rdsk = {(rname,): (np.linalg.matrix_rank, (r.name, 0, 0))}
    graph = HighLevelGraph.from_collections(rname, rdsk, dependencies=[r])
    # rank must be an integer
    rank = Array(graph, rname, shape=(), chunks=(), dtype=int)

    # singular
    sname = ""lstsq-singular-"" + token
    rt = r.T.conj()
    sdsk = {
        (sname, 0): (
            _reverse,
            (np.sqrt, (np.linalg.eigvalsh, (np.dot, (rt.name, 0, 0), (r.name, 0, 0)))),
        )
    }
    graph = HighLevelGraph.from_collections(sname, sdsk, dependencies=[rt, r])
    meta = meta_from_array(residuals, 1)
    s = Array(graph, sname, shape=(r.shape[0],), chunks=r.shape[0], meta=meta)

    return x, residuals, rank, s


@derived_from(np.linalg)
        ```
    ###Assistant : 
    Return the least-squares solution to a linear matrix equation using
    QR decomposition.

    Solves the equation `a x = b` by computing a vector `x` that
    minimizes the Euclidean 2-norm `|| b - a x ||^2`.  The equation may
    be under-, well-, or over- determined (i.e., the number of
    linearly independent rows of `a` can be less than, equal to, or
    greater than its number of linearly independent columns).  If `a`
    is square and of full rank, then `x` (but for round-off error) is
    the ""exact"" solution of the equation.

    Parameters
    ----------
    a : (M, N) array_like
        ""Coefficient"" matrix.
    b : {(M,), (M, K)} array_like
        Ordinate or ""dependent variable"" values. If `b` is two-dimensional,
        the least-squares solution is calculated for each of the `K` columns
        of `b`.

    Returns
    -------
    x : {(N,), (N, K)} Array
        Least-squares solution. If `b` is two-dimensional,
        the solutions are in the `K` columns of `x`.
    residuals : {(1,), (K,)} Array
        Sums of residuals; squared Euclidean 2-norm for each column in
        ``b - a*x``.
        If `b` is 1-dimensional, this is a (1,) shape array.
        Otherwise the shape is (K,).
    rank : Array
        Rank of matrix `a`.
    s : (min(M, N),) Array
        Singular values of `a`.
    
    "
2279,"def docker_environment():  # type: () -> t.Dict[str, str]
    
    env = common_environment()
    env.update(dict((key, os.environ[key]) for key in os.environ if key.startswith('DOCKER_') or key.startswith('CONTAINER_')))
    return env
",Return a dictionary of docker related environment variables found in the current environment.,13,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def docker_environment():  # type: () -> t.Dict[str, str]
    
    env = common_environment()
    env.update(dict((key, os.environ[key]) for key in os.environ if key.startswith('DOCKER_') or key.startswith('CONTAINER_')))
    return env

        ```
    ###Assistant : Return a dictionary of docker related environment variables found in the current environment.
    "
2280,"def test_remove_from_figure(use_gridspec):
    
    fig, ax = plt.subplots()
    sc = ax.scatter([1, 2], [3, 4])
    sc.set_array(np.array([5, 6]))
    pre_position = ax.get_position()
    cb = fig.colorbar(sc, use_gridspec=use_gridspec)
    fig.subplots_adjust()
    cb.remove()
    fig.subplots_adjust()
    post_position = ax.get_position()
    assert (pre_position.get_points() == post_position.get_points()).all()

","
    Test `remove` with the specified ``use_gridspec`` setting
    ",7,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_remove_from_figure(use_gridspec):
    
    fig, ax = plt.subplots()
    sc = ax.scatter([1, 2], [3, 4])
    sc.set_array(np.array([5, 6]))
    pre_position = ax.get_position()
    cb = fig.colorbar(sc, use_gridspec=use_gridspec)
    fig.subplots_adjust()
    cb.remove()
    fig.subplots_adjust()
    post_position = ax.get_position()
    assert (pre_position.get_points() == post_position.get_points()).all()


        ```
    ###Assistant : 
    Test `remove` with the specified ``use_gridspec`` setting
    
    "
2281,"def groupby(func, seq):
    

    d = {}
    for item in seq:
        key = func(item)
        if key not in d:
            d[key] = []
        d[key].append(item)
    return d
"," Group a collection by a key function

    >>> from sympy.multipledispatch.utils import groupby
    >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank']
    >>> groupby(len, names)  # doctest: +SKIP
    {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}

    >>> iseven = lambda x: x % 2 == 0
    >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
    {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}

    See Also:
        ``countby``
    ",72,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def groupby(func, seq):
    

    d = {}
    for item in seq:
        key = func(item)
        if key not in d:
            d[key] = []
        d[key].append(item)
    return d

        ```
    ###Assistant :  Group a collection by a key function

    >>> from sympy.multipledispatch.utils import groupby
    >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank']
    >>> groupby(len, names)  # doctest: +SKIP
    {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}

    >>> iseven = lambda x: x % 2 == 0
    >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
    {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}

    See Also:
        ``countby``
    
    "
2282,"def input_files(self) -> List[str]:
        
        metadata = self._plan.execute().get_metadata()
        files = set()
        for m in metadata:
            for f in m.input_files:
                files.add(f)
        return list(files)
","Return the list of input files for the dataset.

        Time complexity: O(num input files)

        Returns:
            The list of input files used to create the dataset, or an empty
            list if the input files is not known.
        ",36,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def input_files(self) -> List[str]:
        
        metadata = self._plan.execute().get_metadata()
        files = set()
        for m in metadata:
            for f in m.input_files:
                files.add(f)
        return list(files)

        ```
    ###Assistant : Return the list of input files for the dataset.

        Time complexity: O(num input files)

        Returns:
            The list of input files used to create the dataset, or an empty
            list if the input files is not known.
        
    "
2283,"def get_nccl_reduce_op(reduce_op):
    
    if reduce_op not in NCCL_REDUCE_OP_MAP:
        raise RuntimeError(""NCCL does not support reduce op: '{}'."".format(reduce_op))
    return NCCL_REDUCE_OP_MAP[reduce_op]

","Map the reduce op to NCCL reduce op type.

    Args:
        reduce_op (ReduceOp): ReduceOp Enum (SUM/PRODUCT/MIN/MAX).
    Returns:
        (nccl.ncclRedOp_t): the mapped NCCL reduce op.
    ",22,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_nccl_reduce_op(reduce_op):
    
    if reduce_op not in NCCL_REDUCE_OP_MAP:
        raise RuntimeError(""NCCL does not support reduce op: '{}'."".format(reduce_op))
    return NCCL_REDUCE_OP_MAP[reduce_op]


        ```
    ###Assistant : Map the reduce op to NCCL reduce op type.

    Args:
        reduce_op (ReduceOp): ReduceOp Enum (SUM/PRODUCT/MIN/MAX).
    Returns:
        (nccl.ncclRedOp_t): the mapped NCCL reduce op.
    
    "
2284,"def aligned(self) -> AlignedFace:
        
        assert self._aligned is not None
        return self._aligned
", The aligned face connected to this detected face. ,8,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def aligned(self) -> AlignedFace:
        
        assert self._aligned is not None
        return self._aligned

        ```
    ###Assistant :  The aligned face connected to this detected face. 
    "
2285,"def upgrade():
    
    conn = op.get_bind()
    if conn.dialect.name == ""sqlite"":
        # in sqlite TEXT and STRING column types are the same
        return
    if conn.dialect.name == ""mysql"":
        op.alter_column(
            'connection',
            'description',
            existing_type=sa.String(length=5000),
            type_=sa.Text(length=5000),
            existing_nullable=True,
        )
    else:
        # postgres does not allow size modifier for text type
        op.alter_column('connection', 'description', existing_type=sa.String(length=5000), type_=sa.Text())

",Apply Fix description field in ``connection`` to be ``text``,9,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def upgrade():
    
    conn = op.get_bind()
    if conn.dialect.name == ""sqlite"":
        # in sqlite TEXT and STRING column types are the same
        return
    if conn.dialect.name == ""mysql"":
        op.alter_column(
            'connection',
            'description',
            existing_type=sa.String(length=5000),
            type_=sa.Text(length=5000),
            existing_nullable=True,
        )
    else:
        # postgres does not allow size modifier for text type
        op.alter_column('connection', 'description', existing_type=sa.String(length=5000), type_=sa.Text())


        ```
    ###Assistant : Apply Fix description field in ``connection`` to be ``text``
    "
2286,"def test_memory(self):
        

        params = sum(map(list, six.itervalues(self.net.params)), [])
        blobs = self.net.blobs.values()
        del self.net

        # now sum everything (forcing all memory to be read)
        total = 0
        for p in params:
            total += p.data.sum() + p.diff.sum()
        for bl in blobs:
            total += bl.data.sum() + bl.diff.sum()
",Check that holding onto blob data beyond the life of a Net is OK,14,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_memory(self):
        

        params = sum(map(list, six.itervalues(self.net.params)), [])
        blobs = self.net.blobs.values()
        del self.net

        # now sum everything (forcing all memory to be read)
        total = 0
        for p in params:
            total += p.data.sum() + p.diff.sum()
        for bl in blobs:
            total += bl.data.sum() + bl.diff.sum()

        ```
    ###Assistant : Check that holding onto blob data beyond the life of a Net is OK
    "
2287,"async def test_timeout_stops_execution_in_sync_subflows(self, tmp_path):
        
        canary_file = tmp_path / ""canary""
","
        Sync flow runs can be cancelled after a timeout once a task is called
        ",14,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_timeout_stops_execution_in_sync_subflows(self, tmp_path):
        
        canary_file = tmp_path / ""canary""

        ```
    ###Assistant : 
        Sync flow runs can be cancelled after a timeout once a task is called
        
    "
2288,"def get_income_account(doctype, txt, searchfield, start, page_len, filters):
	from erpnext.controllers.queries import get_match_cond

	# income account can be any Credit account,
	# but can also be a Asset account with account_type='Income Account' in special circumstances.
	# Hence the first condition is an ""OR""
	if not filters:
		filters = {}

	condition = """"
	if filters.get(""company""):
		condition += ""and tabAccount.company = %(company)s""

	return frappe.db.sql(
		.format(
			condition=condition, match_condition=get_match_cond(doctype), key=searchfield
		),
		{""txt"": ""%"" + txt + ""%"", ""company"": filters.get(""company"", """")},
	)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs","select tabAccount.name from `tabAccount`
			where (tabAccount.report_type = ""Profit and Loss""
					or tabAccount.account_type in (""Income Account"", ""Temporary""))
				and tabAccount.is_group=0
				and tabAccount.`{key}` LIKE %(txt)s
				{condition} {match_condition}
			order by idx desc, name",29,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_income_account(doctype, txt, searchfield, start, page_len, filters):
	from erpnext.controllers.queries import get_match_cond

	# income account can be any Credit account,
	# but can also be a Asset account with account_type='Income Account' in special circumstances.
	# Hence the first condition is an ""OR""
	if not filters:
		filters = {}

	condition = """"
	if filters.get(""company""):
		condition += ""and tabAccount.company = %(company)s""

	return frappe.db.sql(
		.format(
			condition=condition, match_condition=get_match_cond(doctype), key=searchfield
		),
		{""txt"": ""%"" + txt + ""%"", ""company"": filters.get(""company"", """")},
	)


@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs
        ```
    ###Assistant : select tabAccount.name from `tabAccount`
			where (tabAccount.report_type = ""Profit and Loss""
					or tabAccount.account_type in (""Income Account"", ""Temporary""))
				and tabAccount.is_group=0
				and tabAccount.`{key}` LIKE %(txt)s
				{condition} {match_condition}
			order by idx desc, name
    "
2289,"def win_find_exe(filename, installsubdir=None, env=""ProgramFiles""):
    # type: (str, Optional[Any], str) -> str
    
    fns = [filename] if filename.endswith("".exe"") else [filename + "".exe"", filename]  # noqa: E501
    for fn in fns:
        try:
            if installsubdir is None:
                path = _where(fn)
            else:
                path = _where(fn, dirs=[os.path.join(os.environ[env], installsubdir)])  # noqa: E501
        except IOError:
            path = None
        else:
            break
    return path or """"

","Find executable in current dir, system path or in the
    given ProgramFiles subdir, and retuen its absolute path.
    ",18,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def win_find_exe(filename, installsubdir=None, env=""ProgramFiles""):
    # type: (str, Optional[Any], str) -> str
    
    fns = [filename] if filename.endswith("".exe"") else [filename + "".exe"", filename]  # noqa: E501
    for fn in fns:
        try:
            if installsubdir is None:
                path = _where(fn)
            else:
                path = _where(fn, dirs=[os.path.join(os.environ[env], installsubdir)])  # noqa: E501
        except IOError:
            path = None
        else:
            break
    return path or """"


        ```
    ###Assistant : Find executable in current dir, system path or in the
    given ProgramFiles subdir, and retuen its absolute path.
    
    "
2290,"def test_custom_page_queryset(self):
        
        self.assertIs(type(CustomManagerPage.objects.all()), CustomPageQuerySet)
        self.assertIs(type(CustomManagerPage.objects.about_spam()), CustomPageQuerySet)
        self.assertIs(
            type(CustomManagerPage.objects.all().about_spam()), CustomPageQuerySet
        )
        self.assertIs(
            type(CustomManagerPage.objects.about_spam().all()), CustomPageQuerySet
        )
","
        Managers that are constructed from a custom PageQuerySet
        (via PageManager.from_queryset(CustomPageQuerySet)) should return
        querysets of that type
        ",16,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_custom_page_queryset(self):
        
        self.assertIs(type(CustomManagerPage.objects.all()), CustomPageQuerySet)
        self.assertIs(type(CustomManagerPage.objects.about_spam()), CustomPageQuerySet)
        self.assertIs(
            type(CustomManagerPage.objects.all().about_spam()), CustomPageQuerySet
        )
        self.assertIs(
            type(CustomManagerPage.objects.about_spam().all()), CustomPageQuerySet
        )

        ```
    ###Assistant : 
        Managers that are constructed from a custom PageQuerySet
        (via PageManager.from_queryset(CustomPageQuerySet)) should return
        querysets of that type
        
    "
2291,"def distro_release_info(self):
        # type: () -> Dict[str, str]
        
        return self._distro_release_info
","
        Return a dictionary containing key-value pairs for the information
        items from the distro release file data source of the OS
        distribution.

        For details, see :func:`distro.distro_release_info`.
        ",25,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def distro_release_info(self):
        # type: () -> Dict[str, str]
        
        return self._distro_release_info

        ```
    ###Assistant : 
        Return a dictionary containing key-value pairs for the information
        items from the distro release file data source of the OS
        distribution.

        For details, see :func:`distro.distro_release_info`.
        
    "
2292,"def user_documents_dir(self) -> str:
        
        return os.path.normpath(get_win_folder(""CSIDL_PERSONAL""))
","
        :return: documents directory tied to the user e.g. ``%USERPROFILE%\\Documents``
        ",9,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def user_documents_dir(self) -> str:
        
        return os.path.normpath(get_win_folder(""CSIDL_PERSONAL""))

        ```
    ###Assistant : 
        :return: documents directory tied to the user e.g. ``%USERPROFILE%\\Documents``
        
    "
2293,"def round_robin_reduce_idx_iterator(self):
        
        idx = 0
        round_idx = 0
        while idx < self.output_num_blocks:
            for merge_idx in range(self.num_merge_tasks_per_round):
                if merge_idx < self._partitions_with_extra_task:
                    reduce_idx = merge_idx * (self.merge_partition_size + 1)
                    partition_size = self.merge_partition_size + 1
                else:
                    reduce_idx = self._partitions_with_extra_task * (
                        self.merge_partition_size + 1
                    )
                    merge_idx -= self._partitions_with_extra_task
                    reduce_idx += merge_idx * self.merge_partition_size
                    partition_size = self.merge_partition_size

                if round_idx >= partition_size:
                    continue

                reduce_idx += round_idx
                yield reduce_idx
                idx += 1
            round_idx += 1

","
        When there are multiple nodes, merge tasks are spread throughout the
        cluster to improve load-balancing. Each merge task produces outputs for
        a contiguous partition of reduce tasks. This method creates an iterator
        that returns reduce task indices round-robin across the merge tasks.
        This can be used to submit reduce tasks in a way that spreads the load
        evenly across the cluster.
        ",61,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def round_robin_reduce_idx_iterator(self):
        
        idx = 0
        round_idx = 0
        while idx < self.output_num_blocks:
            for merge_idx in range(self.num_merge_tasks_per_round):
                if merge_idx < self._partitions_with_extra_task:
                    reduce_idx = merge_idx * (self.merge_partition_size + 1)
                    partition_size = self.merge_partition_size + 1
                else:
                    reduce_idx = self._partitions_with_extra_task * (
                        self.merge_partition_size + 1
                    )
                    merge_idx -= self._partitions_with_extra_task
                    reduce_idx += merge_idx * self.merge_partition_size
                    partition_size = self.merge_partition_size

                if round_idx >= partition_size:
                    continue

                reduce_idx += round_idx
                yield reduce_idx
                idx += 1
            round_idx += 1


        ```
    ###Assistant : 
        When there are multiple nodes, merge tasks are spread throughout the
        cluster to improve load-balancing. Each merge task produces outputs for
        a contiguous partition of reduce tasks. This method creates an iterator
        that returns reduce task indices round-robin across the merge tasks.
        This can be used to submit reduce tasks in a way that spreads the load
        evenly across the cluster.
        
    "
2294,"def _check_index_name(self, result):
        
        if self._by is not None:
            # pandas does not name the index for this case
            result._query_compiler.set_index_name(None)
        return result
","
        Check the result of groupby aggregation on the need of resetting index name.

        Parameters
        ----------
        result : DataFrame
            Group by aggregation result.

        Returns
        -------
        DataFrame
        ",25,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_index_name(self, result):
        
        if self._by is not None:
            # pandas does not name the index for this case
            result._query_compiler.set_index_name(None)
        return result

        ```
    ###Assistant : 
        Check the result of groupby aggregation on the need of resetting index name.

        Parameters
        ----------
        result : DataFrame
            Group by aggregation result.

        Returns
        -------
        DataFrame
        
    "
2295,"def check_points_in_rotated_boxes(points, boxes):
    
    # [B, N, 5] -> [B, N, 4, 2]
    corners = box2corners(boxes)
    # [1, L, 2] -> [1, 1, L, 2]
    points = points.unsqueeze(0)
    # [B, N, 4, 2] -> [B, N, 1, 2]
    a, b, c, d = corners.split(4, axis=2)
    ab = b - a
    ad = d - a
    # [B, N, L, 2]
    ap = points - a
    # [B, N, L]
    norm_ab = paddle.sum(ab * ab, axis=-1)
    # [B, N, L]
    norm_ad = paddle.sum(ad * ad, axis=-1)
    # [B, N, L] dot product
    ap_dot_ab = paddle.sum(ap * ab, axis=-1)
    # [B, N, L] dot product
    ap_dot_ad = paddle.sum(ap * ad, axis=-1)
    # [B, N, L] <A, B> = |A|*|B|*cos(theta) 
    is_in_box = (ap_dot_ab >= 0) & (ap_dot_ab <= norm_ab) & (ap_dot_ad >= 0) & (
        ap_dot_ad <= norm_ad)
    return is_in_box

","Check whether point is in rotated boxes

    Args:
        points (tensor): (1, L, 2) anchor points
        boxes (tensor): [B, N, 5] gt_bboxes
        eps (float): default 1e-9
    
    Returns:
        is_in_box (tensor): (B, N, L)

    ",31,136,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_points_in_rotated_boxes(points, boxes):
    
    # [B, N, 5] -> [B, N, 4, 2]
    corners = box2corners(boxes)
    # [1, L, 2] -> [1, 1, L, 2]
    points = points.unsqueeze(0)
    # [B, N, 4, 2] -> [B, N, 1, 2]
    a, b, c, d = corners.split(4, axis=2)
    ab = b - a
    ad = d - a
    # [B, N, L, 2]
    ap = points - a
    # [B, N, L]
    norm_ab = paddle.sum(ab * ab, axis=-1)
    # [B, N, L]
    norm_ad = paddle.sum(ad * ad, axis=-1)
    # [B, N, L] dot product
    ap_dot_ab = paddle.sum(ap * ab, axis=-1)
    # [B, N, L] dot product
    ap_dot_ad = paddle.sum(ap * ad, axis=-1)
    # [B, N, L] <A, B> = |A|*|B|*cos(theta) 
    is_in_box = (ap_dot_ab >= 0) & (ap_dot_ab <= norm_ab) & (ap_dot_ad >= 0) & (
        ap_dot_ad <= norm_ad)
    return is_in_box


        ```
    ###Assistant : Check whether point is in rotated boxes

    Args:
        points (tensor): (1, L, 2) anchor points
        boxes (tensor): [B, N, 5] gt_bboxes
        eps (float): default 1e-9
    
    Returns:
        is_in_box (tensor): (B, N, L)

    
    "
2296,"def write_ssh_wrapper(module):
    
    try:
        # make sure we have full permission to the module_dir, which
        # may not be the case if we're sudo'ing to a non-root user
        if os.access(module.tmpdir, os.W_OK | os.R_OK | os.X_OK):
            fd, wrapper_path = tempfile.mkstemp(prefix=module.tmpdir + '/')
        else:
            raise OSError
    except (IOError, OSError):
        fd, wrapper_path = tempfile.mkstemp()

    # use existing git_ssh/ssh_command, fallback to 'ssh'
    template = b( % os.environ.get('GIT_SSH', os.environ.get('GIT_SSH_COMMAND', 'ssh')))

    # write it
    with os.fdopen(fd, 'w+b') as fh:
        fh.write(template)

    # set execute
    st = os.stat(wrapper_path)
    os.chmod(wrapper_path, st.st_mode | stat.S_IEXEC)

    module.debug('Wrote temp git ssh wrapper (%s): %s' % (wrapper_path, template))

    # ensure we cleanup after ourselves
    module.add_cleanup_file(path=wrapper_path)

    return wrapper_path

","
        This writes an shell wrapper for ssh options to be used with git
        this is only relevant for older versions of gitthat cannot
        handle the options themselves. Returns path to the script
    #!/bin/sh
%s $GIT_SSH_OPTS
",35,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def write_ssh_wrapper(module):
    
    try:
        # make sure we have full permission to the module_dir, which
        # may not be the case if we're sudo'ing to a non-root user
        if os.access(module.tmpdir, os.W_OK | os.R_OK | os.X_OK):
            fd, wrapper_path = tempfile.mkstemp(prefix=module.tmpdir + '/')
        else:
            raise OSError
    except (IOError, OSError):
        fd, wrapper_path = tempfile.mkstemp()

    # use existing git_ssh/ssh_command, fallback to 'ssh'
    template = b( % os.environ.get('GIT_SSH', os.environ.get('GIT_SSH_COMMAND', 'ssh')))

    # write it
    with os.fdopen(fd, 'w+b') as fh:
        fh.write(template)

    # set execute
    st = os.stat(wrapper_path)
    os.chmod(wrapper_path, st.st_mode | stat.S_IEXEC)

    module.debug('Wrote temp git ssh wrapper (%s): %s' % (wrapper_path, template))

    # ensure we cleanup after ourselves
    module.add_cleanup_file(path=wrapper_path)

    return wrapper_path


        ```
    ###Assistant : 
        This writes an shell wrapper for ssh options to be used with git
        this is only relevant for older versions of gitthat cannot
        handle the options themselves. Returns path to the script
    #!/bin/sh
%s $GIT_SSH_OPTS

    "
2297,"def alpha_composite(self, im, dest=(0, 0), source=(0, 0)):
        

        if not isinstance(source, (list, tuple)):
            msg = ""Source must be a tuple""
            raise ValueError(msg)
        if not isinstance(dest, (list, tuple)):
            msg = ""Destination must be a tuple""
            raise ValueError(msg)
        if not len(source) in (2, 4):
            msg = ""Source must be a 2 or 4-tuple""
            raise ValueError(msg)
        if not len(dest) == 2:
            msg = ""Destination must be a 2-tuple""
            raise ValueError(msg)
        if min(source) < 0:
            msg = ""Source must be non-negative""
            raise ValueError(msg)

        if len(source) == 2:
            source = source + im.size

        # over image, crop if it's not the whole thing.
        if source == (0, 0) + im.size:
            overlay = im
        else:
            overlay = im.crop(source)

        # target for the paste
        box = dest + (dest[0] + overlay.width, dest[1] + overlay.height)

        # destination image. don't copy if we're using the whole image.
        if box == (0, 0) + self.size:
            background = self
        else:
            background = self.crop(box)

        result = alpha_composite(background, overlay)
        self.paste(result, box)
","'In-place' analog of Image.alpha_composite. Composites an image
        onto this image.

        :param im: image to composite over this one
        :param dest: Optional 2 tuple (left, top) specifying the upper
          left corner in this (destination) image.
        :param source: Optional 2 (left, top) tuple for the upper left
          corner in the overlay source image, or 4 tuple (left, top, right,
          bottom) for the bounds of the source rectangle

        Performance Note: Not currently implemented in-place in the core layer.
        ",75,157,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def alpha_composite(self, im, dest=(0, 0), source=(0, 0)):
        

        if not isinstance(source, (list, tuple)):
            msg = ""Source must be a tuple""
            raise ValueError(msg)
        if not isinstance(dest, (list, tuple)):
            msg = ""Destination must be a tuple""
            raise ValueError(msg)
        if not len(source) in (2, 4):
            msg = ""Source must be a 2 or 4-tuple""
            raise ValueError(msg)
        if not len(dest) == 2:
            msg = ""Destination must be a 2-tuple""
            raise ValueError(msg)
        if min(source) < 0:
            msg = ""Source must be non-negative""
            raise ValueError(msg)

        if len(source) == 2:
            source = source + im.size

        # over image, crop if it's not the whole thing.
        if source == (0, 0) + im.size:
            overlay = im
        else:
            overlay = im.crop(source)

        # target for the paste
        box = dest + (dest[0] + overlay.width, dest[1] + overlay.height)

        # destination image. don't copy if we're using the whole image.
        if box == (0, 0) + self.size:
            background = self
        else:
            background = self.crop(box)

        result = alpha_composite(background, overlay)
        self.paste(result, box)

        ```
    ###Assistant : 'In-place' analog of Image.alpha_composite. Composites an image
        onto this image.

        :param im: image to composite over this one
        :param dest: Optional 2 tuple (left, top) specifying the upper
          left corner in this (destination) image.
        :param source: Optional 2 (left, top) tuple for the upper left
          corner in the overlay source image, or 4 tuple (left, top, right,
          bottom) for the bounds of the source rectangle

        Performance Note: Not currently implemented in-place in the core layer.
        
    "
2298,"def test_form_field_clean_name_override(self):
        

        field = ExtendedFormField.objects.create(
            page=self.form_page,
            sort_order=1,
            label=""quanti ge·là·to?"",
            field_type=""number"",  # only number fields will add the ID as a prefix to the clean_name
            required=True,
        )

        self.assertEqual(field.clean_name, ""number_field--quanti_gelato"")
","
        Creating a new field should use the overridden method
        See ExtendedFormField get_field_clean_name method
        ",13,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_form_field_clean_name_override(self):
        

        field = ExtendedFormField.objects.create(
            page=self.form_page,
            sort_order=1,
            label=""quanti ge·là·to?"",
            field_type=""number"",  # only number fields will add the ID as a prefix to the clean_name
            required=True,
        )

        self.assertEqual(field.clean_name, ""number_field--quanti_gelato"")

        ```
    ###Assistant : 
        Creating a new field should use the overridden method
        See ExtendedFormField get_field_clean_name method
        
    "
2299,"def polyder(p, m=1):
  _check_arraylike(""polyder"", p)
  m = core.concrete_or_error(operator.index, m, ""'m' argument of jnp.polyder"")
  p, = _promote_dtypes_inexact(p)
  if m < 0:
    raise ValueError(""Order of derivative must be positive"")
  if m == 0:
    return p
  coeff = (arange(len(p), m, -1)[np.newaxis, :] - 1 - arange(m)[:, np.newaxis]).prod(0)
  return p[:-m] * coeff


_LEADING_ZEROS_DOC = 

@_wraps(np.polymul, lax_description=_LEADING_ZEROS_DOC)","\
Setting trim_leading_zeros=True makes the output match that of numpy.
But prevents the function from being able to be used in compiled code.
",23,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def polyder(p, m=1):
  _check_arraylike(""polyder"", p)
  m = core.concrete_or_error(operator.index, m, ""'m' argument of jnp.polyder"")
  p, = _promote_dtypes_inexact(p)
  if m < 0:
    raise ValueError(""Order of derivative must be positive"")
  if m == 0:
    return p
  coeff = (arange(len(p), m, -1)[np.newaxis, :] - 1 - arange(m)[:, np.newaxis]).prod(0)
  return p[:-m] * coeff


_LEADING_ZEROS_DOC = 

@_wraps(np.polymul, lax_description=_LEADING_ZEROS_DOC)
        ```
    ###Assistant : \
Setting trim_leading_zeros=True makes the output match that of numpy.
But prevents the function from being able to be used in compiled code.

    "
2300,"def probiou_loss(pred, target, eps=1e-3, mode='l1'):
    

    gbboxes1 = gbb_form(pred)
    gbboxes2 = gbb_form(target)

    x1, y1, a1_, b1_, c1_ = gbboxes1[:,
                                     0], gbboxes1[:,
                                                  1], gbboxes1[:,
                                                               2], gbboxes1[:,
                                                                            3], gbboxes1[:,
                                                                                         4]
    x2, y2, a2_, b2_, c2_ = gbboxes2[:,
                                     0], gbboxes2[:,
                                                  1], gbboxes2[:,
                                                               2], gbboxes2[:,
                                                                            3], gbboxes2[:,
                                                                                         4]

    a1, b1, c1 = rotated_form(a1_, b1_, c1_)
    a2, b2, c2 = rotated_form(a2_, b2_, c2_)

    t1 = 0.25 * ((a1 + a2) * (paddle.pow(y1 - y2, 2)) + (b1 + b2) * (paddle.pow(x1 - x2, 2))) + \
         0.5 * ((c1+c2)*(x2-x1)*(y1-y2))
    t2 = (a1 + a2) * (b1 + b2) - paddle.pow(c1 + c2, 2)
    t3_ = (a1 * b1 - c1 * c1) * (a2 * b2 - c2 * c2)
    t3 = 0.5 * paddle.log(t2 / (4 * paddle.sqrt(F.relu(t3_)) + eps))

    B_d = (t1 / t2) + t3
    # B_d = t1 + t2 + t3

    B_d = paddle.clip(B_d, min=eps, max=100.0)
    l1 = paddle.sqrt(1.0 - paddle.exp(-B_d) + eps)
    l_i = paddle.pow(l1, 2.0)
    l2 = -paddle.log(1.0 - l_i + eps)

    if mode == 'l1':
        probiou = l1
    if mode == 'l2':
        probiou = l2

    return probiou


@serializable
@register","
        pred    -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours predicted box ;in case of HBB angle == 0
        target  -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours target    box ;in case of HBB angle == 0
        eps     -> threshold to avoid infinite values
        mode    -> ('l1' in [0,1] or 'l2' in [0,inf]) metrics according our paper

    ",58,181,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def probiou_loss(pred, target, eps=1e-3, mode='l1'):
    

    gbboxes1 = gbb_form(pred)
    gbboxes2 = gbb_form(target)

    x1, y1, a1_, b1_, c1_ = gbboxes1[:,
                                     0], gbboxes1[:,
                                                  1], gbboxes1[:,
                                                               2], gbboxes1[:,
                                                                            3], gbboxes1[:,
                                                                                         4]
    x2, y2, a2_, b2_, c2_ = gbboxes2[:,
                                     0], gbboxes2[:,
                                                  1], gbboxes2[:,
                                                               2], gbboxes2[:,
                                                                            3], gbboxes2[:,
                                                                                         4]

    a1, b1, c1 = rotated_form(a1_, b1_, c1_)
    a2, b2, c2 = rotated_form(a2_, b2_, c2_)

    t1 = 0.25 * ((a1 + a2) * (paddle.pow(y1 - y2, 2)) + (b1 + b2) * (paddle.pow(x1 - x2, 2))) + \
         0.5 * ((c1+c2)*(x2-x1)*(y1-y2))
    t2 = (a1 + a2) * (b1 + b2) - paddle.pow(c1 + c2, 2)
    t3_ = (a1 * b1 - c1 * c1) * (a2 * b2 - c2 * c2)
    t3 = 0.5 * paddle.log(t2 / (4 * paddle.sqrt(F.relu(t3_)) + eps))

    B_d = (t1 / t2) + t3
    # B_d = t1 + t2 + t3

    B_d = paddle.clip(B_d, min=eps, max=100.0)
    l1 = paddle.sqrt(1.0 - paddle.exp(-B_d) + eps)
    l_i = paddle.pow(l1, 2.0)
    l2 = -paddle.log(1.0 - l_i + eps)

    if mode == 'l1':
        probiou = l1
    if mode == 'l2':
        probiou = l2

    return probiou


@serializable
@register
        ```
    ###Assistant : 
        pred    -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours predicted box ;in case of HBB angle == 0
        target  -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours target    box ;in case of HBB angle == 0
        eps     -> threshold to avoid infinite values
        mode    -> ('l1' in [0,1] or 'l2' in [0,inf]) metrics according our paper

    
    "
2301,"def mayDisableConsoleWindow():
    

    # TODO: What about MSYS2?
    return isWin32Windows() or isMacOS()

",":returns: bool derived from platform support of disabling the console,",10,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mayDisableConsoleWindow():
    

    # TODO: What about MSYS2?
    return isWin32Windows() or isMacOS()


        ```
    ###Assistant : :returns: bool derived from platform support of disabling the console,
    "
2302,"def compat_cfg(cfg):
    
    cfg = copy.deepcopy(cfg)
    cfg = compat_imgs_per_gpu(cfg)
    cfg = compat_loader_args(cfg)
    cfg = compat_runner_args(cfg)
    return cfg

","This function would modify some filed to keep the compatibility of
    config.

    For example, it will move some args which will be deprecated to the correct
    fields.
    ",27,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compat_cfg(cfg):
    
    cfg = copy.deepcopy(cfg)
    cfg = compat_imgs_per_gpu(cfg)
    cfg = compat_loader_args(cfg)
    cfg = compat_runner_args(cfg)
    return cfg


        ```
    ###Assistant : This function would modify some filed to keep the compatibility of
    config.

    For example, it will move some args which will be deprecated to the correct
    fields.
    
    "
2303,"def getsourcelines(object):
    
    object = unwrap(object)
    lines, lnum = findsource(object)

    if istraceback(object):
        object = object.tb_frame

    # for module or frame that corresponds to module, return all source lines
    if (ismodule(object) or
        (isframe(object) and object.f_code.co_name == ""<module>"")):
        return lines, 0
    else:
        return getblock(lines[lnum:]), lnum + 1
","Return a list of source lines and starting line number for an object.

    The argument may be a module, class, method, function, traceback, frame,
    or code object.  The source code is returned as a list of the lines
    corresponding to the object and the line number indicates where in the
    original source file the first line of code was found.  An OSError is
    raised if the source code cannot be retrieved.",71,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getsourcelines(object):
    
    object = unwrap(object)
    lines, lnum = findsource(object)

    if istraceback(object):
        object = object.tb_frame

    # for module or frame that corresponds to module, return all source lines
    if (ismodule(object) or
        (isframe(object) and object.f_code.co_name == ""<module>"")):
        return lines, 0
    else:
        return getblock(lines[lnum:]), lnum + 1

        ```
    ###Assistant : Return a list of source lines and starting line number for an object.

    The argument may be a module, class, method, function, traceback, frame,
    or code object.  The source code is returned as a list of the lines
    corresponding to the object and the line number indicates where in the
    original source file the first line of code was found.  An OSError is
    raised if the source code cannot be retrieved.
    "
2304,"def get_del_batches(self, objs, fields):
        
        field_names = [field.name for field in fields]
        conn_batch_size = max(
            connections[self.using].ops.bulk_batch_size(field_names, objs), 1
        )
        if len(objs) > conn_batch_size:
            return [
                objs[i : i + conn_batch_size]
                for i in range(0, len(objs), conn_batch_size)
            ]
        else:
            return [objs]
","
        Return the objs in suitably sized batches for the used connection.
        ",11,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_del_batches(self, objs, fields):
        
        field_names = [field.name for field in fields]
        conn_batch_size = max(
            connections[self.using].ops.bulk_batch_size(field_names, objs), 1
        )
        if len(objs) > conn_batch_size:
            return [
                objs[i : i + conn_batch_size]
                for i in range(0, len(objs), conn_batch_size)
            ]
        else:
            return [objs]

        ```
    ###Assistant : 
        Return the objs in suitably sized batches for the used connection.
        
    "
2305,"def save(self, global_step):
        
        save_path = osp.join(self.directory, f""{global_step:09d}.ckpt"")
        self.checkpoint.save(save_path)
        self.latest_checkpoint = save_path
        self.queue.put(True)
","Create a new checkpoint.

        Args:
          global_step (int): The iteration number which will be used
            to name the checkpoint.
        ",18,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save(self, global_step):
        
        save_path = osp.join(self.directory, f""{global_step:09d}.ckpt"")
        self.checkpoint.save(save_path)
        self.latest_checkpoint = save_path
        self.queue.put(True)

        ```
    ###Assistant : Create a new checkpoint.

        Args:
          global_step (int): The iteration number which will be used
            to name the checkpoint.
        
    "
2306,"def versions_from_file(filename):
    
    try:
        with open(filename) as f:
            contents = f.read()
    except OSError:
        raise NotThisMethod(""unable to read _version.py"")
    mo = re.search(r""version_json =   # END VERSION_JSON"",
                   contents, re.M | re.S)
    if not mo:
        mo = re.search(r""version_json =   # END VERSION_JSON"",
                       contents, re.M | re.S)
    if not mo:
        raise NotThisMethod(""no version_json in _version.py"")
    return json.loads(mo.group(1))

",Try to determine the version from _version.py if present.\n(.*)\r\n(.*),9,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def versions_from_file(filename):
    
    try:
        with open(filename) as f:
            contents = f.read()
    except OSError:
        raise NotThisMethod(""unable to read _version.py"")
    mo = re.search(r""version_json =   # END VERSION_JSON"",
                   contents, re.M | re.S)
    if not mo:
        mo = re.search(r""version_json =   # END VERSION_JSON"",
                       contents, re.M | re.S)
    if not mo:
        raise NotThisMethod(""no version_json in _version.py"")
    return json.loads(mo.group(1))


        ```
    ###Assistant : Try to determine the version from _version.py if present.\n(.*)\r\n(.*)
    "
2307,"def get_total_allocated_amount(payment_entry):
	return frappe.db.sql(
		,
		(payment_entry.payment_document, payment_entry.payment_entry),
		as_dict=True,
	)

","
		SELECT
			SUM(btp.allocated_amount) as allocated_amount,
			bt.name
		FROM
			`tabBank Transaction Payments` as btp
		LEFT JOIN
			`tabBank Transaction` bt ON bt.name=btp.parent
		WHERE
			btp.payment_document = %s
		AND
			btp.payment_entry = %s
		AND
			bt.docstatus = 1",30,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_total_allocated_amount(payment_entry):
	return frappe.db.sql(
		,
		(payment_entry.payment_document, payment_entry.payment_entry),
		as_dict=True,
	)


        ```
    ###Assistant : 
		SELECT
			SUM(btp.allocated_amount) as allocated_amount,
			bt.name
		FROM
			`tabBank Transaction Payments` as btp
		LEFT JOIN
			`tabBank Transaction` bt ON bt.name=btp.parent
		WHERE
			btp.payment_document = %s
		AND
			btp.payment_entry = %s
		AND
			bt.docstatus = 1
    "
2308,"def min_weight_matching(G, maxcardinality=False, weight=""weight""):
    
    if len(G.edges) == 0:
        return max_weight_matching(G, maxcardinality, weight)
    G_edges = G.edges(data=weight, default=1)
    min_weight = min(w for _, _, w in G_edges)
    InvG = nx.Graph()
    edges = ((u, v, 1 / (1 + w - min_weight)) for u, v, w in G_edges)
    InvG.add_weighted_edges_from(edges, weight=weight)
    return max_weight_matching(InvG, maxcardinality, weight)


@not_implemented_for(""multigraph"")
@not_implemented_for(""directed"")","Computing a minimum-weight maximal matching of G.

    Use reciprocal edge weights with the maximum-weight algorithm.

    A matching is a subset of edges in which no node occurs more than once.
    The weight of a matching is the sum of the weights of its edges.
    A maximal matching cannot add more edges and still be a matching.
    The cardinality of a matching is the number of matched edges.

    This method replaces the weights with their reciprocal and
    then runs :func:`max_weight_matching`.
    Read the documentation of max_weight_matching for more information.

    Parameters
    ----------
    G : NetworkX graph
      Undirected graph

    maxcardinality: bool, optional (default=False)
       If maxcardinality is True, compute the maximum-cardinality matching
       with minimum weight among all maximum-cardinality matchings.

    weight: string, optional (default='weight')
       Edge data key corresponding to the edge weight.
       If key not found, uses 1 as weight.

    Returns
    -------
    matching : set
        A minimal weight matching of the graph.
    ",146,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def min_weight_matching(G, maxcardinality=False, weight=""weight""):
    
    if len(G.edges) == 0:
        return max_weight_matching(G, maxcardinality, weight)
    G_edges = G.edges(data=weight, default=1)
    min_weight = min(w for _, _, w in G_edges)
    InvG = nx.Graph()
    edges = ((u, v, 1 / (1 + w - min_weight)) for u, v, w in G_edges)
    InvG.add_weighted_edges_from(edges, weight=weight)
    return max_weight_matching(InvG, maxcardinality, weight)


@not_implemented_for(""multigraph"")
@not_implemented_for(""directed"")
        ```
    ###Assistant : Computing a minimum-weight maximal matching of G.

    Use reciprocal edge weights with the maximum-weight algorithm.

    A matching is a subset of edges in which no node occurs more than once.
    The weight of a matching is the sum of the weights of its edges.
    A maximal matching cannot add more edges and still be a matching.
    The cardinality of a matching is the number of matched edges.

    This method replaces the weights with their reciprocal and
    then runs :func:`max_weight_matching`.
    Read the documentation of max_weight_matching for more information.

    Parameters
    ----------
    G : NetworkX graph
      Undirected graph

    maxcardinality: bool, optional (default=False)
       If maxcardinality is True, compute the maximum-cardinality matching
       with minimum weight among all maximum-cardinality matchings.

    weight: string, optional (default='weight')
       Edge data key corresponding to the edge weight.
       If key not found, uses 1 as weight.

    Returns
    -------
    matching : set
        A minimal weight matching of the graph.
    
    "
2309,"def site_config_dir(self) -> str:
        
        return self._append_app_name_and_version(""/Library/Preferences"")
",":return: config directory shared by the users, e.g. ``/Library/Preferences/$appname``",9,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def site_config_dir(self) -> str:
        
        return self._append_app_name_and_version(""/Library/Preferences"")

        ```
    ###Assistant : :return: config directory shared by the users, e.g. ``/Library/Preferences/$appname``
    "
2310,"def _cmp_op(self, other, op_name):
        
        lhs_dtype_class = self._get_dtype_cmp_class(self._dtype)
        rhs_dtype_class = self._get_dtype_cmp_class(other._dtype)
        res_dtype = get_dtype(bool)
        # In HDK comparison with NULL always results in NULL,
        # but in pandas it is True for 'ne' comparison and False
        # for others.
        # Also pandas allows 'eq' and 'ne' comparison for values
        # of incompatible types which doesn't work in HDK.
        if lhs_dtype_class != rhs_dtype_class:
            if op_name == ""eq"" or op_name == ""ne"":
                return LiteralExpr(op_name == ""ne"")
            else:
                raise TypeError(
                    f""Invalid comparison between {self._dtype} and {other._dtype}""
                )
        else:
            cmp = OpExpr(self.binary_operations[op_name], [self, other], res_dtype)
            return build_if_then_else(
                self.is_null(), LiteralExpr(op_name == ""ne""), cmp, res_dtype
            )
","
        Build a comparison expression.

        Parameters
        ----------
        other : BaseExpr
            A value to compare with.
        op_name : str
            The comparison operation name.

        Returns
        -------
        BaseExpr
            The resulting comparison expression.
        ",28,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _cmp_op(self, other, op_name):
        
        lhs_dtype_class = self._get_dtype_cmp_class(self._dtype)
        rhs_dtype_class = self._get_dtype_cmp_class(other._dtype)
        res_dtype = get_dtype(bool)
        # In HDK comparison with NULL always results in NULL,
        # but in pandas it is True for 'ne' comparison and False
        # for others.
        # Also pandas allows 'eq' and 'ne' comparison for values
        # of incompatible types which doesn't work in HDK.
        if lhs_dtype_class != rhs_dtype_class:
            if op_name == ""eq"" or op_name == ""ne"":
                return LiteralExpr(op_name == ""ne"")
            else:
                raise TypeError(
                    f""Invalid comparison between {self._dtype} and {other._dtype}""
                )
        else:
            cmp = OpExpr(self.binary_operations[op_name], [self, other], res_dtype)
            return build_if_then_else(
                self.is_null(), LiteralExpr(op_name == ""ne""), cmp, res_dtype
            )

        ```
    ###Assistant : 
        Build a comparison expression.

        Parameters
        ----------
        other : BaseExpr
            A value to compare with.
        op_name : str
            The comparison operation name.

        Returns
        -------
        BaseExpr
            The resulting comparison expression.
        
    "
2311,"def set_active(self, index):
        
        if index not in range(len(self.labels)):
            raise ValueError(f'Invalid CheckButton index: {index}')

        if colors.same_color(
                self._crosses.get_facecolor()[index], colors.to_rgba(""none"")
        ):
            self._crosses.get_facecolor()[index] = colors.to_rgba(""k"")
        else:
            self._crosses.get_facecolor()[index] = colors.to_rgba(""none"")

        if hasattr(self, ""_rectangles""):
            for i, p in enumerate(self._rectangles):
                p.set_facecolor(""k"" if colors.same_color(
                    p.get_facecolor(), colors.to_rgba(""none""))
                                else ""none"")

        if self.drawon:
            self.ax.figure.canvas.draw()

        if self.eventson:
            self._observers.process('clicked', self.labels[index].get_text())
","
        Toggle (activate or deactivate) a check button by index.

        Callbacks will be triggered if :attr:`eventson` is True.

        Parameters
        ----------
        index : int
            Index of the check button to toggle.

        Raises
        ------
        ValueError
            If *index* is invalid.
        ",36,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_active(self, index):
        
        if index not in range(len(self.labels)):
            raise ValueError(f'Invalid CheckButton index: {index}')

        if colors.same_color(
                self._crosses.get_facecolor()[index], colors.to_rgba(""none"")
        ):
            self._crosses.get_facecolor()[index] = colors.to_rgba(""k"")
        else:
            self._crosses.get_facecolor()[index] = colors.to_rgba(""none"")

        if hasattr(self, ""_rectangles""):
            for i, p in enumerate(self._rectangles):
                p.set_facecolor(""k"" if colors.same_color(
                    p.get_facecolor(), colors.to_rgba(""none""))
                                else ""none"")

        if self.drawon:
            self.ax.figure.canvas.draw()

        if self.eventson:
            self._observers.process('clicked', self.labels[index].get_text())

        ```
    ###Assistant : 
        Toggle (activate or deactivate) a check button by index.

        Callbacks will be triggered if :attr:`eventson` is True.

        Parameters
        ----------
        index : int
            Index of the check button to toggle.

        Raises
        ------
        ValueError
            If *index* is invalid.
        
    "
2312,"async def async_update(self) -> None:
        
        await self.ebox_data.async_update()
        if self.entity_description.key in self.ebox_data.data:
            self._attr_native_value = round(
                self.ebox_data.data[self.entity_description.key], 2
            )

",Get the latest data from EBox and update the state.,10,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_update(self) -> None:
        
        await self.ebox_data.async_update()
        if self.entity_description.key in self.ebox_data.data:
            self._attr_native_value = round(
                self.ebox_data.data[self.entity_description.key], 2
            )


        ```
    ###Assistant : Get the latest data from EBox and update the state.
    "
2313,"def panoptic_evaluate(self, dataset, results, topk=20):
        
        # image to annotations
        gt_json = dataset.coco.img_ann_map

        result_files, tmp_dir = dataset.format_results(results)
        pred_json = mmcv.load(result_files['panoptic'])['annotations']
        pred_folder = osp.join(tmp_dir.name, 'panoptic')
        gt_folder = dataset.seg_prefix

        pqs = {}
        prog_bar = mmcv.ProgressBar(len(results))
        for i in range(len(results)):
            data_info = dataset.prepare_train_img(i)
            image_id = data_info['img_info']['id']
            gt_ann = {
                'image_id': image_id,
                'segments_info': gt_json[image_id],
                'file_name': data_info['img_info']['segm_file']
            }
            pred_ann = pred_json[i]
            pq_stat = pq_compute_single_core(
                i, [(gt_ann, pred_ann)],
                gt_folder,
                pred_folder,
                dataset.categories,
                dataset.file_client,
                print_log=False)
            pq_results, classwise_results = pq_stat.pq_average(
                dataset.categories, isthing=None)
            pqs[i] = pq_results['pq']
            prog_bar.update()

        if tmp_dir is not None:
            tmp_dir.cleanup()

        # descending select topk image
        pqs = list(sorted(pqs.items(), key=lambda kv: kv[1]))
        good_pqs = pqs[-topk:]
        bad_pqs = pqs[:topk]

        return good_pqs, bad_pqs

","Evaluation for panoptic segmentation.

        Args:
            dataset (Dataset): A PyTorch dataset.
            results (list): Panoptic segmentation results from test
                results pkl file.
            topk (int): Number of the highest topk and
                lowest topk after evaluation index sorting. Default: 20.

        Returns:
            tuple: A tuple contains good samples and bad samples.
                good_pqs (dict[int, float]): A dict contains good
                    samples's indices in dataset and model's
                    performance on them.
                bad_pqs (dict[int, float]): A dict contains bad
                    samples's indices in dataset and model's
                    performance on them.
        ",78,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def panoptic_evaluate(self, dataset, results, topk=20):
        
        # image to annotations
        gt_json = dataset.coco.img_ann_map

        result_files, tmp_dir = dataset.format_results(results)
        pred_json = mmcv.load(result_files['panoptic'])['annotations']
        pred_folder = osp.join(tmp_dir.name, 'panoptic')
        gt_folder = dataset.seg_prefix

        pqs = {}
        prog_bar = mmcv.ProgressBar(len(results))
        for i in range(len(results)):
            data_info = dataset.prepare_train_img(i)
            image_id = data_info['img_info']['id']
            gt_ann = {
                'image_id': image_id,
                'segments_info': gt_json[image_id],
                'file_name': data_info['img_info']['segm_file']
            }
            pred_ann = pred_json[i]
            pq_stat = pq_compute_single_core(
                i, [(gt_ann, pred_ann)],
                gt_folder,
                pred_folder,
                dataset.categories,
                dataset.file_client,
                print_log=False)
            pq_results, classwise_results = pq_stat.pq_average(
                dataset.categories, isthing=None)
            pqs[i] = pq_results['pq']
            prog_bar.update()

        if tmp_dir is not None:
            tmp_dir.cleanup()

        # descending select topk image
        pqs = list(sorted(pqs.items(), key=lambda kv: kv[1]))
        good_pqs = pqs[-topk:]
        bad_pqs = pqs[:topk]

        return good_pqs, bad_pqs


        ```
    ###Assistant : Evaluation for panoptic segmentation.

        Args:
            dataset (Dataset): A PyTorch dataset.
            results (list): Panoptic segmentation results from test
                results pkl file.
            topk (int): Number of the highest topk and
                lowest topk after evaluation index sorting. Default: 20.

        Returns:
            tuple: A tuple contains good samples and bad samples.
                good_pqs (dict[int, float]): A dict contains good
                    samples's indices in dataset and model's
                    performance on them.
                bad_pqs (dict[int, float]): A dict contains bad
                    samples's indices in dataset and model's
                    performance on them.
        
    "
2314,"def _compute_mi_cd(c, d, n_neighbors):
    
    n_samples = c.shape[0]
    c = c.reshape((-1, 1))

    radius = np.empty(n_samples)
    label_counts = np.empty(n_samples)
    k_all = np.empty(n_samples)
    nn = NearestNeighbors()
    for label in np.unique(d):
        mask = d == label
        count = np.sum(mask)
        if count > 1:
            k = min(n_neighbors, count - 1)
            nn.set_params(n_neighbors=k)
            nn.fit(c[mask])
            r = nn.kneighbors()[0]
            radius[mask] = np.nextafter(r[:, -1], 0)
            k_all[mask] = k
        label_counts[mask] = count

    # Ignore points with unique labels.
    mask = label_counts > 1
    n_samples = np.sum(mask)
    label_counts = label_counts[mask]
    k_all = k_all[mask]
    c = c[mask]
    radius = radius[mask]

    kd = KDTree(c)
    m_all = kd.query_radius(c, radius, count_only=True, return_distance=False)
    m_all = np.array(m_all)

    mi = (
        digamma(n_samples)
        + np.mean(digamma(k_all))
        - np.mean(digamma(label_counts))
        - np.mean(digamma(m_all))
    )

    return max(0, mi)

","Compute mutual information between continuous and discrete variables.

    Parameters
    ----------
    c : ndarray, shape (n_samples,)
        Samples of a continuous random variable.

    d : ndarray, shape (n_samples,)
        Samples of a discrete random variable.

    n_neighbors : int
        Number of nearest neighbors to search for each point, see [1]_.

    Returns
    -------
    mi : float
        Estimated mutual information. If it turned out to be negative it is
        replace by 0.

    Notes
    -----
    True mutual information can't be negative. If its estimate by a numerical
    method is negative, it means (providing the method is adequate) that the
    mutual information is close to 0 and replacing it by 0 is a reasonable
    strategy.

    References
    ----------
    .. [1] B. C. Ross ""Mutual Information between Discrete and Continuous
       Data Sets"". PLoS ONE 9(2), 2014.
    ",126,113,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _compute_mi_cd(c, d, n_neighbors):
    
    n_samples = c.shape[0]
    c = c.reshape((-1, 1))

    radius = np.empty(n_samples)
    label_counts = np.empty(n_samples)
    k_all = np.empty(n_samples)
    nn = NearestNeighbors()
    for label in np.unique(d):
        mask = d == label
        count = np.sum(mask)
        if count > 1:
            k = min(n_neighbors, count - 1)
            nn.set_params(n_neighbors=k)
            nn.fit(c[mask])
            r = nn.kneighbors()[0]
            radius[mask] = np.nextafter(r[:, -1], 0)
            k_all[mask] = k
        label_counts[mask] = count

    # Ignore points with unique labels.
    mask = label_counts > 1
    n_samples = np.sum(mask)
    label_counts = label_counts[mask]
    k_all = k_all[mask]
    c = c[mask]
    radius = radius[mask]

    kd = KDTree(c)
    m_all = kd.query_radius(c, radius, count_only=True, return_distance=False)
    m_all = np.array(m_all)

    mi = (
        digamma(n_samples)
        + np.mean(digamma(k_all))
        - np.mean(digamma(label_counts))
        - np.mean(digamma(m_all))
    )

    return max(0, mi)


        ```
    ###Assistant : Compute mutual information between continuous and discrete variables.

    Parameters
    ----------
    c : ndarray, shape (n_samples,)
        Samples of a continuous random variable.

    d : ndarray, shape (n_samples,)
        Samples of a discrete random variable.

    n_neighbors : int
        Number of nearest neighbors to search for each point, see [1]_.

    Returns
    -------
    mi : float
        Estimated mutual information. If it turned out to be negative it is
        replace by 0.

    Notes
    -----
    True mutual information can't be negative. If its estimate by a numerical
    method is negative, it means (providing the method is adequate) that the
    mutual information is close to 0 and replacing it by 0 is a reasonable
    strategy.

    References
    ----------
    .. [1] B. C. Ross ""Mutual Information between Discrete and Continuous
       Data Sets"". PLoS ONE 9(2), 2014.
    
    "
2315,"def handle_app_config(self, app_config, **options):
        
        raise NotImplementedError(
            ""Subclasses of AppCommand must provide a handle_app_config() method.""
        )

","
        Perform the command's actions for app_config, an AppConfig instance
        corresponding to an application label given on the command line.
        ",19,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def handle_app_config(self, app_config, **options):
        
        raise NotImplementedError(
            ""Subclasses of AppCommand must provide a handle_app_config() method.""
        )


        ```
    ###Assistant : 
        Perform the command's actions for app_config, an AppConfig instance
        corresponding to an application label given on the command line.
        
    "
2316,"def test_inferred_max_features_integer(max_features):
    
    clf = RandomForestClassifier(n_estimators=5, random_state=0)
    transformer = SelectFromModel(
        estimator=clf, max_features=max_features, threshold=-np.inf
    )
    X_trans = transformer.fit_transform(data, y)
    assert transformer.max_features_ == max_features
    assert X_trans.shape[1] == transformer.max_features_


@pytest.mark.parametrize(
    ""max_features"",
    [lambda X: 1, lambda X: X.shape[1], lambda X: min(X.shape[1], 10000)],
)",Check max_features_ and output shape for integer max_features.,8,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_inferred_max_features_integer(max_features):
    
    clf = RandomForestClassifier(n_estimators=5, random_state=0)
    transformer = SelectFromModel(
        estimator=clf, max_features=max_features, threshold=-np.inf
    )
    X_trans = transformer.fit_transform(data, y)
    assert transformer.max_features_ == max_features
    assert X_trans.shape[1] == transformer.max_features_


@pytest.mark.parametrize(
    ""max_features"",
    [lambda X: 1, lambda X: X.shape[1], lambda X: min(X.shape[1], 10000)],
)
        ```
    ###Assistant : Check max_features_ and output shape for integer max_features.
    "
2317,"def test_naive_all_pairs_lowest_common_ancestor3(self):
        
        all_pairs = product(self.DG.nodes(), self.DG.nodes())
        ans = naive_all_pairs_lca(self.DG, pairs=all_pairs)
        self.assert_lca_dicts_same(dict(ans), self.gold)
",Produces the correct results when all pairs given as a generator.,11,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_naive_all_pairs_lowest_common_ancestor3(self):
        
        all_pairs = product(self.DG.nodes(), self.DG.nodes())
        ans = naive_all_pairs_lca(self.DG, pairs=all_pairs)
        self.assert_lca_dicts_same(dict(ans), self.gold)

        ```
    ###Assistant : Produces the correct results when all pairs given as a generator.
    "
2318,"def _resolve_dependency(dependency):
        
        if dependency[0] != ""__setting__"":
            return dependency, False
        resolved_app_label, resolved_object_name = getattr(
            settings, dependency[1]
        ).split(""."")
        return (resolved_app_label, resolved_object_name.lower()) + dependency[2:], True
","
        Return the resolved dependency and a boolean denoting whether or not
        it was swappable.
        ",14,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _resolve_dependency(dependency):
        
        if dependency[0] != ""__setting__"":
            return dependency, False
        resolved_app_label, resolved_object_name = getattr(
            settings, dependency[1]
        ).split(""."")
        return (resolved_app_label, resolved_object_name.lower()) + dependency[2:], True

        ```
    ###Assistant : 
        Return the resolved dependency and a boolean denoting whether or not
        it was swappable.
        
    "
2319,"def lowest_common_ancestor(G, node1, node2, default=None):
    
    ans = list(all_pairs_lowest_common_ancestor(G, pairs=[(node1, node2)]))
    if ans:
        assert len(ans) == 1
        return ans[0][1]
    else:
        return default


@not_implemented_for(""undirected"")
@not_implemented_for(""multigraph"")","Compute the lowest common ancestor of the given pair of nodes.

    Parameters
    ----------
    G : NetworkX directed graph

    node1, node2 : nodes in the graph.

    default : object
        Returned if no common ancestor between `node1` and `node2`

    Returns
    -------
    The lowest common ancestor of node1 and node2,
    or default if they have no common ancestors.

    Examples
    --------
    >>> G = nx.DiGraph([(0, 1), (0, 2), (2, 3), (2, 4), (1, 6), (4, 5)])
    >>> nx.lowest_common_ancestor(G, 3, 5)
    2

    We can also set `default` argument as below. The value of default is returned
    if there are no common ancestors of given two nodes.

    >>> G = nx.DiGraph([(4, 5), (12, 13)])
    >>> nx.lowest_common_ancestor(G, 12, 5, default=""No common ancestors!"")
    'No common ancestors!'

    Notes
    -----
    Only defined on non-null directed acyclic graphs.
    Takes n log(n) time in the size of the graph.
    See `all_pairs_lowest_common_ancestor` when you have
    more than one pair of nodes of interest.

    See Also
    --------
    tree_all_pairs_lowest_common_ancestor
    all_pairs_lowest_common_ancestor
    ",155,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def lowest_common_ancestor(G, node1, node2, default=None):
    
    ans = list(all_pairs_lowest_common_ancestor(G, pairs=[(node1, node2)]))
    if ans:
        assert len(ans) == 1
        return ans[0][1]
    else:
        return default


@not_implemented_for(""undirected"")
@not_implemented_for(""multigraph"")
        ```
    ###Assistant : Compute the lowest common ancestor of the given pair of nodes.

    Parameters
    ----------
    G : NetworkX directed graph

    node1, node2 : nodes in the graph.

    default : object
        Returned if no common ancestor between `node1` and `node2`

    Returns
    -------
    The lowest common ancestor of node1 and node2,
    or default if they have no common ancestors.

    Examples
    --------
    >>> G = nx.DiGraph([(0, 1), (0, 2), (2, 3), (2, 4), (1, 6), (4, 5)])
    >>> nx.lowest_common_ancestor(G, 3, 5)
    2

    We can also set `default` argument as below. The value of default is returned
    if there are no common ancestors of given two nodes.

    >>> G = nx.DiGraph([(4, 5), (12, 13)])
    >>> nx.lowest_common_ancestor(G, 12, 5, default=""No common ancestors!"")
    'No common ancestors!'

    Notes
    -----
    Only defined on non-null directed acyclic graphs.
    Takes n log(n) time in the size of the graph.
    See `all_pairs_lowest_common_ancestor` when you have
    more than one pair of nodes of interest.

    See Also
    --------
    tree_all_pairs_lowest_common_ancestor
    all_pairs_lowest_common_ancestor
    
    "
2320,"def get_data(filters=None):
	data = []

	conditions = get_filter_conditions(filters)

	fee_details = frappe.db.sql(
		
		% (conditions),
		as_dict=1,
	)

	for entry in fee_details:
		data.append(
			{
				""program"": entry.program,
				""fees_collected"": entry.paid_amount,
				""outstanding_amount"": entry.outstanding_amount,
				""grand_total"": entry.grand_total,
			}
		)

	return data

","
			SELECT
				FeesCollected.program,
				FeesCollected.paid_amount,
				FeesCollected.outstanding_amount,
				FeesCollected.grand_total
			FROM (
				SELECT
					sum(grand_total) - sum(outstanding_amount) AS paid_amount, program,
					sum(outstanding_amount) AS outstanding_amount,
					sum(grand_total) AS grand_total
				FROM `tabFees`
				WHERE
					docstatus = 1 and
					program IS NOT NULL
					%s
				GROUP BY program
			) AS FeesCollected
			ORDER BY FeesCollected.paid_amount DESC
		",42,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_data(filters=None):
	data = []

	conditions = get_filter_conditions(filters)

	fee_details = frappe.db.sql(
		
		% (conditions),
		as_dict=1,
	)

	for entry in fee_details:
		data.append(
			{
				""program"": entry.program,
				""fees_collected"": entry.paid_amount,
				""outstanding_amount"": entry.outstanding_amount,
				""grand_total"": entry.grand_total,
			}
		)

	return data


        ```
    ###Assistant : 
			SELECT
				FeesCollected.program,
				FeesCollected.paid_amount,
				FeesCollected.outstanding_amount,
				FeesCollected.grand_total
			FROM (
				SELECT
					sum(grand_total) - sum(outstanding_amount) AS paid_amount, program,
					sum(outstanding_amount) AS outstanding_amount,
					sum(grand_total) AS grand_total
				FROM `tabFees`
				WHERE
					docstatus = 1 and
					program IS NOT NULL
					%s
				GROUP BY program
			) AS FeesCollected
			ORDER BY FeesCollected.paid_amount DESC
		
    "
2321,"def get_dependencies_from_json(ireq):
    

    if ireq.editable or not is_pinned_requirement(ireq):
        return

    # It is technically possible to parse extras out of the JSON API's
    # requirement format, but it is such a chore let's just use the simple API.
    if ireq.extras:
        return

    session = requests.session()
    atexit.register(session.close)
    version = str(ireq.req.specifier).lstrip(""="")
","Retrieves dependencies for the given install requirement from the json
    api.

    :param ireq: A single InstallRequirement
    :type ireq: :class:`~pipenv.patched.pip._internal.req.req_install.InstallRequirement`
    :return: A set of dependency lines for generating new InstallRequirements.
    :rtype: set(str) or None
    ",33,46,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_dependencies_from_json(ireq):
    

    if ireq.editable or not is_pinned_requirement(ireq):
        return

    # It is technically possible to parse extras out of the JSON API's
    # requirement format, but it is such a chore let's just use the simple API.
    if ireq.extras:
        return

    session = requests.session()
    atexit.register(session.close)
    version = str(ireq.req.specifier).lstrip(""="")

        ```
    ###Assistant : Retrieves dependencies for the given install requirement from the json
    api.

    :param ireq: A single InstallRequirement
    :type ireq: :class:`~pipenv.patched.pip._internal.req.req_install.InstallRequirement`
    :return: A set of dependency lines for generating new InstallRequirements.
    :rtype: set(str) or None
    
    "
2322,"def assign(self, **kwargs) -> DataFrame:
        r
        data = self.copy(deep=None)

        for k, v in kwargs.items():
            data[k] = com.apply_if_callable(v, data)
        return data
","
        Assign new columns to a DataFrame.

        Returns a new object with all original columns in addition to new ones.
        Existing columns that are re-assigned will be overwritten.

        Parameters
        ----------
        **kwargs : dict of {str: callable or Series}
            The column names are keywords. If the values are
            callable, they are computed on the DataFrame and
            assigned to the new columns. The callable must not
            change input DataFrame (though pandas doesn't check it).
            If the values are not callable, (e.g. a Series, scalar, or array),
            they are simply assigned.

        Returns
        -------
        DataFrame
            A new DataFrame with the new columns in addition to
            all the existing columns.

        Notes
        -----
        Assigning multiple columns within the same ``assign`` is possible.
        Later items in '\*\*kwargs' may refer to newly created or modified
        columns in 'df'; items are computed and assigned into 'df' in order.

        Examples
        --------
        >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
        ...                   index=['Portland', 'Berkeley'])
        >>> df
                  temp_c
        Portland    17.0
        Berkeley    25.0

        Where the value is a callable, evaluated on `df`:

        >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
                  temp_c  temp_f
        Portland    17.0    62.6
        Berkeley    25.0    77.0

        Alternatively, the same behavior can be achieved by directly
        referencing an existing Series or sequence:

        >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
                  temp_c  temp_f
        Portland    17.0    62.6
        Berkeley    25.0    77.0

        You can create multiple columns within the same assign where one
        of the columns depends on another one defined within the same assign:

        >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
        ...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
                  temp_c  temp_f  temp_k
        Portland    17.0    62.6  290.15
        Berkeley    25.0    77.0  298.15
        ",268,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def assign(self, **kwargs) -> DataFrame:
        r
        data = self.copy(deep=None)

        for k, v in kwargs.items():
            data[k] = com.apply_if_callable(v, data)
        return data

        ```
    ###Assistant : 
        Assign new columns to a DataFrame.

        Returns a new object with all original columns in addition to new ones.
        Existing columns that are re-assigned will be overwritten.

        Parameters
        ----------
        **kwargs : dict of {str: callable or Series}
            The column names are keywords. If the values are
            callable, they are computed on the DataFrame and
            assigned to the new columns. The callable must not
            change input DataFrame (though pandas doesn't check it).
            If the values are not callable, (e.g. a Series, scalar, or array),
            they are simply assigned.

        Returns
        -------
        DataFrame
            A new DataFrame with the new columns in addition to
            all the existing columns.

        Notes
        -----
        Assigning multiple columns within the same ``assign`` is possible.
        Later items in '\*\*kwargs' may refer to newly created or modified
        columns in 'df'; items are computed and assigned into 'df' in order.

        Examples
        --------
        >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
        ...                   index=['Portland', 'Berkeley'])
        >>> df
                  temp_c
        Portland    17.0
        Berkeley    25.0

        Where the value is a callable, evaluated on `df`:

        >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
                  temp_c  temp_f
        Portland    17.0    62.6
        Berkeley    25.0    77.0

        Alternatively, the same behavior can be achieved by directly
        referencing an existing Series or sequence:

        >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
                  temp_c  temp_f
        Portland    17.0    62.6
        Berkeley    25.0    77.0

        You can create multiple columns within the same assign where one
        of the columns depends on another one defined within the same assign:

        >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
        ...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
                  temp_c  temp_f  temp_k
        Portland    17.0    62.6  290.15
        Berkeley    25.0    77.0  298.15
        
    "
2323,"def resize_feats(self, feats):
        
        out = []
        for i in range(len(feats)):
            if i == 0:
                out.append(
                    F.interpolate(
                        feats[0],
                        size=feats[i + 1].shape[-2:],
                        mode='bilinear',
                        align_corners=False))
            elif i == len(feats) - 1:
                out.append(
                    F.interpolate(
                        feats[i],
                        size=feats[i - 1].shape[-2:],
                        mode='bilinear',
                        align_corners=False))
            else:
                out.append(feats[i])
        return out
",Downsample the first feat and upsample last feat in feats.,10,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resize_feats(self, feats):
        
        out = []
        for i in range(len(feats)):
            if i == 0:
                out.append(
                    F.interpolate(
                        feats[0],
                        size=feats[i + 1].shape[-2:],
                        mode='bilinear',
                        align_corners=False))
            elif i == len(feats) - 1:
                out.append(
                    F.interpolate(
                        feats[i],
                        size=feats[i - 1].shape[-2:],
                        mode='bilinear',
                        align_corners=False))
            else:
                out.append(feats[i])
        return out

        ```
    ###Assistant : Downsample the first feat and upsample last feat in feats.
    "
2324,"def get_bin_list(filters):
	conditions = []

	if filters.item_code:
		conditions.append(""item_code = '%s' "" % filters.item_code)

	if filters.warehouse:
		warehouse_details = frappe.db.get_value(
			""Warehouse"", filters.warehouse, [""lft"", ""rgt""], as_dict=1
		)

		if warehouse_details:
			conditions.append(
				"" exists (select name from `tabWarehouse` wh \
				where wh.lft >= %s and wh.rgt <= %s and bin.warehouse = wh.name)""
				% (warehouse_details.lft, warehouse_details.rgt)
			)

	bin_list = frappe.db.sql(
		.format(
			conditions="" where "" + "" and "".join(conditions) if conditions else """"
		),
		as_dict=1,
	)

	return bin_list

","select item_code, warehouse, actual_qty, planned_qty, indented_qty,
		ordered_qty, reserved_qty, reserved_qty_for_production, reserved_qty_for_sub_contract, projected_qty
		from tabBin bin {conditions} order by item_code, warehouse
		",19,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_bin_list(filters):
	conditions = []

	if filters.item_code:
		conditions.append(""item_code = '%s' "" % filters.item_code)

	if filters.warehouse:
		warehouse_details = frappe.db.get_value(
			""Warehouse"", filters.warehouse, [""lft"", ""rgt""], as_dict=1
		)

		if warehouse_details:
			conditions.append(
				"" exists (select name from `tabWarehouse` wh \
				where wh.lft >= %s and wh.rgt <= %s and bin.warehouse = wh.name)""
				% (warehouse_details.lft, warehouse_details.rgt)
			)

	bin_list = frappe.db.sql(
		.format(
			conditions="" where "" + "" and "".join(conditions) if conditions else """"
		),
		as_dict=1,
	)

	return bin_list


        ```
    ###Assistant : select item_code, warehouse, actual_qty, planned_qty, indented_qty,
		ordered_qty, reserved_qty, reserved_qty_for_production, reserved_qty_for_sub_contract, projected_qty
		from tabBin bin {conditions} order by item_code, warehouse
		
    "
2325,"def run_eagerly(self):
        
        if (
            self.dynamic and self._run_eagerly is False
        ):  # pylint:disable=g-bool-id-comparison
            # TODO(fchollet): consider using py_func to enable this.
            raise ValueError(
                ""Your model contains layers that can only be ""
                ""successfully run in eager execution (layers ""
                ""constructed with `dynamic=True`). ""
                ""You cannot set `run_eagerly=False`.""
            )

        if self._cluster_coordinator and self._run_eagerly:
            raise ValueError(
                ""When using `Model` with `ParameterServerStrategy`, ""
                ""`run_eagerly` is not supported.""
            )

        # Run eagerly logic, by priority:
        # (1) Dynamic models must be run eagerly.
        # (2) Explicitly setting run_eagerly causes a Model to be run eagerly.
        # (3) Not explicitly setting run_eagerly defaults to TF's global setting.
        return (
            self.dynamic
            or self._run_eagerly
            or (tf.config.functions_run_eagerly() and self._run_eagerly is None)
        )
","Settable attribute indicating whether the model should run eagerly.

        Running eagerly means that your model will be run step by step,
        like Python code. Your model might run slower, but it should become easier
        for you to debug it by stepping into individual layer calls.

        By default, we will attempt to compile your model to a static graph to
        deliver the best execution performance.

        Returns:
          Boolean, whether the model should run eagerly.
        ",72,113,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def run_eagerly(self):
        
        if (
            self.dynamic and self._run_eagerly is False
        ):  # pylint:disable=g-bool-id-comparison
            # TODO(fchollet): consider using py_func to enable this.
            raise ValueError(
                ""Your model contains layers that can only be ""
                ""successfully run in eager execution (layers ""
                ""constructed with `dynamic=True`). ""
                ""You cannot set `run_eagerly=False`.""
            )

        if self._cluster_coordinator and self._run_eagerly:
            raise ValueError(
                ""When using `Model` with `ParameterServerStrategy`, ""
                ""`run_eagerly` is not supported.""
            )

        # Run eagerly logic, by priority:
        # (1) Dynamic models must be run eagerly.
        # (2) Explicitly setting run_eagerly causes a Model to be run eagerly.
        # (3) Not explicitly setting run_eagerly defaults to TF's global setting.
        return (
            self.dynamic
            or self._run_eagerly
            or (tf.config.functions_run_eagerly() and self._run_eagerly is None)
        )

        ```
    ###Assistant : Settable attribute indicating whether the model should run eagerly.

        Running eagerly means that your model will be run step by step,
        like Python code. Your model might run slower, but it should become easier
        for you to debug it by stepping into individual layer calls.

        By default, we will attempt to compile your model to a static graph to
        deliver the best execution performance.

        Returns:
          Boolean, whether the model should run eagerly.
        
    "
2326,"def get_preview_context(self, request, *args, **kwargs):
        
        return {""object"": self, ""request"": request}
","
        Returns a context dictionary for use in templates for previewing this object.
        ",12,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_preview_context(self, request, *args, **kwargs):
        
        return {""object"": self, ""request"": request}

        ```
    ###Assistant : 
        Returns a context dictionary for use in templates for previewing this object.
        
    "
2327,"async def test_default_disabling_entity(hass, create_registrations, webhook_client):
    
    webhook_id = create_registrations[1][""webhook_id""]
    webhook_url = f""/api/webhook/{webhook_id}""

    reg_resp = await webhook_client.post(
        webhook_url,
        json={
            ""type"": ""register_sensor"",
            ""data"": {
                ""name"": ""Battery State"",
                ""type"": ""sensor"",
                ""unique_id"": ""battery_state"",
                ""default_disabled"": True,
            },
        },
    )

    assert reg_resp.status == HTTPStatus.CREATED

    json = await reg_resp.json()
    assert json == {""success"": True}
    await hass.async_block_till_done()

    entity = hass.states.get(""sensor.test_1_battery_state"")
    assert entity is None

    assert (
        er.async_get(hass).async_get(""sensor.test_1_battery_state"").disabled_by
        == er.RegistryEntryDisabler.INTEGRATION
    )
",Test that sensors can be disabled by default upon registration.,10,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_default_disabling_entity(hass, create_registrations, webhook_client):
    
    webhook_id = create_registrations[1][""webhook_id""]
    webhook_url = f""/api/webhook/{webhook_id}""

    reg_resp = await webhook_client.post(
        webhook_url,
        json={
            ""type"": ""register_sensor"",
            ""data"": {
                ""name"": ""Battery State"",
                ""type"": ""sensor"",
                ""unique_id"": ""battery_state"",
                ""default_disabled"": True,
            },
        },
    )

    assert reg_resp.status == HTTPStatus.CREATED

    json = await reg_resp.json()
    assert json == {""success"": True}
    await hass.async_block_till_done()

    entity = hass.states.get(""sensor.test_1_battery_state"")
    assert entity is None

    assert (
        er.async_get(hass).async_get(""sensor.test_1_battery_state"").disabled_by
        == er.RegistryEntryDisabler.INTEGRATION
    )

        ```
    ###Assistant : Test that sensors can be disabled by default upon registration.
    "
2328,"def _create_dd_meta(cls, dataset_info):
        

        # Collect necessary information from dataset_info
        schema = dataset_info[""schema""]
        index = dataset_info[""index""]
        categories = dataset_info[""categories""]
        partition_obj = dataset_info[""partitions""]
        partitions = dataset_info[""partition_names""]
        physical_column_names = dataset_info.get(""physical_schema"", schema).names
        columns = None

        # Set index and column names using
        # pandas metadata (when available)
        pandas_metadata = _get_pandas_metadata(schema)
        if pandas_metadata:
            (
                index_names,
                column_names,
                storage_name_mapping,
                column_index_names,
            ) = _parse_pandas_metadata(pandas_metadata)
            if categories is None:
                categories = []
                for col in pandas_metadata[""columns""]:
                    if (col[""pandas_type""] == ""categorical"") and (
                        col[""name""] not in categories
                    ):
                        categories.append(col[""name""])
        else:
            # No pandas metadata implies no index, unless selected by the user
            index_names = []
            column_names = physical_column_names
            storage_name_mapping = {k: k for k in column_names}
            column_index_names = [None]
        if index is None and index_names:
            # Pandas metadata has provided the index name for us
            index = index_names

        # Ensure that there is no overlap between partition columns
        # and explicit column storage
        if partitions:
            _partitions = [p for p in partitions if p not in physical_column_names]
            if not _partitions:
                partitions = []
                dataset_info[""partitions""] = None
                dataset_info[""partition_keys""] = {}
                dataset_info[""partition_names""] = partitions
            elif len(_partitions) != len(partitions):
                raise ValueError(
                    ""No partition-columns should be written in the \n""
                    ""file unless they are ALL written in the file.\n""
                    ""physical columns: {} | partitions: {}"".format(
                        physical_column_names, partitions
                    )
                )

        column_names, index_names = _normalize_index_columns(
            columns, column_names + partitions, index, index_names
        )

        all_columns = index_names + column_names

        # Check that categories are included in columns
        if categories and not set(categories).intersection(all_columns):
            raise ValueError(
                ""categories not in available columns.\n""
                ""categories: {} | columns: {}"".format(categories, list(all_columns))
            )

        dtypes = _get_pyarrow_dtypes(schema, categories)
        dtypes = {storage_name_mapping.get(k, k): v for k, v in dtypes.items()}

        index_cols = index or ()
        meta = _meta_from_dtypes(all_columns, dtypes, index_cols, column_index_names)
        if categories:
            # Make sure all categories are set to ""unknown"".
            # Cannot include index names in the `cols` argument.
            meta = clear_known_categories(
                meta, cols=[c for c in categories if c not in meta.index.names]
            )

        if partition_obj:

            for partition in partition_obj:
                if isinstance(index, list) and partition.name == index[0]:
                    # Index from directory structure
                    meta.index = pd.CategoricalIndex(
                        [], categories=partition.keys, name=index[0]
                    )
                elif partition.name == meta.index.name:
                    # Index created from a categorical column
                    meta.index = pd.CategoricalIndex(
                        [], categories=partition.keys, name=meta.index.name
                    )
                elif partition.name in meta.columns:
                    meta[partition.name] = pd.Series(
                        pd.Categorical(categories=partition.keys, values=[]),
                        index=meta.index,
                    )

        # Update `dataset_info` and return `meta`
        dataset_info[""index""] = index
        dataset_info[""index_cols""] = index_cols
        dataset_info[""categories""] = categories

        return meta
","Use parquet schema and hive-partition information
        (stored in dataset_info) to construct DataFrame metadata.

        This method is used by both arrow engines.
        ",21,379,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_dd_meta(cls, dataset_info):
        

        # Collect necessary information from dataset_info
        schema = dataset_info[""schema""]
        index = dataset_info[""index""]
        categories = dataset_info[""categories""]
        partition_obj = dataset_info[""partitions""]
        partitions = dataset_info[""partition_names""]
        physical_column_names = dataset_info.get(""physical_schema"", schema).names
        columns = None

        # Set index and column names using
        # pandas metadata (when available)
        pandas_metadata = _get_pandas_metadata(schema)
        if pandas_metadata:
            (
                index_names,
                column_names,
                storage_name_mapping,
                column_index_names,
            ) = _parse_pandas_metadata(pandas_metadata)
            if categories is None:
                categories = []
                for col in pandas_metadata[""columns""]:
                    if (col[""pandas_type""] == ""categorical"") and (
                        col[""name""] not in categories
                    ):
                        categories.append(col[""name""])
        else:
            # No pandas metadata implies no index, unless selected by the user
            index_names = []
            column_names = physical_column_names
            storage_name_mapping = {k: k for k in column_names}
            column_index_names = [None]
        if index is None and index_names:
            # Pandas metadata has provided the index name for us
            index = index_names

        # Ensure that there is no overlap between partition columns
        # and explicit column storage
        if partitions:
            _partitions = [p for p in partitions if p not in physical_column_names]
            if not _partitions:
                partitions = []
                dataset_info[""partitions""] = None
                dataset_info[""partition_keys""] = {}
                dataset_info[""partition_names""] = partitions
            elif len(_partitions) != len(partitions):
                raise ValueError(
                    ""No partition-columns should be written in the \n""
                    ""file unless they are ALL written in the file.\n""
                    ""physical columns: {} | partitions: {}"".format(
                        physical_column_names, partitions
                    )
                )

        column_names, index_names = _normalize_index_columns(
            columns, column_names + partitions, index, index_names
        )

        all_columns = index_names + column_names

        # Check that categories are included in columns
        if categories and not set(categories).intersection(all_columns):
            raise ValueError(
                ""categories not in available columns.\n""
                ""categories: {} | columns: {}"".format(categories, list(all_columns))
            )

        dtypes = _get_pyarrow_dtypes(schema, categories)
        dtypes = {storage_name_mapping.get(k, k): v for k, v in dtypes.items()}

        index_cols = index or ()
        meta = _meta_from_dtypes(all_columns, dtypes, index_cols, column_index_names)
        if categories:
            # Make sure all categories are set to ""unknown"".
            # Cannot include index names in the `cols` argument.
            meta = clear_known_categories(
                meta, cols=[c for c in categories if c not in meta.index.names]
            )

        if partition_obj:

            for partition in partition_obj:
                if isinstance(index, list) and partition.name == index[0]:
                    # Index from directory structure
                    meta.index = pd.CategoricalIndex(
                        [], categories=partition.keys, name=index[0]
                    )
                elif partition.name == meta.index.name:
                    # Index created from a categorical column
                    meta.index = pd.CategoricalIndex(
                        [], categories=partition.keys, name=meta.index.name
                    )
                elif partition.name in meta.columns:
                    meta[partition.name] = pd.Series(
                        pd.Categorical(categories=partition.keys, values=[]),
                        index=meta.index,
                    )

        # Update `dataset_info` and return `meta`
        dataset_info[""index""] = index
        dataset_info[""index_cols""] = index_cols
        dataset_info[""categories""] = categories

        return meta

        ```
    ###Assistant : Use parquet schema and hive-partition information
        (stored in dataset_info) to construct DataFrame metadata.

        This method is used by both arrow engines.
        
    "
2329,"def convert_type(self, value, schema_type, stringify_dict=True):
        
        if isinstance(value, datetime.datetime):
            iso_format_value = value.isoformat()
            if value.tzinfo is None:
                return iso_format_value
            return pendulum.parse(iso_format_value).float_timestamp
        if isinstance(value, datetime.date):
            return value.isoformat()
        if isinstance(value, datetime.time):
            formatted_time = time.strptime(str(value), ""%H:%M:%S"")
            time_delta = datetime.timedelta(
                hours=formatted_time.tm_hour, minutes=formatted_time.tm_min, seconds=formatted_time.tm_sec
            )
            return str(time_delta)
        if stringify_dict and isinstance(value, dict):
            return json.dumps(value)
        if isinstance(value, Decimal):
            return float(value)
        return value
","
        Takes a value from Postgres, and converts it to a value that's safe for
        JSON/Google Cloud Storage/BigQuery.
        Timezone aware Datetime are converted to UTC seconds.
        Unaware Datetime, Date and Time are converted to ISO formatted strings.
        Decimals are converted to floats.

        :param value: Postgres column value.
        :param schema_type: BigQuery data type.
        :param stringify_dict: Specify whether to convert dict to string.
        ",60,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_type(self, value, schema_type, stringify_dict=True):
        
        if isinstance(value, datetime.datetime):
            iso_format_value = value.isoformat()
            if value.tzinfo is None:
                return iso_format_value
            return pendulum.parse(iso_format_value).float_timestamp
        if isinstance(value, datetime.date):
            return value.isoformat()
        if isinstance(value, datetime.time):
            formatted_time = time.strptime(str(value), ""%H:%M:%S"")
            time_delta = datetime.timedelta(
                hours=formatted_time.tm_hour, minutes=formatted_time.tm_min, seconds=formatted_time.tm_sec
            )
            return str(time_delta)
        if stringify_dict and isinstance(value, dict):
            return json.dumps(value)
        if isinstance(value, Decimal):
            return float(value)
        return value

        ```
    ###Assistant : 
        Takes a value from Postgres, and converts it to a value that's safe for
        JSON/Google Cloud Storage/BigQuery.
        Timezone aware Datetime are converted to UTC seconds.
        Unaware Datetime, Date and Time are converted to ISO formatted strings.
        Decimals are converted to floats.

        :param value: Postgres column value.
        :param schema_type: BigQuery data type.
        :param stringify_dict: Specify whether to convert dict to string.
        
    "
2330,"def _filter_top_k(x, k):
    
    _, top_k_idx = tf.math.top_k(x, k, sorted=False)
    top_k_mask = tf.reduce_sum(
        tf.one_hot(top_k_idx, tf.shape(x)[-1], axis=-1), axis=-2
    )
    return x * top_k_mask + NEG_INF * (1 - top_k_mask)

","Filters top-k values in the last dim of x and set the rest to NEG_INF.

    Used for computing top-k prediction values in dense labels (which has the same
    shape as predictions) for recall and precision top-k metrics.

    Args:
      x: tensor with any dimensions.
      k: the number of values to keep.

    Returns:
      tensor with same shape and dtype as x.
    ",59,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _filter_top_k(x, k):
    
    _, top_k_idx = tf.math.top_k(x, k, sorted=False)
    top_k_mask = tf.reduce_sum(
        tf.one_hot(top_k_idx, tf.shape(x)[-1], axis=-1), axis=-2
    )
    return x * top_k_mask + NEG_INF * (1 - top_k_mask)


        ```
    ###Assistant : Filters top-k values in the last dim of x and set the rest to NEG_INF.

    Used for computing top-k prediction values in dense labels (which has the same
    shape as predictions) for recall and precision top-k metrics.

    Args:
      x: tensor with any dimensions.
      k: the number of values to keep.

    Returns:
      tensor with same shape and dtype as x.
    
    "
2331,"def test_pick_two_individuals_eligible_for_crossover_bad():
    

    ind1 = creator.Individual.from_string(
        'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)',
        tpot_obj._pset
    )
    ind2 = creator.Individual.from_string(
        'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)',
        tpot_obj._pset
    )
    ind3 = creator.Individual.from_string(
        'GaussianNB(input_matrix)',
        tpot_obj._pset
    )

    # Ind1 and ind2 are not a pair because they are the same, ind3 shares no primitive
    pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1, ind2, ind3])
    assert pick1 is None and pick2 is None

    # You can not do crossover with a population of only 1.
    pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1])
    assert pick1 is None and pick2 is None

    # You can not do crossover with a population of 0.
    pick1, pick2 = pick_two_individuals_eligible_for_crossover([])
    assert pick1 is None and pick2 is None

",Assert that pick_two_individuals_eligible_for_crossover() returns the right output when no pair is eligible,12,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pick_two_individuals_eligible_for_crossover_bad():
    

    ind1 = creator.Individual.from_string(
        'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)',
        tpot_obj._pset
    )
    ind2 = creator.Individual.from_string(
        'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)',
        tpot_obj._pset
    )
    ind3 = creator.Individual.from_string(
        'GaussianNB(input_matrix)',
        tpot_obj._pset
    )

    # Ind1 and ind2 are not a pair because they are the same, ind3 shares no primitive
    pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1, ind2, ind3])
    assert pick1 is None and pick2 is None

    # You can not do crossover with a population of only 1.
    pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1])
    assert pick1 is None and pick2 is None

    # You can not do crossover with a population of 0.
    pick1, pick2 = pick_two_individuals_eligible_for_crossover([])
    assert pick1 is None and pick2 is None


        ```
    ###Assistant : Assert that pick_two_individuals_eligible_for_crossover() returns the right output when no pair is eligible
    "
2332,"def get_ips(v6=False):
    # type: (bool) -> Dict[NetworkInterface, List[str]]
    
    res = {}
    for iface in six.itervalues(conf.ifaces):
        if v6:
            res[iface] = iface.ips[6]
        else:
            res[iface] = iface.ips[4]
    return res

","Returns all available IPs matching to interfaces, using the windows system.
    Should only be used as a WinPcapy fallback.

    :param v6: IPv6 addresses
    ",23,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ips(v6=False):
    # type: (bool) -> Dict[NetworkInterface, List[str]]
    
    res = {}
    for iface in six.itervalues(conf.ifaces):
        if v6:
            res[iface] = iface.ips[6]
        else:
            res[iface] = iface.ips[4]
    return res


        ```
    ###Assistant : Returns all available IPs matching to interfaces, using the windows system.
    Should only be used as a WinPcapy fallback.

    :param v6: IPv6 addresses
    
    "
2333,"def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (OPTDecoder)):
            module.gradient_checkpointing = value


OPT_GENERATION_EXAMPLE = r

OPT_INPUTS_DOCSTRING = r

","
    Generation example:

    ```python
    >>> from transformers import AutoTokenizer, AutoModelForCausalLM

    >>> model = OPTForCausalLM.from_pretrained(""ArthurZ/opt-350m"")
    >>> tokenizer = GPT2Tokenizer.from_pretrained(""patrickvonplaten/opt_gpt2_tokenizer"")

    >>> TEXTS_TO_GENERATE = ""Hey, are you consciours? Can you talk to me?"" ""Hi there, my name is Barack""
    >>> inputs = tokenizer([TEXTS_TO_GENERATE], max_length=1024, return_tensors=""pt"")

    >>> # Generate
    >>> generate_ids = model.generate(inputs[""input_ids""], num_beams=2, min_length=0, max_length=20)
    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    'I'm not conscious.<\s>'
    ```

    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_inputs`] and modify
            to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the
            default strategy.
        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
",470,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (OPTDecoder)):
            module.gradient_checkpointing = value


OPT_GENERATION_EXAMPLE = r

OPT_INPUTS_DOCSTRING = r


        ```
    ###Assistant : 
    Generation example:

    ```python
    >>> from transformers import AutoTokenizer, AutoModelForCausalLM

    >>> model = OPTForCausalLM.from_pretrained(""ArthurZ/opt-350m"")
    >>> tokenizer = GPT2Tokenizer.from_pretrained(""patrickvonplaten/opt_gpt2_tokenizer"")

    >>> TEXTS_TO_GENERATE = ""Hey, are you consciours? Can you talk to me?"" ""Hi there, my name is Barack""
    >>> inputs = tokenizer([TEXTS_TO_GENERATE], max_length=1024, return_tensors=""pt"")

    >>> # Generate
    >>> generate_ids = model.generate(inputs[""input_ids""], num_beams=2, min_length=0, max_length=20)
    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    'I'm not conscious.<\s>'
    ```

    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_inputs`] and modify
            to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the
            default strategy.
        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

    "
2334,"def to(self, device=None, dtype=None) -> None:
        r
        # .to() on the tensors handles None correctly
        self.shadow_params = [
            p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
            for p in self.shadow_params
        ]

","Move internal buffers of the ExponentialMovingAverage to `device`.

        Args:
            device: like `device` argument to `torch.Tensor.to`
        ",15,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def to(self, device=None, dtype=None) -> None:
        r
        # .to() on the tensors handles None correctly
        self.shadow_params = [
            p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
            for p in self.shadow_params
        ]


        ```
    ###Assistant : Move internal buffers of the ExponentialMovingAverage to `device`.

        Args:
            device: like `device` argument to `torch.Tensor.to`
        
    "
2335,"def get_granger_causality(dependent_series, independent_series, lags):
    
    granger_set = pd.concat([dependent_series, independent_series], axis=1)

    granger = grangercausalitytests(granger_set, [lags], verbose=False)

    return granger

","Calculate granger tests

    Parameters
    ----------
    dependent_series: Series
        The series you want to test Granger Causality for.
    independent_series: Series
        The series that you want to test whether it Granger-causes time_series_y
    lags : int
        The amount of lags for the Granger test. By default, this is set to 3.
    ",47,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_granger_causality(dependent_series, independent_series, lags):
    
    granger_set = pd.concat([dependent_series, independent_series], axis=1)

    granger = grangercausalitytests(granger_set, [lags], verbose=False)

    return granger


        ```
    ###Assistant : Calculate granger tests

    Parameters
    ----------
    dependent_series: Series
        The series you want to test Granger Causality for.
    independent_series: Series
        The series that you want to test whether it Granger-causes time_series_y
    lags : int
        The amount of lags for the Granger test. By default, this is set to 3.
    
    "
2336,"def _multi_decorate(decorators, method):
    
    if hasattr(decorators, ""__iter__""):
        # Apply a list/tuple of decorators if 'decorators' is one. Decorator
        # functions are applied so that the call order is the same as the
        # order in which they appear in the iterable.
        decorators = decorators[::-1]
    else:
        decorators = [decorators]
","
    Decorate `method` with one or more function decorators. `decorators` can be
    a single decorator or an iterable of decorators.
    ",19,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _multi_decorate(decorators, method):
    
    if hasattr(decorators, ""__iter__""):
        # Apply a list/tuple of decorators if 'decorators' is one. Decorator
        # functions are applied so that the call order is the same as the
        # order in which they appear in the iterable.
        decorators = decorators[::-1]
    else:
        decorators = [decorators]

        ```
    ###Assistant : 
    Decorate `method` with one or more function decorators. `decorators` can be
    a single decorator or an iterable of decorators.
    
    "
2337,"def generate_level_targets(self, img_size, text_polys, ignore_polys):
        
        h, w = img_size
        lv_size_divs = self.level_size_divisors
        lv_proportion_range = self.level_proportion_range
        lv_text_polys = [[] for i in range(len(lv_size_divs))]
        lv_ignore_polys = [[] for i in range(len(lv_size_divs))]
        level_maps = []
        for poly in text_polys:
            # assert len(poly) == 1
            # text_instance = [[poly[i], poly[i + 1]]
            #                  for i in range(0, len(poly), 2)]
            polygon = np.array(poly, dtype=np.int).reshape((1, -1, 2))
            _, _, box_w, box_h = cv2.boundingRect(polygon)
            proportion = max(box_h, box_w) / (h + 1e-8)

            for ind, proportion_range in enumerate(lv_proportion_range):
                if proportion_range[0] < proportion < proportion_range[1]:
                    lv_text_polys[ind].append(poly / lv_size_divs[ind])

        for ignore_poly in ignore_polys:
            # assert len(ignore_poly) == 1
            # text_instance = [[ignore_poly[i], ignore_poly[i + 1]]
            #                  for i in range(0, len(ignore_poly), 2)]
            polygon = np.array(ignore_poly, dtype=np.int).reshape((1, -1, 2))
            _, _, box_w, box_h = cv2.boundingRect(polygon)
            proportion = max(box_h, box_w) / (h + 1e-8)

            for ind, proportion_range in enumerate(lv_proportion_range):
                if proportion_range[0] < proportion < proportion_range[1]:
                    lv_ignore_polys[ind].append(ignore_poly / lv_size_divs[ind])

        for ind, size_divisor in enumerate(lv_size_divs):
            current_level_maps = []
            level_img_size = (h // size_divisor, w // size_divisor)

            text_region = self.generate_text_region_mask(
                level_img_size, lv_text_polys[ind])[None]
            current_level_maps.append(text_region)

            center_region = self.generate_center_region_mask(
                level_img_size, lv_text_polys[ind])[None]
            current_level_maps.append(center_region)

            effective_mask = self.generate_effective_mask(
                level_img_size, lv_ignore_polys[ind])[None]
            current_level_maps.append(effective_mask)

            fourier_real_map, fourier_image_maps = self.generate_fourier_maps(
                level_img_size, lv_text_polys[ind])
            current_level_maps.append(fourier_real_map)
            current_level_maps.append(fourier_image_maps)

            level_maps.append(np.concatenate(current_level_maps))

        return level_maps
","Generate ground truth target on each level.

        Args:
            img_size (list[int]): Shape of input image.
            text_polys (list[list[ndarray]]): A list of ground truth polygons.
            ignore_polys (list[list[ndarray]]): A list of ignored polygons.
        Returns:
            level_maps (list(ndarray)): A list of ground target on each level.
        ",40,191,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_level_targets(self, img_size, text_polys, ignore_polys):
        
        h, w = img_size
        lv_size_divs = self.level_size_divisors
        lv_proportion_range = self.level_proportion_range
        lv_text_polys = [[] for i in range(len(lv_size_divs))]
        lv_ignore_polys = [[] for i in range(len(lv_size_divs))]
        level_maps = []
        for poly in text_polys:
            # assert len(poly) == 1
            # text_instance = [[poly[i], poly[i + 1]]
            #                  for i in range(0, len(poly), 2)]
            polygon = np.array(poly, dtype=np.int).reshape((1, -1, 2))
            _, _, box_w, box_h = cv2.boundingRect(polygon)
            proportion = max(box_h, box_w) / (h + 1e-8)

            for ind, proportion_range in enumerate(lv_proportion_range):
                if proportion_range[0] < proportion < proportion_range[1]:
                    lv_text_polys[ind].append(poly / lv_size_divs[ind])

        for ignore_poly in ignore_polys:
            # assert len(ignore_poly) == 1
            # text_instance = [[ignore_poly[i], ignore_poly[i + 1]]
            #                  for i in range(0, len(ignore_poly), 2)]
            polygon = np.array(ignore_poly, dtype=np.int).reshape((1, -1, 2))
            _, _, box_w, box_h = cv2.boundingRect(polygon)
            proportion = max(box_h, box_w) / (h + 1e-8)

            for ind, proportion_range in enumerate(lv_proportion_range):
                if proportion_range[0] < proportion < proportion_range[1]:
                    lv_ignore_polys[ind].append(ignore_poly / lv_size_divs[ind])

        for ind, size_divisor in enumerate(lv_size_divs):
            current_level_maps = []
            level_img_size = (h // size_divisor, w // size_divisor)

            text_region = self.generate_text_region_mask(
                level_img_size, lv_text_polys[ind])[None]
            current_level_maps.append(text_region)

            center_region = self.generate_center_region_mask(
                level_img_size, lv_text_polys[ind])[None]
            current_level_maps.append(center_region)

            effective_mask = self.generate_effective_mask(
                level_img_size, lv_ignore_polys[ind])[None]
            current_level_maps.append(effective_mask)

            fourier_real_map, fourier_image_maps = self.generate_fourier_maps(
                level_img_size, lv_text_polys[ind])
            current_level_maps.append(fourier_real_map)
            current_level_maps.append(fourier_image_maps)

            level_maps.append(np.concatenate(current_level_maps))

        return level_maps

        ```
    ###Assistant : Generate ground truth target on each level.

        Args:
            img_size (list[int]): Shape of input image.
            text_polys (list[list[ndarray]]): A list of ground truth polygons.
            ignore_polys (list[list[ndarray]]): A list of ignored polygons.
        Returns:
            level_maps (list(ndarray)): A list of ground target on each level.
        
    "
2338,"def get_reserved_qty(item_code, warehouse):
	reserved_qty = frappe.db.sql(
		,
		(item_code, warehouse, item_code, warehouse),
	)

	return flt(reserved_qty[0][0]) if reserved_qty else 0

","
		select
			sum(dnpi_qty * ((so_item_qty - so_item_delivered_qty) / so_item_qty))
		from
			(
				(select
					qty as dnpi_qty,
					(
						select qty from `tabSales Order Item`
						where name = dnpi.parent_detail_docname
						and (delivered_by_supplier is null or delivered_by_supplier = 0)
					) as so_item_qty,
					(
						select delivered_qty from `tabSales Order Item`
						where name = dnpi.parent_detail_docname
						and delivered_by_supplier = 0
					) as so_item_delivered_qty,
					parent, name
				from
				(
					select qty, parent_detail_docname, parent, name
					from `tabPacked Item` dnpi_in
					where item_code = %s and warehouse = %s
					and parenttype=""Sales Order""
					and item_code != parent_item
					and exists (select * from `tabSales Order` so
					where name = dnpi_in.parent and docstatus = 1 and status != 'Closed')
				) dnpi)
			union
				(select stock_qty as dnpi_qty, qty as so_item_qty,
					delivered_qty as so_item_delivered_qty, parent, name
				from `tabSales Order Item` so_item
				where item_code = %s and warehouse = %s
				and (so_item.delivered_by_supplier is null or so_item.delivered_by_supplier = 0)
				and exists(select * from `tabSales Order` so
					where so.name = so_item.parent and so.docstatus = 1
					and so.status != 'Closed'))
			) tab
		where
			so_item_qty >= so_item_delivered_qty
	",163,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_reserved_qty(item_code, warehouse):
	reserved_qty = frappe.db.sql(
		,
		(item_code, warehouse, item_code, warehouse),
	)

	return flt(reserved_qty[0][0]) if reserved_qty else 0


        ```
    ###Assistant : 
		select
			sum(dnpi_qty * ((so_item_qty - so_item_delivered_qty) / so_item_qty))
		from
			(
				(select
					qty as dnpi_qty,
					(
						select qty from `tabSales Order Item`
						where name = dnpi.parent_detail_docname
						and (delivered_by_supplier is null or delivered_by_supplier = 0)
					) as so_item_qty,
					(
						select delivered_qty from `tabSales Order Item`
						where name = dnpi.parent_detail_docname
						and delivered_by_supplier = 0
					) as so_item_delivered_qty,
					parent, name
				from
				(
					select qty, parent_detail_docname, parent, name
					from `tabPacked Item` dnpi_in
					where item_code = %s and warehouse = %s
					and parenttype=""Sales Order""
					and item_code != parent_item
					and exists (select * from `tabSales Order` so
					where name = dnpi_in.parent and docstatus = 1 and status != 'Closed')
				) dnpi)
			union
				(select stock_qty as dnpi_qty, qty as so_item_qty,
					delivered_qty as so_item_delivered_qty, parent, name
				from `tabSales Order Item` so_item
				where item_code = %s and warehouse = %s
				and (so_item.delivered_by_supplier is null or so_item.delivered_by_supplier = 0)
				and exists(select * from `tabSales Order` so
					where so.name = so_item.parent and so.docstatus = 1
					and so.status != 'Closed'))
			) tab
		where
			so_item_qty >= so_item_delivered_qty
	
    "
2339,"def cool():
    
    set_cmap('cool')


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.","
    Set the colormap to 'cool'.

    This changes the default colormap as well as the colormap of the current
    image if there is one. See ``help(colormaps)`` for more information.
    ",28,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cool():
    
    set_cmap('cool')


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
        ```
    ###Assistant : 
    Set the colormap to 'cool'.

    This changes the default colormap as well as the colormap of the current
    image if there is one. See ``help(colormaps)`` for more information.
    
    "
2340,"def add_parent(self, parent):
        # type: (Packet) -> None
        
        self.parent = parent
","Set packet parent.
        When packet is an element in PacketListField, parent field would
        point to the list owner packet.",19,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def add_parent(self, parent):
        # type: (Packet) -> None
        
        self.parent = parent

        ```
    ###Assistant : Set packet parent.
        When packet is an element in PacketListField, parent field would
        point to the list owner packet.
    "
2341,"def make_tempfile(name):
    
    open(name, 'w', encoding='utf-8').close()
    try:
        yield
    finally:
        os.unlink(name)
"," Create an empty, named, temporary file for the duration of the context.
    ",12,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_tempfile(name):
    
    open(name, 'w', encoding='utf-8').close()
    try:
        yield
    finally:
        os.unlink(name)

        ```
    ###Assistant :  Create an empty, named, temporary file for the duration of the context.
    
    "
2342,"def update_cached_response(self, request, response):
        
        cache_url = self.cache_url(request.url)

        cached_response = self.serializer.loads(request, self.cache.get(cache_url))

        if not cached_response:
            # we didn't have a cached response
            return response

        # Lets update our headers with the headers from the new request:
        # http://tools.ietf.org/html/draft-ietf-httpbis-p4-conditional-26#section-4.1
        #
        # The server isn't supposed to send headers that would make
        # the cached body invalid. But... just in case, we'll be sure
        # to strip out ones we know that might be problmatic due to
        # typical assumptions.
        excluded_headers = [""content-length""]

        cached_response.headers.update(
            dict(
                (k, v)
                for k, v in response.headers.items()
                if k.lower() not in excluded_headers
            )
        )

        # we want a 200 b/c we have content via the cache
        cached_response.status = 200

        # update our cache
        self._cache_set(cache_url, request, cached_response)

        return cached_response
","On a 304 we will get a new set of headers that we want to
        update our cached value with, assuming we have one.

        This should only ever be called when we've sent an ETag and
        gotten a 304 as the response.
        ",42,120,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_cached_response(self, request, response):
        
        cache_url = self.cache_url(request.url)

        cached_response = self.serializer.loads(request, self.cache.get(cache_url))

        if not cached_response:
            # we didn't have a cached response
            return response

        # Lets update our headers with the headers from the new request:
        # http://tools.ietf.org/html/draft-ietf-httpbis-p4-conditional-26#section-4.1
        #
        # The server isn't supposed to send headers that would make
        # the cached body invalid. But... just in case, we'll be sure
        # to strip out ones we know that might be problmatic due to
        # typical assumptions.
        excluded_headers = [""content-length""]

        cached_response.headers.update(
            dict(
                (k, v)
                for k, v in response.headers.items()
                if k.lower() not in excluded_headers
            )
        )

        # we want a 200 b/c we have content via the cache
        cached_response.status = 200

        # update our cache
        self._cache_set(cache_url, request, cached_response)

        return cached_response

        ```
    ###Assistant : On a 304 we will get a new set of headers that we want to
        update our cached value with, assuming we have one.

        This should only ever be called when we've sent an ETag and
        gotten a 304 as the response.
        
    "
2343,"def binary_xloss(logits, labels, ignore=None):
    
    logits, labels = flatten_binary_scores(logits, labels, ignore)
    loss = StableBCELoss()(logits, Variable(labels.float()))
    return loss


# --------------------------- MULTICLASS LOSSES ---------------------------

","
    Binary Cross entropy loss
      logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
      labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
      ignore: void class id
    ",33,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def binary_xloss(logits, labels, ignore=None):
    
    logits, labels = flatten_binary_scores(logits, labels, ignore)
    loss = StableBCELoss()(logits, Variable(labels.float()))
    return loss


# --------------------------- MULTICLASS LOSSES ---------------------------


        ```
    ###Assistant : 
    Binary Cross entropy loss
      logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
      labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
      ignore: void class id
    
    "
2344,"def generate_navigator_js(os=None, navigator=None, platform=None, device_type=None):
    

    config = generate_navigator(
        os=os, navigator=navigator, platform=platform, device_type=device_type
    )
    return {
        ""appCodeName"": config[""app_code_name""],
        ""appName"": config[""app_name""],
        ""appVersion"": config[""app_version""],
        ""platform"": config[""platform""],
        ""userAgent"": config[""user_agent""],
        ""oscpu"": config[""oscpu""],
        ""product"": config[""product""],
        ""productSub"": config[""product_sub""],
        ""vendor"": config[""vendor""],
        ""vendorSub"": config[""vendor_sub""],
        ""buildID"": config[""build_id""],
    }
","
    Generates web navigator's config with keys corresponding
    to keys of `windows.navigator` JavaScript object.

    :param os: limit list of oses for generation
    :type os: string or list/tuple or None
    :param navigator: limit list of browser engines for generation
    :type navigator: string or list/tuple or None
    :param device_type: limit possible oses by device type
    :type device_type: list/tuple or None, possible values:
        ""desktop"", ""smartphone"", ""tablet"", ""all""
    :return: User-Agent config
    :rtype: dict with keys (TODO)
    :raises InvalidOption: if could not generate user-agent for
        any combination of allowed oses and navigators
    :raise InvalidOption: if any of passed options is invalid
    ",95,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def generate_navigator_js(os=None, navigator=None, platform=None, device_type=None):
    

    config = generate_navigator(
        os=os, navigator=navigator, platform=platform, device_type=device_type
    )
    return {
        ""appCodeName"": config[""app_code_name""],
        ""appName"": config[""app_name""],
        ""appVersion"": config[""app_version""],
        ""platform"": config[""platform""],
        ""userAgent"": config[""user_agent""],
        ""oscpu"": config[""oscpu""],
        ""product"": config[""product""],
        ""productSub"": config[""product_sub""],
        ""vendor"": config[""vendor""],
        ""vendorSub"": config[""vendor_sub""],
        ""buildID"": config[""build_id""],
    }

        ```
    ###Assistant : 
    Generates web navigator's config with keys corresponding
    to keys of `windows.navigator` JavaScript object.

    :param os: limit list of oses for generation
    :type os: string or list/tuple or None
    :param navigator: limit list of browser engines for generation
    :type navigator: string or list/tuple or None
    :param device_type: limit possible oses by device type
    :type device_type: list/tuple or None, possible values:
        ""desktop"", ""smartphone"", ""tablet"", ""all""
    :return: User-Agent config
    :rtype: dict with keys (TODO)
    :raises InvalidOption: if could not generate user-agent for
        any combination of allowed oses and navigators
    :raise InvalidOption: if any of passed options is invalid
    
    "
2345,"def printable_text(text):
    

    # These functions want `str` for both Python2 and Python3, but in one case
    # it's a Unicode string and in the other it's a byte string.
    if six.PY3:
        if isinstance(text, str):
            return text
        elif isinstance(text, bytes):
            return text.decode(""utf-8"", ""ignore"")
        else:
            raise ValueError(""Unsupported string type: %s"" % (type(text)))
    elif six.PY2:
        if isinstance(text, str):
            return text
        elif isinstance(text, unicode):
            return text.encode(""utf-8"")
        else:
            raise ValueError(""Unsupported string type: %s"" % (type(text)))
    else:
        raise ValueError(""Not running on Python2 or Python 3?"")

",Returns text encoded in a way suitable for print or `tf.logging`.,11,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def printable_text(text):
    

    # These functions want `str` for both Python2 and Python3, but in one case
    # it's a Unicode string and in the other it's a byte string.
    if six.PY3:
        if isinstance(text, str):
            return text
        elif isinstance(text, bytes):
            return text.decode(""utf-8"", ""ignore"")
        else:
            raise ValueError(""Unsupported string type: %s"" % (type(text)))
    elif six.PY2:
        if isinstance(text, str):
            return text
        elif isinstance(text, unicode):
            return text.encode(""utf-8"")
        else:
            raise ValueError(""Unsupported string type: %s"" % (type(text)))
    else:
        raise ValueError(""Not running on Python2 or Python 3?"")


        ```
    ###Assistant : Returns text encoded in a way suitable for print or `tf.logging`.
    "
2346,"def is_tradesignal(self, action):
        # trade signal 
        
        return not ((action == Actions.Neutral.value and self._position == Positions.Neutral)
                    or (action == Actions.Short.value and self._position == Positions.Short)
                    or (action == Actions.Long.value and self._position == Positions.Long))
    ","
        not trade signal is :
        Action: Neutral, position: Neutral -> Nothing 
        Action: Long, position: Long -> Hold Long
        Action: Short, position: Short -> Hold Short
        ",25,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_tradesignal(self, action):
        # trade signal 
        
        return not ((action == Actions.Neutral.value and self._position == Positions.Neutral)
                    or (action == Actions.Short.value and self._position == Positions.Short)
                    or (action == Actions.Long.value and self._position == Positions.Long))
    
        ```
    ###Assistant : 
        not trade signal is :
        Action: Neutral, position: Neutral -> Nothing 
        Action: Long, position: Long -> Hold Long
        Action: Short, position: Short -> Hold Short
        
    "
2347,"def log_message(self, format, *args):
        

        sys.stderr.write(""%s - - [%s] %s\n"" %
                         (self.address_string(),
                          self.log_date_time_string(),
                          format%args))
","Log an arbitrary message.

        This is used by all other logging functions.  Override
        it if you have specific logging wishes.

        The first argument, FORMAT, is a format string for the
        message to be logged.  If the format string contains
        any % escapes requiring parameters, they should be
        specified as subsequent arguments (it's just like
        printf!).

        The client ip and current date/time are prefixed to
        every message.

        ",66,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def log_message(self, format, *args):
        

        sys.stderr.write(""%s - - [%s] %s\n"" %
                         (self.address_string(),
                          self.log_date_time_string(),
                          format%args))

        ```
    ###Assistant : Log an arbitrary message.

        This is used by all other logging functions.  Override
        it if you have specific logging wishes.

        The first argument, FORMAT, is a format string for the
        message to be logged.  If the format string contains
        any % escapes requiring parameters, they should be
        specified as subsequent arguments (it's just like
        printf!).

        The client ip and current date/time are prefixed to
        every message.

        
    "
2348,"def warning_advice(self, *args, **kwargs):
    
    no_advisory_warnings = os.getenv(""DIFFUSERS_NO_ADVISORY_WARNINGS"", False)
    if no_advisory_warnings:
        return
    self.warning(*args, **kwargs)


logging.Logger.warning_advice = warning_advice

","
    This method is identical to `logger.warninging()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
    warning will not be printed
    ",19,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def warning_advice(self, *args, **kwargs):
    
    no_advisory_warnings = os.getenv(""DIFFUSERS_NO_ADVISORY_WARNINGS"", False)
    if no_advisory_warnings:
        return
    self.warning(*args, **kwargs)


logging.Logger.warning_advice = warning_advice


        ```
    ###Assistant : 
    This method is identical to `logger.warninging()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
    warning will not be printed
    
    "
2349,"def zip_row_op(self, i, k, f):
        
        for j in range(self.cols):
            self[i, j] = f(self[i, j], self[k, j])
","In-place operation on row ``i`` using two-arg functor whose args are
        interpreted as ``(self[i, j], self[k, j])``.

        Examples
        ========

        >>> from sympy import eye
        >>> M = eye(3)
        >>> M.zip_row_op(1, 0, lambda v, u: v + 2*u); M
        Matrix([
        [1, 0, 0],
        [2, 1, 0],
        [0, 0, 1]])

        See Also
        ========
        row
        row_op
        col_op

        ",54,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def zip_row_op(self, i, k, f):
        
        for j in range(self.cols):
            self[i, j] = f(self[i, j], self[k, j])

        ```
    ###Assistant : In-place operation on row ``i`` using two-arg functor whose args are
        interpreted as ``(self[i, j], self[k, j])``.

        Examples
        ========

        >>> from sympy import eye
        >>> M = eye(3)
        >>> M.zip_row_op(1, 0, lambda v, u: v + 2*u); M
        Matrix([
        [1, 0, 0],
        [2, 1, 0],
        [0, 0, 1]])

        See Also
        ========
        row
        row_op
        col_op

        
    "
2350,"def legendre_poly(n, x=None, polys=False):
    r
    return named_poly(n, dup_legendre, QQ, ""Legendre polynomial"", (x,), polys)

","Generates the Legendre polynomial `P_n(x)`.

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    ",31,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def legendre_poly(n, x=None, polys=False):
    r
    return named_poly(n, dup_legendre, QQ, ""Legendre polynomial"", (x,), polys)


        ```
    ###Assistant : Generates the Legendre polynomial `P_n(x)`.

    Parameters
    ==========

    n : int
        Degree of the polynomial.
    x : optional
    polys : bool, optional
        If True, return a Poly, otherwise (default) return an expression.
    
    "
2351,"def _concat_short_text_reuslts(self, input_texts, results):
        
        long_text_lens = [len(text) for text in input_texts]
        concat_results = []
        single_results = {}
        count = 0
        for text in input_texts:
            text_len = len(text)
            while True:
                if len(single_results) == 0 or len(single_results[
                        ""text""]) < text_len:
                    if len(single_results) == 0:
                        single_results = copy.deepcopy(results[count])
                    else:
                        single_results[""text""] += results[count][""text""]
                        single_results[""items""].extend(results[count][""items""])
                    count += 1
                elif len(single_results[""text""]) == text_len:
                    concat_results.append(single_results)
                    single_results = {}
                    break
                else:
                    raise Exception(
                        ""The length of input text and raw text is not equal."")
        for result in concat_results:
            pred_words = result['items']
            pred_words = self._reset_offset(pred_words)
            result['items'] = pred_words
        return concat_results
","
        Concat the model output of short texts to the total result of long text.
        ",14,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _concat_short_text_reuslts(self, input_texts, results):
        
        long_text_lens = [len(text) for text in input_texts]
        concat_results = []
        single_results = {}
        count = 0
        for text in input_texts:
            text_len = len(text)
            while True:
                if len(single_results) == 0 or len(single_results[
                        ""text""]) < text_len:
                    if len(single_results) == 0:
                        single_results = copy.deepcopy(results[count])
                    else:
                        single_results[""text""] += results[count][""text""]
                        single_results[""items""].extend(results[count][""items""])
                    count += 1
                elif len(single_results[""text""]) == text_len:
                    concat_results.append(single_results)
                    single_results = {}
                    break
                else:
                    raise Exception(
                        ""The length of input text and raw text is not equal."")
        for result in concat_results:
            pred_words = result['items']
            pred_words = self._reset_offset(pred_words)
            result['items'] = pred_words
        return concat_results

        ```
    ###Assistant : 
        Concat the model output of short texts to the total result of long text.
        
    "
2352,"def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
        
        origin_shape = paddle.floor(im_shape / scale_factor + 0.5)

        origin_shape_list = []
        scale_factor_list = []
        # scale_factor: scale_y, scale_x
        for i in range(bbox_num.shape[0]):
            expand_shape = paddle.expand(origin_shape[i:i + 1, :],
                                         [bbox_num[i], 2])
            scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
            scale = paddle.concat([
                scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x,
                scale_y
            ])
            expand_scale = paddle.expand(scale, [bbox_num[i], 8])
            origin_shape_list.append(expand_shape)
            scale_factor_list.append(expand_scale)

        origin_shape_list = paddle.concat(origin_shape_list)
        scale_factor_list = paddle.concat(scale_factor_list)

        # bboxes: [N, 10], label, score, bbox
        pred_label_score = bboxes[:, 0:2]
        pred_bbox = bboxes[:, 2:]

        # rescale bbox to original image
        pred_bbox = pred_bbox.reshape([-1, 8])
        scaled_bbox = pred_bbox / scale_factor_list
        origin_h = origin_shape_list[:, 0]
        origin_w = origin_shape_list[:, 1]

        bboxes = scaled_bbox
        zeros = paddle.zeros_like(origin_h)
        x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros)
        y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros)
        x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros)
        y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros)
        x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros)
        y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros)
        x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros)
        y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros)
        pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1)
        pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
        return pred_result
","
        Rescale, clip and filter the bbox from the output of NMS to
        get final prediction.
        Args:
            bboxes(Tensor): bboxes [N, 10]
            bbox_num(Tensor): bbox_num
            im_shape(Tensor): [1 2]
            scale_factor(Tensor): [1 2]
        Returns:
            bbox_pred(Tensor): The output is the prediction with shape [N, 8]
                               including labels, scores and bboxes. The size of
                               bboxes are corresponding to the original image.
        ",54,191,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
        
        origin_shape = paddle.floor(im_shape / scale_factor + 0.5)

        origin_shape_list = []
        scale_factor_list = []
        # scale_factor: scale_y, scale_x
        for i in range(bbox_num.shape[0]):
            expand_shape = paddle.expand(origin_shape[i:i + 1, :],
                                         [bbox_num[i], 2])
            scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
            scale = paddle.concat([
                scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x,
                scale_y
            ])
            expand_scale = paddle.expand(scale, [bbox_num[i], 8])
            origin_shape_list.append(expand_shape)
            scale_factor_list.append(expand_scale)

        origin_shape_list = paddle.concat(origin_shape_list)
        scale_factor_list = paddle.concat(scale_factor_list)

        # bboxes: [N, 10], label, score, bbox
        pred_label_score = bboxes[:, 0:2]
        pred_bbox = bboxes[:, 2:]

        # rescale bbox to original image
        pred_bbox = pred_bbox.reshape([-1, 8])
        scaled_bbox = pred_bbox / scale_factor_list
        origin_h = origin_shape_list[:, 0]
        origin_w = origin_shape_list[:, 1]

        bboxes = scaled_bbox
        zeros = paddle.zeros_like(origin_h)
        x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros)
        y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros)
        x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros)
        y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros)
        x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros)
        y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros)
        x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros)
        y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros)
        pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1)
        pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
        return pred_result

        ```
    ###Assistant : 
        Rescale, clip and filter the bbox from the output of NMS to
        get final prediction.
        Args:
            bboxes(Tensor): bboxes [N, 10]
            bbox_num(Tensor): bbox_num
            im_shape(Tensor): [1 2]
            scale_factor(Tensor): [1 2]
        Returns:
            bbox_pred(Tensor): The output is the prediction with shape [N, 8]
                               including labels, scores and bboxes. The size of
                               bboxes are corresponding to the original image.
        
    "
2353,"def unset_existing_data(company):
	linked = frappe.db.sql(
		,
		as_dict=True,
	)

	# remove accounts data from company
	update_values = {d.fieldname: """" for d in linked}
	frappe.db.set_value(""Company"", company, update_values, update_values)

	# remove accounts data from various doctypes
	for doctype in [
		""Account"",
		""Party Account"",
		""Mode of Payment Account"",
		""Tax Withholding Account"",
		""Sales Taxes and Charges Template"",
		""Purchase Taxes and Charges Template"",
	]:
		frappe.db.sql(
			.format(doctype) % (company)  # nosec
		)

","select fieldname from tabDocField
		where fieldtype=""Link"" and options=""Account"" and parent=""Company""delete from `tab{0}` where `company`=""%s""",14,65,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unset_existing_data(company):
	linked = frappe.db.sql(
		,
		as_dict=True,
	)

	# remove accounts data from company
	update_values = {d.fieldname: """" for d in linked}
	frappe.db.set_value(""Company"", company, update_values, update_values)

	# remove accounts data from various doctypes
	for doctype in [
		""Account"",
		""Party Account"",
		""Mode of Payment Account"",
		""Tax Withholding Account"",
		""Sales Taxes and Charges Template"",
		""Purchase Taxes and Charges Template"",
	]:
		frappe.db.sql(
			.format(doctype) % (company)  # nosec
		)


        ```
    ###Assistant : select fieldname from tabDocField
		where fieldtype=""Link"" and options=""Account"" and parent=""Company""delete from `tab{0}` where `company`=""%s""
    "
2354,"def simple_test(self, feats, batch_img_metas, rescale=False):
        
        outs = self.forward(feats)
        results_list = self.get_results(
            *outs, batch_img_metas=batch_img_metas, rescale=rescale)
        return results_list
","Test function without test-time augmentation.

        Args:
            feats (tuple[torch.Tensor]): Multi-level features from the
                upstream network, each is a 4D-tensor.
            batch_img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[obj:`InstanceData`]: Detection results of each image
            after the post process.
            Each item usually contains following keys.

            - scores (Tensor): Classification scores, has a shape
              (num_instance, )
            - labels (Tensor): Labels of bboxes, has a shape
              (num_instances, ).
            - bboxes (Tensor): Has a shape (num_instances, 4),
              the last dimension 4 arrange as (x1, y1, x2, y2).
        ",91,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def simple_test(self, feats, batch_img_metas, rescale=False):
        
        outs = self.forward(feats)
        results_list = self.get_results(
            *outs, batch_img_metas=batch_img_metas, rescale=rescale)
        return results_list

        ```
    ###Assistant : Test function without test-time augmentation.

        Args:
            feats (tuple[torch.Tensor]): Multi-level features from the
                upstream network, each is a 4D-tensor.
            batch_img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[obj:`InstanceData`]: Detection results of each image
            after the post process.
            Each item usually contains following keys.

            - scores (Tensor): Classification scores, has a shape
              (num_instance, )
            - labels (Tensor): Labels of bboxes, has a shape
              (num_instances, ).
            - bboxes (Tensor): Has a shape (num_instances, 4),
              the last dimension 4 arrange as (x1, y1, x2, y2).
        
    "
2355,"def compare_pt_tf_models(pt_model, pt_input, tf_model, tf_input):
        
        pt_outputs = pt_model(**pt_input, output_hidden_states=True)
        tf_outputs = tf_model(**tf_input, output_hidden_states=True)

        # 1. All output attributes must be the same
        pt_out_attrs = set(pt_outputs.keys())
        tf_out_attrs = set(tf_outputs.keys())
        if pt_out_attrs != tf_out_attrs:
            raise ValueError(
                f""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:""
                f"" {tf_out_attrs})""
            )

        # 2. For each output attribute, ALL values must be the same","
        Compares the TensorFlow and PyTorch models, given their inputs, returning a tuple with the maximum observed
        difference and its source.
        ",20,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compare_pt_tf_models(pt_model, pt_input, tf_model, tf_input):
        
        pt_outputs = pt_model(**pt_input, output_hidden_states=True)
        tf_outputs = tf_model(**tf_input, output_hidden_states=True)

        # 1. All output attributes must be the same
        pt_out_attrs = set(pt_outputs.keys())
        tf_out_attrs = set(tf_outputs.keys())
        if pt_out_attrs != tf_out_attrs:
            raise ValueError(
                f""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:""
                f"" {tf_out_attrs})""
            )

        # 2. For each output attribute, ALL values must be the same
        ```
    ###Assistant : 
        Compares the TensorFlow and PyTorch models, given their inputs, returning a tuple with the maximum observed
        difference and its source.
        
    "
2356,"def _get_all_parser_float_precision_combinations():
    
    params = []
    ids = []
    for parser, parser_id in zip(_all_parsers, _all_parser_ids):
        if hasattr(parser, ""values""):
            # Wrapped in pytest.param, get the actual parser back
            parser = parser.values[0]
        for precision in parser.float_precision_choices:
            # Re-wrap in pytest.param for pyarrow
            mark = pytest.mark.single_cpu if parser.engine == ""pyarrow"" else ()
            param = pytest.param((parser(), precision), marks=mark)
            params.append(param)
            ids.append(f""{parser_id}-{precision}"")

    return {""params"": params, ""ids"": ids}


@pytest.fixture(
    params=_get_all_parser_float_precision_combinations()[""params""],
    ids=_get_all_parser_float_precision_combinations()[""ids""],
)","
    Return all allowable parser and float precision
    combinations and corresponding ids.
    ",11,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_all_parser_float_precision_combinations():
    
    params = []
    ids = []
    for parser, parser_id in zip(_all_parsers, _all_parser_ids):
        if hasattr(parser, ""values""):
            # Wrapped in pytest.param, get the actual parser back
            parser = parser.values[0]
        for precision in parser.float_precision_choices:
            # Re-wrap in pytest.param for pyarrow
            mark = pytest.mark.single_cpu if parser.engine == ""pyarrow"" else ()
            param = pytest.param((parser(), precision), marks=mark)
            params.append(param)
            ids.append(f""{parser_id}-{precision}"")

    return {""params"": params, ""ids"": ids}


@pytest.fixture(
    params=_get_all_parser_float_precision_combinations()[""params""],
    ids=_get_all_parser_float_precision_combinations()[""ids""],
)
        ```
    ###Assistant : 
    Return all allowable parser and float precision
    combinations and corresponding ids.
    
    "
2357,"def test_egg3(self):
        
        egg_name = ""%s/omelet.egg"" % self.egg_dir
        with extend_sys_path(egg_name):
            with self.settings(INSTALLED_APPS=[""omelet.app_with_models""]):
                models_module = apps.get_app_config(""app_with_models"").models_module
                self.assertIsNotNone(models_module)
        del apps.all_models[""app_with_models""]
",Models module can be loaded from an app located under an egg's top-level package,14,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_egg3(self):
        
        egg_name = ""%s/omelet.egg"" % self.egg_dir
        with extend_sys_path(egg_name):
            with self.settings(INSTALLED_APPS=[""omelet.app_with_models""]):
                models_module = apps.get_app_config(""app_with_models"").models_module
                self.assertIsNotNone(models_module)
        del apps.all_models[""app_with_models""]

        ```
    ###Assistant : Models module can be loaded from an app located under an egg's top-level package
    "
2358,"def test_auditing_case_names(lgpo, setting_name, setting, enable_legacy_auditing):
    
    lgpo.set_computer_policy(setting_name, setting)
    result = lgpo.get_policy(setting_name, ""machine"")
    assert result == setting


@pytest.mark.parametrize(""setting"", [""Enabled"", ""Disabled""])","
    Helper function to set an audit setting and assert that it was successful
    ",13,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_auditing_case_names(lgpo, setting_name, setting, enable_legacy_auditing):
    
    lgpo.set_computer_policy(setting_name, setting)
    result = lgpo.get_policy(setting_name, ""machine"")
    assert result == setting


@pytest.mark.parametrize(""setting"", [""Enabled"", ""Disabled""])
        ```
    ###Assistant : 
    Helper function to set an audit setting and assert that it was successful
    
    "
2359,"def test_get(self):
        
        # Generate signature
        signature = generate_signature(self.image.id, ""fill-800x600"")

        # Get the image
        response = self.client.get(
            reverse(
                ""wagtailimages_serve"", args=(signature, self.image.id, ""fill-800x600"")
            )
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertTrue(response.streaming)
        self.assertEqual(response[""Content-Type""], ""image/png"")
","
        Test a valid GET request to the view
        ",8,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get(self):
        
        # Generate signature
        signature = generate_signature(self.image.id, ""fill-800x600"")

        # Get the image
        response = self.client.get(
            reverse(
                ""wagtailimages_serve"", args=(signature, self.image.id, ""fill-800x600"")
            )
        )

        # Check response
        self.assertEqual(response.status_code, 200)
        self.assertTrue(response.streaming)
        self.assertEqual(response[""Content-Type""], ""image/png"")

        ```
    ###Assistant : 
        Test a valid GET request to the view
        
    "
2360,"def _object2proto(self) -> Slice_PB:
        
        slice_pb = Slice_PB()
        if self.start:
            slice_pb.start = self.start
            slice_pb.has_start = True

        if self.stop:
            slice_pb.stop = self.stop
            slice_pb.has_stop = True

        if self.step:
            slice_pb.step = self.step
            slice_pb.has_step = True

        slice_pb.id.CopyFrom(serialize(obj=self._id))

        return slice_pb
","
        Serialize  the Slice object instance returning a protobuf.

        Returns:
            Slice_PB: returns a protobuf object class representing this Slice object.
        ",19,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _object2proto(self) -> Slice_PB:
        
        slice_pb = Slice_PB()
        if self.start:
            slice_pb.start = self.start
            slice_pb.has_start = True

        if self.stop:
            slice_pb.stop = self.stop
            slice_pb.has_stop = True

        if self.step:
            slice_pb.step = self.step
            slice_pb.has_step = True

        slice_pb.id.CopyFrom(serialize(obj=self._id))

        return slice_pb

        ```
    ###Assistant : 
        Serialize  the Slice object instance returning a protobuf.

        Returns:
            Slice_PB: returns a protobuf object class representing this Slice object.
        
    "
2361,"def __call__(self) -> List[Tuple[int, int]]:
        
        logger.info(""Sorting face distances. Depending on your dataset this may take some time..."")
        if self._threshold:
            self._threshold = self._result_linkage[:, 2].max() * self._threshold
        result_order = self._seriation(self._result_linkage,
                                       self._num_predictions,
                                       self._num_predictions + self._num_predictions - 2)
        return result_order
"," Process the linkages.

        Transforms a distance matrix into a sorted distance matrix according to the order implied
        by the hierarchical tree (dendrogram).

        Returns
        -------
        list:
            List of indices with the order implied by the hierarchical tree or list of tuples of
            (`index`, `bin`) if a binning threshold was provided
        ",49,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self) -> List[Tuple[int, int]]:
        
        logger.info(""Sorting face distances. Depending on your dataset this may take some time..."")
        if self._threshold:
            self._threshold = self._result_linkage[:, 2].max() * self._threshold
        result_order = self._seriation(self._result_linkage,
                                       self._num_predictions,
                                       self._num_predictions + self._num_predictions - 2)
        return result_order

        ```
    ###Assistant :  Process the linkages.

        Transforms a distance matrix into a sorted distance matrix according to the order implied
        by the hierarchical tree (dendrogram).

        Returns
        -------
        list:
            List of indices with the order implied by the hierarchical tree or list of tuples of
            (`index`, `bin`) if a binning threshold was provided
        
    "
2362,"def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
        
        if self.export_eb:
            # enable rcnn models for edgeboard hw to skip the following postprocess.
            return bboxes, bboxes, bbox_num

        if not self.export_onnx:
            bboxes_list = []
            bbox_num_list = []
            id_start = 0
            fake_bboxes = paddle.to_tensor(
                np.array(
                    [[0., 0.0, 0.0, 0.0, 1.0, 1.0]], dtype='float32'))
            fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))

            # add fake bbox when output is empty for each batch
            for i in range(bbox_num.shape[0]):
                if bbox_num[i] == 0:
                    bboxes_i = fake_bboxes
                    bbox_num_i = fake_bbox_num
                else:
                    bboxes_i = bboxes[id_start:id_start + bbox_num[i], :]
                    bbox_num_i = bbox_num[i]
                    id_start += bbox_num[i]
                bboxes_list.append(bboxes_i)
                bbox_num_list.append(bbox_num_i)
            bboxes = paddle.concat(bboxes_list)
            bbox_num = paddle.concat(bbox_num_list)

        origin_shape = paddle.floor(im_shape / scale_factor + 0.5)

        if not self.export_onnx:
            origin_shape_list = []
            scale_factor_list = []
            # scale_factor: scale_y, scale_x
            for i in range(bbox_num.shape[0]):
                expand_shape = paddle.expand(origin_shape[i:i + 1, :],
                                             [bbox_num[i], 2])
                scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
                scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
                expand_scale = paddle.expand(scale, [bbox_num[i], 4])
                origin_shape_list.append(expand_shape)
                scale_factor_list.append(expand_scale)

            self.origin_shape_list = paddle.concat(origin_shape_list)
            scale_factor_list = paddle.concat(scale_factor_list)

        else:
            # simplify the computation for bs=1 when exporting onnx
            scale_y, scale_x = scale_factor[0][0], scale_factor[0][1]
            scale = paddle.concat(
                [scale_x, scale_y, scale_x, scale_y]).unsqueeze(0)
            self.origin_shape_list = paddle.expand(origin_shape,
                                                   [bbox_num[0], 2])
            scale_factor_list = paddle.expand(scale, [bbox_num[0], 4])

        # bboxes: [N, 6], label, score, bbox
        pred_label = bboxes[:, 0:1]
        pred_score = bboxes[:, 1:2]
        pred_bbox = bboxes[:, 2:]
        # rescale bbox to original image
        scaled_bbox = pred_bbox / scale_factor_list
        origin_h = self.origin_shape_list[:, 0]
        origin_w = self.origin_shape_list[:, 1]
        zeros = paddle.zeros_like(origin_h)
        # clip bbox to [0, original_size]
        x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
        y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
        x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
        y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
        pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
        # filter empty bbox
        keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
        keep_mask = paddle.unsqueeze(keep_mask, [1])
        pred_label = paddle.where(keep_mask, pred_label,
                                  paddle.ones_like(pred_label) * -1)
        pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
        return bboxes, pred_result, bbox_num
","
        Rescale, clip and filter the bbox from the output of NMS to 
        get final prediction. 

        Notes:
        Currently only support bs = 1.

        Args:
            bboxes (Tensor): The output bboxes with shape [N, 6] after decode
                and NMS, including labels, scores and bboxes.
            bbox_num (Tensor): The number of prediction boxes of each batch with
                shape [1], and is N.
            im_shape (Tensor): The shape of the input image.
            scale_factor (Tensor): The scale factor of the input image.
        Returns:
            pred_result (Tensor): The final prediction results with shape [N, 6]
                including labels, scores and bboxes.
        ",90,292,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
        
        if self.export_eb:
            # enable rcnn models for edgeboard hw to skip the following postprocess.
            return bboxes, bboxes, bbox_num

        if not self.export_onnx:
            bboxes_list = []
            bbox_num_list = []
            id_start = 0
            fake_bboxes = paddle.to_tensor(
                np.array(
                    [[0., 0.0, 0.0, 0.0, 1.0, 1.0]], dtype='float32'))
            fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))

            # add fake bbox when output is empty for each batch
            for i in range(bbox_num.shape[0]):
                if bbox_num[i] == 0:
                    bboxes_i = fake_bboxes
                    bbox_num_i = fake_bbox_num
                else:
                    bboxes_i = bboxes[id_start:id_start + bbox_num[i], :]
                    bbox_num_i = bbox_num[i]
                    id_start += bbox_num[i]
                bboxes_list.append(bboxes_i)
                bbox_num_list.append(bbox_num_i)
            bboxes = paddle.concat(bboxes_list)
            bbox_num = paddle.concat(bbox_num_list)

        origin_shape = paddle.floor(im_shape / scale_factor + 0.5)

        if not self.export_onnx:
            origin_shape_list = []
            scale_factor_list = []
            # scale_factor: scale_y, scale_x
            for i in range(bbox_num.shape[0]):
                expand_shape = paddle.expand(origin_shape[i:i + 1, :],
                                             [bbox_num[i], 2])
                scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
                scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
                expand_scale = paddle.expand(scale, [bbox_num[i], 4])
                origin_shape_list.append(expand_shape)
                scale_factor_list.append(expand_scale)

            self.origin_shape_list = paddle.concat(origin_shape_list)
            scale_factor_list = paddle.concat(scale_factor_list)

        else:
            # simplify the computation for bs=1 when exporting onnx
            scale_y, scale_x = scale_factor[0][0], scale_factor[0][1]
            scale = paddle.concat(
                [scale_x, scale_y, scale_x, scale_y]).unsqueeze(0)
            self.origin_shape_list = paddle.expand(origin_shape,
                                                   [bbox_num[0], 2])
            scale_factor_list = paddle.expand(scale, [bbox_num[0], 4])

        # bboxes: [N, 6], label, score, bbox
        pred_label = bboxes[:, 0:1]
        pred_score = bboxes[:, 1:2]
        pred_bbox = bboxes[:, 2:]
        # rescale bbox to original image
        scaled_bbox = pred_bbox / scale_factor_list
        origin_h = self.origin_shape_list[:, 0]
        origin_w = self.origin_shape_list[:, 1]
        zeros = paddle.zeros_like(origin_h)
        # clip bbox to [0, original_size]
        x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
        y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
        x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
        y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
        pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
        # filter empty bbox
        keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
        keep_mask = paddle.unsqueeze(keep_mask, [1])
        pred_label = paddle.where(keep_mask, pred_label,
                                  paddle.ones_like(pred_label) * -1)
        pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
        return bboxes, pred_result, bbox_num

        ```
    ###Assistant : 
        Rescale, clip and filter the bbox from the output of NMS to 
        get final prediction. 

        Notes:
        Currently only support bs = 1.

        Args:
            bboxes (Tensor): The output bboxes with shape [N, 6] after decode
                and NMS, including labels, scores and bboxes.
            bbox_num (Tensor): The number of prediction boxes of each batch with
                shape [1], and is N.
            im_shape (Tensor): The shape of the input image.
            scale_factor (Tensor): The scale factor of the input image.
        Returns:
            pred_result (Tensor): The final prediction results with shape [N, 6]
                including labels, scores and bboxes.
        
    "
2363,"def _signal_zone_update(self):
        
        async_dispatcher_send(self.hass, f""{SIGNAL_ZONE_UPDATE}-{self._zone.zone_id}"")
","Signal a zone update.

        Whenever the underlying library does an action against
        a zone, the data for the zone is updated.

        Update a single zone.
        ",25,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _signal_zone_update(self):
        
        async_dispatcher_send(self.hass, f""{SIGNAL_ZONE_UPDATE}-{self._zone.zone_id}"")

        ```
    ###Assistant : Signal a zone update.

        Whenever the underlying library does an action against
        a zone, the data for the zone is updated.

        Update a single zone.
        
    "
2364,"def test_asymmetric_error(quantile):
    
    n_samples = 10_000
    rng = np.random.RandomState(42)
    # take care that X @ coef + intercept > 0
    X = np.concatenate(
        (
            np.abs(rng.randn(n_samples)[:, None]),
            -rng.randint(2, size=(n_samples, 1)),
        ),
        axis=1,
    )
    intercept = 1.23
    coef = np.array([0.5, -2])
    # For an exponential distribution with rate lambda, e.g. exp(-lambda * x),
    # the quantile at level q is:
    #   quantile(q) = - log(1 - q) / lambda
    #   scale = 1/lambda = -quantile(q) / log(1-q)
    y = rng.exponential(
        scale=-(X @ coef + intercept) / np.log(1 - quantile), size=n_samples
    )
    model = HistGradientBoostingRegressor(
        loss=""quantile"",
        quantile=quantile,
        max_iter=25,
        random_state=0,
        max_leaf_nodes=10,
    ).fit(X, y)
    assert_allclose(np.mean(model.predict(X) > y), quantile, rtol=1e-2)

    pinball_loss = PinballLoss(quantile=quantile)
    loss_true_quantile = pinball_loss(y, X @ coef + intercept)
    loss_pred_quantile = pinball_loss(y, model.predict(X))
    # we are overfitting
    assert loss_pred_quantile <= loss_true_quantile


@pytest.mark.parametrize(""y"", [([1.0, -2.0, 0.0]), ([0.0, 0.0, 0.0])])",Test quantile regression for asymmetric distributed targets.,7,133,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_asymmetric_error(quantile):
    
    n_samples = 10_000
    rng = np.random.RandomState(42)
    # take care that X @ coef + intercept > 0
    X = np.concatenate(
        (
            np.abs(rng.randn(n_samples)[:, None]),
            -rng.randint(2, size=(n_samples, 1)),
        ),
        axis=1,
    )
    intercept = 1.23
    coef = np.array([0.5, -2])
    # For an exponential distribution with rate lambda, e.g. exp(-lambda * x),
    # the quantile at level q is:
    #   quantile(q) = - log(1 - q) / lambda
    #   scale = 1/lambda = -quantile(q) / log(1-q)
    y = rng.exponential(
        scale=-(X @ coef + intercept) / np.log(1 - quantile), size=n_samples
    )
    model = HistGradientBoostingRegressor(
        loss=""quantile"",
        quantile=quantile,
        max_iter=25,
        random_state=0,
        max_leaf_nodes=10,
    ).fit(X, y)
    assert_allclose(np.mean(model.predict(X) > y), quantile, rtol=1e-2)

    pinball_loss = PinballLoss(quantile=quantile)
    loss_true_quantile = pinball_loss(y, X @ coef + intercept)
    loss_pred_quantile = pinball_loss(y, model.predict(X))
    # we are overfitting
    assert loss_pred_quantile <= loss_true_quantile


@pytest.mark.parametrize(""y"", [([1.0, -2.0, 0.0]), ([0.0, 0.0, 0.0])])
        ```
    ###Assistant : Test quantile regression for asymmetric distributed targets.
    "
2365,"def update(self) -> None:
        
        with self.lock:
            # Fetch valid stop information once
            if not self._origin:
                stops = self._pygtfs.stops_by_id(self.origin)
                if not stops:
                    self._available = False
                    _LOGGER.warning(""Origin stop ID %s not found"", self.origin)
                    return
                self._origin = stops[0]

            if not self._destination:
                stops = self._pygtfs.stops_by_id(self.destination)
                if not stops:
                    self._available = False
                    _LOGGER.warning(
                        ""Destination stop ID %s not found"", self.destination
                    )
                    return
                self._destination = stops[0]

            self._available = True

            # Fetch next departure
            self._departure = get_next_departure(
                self._pygtfs,
                self.origin,
                self.destination,
                self._offset,
                self._include_tomorrow,
            )

            # Define the state as a UTC timestamp with ISO 8601 format
            if not self._departure:
                self._state = None
            else:
                self._state = self._departure[""departure_time""].replace(
                    tzinfo=dt_util.UTC
                )

            # Fetch trip and route details once, unless updated
            if not self._departure:
                self._trip = None
            else:
                trip_id = self._departure[""trip_id""]
                if not self._trip or self._trip.trip_id != trip_id:
                    _LOGGER.debug(""Fetching trip details for %s"", trip_id)
                    self._trip = self._pygtfs.trips_by_id(trip_id)[0]

                route_id = self._departure[""route_id""]
                if not self._route or self._route.route_id != route_id:
                    _LOGGER.debug(""Fetching route details for %s"", route_id)
                    self._route = self._pygtfs.routes_by_id(route_id)[0]

            # Fetch agency details exactly once
            if self._agency is None and self._route:
                _LOGGER.debug(""Fetching agency details for %s"", self._route.agency_id)
                try:
                    self._agency = self._pygtfs.agencies_by_id(self._route.agency_id)[0]
                except IndexError:
                    _LOGGER.warning(
                        ""Agency ID '%s' was not found in agency table, ""
                        ""you may want to update the routes database table ""
                        ""to fix this missing reference"",
                        self._route.agency_id,
                    )
                    self._agency = False

            # Assign attributes, icon and name
            self.update_attributes()

            if self._agency:
                self._attr_attribution = self._agency.agency_name
            else:
                self._attr_attribution = None

            if self._route:
                self._icon = ICONS.get(self._route.route_type, ICON)
            else:
                self._icon = ICON

            name = (
                f""{getattr(self._agency, 'agency_name', DEFAULT_NAME)} ""
                f""{self.origin} to {self.destination} next departure""
            )
            if not self._departure:
                name = f""{DEFAULT_NAME}""
            self._name = self._custom_name or name
",Get the latest data from GTFS and update the states.,10,259,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self) -> None:
        
        with self.lock:
            # Fetch valid stop information once
            if not self._origin:
                stops = self._pygtfs.stops_by_id(self.origin)
                if not stops:
                    self._available = False
                    _LOGGER.warning(""Origin stop ID %s not found"", self.origin)
                    return
                self._origin = stops[0]

            if not self._destination:
                stops = self._pygtfs.stops_by_id(self.destination)
                if not stops:
                    self._available = False
                    _LOGGER.warning(
                        ""Destination stop ID %s not found"", self.destination
                    )
                    return
                self._destination = stops[0]

            self._available = True

            # Fetch next departure
            self._departure = get_next_departure(
                self._pygtfs,
                self.origin,
                self.destination,
                self._offset,
                self._include_tomorrow,
            )

            # Define the state as a UTC timestamp with ISO 8601 format
            if not self._departure:
                self._state = None
            else:
                self._state = self._departure[""departure_time""].replace(
                    tzinfo=dt_util.UTC
                )

            # Fetch trip and route details once, unless updated
            if not self._departure:
                self._trip = None
            else:
                trip_id = self._departure[""trip_id""]
                if not self._trip or self._trip.trip_id != trip_id:
                    _LOGGER.debug(""Fetching trip details for %s"", trip_id)
                    self._trip = self._pygtfs.trips_by_id(trip_id)[0]

                route_id = self._departure[""route_id""]
                if not self._route or self._route.route_id != route_id:
                    _LOGGER.debug(""Fetching route details for %s"", route_id)
                    self._route = self._pygtfs.routes_by_id(route_id)[0]

            # Fetch agency details exactly once
            if self._agency is None and self._route:
                _LOGGER.debug(""Fetching agency details for %s"", self._route.agency_id)
                try:
                    self._agency = self._pygtfs.agencies_by_id(self._route.agency_id)[0]
                except IndexError:
                    _LOGGER.warning(
                        ""Agency ID '%s' was not found in agency table, ""
                        ""you may want to update the routes database table ""
                        ""to fix this missing reference"",
                        self._route.agency_id,
                    )
                    self._agency = False

            # Assign attributes, icon and name
            self.update_attributes()

            if self._agency:
                self._attr_attribution = self._agency.agency_name
            else:
                self._attr_attribution = None

            if self._route:
                self._icon = ICONS.get(self._route.route_type, ICON)
            else:
                self._icon = ICON

            name = (
                f""{getattr(self._agency, 'agency_name', DEFAULT_NAME)} ""
                f""{self.origin} to {self.destination} next departure""
            )
            if not self._departure:
                name = f""{DEFAULT_NAME}""
            self._name = self._custom_name or name

        ```
    ###Assistant : Get the latest data from GTFS and update the states.
    "
2366,"async def test_carbon_monoxide_sensor_read_state(hass, utcnow):
    
    helper = await setup_test_component(hass, create_carbon_monoxide_sensor_service)

    await helper.async_update(
        ServicesTypes.CARBON_MONOXIDE_SENSOR,
        {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 0},
    )
    state = await helper.poll_and_get_state()
    assert state.state == ""off""

    await helper.async_update(
        ServicesTypes.CARBON_MONOXIDE_SENSOR,
        {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 1},
    )
    state = await helper.poll_and_get_state()
    assert state.state == ""on""

    assert state.attributes[""device_class""] == BinarySensorDeviceClass.CO

",Test that we can read the state of a HomeKit contact accessory.,12,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_carbon_monoxide_sensor_read_state(hass, utcnow):
    
    helper = await setup_test_component(hass, create_carbon_monoxide_sensor_service)

    await helper.async_update(
        ServicesTypes.CARBON_MONOXIDE_SENSOR,
        {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 0},
    )
    state = await helper.poll_and_get_state()
    assert state.state == ""off""

    await helper.async_update(
        ServicesTypes.CARBON_MONOXIDE_SENSOR,
        {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 1},
    )
    state = await helper.poll_and_get_state()
    assert state.state == ""on""

    assert state.attributes[""device_class""] == BinarySensorDeviceClass.CO


        ```
    ###Assistant : Test that we can read the state of a HomeKit contact accessory.
    "
2367,"def save_config(self) -> TritonArtifact:
        
        device = self.device
        if self.inference_stage != PREDICTOR:
            device = ""cpu""
        self.config = TritonConfig(
            self.full_model_name,
            self.input_features,
            self.output_features,
            self.max_batch_size,
            self.max_queue_delay_microseconds,
            device,
            self.model_instance_count,
            self.inference_stage,
        )

        config_path = os.path.join(self.base_path, ""config.pbtxt"")
        with open(config_path, ""w"") as f:
            formatted_config = remove_empty_lines(self.config.get_model_config())
            f.write(formatted_config)

        config_artifact = TritonArtifact(
            model_name=self.full_model_name,
            model_version=self.model_version,
            platform=""pytorch_libtorch"",
            path=config_path,
            content_type=""text/x-protobuf"",
            content_length=os.path.getsize(config_path),
        )

        return config_artifact


@dataclass","Save the Triton config.

        Return the appropriate artifact.
        ",8,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_config(self) -> TritonArtifact:
        
        device = self.device
        if self.inference_stage != PREDICTOR:
            device = ""cpu""
        self.config = TritonConfig(
            self.full_model_name,
            self.input_features,
            self.output_features,
            self.max_batch_size,
            self.max_queue_delay_microseconds,
            device,
            self.model_instance_count,
            self.inference_stage,
        )

        config_path = os.path.join(self.base_path, ""config.pbtxt"")
        with open(config_path, ""w"") as f:
            formatted_config = remove_empty_lines(self.config.get_model_config())
            f.write(formatted_config)

        config_artifact = TritonArtifact(
            model_name=self.full_model_name,
            model_version=self.model_version,
            platform=""pytorch_libtorch"",
            path=config_path,
            content_type=""text/x-protobuf"",
            content_length=os.path.getsize(config_path),
        )

        return config_artifact


@dataclass
        ```
    ###Assistant : Save the Triton config.

        Return the appropriate artifact.
        
    "
2368,"def test_unknown_device(self) -> None:
        
        url = ""/_synapse/admin/v2/users/%s/devices/unknown_device"" % urllib.parse.quote(
            self.other_user
        )

        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])

        channel = self.make_request(
            ""PUT"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(200, channel.code, msg=channel.json_body)

        channel = self.make_request(
            ""DELETE"",
            url,
            access_token=self.admin_user_tok,
        )

        # Delete unknown device returns status 200
        self.assertEqual(200, channel.code, msg=channel.json_body)
","
        Tests that a lookup for a device that does not exist returns either 404 or 200.
        ",16,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unknown_device(self) -> None:
        
        url = ""/_synapse/admin/v2/users/%s/devices/unknown_device"" % urllib.parse.quote(
            self.other_user
        )

        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])

        channel = self.make_request(
            ""PUT"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(200, channel.code, msg=channel.json_body)

        channel = self.make_request(
            ""DELETE"",
            url,
            access_token=self.admin_user_tok,
        )

        # Delete unknown device returns status 200
        self.assertEqual(200, channel.code, msg=channel.json_body)

        ```
    ###Assistant : 
        Tests that a lookup for a device that does not exist returns either 404 or 200.
        
    "
2369,"def complete_code(accelerator, model, tokenizer, dataloader, n_tasks, batch_size=20, **gen_kwargs):
    
    gen_token_dict = defaultdict(list)  # dict of list of generated tokens
    for step, batch in tqdm(enumerate(dataloader)):
        with torch.no_grad():
            gen_kwargs[""stopping_criteria""][0].start_length = batch[""ids""].shape[-1]
            generated_tokens = accelerator.unwrap_model(model).generate(
                input_ids=batch[""ids""][:, : batch[""input_len""]], num_return_sequences=batch_size, **gen_kwargs
            )
            # each task is generated batch_size times
            generated_tasks = batch[""task_id""].repeat(batch_size)
            generated_tokens = accelerator.pad_across_processes(
                generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
            )

            generated_tokens, generated_tasks = accelerator.gather((generated_tokens, generated_tasks))
            generated_tokens = generated_tokens.cpu().numpy()
            generated_tasks = generated_tasks.cpu().numpy()

            for task, generated_tokens in zip(generated_tasks, generated_tokens):
                gen_token_dict[task].append(generated_tokens)

    code_gens = [[] for _ in range(n_tasks)]
    for task, generated_tokens in gen_token_dict.items():
        for s in generated_tokens:
            gen_code = tokenizer.decode(s, skip_special_tokens=True, clean_up_tokenization_spaces=True)
            code_gens[task].append(remove_last_block(gen_code))
    return code_gens

","Generate multiple codes for each task in the dataset. This function leverage accelerator to distribute
    the processing to multiple GPUs.
    dataloader, a wrapper around a TokenizeDataset objectm is supposed to send all the prompts from
    the evalution dataset to the modelm as the following:
    [p_0_0, p_0_1, ..., p_0_nc-1, p_1_0, ..., p_nt-1_nc-1]
    where nc is the number of copies of the prompt, and nt is the number of tasks.
    nc is such that num_sample = nc * batch_size

    Parameters
    ----------
    accelerator: Accelerator

    model: transformers.PreTrainedModel
        Code generation model. AutoTokenizer.from_pretrained(model_ckpt), ex model_ckpt = ""lvwerra/codeparrot""

    tokenizer: transformers.AutoTokenizer
        The tokenizer used to train model

    dataloader: DataLoader
        The dataloader is a wrapper around a TokenizeDataset object. It is designed to be used with multiple GPUs.

    n_tasks: int
        The number of tasks in the dataset. It is used to determine the length of the output.
        Should be aligned with the number of tasks in the TokenizeDataset.

    batch_size: int
        num_return_sequences per copy of the prompt such that num_sample = batch_size * n_copies

    gen_kwargs: dict
        Keyword arguments for the generation function of the model.

    Returns
    -------
    code_gens: list of list of str, of length n_tasks
        List of generated codes for each task.
        Each element is a list of generated codes for each task, with length num_samples
    ",207,96,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def complete_code(accelerator, model, tokenizer, dataloader, n_tasks, batch_size=20, **gen_kwargs):
    
    gen_token_dict = defaultdict(list)  # dict of list of generated tokens
    for step, batch in tqdm(enumerate(dataloader)):
        with torch.no_grad():
            gen_kwargs[""stopping_criteria""][0].start_length = batch[""ids""].shape[-1]
            generated_tokens = accelerator.unwrap_model(model).generate(
                input_ids=batch[""ids""][:, : batch[""input_len""]], num_return_sequences=batch_size, **gen_kwargs
            )
            # each task is generated batch_size times
            generated_tasks = batch[""task_id""].repeat(batch_size)
            generated_tokens = accelerator.pad_across_processes(
                generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
            )

            generated_tokens, generated_tasks = accelerator.gather((generated_tokens, generated_tasks))
            generated_tokens = generated_tokens.cpu().numpy()
            generated_tasks = generated_tasks.cpu().numpy()

            for task, generated_tokens in zip(generated_tasks, generated_tokens):
                gen_token_dict[task].append(generated_tokens)

    code_gens = [[] for _ in range(n_tasks)]
    for task, generated_tokens in gen_token_dict.items():
        for s in generated_tokens:
            gen_code = tokenizer.decode(s, skip_special_tokens=True, clean_up_tokenization_spaces=True)
            code_gens[task].append(remove_last_block(gen_code))
    return code_gens


        ```
    ###Assistant : Generate multiple codes for each task in the dataset. This function leverage accelerator to distribute
    the processing to multiple GPUs.
    dataloader, a wrapper around a TokenizeDataset objectm is supposed to send all the prompts from
    the evalution dataset to the modelm as the following:
    [p_0_0, p_0_1, ..., p_0_nc-1, p_1_0, ..., p_nt-1_nc-1]
    where nc is the number of copies of the prompt, and nt is the number of tasks.
    nc is such that num_sample = nc * batch_size

    Parameters
    ----------
    accelerator: Accelerator

    model: transformers.PreTrainedModel
        Code generation model. AutoTokenizer.from_pretrained(model_ckpt), ex model_ckpt = ""lvwerra/codeparrot""

    tokenizer: transformers.AutoTokenizer
        The tokenizer used to train model

    dataloader: DataLoader
        The dataloader is a wrapper around a TokenizeDataset object. It is designed to be used with multiple GPUs.

    n_tasks: int
        The number of tasks in the dataset. It is used to determine the length of the output.
        Should be aligned with the number of tasks in the TokenizeDataset.

    batch_size: int
        num_return_sequences per copy of the prompt such that num_sample = batch_size * n_copies

    gen_kwargs: dict
        Keyword arguments for the generation function of the model.

    Returns
    -------
    code_gens: list of list of str, of length n_tasks
        List of generated codes for each task.
        Each element is a list of generated codes for each task, with length num_samples
    
    "
2370,"def get_status(start_date, end_date):
	

	if not end_date:
		return ""Active""

	start_date = getdate(start_date)
	end_date = getdate(end_date)
	now_date = getdate(nowdate())

	return ""Active"" if start_date <= now_date <= end_date else ""Inactive""

","
	Get a Contract's status based on the start, current and end dates

	Args:
	        start_date (str): The start date of the contract
	        end_date (str): The end date of the contract

	Returns:
	        str: 'Active' if within range, otherwise 'Inactive'
	",37,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_status(start_date, end_date):
	

	if not end_date:
		return ""Active""

	start_date = getdate(start_date)
	end_date = getdate(end_date)
	now_date = getdate(nowdate())

	return ""Active"" if start_date <= now_date <= end_date else ""Inactive""


        ```
    ###Assistant : 
	Get a Contract's status based on the start, current and end dates

	Args:
	        start_date (str): The start date of the contract
	        end_date (str): The end date of the contract

	Returns:
	        str: 'Active' if within range, otherwise 'Inactive'
	
    "
2371,"def calc_position(self, s):
        
        x = self.sx.calc_position(s)
        y = self.sy.calc_position(s)

        return x, y
","
        calc position

        Parameters
        ----------
        s : float
            distance from the start point. if `s` is outside the data point's
            range, return None.

        Returns
        -------
        x : float
            x position for given s.
        y : float
            y position for given s.
        ",40,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def calc_position(self, s):
        
        x = self.sx.calc_position(s)
        y = self.sy.calc_position(s)

        return x, y

        ```
    ###Assistant : 
        calc position

        Parameters
        ----------
        s : float
            distance from the start point. if `s` is outside the data point's
            range, return None.

        Returns
        -------
        x : float
            x position for given s.
        y : float
            y position for given s.
        
    "
2372,"def push(self, exit):
        
        # We use an unbound method rather than a bound method to follow
        # the standard lookup behaviour for special methods.
        _cb_type = type(exit)

        try:
            exit_method = _cb_type.__exit__
        except AttributeError:
            # Not a context manager, so assume it's a callable.
            self._push_exit_callback(exit)
        else:
            self._push_cm_exit(exit, exit_method)
        return exit  # Allow use as a decorator.
","Registers a callback with the standard __exit__ method signature.

        Can suppress exceptions the same way __exit__ method can.
        Also accepts any object with an __exit__ method (registering a call
        to the method instead of the object itself).
        ",37,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def push(self, exit):
        
        # We use an unbound method rather than a bound method to follow
        # the standard lookup behaviour for special methods.
        _cb_type = type(exit)

        try:
            exit_method = _cb_type.__exit__
        except AttributeError:
            # Not a context manager, so assume it's a callable.
            self._push_exit_callback(exit)
        else:
            self._push_cm_exit(exit, exit_method)
        return exit  # Allow use as a decorator.

        ```
    ###Assistant : Registers a callback with the standard __exit__ method signature.

        Can suppress exceptions the same way __exit__ method can.
        Also accepts any object with an __exit__ method (registering a call
        to the method instead of the object itself).
        
    "
2373,"async def async_media_play(self) -> None:
        
        if self._status[""state""] == ""pause"":
            await self._client.pause(0)
        else:
            await self._client.play()
",Service to send the MPD the command for play/pause.,9,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_media_play(self) -> None:
        
        if self._status[""state""] == ""pause"":
            await self._client.pause(0)
        else:
            await self._client.play()

        ```
    ###Assistant : Service to send the MPD the command for play/pause.
    "
2374,"def download_datev_csv(filters):
	
	if isinstance(filters, str):
		filters = json.loads(filters)

	validate(filters)
	company = filters.get(""company"")

	fiscal_year = get_fiscal_year(date=filters.get(""from_date""), company=company)
	filters[""fiscal_year_start""] = fiscal_year[1]

	# set chart of accounts used
	coa = frappe.get_value(""Company"", company, ""chart_of_accounts"")
	filters[""skr""] = ""04"" if ""SKR04"" in coa else (""03"" if ""SKR03"" in coa else """")

	datev_settings = frappe.get_doc(""DATEV Settings"", company)
	filters[""account_number_length""] = datev_settings.account_number_length
	filters[""temporary_against_account_number""] = datev_settings.temporary_against_account_number

	transactions = get_transactions(filters)
	account_names = get_account_names(filters)
	customers = get_customers(filters)
	suppliers = get_suppliers(filters)

	zip_name = ""{} DATEV.zip"".format(frappe.utils.datetime.date.today())
	zip_and_download(
		zip_name,
		[
			{
				""file_name"": ""EXTF_Buchungsstapel.csv"",
				""csv_data"": get_datev_csv(transactions, filters, csv_class=Transactions),
			},
			{
				""file_name"": ""EXTF_Kontenbeschriftungen.csv"",
				""csv_data"": get_datev_csv(account_names, filters, csv_class=AccountNames),
			},
			{
				""file_name"": ""EXTF_Kunden.csv"",
				""csv_data"": get_datev_csv(customers, filters, csv_class=DebtorsCreditors),
			},
			{
				""file_name"": ""EXTF_Lieferanten.csv"",
				""csv_data"": get_datev_csv(suppliers, filters, csv_class=DebtorsCreditors),
			},
		],
	)
","
	Provide accounting entries for download in DATEV format.

	Validate the filters, get the data, produce the CSV file and provide it for
	download. Can be called like this:

	GET /api/method/erpnext.regional.report.datev.datev.download_datev_csv

	Arguments / Params:
	filters -- dict of filters to be passed to the sql query
	",45,109,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def download_datev_csv(filters):
	
	if isinstance(filters, str):
		filters = json.loads(filters)

	validate(filters)
	company = filters.get(""company"")

	fiscal_year = get_fiscal_year(date=filters.get(""from_date""), company=company)
	filters[""fiscal_year_start""] = fiscal_year[1]

	# set chart of accounts used
	coa = frappe.get_value(""Company"", company, ""chart_of_accounts"")
	filters[""skr""] = ""04"" if ""SKR04"" in coa else (""03"" if ""SKR03"" in coa else """")

	datev_settings = frappe.get_doc(""DATEV Settings"", company)
	filters[""account_number_length""] = datev_settings.account_number_length
	filters[""temporary_against_account_number""] = datev_settings.temporary_against_account_number

	transactions = get_transactions(filters)
	account_names = get_account_names(filters)
	customers = get_customers(filters)
	suppliers = get_suppliers(filters)

	zip_name = ""{} DATEV.zip"".format(frappe.utils.datetime.date.today())
	zip_and_download(
		zip_name,
		[
			{
				""file_name"": ""EXTF_Buchungsstapel.csv"",
				""csv_data"": get_datev_csv(transactions, filters, csv_class=Transactions),
			},
			{
				""file_name"": ""EXTF_Kontenbeschriftungen.csv"",
				""csv_data"": get_datev_csv(account_names, filters, csv_class=AccountNames),
			},
			{
				""file_name"": ""EXTF_Kunden.csv"",
				""csv_data"": get_datev_csv(customers, filters, csv_class=DebtorsCreditors),
			},
			{
				""file_name"": ""EXTF_Lieferanten.csv"",
				""csv_data"": get_datev_csv(suppliers, filters, csv_class=DebtorsCreditors),
			},
		],
	)

        ```
    ###Assistant : 
	Provide accounting entries for download in DATEV format.

	Validate the filters, get the data, produce the CSV file and provide it for
	download. Can be called like this:

	GET /api/method/erpnext.regional.report.datev.datev.download_datev_csv

	Arguments / Params:
	filters -- dict of filters to be passed to the sql query
	
    "
2375,"def taggedsent_to_conll(sentence):
    
    for (i, (word, tag)) in enumerate(sentence, start=1):
        input_str = [str(i), word, ""_"", tag, tag, ""_"", ""0"", ""a"", ""_"", ""_""]
        input_str = ""\t"".join(input_str) + ""\n""
        yield input_str

","
    A module to convert a single POS tagged sentence into CONLL format.

    >>> from nltk import word_tokenize, pos_tag
    >>> text = ""This is a foobar sentence.""
    >>> for line in taggedsent_to_conll(pos_tag(word_tokenize(text))): # doctest: +NORMALIZE_WHITESPACE
    ... 	print(line, end="""")
        1	This	_	DT	DT	_	0	a	_	_
        2	is	_	VBZ	VBZ	_	0	a	_	_
        3	a	_	DT	DT	_	0	a	_	_
        4	foobar	_	JJ	JJ	_	0	a	_	_
        5	sentence	_	NN	NN	_	0	a	_	_
        6	.		_	.	.	_	0	a	_	_

    :param sentence: A single input sentence to parse
    :type sentence: list(tuple(str, str))
    :rtype: iter(str)
    :return: a generator yielding a single sentence in CONLL format.
    ",121,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def taggedsent_to_conll(sentence):
    
    for (i, (word, tag)) in enumerate(sentence, start=1):
        input_str = [str(i), word, ""_"", tag, tag, ""_"", ""0"", ""a"", ""_"", ""_""]
        input_str = ""\t"".join(input_str) + ""\n""
        yield input_str


        ```
    ###Assistant : 
    A module to convert a single POS tagged sentence into CONLL format.

    >>> from nltk import word_tokenize, pos_tag
    >>> text = ""This is a foobar sentence.""
    >>> for line in taggedsent_to_conll(pos_tag(word_tokenize(text))): # doctest: +NORMALIZE_WHITESPACE
    ... 	print(line, end="""")
        1	This	_	DT	DT	_	0	a	_	_
        2	is	_	VBZ	VBZ	_	0	a	_	_
        3	a	_	DT	DT	_	0	a	_	_
        4	foobar	_	JJ	JJ	_	0	a	_	_
        5	sentence	_	NN	NN	_	0	a	_	_
        6	.		_	.	.	_	0	a	_	_

    :param sentence: A single input sentence to parse
    :type sentence: list(tuple(str, str))
    :rtype: iter(str)
    :return: a generator yielding a single sentence in CONLL format.
    
    "
2376,"def get_system_encoding():
    
    try:
        encoding = locale.getdefaultlocale()[1] or ""ascii""
        codecs.lookup(encoding)
    except Exception:
        encoding = ""ascii""
    return encoding


DEFAULT_LOCALE_ENCODING = get_system_encoding()
","
    The encoding of the default system locale. Fallback to 'ascii' if the
    #encoding is unsupported by Python or could not be determined. See tickets
    #10335 and #5846.
    ",27,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_system_encoding():
    
    try:
        encoding = locale.getdefaultlocale()[1] or ""ascii""
        codecs.lookup(encoding)
    except Exception:
        encoding = ""ascii""
    return encoding


DEFAULT_LOCALE_ENCODING = get_system_encoding()

        ```
    ###Assistant : 
    The encoding of the default system locale. Fallback to 'ascii' if the
    #encoding is unsupported by Python or could not be determined. See tickets
    #10335 and #5846.
    
    "
2377,"def get_menu_item(self):
        
        if self.modeladmin_instances:
            submenu = Menu(items=self.get_submenu_items())
            return GroupMenuItem(self, self.get_menu_order(), submenu)
","
        Utilised by Wagtail's 'register_menu_item' hook to create a menu
        for this group with a submenu linking to listing pages for any
        associated ModelAdmin instances
        ",24,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_menu_item(self):
        
        if self.modeladmin_instances:
            submenu = Menu(items=self.get_submenu_items())
            return GroupMenuItem(self, self.get_menu_order(), submenu)

        ```
    ###Assistant : 
        Utilised by Wagtail's 'register_menu_item' hook to create a menu
        for this group with a submenu linking to listing pages for any
        associated ModelAdmin instances
        
    "
2378,"def _draw_text_as_path(self, gc, x, y, s, prop, angle, ismath):
        
        path, transform = self._get_text_path_transform(
            x, y, s, prop, angle, ismath)
        color = gc.get_rgb()
        gc.set_linewidth(0.0)
        self.draw_path(gc, path, transform, rgbFace=color)
","
        Draw the text by converting them to paths using `.TextToPath`.

        Parameters
        ----------
        x : float
            The x location of the text in display coords.
        y : float
            The y location of the text baseline in display coords.
        s : str
            The text to be converted.
        prop : `~matplotlib.font_manager.FontProperties`
            The font property.
        angle : float
            Angle in degrees to render the text at.
        ismath : bool or ""TeX""
            If True, use mathtext parser. If ""TeX"", use tex for rendering.
        ",78,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _draw_text_as_path(self, gc, x, y, s, prop, angle, ismath):
        
        path, transform = self._get_text_path_transform(
            x, y, s, prop, angle, ismath)
        color = gc.get_rgb()
        gc.set_linewidth(0.0)
        self.draw_path(gc, path, transform, rgbFace=color)

        ```
    ###Assistant : 
        Draw the text by converting them to paths using `.TextToPath`.

        Parameters
        ----------
        x : float
            The x location of the text in display coords.
        y : float
            The y location of the text baseline in display coords.
        s : str
            The text to be converted.
        prop : `~matplotlib.font_manager.FontProperties`
            The font property.
        angle : float
            Angle in degrees to render the text at.
        ismath : bool or ""TeX""
            If True, use mathtext parser. If ""TeX"", use tex for rendering.
        
    "
2379,"def __call__(self, match_quality_matrix):
        
        assert match_quality_matrix.dim() == 2
        if match_quality_matrix.numel() == 0:
            default_matches = match_quality_matrix.new_full((match_quality_matrix.size(1),), 0, dtype=torch.int64)
            # When no gt boxes exist, we define IOU = 0 and therefore set labels
            # to `self.labels[0]`, which usually defaults to background class 0
            # To choose to ignore instead,
            # can make labels=[-1,0,-1,1] + set appropriate thresholds
            default_match_labels = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
            )
            return default_matches, default_match_labels

        assert torch.all(match_quality_matrix >= 0)

        # match_quality_matrix is M (gt) x N (predicted)
        # Max over gt elements (dim 0) to find best gt candidate for each prediction
        matched_vals, matches = match_quality_matrix.max(dim=0)

        match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)

        for l, low, high in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
            low_high = (matched_vals >= low) & (matched_vals < high)
            match_labels[low_high] = l

        if self.allow_low_quality_matches:
            self.set_low_quality_matches_(match_labels, match_quality_matrix)

        return matches, match_labels
","
        Args:
            match_quality_matrix (Tensor[float]): an MxN tensor, containing the pairwise quality between M ground-truth elements and N predicted
                elements. All elements must be >= 0 (due to the us of `torch.nonzero` for selecting indices in :meth:`set_low_quality_matches_`).
        Returns:
            matches (Tensor[int64]): a vector of length N, where matches[i] is a matched ground-truth index in [0, M)
            match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates true or false positive or ignored
        ",69,128,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self, match_quality_matrix):
        
        assert match_quality_matrix.dim() == 2
        if match_quality_matrix.numel() == 0:
            default_matches = match_quality_matrix.new_full((match_quality_matrix.size(1),), 0, dtype=torch.int64)
            # When no gt boxes exist, we define IOU = 0 and therefore set labels
            # to `self.labels[0]`, which usually defaults to background class 0
            # To choose to ignore instead,
            # can make labels=[-1,0,-1,1] + set appropriate thresholds
            default_match_labels = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
            )
            return default_matches, default_match_labels

        assert torch.all(match_quality_matrix >= 0)

        # match_quality_matrix is M (gt) x N (predicted)
        # Max over gt elements (dim 0) to find best gt candidate for each prediction
        matched_vals, matches = match_quality_matrix.max(dim=0)

        match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)

        for l, low, high in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
            low_high = (matched_vals >= low) & (matched_vals < high)
            match_labels[low_high] = l

        if self.allow_low_quality_matches:
            self.set_low_quality_matches_(match_labels, match_quality_matrix)

        return matches, match_labels

        ```
    ###Assistant : 
        Args:
            match_quality_matrix (Tensor[float]): an MxN tensor, containing the pairwise quality between M ground-truth elements and N predicted
                elements. All elements must be >= 0 (due to the us of `torch.nonzero` for selecting indices in :meth:`set_low_quality_matches_`).
        Returns:
            matches (Tensor[int64]): a vector of length N, where matches[i] is a matched ground-truth index in [0, M)
            match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates true or false positive or ignored
        
    "
2380,"def ismemberdescriptor(object):
        
        return isinstance(object, types.MemberDescriptorType)
else:
    # Other implementations","Return true if the object is a member descriptor.

        Member descriptors are specialized descriptors defined in extension
        modules.",18,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ismemberdescriptor(object):
        
        return isinstance(object, types.MemberDescriptorType)
else:
    # Other implementations
        ```
    ###Assistant : Return true if the object is a member descriptor.

        Member descriptors are specialized descriptors defined in extension
        modules.
    "
2381,"def pandas_dtype_to_arrow_c(dtype) -> str:
    
    if isinstance(dtype, pandas.CategoricalDtype):
        return ArrowCTypes.INT64
    elif dtype == np.dtype(""O""):
        return ArrowCTypes.STRING

    format_str = getattr(ArrowCTypes, dtype.name.upper(), None)
    if format_str is not None:
        return format_str

    if is_datetime64_dtype(dtype):
        # Selecting the first char of resolution string:
        # dtype.str -> '<M8[ns]'
        resolution = re.findall(r""\[(.*)\]"", dtype.str)[0][:1]
        return ArrowCTypes.TIMESTAMP.format(resolution=resolution, tz="""")

    raise NotImplementedError(
        f""Convertion of {dtype} to Arrow C format string is not implemented.""
    )
","
    Represent pandas `dtype` as a format string in Apache Arrow C notation.

    Parameters
    ----------
    dtype : np.dtype
        Datatype of pandas DataFrame to represent.

    Returns
    -------
    str
        Format string in Apache Arrow C notation of the given `dtype`.
    ",37,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pandas_dtype_to_arrow_c(dtype) -> str:
    
    if isinstance(dtype, pandas.CategoricalDtype):
        return ArrowCTypes.INT64
    elif dtype == np.dtype(""O""):
        return ArrowCTypes.STRING

    format_str = getattr(ArrowCTypes, dtype.name.upper(), None)
    if format_str is not None:
        return format_str

    if is_datetime64_dtype(dtype):
        # Selecting the first char of resolution string:
        # dtype.str -> '<M8[ns]'
        resolution = re.findall(r""\[(.*)\]"", dtype.str)[0][:1]
        return ArrowCTypes.TIMESTAMP.format(resolution=resolution, tz="""")

    raise NotImplementedError(
        f""Convertion of {dtype} to Arrow C format string is not implemented.""
    )

        ```
    ###Assistant : 
    Represent pandas `dtype` as a format string in Apache Arrow C notation.

    Parameters
    ----------
    dtype : np.dtype
        Datatype of pandas DataFrame to represent.

    Returns
    -------
    str
        Format string in Apache Arrow C notation of the given `dtype`.
    
    "
2382,"def is_file_like(obj) -> bool:
    
    if not (hasattr(obj, ""read"") or hasattr(obj, ""write"")):
        return False

    return bool(hasattr(obj, ""__iter__""))

","
    Check if the object is a file-like object.

    For objects to be considered file-like, they must
    be an iterator AND have either a `read` and/or `write`
    method as an attribute.

    Note: file-like objects must be iterable, but
    iterable objects need not be file-like.

    Parameters
    ----------
    obj : The object to check

    Returns
    -------
    bool
        Whether `obj` has file-like properties.

    Examples
    --------
    >>> import io
    >>> buffer = io.StringIO(""data"")
    >>> is_file_like(buffer)
    True
    >>> is_file_like([1, 2, 3])
    False
    ",76,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_file_like(obj) -> bool:
    
    if not (hasattr(obj, ""read"") or hasattr(obj, ""write"")):
        return False

    return bool(hasattr(obj, ""__iter__""))


        ```
    ###Assistant : 
    Check if the object is a file-like object.

    For objects to be considered file-like, they must
    be an iterator AND have either a `read` and/or `write`
    method as an attribute.

    Note: file-like objects must be iterable, but
    iterable objects need not be file-like.

    Parameters
    ----------
    obj : The object to check

    Returns
    -------
    bool
        Whether `obj` has file-like properties.

    Examples
    --------
    >>> import io
    >>> buffer = io.StringIO(""data"")
    >>> is_file_like(buffer)
    True
    >>> is_file_like([1, 2, 3])
    False
    
    "
2383,"def update(self) -> Union[SourceRead, DestinationRead, ConnectionRead]:
        
        return self._create_or_update(self._update_fn, self.update_payload)
","Public function to update the resource on the remote Airbyte instance.

        Returns:
            Union[SourceRead, DestinationRead, ConnectionRead]: The updated resource.
        ",18,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self) -> Union[SourceRead, DestinationRead, ConnectionRead]:
        
        return self._create_or_update(self._update_fn, self.update_payload)

        ```
    ###Assistant : Public function to update the resource on the remote Airbyte instance.

        Returns:
            Union[SourceRead, DestinationRead, ConnectionRead]: The updated resource.
        
    "
2384,"def test_callback_session(self) -> None:
        
        request = Mock(spec=[""args"", ""getCookie"", ""cookies""])

        # Missing cookie
        request.args = {}
        request.getCookie.return_value = None
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""missing_session"", ""No session cookie found"")

        # Missing session parameter
        request.args = {}
        request.getCookie.return_value = ""session""
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""invalid_request"", ""State parameter is missing"")

        # Invalid cookie
        request.args = {}
        request.args[b""state""] = [b""state""]
        request.getCookie.return_value = ""session""
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""invalid_session"")

        # Mismatching session
        session = self._generate_oidc_session_token(
            state=""state"",
            nonce=""nonce"",
            client_redirect_url=""http://client/redirect"",
        )
        request.args = {}
        request.args[b""state""] = [b""mismatching state""]
        request.getCookie.return_value = session
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""mismatching_session"")

        # Valid session
        request.args = {}
        request.args[b""state""] = [b""state""]
        request.getCookie.return_value = session
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""invalid_request"")
",The callback verifies the session presence and validity,8,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_callback_session(self) -> None:
        
        request = Mock(spec=[""args"", ""getCookie"", ""cookies""])

        # Missing cookie
        request.args = {}
        request.getCookie.return_value = None
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""missing_session"", ""No session cookie found"")

        # Missing session parameter
        request.args = {}
        request.getCookie.return_value = ""session""
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""invalid_request"", ""State parameter is missing"")

        # Invalid cookie
        request.args = {}
        request.args[b""state""] = [b""state""]
        request.getCookie.return_value = ""session""
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""invalid_session"")

        # Mismatching session
        session = self._generate_oidc_session_token(
            state=""state"",
            nonce=""nonce"",
            client_redirect_url=""http://client/redirect"",
        )
        request.args = {}
        request.args[b""state""] = [b""mismatching state""]
        request.getCookie.return_value = session
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""mismatching_session"")

        # Valid session
        request.args = {}
        request.args[b""state""] = [b""state""]
        request.getCookie.return_value = session
        self.get_success(self.handler.handle_oidc_callback(request))
        self.assertRenderedError(""invalid_request"")

        ```
    ###Assistant : The callback verifies the session presence and validity
    "
2385,"def _create_repo_url(self) -> str:
        
        url_components = urllib.parse.urlparse(self.repository_url)
        if url_components.scheme == ""https"" and self.credentials is not None:
            repo_url = url_components.netloc + url_components.path
            updated_components = url_components._replace(
                netloc=f""{self.credentials.get_secret_value()}@{url_components.netloc}""
            )
            full_url = urllib.parse.urlunparse(updated_components)
        else:
            full_url = self.repository_url

        return full_url
","Format the URL provided to the `git clone` command.

        For private repos: https://<oauth-key>@github.com/<username>/<repo>.git
        All other repos should be the same as `self.repository`.
        ",22,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_repo_url(self) -> str:
        
        url_components = urllib.parse.urlparse(self.repository_url)
        if url_components.scheme == ""https"" and self.credentials is not None:
            repo_url = url_components.netloc + url_components.path
            updated_components = url_components._replace(
                netloc=f""{self.credentials.get_secret_value()}@{url_components.netloc}""
            )
            full_url = urllib.parse.urlunparse(updated_components)
        else:
            full_url = self.repository_url

        return full_url

        ```
    ###Assistant : Format the URL provided to the `git clone` command.

        For private repos: https://<oauth-key>@github.com/<username>/<repo>.git
        All other repos should be the same as `self.repository`.
        
    "
2386,"def test_lookup_with_dynamic_value(self):
        
        modeladmin = DepartmentFilterDynamicValueBookAdmin(Book, site)
","
        Ensure SimpleListFilter can access self.value() inside the lookup.
        ",8,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_lookup_with_dynamic_value(self):
        
        modeladmin = DepartmentFilterDynamicValueBookAdmin(Book, site)

        ```
    ###Assistant : 
        Ensure SimpleListFilter can access self.value() inside the lookup.
        
    "
2387,"def test_batch_encode_dynamic_overflowing(self):
        
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

            with self.subTest(f""{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})""):

                if is_torch_available():
                    returned_tensor = ""pt""
                elif is_tf_available():
                    returned_tensor = ""tf""
                elif is_flax_available():
                    returned_tensor = ""jax""
                else:
                    return

                if not tokenizer.pad_token or tokenizer.pad_token_id < 0:
                    return

                tokens = tokenizer.encode_plus(
                    ""HuggingFace is solving NLP one commit at a time"",
                    max_length=6,
                    padding=True,
                    truncation=True,
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: ""overflow_to_sample_mapping"" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)

                # Mono sample
                tokens = tokenizer.batch_encode_plus(
                    [""HuggingFace is solving NLP one commit at a time""],
                    max_length=6,
                    padding=True,
                    truncation=""only_first"",
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: ""overflow_to_sample_mapping"" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)
                    self.assertEqual(tokens[key].shape[-1], 6)

                # Multi sample
                tokens = tokenizer.batch_encode_plus(
                    [""HuggingFace is solving NLP one commit at a time"", ""Very tiny input""],
                    max_length=6,
                    padding=True,
                    truncation=""only_first"",
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: ""overflow_to_sample_mapping"" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)
                    self.assertEqual(tokens[key].shape[-1], 6)
","
        When calling batch_encode with multiple sequence it can returns different number of
        overflowing encoding for each sequence:
        [
          Sequence 1: [Encoding 1, Encoding 2],
          Sequence 2: [Encoding 1],
          Sequence 3: [Encoding 1, Encoding 2, ... Encoding N]
        ]
        This needs to be padded so that it can represented as a tensor
        ",51,144,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_batch_encode_dynamic_overflowing(self):
        
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

            with self.subTest(f""{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})""):

                if is_torch_available():
                    returned_tensor = ""pt""
                elif is_tf_available():
                    returned_tensor = ""tf""
                elif is_flax_available():
                    returned_tensor = ""jax""
                else:
                    return

                if not tokenizer.pad_token or tokenizer.pad_token_id < 0:
                    return

                tokens = tokenizer.encode_plus(
                    ""HuggingFace is solving NLP one commit at a time"",
                    max_length=6,
                    padding=True,
                    truncation=True,
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: ""overflow_to_sample_mapping"" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)

                # Mono sample
                tokens = tokenizer.batch_encode_plus(
                    [""HuggingFace is solving NLP one commit at a time""],
                    max_length=6,
                    padding=True,
                    truncation=""only_first"",
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: ""overflow_to_sample_mapping"" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)
                    self.assertEqual(tokens[key].shape[-1], 6)

                # Multi sample
                tokens = tokenizer.batch_encode_plus(
                    [""HuggingFace is solving NLP one commit at a time"", ""Very tiny input""],
                    max_length=6,
                    padding=True,
                    truncation=""only_first"",
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: ""overflow_to_sample_mapping"" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)
                    self.assertEqual(tokens[key].shape[-1], 6)

        ```
    ###Assistant : 
        When calling batch_encode with multiple sequence it can returns different number of
        overflowing encoding for each sequence:
        [
          Sequence 1: [Encoding 1, Encoding 2],
          Sequence 2: [Encoding 1],
          Sequence 3: [Encoding 1, Encoding 2, ... Encoding N]
        ]
        This needs to be padded so that it can represented as a tensor
        
    "
2388,"def deserialize(config, custom_objects=None):
    
    # loss_scale_optimizer has a direct dependency of optimizer, import here
    # rather than top to avoid the cyclic dependency.
    from keras.mixed_precision import (
        loss_scale_optimizer,
    )  # pylint: disable=g-import-not-at-top

    all_classes = {
        ""adadelta"": adadelta_v2.Adadelta,
        ""adagrad"": adagrad_v2.Adagrad,
        ""adam"": adam_v2.Adam,
        ""adamax"": adamax_v2.Adamax,
        ""experimentaladadelta"": adadelta_experimental.Adadelta,
        ""experimentaladagrad"": adagrad_experimental.Adagrad,
        ""experimentaladam"": adam_experimental.Adam,
        ""experimentalsgd"": sgd_experimental.SGD,
        ""nadam"": nadam_v2.Nadam,
        ""rmsprop"": rmsprop_v2.RMSprop,
        ""sgd"": gradient_descent_v2.SGD,
        ""ftrl"": ftrl.Ftrl,
        ""lossscaleoptimizer"": loss_scale_optimizer.LossScaleOptimizer,
        ""lossscaleoptimizerv3"": loss_scale_optimizer.LossScaleOptimizerV3,
        # LossScaleOptimizerV1 was an old version of LSO that was removed.
        # Deserializing it turns it into a LossScaleOptimizer
        ""lossscaleoptimizerv1"": loss_scale_optimizer.LossScaleOptimizer,
    }

    # Make deserialization case-insensitive for built-in optimizers.
    if config[""class_name""].lower() in all_classes:
        config[""class_name""] = config[""class_name""].lower()
    return deserialize_keras_object(
        config,
        module_objects=all_classes,
        custom_objects=custom_objects,
        printable_module_name=""optimizer"",
    )


@keras_export(""keras.optimizers.get"")","Inverse of the `serialize` function.

    Args:
        config: Optimizer configuration dictionary.
        custom_objects: Optional dictionary mapping names (strings) to custom
          objects (classes and functions) to be considered during deserialization.

    Returns:
        A Keras Optimizer instance.
    ",32,106,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deserialize(config, custom_objects=None):
    
    # loss_scale_optimizer has a direct dependency of optimizer, import here
    # rather than top to avoid the cyclic dependency.
    from keras.mixed_precision import (
        loss_scale_optimizer,
    )  # pylint: disable=g-import-not-at-top

    all_classes = {
        ""adadelta"": adadelta_v2.Adadelta,
        ""adagrad"": adagrad_v2.Adagrad,
        ""adam"": adam_v2.Adam,
        ""adamax"": adamax_v2.Adamax,
        ""experimentaladadelta"": adadelta_experimental.Adadelta,
        ""experimentaladagrad"": adagrad_experimental.Adagrad,
        ""experimentaladam"": adam_experimental.Adam,
        ""experimentalsgd"": sgd_experimental.SGD,
        ""nadam"": nadam_v2.Nadam,
        ""rmsprop"": rmsprop_v2.RMSprop,
        ""sgd"": gradient_descent_v2.SGD,
        ""ftrl"": ftrl.Ftrl,
        ""lossscaleoptimizer"": loss_scale_optimizer.LossScaleOptimizer,
        ""lossscaleoptimizerv3"": loss_scale_optimizer.LossScaleOptimizerV3,
        # LossScaleOptimizerV1 was an old version of LSO that was removed.
        # Deserializing it turns it into a LossScaleOptimizer
        ""lossscaleoptimizerv1"": loss_scale_optimizer.LossScaleOptimizer,
    }

    # Make deserialization case-insensitive for built-in optimizers.
    if config[""class_name""].lower() in all_classes:
        config[""class_name""] = config[""class_name""].lower()
    return deserialize_keras_object(
        config,
        module_objects=all_classes,
        custom_objects=custom_objects,
        printable_module_name=""optimizer"",
    )


@keras_export(""keras.optimizers.get"")
        ```
    ###Assistant : Inverse of the `serialize` function.

    Args:
        config: Optimizer configuration dictionary.
        custom_objects: Optional dictionary mapping names (strings) to custom
          objects (classes and functions) to be considered during deserialization.

    Returns:
        A Keras Optimizer instance.
    
    "
2389,"def commutes_with(self, other):
        
        a = self.array_form
        b = other.array_form
        return _af_commutes_with(a, b)
","
        Checks if the elements are commuting.

        Examples
        ========

        >>> from sympy.combinatorics import Permutation
        >>> a = Permutation([1, 4, 3, 0, 2, 5])
        >>> b = Permutation([0, 1, 2, 3, 4, 5])
        >>> a.commutes_with(b)
        True
        >>> b = Permutation([2, 3, 5, 4, 1, 0])
        >>> a.commutes_with(b)
        False
        ",46,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def commutes_with(self, other):
        
        a = self.array_form
        b = other.array_form
        return _af_commutes_with(a, b)

        ```
    ###Assistant : 
        Checks if the elements are commuting.

        Examples
        ========

        >>> from sympy.combinatorics import Permutation
        >>> a = Permutation([1, 4, 3, 0, 2, 5])
        >>> b = Permutation([0, 1, 2, 3, 4, 5])
        >>> a.commutes_with(b)
        True
        >>> b = Permutation([2, 3, 5, 4, 1, 0])
        >>> a.commutes_with(b)
        False
        
    "
2390,"def get_all_styles():
    
    yield from STYLE_MAP
    for name, _ in find_plugin_styles():
        yield name
","Return a generator for all styles by name,
    both builtin and plugin.",12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all_styles():
    
    yield from STYLE_MAP
    for name, _ in find_plugin_styles():
        yield name

        ```
    ###Assistant : Return a generator for all styles by name,
    both builtin and plugin.
    "
2391,"def lift(cooccurrence):
    

    diag_rows, diag_cols = _get_row_and_column_matrix(cooccurrence.diagonal())

    with np.errstate(invalid=""ignore"", divide=""ignore""):
        result = cooccurrence / (diag_rows * diag_cols)

    return np.array(result)

","Helper method to calculate the Lift of a matrix of
    co-occurrences. In comparison with basic co-occurrence and Jaccard
    similarity, lift favours discoverability and serendipity, as
    opposed to co-occurrence that favours the most popular items, and
    Jaccard that is a compromise between the two.

    Args:
        cooccurrence (numpy.ndarray): The symmetric matrix of co-occurrences of items.

    Returns:
        numpy.ndarray: The matrix of Lifts between any two items.

    ",63,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def lift(cooccurrence):
    

    diag_rows, diag_cols = _get_row_and_column_matrix(cooccurrence.diagonal())

    with np.errstate(invalid=""ignore"", divide=""ignore""):
        result = cooccurrence / (diag_rows * diag_cols)

    return np.array(result)


        ```
    ###Assistant : Helper method to calculate the Lift of a matrix of
    co-occurrences. In comparison with basic co-occurrence and Jaccard
    similarity, lift favours discoverability and serendipity, as
    opposed to co-occurrence that favours the most popular items, and
    Jaccard that is a compromise between the two.

    Args:
        cooccurrence (numpy.ndarray): The symmetric matrix of co-occurrences of items.

    Returns:
        numpy.ndarray: The matrix of Lifts between any two items.

    
    "
2392,"def update(self, props):
        
        return self._update_props(
            props, ""{cls.__name__!r} object has no property {prop_name!r}"")
","
        Update this artist's properties from the dict *props*.

        Parameters
        ----------
        props : dict
        ",13,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update(self, props):
        
        return self._update_props(
            props, ""{cls.__name__!r} object has no property {prop_name!r}"")

        ```
    ###Assistant : 
        Update this artist's properties from the dict *props*.

        Parameters
        ----------
        props : dict
        
    "
2393,"def new_workers_size(self):
        
        remote_resources = ray.available_resources()
        max_remote_workers = self._max_workers
        new_remote_workers = min(remote_resources.get(""CPU"", 0), max_remote_workers)
        if self._use_gpu:
            new_remote_workers = min(remote_resources.get(""GPU"", 0), new_remote_workers)
        return new_remote_workers
",Returns number of workers to create based on available resources.,10,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def new_workers_size(self):
        
        remote_resources = ray.available_resources()
        max_remote_workers = self._max_workers
        new_remote_workers = min(remote_resources.get(""CPU"", 0), max_remote_workers)
        if self._use_gpu:
            new_remote_workers = min(remote_resources.get(""GPU"", 0), new_remote_workers)
        return new_remote_workers

        ```
    ###Assistant : Returns number of workers to create based on available resources.
    "
2394,"def setmonitor(self, enable=True):
        # type: (bool) -> bool
        
        # We must reset the monitor cache
        if enable:
            res = self.setmode('monitor')
        else:
            res = self.setmode('managed')
        if not res:
            log_runtime.error(""Npcap WlanHelper returned with an error code !"")
        self.cache_mode = None
        tmp = self.cache_mode = self.ismonitor()
        return tmp if enable else (not tmp)
","Alias for setmode('monitor') or setmode('managed')
        Only available with Npcap",9,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setmonitor(self, enable=True):
        # type: (bool) -> bool
        
        # We must reset the monitor cache
        if enable:
            res = self.setmode('monitor')
        else:
            res = self.setmode('managed')
        if not res:
            log_runtime.error(""Npcap WlanHelper returned with an error code !"")
        self.cache_mode = None
        tmp = self.cache_mode = self.ismonitor()
        return tmp if enable else (not tmp)

        ```
    ###Assistant : Alias for setmode('monitor') or setmode('managed')
        Only available with Npcap
    "
2395,"def _rotated_rect_with_max_area(h, w, angle):
        

        angle = math.radians(angle)
        width_is_longer = w >= h
        side_long, side_short = (w, h) if width_is_longer else (h, w)

        # since the solutions for angle, -angle and 180-angle are all the same,
        # it is sufficient to look at the first quadrant and the absolute values of sin,cos:
        sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
        if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
            # half constrained case: two crop corners touch the longer side,
            # the other two corners are on the mid-line parallel to the longer line
            x = 0.5 * side_short
            wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
        else:
            # fully constrained case: crop touches all 4 sides
            cos_2a = cos_a * cos_a - sin_a * sin_a
            wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a

        return dict(
            x_min=max(0, int(w / 2 - wr / 2)),
            x_max=min(w, int(w / 2 + wr / 2)),
            y_min=max(0, int(h / 2 - hr / 2)),
            y_max=min(h, int(h / 2 + hr / 2)),
        )
","
        Given a rectangle of size wxh that has been rotated by 'angle' (in
        degrees), computes the width and height of the largest possible
        axis-aligned rectangle (maximal area) within the rotated rectangle.

        Code from: https://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
        ",34,195,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _rotated_rect_with_max_area(h, w, angle):
        

        angle = math.radians(angle)
        width_is_longer = w >= h
        side_long, side_short = (w, h) if width_is_longer else (h, w)

        # since the solutions for angle, -angle and 180-angle are all the same,
        # it is sufficient to look at the first quadrant and the absolute values of sin,cos:
        sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
        if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
            # half constrained case: two crop corners touch the longer side,
            # the other two corners are on the mid-line parallel to the longer line
            x = 0.5 * side_short
            wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
        else:
            # fully constrained case: crop touches all 4 sides
            cos_2a = cos_a * cos_a - sin_a * sin_a
            wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a

        return dict(
            x_min=max(0, int(w / 2 - wr / 2)),
            x_max=min(w, int(w / 2 + wr / 2)),
            y_min=max(0, int(h / 2 - hr / 2)),
            y_max=min(h, int(h / 2 + hr / 2)),
        )

        ```
    ###Assistant : 
        Given a rectangle of size wxh that has been rotated by 'angle' (in
        degrees), computes the width and height of the largest possible
        axis-aligned rectangle (maximal area) within the rotated rectangle.

        Code from: https://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
        
    "
2396,"def using(_other, **kwargs):
    
    gt_kwargs = {}
    if 'state' in kwargs:
        s = kwargs.pop('state')
        if isinstance(s, (list, tuple)):
            gt_kwargs['stack'] = s
        else:
            gt_kwargs['stack'] = ('root', s)

    if _other is this:","
    Callback that processes the match with a different lexer.

    The keyword arguments are forwarded to the lexer, except `state` which
    is handled separately.

    `state` specifies the state that the new lexer will start in, and can
    be an enumerable such as ('root', 'inline', 'string') or a simple
    string which is assumed to be on top of the root state.

    Note: For that to work, `_other` must not be an `ExtendedRegexLexer`.
    ",70,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def using(_other, **kwargs):
    
    gt_kwargs = {}
    if 'state' in kwargs:
        s = kwargs.pop('state')
        if isinstance(s, (list, tuple)):
            gt_kwargs['stack'] = s
        else:
            gt_kwargs['stack'] = ('root', s)

    if _other is this:
        ```
    ###Assistant : 
    Callback that processes the match with a different lexer.

    The keyword arguments are forwarded to the lexer, except `state` which
    is handled separately.

    `state` specifies the state that the new lexer will start in, and can
    be an enumerable such as ('root', 'inline', 'string') or a simple
    string which is assumed to be on top of the root state.

    Note: For that to work, `_other` must not be an `ExtendedRegexLexer`.
    
    "
2397,"def test_unset_document_storage_path(self):
        
        self.assertEqual(Document.objects.filter(storage_path=None).count(), 5)

        bulk_edit.set_storage_path(
            [self.doc1.id],
            self.sp1.id,
        )

        self.assertEqual(Document.objects.filter(storage_path=None).count(), 4)

        bulk_edit.set_storage_path(
            [self.doc1.id],
            None,
        )

        self.assertEqual(Document.objects.filter(storage_path=None).count(), 5)

        self.async_task.assert_called()
        args, kwargs = self.async_task.call_args

        self.assertCountEqual(kwargs[""document_ids""], [self.doc1.id])
","
        GIVEN:
            - 4 documents without defined storage path
            - 1 document with a defined storage
        WHEN:
            - Bulk edit called to remove storage path from 1 document
        THEN:
            - Single document storage path removed
        ",34,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_unset_document_storage_path(self):
        
        self.assertEqual(Document.objects.filter(storage_path=None).count(), 5)

        bulk_edit.set_storage_path(
            [self.doc1.id],
            self.sp1.id,
        )

        self.assertEqual(Document.objects.filter(storage_path=None).count(), 4)

        bulk_edit.set_storage_path(
            [self.doc1.id],
            None,
        )

        self.assertEqual(Document.objects.filter(storage_path=None).count(), 5)

        self.async_task.assert_called()
        args, kwargs = self.async_task.call_args

        self.assertCountEqual(kwargs[""document_ids""], [self.doc1.id])

        ```
    ###Assistant : 
        GIVEN:
            - 4 documents without defined storage path
            - 1 document with a defined storage
        WHEN:
            - Bulk edit called to remove storage path from 1 document
        THEN:
            - Single document storage path removed
        
    "
2398,"def test_with_spinner(self):
        
        function_with_spinner()
        self.assertFalse(self.forward_msg_queue.is_empty())
","If the show_spinner flag is set, there should be one element in the
        report queue.
        ",15,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_with_spinner(self):
        
        function_with_spinner()
        self.assertFalse(self.forward_msg_queue.is_empty())

        ```
    ###Assistant : If the show_spinner flag is set, there should be one element in the
        report queue.
        
    "
2399,"def test_update_device_too_long_display_name(self) -> None:
        
        # Set iniital display name.
        update = {""display_name"": ""new display""}
        self.get_success(
            self.handler.update_device(
                self.other_user, self.other_user_device_id, update
            )
        )

        # Request to update a device display name with a new value that is longer than allowed.
        update = {
            ""display_name"": ""a""
            * (synapse.handlers.device.MAX_DEVICE_DISPLAY_NAME_LEN + 1)
        }

        channel = self.make_request(
            ""PUT"",
            self.url,
            access_token=self.admin_user_tok,
            content=update,
        )

        self.assertEqual(HTTPStatus.BAD_REQUEST, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.TOO_LARGE, channel.json_body[""errcode""])

        # Ensure the display name was not updated.
        channel = self.make_request(
            ""GET"",
            self.url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(""new display"", channel.json_body[""display_name""])
","
        Update a device with a display name that is invalid (too long).
        ",12,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_update_device_too_long_display_name(self) -> None:
        
        # Set iniital display name.
        update = {""display_name"": ""new display""}
        self.get_success(
            self.handler.update_device(
                self.other_user, self.other_user_device_id, update
            )
        )

        # Request to update a device display name with a new value that is longer than allowed.
        update = {
            ""display_name"": ""a""
            * (synapse.handlers.device.MAX_DEVICE_DISPLAY_NAME_LEN + 1)
        }

        channel = self.make_request(
            ""PUT"",
            self.url,
            access_token=self.admin_user_tok,
            content=update,
        )

        self.assertEqual(HTTPStatus.BAD_REQUEST, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.TOO_LARGE, channel.json_body[""errcode""])

        # Ensure the display name was not updated.
        channel = self.make_request(
            ""GET"",
            self.url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(200, channel.code, msg=channel.json_body)
        self.assertEqual(""new display"", channel.json_body[""display_name""])

        ```
    ###Assistant : 
        Update a device with a display name that is invalid (too long).
        
    "
2400,"def xatom(self, name, *args):
        
        name = name.upper()
        #if not name in self.capabilities:      # Let the server decide!
        #    raise self.error('unknown extension command: %s' % name)
        if not name in Commands:
            Commands[name] = (self.state,)
        return self._simple_command(name, *args)



    #       Private methods

","Allow simple extension commands
                notified by server in CAPABILITY response.

        Assumes command is legal in current state.

        (typ, [data]) = <instance>.xatom(name, arg, ...)

        Returns response appropriate to extension command `name'.
        ",30,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def xatom(self, name, *args):
        
        name = name.upper()
        #if not name in self.capabilities:      # Let the server decide!
        #    raise self.error('unknown extension command: %s' % name)
        if not name in Commands:
            Commands[name] = (self.state,)
        return self._simple_command(name, *args)



    #       Private methods


        ```
    ###Assistant : Allow simple extension commands
                notified by server in CAPABILITY response.

        Assumes command is legal in current state.

        (typ, [data]) = <instance>.xatom(name, arg, ...)

        Returns response appropriate to extension command `name'.
        
    "
2401,"def forward(self, outputs, targets):
        
        outputs = outputs.clip(self.epsilon, 1 - self.epsilon)
        log_loss = targets * dp_log(outputs) + ((targets * -1) + 1) * dp_log((outputs * -1) + 1)
        log_loss = log_loss.sum(axis=1) * -1
        return log_loss.mean()
","Forward pass.

        .. math:: L = -t \\log(p) - (1 - t) \\log(1 - p)

        Parameters
        ----------
        outputs : numpy.array
            Predictions in (0, 1), such as sigmoidal output of a neural network.
        targets : numpy.array
            Targets in [0, 1], such as ground truth labels.
        ",44,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def forward(self, outputs, targets):
        
        outputs = outputs.clip(self.epsilon, 1 - self.epsilon)
        log_loss = targets * dp_log(outputs) + ((targets * -1) + 1) * dp_log((outputs * -1) + 1)
        log_loss = log_loss.sum(axis=1) * -1
        return log_loss.mean()

        ```
    ###Assistant : Forward pass.

        .. math:: L = -t \\log(p) - (1 - t) \\log(1 - p)

        Parameters
        ----------
        outputs : numpy.array
            Predictions in (0, 1), such as sigmoidal output of a neural network.
        targets : numpy.array
            Targets in [0, 1], such as ground truth labels.
        
    "
2402,"def numeric_assortativity_coefficient(G, attribute, nodes=None):
    
    if nodes is None:
        nodes = G.nodes
    vals = {G.nodes[n][attribute] for n in nodes}
    mapping = {d: i for i, d, in enumerate(vals)}
    M = attribute_mixing_matrix(G, attribute, nodes, mapping)
    return _numeric_ac(M, mapping)

","Compute assortativity for numerical node attributes.

    Assortativity measures the similarity of connections
    in the graph with respect to the given numeric attribute.

    Parameters
    ----------
    G : NetworkX graph

    attribute : string
        Node attribute key.

    nodes: list or iterable (optional)
        Compute numeric assortativity only for attributes of nodes in
        container. The default is all nodes.

    Returns
    -------
    r: float
       Assortativity of graph for given attribute

    Examples
    --------
    >>> G = nx.Graph()
    >>> G.add_nodes_from([0, 1], size=2)
    >>> G.add_nodes_from([2, 3], size=3)
    >>> G.add_edges_from([(0, 1), (2, 3)])
    >>> print(nx.numeric_assortativity_coefficient(G, ""size""))
    1.0

    Notes
    -----
    This computes Eq. (21) in Ref. [1]_ , which is the Pearson correlation
    coefficient of the specified (scalar valued) attribute across edges.

    References
    ----------
    .. [1] M. E. J. Newman, Mixing patterns in networks
           Physical Review E, 67 026126, 2003
    ",129,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def numeric_assortativity_coefficient(G, attribute, nodes=None):
    
    if nodes is None:
        nodes = G.nodes
    vals = {G.nodes[n][attribute] for n in nodes}
    mapping = {d: i for i, d, in enumerate(vals)}
    M = attribute_mixing_matrix(G, attribute, nodes, mapping)
    return _numeric_ac(M, mapping)


        ```
    ###Assistant : Compute assortativity for numerical node attributes.

    Assortativity measures the similarity of connections
    in the graph with respect to the given numeric attribute.

    Parameters
    ----------
    G : NetworkX graph

    attribute : string
        Node attribute key.

    nodes: list or iterable (optional)
        Compute numeric assortativity only for attributes of nodes in
        container. The default is all nodes.

    Returns
    -------
    r: float
       Assortativity of graph for given attribute

    Examples
    --------
    >>> G = nx.Graph()
    >>> G.add_nodes_from([0, 1], size=2)
    >>> G.add_nodes_from([2, 3], size=3)
    >>> G.add_edges_from([(0, 1), (2, 3)])
    >>> print(nx.numeric_assortativity_coefficient(G, ""size""))
    1.0

    Notes
    -----
    This computes Eq. (21) in Ref. [1]_ , which is the Pearson correlation
    coefficient of the specified (scalar valued) attribute across edges.

    References
    ----------
    .. [1] M. E. J. Newman, Mixing patterns in networks
           Physical Review E, 67 026126, 2003
    
    "
2403,"def write_filepath(filepath, strategy):
    
    dirpath = os.path.dirname(filepath)
    base = os.path.basename(filepath)
    return os.path.join(write_dirpath(dirpath, strategy), base)

","Returns the writing file path to be used to save file distributedly.

    Directory to contain `filepath` would be created if it doesn't exist.

    Args:
      filepath: Original filepath that would be used without distribution.
      strategy: The tf.distribute strategy object currently used.

    Returns:
      The writing filepath that should be used to save file with distribution.
    ",53,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def write_filepath(filepath, strategy):
    
    dirpath = os.path.dirname(filepath)
    base = os.path.basename(filepath)
    return os.path.join(write_dirpath(dirpath, strategy), base)


        ```
    ###Assistant : Returns the writing file path to be used to save file distributedly.

    Directory to contain `filepath` would be created if it doesn't exist.

    Args:
      filepath: Original filepath that would be used without distribution.
      strategy: The tf.distribute strategy object currently used.

    Returns:
      The writing filepath that should be used to save file with distribution.
    
    "
2404,"def serving(self, inputs):
        
        output = self.call(inputs)
        return self.serving_output(output)


CONVNEXT_START_DOCSTRING = r

CONVNEXT_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare ConvNext model outputting raw features without any specific head on top."",
    CONVNEXT_START_DOCSTRING,
)","
        Method used for serving the model.

        Args:
            inputs (`Dict[str, tf.Tensor]`):
                The input of the saved model as a dictionary of tensors.
        
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.

    <Tip>

    TF 2.0 models accepts two formats as inputs:

    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional arguments.

    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.

    </Tip>

    Parameters:
        config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`ConvNextFeatureExtractor`]. See
            [`ConvNextFeatureExtractor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
",298,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serving(self, inputs):
        
        output = self.call(inputs)
        return self.serving_output(output)


CONVNEXT_START_DOCSTRING = r

CONVNEXT_INPUTS_DOCSTRING = r


@add_start_docstrings(
    ""The bare ConvNext model outputting raw features without any specific head on top."",
    CONVNEXT_START_DOCSTRING,
)
        ```
    ###Assistant : 
        Method used for serving the model.

        Args:
            inputs (`Dict[str, tf.Tensor]`):
                The input of the saved model as a dictionary of tensors.
        
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.

    <Tip>

    TF 2.0 models accepts two formats as inputs:

    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional arguments.

    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.

    </Tip>

    Parameters:
        config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.

    Args:
        pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`ConvNextFeatureExtractor`]. See
            [`ConvNextFeatureExtractor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.

    "
2405,"def getclasstree(classes, unique=False):
    
    children = {}
    roots = []
    for c in classes:
        if c.__bases__:
            for parent in c.__bases__:
                if parent not in children:
                    children[parent] = []
                if c not in children[parent]:
                    children[parent].append(c)
                if unique and parent in classes: break
        elif c not in roots:
            roots.append(c)
    for parent in children:
        if parent not in classes:
            roots.append(parent)
    return walktree(roots, children, None)

# ------------------------------------------------ argument list extraction
Arguments = namedtuple('Arguments', 'args, varargs, varkw')
","Arrange the given list of classes into a hierarchy of nested lists.

    Where a nested list appears, it contains classes derived from the class
    whose entry immediately precedes the list.  Each entry is a 2-tuple
    containing a class and a tuple of its base classes.  If the 'unique'
    argument is true, exactly one entry appears in the returned structure
    for each class in the given list.  Otherwise, classes using multiple
    inheritance and their descendants will appear multiple times.",78,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getclasstree(classes, unique=False):
    
    children = {}
    roots = []
    for c in classes:
        if c.__bases__:
            for parent in c.__bases__:
                if parent not in children:
                    children[parent] = []
                if c not in children[parent]:
                    children[parent].append(c)
                if unique and parent in classes: break
        elif c not in roots:
            roots.append(c)
    for parent in children:
        if parent not in classes:
            roots.append(parent)
    return walktree(roots, children, None)

# ------------------------------------------------ argument list extraction
Arguments = namedtuple('Arguments', 'args, varargs, varkw')

        ```
    ###Assistant : Arrange the given list of classes into a hierarchy of nested lists.

    Where a nested list appears, it contains classes derived from the class
    whose entry immediately precedes the list.  Each entry is a 2-tuple
    containing a class and a tuple of its base classes.  If the 'unique'
    argument is true, exactly one entry appears in the returned structure
    for each class in the given list.  Otherwise, classes using multiple
    inheritance and their descendants will appear multiple times.
    "
2406,"def get_revision(cls, location):
        # type: (str) -> str
        
        raise NotImplementedError
","
        Return the current commit id of the files at the given location.
        ",12,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_revision(cls, location):
        # type: (str) -> str
        
        raise NotImplementedError

        ```
    ###Assistant : 
        Return the current commit id of the files at the given location.
        
    "
2407,"def get_current_settings() -> Settings:
    
    from prefect.context import ProfileContext

    profile = ProfileContext.get()
    if profile is not None:
        return profile.settings

    return get_settings_from_env()

","
    Returns a settings object populated with values from the current profile or, if no
    profile is active, the environment.
    ",19,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_current_settings() -> Settings:
    
    from prefect.context import ProfileContext

    profile = ProfileContext.get()
    if profile is not None:
        return profile.settings

    return get_settings_from_env()


        ```
    ###Assistant : 
    Returns a settings object populated with values from the current profile or, if no
    profile is active, the environment.
    
    "
2408,"def test_compare_key_greater(self):
        
        self.not_at_dest_sync_strategy.determine_should_sync.return_value = False

        # Try when the sync strategy says to sync the file.
        self.not_at_src_sync_strategy.determine_should_sync.return_value = True

        src_files = []
        dest_files = []
        ref_list = []
        result_list = []
        time = datetime.datetime.now()
        src_file = FileStat(src='', dest='',
                            compare_key='domparator_test.py', size=10,
                            last_update=time, src_type='local',
                            dest_type='s3', operation_name='upload')
        dest_file = FileStat(src='', dest='',
                             compare_key='comparator_test.py', size=10,
                             last_update=time, src_type='s3',
                             dest_type='local', operation_name='')
        src_files.append(src_file)
        dest_files.append(dest_file)
        ref_list.append(dest_file)
        files = self.comparator.call(iter(src_files), iter(dest_files))
        for filename in files:
            result_list.append(filename)
        self.assertEqual(result_list, ref_list)

        # Now try when the sync strategy says not to sync the file.
        self.not_at_src_sync_strategy.determine_should_sync.return_value = False
        result_list = []
        ref_list = []
        files = self.comparator.call(iter(src_files), iter(dest_files))
        for filename in files:
            result_list.append(filename)
        self.assertEqual(result_list, ref_list)

","
        Confirm the appropriate action is taken when the soruce compare key
        is greater than the destination compare key.
        ",18,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_compare_key_greater(self):
        
        self.not_at_dest_sync_strategy.determine_should_sync.return_value = False

        # Try when the sync strategy says to sync the file.
        self.not_at_src_sync_strategy.determine_should_sync.return_value = True

        src_files = []
        dest_files = []
        ref_list = []
        result_list = []
        time = datetime.datetime.now()
        src_file = FileStat(src='', dest='',
                            compare_key='domparator_test.py', size=10,
                            last_update=time, src_type='local',
                            dest_type='s3', operation_name='upload')
        dest_file = FileStat(src='', dest='',
                             compare_key='comparator_test.py', size=10,
                             last_update=time, src_type='s3',
                             dest_type='local', operation_name='')
        src_files.append(src_file)
        dest_files.append(dest_file)
        ref_list.append(dest_file)
        files = self.comparator.call(iter(src_files), iter(dest_files))
        for filename in files:
            result_list.append(filename)
        self.assertEqual(result_list, ref_list)

        # Now try when the sync strategy says not to sync the file.
        self.not_at_src_sync_strategy.determine_should_sync.return_value = False
        result_list = []
        ref_list = []
        files = self.comparator.call(iter(src_files), iter(dest_files))
        for filename in files:
            result_list.append(filename)
        self.assertEqual(result_list, ref_list)


        ```
    ###Assistant : 
        Confirm the appropriate action is taken when the soruce compare key
        is greater than the destination compare key.
        
    "
2409,"def test_custom_exact_lookup_none_rhs(self):
        
        field = Author._meta.get_field(""birthdate"")
        OldExactLookup = field.get_lookup(""exact"")
        author = Author.objects.create(name=""author"", birthdate=None)
        try:
            field.register_lookup(Exactly, ""exact"")
            self.assertEqual(Author.objects.get(birthdate__exact=None), author)
        finally:
            field.register_lookup(OldExactLookup, ""exact"")
","
        __exact=None is transformed to __isnull=True if a custom lookup class
        with lookup_name != 'exact' is registered as the `exact` lookup.
        ",20,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_custom_exact_lookup_none_rhs(self):
        
        field = Author._meta.get_field(""birthdate"")
        OldExactLookup = field.get_lookup(""exact"")
        author = Author.objects.create(name=""author"", birthdate=None)
        try:
            field.register_lookup(Exactly, ""exact"")
            self.assertEqual(Author.objects.get(birthdate__exact=None), author)
        finally:
            field.register_lookup(OldExactLookup, ""exact"")

        ```
    ###Assistant : 
        __exact=None is transformed to __isnull=True if a custom lookup class
        with lookup_name != 'exact' is registered as the `exact` lookup.
        
    "
2410,"def batch_pairwise_distances(U, V):
    
    with tf.variable_scope('pairwise_dist_block'):
        # Squared norms of each row in U and V.
        norm_u = tf.reduce_sum(tf.square(U), 1)
        norm_v = tf.reduce_sum(tf.square(V), 1)

        # norm_u as a row and norm_v as a column vectors.
        norm_u = tf.reshape(norm_u, [-1, 1])
        norm_v = tf.reshape(norm_v, [1, -1])

        # Pairwise squared Euclidean distances.
        D = tf.maximum(norm_u - 2*tf.matmul(U, V, False, True) + norm_v, 0.0)

    return D

#----------------------------------------------------------------------------
", Compute pairwise distances between two batches of feature vectors.,9,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def batch_pairwise_distances(U, V):
    
    with tf.variable_scope('pairwise_dist_block'):
        # Squared norms of each row in U and V.
        norm_u = tf.reduce_sum(tf.square(U), 1)
        norm_v = tf.reduce_sum(tf.square(V), 1)

        # norm_u as a row and norm_v as a column vectors.
        norm_u = tf.reshape(norm_u, [-1, 1])
        norm_v = tf.reshape(norm_v, [1, -1])

        # Pairwise squared Euclidean distances.
        D = tf.maximum(norm_u - 2*tf.matmul(U, V, False, True) + norm_v, 0.0)

    return D

#----------------------------------------------------------------------------

        ```
    ###Assistant :  Compute pairwise distances between two batches of feature vectors.
    "
2411,"def expand_dims(self, image):
        
        self._ensure_format_supported(image)

        # Do nothing if PIL image
        if isinstance(image, PIL.Image.Image):
            return image

        if is_torch_tensor(image):
            image = image.unsqueeze(0)
        else:
            image = np.expand_dims(image, axis=0)
        return image
","
        Expands 2-dimensional `image` to 3 dimensions.

        Args:
            image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                The image to expand.
        ",17,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def expand_dims(self, image):
        
        self._ensure_format_supported(image)

        # Do nothing if PIL image
        if isinstance(image, PIL.Image.Image):
            return image

        if is_torch_tensor(image):
            image = image.unsqueeze(0)
        else:
            image = np.expand_dims(image, axis=0)
        return image

        ```
    ###Assistant : 
        Expands 2-dimensional `image` to 3 dimensions.

        Args:
            image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                The image to expand.
        
    "
2412,"def test_find_executable_task_instances_order_execution_date(self, dag_maker):
        
        dag_id_1 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-a'
        dag_id_2 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-b'
        task_id = 'task-a'
        session = settings.Session()
        with dag_maker(dag_id=dag_id_1, max_active_tasks=16, session=session):
            EmptyOperator(task_id=task_id)
        dr1 = dag_maker.create_dagrun(execution_date=DEFAULT_DATE + timedelta(hours=1))

        with dag_maker(dag_id=dag_id_2, max_active_tasks=16, session=session):
            EmptyOperator(task_id=task_id)
        dr2 = dag_maker.create_dagrun()

        dr1 = session.merge(dr1, load=False)

        self.scheduler_job = SchedulerJob(subdir=os.devnull)

        tis = dr1.task_instances + dr2.task_instances
        for ti in tis:
            ti.state = State.SCHEDULED
            session.merge(ti)
        session.flush()

        res = self.scheduler_job._executable_task_instances_to_queued(max_tis=1, session=session)
        session.flush()
        assert [ti.key for ti in res] == [tis[1].key]
        session.rollback()
","
        Test that task instances follow execution_date order priority. If two dagruns with
        different execution dates are scheduled, tasks with earliest dagrun execution date will first
        be executed
        ",27,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_find_executable_task_instances_order_execution_date(self, dag_maker):
        
        dag_id_1 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-a'
        dag_id_2 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-b'
        task_id = 'task-a'
        session = settings.Session()
        with dag_maker(dag_id=dag_id_1, max_active_tasks=16, session=session):
            EmptyOperator(task_id=task_id)
        dr1 = dag_maker.create_dagrun(execution_date=DEFAULT_DATE + timedelta(hours=1))

        with dag_maker(dag_id=dag_id_2, max_active_tasks=16, session=session):
            EmptyOperator(task_id=task_id)
        dr2 = dag_maker.create_dagrun()

        dr1 = session.merge(dr1, load=False)

        self.scheduler_job = SchedulerJob(subdir=os.devnull)

        tis = dr1.task_instances + dr2.task_instances
        for ti in tis:
            ti.state = State.SCHEDULED
            session.merge(ti)
        session.flush()

        res = self.scheduler_job._executable_task_instances_to_queued(max_tis=1, session=session)
        session.flush()
        assert [ti.key for ti in res] == [tis[1].key]
        session.rollback()

        ```
    ###Assistant : 
        Test that task instances follow execution_date order priority. If two dagruns with
        different execution dates are scheduled, tasks with earliest dagrun execution date will first
        be executed
        
    "
2413,"def dispatch(self, request, *args, **kwargs):
        

        self.determine_active_organization(request, kwargs.get(""organization_slug"", None))

        if self.csrf_protect:
            if hasattr(self.dispatch.__func__, ""csrf_exempt""):
                delattr(self.dispatch.__func__, ""csrf_exempt"")
            response = self.test_csrf(request)
            if response:
                return response

        if self.is_auth_required(request, *args, **kwargs):
            return self.handle_auth_required(request, *args, **kwargs)

        if self.is_sudo_required(request, *args, **kwargs):
            return self.handle_sudo_required(request, *args, **kwargs)

        args, kwargs = self.convert_args(request, *args, **kwargs)

        request.access = self.get_access(request, *args, **kwargs)

        if not self.has_permission(request, *args, **kwargs):
            return self.handle_permission_required(request, *args, **kwargs)

        if ""organization"" in kwargs:
            org = kwargs[""organization""]
            if self.is_member_disabled_from_limit(request, org):
                return self.handle_disabled_member(org)
            if self.is_not_2fa_compliant(request, org):
                return self.handle_not_2fa_compliant(request, *args, **kwargs)

        self.request = request
        self.default_context = self.get_context_data(request, *args, **kwargs)

        return self.handle(request, *args, **kwargs)
","
        A note on the CSRF protection process.

        Because the CSRF decorators don't work well with view subclasses, we
        allow them to control whether a CSRF check is done by setting
        self.csrf_protect. This has a couple of implications:

        1. We need to mark this method as @csrf_exempt so that when the CSRF
           middleware checks it as part of the regular middleware sequence, it
           always passes.
        2. If self.csrf_protect is set, we will re-run the CSRF check ourselves
           using CsrfViewMiddleware().process_view()
        3. But first we must remove the csrf_exempt attribute that was set by
           the decorator so that the middleware doesn't shortcut and pass the
           check unconditionally again.

        ",105,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dispatch(self, request, *args, **kwargs):
        

        self.determine_active_organization(request, kwargs.get(""organization_slug"", None))

        if self.csrf_protect:
            if hasattr(self.dispatch.__func__, ""csrf_exempt""):
                delattr(self.dispatch.__func__, ""csrf_exempt"")
            response = self.test_csrf(request)
            if response:
                return response

        if self.is_auth_required(request, *args, **kwargs):
            return self.handle_auth_required(request, *args, **kwargs)

        if self.is_sudo_required(request, *args, **kwargs):
            return self.handle_sudo_required(request, *args, **kwargs)

        args, kwargs = self.convert_args(request, *args, **kwargs)

        request.access = self.get_access(request, *args, **kwargs)

        if not self.has_permission(request, *args, **kwargs):
            return self.handle_permission_required(request, *args, **kwargs)

        if ""organization"" in kwargs:
            org = kwargs[""organization""]
            if self.is_member_disabled_from_limit(request, org):
                return self.handle_disabled_member(org)
            if self.is_not_2fa_compliant(request, org):
                return self.handle_not_2fa_compliant(request, *args, **kwargs)

        self.request = request
        self.default_context = self.get_context_data(request, *args, **kwargs)

        return self.handle(request, *args, **kwargs)

        ```
    ###Assistant : 
        A note on the CSRF protection process.

        Because the CSRF decorators don't work well with view subclasses, we
        allow them to control whether a CSRF check is done by setting
        self.csrf_protect. This has a couple of implications:

        1. We need to mark this method as @csrf_exempt so that when the CSRF
           middleware checks it as part of the regular middleware sequence, it
           always passes.
        2. If self.csrf_protect is set, we will re-run the CSRF check ourselves
           using CsrfViewMiddleware().process_view()
        3. But first we must remove the csrf_exempt attribute that was set by
           the decorator so that the middleware doesn't shortcut and pass the
           check unconditionally again.

        
    "
2414,"def interpolator(self) -> int:
        
        assert self._interpolator is not None
        return self._interpolator
", int: The cv2 interpolator required to transpose the mask to a full frame. ,13,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def interpolator(self) -> int:
        
        assert self._interpolator is not None
        return self._interpolator

        ```
    ###Assistant :  int: The cv2 interpolator required to transpose the mask to a full frame. 
    "
2415,"def test_cluster_interrupt(start_connected_cluster, tmpdir):
    
    cluster = start_connected_cluster
    dirpath = str(tmpdir)

    # Needs to be in scope for pytest","Tests run_experiment on cluster shutdown with actual interrupt.

    This is an end-to-end test.
    ",13,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_cluster_interrupt(start_connected_cluster, tmpdir):
    
    cluster = start_connected_cluster
    dirpath = str(tmpdir)

    # Needs to be in scope for pytest
        ```
    ###Assistant : Tests run_experiment on cluster shutdown with actual interrupt.

    This is an end-to-end test.
    
    "
2416,"def test_version(self) -> None:
        
        for version in (""1.0"", 1.0, 1):
            result = self.parse_response({""version"": version, ""type"": ""link""})
            # An empty Open Graph response is an error, ensure the URL is included.
            self.assertIn(""og:url"", result.open_graph_result)

        # A missing version should be treated as 1.0.
        result = self.parse_response({""type"": ""link""})
        self.assertIn(""og:url"", result.open_graph_result)

        # Invalid versions should be rejected.
        for version in (""2.0"", ""1"", 1.1, 0, None, {}, []):
            result = self.parse_response({""version"": version, ""type"": ""link""})
            # An empty Open Graph response is an error, ensure the URL is included.
            self.assertEqual({}, result.open_graph_result)
",Accept versions that are similar to 1.0 as a string or int (or missing).,14,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_version(self) -> None:
        
        for version in (""1.0"", 1.0, 1):
            result = self.parse_response({""version"": version, ""type"": ""link""})
            # An empty Open Graph response is an error, ensure the URL is included.
            self.assertIn(""og:url"", result.open_graph_result)

        # A missing version should be treated as 1.0.
        result = self.parse_response({""type"": ""link""})
        self.assertIn(""og:url"", result.open_graph_result)

        # Invalid versions should be rejected.
        for version in (""2.0"", ""1"", 1.1, 0, None, {}, []):
            result = self.parse_response({""version"": version, ""type"": ""link""})
            # An empty Open Graph response is an error, ensure the URL is included.
            self.assertEqual({}, result.open_graph_result)

        ```
    ###Assistant : Accept versions that are similar to 1.0 as a string or int (or missing).
    "
2417,"def test_page_allowing_subpages(self):
        response = self.client.get(
            reverse(""wagtailadmin_userbar_frontend"", args=(self.event_index.id,))
        )

        # page allows subpages, so the 'add page' button should show
        expected_url = reverse(
            ""wagtailadmin_pages:add_subpage"", args=(self.event_index.id,)
        )
        needle = f
        self.assertTagInHTML(needle, str(response.content))
","
            <a href=""{expected_url}"" target=""_parent"" role=""menuitem"">
                <svg class=""icon icon-plus wagtail-action-icon"" aria-hidden=""true"" focusable=""false"">
                    <use href=""#icon-plus""></use>
                </svg>
                Add a child page
            </a>
            ",18,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_page_allowing_subpages(self):
        response = self.client.get(
            reverse(""wagtailadmin_userbar_frontend"", args=(self.event_index.id,))
        )

        # page allows subpages, so the 'add page' button should show
        expected_url = reverse(
            ""wagtailadmin_pages:add_subpage"", args=(self.event_index.id,)
        )
        needle = f
        self.assertTagInHTML(needle, str(response.content))

        ```
    ###Assistant : 
            <a href=""{expected_url}"" target=""_parent"" role=""menuitem"">
                <svg class=""icon icon-plus wagtail-action-icon"" aria-hidden=""true"" focusable=""false"">
                    <use href=""#icon-plus""></use>
                </svg>
                Add a child page
            </a>
            
    "
2418,"def render_output_ui(self, streamlit_app, input) -> None:  # type: ignore
        
        src, result = self.__root__
        
        streamlit_app.subheader(""Synthesized Audio"")
        streamlit_app.audio(result.content, format=""audio/wav"")

        fig, ax = plt.subplots()
        ax.imshow(src.mel, aspect=""equal"", interpolation=""none"")
        ax.set_title(""mel spectrogram(Source Audio)"")
        streamlit_app.pyplot(fig)
        fig, ax = plt.subplots()
        ax.imshow(result.mel, aspect=""equal"", interpolation=""none"")
        ax.set_title(""mel spectrogram(Result Audio)"")
        streamlit_app.pyplot(fig)

","Custom output UI.
        If this method is implmeneted, it will be used instead of the default Output UI renderer.
        ",19,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def render_output_ui(self, streamlit_app, input) -> None:  # type: ignore
        
        src, result = self.__root__
        
        streamlit_app.subheader(""Synthesized Audio"")
        streamlit_app.audio(result.content, format=""audio/wav"")

        fig, ax = plt.subplots()
        ax.imshow(src.mel, aspect=""equal"", interpolation=""none"")
        ax.set_title(""mel spectrogram(Source Audio)"")
        streamlit_app.pyplot(fig)
        fig, ax = plt.subplots()
        ax.imshow(result.mel, aspect=""equal"", interpolation=""none"")
        ax.set_title(""mel spectrogram(Result Audio)"")
        streamlit_app.pyplot(fig)


        ```
    ###Assistant : Custom output UI.
        If this method is implmeneted, it will be used instead of the default Output UI renderer.
        
    "
2419,"def get_default_mesh(self):
        
        return self._default_mesh


LayoutMap.get.__doc__ = LayoutMap.__getitem__.__doc__


@keras_export(""keras.dtensor.experimental.layout_map_scope"", v1=[])
@contextlib.contextmanager","Return the default `Mesh` set at instance creation.

        The `Mesh` can be used to create default replicated `Layout` when there
        isn't a match of the input string query.
        ",28,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_default_mesh(self):
        
        return self._default_mesh


LayoutMap.get.__doc__ = LayoutMap.__getitem__.__doc__


@keras_export(""keras.dtensor.experimental.layout_map_scope"", v1=[])
@contextlib.contextmanager
        ```
    ###Assistant : Return the default `Mesh` set at instance creation.

        The `Mesh` can be used to create default replicated `Layout` when there
        isn't a match of the input string query.
        
    "
2420,"def __setitem__(self, key, item):  # pragma: no cover
        
        raise NotImplementedError(""Implemented by subclasses"")
","
        Assign `item` value to dataset located by `key`.

        Parameters
        ----------
        key : callable or tuple
            The global row numbers to assign data to.
        item : modin.pandas.DataFrame, modin.pandas.Series or scalar
            Value that should be assigned to located dataset.

        See Also
        --------
        pandas.DataFrame.iloc
        ",41,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __setitem__(self, key, item):  # pragma: no cover
        
        raise NotImplementedError(""Implemented by subclasses"")

        ```
    ###Assistant : 
        Assign `item` value to dataset located by `key`.

        Parameters
        ----------
        key : callable or tuple
            The global row numbers to assign data to.
        item : modin.pandas.DataFrame, modin.pandas.Series or scalar
            Value that should be assigned to located dataset.

        See Also
        --------
        pandas.DataFrame.iloc
        
    "
2421,"def test_bad_origin_cannot_be_parsed(self):
        
        req = self._get_POST_request_with_token()
        req.META[""HTTP_HOST""] = ""www.example.com""
        req.META[""HTTP_ORIGIN""] = ""https://[""
        mw = CsrfViewMiddleware(post_form_view)
        self._check_referer_rejects(mw, req)
        self.assertIs(mw._origin_verified(req), False)
        with self.assertLogs(""django.security.csrf"", ""WARNING"") as cm:
            response = mw.process_view(req, post_form_view, (), {})
        self.assertEqual(response.status_code, 403)
        msg = REASON_BAD_ORIGIN % req.META[""HTTP_ORIGIN""]
        self.assertEqual(cm.records[0].getMessage(), ""Forbidden (%s): "" % msg)
","
        A POST request with an origin that can't be parsed by urlparse() is
        rejected.
        ",14,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_bad_origin_cannot_be_parsed(self):
        
        req = self._get_POST_request_with_token()
        req.META[""HTTP_HOST""] = ""www.example.com""
        req.META[""HTTP_ORIGIN""] = ""https://[""
        mw = CsrfViewMiddleware(post_form_view)
        self._check_referer_rejects(mw, req)
        self.assertIs(mw._origin_verified(req), False)
        with self.assertLogs(""django.security.csrf"", ""WARNING"") as cm:
            response = mw.process_view(req, post_form_view, (), {})
        self.assertEqual(response.status_code, 403)
        msg = REASON_BAD_ORIGIN % req.META[""HTTP_ORIGIN""]
        self.assertEqual(cm.records[0].getMessage(), ""Forbidden (%s): "" % msg)

        ```
    ###Assistant : 
        A POST request with an origin that can't be parsed by urlparse() is
        rejected.
        
    "
2422,"def move_from_center(coord, centers, deltas, axmask=(True, True, True)):
    
    return _move_from_center(coord, centers, deltas, axmask=axmask)

","
    For each coordinate where *axmask* is True, move *coord* away from
    *centers* by *deltas*.
    ",14,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def move_from_center(coord, centers, deltas, axmask=(True, True, True)):
    
    return _move_from_center(coord, centers, deltas, axmask=axmask)


        ```
    ###Assistant : 
    For each coordinate where *axmask* is True, move *coord* away from
    *centers* by *deltas*.
    
    "
2423,"def get_execution_info(self, job_id, function_descriptor):
        
        function_id = function_descriptor.function_id
        # If the function has already been loaded,
        # There's no need to load again
        if function_id in self._function_execution_info:
            return self._function_execution_info[function_id]
        if self._worker.load_code_from_local:
            # Load function from local code.
            if not function_descriptor.is_actor_method():
                # If the function is not able to be loaded,
                # try to load it from GCS,
                # even if load_code_from_local is set True
                if self._load_function_from_local(function_descriptor) is True:
                    return self._function_execution_info[function_id]
        # Load function from GCS.
        # Wait until the function to be executed has actually been
        # registered on this worker. We will push warnings to the user if
        # we spend too long in this loop.
        # The driver function may not be found in sys.path. Try to load
        # the function from GCS.
        with profiling.profile(""wait_for_function""):
            self._wait_for_function(function_descriptor, job_id)
        try:
            function_id = function_descriptor.function_id
            info = self._function_execution_info[function_id]
        except KeyError as e:
            message = (
                ""Error occurs in get_execution_info: ""
                ""job_id: %s, function_descriptor: %s. Message: %s""
                % (job_id, function_descriptor, e)
            )
            raise KeyError(message)
        return info
","Get the FunctionExecutionInfo of a remote function.
        Args:
            job_id: ID of the job that the function belongs to.
            function_descriptor: The FunctionDescriptor of the function to get.
        Returns:
            A FunctionExecutionInfo object.
        ",30,162,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_execution_info(self, job_id, function_descriptor):
        
        function_id = function_descriptor.function_id
        # If the function has already been loaded,
        # There's no need to load again
        if function_id in self._function_execution_info:
            return self._function_execution_info[function_id]
        if self._worker.load_code_from_local:
            # Load function from local code.
            if not function_descriptor.is_actor_method():
                # If the function is not able to be loaded,
                # try to load it from GCS,
                # even if load_code_from_local is set True
                if self._load_function_from_local(function_descriptor) is True:
                    return self._function_execution_info[function_id]
        # Load function from GCS.
        # Wait until the function to be executed has actually been
        # registered on this worker. We will push warnings to the user if
        # we spend too long in this loop.
        # The driver function may not be found in sys.path. Try to load
        # the function from GCS.
        with profiling.profile(""wait_for_function""):
            self._wait_for_function(function_descriptor, job_id)
        try:
            function_id = function_descriptor.function_id
            info = self._function_execution_info[function_id]
        except KeyError as e:
            message = (
                ""Error occurs in get_execution_info: ""
                ""job_id: %s, function_descriptor: %s. Message: %s""
                % (job_id, function_descriptor, e)
            )
            raise KeyError(message)
        return info

        ```
    ###Assistant : Get the FunctionExecutionInfo of a remote function.
        Args:
            job_id: ID of the job that the function belongs to.
            function_descriptor: The FunctionDescriptor of the function to get.
        Returns:
            A FunctionExecutionInfo object.
        
    "
2424,"def exclude_all_devices(self) -> bool:
        
        return all(idx in _EXCLUDE_DEVICES for idx in range(self._device_count))
", bool: ``True`` if all GPU devices have been explicitly disabled otherwise ``False`` ,12,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def exclude_all_devices(self) -> bool:
        
        return all(idx in _EXCLUDE_DEVICES for idx in range(self._device_count))

        ```
    ###Assistant :  bool: ``True`` if all GPU devices have been explicitly disabled otherwise ``False`` 
    "
2425,"def serialize_labels(self, resources):
        
        labels = []
        for label in resources:
            if label in AlexaGlobalCatalog.__dict__.values():
                label = {""@type"": ""asset"", ""value"": {""assetId"": label}}
            else:
                label = {""@type"": ""text"", ""value"": {""text"": label, ""locale"": ""en-US""}}

            labels.append(label)

        return {""friendlyNames"": labels}

",Return resource label objects for friendlyNames serialized for an API response.,11,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serialize_labels(self, resources):
        
        labels = []
        for label in resources:
            if label in AlexaGlobalCatalog.__dict__.values():
                label = {""@type"": ""asset"", ""value"": {""assetId"": label}}
            else:
                label = {""@type"": ""text"", ""value"": {""text"": label, ""locale"": ""en-US""}}

            labels.append(label)

        return {""friendlyNames"": labels}


        ```
    ###Assistant : Return resource label objects for friendlyNames serialized for an API response.
    "
2426,"def _decode_bitonal(self):
        
        data = bytearray()
        total_bytes = self.state.xsize * self.state.ysize

        comment_spans = False
        while len(data) != total_bytes:
            block = self._read_block()  # read next block
            if not block:
                # eof
                break

            while block and comment_spans:
                comment_end = self._find_comment_end(block)
                if comment_end != -1:  # comment ends in this block
                    block = block[comment_end + 1 :]  # delete tail of previous comment
                    break
                else:  # comment spans whole block
                    block = self._read_block()

            block, comment_spans = self._ignore_comments(block)

            tokens = b"""".join(block.split())
            for token in tokens:
                if token not in (48, 49):
                    raise ValueError(f""Invalid token for this mode: {bytes([token])}"")
            data = (data + tokens)[:total_bytes]
        invert = bytes.maketrans(b""01"", b""\xFF\x00"")
        return data.translate(invert)
","
        This is a separate method because in the plain PBM format, all data tokens are
        exactly one byte, so the inter-token whitespace is optional.
        ",24,104,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _decode_bitonal(self):
        
        data = bytearray()
        total_bytes = self.state.xsize * self.state.ysize

        comment_spans = False
        while len(data) != total_bytes:
            block = self._read_block()  # read next block
            if not block:
                # eof
                break

            while block and comment_spans:
                comment_end = self._find_comment_end(block)
                if comment_end != -1:  # comment ends in this block
                    block = block[comment_end + 1 :]  # delete tail of previous comment
                    break
                else:  # comment spans whole block
                    block = self._read_block()

            block, comment_spans = self._ignore_comments(block)

            tokens = b"""".join(block.split())
            for token in tokens:
                if token not in (48, 49):
                    raise ValueError(f""Invalid token for this mode: {bytes([token])}"")
            data = (data + tokens)[:total_bytes]
        invert = bytes.maketrans(b""01"", b""\xFF\x00"")
        return data.translate(invert)

        ```
    ###Assistant : 
        This is a separate method because in the plain PBM format, all data tokens are
        exactly one byte, so the inter-token whitespace is optional.
        
    "
2427,"def recast_to_symbols(eqs, symbols):
    
    if not iterable(eqs) and iterable(symbols):
        raise ValueError('Both eqs and symbols must be iterable')
    orig = list(symbols)
    symbols = list(ordered(symbols))
    swap_sym = {}
    i = 0
    for j, s in enumerate(symbols):
        if not isinstance(s, Symbol) and s not in swap_sym:
            swap_sym[s] = Dummy('X%d' % i)
            i += 1
    new_f = []
    for i in eqs:
        isubs = getattr(i, 'subs', None)
        if isubs is not None:
            new_f.append(isubs(swap_sym))
        else:
            new_f.append(i)
    restore = {v: k for k, v in swap_sym.items()}
    return new_f, [swap_sym.get(i, i) for i in orig], restore

","
    Return (e, s, d) where e and s are versions of *eqs* and
    *symbols* in which any non-Symbol objects in *symbols* have
    been replaced with generic Dummy symbols and d is a dictionary
    that can be used to restore the original expressions.

    Examples
    ========

    >>> from sympy.solvers.solvers import recast_to_symbols
    >>> from sympy import symbols, Function
    >>> x, y = symbols('x y')
    >>> fx = Function('f')(x)
    >>> eqs, syms = [fx + 1, x, y], [fx, y]
    >>> e, s, d = recast_to_symbols(eqs, syms); (e, s, d)
    ([_X0 + 1, x, y], [_X0, y], {_X0: f(x)})

    The original equations and symbols can be restored using d:

    >>> assert [i.xreplace(d) for i in eqs] == eqs
    >>> assert [d.get(i, i) for i in s] == syms

    ",124,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def recast_to_symbols(eqs, symbols):
    
    if not iterable(eqs) and iterable(symbols):
        raise ValueError('Both eqs and symbols must be iterable')
    orig = list(symbols)
    symbols = list(ordered(symbols))
    swap_sym = {}
    i = 0
    for j, s in enumerate(symbols):
        if not isinstance(s, Symbol) and s not in swap_sym:
            swap_sym[s] = Dummy('X%d' % i)
            i += 1
    new_f = []
    for i in eqs:
        isubs = getattr(i, 'subs', None)
        if isubs is not None:
            new_f.append(isubs(swap_sym))
        else:
            new_f.append(i)
    restore = {v: k for k, v in swap_sym.items()}
    return new_f, [swap_sym.get(i, i) for i in orig], restore


        ```
    ###Assistant : 
    Return (e, s, d) where e and s are versions of *eqs* and
    *symbols* in which any non-Symbol objects in *symbols* have
    been replaced with generic Dummy symbols and d is a dictionary
    that can be used to restore the original expressions.

    Examples
    ========

    >>> from sympy.solvers.solvers import recast_to_symbols
    >>> from sympy import symbols, Function
    >>> x, y = symbols('x y')
    >>> fx = Function('f')(x)
    >>> eqs, syms = [fx + 1, x, y], [fx, y]
    >>> e, s, d = recast_to_symbols(eqs, syms); (e, s, d)
    ([_X0 + 1, x, y], [_X0, y], {_X0: f(x)})

    The original equations and symbols can be restored using d:

    >>> assert [i.xreplace(d) for i in eqs] == eqs
    >>> assert [d.get(i, i) for i in s] == syms

    
    "
2428,"def model_is_indexable(cls, model, allow_child_models=False):
        
        if getattr(model, ""wagtail_reference_index_ignore"", False):
            return False

        # Don't check any models that have a parental key, references from these will be collected from the parent
        if not allow_child_models and any(
            [isinstance(field, ParentalKey) for field in model._meta.get_fields()]
        ):
            return False

        for field in model._meta.get_fields():
            if field.is_relation and field.many_to_one:
                if getattr(field, ""wagtail_reference_index_ignore"", False):
                    continue

                if getattr(
                    field.related_model, ""wagtail_reference_index_ignore"", False
                ):
                    continue

                if isinstance(field, (ParentalKey, GenericRel)):
                    continue

                return True

            if hasattr(field, ""extract_references""):
                return True

        if issubclass(model, ClusterableModel):
            for child_relation in get_all_child_relations(model):
                if cls.model_is_indexable(
                    child_relation.related_model,
                    allow_child_models=True,
                ):
                    return True

        return False
","
        Returns True if the given model may have outbound references that we would be interested in recording in the index.


        Args:
            model (type): a Django model class
            allow_child_models (boolean): Child models are not indexable on their own. If you are looking at
                                          a child model from the perspective of indexing it through its parent,
                                          set this to True to disable checking for this. Default False.
        ",65,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def model_is_indexable(cls, model, allow_child_models=False):
        
        if getattr(model, ""wagtail_reference_index_ignore"", False):
            return False

        # Don't check any models that have a parental key, references from these will be collected from the parent
        if not allow_child_models and any(
            [isinstance(field, ParentalKey) for field in model._meta.get_fields()]
        ):
            return False

        for field in model._meta.get_fields():
            if field.is_relation and field.many_to_one:
                if getattr(field, ""wagtail_reference_index_ignore"", False):
                    continue

                if getattr(
                    field.related_model, ""wagtail_reference_index_ignore"", False
                ):
                    continue

                if isinstance(field, (ParentalKey, GenericRel)):
                    continue

                return True

            if hasattr(field, ""extract_references""):
                return True

        if issubclass(model, ClusterableModel):
            for child_relation in get_all_child_relations(model):
                if cls.model_is_indexable(
                    child_relation.related_model,
                    allow_child_models=True,
                ):
                    return True

        return False

        ```
    ###Assistant : 
        Returns True if the given model may have outbound references that we would be interested in recording in the index.


        Args:
            model (type): a Django model class
            allow_child_models (boolean): Child models are not indexable on their own. If you are looking at
                                          a child model from the perspective of indexing it through its parent,
                                          set this to True to disable checking for this. Default False.
        
    "
2429,"def get_encodings_from_content(content):
    
    warnings.warn(
        (
            ""In requests 3.0, get_encodings_from_content will be removed. For ""
            ""more information, please see the discussion on issue #2266. (This""
            "" warning should only appear once.)""
        ),
        DeprecationWarning,
    )

    charset_re = re.compile(r'<meta.*?charset=[""\']*(.+?)[""\'>]', flags=re.I)
    pragma_re = re.compile(r'<meta.*?content=[""\']*;?charset=(.+?)[""\'>]', flags=re.I)
    xml_re = re.compile(r'^<\?xml.*?encoding=[""\']*(.+?)[""\'>]')

    return (
        charset_re.findall(content)
        + pragma_re.findall(content)
        + xml_re.findall(content)
    )

","Returns encodings from given content string.

    :param content: bytestring to extract encodings from.
    ",13,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_encodings_from_content(content):
    
    warnings.warn(
        (
            ""In requests 3.0, get_encodings_from_content will be removed. For ""
            ""more information, please see the discussion on issue #2266. (This""
            "" warning should only appear once.)""
        ),
        DeprecationWarning,
    )

    charset_re = re.compile(r'<meta.*?charset=[""\']*(.+?)[""\'>]', flags=re.I)
    pragma_re = re.compile(r'<meta.*?content=[""\']*;?charset=(.+?)[""\'>]', flags=re.I)
    xml_re = re.compile(r'^<\?xml.*?encoding=[""\']*(.+?)[""\'>]')

    return (
        charset_re.findall(content)
        + pragma_re.findall(content)
        + xml_re.findall(content)
    )


        ```
    ###Assistant : Returns encodings from given content string.

    :param content: bytestring to extract encodings from.
    
    "
2430,"def _scale_axis_limits(self, scale_x, scale_y, scale_z):
        
        # Get the axis limits and centers
        minx, maxx, miny, maxy, minz, maxz = self.get_w_lims()
        cx = (maxx + minx)/2
        cy = (maxy + miny)/2
        cz = (maxz + minz)/2

        # Scale the data range
        dx = (maxx - minx)*scale_x
        dy = (maxy - miny)*scale_y
        dz = (maxz - minz)*scale_z

        # Set the scaled axis limits
        self.set_xlim3d(cx - dx/2, cx + dx/2)
        self.set_ylim3d(cy - dy/2, cy + dy/2)
        self.set_zlim3d(cz - dz/2, cz + dz/2)
","
        Keeping the center of the x, y, and z data axes fixed, scale their
        limits by scale factors. A scale factor > 1 zooms out and a scale
        factor < 1 zooms in.

        Parameters
        ----------
        scale_x : float
            Scale factor for the x data axis.
        scale_y : float
            Scale factor for the y data axis.
        scale_z : float
            Scale factor for the z data axis.
        ",65,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _scale_axis_limits(self, scale_x, scale_y, scale_z):
        
        # Get the axis limits and centers
        minx, maxx, miny, maxy, minz, maxz = self.get_w_lims()
        cx = (maxx + minx)/2
        cy = (maxy + miny)/2
        cz = (maxz + minz)/2

        # Scale the data range
        dx = (maxx - minx)*scale_x
        dy = (maxy - miny)*scale_y
        dz = (maxz - minz)*scale_z

        # Set the scaled axis limits
        self.set_xlim3d(cx - dx/2, cx + dx/2)
        self.set_ylim3d(cy - dy/2, cy + dy/2)
        self.set_zlim3d(cz - dz/2, cz + dz/2)

        ```
    ###Assistant : 
        Keeping the center of the x, y, and z data axes fixed, scale their
        limits by scale factors. A scale factor > 1 zooms out and a scale
        factor < 1 zooms in.

        Parameters
        ----------
        scale_x : float
            Scale factor for the x data axis.
        scale_y : float
            Scale factor for the y data axis.
        scale_z : float
            Scale factor for the z data axis.
        
    "
2431,"def layers(self) -> tuple[str, ...]:
        
        for node in self.ancestors:
            if not isinstance(node, Widget):
                break
            if node.styles.has_rule(""layers""):
                return node.styles.layers
        return (""default"",)
","Layers of from parent.

        Returns:
            tuple[str, ...]: Tuple of layer names.
        ",11,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def layers(self) -> tuple[str, ...]:
        
        for node in self.ancestors:
            if not isinstance(node, Widget):
                break
            if node.styles.has_rule(""layers""):
                return node.styles.layers
        return (""default"",)

        ```
    ###Assistant : Layers of from parent.

        Returns:
            tuple[str, ...]: Tuple of layer names.
        
    "
2432,"def get_roi_head_cfg(fname):
    
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)

    roi_head = model.roi_head
    train_cfg = None if model.train_cfg is None else model.train_cfg.rcnn
    test_cfg = None if model.test_cfg is None else model.test_cfg.rcnn
    roi_head.update(dict(train_cfg=train_cfg, test_cfg=test_cfg))
    return roi_head

","Grab configs necessary to create a roi_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    ",22,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_roi_head_cfg(fname):
    
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)

    roi_head = model.roi_head
    train_cfg = None if model.train_cfg is None else model.train_cfg.rcnn
    test_cfg = None if model.test_cfg is None else model.test_cfg.rcnn
    roi_head.update(dict(train_cfg=train_cfg, test_cfg=test_cfg))
    return roi_head


        ```
    ###Assistant : Grab configs necessary to create a roi_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    
    "
2433,"def print_as_log(*args, **kwargs):
    
    from prefect.context import FlowRunContext, TaskRunContext

    context = TaskRunContext.get() or FlowRunContext.get()
    if not context or not context.log_prints:
        return print(*args, **kwargs)

    logger = get_run_logger()

    # Print to an in-memory buffer; so we do not need to implement `print`
    buffer = io.StringIO()
    kwargs[""file""] = buffer
    print(*args, **kwargs)

    # Remove trailing whitespace to prevent duplicates
    logger.info(buffer.getvalue().rstrip())


@contextmanager","
    A patch for `print` to send printed messages to the Prefect run logger.

    If no run is active, `print` will behave as if it were not patched.
    ",27,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_as_log(*args, **kwargs):
    
    from prefect.context import FlowRunContext, TaskRunContext

    context = TaskRunContext.get() or FlowRunContext.get()
    if not context or not context.log_prints:
        return print(*args, **kwargs)

    logger = get_run_logger()

    # Print to an in-memory buffer; so we do not need to implement `print`
    buffer = io.StringIO()
    kwargs[""file""] = buffer
    print(*args, **kwargs)

    # Remove trailing whitespace to prevent duplicates
    logger.info(buffer.getvalue().rstrip())


@contextmanager
        ```
    ###Assistant : 
    A patch for `print` to send printed messages to the Prefect run logger.

    If no run is active, `print` will behave as if it were not patched.
    
    "
2434,"def test_image():
    # Test fails for matplotlib 1.5+ because the size of the image
    # generated by matplotlib has changed.
    if Version(matplotlib.__version__) == Version(""3.4.1""):
        image_size = 432
    else:
        pytest.skip(""Test fails for older matplotlib"")
    np.random.seed(0)  # image size depends on the seed
    fig, ax = plt.subplots(figsize=(2, 2))
    ax.imshow(np.random.random((10, 10)), cmap=plt.cm.jet, interpolation=""nearest"")
    _assert_output_equal(
        fake_renderer_output(fig, FakeRenderer),
        f,
    )

","
                         opening figure
                         opening axes
                         draw image of size {image_size} 
                         closing axes
                         closing figure
                         ",13,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_image():
    # Test fails for matplotlib 1.5+ because the size of the image
    # generated by matplotlib has changed.
    if Version(matplotlib.__version__) == Version(""3.4.1""):
        image_size = 432
    else:
        pytest.skip(""Test fails for older matplotlib"")
    np.random.seed(0)  # image size depends on the seed
    fig, ax = plt.subplots(figsize=(2, 2))
    ax.imshow(np.random.random((10, 10)), cmap=plt.cm.jet, interpolation=""nearest"")
    _assert_output_equal(
        fake_renderer_output(fig, FakeRenderer),
        f,
    )


        ```
    ###Assistant : 
                         opening figure
                         opening axes
                         draw image of size {image_size} 
                         closing axes
                         closing figure
                         
    "
2435,"def ignore_cidr(vm_, ip):
    
    from ipaddress import ip_address, ip_network

    cidrs = config.get_cloud_config_value(
        ""ignore_cidr"", vm_, __opts__, default=[], search_global=False
    )
    if cidrs and isinstance(cidrs, str):
        cidrs = [cidrs]
    for cidr in cidrs or []:
        if ip_address(ip) in ip_network(cidr):
            log.warning(""IP %r found within %r; ignoring it."", ip, cidr)
            return True

    return False

","
    Return True if we are to ignore the specified IP.
    ",10,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ignore_cidr(vm_, ip):
    
    from ipaddress import ip_address, ip_network

    cidrs = config.get_cloud_config_value(
        ""ignore_cidr"", vm_, __opts__, default=[], search_global=False
    )
    if cidrs and isinstance(cidrs, str):
        cidrs = [cidrs]
    for cidr in cidrs or []:
        if ip_address(ip) in ip_network(cidr):
            log.warning(""IP %r found within %r; ignoring it."", ip, cidr)
            return True

    return False


        ```
    ###Assistant : 
    Return True if we are to ignore the specified IP.
    
    "
2436,"def encode_nested_example(schema, obj):
    
    # Nested structures: we allow dict, list/tuples, sequences
    if isinstance(schema, dict):
        return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)}
    elif isinstance(schema, (list, tuple)):
        sub_schema = schema[0]
        if obj is None:
            return None
        else:
            if len(obj) > 0:
                for first_elmt in obj:
                    if _check_non_null_non_empty_recursive(first_elmt, sub_schema):
                        break
                if encode_nested_example(sub_schema, first_elmt) != first_elmt:
                    return [encode_nested_example(sub_schema, o) for o in obj]
            return list(obj)
    elif isinstance(schema, Sequence):
        # We allow to reverse list of dict => dict of list for compatiblity with tfds
        if isinstance(schema.feature, dict):
            # dict of list to fill
            list_dict = {}
            if isinstance(obj, (list, tuple)):
                # obj is a list of dict
                for k, dict_tuples in zip_dict(schema.feature, *obj):
                    list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
                return list_dict
            else:
                # obj is a single dict
                for k, (sub_schema, sub_objs) in zip_dict(schema.feature, obj):
                    list_dict[k] = [encode_nested_example(sub_schema, o) for o in sub_objs]
                return list_dict
        # schema.feature is not a dict
        if isinstance(obj, str):  # don't interpret a string as a list
            raise ValueError(f""Got a string but expected a list instead: '{obj}'"")
        if obj is None:
            return None
        else:
            if len(obj) > 0:
                for first_elmt in obj:
                    if _check_non_null_non_empty_recursive(first_elmt, schema.feature):
                        break
                # be careful when comparing tensors here
                if not isinstance(first_elmt, list) or encode_nested_example(schema.feature, first_elmt) != first_elmt:
                    return [encode_nested_example(schema.feature, o) for o in obj]
            return list(obj)
    # Object with special encoding:
    # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks
    elif isinstance(schema, (Audio, Image, ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):
        return schema.encode_example(obj) if obj is not None else None
    # Other object should be directly convertible to a native Arrow type (like Translation and Translation)
    return obj

","Encode a nested example.
    This is used since some features (in particular ClassLabel) have some logic during encoding.

    To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be encoded.
    If the first element needs to be encoded, then all the elements of the list will be encoded, otherwise they'll stay the same.
    ",71,270,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def encode_nested_example(schema, obj):
    
    # Nested structures: we allow dict, list/tuples, sequences
    if isinstance(schema, dict):
        return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)}
    elif isinstance(schema, (list, tuple)):
        sub_schema = schema[0]
        if obj is None:
            return None
        else:
            if len(obj) > 0:
                for first_elmt in obj:
                    if _check_non_null_non_empty_recursive(first_elmt, sub_schema):
                        break
                if encode_nested_example(sub_schema, first_elmt) != first_elmt:
                    return [encode_nested_example(sub_schema, o) for o in obj]
            return list(obj)
    elif isinstance(schema, Sequence):
        # We allow to reverse list of dict => dict of list for compatiblity with tfds
        if isinstance(schema.feature, dict):
            # dict of list to fill
            list_dict = {}
            if isinstance(obj, (list, tuple)):
                # obj is a list of dict
                for k, dict_tuples in zip_dict(schema.feature, *obj):
                    list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
                return list_dict
            else:
                # obj is a single dict
                for k, (sub_schema, sub_objs) in zip_dict(schema.feature, obj):
                    list_dict[k] = [encode_nested_example(sub_schema, o) for o in sub_objs]
                return list_dict
        # schema.feature is not a dict
        if isinstance(obj, str):  # don't interpret a string as a list
            raise ValueError(f""Got a string but expected a list instead: '{obj}'"")
        if obj is None:
            return None
        else:
            if len(obj) > 0:
                for first_elmt in obj:
                    if _check_non_null_non_empty_recursive(first_elmt, schema.feature):
                        break
                # be careful when comparing tensors here
                if not isinstance(first_elmt, list) or encode_nested_example(schema.feature, first_elmt) != first_elmt:
                    return [encode_nested_example(schema.feature, o) for o in obj]
            return list(obj)
    # Object with special encoding:
    # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks
    elif isinstance(schema, (Audio, Image, ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):
        return schema.encode_example(obj) if obj is not None else None
    # Other object should be directly convertible to a native Arrow type (like Translation and Translation)
    return obj


        ```
    ###Assistant : Encode a nested example.
    This is used since some features (in particular ClassLabel) have some logic during encoding.

    To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be encoded.
    If the first element needs to be encoded, then all the elements of the list will be encoded, otherwise they'll stay the same.
    
    "
2437,"def _configure_matplotlib(cls):
        
        rcParams[""keymap.fullscreen""] = [k for k in rcParams[""keymap.fullscreen""] if k != ""f""]
        rcParams[""keymap.save""] = [k for k in rcParams[""keymap.save""] if k != ""s""]
        rcParams[""keymap.home""] = [k for k in rcParams[""keymap.home""] if k != ""r""]
        rcParams[""figure.raise_window""] = False
"," Remove `F`, 'S' and 'R' from their default bindings and stop Matplotlib from stealing
        focus ",15,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _configure_matplotlib(cls):
        
        rcParams[""keymap.fullscreen""] = [k for k in rcParams[""keymap.fullscreen""] if k != ""f""]
        rcParams[""keymap.save""] = [k for k in rcParams[""keymap.save""] if k != ""s""]
        rcParams[""keymap.home""] = [k for k in rcParams[""keymap.home""] if k != ""r""]
        rcParams[""figure.raise_window""] = False

        ```
    ###Assistant :  Remove `F`, 'S' and 'R' from their default bindings and stop Matplotlib from stealing
        focus 
    "
2438,"def set_permission_cache(user, key, value):
    
    from django.core.cache import cache

    # store this key, so we can clean it when required
    cache_key = get_cache_key(user, key)
    cache.set(cache_key, value,
              get_cms_setting('CACHE_DURATIONS')['permissions'],
              version=get_cache_permission_version())

","
    Helper method for storing values in cache. Stores used keys so
    all of them can be cleaned when clean_permission_cache gets called.
    ",21,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_permission_cache(user, key, value):
    
    from django.core.cache import cache

    # store this key, so we can clean it when required
    cache_key = get_cache_key(user, key)
    cache.set(cache_key, value,
              get_cms_setting('CACHE_DURATIONS')['permissions'],
              version=get_cache_permission_version())


        ```
    ###Assistant : 
    Helper method for storing values in cache. Stores used keys so
    all of them can be cleaned when clean_permission_cache gets called.
    
    "
2439,"def cookies(self) -> multidict.MultiDictView[str, tuple[str, multidict.MultiDict[str, Optional[str]]]]:
        
        return multidict.MultiDictView(
            self._get_cookies,
            self._set_cookies
        )
","
        The response cookies. A possibly empty `MultiDictView`, where the keys are cookie
        name strings, and values are `(cookie value, attributes)` tuples. Within
        attributes, unary attributes (e.g. `HTTPOnly`) are indicated by a `None` value.
        Modifications to the MultiDictView update `Response.headers`, and vice versa.

        *Warning:* Changes to `attributes` will not be picked up unless you also reassign
        the `(cookie value, attributes)` tuple directly in the `MultiDictView`.
        ",64,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cookies(self) -> multidict.MultiDictView[str, tuple[str, multidict.MultiDict[str, Optional[str]]]]:
        
        return multidict.MultiDictView(
            self._get_cookies,
            self._set_cookies
        )

        ```
    ###Assistant : 
        The response cookies. A possibly empty `MultiDictView`, where the keys are cookie
        name strings, and values are `(cookie value, attributes)` tuples. Within
        attributes, unary attributes (e.g. `HTTPOnly`) are indicated by a `None` value.
        Modifications to the MultiDictView update `Response.headers`, and vice versa.

        *Warning:* Changes to `attributes` will not be picked up unless you also reassign
        the `(cookie value, attributes)` tuple directly in the `MultiDictView`.
        
    "
2440,"def _normalize_entries(entries, separators=None):
	
	norm_files = {}
	for entry in entries:
		norm_files[normalize_file(entry.path, separators=separators)] = entry
	return norm_files

","
	Normalizes the entry paths to use the POSIX path separator.

	*entries* (:class:`~collections.abc.Iterable` of :class:`.TreeEntry`)
	contains the entries to be normalized.

	*separators* (:class:`~collections.abc.Collection` of :class:`str`; or
	:data:`None`) optionally contains the path separators to normalize.
	See :func:`normalize_file` for more information.

	Returns a :class:`dict` mapping the each normalized file path (:class:`str`)
	to the entry (:class:`.TreeEntry`)
	",52,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _normalize_entries(entries, separators=None):
	
	norm_files = {}
	for entry in entries:
		norm_files[normalize_file(entry.path, separators=separators)] = entry
	return norm_files


        ```
    ###Assistant : 
	Normalizes the entry paths to use the POSIX path separator.

	*entries* (:class:`~collections.abc.Iterable` of :class:`.TreeEntry`)
	contains the entries to be normalized.

	*separators* (:class:`~collections.abc.Collection` of :class:`str`; or
	:data:`None`) optionally contains the path separators to normalize.
	See :func:`normalize_file` for more information.

	Returns a :class:`dict` mapping the each normalized file path (:class:`str`)
	to the entry (:class:`.TreeEntry`)
	
    "
2441,"def assertXMLNotEqual(self, xml1, xml2, msg=None):
        
        try:
            result = compare_xml(xml1, xml2)
        except Exception as e:
            standardMsg = ""First or second argument is not valid XML\n%s"" % e
            self.fail(self._formatMessage(msg, standardMsg))
        else:
            if result:
                standardMsg = ""%s == %s"" % (
                    safe_repr(xml1, True),
                    safe_repr(xml2, True),
                )
                self.fail(self._formatMessage(msg, standardMsg))

","
        Assert that two XML snippets are not semantically equivalent.
        Whitespace in most cases is ignored and attribute ordering is not
        significant. The arguments must be valid XML.
        ",27,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def assertXMLNotEqual(self, xml1, xml2, msg=None):
        
        try:
            result = compare_xml(xml1, xml2)
        except Exception as e:
            standardMsg = ""First or second argument is not valid XML\n%s"" % e
            self.fail(self._formatMessage(msg, standardMsg))
        else:
            if result:
                standardMsg = ""%s == %s"" % (
                    safe_repr(xml1, True),
                    safe_repr(xml2, True),
                )
                self.fail(self._formatMessage(msg, standardMsg))


        ```
    ###Assistant : 
        Assert that two XML snippets are not semantically equivalent.
        Whitespace in most cases is ignored and attribute ordering is not
        significant. The arguments must be valid XML.
        
    "
2442,"def token_kwargs(bits, parser, support_legacy=False):
    
    if not bits:
        return {}
    match = kwarg_re.match(bits[0])
    kwarg_format = match and match[1]
    if not kwarg_format:
        if not support_legacy:
            return {}
        if len(bits) < 3 or bits[1] != ""as"":
            return {}

    kwargs = {}
    while bits:
        if kwarg_format:
            match = kwarg_re.match(bits[0])
            if not match or not match[1]:
                return kwargs
            key, value = match.groups()
            del bits[:1]
        else:
            if len(bits) < 3 or bits[1] != ""as"":
                return kwargs
            key, value = bits[2], bits[0]
            del bits[:3]
        kwargs[key] = parser.compile_filter(value)
        if bits and not kwarg_format:
            if bits[0] != ""and"":
                return kwargs
            del bits[:1]
    return kwargs
","
    Parse token keyword arguments and return a dictionary of the arguments
    retrieved from the ``bits`` token list.

    `bits` is a list containing the remainder of the token (split by spaces)
    that is to be checked for arguments. Valid arguments are removed from this
    list.

    `support_legacy` - if True, the legacy format ``1 as foo`` is accepted.
    Otherwise, only the standard ``foo=1`` format is allowed.

    There is no requirement for all remaining token ``bits`` to be keyword
    arguments, so return the dictionary as soon as an invalid argument format
    is reached.
    ",90,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def token_kwargs(bits, parser, support_legacy=False):
    
    if not bits:
        return {}
    match = kwarg_re.match(bits[0])
    kwarg_format = match and match[1]
    if not kwarg_format:
        if not support_legacy:
            return {}
        if len(bits) < 3 or bits[1] != ""as"":
            return {}

    kwargs = {}
    while bits:
        if kwarg_format:
            match = kwarg_re.match(bits[0])
            if not match or not match[1]:
                return kwargs
            key, value = match.groups()
            del bits[:1]
        else:
            if len(bits) < 3 or bits[1] != ""as"":
                return kwargs
            key, value = bits[2], bits[0]
            del bits[:3]
        kwargs[key] = parser.compile_filter(value)
        if bits and not kwarg_format:
            if bits[0] != ""and"":
                return kwargs
            del bits[:1]
    return kwargs

        ```
    ###Assistant : 
    Parse token keyword arguments and return a dictionary of the arguments
    retrieved from the ``bits`` token list.

    `bits` is a list containing the remainder of the token (split by spaces)
    that is to be checked for arguments. Valid arguments are removed from this
    list.

    `support_legacy` - if True, the legacy format ``1 as foo`` is accepted.
    Otherwise, only the standard ``foo=1`` format is allowed.

    There is no requirement for all remaining token ``bits`` to be keyword
    arguments, so return the dictionary as soon as an invalid argument format
    is reached.
    
    "
2443,"def get_cost_of_delayed_shipments(scorecard):
	
	return get_total_cost_of_shipments(scorecard) - get_cost_of_on_time_shipments(scorecard)

",Gets the total cost of all delayed shipments in the period (based on Purchase Receipts - POs),17,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_cost_of_delayed_shipments(scorecard):
	
	return get_total_cost_of_shipments(scorecard) - get_cost_of_on_time_shipments(scorecard)


        ```
    ###Assistant : Gets the total cost of all delayed shipments in the period (based on Purchase Receipts - POs)
    "
2444,"def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
    
    # if weight is specified, apply element-wise weight
    if weight is not None:
        loss = loss * weight

    # if avg_factor is not specified, just reduce the loss
    if avg_factor is None:
        loss = reduce_loss(loss, reduction)
    else:
        # if reduction is mean, then average the loss by avg_factor
        if reduction == 'mean':
            # Avoid causing ZeroDivisionError when avg_factor is 0.0,
            # i.e., all labels of an image belong to ignore index.
            eps = torch.finfo(torch.float32).eps
            loss = loss.sum() / (avg_factor + eps)
        # if reduction is 'none', then do nothing, otherwise raise an error
        elif reduction != 'none':
            raise ValueError('avg_factor can not be used with reduction=""sum""')
    return loss

","Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights.
        reduction (str): Same as built-in losses of PyTorch.
        avg_factor (float): Average factor when computing the mean of losses.

    Returns:
        Tensor: Processed loss values.
    ",38,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
    
    # if weight is specified, apply element-wise weight
    if weight is not None:
        loss = loss * weight

    # if avg_factor is not specified, just reduce the loss
    if avg_factor is None:
        loss = reduce_loss(loss, reduction)
    else:
        # if reduction is mean, then average the loss by avg_factor
        if reduction == 'mean':
            # Avoid causing ZeroDivisionError when avg_factor is 0.0,
            # i.e., all labels of an image belong to ignore index.
            eps = torch.finfo(torch.float32).eps
            loss = loss.sum() / (avg_factor + eps)
        # if reduction is 'none', then do nothing, otherwise raise an error
        elif reduction != 'none':
            raise ValueError('avg_factor can not be used with reduction=""sum""')
    return loss


        ```
    ###Assistant : Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights.
        reduction (str): Same as built-in losses of PyTorch.
        avg_factor (float): Average factor when computing the mean of losses.

    Returns:
        Tensor: Processed loss values.
    
    "
2445,"def test_failure_to_run_iterations():
    
    rnd = np.random.RandomState(0)
    X = rnd.standard_normal((100, 10))
    A = X @ X.T
    Q = rnd.standard_normal((X.shape[0], 4))
    with pytest.warns(UserWarning, match=""Exited at iteration""):
        eigenvalues, _ = lobpcg(A, Q, maxiter=20)
    assert(np.max(eigenvalues) > 0)


@pytest.mark.filterwarnings(""ignore:The problem size"")","Check that the code exists gracefully without breaking. Issue #10974.
    ",10,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_failure_to_run_iterations():
    
    rnd = np.random.RandomState(0)
    X = rnd.standard_normal((100, 10))
    A = X @ X.T
    Q = rnd.standard_normal((X.shape[0], 4))
    with pytest.warns(UserWarning, match=""Exited at iteration""):
        eigenvalues, _ = lobpcg(A, Q, maxiter=20)
    assert(np.max(eigenvalues) > 0)


@pytest.mark.filterwarnings(""ignore:The problem size"")
        ```
    ###Assistant : Check that the code exists gracefully without breaking. Issue #10974.
    
    "
2446,"def test_predictor_tableau_header(self, mock_handler):
        df = pd.DataFrame([
            {'a': 1, 'b': 'one'},
            {'a': 2, 'b': 'two'},
            {'a': 1, 'b': 'three'},
        ])
        self.set_handler(mock_handler, name='pg', tables={'tasks': df})

        # --- use predictor ---
        predicted_value = 5
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical
            },
            'predicted_value': predicted_value
        }
        self.set_predictor(predictor)
        ret = self.command_executor.execute_command(parse_sql(f, dialect='mindsdb'))

        # second column is having last value of 'b'
        # 3: count rows, 4: sum of 'a', 5 max of prediction
        assert ret.data[0] == [3, 4, 5]
","
           SELECT 
              SUM(1) AS `cnt__0B4A4E8BD11C48FFB4730D4D2C32191A_ok`,
              sum(`Custom SQL Query`.`a`) AS `sum_height_ok`,
              max(`Custom SQL Query`.`p`) AS `sum_length1_ok`
            FROM (
              SELECT res.a, res.p 
               FROM pg.tasks as source
               JOIN mindsdb.task_model as res
            ) `Custom SQL Query`
            HAVING (COUNT(1) > 0)
                ",35,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_predictor_tableau_header(self, mock_handler):
        df = pd.DataFrame([
            {'a': 1, 'b': 'one'},
            {'a': 2, 'b': 'two'},
            {'a': 1, 'b': 'three'},
        ])
        self.set_handler(mock_handler, name='pg', tables={'tasks': df})

        # --- use predictor ---
        predicted_value = 5
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical
            },
            'predicted_value': predicted_value
        }
        self.set_predictor(predictor)
        ret = self.command_executor.execute_command(parse_sql(f, dialect='mindsdb'))

        # second column is having last value of 'b'
        # 3: count rows, 4: sum of 'a', 5 max of prediction
        assert ret.data[0] == [3, 4, 5]

        ```
    ###Assistant : 
           SELECT 
              SUM(1) AS `cnt__0B4A4E8BD11C48FFB4730D4D2C32191A_ok`,
              sum(`Custom SQL Query`.`a`) AS `sum_height_ok`,
              max(`Custom SQL Query`.`p`) AS `sum_length1_ok`
            FROM (
              SELECT res.a, res.p 
               FROM pg.tasks as source
               JOIN mindsdb.task_model as res
            ) `Custom SQL Query`
            HAVING (COUNT(1) > 0)
                
    "
2447,"def test_conflicting_specified_basename_different_models(self):
        
        self.router.register(r'notes', NoteViewSet)
        with pytest.raises(ImproperlyConfigured):
            self.router.register(r'notes_basename', BasenameViewSet, basename='routertestmodel')
","
        Ensure 2 routers with different models, and a conflicting basename specified
        throws an exception
        ",14,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_conflicting_specified_basename_different_models(self):
        
        self.router.register(r'notes', NoteViewSet)
        with pytest.raises(ImproperlyConfigured):
            self.router.register(r'notes_basename', BasenameViewSet, basename='routertestmodel')

        ```
    ###Assistant : 
        Ensure 2 routers with different models, and a conflicting basename specified
        throws an exception
        
    "
2448,"def _get_boosted_releases(self) -> BoostedReleases:
        
        boosted_releases = BoostedReleases()
        for boosted_release_cache_key, timestamp in self.redis_client.hgetall(
            self._generate_cache_key_for_boosted_releases_hash()
        ).items():
            extracted_data = self._extract_data_from_cache_key(boosted_release_cache_key)
            if extracted_data:
                release_id, environment = extracted_data
                boosted_releases.add_release(
                    cache_key=boosted_release_cache_key,
                    id=release_id,
                    timestamp=float(timestamp),
                    environment=environment,
                )

        return boosted_releases
","
        Returns all the boosted releases and parses them based on key and value data.

        This method should not be called directly as the boosted releases are not extended, thus they contain only a
        subset of information.
        ",36,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_boosted_releases(self) -> BoostedReleases:
        
        boosted_releases = BoostedReleases()
        for boosted_release_cache_key, timestamp in self.redis_client.hgetall(
            self._generate_cache_key_for_boosted_releases_hash()
        ).items():
            extracted_data = self._extract_data_from_cache_key(boosted_release_cache_key)
            if extracted_data:
                release_id, environment = extracted_data
                boosted_releases.add_release(
                    cache_key=boosted_release_cache_key,
                    id=release_id,
                    timestamp=float(timestamp),
                    environment=environment,
                )

        return boosted_releases

        ```
    ###Assistant : 
        Returns all the boosted releases and parses them based on key and value data.

        This method should not be called directly as the boosted releases are not extended, thus they contain only a
        subset of information.
        
    "
2449,"def store_stats_summary(reply):
    
    store_summary = ""--- Aggregate object store stats across all nodes ---\n""
    # TODO(ekl) it would be nice if we could provide a full memory usage
    # breakdown by type (e.g., pinned by worker, primary, etc.)
    store_summary += (
        ""Plasma memory usage {} MiB, {} objects, {}% full, {}% ""
        ""needed\n"".format(
            int(reply.store_stats.object_store_bytes_used / (1024 * 1024)),
            reply.store_stats.num_local_objects,
            round(
                100
                * reply.store_stats.object_store_bytes_used
                / reply.store_stats.object_store_bytes_avail,
                2,
            ),
            round(
                100
                * reply.store_stats.object_store_bytes_primary_copy
                / reply.store_stats.object_store_bytes_avail,
                2,
            ),
        )
    )
    if reply.store_stats.object_store_bytes_fallback > 0:
        store_summary += ""Plasma filesystem mmap usage: {} MiB\n"".format(
            int(reply.store_stats.object_store_bytes_fallback / (1024 * 1024))
        )
    if reply.store_stats.spill_time_total_s > 0:
        store_summary += (
            ""Spilled {} MiB, {} objects, avg write throughput {} MiB/s\n"".format(
                int(reply.store_stats.spilled_bytes_total / (1024 * 1024)),
                reply.store_stats.spilled_objects_total,
                int(
                    reply.store_stats.spilled_bytes_total
                    / (1024 * 1024)
                    / reply.store_stats.spill_time_total_s
                ),
            )
        )
    if reply.store_stats.restore_time_total_s > 0:
        store_summary += (
            ""Restored {} MiB, {} objects, avg read throughput {} MiB/s\n"".format(
                int(reply.store_stats.restored_bytes_total / (1024 * 1024)),
                reply.store_stats.restored_objects_total,
                int(
                    reply.store_stats.restored_bytes_total
                    / (1024 * 1024)
                    / reply.store_stats.restore_time_total_s
                ),
            )
        )
    if reply.store_stats.consumed_bytes > 0:
        store_summary += ""Objects consumed by Ray tasks: {} MiB.\n"".format(
            int(reply.store_stats.consumed_bytes / (1024 * 1024))
        )
    if reply.store_stats.object_pulls_queued:
        store_summary += ""Object fetches queued, waiting for available memory.""

    return store_summary

",Returns formatted string describing object store stats in all nodes.,10,194,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def store_stats_summary(reply):
    
    store_summary = ""--- Aggregate object store stats across all nodes ---\n""
    # TODO(ekl) it would be nice if we could provide a full memory usage
    # breakdown by type (e.g., pinned by worker, primary, etc.)
    store_summary += (
        ""Plasma memory usage {} MiB, {} objects, {}% full, {}% ""
        ""needed\n"".format(
            int(reply.store_stats.object_store_bytes_used / (1024 * 1024)),
            reply.store_stats.num_local_objects,
            round(
                100
                * reply.store_stats.object_store_bytes_used
                / reply.store_stats.object_store_bytes_avail,
                2,
            ),
            round(
                100
                * reply.store_stats.object_store_bytes_primary_copy
                / reply.store_stats.object_store_bytes_avail,
                2,
            ),
        )
    )
    if reply.store_stats.object_store_bytes_fallback > 0:
        store_summary += ""Plasma filesystem mmap usage: {} MiB\n"".format(
            int(reply.store_stats.object_store_bytes_fallback / (1024 * 1024))
        )
    if reply.store_stats.spill_time_total_s > 0:
        store_summary += (
            ""Spilled {} MiB, {} objects, avg write throughput {} MiB/s\n"".format(
                int(reply.store_stats.spilled_bytes_total / (1024 * 1024)),
                reply.store_stats.spilled_objects_total,
                int(
                    reply.store_stats.spilled_bytes_total
                    / (1024 * 1024)
                    / reply.store_stats.spill_time_total_s
                ),
            )
        )
    if reply.store_stats.restore_time_total_s > 0:
        store_summary += (
            ""Restored {} MiB, {} objects, avg read throughput {} MiB/s\n"".format(
                int(reply.store_stats.restored_bytes_total / (1024 * 1024)),
                reply.store_stats.restored_objects_total,
                int(
                    reply.store_stats.restored_bytes_total
                    / (1024 * 1024)
                    / reply.store_stats.restore_time_total_s
                ),
            )
        )
    if reply.store_stats.consumed_bytes > 0:
        store_summary += ""Objects consumed by Ray tasks: {} MiB.\n"".format(
            int(reply.store_stats.consumed_bytes / (1024 * 1024))
        )
    if reply.store_stats.object_pulls_queued:
        store_summary += ""Object fetches queued, waiting for available memory.""

    return store_summary


        ```
    ###Assistant : Returns formatted string describing object store stats in all nodes.
    "
2450,"def test_spam_checker_deny(self) -> None:
        
        self.get_failure(self.handler.register_user(localpart=""user""), SynapseError)
","A spam checker can deny registration, which results in an error.",11,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_spam_checker_deny(self) -> None:
        
        self.get_failure(self.handler.register_user(localpart=""user""), SynapseError)

        ```
    ###Assistant : A spam checker can deny registration, which results in an error.
    "
2451,"def _get_pitch_yaw_roll(self) -> None:
        
        proj_matrix = np.zeros((3, 4), dtype=""float32"")
        proj_matrix[:3, :3] = cv2.Rodrigues(self._rotation)[0]
        euler = cv2.decomposeProjectionMatrix(proj_matrix)[-1]
        self._pitch_yaw_roll = cast(Tuple[float, float, float], tuple(euler.squeeze()))
        logger.trace(""yaw_pitch: %s"", self._pitch_yaw_roll)  # type: ignore
"," Obtain the yaw, roll and pitch from the :attr:`_rotation` in eular angles. ",12,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_pitch_yaw_roll(self) -> None:
        
        proj_matrix = np.zeros((3, 4), dtype=""float32"")
        proj_matrix[:3, :3] = cv2.Rodrigues(self._rotation)[0]
        euler = cv2.decomposeProjectionMatrix(proj_matrix)[-1]
        self._pitch_yaw_roll = cast(Tuple[float, float, float], tuple(euler.squeeze()))
        logger.trace(""yaw_pitch: %s"", self._pitch_yaw_roll)  # type: ignore

        ```
    ###Assistant :  Obtain the yaw, roll and pitch from the :attr:`_rotation` in eular angles. 
    "
2452,"def aiohttp_notify_servers_mock() -> Iterable[Mock]:
    
    with patch(
        ""homeassistant.components.dlna_dmr.data.AiohttpNotifyServer""
    ) as mock_constructor:
        servers = []
","Construct mock AiohttpNotifyServer on demand, eliminating network use.

    This fixture provides a list of the constructed servers.
    ",17,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def aiohttp_notify_servers_mock() -> Iterable[Mock]:
    
    with patch(
        ""homeassistant.components.dlna_dmr.data.AiohttpNotifyServer""
    ) as mock_constructor:
        servers = []

        ```
    ###Assistant : Construct mock AiohttpNotifyServer on demand, eliminating network use.

    This fixture provides a list of the constructed servers.
    
    "
2453,"def line_collection_2d_to_3d(col, zs=0, zdir='z'):
    
    segments3d = _paths_to_3d_segments(col.get_paths(), zs, zdir)
    col.__class__ = Line3DCollection
    col.set_segments(segments3d)

",Convert a `.LineCollection` to a `.Line3DCollection` object.,7,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def line_collection_2d_to_3d(col, zs=0, zdir='z'):
    
    segments3d = _paths_to_3d_segments(col.get_paths(), zs, zdir)
    col.__class__ = Line3DCollection
    col.set_segments(segments3d)


        ```
    ###Assistant : Convert a `.LineCollection` to a `.Line3DCollection` object.
    "
2454,"def execute():
    
    frappe.reload_doc(""e_commerce"", ""web_template"", ""item_card_group"")

    blocks = frappe.db.get_all(
        ""Web Page Block"",
        filters={""web_template"": ""Item Card Group""},
        fields=[""parent"", ""web_template_values"", ""name""]
    )

    fields = generate_fields_to_edit()

    for block in blocks:
        web_template_value = json.loads(block.get('web_template_values'))

        for field in fields:
            item = web_template_value.get(field)
            if not item:
                continue

            if frappe.db.exists(""Website Item"", {""item_code"": item}):
                website_item = frappe.db.get_value(""Website Item"", {""item_code"": item})
            else:
                website_item = make_new_website_item(item, web_template_value, field)
                continue

            if website_item:
                web_template_value[field] = website_item

        frappe.db.set_value(""Web Page Block"", block.name, ""web_template_values"", json.dumps(web_template_value))
","
        Convert all Item links to Website Item link values in
        exisitng 'Item Card Group' Web Page Block data.
    ",18,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def execute():
    
    frappe.reload_doc(""e_commerce"", ""web_template"", ""item_card_group"")

    blocks = frappe.db.get_all(
        ""Web Page Block"",
        filters={""web_template"": ""Item Card Group""},
        fields=[""parent"", ""web_template_values"", ""name""]
    )

    fields = generate_fields_to_edit()

    for block in blocks:
        web_template_value = json.loads(block.get('web_template_values'))

        for field in fields:
            item = web_template_value.get(field)
            if not item:
                continue

            if frappe.db.exists(""Website Item"", {""item_code"": item}):
                website_item = frappe.db.get_value(""Website Item"", {""item_code"": item})
            else:
                website_item = make_new_website_item(item, web_template_value, field)
                continue

            if website_item:
                web_template_value[field] = website_item

        frappe.db.set_value(""Web Page Block"", block.name, ""web_template_values"", json.dumps(web_template_value))

        ```
    ###Assistant : 
        Convert all Item links to Website Item link values in
        exisitng 'Item Card Group' Web Page Block data.
    
    "
2455,"def set_pickradius(self, pickradius):
        
        if not isinstance(pickradius, Number) or pickradius < 0:
            raise ValueError(""pick radius should be a distance"")
        self._pickradius = pickradius

    pickradius = property(get_pickradius, set_pickradius)
","
        Set the pick radius used for containment tests.

        See `.contains` for more details.

        Parameters
        ----------
        pickradius : float
            Pick radius, in points.
        ",22,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_pickradius(self, pickradius):
        
        if not isinstance(pickradius, Number) or pickradius < 0:
            raise ValueError(""pick radius should be a distance"")
        self._pickradius = pickradius

    pickradius = property(get_pickradius, set_pickradius)

        ```
    ###Assistant : 
        Set the pick radius used for containment tests.

        See `.contains` for more details.

        Parameters
        ----------
        pickradius : float
            Pick radius, in points.
        
    "
2456,"def _predict(self):
        
        with self._lock:
            self._predicted_images = []
            for frame in self._input_images:
                self._predictor.in_queue.put(frame)
            idx = 0
            while idx < self._sample_size:
                logger.debug(""Predicting face %s of %s"", idx + 1, self._sample_size)
                items = self._predictor.out_queue.get()
                if items == ""EOF"":
                    logger.debug(""Received EOF"")
                    break
                for item in items:
                    self._predicted_images.append(item)
                    logger.debug(""Predicted face %s of %s"", idx + 1, self._sample_size)
                    idx += 1
        logger.debug(""Predicted faces"")

"," Predict from the loaded frames.

        With a threading lock (to prevent stacking), run the selected faces through the Faceswap
        model predict function and add the output to :attr:`predicted`
        ",28,57,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _predict(self):
        
        with self._lock:
            self._predicted_images = []
            for frame in self._input_images:
                self._predictor.in_queue.put(frame)
            idx = 0
            while idx < self._sample_size:
                logger.debug(""Predicting face %s of %s"", idx + 1, self._sample_size)
                items = self._predictor.out_queue.get()
                if items == ""EOF"":
                    logger.debug(""Received EOF"")
                    break
                for item in items:
                    self._predicted_images.append(item)
                    logger.debug(""Predicted face %s of %s"", idx + 1, self._sample_size)
                    idx += 1
        logger.debug(""Predicted faces"")


        ```
    ###Assistant :  Predict from the loaded frames.

        With a threading lock (to prevent stacking), run the selected faces through the Faceswap
        model predict function and add the output to :attr:`predicted`
        
    "
2457,"def exclude(f):
        
        J, new = f.rep.exclude()
        gens = [gen for j, gen in enumerate(f.gens) if j not in J]

        return f.per(new, gens=gens)
","
        Remove unnecessary generators from ``f``.

        Examples
        ========

        >>> from sympy import Poly
        >>> from sympy.abc import a, b, c, d, x

        >>> Poly(a + x, a, b, c, d, x).exclude()
        Poly(a + x, a, x, domain='ZZ')

        ",36,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def exclude(f):
        
        J, new = f.rep.exclude()
        gens = [gen for j, gen in enumerate(f.gens) if j not in J]

        return f.per(new, gens=gens)

        ```
    ###Assistant : 
        Remove unnecessary generators from ``f``.

        Examples
        ========

        >>> from sympy import Poly
        >>> from sympy.abc import a, b, c, d, x

        >>> Poly(a + x, a, b, c, d, x).exclude()
        Poly(a + x, a, x, domain='ZZ')

        
    "
2458,"def set_ticks(self, ticks, labels=None, *, minor=False, **kwargs):
        
        result = self._set_tick_locations(ticks, minor=minor)
        if labels is not None:
            self.set_ticklabels(labels, minor=minor, **kwargs)
        return result
","
        Set this Axis' tick locations and optionally labels.

        If necessary, the view limits of the Axis are expanded so that all
        given ticks are visible.

        Parameters
        ----------
        ticks : list of floats
            List of tick locations.  The axis `.Locator` is replaced by a
            `~.ticker.FixedLocator`.

            Some tick formatters will not label arbitrary tick positions;
            e.g. log formatters only label decade ticks by default. In
            such a case you can set a formatter explicitly on the axis
            using `.Axis.set_major_formatter` or provide formatted
            *labels* yourself.
        labels : list of str, optional
            List of tick labels. If not set, the labels are generated with
            the axis tick `.Formatter`.
        minor : bool, default: False
            If ``False``, set the major ticks; if ``True``, the minor ticks.
        **kwargs
            `.Text` properties for the labels. These take effect only if you
            pass *labels*. In other cases, please use `~.Axes.tick_params`.

        Notes
        -----
        The mandatory expansion of the view limits is an intentional design
        choice to prevent the surprise of a non-visible tick. If you need
        other limits, you should set the limits explicitly after setting the
        ticks.
        ",177,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_ticks(self, ticks, labels=None, *, minor=False, **kwargs):
        
        result = self._set_tick_locations(ticks, minor=minor)
        if labels is not None:
            self.set_ticklabels(labels, minor=minor, **kwargs)
        return result

        ```
    ###Assistant : 
        Set this Axis' tick locations and optionally labels.

        If necessary, the view limits of the Axis are expanded so that all
        given ticks are visible.

        Parameters
        ----------
        ticks : list of floats
            List of tick locations.  The axis `.Locator` is replaced by a
            `~.ticker.FixedLocator`.

            Some tick formatters will not label arbitrary tick positions;
            e.g. log formatters only label decade ticks by default. In
            such a case you can set a formatter explicitly on the axis
            using `.Axis.set_major_formatter` or provide formatted
            *labels* yourself.
        labels : list of str, optional
            List of tick labels. If not set, the labels are generated with
            the axis tick `.Formatter`.
        minor : bool, default: False
            If ``False``, set the major ticks; if ``True``, the minor ticks.
        **kwargs
            `.Text` properties for the labels. These take effect only if you
            pass *labels*. In other cases, please use `~.Axes.tick_params`.

        Notes
        -----
        The mandatory expansion of the view limits is an intentional design
        choice to prevent the surprise of a non-visible tick. If you need
        other limits, you should set the limits explicitly after setting the
        ticks.
        
    "
2459,"def get_all_customers(date_range, company, field, limit=None):
	if field == ""outstanding_amount"":
		filters = [[""docstatus"", ""="", ""1""], [""company"", ""="", company]]
		if date_range:
			date_range = frappe.parse_json(date_range)
			filters.append([""posting_date"", "">="", ""between"", [date_range[0], date_range[1]]])
		return frappe.db.get_all(
			""Sales Invoice"",
			fields=[""customer as name"", ""sum(outstanding_amount) as value""],
			filters=filters,
			group_by=""customer"",
			order_by=""value desc"",
			limit=limit,
		)
	else:
		if field == ""total_sales_amount"":
			select_field = ""sum(so_item.base_net_amount)""
		elif field == ""total_qty_sold"":
			select_field = ""sum(so_item.stock_qty)""

		date_condition = get_date_condition(date_range, ""so.transaction_date"")

		return frappe.db.sql(
			.format(
				select_field, date_condition
			),
			(company, cint(limit)),
			as_dict=1,
		)


@frappe.whitelist()","
			select so.customer as name, {0} as value
			FROM `tabSales Order` as so JOIN `tabSales Order Item` as so_item
				ON so.name = so_item.parent
			where so.docstatus = 1 {1} and so.company = %s
			group by so.customer
			order by value DESC
			limit %s
		",40,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all_customers(date_range, company, field, limit=None):
	if field == ""outstanding_amount"":
		filters = [[""docstatus"", ""="", ""1""], [""company"", ""="", company]]
		if date_range:
			date_range = frappe.parse_json(date_range)
			filters.append([""posting_date"", "">="", ""between"", [date_range[0], date_range[1]]])
		return frappe.db.get_all(
			""Sales Invoice"",
			fields=[""customer as name"", ""sum(outstanding_amount) as value""],
			filters=filters,
			group_by=""customer"",
			order_by=""value desc"",
			limit=limit,
		)
	else:
		if field == ""total_sales_amount"":
			select_field = ""sum(so_item.base_net_amount)""
		elif field == ""total_qty_sold"":
			select_field = ""sum(so_item.stock_qty)""

		date_condition = get_date_condition(date_range, ""so.transaction_date"")

		return frappe.db.sql(
			.format(
				select_field, date_condition
			),
			(company, cint(limit)),
			as_dict=1,
		)


@frappe.whitelist()
        ```
    ###Assistant : 
			select so.customer as name, {0} as value
			FROM `tabSales Order` as so JOIN `tabSales Order Item` as so_item
				ON so.name = so_item.parent
			where so.docstatus = 1 {1} and so.company = %s
			group by so.customer
			order by value DESC
			limit %s
		
    "
2460,"async def _async_create_radio_entity(self) -> FlowResult:
        
        assert self._title is not None
        assert self._radio_type is not None
        assert self._device_path is not None
        assert self._device_settings is not None

        device_settings = self._device_settings.copy()
        device_settings[CONF_DEVICE_PATH] = await self.hass.async_add_executor_job(
            usb.get_serial_by_id, self._device_path
        )

        return self.async_create_entry(
            title=self._title,
            data={
                CONF_DEVICE: device_settings,
                CONF_RADIO_TYPE: self._radio_type.name,
            },
        )
",Create a config entity with the current flow state.,9,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _async_create_radio_entity(self) -> FlowResult:
        
        assert self._title is not None
        assert self._radio_type is not None
        assert self._device_path is not None
        assert self._device_settings is not None

        device_settings = self._device_settings.copy()
        device_settings[CONF_DEVICE_PATH] = await self.hass.async_add_executor_job(
            usb.get_serial_by_id, self._device_path
        )

        return self.async_create_entry(
            title=self._title,
            data={
                CONF_DEVICE: device_settings,
                CONF_RADIO_TYPE: self._radio_type.name,
            },
        )

        ```
    ###Assistant : Create a config entity with the current flow state.
    "
2461,"def publish_daemon(self, publish_payload, *args, **kwargs):
        
        context = zmq.Context(1)
        ioloop = salt.ext.tornado.ioloop.IOLoop()
        ioloop.make_current()
        # Set up the context","
        Bind to the interface specified in the configuration file
        ",9,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def publish_daemon(self, publish_payload, *args, **kwargs):
        
        context = zmq.Context(1)
        ioloop = salt.ext.tornado.ioloop.IOLoop()
        ioloop.make_current()
        # Set up the context
        ```
    ###Assistant : 
        Bind to the interface specified in the configuration file
        
    "
2462,"def _print_Pow(self, expr, rational=False):
        
        PREC = precedence(expr)

        if expr.exp is S.Half and not rational:
            return ""sqrt(%s)"" % self._print(expr.base)

        if expr.is_commutative:
            if -expr.exp is S.Half and not rational:
                # Note: Don't test ""expr.exp == -S.Half"" here, because that will
                # match -0.5, which we don't want.
                return ""%s/sqrt(%s)"" % tuple(map(lambda arg: self._print(arg), (S.One, expr.base)))
            if expr.exp is -S.One:
                # Similarly to the S.Half case, don't test with ""=="" here.
                return '%s/%s' % (self._print(S.One),
                                  self.parenthesize(expr.base, PREC, strict=False))

        e = self.parenthesize(expr.exp, PREC, strict=False)
        if self.printmethod == '_sympyrepr' and expr.exp.is_Rational and expr.exp.q != 1:
            # the parenthesized exp should be '(Rational(a, b))' so strip parens,
            # but just check to be sure.
            if e.startswith('(Rational'):
                return '%s**%s' % (self.parenthesize(expr.base, PREC, strict=False), e[1:-1])
        return '%s**%s' % (self.parenthesize(expr.base, PREC, strict=False), e)
","Printing helper function for ``Pow``

        Parameters
        ==========

        rational : bool, optional
            If ``True``, it will not attempt printing ``sqrt(x)`` or
            ``x**S.Half`` as ``sqrt``, and will use ``x**(1/2)``
            instead.

            See examples for additional details

        Examples
        ========

        >>> from sympy import sqrt, StrPrinter
        >>> from sympy.abc import x

        How ``rational`` keyword works with ``sqrt``:

        >>> printer = StrPrinter()
        >>> printer._print_Pow(sqrt(x), rational=True)
        'x**(1/2)'
        >>> printer._print_Pow(sqrt(x), rational=False)
        'sqrt(x)'
        >>> printer._print_Pow(1/sqrt(x), rational=True)
        'x**(-1/2)'
        >>> printer._print_Pow(1/sqrt(x), rational=False)
        '1/sqrt(x)'

        Notes
        =====

        ``sqrt(x)`` is canonicalized as ``Pow(x, S.Half)`` in SymPy,
        so there is no need of defining a separate printer for ``sqrt``.
        Instead, it should be handled here as well.
        ",102,124,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _print_Pow(self, expr, rational=False):
        
        PREC = precedence(expr)

        if expr.exp is S.Half and not rational:
            return ""sqrt(%s)"" % self._print(expr.base)

        if expr.is_commutative:
            if -expr.exp is S.Half and not rational:
                # Note: Don't test ""expr.exp == -S.Half"" here, because that will
                # match -0.5, which we don't want.
                return ""%s/sqrt(%s)"" % tuple(map(lambda arg: self._print(arg), (S.One, expr.base)))
            if expr.exp is -S.One:
                # Similarly to the S.Half case, don't test with ""=="" here.
                return '%s/%s' % (self._print(S.One),
                                  self.parenthesize(expr.base, PREC, strict=False))

        e = self.parenthesize(expr.exp, PREC, strict=False)
        if self.printmethod == '_sympyrepr' and expr.exp.is_Rational and expr.exp.q != 1:
            # the parenthesized exp should be '(Rational(a, b))' so strip parens,
            # but just check to be sure.
            if e.startswith('(Rational'):
                return '%s**%s' % (self.parenthesize(expr.base, PREC, strict=False), e[1:-1])
        return '%s**%s' % (self.parenthesize(expr.base, PREC, strict=False), e)

        ```
    ###Assistant : Printing helper function for ``Pow``

        Parameters
        ==========

        rational : bool, optional
            If ``True``, it will not attempt printing ``sqrt(x)`` or
            ``x**S.Half`` as ``sqrt``, and will use ``x**(1/2)``
            instead.

            See examples for additional details

        Examples
        ========

        >>> from sympy import sqrt, StrPrinter
        >>> from sympy.abc import x

        How ``rational`` keyword works with ``sqrt``:

        >>> printer = StrPrinter()
        >>> printer._print_Pow(sqrt(x), rational=True)
        'x**(1/2)'
        >>> printer._print_Pow(sqrt(x), rational=False)
        'sqrt(x)'
        >>> printer._print_Pow(1/sqrt(x), rational=True)
        'x**(-1/2)'
        >>> printer._print_Pow(1/sqrt(x), rational=False)
        '1/sqrt(x)'

        Notes
        =====

        ``sqrt(x)`` is canonicalized as ``Pow(x, S.Half)`` in SymPy,
        so there is no need of defining a separate printer for ``sqrt``.
        Instead, it should be handled here as well.
        
    "
2463,"def _save_tab(self, tab, active, minimal=False):
        
        data: _JsonType = {'history': []}
        if active:
            data['active'] = True

        if minimal:
            history = [tab.history.current_item()]
        else:
            history = tab.history

        for idx, item in enumerate(history):
            qtutils.ensure_valid(item)
            item_data = self._save_tab_item(tab, idx, item)
            if item.url().scheme() == 'qute' and item.url().host() == 'back':
                # don't add qute://back to the session file
                if item_data.get('active', False) and data['history']:
                    # mark entry before qute://back as active
                    data['history'][-1]['active'] = True
            else:
                data['history'].append(item_data)
        return data
","Get a dict with data for a single tab.

        Args:
            tab: The WebView to save.
            active: Whether the tab is currently active.
        ",22,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _save_tab(self, tab, active, minimal=False):
        
        data: _JsonType = {'history': []}
        if active:
            data['active'] = True

        if minimal:
            history = [tab.history.current_item()]
        else:
            history = tab.history

        for idx, item in enumerate(history):
            qtutils.ensure_valid(item)
            item_data = self._save_tab_item(tab, idx, item)
            if item.url().scheme() == 'qute' and item.url().host() == 'back':
                # don't add qute://back to the session file
                if item_data.get('active', False) and data['history']:
                    # mark entry before qute://back as active
                    data['history'][-1]['active'] = True
            else:
                data['history'].append(item_data)
        return data

        ```
    ###Assistant : Get a dict with data for a single tab.

        Args:
            tab: The WebView to save.
            active: Whether the tab is currently active.
        
    "
2464,"def testBestCheckpoints(self):
        
        keep_checkpoints_num = 4
        checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num)
        checkpoints = [
            Checkpoint(Checkpoint.PERSISTENT, i, self.mock_result(i)) for i in range(16)
        ]
        random.shuffle(checkpoints)

        for checkpoint in checkpoints:
            checkpoint_manager.on_checkpoint(checkpoint)

        best_checkpoints = checkpoint_manager.best_checkpoints()
        self.assertEqual(len(best_checkpoints), keep_checkpoints_num)
        for i in range(len(best_checkpoints)):
            self.assertEqual(best_checkpoints[i].value, i + 12)
","
        Tests that the best checkpoints are tracked and ordered correctly.
        ",10,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def testBestCheckpoints(self):
        
        keep_checkpoints_num = 4
        checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num)
        checkpoints = [
            Checkpoint(Checkpoint.PERSISTENT, i, self.mock_result(i)) for i in range(16)
        ]
        random.shuffle(checkpoints)

        for checkpoint in checkpoints:
            checkpoint_manager.on_checkpoint(checkpoint)

        best_checkpoints = checkpoint_manager.best_checkpoints()
        self.assertEqual(len(best_checkpoints), keep_checkpoints_num)
        for i in range(len(best_checkpoints)):
            self.assertEqual(best_checkpoints[i].value, i + 12)

        ```
    ###Assistant : 
        Tests that the best checkpoints are tracked and ordered correctly.
        
    "
2465,"def save(self):
        
        s = self._read_from_storage()  # type: _Settings

        for k, v in self.__dict__.items():
            if k[0] == '_':
                continue
            if hasattr(s, k):
                setattr(s, k, v)

        log.debug(""_ConfigSQL updating storage"")
        self._session.merge(s)
        try:
            self._session.commit()
        except OperationalError as e:
            log.error('Database error: %s', e)
            self._session.rollback()
        self.load()
",Apply all configuration values to the underlying storage.,8,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save(self):
        
        s = self._read_from_storage()  # type: _Settings

        for k, v in self.__dict__.items():
            if k[0] == '_':
                continue
            if hasattr(s, k):
                setattr(s, k, v)

        log.debug(""_ConfigSQL updating storage"")
        self._session.merge(s)
        try:
            self._session.commit()
        except OperationalError as e:
            log.error('Database error: %s', e)
            self._session.rollback()
        self.load()

        ```
    ###Assistant : Apply all configuration values to the underlying storage.
    "
2466,"def test_transactions(self):
        prev_hour = timezone.now() - timedelta(hours=1)
        event = self.transaction_data.copy()
        event.update(
            {
                ""start_timestamp"": iso_format(prev_hour - timedelta(minutes=1)),
                ""timestamp"": iso_format(prev_hour),
                ""tags"": {""foo"": ""bar""},
                ""transaction"": ""this is where a transaction's 'message' is stored"",
            }
        )
        transaction = self.store_event(project_id=self.project.id, data=event)

        perf_issue = transaction.groups[0]
        perf_issue.update(first_seen=prev_hour)
        Activity.objects.create(
            project=self.project,
            group=perf_issue,
            type=ActivityType.SET_REGRESSION.value,
            datetime=prev_hour,
            data={""event_id"": transaction.event_id},
        )
        conditions = [{""id"": ""sentry.rules.conditions.regression_event.RegressionEventCondition""}]
        filters = [
            {
                ""id"": ""sentry.rules.filters.tagged_event.TaggedEventFilter"",
                ""key"": ""foo"",
                ""match"": ""eq"",
                ""value"": ""bar"",
            }
        ]
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id in result

        filters[0][""value""] = ""baz""
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id not in result

        filters = [
            {
                ""id"": ""sentry.rules.filters.event_attribute.EventAttributeFilter"",
                ""attribute"": ""message"",
                ""match"": ""eq"",
                ""value"": ""this is where a transaction's 'message' is stored"",
            }
        ]
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id in result

        filters[0][""value""] = ""wrong message""
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id not in result
        # this can be tested when SNS-1891 is fixed
        
","
        conditions = [{""id"": ""sentry.rules.conditions.first_seen_event.FirstSeenEventCondition""}]
        filters = [{
            ""id"": ""sentry.rules.filters.tagged_event.TaggedEventFilter"",
            ""key"": ""foo"",
            ""match"": ""eq"",
            ""value"": ""bar"",
        }]
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id in result
        ",28,153,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_transactions(self):
        prev_hour = timezone.now() - timedelta(hours=1)
        event = self.transaction_data.copy()
        event.update(
            {
                ""start_timestamp"": iso_format(prev_hour - timedelta(minutes=1)),
                ""timestamp"": iso_format(prev_hour),
                ""tags"": {""foo"": ""bar""},
                ""transaction"": ""this is where a transaction's 'message' is stored"",
            }
        )
        transaction = self.store_event(project_id=self.project.id, data=event)

        perf_issue = transaction.groups[0]
        perf_issue.update(first_seen=prev_hour)
        Activity.objects.create(
            project=self.project,
            group=perf_issue,
            type=ActivityType.SET_REGRESSION.value,
            datetime=prev_hour,
            data={""event_id"": transaction.event_id},
        )
        conditions = [{""id"": ""sentry.rules.conditions.regression_event.RegressionEventCondition""}]
        filters = [
            {
                ""id"": ""sentry.rules.filters.tagged_event.TaggedEventFilter"",
                ""key"": ""foo"",
                ""match"": ""eq"",
                ""value"": ""bar"",
            }
        ]
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id in result

        filters[0][""value""] = ""baz""
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id not in result

        filters = [
            {
                ""id"": ""sentry.rules.filters.event_attribute.EventAttributeFilter"",
                ""attribute"": ""message"",
                ""match"": ""eq"",
                ""value"": ""this is where a transaction's 'message' is stored"",
            }
        ]
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id in result

        filters[0][""value""] = ""wrong message""
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id not in result
        # this can be tested when SNS-1891 is fixed
        

        ```
    ###Assistant : 
        conditions = [{""id"": ""sentry.rules.conditions.first_seen_event.FirstSeenEventCondition""}]
        filters = [{
            ""id"": ""sentry.rules.filters.tagged_event.TaggedEventFilter"",
            ""key"": ""foo"",
            ""match"": ""eq"",
            ""value"": ""bar"",
        }]
        result = preview(self.project, conditions, filters, ""all"", ""all"", 0)
        assert perf_issue.id in result
        
    "
2467,"def test_follows_semver_all_releases_semver_and_missing_package_semver_release_version(self):
        
        assert (
            follows_semver_versioning_scheme(
                org_id=self.org.id, project_id=self.proj_1.id, release_version=""2.0.0""
            )
            is False
        )
","
        Test that ensures that even if a project is following semver, then if the release_version
        supplied lacks a package, then for that specific release we opt the project out of being
        considered a semver project
        ",35,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_follows_semver_all_releases_semver_and_missing_package_semver_release_version(self):
        
        assert (
            follows_semver_versioning_scheme(
                org_id=self.org.id, project_id=self.proj_1.id, release_version=""2.0.0""
            )
            is False
        )

        ```
    ###Assistant : 
        Test that ensures that even if a project is following semver, then if the release_version
        supplied lacks a package, then for that specific release we opt the project out of being
        considered a semver project
        
    "
2468,"def MultivariateT(syms, mu, sigma, v):
    
    return multivariate_rv(MultivariateTDistribution, syms, mu, sigma, v)


#-------------------------------------------------------------------------------
# Multivariate Normal Gamma distribution ---------------------------------------
","
    Creates a joint random variable with multivariate T-distribution.

    Parameters
    ==========

    syms : A symbol/str
        For identifying the random variable.
    mu : A list/matrix
        Representing the location vector
    sigma : The shape matrix for the distribution

    Examples
    ========

    >>> from sympy.stats import density, MultivariateT
    >>> from sympy import Symbol

    >>> x = Symbol(""x"")
    >>> X = MultivariateT(""x"", [1, 1], [[1, 0], [0, 1]], 2)

    >>> density(X)(1, 2)
    2/(9*pi)

    Returns
    =======

    RandomSymbol

    ",70,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def MultivariateT(syms, mu, sigma, v):
    
    return multivariate_rv(MultivariateTDistribution, syms, mu, sigma, v)


#-------------------------------------------------------------------------------
# Multivariate Normal Gamma distribution ---------------------------------------

        ```
    ###Assistant : 
    Creates a joint random variable with multivariate T-distribution.

    Parameters
    ==========

    syms : A symbol/str
        For identifying the random variable.
    mu : A list/matrix
        Representing the location vector
    sigma : The shape matrix for the distribution

    Examples
    ========

    >>> from sympy.stats import density, MultivariateT
    >>> from sympy import Symbol

    >>> x = Symbol(""x"")
    >>> X = MultivariateT(""x"", [1, 1], [[1, 0], [0, 1]], 2)

    >>> density(X)(1, 2)
    2/(9*pi)

    Returns
    =======

    RandomSymbol

    
    "
2469,"def _can_hold_identifiers_and_holds_name(self, name) -> bool:
        
        if self.is_object() or is_string_dtype(self.dtype) or self.is_categorical():
            return name in self
        return False
","
        Faster check for ``name in self`` when we know `name` is a Python
        identifier (e.g. in NDFrame.__getattr__, which hits this to support
        . key lookup). For indexes that can't hold identifiers (everything
        but object & categorical) we just return False.

        https://github.com/pandas-dev/pandas/issues/19764
        ",41,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _can_hold_identifiers_and_holds_name(self, name) -> bool:
        
        if self.is_object() or is_string_dtype(self.dtype) or self.is_categorical():
            return name in self
        return False

        ```
    ###Assistant : 
        Faster check for ``name in self`` when we know `name` is a Python
        identifier (e.g. in NDFrame.__getattr__, which hits this to support
        . key lookup). For indexes that can't hold identifiers (everything
        but object & categorical) we just return False.

        https://github.com/pandas-dev/pandas/issues/19764
        
    "
2470,"def test_remove_other_alias(self) -> None:
        
        # Create a second alias.
        other_test_alias = ""#test2:test""
        other_room_alias = self._add_alias(other_test_alias)

        # Set the alias as the canonical alias for this room.
        self._set_canonical_alias(
            {
                ""alias"": self.test_alias,
                ""alt_aliases"": [self.test_alias, other_test_alias],
            }
        )

        data = self._get_canonical_alias()
        self.assertEqual(data[""content""][""alias""], self.test_alias)
        self.assertEqual(
            data[""content""][""alt_aliases""], [self.test_alias, other_test_alias]
        )

        # Delete the second alias.
        self.get_success(
            self.handler.delete_association(
                create_requester(self.admin_user), other_room_alias
            )
        )

        data = self._get_canonical_alias()
        self.assertEqual(data[""content""][""alias""], self.test_alias)
        self.assertEqual(data[""content""][""alt_aliases""], [self.test_alias])

",Removing an alias listed as in alt_aliases should remove it there too.,12,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_remove_other_alias(self) -> None:
        
        # Create a second alias.
        other_test_alias = ""#test2:test""
        other_room_alias = self._add_alias(other_test_alias)

        # Set the alias as the canonical alias for this room.
        self._set_canonical_alias(
            {
                ""alias"": self.test_alias,
                ""alt_aliases"": [self.test_alias, other_test_alias],
            }
        )

        data = self._get_canonical_alias()
        self.assertEqual(data[""content""][""alias""], self.test_alias)
        self.assertEqual(
            data[""content""][""alt_aliases""], [self.test_alias, other_test_alias]
        )

        # Delete the second alias.
        self.get_success(
            self.handler.delete_association(
                create_requester(self.admin_user), other_room_alias
            )
        )

        data = self._get_canonical_alias()
        self.assertEqual(data[""content""][""alias""], self.test_alias)
        self.assertEqual(data[""content""][""alt_aliases""], [self.test_alias])


        ```
    ###Assistant : Removing an alias listed as in alt_aliases should remove it there too.
    "
2471,"def test_get_comments_no_doc(self):
        
        response = self.client.get(
            ""/api/documents/500/comments/"",
            format=""json"",
        )
        self.assertEqual(response.status_code, 404)

","
        GIVEN:
            - A request to get comments from a non-existent document
        WHEN:
            - API request for document comments is made
        THEN:
            - HTTP 404 is returned
        ",26,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_comments_no_doc(self):
        
        response = self.client.get(
            ""/api/documents/500/comments/"",
            format=""json"",
        )
        self.assertEqual(response.status_code, 404)


        ```
    ###Assistant : 
        GIVEN:
            - A request to get comments from a non-existent document
        WHEN:
            - API request for document comments is made
        THEN:
            - HTTP 404 is returned
        
    "
2472,"def test_mapping_keypad(self, config_stub, keyparser):
        
        config_stub.val.bindings.commands = {'normal': {'a': 'nop'}}
        config_stub.val.bindings.key_mappings = {'1': 'a'}

        info = keyutils.KeyInfo(Qt.Key.Key_1, Qt.KeyboardModifier.KeypadModifier)
        keyparser.handle(info.to_event())
        keyparser.execute.assert_called_once_with('nop', None)
",Make sure falling back to non-numpad keys works with mappings.,10,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_mapping_keypad(self, config_stub, keyparser):
        
        config_stub.val.bindings.commands = {'normal': {'a': 'nop'}}
        config_stub.val.bindings.key_mappings = {'1': 'a'}

        info = keyutils.KeyInfo(Qt.Key.Key_1, Qt.KeyboardModifier.KeypadModifier)
        keyparser.handle(info.to_event())
        keyparser.execute.assert_called_once_with('nop', None)

        ```
    ###Assistant : Make sure falling back to non-numpad keys works with mappings.
    "
2473,"def verify_liked_image(browser, logger):
    

    browser.refresh()
    unlike_xpath = read_xpath(like_image.__name__, ""unlike"")
    like_elem = browser.find_elements(By.XPATH, unlike_xpath)

    if len(like_elem) == 1:
        return True
    else:
        logger.warning(""--> Image was NOT liked! You have a BLOCK on likes!"")
        return False

",Check for a ban on likes using the last liked image,11,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def verify_liked_image(browser, logger):
    

    browser.refresh()
    unlike_xpath = read_xpath(like_image.__name__, ""unlike"")
    like_elem = browser.find_elements(By.XPATH, unlike_xpath)

    if len(like_elem) == 1:
        return True
    else:
        logger.warning(""--> Image was NOT liked! You have a BLOCK on likes!"")
        return False


        ```
    ###Assistant : Check for a ban on likes using the last liked image
    "
2474,"def get_bboxes(self, dst_type='hbb'):
        
        from ..bbox import get_box_type
        _, box_type_cls = get_box_type(dst_type)
        return box_type_cls.from_instance_masks(self)

","Get the certain type boxes from masks.

        Please refer to ``mmdet.structures.bbox.box_type`` for more details of
        the box type.

        Args:
            dst_type: Destination box type.

        Returns:
            :obj:`BaseBoxes`: Certain type boxes.
        ",28,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_bboxes(self, dst_type='hbb'):
        
        from ..bbox import get_box_type
        _, box_type_cls = get_box_type(dst_type)
        return box_type_cls.from_instance_masks(self)


        ```
    ###Assistant : Get the certain type boxes from masks.

        Please refer to ``mmdet.structures.bbox.box_type`` for more details of
        the box type.

        Args:
            dst_type: Destination box type.

        Returns:
            :obj:`BaseBoxes`: Certain type boxes.
        
    "
2475,"def deprecate_call():
    sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.5"",
        active_deprecations_target=""deprecated-tensor-fun-eval"",
        stacklevel=4,
    )

","
        Calling a tensor like Tensor(*indices) is deprecated. Use
        Tensor.substitute_indices() instead.
        ",10,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deprecate_call():
    sympy_deprecation_warning(
        ,
        deprecated_since_version=""1.5"",
        active_deprecations_target=""deprecated-tensor-fun-eval"",
        stacklevel=4,
    )


        ```
    ###Assistant : 
        Calling a tensor like Tensor(*indices) is deprecated. Use
        Tensor.substitute_indices() instead.
        
    "
2476,"def assign_proto(proto, name, val):
    

    is_repeated_field = hasattr(getattr(proto, name), 'extend')
    if is_repeated_field and not isinstance(val, list):
        val = [val]
    if isinstance(val, list):
        if isinstance(val[0], dict):
            for item in val:
                proto_item = getattr(proto, name).add()
                for k, v in six.iteritems(item):
                    assign_proto(proto_item, k, v)
        else:
            getattr(proto, name).extend(val)
    elif isinstance(val, dict):
        for k, v in six.iteritems(val):
            assign_proto(getattr(proto, name), k, v)
    else:
        setattr(proto, name, val)

","Assign a Python object to a protobuf message, based on the Python
    type (in recursive fashion). Lists become repeated fields/messages, dicts
    become messages, and other types are assigned directly. For convenience,
    repeated fields whose values are not lists are converted to single-element
    lists; e.g., `my_repeated_int_field=3` is converted to
    `my_repeated_int_field=[3]`.",49,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def assign_proto(proto, name, val):
    

    is_repeated_field = hasattr(getattr(proto, name), 'extend')
    if is_repeated_field and not isinstance(val, list):
        val = [val]
    if isinstance(val, list):
        if isinstance(val[0], dict):
            for item in val:
                proto_item = getattr(proto, name).add()
                for k, v in six.iteritems(item):
                    assign_proto(proto_item, k, v)
        else:
            getattr(proto, name).extend(val)
    elif isinstance(val, dict):
        for k, v in six.iteritems(val):
            assign_proto(getattr(proto, name), k, v)
    else:
        setattr(proto, name, val)


        ```
    ###Assistant : Assign a Python object to a protobuf message, based on the Python
    type (in recursive fashion). Lists become repeated fields/messages, dicts
    become messages, and other types are assigned directly. For convenience,
    repeated fields whose values are not lists are converted to single-element
    lists; e.g., `my_repeated_int_field=3` is converted to
    `my_repeated_int_field=[3]`.
    "
2477,"def __new__(cls, *args, **kwargs):
        sympy_deprecation_warning(
            ,
            deprecated_since_version=""1.8"",
            active_deprecations_target='deprecated-askhandler',
        )
        return super().__new__(cls, *args, **kwargs)

","
            The AskHandler system is deprecated. The AskHandler class should
            be replaced with the multipledispatch handler of Predicate
            ",17,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __new__(cls, *args, **kwargs):
        sympy_deprecation_warning(
            ,
            deprecated_since_version=""1.8"",
            active_deprecations_target='deprecated-askhandler',
        )
        return super().__new__(cls, *args, **kwargs)


        ```
    ###Assistant : 
            The AskHandler system is deprecated. The AskHandler class should
            be replaced with the multipledispatch handler of Predicate
            
    "
2478,"def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
    
    _raise_error_wrong_axis(axis)

    if not isinstance(X, (sp.csr_matrix, sp.csc_matrix)):
        _raise_typeerror(X)

    if np.size(last_n) == 1:
        last_n = np.full(last_mean.shape, last_n, dtype=last_mean.dtype)

    if not (np.size(last_mean) == np.size(last_var) == np.size(last_n)):
        raise ValueError(""last_mean, last_var, last_n do not have the same shapes."")

    if axis == 1:
        if np.size(last_mean) != X.shape[0]:
            raise ValueError(
                ""If axis=1, then last_mean, last_n, last_var should be of ""
                f""size n_samples {X.shape[0]} (Got {np.size(last_mean)}).""
            )
    else:  # axis == 0
        if np.size(last_mean) != X.shape[1]:
            raise ValueError(
                ""If axis=0, then last_mean, last_n, last_var should be of ""
                f""size n_features {X.shape[1]} (Got {np.size(last_mean)}).""
            )

    X = X.T if axis == 1 else X

    if weights is not None:
        weights = _check_sample_weight(weights, X, dtype=X.dtype)

    return _incr_mean_var_axis0(
        X, last_mean=last_mean, last_var=last_var, last_n=last_n, weights=weights
    )

","Compute incremental mean and variance along an axis on a CSR or CSC matrix.

    last_mean, last_var are the statistics computed at the last step by this
    function. Both must be initialized to 0-arrays of the proper size, i.e.
    the number of features in X. last_n is the number of samples encountered
    until now.

    Parameters
    ----------
    X : CSR or CSC sparse matrix of shape (n_samples, n_features)
        Input data.

    axis : {0, 1}
        Axis along which the axis should be computed.

    last_mean : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Array of means to update with the new data X.
        Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.

    last_var : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Array of variances to update with the new data X.
        Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.

    last_n : float or ndarray of shape (n_features,) or (n_samples,), \
            dtype=floating
        Sum of the weights seen so far, excluding the current weights
        If not float, it should be of shape (n_features,) if
        axis=0 or (n_samples,) if axis=1. If float it corresponds to
        having same weights for all samples (or features).

    weights : ndarray of shape (n_samples,) or (n_features,), default=None
        If axis is set to 0 shape is (n_samples,) or
        if axis is set to 1 shape is (n_features,).
        If it is set to None, then samples are equally weighted.

        .. versionadded:: 0.24

    Returns
    -------
    means : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Updated feature-wise means if axis = 0 or
        sample-wise means if axis = 1.

    variances : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Updated feature-wise variances if axis = 0 or
        sample-wise variances if axis = 1.

    n : ndarray of shape (n_features,) or (n_samples,), dtype=integral
        Updated number of seen samples per feature if axis=0
        or number of seen features per sample if axis=1.

        If weights is not None, n is a sum of the weights of the seen
        samples or features instead of the actual number of seen
        samples or features.

    Notes
    -----
    NaNs are ignored in the algorithm.
    ",344,121,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
    
    _raise_error_wrong_axis(axis)

    if not isinstance(X, (sp.csr_matrix, sp.csc_matrix)):
        _raise_typeerror(X)

    if np.size(last_n) == 1:
        last_n = np.full(last_mean.shape, last_n, dtype=last_mean.dtype)

    if not (np.size(last_mean) == np.size(last_var) == np.size(last_n)):
        raise ValueError(""last_mean, last_var, last_n do not have the same shapes."")

    if axis == 1:
        if np.size(last_mean) != X.shape[0]:
            raise ValueError(
                ""If axis=1, then last_mean, last_n, last_var should be of ""
                f""size n_samples {X.shape[0]} (Got {np.size(last_mean)}).""
            )
    else:  # axis == 0
        if np.size(last_mean) != X.shape[1]:
            raise ValueError(
                ""If axis=0, then last_mean, last_n, last_var should be of ""
                f""size n_features {X.shape[1]} (Got {np.size(last_mean)}).""
            )

    X = X.T if axis == 1 else X

    if weights is not None:
        weights = _check_sample_weight(weights, X, dtype=X.dtype)

    return _incr_mean_var_axis0(
        X, last_mean=last_mean, last_var=last_var, last_n=last_n, weights=weights
    )


        ```
    ###Assistant : Compute incremental mean and variance along an axis on a CSR or CSC matrix.

    last_mean, last_var are the statistics computed at the last step by this
    function. Both must be initialized to 0-arrays of the proper size, i.e.
    the number of features in X. last_n is the number of samples encountered
    until now.

    Parameters
    ----------
    X : CSR or CSC sparse matrix of shape (n_samples, n_features)
        Input data.

    axis : {0, 1}
        Axis along which the axis should be computed.

    last_mean : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Array of means to update with the new data X.
        Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.

    last_var : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Array of variances to update with the new data X.
        Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.

    last_n : float or ndarray of shape (n_features,) or (n_samples,), \
            dtype=floating
        Sum of the weights seen so far, excluding the current weights
        If not float, it should be of shape (n_features,) if
        axis=0 or (n_samples,) if axis=1. If float it corresponds to
        having same weights for all samples (or features).

    weights : ndarray of shape (n_samples,) or (n_features,), default=None
        If axis is set to 0 shape is (n_samples,) or
        if axis is set to 1 shape is (n_features,).
        If it is set to None, then samples are equally weighted.

        .. versionadded:: 0.24

    Returns
    -------
    means : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Updated feature-wise means if axis = 0 or
        sample-wise means if axis = 1.

    variances : ndarray of shape (n_features,) or (n_samples,), dtype=floating
        Updated feature-wise variances if axis = 0 or
        sample-wise variances if axis = 1.

    n : ndarray of shape (n_features,) or (n_samples,), dtype=integral
        Updated number of seen samples per feature if axis=0
        or number of seen features per sample if axis=1.

        If weights is not None, n is a sum of the weights of the seen
        samples or features instead of the actual number of seen
        samples or features.

    Notes
    -----
    NaNs are ignored in the algorithm.
    
    "
2479,"async def test_thermostat_with_no_off_after_recheck(hass, hk_driver, events):
    
    entity_id = ""climate.test""

    # support_auto = True
    hass.states.async_set(
        entity_id,
        HVACMode.COOL,
        {
            ATTR_SUPPORTED_FEATURES: SUPPORT_TARGET_TEMPERATURE
            | SUPPORT_TARGET_TEMPERATURE_RANGE,
            ATTR_HVAC_MODES: [],
        },
    )
    await hass.async_block_till_done()
    acc = Thermostat(hass, hk_driver, ""Climate"", entity_id, 1, None)
    hk_driver.add_accessory(acc)

    await acc.run()
    await hass.async_block_till_done()

    assert acc.char_cooling_thresh_temp.value == 23.0
    assert acc.char_heating_thresh_temp.value == 19.0

    assert acc.char_cooling_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
    assert acc.char_cooling_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
    assert acc.char_cooling_thresh_temp.properties[PROP_MIN_STEP] == 0.1
    assert acc.char_heating_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
    assert acc.char_heating_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
    assert acc.char_heating_thresh_temp.properties[PROP_MIN_STEP] == 0.1

    assert acc.char_target_heat_cool.value == 2

    hass.states.async_set(
        entity_id,
        HVACMode.HEAT_COOL,
        {
            ATTR_TARGET_TEMP_HIGH: 22.0,
            ATTR_TARGET_TEMP_LOW: 20.0,
            ATTR_CURRENT_TEMPERATURE: 18.0,
            ATTR_HVAC_ACTION: HVACAction.HEATING,
            ATTR_HVAC_MODES: [HVACMode.HEAT_COOL, HVACMode.AUTO],
        },
    )
    await hass.async_block_till_done()
    assert acc.char_heating_thresh_temp.value == 20.0
    assert acc.char_cooling_thresh_temp.value == 22.0
    assert acc.char_current_heat_cool.value == 1
    assert acc.char_target_heat_cool.value == 3
    assert acc.char_current_temp.value == 18.0
    assert acc.char_display_units.value == 0

",Test if a thermostat that is not ready when we first see it that actually does not have off.,19,118,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_thermostat_with_no_off_after_recheck(hass, hk_driver, events):
    
    entity_id = ""climate.test""

    # support_auto = True
    hass.states.async_set(
        entity_id,
        HVACMode.COOL,
        {
            ATTR_SUPPORTED_FEATURES: SUPPORT_TARGET_TEMPERATURE
            | SUPPORT_TARGET_TEMPERATURE_RANGE,
            ATTR_HVAC_MODES: [],
        },
    )
    await hass.async_block_till_done()
    acc = Thermostat(hass, hk_driver, ""Climate"", entity_id, 1, None)
    hk_driver.add_accessory(acc)

    await acc.run()
    await hass.async_block_till_done()

    assert acc.char_cooling_thresh_temp.value == 23.0
    assert acc.char_heating_thresh_temp.value == 19.0

    assert acc.char_cooling_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
    assert acc.char_cooling_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
    assert acc.char_cooling_thresh_temp.properties[PROP_MIN_STEP] == 0.1
    assert acc.char_heating_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
    assert acc.char_heating_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
    assert acc.char_heating_thresh_temp.properties[PROP_MIN_STEP] == 0.1

    assert acc.char_target_heat_cool.value == 2

    hass.states.async_set(
        entity_id,
        HVACMode.HEAT_COOL,
        {
            ATTR_TARGET_TEMP_HIGH: 22.0,
            ATTR_TARGET_TEMP_LOW: 20.0,
            ATTR_CURRENT_TEMPERATURE: 18.0,
            ATTR_HVAC_ACTION: HVACAction.HEATING,
            ATTR_HVAC_MODES: [HVACMode.HEAT_COOL, HVACMode.AUTO],
        },
    )
    await hass.async_block_till_done()
    assert acc.char_heating_thresh_temp.value == 20.0
    assert acc.char_cooling_thresh_temp.value == 22.0
    assert acc.char_current_heat_cool.value == 1
    assert acc.char_target_heat_cool.value == 3
    assert acc.char_current_temp.value == 18.0
    assert acc.char_display_units.value == 0


        ```
    ###Assistant : Test if a thermostat that is not ready when we first see it that actually does not have off.
    "
2480,"def save(self, *args, **kwargs):
        

        is_new = self.pk is None
        if is_new:
            clean_name = get_field_clean_name(self.label)
            self.clean_name = clean_name

        super().save(*args, **kwargs)
","
        When new fields are created, generate a template safe ascii name to use as the
        JSON storage reference for this field. Previously created fields will be updated
        to use the legacy unidecode method via checks & _migrate_legacy_clean_name.
        We do not want to update the clean name on any subsequent changes to the label
        as this would invalidate any previously submitted data.
        ",61,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save(self, *args, **kwargs):
        

        is_new = self.pk is None
        if is_new:
            clean_name = get_field_clean_name(self.label)
            self.clean_name = clean_name

        super().save(*args, **kwargs)

        ```
    ###Assistant : 
        When new fields are created, generate a template safe ascii name to use as the
        JSON storage reference for this field. Previously created fields will be updated
        to use the legacy unidecode method via checks & _migrate_legacy_clean_name.
        We do not want to update the clean name on any subsequent changes to the label
        as this would invalidate any previously submitted data.
        
    "
2481,"def dask_task_wrapper(func, repack, key, ray_pretask_cbs, ray_posttask_cbs, *args):
    
    if ray_pretask_cbs is not None:
        pre_states = [
            cb(key, args) if cb is not None else None for cb in ray_pretask_cbs
        ]
    repacked_args, repacked_deps = repack(args)
    # Recursively execute Dask-inlined tasks.
    actual_args = [_execute_task(a, repacked_deps) for a in repacked_args]
    # Execute the actual underlying Dask task.
    result = func(*actual_args)

    if ray_posttask_cbs is not None:
        for cb, pre_state in zip(ray_posttask_cbs, pre_states):
            if cb is not None:
                cb(key, result, pre_state)

    return result

","
    A Ray remote function acting as a Dask task wrapper. This function will
    repackage the given flat `args` into its original data structures using
    `repack`, execute any Dask subtasks within the repackaged arguments
    (inlined by Dask's optimization pass), and then pass the concrete task
    arguments to the provide Dask task function, `func`.

    Args:
        func (callable): The Dask task function to execute.
        repack (callable): A function that repackages the provided args into
            the original (possibly nested) Python objects.
        key (str): The Dask key for this task.
        ray_pretask_cbs (callable): Pre-task execution callbacks.
        ray_posttask_cbs (callable): Post-task execution callback.
        *args (ObjectRef): Ray object references representing the Dask task's
            arguments.

    Returns:
        The output of the Dask task. In the context of Ray, a
        dask_task_wrapper.remote() invocation will return a Ray object
        reference representing the Ray task's result.
    ",131,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dask_task_wrapper(func, repack, key, ray_pretask_cbs, ray_posttask_cbs, *args):
    
    if ray_pretask_cbs is not None:
        pre_states = [
            cb(key, args) if cb is not None else None for cb in ray_pretask_cbs
        ]
    repacked_args, repacked_deps = repack(args)
    # Recursively execute Dask-inlined tasks.
    actual_args = [_execute_task(a, repacked_deps) for a in repacked_args]
    # Execute the actual underlying Dask task.
    result = func(*actual_args)

    if ray_posttask_cbs is not None:
        for cb, pre_state in zip(ray_posttask_cbs, pre_states):
            if cb is not None:
                cb(key, result, pre_state)

    return result


        ```
    ###Assistant : 
    A Ray remote function acting as a Dask task wrapper. This function will
    repackage the given flat `args` into its original data structures using
    `repack`, execute any Dask subtasks within the repackaged arguments
    (inlined by Dask's optimization pass), and then pass the concrete task
    arguments to the provide Dask task function, `func`.

    Args:
        func (callable): The Dask task function to execute.
        repack (callable): A function that repackages the provided args into
            the original (possibly nested) Python objects.
        key (str): The Dask key for this task.
        ray_pretask_cbs (callable): Pre-task execution callbacks.
        ray_posttask_cbs (callable): Post-task execution callback.
        *args (ObjectRef): Ray object references representing the Dask task's
            arguments.

    Returns:
        The output of the Dask task. In the context of Ray, a
        dask_task_wrapper.remote() invocation will return a Ray object
        reference representing the Ray task's result.
    
    "
2482,"def words(count, common=True):
    
    word_list = list(COMMON_WORDS) if common else []
    c = len(word_list)
    if count > c:
        count -= c
        while count > 0:
            c = min(count, len(WORDS))
            count -= c
            word_list += random.sample(WORDS, c)
    else:
        word_list = word_list[:count]
    return "" "".join(word_list)
","
    Return a string of `count` lorem ipsum words separated by a single space.

    If `common` is True, then the first 19 words will be the standard
    'lorem ipsum' words. Otherwise, all words will be selected randomly.
    ",36,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def words(count, common=True):
    
    word_list = list(COMMON_WORDS) if common else []
    c = len(word_list)
    if count > c:
        count -= c
        while count > 0:
            c = min(count, len(WORDS))
            count -= c
            word_list += random.sample(WORDS, c)
    else:
        word_list = word_list[:count]
    return "" "".join(word_list)

        ```
    ###Assistant : 
    Return a string of `count` lorem ipsum words separated by a single space.

    If `common` is True, then the first 19 words will be the standard
    'lorem ipsum' words. Otherwise, all words will be selected randomly.
    
    "
2483,"def jumpTo(self, bytes):
        
        try:
            self._position = self.index(bytes, self.position) + len(bytes) - 1
        except ValueError:
            raise StopIteration
        return True

","Look for the next sequence of bytes matching a given sequence. If
        a match is found advance the position to the last byte of the match",26,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def jumpTo(self, bytes):
        
        try:
            self._position = self.index(bytes, self.position) + len(bytes) - 1
        except ValueError:
            raise StopIteration
        return True


        ```
    ###Assistant : Look for the next sequence of bytes matching a given sequence. If
        a match is found advance the position to the last byte of the match
    "
2484,"def available(self) -> bool:
        
        expire_after: int | None = self._config.get(CONF_EXPIRE_AFTER)
        # mypy doesn't know about fget: https://github.com/python/mypy/issues/6185
        return MqttAvailability.available.fget(self) and (  # type: ignore[attr-defined]
            expire_after is None or not self._expired
        )
",Return true if the device is available and value has not expired.,12,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def available(self) -> bool:
        
        expire_after: int | None = self._config.get(CONF_EXPIRE_AFTER)
        # mypy doesn't know about fget: https://github.com/python/mypy/issues/6185
        return MqttAvailability.available.fget(self) and (  # type: ignore[attr-defined]
            expire_after is None or not self._expired
        )

        ```
    ###Assistant : Return true if the device is available and value has not expired.
    "
2485,"def Logistic(name, mu, s):
    r

    return rv(name, LogisticDistribution, (mu, s))

#-------------------------------------------------------------------------------
# Log-logistic distribution --------------------------------------------------------

","
    Create a continuous random variable with a logistic distribution.

    Explanation
    ===========

    The density of the logistic distribution is given by

    .. math::
        f(x) := \frac{e^{-(x-\mu)/s}} {s\left(1+e^{-(x-\mu)/s}\right)^2}

    Parameters
    ==========

    mu : Real number, the location (mean)
    s : Real number, `s > 0`, a scale

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import Logistic, density, cdf
    >>> from sympy import Symbol

    >>> mu = Symbol(""mu"", real=True)
    >>> s = Symbol(""s"", positive=True)
    >>> z = Symbol(""z"")

    >>> X = Logistic(""x"", mu, s)

    >>> density(X)(z)
    exp((mu - z)/s)/(s*(exp((mu - z)/s) + 1)**2)

    >>> cdf(X)(z)
    1/(exp((mu - z)/s) + 1)

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Logistic_distribution
    .. [2] http://mathworld.wolfram.com/LogisticDistribution.html

    ",105,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def Logistic(name, mu, s):
    r

    return rv(name, LogisticDistribution, (mu, s))

#-------------------------------------------------------------------------------
# Log-logistic distribution --------------------------------------------------------


        ```
    ###Assistant : 
    Create a continuous random variable with a logistic distribution.

    Explanation
    ===========

    The density of the logistic distribution is given by

    .. math::
        f(x) := \frac{e^{-(x-\mu)/s}} {s\left(1+e^{-(x-\mu)/s}\right)^2}

    Parameters
    ==========

    mu : Real number, the location (mean)
    s : Real number, `s > 0`, a scale

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import Logistic, density, cdf
    >>> from sympy import Symbol

    >>> mu = Symbol(""mu"", real=True)
    >>> s = Symbol(""s"", positive=True)
    >>> z = Symbol(""z"")

    >>> X = Logistic(""x"", mu, s)

    >>> density(X)(z)
    exp((mu - z)/s)/(s*(exp((mu - z)/s) + 1)**2)

    >>> cdf(X)(z)
    1/(exp((mu - z)/s) + 1)

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Logistic_distribution
    .. [2] http://mathworld.wolfram.com/LogisticDistribution.html

    
    "
2486,"def __call__(self, feat_maps, comp_attribs):
        

        assert isinstance(feat_maps, paddle.Tensor)
        assert comp_attribs.ndim == 3
        assert comp_attribs.shape[2] == 8

        sorted_dist_inds_batch = []
        local_graph_batch = []
        knn_batch = []
        node_feat_batch = []
        node_label_batch = []

        for batch_ind in range(comp_attribs.shape[0]):
            num_comps = int(comp_attribs[batch_ind, 0, 0])
            comp_geo_attribs = comp_attribs[batch_ind, :num_comps, 1:7]
            node_labels = comp_attribs[batch_ind, :num_comps, 7].astype(
                np.int32)

            comp_centers = comp_geo_attribs[:, 0:2]
            distance_matrix = euclidean_distance_matrix(comp_centers,
                                                        comp_centers)

            batch_id = np.zeros(
                (comp_geo_attribs.shape[0], 1), dtype=np.float32) * batch_ind
            comp_geo_attribs[:, -2] = np.clip(comp_geo_attribs[:, -2], -1, 1)
            angle = np.arccos(comp_geo_attribs[:, -2]) * np.sign(
                comp_geo_attribs[:, -1])
            angle = angle.reshape((-1, 1))
            rotated_rois = np.hstack(
                [batch_id, comp_geo_attribs[:, :-2], angle])
            rois = paddle.to_tensor(rotated_rois)
            content_feats = self.pooling(feat_maps[batch_ind].unsqueeze(0),
                                         rois)

            content_feats = content_feats.reshape([content_feats.shape[0], -1])
            geo_feats = feature_embedding(comp_geo_attribs,
                                          self.node_geo_feat_dim)
            geo_feats = paddle.to_tensor(geo_feats)
            node_feats = paddle.concat([content_feats, geo_feats], axis=-1)

            sorted_dist_inds = np.argsort(distance_matrix, axis=1)
            pivot_local_graphs, pivot_knns = self.generate_local_graphs(
                sorted_dist_inds, node_labels)

            node_feat_batch.append(node_feats)
            node_label_batch.append(node_labels)
            local_graph_batch.append(pivot_local_graphs)
            knn_batch.append(pivot_knns)
            sorted_dist_inds_batch.append(sorted_dist_inds)

        (node_feats, adjacent_matrices, knn_inds, gt_linkage) = \
            self.generate_gcn_input(node_feat_batch,
                                    node_label_batch,
                                    local_graph_batch,
                                    knn_batch,
                                    sorted_dist_inds_batch)

        return node_feats, adjacent_matrices, knn_inds, gt_linkage
","Generate local graphs as GCN input.

        Args:
            feat_maps (Tensor): The feature maps to extract the content
                features of text components.
            comp_attribs (ndarray): The text component attributes.

        Returns:
            local_graphs_node_feat (Tensor): The node features of graph.
            adjacent_matrices (Tensor): The adjacent matrices of local graphs.
            pivots_knn_inds (Tensor): The k-nearest neighbor indices in local
                graph.
            gt_linkage (Tensor): The surpervision signal of GCN for linkage
                prediction.
        ",61,146,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self, feat_maps, comp_attribs):
        

        assert isinstance(feat_maps, paddle.Tensor)
        assert comp_attribs.ndim == 3
        assert comp_attribs.shape[2] == 8

        sorted_dist_inds_batch = []
        local_graph_batch = []
        knn_batch = []
        node_feat_batch = []
        node_label_batch = []

        for batch_ind in range(comp_attribs.shape[0]):
            num_comps = int(comp_attribs[batch_ind, 0, 0])
            comp_geo_attribs = comp_attribs[batch_ind, :num_comps, 1:7]
            node_labels = comp_attribs[batch_ind, :num_comps, 7].astype(
                np.int32)

            comp_centers = comp_geo_attribs[:, 0:2]
            distance_matrix = euclidean_distance_matrix(comp_centers,
                                                        comp_centers)

            batch_id = np.zeros(
                (comp_geo_attribs.shape[0], 1), dtype=np.float32) * batch_ind
            comp_geo_attribs[:, -2] = np.clip(comp_geo_attribs[:, -2], -1, 1)
            angle = np.arccos(comp_geo_attribs[:, -2]) * np.sign(
                comp_geo_attribs[:, -1])
            angle = angle.reshape((-1, 1))
            rotated_rois = np.hstack(
                [batch_id, comp_geo_attribs[:, :-2], angle])
            rois = paddle.to_tensor(rotated_rois)
            content_feats = self.pooling(feat_maps[batch_ind].unsqueeze(0),
                                         rois)

            content_feats = content_feats.reshape([content_feats.shape[0], -1])
            geo_feats = feature_embedding(comp_geo_attribs,
                                          self.node_geo_feat_dim)
            geo_feats = paddle.to_tensor(geo_feats)
            node_feats = paddle.concat([content_feats, geo_feats], axis=-1)

            sorted_dist_inds = np.argsort(distance_matrix, axis=1)
            pivot_local_graphs, pivot_knns = self.generate_local_graphs(
                sorted_dist_inds, node_labels)

            node_feat_batch.append(node_feats)
            node_label_batch.append(node_labels)
            local_graph_batch.append(pivot_local_graphs)
            knn_batch.append(pivot_knns)
            sorted_dist_inds_batch.append(sorted_dist_inds)

        (node_feats, adjacent_matrices, knn_inds, gt_linkage) = \
            self.generate_gcn_input(node_feat_batch,
                                    node_label_batch,
                                    local_graph_batch,
                                    knn_batch,
                                    sorted_dist_inds_batch)

        return node_feats, adjacent_matrices, knn_inds, gt_linkage

        ```
    ###Assistant : Generate local graphs as GCN input.

        Args:
            feat_maps (Tensor): The feature maps to extract the content
                features of text components.
            comp_attribs (ndarray): The text component attributes.

        Returns:
            local_graphs_node_feat (Tensor): The node features of graph.
            adjacent_matrices (Tensor): The adjacent matrices of local graphs.
            pivots_knn_inds (Tensor): The k-nearest neighbor indices in local
                graph.
            gt_linkage (Tensor): The surpervision signal of GCN for linkage
                prediction.
        
    "
2487,"def _is_zero_copy_arrow_op(cls, op) -> bool:
        
        is_zero_copy_op = False
        if isinstance(op, (FrameNode, TransformNode, UnionNode)):
            # - FrameNode: already materialized PyArrow table
            # - TransformNode: select certain columns of the table, implemented zero-copy (``df._arrow_select``)
            # - UnionNode: concatenate PyArrow tables, implemented zero-copy (``df._arrow_concat``)
            is_zero_copy_op = True
        elif isinstance(op, MaskNode) and (
            isinstance(op.row_positions, slice) or is_range_like(op.row_positions)
        ):
            # Can select rows zero-copy if indexer is a slice-like (``df._arrow_row_slice``)
            is_zero_copy_op = True
        return is_zero_copy_op and all(
            # Walk the computation tree
            cls._is_zero_copy_arrow_op(_op)
            for _op in getattr(op, ""inputs"", [])
        )
","
        Check whether the passed node of the delayed computation tree could be executed zero-copy via PyArrow execution.

        Parameters
        ----------
        op : DFAlgNode

        Returns
        -------
        bool
        ",25,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _is_zero_copy_arrow_op(cls, op) -> bool:
        
        is_zero_copy_op = False
        if isinstance(op, (FrameNode, TransformNode, UnionNode)):
            # - FrameNode: already materialized PyArrow table
            # - TransformNode: select certain columns of the table, implemented zero-copy (``df._arrow_select``)
            # - UnionNode: concatenate PyArrow tables, implemented zero-copy (``df._arrow_concat``)
            is_zero_copy_op = True
        elif isinstance(op, MaskNode) and (
            isinstance(op.row_positions, slice) or is_range_like(op.row_positions)
        ):
            # Can select rows zero-copy if indexer is a slice-like (``df._arrow_row_slice``)
            is_zero_copy_op = True
        return is_zero_copy_op and all(
            # Walk the computation tree
            cls._is_zero_copy_arrow_op(_op)
            for _op in getattr(op, ""inputs"", [])
        )

        ```
    ###Assistant : 
        Check whether the passed node of the delayed computation tree could be executed zero-copy via PyArrow execution.

        Parameters
        ----------
        op : DFAlgNode

        Returns
        -------
        bool
        
    "
2488,"def batch_p_dist(x, y, p=2):
    
    x = x.unsqueeze(1)
    diff = x - y
    return paddle.norm(diff, p=p, axis=list(range(2, diff.dim())))


@register","
    calculate pairwise p_dist, the first index of x and y are batch
    return [x.shape[0], y.shape[0]]
    ",15,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def batch_p_dist(x, y, p=2):
    
    x = x.unsqueeze(1)
    diff = x - y
    return paddle.norm(diff, p=p, axis=list(range(2, diff.dim())))


@register
        ```
    ###Assistant : 
    calculate pairwise p_dist, the first index of x and y are batch
    return [x.shape[0], y.shape[0]]
    
    "
2489,"def get_employee_shift(employee, for_timestamp=None, consider_default_shift=False, next_shift_direction=None):
	
	if for_timestamp is None:
		for_timestamp = now_datetime()

	shift_details = get_shift_for_timestamp(employee, for_timestamp)

	# if shift assignment is not found, consider default shift
	default_shift = frappe.db.get_value('Employee', employee, 'default_shift')
	if not shift_details and consider_default_shift:
		shift_details = get_shift_details(default_shift, for_timestamp.date())

	# if its a holiday, reset
	if shift_details and is_holiday_date(employee, shift_details):
		shift_details = None

	# if no shift is found, find next or prev shift based on direction
	if not shift_details and next_shift_direction:
		shift_details = get_prev_or_next_shift(employee, for_timestamp, consider_default_shift, default_shift, next_shift_direction)

	return shift_details

","Returns a Shift Type for the given employee on the given date. (excluding the holidays)

	:param employee: Employee for which shift is required.
	:param for_timestamp: DateTime on which shift is required
	:param consider_default_shift: If set to true, default shift is taken when no shift assignment is found.
	:param next_shift_direction: One of: None, 'forward', 'reverse'. Direction to look for next shift if shift not found on given date.
	",67,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_employee_shift(employee, for_timestamp=None, consider_default_shift=False, next_shift_direction=None):
	
	if for_timestamp is None:
		for_timestamp = now_datetime()

	shift_details = get_shift_for_timestamp(employee, for_timestamp)

	# if shift assignment is not found, consider default shift
	default_shift = frappe.db.get_value('Employee', employee, 'default_shift')
	if not shift_details and consider_default_shift:
		shift_details = get_shift_details(default_shift, for_timestamp.date())

	# if its a holiday, reset
	if shift_details and is_holiday_date(employee, shift_details):
		shift_details = None

	# if no shift is found, find next or prev shift based on direction
	if not shift_details and next_shift_direction:
		shift_details = get_prev_or_next_shift(employee, for_timestamp, consider_default_shift, default_shift, next_shift_direction)

	return shift_details


        ```
    ###Assistant : Returns a Shift Type for the given employee on the given date. (excluding the holidays)

	:param employee: Employee for which shift is required.
	:param for_timestamp: DateTime on which shift is required
	:param consider_default_shift: If set to true, default shift is taken when no shift assignment is found.
	:param next_shift_direction: One of: None, 'forward', 'reverse'. Direction to look for next shift if shift not found on given date.
	
    "
2490,"def get_create_form_class(self):
        
        self.create_model = self.get_create_model()
        if self.create_model:
            return get_task_form_class(self.create_model)
        else:
            return None
","
        To be called after dispatch(); returns the form class for creating a new task
        ",14,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_create_form_class(self):
        
        self.create_model = self.get_create_model()
        if self.create_model:
            return get_task_form_class(self.create_model)
        else:
            return None

        ```
    ###Assistant : 
        To be called after dispatch(); returns the form class for creating a new task
        
    "
2491,"def retrieve_image(self):
        
        image = self.storage.open(self.image_path, ""rb"")
        image_format = self.get_image_metadata_from_file(image)
        return (Image.open(image), image_format)
",Return a PIL Image instance stored at `image_path`.,8,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def retrieve_image(self):
        
        image = self.storage.open(self.image_path, ""rb"")
        image_format = self.get_image_metadata_from_file(image)
        return (Image.open(image), image_format)

        ```
    ###Assistant : Return a PIL Image instance stored at `image_path`.
    "
2492,"def chebval(x, c, tensor=True):
    
    c = np.array(c, ndmin=1, copy=True)
    if c.dtype.char in '?bBhHiIlLqQpP':
        c = c.astype(np.double)
    if isinstance(x, (tuple, list)):
        x = np.asarray(x)
    if isinstance(x, np.ndarray) and tensor:
        c = c.reshape(c.shape + (1,)*x.ndim)

    if len(c) == 1:
        c0 = c[0]
        c1 = 0
    elif len(c) == 2:
        c0 = c[0]
        c1 = c[1]
    else:
        x2 = 2*x
        c0 = c[-2]
        c1 = c[-1]
        for i in range(3, len(c) + 1):
            tmp = c0
            c0 = c[-i] - c1
            c1 = tmp + c1*x2
    return c0 + c1*x

","
    Evaluate a Chebyshev series at points x.

    If `c` is of length `n + 1`, this function returns the value:

    .. math:: p(x) = c_0 * T_0(x) + c_1 * T_1(x) + ... + c_n * T_n(x)

    The parameter `x` is converted to an array only if it is a tuple or a
    list, otherwise it is treated as a scalar. In either case, either `x`
    or its elements must support multiplication and addition both with
    themselves and with the elements of `c`.

    If `c` is a 1-D array, then `p(x)` will have the same shape as `x`.  If
    `c` is multidimensional, then the shape of the result depends on the
    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
    scalars have shape (,).

    Trailing zeros in the coefficients will be used in the evaluation, so
    they should be avoided if efficiency is a concern.

    Parameters
    ----------
    x : array_like, compatible object
        If `x` is a list or tuple, it is converted to an ndarray, otherwise
        it is left unchanged and treated as a scalar. In either case, `x`
        or its elements must support addition and multiplication with
        themselves and with the elements of `c`.
    c : array_like
        Array of coefficients ordered so that the coefficients for terms of
        degree n are contained in c[n]. If `c` is multidimensional the
        remaining indices enumerate multiple polynomials. In the two
        dimensional case the coefficients may be thought of as stored in
        the columns of `c`.
    tensor : boolean, optional
        If True, the shape of the coefficient array is extended with ones
        on the right, one for each dimension of `x`. Scalars have dimension 0
        for this action. The result is that every column of coefficients in
        `c` is evaluated for every element of `x`. If False, `x` is broadcast
        over the columns of `c` for the evaluation.  This keyword is useful
        when `c` is multidimensional. The default value is True.

        .. versionadded:: 1.7.0

    Returns
    -------
    values : ndarray, algebra_like
        The shape of the return value is described above.

    See Also
    --------
    chebval2d, chebgrid2d, chebval3d, chebgrid3d

    Notes
    -----
    The evaluation uses Clenshaw recursion, aka synthetic division.

    ",369,87,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def chebval(x, c, tensor=True):
    
    c = np.array(c, ndmin=1, copy=True)
    if c.dtype.char in '?bBhHiIlLqQpP':
        c = c.astype(np.double)
    if isinstance(x, (tuple, list)):
        x = np.asarray(x)
    if isinstance(x, np.ndarray) and tensor:
        c = c.reshape(c.shape + (1,)*x.ndim)

    if len(c) == 1:
        c0 = c[0]
        c1 = 0
    elif len(c) == 2:
        c0 = c[0]
        c1 = c[1]
    else:
        x2 = 2*x
        c0 = c[-2]
        c1 = c[-1]
        for i in range(3, len(c) + 1):
            tmp = c0
            c0 = c[-i] - c1
            c1 = tmp + c1*x2
    return c0 + c1*x


        ```
    ###Assistant : 
    Evaluate a Chebyshev series at points x.

    If `c` is of length `n + 1`, this function returns the value:

    .. math:: p(x) = c_0 * T_0(x) + c_1 * T_1(x) + ... + c_n * T_n(x)

    The parameter `x` is converted to an array only if it is a tuple or a
    list, otherwise it is treated as a scalar. In either case, either `x`
    or its elements must support multiplication and addition both with
    themselves and with the elements of `c`.

    If `c` is a 1-D array, then `p(x)` will have the same shape as `x`.  If
    `c` is multidimensional, then the shape of the result depends on the
    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
    scalars have shape (,).

    Trailing zeros in the coefficients will be used in the evaluation, so
    they should be avoided if efficiency is a concern.

    Parameters
    ----------
    x : array_like, compatible object
        If `x` is a list or tuple, it is converted to an ndarray, otherwise
        it is left unchanged and treated as a scalar. In either case, `x`
        or its elements must support addition and multiplication with
        themselves and with the elements of `c`.
    c : array_like
        Array of coefficients ordered so that the coefficients for terms of
        degree n are contained in c[n]. If `c` is multidimensional the
        remaining indices enumerate multiple polynomials. In the two
        dimensional case the coefficients may be thought of as stored in
        the columns of `c`.
    tensor : boolean, optional
        If True, the shape of the coefficient array is extended with ones
        on the right, one for each dimension of `x`. Scalars have dimension 0
        for this action. The result is that every column of coefficients in
        `c` is evaluated for every element of `x`. If False, `x` is broadcast
        over the columns of `c` for the evaluation.  This keyword is useful
        when `c` is multidimensional. The default value is True.

        .. versionadded:: 1.7.0

    Returns
    -------
    values : ndarray, algebra_like
        The shape of the return value is described above.

    See Also
    --------
    chebval2d, chebgrid2d, chebval3d, chebgrid3d

    Notes
    -----
    The evaluation uses Clenshaw recursion, aka synthetic division.

    
    "
2493,"def dagrun_queued(self):
        
        dag_id = request.form.get('dag_id')
        dag_run_id = request.form.get('dag_run_id')
        confirmed = request.form.get('confirmed') == 'true'
        origin = get_safe_url(request.form.get('origin'))
        return self._mark_dagrun_state_as_queued(dag_id, dag_run_id, confirmed, origin)
",Queue DagRun so tasks that haven't run yet can be started.,11,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dagrun_queued(self):
        
        dag_id = request.form.get('dag_id')
        dag_run_id = request.form.get('dag_run_id')
        confirmed = request.form.get('confirmed') == 'true'
        origin = get_safe_url(request.form.get('origin'))
        return self._mark_dagrun_state_as_queued(dag_id, dag_run_id, confirmed, origin)

        ```
    ###Assistant : Queue DagRun so tasks that haven't run yet can be started.
    "
2494,"def data_to_internal(data):
    
    internal = data.copy()
    if 'extra_vars' in data:
        internal['extra_vars'] = json.loads(data['extra_vars'])
    if 'credentials' in data:
        internal['credentials'] = set(Credential.objects.get(pk=_id) for _id in data['credentials'])
    if 'inventory' in data:
        internal['inventory'] = Inventory.objects.get(pk=data['inventory'])
    if 'execution_environment' in data:
        internal['execution_environment'] = ExecutionEnvironment.objects.get(pk=data['execution_environment'])
    if 'labels' in data:
        internal['labels'] = [Label.objects.get(pk=_id) for _id in data['labels']]
    if 'instance_groups' in data:
        internal['instance_groups'] = [InstanceGroup.objects.get(pk=_id) for _id in data['instance_groups']]
    return internal


# End of setup, tests start here
@pytest.mark.django_db
@pytest.mark.job_runtime_vars","
    returns internal representation, model objects, dictionaries, etc
    as opposed to integer primary keys and JSON strings
    ",16,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def data_to_internal(data):
    
    internal = data.copy()
    if 'extra_vars' in data:
        internal['extra_vars'] = json.loads(data['extra_vars'])
    if 'credentials' in data:
        internal['credentials'] = set(Credential.objects.get(pk=_id) for _id in data['credentials'])
    if 'inventory' in data:
        internal['inventory'] = Inventory.objects.get(pk=data['inventory'])
    if 'execution_environment' in data:
        internal['execution_environment'] = ExecutionEnvironment.objects.get(pk=data['execution_environment'])
    if 'labels' in data:
        internal['labels'] = [Label.objects.get(pk=_id) for _id in data['labels']]
    if 'instance_groups' in data:
        internal['instance_groups'] = [InstanceGroup.objects.get(pk=_id) for _id in data['instance_groups']]
    return internal


# End of setup, tests start here
@pytest.mark.django_db
@pytest.mark.job_runtime_vars
        ```
    ###Assistant : 
    returns internal representation, model objects, dictionaries, etc
    as opposed to integer primary keys and JSON strings
    
    "
2495,"def _generate_sys_info_str(*sys_info) -> str:
    
    if sys_info:
        return ""<br>"".join(sys_info).replace(""\n"", ""<br>"")
    return """"

","Format system info into a string.
        *sys_info: System info strings to be included.

    Returns:
        Formatted string containing system information.
    ",19,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _generate_sys_info_str(*sys_info) -> str:
    
    if sys_info:
        return ""<br>"".join(sys_info).replace(""\n"", ""<br>"")
    return """"


        ```
    ###Assistant : Format system info into a string.
        *sys_info: System info strings to be included.

    Returns:
        Formatted string containing system information.
    
    "
2496,"def default(self, obj):
        
        if isinstance(obj, tf.TensorShape):
            items = obj.as_list() if obj.rank is not None else None
            return {""class_name"": ""TensorShape"", ""items"": items}
        return get_json_type(obj)
","Encodes objects for types that aren't handled by the default
        encoder.",11,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def default(self, obj):
        
        if isinstance(obj, tf.TensorShape):
            items = obj.as_list() if obj.rank is not None else None
            return {""class_name"": ""TensorShape"", ""items"": items}
        return get_json_type(obj)

        ```
    ###Assistant : Encodes objects for types that aren't handled by the default
        encoder.
    "
2497,"async def _pause_and_wait_for_callback(self):
        
        self._pause_requested = True
        await self.async_media_pause()
        try:",Send pause and wait for the pause callback to be received.,11,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _pause_and_wait_for_callback(self):
        
        self._pause_requested = True
        await self.async_media_pause()
        try:
        ```
    ###Assistant : Send pause and wait for the pause callback to be received.
    "
2498,"def delete_checkpoint(self, checkpoint_path):
        
        # Ensure TrialCheckpoints are converted
        if isinstance(checkpoint_path, TrialCheckpoint):
            checkpoint_path = checkpoint_path.local_path

        try:
            checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
        except FileNotFoundError:
            # The checkpoint won't exist locally if the
            # trial was rescheduled to another worker.
            logger.debug(
                f""Local checkpoint not found during garbage collection: ""
                f""{self.trial_id} - {checkpoint_path}""
            )
            return
        else:
            if self.uses_cloud_checkpointing:
                self.storage_client.delete(self._storage_path(checkpoint_dir))

        if os.path.exists(checkpoint_dir):
            shutil.rmtree(checkpoint_dir)
","Deletes local copy of checkpoint.

        Args:
            checkpoint_path (str): Path to checkpoint.
        ",11,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_checkpoint(self, checkpoint_path):
        
        # Ensure TrialCheckpoints are converted
        if isinstance(checkpoint_path, TrialCheckpoint):
            checkpoint_path = checkpoint_path.local_path

        try:
            checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
        except FileNotFoundError:
            # The checkpoint won't exist locally if the
            # trial was rescheduled to another worker.
            logger.debug(
                f""Local checkpoint not found during garbage collection: ""
                f""{self.trial_id} - {checkpoint_path}""
            )
            return
        else:
            if self.uses_cloud_checkpointing:
                self.storage_client.delete(self._storage_path(checkpoint_dir))

        if os.path.exists(checkpoint_dir):
            shutil.rmtree(checkpoint_dir)

        ```
    ###Assistant : Deletes local copy of checkpoint.

        Args:
            checkpoint_path (str): Path to checkpoint.
        
    "
2499,"def _resize_worker_group(self, state_dict, max_retries=10):
        
        old_workers = self.worker_group.num_workers
        self.worker_group.reset()

        time.sleep(1)
        for i in range(max_retries):
            new_workers = self.worker_group.new_workers_size()
            if new_workers:
                self._last_resize = time.time()
                startup_success = self._start_workers(int(new_workers))
                if not startup_success:
                    logger.info(
                        f""Worker startup failed. Retrying ""
                        f""{max_retries-i-1} more times.""
                    )
                    self.worker_group.reset()
                    continue
                self.load_state_dict(state_dict, blocking=True)
                if self.use_local and new_workers == 1 and old_workers > 1:
                    # Major hack. If we go from LocalDistributedRunner to a
                    # standard TorchRunner we have to manually reset the
                    # dummy actor handle global vars.
                    # TODO(amog): Refactor LocalDistributedTorchRunner to
                    #  not use global variables for resource reservation.
                    ray.util.sgd.torch.distributed_torch_runner._dummy_cuda_actor = None
                    ray.util.sgd.torch.distributed_torch_runner._dummy_cpu_actor = None
                return
            else:
                delay = 2 ** i
                logger.warning(""No new workers found. Retrying in %d sec."" % delay)
                time.sleep(delay)
        raise RuntimeError(""Exceeded max_retries for relaunching workers."")
","Resizes the number of remote workers based on available resources.
        Total number of workers will never exceed `num_workers` amount.

        Args:
            state_dict (dict): The state dict to load to all workers.
            max_retries (int): How many times to attempt to resize workers
                before failing.
        ",42,119,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _resize_worker_group(self, state_dict, max_retries=10):
        
        old_workers = self.worker_group.num_workers
        self.worker_group.reset()

        time.sleep(1)
        for i in range(max_retries):
            new_workers = self.worker_group.new_workers_size()
            if new_workers:
                self._last_resize = time.time()
                startup_success = self._start_workers(int(new_workers))
                if not startup_success:
                    logger.info(
                        f""Worker startup failed. Retrying ""
                        f""{max_retries-i-1} more times.""
                    )
                    self.worker_group.reset()
                    continue
                self.load_state_dict(state_dict, blocking=True)
                if self.use_local and new_workers == 1 and old_workers > 1:
                    # Major hack. If we go from LocalDistributedRunner to a
                    # standard TorchRunner we have to manually reset the
                    # dummy actor handle global vars.
                    # TODO(amog): Refactor LocalDistributedTorchRunner to
                    #  not use global variables for resource reservation.
                    ray.util.sgd.torch.distributed_torch_runner._dummy_cuda_actor = None
                    ray.util.sgd.torch.distributed_torch_runner._dummy_cpu_actor = None
                return
            else:
                delay = 2 ** i
                logger.warning(""No new workers found. Retrying in %d sec."" % delay)
                time.sleep(delay)
        raise RuntimeError(""Exceeded max_retries for relaunching workers."")

        ```
    ###Assistant : Resizes the number of remote workers based on available resources.
        Total number of workers will never exceed `num_workers` amount.

        Args:
            state_dict (dict): The state dict to load to all workers.
            max_retries (int): How many times to attempt to resize workers
                before failing.
        
    "
2500,"def check_result_same(results, pipeline_results, check_keys):
    
    for key in check_keys:
        if results.get(key, None) is None:
            continue
        if isinstance(results[key], (BitmapMasks, PolygonMasks)):
            assert_allclose(pipeline_results[key].to_ndarray(),
                            results[key].to_ndarray())
        else:
            assert_allclose(pipeline_results[key], results[key])
","Check whether the ``pipeline_results`` is the same with the predefined
    ``results``.

    Args:
        results (dict): Predefined results which should be the standard
            output of the transform pipeline.
        pipeline_results (dict): Results processed by the transform
            pipeline.
        check_keys (tuple): Keys that need to be checked between
            results and pipeline_results.
    ",46,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_result_same(results, pipeline_results, check_keys):
    
    for key in check_keys:
        if results.get(key, None) is None:
            continue
        if isinstance(results[key], (BitmapMasks, PolygonMasks)):
            assert_allclose(pipeline_results[key].to_ndarray(),
                            results[key].to_ndarray())
        else:
            assert_allclose(pipeline_results[key], results[key])

        ```
    ###Assistant : Check whether the ``pipeline_results`` is the same with the predefined
    ``results``.

    Args:
        results (dict): Predefined results which should be the standard
            output of the transform pipeline.
        pipeline_results (dict): Results processed by the transform
            pipeline.
        check_keys (tuple): Keys that need to be checked between
            results and pipeline_results.
    
    "
2501,"def get_rasa_sdk_version() -> Text:
    

    dependencies_filename = ""pyproject.toml""
    toml_data = toml.load(project_root() / dependencies_filename)
    try:
        sdk_version = toml_data[""tool""][""poetry""][""dependencies""][""rasa-sdk""]
        return sdk_version[1:].strip()
    except AttributeError:
        raise Exception(f""Failed to find Rasa SDK version in {dependencies_filename}"")

",Find out what the referenced version of the Rasa SDK is.,11,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_rasa_sdk_version() -> Text:
    

    dependencies_filename = ""pyproject.toml""
    toml_data = toml.load(project_root() / dependencies_filename)
    try:
        sdk_version = toml_data[""tool""][""poetry""][""dependencies""][""rasa-sdk""]
        return sdk_version[1:].strip()
    except AttributeError:
        raise Exception(f""Failed to find Rasa SDK version in {dependencies_filename}"")


        ```
    ###Assistant : Find out what the referenced version of the Rasa SDK is.
    "
2502,"def test_update_omitted_version(self) -> None:
        
        version = self.get_success(
            self.handler.create_version(
                self.local_user,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""first_version_auth_data"",
                },
            )
        )
        self.assertEqual(version, ""1"")

        self.get_success(
            self.handler.update_version(
                self.local_user,
                version,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""revised_first_version_auth_data"",
                },
            )
        )

        # check we can retrieve it as the current version
        res = self.get_success(self.handler.get_version_info(self.local_user))
        del res[""etag""]  # etag is opaque, so don't test its contents
        self.assertDictEqual(
            res,
            {
                ""algorithm"": ""m.megolm_backup.v1"",
                ""auth_data"": ""revised_first_version_auth_data"",
                ""version"": version,
                ""count"": 0,
            },
        )
",Check that the update succeeds if the version is missing from the body,13,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_update_omitted_version(self) -> None:
        
        version = self.get_success(
            self.handler.create_version(
                self.local_user,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""first_version_auth_data"",
                },
            )
        )
        self.assertEqual(version, ""1"")

        self.get_success(
            self.handler.update_version(
                self.local_user,
                version,
                {
                    ""algorithm"": ""m.megolm_backup.v1"",
                    ""auth_data"": ""revised_first_version_auth_data"",
                },
            )
        )

        # check we can retrieve it as the current version
        res = self.get_success(self.handler.get_version_info(self.local_user))
        del res[""etag""]  # etag is opaque, so don't test its contents
        self.assertDictEqual(
            res,
            {
                ""algorithm"": ""m.megolm_backup.v1"",
                ""auth_data"": ""revised_first_version_auth_data"",
                ""version"": version,
                ""count"": 0,
            },
        )

        ```
    ###Assistant : Check that the update succeeds if the version is missing from the body
    "
2503,"def parameter_value(self, other, u, v=None):
        
        from sympy.geometry.point import Point
        if not isinstance(other, GeometryEntity):
            other = Point(other, dim=self.ambient_dimension)
        if not isinstance(other, Point):
            raise ValueError(""other must be a point"")
        if other == self.p1:
            return other
        if isinstance(u, Symbol) and v is None:
            delta = self.arbitrary_point(u) - self.p1
            eq = delta - (other - self.p1).unit
            sol = solve(eq, u, dict=True)
        elif isinstance(u, Symbol) and isinstance(v, Symbol):
            pt = self.arbitrary_point(u, v)
            sol = solve(pt - other, (u, v), dict=True)
        else:
            raise ValueError('expecting 1 or 2 symbols')
        if not sol:
            raise ValueError(""Given point is not on %s"" % func_name(self))
        return sol[0]  # {t: tval} or {u: uval, v: vval}
","Return the parameter(s) corresponding to the given point.

        Examples
        ========

        >>> from sympy import pi, Plane
        >>> from sympy.abc import t, u, v
        >>> p = Plane((2, 0, 0), (0, 0, 1), (0, 1, 0))

        By default, the parameter value returned defines a point
        that is a distance of 1 from the Plane's p1 value and
        in line with the given point:

        >>> on_circle = p.arbitrary_point(t).subs(t, pi/4)
        >>> on_circle.distance(p.p1)
        1
        >>> p.parameter_value(on_circle, t)
        {t: pi/4}

        Moving the point twice as far from p1 does not change
        the parameter value:

        >>> off_circle = p.p1 + (on_circle - p.p1)*2
        >>> off_circle.distance(p.p1)
        2
        >>> p.parameter_value(off_circle, t)
        {t: pi/4}

        If the 2-value parameter is desired, supply the two
        parameter symbols and a replacement dictionary will
        be returned:

        >>> p.parameter_value(on_circle, u, v)
        {u: sqrt(10)/10, v: sqrt(10)/30}
        >>> p.parameter_value(off_circle, u, v)
        {u: sqrt(10)/5, v: sqrt(10)/15}
        ",139,104,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parameter_value(self, other, u, v=None):
        
        from sympy.geometry.point import Point
        if not isinstance(other, GeometryEntity):
            other = Point(other, dim=self.ambient_dimension)
        if not isinstance(other, Point):
            raise ValueError(""other must be a point"")
        if other == self.p1:
            return other
        if isinstance(u, Symbol) and v is None:
            delta = self.arbitrary_point(u) - self.p1
            eq = delta - (other - self.p1).unit
            sol = solve(eq, u, dict=True)
        elif isinstance(u, Symbol) and isinstance(v, Symbol):
            pt = self.arbitrary_point(u, v)
            sol = solve(pt - other, (u, v), dict=True)
        else:
            raise ValueError('expecting 1 or 2 symbols')
        if not sol:
            raise ValueError(""Given point is not on %s"" % func_name(self))
        return sol[0]  # {t: tval} or {u: uval, v: vval}

        ```
    ###Assistant : Return the parameter(s) corresponding to the given point.

        Examples
        ========

        >>> from sympy import pi, Plane
        >>> from sympy.abc import t, u, v
        >>> p = Plane((2, 0, 0), (0, 0, 1), (0, 1, 0))

        By default, the parameter value returned defines a point
        that is a distance of 1 from the Plane's p1 value and
        in line with the given point:

        >>> on_circle = p.arbitrary_point(t).subs(t, pi/4)
        >>> on_circle.distance(p.p1)
        1
        >>> p.parameter_value(on_circle, t)
        {t: pi/4}

        Moving the point twice as far from p1 does not change
        the parameter value:

        >>> off_circle = p.p1 + (on_circle - p.p1)*2
        >>> off_circle.distance(p.p1)
        2
        >>> p.parameter_value(off_circle, t)
        {t: pi/4}

        If the 2-value parameter is desired, supply the two
        parameter symbols and a replacement dictionary will
        be returned:

        >>> p.parameter_value(on_circle, u, v)
        {u: sqrt(10)/10, v: sqrt(10)/30}
        >>> p.parameter_value(off_circle, u, v)
        {u: sqrt(10)/5, v: sqrt(10)/15}
        
    "
2504,"def orthographic_projection(X, camera):
    
    camera = camera.reshape((-1, 1, 3))
    X_trans = X[:, :, :2] + camera[:, :, 1:]
    shape = paddle.shape(X_trans)
    X_2d = (camera[:, :, 0] * X_trans.reshape((shape[0], -1))).reshape(shape)
    return X_2d


@register","Perform orthographic projection of 3D points X using the camera parameters
    Args:
        X: size = [B, N, 3]
        camera: size = [B, 3]
    Returns:
        Projected 2D points -- size = [B, N, 2]
    ",33,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def orthographic_projection(X, camera):
    
    camera = camera.reshape((-1, 1, 3))
    X_trans = X[:, :, :2] + camera[:, :, 1:]
    shape = paddle.shape(X_trans)
    X_2d = (camera[:, :, 0] * X_trans.reshape((shape[0], -1))).reshape(shape)
    return X_2d


@register
        ```
    ###Assistant : Perform orthographic projection of 3D points X using the camera parameters
    Args:
        X: size = [B, N, 3]
        camera: size = [B, 3]
    Returns:
        Projected 2D points -- size = [B, N, 2]
    
    "
2505,"def _load_state_id(self, state_id):
        
        remote_calls = [
            worker.load_state_stream.remote(state_id) for worker in self.remote_workers
        ]
        return remote_calls
",Loads the object with id `state_id` to all workers.,9,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _load_state_id(self, state_id):
        
        remote_calls = [
            worker.load_state_stream.remote(state_id) for worker in self.remote_workers
        ]
        return remote_calls

        ```
    ###Assistant : Loads the object with id `state_id` to all workers.
    "
2506,"def __new__(cls, p1, pt=None, angle=None, **kwargs):
        p1 = Point(p1, dim=2)
        if pt is not None and angle is None:
            try:
                p2 = Point(pt, dim=2)
            except (NotImplementedError, TypeError, ValueError):
                raise ValueError(filldedent())
            if p1 == p2:
                raise ValueError('A Ray requires two distinct points.')
        elif angle is not None and pt is None:
            # we need to know if the angle is an odd multiple of pi/2
            angle = sympify(angle)
            c = _pi_coeff(angle)
            p2 = None
            if c is not None:
                if c.is_Rational:
                    if c.q == 2:
                        if c.p == 1:
                            p2 = p1 + Point(0, 1)
                        elif c.p == 3:
                            p2 = p1 + Point(0, -1)
                    elif c.q == 1:
                        if c.p == 0:
                            p2 = p1 + Point(1, 0)
                        elif c.p == 1:
                            p2 = p1 + Point(-1, 0)
                if p2 is None:
                    c *= S.Pi
            else:
                c = angle % (2*S.Pi)
            if not p2:
                m = 2*c/S.Pi
                left = And(1 < m, m < 3)  # is it in quadrant 2 or 3?
                x = Piecewise((-1, left), (Piecewise((0, Eq(m % 1, 0)), (1, True)), True))
                y = Piecewise((-tan(c), left), (Piecewise((1, Eq(m, 1)), (-1, Eq(m, 3)), (tan(c), True)), True))
                p2 = p1 + Point(x, y)
        else:
            raise ValueError('A 2nd point or keyword ""angle"" must be used.')

        return LinearEntity2D.__new__(cls, p1, p2, **kwargs)
","
                    The 2nd argument was not a valid Point; if
                    it was meant to be an angle it should be
                    given with keyword ""angle"".",23,210,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __new__(cls, p1, pt=None, angle=None, **kwargs):
        p1 = Point(p1, dim=2)
        if pt is not None and angle is None:
            try:
                p2 = Point(pt, dim=2)
            except (NotImplementedError, TypeError, ValueError):
                raise ValueError(filldedent())
            if p1 == p2:
                raise ValueError('A Ray requires two distinct points.')
        elif angle is not None and pt is None:
            # we need to know if the angle is an odd multiple of pi/2
            angle = sympify(angle)
            c = _pi_coeff(angle)
            p2 = None
            if c is not None:
                if c.is_Rational:
                    if c.q == 2:
                        if c.p == 1:
                            p2 = p1 + Point(0, 1)
                        elif c.p == 3:
                            p2 = p1 + Point(0, -1)
                    elif c.q == 1:
                        if c.p == 0:
                            p2 = p1 + Point(1, 0)
                        elif c.p == 1:
                            p2 = p1 + Point(-1, 0)
                if p2 is None:
                    c *= S.Pi
            else:
                c = angle % (2*S.Pi)
            if not p2:
                m = 2*c/S.Pi
                left = And(1 < m, m < 3)  # is it in quadrant 2 or 3?
                x = Piecewise((-1, left), (Piecewise((0, Eq(m % 1, 0)), (1, True)), True))
                y = Piecewise((-tan(c), left), (Piecewise((1, Eq(m, 1)), (-1, Eq(m, 3)), (tan(c), True)), True))
                p2 = p1 + Point(x, y)
        else:
            raise ValueError('A 2nd point or keyword ""angle"" must be used.')

        return LinearEntity2D.__new__(cls, p1, p2, **kwargs)

        ```
    ###Assistant : 
                    The 2nd argument was not a valid Point; if
                    it was meant to be an angle it should be
                    given with keyword ""angle"".
    "
2507,"def delegate_command(args, host_state, exclude, require):  # type: (EnvironmentConfig, HostState, t.List[str], t.List[str]) -> None
    
    con = host_state.controller_profile.get_origin_controller_connection()
    working_directory = host_state.controller_profile.get_working_directory()
    host_delegation = not isinstance(args.controller, OriginConfig)

    if host_delegation:
        if data_context().content.collection:
            content_root = os.path.join(working_directory, data_context().content.collection.directory)
        else:
            content_root = os.path.join(working_directory, 'ansible')

        ansible_bin_path = os.path.join(working_directory, 'ansible', 'bin')

        with tempfile.NamedTemporaryFile(prefix='ansible-source-', suffix='.tgz') as payload_file:
            create_payload(args, payload_file.name)
            con.extract_archive(chdir=working_directory, src=payload_file)
    else:
        content_root = working_directory
        ansible_bin_path = ANSIBLE_BIN_PATH

    command = generate_command(args, host_state.controller_profile.python, ansible_bin_path, content_root, exclude, require)

    if isinstance(con, SshConnection):
        ssh = con.settings
    else:
        ssh = None

    options = []

    if isinstance(args, IntegrationConfig) and args.controller.is_managed and all(target.is_managed for target in args.targets):
        if not args.allow_destructive:
            options.append('--allow-destructive')

    with support_container_context(args, ssh) as containers:  # type: t.Optional[ContainerDatabase]
        if containers:
            options.extend(['--containers', json.dumps(containers.to_dict())])

        # Run unit tests unprivileged to prevent stray writes to the source tree.
        # Also disconnect from the network once requirements have been installed.
        if isinstance(args, UnitsConfig) and isinstance(con, DockerConnection):
            pytest_user = 'pytest'

            writable_dirs = [
                os.path.join(content_root, ResultType.JUNIT.relative_path),
                os.path.join(content_root, ResultType.COVERAGE.relative_path),
            ]

            con.run(['mkdir', '-p'] + writable_dirs)
            con.run(['chmod', '777'] + writable_dirs)
            con.run(['chmod', '755', working_directory])
            con.run(['chmod', '644', os.path.join(content_root, args.metadata_path)])
            con.run(['useradd', pytest_user, '--create-home'])
            con.run(insert_options(command, options + ['--requirements-mode', 'only']))

            container = con.inspect()
            networks = container.get_network_names()

            if networks is not None:
                for network in networks:
                    con.disconnect_network(network)
            else:
                display.warning('Network disconnection is not supported (this is normal under podman). '
                                'Tests will not be isolated from the network. Network-related tests may misbehave.')

            options.extend(['--requirements-mode', 'skip'])

            con.user = pytest_user

        success = False

        try:
            con.run(insert_options(command, options))
            success = True
        finally:
            if host_delegation:
                download_results(args, con, content_root, success)

",Delegate execution based on the provided host state.,8,231,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delegate_command(args, host_state, exclude, require):  # type: (EnvironmentConfig, HostState, t.List[str], t.List[str]) -> None
    
    con = host_state.controller_profile.get_origin_controller_connection()
    working_directory = host_state.controller_profile.get_working_directory()
    host_delegation = not isinstance(args.controller, OriginConfig)

    if host_delegation:
        if data_context().content.collection:
            content_root = os.path.join(working_directory, data_context().content.collection.directory)
        else:
            content_root = os.path.join(working_directory, 'ansible')

        ansible_bin_path = os.path.join(working_directory, 'ansible', 'bin')

        with tempfile.NamedTemporaryFile(prefix='ansible-source-', suffix='.tgz') as payload_file:
            create_payload(args, payload_file.name)
            con.extract_archive(chdir=working_directory, src=payload_file)
    else:
        content_root = working_directory
        ansible_bin_path = ANSIBLE_BIN_PATH

    command = generate_command(args, host_state.controller_profile.python, ansible_bin_path, content_root, exclude, require)

    if isinstance(con, SshConnection):
        ssh = con.settings
    else:
        ssh = None

    options = []

    if isinstance(args, IntegrationConfig) and args.controller.is_managed and all(target.is_managed for target in args.targets):
        if not args.allow_destructive:
            options.append('--allow-destructive')

    with support_container_context(args, ssh) as containers:  # type: t.Optional[ContainerDatabase]
        if containers:
            options.extend(['--containers', json.dumps(containers.to_dict())])

        # Run unit tests unprivileged to prevent stray writes to the source tree.
        # Also disconnect from the network once requirements have been installed.
        if isinstance(args, UnitsConfig) and isinstance(con, DockerConnection):
            pytest_user = 'pytest'

            writable_dirs = [
                os.path.join(content_root, ResultType.JUNIT.relative_path),
                os.path.join(content_root, ResultType.COVERAGE.relative_path),
            ]

            con.run(['mkdir', '-p'] + writable_dirs)
            con.run(['chmod', '777'] + writable_dirs)
            con.run(['chmod', '755', working_directory])
            con.run(['chmod', '644', os.path.join(content_root, args.metadata_path)])
            con.run(['useradd', pytest_user, '--create-home'])
            con.run(insert_options(command, options + ['--requirements-mode', 'only']))

            container = con.inspect()
            networks = container.get_network_names()

            if networks is not None:
                for network in networks:
                    con.disconnect_network(network)
            else:
                display.warning('Network disconnection is not supported (this is normal under podman). '
                                'Tests will not be isolated from the network. Network-related tests may misbehave.')

            options.extend(['--requirements-mode', 'skip'])

            con.user = pytest_user

        success = False

        try:
            con.run(insert_options(command, options))
            success = True
        finally:
            if host_delegation:
                download_results(args, con, content_root, success)


        ```
    ###Assistant : Delegate execution based on the provided host state.
    "
2508,"def test_in_predicate_requires_an_iterable(tmp_path, engine, filter_value):
    
    path = tmp_path / ""gh_8720_pandas.parquet""
    df = pd.DataFrame(
        {""A"": [1, 2, 3, 4], ""B"": [1, 1, 2, 2]},
    )
    df.to_parquet(path, engine=engine)
    with pytest.raises(TypeError, match=""Value of 'in' filter""):
        dd.read_parquet(path, engine=engine, filters=filter_value)

    # pandas to_parquet outputs a single file, dask outputs a folder with global
    # metadata that changes the filtering code path
    ddf = dd.from_pandas(df, npartitions=2)
    path = tmp_path / ""gh_8720_dask.parquet""
    ddf.to_parquet(path, engine=engine)
    with pytest.raises(TypeError, match=""Value of 'in' filter""):
        dd.read_parquet(path, engine=engine, filters=filter_value)
",Regression test for https://github.com/dask/dask/issues/8720,4,75,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_in_predicate_requires_an_iterable(tmp_path, engine, filter_value):
    
    path = tmp_path / ""gh_8720_pandas.parquet""
    df = pd.DataFrame(
        {""A"": [1, 2, 3, 4], ""B"": [1, 1, 2, 2]},
    )
    df.to_parquet(path, engine=engine)
    with pytest.raises(TypeError, match=""Value of 'in' filter""):
        dd.read_parquet(path, engine=engine, filters=filter_value)

    # pandas to_parquet outputs a single file, dask outputs a folder with global
    # metadata that changes the filtering code path
    ddf = dd.from_pandas(df, npartitions=2)
    path = tmp_path / ""gh_8720_dask.parquet""
    ddf.to_parquet(path, engine=engine)
    with pytest.raises(TypeError, match=""Value of 'in' filter""):
        dd.read_parquet(path, engine=engine, filters=filter_value)

        ```
    ###Assistant : Regression test for https://github.com/dask/dask/issues/8720
    "
2509,"def only_targets(self, target_type):  # type: (t.Type[THostConfig]) -> t.List[THostConfig]
        
        if not self.targets:
            raise Exception('There must be one or more targets.')

        assert type_guard(self.targets, target_type)

        return t.cast(t.List[THostConfig], self.targets)
","
        Return a list of target host configurations.
        Requires that there are one or more targets, all the specified type.
        ",19,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def only_targets(self, target_type):  # type: (t.Type[THostConfig]) -> t.List[THostConfig]
        
        if not self.targets:
            raise Exception('There must be one or more targets.')

        assert type_guard(self.targets, target_type)

        return t.cast(t.List[THostConfig], self.targets)

        ```
    ###Assistant : 
        Return a list of target host configurations.
        Requires that there are one or more targets, all the specified type.
        
    "
2510,"def _get_animated_artists(self):
        
        return tuple([a for ax_ in self.ax.get_figure().get_axes()
                      for a in ax_.get_children()
                      if a.get_animated() and a not in self.artists])
","
        Convenience method to get all animated artists of a figure, except
        those already present in self.artists. 'z_order' is ignored.
        ",19,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_animated_artists(self):
        
        return tuple([a for ax_ in self.ax.get_figure().get_axes()
                      for a in ax_.get_children()
                      if a.get_animated() and a not in self.artists])

        ```
    ###Assistant : 
        Convenience method to get all animated artists of a figure, except
        those already present in self.artists. 'z_order' is ignored.
        
    "
2511,"def new_gridlines(self, ax):
        
        gridlines = GridlinesCollection(
            None, transform=ax.transData, colors=mpl.rcParams['grid.color'],
            linestyles=mpl.rcParams['grid.linestyle'],
            linewidths=mpl.rcParams['grid.linewidth'])
        ax._set_artist_props(gridlines)
        gridlines.set_grid_helper(self)

        ax.axes._set_artist_props(gridlines)
        # gridlines.set_clip_path(self.axes.patch)
        # set_clip_path need to be deferred after Axes.cla is completed.
        # It is done inside the cla.

        return gridlines

","
        Create and return a new GridlineCollection instance.

        *which* : ""major"" or ""minor""
        *axis* : ""both"", ""x"" or ""y""

        ",18,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def new_gridlines(self, ax):
        
        gridlines = GridlinesCollection(
            None, transform=ax.transData, colors=mpl.rcParams['grid.color'],
            linestyles=mpl.rcParams['grid.linestyle'],
            linewidths=mpl.rcParams['grid.linewidth'])
        ax._set_artist_props(gridlines)
        gridlines.set_grid_helper(self)

        ax.axes._set_artist_props(gridlines)
        # gridlines.set_clip_path(self.axes.patch)
        # set_clip_path need to be deferred after Axes.cla is completed.
        # It is done inside the cla.

        return gridlines


        ```
    ###Assistant : 
        Create and return a new GridlineCollection instance.

        *which* : ""major"" or ""minor""
        *axis* : ""both"", ""x"" or ""y""

        
    "
2512,"def duplicates_removed(it, already_seen=()):
    
    lst = []
    seen = set()
    for i in it:
        if i in seen or i in already_seen:
            continue
        lst.append(i)
        seen.add(i)
    return lst

","
    Returns a list with duplicates removed from the iterable `it`.

    Order is preserved.
    ",13,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def duplicates_removed(it, already_seen=()):
    
    lst = []
    seen = set()
    for i in it:
        if i in seen or i in already_seen:
            continue
        lst.append(i)
        seen.add(i)
    return lst


        ```
    ###Assistant : 
    Returns a list with duplicates removed from the iterable `it`.

    Order is preserved.
    
    "
2513,"def masked_all(shape, dtype=float):
    
    a = masked_array(np.empty(shape, dtype),
                     mask=np.ones(shape, make_mask_descr(dtype)))
    return a

","
    Empty masked array with all elements masked.

    Return an empty masked array of the given shape and dtype, where all the
    data are masked.

    Parameters
    ----------
    shape : int or tuple of ints
        Shape of the required MaskedArray, e.g., ``(2, 3)`` or ``2``.
    dtype : dtype, optional
        Data type of the output.

    Returns
    -------
    a : MaskedArray
        A masked array with all data masked.

    See Also
    --------
    masked_all_like : Empty masked array modelled on an existing array.

    Examples
    --------
    >>> import numpy.ma as ma
    >>> ma.masked_all((3, 3))
    masked_array(
      data=[[--, --, --],
            [--, --, --],
            [--, --, --]],
      mask=[[ True,  True,  True],
            [ True,  True,  True],
            [ True,  True,  True]],
      fill_value=1e+20,
      dtype=float64)

    The `dtype` parameter defines the underlying data type.

    >>> a = ma.masked_all((3, 3))
    >>> a.dtype
    dtype('float64')
    >>> a = ma.masked_all((3, 3), dtype=np.int32)
    >>> a.dtype
    dtype('int32')

    ",136,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def masked_all(shape, dtype=float):
    
    a = masked_array(np.empty(shape, dtype),
                     mask=np.ones(shape, make_mask_descr(dtype)))
    return a


        ```
    ###Assistant : 
    Empty masked array with all elements masked.

    Return an empty masked array of the given shape and dtype, where all the
    data are masked.

    Parameters
    ----------
    shape : int or tuple of ints
        Shape of the required MaskedArray, e.g., ``(2, 3)`` or ``2``.
    dtype : dtype, optional
        Data type of the output.

    Returns
    -------
    a : MaskedArray
        A masked array with all data masked.

    See Also
    --------
    masked_all_like : Empty masked array modelled on an existing array.

    Examples
    --------
    >>> import numpy.ma as ma
    >>> ma.masked_all((3, 3))
    masked_array(
      data=[[--, --, --],
            [--, --, --],
            [--, --, --]],
      mask=[[ True,  True,  True],
            [ True,  True,  True],
            [ True,  True,  True]],
      fill_value=1e+20,
      dtype=float64)

    The `dtype` parameter defines the underlying data type.

    >>> a = ma.masked_all((3, 3))
    >>> a.dtype
    dtype('float64')
    >>> a = ma.masked_all((3, 3), dtype=np.int32)
    >>> a.dtype
    dtype('int32')

    
    "
2514,"def closeness_centrality(G, u=None, distance=None, wf_improved=True):
    r
    if G.is_directed():
        G = G.reverse()  # create a reversed graph view

    if distance is not None:
        # use Dijkstra's algorithm with specified attribute as edge weight
        path_length = functools.partial(
            nx.single_source_dijkstra_path_length, weight=distance
        )
    else:
        path_length = nx.single_source_shortest_path_length

    if u is None:
        nodes = G.nodes
    else:
        nodes = [u]
    closeness_centrality = {}
    for n in nodes:
        sp = path_length(G, n)
        totsp = sum(sp.values())
        len_G = len(G)
        _closeness_centrality = 0.0
        if totsp > 0.0 and len_G > 1:
            _closeness_centrality = (len(sp) - 1.0) / totsp
            # normalize to number of nodes-1 in connected part
            if wf_improved:
                s = (len(sp) - 1.0) / (len_G - 1)
                _closeness_centrality *= s
        closeness_centrality[n] = _closeness_centrality
    if u is not None:
        return closeness_centrality[u]
    else:
        return closeness_centrality


@not_implemented_for(""directed"")","Compute closeness centrality for nodes.

    Closeness centrality [1]_ of a node `u` is the reciprocal of the
    average shortest path distance to `u` over all `n-1` reachable nodes.

    .. math::

        C(u) = \frac{n - 1}{\sum_{v=1}^{n-1} d(v, u)},

    where `d(v, u)` is the shortest-path distance between `v` and `u`,
    and `n-1` is the number of nodes reachable from `u`. Notice that the
    closeness distance function computes the incoming distance to `u`
    for directed graphs. To use outward distance, act on `G.reverse()`.

    Notice that higher values of closeness indicate higher centrality.

    Wasserman and Faust propose an improved formula for graphs with
    more than one connected component. The result is ""a ratio of the
    fraction of actors in the group who are reachable, to the average
    distance"" from the reachable actors [2]_. You might think this
    scale factor is inverted but it is not. As is, nodes from small
    components receive a smaller closeness value. Letting `N` denote
    the number of nodes in the graph,

    .. math::

        C_{WF}(u) = \frac{n-1}{N-1} \frac{n - 1}{\sum_{v=1}^{n-1} d(v, u)},

    Parameters
    ----------
    G : graph
      A NetworkX graph

    u : node, optional
      Return only the value for node u

    distance : edge attribute key, optional (default=None)
      Use the specified edge attribute as the edge distance in shortest
      path calculations

    wf_improved : bool, optional (default=True)
      If True, scale by the fraction of nodes reachable. This gives the
      Wasserman and Faust improved formula. For single component graphs
      it is the same as the original formula.

    Returns
    -------
    nodes : dictionary
      Dictionary of nodes with closeness centrality as the value.

    Examples
    --------
    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
    >>> nx.closeness_centrality(G)
    {0: 1.0, 1: 1.0, 2: 0.75, 3: 0.75}

    See Also
    --------
    betweenness_centrality, load_centrality, eigenvector_centrality,
    degree_centrality, incremental_closeness_centrality

    Notes
    -----
    The closeness centrality is normalized to `(n-1)/(|G|-1)` where
    `n` is the number of nodes in the connected part of graph
    containing the node.  If the graph is not completely connected,
    this algorithm computes the closeness centrality for each
    connected part separately scaled by that parts size.

    If the 'distance' keyword is set to an edge attribute key then the
    shortest-path length will be computed using Dijkstra's algorithm with
    that edge attribute as the edge weight.

    The closeness centrality uses *inward* distance to a node, not outward.
    If you want to use outword distances apply the function to `G.reverse()`

    In NetworkX 2.2 and earlier a bug caused Dijkstra's algorithm to use the
    outward distance rather than the inward distance. If you use a 'distance'
    keyword and a DiGraph, your results will change between v2.2 and v2.3.

    References
    ----------
    .. [1] Linton C. Freeman: Centrality in networks: I.
       Conceptual clarification. Social Networks 1:215-239, 1979.
       https://doi.org/10.1016/0378-8733(78)90021-7
    .. [2] pg. 201 of Wasserman, S. and Faust, K.,
       Social Network Analysis: Methods and Applications, 1994,
       Cambridge University Press.
    ",467,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def closeness_centrality(G, u=None, distance=None, wf_improved=True):
    r
    if G.is_directed():
        G = G.reverse()  # create a reversed graph view

    if distance is not None:
        # use Dijkstra's algorithm with specified attribute as edge weight
        path_length = functools.partial(
            nx.single_source_dijkstra_path_length, weight=distance
        )
    else:
        path_length = nx.single_source_shortest_path_length

    if u is None:
        nodes = G.nodes
    else:
        nodes = [u]
    closeness_centrality = {}
    for n in nodes:
        sp = path_length(G, n)
        totsp = sum(sp.values())
        len_G = len(G)
        _closeness_centrality = 0.0
        if totsp > 0.0 and len_G > 1:
            _closeness_centrality = (len(sp) - 1.0) / totsp
            # normalize to number of nodes-1 in connected part
            if wf_improved:
                s = (len(sp) - 1.0) / (len_G - 1)
                _closeness_centrality *= s
        closeness_centrality[n] = _closeness_centrality
    if u is not None:
        return closeness_centrality[u]
    else:
        return closeness_centrality


@not_implemented_for(""directed"")
        ```
    ###Assistant : Compute closeness centrality for nodes.

    Closeness centrality [1]_ of a node `u` is the reciprocal of the
    average shortest path distance to `u` over all `n-1` reachable nodes.

    .. math::

        C(u) = \frac{n - 1}{\sum_{v=1}^{n-1} d(v, u)},

    where `d(v, u)` is the shortest-path distance between `v` and `u`,
    and `n-1` is the number of nodes reachable from `u`. Notice that the
    closeness distance function computes the incoming distance to `u`
    for directed graphs. To use outward distance, act on `G.reverse()`.

    Notice that higher values of closeness indicate higher centrality.

    Wasserman and Faust propose an improved formula for graphs with
    more than one connected component. The result is ""a ratio of the
    fraction of actors in the group who are reachable, to the average
    distance"" from the reachable actors [2]_. You might think this
    scale factor is inverted but it is not. As is, nodes from small
    components receive a smaller closeness value. Letting `N` denote
    the number of nodes in the graph,

    .. math::

        C_{WF}(u) = \frac{n-1}{N-1} \frac{n - 1}{\sum_{v=1}^{n-1} d(v, u)},

    Parameters
    ----------
    G : graph
      A NetworkX graph

    u : node, optional
      Return only the value for node u

    distance : edge attribute key, optional (default=None)
      Use the specified edge attribute as the edge distance in shortest
      path calculations

    wf_improved : bool, optional (default=True)
      If True, scale by the fraction of nodes reachable. This gives the
      Wasserman and Faust improved formula. For single component graphs
      it is the same as the original formula.

    Returns
    -------
    nodes : dictionary
      Dictionary of nodes with closeness centrality as the value.

    Examples
    --------
    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
    >>> nx.closeness_centrality(G)
    {0: 1.0, 1: 1.0, 2: 0.75, 3: 0.75}

    See Also
    --------
    betweenness_centrality, load_centrality, eigenvector_centrality,
    degree_centrality, incremental_closeness_centrality

    Notes
    -----
    The closeness centrality is normalized to `(n-1)/(|G|-1)` where
    `n` is the number of nodes in the connected part of graph
    containing the node.  If the graph is not completely connected,
    this algorithm computes the closeness centrality for each
    connected part separately scaled by that parts size.

    If the 'distance' keyword is set to an edge attribute key then the
    shortest-path length will be computed using Dijkstra's algorithm with
    that edge attribute as the edge weight.

    The closeness centrality uses *inward* distance to a node, not outward.
    If you want to use outword distances apply the function to `G.reverse()`

    In NetworkX 2.2 and earlier a bug caused Dijkstra's algorithm to use the
    outward distance rather than the inward distance. If you use a 'distance'
    keyword and a DiGraph, your results will change between v2.2 and v2.3.

    References
    ----------
    .. [1] Linton C. Freeman: Centrality in networks: I.
       Conceptual clarification. Social Networks 1:215-239, 1979.
       https://doi.org/10.1016/0378-8733(78)90021-7
    .. [2] pg. 201 of Wasserman, S. and Faust, K.,
       Social Network Analysis: Methods and Applications, 1994,
       Cambridge University Press.
    
    "
2515,"async def _remove_old_push_actions_that_have_rotated(self) -> None:
        

        # We want to clear out anything that is older than a day that *has* already
        # been rotated.
        rotated_upto_stream_ordering = await self.db_pool.simple_select_one_onecol(
            table=""event_push_summary_stream_ordering"",
            keyvalues={},
            retcol=""stream_ordering"",
        )

        max_stream_ordering_to_delete = min(
            rotated_upto_stream_ordering, self.stream_ordering_day_ago
        )
",Clear out old push actions that have been summarised.,9,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def _remove_old_push_actions_that_have_rotated(self) -> None:
        

        # We want to clear out anything that is older than a day that *has* already
        # been rotated.
        rotated_upto_stream_ordering = await self.db_pool.simple_select_one_onecol(
            table=""event_push_summary_stream_ordering"",
            keyvalues={},
            retcol=""stream_ordering"",
        )

        max_stream_ordering_to_delete = min(
            rotated_upto_stream_ordering, self.stream_ordering_day_ago
        )

        ```
    ###Assistant : Clear out old push actions that have been summarised.
    "
2516,"def test_indent():
    
    multiline_string = 

    indented_multiline_string = 

    assert indented_multiline_string == _indent(multiline_string, 4)

","Assert that indenting a multiline string by 4 spaces prepends 4 spaces before each new line.test
test1
test2
test3    test
    test1
    test2
    test3",23,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_indent():
    
    multiline_string = 

    indented_multiline_string = 

    assert indented_multiline_string == _indent(multiline_string, 4)


        ```
    ###Assistant : Assert that indenting a multiline string by 4 spaces prepends 4 spaces before each new line.test
test1
test2
test3    test
    test1
    test2
    test3
    "
2517,"def update_inputs_outputs_dims(model, input_dims, output_dims):  # type: (ModelProto, Dict[Text, List[Any]], Dict[Text, List[Any]]) -> ModelProto
    
    dim_param_set = set()  # type: Set[Text]
","
        This function updates the dimension sizes of the model's inputs and outputs to the values
        provided in input_dims and output_dims. if the dim value provided is negative, a unique dim_param
        will be set for that dimension.

        Example. if we have the following shape for inputs and outputs:
                shape(input_1) = ('b', 3, 'w', 'h')
                shape(input_2) = ('b', 4)
                and shape(output)  = ('b', 'd', 5)

            The parameters can be provided as:
                input_dims = {
                    ""input_1"": ['b', 3, 'w', 'h'],
                    ""input_2"": ['b', 4],
                }
                output_dims = {
                    ""output"": ['b', -1, 5]
                }

            Putting it together:
                model = onnx.load('model.onnx')
                updated_model = update_inputs_outputs_dims(model, input_dims, output_dims)
                onnx.save(updated_model, 'model.onnx')
    ",102,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_inputs_outputs_dims(model, input_dims, output_dims):  # type: (ModelProto, Dict[Text, List[Any]], Dict[Text, List[Any]]) -> ModelProto
    
    dim_param_set = set()  # type: Set[Text]

        ```
    ###Assistant : 
        This function updates the dimension sizes of the model's inputs and outputs to the values
        provided in input_dims and output_dims. if the dim value provided is negative, a unique dim_param
        will be set for that dimension.

        Example. if we have the following shape for inputs and outputs:
                shape(input_1) = ('b', 3, 'w', 'h')
                shape(input_2) = ('b', 4)
                and shape(output)  = ('b', 'd', 5)

            The parameters can be provided as:
                input_dims = {
                    ""input_1"": ['b', 3, 'w', 'h'],
                    ""input_2"": ['b', 4],
                }
                output_dims = {
                    ""output"": ['b', -1, 5]
                }

            Putting it together:
                model = onnx.load('model.onnx')
                updated_model = update_inputs_outputs_dims(model, input_dims, output_dims)
                onnx.save(updated_model, 'model.onnx')
    
    "
2518,"def get_aliased_columns(aliased_columns, model_alias, targets, mode=None):
    
    for col in targets:
        if mode == 'input':
            if str(col.parts[0]) != model_alias and col.alias is not None:
                aliased_columns[aliased_columns.index(col.parts[-1])] = str(col.alias)

        if mode == 'output':
            if str(col.parts[0]) == model_alias and col.alias is not None:
                aliased_columns[aliased_columns.index('prediction')] = str(col.alias)

    return aliased_columns", This method assumes mdb_sql will alert if there are two columns with the same alias ,15,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_aliased_columns(aliased_columns, model_alias, targets, mode=None):
    
    for col in targets:
        if mode == 'input':
            if str(col.parts[0]) != model_alias and col.alias is not None:
                aliased_columns[aliased_columns.index(col.parts[-1])] = str(col.alias)

        if mode == 'output':
            if str(col.parts[0]) == model_alias and col.alias is not None:
                aliased_columns[aliased_columns.index('prediction')] = str(col.alias)

    return aliased_columns
        ```
    ###Assistant :  This method assumes mdb_sql will alert if there are two columns with the same alias 
    "
2519,"def get_model_urls(app_label, model_name):
    
    paths = []

    # Retrieve registered views for this model
    try:
        views = registry['views'][app_label][model_name]
    except KeyError:
        # No views have been registered for this model
        views = []

    for view in views:
        # Import the view class or function
        callable = import_string(view['path'])
        if issubclass(callable, View):
            callable = callable.as_view()
        # Create a path to the view
        paths.append(
            path(f""{view['name']}/"", callable, name=f""{model_name}_{view['name']}"", kwargs=view['kwargs'])
        )

    return paths
","
    Return a list of URL paths for detail views registered to the given model.

    Args:
        app_label: App/plugin name
        model_name: Model name
    ",21,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_model_urls(app_label, model_name):
    
    paths = []

    # Retrieve registered views for this model
    try:
        views = registry['views'][app_label][model_name]
    except KeyError:
        # No views have been registered for this model
        views = []

    for view in views:
        # Import the view class or function
        callable = import_string(view['path'])
        if issubclass(callable, View):
            callable = callable.as_view()
        # Create a path to the view
        paths.append(
            path(f""{view['name']}/"", callable, name=f""{model_name}_{view['name']}"", kwargs=view['kwargs'])
        )

    return paths

        ```
    ###Assistant : 
    Return a list of URL paths for detail views registered to the given model.

    Args:
        app_label: App/plugin name
        model_name: Model name
    
    "
2520,"def head(self, url, **kwargs):
        r

        kwargs.setdefault(""allow_redirects"", False)
        return self.request(""HEAD"", url, **kwargs)
","Sends a HEAD request. Returns :class:`Response` object.

        :param url: URL for the new :class:`Request` object.
        :param \*\*kwargs: Optional arguments that ``request`` takes.
        :rtype: requests.Response
        ",24,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def head(self, url, **kwargs):
        r

        kwargs.setdefault(""allow_redirects"", False)
        return self.request(""HEAD"", url, **kwargs)

        ```
    ###Assistant : Sends a HEAD request. Returns :class:`Response` object.

        :param url: URL for the new :class:`Request` object.
        :param \*\*kwargs: Optional arguments that ``request`` takes.
        :rtype: requests.Response
        
    "
2521,"async def get_and_submit_flow_runs(self) -> List[FlowRun]:
        
        if not self.started:
            raise RuntimeError(""Agent is not started. Use `async with OrionAgent()...`"")

        self.logger.debug(""Checking for flow runs..."")

        submittable_runs = await self.client.read_flow_runs(
            sort=FlowRunSort.NEXT_SCHEDULED_START_TIME_ASC,
            flow_run_filter=self.flow_run_query_filter(),
        )

        for flow_run in submittable_runs:
            self.logger.info(f""Submitting flow run '{flow_run.id}'"")
            self.submitting_flow_run_ids.add(flow_run.id)
            self.task_group.start_soon(
                self.submit_run,
                flow_run,
            )
        return submittable_runs
","
        Queries for scheduled flow runs and submits them for execution in parallel
        ",12,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def get_and_submit_flow_runs(self) -> List[FlowRun]:
        
        if not self.started:
            raise RuntimeError(""Agent is not started. Use `async with OrionAgent()...`"")

        self.logger.debug(""Checking for flow runs..."")

        submittable_runs = await self.client.read_flow_runs(
            sort=FlowRunSort.NEXT_SCHEDULED_START_TIME_ASC,
            flow_run_filter=self.flow_run_query_filter(),
        )

        for flow_run in submittable_runs:
            self.logger.info(f""Submitting flow run '{flow_run.id}'"")
            self.submitting_flow_run_ids.add(flow_run.id)
            self.task_group.start_soon(
                self.submit_run,
                flow_run,
            )
        return submittable_runs

        ```
    ###Assistant : 
        Queries for scheduled flow runs and submits them for execution in parallel
        
    "
2522,"def get_quoted_string(value):
    
    quoted_string = QuotedString()
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        quoted_string.append(token)
    token, value = get_bare_quoted_string(value)
    quoted_string.append(token)
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        quoted_string.append(token)
    return quoted_string, value
","quoted-string = [CFWS] <bare-quoted-string> [CFWS]

    'bare-quoted-string' is an intermediate class defined by this
    parser and not by the RFC grammar.  It is the quoted string
    without any attached CFWS.
    ",29,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_quoted_string(value):
    
    quoted_string = QuotedString()
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        quoted_string.append(token)
    token, value = get_bare_quoted_string(value)
    quoted_string.append(token)
    if value and value[0] in CFWS_LEADER:
        token, value = get_cfws(value)
        quoted_string.append(token)
    return quoted_string, value

        ```
    ###Assistant : quoted-string = [CFWS] <bare-quoted-string> [CFWS]

    'bare-quoted-string' is an intermediate class defined by this
    parser and not by the RFC grammar.  It is the quoted string
    without any attached CFWS.
    
    "
2523,"def test_defined_keyword_exist_in_schema(self, keyword, discovered_catalog):
        
        schemas_errors = []
        for stream_name, stream in discovered_catalog.items():
            check_result = find_keyword_schema(stream.json_schema, key=keyword)
            if check_result:
                schemas_errors.append(stream_name)

        assert not schemas_errors, f""Found not allowed `{keyword}` keyword for selected streams: {schemas_errors}.""
",Checking for the presence of not allowed keywords within each json schema,12,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_defined_keyword_exist_in_schema(self, keyword, discovered_catalog):
        
        schemas_errors = []
        for stream_name, stream in discovered_catalog.items():
            check_result = find_keyword_schema(stream.json_schema, key=keyword)
            if check_result:
                schemas_errors.append(stream_name)

        assert not schemas_errors, f""Found not allowed `{keyword}` keyword for selected streams: {schemas_errors}.""

        ```
    ###Assistant : Checking for the presence of not allowed keywords within each json schema
    "
2524,"def normalize_path_patterns(patterns):
    
    patterns = [os.path.normcase(p) for p in patterns]
    dir_suffixes = {""%s*"" % path_sep for path_sep in {""/"", os.sep}}
    norm_patterns = []
    for pattern in patterns:
        for dir_suffix in dir_suffixes:
            if pattern.endswith(dir_suffix):
                norm_patterns.append(pattern[: -len(dir_suffix)])
                break
        else:
            norm_patterns.append(pattern)
    return norm_patterns

",Normalize an iterable of glob style patterns based on OS.,10,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def normalize_path_patterns(patterns):
    
    patterns = [os.path.normcase(p) for p in patterns]
    dir_suffixes = {""%s*"" % path_sep for path_sep in {""/"", os.sep}}
    norm_patterns = []
    for pattern in patterns:
        for dir_suffix in dir_suffixes:
            if pattern.endswith(dir_suffix):
                norm_patterns.append(pattern[: -len(dir_suffix)])
                break
        else:
            norm_patterns.append(pattern)
    return norm_patterns


        ```
    ###Assistant : Normalize an iterable of glob style patterns based on OS.
    "
2525,"def get_protobuf_schema() -> GeneratedProtocolMessageType:
        

        return ErrorResponseMessage_PB
","Return the type of protobuf object which stores a class of this type
        As a part of serialization and deserialization, we need the ability to
        lookup the protobuf object type directly from the object type. This
        static method allows us to do this.
        Importantly, this method is also used to create the reverse lookup ability within
        the metaclass of Serializable. In the metaclass, it calls this method and then
        it takes whatever type is returned from this method and adds an attribute to it
        with the type of this class attached to it. See the MetaSerializable class for
        details.
        :return: the type of protobuf object which corresponds to this class.
        :rtype: GeneratedProtocolMessageType
        ",112,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_protobuf_schema() -> GeneratedProtocolMessageType:
        

        return ErrorResponseMessage_PB

        ```
    ###Assistant : Return the type of protobuf object which stores a class of this type
        As a part of serialization and deserialization, we need the ability to
        lookup the protobuf object type directly from the object type. This
        static method allows us to do this.
        Importantly, this method is also used to create the reverse lookup ability within
        the metaclass of Serializable. In the metaclass, it calls this method and then
        it takes whatever type is returned from this method and adds an attribute to it
        with the type of this class attached to it. See the MetaSerializable class for
        details.
        :return: the type of protobuf object which corresponds to this class.
        :rtype: GeneratedProtocolMessageType
        
    "
2526,"def test_remote_media_cache_retention(self) -> None:
        
        # Advance 31 days (in seconds)
        self.reactor.advance(31 * 24 * 60 * 60)

        # Check that media has been correctly purged.
        # Local media should be unaffected.
        # Remote media accessed <30 days ago should still exist.
        self._assert_if_mxc_uris_purged(
            purged=[
                (self.remote_server_name, self.remote_not_recently_accessed_media),
            ],
            not_purged=[
                (self.remote_server_name, self.remote_recently_accessed_media),
                (self.hs.config.server.server_name, self.local_recently_accessed_media),
                (
                    self.hs.config.server.server_name,
                    self.local_not_recently_accessed_media,
                ),
                (self.hs.config.server.server_name, self.local_never_accessed_media),
            ],
        )
","
        Tests that entries from the remote media cache that have not been accessed
        recently is purged, while local media is unaffected.
        ",21,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_remote_media_cache_retention(self) -> None:
        
        # Advance 31 days (in seconds)
        self.reactor.advance(31 * 24 * 60 * 60)

        # Check that media has been correctly purged.
        # Local media should be unaffected.
        # Remote media accessed <30 days ago should still exist.
        self._assert_if_mxc_uris_purged(
            purged=[
                (self.remote_server_name, self.remote_not_recently_accessed_media),
            ],
            not_purged=[
                (self.remote_server_name, self.remote_recently_accessed_media),
                (self.hs.config.server.server_name, self.local_recently_accessed_media),
                (
                    self.hs.config.server.server_name,
                    self.local_not_recently_accessed_media,
                ),
                (self.hs.config.server.server_name, self.local_never_accessed_media),
            ],
        )

        ```
    ###Assistant : 
        Tests that entries from the remote media cache that have not been accessed
        recently is purged, while local media is unaffected.
        
    "
2527,"def setmodulation(self, modu):
        # type: (int) -> bool
        
        # According to https://nmap.org/npcap/guide/npcap-devguide.html#npcap-feature-dot11  # noqa: E501
        self._check_npcap_requirement()
        _modus = {
            0: ""dsss"",
            1: ""fhss"",
            2: ""irbaseband"",
            3: ""ofdm"",
            4: ""hrdss"",
            5: ""erp"",
            6: ""ht"",
            7: ""vht"",
            8: ""ihv"",
            9: ""mimo-ofdm"",
            10: ""mimo-ofdm"",
        }
        m = _modus.get(modu, ""unknown"") if isinstance(modu, int) else modu
        return self._npcap_set(""modu"", str(m))

","Set the interface modulation. It can be:
           - 0: dsss
           - 1: fhss
           - 2: irbaseband
           - 3: ofdm
           - 4: hrdss
           - 5: erp
           - 6: ht
           - 7: vht
           - 8: ihv
           - 9: mimo-ofdm
           - 10: mimo-ofdm
           - the value directly
        Only available with Npcap.",48,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def setmodulation(self, modu):
        # type: (int) -> bool
        
        # According to https://nmap.org/npcap/guide/npcap-devguide.html#npcap-feature-dot11  # noqa: E501
        self._check_npcap_requirement()
        _modus = {
            0: ""dsss"",
            1: ""fhss"",
            2: ""irbaseband"",
            3: ""ofdm"",
            4: ""hrdss"",
            5: ""erp"",
            6: ""ht"",
            7: ""vht"",
            8: ""ihv"",
            9: ""mimo-ofdm"",
            10: ""mimo-ofdm"",
        }
        m = _modus.get(modu, ""unknown"") if isinstance(modu, int) else modu
        return self._npcap_set(""modu"", str(m))


        ```
    ###Assistant : Set the interface modulation. It can be:
           - 0: dsss
           - 1: fhss
           - 2: irbaseband
           - 3: ofdm
           - 4: hrdss
           - 5: erp
           - 6: ht
           - 7: vht
           - 8: ihv
           - 9: mimo-ofdm
           - 10: mimo-ofdm
           - the value directly
        Only available with Npcap.
    "
2528,"def _on_connection_error(self, connection, exception):
        
        log.error(""Failed to connect"", exc_info=True)
","
        Invoked by pika when connection on connection error
        :param connection:
        :param exception:
        :return:
        ",13,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _on_connection_error(self, connection, exception):
        
        log.error(""Failed to connect"", exc_info=True)

        ```
    ###Assistant : 
        Invoked by pika when connection on connection error
        :param connection:
        :param exception:
        :return:
        
    "
2529,"def is_monotonic_decreasing(self) -> bool:
        
        # monotonic decreasing if and only if reverse is monotonic increasing
        return self[::-1].is_monotonic_increasing
","
        Return a boolean if the values are equal or decreasing.
        ",10,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def is_monotonic_decreasing(self) -> bool:
        
        # monotonic decreasing if and only if reverse is monotonic increasing
        return self[::-1].is_monotonic_increasing

        ```
    ###Assistant : 
        Return a boolean if the values are equal or decreasing.
        
    "
2530,"def test_sends_assignment_notification(self):
        

        url = f""/api/0/issues/{self.group.id}/""
        with self.tasks():
            response = self.client.put(url, format=""json"", data={""assignedTo"": self.user.username})
        assert response.status_code == 200, response.content

        msg = mail.outbox[0]
        # check the txt version
        assert f""assigned {self.short_id} to themselves"" in msg.body
        # check the html version
        assert f""{self.short_id}</a> to themselves</p>"" in msg.alternatives[0][0]

        attachment, text = get_attachment()

        assert text == f""Issue assigned to {self.name} by themselves""
        assert attachment[""title""] == self.group.title
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=assigned_activity-slack-user|Notification Settings>""
        )
","
        Test that an email AND Slack notification are sent with
        the expected values when an issue is assigned.
        ",18,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_sends_assignment_notification(self):
        

        url = f""/api/0/issues/{self.group.id}/""
        with self.tasks():
            response = self.client.put(url, format=""json"", data={""assignedTo"": self.user.username})
        assert response.status_code == 200, response.content

        msg = mail.outbox[0]
        # check the txt version
        assert f""assigned {self.short_id} to themselves"" in msg.body
        # check the html version
        assert f""{self.short_id}</a> to themselves</p>"" in msg.alternatives[0][0]

        attachment, text = get_attachment()

        assert text == f""Issue assigned to {self.name} by themselves""
        assert attachment[""title""] == self.group.title
        assert (
            attachment[""footer""]
            == f""{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=assigned_activity-slack-user|Notification Settings>""
        )

        ```
    ###Assistant : 
        Test that an email AND Slack notification are sent with
        the expected values when an issue is assigned.
        
    "
2531,"def _ask_default(self, default=''):
        
        self.prompt_output.write('Please enter the default value as valid Python.')
        if default:
            self.prompt_output.write(
                f""Accept the default '{default}' by pressing 'Enter' or ""
                f""provide another value.""
            )
        self.prompt_output.write(
            'The datetime and django.utils.timezone modules are available, so '
            'it is possible to provide e.g. timezone.now as a value.'
        )
        self.prompt_output.write(""Type 'exit' to exit this prompt"")
        while True:
            if default:
                prompt = ""[default: {}] >>> "".format(default)
            else:
                prompt = "">>> ""
            self.prompt_output.write(prompt, ending='')
            code = input()
            if not code and default:
                code = default
            if not code:
                self.prompt_output.write(""Please enter some code, or 'exit' (without quotes) to exit."")
            elif code == ""exit"":
                sys.exit(1)
            else:
                try:
                    return eval(code, {}, {'datetime': datetime, 'timezone': timezone})
                except (SyntaxError, NameError) as e:
                    self.prompt_output.write('Invalid input: %s' % e)
","
        Prompt for a default value.

        The ``default`` argument allows providing a custom default value (as a
        string) which will be shown to the user and used as the return value
        if the user doesn't provide any other input.
        ",38,119,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _ask_default(self, default=''):
        
        self.prompt_output.write('Please enter the default value as valid Python.')
        if default:
            self.prompt_output.write(
                f""Accept the default '{default}' by pressing 'Enter' or ""
                f""provide another value.""
            )
        self.prompt_output.write(
            'The datetime and django.utils.timezone modules are available, so '
            'it is possible to provide e.g. timezone.now as a value.'
        )
        self.prompt_output.write(""Type 'exit' to exit this prompt"")
        while True:
            if default:
                prompt = ""[default: {}] >>> "".format(default)
            else:
                prompt = "">>> ""
            self.prompt_output.write(prompt, ending='')
            code = input()
            if not code and default:
                code = default
            if not code:
                self.prompt_output.write(""Please enter some code, or 'exit' (without quotes) to exit."")
            elif code == ""exit"":
                sys.exit(1)
            else:
                try:
                    return eval(code, {}, {'datetime': datetime, 'timezone': timezone})
                except (SyntaxError, NameError) as e:
                    self.prompt_output.write('Invalid input: %s' % e)

        ```
    ###Assistant : 
        Prompt for a default value.

        The ``default`` argument allows providing a custom default value (as a
        string) which will be shown to the user and used as the return value
        if the user doesn't provide any other input.
        
    "
2532,"def reset_modules(self) -> None:
        
        self.modules = {}
        self.update_modules()
        self.parse_modules()
","Reset the loaded modules list. This is called from cleanup to clear
        temporarily loaded modules.",15,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reset_modules(self) -> None:
        
        self.modules = {}
        self.update_modules()
        self.parse_modules()

        ```
    ###Assistant : Reset the loaded modules list. This is called from cleanup to clear
        temporarily loaded modules.
    "
2533,"def test_ddppo_compilation(self):
        
        config = ppo.ddppo.DEFAULT_CONFIG.copy()
        config[""num_gpus_per_worker""] = 0
        num_iterations = 2

        for _ in framework_iterator(config, frameworks=""torch""):
            trainer = ppo.ddppo.DDPPOTrainer(config=config, env=""CartPole-v0"")
            for i in range(num_iterations):
                results = trainer.train()
                check_train_results(results)
                print(results)
                # Make sure, weights on all workers are the same (including
                # local one).
                weights = trainer.workers.foreach_worker(lambda w: w.get_weights())
                for w in weights[1:]:
                    check(w, weights[0])

            check_compute_single_action(trainer)
            trainer.stop()
",Test whether a DDPPOTrainer can be built with both frameworks.,10,56,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_ddppo_compilation(self):
        
        config = ppo.ddppo.DEFAULT_CONFIG.copy()
        config[""num_gpus_per_worker""] = 0
        num_iterations = 2

        for _ in framework_iterator(config, frameworks=""torch""):
            trainer = ppo.ddppo.DDPPOTrainer(config=config, env=""CartPole-v0"")
            for i in range(num_iterations):
                results = trainer.train()
                check_train_results(results)
                print(results)
                # Make sure, weights on all workers are the same (including
                # local one).
                weights = trainer.workers.foreach_worker(lambda w: w.get_weights())
                for w in weights[1:]:
                    check(w, weights[0])

            check_compute_single_action(trainer)
            trainer.stop()

        ```
    ###Assistant : Test whether a DDPPOTrainer can be built with both frameworks.
    "
2534,"def log_cosh(y_true, y_pred):
  
  y_pred = tf.convert_to_tensor(y_pred)
  y_true = tf.cast(y_true, y_pred.dtype)

  def _logcosh(x):
    return x + tf.math.softplus(-2. * x) - tf.cast(
        tf.math.log(2.), x.dtype)

  return backend.mean(_logcosh(y_pred - y_true), axis=-1)


@keras_export('keras.metrics.categorical_crossentropy',
              'keras.losses.categorical_crossentropy')
@tf.__internal__.dispatch.add_dispatch_support","Logarithm of the hyperbolic cosine of the prediction error.

  `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
  to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
  like the mean squared error, but will not be so strongly affected by the
  occasional wildly incorrect prediction.

  Standalone usage:

  >>> y_true = np.random.random(size=(2, 3))
  >>> y_pred = np.random.random(size=(2, 3))
  >>> loss = tf.keras.losses.logcosh(y_true, y_pred)
  >>> assert loss.shape == (2,)
  >>> x = y_pred - y_true
  >>> assert np.allclose(
  ...     loss.numpy(),
  ...     np.mean(x + np.log(np.exp(-2. * x) + 1.) - tf.math.log(2.), axis=-1),
  ...     atol=1e-5)

  Args:
    y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
    y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.

  Returns:
    Logcosh error values. shape = `[batch_size, d0, .. dN-1]`.
  ",131,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def log_cosh(y_true, y_pred):
  
  y_pred = tf.convert_to_tensor(y_pred)
  y_true = tf.cast(y_true, y_pred.dtype)

  def _logcosh(x):
    return x + tf.math.softplus(-2. * x) - tf.cast(
        tf.math.log(2.), x.dtype)

  return backend.mean(_logcosh(y_pred - y_true), axis=-1)


@keras_export('keras.metrics.categorical_crossentropy',
              'keras.losses.categorical_crossentropy')
@tf.__internal__.dispatch.add_dispatch_support
        ```
    ###Assistant : Logarithm of the hyperbolic cosine of the prediction error.

  `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
  to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
  like the mean squared error, but will not be so strongly affected by the
  occasional wildly incorrect prediction.

  Standalone usage:

  >>> y_true = np.random.random(size=(2, 3))
  >>> y_pred = np.random.random(size=(2, 3))
  >>> loss = tf.keras.losses.logcosh(y_true, y_pred)
  >>> assert loss.shape == (2,)
  >>> x = y_pred - y_true
  >>> assert np.allclose(
  ...     loss.numpy(),
  ...     np.mean(x + np.log(np.exp(-2. * x) + 1.) - tf.math.log(2.), axis=-1),
  ...     atol=1e-5)

  Args:
    y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
    y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.

  Returns:
    Logcosh error values. shape = `[batch_size, d0, .. dN-1]`.
  
    "
2535,"def make_predict_function(self, force=False):
        
        if self.predict_function is not None and not force:
            return self.predict_function
","Creates a function that executes one step of inference.

        This method can be overridden to support custom inference logic.
        This method is called by `Model.predict` and `Model.predict_on_batch`.

        Typically, this method directly controls `tf.function` and
        `tf.distribute.Strategy` settings, and delegates the actual evaluation
        logic to `Model.predict_step`.

        This function is cached the first time `Model.predict` or
        `Model.predict_on_batch` is called. The cache is cleared whenever
        `Model.compile` is called. You can skip the cache and generate again the
        function with `force=True`.

        Args:
          force: Whether to regenerate the predict function and skip the cached
            function if available.

        Returns:
          Function. The function created by this method should accept a
          `tf.data.Iterator`, and return the outputs of the `Model`.
        ",110,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def make_predict_function(self, force=False):
        
        if self.predict_function is not None and not force:
            return self.predict_function

        ```
    ###Assistant : Creates a function that executes one step of inference.

        This method can be overridden to support custom inference logic.
        This method is called by `Model.predict` and `Model.predict_on_batch`.

        Typically, this method directly controls `tf.function` and
        `tf.distribute.Strategy` settings, and delegates the actual evaluation
        logic to `Model.predict_step`.

        This function is cached the first time `Model.predict` or
        `Model.predict_on_batch` is called. The cache is cleared whenever
        `Model.compile` is called. You can skip the cache and generate again the
        function with `force=True`.

        Args:
          force: Whether to regenerate the predict function and skip the cached
            function if available.

        Returns:
          Function. The function created by this method should accept a
          `tf.data.Iterator`, and return the outputs of the `Model`.
        
    "
2536,"def fit(self, X, y, **fit_params):
        
        self._validate_params()
        return self._fit(X, y, **fit_params)
","Fit the RFE model and then the underlying estimator on the selected features.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples.

        y : array-like of shape (n_samples,)
            The target values.

        **fit_params : dict
            Additional parameters passed to the `fit` method of the underlying
            estimator.

        Returns
        -------
        self : object
            Fitted estimator.
        ",58,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y, **fit_params):
        
        self._validate_params()
        return self._fit(X, y, **fit_params)

        ```
    ###Assistant : Fit the RFE model and then the underlying estimator on the selected features.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples.

        y : array-like of shape (n_samples,)
            The target values.

        **fit_params : dict
            Additional parameters passed to the `fit` method of the underlying
            estimator.

        Returns
        -------
        self : object
            Fitted estimator.
        
    "
2537,"def test_invalid_number_selection_fails():
    
    number_string = ""99999999""
    result = get_first_menu_and_fail(number_string)
    lines = result.stdout.splitlines()
    # Strange string addition are due to coloring, I believe
    assert lines[-1] == f""\x1b[31mInvalid selection {number_string}\x1b[0m""
    assert result.exit_code == 1

","
    We need to make sure that if we give an invalid number that the CLI
    will exit.
    ",17,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_invalid_number_selection_fails():
    
    number_string = ""99999999""
    result = get_first_menu_and_fail(number_string)
    lines = result.stdout.splitlines()
    # Strange string addition are due to coloring, I believe
    assert lines[-1] == f""\x1b[31mInvalid selection {number_string}\x1b[0m""
    assert result.exit_code == 1


        ```
    ###Assistant : 
    We need to make sure that if we give an invalid number that the CLI
    will exit.
    
    "
2538,"def filldedent(s, w=70, **kwargs):
    
    return '\n' + fill(dedent(str(s)).strip('\n'), width=w, **kwargs)

","
    Strips leading and trailing empty lines from a copy of ``s``, then dedents,
    fills and returns it.

    Empty line stripping serves to deal with docstrings like this one that
    start with a newline after the initial triple quote, inserting an empty
    line at the beginning of the string.

    Additional keyword arguments will be passed to ``textwrap.fill()``.

    See Also
    ========
    strlines, rawlines

    ",61,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def filldedent(s, w=70, **kwargs):
    
    return '\n' + fill(dedent(str(s)).strip('\n'), width=w, **kwargs)


        ```
    ###Assistant : 
    Strips leading and trailing empty lines from a copy of ``s``, then dedents,
    fills and returns it.

    Empty line stripping serves to deal with docstrings like this one that
    start with a newline after the initial triple quote, inserting an empty
    line at the beginning of the string.

    Additional keyword arguments will be passed to ``textwrap.fill()``.

    See Also
    ========
    strlines, rawlines

    
    "
2539,"def in1d(ar1, ar2, assume_unique=False, invert=False, kind=None):
    
    # Ravel both arrays, behavior for the first array could be different
    ar1 = np.asarray(ar1).ravel()
    ar2 = np.asarray(ar2).ravel()

    # Ensure that iteration through object arrays yields size-1 arrays
    if ar2.dtype == object:
        ar2 = ar2.reshape(-1, 1)
    # Convert booleans to uint8 so we can use the fast integer algorithm
    if ar1.dtype == bool:
        ar1 = ar1.view(np.uint8)
    if ar2.dtype == bool:
        ar2 = ar2.view(np.uint8)

    # Check if we can use a fast integer algorithm:
    integer_arrays = (np.issubdtype(ar1.dtype, np.integer) and
                      np.issubdtype(ar2.dtype, np.integer))

    if kind not in {None, 'sort', 'table'}:
        raise ValueError(
            ""Invalid kind: {0}. "".format(kind)
            + ""Please use None, 'sort' or 'table'."")

    if integer_arrays and kind in {None, 'table'}:
        ar2_min = np.min(ar2)
        ar2_max = np.max(ar2)

        ar2_range = int(ar2_max) - int(ar2_min)

        # Constraints on whether we can actually use the table method:
        range_safe_from_overflow = ar2_range < np.iinfo(ar2.dtype).max
        below_memory_constraint = ar2_range <= 6 * (ar1.size + ar2.size)

        # Optimal performance is for approximately
        # log10(size) > (log10(range) - 2.27) / 0.927.
        # However, here we set the requirement that by default
        # the intermediate array can only be 6x
        # the combined memory allocation of the original
        # arrays. See discussion on 
        # https://github.com/numpy/numpy/pull/12065.

        if (
            range_safe_from_overflow and 
            (below_memory_constraint or kind == 'table')
        ):

            if invert:
                outgoing_array = np.ones_like(ar1, dtype=bool)
            else:
                outgoing_array = np.zeros_like(ar1, dtype=bool)

            # Make elements 1 where the integer exists in ar2
            if invert:
                isin_helper_ar = np.ones(ar2_range + 1, dtype=bool)
                isin_helper_ar[ar2 - ar2_min] = 0
            else:
                isin_helper_ar = np.zeros(ar2_range + 1, dtype=bool)
                isin_helper_ar[ar2 - ar2_min] = 1

            # Mask out elements we know won't work
            basic_mask = (ar1 <= ar2_max) & (ar1 >= ar2_min)
            outgoing_array[basic_mask] = isin_helper_ar[ar1[basic_mask] -
                                                        ar2_min]

            return outgoing_array
        elif kind == 'table':  # not range_safe_from_overflow
            raise RuntimeError(
                ""You have specified kind='table', ""
                ""but the range of values in `ar2` exceeds the ""
                ""maximum integer of the datatype. ""
                ""Please set `kind` to None or 'sort'.""
            )
    elif kind == 'table':
        raise ValueError(
            ""The 'table' method is only ""
            ""supported for boolean or integer arrays. ""
            ""Please select 'sort' or None for kind.""
        )


    # Check if one of the arrays may contain arbitrary objects
    contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject

    # This code is run when
    # a) the first condition is true, making the code significantly faster
    # b) the second condition is true (i.e. `ar1` or `ar2` may contain
    #    arbitrary objects), since then sorting is not guaranteed to work
    if len(ar2) < 10 * len(ar1) ** 0.145 or contains_object:
        if invert:
            mask = np.ones(len(ar1), dtype=bool)
            for a in ar2:
                mask &= (ar1 != a)
        else:
            mask = np.zeros(len(ar1), dtype=bool)
            for a in ar2:
                mask |= (ar1 == a)
        return mask

    # Otherwise use sorting
    if not assume_unique:
        ar1, rev_idx = np.unique(ar1, return_inverse=True)
        ar2 = np.unique(ar2)

    ar = np.concatenate((ar1, ar2))
    # We need this to be a stable sort, so always use 'mergesort'
    # here. The values from the first array should always come before
    # the values from the second array.
    order = ar.argsort(kind='mergesort')
    sar = ar[order]
    if invert:
        bool_ar = (sar[1:] != sar[:-1])
    else:
        bool_ar = (sar[1:] == sar[:-1])
    flag = np.concatenate((bool_ar, [invert]))
    ret = np.empty(ar.shape, dtype=bool)
    ret[order] = flag

    if assume_unique:
        return ret[:len(ar1)]
    else:
        return ret[rev_idx]

","
    Test whether each element of a 1-D array is also present in a second array.

    Returns a boolean array the same length as `ar1` that is True
    where an element of `ar1` is in `ar2` and False otherwise.

    We recommend using :func:`isin` instead of `in1d` for new code.

    Parameters
    ----------
    ar1 : (M,) array_like
        Input array.
    ar2 : array_like
        The values against which to test each value of `ar1`.
    assume_unique : bool, optional
        If True, the input arrays are both assumed to be unique, which
        can speed up the calculation.  Default is False.
    invert : bool, optional
        If True, the values in the returned array are inverted (that is,
        False where an element of `ar1` is in `ar2` and True otherwise).
        Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
        to (but is faster than) ``np.invert(in1d(a, b))``.
    kind : {None, 'sort', 'table'}, optional
        The algorithm to use. This will not affect the final result,
        but will affect the speed. Default will select automatically
        based on memory considerations.

        * If 'sort', will use a mergesort-based approach. This will have
          a memory usage of roughly 6 times the sum of the sizes of
          `ar1` and `ar2`, not accounting for size of dtypes.
        * If 'table', will use a key-dictionary approach similar
          to a counting sort. This is only available for boolean and
          integer arrays. This will have a memory usage of the
          size of `ar1` plus the max-min value of `ar2`. This tends
          to be the faster method if the following formula is true:
          ``log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927``,
          but may use greater memory.
        * If `None`, will automatically choose 'table' if
          the required memory allocation is less than or equal to
          6 times the sum of the sizes of `ar1` and `ar2`,
          otherwise will use 'sort'. This is done to not use
          a large amount of memory by default, even though
          'table' may be faster in most cases.

        .. versionadded:: 1.8.0

    Returns
    -------
    in1d : (M,) ndarray, bool
        The values `ar1[in1d]` are in `ar2`.

    See Also
    --------
    isin                  : Version of this function that preserves the
                            shape of ar1.
    numpy.lib.arraysetops : Module with a number of other functions for
                            performing set operations on arrays.

    Notes
    -----
    `in1d` can be considered as an element-wise function version of the
    python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
    equivalent to ``np.array([item in b for item in a])``.
    However, this idea fails if `ar2` is a set, or similar (non-sequence)
    container:  As ``ar2`` is converted to an array, in those cases
    ``asarray(ar2)`` is an object array rather than the expected array of
    contained values.

    .. versionadded:: 1.4.0

    Examples
    --------
    >>> test = np.array([0, 1, 2, 5, 0])
    >>> states = [0, 2]
    >>> mask = np.in1d(test, states)
    >>> mask
    array([ True, False,  True, False,  True])
    >>> test[mask]
    array([0, 2, 0])
    >>> mask = np.in1d(test, states, invert=True)
    >>> mask
    array([False,  True, False,  True, False])
    >>> test[mask]
    array([1, 5])
    ",485,528,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def in1d(ar1, ar2, assume_unique=False, invert=False, kind=None):
    
    # Ravel both arrays, behavior for the first array could be different
    ar1 = np.asarray(ar1).ravel()
    ar2 = np.asarray(ar2).ravel()

    # Ensure that iteration through object arrays yields size-1 arrays
    if ar2.dtype == object:
        ar2 = ar2.reshape(-1, 1)
    # Convert booleans to uint8 so we can use the fast integer algorithm
    if ar1.dtype == bool:
        ar1 = ar1.view(np.uint8)
    if ar2.dtype == bool:
        ar2 = ar2.view(np.uint8)

    # Check if we can use a fast integer algorithm:
    integer_arrays = (np.issubdtype(ar1.dtype, np.integer) and
                      np.issubdtype(ar2.dtype, np.integer))

    if kind not in {None, 'sort', 'table'}:
        raise ValueError(
            ""Invalid kind: {0}. "".format(kind)
            + ""Please use None, 'sort' or 'table'."")

    if integer_arrays and kind in {None, 'table'}:
        ar2_min = np.min(ar2)
        ar2_max = np.max(ar2)

        ar2_range = int(ar2_max) - int(ar2_min)

        # Constraints on whether we can actually use the table method:
        range_safe_from_overflow = ar2_range < np.iinfo(ar2.dtype).max
        below_memory_constraint = ar2_range <= 6 * (ar1.size + ar2.size)

        # Optimal performance is for approximately
        # log10(size) > (log10(range) - 2.27) / 0.927.
        # However, here we set the requirement that by default
        # the intermediate array can only be 6x
        # the combined memory allocation of the original
        # arrays. See discussion on 
        # https://github.com/numpy/numpy/pull/12065.

        if (
            range_safe_from_overflow and 
            (below_memory_constraint or kind == 'table')
        ):

            if invert:
                outgoing_array = np.ones_like(ar1, dtype=bool)
            else:
                outgoing_array = np.zeros_like(ar1, dtype=bool)

            # Make elements 1 where the integer exists in ar2
            if invert:
                isin_helper_ar = np.ones(ar2_range + 1, dtype=bool)
                isin_helper_ar[ar2 - ar2_min] = 0
            else:
                isin_helper_ar = np.zeros(ar2_range + 1, dtype=bool)
                isin_helper_ar[ar2 - ar2_min] = 1

            # Mask out elements we know won't work
            basic_mask = (ar1 <= ar2_max) & (ar1 >= ar2_min)
            outgoing_array[basic_mask] = isin_helper_ar[ar1[basic_mask] -
                                                        ar2_min]

            return outgoing_array
        elif kind == 'table':  # not range_safe_from_overflow
            raise RuntimeError(
                ""You have specified kind='table', ""
                ""but the range of values in `ar2` exceeds the ""
                ""maximum integer of the datatype. ""
                ""Please set `kind` to None or 'sort'.""
            )
    elif kind == 'table':
        raise ValueError(
            ""The 'table' method is only ""
            ""supported for boolean or integer arrays. ""
            ""Please select 'sort' or None for kind.""
        )


    # Check if one of the arrays may contain arbitrary objects
    contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject

    # This code is run when
    # a) the first condition is true, making the code significantly faster
    # b) the second condition is true (i.e. `ar1` or `ar2` may contain
    #    arbitrary objects), since then sorting is not guaranteed to work
    if len(ar2) < 10 * len(ar1) ** 0.145 or contains_object:
        if invert:
            mask = np.ones(len(ar1), dtype=bool)
            for a in ar2:
                mask &= (ar1 != a)
        else:
            mask = np.zeros(len(ar1), dtype=bool)
            for a in ar2:
                mask |= (ar1 == a)
        return mask

    # Otherwise use sorting
    if not assume_unique:
        ar1, rev_idx = np.unique(ar1, return_inverse=True)
        ar2 = np.unique(ar2)

    ar = np.concatenate((ar1, ar2))
    # We need this to be a stable sort, so always use 'mergesort'
    # here. The values from the first array should always come before
    # the values from the second array.
    order = ar.argsort(kind='mergesort')
    sar = ar[order]
    if invert:
        bool_ar = (sar[1:] != sar[:-1])
    else:
        bool_ar = (sar[1:] == sar[:-1])
    flag = np.concatenate((bool_ar, [invert]))
    ret = np.empty(ar.shape, dtype=bool)
    ret[order] = flag

    if assume_unique:
        return ret[:len(ar1)]
    else:
        return ret[rev_idx]


        ```
    ###Assistant : 
    Test whether each element of a 1-D array is also present in a second array.

    Returns a boolean array the same length as `ar1` that is True
    where an element of `ar1` is in `ar2` and False otherwise.

    We recommend using :func:`isin` instead of `in1d` for new code.

    Parameters
    ----------
    ar1 : (M,) array_like
        Input array.
    ar2 : array_like
        The values against which to test each value of `ar1`.
    assume_unique : bool, optional
        If True, the input arrays are both assumed to be unique, which
        can speed up the calculation.  Default is False.
    invert : bool, optional
        If True, the values in the returned array are inverted (that is,
        False where an element of `ar1` is in `ar2` and True otherwise).
        Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
        to (but is faster than) ``np.invert(in1d(a, b))``.
    kind : {None, 'sort', 'table'}, optional
        The algorithm to use. This will not affect the final result,
        but will affect the speed. Default will select automatically
        based on memory considerations.

        * If 'sort', will use a mergesort-based approach. This will have
          a memory usage of roughly 6 times the sum of the sizes of
          `ar1` and `ar2`, not accounting for size of dtypes.
        * If 'table', will use a key-dictionary approach similar
          to a counting sort. This is only available for boolean and
          integer arrays. This will have a memory usage of the
          size of `ar1` plus the max-min value of `ar2`. This tends
          to be the faster method if the following formula is true:
          ``log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927``,
          but may use greater memory.
        * If `None`, will automatically choose 'table' if
          the required memory allocation is less than or equal to
          6 times the sum of the sizes of `ar1` and `ar2`,
          otherwise will use 'sort'. This is done to not use
          a large amount of memory by default, even though
          'table' may be faster in most cases.

        .. versionadded:: 1.8.0

    Returns
    -------
    in1d : (M,) ndarray, bool
        The values `ar1[in1d]` are in `ar2`.

    See Also
    --------
    isin                  : Version of this function that preserves the
                            shape of ar1.
    numpy.lib.arraysetops : Module with a number of other functions for
                            performing set operations on arrays.

    Notes
    -----
    `in1d` can be considered as an element-wise function version of the
    python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
    equivalent to ``np.array([item in b for item in a])``.
    However, this idea fails if `ar2` is a set, or similar (non-sequence)
    container:  As ``ar2`` is converted to an array, in those cases
    ``asarray(ar2)`` is an object array rather than the expected array of
    contained values.

    .. versionadded:: 1.4.0

    Examples
    --------
    >>> test = np.array([0, 1, 2, 5, 0])
    >>> states = [0, 2]
    >>> mask = np.in1d(test, states)
    >>> mask
    array([ True, False,  True, False,  True])
    >>> test[mask]
    array([0, 2, 0])
    >>> mask = np.in1d(test, states, invert=True)
    >>> mask
    array([False,  True, False,  True, False])
    >>> test[mask]
    array([1, 5])
    
    "
2540,"def test_devices(self) -> None:
        
        # Login in as the user
        self._get_token()

        # Check that we don't see a new device in our devices list
        channel = self.make_request(
            ""GET"", ""devices"", b""{}"", access_token=self.other_user_tok
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)

        # We should only see the one device (from the login in `prepare`)
        self.assertEqual(len(channel.json_body[""devices""]), 1)
",Tests that logging in as a user doesn't create a new device for them.,14,50,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_devices(self) -> None:
        
        # Login in as the user
        self._get_token()

        # Check that we don't see a new device in our devices list
        channel = self.make_request(
            ""GET"", ""devices"", b""{}"", access_token=self.other_user_tok
        )
        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)

        # We should only see the one device (from the login in `prepare`)
        self.assertEqual(len(channel.json_body[""devices""]), 1)

        ```
    ###Assistant : Tests that logging in as a user doesn't create a new device for them.
    "
2541,"def in_ipython() -> bool:
    
    try:
        eval('__IPYTHON__')
    except NameError:
        return False
    else:  # pragma: no cover
        return True
","
    Check whether we're in an ipython environment, including jupyter notebooks.
    ",10,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def in_ipython() -> bool:
    
    try:
        eval('__IPYTHON__')
    except NameError:
        return False
    else:  # pragma: no cover
        return True

        ```
    ###Assistant : 
    Check whether we're in an ipython environment, including jupyter notebooks.
    
    "
2542,"def test_stroptions_deprecated_internal_subset():
    
    with pytest.raises(ValueError, match=""deprecated options must be a subset""):
        StrOptions({""a"", ""b"", ""c""}, deprecated={""a"", ""d""})

    with pytest.raises(ValueError, match=""internal options must be a subset""):
        StrOptions({""a"", ""b"", ""c""}, internal={""a"", ""d""})
",Check that the deprecated and internal parameters must be subsets of options.,12,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stroptions_deprecated_internal_subset():
    
    with pytest.raises(ValueError, match=""deprecated options must be a subset""):
        StrOptions({""a"", ""b"", ""c""}, deprecated={""a"", ""d""})

    with pytest.raises(ValueError, match=""internal options must be a subset""):
        StrOptions({""a"", ""b"", ""c""}, internal={""a"", ""d""})

        ```
    ###Assistant : Check that the deprecated and internal parameters must be subsets of options.
    "
2543,"def test_change_view_without_object_change_permission(self):
        
        change_url = reverse(""admin9:admin_views_article_change"", args=(self.a1.pk,))
        self.client.force_login(self.viewuser)
        response = self.client.get(change_url)
        self.assertEqual(response.context[""title""], ""View article"")
        self.assertContains(response, ""<title>View article | Django site admin</title>"")
        self.assertContains(response, ""<h1>View article</h1>"")
        self.assertContains(
            response,
            '<a href=""/test_admin/admin9/admin_views/article/"" class=""closelink"">Close</a>',
        )
","
        The object should be read-only if the user has permission to view it
        and change objects of that type but not to change the current object.
        ",26,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_change_view_without_object_change_permission(self):
        
        change_url = reverse(""admin9:admin_views_article_change"", args=(self.a1.pk,))
        self.client.force_login(self.viewuser)
        response = self.client.get(change_url)
        self.assertEqual(response.context[""title""], ""View article"")
        self.assertContains(response, ""<title>View article | Django site admin</title>"")
        self.assertContains(response, ""<h1>View article</h1>"")
        self.assertContains(
            response,
            '<a href=""/test_admin/admin9/admin_views/article/"" class=""closelink"">Close</a>',
        )

        ```
    ###Assistant : 
        The object should be read-only if the user has permission to view it
        and change objects of that type but not to change the current object.
        
    "
2544,"def __getattr__(name):
    import warnings

    if name in __deprecated_num_index_names:
        warnings.warn(
            f""pandas.{name} is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Use pandas.Index with the appropriate dtype instead."",
            FutureWarning,
            stacklevel=2,
        )
        from pandas.core.api import Float64Index, Int64Index, UInt64Index

        return {
            ""Float64Index"": Float64Index,
            ""Int64Index"": Int64Index,
            ""UInt64Index"": UInt64Index,
        }[name]
    elif name == ""datetime"":
        warnings.warn(
            ""The pandas.datetime class is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Import from datetime module instead."",
            FutureWarning,
            stacklevel=2,
        )

        from datetime import datetime as dt

        return dt

    elif name == ""np"":

        warnings.warn(
            ""The pandas.np module is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Import numpy directly instead."",
            FutureWarning,
            stacklevel=2,
        )
        import numpy as np

        return np

    elif name in {""SparseSeries"", ""SparseDataFrame""}:
        warnings.warn(
            f""The {name} class is removed from pandas. Accessing it from ""
            ""the top-level namespace will also be removed in the next version."",
            FutureWarning,
            stacklevel=2,
        )

        return type(name, (), {})

    elif name == ""SparseArray"":

        warnings.warn(
            ""The pandas.SparseArray class is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Use pandas.arrays.SparseArray instead."",
            FutureWarning,
            stacklevel=2,
        )
        from pandas.core.arrays.sparse import SparseArray as _SparseArray

        return _SparseArray

    raise AttributeError(f""module 'pandas' has no attribute '{name}'"")


# module level doc-string
__doc__ = 

# Use __all__ to let type checkers know what is part of the public API.
# Pandas is not (yet) a py.typed library: the public API is determined
# based on the documentation.
__all__ = [
    ""BooleanDtype"",
    ""Categorical"",
    ""CategoricalDtype"",
    ""CategoricalIndex"",
    ""DataFrame"",
    ""DateOffset"",
    ""DatetimeIndex"",
    ""DatetimeTZDtype"",
    ""ExcelFile"",
    ""ExcelWriter"",
    ""Flags"",
    ""Float32Dtype"",
    ""Float64Dtype"",
    ""Grouper"",
    ""HDFStore"",
    ""Index"",
    ""IndexSlice"",
    ""Int16Dtype"",
    ""Int32Dtype"",
    ""Int64Dtype"",
    ""Int8Dtype"",
    ""Interval"",
    ""IntervalDtype"",
    ""IntervalIndex"",
    ""MultiIndex"",
    ""NA"",
    ""NaT"",
    ""NamedAgg"",
    ""Period"",
    ""PeriodDtype"",
    ""PeriodIndex"",
    ""RangeIndex"",
    ""Series"",
    ""SparseDtype"",
    ""StringDtype"",
    ""Timedelta"",
    ""TimedeltaIndex"",
    ""Timestamp"",
    ""UInt16Dtype"",
    ""UInt32Dtype"",
    ""UInt64Dtype"",
    ""UInt8Dtype"",
    ""api"",
    ""array"",
    ""arrays"",
    ""bdate_range"",
    ""concat"",
    ""crosstab"",
    ""cut"",
    ""date_range"",
    ""describe_option"",
    ""errors"",
    ""eval"",
    ""factorize"",
    ""get_dummies"",
    ""get_option"",
    ""infer_freq"",
    ""interval_range"",
    ""io"",
    ""isna"",
    ""isnull"",
    ""json_normalize"",
    ""lreshape"",
    ""melt"",
    ""merge"",
    ""merge_asof"",
    ""merge_ordered"",
    ""notna"",
    ""notnull"",
    ""offsets"",
    ""option_context"",
    ""options"",
    ""period_range"",
    ""pivot"",
    ""pivot_table"",
    ""plotting"",
    ""qcut"",
    ""read_clipboard"",
    ""read_csv"",
    ""read_excel"",
    ""read_feather"",
    ""read_fwf"",
    ""read_gbq"",
    ""read_hdf"",
    ""read_html"",
    ""read_json"",
    ""read_orc"",
    ""read_parquet"",
    ""read_pickle"",
    ""read_sas"",
    ""read_spss"",
    ""read_sql"",
    ""read_sql_query"",
    ""read_sql_table"",
    ""read_stata"",
    ""read_table"",
    ""read_xml"",
    ""reset_option"",
    ""set_eng_float_format"",
    ""set_option"",
    ""show_versions"",
    ""test"",
    ""testing"",
    ""timedelta_range"",
    ""to_datetime"",
    ""to_numeric"",
    ""to_pickle"",
    ""to_timedelta"",
    ""tseries"",
    ""unique"",
    ""value_counts"",
    ""wide_to_long"",
]
","
pandas - a powerful data analysis and manipulation library for Python
=====================================================================

**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with ""relational"" or ""labeled"" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way toward this goal.

Main Features
-------------
Here are just a few of the things that pandas does well:

  - Easy handling of missing data in floating point as well as non-floating
    point data.
  - Size mutability: columns can be inserted and deleted from DataFrame and
    higher dimensional objects
  - Automatic and explicit data alignment: objects can be explicitly aligned
    to a set of labels, or the user can simply ignore the labels and let
    `Series`, `DataFrame`, etc. automatically align the data for you in
    computations.
  - Powerful, flexible group by functionality to perform split-apply-combine
    operations on data sets, for both aggregating and transforming data.
  - Make it easy to convert ragged, differently-indexed data in other Python
    and NumPy data structures into DataFrame objects.
  - Intelligent label-based slicing, fancy indexing, and subsetting of large
    data sets.
  - Intuitive merging and joining data sets.
  - Flexible reshaping and pivoting of data sets.
  - Hierarchical labeling of axes (possible to have multiple labels per tick).
  - Robust IO tools for loading data from flat files (CSV and delimited),
    Excel files, databases, and saving/loading data from the ultrafast HDF5
    format.
  - Time series-specific functionality: date range generation and frequency
    conversion, moving window statistics, date shifting and lagging.
",289,355,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __getattr__(name):
    import warnings

    if name in __deprecated_num_index_names:
        warnings.warn(
            f""pandas.{name} is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Use pandas.Index with the appropriate dtype instead."",
            FutureWarning,
            stacklevel=2,
        )
        from pandas.core.api import Float64Index, Int64Index, UInt64Index

        return {
            ""Float64Index"": Float64Index,
            ""Int64Index"": Int64Index,
            ""UInt64Index"": UInt64Index,
        }[name]
    elif name == ""datetime"":
        warnings.warn(
            ""The pandas.datetime class is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Import from datetime module instead."",
            FutureWarning,
            stacklevel=2,
        )

        from datetime import datetime as dt

        return dt

    elif name == ""np"":

        warnings.warn(
            ""The pandas.np module is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Import numpy directly instead."",
            FutureWarning,
            stacklevel=2,
        )
        import numpy as np

        return np

    elif name in {""SparseSeries"", ""SparseDataFrame""}:
        warnings.warn(
            f""The {name} class is removed from pandas. Accessing it from ""
            ""the top-level namespace will also be removed in the next version."",
            FutureWarning,
            stacklevel=2,
        )

        return type(name, (), {})

    elif name == ""SparseArray"":

        warnings.warn(
            ""The pandas.SparseArray class is deprecated ""
            ""and will be removed from pandas in a future version. ""
            ""Use pandas.arrays.SparseArray instead."",
            FutureWarning,
            stacklevel=2,
        )
        from pandas.core.arrays.sparse import SparseArray as _SparseArray

        return _SparseArray

    raise AttributeError(f""module 'pandas' has no attribute '{name}'"")


# module level doc-string
__doc__ = 

# Use __all__ to let type checkers know what is part of the public API.
# Pandas is not (yet) a py.typed library: the public API is determined
# based on the documentation.
__all__ = [
    ""BooleanDtype"",
    ""Categorical"",
    ""CategoricalDtype"",
    ""CategoricalIndex"",
    ""DataFrame"",
    ""DateOffset"",
    ""DatetimeIndex"",
    ""DatetimeTZDtype"",
    ""ExcelFile"",
    ""ExcelWriter"",
    ""Flags"",
    ""Float32Dtype"",
    ""Float64Dtype"",
    ""Grouper"",
    ""HDFStore"",
    ""Index"",
    ""IndexSlice"",
    ""Int16Dtype"",
    ""Int32Dtype"",
    ""Int64Dtype"",
    ""Int8Dtype"",
    ""Interval"",
    ""IntervalDtype"",
    ""IntervalIndex"",
    ""MultiIndex"",
    ""NA"",
    ""NaT"",
    ""NamedAgg"",
    ""Period"",
    ""PeriodDtype"",
    ""PeriodIndex"",
    ""RangeIndex"",
    ""Series"",
    ""SparseDtype"",
    ""StringDtype"",
    ""Timedelta"",
    ""TimedeltaIndex"",
    ""Timestamp"",
    ""UInt16Dtype"",
    ""UInt32Dtype"",
    ""UInt64Dtype"",
    ""UInt8Dtype"",
    ""api"",
    ""array"",
    ""arrays"",
    ""bdate_range"",
    ""concat"",
    ""crosstab"",
    ""cut"",
    ""date_range"",
    ""describe_option"",
    ""errors"",
    ""eval"",
    ""factorize"",
    ""get_dummies"",
    ""get_option"",
    ""infer_freq"",
    ""interval_range"",
    ""io"",
    ""isna"",
    ""isnull"",
    ""json_normalize"",
    ""lreshape"",
    ""melt"",
    ""merge"",
    ""merge_asof"",
    ""merge_ordered"",
    ""notna"",
    ""notnull"",
    ""offsets"",
    ""option_context"",
    ""options"",
    ""period_range"",
    ""pivot"",
    ""pivot_table"",
    ""plotting"",
    ""qcut"",
    ""read_clipboard"",
    ""read_csv"",
    ""read_excel"",
    ""read_feather"",
    ""read_fwf"",
    ""read_gbq"",
    ""read_hdf"",
    ""read_html"",
    ""read_json"",
    ""read_orc"",
    ""read_parquet"",
    ""read_pickle"",
    ""read_sas"",
    ""read_spss"",
    ""read_sql"",
    ""read_sql_query"",
    ""read_sql_table"",
    ""read_stata"",
    ""read_table"",
    ""read_xml"",
    ""reset_option"",
    ""set_eng_float_format"",
    ""set_option"",
    ""show_versions"",
    ""test"",
    ""testing"",
    ""timedelta_range"",
    ""to_datetime"",
    ""to_numeric"",
    ""to_pickle"",
    ""to_timedelta"",
    ""tseries"",
    ""unique"",
    ""value_counts"",
    ""wide_to_long"",
]

        ```
    ###Assistant : 
pandas - a powerful data analysis and manipulation library for Python
=====================================================================

**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with ""relational"" or ""labeled"" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way toward this goal.

Main Features
-------------
Here are just a few of the things that pandas does well:

  - Easy handling of missing data in floating point as well as non-floating
    point data.
  - Size mutability: columns can be inserted and deleted from DataFrame and
    higher dimensional objects
  - Automatic and explicit data alignment: objects can be explicitly aligned
    to a set of labels, or the user can simply ignore the labels and let
    `Series`, `DataFrame`, etc. automatically align the data for you in
    computations.
  - Powerful, flexible group by functionality to perform split-apply-combine
    operations on data sets, for both aggregating and transforming data.
  - Make it easy to convert ragged, differently-indexed data in other Python
    and NumPy data structures into DataFrame objects.
  - Intelligent label-based slicing, fancy indexing, and subsetting of large
    data sets.
  - Intuitive merging and joining data sets.
  - Flexible reshaping and pivoting of data sets.
  - Hierarchical labeling of axes (possible to have multiple labels per tick).
  - Robust IO tools for loading data from flat files (CSV and delimited),
    Excel files, databases, and saving/loading data from the ultrafast HDF5
    format.
  - Time series-specific functionality: date range generation and frequency
    conversion, moving window statistics, date shifting and lagging.

    "
2545,"def readlink(path, canonicalize=False):
    
    path = os.path.expanduser(path)

    if not os.path.isabs(path):
        raise SaltInvocationError(""Path to link must be absolute."")

    if not os.path.islink(path):
        raise SaltInvocationError(""A valid link was not specified."")

    if canonicalize:
        return os.path.realpath(path)
    else:
        return salt.utils.path.readlink(path)

","
    .. versionadded:: 2014.1.0

    Return the path that a symlink points to
    If canonicalize is set to True, then it return the final target

    CLI Example:

    .. code-block:: bash

        salt '*' file.readlink /path/to/link
    ",32,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def readlink(path, canonicalize=False):
    
    path = os.path.expanduser(path)

    if not os.path.isabs(path):
        raise SaltInvocationError(""Path to link must be absolute."")

    if not os.path.islink(path):
        raise SaltInvocationError(""A valid link was not specified."")

    if canonicalize:
        return os.path.realpath(path)
    else:
        return salt.utils.path.readlink(path)


        ```
    ###Assistant : 
    .. versionadded:: 2014.1.0

    Return the path that a symlink points to
    If canonicalize is set to True, then it return the final target

    CLI Example:

    .. code-block:: bash

        salt '*' file.readlink /path/to/link
    
    "
2546,"def warns_deprecated_sympy():
    
    with warns(SymPyDeprecationWarning):
        yield

@contextlib.contextmanager","
    Shorthand for ``warns(SymPyDeprecationWarning)``

    This is the recommended way to test that ``SymPyDeprecationWarning`` is
    emitted for deprecated features in SymPy. To test for other warnings use
    ``warns``. To suppress warnings without asserting that they are emitted
    use ``ignore_warnings``.

    .. note::

       ``warns_deprecated_sympy()`` is only intended for internal use in the
       SymPy test suite to test that a deprecation warning triggers properly.
       All other code in the SymPy codebase, including documentation examples,
       should not use deprecated behavior.

       If you are a user of SymPy and you want to disable
       SymPyDeprecationWarnings, use ``warnings`` filters (see
       :ref:`silencing-sympy-deprecation-warnings`).

    >>> from sympy.testing.pytest import warns_deprecated_sympy
    >>> from sympy.utilities.exceptions import SymPyDeprecationWarning
    >>> with warns_deprecated_sympy():
    ...     SymPyDeprecationWarning(""Don't use"", feature=""old thing"",
    ...         deprecated_since_version=""1.0"", issue=123).warn()

    >>> with warns_deprecated_sympy():
    ...     pass
    Traceback (most recent call last):
    ...
    Failed: DID NOT WARN. No warnings of type \
    SymPyDeprecationWarning was emitted. The list of emitted warnings is: [].

    ",143,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def warns_deprecated_sympy():
    
    with warns(SymPyDeprecationWarning):
        yield

@contextlib.contextmanager
        ```
    ###Assistant : 
    Shorthand for ``warns(SymPyDeprecationWarning)``

    This is the recommended way to test that ``SymPyDeprecationWarning`` is
    emitted for deprecated features in SymPy. To test for other warnings use
    ``warns``. To suppress warnings without asserting that they are emitted
    use ``ignore_warnings``.

    .. note::

       ``warns_deprecated_sympy()`` is only intended for internal use in the
       SymPy test suite to test that a deprecation warning triggers properly.
       All other code in the SymPy codebase, including documentation examples,
       should not use deprecated behavior.

       If you are a user of SymPy and you want to disable
       SymPyDeprecationWarnings, use ``warnings`` filters (see
       :ref:`silencing-sympy-deprecation-warnings`).

    >>> from sympy.testing.pytest import warns_deprecated_sympy
    >>> from sympy.utilities.exceptions import SymPyDeprecationWarning
    >>> with warns_deprecated_sympy():
    ...     SymPyDeprecationWarning(""Don't use"", feature=""old thing"",
    ...         deprecated_since_version=""1.0"", issue=123).warn()

    >>> with warns_deprecated_sympy():
    ...     pass
    Traceback (most recent call last):
    ...
    Failed: DID NOT WARN. No warnings of type \
    SymPyDeprecationWarning was emitted. The list of emitted warnings is: [].

    
    "
2547,"def assert_array_equal(x, y, err_msg='', verbose=True, *, strict=False):
    
    __tracebackhide__ = True  # Hide traceback for py.test
    assert_array_compare(operator.__eq__, x, y, err_msg=err_msg,
                         verbose=verbose, header='Arrays are not equal',
                         strict=strict)

","
    Raises an AssertionError if two array_like objects are not equal.

    Given two array_like objects, check that the shape is equal and all
    elements of these objects are equal (but see the Notes for the special
    handling of a scalar). An exception is raised at shape mismatch or
    conflicting values. In contrast to the standard usage in numpy, NaNs
    are compared like numbers, no assertion is raised if both objects have
    NaNs in the same positions.

    The usual caution for verifying equality with floating point numbers is
    advised.

    Parameters
    ----------
    x : array_like
        The actual object to check.
    y : array_like
        The desired, expected object.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.
    strict : bool, optional
        If True, raise an AssertionError when either the shape or the data
        type of the array_like objects does not match. The special
        handling for scalars mentioned in the Notes section is disabled.

    Raises
    ------
    AssertionError
        If actual and desired objects are not equal.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Notes
    -----
    When one of `x` and `y` is a scalar and the other is array_like, the
    function checks that each element of the array_like object is equal to
    the scalar. This behaviour can be disabled with the `strict` parameter.

    Examples
    --------
    The first assert does not raise an exception:

    >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
    ...                               [np.exp(0),2.33333, np.nan])

    Assert fails with numerical imprecision with floats:

    >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
    ...                               [1, np.sqrt(np.pi)**2, np.nan])
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    Mismatched elements: 1 / 3 (33.3%)
    Max absolute difference: 4.4408921e-16
    Max relative difference: 1.41357986e-16
     x: array([1.      , 3.141593,      nan])
     y: array([1.      , 3.141593,      nan])

    Use `assert_allclose` or one of the nulp (number of floating point values)
    functions for these cases instead:

    >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
    ...                            [1, np.sqrt(np.pi)**2, np.nan],
    ...                            rtol=1e-10, atol=0)

    As mentioned in the Notes section, `assert_array_equal` has special
    handling for scalars. Here the test checks that each value in `x` is 3:

    >>> x = np.full((2, 5), fill_value=3)
    >>> np.testing.assert_array_equal(x, 3)

    Use `strict` to raise an AssertionError when comparing a scalar with an
    array:

    >>> np.testing.assert_array_equal(x, 3, strict=True)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    (shapes (2, 5), () mismatch)
     x: array([[3, 3, 3, 3, 3],
           [3, 3, 3, 3, 3]])
     y: array(3)

    The `strict` parameter also ensures that the array data types match:

    >>> x = np.array([2, 2, 2])
    >>> y = np.array([2., 2., 2.], dtype=np.float32)
    >>> np.testing.assert_array_equal(x, y, strict=True)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    (dtypes int64, float32 mismatch)
     x: array([2, 2, 2])
     y: array([2., 2., 2.], dtype=float32)
    ",461,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def assert_array_equal(x, y, err_msg='', verbose=True, *, strict=False):
    
    __tracebackhide__ = True  # Hide traceback for py.test
    assert_array_compare(operator.__eq__, x, y, err_msg=err_msg,
                         verbose=verbose, header='Arrays are not equal',
                         strict=strict)


        ```
    ###Assistant : 
    Raises an AssertionError if two array_like objects are not equal.

    Given two array_like objects, check that the shape is equal and all
    elements of these objects are equal (but see the Notes for the special
    handling of a scalar). An exception is raised at shape mismatch or
    conflicting values. In contrast to the standard usage in numpy, NaNs
    are compared like numbers, no assertion is raised if both objects have
    NaNs in the same positions.

    The usual caution for verifying equality with floating point numbers is
    advised.

    Parameters
    ----------
    x : array_like
        The actual object to check.
    y : array_like
        The desired, expected object.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.
    strict : bool, optional
        If True, raise an AssertionError when either the shape or the data
        type of the array_like objects does not match. The special
        handling for scalars mentioned in the Notes section is disabled.

    Raises
    ------
    AssertionError
        If actual and desired objects are not equal.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Notes
    -----
    When one of `x` and `y` is a scalar and the other is array_like, the
    function checks that each element of the array_like object is equal to
    the scalar. This behaviour can be disabled with the `strict` parameter.

    Examples
    --------
    The first assert does not raise an exception:

    >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
    ...                               [np.exp(0),2.33333, np.nan])

    Assert fails with numerical imprecision with floats:

    >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
    ...                               [1, np.sqrt(np.pi)**2, np.nan])
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    Mismatched elements: 1 / 3 (33.3%)
    Max absolute difference: 4.4408921e-16
    Max relative difference: 1.41357986e-16
     x: array([1.      , 3.141593,      nan])
     y: array([1.      , 3.141593,      nan])

    Use `assert_allclose` or one of the nulp (number of floating point values)
    functions for these cases instead:

    >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
    ...                            [1, np.sqrt(np.pi)**2, np.nan],
    ...                            rtol=1e-10, atol=0)

    As mentioned in the Notes section, `assert_array_equal` has special
    handling for scalars. Here the test checks that each value in `x` is 3:

    >>> x = np.full((2, 5), fill_value=3)
    >>> np.testing.assert_array_equal(x, 3)

    Use `strict` to raise an AssertionError when comparing a scalar with an
    array:

    >>> np.testing.assert_array_equal(x, 3, strict=True)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    (shapes (2, 5), () mismatch)
     x: array([[3, 3, 3, 3, 3],
           [3, 3, 3, 3, 3]])
     y: array(3)

    The `strict` parameter also ensures that the array data types match:

    >>> x = np.array([2, 2, 2])
    >>> y = np.array([2., 2., 2.], dtype=np.float32)
    >>> np.testing.assert_array_equal(x, y, strict=True)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    (dtypes int64, float32 mismatch)
     x: array([2, 2, 2])
     y: array([2., 2., 2.], dtype=float32)
    
    "
2548,"def update_from_data_x(self, x, ignore=None):
        
        x = np.ravel(x)
        self.update_from_data_xy(np.column_stack([x, np.ones(x.size)]),
                                 ignore=ignore, updatey=False)
","
        Update the x-bounds of the `Bbox` based on the passed in data. After
        updating, the bounds will have positive *width*, and *x0* will be the
        minimal value.

        Parameters
        ----------
        x : `~numpy.ndarray`
            Array of x-values.

        ignore : bool, optional
           - When ``True``, ignore the existing bounds of the `Bbox`.
           - When ``False``, include the existing bounds of the `Bbox`.
           - When ``None``, use the last value passed to :meth:`ignore`.
        ",69,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_from_data_x(self, x, ignore=None):
        
        x = np.ravel(x)
        self.update_from_data_xy(np.column_stack([x, np.ones(x.size)]),
                                 ignore=ignore, updatey=False)

        ```
    ###Assistant : 
        Update the x-bounds of the `Bbox` based on the passed in data. After
        updating, the bounds will have positive *width*, and *x0* will be the
        minimal value.

        Parameters
        ----------
        x : `~numpy.ndarray`
            Array of x-values.

        ignore : bool, optional
           - When ``True``, ignore the existing bounds of the `Bbox`.
           - When ``False``, include the existing bounds of the `Bbox`.
           - When ``None``, use the last value passed to :meth:`ignore`.
        
    "
2549,"def test_https_malformed_referer(self):
        
        malformed_referer_msg = ""Referer checking failed - Referer is malformed.""
        req = self._get_POST_request_with_token()
        req._is_secure_override = True
        req.META[""HTTP_REFERER""] = ""http://http://www.example.com/""
        mw = CsrfViewMiddleware(post_form_view)
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(
            response,
            ""Referer checking failed - Referer is insecure while host is secure."",
            status_code=403,
        )
        # Empty
        req.META[""HTTP_REFERER""] = """"
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # Non-ASCII
        req.META[""HTTP_REFERER""] = ""ØBöIß""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # missing scheme
        # >>> urlparse('//example.com/')
        # ParseResult(scheme='', netloc='example.com', path='/', params='', query='', fragment='')
        req.META[""HTTP_REFERER""] = ""//example.com/""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # missing netloc
        # >>> urlparse('https://')
        # ParseResult(scheme='https', netloc='', path='', params='', query='', fragment='')
        req.META[""HTTP_REFERER""] = ""https://""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # Invalid URL
        # >>> urlparse('https://[')
        # ValueError: Invalid IPv6 URL
        req.META[""HTTP_REFERER""] = ""https://[""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
","
        A POST HTTPS request with a bad referer is rejected.
        ",10,157,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_https_malformed_referer(self):
        
        malformed_referer_msg = ""Referer checking failed - Referer is malformed.""
        req = self._get_POST_request_with_token()
        req._is_secure_override = True
        req.META[""HTTP_REFERER""] = ""http://http://www.example.com/""
        mw = CsrfViewMiddleware(post_form_view)
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(
            response,
            ""Referer checking failed - Referer is insecure while host is secure."",
            status_code=403,
        )
        # Empty
        req.META[""HTTP_REFERER""] = """"
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # Non-ASCII
        req.META[""HTTP_REFERER""] = ""ØBöIß""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # missing scheme
        # >>> urlparse('//example.com/')
        # ParseResult(scheme='', netloc='example.com', path='/', params='', query='', fragment='')
        req.META[""HTTP_REFERER""] = ""//example.com/""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # missing netloc
        # >>> urlparse('https://')
        # ParseResult(scheme='https', netloc='', path='', params='', query='', fragment='')
        req.META[""HTTP_REFERER""] = ""https://""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)
        # Invalid URL
        # >>> urlparse('https://[')
        # ValueError: Invalid IPv6 URL
        req.META[""HTTP_REFERER""] = ""https://[""
        self._check_referer_rejects(mw, req)
        response = mw.process_view(req, post_form_view, (), {})
        self.assertContains(response, malformed_referer_msg, status_code=403)

        ```
    ###Assistant : 
        A POST HTTPS request with a bad referer is rejected.
        
    "
2550,"def test_missing_cpp_namespace(self) -> None:
        yaml_str = 
        output_error = self.get_errors_from_gen_backend_stubs(yaml_str)
        self.assertExpectedInline(output_error, )
","\
backend: XLA
supported:
- absYou must provide a value for ""cpp_namespace""",12,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_missing_cpp_namespace(self) -> None:
        yaml_str = 
        output_error = self.get_errors_from_gen_backend_stubs(yaml_str)
        self.assertExpectedInline(output_error, )

        ```
    ###Assistant : \
backend: XLA
supported:
- absYou must provide a value for ""cpp_namespace""
    "
2551,"def single_source_dijkstra_path_length(G, source, cutoff=None, weight=""weight""):
    
    return multi_source_dijkstra_path_length(G, {source}, cutoff=cutoff, weight=weight)

","Find shortest weighted path lengths in G from a source node.

    Compute the shortest path length between source and all other
    reachable nodes for a weighted graph.

    Parameters
    ----------
    G : NetworkX graph

    source : node label
        Starting node for path

    cutoff : integer or float, optional
        Length (sum of edge weights) at which the search is stopped.
        If cutoff is provided, only return paths with summed weight <= cutoff.

    weight : string or function
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number or None to indicate a hidden edge.

    Returns
    -------
    length : dict
        Dict keyed by node to shortest path length from source.

    Raises
    ------
    NodeNotFound
        If `source` is not in `G`.

    Examples
    --------
    >>> G = nx.path_graph(5)
    >>> length = nx.single_source_dijkstra_path_length(G, 0)
    >>> length[4]
    4
    >>> for node in [0, 1, 2, 3, 4]:
    ...     print(f""{node}: {length[node]}"")
    0: 0
    1: 1
    2: 2
    3: 3
    4: 4

    Notes
    -----
    Edge weight attributes must be numerical.
    Distances are calculated as sums of weighted edges traversed.

    The weight function can be used to hide edges by returning None.
    So ``weight = lambda u, v, d: 1 if d['color']==""red"" else None``
    will find the shortest red path.

    See Also
    --------
    single_source_dijkstra, single_source_bellman_ford_path_length

    ",289,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def single_source_dijkstra_path_length(G, source, cutoff=None, weight=""weight""):
    
    return multi_source_dijkstra_path_length(G, {source}, cutoff=cutoff, weight=weight)


        ```
    ###Assistant : Find shortest weighted path lengths in G from a source node.

    Compute the shortest path length between source and all other
    reachable nodes for a weighted graph.

    Parameters
    ----------
    G : NetworkX graph

    source : node label
        Starting node for path

    cutoff : integer or float, optional
        Length (sum of edge weights) at which the search is stopped.
        If cutoff is provided, only return paths with summed weight <= cutoff.

    weight : string or function
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number or None to indicate a hidden edge.

    Returns
    -------
    length : dict
        Dict keyed by node to shortest path length from source.

    Raises
    ------
    NodeNotFound
        If `source` is not in `G`.

    Examples
    --------
    >>> G = nx.path_graph(5)
    >>> length = nx.single_source_dijkstra_path_length(G, 0)
    >>> length[4]
    4
    >>> for node in [0, 1, 2, 3, 4]:
    ...     print(f""{node}: {length[node]}"")
    0: 0
    1: 1
    2: 2
    3: 3
    4: 4

    Notes
    -----
    Edge weight attributes must be numerical.
    Distances are calculated as sums of weighted edges traversed.

    The weight function can be used to hide edges by returning None.
    So ``weight = lambda u, v, d: 1 if d['color']==""red"" else None``
    will find the shortest red path.

    See Also
    --------
    single_source_dijkstra, single_source_bellman_ford_path_length

    
    "
2552,"def print_help(self):
        has_ticker_start = """" if self.ticker else ""[unvl]""
        has_ticker_end = """" if self.ticker else ""[/unvl]""
        help_text = f
        console.print(text=help_text, menu=""Stocks - Behavioural Analysis"")
","[cmds]
    load           load a specific stock ticker for analysis

[param]Ticker: [/param]{self.ticker.upper() or None}
{has_ticker_start}
[src][Finbrain][/src]
    headlines     sentiment from 15+ major news headlines
[src][Finnhub][/src]
    stats         sentiment stats including comparison with sector{has_ticker_end}
[src][Reddit][/src]
    wsb           show what WSB gang is up to in subreddit wallstreetbets
    watchlist     show other users watchlist
    popular       show popular tickers
    spac_c        show other users spacs announcements from subreddit SPACs community
    spac          show other users spacs announcements from other subs{has_ticker_start}
    getdd         gets due diligence from another user's post{has_ticker_end}
[src][Stocktwits][/src]
    trending      trending stocks
    stalker       stalk stocktwits user's last messages{has_ticker_start}
    bullbear      estimate quick sentiment from last 30 messages on board
    messages      output up to the 30 last messages on the board
[src][Twitter][/src]
    infer         infer about stock's sentiment from latest tweets
    sentiment     in-depth sentiment prediction from tweets over time
[src][Google][/src]
    mentions      interest over time based on stock's mentions
    regions       regions that show highest interest in stock
    queries       top related queries with this stock
    rise          top rising related queries with stock{has_ticker_end}
[src][SentimentInvestor][/src]
    popularsi     show most popular stocks on social media right now
    emerging      show stocks that are being talked about more than usual{has_ticker_start}
    metrics       core social sentiment metrics for this stock
    social        social media figures for stock popularity
    historical    plot the past week of data for a selected metric{has_ticker_end}[/cmds]
        ",205,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        has_ticker_start = """" if self.ticker else ""[unvl]""
        has_ticker_end = """" if self.ticker else ""[/unvl]""
        help_text = f
        console.print(text=help_text, menu=""Stocks - Behavioural Analysis"")

        ```
    ###Assistant : [cmds]
    load           load a specific stock ticker for analysis

[param]Ticker: [/param]{self.ticker.upper() or None}
{has_ticker_start}
[src][Finbrain][/src]
    headlines     sentiment from 15+ major news headlines
[src][Finnhub][/src]
    stats         sentiment stats including comparison with sector{has_ticker_end}
[src][Reddit][/src]
    wsb           show what WSB gang is up to in subreddit wallstreetbets
    watchlist     show other users watchlist
    popular       show popular tickers
    spac_c        show other users spacs announcements from subreddit SPACs community
    spac          show other users spacs announcements from other subs{has_ticker_start}
    getdd         gets due diligence from another user's post{has_ticker_end}
[src][Stocktwits][/src]
    trending      trending stocks
    stalker       stalk stocktwits user's last messages{has_ticker_start}
    bullbear      estimate quick sentiment from last 30 messages on board
    messages      output up to the 30 last messages on the board
[src][Twitter][/src]
    infer         infer about stock's sentiment from latest tweets
    sentiment     in-depth sentiment prediction from tweets over time
[src][Google][/src]
    mentions      interest over time based on stock's mentions
    regions       regions that show highest interest in stock
    queries       top related queries with this stock
    rise          top rising related queries with stock{has_ticker_end}
[src][SentimentInvestor][/src]
    popularsi     show most popular stocks on social media right now
    emerging      show stocks that are being talked about more than usual{has_ticker_start}
    metrics       core social sentiment metrics for this stock
    social        social media figures for stock popularity
    historical    plot the past week of data for a selected metric{has_ticker_end}[/cmds]
        
    "
2553,"def iterate_instructions(code_object):
    
    # The arg extension the EXTENDED_ARG opcode represents is automatically handled by get_instructions() but the
    # instruction is left in. Get rid of it to make subsequent parsing easier/safer.
    yield from (i for i in get_instructions(code_object) if i.opname != ""EXTENDED_ARG"")

    yield None

    # For each constant in this code object that is itself a code object,
    # parse this constant in the same manner.
    for constant in code_object.co_consts:
        if inspect.iscode(constant):
            yield from iterate_instructions(constant)
","Delivers the byte-code instructions as a continuous stream.

    Yields `dis.Instruction`. After each code-block (`co_code`), `None` is
    yielded to mark the end of the block and to interrupt the steam.
    ",29,75,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def iterate_instructions(code_object):
    
    # The arg extension the EXTENDED_ARG opcode represents is automatically handled by get_instructions() but the
    # instruction is left in. Get rid of it to make subsequent parsing easier/safer.
    yield from (i for i in get_instructions(code_object) if i.opname != ""EXTENDED_ARG"")

    yield None

    # For each constant in this code object that is itself a code object,
    # parse this constant in the same manner.
    for constant in code_object.co_consts:
        if inspect.iscode(constant):
            yield from iterate_instructions(constant)

        ```
    ###Assistant : Delivers the byte-code instructions as a continuous stream.

    Yields `dis.Instruction`. After each code-block (`co_code`), `None` is
    yielded to mark the end of the block and to interrupt the steam.
    
    "
2554,"def fit(self) -> ResultGrid:
        

        if not self._is_ray_client:
            try:
                return self._local_tuner.fit()
            except Exception as e:
                raise TuneError(
                    f""Tune run failed. ""
                    f'Please use tuner = Tuner.restore(""'
                    f'{self._local_tuner.get_experiment_checkpoint_dir()}"") to resume.'
                ) from e
        else:
            experiment_checkpoint_dir = ray.get(
                self._remote_tuner.get_experiment_checkpoint_dir.remote()
            )
            try:
                return ray.get(self._remote_tuner.fit.remote())
            except Exception as e:
                raise TuneError(
                    f""Tune run failed. ""
                    f'Please use tuner = Tuner.restore(""'
                    f'{experiment_checkpoint_dir}"") to resume.'
                ) from e
","Executes hyperparameter tuning job as configured and returns result.

        Failure handling:
        For the kind of exception that happens during the execution of a trial,
        one may inspect it together with stacktrace through the returned result grid.
        See ``ResultGrid`` for reference. Each trial may fail up to a certain number.
        This is configured by ``RunConfig.FailureConfig.max_failures``.

        Exception that happens beyond trials will be thrown by this method as well.
        In such cases, there will be instruction like the following printed out
        at the end of console output to inform users on how to resume.

        Please use tuner = Tuner.restore(""~/ray_results/tuner_resume"")
        to resume.

        Raises:
            RayTaskError: If user-provided trainable raises an exception
            TuneError: General Ray Tune error.
        ",112,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self) -> ResultGrid:
        

        if not self._is_ray_client:
            try:
                return self._local_tuner.fit()
            except Exception as e:
                raise TuneError(
                    f""Tune run failed. ""
                    f'Please use tuner = Tuner.restore(""'
                    f'{self._local_tuner.get_experiment_checkpoint_dir()}"") to resume.'
                ) from e
        else:
            experiment_checkpoint_dir = ray.get(
                self._remote_tuner.get_experiment_checkpoint_dir.remote()
            )
            try:
                return ray.get(self._remote_tuner.fit.remote())
            except Exception as e:
                raise TuneError(
                    f""Tune run failed. ""
                    f'Please use tuner = Tuner.restore(""'
                    f'{experiment_checkpoint_dir}"") to resume.'
                ) from e

        ```
    ###Assistant : Executes hyperparameter tuning job as configured and returns result.

        Failure handling:
        For the kind of exception that happens during the execution of a trial,
        one may inspect it together with stacktrace through the returned result grid.
        See ``ResultGrid`` for reference. Each trial may fail up to a certain number.
        This is configured by ``RunConfig.FailureConfig.max_failures``.

        Exception that happens beyond trials will be thrown by this method as well.
        In such cases, there will be instruction like the following printed out
        at the end of console output to inform users on how to resume.

        Please use tuner = Tuner.restore(""~/ray_results/tuner_resume"")
        to resume.

        Raises:
            RayTaskError: If user-provided trainable raises an exception
            TuneError: General Ray Tune error.
        
    "
2555,"def _handle_coordinator_update(self) -> None:
        
        self._refresh()
        super()._handle_coordinator_update()
","
        Handle updated data from the coordinator.

        Tests fails without this method.
        ",11,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _handle_coordinator_update(self) -> None:
        
        self._refresh()
        super()._handle_coordinator_update()

        ```
    ###Assistant : 
        Handle updated data from the coordinator.

        Tests fails without this method.
        
    "
2556,"def _skew_1d(self, column, bias=True, nan_policy=""propagate""):
        
        # import depends on scipy, not installed by default
        from dask.array import stats as da_stats

        if pd.Int64Dtype.is_dtype(column._meta_nonempty):
            column = column.astype(""f8"")

        if not np.issubdtype(column.dtype, np.number):
            column = column.astype(""f8"")

        name = self._token_prefix + ""skew-1d-"" + tokenize(column)

        array_skew = da_stats.skew(
            column.values, axis=0, bias=bias, nan_policy=nan_policy
        )

        layer = {(name, 0): (methods.wrap_skew_reduction, (array_skew._name,), None)}
        graph = HighLevelGraph.from_collections(name, layer, dependencies=[array_skew])

        return new_dd_object(
            graph, name, column._meta_nonempty.skew(), divisions=[None, None]
        )
","1D version of the skew calculation.

        Uses the array version from da.stats in case we are passing in a single series
        ",21,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _skew_1d(self, column, bias=True, nan_policy=""propagate""):
        
        # import depends on scipy, not installed by default
        from dask.array import stats as da_stats

        if pd.Int64Dtype.is_dtype(column._meta_nonempty):
            column = column.astype(""f8"")

        if not np.issubdtype(column.dtype, np.number):
            column = column.astype(""f8"")

        name = self._token_prefix + ""skew-1d-"" + tokenize(column)

        array_skew = da_stats.skew(
            column.values, axis=0, bias=bias, nan_policy=nan_policy
        )

        layer = {(name, 0): (methods.wrap_skew_reduction, (array_skew._name,), None)}
        graph = HighLevelGraph.from_collections(name, layer, dependencies=[array_skew])

        return new_dd_object(
            graph, name, column._meta_nonempty.skew(), divisions=[None, None]
        )

        ```
    ###Assistant : 1D version of the skew calculation.

        Uses the array version from da.stats in case we are passing in a single series
        
    "
2557,"def test_device_classes_aligned():
    

    for device_class in NumberDeviceClass:
        assert hasattr(SensorDeviceClass, device_class.name)
        assert getattr(SensorDeviceClass, device_class.name).value == device_class.value
",Make sure all number device classes are also available in SensorDeviceClass.,11,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_device_classes_aligned():
    

    for device_class in NumberDeviceClass:
        assert hasattr(SensorDeviceClass, device_class.name)
        assert getattr(SensorDeviceClass, device_class.name).value == device_class.value

        ```
    ###Assistant : Make sure all number device classes are also available in SensorDeviceClass.
    "
2558,"def intersection_all(graphs):
    
    R = None

    for i, G in enumerate(graphs):
        G_nodes_set = set(G.nodes)
        G_edges_set = set(G.edges(keys=True) if G.is_multigraph() else G.edges())
        if i == 0:
            # create new graph
            R = G.__class__()
            node_intersection = G_nodes_set
            edge_intersection = G_edges_set
        elif G.is_multigraph() != R.is_multigraph():
            raise nx.NetworkXError(""All graphs must be graphs or multigraphs."")
        else:
            node_intersection &= G_nodes_set
            edge_intersection &= G_edges_set

        R.graph.update(G.graph)

    if R is None:
        raise ValueError(""cannot apply intersection_all to an empty list"")

    R.add_nodes_from(node_intersection)
    R.add_edges_from(edge_intersection)

    return R
","Returns a new graph that contains only the nodes and the edges that exist in
    all graphs.

    Parameters
    ----------
    graphs : iterable
       Iterable of NetworkX graphs

    Returns
    -------
    R : A new graph with the same type as the first graph in list

    Raises
    ------
    ValueError
       If `graphs` is an empty list.

    Notes
    -----
    Attributes from the graph, nodes, and edges are not copied to the new
    graph.
    ",68,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def intersection_all(graphs):
    
    R = None

    for i, G in enumerate(graphs):
        G_nodes_set = set(G.nodes)
        G_edges_set = set(G.edges(keys=True) if G.is_multigraph() else G.edges())
        if i == 0:
            # create new graph
            R = G.__class__()
            node_intersection = G_nodes_set
            edge_intersection = G_edges_set
        elif G.is_multigraph() != R.is_multigraph():
            raise nx.NetworkXError(""All graphs must be graphs or multigraphs."")
        else:
            node_intersection &= G_nodes_set
            edge_intersection &= G_edges_set

        R.graph.update(G.graph)

    if R is None:
        raise ValueError(""cannot apply intersection_all to an empty list"")

    R.add_nodes_from(node_intersection)
    R.add_edges_from(edge_intersection)

    return R

        ```
    ###Assistant : Returns a new graph that contains only the nodes and the edges that exist in
    all graphs.

    Parameters
    ----------
    graphs : iterable
       Iterable of NetworkX graphs

    Returns
    -------
    R : A new graph with the same type as the first graph in list

    Raises
    ------
    ValueError
       If `graphs` is an empty list.

    Notes
    -----
    Attributes from the graph, nodes, and edges are not copied to the new
    graph.
    
    "
2559,"def _looks_like_red_hat_scheme() -> bool:
    
    from distutils.command.install import install
    from distutils.dist import Distribution

    cmd: Any = install(Distribution())
    cmd.finalize_options()
    return (
        cmd.exec_prefix == f""{os.path.normpath(sys.exec_prefix)}/local""
        and cmd.prefix == f""{os.path.normpath(sys.prefix)}/local""
    )


@functools.lru_cache(maxsize=None)","Red Hat patches ``sys.prefix`` and ``sys.exec_prefix``.

    Red Hat's ``00251-change-user-install-location.patch`` changes the install
    command's ``prefix`` and ``exec_prefix`` to append ``""/local""``. This is
    (fortunately?) done quite unconditionally, so we create a default command
    object without any configuration to detect this.
    ",38,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _looks_like_red_hat_scheme() -> bool:
    
    from distutils.command.install import install
    from distutils.dist import Distribution

    cmd: Any = install(Distribution())
    cmd.finalize_options()
    return (
        cmd.exec_prefix == f""{os.path.normpath(sys.exec_prefix)}/local""
        and cmd.prefix == f""{os.path.normpath(sys.prefix)}/local""
    )


@functools.lru_cache(maxsize=None)
        ```
    ###Assistant : Red Hat patches ``sys.prefix`` and ``sys.exec_prefix``.

    Red Hat's ``00251-change-user-install-location.patch`` changes the install
    command's ``prefix`` and ``exec_prefix`` to append ``""/local""``. This is
    (fortunately?) done quite unconditionally, so we create a default command
    object without any configuration to detect this.
    
    "
2560,"def _create_sql_query(self) -> str:
        escaper = ParamEscaper()
        maybe_with = """"
        if self._encryption is not None or self._credential is not None:
            maybe_encryption = """"
            if self._encryption is not None:
                maybe_encryption = self._generate_options(""ENCRYPTION"", escaper, self._encryption, False)
            maybe_credential = """"
            if self._credential is not None:
                maybe_credential = self._generate_options(""CREDENTIAL"", escaper, self._credential, False)
            maybe_with = f"" WITH ({maybe_credential} {maybe_encryption})""
        location = escaper.escape_item(self._file_location) + maybe_with
        if self._expression_list is not None:
            location = f""(SELECT {self._expression_list} FROM {location})""
        files_or_pattern = """"
        if self._pattern is not None:
            files_or_pattern = f""PATTERN = {escaper.escape_item(self._pattern)}\n""
        elif self._files is not None:
            files_or_pattern = f""FILES = {escaper.escape_item(self._files)}\n""
        format_options = self._generate_options(""FORMAT_OPTIONS"", escaper, self._format_options) + ""\n""
        copy_options = self._generate_options(""COPY_OPTIONS"", escaper, self._copy_options) + ""\n""
        validation = """"
        if self._validate is not None:
            if isinstance(self._validate, bool):
                if self._validate:
                    validation = ""VALIDATE ALL\n""
            elif isinstance(self._validate, int):
                if self._validate < 0:
                    raise AirflowException(
                        ""Number of rows for validation should be positive, got: "" + str(self._validate)
                    )
                validation = f""VALIDATE {self._validate} ROWS\n""
            else:
                raise AirflowException(""Incorrect data type for validate parameter: "" + type(self._validate))
        # TODO: think on how to make sure that table_name and expression_list aren't used for SQL injection
        sql = f
        return sql.strip()
","COPY INTO {self._table_name}
FROM {location}
FILEFORMAT = {self._file_format}
{validation}{files_or_pattern}{format_options}{copy_options}
",9,184,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_sql_query(self) -> str:
        escaper = ParamEscaper()
        maybe_with = """"
        if self._encryption is not None or self._credential is not None:
            maybe_encryption = """"
            if self._encryption is not None:
                maybe_encryption = self._generate_options(""ENCRYPTION"", escaper, self._encryption, False)
            maybe_credential = """"
            if self._credential is not None:
                maybe_credential = self._generate_options(""CREDENTIAL"", escaper, self._credential, False)
            maybe_with = f"" WITH ({maybe_credential} {maybe_encryption})""
        location = escaper.escape_item(self._file_location) + maybe_with
        if self._expression_list is not None:
            location = f""(SELECT {self._expression_list} FROM {location})""
        files_or_pattern = """"
        if self._pattern is not None:
            files_or_pattern = f""PATTERN = {escaper.escape_item(self._pattern)}\n""
        elif self._files is not None:
            files_or_pattern = f""FILES = {escaper.escape_item(self._files)}\n""
        format_options = self._generate_options(""FORMAT_OPTIONS"", escaper, self._format_options) + ""\n""
        copy_options = self._generate_options(""COPY_OPTIONS"", escaper, self._copy_options) + ""\n""
        validation = """"
        if self._validate is not None:
            if isinstance(self._validate, bool):
                if self._validate:
                    validation = ""VALIDATE ALL\n""
            elif isinstance(self._validate, int):
                if self._validate < 0:
                    raise AirflowException(
                        ""Number of rows for validation should be positive, got: "" + str(self._validate)
                    )
                validation = f""VALIDATE {self._validate} ROWS\n""
            else:
                raise AirflowException(""Incorrect data type for validate parameter: "" + type(self._validate))
        # TODO: think on how to make sure that table_name and expression_list aren't used for SQL injection
        sql = f
        return sql.strip()

        ```
    ###Assistant : COPY INTO {self._table_name}
FROM {location}
FILEFORMAT = {self._file_format}
{validation}{files_or_pattern}{format_options}{copy_options}

    "
2561,"def predict(self, x, **kwargs):
        
        proba = self.model.predict(x, **kwargs)
        if proba.shape[-1] > 1:
            classes = proba.argmax(axis=-1)
        else:
            classes = (proba > 0.5).astype(""int32"")
        return self.classes_[classes]
","Returns the class predictions for the given test data.

        Args:
            x: array-like, shape `(n_samples, n_features)`
                Test samples where `n_samples` is the number of samples
                and `n_features` is the number of features.
            **kwargs: dictionary arguments
                Legal arguments are the arguments
                of `Sequential.predict`.

        Returns:
            preds: array-like, shape `(n_samples,)`
                Class predictions.
        ",48,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def predict(self, x, **kwargs):
        
        proba = self.model.predict(x, **kwargs)
        if proba.shape[-1] > 1:
            classes = proba.argmax(axis=-1)
        else:
            classes = (proba > 0.5).astype(""int32"")
        return self.classes_[classes]

        ```
    ###Assistant : Returns the class predictions for the given test data.

        Args:
            x: array-like, shape `(n_samples, n_features)`
                Test samples where `n_samples` is the number of samples
                and `n_features` is the number of features.
            **kwargs: dictionary arguments
                Legal arguments are the arguments
                of `Sequential.predict`.

        Returns:
            preds: array-like, shape `(n_samples,)`
                Class predictions.
        
    "
2562,"def resize_image_type0(self, img):
        
        limit_side_len = self.max_side_len
        h, w, _ = img.shape

        # limit the max side
        if max(h, w) > limit_side_len:
            if h > w:
                ratio = float(limit_side_len) / h
            else:
                ratio = float(limit_side_len) / w
        else:
            ratio = 1.
        resize_h = int(h * ratio)
        resize_w = int(w * ratio)

        resize_h = int(round(resize_h / 32) * 32)
        resize_w = int(round(resize_w / 32) * 32)

        try:
            if int(resize_w) <= 0 or int(resize_h) <= 0:
                return None, (None, None)
            img = cv2.resize(img, (int(resize_w), int(resize_h)))
        except:
            print(img.shape, resize_w, resize_h)
            sys.exit(0)
        ratio_h = resize_h / float(h)
        ratio_w = resize_w / float(w)
        # return img, np.array([h, w])
        return img, [ratio_h, ratio_w]
","
        resize image to a size multiple of 32 which is required by the network
        args:
            img(array): array with shape [h, w, c]
        return(tuple):
            img, (ratio_h, ratio_w)
        ",26,106,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resize_image_type0(self, img):
        
        limit_side_len = self.max_side_len
        h, w, _ = img.shape

        # limit the max side
        if max(h, w) > limit_side_len:
            if h > w:
                ratio = float(limit_side_len) / h
            else:
                ratio = float(limit_side_len) / w
        else:
            ratio = 1.
        resize_h = int(h * ratio)
        resize_w = int(w * ratio)

        resize_h = int(round(resize_h / 32) * 32)
        resize_w = int(round(resize_w / 32) * 32)

        try:
            if int(resize_w) <= 0 or int(resize_h) <= 0:
                return None, (None, None)
            img = cv2.resize(img, (int(resize_w), int(resize_h)))
        except:
            print(img.shape, resize_w, resize_h)
            sys.exit(0)
        ratio_h = resize_h / float(h)
        ratio_w = resize_w / float(w)
        # return img, np.array([h, w])
        return img, [ratio_h, ratio_w]

        ```
    ###Assistant : 
        resize image to a size multiple of 32 which is required by the network
        args:
            img(array): array with shape [h, w, c]
        return(tuple):
            img, (ratio_h, ratio_w)
        
    "
2563,"def metrics(self):
        
        metrics = []
        if self._is_compiled:
            # TODO(omalleyt): Track `LossesContainer` and `MetricsContainer` objects
            # so that attr names are not load-bearing.
            if self.compiled_loss is not None:
                metrics += self.compiled_loss.metrics
            if self.compiled_metrics is not None:
                metrics += self.compiled_metrics.metrics

        for l in self._flatten_layers():
            metrics.extend(l._metrics)  # pylint: disable=protected-access
        return metrics
","Returns the model's metrics added using `compile()`, `add_metric()` APIs.

        Note: Metrics passed to `compile()` are available only after a `keras.Model`
        has been trained/evaluated on actual data.

        Examples:

        >>> inputs = tf.keras.layers.Input(shape=(3,))
        >>> outputs = tf.keras.layers.Dense(2)(inputs)
        >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
        >>> model.compile(optimizer=""Adam"", loss=""mse"", metrics=[""mae""])
        >>> [m.name for m in model.metrics]
        []

        >>> x = np.random.random((2, 3))
        >>> y = np.random.randint(0, 2, (2, 2))
        >>> model.fit(x, y)
        >>> [m.name for m in model.metrics]
        ['loss', 'mae']

        >>> inputs = tf.keras.layers.Input(shape=(3,))
        >>> d = tf.keras.layers.Dense(2, name='out')
        >>> output_1 = d(inputs)
        >>> output_2 = d(inputs)
        >>> model = tf.keras.models.Model(
        ...    inputs=inputs, outputs=[output_1, output_2])
        >>> model.add_metric(
        ...    tf.reduce_sum(output_2), name='mean', aggregation='mean')
        >>> model.compile(optimizer=""Adam"", loss=""mse"", metrics=[""mae"", ""acc""])
        >>> model.fit(x, (y, y))
        >>> [m.name for m in model.metrics]
        ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
        'out_1_acc', 'mean']

        ",128,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def metrics(self):
        
        metrics = []
        if self._is_compiled:
            # TODO(omalleyt): Track `LossesContainer` and `MetricsContainer` objects
            # so that attr names are not load-bearing.
            if self.compiled_loss is not None:
                metrics += self.compiled_loss.metrics
            if self.compiled_metrics is not None:
                metrics += self.compiled_metrics.metrics

        for l in self._flatten_layers():
            metrics.extend(l._metrics)  # pylint: disable=protected-access
        return metrics

        ```
    ###Assistant : Returns the model's metrics added using `compile()`, `add_metric()` APIs.

        Note: Metrics passed to `compile()` are available only after a `keras.Model`
        has been trained/evaluated on actual data.

        Examples:

        >>> inputs = tf.keras.layers.Input(shape=(3,))
        >>> outputs = tf.keras.layers.Dense(2)(inputs)
        >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
        >>> model.compile(optimizer=""Adam"", loss=""mse"", metrics=[""mae""])
        >>> [m.name for m in model.metrics]
        []

        >>> x = np.random.random((2, 3))
        >>> y = np.random.randint(0, 2, (2, 2))
        >>> model.fit(x, y)
        >>> [m.name for m in model.metrics]
        ['loss', 'mae']

        >>> inputs = tf.keras.layers.Input(shape=(3,))
        >>> d = tf.keras.layers.Dense(2, name='out')
        >>> output_1 = d(inputs)
        >>> output_2 = d(inputs)
        >>> model = tf.keras.models.Model(
        ...    inputs=inputs, outputs=[output_1, output_2])
        >>> model.add_metric(
        ...    tf.reduce_sum(output_2), name='mean', aggregation='mean')
        >>> model.compile(optimizer=""Adam"", loss=""mse"", metrics=[""mae"", ""acc""])
        >>> model.fit(x, (y, y))
        >>> [m.name for m in model.metrics]
        ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
        'out_1_acc', 'mean']

        
    "
2564,"def set_level(request, level):
    
    if not hasattr(request, ""_messages""):
        return False
    request._messages.level = level
    return True

","
    Set the minimum level of messages to be recorded, and return ``True`` if
    the level was recorded successfully.

    If set to ``None``, use the default level (see the get_level() function).
    ",30,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_level(request, level):
    
    if not hasattr(request, ""_messages""):
        return False
    request._messages.level = level
    return True


        ```
    ###Assistant : 
    Set the minimum level of messages to be recorded, and return ``True`` if
    the level was recorded successfully.

    If set to ``None``, use the default level (see the get_level() function).
    
    "
2565,"def all_pairs_lowest_common_ancestor(G, pairs=None):
    
    if not nx.is_directed_acyclic_graph(G):
        raise nx.NetworkXError(""LCA only defined on directed acyclic graphs."")
    if len(G) == 0:
        raise nx.NetworkXPointlessConcept(""LCA meaningless on null graphs."")

    if pairs is None:
        pairs = combinations_with_replacement(G, 2)
    else:
        # Convert iterator to iterable, if necessary. Trim duplicates.
        pairs = dict.fromkeys(pairs)
        # Verify that each of the nodes in the provided pairs is in G
        nodeset = set(G)
        for pair in pairs:
            if set(pair) - nodeset:
                raise nx.NodeNotFound(
                    f""Node(s) {set(pair) - nodeset} from pair {pair} not in G.""
                )

    # Once input validation is done, construct the generator","Return the lowest common ancestor of all pairs or the provided pairs

    Parameters
    ----------
    G : NetworkX directed graph

    pairs : iterable of pairs of nodes, optional (default: all pairs)
        The pairs of nodes of interest.
        If None, will find the LCA of all pairs of nodes.

    Yields
    ------
    ((node1, node2), lca) : 2-tuple
        Where lca is least common ancestor of node1 and node2.
        Note that for the default case, the order of the node pair is not considered,
        e.g. you will not get both ``(a, b)`` and ``(b, a)``

    Raises
    ------
    NetworkXPointlessConcept
        If `G` is null.
    NetworkXError
        If `G` is not a DAG.

    Examples
    --------
    The default behavior is to yield the lowest common ancestor for all
    possible combinations of nodes in `G`, including self-pairings:

    >>> G = nx.DiGraph([(0, 1), (0, 3), (1, 2)])
    >>> dict(nx.all_pairs_lowest_common_ancestor(G))
    {(0, 0): 0, (0, 1): 0, (0, 3): 0, (0, 2): 0, (1, 1): 1, (1, 3): 0, (1, 2): 1, (3, 3): 3, (3, 2): 0, (2, 2): 2}

    The pairs argument can be used to limit the output to only the
    specified node pairings:

    >>> dict(nx.all_pairs_lowest_common_ancestor(G, pairs=[(1, 2), (2, 3)]))
    {(1, 2): 1, (2, 3): 0}

    Notes
    -----
    Only defined on non-null directed acyclic graphs.

    See Also
    --------
    lowest_common_ancestor
    ",208,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def all_pairs_lowest_common_ancestor(G, pairs=None):
    
    if not nx.is_directed_acyclic_graph(G):
        raise nx.NetworkXError(""LCA only defined on directed acyclic graphs."")
    if len(G) == 0:
        raise nx.NetworkXPointlessConcept(""LCA meaningless on null graphs."")

    if pairs is None:
        pairs = combinations_with_replacement(G, 2)
    else:
        # Convert iterator to iterable, if necessary. Trim duplicates.
        pairs = dict.fromkeys(pairs)
        # Verify that each of the nodes in the provided pairs is in G
        nodeset = set(G)
        for pair in pairs:
            if set(pair) - nodeset:
                raise nx.NodeNotFound(
                    f""Node(s) {set(pair) - nodeset} from pair {pair} not in G.""
                )

    # Once input validation is done, construct the generator
        ```
    ###Assistant : Return the lowest common ancestor of all pairs or the provided pairs

    Parameters
    ----------
    G : NetworkX directed graph

    pairs : iterable of pairs of nodes, optional (default: all pairs)
        The pairs of nodes of interest.
        If None, will find the LCA of all pairs of nodes.

    Yields
    ------
    ((node1, node2), lca) : 2-tuple
        Where lca is least common ancestor of node1 and node2.
        Note that for the default case, the order of the node pair is not considered,
        e.g. you will not get both ``(a, b)`` and ``(b, a)``

    Raises
    ------
    NetworkXPointlessConcept
        If `G` is null.
    NetworkXError
        If `G` is not a DAG.

    Examples
    --------
    The default behavior is to yield the lowest common ancestor for all
    possible combinations of nodes in `G`, including self-pairings:

    >>> G = nx.DiGraph([(0, 1), (0, 3), (1, 2)])
    >>> dict(nx.all_pairs_lowest_common_ancestor(G))
    {(0, 0): 0, (0, 1): 0, (0, 3): 0, (0, 2): 0, (1, 1): 1, (1, 3): 0, (1, 2): 1, (3, 3): 3, (3, 2): 0, (2, 2): 2}

    The pairs argument can be used to limit the output to only the
    specified node pairings:

    >>> dict(nx.all_pairs_lowest_common_ancestor(G, pairs=[(1, 2), (2, 3)]))
    {(1, 2): 1, (2, 3): 0}

    Notes
    -----
    Only defined on non-null directed acyclic graphs.

    See Also
    --------
    lowest_common_ancestor
    
    "
2566,"async def test_is_pickleable_after_start(self, task_runner):
        
        task_runner.client_kwargs[""set_as_default""] = True","
        The task_runner must be picklable as it is attached to `PrefectFuture` objects
        Reimplemented to set Dask client as default to allow unpickling
        ",22,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_is_pickleable_after_start(self, task_runner):
        
        task_runner.client_kwargs[""set_as_default""] = True
        ```
    ###Assistant : 
        The task_runner must be picklable as it is attached to `PrefectFuture` objects
        Reimplemented to set Dask client as default to allow unpickling
        
    "
2567,"def _add_callback_signalsafe(self, handle):
        
        self._add_callback(handle)
        self._write_to_self()
",Like _add_callback() but called from a signal handler.,8,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _add_callback_signalsafe(self, handle):
        
        self._add_callback(handle)
        self._write_to_self()

        ```
    ###Assistant : Like _add_callback() but called from a signal handler.
    "
2568,"def get_local_ffmpeg() -> Optional[Path]:
    

    ffmpeg_path = Path(
        get_spotdl_path(), ""ffmpeg"" + "".exe"" if platform.system() == ""Windows"" else """"
    )

    if ffmpeg_path.is_file():
        return ffmpeg_path

    return None

","
    Get local ffmpeg binary path or None if not found.
    ",10,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_local_ffmpeg() -> Optional[Path]:
    

    ffmpeg_path = Path(
        get_spotdl_path(), ""ffmpeg"" + "".exe"" if platform.system() == ""Windows"" else """"
    )

    if ffmpeg_path.is_file():
        return ffmpeg_path

    return None


        ```
    ###Assistant : 
    Get local ffmpeg binary path or None if not found.
    
    "
2569,"def _render_cmd(cmd, cwd, template, saltenv=None, pillarenv=None, pillar_override=None):
    
    if saltenv is None:
        saltenv = __opts__.get(""saltenv"", ""base"")
    if not template:
        return (cmd, cwd)

    # render the path as a template using path_template_engine as the engine
    if template not in salt.utils.templates.TEMPLATE_REGISTRY:
        raise CommandExecutionError(
            ""Attempted to render file paths with unavailable engine {}"".format(template)
        )

    kwargs = {}
    kwargs[""salt""] = __salt__
    if pillarenv is not None or pillar_override is not None:
        pillarenv = pillarenv or __opts__[""pillarenv""]
        kwargs[""pillar""] = _gather_pillar(pillarenv, pillar_override)
    else:
        kwargs[""pillar""] = __pillar__
    kwargs[""grains""] = __grains__
    kwargs[""opts""] = __opts__
    kwargs[""saltenv""] = saltenv
","
    If template is a valid template engine, process the cmd and cwd through
    that engine.
    ",15,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _render_cmd(cmd, cwd, template, saltenv=None, pillarenv=None, pillar_override=None):
    
    if saltenv is None:
        saltenv = __opts__.get(""saltenv"", ""base"")
    if not template:
        return (cmd, cwd)

    # render the path as a template using path_template_engine as the engine
    if template not in salt.utils.templates.TEMPLATE_REGISTRY:
        raise CommandExecutionError(
            ""Attempted to render file paths with unavailable engine {}"".format(template)
        )

    kwargs = {}
    kwargs[""salt""] = __salt__
    if pillarenv is not None or pillar_override is not None:
        pillarenv = pillarenv or __opts__[""pillarenv""]
        kwargs[""pillar""] = _gather_pillar(pillarenv, pillar_override)
    else:
        kwargs[""pillar""] = __pillar__
    kwargs[""grains""] = __grains__
    kwargs[""opts""] = __opts__
    kwargs[""saltenv""] = saltenv

        ```
    ###Assistant : 
    If template is a valid template engine, process the cmd and cwd through
    that engine.
    
    "
2570,"def test_windows_1252(self) -> None:
        
        html = b
        tree = decode_body(html, ""http://example.com/test.html"")
        og = parse_html_to_open_graph(tree, ""http://example.com/test.html"")
        self.assertEqual(og, {""og:title"": ""ó"", ""og:description"": ""Some text.""})

","A body which uses cp1252, but doesn't declare that.
        <html>
        <head><title>\xf3</title></head>
        <body>
        Some text.
        </body>
        </html>
        ",16,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_windows_1252(self) -> None:
        
        html = b
        tree = decode_body(html, ""http://example.com/test.html"")
        og = parse_html_to_open_graph(tree, ""http://example.com/test.html"")
        self.assertEqual(og, {""og:title"": ""ó"", ""og:description"": ""Some text.""})


        ```
    ###Assistant : A body which uses cp1252, but doesn't declare that.
        <html>
        <head><title>\xf3</title></head>
        <body>
        Some text.
        </body>
        </html>
        
    "
2571,"def square_root(value, default=_SENTINEL):
    
    try:
        return math.sqrt(float(value))
    except (ValueError, TypeError):
        if default is _SENTINEL:
            raise_no_default(""sqrt"", value)
        return default

",Filter and function to get square root of the value.,10,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def square_root(value, default=_SENTINEL):
    
    try:
        return math.sqrt(float(value))
    except (ValueError, TypeError):
        if default is _SENTINEL:
            raise_no_default(""sqrt"", value)
        return default


        ```
    ###Assistant : Filter and function to get square root of the value.
    "
2572,"async def test_track_task_functions(event_loop):
    
    hass = ha.HomeAssistant()
    try:
        assert hass._track_task

        hass.async_stop_track_tasks()
        assert not hass._track_task

        hass.async_track_tasks()
        assert hass._track_task
    finally:
        await hass.async_stop()

",Test function to start/stop track task and initial state.,9,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_track_task_functions(event_loop):
    
    hass = ha.HomeAssistant()
    try:
        assert hass._track_task

        hass.async_stop_track_tasks()
        assert not hass._track_task

        hass.async_track_tasks()
        assert hass._track_task
    finally:
        await hass.async_stop()


        ```
    ###Assistant : Test function to start/stop track task and initial state.
    "
2573,"def media_position_updated_at(self) -> datetime | None:
        
        if self._device.movie.play_status in KALEIDESCAPE_PLAYING_STATES:
            return utcnow()
        return None
",When was the position of the current playing media valid.,10,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def media_position_updated_at(self) -> datetime | None:
        
        if self._device.movie.play_status in KALEIDESCAPE_PLAYING_STATES:
            return utcnow()
        return None

        ```
    ###Assistant : When was the position of the current playing media valid.
    "
2574,"def write(self, data):
        
        if not isinstance(data, (bytes, bytearray, memoryview)):
            raise TypeError(f""data: expecting a bytes-like instance, ""
                            f""got {type(data).__name__}"")
        if not data:
            return
        self._ssl_protocol._write_appdata(data)
","Write some data bytes to the transport.

        This does not block; it buffers the data and arranges for it
        to be sent out asynchronously.
        ",24,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def write(self, data):
        
        if not isinstance(data, (bytes, bytearray, memoryview)):
            raise TypeError(f""data: expecting a bytes-like instance, ""
                            f""got {type(data).__name__}"")
        if not data:
            return
        self._ssl_protocol._write_appdata(data)

        ```
    ###Assistant : Write some data bytes to the transport.

        This does not block; it buffers the data and arranges for it
        to be sent out asynchronously.
        
    "
2575,"def _extract_color_tags(self):
        
        tags = re.finditer(
            r'<color\s+col=""([^""]+)""(\s+offset=""([^""]+)"")?>(.+?)</color>',
            self.original_text,
            re.S,
        )

        colormap = []
        for tag in tags:
            start = self._count_real_chars(self.original_text[: tag.start(0)])
            end = start + self._count_real_chars(tag.group(4))
            offsets = tag.group(3).split("","") if tag.group(3) else [0]
            start_offset = int(offsets[0]) if offsets[0] else 0
            end_offset = int(offsets[1]) if len(offsets) == 2 and offsets[1] else 0

            colormap.append(
                {
                    ""start"": start,
                    ""end"": end,
                    ""color"": tag.group(1),
                    ""start_offset"": start_offset,
                    ""end_offset"": end_offset,
                },
            )
        self.text = re.sub(""<color[^>]+>(.+?)</color>"", r""\1"", self.text, 0, re.S)
        return colormap
","Used to determine which parts (if any) of the string should be formatted
        with a custom color.

        Removes the ``<color>`` tag, as it is not part of Pango's markup and would cause an error.

        Note: Using the ``<color>`` tags is deprecated. As soon as the legacy syntax is gone, this function
        will be removed.
        ",54,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _extract_color_tags(self):
        
        tags = re.finditer(
            r'<color\s+col=""([^""]+)""(\s+offset=""([^""]+)"")?>(.+?)</color>',
            self.original_text,
            re.S,
        )

        colormap = []
        for tag in tags:
            start = self._count_real_chars(self.original_text[: tag.start(0)])
            end = start + self._count_real_chars(tag.group(4))
            offsets = tag.group(3).split("","") if tag.group(3) else [0]
            start_offset = int(offsets[0]) if offsets[0] else 0
            end_offset = int(offsets[1]) if len(offsets) == 2 and offsets[1] else 0

            colormap.append(
                {
                    ""start"": start,
                    ""end"": end,
                    ""color"": tag.group(1),
                    ""start_offset"": start_offset,
                    ""end_offset"": end_offset,
                },
            )
        self.text = re.sub(""<color[^>]+>(.+?)</color>"", r""\1"", self.text, 0, re.S)
        return colormap

        ```
    ###Assistant : Used to determine which parts (if any) of the string should be formatted
        with a custom color.

        Removes the ``<color>`` tag, as it is not part of Pango's markup and would cause an error.

        Note: Using the ``<color>`` tags is deprecated. As soon as the legacy syntax is gone, this function
        will be removed.
        
    "
2576,"def write_readme(self, file_path, parametric_eq_peqs=None, fixed_band_eq_peq=None):
        
        file_path = os.path.abspath(file_path)
        dir_path = os.path.dirname(file_path)
        model = self.name

        # Write model
        s = '# {}\n'.format(model)
        s += 'See [usage instructions](https://github.com/jaakkopasanen/AutoEq#usage) for more options and info.\n\n'

        # Add parametric EQ settings
        if parametric_eq_peqs is not None:
            s += '### Parametric EQs\n'
            if len(parametric_eq_peqs) > 1:
                compound = PEQ(self.frequency.copy(), parametric_eq_peqs[0].fs, [])
                n = 0
                filter_ranges = ''
                preamps = ''
                for i, peq in enumerate(parametric_eq_peqs):
                    for filt in peq.filters:
                        compound.add_filter(filt)
                    filter_ranges += f'1-{len(peq.filters) + n}'
                    preamps += f'{-compound.max_gain - 0.1:.1f} dB'
                    if i < len(parametric_eq_peqs) - 2:
                        filter_ranges += ', '
                        preamps += ', '
                    elif i == len(parametric_eq_peqs) - 2:
                        filter_ranges += ' or '
                        preamps += ' or '
                    n += len(peq.filters)
                s += f'You can use filters {filter_ranges}. Apply preamp of {preamps}, respectively.\n\n'
            else:
                compound = PEQ(self.frequency.copy(), parametric_eq_peqs[0].fs, [])
                for peq in parametric_eq_peqs:
                    for filt in peq.filters:
                        compound.add_filter(filt)
                s += f'Apply preamp of -{compound.max_gain + 0.1:.1f} dB when using parametric equalizer.\n\n'
            s += compound.markdown_table() + '\n\n'

        # Add fixed band eq
        if fixed_band_eq_peq is not None:
            s += f'### Fixed Band EQs\nWhen using fixed band (also called graphic) equalizer, apply preamp of ' \
                 f'**-{fixed_band_eq_peq.max_gain + 0.1:.1f} dB** (if available) and set gains manually with these ' \
                 f'parameters.\n\n{fixed_band_eq_peq.markdown_table()}\n\n'

        # Write image link
        img_path = os.path.join(dir_path, model + '.png')
        if os.path.isfile(img_path):
            img_url = f'./{os.path.split(img_path)[1]}'
            img_url = urllib.parse.quote(img_url, safe=""%/:=&?~#+!$,;'@()*[]"")
            s += f'### Graphs\n![]({img_url})\n'

        # Write file
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(s)
",Writes README.md with picture and Equalizer APO settings.,8,239,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def write_readme(self, file_path, parametric_eq_peqs=None, fixed_band_eq_peq=None):
        
        file_path = os.path.abspath(file_path)
        dir_path = os.path.dirname(file_path)
        model = self.name

        # Write model
        s = '# {}\n'.format(model)
        s += 'See [usage instructions](https://github.com/jaakkopasanen/AutoEq#usage) for more options and info.\n\n'

        # Add parametric EQ settings
        if parametric_eq_peqs is not None:
            s += '### Parametric EQs\n'
            if len(parametric_eq_peqs) > 1:
                compound = PEQ(self.frequency.copy(), parametric_eq_peqs[0].fs, [])
                n = 0
                filter_ranges = ''
                preamps = ''
                for i, peq in enumerate(parametric_eq_peqs):
                    for filt in peq.filters:
                        compound.add_filter(filt)
                    filter_ranges += f'1-{len(peq.filters) + n}'
                    preamps += f'{-compound.max_gain - 0.1:.1f} dB'
                    if i < len(parametric_eq_peqs) - 2:
                        filter_ranges += ', '
                        preamps += ', '
                    elif i == len(parametric_eq_peqs) - 2:
                        filter_ranges += ' or '
                        preamps += ' or '
                    n += len(peq.filters)
                s += f'You can use filters {filter_ranges}. Apply preamp of {preamps}, respectively.\n\n'
            else:
                compound = PEQ(self.frequency.copy(), parametric_eq_peqs[0].fs, [])
                for peq in parametric_eq_peqs:
                    for filt in peq.filters:
                        compound.add_filter(filt)
                s += f'Apply preamp of -{compound.max_gain + 0.1:.1f} dB when using parametric equalizer.\n\n'
            s += compound.markdown_table() + '\n\n'

        # Add fixed band eq
        if fixed_band_eq_peq is not None:
            s += f'### Fixed Band EQs\nWhen using fixed band (also called graphic) equalizer, apply preamp of ' \
                 f'**-{fixed_band_eq_peq.max_gain + 0.1:.1f} dB** (if available) and set gains manually with these ' \
                 f'parameters.\n\n{fixed_band_eq_peq.markdown_table()}\n\n'

        # Write image link
        img_path = os.path.join(dir_path, model + '.png')
        if os.path.isfile(img_path):
            img_url = f'./{os.path.split(img_path)[1]}'
            img_url = urllib.parse.quote(img_url, safe=""%/:=&?~#+!$,;'@()*[]"")
            s += f'### Graphs\n![]({img_url})\n'

        # Write file
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(s)

        ```
    ###Assistant : Writes README.md with picture and Equalizer APO settings.
    "
2577,"def _get_execution_environment():
    
    if os.environ.get(""CI"", ""False"").lower() == ""true"":
        execution_env = ""ci""
    elif ""google.colab"" in sys.modules:
        execution_env = ""colab""
    elif ""KUBERNETES_SERVICE_HOST"" in os.environ:
        execution_env = ""kubernetes""
    elif HAYSTACK_DOCKER_CONTAINER in os.environ:
        execution_env = os.environ.get(HAYSTACK_DOCKER_CONTAINER)
    # check if pytest is imported
    elif ""pytest"" in sys.modules:
        execution_env = ""test""
    else:
        try:
            execution_env = get_ipython().__class__.__name__  # pylint: disable=undefined-variable
        except NameError:
            execution_env = ""script""
    return execution_env

","
    Identifies the execution environment that Haystack is running in.
    Options are: colab notebook, kubernetes, CPU/GPU docker container, test environment, jupyter notebook, python script
    ",23,59,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_execution_environment():
    
    if os.environ.get(""CI"", ""False"").lower() == ""true"":
        execution_env = ""ci""
    elif ""google.colab"" in sys.modules:
        execution_env = ""colab""
    elif ""KUBERNETES_SERVICE_HOST"" in os.environ:
        execution_env = ""kubernetes""
    elif HAYSTACK_DOCKER_CONTAINER in os.environ:
        execution_env = os.environ.get(HAYSTACK_DOCKER_CONTAINER)
    # check if pytest is imported
    elif ""pytest"" in sys.modules:
        execution_env = ""test""
    else:
        try:
            execution_env = get_ipython().__class__.__name__  # pylint: disable=undefined-variable
        except NameError:
            execution_env = ""script""
    return execution_env


        ```
    ###Assistant : 
    Identifies the execution environment that Haystack is running in.
    Options are: colab notebook, kubernetes, CPU/GPU docker container, test environment, jupyter notebook, python script
    
    "
2578,"def _ensure_html_response(url, session):
    # type: (str, PipSession) -> None
    
    scheme, netloc, path, query, fragment = urllib.parse.urlsplit(url)
    if scheme not in {'http', 'https'}:
        raise _NotHTTP()

    resp = session.head(url, allow_redirects=True)
    raise_for_status(resp)

    _ensure_html_header(resp)

","Send a HEAD request to the URL, and ensure the response contains HTML.

    Raises `_NotHTTP` if the URL is not available for a HEAD request, or
    `_NotHTML` if the content type is not text/html.
    ",34,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _ensure_html_response(url, session):
    # type: (str, PipSession) -> None
    
    scheme, netloc, path, query, fragment = urllib.parse.urlsplit(url)
    if scheme not in {'http', 'https'}:
        raise _NotHTTP()

    resp = session.head(url, allow_redirects=True)
    raise_for_status(resp)

    _ensure_html_header(resp)


        ```
    ###Assistant : Send a HEAD request to the URL, and ensure the response contains HTML.

    Raises `_NotHTTP` if the URL is not available for a HEAD request, or
    `_NotHTML` if the content type is not text/html.
    
    "
2579,"def _parse_item(self) -> Optional[Tuple[Optional[Key], Item]]:
        
        self.mark()
        with self._state as state:
            while True:
                c = self._current
                if c == ""\n"":
                    # Found a newline; Return all whitespace found up to this point.
                    self.inc()

                    return None, Whitespace(self.extract())
                elif c in "" \t\r"":
                    # Skip whitespace.
                    if not self.inc():
                        return None, Whitespace(self.extract())
                elif c == ""#"":
                    # Found a comment, parse it
                    indent = self.extract()
                    cws, comment, trail = self._parse_comment_trail()

                    return None, Comment(Trivia(indent, cws, comment, trail))
                elif c == ""["":
                    # Found a table, delegate to the calling function.
                    return
                else:
                    # Begining of a KV pair.
                    # Return to beginning of whitespace so it gets included
                    # as indentation for the KV about to be parsed.
                    state.restore = True
                    break

        return self._parse_key_value(True)
","
        Attempts to parse the next item and returns it, along with its key
        if the item is value-like.
        ",18,120,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _parse_item(self) -> Optional[Tuple[Optional[Key], Item]]:
        
        self.mark()
        with self._state as state:
            while True:
                c = self._current
                if c == ""\n"":
                    # Found a newline; Return all whitespace found up to this point.
                    self.inc()

                    return None, Whitespace(self.extract())
                elif c in "" \t\r"":
                    # Skip whitespace.
                    if not self.inc():
                        return None, Whitespace(self.extract())
                elif c == ""#"":
                    # Found a comment, parse it
                    indent = self.extract()
                    cws, comment, trail = self._parse_comment_trail()

                    return None, Comment(Trivia(indent, cws, comment, trail))
                elif c == ""["":
                    # Found a table, delegate to the calling function.
                    return
                else:
                    # Begining of a KV pair.
                    # Return to beginning of whitespace so it gets included
                    # as indentation for the KV about to be parsed.
                    state.restore = True
                    break

        return self._parse_key_value(True)

        ```
    ###Assistant : 
        Attempts to parse the next item and returns it, along with its key
        if the item is value-like.
        
    "
2580,"def _xreplace(self, rule):
        
        if self in rule:
            return rule[self], True
        elif rule:
            rule = self._dedupe_indices_in_rule(rule)
            args = []
            changed = False
            for a in self.args:
                _xreplace = getattr(a, '_xreplace', None)
                if _xreplace is not None:
                    a_xr = _xreplace(rule)
                    args.append(a_xr[0])
                    changed |= a_xr[1]
                else:
                    args.append(a)
            args = tuple(args)
            if changed:
                return self.func(*args), True
        return self, False
","
        Helper for xreplace. Tracks whether a replacement actually occurred.

        Given that the rule has entries {old:new, ...}, this handles the fact
        that if a dummy index in new is the same as an index in self, the
        dummy index in new must be renamed.
        ",44,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _xreplace(self, rule):
        
        if self in rule:
            return rule[self], True
        elif rule:
            rule = self._dedupe_indices_in_rule(rule)
            args = []
            changed = False
            for a in self.args:
                _xreplace = getattr(a, '_xreplace', None)
                if _xreplace is not None:
                    a_xr = _xreplace(rule)
                    args.append(a_xr[0])
                    changed |= a_xr[1]
                else:
                    args.append(a)
            args = tuple(args)
            if changed:
                return self.func(*args), True
        return self, False

        ```
    ###Assistant : 
        Helper for xreplace. Tracks whether a replacement actually occurred.

        Given that the rule has entries {old:new, ...}, this handles the fact
        that if a dummy index in new is the same as an index in self, the
        dummy index in new must be renamed.
        
    "
2581,"async def async_turn_on(self) -> None:
        
        await self._client.play()
        await self._update_playlists(no_throttle=True)
",Service to send the MPD the command to start playing.,10,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def async_turn_on(self) -> None:
        
        await self._client.play()
        await self._update_playlists(no_throttle=True)

        ```
    ###Assistant : Service to send the MPD the command to start playing.
    "
2582,"def _build_network_on_replica(model, mode, inputs=None, targets=None):
    
    # Need to do imports here since we run into a circular dependency error.
    from keras import models  # pylint: disable=g-import-not-at-top
    from keras.engine import sequential  # pylint: disable=g-import-not-at-top

    # We rely on the internal methods to avoid having share_weights weights in the
    # public API.
    if isinstance(model, sequential.Sequential):
        updated_model = models._clone_sequential_model(
            model, input_tensors=inputs, layer_fn=models.share_weights
        )
    else:
        updated_model = models._clone_functional_model(
            model, input_tensors=inputs, layer_fn=models.share_weights
        )
        # Callable losses added directly to a functional Model need to be added
        # here.
        updated_model._callable_losses = model._callable_losses

    # Recast all low precision outputs back to float32 since we only casted
    # the inputs to bfloat16 and not targets. This is done so that we can preserve
    # precision when calculating the loss value.","Build an updated model on replicas.

    We create a new Keras model while sharing the variables from the old graph.
    Building a new sub-graph is required since the original keras model creates
    placeholders for the input and the output that are not accessible till we
    call iterator.get_next() inside the step_fn for `fit`/`evaluate`/`predict`.

    The sharing of weights and layers between the old and the new model guarantee
    that we're using Strategy variables and any updates on either model are
    reflected correctly in callbacks and loop iterations.

    We need to make sure we share the optimizers between the old and the new model
    as well so that optimizer state is not lost if the user is running fit
    multiple times.

    Args:
      model: Model to be replicated across Replicas
      mode: Which of fit/eval/predict is building the distributed network
      inputs: Input variables to be passed to the model
      targets: Target tensor to be passed to model.compile

    Returns:
      A new model with shared layers with the old model.
    ",163,122,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _build_network_on_replica(model, mode, inputs=None, targets=None):
    
    # Need to do imports here since we run into a circular dependency error.
    from keras import models  # pylint: disable=g-import-not-at-top
    from keras.engine import sequential  # pylint: disable=g-import-not-at-top

    # We rely on the internal methods to avoid having share_weights weights in the
    # public API.
    if isinstance(model, sequential.Sequential):
        updated_model = models._clone_sequential_model(
            model, input_tensors=inputs, layer_fn=models.share_weights
        )
    else:
        updated_model = models._clone_functional_model(
            model, input_tensors=inputs, layer_fn=models.share_weights
        )
        # Callable losses added directly to a functional Model need to be added
        # here.
        updated_model._callable_losses = model._callable_losses

    # Recast all low precision outputs back to float32 since we only casted
    # the inputs to bfloat16 and not targets. This is done so that we can preserve
    # precision when calculating the loss value.
        ```
    ###Assistant : Build an updated model on replicas.

    We create a new Keras model while sharing the variables from the old graph.
    Building a new sub-graph is required since the original keras model creates
    placeholders for the input and the output that are not accessible till we
    call iterator.get_next() inside the step_fn for `fit`/`evaluate`/`predict`.

    The sharing of weights and layers between the old and the new model guarantee
    that we're using Strategy variables and any updates on either model are
    reflected correctly in callbacks and loop iterations.

    We need to make sure we share the optimizers between the old and the new model
    as well so that optimizer state is not lost if the user is running fit
    multiple times.

    Args:
      model: Model to be replicated across Replicas
      mode: Which of fit/eval/predict is building the distributed network
      inputs: Input variables to be passed to the model
      targets: Target tensor to be passed to model.compile

    Returns:
      A new model with shared layers with the old model.
    
    "
2583,"def can_jit_compile(warn=False):
    
    if platform.system() == ""Darwin"" and ""arm"" in platform.processor().lower():
        if warn:
            logging.warning(
                ""Tensorflow is not compiled with XLA on Mac M1 Arm processors, ""
                ""so cannot set `jit_compile` to True.""
            )
        return False
    return True
",Returns True if TensorFlow XLA is available for the platform.,10,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def can_jit_compile(warn=False):
    
    if platform.system() == ""Darwin"" and ""arm"" in platform.processor().lower():
        if warn:
            logging.warning(
                ""Tensorflow is not compiled with XLA on Mac M1 Arm processors, ""
                ""so cannot set `jit_compile` to True.""
            )
        return False
    return True

        ```
    ###Assistant : Returns True if TensorFlow XLA is available for the platform.
    "
2584,"def __setstate__(self, state) -> None:
        
        # TODO (sven): Validate that our config and the config in state are compatible.
        #  For example, the model architectures may differ.
        #  Also, what should the behavior be if e.g. some training parameter
        #  (e.g. lr) changed?

        if hasattr(self, ""workers"") and ""worker"" in state:
            self.workers.local_worker().set_state(state[""worker""])
            remote_state = ray.put(state[""worker""])
            for r in self.workers.remote_workers():
                r.set_state.remote(remote_state)
            if self.evaluation_workers:
                # If evaluation workers are used, also restore the policies
                # there in case they are used for evaluation purpose.
                for r in self.evaluation_workers.remote_workers():
                    r.set_state.remote(remote_state)
        # If necessary, restore replay data as well.
        if self.local_replay_buffer is not None:
            # TODO: Experimental functionality: Restore contents of replay
            #  buffer from checkpoint, only if user has configured this.
            if self.config.get(""store_buffer_in_checkpoints""):
                if ""local_replay_buffer"" in state:
                    self.local_replay_buffer.set_state(state[""local_replay_buffer""])
                else:
                    logger.warning(
                        ""`store_buffer_in_checkpoints` is True, but no replay ""
                        ""data found in state!""
                    )
            elif ""local_replay_buffer"" in state and log_once(
                ""no_store_buffer_in_checkpoints_but_data_found""
            ):
                logger.warning(
                    ""`store_buffer_in_checkpoints` is False, but some replay ""
                    ""data found in state!""
                )

        if self.train_exec_impl is not None:
            self.train_exec_impl.shared_metrics.get().restore(state[""train_exec_impl""])
","Sets the algorithm to the provided state.

        Args:
            state: The state dict to restore this Algorithm instance to. `state` may
                have been returned by a call to an Algorithm's `__getstate__()` method.
        ",31,165,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __setstate__(self, state) -> None:
        
        # TODO (sven): Validate that our config and the config in state are compatible.
        #  For example, the model architectures may differ.
        #  Also, what should the behavior be if e.g. some training parameter
        #  (e.g. lr) changed?

        if hasattr(self, ""workers"") and ""worker"" in state:
            self.workers.local_worker().set_state(state[""worker""])
            remote_state = ray.put(state[""worker""])
            for r in self.workers.remote_workers():
                r.set_state.remote(remote_state)
            if self.evaluation_workers:
                # If evaluation workers are used, also restore the policies
                # there in case they are used for evaluation purpose.
                for r in self.evaluation_workers.remote_workers():
                    r.set_state.remote(remote_state)
        # If necessary, restore replay data as well.
        if self.local_replay_buffer is not None:
            # TODO: Experimental functionality: Restore contents of replay
            #  buffer from checkpoint, only if user has configured this.
            if self.config.get(""store_buffer_in_checkpoints""):
                if ""local_replay_buffer"" in state:
                    self.local_replay_buffer.set_state(state[""local_replay_buffer""])
                else:
                    logger.warning(
                        ""`store_buffer_in_checkpoints` is True, but no replay ""
                        ""data found in state!""
                    )
            elif ""local_replay_buffer"" in state and log_once(
                ""no_store_buffer_in_checkpoints_but_data_found""
            ):
                logger.warning(
                    ""`store_buffer_in_checkpoints` is False, but some replay ""
                    ""data found in state!""
                )

        if self.train_exec_impl is not None:
            self.train_exec_impl.shared_metrics.get().restore(state[""train_exec_impl""])

        ```
    ###Assistant : Sets the algorithm to the provided state.

        Args:
            state: The state dict to restore this Algorithm instance to. `state` may
                have been returned by a call to an Algorithm's `__getstate__()` method.
        
    "
2585,"def test_http2_client_aborts(tctx, stream, when, how):
    
    server = Placeholder(Server)
    flow = Placeholder(HTTPFlow)
    playbook, cff = start_h2_client(tctx)
    resp = Placeholder(bytes)
","
    Test handling of the case where a client aborts during request or response transmission.

    If the client aborts the request transmission, we must trigger an error hook,
    if the client disconnects during response transmission, no error hook is triggered.
    ",39,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_http2_client_aborts(tctx, stream, when, how):
    
    server = Placeholder(Server)
    flow = Placeholder(HTTPFlow)
    playbook, cff = start_h2_client(tctx)
    resp = Placeholder(bytes)

        ```
    ###Assistant : 
    Test handling of the case where a client aborts during request or response transmission.

    If the client aborts the request transmission, we must trigger an error hook,
    if the client disconnects during response transmission, no error hook is triggered.
    
    "
2586,"def get_changelist_instance(self, request):
        
        list_display = self.get_list_display(request)
        list_display_links = self.get_list_display_links(request, list_display)
        # Add the action checkboxes if any actions are available.
        if self.get_actions(request):
            list_display = [""action_checkbox"", *list_display]
        sortable_by = self.get_sortable_by(request)
        ChangeList = self.get_changelist(request)
        return ChangeList(
            request,
            self.model,
            list_display,
            list_display_links,
            self.get_list_filter(request),
            self.date_hierarchy,
            self.get_search_fields(request),
            self.get_list_select_related(request),
            self.list_per_page,
            self.list_max_show_all,
            self.list_editable,
            self,
            sortable_by,
            self.search_help_text,
        )
","
        Return a `ChangeList` instance based on `request`. May raise
        `IncorrectLookupParameters`.
        ",10,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_changelist_instance(self, request):
        
        list_display = self.get_list_display(request)
        list_display_links = self.get_list_display_links(request, list_display)
        # Add the action checkboxes if any actions are available.
        if self.get_actions(request):
            list_display = [""action_checkbox"", *list_display]
        sortable_by = self.get_sortable_by(request)
        ChangeList = self.get_changelist(request)
        return ChangeList(
            request,
            self.model,
            list_display,
            list_display_links,
            self.get_list_filter(request),
            self.date_hierarchy,
            self.get_search_fields(request),
            self.get_list_select_related(request),
            self.list_per_page,
            self.list_max_show_all,
            self.list_editable,
            self,
            sortable_by,
            self.search_help_text,
        )

        ```
    ###Assistant : 
        Return a `ChangeList` instance based on `request`. May raise
        `IncorrectLookupParameters`.
        
    "
2587,"def _use_cholesky(u, m, n, params):
  
  a, b, c = params
  _, N = u.shape
  x = c * (u.T.conj() @ u) + jnp.eye(N, dtype=jnp.dtype(u))
  # Pads the lower-right corner with the identity matrix to prevent the Cholesky
  # decomposition from failing due to the matrix not being PSD if padded with
  # zeros.
  x = _mask(x, (n, n), jnp.eye(N, dtype=x.dtype))

  # `y` is lower triangular.
  y = lax_linalg.cholesky(x, symmetrize_input=False)

  z = lax_linalg.triangular_solve(
      y, u.T, left_side=True, lower=True, conjugate_a=True).conj()

  z = lax_linalg.triangular_solve(y, z, left_side=True, lower=True,
                                  transpose_a=True, conjugate_a=True).T.conj()

  e = b / c
  u = e * u + (a - e) * z
  return u
","QDWH iteration using Cholesky decomposition.

  Args:
  u: a matrix, with static (padded) shape M x N
  m, n: the dynamic shape of the matrix, where m <= M and n <= N.
  params: the QDWH parameters.
  ",36,103,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _use_cholesky(u, m, n, params):
  
  a, b, c = params
  _, N = u.shape
  x = c * (u.T.conj() @ u) + jnp.eye(N, dtype=jnp.dtype(u))
  # Pads the lower-right corner with the identity matrix to prevent the Cholesky
  # decomposition from failing due to the matrix not being PSD if padded with
  # zeros.
  x = _mask(x, (n, n), jnp.eye(N, dtype=x.dtype))

  # `y` is lower triangular.
  y = lax_linalg.cholesky(x, symmetrize_input=False)

  z = lax_linalg.triangular_solve(
      y, u.T, left_side=True, lower=True, conjugate_a=True).conj()

  z = lax_linalg.triangular_solve(y, z, left_side=True, lower=True,
                                  transpose_a=True, conjugate_a=True).T.conj()

  e = b / c
  u = e * u + (a - e) * z
  return u

        ```
    ###Assistant : QDWH iteration using Cholesky decomposition.

  Args:
  u: a matrix, with static (padded) shape M x N
  m, n: the dynamic shape of the matrix, where m <= M and n <= N.
  params: the QDWH parameters.
  
    "
2588,"def piecewise_integrate(self, x, **kwargs):
        
        from sympy.integrals import integrate
        return self.func(*[(integrate(e, x, **kwargs), c) for e, c in self.args])
","Return the Piecewise with each expression being
        replaced with its antiderivative. To obtain a continuous
        antiderivative, use the :func:`~.integrate` function or method.

        Examples
        ========

        >>> from sympy import Piecewise
        >>> from sympy.abc import x
        >>> p = Piecewise((0, x < 0), (1, x < 1), (2, True))
        >>> p.piecewise_integrate(x)
        Piecewise((0, x < 0), (x, x < 1), (2*x, True))

        Note that this does not give a continuous function, e.g.
        at x = 1 the 3rd condition applies and the antiderivative
        there is 2*x so the value of the antiderivative is 2:

        >>> anti = _
        >>> anti.subs(x, 1)
        2

        The continuous derivative accounts for the integral *up to*
        the point of interest, however:

        >>> p.integrate(x)
        Piecewise((0, x < 0), (x, x < 1), (2*x - 1, True))
        >>> _.subs(x, 1)
        1

        See Also
        ========
        Piecewise._eval_integral
        ",135,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def piecewise_integrate(self, x, **kwargs):
        
        from sympy.integrals import integrate
        return self.func(*[(integrate(e, x, **kwargs), c) for e, c in self.args])

        ```
    ###Assistant : Return the Piecewise with each expression being
        replaced with its antiderivative. To obtain a continuous
        antiderivative, use the :func:`~.integrate` function or method.

        Examples
        ========

        >>> from sympy import Piecewise
        >>> from sympy.abc import x
        >>> p = Piecewise((0, x < 0), (1, x < 1), (2, True))
        >>> p.piecewise_integrate(x)
        Piecewise((0, x < 0), (x, x < 1), (2*x, True))

        Note that this does not give a continuous function, e.g.
        at x = 1 the 3rd condition applies and the antiderivative
        there is 2*x so the value of the antiderivative is 2:

        >>> anti = _
        >>> anti.subs(x, 1)
        2

        The continuous derivative accounts for the integral *up to*
        the point of interest, however:

        >>> p.integrate(x)
        Piecewise((0, x < 0), (x, x < 1), (2*x - 1, True))
        >>> _.subs(x, 1)
        1

        See Also
        ========
        Piecewise._eval_integral
        
    "
2589,"def check_send_to_ereader(entry):
    
    formats = list()
    book_formats = list()
    if len(entry.data):
        for ele in iter(entry.data):
            if ele.uncompressed_size < config.mail_size:
                formats.append(ele.format)
        if 'EPUB' in formats:
            book_formats.append({'format': 'Epub',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Epub')})
        if 'MOBI' in formats:
            book_formats.append({'format': 'Mobi',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Mobi')})
        if 'PDF' in formats:
            book_formats.append({'format': 'Pdf',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Pdf')})
        if 'AZW' in formats:
            book_formats.append({'format': 'Azw',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Azw')})
        if config.config_converterpath:
            book_formats.extend(check_send_to_ereader_with_converter(formats))
        return book_formats
    else:
        log.error(u'Cannot find book entry %d', entry.id)
        return None


# Check if a reader is existing for any of the book formats, if not, return empty list, otherwise return
# list with supported formats","
        returns all available book formats for sending to E-Reader
    ",9,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_send_to_ereader(entry):
    
    formats = list()
    book_formats = list()
    if len(entry.data):
        for ele in iter(entry.data):
            if ele.uncompressed_size < config.mail_size:
                formats.append(ele.format)
        if 'EPUB' in formats:
            book_formats.append({'format': 'Epub',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Epub')})
        if 'MOBI' in formats:
            book_formats.append({'format': 'Mobi',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Mobi')})
        if 'PDF' in formats:
            book_formats.append({'format': 'Pdf',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Pdf')})
        if 'AZW' in formats:
            book_formats.append({'format': 'Azw',
                                 'convert': 0,
                                 'text': _('Send %(format)s to E-Reader', format='Azw')})
        if config.config_converterpath:
            book_formats.extend(check_send_to_ereader_with_converter(formats))
        return book_formats
    else:
        log.error(u'Cannot find book entry %d', entry.id)
        return None


# Check if a reader is existing for any of the book formats, if not, return empty list, otherwise return
# list with supported formats
        ```
    ###Assistant : 
        returns all available book formats for sending to E-Reader
    
    "
2590,"def Multinomial(syms, n, *p):
    
    if not isinstance(p[0], list):
        p = (list(p), )
    return multivariate_rv(MultinomialDistribution, syms, n, p[0])

#-------------------------------------------------------------------------------
# Negative Multinomial Distribution --------------------------------------------
","
    Creates a discrete random variable with Multinomial Distribution.

    The density of the said distribution can be found at [1].

    Parameters
    ==========

    n : Positive integer
        Represents number of trials
    p : List of event probabilites
        Must be in the range of [0, 1]

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import density, Multinomial, marginal_distribution
    >>> from sympy import symbols
    >>> x1, x2, x3 = symbols('x1, x2, x3', nonnegative=True, integer=True)
    >>> p1, p2, p3 = symbols('p1, p2, p3', positive=True)
    >>> M = Multinomial('M', 3, p1, p2, p3)
    >>> density(M)(x1, x2, x3)
    Piecewise((6*p1**x1*p2**x2*p3**x3/(factorial(x1)*factorial(x2)*factorial(x3)),
    Eq(x1 + x2 + x3, 3)), (0, True))
    >>> marginal_distribution(M, M[0])(x1).subs(x1, 1)
    3*p1*p2**2 + 6*p1*p2*p3 + 3*p1*p3**2

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Multinomial_distribution
    .. [2] http://mathworld.wolfram.com/MultinomialDistribution.html

    ",117,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def Multinomial(syms, n, *p):
    
    if not isinstance(p[0], list):
        p = (list(p), )
    return multivariate_rv(MultinomialDistribution, syms, n, p[0])

#-------------------------------------------------------------------------------
# Negative Multinomial Distribution --------------------------------------------

        ```
    ###Assistant : 
    Creates a discrete random variable with Multinomial Distribution.

    The density of the said distribution can be found at [1].

    Parameters
    ==========

    n : Positive integer
        Represents number of trials
    p : List of event probabilites
        Must be in the range of [0, 1]

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import density, Multinomial, marginal_distribution
    >>> from sympy import symbols
    >>> x1, x2, x3 = symbols('x1, x2, x3', nonnegative=True, integer=True)
    >>> p1, p2, p3 = symbols('p1, p2, p3', positive=True)
    >>> M = Multinomial('M', 3, p1, p2, p3)
    >>> density(M)(x1, x2, x3)
    Piecewise((6*p1**x1*p2**x2*p3**x3/(factorial(x1)*factorial(x2)*factorial(x3)),
    Eq(x1 + x2 + x3, 3)), (0, True))
    >>> marginal_distribution(M, M[0])(x1).subs(x1, 1)
    3*p1*p2**2 + 6*p1*p2*p3 + 3*p1*p3**2

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Multinomial_distribution
    .. [2] http://mathworld.wolfram.com/MultinomialDistribution.html

    
    "
2591,"def psi_n(n, x, m, omega):
    

    # sympify arguments
    n, x, m, omega = map(S, [n, x, m, omega])
    nu = m * omega / hbar
    # normalization coefficient
    C = (nu/pi)**Rational(1, 4) * sqrt(1/(2**n*factorial(n)))

    return C * exp(-nu* x**2 /2) * hermite(n, sqrt(nu)*x)

","
    Returns the wavefunction psi_{n} for the One-dimensional harmonic oscillator.

    Parameters
    ==========

    n :
        the ""nodal"" quantum number.  Corresponds to the number of nodes in the
        wavefunction.  ``n >= 0``
    x :
        x coordinate.
    m :
        Mass of the particle.
    omega :
        Angular frequency of the oscillator.

    Examples
    ========

    >>> from sympy.physics.qho_1d import psi_n
    >>> from sympy.abc import m, x, omega
    >>> psi_n(0, x, m, omega)
    (m*omega)**(1/4)*exp(-m*omega*x**2/(2*hbar))/(hbar**(1/4)*pi**(1/4))

    ",66,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def psi_n(n, x, m, omega):
    

    # sympify arguments
    n, x, m, omega = map(S, [n, x, m, omega])
    nu = m * omega / hbar
    # normalization coefficient
    C = (nu/pi)**Rational(1, 4) * sqrt(1/(2**n*factorial(n)))

    return C * exp(-nu* x**2 /2) * hermite(n, sqrt(nu)*x)


        ```
    ###Assistant : 
    Returns the wavefunction psi_{n} for the One-dimensional harmonic oscillator.

    Parameters
    ==========

    n :
        the ""nodal"" quantum number.  Corresponds to the number of nodes in the
        wavefunction.  ``n >= 0``
    x :
        x coordinate.
    m :
        Mass of the particle.
    omega :
        Angular frequency of the oscillator.

    Examples
    ========

    >>> from sympy.physics.qho_1d import psi_n
    >>> from sympy.abc import m, x, omega
    >>> psi_n(0, x, m, omega)
    (m*omega)**(1/4)*exp(-m*omega*x**2/(2*hbar))/(hbar**(1/4)*pi**(1/4))

    
    "
2592,"def getsourcefile(object):
    
    filename = getfile(object)
    all_bytecode_suffixes = importlib.machinery.DEBUG_BYTECODE_SUFFIXES[:]
    all_bytecode_suffixes += importlib.machinery.OPTIMIZED_BYTECODE_SUFFIXES[:]
    if any(filename.endswith(s) for s in all_bytecode_suffixes):
        filename = (os.path.splitext(filename)[0] +
                    importlib.machinery.SOURCE_SUFFIXES[0])
    elif any(filename.endswith(s) for s in
                 importlib.machinery.EXTENSION_SUFFIXES):
        return None
    if os.path.exists(filename):
        return filename
    # only return a non-existent filename if the module has a PEP 302 loader
    module = getmodule(object, filename)
    if getattr(module, '__loader__', None) is not None:
        return filename
    elif getattr(getattr(module, ""__spec__"", None), ""loader"", None) is not None:
        return filename
    # or it is in the linecache
    elif filename in linecache.cache:
        return filename
","Return the filename that can be used to locate an object's source.
    Return None if no way can be identified to get the source.
    ",24,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getsourcefile(object):
    
    filename = getfile(object)
    all_bytecode_suffixes = importlib.machinery.DEBUG_BYTECODE_SUFFIXES[:]
    all_bytecode_suffixes += importlib.machinery.OPTIMIZED_BYTECODE_SUFFIXES[:]
    if any(filename.endswith(s) for s in all_bytecode_suffixes):
        filename = (os.path.splitext(filename)[0] +
                    importlib.machinery.SOURCE_SUFFIXES[0])
    elif any(filename.endswith(s) for s in
                 importlib.machinery.EXTENSION_SUFFIXES):
        return None
    if os.path.exists(filename):
        return filename
    # only return a non-existent filename if the module has a PEP 302 loader
    module = getmodule(object, filename)
    if getattr(module, '__loader__', None) is not None:
        return filename
    elif getattr(getattr(module, ""__spec__"", None), ""loader"", None) is not None:
        return filename
    # or it is in the linecache
    elif filename in linecache.cache:
        return filename

        ```
    ###Assistant : Return the filename that can be used to locate an object's source.
    Return None if no way can be identified to get the source.
    
    "
2593,"def module_repr(self, module):
        
        warnings.warn(""importlib.abc.Loader.module_repr() is deprecated and ""
                      ""slated for removal in Python 3.12"", DeprecationWarning)
        # The exception will cause ModuleType.__repr__ to ignore this method.
        raise NotImplementedError
","Return a module's repr.

        Used by the module type when the method does not raise
        NotImplementedError.

        This method is deprecated.

        ",20,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def module_repr(self, module):
        
        warnings.warn(""importlib.abc.Loader.module_repr() is deprecated and ""
                      ""slated for removal in Python 3.12"", DeprecationWarning)
        # The exception will cause ModuleType.__repr__ to ignore this method.
        raise NotImplementedError

        ```
    ###Assistant : Return a module's repr.

        Used by the module type when the method does not raise
        NotImplementedError.

        This method is deprecated.

        
    "
2594,"def test_need_validated_email(self):
        
        with self.assertRaises(SynapseError) as cm:
            self.get_success_or_raise(
                self.hs.get_pusherpool().add_or_update_pusher(
                    user_id=self.user_id,
                    access_token=self.token_id,
                    kind=""email"",
                    app_id=""m.email"",
                    app_display_name=""Email Notifications"",
                    device_display_name=""b@example.com"",
                    pushkey=""b@example.com"",
                    lang=None,
                    data={},
                )
            )

        self.assertEqual(400, cm.exception.code)
        self.assertEqual(Codes.THREEPID_NOT_FOUND, cm.exception.errcode)
","Test that we can only add an email pusher if the user has validated
        their email.
        ",16,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_need_validated_email(self):
        
        with self.assertRaises(SynapseError) as cm:
            self.get_success_or_raise(
                self.hs.get_pusherpool().add_or_update_pusher(
                    user_id=self.user_id,
                    access_token=self.token_id,
                    kind=""email"",
                    app_id=""m.email"",
                    app_display_name=""Email Notifications"",
                    device_display_name=""b@example.com"",
                    pushkey=""b@example.com"",
                    lang=None,
                    data={},
                )
            )

        self.assertEqual(400, cm.exception.code)
        self.assertEqual(Codes.THREEPID_NOT_FOUND, cm.exception.errcode)

        ```
    ###Assistant : Test that we can only add an email pusher if the user has validated
        their email.
        
    "
2595,"def test_get_existing_comments(self):
        
        doc = Document.objects.create(
            title=""test"",
            mime_type=""application/pdf"",
            content=""this is a document which will have comments!"",
        )
        comment = Comment.objects.create(
            comment=""This is a comment."",
            document=doc,
            user=self.user,
        )

        response = self.client.get(
            f""/api/documents/{doc.pk}/comments/"",
            format=""json"",
        )

        self.assertEqual(response.status_code, 200)

        resp_data = response.json()

        self.assertEqual(len(resp_data), 1)

        resp_data = resp_data[0]
        del resp_data[""created""]

        self.assertDictEqual(
            resp_data,
            {
                ""id"": comment.id,
                ""comment"": comment.comment,
                ""user"": {
                    ""id"": comment.user.id,
                    ""username"": comment.user.username,
                    ""firstname"": comment.user.first_name,
                    ""lastname"": comment.user.last_name,
                },
            },
        )
","
        GIVEN:
            - A document with a single comment
        WHEN:
            - API reuqest for document comments is made
        THEN:
            - The associated comment is returned
        ",24,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_existing_comments(self):
        
        doc = Document.objects.create(
            title=""test"",
            mime_type=""application/pdf"",
            content=""this is a document which will have comments!"",
        )
        comment = Comment.objects.create(
            comment=""This is a comment."",
            document=doc,
            user=self.user,
        )

        response = self.client.get(
            f""/api/documents/{doc.pk}/comments/"",
            format=""json"",
        )

        self.assertEqual(response.status_code, 200)

        resp_data = response.json()

        self.assertEqual(len(resp_data), 1)

        resp_data = resp_data[0]
        del resp_data[""created""]

        self.assertDictEqual(
            resp_data,
            {
                ""id"": comment.id,
                ""comment"": comment.comment,
                ""user"": {
                    ""id"": comment.user.id,
                    ""username"": comment.user.username,
                    ""firstname"": comment.user.first_name,
                    ""lastname"": comment.user.last_name,
                },
            },
        )

        ```
    ###Assistant : 
        GIVEN:
            - A document with a single comment
        WHEN:
            - API reuqest for document comments is made
        THEN:
            - The associated comment is returned
        
    "
2596,"def collate_full_clips(batch):
        
        max_mel_length = max([b[0].shape[1] for b in batch]) if len(batch) > 1 else batch[0][0].shape[1]
        max_audio_length = max([b[1].shape[0] for b in batch]) if len(batch) > 1 else batch[0][1].shape[0]

        mels = torch.zeros([len(batch), batch[0][0].shape[0], max_mel_length])
        audios = torch.zeros([len(batch), max_audio_length])

        for idx, b in enumerate(batch):
            mel = b[0]
            audio = b[1]
            mels[idx, :, : mel.shape[1]] = mel
            audios[idx, : audio.shape[0]] = audio

        return mels, audios
","This is used in tune_wavegrad.py.
        It pads sequences to the max length.",12,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def collate_full_clips(batch):
        
        max_mel_length = max([b[0].shape[1] for b in batch]) if len(batch) > 1 else batch[0][0].shape[1]
        max_audio_length = max([b[1].shape[0] for b in batch]) if len(batch) > 1 else batch[0][1].shape[0]

        mels = torch.zeros([len(batch), batch[0][0].shape[0], max_mel_length])
        audios = torch.zeros([len(batch), max_audio_length])

        for idx, b in enumerate(batch):
            mel = b[0]
            audio = b[1]
            mels[idx, :, : mel.shape[1]] = mel
            audios[idx, : audio.shape[0]] = audio

        return mels, audios

        ```
    ###Assistant : This is used in tune_wavegrad.py.
        It pads sequences to the max length.
    "
2597,"def feature_test(self, name, force_flags=None, macros=[]):
        
        if force_flags is None:
            force_flags = self.feature_flags(name)

        self.dist_log(
            ""testing feature '%s' with flags (%s)"" % (
            name, ' '.join(force_flags)
        ))
        # Each CPU feature must have C source code contains at
        # least one intrinsic or instruction related to this feature.
        test_path = os.path.join(
            self.conf_check_path, ""cpu_%s.c"" % name.lower()
        )
        if not os.path.exists(test_path):
            self.dist_fatal(""feature test file is not exist"", test_path)

        test = self.dist_test(
            test_path, force_flags + self.cc_flags[""werror""], macros=macros
        )
        if not test:
            self.dist_log(""testing failed"", stderr=True)
        return test
","
        Test a certain CPU feature against the compiler through its own
        check file.

        Parameters
        ----------
        name : str
            Supported CPU feature name.

        force_flags : list or None, optional
            If None(default), the returned flags from `feature_flags()`
            will be used.

        macros : list of tuples, optional
            A list of C macro definitions.
        ",50,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def feature_test(self, name, force_flags=None, macros=[]):
        
        if force_flags is None:
            force_flags = self.feature_flags(name)

        self.dist_log(
            ""testing feature '%s' with flags (%s)"" % (
            name, ' '.join(force_flags)
        ))
        # Each CPU feature must have C source code contains at
        # least one intrinsic or instruction related to this feature.
        test_path = os.path.join(
            self.conf_check_path, ""cpu_%s.c"" % name.lower()
        )
        if not os.path.exists(test_path):
            self.dist_fatal(""feature test file is not exist"", test_path)

        test = self.dist_test(
            test_path, force_flags + self.cc_flags[""werror""], macros=macros
        )
        if not test:
            self.dist_log(""testing failed"", stderr=True)
        return test

        ```
    ###Assistant : 
        Test a certain CPU feature against the compiler through its own
        check file.

        Parameters
        ----------
        name : str
            Supported CPU feature name.

        force_flags : list or None, optional
            If None(default), the returned flags from `feature_flags()`
            will be used.

        macros : list of tuples, optional
            A list of C macro definitions.
        
    "
2598,"def get_file_path(self) -> str:
        
        if self.file_name is None:
            raise ValueError(""Must specify file for SVGMobject"")
        return get_full_vector_image_path(self.file_name)
",Search for an existing file based on the specified file name.,11,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_file_path(self) -> str:
        
        if self.file_name is None:
            raise ValueError(""Must specify file for SVGMobject"")
        return get_full_vector_image_path(self.file_name)

        ```
    ###Assistant : Search for an existing file based on the specified file name.
    "
2599,"def default_config(self) -> Dict[str, Any]:
        
        base = super().default_config()
        base[""redis""] = {""enabled"": True}
        return base
","
        Overrides the default config to enable Redis.
        Even if the test only uses make_worker_hs, the main process needs Redis
        enabled otherwise it won't create a Fake Redis server to listen on the
        Redis port and accept fake TCP connections.
        ",39,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def default_config(self) -> Dict[str, Any]:
        
        base = super().default_config()
        base[""redis""] = {""enabled"": True}
        return base

        ```
    ###Assistant : 
        Overrides the default config to enable Redis.
        Even if the test only uses make_worker_hs, the main process needs Redis
        enabled otherwise it won't create a Fake Redis server to listen on the
        Redis port and accept fake TCP connections.
        
    "
2600,"def delete_project_summary_annotations_before_updating_annotation(sender, instance, **kwargs):
    
    try:
        old_annotation = sender.objects.get(id=instance.id)
    except Annotation.DoesNotExist:
        # annotation just created - do nothing
        return
    old_annotation.decrease_project_summary_counters()

    # update task counters if annotation changes it's was_cancelled status
    task = instance.task
    if old_annotation.was_cancelled != instance.was_cancelled:
        if instance.was_cancelled:
            task.cancelled_annotations = task.cancelled_annotations + 1
            task.total_annotations = task.total_annotations - 1
        else:
            task.cancelled_annotations = task.cancelled_annotations - 1
            task.total_annotations = task.total_annotations + 1
        task.update_is_labeled()

        Task.objects.filter(id=instance.task.id).update(
            is_labeled=task.is_labeled,
            total_annotations=task.total_annotations,
            cancelled_annotations=task.cancelled_annotations
        )


@receiver(post_save, sender=Annotation)",Before updating annotation fields - ensure previous info removed from project.summary,11,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_project_summary_annotations_before_updating_annotation(sender, instance, **kwargs):
    
    try:
        old_annotation = sender.objects.get(id=instance.id)
    except Annotation.DoesNotExist:
        # annotation just created - do nothing
        return
    old_annotation.decrease_project_summary_counters()

    # update task counters if annotation changes it's was_cancelled status
    task = instance.task
    if old_annotation.was_cancelled != instance.was_cancelled:
        if instance.was_cancelled:
            task.cancelled_annotations = task.cancelled_annotations + 1
            task.total_annotations = task.total_annotations - 1
        else:
            task.cancelled_annotations = task.cancelled_annotations - 1
            task.total_annotations = task.total_annotations + 1
        task.update_is_labeled()

        Task.objects.filter(id=instance.task.id).update(
            is_labeled=task.is_labeled,
            total_annotations=task.total_annotations,
            cancelled_annotations=task.cancelled_annotations
        )


@receiver(post_save, sender=Annotation)
        ```
    ###Assistant : Before updating annotation fields - ensure previous info removed from project.summary
    "
2601,"def load_config(self, modelpath, use_gpu, gpu_id, use_mkldnn, cpu_threads):
        
        # 对运行位置进行配置
        if use_gpu:
            try:
                int(os.environ.get('CUDA_VISIBLE_DEVICES'))
            except Exception:
                print(
                    )
                use_gpu = False

        if os.path.isdir(modelpath):
            if os.path.exists(os.path.join(modelpath, ""__params__"")):
                # __model__ + __params__
                model = os.path.join(modelpath, ""__model__"")
                params = os.path.join(modelpath, ""__params__"")
                config = Config(model, params)
            elif os.path.exists(os.path.join(modelpath, ""params"")):
                # model + params
                model = os.path.join(modelpath, ""model"")
                params = os.path.join(modelpath, ""params"")
                config = Config(model, params)
            elif os.path.exists(os.path.join(modelpath, ""__model__"")):
                # __model__ + others
                config = Config(modelpath)
            else:
                raise Exception(
                    ""Error! Can\'t find the model in: %s. Please check your model path."" % os.path.abspath(modelpath))
        elif os.path.exists(modelpath + "".pdmodel""):
            # *.pdmodel + *.pdiparams
            model = modelpath + "".pdmodel""
            params = modelpath + "".pdiparams""
            config = Config(model, params)
        elif isinstance(modelpath, Config):
            config = modelpath
        else:
            raise Exception(
                ""Error! Can\'t find the model in: %s. Please check your model path."" % os.path.abspath(modelpath))

        # 设置参数
        if use_gpu:
            config.enable_use_gpu(100, gpu_id)
        else:
            config.disable_gpu()
            config.set_cpu_math_library_num_threads(cpu_threads)
            if use_mkldnn:
                config.enable_mkldnn()

        config.disable_glog_info()

        # 返回配置
        return config

    # 预测器创建函数
","
        load the model config
        modelpath: inference model path
        use_gpu: use gpu or not
        use_mkldnn: use mkldnn or not
        Error! Unable to use GPU. Please set the environment variables ""CUDA_VISIBLE_DEVICES=GPU_id"" to use GPU. Now switch to CPU to continue...",38,151,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load_config(self, modelpath, use_gpu, gpu_id, use_mkldnn, cpu_threads):
        
        # 对运行位置进行配置
        if use_gpu:
            try:
                int(os.environ.get('CUDA_VISIBLE_DEVICES'))
            except Exception:
                print(
                    )
                use_gpu = False

        if os.path.isdir(modelpath):
            if os.path.exists(os.path.join(modelpath, ""__params__"")):
                # __model__ + __params__
                model = os.path.join(modelpath, ""__model__"")
                params = os.path.join(modelpath, ""__params__"")
                config = Config(model, params)
            elif os.path.exists(os.path.join(modelpath, ""params"")):
                # model + params
                model = os.path.join(modelpath, ""model"")
                params = os.path.join(modelpath, ""params"")
                config = Config(model, params)
            elif os.path.exists(os.path.join(modelpath, ""__model__"")):
                # __model__ + others
                config = Config(modelpath)
            else:
                raise Exception(
                    ""Error! Can\'t find the model in: %s. Please check your model path."" % os.path.abspath(modelpath))
        elif os.path.exists(modelpath + "".pdmodel""):
            # *.pdmodel + *.pdiparams
            model = modelpath + "".pdmodel""
            params = modelpath + "".pdiparams""
            config = Config(model, params)
        elif isinstance(modelpath, Config):
            config = modelpath
        else:
            raise Exception(
                ""Error! Can\'t find the model in: %s. Please check your model path."" % os.path.abspath(modelpath))

        # 设置参数
        if use_gpu:
            config.enable_use_gpu(100, gpu_id)
        else:
            config.disable_gpu()
            config.set_cpu_math_library_num_threads(cpu_threads)
            if use_mkldnn:
                config.enable_mkldnn()

        config.disable_glog_info()

        # 返回配置
        return config

    # 预测器创建函数
        ```
    ###Assistant : 
        load the model config
        modelpath: inference model path
        use_gpu: use gpu or not
        use_mkldnn: use mkldnn or not
        Error! Unable to use GPU. Please set the environment variables ""CUDA_VISIBLE_DEVICES=GPU_id"" to use GPU. Now switch to CPU to continue...
    "
2602,"def trim_line(line, column=0):
    
    line = line.strip(""\n"")
    ll = len(line)
    if ll <= 150:
        return line
    if column > ll:
        column = ll
    start = max(column - 60, 0)
    # Round down if it brings us close to the edge
    if start < 5:
        start = 0
    end = min(start + 140, ll)
    # Round up to the end if it's close
    if end > ll - 5:
        end = ll
    # If we are bumped all the way to the end,
    # make sure we still get a full 140 characters in the line
    if end == ll:
        start = max(end - 140, 0)
    line = line[start:end]
    if end < ll:
        # we've snipped from the end
        line += "" {snip}""
    if start > 0:
        # we've snipped from the beginning
        line = ""{snip} "" + line
    return line

","
    Trims a line down to a goal of 140 characters, with a little
    wiggle room to be sensible and tries to trim around the given
    `column`. So it tries to extract 60 characters before and after
    the provided `column` and yield a better context.
    ",44,139,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def trim_line(line, column=0):
    
    line = line.strip(""\n"")
    ll = len(line)
    if ll <= 150:
        return line
    if column > ll:
        column = ll
    start = max(column - 60, 0)
    # Round down if it brings us close to the edge
    if start < 5:
        start = 0
    end = min(start + 140, ll)
    # Round up to the end if it's close
    if end > ll - 5:
        end = ll
    # If we are bumped all the way to the end,
    # make sure we still get a full 140 characters in the line
    if end == ll:
        start = max(end - 140, 0)
    line = line[start:end]
    if end < ll:
        # we've snipped from the end
        line += "" {snip}""
    if start > 0:
        # we've snipped from the beginning
        line = ""{snip} "" + line
    return line


        ```
    ###Assistant : 
    Trims a line down to a goal of 140 characters, with a little
    wiggle room to be sensible and tries to trim around the given
    `column`. So it tries to extract 60 characters before and after
    the provided `column` and yield a better context.
    
    "
2603,"def test_background_update_default_batch_set_by_config(self):
        

        self.get_success(
            self.store.db_pool.simple_insert(
                ""background_updates"",
                values={""update_name"": ""test_update"", ""progress_json"": '{""my_key"": 1}'},
            )
        )

        self.update_handler.side_effect = self.update
        self.update_handler.reset_mock()
        res = self.get_success(
            self.updates.do_next_background_update(False),
            by=0.01,
        )
        self.assertFalse(res)

        # on the first call, we should get run with the default background update size specified in the config
        self.update_handler.assert_called_once_with({""my_key"": 1}, 20)
","
        Test that the background update is run with the default_batch_size set by the config
        ",14,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_background_update_default_batch_set_by_config(self):
        

        self.get_success(
            self.store.db_pool.simple_insert(
                ""background_updates"",
                values={""update_name"": ""test_update"", ""progress_json"": '{""my_key"": 1}'},
            )
        )

        self.update_handler.side_effect = self.update
        self.update_handler.reset_mock()
        res = self.get_success(
            self.updates.do_next_background_update(False),
            by=0.01,
        )
        self.assertFalse(res)

        # on the first call, we should get run with the default background update size specified in the config
        self.update_handler.assert_called_once_with({""my_key"": 1}, 20)

        ```
    ###Assistant : 
        Test that the background update is run with the default_batch_size set by the config
        
    "
2604,"def _expiry_date(self, session_data):
        
        return session_data.get(""_session_expiry"") or (
            self._last_modification()
            + datetime.timedelta(seconds=self.get_session_cookie_age())
        )
","
        Return the expiry time of the file storing the session's content.
        ",11,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _expiry_date(self, session_data):
        
        return session_data.get(""_session_expiry"") or (
            self._last_modification()
            + datetime.timedelta(seconds=self.get_session_cookie_age())
        )

        ```
    ###Assistant : 
        Return the expiry time of the file storing the session's content.
        
    "
2605,"def __call__(self, *args, **kwargs):
        
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*args, **kwargs)

        images = kwargs.pop(""images"", None)
        text = kwargs.pop(""text"", None)
        if len(args) > 0:
            images = args[0]
            args = args[1:]

        if images is None and text is None:
            raise ValueError(""You need to specify either an `images` or `text` input to process."")

        if images is not None:
            inputs = self.feature_extractor(images, *args, **kwargs)
        if text is not None:
            encodings = self.tokenizer(text, **kwargs)

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            inputs[""labels""] = encodings[""input_ids""]
            return inputs
","
        When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor's
        [`~AutoFeatureExtractor.__call__`] and returns its output. If used in the context
        [`~TrOCRProcessor.as_target_processor`] this method forwards all its arguments to TrOCRTokenizer's
        [`~TrOCRTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
        ",46,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self, *args, **kwargs):
        
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*args, **kwargs)

        images = kwargs.pop(""images"", None)
        text = kwargs.pop(""text"", None)
        if len(args) > 0:
            images = args[0]
            args = args[1:]

        if images is None and text is None:
            raise ValueError(""You need to specify either an `images` or `text` input to process."")

        if images is not None:
            inputs = self.feature_extractor(images, *args, **kwargs)
        if text is not None:
            encodings = self.tokenizer(text, **kwargs)

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            inputs[""labels""] = encodings[""input_ids""]
            return inputs

        ```
    ###Assistant : 
        When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor's
        [`~AutoFeatureExtractor.__call__`] and returns its output. If used in the context
        [`~TrOCRProcessor.as_target_processor`] this method forwards all its arguments to TrOCRTokenizer's
        [`~TrOCRTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
        
    "
2606,"def get_report(module_name, report_name):
    
    reports = get_reports()
    module = reports.get(module_name)

    if module is None:
        return None

    report = module.get(report_name)

    if report is None:
        return None

    return report

","
    Return a specific report from within a module.
    ",8,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_report(module_name, report_name):
    
    reports = get_reports()
    module = reports.get(module_name)

    if module is None:
        return None

    report = module.get(report_name)

    if report is None:
        return None

    return report


        ```
    ###Assistant : 
    Return a specific report from within a module.
    
    "
2607,"def test_worker_duty_configs(self) -> None:
        

        worker1_config = self._make_worker_config(
            worker_app=""synapse.app.generic_worker"",
            worker_name=""worker1"",
            extras={
                ""notify_appservices_from_worker"": ""worker2"",
                ""update_user_directory_from_worker"": ""worker1"",
            },
        )
        self.assertFalse(worker1_config.should_notify_appservices)
        self.assertTrue(worker1_config.should_update_user_directory)

        worker2_config = self._make_worker_config(
            worker_app=""synapse.app.generic_worker"",
            worker_name=""worker2"",
            extras={
                ""notify_appservices_from_worker"": ""worker2"",
                ""update_user_directory_from_worker"": ""worker1"",
            },
        )
        self.assertTrue(worker2_config.should_notify_appservices)
        self.assertFalse(worker2_config.should_update_user_directory)
","
        Additional tests for the worker duties
        ",6,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_worker_duty_configs(self) -> None:
        

        worker1_config = self._make_worker_config(
            worker_app=""synapse.app.generic_worker"",
            worker_name=""worker1"",
            extras={
                ""notify_appservices_from_worker"": ""worker2"",
                ""update_user_directory_from_worker"": ""worker1"",
            },
        )
        self.assertFalse(worker1_config.should_notify_appservices)
        self.assertTrue(worker1_config.should_update_user_directory)

        worker2_config = self._make_worker_config(
            worker_app=""synapse.app.generic_worker"",
            worker_name=""worker2"",
            extras={
                ""notify_appservices_from_worker"": ""worker2"",
                ""update_user_directory_from_worker"": ""worker1"",
            },
        )
        self.assertTrue(worker2_config.should_notify_appservices)
        self.assertFalse(worker2_config.should_update_user_directory)

        ```
    ###Assistant : 
        Additional tests for the worker duties
        
    "
2608,"def actor_id(self):
        
        # only worker mode has actor_id
        assert (
            self.worker.mode == ray.worker.WORKER_MODE
        ), f""This method is only available when the process is a\
                 worker. Current mode: {self.worker.mode}""
        actor_id = self.worker.actor_id
        return actor_id if not actor_id.is_nil() else None
","Get the current actor ID in this worker.

        ID of the actor of the current process.
        This shouldn't be used in a driver process.

        Returns:
            The current actor id in this worker. None if there's no actor id.
        ",38,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def actor_id(self):
        
        # only worker mode has actor_id
        assert (
            self.worker.mode == ray.worker.WORKER_MODE
        ), f""This method is only available when the process is a\
                 worker. Current mode: {self.worker.mode}""
        actor_id = self.worker.actor_id
        return actor_id if not actor_id.is_nil() else None

        ```
    ###Assistant : Get the current actor ID in this worker.

        ID of the actor of the current process.
        This shouldn't be used in a driver process.

        Returns:
            The current actor id in this worker. None if there's no actor id.
        
    "
2609,"def test_redirect_to_current(self):
        
        start_url = reverse(""wagtailsettings:edit"", args=[""tests"", ""testsetting""])
        dest_url = reverse(
            ""wagtailsettings:edit"", args=[""tests"", ""testsetting"", self.other_site.pk]
        )
        response = self.client.get(
            start_url, follow=True, HTTP_HOST=self.other_site.hostname
        )
        self.assertRedirects(
            response, dest_url, status_code=302, fetch_redirect_response=False
        )
","
        Should redirect to the setting for the current site taken from the URL,
        by default
        ",15,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_redirect_to_current(self):
        
        start_url = reverse(""wagtailsettings:edit"", args=[""tests"", ""testsetting""])
        dest_url = reverse(
            ""wagtailsettings:edit"", args=[""tests"", ""testsetting"", self.other_site.pk]
        )
        response = self.client.get(
            start_url, follow=True, HTTP_HOST=self.other_site.hostname
        )
        self.assertRedirects(
            response, dest_url, status_code=302, fetch_redirect_response=False
        )

        ```
    ###Assistant : 
        Should redirect to the setting for the current site taken from the URL,
        by default
        
    "
2610,"def fit(self, X, y, sample_weight=None, fit_params=None):
        

        if not hasattr(self.estimator, ""fit""):
            raise ValueError(""The base estimator should implement a fit method"")

        y = self._validate_data(X=""no_validation"", y=y, multi_output=True)

        if is_classifier(self):
            check_classification_targets(y)

        if y.ndim == 1:
            raise ValueError(
                ""y must have at least two dimensions for ""
                ""multi-output regression but has only one.""
            )

        if sample_weight is not None and not has_fit_parameter(
            self.estimator, ""sample_weight""
        ):
            raise ValueError(""Underlying estimator does not support sample weights."")

        if not fit_params:
            fit_params = [None] * y.shape[1]

        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
            delayed(_fit_estimator)(
                self.estimator, X, y[:, i], sample_weight, **fit_params[i]
            )
            for i in range(y.shape[1])
        )

        self.classes_ = []
        for estimator in self.estimators_:
            self.classes_.extend(estimator.classes_)

        if hasattr(self.estimators_[0], ""n_features_in_""):
            self.n_features_in_ = self.estimators_[0].n_features_in_
        if hasattr(self.estimators_[0], ""feature_names_in_""):
            self.feature_names_in_ = self.estimators_[0].feature_names_in_

        return self
","Fit the model to data, separately for each output variable.
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input data.
        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
            Multi-output targets. An indicator matrix turns on multilabel
            estimation.
        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If `None`, then samples are equally weighted.
            Only supported if the underlying classifier supports sample
            weights.
        fit_params : A list of dicts for the fit_params
            Parameters passed to the ``estimator.fit`` method of each step.
            Each dict may contain same or different values (e.g. different
            eval_sets or init_models)
            .. versionadded:: 0.23
        Returns
        -------
        self : object
            Returns a fitted instance.
        ",110,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y, sample_weight=None, fit_params=None):
        

        if not hasattr(self.estimator, ""fit""):
            raise ValueError(""The base estimator should implement a fit method"")

        y = self._validate_data(X=""no_validation"", y=y, multi_output=True)

        if is_classifier(self):
            check_classification_targets(y)

        if y.ndim == 1:
            raise ValueError(
                ""y must have at least two dimensions for ""
                ""multi-output regression but has only one.""
            )

        if sample_weight is not None and not has_fit_parameter(
            self.estimator, ""sample_weight""
        ):
            raise ValueError(""Underlying estimator does not support sample weights."")

        if not fit_params:
            fit_params = [None] * y.shape[1]

        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
            delayed(_fit_estimator)(
                self.estimator, X, y[:, i], sample_weight, **fit_params[i]
            )
            for i in range(y.shape[1])
        )

        self.classes_ = []
        for estimator in self.estimators_:
            self.classes_.extend(estimator.classes_)

        if hasattr(self.estimators_[0], ""n_features_in_""):
            self.n_features_in_ = self.estimators_[0].n_features_in_
        if hasattr(self.estimators_[0], ""feature_names_in_""):
            self.feature_names_in_ = self.estimators_[0].feature_names_in_

        return self

        ```
    ###Assistant : Fit the model to data, separately for each output variable.
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input data.
        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
            Multi-output targets. An indicator matrix turns on multilabel
            estimation.
        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If `None`, then samples are equally weighted.
            Only supported if the underlying classifier supports sample
            weights.
        fit_params : A list of dicts for the fit_params
            Parameters passed to the ``estimator.fit`` method of each step.
            Each dict may contain same or different values (e.g. different
            eval_sets or init_models)
            .. versionadded:: 0.23
        Returns
        -------
        self : object
            Returns a fitted instance.
        
    "
2611,"def inner_choices(self) -> Iterable['ValueChoice']:
        
        for arg in self.arguments:
            if isinstance(arg, ValueChoiceX):
                yield from arg.inner_choices()
","
        Return an iterable of all leaf value choices.
        Useful for composition of value choices.
        No deduplication on labels. Mutators should take care.
        ",22,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def inner_choices(self) -> Iterable['ValueChoice']:
        
        for arg in self.arguments:
            if isinstance(arg, ValueChoiceX):
                yield from arg.inner_choices()

        ```
    ###Assistant : 
        Return an iterable of all leaf value choices.
        Useful for composition of value choices.
        No deduplication on labels. Mutators should take care.
        
    "
2612,"def get_indices(expr):
    
    # We call ourself recursively to determine indices of sub expressions.

    # break recursion
    if isinstance(expr, Indexed):
        c = expr.indices
        inds, dummies = _remove_repeated(c)
        return inds, {}
    elif expr is None:
        return set(), {}
    elif isinstance(expr, Idx):
        return {expr}, {}
    elif expr.is_Atom:
        return set(), {}


    # recurse via specialized functions
    else:
        if expr.is_Mul:
            return _get_indices_Mul(expr)
        elif expr.is_Add:
            return _get_indices_Add(expr)
        elif expr.is_Pow or isinstance(expr, exp):
            return _get_indices_Pow(expr)

        elif isinstance(expr, Piecewise):
            # FIXME:  No support for Piecewise yet
            return set(), {}
        elif isinstance(expr, Function):
            # Support ufunc like behaviour by returning indices from arguments.
            # Functions do not interpret repeated indices across arguments
            # as summation
            ind0 = set()
            for arg in expr.args:
                ind, sym = get_indices(arg)
                ind0 |= ind
            return ind0, sym

        # this test is expensive, so it should be at the end
        elif not expr.has(Indexed):
            return set(), {}
        raise NotImplementedError(
            ""FIXME: No specialized handling of type %s"" % type(expr))

","Determine the outer indices of expression ``expr``

    By *outer* we mean indices that are not summation indices.  Returns a set
    and a dict.  The set contains outer indices and the dict contains
    information about index symmetries.

    Examples
    ========

    >>> from sympy.tensor.index_methods import get_indices
    >>> from sympy import symbols
    >>> from sympy.tensor import IndexedBase
    >>> x, y, A = map(IndexedBase, ['x', 'y', 'A'])
    >>> i, j, a, z = symbols('i j a z', integer=True)

    The indices of the total expression is determined, Repeated indices imply a
    summation, for instance the trace of a matrix A:

    >>> get_indices(A[i, i])
    (set(), {})

    In the case of many terms, the terms are required to have identical
    outer indices.  Else an IndexConformanceException is raised.

    >>> get_indices(x[i] + A[i, j]*y[j])
    ({i}, {})

    :Exceptions:

    An IndexConformanceException means that the terms ar not compatible, e.g.

    >>> get_indices(x[i] + y[j])                #doctest: +SKIP
            (...)
    IndexConformanceException: Indices are not consistent: x(i) + y(j)

    .. warning::
       The concept of *outer* indices applies recursively, starting on the deepest
       level.  This implies that dummies inside parenthesis are assumed to be
       summed first, so that the following expression is handled gracefully:

       >>> get_indices((x[i] + A[i, j]*y[j])*x[j])
       ({i, j}, {})

       This is correct and may appear convenient, but you need to be careful
       with this as SymPy will happily .expand() the product, if requested.  The
       resulting expression would mix the outer ``j`` with the dummies inside
       the parenthesis, which makes it a different expression.  To be on the
       safe side, it is best to avoid such ambiguities by using unique indices
       for all contractions that should be held separate.

    ",263,152,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_indices(expr):
    
    # We call ourself recursively to determine indices of sub expressions.

    # break recursion
    if isinstance(expr, Indexed):
        c = expr.indices
        inds, dummies = _remove_repeated(c)
        return inds, {}
    elif expr is None:
        return set(), {}
    elif isinstance(expr, Idx):
        return {expr}, {}
    elif expr.is_Atom:
        return set(), {}


    # recurse via specialized functions
    else:
        if expr.is_Mul:
            return _get_indices_Mul(expr)
        elif expr.is_Add:
            return _get_indices_Add(expr)
        elif expr.is_Pow or isinstance(expr, exp):
            return _get_indices_Pow(expr)

        elif isinstance(expr, Piecewise):
            # FIXME:  No support for Piecewise yet
            return set(), {}
        elif isinstance(expr, Function):
            # Support ufunc like behaviour by returning indices from arguments.
            # Functions do not interpret repeated indices across arguments
            # as summation
            ind0 = set()
            for arg in expr.args:
                ind, sym = get_indices(arg)
                ind0 |= ind
            return ind0, sym

        # this test is expensive, so it should be at the end
        elif not expr.has(Indexed):
            return set(), {}
        raise NotImplementedError(
            ""FIXME: No specialized handling of type %s"" % type(expr))


        ```
    ###Assistant : Determine the outer indices of expression ``expr``

    By *outer* we mean indices that are not summation indices.  Returns a set
    and a dict.  The set contains outer indices and the dict contains
    information about index symmetries.

    Examples
    ========

    >>> from sympy.tensor.index_methods import get_indices
    >>> from sympy import symbols
    >>> from sympy.tensor import IndexedBase
    >>> x, y, A = map(IndexedBase, ['x', 'y', 'A'])
    >>> i, j, a, z = symbols('i j a z', integer=True)

    The indices of the total expression is determined, Repeated indices imply a
    summation, for instance the trace of a matrix A:

    >>> get_indices(A[i, i])
    (set(), {})

    In the case of many terms, the terms are required to have identical
    outer indices.  Else an IndexConformanceException is raised.

    >>> get_indices(x[i] + A[i, j]*y[j])
    ({i}, {})

    :Exceptions:

    An IndexConformanceException means that the terms ar not compatible, e.g.

    >>> get_indices(x[i] + y[j])                #doctest: +SKIP
            (...)
    IndexConformanceException: Indices are not consistent: x(i) + y(j)

    .. warning::
       The concept of *outer* indices applies recursively, starting on the deepest
       level.  This implies that dummies inside parenthesis are assumed to be
       summed first, so that the following expression is handled gracefully:

       >>> get_indices((x[i] + A[i, j]*y[j])*x[j])
       ({i, j}, {})

       This is correct and may appear convenient, but you need to be careful
       with this as SymPy will happily .expand() the product, if requested.  The
       resulting expression would mix the outer ``j`` with the dummies inside
       the parenthesis, which makes it a different expression.  To be on the
       safe side, it is best to avoid such ambiguities by using unique indices
       for all contractions that should be held separate.

    
    "
2613,"def dist_location(dist):
    # type: (Distribution) -> str
    
    egg_link = egg_link_path(dist)
    if egg_link:
        return normalize_path(egg_link)
    return normalize_path(dist.location)

","
    Get the site-packages location of this distribution. Generally
    this is dist.location, except in the case of develop-installed
    packages, where dist.location is the source code location, and we
    want to know where the egg-link file is.

    The returned location is normalized (in particular, with symlinks removed).
    ",45,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dist_location(dist):
    # type: (Distribution) -> str
    
    egg_link = egg_link_path(dist)
    if egg_link:
        return normalize_path(egg_link)
    return normalize_path(dist.location)


        ```
    ###Assistant : 
    Get the site-packages location of this distribution. Generally
    this is dist.location, except in the case of develop-installed
    packages, where dist.location is the source code location, and we
    want to know where the egg-link file is.

    The returned location is normalized (in particular, with symlinks removed).
    
    "
2614,"def can_connect(url, error_classes=None):
    
    if error_classes is None:
        error_classes = _get_default_network_errors()

    try:
        with urlopen(url, timeout=20) as response:
            # Timeout just in case rate-limiting is applied
            if response.status != 200:
                return False
    except error_classes:
        return False
    else:
        return True


# ------------------------------------------------------------------
# File-IO

","
    Try to connect to the given url. True if succeeds, False if OSError
    raised

    Parameters
    ----------
    url : basestring
        The URL to try to connect to

    Returns
    -------
    connectable : bool
        Return True if no OSError (unable to connect) or URLError (bad url) was
        raised
    ",45,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def can_connect(url, error_classes=None):
    
    if error_classes is None:
        error_classes = _get_default_network_errors()

    try:
        with urlopen(url, timeout=20) as response:
            # Timeout just in case rate-limiting is applied
            if response.status != 200:
                return False
    except error_classes:
        return False
    else:
        return True


# ------------------------------------------------------------------
# File-IO


        ```
    ###Assistant : 
    Try to connect to the given url. True if succeeds, False if OSError
    raised

    Parameters
    ----------
    url : basestring
        The URL to try to connect to

    Returns
    -------
    connectable : bool
        Return True if no OSError (unable to connect) or URLError (bad url) was
        raised
    
    "
2615,"def test_valid_slack_channel_id(self):
        
        integration = Integration.objects.create(
            external_id=""1"",
            provider=""slack"",
            metadata={""access_token"": ""xoxp-xxxxxxxxx-xxxxxxxxxx-xxxxxxxxxxxx""},
        )
        integration.add_organization(self.organization, self.user)

        base_params = self.valid_params.copy()
        base_params.update(
            {
                ""type"": AlertRuleTriggerAction.get_registered_type(
                    AlertRuleTriggerAction.Type.SLACK
                ).slug,
                ""targetType"": ACTION_TARGET_TYPE_TO_STRING[
                    AlertRuleTriggerAction.TargetType.SPECIFIC
                ],
                ""targetIdentifier"": ""merp"",
                ""integration"": str(integration.id),
            }
        )
        context = self.context.copy()
        context.update({""input_channel_id"": ""CSVK0921""})
        responses.add(
            method=responses.GET,
            url=""https://slack.com/api/conversations.info"",
            status=200,
            content_type=""application/json"",
            body=json.dumps({""ok"": ""true"", ""channel"": {""name"": ""merp"", ""id"": ""CSVK0921""}}),
        )
        serializer = AlertRuleTriggerActionSerializer(context=context, data=base_params)
        assert serializer.is_valid()

        serializer.save()

        # # Make sure the action was created.
        alert_rule_trigger_actions = list(
            AlertRuleTriggerAction.objects.filter(integration=integration)
        )
        assert len(alert_rule_trigger_actions) == 1
","
        Test that when a valid Slack channel ID is provided, we look up the channel name and validate it against the targetIdentifier.
        ",22,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_valid_slack_channel_id(self):
        
        integration = Integration.objects.create(
            external_id=""1"",
            provider=""slack"",
            metadata={""access_token"": ""xoxp-xxxxxxxxx-xxxxxxxxxx-xxxxxxxxxxxx""},
        )
        integration.add_organization(self.organization, self.user)

        base_params = self.valid_params.copy()
        base_params.update(
            {
                ""type"": AlertRuleTriggerAction.get_registered_type(
                    AlertRuleTriggerAction.Type.SLACK
                ).slug,
                ""targetType"": ACTION_TARGET_TYPE_TO_STRING[
                    AlertRuleTriggerAction.TargetType.SPECIFIC
                ],
                ""targetIdentifier"": ""merp"",
                ""integration"": str(integration.id),
            }
        )
        context = self.context.copy()
        context.update({""input_channel_id"": ""CSVK0921""})
        responses.add(
            method=responses.GET,
            url=""https://slack.com/api/conversations.info"",
            status=200,
            content_type=""application/json"",
            body=json.dumps({""ok"": ""true"", ""channel"": {""name"": ""merp"", ""id"": ""CSVK0921""}}),
        )
        serializer = AlertRuleTriggerActionSerializer(context=context, data=base_params)
        assert serializer.is_valid()

        serializer.save()

        # # Make sure the action was created.
        alert_rule_trigger_actions = list(
            AlertRuleTriggerAction.objects.filter(integration=integration)
        )
        assert len(alert_rule_trigger_actions) == 1

        ```
    ###Assistant : 
        Test that when a valid Slack channel ID is provided, we look up the channel name and validate it against the targetIdentifier.
        
    "
2616,"def get_legacy(members):
    
    if AIX_ABI == 64:
        # AIX 64-bit member is one of shr64.o, shr_64.o, or shr4_64.o
        expr = r'shr4?_?64\.o'
        member = get_one_match(expr, members)
        if member:
            return member
    else:
        # 32-bit legacy names - both shr.o and shr4.o exist.
        # shr.o is the preferred name so we look for shr.o first
        #  i.e., shr4.o is returned only when shr.o does not exist
        for name in ['shr.o', 'shr4.o']:
            member = get_one_match(re.escape(name), members)
            if member:
                return member
    return None
","
    This routine provides historical aka legacy naming schemes started
    in AIX4 shared library support for library members names.
    e.g., in /usr/lib/libc.a the member name shr.o for 32-bit binary and
    shr_64.o for 64-bit binary.
    ",33,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_legacy(members):
    
    if AIX_ABI == 64:
        # AIX 64-bit member is one of shr64.o, shr_64.o, or shr4_64.o
        expr = r'shr4?_?64\.o'
        member = get_one_match(expr, members)
        if member:
            return member
    else:
        # 32-bit legacy names - both shr.o and shr4.o exist.
        # shr.o is the preferred name so we look for shr.o first
        #  i.e., shr4.o is returned only when shr.o does not exist
        for name in ['shr.o', 'shr4.o']:
            member = get_one_match(re.escape(name), members)
            if member:
                return member
    return None

        ```
    ###Assistant : 
    This routine provides historical aka legacy naming schemes started
    in AIX4 shared library support for library members names.
    e.g., in /usr/lib/libc.a the member name shr.o for 32-bit binary and
    shr_64.o for 64-bit binary.
    
    "
2617,"def set_policy(name, table=""filter"", family=""ipv4"", **kwargs):
    
    ret = {""name"": name, ""changes"": {}, ""result"": None, ""comment"": """"}

    for ignore in _STATE_INTERNAL_KEYWORDS:
        if ignore in kwargs:
            del kwargs[ignore]

    if (
        __salt__[""iptables.get_policy""](table, kwargs[""chain""], family)
        == kwargs[""policy""]
    ):
        ret[""result""] = True
        ret[
            ""comment""
        ] = ""iptables default policy for chain {} on table {} for {} already set to {}"".format(
            kwargs[""chain""], table, family, kwargs[""policy""]
        )
        return ret
    if __opts__[""test""]:
        ret[""comment""] = (
            ""iptables default policy for chain {} on table {} for {} needs to be set""
            "" to {}"".format(kwargs[""chain""], table, family, kwargs[""policy""])
        )
        return ret
    if not __salt__[""iptables.set_policy""](
        table, kwargs[""chain""], kwargs[""policy""], family
    ):
        ret[""changes""] = {""locale"": name}
        ret[""result""] = True
        ret[""comment""] = ""Set default policy for {} to {} family {}"".format(
            kwargs[""chain""], kwargs[""policy""], family
        )
        if ""save"" in kwargs and kwargs[""save""]:
            if kwargs[""save""] is not True:
                filename = kwargs[""save""]
            else:
                filename = None
            __salt__[""iptables.save""](filename=filename, family=family)
            ret[
                ""comment""
            ] = ""Set and saved default policy for {} to {} family {}"".format(
                kwargs[""chain""], kwargs[""policy""], family
            )
        return ret
    else:
        ret[""result""] = False
        ret[""comment""] = ""Failed to set iptables default policy""
        return ret

","
    .. versionadded:: 2014.1.0

    Sets the default policy for iptables firewall tables

    table
        The table that owns the chain that should be modified

    family
        Networking family, either ipv4 or ipv6

    policy
        The requested table policy

    save
        If set to a true value, the new iptables rules for the given family
        will be saved to a file. See the ``append`` state for more details.

    ",62,176,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_policy(name, table=""filter"", family=""ipv4"", **kwargs):
    
    ret = {""name"": name, ""changes"": {}, ""result"": None, ""comment"": """"}

    for ignore in _STATE_INTERNAL_KEYWORDS:
        if ignore in kwargs:
            del kwargs[ignore]

    if (
        __salt__[""iptables.get_policy""](table, kwargs[""chain""], family)
        == kwargs[""policy""]
    ):
        ret[""result""] = True
        ret[
            ""comment""
        ] = ""iptables default policy for chain {} on table {} for {} already set to {}"".format(
            kwargs[""chain""], table, family, kwargs[""policy""]
        )
        return ret
    if __opts__[""test""]:
        ret[""comment""] = (
            ""iptables default policy for chain {} on table {} for {} needs to be set""
            "" to {}"".format(kwargs[""chain""], table, family, kwargs[""policy""])
        )
        return ret
    if not __salt__[""iptables.set_policy""](
        table, kwargs[""chain""], kwargs[""policy""], family
    ):
        ret[""changes""] = {""locale"": name}
        ret[""result""] = True
        ret[""comment""] = ""Set default policy for {} to {} family {}"".format(
            kwargs[""chain""], kwargs[""policy""], family
        )
        if ""save"" in kwargs and kwargs[""save""]:
            if kwargs[""save""] is not True:
                filename = kwargs[""save""]
            else:
                filename = None
            __salt__[""iptables.save""](filename=filename, family=family)
            ret[
                ""comment""
            ] = ""Set and saved default policy for {} to {} family {}"".format(
                kwargs[""chain""], kwargs[""policy""], family
            )
        return ret
    else:
        ret[""result""] = False
        ret[""comment""] = ""Failed to set iptables default policy""
        return ret


        ```
    ###Assistant : 
    .. versionadded:: 2014.1.0

    Sets the default policy for iptables firewall tables

    table
        The table that owns the chain that should be modified

    family
        Networking family, either ipv4 or ipv6

    policy
        The requested table policy

    save
        If set to a true value, the new iptables rules for the given family
        will be saved to a file. See the ``append`` state for more details.

    
    "
2618,"def _after_start(self):
        
        delay = self.request.config.getoption('--qute-delay-start')
        if delay:
            with self.disable_capturing():
                print(f""- waiting {delay}ms for quteprocess ""
                      f""(PID: {self.proc.processId()})..."")
            time.sleep(delay / 1000)
","Wait before continuing if requested, e.g. for debugger attachment.",9,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _after_start(self):
        
        delay = self.request.config.getoption('--qute-delay-start')
        if delay:
            with self.disable_capturing():
                print(f""- waiting {delay}ms for quteprocess ""
                      f""(PID: {self.proc.processId()})..."")
            time.sleep(delay / 1000)

        ```
    ###Assistant : Wait before continuing if requested, e.g. for debugger attachment.
    "
2619,"def list_secrets(path, default=None):
    
    if default is None:
        default = CommandExecutionError
    log.debug(""Listing vault secret keys for %s in %s"", __grains__[""id""], path)
    version2 = __utils__[""vault.is_v2""](path)
    if version2[""v2""]:
        path = version2[""metadata""]
    try:
        url = ""v1/{}"".format(path)
        response = __utils__[""vault.make_request""](""LIST"", url)
        if response.status_code != 200:
            response.raise_for_status()
        return response.json()[""data""]
    except Exception as err:  # pylint: disable=broad-except
        if default is CommandExecutionError:
            raise CommandExecutionError(
                ""Failed to list secrets! {}: {}"".format(type(err).__name__, err)
            )
        return default

","
    .. versionchanged:: 3001
        The ``default`` argument has been added. When the path or path/key
        combination is not found, an exception will be raised, unless a default
        is provided.

    List secret keys at the path in vault. The vault policy used must allow this.
    The path should end with a trailing slash.

    CLI Example:

    .. code-block:: bash

            salt '*' vault.list_secrets ""secret/my/""
    ",60,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list_secrets(path, default=None):
    
    if default is None:
        default = CommandExecutionError
    log.debug(""Listing vault secret keys for %s in %s"", __grains__[""id""], path)
    version2 = __utils__[""vault.is_v2""](path)
    if version2[""v2""]:
        path = version2[""metadata""]
    try:
        url = ""v1/{}"".format(path)
        response = __utils__[""vault.make_request""](""LIST"", url)
        if response.status_code != 200:
            response.raise_for_status()
        return response.json()[""data""]
    except Exception as err:  # pylint: disable=broad-except
        if default is CommandExecutionError:
            raise CommandExecutionError(
                ""Failed to list secrets! {}: {}"".format(type(err).__name__, err)
            )
        return default


        ```
    ###Assistant : 
    .. versionchanged:: 3001
        The ``default`` argument has been added. When the path or path/key
        combination is not found, an exception will be raised, unless a default
        is provided.

    List secret keys at the path in vault. The vault policy used must allow this.
    The path should end with a trailing slash.

    CLI Example:

    .. code-block:: bash

            salt '*' vault.list_secrets ""secret/my/""
    
    "
2620,"def test_custom_kwargs_sharded(tmpdir, cls):
    
    strategy = cls(reduce_fp16=True)
    strategy.model = Mock(spec=LightningModule)
    strategy.model.trainer = Mock()
    class_name = ""sharded"" if isinstance(strategy, DDPShardedStrategy) else ""sharded_spawn""

    with mock.patch(f""pytorch_lightning.strategies.{class_name}.ShardedDataParallel"", autospec=True) as mock_sharded:
        strategy.configure_ddp()
    args, kwargs = mock_sharded.call_args
    assert ""reduce_fp16"" in kwargs
    assert kwargs[""reduce_fp16""]


@RunIf(skip_windows=True, fairscale=True)
@mock.patch(""pytorch_lightning.strategies.DDPShardedStrategy._wrap_optimizers"", autospec=True)
@pytest.mark.parametrize([""params"", ""expected_buffer_size""], [(dict(), 0), (dict(reduce_buffer_size=128), 128)])
@pytest.mark.parametrize(""num_nodes"", [1, 2])","Tests to ensure that if custom kwargs are passed, they are set correctly.",13,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_custom_kwargs_sharded(tmpdir, cls):
    
    strategy = cls(reduce_fp16=True)
    strategy.model = Mock(spec=LightningModule)
    strategy.model.trainer = Mock()
    class_name = ""sharded"" if isinstance(strategy, DDPShardedStrategy) else ""sharded_spawn""

    with mock.patch(f""pytorch_lightning.strategies.{class_name}.ShardedDataParallel"", autospec=True) as mock_sharded:
        strategy.configure_ddp()
    args, kwargs = mock_sharded.call_args
    assert ""reduce_fp16"" in kwargs
    assert kwargs[""reduce_fp16""]


@RunIf(skip_windows=True, fairscale=True)
@mock.patch(""pytorch_lightning.strategies.DDPShardedStrategy._wrap_optimizers"", autospec=True)
@pytest.mark.parametrize([""params"", ""expected_buffer_size""], [(dict(), 0), (dict(reduce_buffer_size=128), 128)])
@pytest.mark.parametrize(""num_nodes"", [1, 2])
        ```
    ###Assistant : Tests to ensure that if custom kwargs are passed, they are set correctly.
    "
2621,"def versions_from_parentdir(parentdir_prefix, root, verbose):
    
    rootdirs = []

    for _ in range(3):
        dirname = os.path.basename(root)
        if dirname.startswith(parentdir_prefix):
            return {""version"": dirname[len(parentdir_prefix):],
                    ""full-revisionid"": None,
                    ""dirty"": False, ""error"": None, ""date"": None}
        rootdirs.append(root)
        root = os.path.dirname(root)  # up a level

    if verbose:
        print(""Tried directories %s but none started with prefix %s"" %
              (str(rootdirs), parentdir_prefix))
    raise NotThisMethod(""rootdir doesn't start with parentdir_prefix"")


SHORT_VERSION_PY = 
%s
","Try to determine the version from the parent directory name.

    Source tarballs conventionally unpack into a directory that includes both
    the project name and a version string. We will also support searching up
    two directory levels for an appropriately named parent directory
    
# This file was generated by 'versioneer.py' (0.21) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.

import json

version_json =   # END VERSION_JSON

",84,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def versions_from_parentdir(parentdir_prefix, root, verbose):
    
    rootdirs = []

    for _ in range(3):
        dirname = os.path.basename(root)
        if dirname.startswith(parentdir_prefix):
            return {""version"": dirname[len(parentdir_prefix):],
                    ""full-revisionid"": None,
                    ""dirty"": False, ""error"": None, ""date"": None}
        rootdirs.append(root)
        root = os.path.dirname(root)  # up a level

    if verbose:
        print(""Tried directories %s but none started with prefix %s"" %
              (str(rootdirs), parentdir_prefix))
    raise NotThisMethod(""rootdir doesn't start with parentdir_prefix"")


SHORT_VERSION_PY = 
%s

        ```
    ###Assistant : Try to determine the version from the parent directory name.

    Source tarballs conventionally unpack into a directory that includes both
    the project name and a version string. We will also support searching up
    two directory levels for an appropriately named parent directory
    
# This file was generated by 'versioneer.py' (0.21) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.

import json

version_json =   # END VERSION_JSON


    "
2622,"def response_add(self, request, obj, post_url_continue=None):
        
        opts = obj._meta
        preserved_filters = self.get_preserved_filters(request)
        obj_url = reverse(
            ""admin:%s_%s_change"" % (opts.app_label, opts.model_name),
            args=(quote(obj.pk),),
            current_app=self.admin_site.name,
        )
        # Add a link to the object's change form if the user can edit the obj.
        if self.has_change_permission(request, obj):
            obj_repr = format_html('<a href=""{}"">{}</a>', urlquote(obj_url), obj)
        else:
            obj_repr = str(obj)
        msg_dict = {
            ""name"": opts.verbose_name,
            ""obj"": obj_repr,
        }
        # Here, we distinguish between different save types by checking for
        # the presence of keys in request.POST.

        if IS_POPUP_VAR in request.POST:
            to_field = request.POST.get(TO_FIELD_VAR)
            if to_field:
                attr = str(to_field)
            else:
                attr = obj._meta.pk.attname
            value = obj.serializable_value(attr)
            popup_response_data = json.dumps(
                {
                    ""value"": str(value),
                    ""obj"": str(obj),
                }
            )
            return TemplateResponse(
                request,
                self.popup_response_template
                or [
                    ""admin/%s/%s/popup_response.html""
                    % (opts.app_label, opts.model_name),
                    ""admin/%s/popup_response.html"" % opts.app_label,
                    ""admin/popup_response.html"",
                ],
                {
                    ""popup_response_data"": popup_response_data,
                },
            )

        elif ""_continue"" in request.POST or (
            # Redirecting after ""Save as new"".
            ""_saveasnew"" in request.POST
            and self.save_as_continue
            and self.has_change_permission(request, obj)
        ):
            msg = _(""The {name} “{obj}” was added successfully."")
            if self.has_change_permission(request, obj):
                msg += "" "" + _(""You may edit it again below."")
            self.message_user(request, format_html(msg, **msg_dict), messages.SUCCESS)
            if post_url_continue is None:
                post_url_continue = obj_url
            post_url_continue = add_preserved_filters(
                {""preserved_filters"": preserved_filters, ""opts"": opts},
                post_url_continue,
            )
            return HttpResponseRedirect(post_url_continue)

        elif ""_addanother"" in request.POST:
            msg = format_html(
                _(
                    ""The {name} “{obj}” was added successfully. You may add another {name} below.""
                ),
                **msg_dict,
            )
            self.message_user(request, msg, messages.SUCCESS)
            redirect_url = request.path
            redirect_url = add_preserved_filters(
                {""preserved_filters"": preserved_filters, ""opts"": opts}, redirect_url
            )
            return HttpResponseRedirect(redirect_url)

        else:
            msg = format_html(
                _(""The {name} “{obj}” was added successfully.""), **msg_dict
            )
            self.message_user(request, msg, messages.SUCCESS)
            return self.response_post_save_add(request, obj)
","
        Determine the HttpResponse for the add_view stage.
        ",7,248,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def response_add(self, request, obj, post_url_continue=None):
        
        opts = obj._meta
        preserved_filters = self.get_preserved_filters(request)
        obj_url = reverse(
            ""admin:%s_%s_change"" % (opts.app_label, opts.model_name),
            args=(quote(obj.pk),),
            current_app=self.admin_site.name,
        )
        # Add a link to the object's change form if the user can edit the obj.
        if self.has_change_permission(request, obj):
            obj_repr = format_html('<a href=""{}"">{}</a>', urlquote(obj_url), obj)
        else:
            obj_repr = str(obj)
        msg_dict = {
            ""name"": opts.verbose_name,
            ""obj"": obj_repr,
        }
        # Here, we distinguish between different save types by checking for
        # the presence of keys in request.POST.

        if IS_POPUP_VAR in request.POST:
            to_field = request.POST.get(TO_FIELD_VAR)
            if to_field:
                attr = str(to_field)
            else:
                attr = obj._meta.pk.attname
            value = obj.serializable_value(attr)
            popup_response_data = json.dumps(
                {
                    ""value"": str(value),
                    ""obj"": str(obj),
                }
            )
            return TemplateResponse(
                request,
                self.popup_response_template
                or [
                    ""admin/%s/%s/popup_response.html""
                    % (opts.app_label, opts.model_name),
                    ""admin/%s/popup_response.html"" % opts.app_label,
                    ""admin/popup_response.html"",
                ],
                {
                    ""popup_response_data"": popup_response_data,
                },
            )

        elif ""_continue"" in request.POST or (
            # Redirecting after ""Save as new"".
            ""_saveasnew"" in request.POST
            and self.save_as_continue
            and self.has_change_permission(request, obj)
        ):
            msg = _(""The {name} “{obj}” was added successfully."")
            if self.has_change_permission(request, obj):
                msg += "" "" + _(""You may edit it again below."")
            self.message_user(request, format_html(msg, **msg_dict), messages.SUCCESS)
            if post_url_continue is None:
                post_url_continue = obj_url
            post_url_continue = add_preserved_filters(
                {""preserved_filters"": preserved_filters, ""opts"": opts},
                post_url_continue,
            )
            return HttpResponseRedirect(post_url_continue)

        elif ""_addanother"" in request.POST:
            msg = format_html(
                _(
                    ""The {name} “{obj}” was added successfully. You may add another {name} below.""
                ),
                **msg_dict,
            )
            self.message_user(request, msg, messages.SUCCESS)
            redirect_url = request.path
            redirect_url = add_preserved_filters(
                {""preserved_filters"": preserved_filters, ""opts"": opts}, redirect_url
            )
            return HttpResponseRedirect(redirect_url)

        else:
            msg = format_html(
                _(""The {name} “{obj}” was added successfully.""), **msg_dict
            )
            self.message_user(request, msg, messages.SUCCESS)
            return self.response_post_save_add(request, obj)

        ```
    ###Assistant : 
        Determine the HttpResponse for the add_view stage.
        
    "
2623,"def test_tika_parse_unreachable(self):
        
        html = '<html><head><meta http-equiv=""content-type"" content=""text/html; charset=UTF-8""></head><body><p>Some Text</p></body></html>'

        # Check if exception is raised when Tika cannot be reached.
        self.parser.tika_server = """"
        self.assertRaises(ParseError, self.parser.tika_parse, html)
","
        GIVEN:
            - Fresh start
        WHEN:
            - tika parsing is called but tika is not available
        THEN:
            - a ParseError Exception is thrown
        ",22,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_tika_parse_unreachable(self):
        
        html = '<html><head><meta http-equiv=""content-type"" content=""text/html; charset=UTF-8""></head><body><p>Some Text</p></body></html>'

        # Check if exception is raised when Tika cannot be reached.
        self.parser.tika_server = """"
        self.assertRaises(ParseError, self.parser.tika_parse, html)

        ```
    ###Assistant : 
        GIVEN:
            - Fresh start
        WHEN:
            - tika parsing is called but tika is not available
        THEN:
            - a ParseError Exception is thrown
        
    "
2624,"def test_read_config_file_2():
    
    tpot_obj = TPOTRegressor()
    assert_raises(ValueError, tpot_obj._read_config_file, ""tests/test_config.py.bad"")

",Assert that _read_config_file rasies ValueError with wrong dictionary format,9,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_read_config_file_2():
    
    tpot_obj = TPOTRegressor()
    assert_raises(ValueError, tpot_obj._read_config_file, ""tests/test_config.py.bad"")


        ```
    ###Assistant : Assert that _read_config_file rasies ValueError with wrong dictionary format
    "
2625,"def pre_delete_handler(self, sender, instance, **kwargs):
        
        key = self.get_key_for_instance(instance)
        object_type = instance._meta.verbose_name

        # Delete an existing object
        logger.debug(f""[{self.branch}] Staging deletion of {object_type} {instance} (PK: {instance.pk})"")
        self.queue[key] = (ChangeActionChoices.ACTION_DELETE, None)
","
        Hooks to the pre_delete signal when a branch is active to queue delete actions.
        ",14,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pre_delete_handler(self, sender, instance, **kwargs):
        
        key = self.get_key_for_instance(instance)
        object_type = instance._meta.verbose_name

        # Delete an existing object
        logger.debug(f""[{self.branch}] Staging deletion of {object_type} {instance} (PK: {instance.pk})"")
        self.queue[key] = (ChangeActionChoices.ACTION_DELETE, None)

        ```
    ###Assistant : 
        Hooks to the pre_delete signal when a branch is active to queue delete actions.
        
    "
2626,"def _reset_state(self):
        
        self.cache = {}
        self.resolved_nodes = 0
        self.finished_last_inference = True

        # maps DAGNode uuid to unique instance of a gradio block
        self.node_to_block: Dict[DAGNode, Any] = {}
        # maps InputAttributeNodes to unique instance of interactive gradio block
        self.input_key_to_blocks: Dict[int, Any] = {}
",Resets state for each new RayServeHandle representing a new DAG.,10,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _reset_state(self):
        
        self.cache = {}
        self.resolved_nodes = 0
        self.finished_last_inference = True

        # maps DAGNode uuid to unique instance of a gradio block
        self.node_to_block: Dict[DAGNode, Any] = {}
        # maps InputAttributeNodes to unique instance of interactive gradio block
        self.input_key_to_blocks: Dict[int, Any] = {}

        ```
    ###Assistant : Resets state for each new RayServeHandle representing a new DAG.
    "
2627,"def find_element(self, selector):
        
        return self.driver.find_element(By.CSS_SELECTOR, selector)
","find_element returns the first found element by the css `selector`
        shortcut to `driver.find_element(By.CSS_SELECTOR, ...)`.",14,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_element(self, selector):
        
        return self.driver.find_element(By.CSS_SELECTOR, selector)

        ```
    ###Assistant : find_element returns the first found element by the css `selector`
        shortcut to `driver.find_element(By.CSS_SELECTOR, ...)`.
    "
2628,"def test_bad_persist_value(self):
        
        with self.assertRaises(StreamlitAPIException) as e:
",Throw an error if an invalid value is passed to 'persist'.,11,6,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_bad_persist_value(self):
        
        with self.assertRaises(StreamlitAPIException) as e:

        ```
    ###Assistant : Throw an error if an invalid value is passed to 'persist'.
    "
2629,"def _getDataFileTagsOptionHelp():
    return  % "", "".join(
        ""'%s' (%s)"" % d for d in data_files_tags
    )


data_file_tags_option = data_group.add_option(
    ""--data-file-tags"",
    action=""append"",
    dest=""data_file_tags"",
    metavar=""DATA_TAGS"",
    default=[],
)

parser.add_option_group(data_group)


execute_group = OptionGroup(parser, ""Immediate execution after compilation"")

execute_group.add_option(
    ""--run"",
    action=""store_true"",
    dest=""immediate_execution"",
    default=is_nuitka_run,
    help=
    % (""on"" if is_nuitka_run else ""off""),
)

execute_group.add_option(
    ""--debugger"",
    ""--gdb"",
    action=""store_true"",
    dest=""debugger"",
    default=False,
    help=,
)

execute_group.add_option(
    ""--execute-with-pythonpath"",
    action=""store_true"",
    dest=""keep_pythonpath"",
    default=False,
    help=,
)

parser.add_option_group(execute_group)

dump_group = OptionGroup(parser, ""Dump options for internal tree"")

dump_group.add_option(
    ""--xml"",
    action=""store_true"",
    dest=""dump_xml"",
    default=False,
    help=""Dump the final result of optimization as XML, then exit."",
)


parser.add_option_group(dump_group)


codegen_group = OptionGroup(parser, ""Code generation choices"")

codegen_group.add_option(
    ""--disable-bytecode-cache"",
    action=""store_true"",
    dest=""disable_bytecode_cache"",
    default=False,
    help=,
)

codegen_group.add_option(
    ""--full-compat"",
    action=""store_false"",
    dest=""improved"",
    default=True,
    help=,
)

codegen_group.add_option(
    ""--file-reference-choice"",
    action=""store"",
    dest=""file_reference_mode"",
    metavar=""MODE"",
    choices=(""original"", ""runtime"", ""frozen""),
    default=None,
    help=,
)

codegen_group.add_option(
    ""--module-name-choice"",
    action=""store"",
    dest=""module_name_mode"",
    metavar=""MODE"",
    choices=(""original"", ""runtime""),
    default=None,
    help=,
)


parser.add_option_group(codegen_group)

output_group = OptionGroup(parser, ""Output choices"")

output_group.add_option(
    ""-o"",
    action=""store"",
    dest=""output_filename"",
    metavar=""FILENAME"",
    default=None,
    help=
    % ""<program_name>""
    + ("".exe"" if getOS() == ""Windows"" else "".bin""),
)

output_group.add_option(
    ""--output-dir"",
    action=""store"",
    dest=""output_dir"",
    metavar=""DIRECTORY"",
    default="""",
    help=,
)

output_group.add_option(
    ""--remove-output"",
    action=""store_true"",
    dest=""remove_build"",
    default=False,
    help=,
)

output_group.add_option(
    ""--no-pyi-file"",
    action=""store_false"",
    dest=""pyi_file"",
    default=True,
    help=,
)

parser.add_option_group(output_group)


debug_group = OptionGroup(parser, ""Debug features"")

debug_group.add_option(
    ""--debug"",
    action=""store_true"",
    dest=""debug"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--unstripped"",
    action=""store_true"",
    dest=""unstripped"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--profile"",
    action=""store_true"",
    dest=""profile"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--internal-graph"",
    action=""store_true"",
    dest=""graph"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--trace-execution"",
    action=""store_true"",
    dest=""trace_execution"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--recompile-c-only"",
    action=""store_true"",
    dest=""recompile_c_only"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--generate-c-only"",
    action=""store_true"",
    dest=""generate_c_only"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--experimental"",
    action=""append"",
    dest=""experimental"",
    metavar=""FLAG"",
    default=[],
    help=,
)

debug_group.add_option(
    ""--explain-imports"",
    action=""store_true"",
    dest=""explain_imports"",
    default=False,
    help=SUPPRESS_HELP,
)

debug_group.add_option(
    ""--low-memory"",
    action=""store_true"",
    dest=""low_memory"",
    default=False,
    help=,
)

if os.name == ""nt"":
    debug_group.add_option(
        ""--disable-dll-dependency-cache"",
        action=""store_true"",
        dest=""no_dependency_cache"",
        default=False,
        help=,
    )

    debug_group.add_option(
        ""--force-dll-dependency-cache-update"",
        action=""store_true"",
        dest=""update_dependency_cache"",
        default=False,
        help=,
    )

# This is for testing framework, ""coverage.py"" hates to loose the process. And
# we can use it to make sure it's not done unknowingly.
parser.add_option(
    ""--must-not-re-execute"",
    action=""store_false"",
    dest=""allow_reexecute"",
    default=True,
    help=SUPPRESS_HELP,
)


parser.add_option_group(debug_group)

c_compiler_group = OptionGroup(parser, ""Backend C compiler choice"")


c_compiler_group.add_option(
    ""--clang"",
    action=""store_true"",
    dest=""clang"",
    default=False,
    help=,
)

c_compiler_group.add_option(
    ""--mingw64"",
    action=""store_true"",
    dest=""mingw64"",
    default=False,
    help=,
)

c_compiler_group.add_option(
    ""--msvc"",
    action=""store"",
    dest=""msvc_version"",
    default=None,
    help=,
)

c_compiler_group.add_option(
    ""-j"",
    ""--jobs"",
    action=""store"",
    dest=""jobs"",
    metavar=""N"",
    default=None,
    help=,
)

c_compiler_group.add_option(
    ""--lto"",
    action=""store"",
    dest=""lto"",
    metavar=""choice"",
    default=""auto"",
    choices=(""yes"", ""no"", ""auto""),
    help=,
)

c_compiler_group.add_option(
    ""--static-libpython"",
    action=""store"",
    dest=""static_libpython"",
    metavar=""choice"",
    default=""auto"",
    choices=(""yes"", ""no"", ""auto""),
    help=,
)

c_compiler_group.add_option(
    ""--disable-ccache"",
    action=""store_true"",
    dest=""disable_ccache"",
    default=False,
    help=,
)

parser.add_option_group(c_compiler_group)

pgo_group = OptionGroup(parser, ""PGO compilation choices"")

pgo_group.add_option(
    ""--pgo"",
    action=""store_true"",
    dest=""is_c_pgo"",
    default=False,
    help=,
)

pgo_group.add_option(
    ""--pgo-python"",
    action=""store_true"",
    dest=""is_python_pgo"",
    default=False,
    help=SUPPRESS_HELP,
)

pgo_group.add_option(
    ""--pgo-python-input"",
    action=""store"",
    dest=""python_pgo_input"",
    default=None,
    help=SUPPRESS_HELP,
)

pgo_group.add_option(
    ""--pgo-python-policy-unused-module"",
    action=""store"",
    dest=""python_pgo_policy_unused_module"",
    choices=(""include"", ""exclude"", ""bytecode""),
    default=""include"",
    help=SUPPRESS_HELP,
)

pgo_group.add_option(
    ""--pgo-args"",
    action=""store"",
    dest=""pgo_args"",
    default="""",
    help=,
)

pgo_group.add_option(
    ""--pgo-executable"",
    action=""store"",
    dest=""pgo_executable"",
    default=None,
    help=,
)


parser.add_option_group(pgo_group)

tracing_group = OptionGroup(parser, ""Tracing features"")

tracing_group.add_option(
    ""--quiet"",
    action=""store_true"",
    dest=""quiet"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--show-scons"",
    action=""store_true"",
    dest=""show_scons"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--show-progress"",
    action=""store_true"",
    dest=""show_progress"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--no-progressbar"",
    action=""store_false"",
    dest=""progress_bar"",
    default=True,
    help=,
)


tracing_group.add_option(
    ""--show-memory"",
    action=""store_true"",
    dest=""show_memory"",
    default=False,
    help=,
)


tracing_group.add_option(
    ""--show-modules"",
    action=""store_true"",
    dest=""show_inclusion"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--show-modules-output"",
    action=""store"",
    dest=""show_inclusion_output"",
    metavar=""PATH"",
    default=None,
    help=,
)

tracing_group.add_option(
    ""--report"",
    action=""store"",
    dest=""compilation_report_filename"",
    default=None,
    help=,
)

tracing_group.add_option(
    ""--verbose"",
    action=""store_true"",
    dest=""verbose"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--verbose-output"",
    action=""store"",
    dest=""verbose_output"",
    metavar=""PATH"",
    default=None,
    help=,
)

parser.add_option_group(tracing_group)

windows_group = OptionGroup(parser, ""Windows specific controls"")

windows_group.add_option(
    ""--windows-dependency-tool"",
    action=""store"",
    dest=""dependency_tool"",
    default=None,
    help=SUPPRESS_HELP,
)

windows_group.add_option(
    ""--windows-disable-console"",
    action=""store_true"",
    dest=""disable_console"",
    default=False,
    help=,
)

windows_group.add_option(
    ""--windows-icon-from-ico"",
    action=""append"",
    dest=""icon_path"",
    metavar=""ICON_PATH"",
    default=[],
    help=,
)

windows_group.add_option(
    ""--windows-icon-from-exe"",
    action=""store"",
    dest=""icon_exe_path"",
    metavar=""ICON_EXE_PATH"",
    default=None,
    help=""Copy executable icons from this existing executable (Windows only)."",
)

windows_group.add_option(
    ""--onefile-windows-splash-screen-image"",
    action=""store"",
    dest=""splash_screen_image"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-uac-admin"",
    action=""store_true"",
    dest=""windows_uac_admin"",
    metavar=""WINDOWS_UAC_ADMIN"",
    default=False,
    help=""Request Windows User Control, to grant admin rights on execution. (Windows only). Defaults to off."",
)

windows_group.add_option(
    ""--windows-uac-uiaccess"",
    action=""store_true"",
    dest=""windows_uac_uiaccess"",
    metavar=""WINDOWS_UAC_UIACCESS"",
    default=False,
    help=,
)

windows_group.add_option(
    ""--windows-company-name"",
    action=""store"",
    dest=""windows_company_name"",
    metavar=""WINDOWS_COMPANY_NAME"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-product-name"",
    action=""store"",
    dest=""windows_product_name"",
    metavar=""WINDOWS_PRODUCT_NAME"",
    default=None,
    help=,
)


windows_group.add_option(
    ""--windows-file-version"",
    action=""store"",
    dest=""windows_file_version"",
    metavar=""WINDOWS_FILE_VERSION"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-product-version"",
    action=""store"",
    dest=""windows_product_version"",
    metavar=""WINDOWS_PRODUCT_VERSION"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-file-description"",
    action=""store"",
    dest=""windows_file_description"",
    metavar=""WINDOWS_FILE_DESCRIPTION"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-onefile-tempdir"",
    ""--onefile-tempdir"",
    action=""store_true"",
    dest=""is_onefile_tempdir"",
    metavar=""ONEFILE_TEMPDIR"",
    default=False,
    help=SUPPRESS_HELP,
)

windows_group.add_option(
    ""--windows-onefile-tempdir-spec"",
    ""--onefile-tempdir-spec"",
    action=""store"",
    dest=""onefile_tempdir_spec"",
    metavar=""ONEFILE_TEMPDIR_SPEC"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-force-stdout-spec"",
    action=""store"",
    dest=""force_stdout_spec"",
    metavar=""WINDOWS_FORCE_STDOUT_SPEC"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-force-stderr-spec"",
    action=""store"",
    dest=""force_stderr_spec"",
    metavar=""WINDOWS_FORCE_STDERR_SPEC"",
    default=None,
    help=,
)


parser.add_option_group(windows_group)

macos_group = OptionGroup(parser, ""macOS specific controls"")

macos_group.add_option(
    ""--macos-target-arch"",
    action=""store"",
    dest=""macos_target_arch"",
    choices=(""universal"", ""arm64"", ""x86_64""),
    metavar=""MACOS_TARGET_ARCH"",
    default=None,
    help=,
)

macos_group.add_option(
    ""--macos-disable-console"",
    ""--disable-console"",
    action=""store_true"",
    dest=""disable_console"",
    default=False,
    help=,
)

macos_group.add_option(
    ""--macos-create-app-bundle"",
    action=""store_true"",
    dest=""macos_create_bundle"",
    default=False,
    help=,
)

macos_group.add_option(
    ""--macos-onefile-icon"",
    action=""append"",
    dest=""icon_path"",
    metavar=""ICON_PATH"",
    default=[],
    help=""Add executable icon for binary to use. Can be given only one time. Defaults to Python icon if available."",
)


macos_group.add_option(
    ""--macos-signed-app-name"",
    action=""store"",
    dest=""macos_signed_app_name"",
    metavar=""MACOS_SIGNED_APP_NAME"",
    default=None,
    help=,
)

macos_group.add_option(
    ""--macos-app-name"",
    action=""store"",
    dest=""macos_app_name"",
    metavar=""MACOS_APP_NAME"",
    default=None,
    help=,
)

macos_group.add_option(
    ""--macos-app-version"",
    action=""store"",
    dest=""macos_app_version"",
    metavar=""MACOS_APP_VERSION"",
    default=None,
    help=,
)


parser.add_option_group(macos_group)

linux_group = OptionGroup(parser, ""Linux specific controls"")

linux_group.add_option(
    ""--linux-onefile-icon"",
    action=""append"",
    dest=""icon_path"",
    metavar=""ICON_PATH"",
    default=[],
    help=""Add executable icon for onefile binary to use. Can be given only one time. Defaults to Python icon if available."",
)

linux_group.add_option(
    ""--linux-onefile-compression"",
    action=""store"",
    dest=""appimage_compression"",
    choices=(""gzip"", ""xz""),
    metavar=""COMPRESSION"",
    default=""gzip"",
    help=""Compression method to use for Linux onefile builds. Defaults to gzip for faster decompression"",
)

parser.add_option_group(linux_group)

plugin_group = OptionGroup(parser, ""Plugin control"")

plugin_group.add_option(
    ""--enable-plugin"",
    ""--plugin-enable"",
    action=""append"",
    dest=""plugins_enabled"",
    metavar=""PLUGIN_NAME"",
    default=[],
    help=,
)

plugin_group.add_option(
    ""--disable-plugin"",
    ""--plugin-disable"",
    action=""append"",
    dest=""plugins_disabled"",
    metavar=""PLUGIN_NAME"",
    default=[],
    help=,
)

plugin_group.add_option(
    ""--plugin-no-detection"",
    action=""store_false"",
    dest=""detect_missing_plugins"",
    default=True,
    help=,
)

plugin_group.add_option(
    ""--plugin-list"",
    action=""store_true"",
    dest=""list_plugins"",
    default=False,
    help=,
)


parser.add_option_group(plugin_group)

plugin_group.add_option(
    ""--user-plugin"",
    action=""append"",
    dest=""user_plugins"",
    metavar=""PATH"",
    default=[],
    help=""The file name of user plugin. Can be given multiple times. Default empty."",
)

plugin_group.add_option(
    ""--persist-source-changes"",
    action=""store_true"",
    dest=""persist_source_changes"",
    default=False,
    help=,
)

","\
For included data files, special handlings can be chosen. With the
commercial plugins, e.g. files can be included directly in the
binary. The list is completed by some plugins. With the current
list of plugins, these are available: %s.
The default is empty.\
Execute immediately the created binary (or import the compiled module).
Defaults to %s.\
Execute inside a debugger, e.g. ""gdb"" or ""lldb"" to automatically get a stack trace.
Defaults to off.\
When immediately executing the created binary (--execute), don't reset
PYTHONPATH. When all modules are successfully included, you ought to not need
PYTHONPATH anymore.\
Do not reuse dependency analysis results for modules, esp. from standard library,
that are included as bytecode.\
Enforce absolute compatibility with CPython. Do not even allow minor
deviations from CPython behavior, e.g. not having better tracebacks
or exception messages which are not really incompatible, but only
different. This is intended for tests only and should not be used
for normal use.\
Select what value ""__file__"" is going to be. With ""runtime"" (default for
standalone binary mode and module mode), the created binaries and modules,
use the location of themselves to deduct the value of ""__file__"". Included
packages pretend to be in directories below that location. This allows you
to include data files in deployments. If you merely seek acceleration, it's
better for you to use the ""original"" value, where the source files location
will be used. With ""frozen"" a notation ""<frozen module_name>"" is used. For
compatibility reasons, the ""__file__"" value will always have "".py"" suffix
independent of what it really is.\
Select what value ""__name__"" and ""__package__"" are going to be. With ""runtime""
(default for module mode), the created module, it uses the parent package to
deduct the value of ""__package__"", to be fully compatible. This allows for more
optimization to happen, but normally any package can be loaded into another one,
but this will raise an import error when it detects that with ""original"" mode.\
Specify how the executable should be named. For extension modules there is no
choice, also not for standalone mode and using it will be an error. This may
include path information that needs to exist though. Defaults to '%s' on this
platform.
\
Specify where intermediate and final output files should be put. The DIRECTORY
will be populated with C files, object files, etc.
Defaults to current directory.
\
Removes the build directory after producing the module or exe file.
Defaults to off.\
Do not create a "".pyi"" file for extension modules created by Nuitka. This is
used to detect implicit imports.
Defaults to off.\
Executing all self checks possible to find errors in Nuitka, do not use for
production. Defaults to off.\
Keep debug info in the resulting object file for better debugger interaction.
Defaults to off.\
Enable vmprof based profiling of time spent. Not working currently. Defaults to off.\
Create graph of optimization process internals, do not use for whole programs, but only
for small test cases. Defaults to off.\
Traced execution output, output the line of code before executing it.
Defaults to off.\
This is not incremental compilation, but for Nuitka development only. Takes
existing files and simply compile them as C again. Allows compiling edited
C files for quick debugging changes to the generated source, e.g. to see if
code is passed by, values output, etc, Defaults to off. Depends on compiling
Python source to determine which files it should look at.\
Generate only C source code, and do not compile it to binary or module. This
is for debugging and code coverage analysis that doesn't waste CPU. Defaults to
off. Do not think you can use this directly.\
Use features declared as 'experimental'. May have no effect if no experimental
features are present in the code. Uses secret tags (check source) per
experimented feature.\
Attempt to use less memory, by forking less C compilation jobs and using
options that use less memory. For use on embedded machines. Use this in
case of out of memory problems. Defaults to off.\
Disable the dependency walker cache. Will result in much longer times to create
the distribution folder, but might be used in case the cache is suspect to cause
errors.
\
For an update of the dependency walker cache. Will result in much longer times
to create the distribution folder, but might be used in case the cache is suspect
to cause errors or known to need an update.
\
Enforce the use of clang. On Windows this requires a working Visual
Studio version to piggy back on. Defaults to off.\
Enforce the use of MinGW64 on Windows. Defaults to off.\
Enforce the use of specific MSVC version on Windows. Allowed values
are e.g. ""14.3"" (MSVC 2022) and other MSVC version numbers, specify
""list"" for a list of installed compilers, or use ""latest"".

Defaults to latest MSVC being used if installed, otherwise MinGW64
is used.\
Specify the allowed number of parallel C compiler jobs. Defaults to the
system CPU count.\
Use link time optimizations (MSVC, gcc, clang). Allowed values are
""yes"", ""no"", and ""auto"" (when it's known to work). Defaults to
""auto"".\
Use static link library of Python. Allowed values are ""yes"", ""no"",
and ""auto"" (when it's known to work). Defaults to ""auto"".\
Do not attempt to use ccache (gcc, clang, etc.) or clcache (MSVC, clangcl).\
Enables C level profile guided optimization (PGO), by executing a dedicated build first
for a profiling run, and then using the result to feedback into the C compilation.
Note: This is experimental and not working with standalone modes of Nuitka yet.
Defaults to off.\
Arguments to be passed in case of profile guided optimization. These are passed to the special
built executable during the PGO profiling run. Default empty.\
Command to execute when collecting profile information. Use this only, if you need to
launch it through a script that prepares it to run. Default use created program.\
Disable all information outputs, but show warnings.
Defaults to off.\
Operate Scons in non-quiet mode, showing the executed commands.
Defaults to off.Provide progress information and statistics.
Defaults to off.Disable progress bar outputs (if tqdm is installed).
Defaults to off.Provide memory information and statistics.
Defaults to off.\
Provide information for included modules and DLLs
Defaults to off.\
Where to output --show-modules, should be a filename. Default is standard output.\
Report module inclusion in an XML output file. Default is off.\
Output details of actions taken, esp. in optimizations. Can become a lot.
Defaults to off.\
Where to output --verbose, should be a filename. Default is standard output.\
When compiling for Windows, disable the console window. Defaults to off.\
Add executable icon. Can be given multiple times for different resolutions
or files with multiple icons inside. In the later case, you may also suffix
with #<n> where n is an integer index starting from 1, specifying a specific
icon to be included, and all others to be ignored.\
When compiling for Windows and onefile, show this while loading the application. Defaults to off.\
Request Windows User Control, to enforce running from a few folders only, remote
desktop access. (Windows only). Defaults to off.\
Name of the company to use in Windows Version information.

One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Name of the product to use in Windows Version information. Defaults to base
filename of the binary.\
File version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Product version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Description of the file use in Windows Version information.

One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to nonsense.\
Use this as a temporary folder. Defaults to '%TEMP%\\onefile_%PID%_%TIME%', i.e. system temporary directory.\
Force standard output of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.out.txt', i.e. file near your program.\
Force standard error of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.err.txt', i.e. file near your program.\
What architectures is this to supposed to run on. Default and limit
is what the running Python allows for. Default is ""native"" which is
the architecture the Python is run with.\
When compiling for macOS, disable the console window and create a GUI
application. Defaults to off.\
When compiling for macOS, create a bundle rather than a plain binary
application. Currently experimental and incomplete. Currently this
is the only way to unlock disabling of console.Defaults to off.\
Name of the application to use for macOS signing. Follow com.yourcompany.appname naming
results for best results, as these have to be globally unique, and will grant protected
API accesses.\
Name of the product to use in macOS bundle information. Defaults to base
filename of the binary.\
Product version to use in macOS bundle information. Defaults to 1.0 if
not given.\
Enabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Disabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Plugins can detect if they might be used, and the you can disable the warning
via ""--disable-plugin=plugin-that-warned"", or you can use this option to disable
the mechanism entirely, which also speeds up compilation slightly of course as
this detection code is run in vain once you are certain of which plugins to
use. Defaults to off.\
Show list of all available plugins and exit. Defaults to off.\
Write source changes to original Python files. Use with care. May need
permissions, best for use in a virtualenv to debug if plugin code
changes work with standard Python or to benefit from bloat removal
even with pure Python. Default False.",1740,859,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _getDataFileTagsOptionHelp():
    return  % "", "".join(
        ""'%s' (%s)"" % d for d in data_files_tags
    )


data_file_tags_option = data_group.add_option(
    ""--data-file-tags"",
    action=""append"",
    dest=""data_file_tags"",
    metavar=""DATA_TAGS"",
    default=[],
)

parser.add_option_group(data_group)


execute_group = OptionGroup(parser, ""Immediate execution after compilation"")

execute_group.add_option(
    ""--run"",
    action=""store_true"",
    dest=""immediate_execution"",
    default=is_nuitka_run,
    help=
    % (""on"" if is_nuitka_run else ""off""),
)

execute_group.add_option(
    ""--debugger"",
    ""--gdb"",
    action=""store_true"",
    dest=""debugger"",
    default=False,
    help=,
)

execute_group.add_option(
    ""--execute-with-pythonpath"",
    action=""store_true"",
    dest=""keep_pythonpath"",
    default=False,
    help=,
)

parser.add_option_group(execute_group)

dump_group = OptionGroup(parser, ""Dump options for internal tree"")

dump_group.add_option(
    ""--xml"",
    action=""store_true"",
    dest=""dump_xml"",
    default=False,
    help=""Dump the final result of optimization as XML, then exit."",
)


parser.add_option_group(dump_group)


codegen_group = OptionGroup(parser, ""Code generation choices"")

codegen_group.add_option(
    ""--disable-bytecode-cache"",
    action=""store_true"",
    dest=""disable_bytecode_cache"",
    default=False,
    help=,
)

codegen_group.add_option(
    ""--full-compat"",
    action=""store_false"",
    dest=""improved"",
    default=True,
    help=,
)

codegen_group.add_option(
    ""--file-reference-choice"",
    action=""store"",
    dest=""file_reference_mode"",
    metavar=""MODE"",
    choices=(""original"", ""runtime"", ""frozen""),
    default=None,
    help=,
)

codegen_group.add_option(
    ""--module-name-choice"",
    action=""store"",
    dest=""module_name_mode"",
    metavar=""MODE"",
    choices=(""original"", ""runtime""),
    default=None,
    help=,
)


parser.add_option_group(codegen_group)

output_group = OptionGroup(parser, ""Output choices"")

output_group.add_option(
    ""-o"",
    action=""store"",
    dest=""output_filename"",
    metavar=""FILENAME"",
    default=None,
    help=
    % ""<program_name>""
    + ("".exe"" if getOS() == ""Windows"" else "".bin""),
)

output_group.add_option(
    ""--output-dir"",
    action=""store"",
    dest=""output_dir"",
    metavar=""DIRECTORY"",
    default="""",
    help=,
)

output_group.add_option(
    ""--remove-output"",
    action=""store_true"",
    dest=""remove_build"",
    default=False,
    help=,
)

output_group.add_option(
    ""--no-pyi-file"",
    action=""store_false"",
    dest=""pyi_file"",
    default=True,
    help=,
)

parser.add_option_group(output_group)


debug_group = OptionGroup(parser, ""Debug features"")

debug_group.add_option(
    ""--debug"",
    action=""store_true"",
    dest=""debug"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--unstripped"",
    action=""store_true"",
    dest=""unstripped"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--profile"",
    action=""store_true"",
    dest=""profile"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--internal-graph"",
    action=""store_true"",
    dest=""graph"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--trace-execution"",
    action=""store_true"",
    dest=""trace_execution"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--recompile-c-only"",
    action=""store_true"",
    dest=""recompile_c_only"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--generate-c-only"",
    action=""store_true"",
    dest=""generate_c_only"",
    default=False,
    help=,
)

debug_group.add_option(
    ""--experimental"",
    action=""append"",
    dest=""experimental"",
    metavar=""FLAG"",
    default=[],
    help=,
)

debug_group.add_option(
    ""--explain-imports"",
    action=""store_true"",
    dest=""explain_imports"",
    default=False,
    help=SUPPRESS_HELP,
)

debug_group.add_option(
    ""--low-memory"",
    action=""store_true"",
    dest=""low_memory"",
    default=False,
    help=,
)

if os.name == ""nt"":
    debug_group.add_option(
        ""--disable-dll-dependency-cache"",
        action=""store_true"",
        dest=""no_dependency_cache"",
        default=False,
        help=,
    )

    debug_group.add_option(
        ""--force-dll-dependency-cache-update"",
        action=""store_true"",
        dest=""update_dependency_cache"",
        default=False,
        help=,
    )

# This is for testing framework, ""coverage.py"" hates to loose the process. And
# we can use it to make sure it's not done unknowingly.
parser.add_option(
    ""--must-not-re-execute"",
    action=""store_false"",
    dest=""allow_reexecute"",
    default=True,
    help=SUPPRESS_HELP,
)


parser.add_option_group(debug_group)

c_compiler_group = OptionGroup(parser, ""Backend C compiler choice"")


c_compiler_group.add_option(
    ""--clang"",
    action=""store_true"",
    dest=""clang"",
    default=False,
    help=,
)

c_compiler_group.add_option(
    ""--mingw64"",
    action=""store_true"",
    dest=""mingw64"",
    default=False,
    help=,
)

c_compiler_group.add_option(
    ""--msvc"",
    action=""store"",
    dest=""msvc_version"",
    default=None,
    help=,
)

c_compiler_group.add_option(
    ""-j"",
    ""--jobs"",
    action=""store"",
    dest=""jobs"",
    metavar=""N"",
    default=None,
    help=,
)

c_compiler_group.add_option(
    ""--lto"",
    action=""store"",
    dest=""lto"",
    metavar=""choice"",
    default=""auto"",
    choices=(""yes"", ""no"", ""auto""),
    help=,
)

c_compiler_group.add_option(
    ""--static-libpython"",
    action=""store"",
    dest=""static_libpython"",
    metavar=""choice"",
    default=""auto"",
    choices=(""yes"", ""no"", ""auto""),
    help=,
)

c_compiler_group.add_option(
    ""--disable-ccache"",
    action=""store_true"",
    dest=""disable_ccache"",
    default=False,
    help=,
)

parser.add_option_group(c_compiler_group)

pgo_group = OptionGroup(parser, ""PGO compilation choices"")

pgo_group.add_option(
    ""--pgo"",
    action=""store_true"",
    dest=""is_c_pgo"",
    default=False,
    help=,
)

pgo_group.add_option(
    ""--pgo-python"",
    action=""store_true"",
    dest=""is_python_pgo"",
    default=False,
    help=SUPPRESS_HELP,
)

pgo_group.add_option(
    ""--pgo-python-input"",
    action=""store"",
    dest=""python_pgo_input"",
    default=None,
    help=SUPPRESS_HELP,
)

pgo_group.add_option(
    ""--pgo-python-policy-unused-module"",
    action=""store"",
    dest=""python_pgo_policy_unused_module"",
    choices=(""include"", ""exclude"", ""bytecode""),
    default=""include"",
    help=SUPPRESS_HELP,
)

pgo_group.add_option(
    ""--pgo-args"",
    action=""store"",
    dest=""pgo_args"",
    default="""",
    help=,
)

pgo_group.add_option(
    ""--pgo-executable"",
    action=""store"",
    dest=""pgo_executable"",
    default=None,
    help=,
)


parser.add_option_group(pgo_group)

tracing_group = OptionGroup(parser, ""Tracing features"")

tracing_group.add_option(
    ""--quiet"",
    action=""store_true"",
    dest=""quiet"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--show-scons"",
    action=""store_true"",
    dest=""show_scons"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--show-progress"",
    action=""store_true"",
    dest=""show_progress"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--no-progressbar"",
    action=""store_false"",
    dest=""progress_bar"",
    default=True,
    help=,
)


tracing_group.add_option(
    ""--show-memory"",
    action=""store_true"",
    dest=""show_memory"",
    default=False,
    help=,
)


tracing_group.add_option(
    ""--show-modules"",
    action=""store_true"",
    dest=""show_inclusion"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--show-modules-output"",
    action=""store"",
    dest=""show_inclusion_output"",
    metavar=""PATH"",
    default=None,
    help=,
)

tracing_group.add_option(
    ""--report"",
    action=""store"",
    dest=""compilation_report_filename"",
    default=None,
    help=,
)

tracing_group.add_option(
    ""--verbose"",
    action=""store_true"",
    dest=""verbose"",
    default=False,
    help=,
)

tracing_group.add_option(
    ""--verbose-output"",
    action=""store"",
    dest=""verbose_output"",
    metavar=""PATH"",
    default=None,
    help=,
)

parser.add_option_group(tracing_group)

windows_group = OptionGroup(parser, ""Windows specific controls"")

windows_group.add_option(
    ""--windows-dependency-tool"",
    action=""store"",
    dest=""dependency_tool"",
    default=None,
    help=SUPPRESS_HELP,
)

windows_group.add_option(
    ""--windows-disable-console"",
    action=""store_true"",
    dest=""disable_console"",
    default=False,
    help=,
)

windows_group.add_option(
    ""--windows-icon-from-ico"",
    action=""append"",
    dest=""icon_path"",
    metavar=""ICON_PATH"",
    default=[],
    help=,
)

windows_group.add_option(
    ""--windows-icon-from-exe"",
    action=""store"",
    dest=""icon_exe_path"",
    metavar=""ICON_EXE_PATH"",
    default=None,
    help=""Copy executable icons from this existing executable (Windows only)."",
)

windows_group.add_option(
    ""--onefile-windows-splash-screen-image"",
    action=""store"",
    dest=""splash_screen_image"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-uac-admin"",
    action=""store_true"",
    dest=""windows_uac_admin"",
    metavar=""WINDOWS_UAC_ADMIN"",
    default=False,
    help=""Request Windows User Control, to grant admin rights on execution. (Windows only). Defaults to off."",
)

windows_group.add_option(
    ""--windows-uac-uiaccess"",
    action=""store_true"",
    dest=""windows_uac_uiaccess"",
    metavar=""WINDOWS_UAC_UIACCESS"",
    default=False,
    help=,
)

windows_group.add_option(
    ""--windows-company-name"",
    action=""store"",
    dest=""windows_company_name"",
    metavar=""WINDOWS_COMPANY_NAME"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-product-name"",
    action=""store"",
    dest=""windows_product_name"",
    metavar=""WINDOWS_PRODUCT_NAME"",
    default=None,
    help=,
)


windows_group.add_option(
    ""--windows-file-version"",
    action=""store"",
    dest=""windows_file_version"",
    metavar=""WINDOWS_FILE_VERSION"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-product-version"",
    action=""store"",
    dest=""windows_product_version"",
    metavar=""WINDOWS_PRODUCT_VERSION"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-file-description"",
    action=""store"",
    dest=""windows_file_description"",
    metavar=""WINDOWS_FILE_DESCRIPTION"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-onefile-tempdir"",
    ""--onefile-tempdir"",
    action=""store_true"",
    dest=""is_onefile_tempdir"",
    metavar=""ONEFILE_TEMPDIR"",
    default=False,
    help=SUPPRESS_HELP,
)

windows_group.add_option(
    ""--windows-onefile-tempdir-spec"",
    ""--onefile-tempdir-spec"",
    action=""store"",
    dest=""onefile_tempdir_spec"",
    metavar=""ONEFILE_TEMPDIR_SPEC"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-force-stdout-spec"",
    action=""store"",
    dest=""force_stdout_spec"",
    metavar=""WINDOWS_FORCE_STDOUT_SPEC"",
    default=None,
    help=,
)

windows_group.add_option(
    ""--windows-force-stderr-spec"",
    action=""store"",
    dest=""force_stderr_spec"",
    metavar=""WINDOWS_FORCE_STDERR_SPEC"",
    default=None,
    help=,
)


parser.add_option_group(windows_group)

macos_group = OptionGroup(parser, ""macOS specific controls"")

macos_group.add_option(
    ""--macos-target-arch"",
    action=""store"",
    dest=""macos_target_arch"",
    choices=(""universal"", ""arm64"", ""x86_64""),
    metavar=""MACOS_TARGET_ARCH"",
    default=None,
    help=,
)

macos_group.add_option(
    ""--macos-disable-console"",
    ""--disable-console"",
    action=""store_true"",
    dest=""disable_console"",
    default=False,
    help=,
)

macos_group.add_option(
    ""--macos-create-app-bundle"",
    action=""store_true"",
    dest=""macos_create_bundle"",
    default=False,
    help=,
)

macos_group.add_option(
    ""--macos-onefile-icon"",
    action=""append"",
    dest=""icon_path"",
    metavar=""ICON_PATH"",
    default=[],
    help=""Add executable icon for binary to use. Can be given only one time. Defaults to Python icon if available."",
)


macos_group.add_option(
    ""--macos-signed-app-name"",
    action=""store"",
    dest=""macos_signed_app_name"",
    metavar=""MACOS_SIGNED_APP_NAME"",
    default=None,
    help=,
)

macos_group.add_option(
    ""--macos-app-name"",
    action=""store"",
    dest=""macos_app_name"",
    metavar=""MACOS_APP_NAME"",
    default=None,
    help=,
)

macos_group.add_option(
    ""--macos-app-version"",
    action=""store"",
    dest=""macos_app_version"",
    metavar=""MACOS_APP_VERSION"",
    default=None,
    help=,
)


parser.add_option_group(macos_group)

linux_group = OptionGroup(parser, ""Linux specific controls"")

linux_group.add_option(
    ""--linux-onefile-icon"",
    action=""append"",
    dest=""icon_path"",
    metavar=""ICON_PATH"",
    default=[],
    help=""Add executable icon for onefile binary to use. Can be given only one time. Defaults to Python icon if available."",
)

linux_group.add_option(
    ""--linux-onefile-compression"",
    action=""store"",
    dest=""appimage_compression"",
    choices=(""gzip"", ""xz""),
    metavar=""COMPRESSION"",
    default=""gzip"",
    help=""Compression method to use for Linux onefile builds. Defaults to gzip for faster decompression"",
)

parser.add_option_group(linux_group)

plugin_group = OptionGroup(parser, ""Plugin control"")

plugin_group.add_option(
    ""--enable-plugin"",
    ""--plugin-enable"",
    action=""append"",
    dest=""plugins_enabled"",
    metavar=""PLUGIN_NAME"",
    default=[],
    help=,
)

plugin_group.add_option(
    ""--disable-plugin"",
    ""--plugin-disable"",
    action=""append"",
    dest=""plugins_disabled"",
    metavar=""PLUGIN_NAME"",
    default=[],
    help=,
)

plugin_group.add_option(
    ""--plugin-no-detection"",
    action=""store_false"",
    dest=""detect_missing_plugins"",
    default=True,
    help=,
)

plugin_group.add_option(
    ""--plugin-list"",
    action=""store_true"",
    dest=""list_plugins"",
    default=False,
    help=,
)


parser.add_option_group(plugin_group)

plugin_group.add_option(
    ""--user-plugin"",
    action=""append"",
    dest=""user_plugins"",
    metavar=""PATH"",
    default=[],
    help=""The file name of user plugin. Can be given multiple times. Default empty."",
)

plugin_group.add_option(
    ""--persist-source-changes"",
    action=""store_true"",
    dest=""persist_source_changes"",
    default=False,
    help=,
)


        ```
    ###Assistant : \
For included data files, special handlings can be chosen. With the
commercial plugins, e.g. files can be included directly in the
binary. The list is completed by some plugins. With the current
list of plugins, these are available: %s.
The default is empty.\
Execute immediately the created binary (or import the compiled module).
Defaults to %s.\
Execute inside a debugger, e.g. ""gdb"" or ""lldb"" to automatically get a stack trace.
Defaults to off.\
When immediately executing the created binary (--execute), don't reset
PYTHONPATH. When all modules are successfully included, you ought to not need
PYTHONPATH anymore.\
Do not reuse dependency analysis results for modules, esp. from standard library,
that are included as bytecode.\
Enforce absolute compatibility with CPython. Do not even allow minor
deviations from CPython behavior, e.g. not having better tracebacks
or exception messages which are not really incompatible, but only
different. This is intended for tests only and should not be used
for normal use.\
Select what value ""__file__"" is going to be. With ""runtime"" (default for
standalone binary mode and module mode), the created binaries and modules,
use the location of themselves to deduct the value of ""__file__"". Included
packages pretend to be in directories below that location. This allows you
to include data files in deployments. If you merely seek acceleration, it's
better for you to use the ""original"" value, where the source files location
will be used. With ""frozen"" a notation ""<frozen module_name>"" is used. For
compatibility reasons, the ""__file__"" value will always have "".py"" suffix
independent of what it really is.\
Select what value ""__name__"" and ""__package__"" are going to be. With ""runtime""
(default for module mode), the created module, it uses the parent package to
deduct the value of ""__package__"", to be fully compatible. This allows for more
optimization to happen, but normally any package can be loaded into another one,
but this will raise an import error when it detects that with ""original"" mode.\
Specify how the executable should be named. For extension modules there is no
choice, also not for standalone mode and using it will be an error. This may
include path information that needs to exist though. Defaults to '%s' on this
platform.
\
Specify where intermediate and final output files should be put. The DIRECTORY
will be populated with C files, object files, etc.
Defaults to current directory.
\
Removes the build directory after producing the module or exe file.
Defaults to off.\
Do not create a "".pyi"" file for extension modules created by Nuitka. This is
used to detect implicit imports.
Defaults to off.\
Executing all self checks possible to find errors in Nuitka, do not use for
production. Defaults to off.\
Keep debug info in the resulting object file for better debugger interaction.
Defaults to off.\
Enable vmprof based profiling of time spent. Not working currently. Defaults to off.\
Create graph of optimization process internals, do not use for whole programs, but only
for small test cases. Defaults to off.\
Traced execution output, output the line of code before executing it.
Defaults to off.\
This is not incremental compilation, but for Nuitka development only. Takes
existing files and simply compile them as C again. Allows compiling edited
C files for quick debugging changes to the generated source, e.g. to see if
code is passed by, values output, etc, Defaults to off. Depends on compiling
Python source to determine which files it should look at.\
Generate only C source code, and do not compile it to binary or module. This
is for debugging and code coverage analysis that doesn't waste CPU. Defaults to
off. Do not think you can use this directly.\
Use features declared as 'experimental'. May have no effect if no experimental
features are present in the code. Uses secret tags (check source) per
experimented feature.\
Attempt to use less memory, by forking less C compilation jobs and using
options that use less memory. For use on embedded machines. Use this in
case of out of memory problems. Defaults to off.\
Disable the dependency walker cache. Will result in much longer times to create
the distribution folder, but might be used in case the cache is suspect to cause
errors.
\
For an update of the dependency walker cache. Will result in much longer times
to create the distribution folder, but might be used in case the cache is suspect
to cause errors or known to need an update.
\
Enforce the use of clang. On Windows this requires a working Visual
Studio version to piggy back on. Defaults to off.\
Enforce the use of MinGW64 on Windows. Defaults to off.\
Enforce the use of specific MSVC version on Windows. Allowed values
are e.g. ""14.3"" (MSVC 2022) and other MSVC version numbers, specify
""list"" for a list of installed compilers, or use ""latest"".

Defaults to latest MSVC being used if installed, otherwise MinGW64
is used.\
Specify the allowed number of parallel C compiler jobs. Defaults to the
system CPU count.\
Use link time optimizations (MSVC, gcc, clang). Allowed values are
""yes"", ""no"", and ""auto"" (when it's known to work). Defaults to
""auto"".\
Use static link library of Python. Allowed values are ""yes"", ""no"",
and ""auto"" (when it's known to work). Defaults to ""auto"".\
Do not attempt to use ccache (gcc, clang, etc.) or clcache (MSVC, clangcl).\
Enables C level profile guided optimization (PGO), by executing a dedicated build first
for a profiling run, and then using the result to feedback into the C compilation.
Note: This is experimental and not working with standalone modes of Nuitka yet.
Defaults to off.\
Arguments to be passed in case of profile guided optimization. These are passed to the special
built executable during the PGO profiling run. Default empty.\
Command to execute when collecting profile information. Use this only, if you need to
launch it through a script that prepares it to run. Default use created program.\
Disable all information outputs, but show warnings.
Defaults to off.\
Operate Scons in non-quiet mode, showing the executed commands.
Defaults to off.Provide progress information and statistics.
Defaults to off.Disable progress bar outputs (if tqdm is installed).
Defaults to off.Provide memory information and statistics.
Defaults to off.\
Provide information for included modules and DLLs
Defaults to off.\
Where to output --show-modules, should be a filename. Default is standard output.\
Report module inclusion in an XML output file. Default is off.\
Output details of actions taken, esp. in optimizations. Can become a lot.
Defaults to off.\
Where to output --verbose, should be a filename. Default is standard output.\
When compiling for Windows, disable the console window. Defaults to off.\
Add executable icon. Can be given multiple times for different resolutions
or files with multiple icons inside. In the later case, you may also suffix
with #<n> where n is an integer index starting from 1, specifying a specific
icon to be included, and all others to be ignored.\
When compiling for Windows and onefile, show this while loading the application. Defaults to off.\
Request Windows User Control, to enforce running from a few folders only, remote
desktop access. (Windows only). Defaults to off.\
Name of the company to use in Windows Version information.

One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Name of the product to use in Windows Version information. Defaults to base
filename of the binary.\
File version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Product version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Description of the file use in Windows Version information.

One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to nonsense.\
Use this as a temporary folder. Defaults to '%TEMP%\\onefile_%PID%_%TIME%', i.e. system temporary directory.\
Force standard output of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.out.txt', i.e. file near your program.\
Force standard error of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.err.txt', i.e. file near your program.\
What architectures is this to supposed to run on. Default and limit
is what the running Python allows for. Default is ""native"" which is
the architecture the Python is run with.\
When compiling for macOS, disable the console window and create a GUI
application. Defaults to off.\
When compiling for macOS, create a bundle rather than a plain binary
application. Currently experimental and incomplete. Currently this
is the only way to unlock disabling of console.Defaults to off.\
Name of the application to use for macOS signing. Follow com.yourcompany.appname naming
results for best results, as these have to be globally unique, and will grant protected
API accesses.\
Name of the product to use in macOS bundle information. Defaults to base
filename of the binary.\
Product version to use in macOS bundle information. Defaults to 1.0 if
not given.\
Enabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Disabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Plugins can detect if they might be used, and the you can disable the warning
via ""--disable-plugin=plugin-that-warned"", or you can use this option to disable
the mechanism entirely, which also speeds up compilation slightly of course as
this detection code is run in vain once you are certain of which plugins to
use. Defaults to off.\
Show list of all available plugins and exit. Defaults to off.\
Write source changes to original Python files. Use with care. May need
permissions, best for use in a virtualenv to debug if plugin code
changes work with standard Python or to benefit from bloat removal
even with pure Python. Default False.
    "
2630,"def test_collect_workflow_action_data_post(self):
        
        response = self.client.post(
            reverse(
                ""wagtailadmin_pages:collect_workflow_action_data"",
                args=(
                    self.page.id,
                    ""approve"",
                    self.page.current_workflow_task_state.id,
                ),
            ),
            {""comment"": ""This is my comment""},
        )
        self.assertEqual(response.status_code, 200)
        response_json = json.loads(response.content)
        self.assertEqual(response_json[""step""], ""success"")
        self.assertEqual(
            response_json[""cleaned_data""], {""comment"": ""This is my comment""}
        )
","
        This tests that a POST request to the collect_workflow_action_data view (for the approve action) returns a modal response with the validated data
        ",22,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_collect_workflow_action_data_post(self):
        
        response = self.client.post(
            reverse(
                ""wagtailadmin_pages:collect_workflow_action_data"",
                args=(
                    self.page.id,
                    ""approve"",
                    self.page.current_workflow_task_state.id,
                ),
            ),
            {""comment"": ""This is my comment""},
        )
        self.assertEqual(response.status_code, 200)
        response_json = json.loads(response.content)
        self.assertEqual(response_json[""step""], ""success"")
        self.assertEqual(
            response_json[""cleaned_data""], {""comment"": ""This is my comment""}
        )

        ```
    ###Assistant : 
        This tests that a POST request to the collect_workflow_action_data view (for the approve action) returns a modal response with the validated data
        
    "
2631,"def test_02_train_predictor(self):
        query = f
        response = self.handler.native_query(query)
        self.assertTrue(response.type == RESPONSE_TYPE.OK)
","
            CREATE PREDICTOR {self.test_model_1}
            FROM {PG_HANDLER_NAME} (SELECT * FROM {self.data_table_1} limit 50)
            PREDICT rental_price
        ",13,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_02_train_predictor(self):
        query = f
        response = self.handler.native_query(query)
        self.assertTrue(response.type == RESPONSE_TYPE.OK)

        ```
    ###Assistant : 
            CREATE PREDICTOR {self.test_model_1}
            FROM {PG_HANDLER_NAME} (SELECT * FROM {self.data_table_1} limit 50)
            PREDICT rental_price
        
    "
2632,"def __call__(self, results):
        

        img = results['img']
        if self.to_float32:
            img = img.astype(np.float32)

        results['img_path'] = None
        results['img'] = img
        height, width = img.shape[:2]
        results['height'] = height
        results['width'] = width
        results['ori_height'] = height
        results['ori_width'] = width
        return results


@TRANSFORMS.register_module()","Call functions to add image meta information.

        Args:
            results (dict): Result dict with Webcam read image in
                ``results['img']``.

        Returns:
            dict: The dict contains loaded image and meta information.
        ",28,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(self, results):
        

        img = results['img']
        if self.to_float32:
            img = img.astype(np.float32)

        results['img_path'] = None
        results['img'] = img
        height, width = img.shape[:2]
        results['height'] = height
        results['width'] = width
        results['ori_height'] = height
        results['ori_width'] = width
        return results


@TRANSFORMS.register_module()
        ```
    ###Assistant : Call functions to add image meta information.

        Args:
            results (dict): Result dict with Webcam read image in
                ``results['img']``.

        Returns:
            dict: The dict contains loaded image and meta information.
        
    "
2633,"def test_jemalloc_env_var_propagate():
    
    gcs_ptype = ray.ray_constants.PROCESS_TYPE_GCS_SERVER
    
    expected = {}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path="""", jemalloc_conf="""", jemalloc_comps=[], process_type=gcs_ptype
    )
    assert actual == expected
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=None,
        jemalloc_conf=""a,b,c"",
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
        process_type=gcs_ptype,
    )
    assert actual == expected
    
    library_path = ""/abc""
    expected = {""LD_PRELOAD"": library_path}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=library_path,
        jemalloc_conf="""",
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
        process_type=gcs_ptype,
    )
    assert actual == expected

    # comps should be a list type.
    with pytest.raises(AssertionError):
        ray._private.services.propagate_jemalloc_env_var(
            jemalloc_path=library_path,
            jemalloc_conf="""",
            jemalloc_comps=""ray.ray_constants.PROCESS_TYPE_GCS_SERVER,"",
            process_type=gcs_ptype,
        )

    # When comps don't match the process_type, it should return an empty dict.
    expected = {}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=library_path,
        jemalloc_conf="""",
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_RAYLET],
        process_type=gcs_ptype,
    )
    
    library_path = ""/abc""
    malloc_conf = ""a,b,c""
    expected = {""LD_PRELOAD"": library_path, ""MALLOC_CONF"": malloc_conf}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=library_path,
        jemalloc_conf=malloc_conf,
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
        process_type=gcs_ptype,
    )
    assert actual == expected

","Test `propagate_jemalloc_env_var`
    If the shared library path is not specified,
    it should return an empty dict.
    
    When the shared library is specified
    
    When the malloc config is specified
    ",28,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_jemalloc_env_var_propagate():
    
    gcs_ptype = ray.ray_constants.PROCESS_TYPE_GCS_SERVER
    
    expected = {}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path="""", jemalloc_conf="""", jemalloc_comps=[], process_type=gcs_ptype
    )
    assert actual == expected
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=None,
        jemalloc_conf=""a,b,c"",
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
        process_type=gcs_ptype,
    )
    assert actual == expected
    
    library_path = ""/abc""
    expected = {""LD_PRELOAD"": library_path}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=library_path,
        jemalloc_conf="""",
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
        process_type=gcs_ptype,
    )
    assert actual == expected

    # comps should be a list type.
    with pytest.raises(AssertionError):
        ray._private.services.propagate_jemalloc_env_var(
            jemalloc_path=library_path,
            jemalloc_conf="""",
            jemalloc_comps=""ray.ray_constants.PROCESS_TYPE_GCS_SERVER,"",
            process_type=gcs_ptype,
        )

    # When comps don't match the process_type, it should return an empty dict.
    expected = {}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=library_path,
        jemalloc_conf="""",
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_RAYLET],
        process_type=gcs_ptype,
    )
    
    library_path = ""/abc""
    malloc_conf = ""a,b,c""
    expected = {""LD_PRELOAD"": library_path, ""MALLOC_CONF"": malloc_conf}
    actual = ray._private.services.propagate_jemalloc_env_var(
        jemalloc_path=library_path,
        jemalloc_conf=malloc_conf,
        jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
        process_type=gcs_ptype,
    )
    assert actual == expected


        ```
    ###Assistant : Test `propagate_jemalloc_env_var`
    If the shared library path is not specified,
    it should return an empty dict.
    
    When the shared library is specified
    
    When the malloc config is specified
    
    "
2634,"def aug_test_bboxes(self, feats, img_metas, rescale=False):
        
        # check with_nms argument
        gb_sig = signature(self.get_results)
        gb_args = [p.name for p in gb_sig.parameters.values()]
        gbs_sig = signature(self._get_results_single)
        gbs_args = [p.name for p in gbs_sig.parameters.values()]
        assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
            f'{self.__class__.__name__}' \
            ' does not support test-time augmentation'

        aug_bboxes = []
        aug_scores = []
        aug_labels = []
        for x, img_meta in zip(feats, img_metas):
            # only one image in the batch
            outs = self.forward(x)
            bbox_outputs = self.get_results(
                *outs,
                img_metas=img_meta,
                cfg=self.test_cfg,
                rescale=False,
                with_nms=False)[0]
            aug_bboxes.append(bbox_outputs.bboxes)
            aug_scores.append(bbox_outputs.scores)
            if len(bbox_outputs) >= 3:
                aug_labels.append(bbox_outputs.labels)

        # after merging, bboxes will be rescaled to the original image size
        merged_bboxes, merged_scores = self.merge_aug_bboxes(
            aug_bboxes, aug_scores, img_metas)
        merged_labels = torch.cat(aug_labels, dim=0) if aug_labels else None

        if merged_bboxes.numel() == 0:
            det_bboxes = torch.cat([merged_bboxes, merged_scores[:, None]], -1)
            return [
                (det_bboxes, merged_labels),
            ]

        det_bboxes, keep_idxs = batched_nms(merged_bboxes, merged_scores,
                                            merged_labels, self.test_cfg.nms)
        det_bboxes = det_bboxes[:self.test_cfg.max_per_img]
        det_labels = merged_labels[keep_idxs][:self.test_cfg.max_per_img]

        if rescale:
            _det_bboxes = det_bboxes
        else:
            _det_bboxes = det_bboxes.clone()
            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
                img_metas[0][0]['scale_factor'])

        results = InstanceData()
        results.bboxes = _det_bboxes[:, :4]
        results.scores = _det_bboxes[:, 4]
        results.labels = det_labels
        return [results]
","Test det bboxes with test time augmentation, can be applied in
        DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
        etc.

        Args:
            feats (list[Tensor]): the outer list indicates test-time
                augmentations and inner Tensor should have a shape NxCxHxW,
                which contains features for all images in the batch.
            img_metas (list[list[dict]]): the outer list indicates test-time
                augs (multiscale, flip, etc.) and the inner list indicates
                images in a batch. each dict has image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
                The first item is ``bboxes`` with shape (n, 5),
                where 5 represent (tl_x, tl_y, br_x, br_y, score).
                The shape of the second tensor in the tuple is ``labels``
                with shape (n,). The length of list should always be 1.
        ",131,171,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def aug_test_bboxes(self, feats, img_metas, rescale=False):
        
        # check with_nms argument
        gb_sig = signature(self.get_results)
        gb_args = [p.name for p in gb_sig.parameters.values()]
        gbs_sig = signature(self._get_results_single)
        gbs_args = [p.name for p in gbs_sig.parameters.values()]
        assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
            f'{self.__class__.__name__}' \
            ' does not support test-time augmentation'

        aug_bboxes = []
        aug_scores = []
        aug_labels = []
        for x, img_meta in zip(feats, img_metas):
            # only one image in the batch
            outs = self.forward(x)
            bbox_outputs = self.get_results(
                *outs,
                img_metas=img_meta,
                cfg=self.test_cfg,
                rescale=False,
                with_nms=False)[0]
            aug_bboxes.append(bbox_outputs.bboxes)
            aug_scores.append(bbox_outputs.scores)
            if len(bbox_outputs) >= 3:
                aug_labels.append(bbox_outputs.labels)

        # after merging, bboxes will be rescaled to the original image size
        merged_bboxes, merged_scores = self.merge_aug_bboxes(
            aug_bboxes, aug_scores, img_metas)
        merged_labels = torch.cat(aug_labels, dim=0) if aug_labels else None

        if merged_bboxes.numel() == 0:
            det_bboxes = torch.cat([merged_bboxes, merged_scores[:, None]], -1)
            return [
                (det_bboxes, merged_labels),
            ]

        det_bboxes, keep_idxs = batched_nms(merged_bboxes, merged_scores,
                                            merged_labels, self.test_cfg.nms)
        det_bboxes = det_bboxes[:self.test_cfg.max_per_img]
        det_labels = merged_labels[keep_idxs][:self.test_cfg.max_per_img]

        if rescale:
            _det_bboxes = det_bboxes
        else:
            _det_bboxes = det_bboxes.clone()
            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
                img_metas[0][0]['scale_factor'])

        results = InstanceData()
        results.bboxes = _det_bboxes[:, :4]
        results.scores = _det_bboxes[:, 4]
        results.labels = det_labels
        return [results]

        ```
    ###Assistant : Test det bboxes with test time augmentation, can be applied in
        DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
        etc.

        Args:
            feats (list[Tensor]): the outer list indicates test-time
                augmentations and inner Tensor should have a shape NxCxHxW,
                which contains features for all images in the batch.
            img_metas (list[list[dict]]): the outer list indicates test-time
                augs (multiscale, flip, etc.) and the inner list indicates
                images in a batch. each dict has image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
                The first item is ``bboxes`` with shape (n, 5),
                where 5 represent (tl_x, tl_y, br_x, br_y, score).
                The shape of the second tensor in the tuple is ``labels``
                with shape (n,). The length of list should always be 1.
        
    "
2635,"def pauseProducing(self) -> None:
        
        logger.info(""[%s] Pause producing"", self.id())
        self.state = ConnectionStates.PAUSED
","This is called when both the kernel send buffer and the twisted
        tcp connection send buffers have become full.

        We don't actually have any control over those sizes, so we buffer some
        commands ourselves before knifing the connection due to the remote
        failing to keep up.
        ",46,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pauseProducing(self) -> None:
        
        logger.info(""[%s] Pause producing"", self.id())
        self.state = ConnectionStates.PAUSED

        ```
    ###Assistant : This is called when both the kernel send buffer and the twisted
        tcp connection send buffers have become full.

        We don't actually have any control over those sizes, so we buffer some
        commands ourselves before knifing the connection due to the remote
        failing to keep up.
        
    "
2636,"def throw(self, typ, val=None, tb=None):
        
        if val is None:
            if tb is None:
                raise typ
            val = typ()
        if tb is not None:
            val = val.with_traceback(tb)
        raise val
","Raise an exception in the coroutine.
        Return next yielded value or raise StopIteration.
        ",13,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def throw(self, typ, val=None, tb=None):
        
        if val is None:
            if tb is None:
                raise typ
            val = typ()
        if tb is not None:
            val = val.with_traceback(tb)
        raise val

        ```
    ###Assistant : Raise an exception in the coroutine.
        Return next yielded value or raise StopIteration.
        
    "
2637,"def _exit_buffer(self) -> None:
        
        self._buffer_index -= 1
        self._check_buffer()
","Leave buffer context, and render content if required.",8,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _exit_buffer(self) -> None:
        
        self._buffer_index -= 1
        self._check_buffer()

        ```
    ###Assistant : Leave buffer context, and render content if required.
    "
2638,"def flatten_sensors_data(sensor):
    
    if ""temp"" in sensor[""data""]:
        sensor[""data""][""temperature""] = sensor[""data""][""temp""][""c""]

    return sensor

",Deconstruct SwitchBot library temp object C/Fº readings from dictionary.,9,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def flatten_sensors_data(sensor):
    
    if ""temp"" in sensor[""data""]:
        sensor[""data""][""temperature""] = sensor[""data""][""temp""][""c""]

    return sensor


        ```
    ###Assistant : Deconstruct SwitchBot library temp object C/Fº readings from dictionary.
    "
2639,"def test_shared_embedding_column_with_non_sequence_categorical(self):
        
        with tf.Graph().as_default():
            vocabulary_size = 3
            sparse_input_a = tf.compat.v1.SparseTensorValue(
                # example 0, ids [2]
                # example 1, ids [0, 1]
                indices=((0, 0), (1, 0), (1, 1)),
                values=(2, 0, 1),
                dense_shape=(2, 2),
            )
            sparse_input_b = tf.compat.v1.SparseTensorValue(
                # example 0, ids [2]
                # example 1, ids [0, 1]
                indices=((0, 0), (1, 0), (1, 1)),
                values=(2, 0, 1),
                dense_shape=(2, 2),
            )

            categorical_column_a = (
                tf.feature_column.categorical_column_with_identity(
                    key=""aaa"", num_buckets=vocabulary_size
                )
            )
            categorical_column_b = (
                tf.feature_column.categorical_column_with_identity(
                    key=""bbb"", num_buckets=vocabulary_size
                )
            )
            shared_embedding_columns = tf.feature_column.shared_embeddings(
                [categorical_column_a, categorical_column_b], dimension=2
            )

            sequence_input_layer = ksfc.SequenceFeatures(
                shared_embedding_columns
            )
            with self.assertRaisesRegex(
                ValueError,
                r""In embedding_column: aaa_shared_embedding\. ""
                r""categorical_column must ""
                r""be of type SequenceCategoricalColumn to use ""
                r""SequenceFeatures\."",
            ):
                _, _ = sequence_input_layer(
                    {""aaa"": sparse_input_a, ""bbb"": sparse_input_b}
                )
","Tests that error is raised for non-sequence shared embedding
        column.",10,115,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_shared_embedding_column_with_non_sequence_categorical(self):
        
        with tf.Graph().as_default():
            vocabulary_size = 3
            sparse_input_a = tf.compat.v1.SparseTensorValue(
                # example 0, ids [2]
                # example 1, ids [0, 1]
                indices=((0, 0), (1, 0), (1, 1)),
                values=(2, 0, 1),
                dense_shape=(2, 2),
            )
            sparse_input_b = tf.compat.v1.SparseTensorValue(
                # example 0, ids [2]
                # example 1, ids [0, 1]
                indices=((0, 0), (1, 0), (1, 1)),
                values=(2, 0, 1),
                dense_shape=(2, 2),
            )

            categorical_column_a = (
                tf.feature_column.categorical_column_with_identity(
                    key=""aaa"", num_buckets=vocabulary_size
                )
            )
            categorical_column_b = (
                tf.feature_column.categorical_column_with_identity(
                    key=""bbb"", num_buckets=vocabulary_size
                )
            )
            shared_embedding_columns = tf.feature_column.shared_embeddings(
                [categorical_column_a, categorical_column_b], dimension=2
            )

            sequence_input_layer = ksfc.SequenceFeatures(
                shared_embedding_columns
            )
            with self.assertRaisesRegex(
                ValueError,
                r""In embedding_column: aaa_shared_embedding\. ""
                r""categorical_column must ""
                r""be of type SequenceCategoricalColumn to use ""
                r""SequenceFeatures\."",
            ):
                _, _ = sequence_input_layer(
                    {""aaa"": sparse_input_a, ""bbb"": sparse_input_b}
                )

        ```
    ###Assistant : Tests that error is raised for non-sequence shared embedding
        column.
    "
2640,"def get_next_stock_market_days(last_stock_day, n_next_days) -> list:
    
    n_days = 0
    l_pred_days = []
    years: list = []
    holidays: list = []
    if isinstance(last_stock_day, datetime):
        while n_days < n_next_days:
            last_stock_day += timedelta(hours=24)
            year = last_stock_day.date().year
            if year not in years:
                years.append(year)
                holidays += us_market_holidays(year)
            # Check if it is a weekend
            if last_stock_day.date().weekday() > 4:
                continue
            # Check if it is a holiday
            if last_stock_day.strftime(""%Y-%m-%d"") in holidays:
                continue
            # Otherwise stock market is open
            n_days += 1
            l_pred_days.append(last_stock_day)
    else:
        while n_days < n_next_days:
            l_pred_days.append(last_stock_day + 1 + n_days)
            n_days += 1

    return l_pred_days

",Gets the next stock market day. Checks against weekends and holidays,11,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_next_stock_market_days(last_stock_day, n_next_days) -> list:
    
    n_days = 0
    l_pred_days = []
    years: list = []
    holidays: list = []
    if isinstance(last_stock_day, datetime):
        while n_days < n_next_days:
            last_stock_day += timedelta(hours=24)
            year = last_stock_day.date().year
            if year not in years:
                years.append(year)
                holidays += us_market_holidays(year)
            # Check if it is a weekend
            if last_stock_day.date().weekday() > 4:
                continue
            # Check if it is a holiday
            if last_stock_day.strftime(""%Y-%m-%d"") in holidays:
                continue
            # Otherwise stock market is open
            n_days += 1
            l_pred_days.append(last_stock_day)
    else:
        while n_days < n_next_days:
            l_pred_days.append(last_stock_day + 1 + n_days)
            n_days += 1

    return l_pred_days


        ```
    ###Assistant : Gets the next stock market day. Checks against weekends and holidays
    "
2641,"async def test_max_concurrent_in_progress_functions(extra_req_num):
    
    max_req = 10
    a = A(max_num_call=max_req)

    # Run more than allowed concurrent async functions should trigger rate limiting
    res_arr = await asyncio.gather(
        *[a.fn1() if i % 2 == 0 else a.fn2() for i in range(max_req + extra_req_num)]
    )
    fail_cnt = 0
    for ok in res_arr:
        fail_cnt += 0 if ok else 1

    expected_fail_cnt = max(0, extra_req_num)
    assert fail_cnt == expected_fail_cnt, (
        f""{expected_fail_cnt} out of {max_req + extra_req_num} ""
        f""concurrent runs should fail with max={max_req} but {fail_cnt}.""
    )

    assert a.num_call_ == 0, ""All requests should be done""


@pytest.mark.asyncio
@pytest.mark.parametrize(
    ""failures"",
    [
        [True, True, True, True, True],
        [False, False, False, False, False],
        [False, True, False, True, False],
        [False, False, False, True, True],
        [True, True, False, False, False],
    ],
)",Test rate limiting for concurrent in-progress requests on StateHead,9,120,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_max_concurrent_in_progress_functions(extra_req_num):
    
    max_req = 10
    a = A(max_num_call=max_req)

    # Run more than allowed concurrent async functions should trigger rate limiting
    res_arr = await asyncio.gather(
        *[a.fn1() if i % 2 == 0 else a.fn2() for i in range(max_req + extra_req_num)]
    )
    fail_cnt = 0
    for ok in res_arr:
        fail_cnt += 0 if ok else 1

    expected_fail_cnt = max(0, extra_req_num)
    assert fail_cnt == expected_fail_cnt, (
        f""{expected_fail_cnt} out of {max_req + extra_req_num} ""
        f""concurrent runs should fail with max={max_req} but {fail_cnt}.""
    )

    assert a.num_call_ == 0, ""All requests should be done""


@pytest.mark.asyncio
@pytest.mark.parametrize(
    ""failures"",
    [
        [True, True, True, True, True],
        [False, False, False, False, False],
        [False, True, False, True, False],
        [False, False, False, True, True],
        [True, True, False, False, False],
    ],
)
        ```
    ###Assistant : Test rate limiting for concurrent in-progress requests on StateHead
    "
2642,"def transpose_qkv(X, num_heads):
    
    # Shape of input `X`:
    # (`batch_size`, no. of queries or key-value pairs, `num_hiddens`).
    # Shape of output `X`:
    # (`batch_size`, no. of queries or key-value pairs, `num_heads`,
    # `num_hiddens` / `num_heads`)
    X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)

    # Shape of output `X`:
    # (`batch_size`, `num_heads`, no. of queries or key-value pairs,
    # `num_hiddens` / `num_heads`)
    X = X.transpose(0, 2, 1, 3)

    # Shape of `output`:
    # (`batch_size` * `num_heads`, no. of queries or key-value pairs,
    # `num_hiddens` / `num_heads`)
    return X.reshape(-1, X.shape[2], X.shape[3])

","Transposition for parallel computation of multiple attention heads.

    Defined in :numref:`sec_multihead-attention`",11,87,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transpose_qkv(X, num_heads):
    
    # Shape of input `X`:
    # (`batch_size`, no. of queries or key-value pairs, `num_hiddens`).
    # Shape of output `X`:
    # (`batch_size`, no. of queries or key-value pairs, `num_heads`,
    # `num_hiddens` / `num_heads`)
    X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)

    # Shape of output `X`:
    # (`batch_size`, `num_heads`, no. of queries or key-value pairs,
    # `num_hiddens` / `num_heads`)
    X = X.transpose(0, 2, 1, 3)

    # Shape of `output`:
    # (`batch_size` * `num_heads`, no. of queries or key-value pairs,
    # `num_hiddens` / `num_heads`)
    return X.reshape(-1, X.shape[2], X.shape[3])


        ```
    ###Assistant : Transposition for parallel computation of multiple attention heads.

    Defined in :numref:`sec_multihead-attention`
    "
2643,"def current_state(self, session=NEW_SESSION) -> str:
        
        return (
            session.query(TaskInstance.state)
            .filter(
                TaskInstance.dag_id == self.dag_id,
                TaskInstance.task_id == self.task_id,
                TaskInstance.run_id == self.run_id,
            )
            .scalar()
        )
","
        Get the very latest state from the database, if a session is passed,
        we use and looking up the state becomes part of the session, otherwise
        a new session is used.

        :param session: SQLAlchemy ORM Session
        ",36,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def current_state(self, session=NEW_SESSION) -> str:
        
        return (
            session.query(TaskInstance.state)
            .filter(
                TaskInstance.dag_id == self.dag_id,
                TaskInstance.task_id == self.task_id,
                TaskInstance.run_id == self.run_id,
            )
            .scalar()
        )

        ```
    ###Assistant : 
        Get the very latest state from the database, if a session is passed,
        we use and looking up the state becomes part of the session, otherwise
        a new session is used.

        :param session: SQLAlchemy ORM Session
        
    "
2644,"def house_graph(create_using=None):
    
    description = [
        ""adjacencylist"",
        ""House Graph"",
        5,
        [[2, 3], [1, 4], [1, 4, 5], [2, 3, 5], [3, 4]],
    ]
    G = make_small_undirected_graph(description, create_using)
    return G

","
    Returns the House graph (square with triangle on top)

    The house graph is a simple undirected graph with
    5 nodes and 6 edges [1]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        House graph in the form of a square with a triangle on top

    References
    ----------
    .. [1] https://mathworld.wolfram.com/HouseGraph.html
    ",68,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def house_graph(create_using=None):
    
    description = [
        ""adjacencylist"",
        ""House Graph"",
        5,
        [[2, 3], [1, 4], [1, 4, 5], [2, 3, 5], [3, 4]],
    ]
    G = make_small_undirected_graph(description, create_using)
    return G


        ```
    ###Assistant : 
    Returns the House graph (square with triangle on top)

    The house graph is a simple undirected graph with
    5 nodes and 6 edges [1]_.

    Parameters
    ----------
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Returns
    -------
    G : networkx Graph
        House graph in the form of a square with a triangle on top

    References
    ----------
    .. [1] https://mathworld.wolfram.com/HouseGraph.html
    
    "
2645,"def check_header_validity(header):
    
    name, value = header

    for part in header:
        if type(part) not in HEADER_VALIDATORS:
            raise InvalidHeader(
                f""Header part ({part!r}) from {{{name!r}: {value!r}}} must be ""
                f""of type str or bytes, not {type(part)}""
            )

    _validate_header_part(name, ""name"", HEADER_VALIDATORS[type(name)][0])
    _validate_header_part(value, ""value"", HEADER_VALIDATORS[type(value)][1])

","Verifies that header parts don't contain leading whitespace
    reserved characters, or return characters.

    :param header: tuple, in the format (name, value).
    ",21,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_header_validity(header):
    
    name, value = header

    for part in header:
        if type(part) not in HEADER_VALIDATORS:
            raise InvalidHeader(
                f""Header part ({part!r}) from {{{name!r}: {value!r}}} must be ""
                f""of type str or bytes, not {type(part)}""
            )

    _validate_header_part(name, ""name"", HEADER_VALIDATORS[type(name)][0])
    _validate_header_part(value, ""value"", HEADER_VALIDATORS[type(value)][1])


        ```
    ###Assistant : Verifies that header parts don't contain leading whitespace
    reserved characters, or return characters.

    :param header: tuple, in the format (name, value).
    
    "
2646,"def test_orderline_query(staff_api_client, permission_manage_orders, fulfilled_order):
    order = fulfilled_order
    query = 
    line = order.lines.first()

    metadata_key = ""md key""
    metadata_value = ""md value""

    line.store_value_in_private_metadata({metadata_key: metadata_value})
    line.store_value_in_metadata({metadata_key: metadata_value})
    line.save()

    staff_api_client.user.user_permissions.add(permission_manage_orders)
    response = staff_api_client.post_graphql(query)
    content = get_graphql_content(response)
    order_data = content[""data""][""orders""][""edges""][0][""node""]
    first_order_data_line = order_data[""lines""][0]
    variant_id = graphene.Node.to_global_id(""ProductVariant"", line.variant.pk)

    assert first_order_data_line[""thumbnail""] is None
    assert first_order_data_line[""variant""][""id""] == variant_id
    assert first_order_data_line[""quantity""] == line.quantity
    assert first_order_data_line[""unitPrice""][""currency""] == line.unit_price.currency
    assert first_order_data_line[""metadata""] == [
        {""key"": metadata_key, ""value"": metadata_value}
    ]
    assert first_order_data_line[""privateMetadata""] == [
        {""key"": metadata_key, ""value"": metadata_value}
    ]
    expected_unit_price = Money(
        amount=str(first_order_data_line[""unitPrice""][""gross""][""amount""]),
        currency=""USD"",
    )
    assert first_order_data_line[""totalPrice""][""currency""] == line.unit_price.currency
    assert expected_unit_price == line.unit_price.gross

    expected_total_price = Money(
        amount=str(first_order_data_line[""totalPrice""][""gross""][""amount""]),
        currency=""USD"",
    )
    assert expected_total_price == line.unit_price.gross * line.quantity

    allocation = line.allocations.first()
    allocation_id = graphene.Node.to_global_id(""Allocation"", allocation.pk)
    warehouse_id = graphene.Node.to_global_id(
        ""Warehouse"", allocation.stock.warehouse.pk
    )
    assert first_order_data_line[""allocations""] == [
        {
            ""id"": allocation_id,
            ""quantity"": allocation.quantity_allocated,
            ""warehouse"": {""id"": warehouse_id},
        }
    ]

","
        query OrdersQuery {
            orders(first: 1) {
                edges {
                    node {
                        lines {
                            thumbnail(size: 540) {
                                url
                            }
                            variant {
                                id
                            }
                            quantity
                            allocations {
                                id
                                quantity
                                warehouse {
                                    id
                                }
                            }
                            unitPrice {
                                currency
                                gross {
                                    amount
                                }
                            }
                            totalPrice {
                                currency
                                gross {
                                    amount
                                }
                            }
                            metadata {
                                key
                                value
                            }
                            privateMetadata {
                                key
                                value
                            }
                        }
                    }
                }
            }
        }
    ",62,129,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_orderline_query(staff_api_client, permission_manage_orders, fulfilled_order):
    order = fulfilled_order
    query = 
    line = order.lines.first()

    metadata_key = ""md key""
    metadata_value = ""md value""

    line.store_value_in_private_metadata({metadata_key: metadata_value})
    line.store_value_in_metadata({metadata_key: metadata_value})
    line.save()

    staff_api_client.user.user_permissions.add(permission_manage_orders)
    response = staff_api_client.post_graphql(query)
    content = get_graphql_content(response)
    order_data = content[""data""][""orders""][""edges""][0][""node""]
    first_order_data_line = order_data[""lines""][0]
    variant_id = graphene.Node.to_global_id(""ProductVariant"", line.variant.pk)

    assert first_order_data_line[""thumbnail""] is None
    assert first_order_data_line[""variant""][""id""] == variant_id
    assert first_order_data_line[""quantity""] == line.quantity
    assert first_order_data_line[""unitPrice""][""currency""] == line.unit_price.currency
    assert first_order_data_line[""metadata""] == [
        {""key"": metadata_key, ""value"": metadata_value}
    ]
    assert first_order_data_line[""privateMetadata""] == [
        {""key"": metadata_key, ""value"": metadata_value}
    ]
    expected_unit_price = Money(
        amount=str(first_order_data_line[""unitPrice""][""gross""][""amount""]),
        currency=""USD"",
    )
    assert first_order_data_line[""totalPrice""][""currency""] == line.unit_price.currency
    assert expected_unit_price == line.unit_price.gross

    expected_total_price = Money(
        amount=str(first_order_data_line[""totalPrice""][""gross""][""amount""]),
        currency=""USD"",
    )
    assert expected_total_price == line.unit_price.gross * line.quantity

    allocation = line.allocations.first()
    allocation_id = graphene.Node.to_global_id(""Allocation"", allocation.pk)
    warehouse_id = graphene.Node.to_global_id(
        ""Warehouse"", allocation.stock.warehouse.pk
    )
    assert first_order_data_line[""allocations""] == [
        {
            ""id"": allocation_id,
            ""quantity"": allocation.quantity_allocated,
            ""warehouse"": {""id"": warehouse_id},
        }
    ]


        ```
    ###Assistant : 
        query OrdersQuery {
            orders(first: 1) {
                edges {
                    node {
                        lines {
                            thumbnail(size: 540) {
                                url
                            }
                            variant {
                                id
                            }
                            quantity
                            allocations {
                                id
                                quantity
                                warehouse {
                                    id
                                }
                            }
                            unitPrice {
                                currency
                                gross {
                                    amount
                                }
                            }
                            totalPrice {
                                currency
                                gross {
                                    amount
                                }
                            }
                            metadata {
                                key
                                value
                            }
                            privateMetadata {
                                key
                                value
                            }
                        }
                    }
                }
            }
        }
    
    "
2647,"def in4_pseudoheader(proto, u, plen):
    # type: (int, IP, int) -> bytes
    
    if u.len is not None:
        if u.ihl is None:
            olen = sum(len(x) for x in u.options)
            ihl = 5 + olen // 4 + (1 if olen % 4 else 0)
        else:
            ihl = u.ihl
        ln = max(u.len - 4 * ihl, 0)
    else:
        ln = plen

    # Filter out IPOption_LSRR and IPOption_SSRR
    sr_options = [opt for opt in u.options if isinstance(opt, IPOption_LSRR) or
                  isinstance(opt, IPOption_SSRR)]
    len_sr_options = len(sr_options)
    if len_sr_options == 1 and len(sr_options[0].routers):
        # The checksum must be computed using the final
        # destination address
        u.dst = sr_options[0].routers[-1]
    elif len_sr_options > 1:
        message = ""Found %d Source Routing Options! ""
        message += ""Falling back to IP.dst for checksum computation.""
        warning(message, len_sr_options)

    return struct.pack(""!4s4sHH"",
                       inet_pton(socket.AF_INET, u.src),
                       inet_pton(socket.AF_INET, u.dst),
                       proto,
                       ln)

","IPv4 Pseudo Header as defined in RFC793 as bytes

    :param proto: value of upper layer protocol
    :param u: IP layer instance
    :param plen: the length of the upper layer and payload
    ",31,132,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def in4_pseudoheader(proto, u, plen):
    # type: (int, IP, int) -> bytes
    
    if u.len is not None:
        if u.ihl is None:
            olen = sum(len(x) for x in u.options)
            ihl = 5 + olen // 4 + (1 if olen % 4 else 0)
        else:
            ihl = u.ihl
        ln = max(u.len - 4 * ihl, 0)
    else:
        ln = plen

    # Filter out IPOption_LSRR and IPOption_SSRR
    sr_options = [opt for opt in u.options if isinstance(opt, IPOption_LSRR) or
                  isinstance(opt, IPOption_SSRR)]
    len_sr_options = len(sr_options)
    if len_sr_options == 1 and len(sr_options[0].routers):
        # The checksum must be computed using the final
        # destination address
        u.dst = sr_options[0].routers[-1]
    elif len_sr_options > 1:
        message = ""Found %d Source Routing Options! ""
        message += ""Falling back to IP.dst for checksum computation.""
        warning(message, len_sr_options)

    return struct.pack(""!4s4sHH"",
                       inet_pton(socket.AF_INET, u.src),
                       inet_pton(socket.AF_INET, u.dst),
                       proto,
                       ln)


        ```
    ###Assistant : IPv4 Pseudo Header as defined in RFC793 as bytes

    :param proto: value of upper layer protocol
    :param u: IP layer instance
    :param plen: the length of the upper layer and payload
    
    "
2648,"def doc_resample_fillna(method, refer_to, params=None, overwrite_template_params=False):
    
    action = f""fill missing values in each group independently using {method} method""
    params_substitution = ""limit : int\n""

    if params:
        params_substitution = (
            params
            if overwrite_template_params
            else format_string(
                ""{params}\n{params_substitution}"",
                params=params,
                params_substitution=params_substitution,
            )
        )

    build_rules = ""- QueryCompiler contains unsampled data with missing values filled.""

    return doc_resample(
        action=action,
        extra_params=params_substitution,
        build_rules=build_rules,
        refer_to=refer_to,
    )


doc_dt = partial(
    doc_qc_method,
    template=,
    one_column_method=True,
    refer_to_module_name=""Series.dt"",
)

doc_dt_timestamp = partial(doc_dt, dt_type=""datetime"")
doc_dt_interval = partial(doc_dt, dt_type=""interval"")
doc_dt_period = partial(doc_dt, dt_type=""period"")

doc_dt_round = partial(
    doc_qc_method,
    template=,
    one_column_method=True,
    refer_to_module_name=""Series.dt"",
)

doc_str_method = partial(
    doc_qc_method,
    template=,
    one_column_method=True,
    refer_to_module_name=""Series.str"",
)

","
    Build decorator which adds docstring for the resample fillna query compiler method.

    Parameters
    ----------
    method : str
        Fillna method name.
    refer_to : str
        Method name in ``modin.pandas.resample.Resampler`` module to refer to for
        more information about parameters and output format.
    params : str, optional
        Method parameters in the NumPy docstyle format to substitute
        to the docstring template.
    overwrite_template_params : bool, default: False
        If `params` is specified indicates whether to overwrite method parameters in
        the docstring template or append then at the end.

    Returns
    -------
    callable
    
    Get {prop} for each {dt_type} value.
    {params}
    Returns
    -------
    BaseQueryCompiler
        New QueryCompiler with the same shape as `self`, where each element is
        {prop} for the corresponding {dt_type} value.
    
    Perform {refer_to} operation on the underlying time-series data to the specified `freq`.

    Parameters
    ----------
    freq : str
    ambiguous : {{""raise"", ""infer"", ""NaT""}} or bool mask, default: ""raise""
    nonexistent : {{""raise"", ""shift_forward"", ""shift_backward"", ""NaT""}} or timedelta, default: ""raise""

    Returns
    -------
    BaseQueryCompiler
        New QueryCompiler with performed {refer_to} operation on every element.
    
    Apply ""{refer_to}"" function to each string value in QueryCompiler.
    {params}
    Returns
    -------
    BaseQueryCompiler
        New QueryCompiler containing the result of execution of the ""{refer_to}"" function
        against each string element.
    ",189,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def doc_resample_fillna(method, refer_to, params=None, overwrite_template_params=False):
    
    action = f""fill missing values in each group independently using {method} method""
    params_substitution = ""limit : int\n""

    if params:
        params_substitution = (
            params
            if overwrite_template_params
            else format_string(
                ""{params}\n{params_substitution}"",
                params=params,
                params_substitution=params_substitution,
            )
        )

    build_rules = ""- QueryCompiler contains unsampled data with missing values filled.""

    return doc_resample(
        action=action,
        extra_params=params_substitution,
        build_rules=build_rules,
        refer_to=refer_to,
    )


doc_dt = partial(
    doc_qc_method,
    template=,
    one_column_method=True,
    refer_to_module_name=""Series.dt"",
)

doc_dt_timestamp = partial(doc_dt, dt_type=""datetime"")
doc_dt_interval = partial(doc_dt, dt_type=""interval"")
doc_dt_period = partial(doc_dt, dt_type=""period"")

doc_dt_round = partial(
    doc_qc_method,
    template=,
    one_column_method=True,
    refer_to_module_name=""Series.dt"",
)

doc_str_method = partial(
    doc_qc_method,
    template=,
    one_column_method=True,
    refer_to_module_name=""Series.str"",
)


        ```
    ###Assistant : 
    Build decorator which adds docstring for the resample fillna query compiler method.

    Parameters
    ----------
    method : str
        Fillna method name.
    refer_to : str
        Method name in ``modin.pandas.resample.Resampler`` module to refer to for
        more information about parameters and output format.
    params : str, optional
        Method parameters in the NumPy docstyle format to substitute
        to the docstring template.
    overwrite_template_params : bool, default: False
        If `params` is specified indicates whether to overwrite method parameters in
        the docstring template or append then at the end.

    Returns
    -------
    callable
    
    Get {prop} for each {dt_type} value.
    {params}
    Returns
    -------
    BaseQueryCompiler
        New QueryCompiler with the same shape as `self`, where each element is
        {prop} for the corresponding {dt_type} value.
    
    Perform {refer_to} operation on the underlying time-series data to the specified `freq`.

    Parameters
    ----------
    freq : str
    ambiguous : {{""raise"", ""infer"", ""NaT""}} or bool mask, default: ""raise""
    nonexistent : {{""raise"", ""shift_forward"", ""shift_backward"", ""NaT""}} or timedelta, default: ""raise""

    Returns
    -------
    BaseQueryCompiler
        New QueryCompiler with performed {refer_to} operation on every element.
    
    Apply ""{refer_to}"" function to each string value in QueryCompiler.
    {params}
    Returns
    -------
    BaseQueryCompiler
        New QueryCompiler containing the result of execution of the ""{refer_to}"" function
        against each string element.
    
    "
2649,"def delete_events(ref_type, ref_name):
	events = (
		frappe.db.sql_list(
			,
			(ref_type, ref_name),
		)
		or []
	)

	if events:
		frappe.delete_doc(""Event"", events, for_reload=True)

"," SELECT
			distinct `tabEvent`.name
		from
			`tabEvent`, `tabEvent Participants`
		where
			`tabEvent`.name = `tabEvent Participants`.parent
			and `tabEvent Participants`.reference_doctype = %s
			and `tabEvent Participants`.reference_docname = %s
		",22,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def delete_events(ref_type, ref_name):
	events = (
		frappe.db.sql_list(
			,
			(ref_type, ref_name),
		)
		or []
	)

	if events:
		frappe.delete_doc(""Event"", events, for_reload=True)


        ```
    ###Assistant :  SELECT
			distinct `tabEvent`.name
		from
			`tabEvent`, `tabEvent Participants`
		where
			`tabEvent`.name = `tabEvent Participants`.parent
			and `tabEvent Participants`.reference_doctype = %s
			and `tabEvent Participants`.reference_docname = %s
		
    "
2650,"def check_started(self) -> ReplicaStartupStatus:
        
        status, version = self._actor.check_ready()

        if status == ReplicaStartupStatus.SUCCEEDED:
            # Re-assign DeploymentVersion if start / update / recover succeeded
            # by reading re-computed version in RayServeReplica
            if version is not None:
                self._version = version

        return status
","Check if the replica has started. If so, transition to RUNNING.

        Should handle the case where the replica has already stopped.

        Returns:
            status: Most recent state of replica by
                querying actor obj ref
        ",33,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_started(self) -> ReplicaStartupStatus:
        
        status, version = self._actor.check_ready()

        if status == ReplicaStartupStatus.SUCCEEDED:
            # Re-assign DeploymentVersion if start / update / recover succeeded
            # by reading re-computed version in RayServeReplica
            if version is not None:
                self._version = version

        return status

        ```
    ###Assistant : Check if the replica has started. If so, transition to RUNNING.

        Should handle the case where the replica has already stopped.

        Returns:
            status: Most recent state of replica by
                querying actor obj ref
        
    "
2651,"async def test_battery_low(hass, utcnow):
    
    helper = await setup_test_component(
        hass, create_battery_level_sensor, suffix=""battery""
    )

    state = await helper.async_update(
        ServicesTypes.BATTERY_SERVICE,
        {
            CharacteristicsTypes.BATTERY_LEVEL: 1,
            CharacteristicsTypes.STATUS_LO_BATT: 0,
        },
    )
    assert state.attributes[""icon""] == ""mdi:battery-10""

    state = await helper.async_update(
        ServicesTypes.BATTERY_SERVICE,
        {
            CharacteristicsTypes.BATTERY_LEVEL: 1,
            CharacteristicsTypes.STATUS_LO_BATT: 1,
        },
    )
    assert state.attributes[""icon""] == ""mdi:battery-alert""

",Test reading the state of a HomeKit battery's low state.,10,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_battery_low(hass, utcnow):
    
    helper = await setup_test_component(
        hass, create_battery_level_sensor, suffix=""battery""
    )

    state = await helper.async_update(
        ServicesTypes.BATTERY_SERVICE,
        {
            CharacteristicsTypes.BATTERY_LEVEL: 1,
            CharacteristicsTypes.STATUS_LO_BATT: 0,
        },
    )
    assert state.attributes[""icon""] == ""mdi:battery-10""

    state = await helper.async_update(
        ServicesTypes.BATTERY_SERVICE,
        {
            CharacteristicsTypes.BATTERY_LEVEL: 1,
            CharacteristicsTypes.STATUS_LO_BATT: 1,
        },
    )
    assert state.attributes[""icon""] == ""mdi:battery-alert""


        ```
    ###Assistant : Test reading the state of a HomeKit battery's low state.
    "
2652,"def _hyab(self, y_true, y_pred):
        
        delta = y_true - y_pred
        root = K.sqrt(K.clip(K.pow(delta[..., 0:1], 2), self._epsilon, None))
        delta_norm = frobenius_norm(delta[..., 1:3])
        return root + delta_norm
"," Compute the HyAB distance between true and predicted images.

        Parameters
        ----------
        y_true: :class:`plaidml.tile.Value`
            The ground truth batch of images in standard or Hunt-adjusted L*A*B* color space
        y_pred: :class:`plaidml.tile.Value`
            The predicted batch of images in in standard or Hunt-adjusted L*A*B* color space

        Returns
        -------
        :class:`plaidml.tile.Value`
            image tensor containing the per-pixel HyAB distances between true and predicted images
        ",56,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _hyab(self, y_true, y_pred):
        
        delta = y_true - y_pred
        root = K.sqrt(K.clip(K.pow(delta[..., 0:1], 2), self._epsilon, None))
        delta_norm = frobenius_norm(delta[..., 1:3])
        return root + delta_norm

        ```
    ###Assistant :  Compute the HyAB distance between true and predicted images.

        Parameters
        ----------
        y_true: :class:`plaidml.tile.Value`
            The ground truth batch of images in standard or Hunt-adjusted L*A*B* color space
        y_pred: :class:`plaidml.tile.Value`
            The predicted batch of images in in standard or Hunt-adjusted L*A*B* color space

        Returns
        -------
        :class:`plaidml.tile.Value`
            image tensor containing the per-pixel HyAB distances between true and predicted images
        
    "
2653,"def test_dry_run(self, run_cleanup_mock, dry_run_arg, expected):
        
        args = self.parser.parse_args(
            [
                'db',
                'clean',
                '--clean-before-timestamp',
                '2021-01-01',
                *dry_run_arg,
            ]
        )
        db_command.cleanup_tables(args)

        run_cleanup_mock.assert_called_once_with(
            table_names=None,
            dry_run=expected,
            clean_before_timestamp=pendulum.parse('2021-01-01 00:00:00Z'),
            verbose=False,
            confirm=True,
            skip_archive=False,
        )
","
        When tz included in the string then default timezone should not be used.
        ",13,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dry_run(self, run_cleanup_mock, dry_run_arg, expected):
        
        args = self.parser.parse_args(
            [
                'db',
                'clean',
                '--clean-before-timestamp',
                '2021-01-01',
                *dry_run_arg,
            ]
        )
        db_command.cleanup_tables(args)

        run_cleanup_mock.assert_called_once_with(
            table_names=None,
            dry_run=expected,
            clean_before_timestamp=pendulum.parse('2021-01-01 00:00:00Z'),
            verbose=False,
            confirm=True,
            skip_archive=False,
        )

        ```
    ###Assistant : 
        When tz included in the string then default timezone should not be used.
        
    "
2654,"def async_dismiss_setup_message(hass, entry_id):
    
    persistent_notification.async_dismiss(hass, entry_id)

",Dismiss persistent notification and remove QR code.,7,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def async_dismiss_setup_message(hass, entry_id):
    
    persistent_notification.async_dismiss(hass, entry_id)


        ```
    ###Assistant : Dismiss persistent notification and remove QR code.
    "
2655,"def get_containing_app_config(self, object_name):
        
        self.check_apps_ready()
        candidates = []
        for app_config in self.app_configs.values():
            if object_name.startswith(app_config.name):
                subpath = object_name[len(app_config.name) :]
                if subpath == """" or subpath[0] == ""."":
                    candidates.append(app_config)
        if candidates:
            return sorted(candidates, key=lambda ac: -len(ac.name))[0]
","
        Look for an app config containing a given object.

        object_name is the dotted Python path to the object.

        Return the app config for the inner application in case of nesting.
        Return None if the object isn't in any registered app config.
        ",41,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_containing_app_config(self, object_name):
        
        self.check_apps_ready()
        candidates = []
        for app_config in self.app_configs.values():
            if object_name.startswith(app_config.name):
                subpath = object_name[len(app_config.name) :]
                if subpath == """" or subpath[0] == ""."":
                    candidates.append(app_config)
        if candidates:
            return sorted(candidates, key=lambda ac: -len(ac.name))[0]

        ```
    ###Assistant : 
        Look for an app config containing a given object.

        object_name is the dotted Python path to the object.

        Return the app config for the inner application in case of nesting.
        Return None if the object isn't in any registered app config.
        
    "
2656,"def _track_variables(self, value):
        
        for val in tf.nest.flatten(value):
            if isinstance(val, tf.Variable):
                self._track_variable(val)
            elif tf_utils.is_extension_type(val):
                # Manually expand extension types to track resource variables.
                nested_vals = tf_utils.type_spec_from_value(val)._to_components(
                    val
                )
                self._track_variables(nested_vals)
",Tracks `Variable`s including `Variable`s in `CompositeTensor`s.,6,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _track_variables(self, value):
        
        for val in tf.nest.flatten(value):
            if isinstance(val, tf.Variable):
                self._track_variable(val)
            elif tf_utils.is_extension_type(val):
                # Manually expand extension types to track resource variables.
                nested_vals = tf_utils.type_spec_from_value(val)._to_components(
                    val
                )
                self._track_variables(nested_vals)

        ```
    ###Assistant : Tracks `Variable`s including `Variable`s in `CompositeTensor`s.
    "
2657,"def _create_vhost_v2(self, node):
        
        addrs = set()
        for param in node.parameters:
            addr = obj.Addr.fromstring(param)
            if addr:
                addrs.add(addr)

        is_ssl = False
        # Exclusion to match the behavior in get_virtual_hosts_v2
        sslengine = node.find_directives(""SSLEngine"", exclude=False)
        if sslengine:
            for directive in sslengine:
                if directive.parameters[0].lower() == ""on"":
                    is_ssl = True
                    break

        # ""SSLEngine on"" might be set outside of <VirtualHost>
        # Treat vhosts with port 443 as ssl vhosts
        for addr in addrs:
            if addr.get_port() == ""443"":
                is_ssl = True

        enabled = apache_util.included_in_paths(node.filepath, self.parsed_paths)

        macro = False
        # Check if the VirtualHost is contained in a mod_macro block
        if node.find_ancestors(""Macro""):
            macro = True
        vhost = obj.VirtualHost(
            node.filepath, None, addrs, is_ssl, enabled, modmacro=macro, node=node
        )
        self._populate_vhost_names_v2(vhost)
        return vhost
","Used by get_virtual_hosts_v2 to create vhost objects using ParserNode
        interfaces.
        :param interfaces.BlockNode node: The BlockNode object of VirtualHost block
        :returns: newly created vhost
        :rtype: :class:`~certbot_apache.obj.VirtualHost`
        ",25,111,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_vhost_v2(self, node):
        
        addrs = set()
        for param in node.parameters:
            addr = obj.Addr.fromstring(param)
            if addr:
                addrs.add(addr)

        is_ssl = False
        # Exclusion to match the behavior in get_virtual_hosts_v2
        sslengine = node.find_directives(""SSLEngine"", exclude=False)
        if sslengine:
            for directive in sslengine:
                if directive.parameters[0].lower() == ""on"":
                    is_ssl = True
                    break

        # ""SSLEngine on"" might be set outside of <VirtualHost>
        # Treat vhosts with port 443 as ssl vhosts
        for addr in addrs:
            if addr.get_port() == ""443"":
                is_ssl = True

        enabled = apache_util.included_in_paths(node.filepath, self.parsed_paths)

        macro = False
        # Check if the VirtualHost is contained in a mod_macro block
        if node.find_ancestors(""Macro""):
            macro = True
        vhost = obj.VirtualHost(
            node.filepath, None, addrs, is_ssl, enabled, modmacro=macro, node=node
        )
        self._populate_vhost_names_v2(vhost)
        return vhost

        ```
    ###Assistant : Used by get_virtual_hosts_v2 to create vhost objects using ParserNode
        interfaces.
        :param interfaces.BlockNode node: The BlockNode object of VirtualHost block
        :returns: newly created vhost
        :rtype: :class:`~certbot_apache.obj.VirtualHost`
        
    "
2658,"def insert_on(self, path, loc=None, replace=False):
        

        loc = loc or self.location
        if not loc:
            return

        nloc = _normalize_cached(loc)
        bdir = os.path.dirname(nloc)
        npath = [(p and _normalize_cached(p) or p) for p in path]

        for p, item in enumerate(npath):
            if item == nloc:
                if replace:
                    break
                else:
                    # don't modify path (even removing duplicates) if
                    # found and not replace
                    return
            elif item == bdir and self.precedence == EGG_DIST:
                # if it's an .egg, give it precedence over its directory
                # UNLESS it's already been added to sys.path and replace=False
                if (not replace) and nloc in npath[p:]:
                    return
                if path is sys.path:
                    self.check_version_conflict()
                path.insert(p, loc)
                npath.insert(p, nloc)
                break
        else:
            if path is sys.path:
                self.check_version_conflict()
            if replace:
                path.insert(0, loc)
            else:
                path.append(loc)
            return

        # p is the spot where we found or inserted loc; now remove duplicates
        while True:
            try:
                np = npath.index(nloc, p + 1)
            except ValueError:
                break
            else:
                del npath[np], path[np]
                # ha!
                p = np

        return
","Ensure self.location is on path

        If replace=False (default):
            - If location is already in path anywhere, do nothing.
            - Else:
              - If it's an egg and its parent directory is on path,
                insert just ahead of the parent.
              - Else: add to the end of path.
        If replace=True:
            - If location is already on path anywhere (not eggs)
              or higher priority than its parent (eggs)
              do nothing.
            - Else:
              - If it's an egg and its parent directory is on path,
                insert just ahead of the parent,
                removing any lower-priority entries.
              - Else: add it to the front of path.
        ",100,154,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def insert_on(self, path, loc=None, replace=False):
        

        loc = loc or self.location
        if not loc:
            return

        nloc = _normalize_cached(loc)
        bdir = os.path.dirname(nloc)
        npath = [(p and _normalize_cached(p) or p) for p in path]

        for p, item in enumerate(npath):
            if item == nloc:
                if replace:
                    break
                else:
                    # don't modify path (even removing duplicates) if
                    # found and not replace
                    return
            elif item == bdir and self.precedence == EGG_DIST:
                # if it's an .egg, give it precedence over its directory
                # UNLESS it's already been added to sys.path and replace=False
                if (not replace) and nloc in npath[p:]:
                    return
                if path is sys.path:
                    self.check_version_conflict()
                path.insert(p, loc)
                npath.insert(p, nloc)
                break
        else:
            if path is sys.path:
                self.check_version_conflict()
            if replace:
                path.insert(0, loc)
            else:
                path.append(loc)
            return

        # p is the spot where we found or inserted loc; now remove duplicates
        while True:
            try:
                np = npath.index(nloc, p + 1)
            except ValueError:
                break
            else:
                del npath[np], path[np]
                # ha!
                p = np

        return

        ```
    ###Assistant : Ensure self.location is on path

        If replace=False (default):
            - If location is already in path anywhere, do nothing.
            - Else:
              - If it's an egg and its parent directory is on path,
                insert just ahead of the parent.
              - Else: add to the end of path.
        If replace=True:
            - If location is already on path anywhere (not eggs)
              or higher priority than its parent (eggs)
              do nothing.
            - Else:
              - If it's an egg and its parent directory is on path,
                insert just ahead of the parent,
                removing any lower-priority entries.
              - Else: add it to the front of path.
        
    "
2659,"def serving_output(self, output):
        pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
        cross_attns = (
            tf.convert_to_tensor(output.cross_attentions)
            if self.config.output_attentions and self.config.add_cross_attention
            else None
        )

        return TFBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=output.hidden_states,
            past_key_values=pkv,
            hidden_states=hs,
            attentions=attns,
            cross_attentions=cross_attns,
        )


@add_start_docstrings(
    ,
    XGLM_START_DOCSTRING,
)","
    The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    ",20,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def serving_output(self, output):
        pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
        cross_attns = (
            tf.convert_to_tensor(output.cross_attentions)
            if self.config.output_attentions and self.config.add_cross_attention
            else None
        )

        return TFBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=output.hidden_states,
            past_key_values=pkv,
            hidden_states=hs,
            attentions=attns,
            cross_attentions=cross_attns,
        )


@add_start_docstrings(
    ,
    XGLM_START_DOCSTRING,
)
        ```
    ###Assistant : 
    The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    
    "
2660,"def _apply_func_to_list_of_partitions(cls, func, partitions, **kwargs):
        
        preprocessed_map_func = cls.preprocess_func(func)
        key_futures = RayWrapper.materialize(
            [
                partition.apply(preprocessed_map_func, **kwargs)
                for partition in partitions
            ]
        )
        gpu_managers = [partition.get_gpu_manager() for partition in partitions]
        return cls._create_partitions(key_futures, gpu_managers)
","
        Apply `func` to a list of remote partitions from `partitions`.

        Parameters
        ----------
        func : callable
            The function to apply.
        partitions : np.ndarray
            NumPy array with partitions.
        **kwargs : dict
            Additional keywords arguments to be passed in `func`.

        Returns
        -------
        np.ndarray
            A NumPy array of ``cuDFOnRayDataframePartition`` objects.

        Notes
        -----
        This preprocesses the `func` first before applying it to the partitions.
        ",59,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _apply_func_to_list_of_partitions(cls, func, partitions, **kwargs):
        
        preprocessed_map_func = cls.preprocess_func(func)
        key_futures = RayWrapper.materialize(
            [
                partition.apply(preprocessed_map_func, **kwargs)
                for partition in partitions
            ]
        )
        gpu_managers = [partition.get_gpu_manager() for partition in partitions]
        return cls._create_partitions(key_futures, gpu_managers)

        ```
    ###Assistant : 
        Apply `func` to a list of remote partitions from `partitions`.

        Parameters
        ----------
        func : callable
            The function to apply.
        partitions : np.ndarray
            NumPy array with partitions.
        **kwargs : dict
            Additional keywords arguments to be passed in `func`.

        Returns
        -------
        np.ndarray
            A NumPy array of ``cuDFOnRayDataframePartition`` objects.

        Notes
        -----
        This preprocesses the `func` first before applying it to the partitions.
        
    "
2661,"def _execute_impl(self, *args, **kwargs) -> ObjectRef:
        
        return self._deployment_function_handle.remote(
            *self._bound_args, **self._bound_kwargs
        )
","Executor of DeploymentNode getting called each time on dag.execute.

        The execute implementation is recursive, that is, the method nodes will
        receive whatever this method returns. We return a handle here so method
        node can directly call upon.
        ",37,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _execute_impl(self, *args, **kwargs) -> ObjectRef:
        
        return self._deployment_function_handle.remote(
            *self._bound_args, **self._bound_kwargs
        )

        ```
    ###Assistant : Executor of DeploymentNode getting called each time on dag.execute.

        The execute implementation is recursive, that is, the method nodes will
        receive whatever this method returns. We return a handle here so method
        node can directly call upon.
        
    "
2662,"def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs):
  
  if data_format is None:
    data_format = backend.image_data_format()
  img = array_to_img(x, data_format=data_format, scale=scale)
  if img.mode == 'RGBA' and (file_format == 'jpg' or file_format == 'jpeg'):
    warnings.warn('The JPG format does not support '
                  'RGBA images, converting to RGB.')
    img = img.convert('RGB')
  img.save(path, format=file_format, **kwargs)


@keras_export('keras.utils.load_img', 'keras.preprocessing.image.load_img')","Saves an image stored as a Numpy array to a path or file object.

  Args:
      path: Path or file object.
      x: Numpy array.
      data_format: Image data format, either ""channels_first"" or
        ""channels_last"".
      file_format: Optional file format override. If omitted, the format to use
        is determined from the filename extension. If a file object was used
        instead of a filename, this parameter should always be used.
      scale: Whether to rescale image values to be within `[0, 255]`.
      **kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
  ",82,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs):
  
  if data_format is None:
    data_format = backend.image_data_format()
  img = array_to_img(x, data_format=data_format, scale=scale)
  if img.mode == 'RGBA' and (file_format == 'jpg' or file_format == 'jpeg'):
    warnings.warn('The JPG format does not support '
                  'RGBA images, converting to RGB.')
    img = img.convert('RGB')
  img.save(path, format=file_format, **kwargs)


@keras_export('keras.utils.load_img', 'keras.preprocessing.image.load_img')
        ```
    ###Assistant : Saves an image stored as a Numpy array to a path or file object.

  Args:
      path: Path or file object.
      x: Numpy array.
      data_format: Image data format, either ""channels_first"" or
        ""channels_last"".
      file_format: Optional file format override. If omitted, the format to use
        is determined from the filename extension. If a file object was used
        instead of a filename, this parameter should always be used.
      scale: Whether to rescale image values to be within `[0, 255]`.
      **kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
  
    "
2663,"def log_commenting_changes(self, changes, revision):
        
        for comment in changes[""new_comments""]:
            comment.log_create(page_revision=revision, user=self.request.user)

        for comment in changes[""edited_comments""]:
            comment.log_edit(page_revision=revision, user=self.request.user)

        for comment in changes[""resolved_comments""]:
            comment.log_resolve(page_revision=revision, user=self.request.user)

        for comment in changes[""deleted_comments""]:
            comment.log_delete(page_revision=revision, user=self.request.user)

        for comment, replies in changes[""new_replies""]:
            for reply in replies:
                reply.log_create(page_revision=revision, user=self.request.user)

        for comment, replies in changes[""edited_replies""]:
            for reply in replies:
                reply.log_edit(page_revision=revision, user=self.request.user)

        for comment, replies in changes[""deleted_replies""]:
            for reply in replies:
                reply.log_delete(page_revision=revision, user=self.request.user)
","
        Generates log entries for any changes made to comments or replies.
        ",11,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def log_commenting_changes(self, changes, revision):
        
        for comment in changes[""new_comments""]:
            comment.log_create(page_revision=revision, user=self.request.user)

        for comment in changes[""edited_comments""]:
            comment.log_edit(page_revision=revision, user=self.request.user)

        for comment in changes[""resolved_comments""]:
            comment.log_resolve(page_revision=revision, user=self.request.user)

        for comment in changes[""deleted_comments""]:
            comment.log_delete(page_revision=revision, user=self.request.user)

        for comment, replies in changes[""new_replies""]:
            for reply in replies:
                reply.log_create(page_revision=revision, user=self.request.user)

        for comment, replies in changes[""edited_replies""]:
            for reply in replies:
                reply.log_edit(page_revision=revision, user=self.request.user)

        for comment, replies in changes[""deleted_replies""]:
            for reply in replies:
                reply.log_delete(page_revision=revision, user=self.request.user)

        ```
    ###Assistant : 
        Generates log entries for any changes made to comments or replies.
        
    "
2664,"def __new__(cls, stylename, **kwargs):
        
        # The ""class"" should have the _style_list attribute, which is a mapping
        # of style names to style classes.
        _list = stylename.replace("" "", """").split("","")
        _name = _list[0].lower()
        try:
            _cls = cls._style_list[_name]
        except KeyError as err:
            raise ValueError(f""Unknown style: {stylename}"") from err
        try:
            _args_pair = [cs.split(""="") for cs in _list[1:]]
            _args = {k: float(v) for k, v in _args_pair}
        except ValueError as err:
            raise ValueError(f""Incorrect style argument: {stylename}"") from err
        return _cls(**{**_args, **kwargs})
",Return the instance of the subclass with the given style name.,11,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __new__(cls, stylename, **kwargs):
        
        # The ""class"" should have the _style_list attribute, which is a mapping
        # of style names to style classes.
        _list = stylename.replace("" "", """").split("","")
        _name = _list[0].lower()
        try:
            _cls = cls._style_list[_name]
        except KeyError as err:
            raise ValueError(f""Unknown style: {stylename}"") from err
        try:
            _args_pair = [cs.split(""="") for cs in _list[1:]]
            _args = {k: float(v) for k, v in _args_pair}
        except ValueError as err:
            raise ValueError(f""Incorrect style argument: {stylename}"") from err
        return _cls(**{**_args, **kwargs})

        ```
    ###Assistant : Return the instance of the subclass with the given style name.
    "
2665,"def losses(self):
        
        collected_losses = []
        for layer in self._flatten_layers():
            # If any eager losses are present, we assume the model to be part of
            # an eager training loop (either a custom one or the one used when
            # `run_eagerly=True`) and so we always return just the eager losses.
            if layer._eager_losses:
                # Filter placeholder losses that may have been added by revived
                # layers.  (see base_layer_utils for details).
                if (
                    layer._eager_losses[0]
                    is not base_layer_utils.REVIVED_LOSS_PLACEHOLDER
                ):
                    collected_losses.extend(layer._eager_losses)
            else:
                collected_losses.extend(layer._losses)
            for regularizer in layer._callable_losses:
                loss_tensor = regularizer()
                if loss_tensor is not None:
                    collected_losses.append(loss_tensor)
        return collected_losses
","List of losses added using the `add_loss()` API.

        Variable regularization tensors are created when this property is
        accessed, so it is eager safe: accessing `losses` under a
        `tf.GradientTape` will propagate gradients back to the corresponding
        variables.

        Examples:

        >>> class MyLayer(tf.keras.layers.Layer):
        ...   def call(self, inputs):
        ...     self.add_loss(tf.abs(tf.reduce_mean(inputs)))
        ...     return inputs
        >>> l = MyLayer()
        >>> l(np.ones((10, 1)))
        >>> l.losses
        [1.0]

        >>> inputs = tf.keras.Input(shape=(10,))
        >>> x = tf.keras.layers.Dense(10)(inputs)
        >>> outputs = tf.keras.layers.Dense(1)(x)
        >>> model = tf.keras.Model(inputs, outputs)
        >>> # Activity regularization.
        >>> len(model.losses)
        0
        >>> model.add_loss(tf.abs(tf.reduce_mean(x)))
        >>> len(model.losses)
        1

        >>> inputs = tf.keras.Input(shape=(10,))
        >>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
        >>> x = d(inputs)
        >>> outputs = tf.keras.layers.Dense(1)(x)
        >>> model = tf.keras.Model(inputs, outputs)
        >>> # Weight regularization.
        >>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
        >>> model.losses
        [<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]

        Returns:
          A list of tensors.
        ",128,93,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def losses(self):
        
        collected_losses = []
        for layer in self._flatten_layers():
            # If any eager losses are present, we assume the model to be part of
            # an eager training loop (either a custom one or the one used when
            # `run_eagerly=True`) and so we always return just the eager losses.
            if layer._eager_losses:
                # Filter placeholder losses that may have been added by revived
                # layers.  (see base_layer_utils for details).
                if (
                    layer._eager_losses[0]
                    is not base_layer_utils.REVIVED_LOSS_PLACEHOLDER
                ):
                    collected_losses.extend(layer._eager_losses)
            else:
                collected_losses.extend(layer._losses)
            for regularizer in layer._callable_losses:
                loss_tensor = regularizer()
                if loss_tensor is not None:
                    collected_losses.append(loss_tensor)
        return collected_losses

        ```
    ###Assistant : List of losses added using the `add_loss()` API.

        Variable regularization tensors are created when this property is
        accessed, so it is eager safe: accessing `losses` under a
        `tf.GradientTape` will propagate gradients back to the corresponding
        variables.

        Examples:

        >>> class MyLayer(tf.keras.layers.Layer):
        ...   def call(self, inputs):
        ...     self.add_loss(tf.abs(tf.reduce_mean(inputs)))
        ...     return inputs
        >>> l = MyLayer()
        >>> l(np.ones((10, 1)))
        >>> l.losses
        [1.0]

        >>> inputs = tf.keras.Input(shape=(10,))
        >>> x = tf.keras.layers.Dense(10)(inputs)
        >>> outputs = tf.keras.layers.Dense(1)(x)
        >>> model = tf.keras.Model(inputs, outputs)
        >>> # Activity regularization.
        >>> len(model.losses)
        0
        >>> model.add_loss(tf.abs(tf.reduce_mean(x)))
        >>> len(model.losses)
        1

        >>> inputs = tf.keras.Input(shape=(10,))
        >>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
        >>> x = d(inputs)
        >>> outputs = tf.keras.layers.Dense(1)(x)
        >>> model = tf.keras.Model(inputs, outputs)
        >>> # Weight regularization.
        >>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
        >>> model.losses
        [<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]

        Returns:
          A list of tensors.
        
    "
2666,"def fit(self, X, y, Xy=None):
        
        self._validate_params()

        X, y = self._validate_data(X, y, y_numeric=True, multi_output=True)

        _normalize = _deprecate_normalize(
            self.normalize, default=True, estimator_name=self.__class__.__name__
        )

        alpha = getattr(self, ""alpha"", 0.0)
        if hasattr(self, ""n_nonzero_coefs""):
            alpha = 0.0  # n_nonzero_coefs parametrization takes priority
            max_iter = self.n_nonzero_coefs
        else:
            max_iter = self.max_iter

        if self.jitter is not None:
            rng = check_random_state(self.random_state)

            noise = rng.uniform(high=self.jitter, size=len(y))
            y = y + noise

        self._fit(
            X,
            y,
            max_iter=max_iter,
            alpha=alpha,
            fit_path=self.fit_path,
            normalize=_normalize,
            Xy=Xy,
        )

        return self

","Fit the model using X, y as training data.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data.

        y : array-like of shape (n_samples,) or (n_samples, n_targets)
            Target values.

        Xy : array-like of shape (n_samples,) or (n_samples, n_targets), \
                default=None
            Xy = np.dot(X.T, y) that can be precomputed. It is useful
            only when the Gram matrix is precomputed.

        Returns
        -------
        self : object
            Returns an instance of self.
        ",70,71,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y, Xy=None):
        
        self._validate_params()

        X, y = self._validate_data(X, y, y_numeric=True, multi_output=True)

        _normalize = _deprecate_normalize(
            self.normalize, default=True, estimator_name=self.__class__.__name__
        )

        alpha = getattr(self, ""alpha"", 0.0)
        if hasattr(self, ""n_nonzero_coefs""):
            alpha = 0.0  # n_nonzero_coefs parametrization takes priority
            max_iter = self.n_nonzero_coefs
        else:
            max_iter = self.max_iter

        if self.jitter is not None:
            rng = check_random_state(self.random_state)

            noise = rng.uniform(high=self.jitter, size=len(y))
            y = y + noise

        self._fit(
            X,
            y,
            max_iter=max_iter,
            alpha=alpha,
            fit_path=self.fit_path,
            normalize=_normalize,
            Xy=Xy,
        )

        return self


        ```
    ###Assistant : Fit the model using X, y as training data.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data.

        y : array-like of shape (n_samples,) or (n_samples, n_targets)
            Target values.

        Xy : array-like of shape (n_samples,) or (n_samples, n_targets), \
                default=None
            Xy = np.dot(X.T, y) that can be precomputed. It is useful
            only when the Gram matrix is precomputed.

        Returns
        -------
        self : object
            Returns an instance of self.
        
    "
2667,"def _decode_block_string(block_string):
        
        assert isinstance(block_string, str)

        ops = block_string.split('_')
        options = {}
        for op in ops:
            splits = re.split(r'(\d.*)', op)
            if len(splits) >= 2:
                key, value = splits[:2]
                options[key] = value

        # Check stride
        cond_1 = ('s' in options and len(options['s']) == 1)
        cond_2 = ((len(options['s']) == 2) and (options['s'][0] == options['s'][1]))
        assert (cond_1 or cond_2)

        return BlockArgs(kernel_size=int(options['k']),
                         num_repeat=int(options['r']),
                         input_filters=int(options['i']),
                         output_filters=int(options['o']),
                         expand_ratio=int(options['e']),
                         id_skip=('noskip' not in block_string),
                         se_ratio=float(options['se']) if 'se' in options else None,
                         stride=[int(options['s'][0])])
", Gets a block through a string notation of arguments. ,9,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _decode_block_string(block_string):
        
        assert isinstance(block_string, str)

        ops = block_string.split('_')
        options = {}
        for op in ops:
            splits = re.split(r'(\d.*)', op)
            if len(splits) >= 2:
                key, value = splits[:2]
                options[key] = value

        # Check stride
        cond_1 = ('s' in options and len(options['s']) == 1)
        cond_2 = ((len(options['s']) == 2) and (options['s'][0] == options['s'][1]))
        assert (cond_1 or cond_2)

        return BlockArgs(kernel_size=int(options['k']),
                         num_repeat=int(options['r']),
                         input_filters=int(options['i']),
                         output_filters=int(options['o']),
                         expand_ratio=int(options['e']),
                         id_skip=('noskip' not in block_string),
                         se_ratio=float(options['se']) if 'se' in options else None,
                         stride=[int(options['s'][0])])

        ```
    ###Assistant :  Gets a block through a string notation of arguments. 
    "
2668,"def copy(a, order='K', subok=False):
    
    return array(a, order=order, subok=subok, copy=True)

# Basic operations

","
    Return an array copy of the given object.

    Parameters
    ----------
    a : array_like
        Input data.
    order : {'C', 'F', 'A', 'K'}, optional
        Controls the memory layout of the copy. 'C' means C-order,
        'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
        'C' otherwise. 'K' means match the layout of `a` as closely
        as possible. (Note that this function and :meth:`ndarray.copy` are very
        similar, but have different default values for their order=
        arguments.)
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise the
        returned array will be forced to be a base-class array (defaults to False).

        .. versionadded:: 1.19.0

    Returns
    -------
    arr : ndarray
        Array interpretation of `a`.

    See Also
    --------
    ndarray.copy : Preferred method for creating an array copy

    Notes
    -----
    This is equivalent to:

    >>> np.array(a, copy=True)  #doctest: +SKIP

    Examples
    --------
    Create an array x, with a reference y and a copy z:

    >>> x = np.array([1, 2, 3])
    >>> y = x
    >>> z = np.copy(x)

    Note that, when we modify x, y changes, but not z:

    >>> x[0] = 10
    >>> x[0] == y[0]
    True
    >>> x[0] == z[0]
    False

    Note that, np.copy clears previously set WRITEABLE=False flag.

    >>> a = np.array([1, 2, 3])
    >>> a.flags[""WRITEABLE""] = False
    >>> b = np.copy(a)
    >>> b.flags[""WRITEABLE""]
    True
    >>> b[0] = 3
    >>> b
    array([3, 2, 3])

    Note that np.copy is a shallow copy and will not copy object
    elements within arrays. This is mainly important for arrays
    containing Python objects. The new array will contain the
    same object which may lead to surprises if that object can
    be modified (is mutable):

    >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
    >>> b = np.copy(a)
    >>> b[2][0] = 10
    >>> a
    array([1, 'm', list([10, 3, 4])], dtype=object)

    To ensure all elements within an ``object`` array are copied,
    use `copy.deepcopy`:

    >>> import copy
    >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
    >>> c = copy.deepcopy(a)
    >>> c[2][0] = 10
    >>> c
    array([1, 'm', list([10, 3, 4])], dtype=object)
    >>> a
    array([1, 'm', list([2, 3, 4])], dtype=object)

    ",340,12,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copy(a, order='K', subok=False):
    
    return array(a, order=order, subok=subok, copy=True)

# Basic operations


        ```
    ###Assistant : 
    Return an array copy of the given object.

    Parameters
    ----------
    a : array_like
        Input data.
    order : {'C', 'F', 'A', 'K'}, optional
        Controls the memory layout of the copy. 'C' means C-order,
        'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
        'C' otherwise. 'K' means match the layout of `a` as closely
        as possible. (Note that this function and :meth:`ndarray.copy` are very
        similar, but have different default values for their order=
        arguments.)
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise the
        returned array will be forced to be a base-class array (defaults to False).

        .. versionadded:: 1.19.0

    Returns
    -------
    arr : ndarray
        Array interpretation of `a`.

    See Also
    --------
    ndarray.copy : Preferred method for creating an array copy

    Notes
    -----
    This is equivalent to:

    >>> np.array(a, copy=True)  #doctest: +SKIP

    Examples
    --------
    Create an array x, with a reference y and a copy z:

    >>> x = np.array([1, 2, 3])
    >>> y = x
    >>> z = np.copy(x)

    Note that, when we modify x, y changes, but not z:

    >>> x[0] = 10
    >>> x[0] == y[0]
    True
    >>> x[0] == z[0]
    False

    Note that, np.copy clears previously set WRITEABLE=False flag.

    >>> a = np.array([1, 2, 3])
    >>> a.flags[""WRITEABLE""] = False
    >>> b = np.copy(a)
    >>> b.flags[""WRITEABLE""]
    True
    >>> b[0] = 3
    >>> b
    array([3, 2, 3])

    Note that np.copy is a shallow copy and will not copy object
    elements within arrays. This is mainly important for arrays
    containing Python objects. The new array will contain the
    same object which may lead to surprises if that object can
    be modified (is mutable):

    >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
    >>> b = np.copy(a)
    >>> b[2][0] = 10
    >>> a
    array([1, 'm', list([10, 3, 4])], dtype=object)

    To ensure all elements within an ``object`` array are copied,
    use `copy.deepcopy`:

    >>> import copy
    >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
    >>> c = copy.deepcopy(a)
    >>> c[2][0] = 10
    >>> c
    array([1, 'm', list([10, 3, 4])], dtype=object)
    >>> a
    array([1, 'm', list([2, 3, 4])], dtype=object)

    
    "
2669,"def test_context_placement_group():
    driver_code = 
    proc = run_string_as_driver_nonblocking(driver_code)
","
import ray
from ray.data.context import DatasetContext
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
from ray._private.test_utils import placement_group_assert_no_leak

ray.init(num_cpus=1)

context = DatasetContext.get_current()
# This placement group will take up all cores of the local cluster.
placement_group = ray.util.placement_group(
    name=""core_hog"",
    strategy=""SPREAD"",
    bundles=[
        {""CPU"": 1},
    ],
)
ray.get(placement_group.ready())
context.scheduling_strategy = PlacementGroupSchedulingStrategy(placement_group)
pipe = ray.data.range(100, parallelism=2) \
    .window(blocks_per_window=1) \
    .map(lambda x: x + 1)
assert pipe.take_all() == list(range(1, 101))
placement_group_assert_no_leak([placement_group])
ray.shutdown()
    ",64,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_context_placement_group():
    driver_code = 
    proc = run_string_as_driver_nonblocking(driver_code)

        ```
    ###Assistant : 
import ray
from ray.data.context import DatasetContext
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
from ray._private.test_utils import placement_group_assert_no_leak

ray.init(num_cpus=1)

context = DatasetContext.get_current()
# This placement group will take up all cores of the local cluster.
placement_group = ray.util.placement_group(
    name=""core_hog"",
    strategy=""SPREAD"",
    bundles=[
        {""CPU"": 1},
    ],
)
ray.get(placement_group.ready())
context.scheduling_strategy = PlacementGroupSchedulingStrategy(placement_group)
pipe = ray.data.range(100, parallelism=2) \
    .window(blocks_per_window=1) \
    .map(lambda x: x + 1)
assert pipe.take_all() == list(range(1, 101))
placement_group_assert_no_leak([placement_group])
ray.shutdown()
    
    "
2670,"def reorder_categories(self, new_categories, ordered=None):
        
        if set(self.dtype.categories) != set(new_categories):
            raise ValueError(
                ""items in new_categories are not the same as in old categories""
            )
        return self.set_categories(new_categories, ordered=ordered)
","
        Reorder categories as specified in new_categories.

        `new_categories` need to include all old categories and no new category
        items.

        Parameters
        ----------
        new_categories : Index-like
           The categories in new order.
        ordered : bool, optional
           Whether or not the categorical is treated as a ordered categorical.
           If not given, do not change the ordered information.

        Returns
        -------
        cat : Categorical
            Categorical with reordered categories.

        Raises
        ------
        ValueError
            If the new categories do not contain all old category items or any
            new ones

        See Also
        --------
        rename_categories : Rename categories.
        add_categories : Add new categories.
        remove_categories : Remove the specified categories.
        remove_unused_categories : Remove categories which are not used.
        set_categories : Set the categories to the specified ones.
        ",114,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def reorder_categories(self, new_categories, ordered=None):
        
        if set(self.dtype.categories) != set(new_categories):
            raise ValueError(
                ""items in new_categories are not the same as in old categories""
            )
        return self.set_categories(new_categories, ordered=ordered)

        ```
    ###Assistant : 
        Reorder categories as specified in new_categories.

        `new_categories` need to include all old categories and no new category
        items.

        Parameters
        ----------
        new_categories : Index-like
           The categories in new order.
        ordered : bool, optional
           Whether or not the categorical is treated as a ordered categorical.
           If not given, do not change the ordered information.

        Returns
        -------
        cat : Categorical
            Categorical with reordered categories.

        Raises
        ------
        ValueError
            If the new categories do not contain all old category items or any
            new ones

        See Also
        --------
        rename_categories : Rename categories.
        add_categories : Add new categories.
        remove_categories : Remove the specified categories.
        remove_unused_categories : Remove categories which are not used.
        set_categories : Set the categories to the specified ones.
        
    "
2671,"def test_get_member_list_no_permission_former_member_with_at_token(self):
        
        # create a room, invite the user and the user joins
        room_id = self.helper.create_room_as(""@alice:red"")
        self.helper.invite(room_id, ""@alice:red"", self.user_id)
        self.helper.join(room_id, self.user_id)

        # sync to get an at token
        channel = self.make_request(""GET"", ""/sync"")
        self.assertEqual(200, channel.code)
        sync_token = channel.json_body[""next_batch""]

        # check that the user can see the member list to start with
        channel = self.make_request(
            ""GET"", ""/rooms/%s/members?at=%s"" % (room_id, sync_token)
        )
        self.assertEqual(200, channel.code, msg=channel.result[""body""])

        # ban the user (Note: the user is actually allowed to see this event and
        # state so that they know they're banned!)
        self.helper.change_membership(room_id, ""@alice:red"", self.user_id, ""ban"")

        # invite a third user and let them join
        self.helper.invite(room_id, ""@alice:red"", ""@bob:red"")
        self.helper.join(room_id, ""@bob:red"")

        # now, with the original user, sync again to get a new at token
        channel = self.make_request(""GET"", ""/sync"")
        self.assertEqual(200, channel.code)
        sync_token = channel.json_body[""next_batch""]

        # check the user can no longer see the updated member list
        channel = self.make_request(
            ""GET"", ""/rooms/%s/members?at=%s"" % (room_id, sync_token)
        )
        self.assertEqual(403, channel.code, msg=channel.result[""body""])
","
        Tests that a former member of the room can not get the member list
        (in the case that they use an at token).
        ",23,150,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_member_list_no_permission_former_member_with_at_token(self):
        
        # create a room, invite the user and the user joins
        room_id = self.helper.create_room_as(""@alice:red"")
        self.helper.invite(room_id, ""@alice:red"", self.user_id)
        self.helper.join(room_id, self.user_id)

        # sync to get an at token
        channel = self.make_request(""GET"", ""/sync"")
        self.assertEqual(200, channel.code)
        sync_token = channel.json_body[""next_batch""]

        # check that the user can see the member list to start with
        channel = self.make_request(
            ""GET"", ""/rooms/%s/members?at=%s"" % (room_id, sync_token)
        )
        self.assertEqual(200, channel.code, msg=channel.result[""body""])

        # ban the user (Note: the user is actually allowed to see this event and
        # state so that they know they're banned!)
        self.helper.change_membership(room_id, ""@alice:red"", self.user_id, ""ban"")

        # invite a third user and let them join
        self.helper.invite(room_id, ""@alice:red"", ""@bob:red"")
        self.helper.join(room_id, ""@bob:red"")

        # now, with the original user, sync again to get a new at token
        channel = self.make_request(""GET"", ""/sync"")
        self.assertEqual(200, channel.code)
        sync_token = channel.json_body[""next_batch""]

        # check the user can no longer see the updated member list
        channel = self.make_request(
            ""GET"", ""/rooms/%s/members?at=%s"" % (room_id, sync_token)
        )
        self.assertEqual(403, channel.code, msg=channel.result[""body""])

        ```
    ###Assistant : 
        Tests that a former member of the room can not get the member list
        (in the case that they use an at token).
        
    "
2672,"def _keep_original_ws(s, tag_s):
    
    return ''.join(
        c if tag_c == "" "" and c.isspace() else tag_c
        for c, tag_c in zip(s, tag_s)
    )


",Replace whitespace with the original whitespace characters in `s`,9,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _keep_original_ws(s, tag_s):
    
    return ''.join(
        c if tag_c == "" "" and c.isspace() else tag_c
        for c, tag_c in zip(s, tag_s)
    )



        ```
    ###Assistant : Replace whitespace with the original whitespace characters in `s`
    "
2673,"def unregister_cmap(name):
    
    cmap = _colormaps.get(name, None)
    _colormaps.unregister(name)
    return cmap

","
    Remove a colormap recognized by :func:`get_cmap`.

    You may not remove built-in colormaps.

    If the named colormap is not registered, returns with no error, raises
    if you try to de-register a default colormap.

    .. warning::

      Colormap names are currently a shared namespace that may be used
      by multiple packages. Use `unregister_cmap` only if you know you
      have registered that name before. In particular, do not
      unregister just in case to clean the name before registering a
      new colormap.

    Parameters
    ----------
    name : str
        The name of the colormap to be un-registered

    Returns
    -------
    ColorMap or None
        If the colormap was registered, return it if not return `None`

    Raises
    ------
    ValueError
       If you try to de-register a default built-in colormap.
    ",118,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unregister_cmap(name):
    
    cmap = _colormaps.get(name, None)
    _colormaps.unregister(name)
    return cmap


        ```
    ###Assistant : 
    Remove a colormap recognized by :func:`get_cmap`.

    You may not remove built-in colormaps.

    If the named colormap is not registered, returns with no error, raises
    if you try to de-register a default colormap.

    .. warning::

      Colormap names are currently a shared namespace that may be used
      by multiple packages. Use `unregister_cmap` only if you know you
      have registered that name before. In particular, do not
      unregister just in case to clean the name before registering a
      new colormap.

    Parameters
    ----------
    name : str
        The name of the colormap to be un-registered

    Returns
    -------
    ColorMap or None
        If the colormap was registered, return it if not return `None`

    Raises
    ------
    ValueError
       If you try to de-register a default built-in colormap.
    
    "
2674,"def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
    
    magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
    f.seek(0)
    for i in range(MAGIC_NUMBER_MAX_LENGTH):
        compression = MAGIC_NUMBER_TO_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
        if compression is not None:  # TODO(QL): raise an error for .tar.gz files as in _get_extraction_protocol
            return compression
        compression = MAGIC_NUMBER_TO_UNSUPPORTED_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
        if compression is not None:
            raise NotImplementedError(f""Compression protocol '{compression}' not implemented."")

",read the magic number from a file-like object and return the compression protocol,13,53,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
    
    magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
    f.seek(0)
    for i in range(MAGIC_NUMBER_MAX_LENGTH):
        compression = MAGIC_NUMBER_TO_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
        if compression is not None:  # TODO(QL): raise an error for .tar.gz files as in _get_extraction_protocol
            return compression
        compression = MAGIC_NUMBER_TO_UNSUPPORTED_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
        if compression is not None:
            raise NotImplementedError(f""Compression protocol '{compression}' not implemented."")


        ```
    ###Assistant : read the magic number from a file-like object and return the compression protocol
    "
2675,"def _get_veths(net_data):
    
    if isinstance(net_data, dict):
        net_data = list(net_data.items())
    nics = salt.utils.odict.OrderedDict()
    current_nic = salt.utils.odict.OrderedDict()
    no_names = True
    for item in net_data:
        if item and isinstance(item, dict):
            item = list(item.items())[0]
        # skip LXC configuration comment lines, and play only with tuples conf
        elif isinstance(item, str):
            # deal with reflection of commented lxc configs
            sitem = item.strip()
            if sitem.startswith(""#"") or not sitem:
                continue
            elif ""="" in item:
                item = tuple(a.strip() for a in item.split(""="", 1))
        if item[0] == ""lxc.network.type"":
            current_nic = salt.utils.odict.OrderedDict()
        if item[0] == ""lxc.network.name"":
            no_names = False
            nics[item[1].strip()] = current_nic
        current_nic[item[0].strip()] = item[1].strip()
    # if not ethernet card name has been collected, assuming we collected
    # data for eth0
    if no_names and current_nic:
        nics[DEFAULT_NIC] = current_nic
    return nics

","
    Parse the nic setup inside lxc conf tuples back to a dictionary indexed by
    network interface
    ",16,118,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_veths(net_data):
    
    if isinstance(net_data, dict):
        net_data = list(net_data.items())
    nics = salt.utils.odict.OrderedDict()
    current_nic = salt.utils.odict.OrderedDict()
    no_names = True
    for item in net_data:
        if item and isinstance(item, dict):
            item = list(item.items())[0]
        # skip LXC configuration comment lines, and play only with tuples conf
        elif isinstance(item, str):
            # deal with reflection of commented lxc configs
            sitem = item.strip()
            if sitem.startswith(""#"") or not sitem:
                continue
            elif ""="" in item:
                item = tuple(a.strip() for a in item.split(""="", 1))
        if item[0] == ""lxc.network.type"":
            current_nic = salt.utils.odict.OrderedDict()
        if item[0] == ""lxc.network.name"":
            no_names = False
            nics[item[1].strip()] = current_nic
        current_nic[item[0].strip()] = item[1].strip()
    # if not ethernet card name has been collected, assuming we collected
    # data for eth0
    if no_names and current_nic:
        nics[DEFAULT_NIC] = current_nic
    return nics


        ```
    ###Assistant : 
    Parse the nic setup inside lxc conf tuples back to a dictionary indexed by
    network interface
    
    "
2676,"def start_stdout_logging() -> None:
    
    if '_stdout_' in _handlers:
        return

    handler = StreamHandler(sys.stdout)
    handler.setFormatter(_StdoutFormatter())

    _handlers['_stdout_'] = handler
    _root_logger.addHandler(handler)
","
    Register the stdout handler.

    This function should be invoked on importing nni.

    It is safe to call it multiple times.
    ",20,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def start_stdout_logging() -> None:
    
    if '_stdout_' in _handlers:
        return

    handler = StreamHandler(sys.stdout)
    handler.setFormatter(_StdoutFormatter())

    _handlers['_stdout_'] = handler
    _root_logger.addHandler(handler)

        ```
    ###Assistant : 
    Register the stdout handler.

    This function should be invoked on importing nni.

    It is safe to call it multiple times.
    
    "
2677,"def get_staffing_plan_detail(designation, company, offer_date):
	detail = frappe.db.sql(
		,
		(designation, company, offer_date),
		as_dict=1,
	)

	return frappe._dict(detail[0]) if (detail and detail[0].parent) else None


@frappe.whitelist()","
		SELECT DISTINCT spd.parent,
			sp.from_date as from_date,
			sp.to_date as to_date,
			sp.name,
			sum(spd.vacancies) as vacancies,
			spd.designation
		FROM `tabStaffing Plan Detail` spd, `tabStaffing Plan` sp
		WHERE
			sp.docstatus=1
			AND spd.designation=%s
			AND sp.company=%s
			AND spd.parent = sp.name
			AND %s between sp.from_date and sp.to_date
	",38,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_staffing_plan_detail(designation, company, offer_date):
	detail = frappe.db.sql(
		,
		(designation, company, offer_date),
		as_dict=1,
	)

	return frappe._dict(detail[0]) if (detail and detail[0].parent) else None


@frappe.whitelist()
        ```
    ###Assistant : 
		SELECT DISTINCT spd.parent,
			sp.from_date as from_date,
			sp.to_date as to_date,
			sp.name,
			sum(spd.vacancies) as vacancies,
			spd.designation
		FROM `tabStaffing Plan Detail` spd, `tabStaffing Plan` sp
		WHERE
			sp.docstatus=1
			AND spd.designation=%s
			AND sp.company=%s
			AND spd.parent = sp.name
			AND %s between sp.from_date and sp.to_date
	
    "
2678,"def test_display_name(self) -> None:
        
        evaluator = self._get_evaluator({""body"": ""foo bar baz""})

        condition = {
            ""kind"": ""contains_display_name"",
        }

        # Blank names are skipped.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", """"))

        # Check a display name that doesn't match.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", ""not found""))

        # Check a display name which matches.
        self.assertTrue(evaluator.matches(condition, ""@user:test"", ""foo""))

        # A display name that matches, but not a full word does not result in a match.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", ""ba""))

        # A display name should not be interpreted as a regular expression.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", ""ba[rz]""))

        # A display name with spaces should work fine.
        self.assertTrue(evaluator.matches(condition, ""@user:test"", ""foo bar""))
",Check for a matching display name in the body of the event.,12,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_display_name(self) -> None:
        
        evaluator = self._get_evaluator({""body"": ""foo bar baz""})

        condition = {
            ""kind"": ""contains_display_name"",
        }

        # Blank names are skipped.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", """"))

        # Check a display name that doesn't match.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", ""not found""))

        # Check a display name which matches.
        self.assertTrue(evaluator.matches(condition, ""@user:test"", ""foo""))

        # A display name that matches, but not a full word does not result in a match.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", ""ba""))

        # A display name should not be interpreted as a regular expression.
        self.assertFalse(evaluator.matches(condition, ""@user:test"", ""ba[rz]""))

        # A display name with spaces should work fine.
        self.assertTrue(evaluator.matches(condition, ""@user:test"", ""foo bar""))

        ```
    ###Assistant : Check for a matching display name in the body of the event.
    "
2679,"def test_readlink_not_a_link(file, source):
    
    with pytest.raises(SaltInvocationError) as exc:
        file.readlink(path=source)
    assert ""A valid link was not specified"" in exc.value.message

","
    Test readlink where the path is not a link
    Should throw a SaltInvocationError
    ",13,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_readlink_not_a_link(file, source):
    
    with pytest.raises(SaltInvocationError) as exc:
        file.readlink(path=source)
    assert ""A valid link was not specified"" in exc.value.message


        ```
    ###Assistant : 
    Test readlink where the path is not a link
    Should throw a SaltInvocationError
    
    "
2680,"def get_periodic_data(entry, filters):
	
	periodic_data = {}
	for d in entry:
		period = get_period(d.posting_date, filters)
		bal_qty = 0

		# if period against item does not exist yet, instantiate it
		# insert existing balance dict against period, and add/subtract to it
		if periodic_data.get(d.item_code) and not periodic_data.get(d.item_code).get(period):
			previous_balance = periodic_data[d.item_code][""balance""].copy()
			periodic_data[d.item_code][period] = previous_balance

		if d.voucher_type == ""Stock Reconciliation"":
			if periodic_data.get(d.item_code) and periodic_data.get(d.item_code).get(""balance"").get(
				d.warehouse
			):
				bal_qty = periodic_data[d.item_code][""balance""][d.warehouse]

			qty_diff = d.qty_after_transaction - bal_qty
		else:
			qty_diff = d.actual_qty

		if filters[""value_quantity""] == ""Quantity"":
			value = qty_diff
		else:
			value = d.stock_value_difference

		# period-warehouse wise balance
		periodic_data.setdefault(d.item_code, {}).setdefault(""balance"", {}).setdefault(d.warehouse, 0.0)
		periodic_data.setdefault(d.item_code, {}).setdefault(period, {}).setdefault(d.warehouse, 0.0)

		periodic_data[d.item_code][""balance""][d.warehouse] += value
		periodic_data[d.item_code][period][d.warehouse] = periodic_data[d.item_code][""balance""][
			d.warehouse
		]

	return periodic_data

","Structured as:
	Item 1
	        - Balance (updated and carried forward):
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jun 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jul 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	Item 2
	        - Balance (updated and carried forward):
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jun 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jul 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	",118,106,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_periodic_data(entry, filters):
	
	periodic_data = {}
	for d in entry:
		period = get_period(d.posting_date, filters)
		bal_qty = 0

		# if period against item does not exist yet, instantiate it
		# insert existing balance dict against period, and add/subtract to it
		if periodic_data.get(d.item_code) and not periodic_data.get(d.item_code).get(period):
			previous_balance = periodic_data[d.item_code][""balance""].copy()
			periodic_data[d.item_code][period] = previous_balance

		if d.voucher_type == ""Stock Reconciliation"":
			if periodic_data.get(d.item_code) and periodic_data.get(d.item_code).get(""balance"").get(
				d.warehouse
			):
				bal_qty = periodic_data[d.item_code][""balance""][d.warehouse]

			qty_diff = d.qty_after_transaction - bal_qty
		else:
			qty_diff = d.actual_qty

		if filters[""value_quantity""] == ""Quantity"":
			value = qty_diff
		else:
			value = d.stock_value_difference

		# period-warehouse wise balance
		periodic_data.setdefault(d.item_code, {}).setdefault(""balance"", {}).setdefault(d.warehouse, 0.0)
		periodic_data.setdefault(d.item_code, {}).setdefault(period, {}).setdefault(d.warehouse, 0.0)

		periodic_data[d.item_code][""balance""][d.warehouse] += value
		periodic_data[d.item_code][period][d.warehouse] = periodic_data[d.item_code][""balance""][
			d.warehouse
		]

	return periodic_data


        ```
    ###Assistant : Structured as:
	Item 1
	        - Balance (updated and carried forward):
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jun 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jul 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	Item 2
	        - Balance (updated and carried forward):
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jun 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	        - Jul 2021 (sum of warehouse quantities used in report)
	                        - Warehouse A : bal_qty/value
	                        - Warehouse B : bal_qty/value
	
    "
2681,"def shuffle(*arrays, random_state=None, n_samples=None):
    
    return resample(
        *arrays, replace=False, n_samples=n_samples, random_state=random_state
    )

","Shuffle arrays or sparse matrices in a consistent way.

    This is a convenience alias to ``resample(*arrays, replace=False)`` to do
    random permutations of the collections.

    Parameters
    ----------
    *arrays : sequence of indexable data-structures
        Indexable data-structures can be arrays, lists, dataframes or scipy
        sparse matrices with consistent first dimension.

    random_state : int, RandomState instance or None, default=None
        Determines random number generation for shuffling
        the data.
        Pass an int for reproducible results across multiple function calls.
        See :term:`Glossary <random_state>`.

    n_samples : int, default=None
        Number of samples to generate. If left to None this is
        automatically set to the first dimension of the arrays.  It should
        not be larger than the length of arrays.

    Returns
    -------
    shuffled_arrays : sequence of indexable data-structures
        Sequence of shuffled copies of the collections. The original arrays
        are not impacted.

    See Also
    --------
    resample : Resample arrays or sparse matrices in a consistent way.

    Examples
    --------
    It is possible to mix sparse and dense arrays in the same run::

      >>> import numpy as np
      >>> X = np.array([[1., 0.], [2., 1.], [0., 0.]])
      >>> y = np.array([0, 1, 2])

      >>> from scipy.sparse import coo_matrix
      >>> X_sparse = coo_matrix(X)

      >>> from sklearn.utils import shuffle
      >>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
      >>> X
      array([[0., 0.],
             [2., 1.],
             [1., 0.]])

      >>> X_sparse
      <3x2 sparse matrix of type '<... 'numpy.float64'>'
          with 3 stored elements in Compressed Sparse Row format>

      >>> X_sparse.toarray()
      array([[0., 0.],
             [2., 1.],
             [1., 0.]])

      >>> y
      array([2, 1, 0])

      >>> shuffle(y, n_samples=2, random_state=0)
      array([0, 1])
    ",248,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def shuffle(*arrays, random_state=None, n_samples=None):
    
    return resample(
        *arrays, replace=False, n_samples=n_samples, random_state=random_state
    )


        ```
    ###Assistant : Shuffle arrays or sparse matrices in a consistent way.

    This is a convenience alias to ``resample(*arrays, replace=False)`` to do
    random permutations of the collections.

    Parameters
    ----------
    *arrays : sequence of indexable data-structures
        Indexable data-structures can be arrays, lists, dataframes or scipy
        sparse matrices with consistent first dimension.

    random_state : int, RandomState instance or None, default=None
        Determines random number generation for shuffling
        the data.
        Pass an int for reproducible results across multiple function calls.
        See :term:`Glossary <random_state>`.

    n_samples : int, default=None
        Number of samples to generate. If left to None this is
        automatically set to the first dimension of the arrays.  It should
        not be larger than the length of arrays.

    Returns
    -------
    shuffled_arrays : sequence of indexable data-structures
        Sequence of shuffled copies of the collections. The original arrays
        are not impacted.

    See Also
    --------
    resample : Resample arrays or sparse matrices in a consistent way.

    Examples
    --------
    It is possible to mix sparse and dense arrays in the same run::

      >>> import numpy as np
      >>> X = np.array([[1., 0.], [2., 1.], [0., 0.]])
      >>> y = np.array([0, 1, 2])

      >>> from scipy.sparse import coo_matrix
      >>> X_sparse = coo_matrix(X)

      >>> from sklearn.utils import shuffle
      >>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
      >>> X
      array([[0., 0.],
             [2., 1.],
             [1., 0.]])

      >>> X_sparse
      <3x2 sparse matrix of type '<... 'numpy.float64'>'
          with 3 stored elements in Compressed Sparse Row format>

      >>> X_sparse.toarray()
      array([[0., 0.],
             [2., 1.],
             [1., 0.]])

      >>> y
      array([2, 1, 0])

      >>> shuffle(y, n_samples=2, random_state=0)
      array([0, 1])
    
    "
2682,"def check_changes(self, args, results):  # type: (SanityConfig, Results) -> None
        
        integration_targets = list(walk_integration_targets())
        module_targets = list(walk_module_targets())

        integration_targets_by_name = dict((target.name, target) for target in integration_targets)
        module_names_by_path = dict((target.path, target.module) for target in module_targets)

        disabled_targets = []
        unstable_targets = []
        unsupported_targets = []

        for command in [command for command in args.metadata.change_description.focused_command_targets if 'integration' in command]:
            for target in args.metadata.change_description.focused_command_targets[command]:
                if self.DISABLED in integration_targets_by_name[target].aliases:
                    disabled_targets.append(target)
                elif self.UNSTABLE in integration_targets_by_name[target].aliases:
                    unstable_targets.append(target)
                elif self.UNSUPPORTED in integration_targets_by_name[target].aliases:
                    unsupported_targets.append(target)

        untested_modules = []

        for path in args.metadata.change_description.no_integration_paths:
            module = module_names_by_path.get(path)

            if module:
                untested_modules.append(module)

        comments = [
            self.format_comment(self.TEMPLATE_DISABLED, disabled_targets),
            self.format_comment(self.TEMPLATE_UNSTABLE, unstable_targets),
            self.format_comment(self.TEMPLATE_UNSUPPORTED, unsupported_targets),
            self.format_comment(self.TEMPLATE_UNTESTED, untested_modules),
        ]

        comments = [comment for comment in comments if comment]

        labels = dict(
            needs_tests=bool(untested_modules),
            disabled_tests=bool(disabled_targets),
            unstable_tests=bool(unstable_targets),
            unsupported_tests=bool(unsupported_targets),
        )

        results.comments += comments
        results.labels.update(labels)
",Check changes and store results in the provided result dictionary.,10,118,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_changes(self, args, results):  # type: (SanityConfig, Results) -> None
        
        integration_targets = list(walk_integration_targets())
        module_targets = list(walk_module_targets())

        integration_targets_by_name = dict((target.name, target) for target in integration_targets)
        module_names_by_path = dict((target.path, target.module) for target in module_targets)

        disabled_targets = []
        unstable_targets = []
        unsupported_targets = []

        for command in [command for command in args.metadata.change_description.focused_command_targets if 'integration' in command]:
            for target in args.metadata.change_description.focused_command_targets[command]:
                if self.DISABLED in integration_targets_by_name[target].aliases:
                    disabled_targets.append(target)
                elif self.UNSTABLE in integration_targets_by_name[target].aliases:
                    unstable_targets.append(target)
                elif self.UNSUPPORTED in integration_targets_by_name[target].aliases:
                    unsupported_targets.append(target)

        untested_modules = []

        for path in args.metadata.change_description.no_integration_paths:
            module = module_names_by_path.get(path)

            if module:
                untested_modules.append(module)

        comments = [
            self.format_comment(self.TEMPLATE_DISABLED, disabled_targets),
            self.format_comment(self.TEMPLATE_UNSTABLE, unstable_targets),
            self.format_comment(self.TEMPLATE_UNSUPPORTED, unsupported_targets),
            self.format_comment(self.TEMPLATE_UNTESTED, untested_modules),
        ]

        comments = [comment for comment in comments if comment]

        labels = dict(
            needs_tests=bool(untested_modules),
            disabled_tests=bool(disabled_targets),
            unstable_tests=bool(unstable_targets),
            unsupported_tests=bool(unsupported_targets),
        )

        results.comments += comments
        results.labels.update(labels)

        ```
    ###Assistant : Check changes and store results in the provided result dictionary.
    "
2683,"def test_overlap_first(business_client, setup_before_upload, show_overlap_first):
    c = business_client
    config = dict(
        title='test_overlap_first',
        is_published=True,
        maximum_annotations=1,
        show_overlap_first=show_overlap_first,
        sampling=""Uniform sampling"",
        label_config=
    )

    project = make_project(config, business_client.user)

    annotation_result = json.dumps([{
        'from_name': 'text_class',
        'to_name': 'text',
        'type': 'choices',
        'value': {'choices': ['class_A']}
    }])

    num_tasks = 1000
    overlap_cohort_percentage = 1

    # set up tasks overlap
    setup_after_upload = True
    if setup_before_upload:
        r = c.patch(
            f'/api/projects/{project.id}/',
            data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
            content_type='application/json'
        )
        assert r.status_code == 200
        setup_after_upload = False

    # create tasks
    tasks = []
    for i in range(num_tasks):
        tasks.append({'data': {'text': f'this is {str(i)}'}})
    r = business_client.post(
        f'/api/projects/{project.id}/tasks/bulk/', data=json.dumps(tasks), content_type='application/json')
    assert r.status_code == 201

    if setup_after_upload:
        r = c.patch(
            f'/api/projects/{project.id}/',
            data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
            content_type='application/json'
        )
        assert r.status_code == 200

    expected_tasks_with_overlap = int(overlap_cohort_percentage / 100. * num_tasks)

    assert Task.objects.filter(Q(project_id=project.id) & Q(overlap__gt=1)).count() == expected_tasks_with_overlap
","
            <View>
              <Text name=""text"" value=""$text""></Text>
              <Choices name=""text_class"" choice=""single"">
                <Choice value=""class_A""></Choice>
                <Choice value=""class_B""></Choice>
              </Choices>
            </View>",13,122,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_overlap_first(business_client, setup_before_upload, show_overlap_first):
    c = business_client
    config = dict(
        title='test_overlap_first',
        is_published=True,
        maximum_annotations=1,
        show_overlap_first=show_overlap_first,
        sampling=""Uniform sampling"",
        label_config=
    )

    project = make_project(config, business_client.user)

    annotation_result = json.dumps([{
        'from_name': 'text_class',
        'to_name': 'text',
        'type': 'choices',
        'value': {'choices': ['class_A']}
    }])

    num_tasks = 1000
    overlap_cohort_percentage = 1

    # set up tasks overlap
    setup_after_upload = True
    if setup_before_upload:
        r = c.patch(
            f'/api/projects/{project.id}/',
            data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
            content_type='application/json'
        )
        assert r.status_code == 200
        setup_after_upload = False

    # create tasks
    tasks = []
    for i in range(num_tasks):
        tasks.append({'data': {'text': f'this is {str(i)}'}})
    r = business_client.post(
        f'/api/projects/{project.id}/tasks/bulk/', data=json.dumps(tasks), content_type='application/json')
    assert r.status_code == 201

    if setup_after_upload:
        r = c.patch(
            f'/api/projects/{project.id}/',
            data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
            content_type='application/json'
        )
        assert r.status_code == 200

    expected_tasks_with_overlap = int(overlap_cohort_percentage / 100. * num_tasks)

    assert Task.objects.filter(Q(project_id=project.id) & Q(overlap__gt=1)).count() == expected_tasks_with_overlap

        ```
    ###Assistant : 
            <View>
              <Text name=""text"" value=""$text""></Text>
              <Choices name=""text_class"" choice=""single"">
                <Choice value=""class_A""></Choice>
                <Choice value=""class_B""></Choice>
              </Choices>
            </View>
    "
2684,"def svd_flip(u, v, u_based_decision=True):
    
    if u_based_decision:
        # columns of u, rows of v
        max_abs_cols = np.argmax(np.abs(u), axis=0)
        signs = np.sign(u[max_abs_cols, range(u.shape[1])])
        u *= signs
        v *= signs[:, np.newaxis]
    else:
        # rows of v, columns of u
        max_abs_rows = np.argmax(np.abs(v), axis=1)
        signs = np.sign(v[range(v.shape[0]), max_abs_rows])
        u *= signs
        v *= signs[:, np.newaxis]
    return u, v

","Sign correction to ensure deterministic output from SVD.

    Adjusts the columns of u and the rows of v such that the loadings in the
    columns in u that are largest in absolute value are always positive.

    Parameters
    ----------
    u : ndarray
        Parameters u and v are the output of `linalg.svd` or
        :func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
        dimensions so one can compute `np.dot(u * s, v)`.

    v : ndarray
        Parameters u and v are the output of `linalg.svd` or
        :func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
        dimensions so one can compute `np.dot(u * s, v)`.
        The input v should really be called vt to be consistent with scipy's
        output.

    u_based_decision : bool, default=True
        If True, use the columns of u as the basis for sign flipping.
        Otherwise, use the rows of v. The choice of which variable to base the
        decision on is generally algorithm dependent.

    Returns
    -------
    u_adjusted : ndarray
        Array u with adjusted columns and the same dimensions as u.

    v_adjusted : ndarray
        Array v with adjusted rows and the same dimensions as v.
    ",171,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def svd_flip(u, v, u_based_decision=True):
    
    if u_based_decision:
        # columns of u, rows of v
        max_abs_cols = np.argmax(np.abs(u), axis=0)
        signs = np.sign(u[max_abs_cols, range(u.shape[1])])
        u *= signs
        v *= signs[:, np.newaxis]
    else:
        # rows of v, columns of u
        max_abs_rows = np.argmax(np.abs(v), axis=1)
        signs = np.sign(v[range(v.shape[0]), max_abs_rows])
        u *= signs
        v *= signs[:, np.newaxis]
    return u, v


        ```
    ###Assistant : Sign correction to ensure deterministic output from SVD.

    Adjusts the columns of u and the rows of v such that the loadings in the
    columns in u that are largest in absolute value are always positive.

    Parameters
    ----------
    u : ndarray
        Parameters u and v are the output of `linalg.svd` or
        :func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
        dimensions so one can compute `np.dot(u * s, v)`.

    v : ndarray
        Parameters u and v are the output of `linalg.svd` or
        :func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
        dimensions so one can compute `np.dot(u * s, v)`.
        The input v should really be called vt to be consistent with scipy's
        output.

    u_based_decision : bool, default=True
        If True, use the columns of u as the basis for sign flipping.
        Otherwise, use the rows of v. The choice of which variable to base the
        decision on is generally algorithm dependent.

    Returns
    -------
    u_adjusted : ndarray
        Array u with adjusted columns and the same dimensions as u.

    v_adjusted : ndarray
        Array v with adjusted rows and the same dimensions as v.
    
    "
2685,"def extra_action_out_fn(self) -> Dict[str, TensorType]:
        
        extra_fetches = {}
        # Action-logp and action-prob.
        if self._sampled_action_logp is not None:
            extra_fetches[SampleBatch.ACTION_PROB] = self._sampled_action_prob
            extra_fetches[SampleBatch.ACTION_LOGP] = self._sampled_action_logp
        # Action-dist inputs.
        if self._dist_inputs is not None:
            extra_fetches[SampleBatch.ACTION_DIST_INPUTS] = self._dist_inputs
        return extra_fetches
","Extra values to fetch and return from compute_actions().

        By default we return action probability/log-likelihood info
        and action distribution inputs (if present).

        Returns:
             Dict[str, TensorType]: An extra fetch-dict to be passed to and
                returned from the compute_actions() call.
        ",37,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def extra_action_out_fn(self) -> Dict[str, TensorType]:
        
        extra_fetches = {}
        # Action-logp and action-prob.
        if self._sampled_action_logp is not None:
            extra_fetches[SampleBatch.ACTION_PROB] = self._sampled_action_prob
            extra_fetches[SampleBatch.ACTION_LOGP] = self._sampled_action_logp
        # Action-dist inputs.
        if self._dist_inputs is not None:
            extra_fetches[SampleBatch.ACTION_DIST_INPUTS] = self._dist_inputs
        return extra_fetches

        ```
    ###Assistant : Extra values to fetch and return from compute_actions().

        By default we return action probability/log-likelihood info
        and action distribution inputs (if present).

        Returns:
             Dict[str, TensorType]: An extra fetch-dict to be passed to and
                returned from the compute_actions() call.
        
    "
2686,"async def cleanup_finished_triggers(self):
        
        for trigger_id, details in list(self.triggers.items()):
            if details[""task""].done():
                # Check to see if it exited for good reasons
                saved_exc = None
                try:
                    result = details[""task""].result()
                except (asyncio.CancelledError, SystemExit, KeyboardInterrupt):
                    # These are ""expected"" exceptions and we stop processing here
                    # If we don't, then the system requesting a trigger be removed -
                    # which turns into CancelledError - results in a failure.
                    del self.triggers[trigger_id]
                    continue
                except BaseException as e:
                    # This is potentially bad, so log it.
                    self.log.exception(""Trigger %s exited with error %s"", details[""name""], e)
                    saved_exc = e
                else:
                    # See if they foolishly returned a TriggerEvent
                    if isinstance(result, TriggerEvent):
                        self.log.error(
                            ""Trigger %s returned a TriggerEvent rather than yielding it"", details[""name""]
                        )
                # See if this exited without sending an event, in which case
                # any task instances depending on it need to be failed
                if details[""events""] == 0:
                    self.log.error(
                        ""Trigger %s exited without sending an event. Dependent tasks will be failed."",
                        details[""name""],
                    )
                    self.failed_triggers.append((trigger_id, saved_exc))
                del self.triggers[trigger_id]
            await asyncio.sleep(0)
","
        Go through all trigger tasks (coroutines) and clean up entries for
        ones that have exited, optionally warning users if the exit was
        not normal.
        ",24,162,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def cleanup_finished_triggers(self):
        
        for trigger_id, details in list(self.triggers.items()):
            if details[""task""].done():
                # Check to see if it exited for good reasons
                saved_exc = None
                try:
                    result = details[""task""].result()
                except (asyncio.CancelledError, SystemExit, KeyboardInterrupt):
                    # These are ""expected"" exceptions and we stop processing here
                    # If we don't, then the system requesting a trigger be removed -
                    # which turns into CancelledError - results in a failure.
                    del self.triggers[trigger_id]
                    continue
                except BaseException as e:
                    # This is potentially bad, so log it.
                    self.log.exception(""Trigger %s exited with error %s"", details[""name""], e)
                    saved_exc = e
                else:
                    # See if they foolishly returned a TriggerEvent
                    if isinstance(result, TriggerEvent):
                        self.log.error(
                            ""Trigger %s returned a TriggerEvent rather than yielding it"", details[""name""]
                        )
                # See if this exited without sending an event, in which case
                # any task instances depending on it need to be failed
                if details[""events""] == 0:
                    self.log.error(
                        ""Trigger %s exited without sending an event. Dependent tasks will be failed."",
                        details[""name""],
                    )
                    self.failed_triggers.append((trigger_id, saved_exc))
                del self.triggers[trigger_id]
            await asyncio.sleep(0)

        ```
    ###Assistant : 
        Go through all trigger tasks (coroutines) and clean up entries for
        ones that have exited, optionally warning users if the exit was
        not normal.
        
    "
2687,"def get_global_travel_time(self):
        
        gtt = 0
        for entity in self.tripinfo:
            gtt += self.get_duration(entity, default=0.0)
        for entity in self.personinfo:
            gtt += self.get_duration(entity, default=0.0)
        return gtt

    ###########################################################################
    # ROUTING
","
        Returns the global travel time computed from SUMO tripinfo data.

        The functions process_tripinfo_file() needs to be called in advance
        to initialize the data structures required.
        ",25,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_global_travel_time(self):
        
        gtt = 0
        for entity in self.tripinfo:
            gtt += self.get_duration(entity, default=0.0)
        for entity in self.personinfo:
            gtt += self.get_duration(entity, default=0.0)
        return gtt

    ###########################################################################
    # ROUTING

        ```
    ###Assistant : 
        Returns the global travel time computed from SUMO tripinfo data.

        The functions process_tripinfo_file() needs to be called in advance
        to initialize the data structures required.
        
    "
2688,"def post(self, url, data=None, json=None, **kwargs):
        r

        return self.request(""POST"", url, data=data, json=json, **kwargs)
","Sends a POST request. Returns :class:`Response` object.

        :param url: URL for the new :class:`Request` object.
        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
            object to send in the body of the :class:`Request`.
        :param json: (optional) json to send in the body of the :class:`Request`.
        :param \*\*kwargs: Optional arguments that ``request`` takes.
        :rtype: requests.Response
        ",55,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def post(self, url, data=None, json=None, **kwargs):
        r

        return self.request(""POST"", url, data=data, json=json, **kwargs)

        ```
    ###Assistant : Sends a POST request. Returns :class:`Response` object.

        :param url: URL for the new :class:`Request` object.
        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
            object to send in the body of the :class:`Request`.
        :param json: (optional) json to send in the body of the :class:`Request`.
        :param \*\*kwargs: Optional arguments that ``request`` takes.
        :rtype: requests.Response
        
    "
2689,"def __getitem__(self, idx):
        # type: (int) -> HPackHdrEntry
        
        assert idx >= 0
        if idx > type(self)._static_entries_last_idx:
            idx -= type(self)._static_entries_last_idx + 1
            if idx >= len(self._dynamic_table):
                raise KeyError(
                    'EINVAL: idx: out-of-bound read: {}; maximum index: {}'.format(idx, len(self._dynamic_table))  # noqa: E501
                )
            return self._dynamic_table[idx]
        return type(self)._static_entries[idx]
","Gets an element from the header tables (static or dynamic indifferently)

        :param int idx: the index number of the entry to retrieve. If the index
        value is superior to the last index of the static entry table, then the
        dynamic entry type is requested, following the procedure described in
        RFC 7541 par2.3.3
        :return: HPackHdrEntry: the entry defined at this requested index. If the entry does not exist, KeyError is  # noqa: E501
          raised
        :raises: KeyError, AssertionError
        ",76,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __getitem__(self, idx):
        # type: (int) -> HPackHdrEntry
        
        assert idx >= 0
        if idx > type(self)._static_entries_last_idx:
            idx -= type(self)._static_entries_last_idx + 1
            if idx >= len(self._dynamic_table):
                raise KeyError(
                    'EINVAL: idx: out-of-bound read: {}; maximum index: {}'.format(idx, len(self._dynamic_table))  # noqa: E501
                )
            return self._dynamic_table[idx]
        return type(self)._static_entries[idx]

        ```
    ###Assistant : Gets an element from the header tables (static or dynamic indifferently)

        :param int idx: the index number of the entry to retrieve. If the index
        value is superior to the last index of the static entry table, then the
        dynamic entry type is requested, following the procedure described in
        RFC 7541 par2.3.3
        :return: HPackHdrEntry: the entry defined at this requested index. If the entry does not exist, KeyError is  # noqa: E501
          raised
        :raises: KeyError, AssertionError
        
    "
2690,"def test_pagination(self):
        
        parent = Parent.objects.create(name=""anything"")
        for i in range(1, 31):
            Child.objects.create(name=""name %s"" % i, parent=parent)
            Child.objects.create(name=""filtered %s"" % i, parent=parent)

        request = self.factory.get(""/child/"")
        request.user = self.superuser

        # Test default queryset
        m = ChildAdmin(Child, custom_site)
        cl = m.get_changelist_instance(request)
        self.assertEqual(cl.queryset.count(), 60)
        self.assertEqual(cl.paginator.count, 60)
        self.assertEqual(list(cl.paginator.page_range), [1, 2, 3, 4, 5, 6])

        # Test custom queryset
        m = FilteredChildAdmin(Child, custom_site)
        cl = m.get_changelist_instance(request)
        self.assertEqual(cl.queryset.count(), 30)
        self.assertEqual(cl.paginator.count, 30)
        self.assertEqual(list(cl.paginator.page_range), [1, 2, 3])
","
        Regression tests for #12893: Pagination in admins changelist doesn't
        use queryset set by modeladmin.
        ",14,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_pagination(self):
        
        parent = Parent.objects.create(name=""anything"")
        for i in range(1, 31):
            Child.objects.create(name=""name %s"" % i, parent=parent)
            Child.objects.create(name=""filtered %s"" % i, parent=parent)

        request = self.factory.get(""/child/"")
        request.user = self.superuser

        # Test default queryset
        m = ChildAdmin(Child, custom_site)
        cl = m.get_changelist_instance(request)
        self.assertEqual(cl.queryset.count(), 60)
        self.assertEqual(cl.paginator.count, 60)
        self.assertEqual(list(cl.paginator.page_range), [1, 2, 3, 4, 5, 6])

        # Test custom queryset
        m = FilteredChildAdmin(Child, custom_site)
        cl = m.get_changelist_instance(request)
        self.assertEqual(cl.queryset.count(), 30)
        self.assertEqual(cl.paginator.count, 30)
        self.assertEqual(list(cl.paginator.page_range), [1, 2, 3])

        ```
    ###Assistant : 
        Regression tests for #12893: Pagination in admins changelist doesn't
        use queryset set by modeladmin.
        
    "
2691,"def transform(self, X):
        
        if self.solver == ""lsqr"":
            raise NotImplementedError(
                ""transform not implemented for 'lsqr' solver (use 'svd' or 'eigen').""
            )
        check_is_fitted(self)

        X = self._validate_data(X, reset=False)
        if self.solver == ""svd"":
            X_new = np.dot(X - self.xbar_, self.scalings_)
        elif self.solver == ""eigen"":
            X_new = np.dot(X, self.scalings_)

        return X_new[:, : self._max_components]
","Project data to maximize class separation.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Input data.

        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components) or \
            (n_samples, min(rank, n_components))
            Transformed data. In the case of the 'svd' solver, the shape
            is (n_samples, min(rank, n_components)).
        ",46,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transform(self, X):
        
        if self.solver == ""lsqr"":
            raise NotImplementedError(
                ""transform not implemented for 'lsqr' solver (use 'svd' or 'eigen').""
            )
        check_is_fitted(self)

        X = self._validate_data(X, reset=False)
        if self.solver == ""svd"":
            X_new = np.dot(X - self.xbar_, self.scalings_)
        elif self.solver == ""eigen"":
            X_new = np.dot(X, self.scalings_)

        return X_new[:, : self._max_components]

        ```
    ###Assistant : Project data to maximize class separation.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Input data.

        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components) or \
            (n_samples, min(rank, n_components))
            Transformed data. In the case of the 'svd' solver, the shape
            is (n_samples, min(rank, n_components)).
        
    "
2692,"async def test_heater_cooler_hvac_mode_vs_hvac_action(hass, utcnow):
    
    helper = await setup_test_component(hass, create_heater_cooler_service)

    # Simulate that current temperature is above target temp
    # Heating might be on, but hvac_action currently 'off'
    await helper.async_update(
        ServicesTypes.HEATER_COOLER,
        {
            CharacteristicsTypes.TEMPERATURE_CURRENT: 22,
            CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
            CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.IDLE,
            CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
            CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
        },
    )

    state = await helper.poll_and_get_state()
    assert state.state == ""heat""
    assert state.attributes[""hvac_action""] == ""idle""

    # Simulate that current temperature is below target temp
    # Heating might be on and hvac_action currently 'heat'
    await helper.async_update(
        ServicesTypes.HEATER_COOLER,
        {
            CharacteristicsTypes.TEMPERATURE_CURRENT: 19,
            CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
            CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.HEATING,
            CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
            CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
        },
    )

    state = await helper.poll_and_get_state()
    assert state.state == ""heat""
    assert state.attributes[""hvac_action""] == ""heating""

",Check that we haven't conflated hvac_mode and hvac_action.,8,101,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_heater_cooler_hvac_mode_vs_hvac_action(hass, utcnow):
    
    helper = await setup_test_component(hass, create_heater_cooler_service)

    # Simulate that current temperature is above target temp
    # Heating might be on, but hvac_action currently 'off'
    await helper.async_update(
        ServicesTypes.HEATER_COOLER,
        {
            CharacteristicsTypes.TEMPERATURE_CURRENT: 22,
            CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
            CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.IDLE,
            CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
            CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
        },
    )

    state = await helper.poll_and_get_state()
    assert state.state == ""heat""
    assert state.attributes[""hvac_action""] == ""idle""

    # Simulate that current temperature is below target temp
    # Heating might be on and hvac_action currently 'heat'
    await helper.async_update(
        ServicesTypes.HEATER_COOLER,
        {
            CharacteristicsTypes.TEMPERATURE_CURRENT: 19,
            CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
            CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.HEATING,
            CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
            CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
        },
    )

    state = await helper.poll_and_get_state()
    assert state.state == ""heat""
    assert state.attributes[""hvac_action""] == ""heating""


        ```
    ###Assistant : Check that we haven't conflated hvac_mode and hvac_action.
    "
2693,"def not_in_timeout(cls, last_triggered, timeout):
        
        return (
            last_triggered is None
            or timeout is None
            or (time.time() - last_triggered > timeout)
        )
",Checks if current error lies not in timeout after last trigger (potential reset of connection).,15,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def not_in_timeout(cls, last_triggered, timeout):
        
        return (
            last_triggered is None
            or timeout is None
            or (time.time() - last_triggered > timeout)
        )

        ```
    ###Assistant : Checks if current error lies not in timeout after last trigger (potential reset of connection).
    "
2694,"def get_rescored_finished(self, n_best=None):
        
        # if we never actually finished, force one
        if not self.finished:
            self.outputs[-1][0] = self.eos
            self.finished.append(
                _HypothesisTail(
                    timestep=len(self.outputs) - 1,
                    hypid=0,
                    score=self.all_scores[-1][0],
                    tokenid=self.outputs[-1][0],
                    token_score=self.token_scores[0, -1]
                    if self.token_scores is not None
                    else None,
                    token_rank=self.token_ranks[0, -1]
                    if self.token_ranks is not None
                    else None,
                )
            )

        rescored_finished = []
        for finished_item in self.finished:
            current_length = finished_item.timestep + 1
            # these weights are from Google NMT paper
            length_penalty = math.pow((1 + current_length) / 6, self.length_penalty)
            rescored_finished.append(
                _HypothesisTail(
                    timestep=finished_item.timestep,
                    hypid=finished_item.hypid,
                    score=finished_item.score / length_penalty,
                    tokenid=finished_item.tokenid,
                    token_score=finished_item.token_score,
                    token_rank=finished_item.token_rank,
                )
            )

        # Note: beam size is almost always pretty small, so sorting is cheap enough
        srted = sorted(rescored_finished, key=attrgetter('score'), reverse=True)

        if n_best is not None:
            srted = srted[:n_best]

        n_best_list = []
        for hyp in srted:
            hyp_data = self._get_hyp_from_finished(hyp)
            token_ids = self._get_pretty_hypothesis(hyp_data)
            token_metadata = (
                self._get_pretty_token_metadata(hyp_data) if self.verbose else None
            )
            n_best_list.append((token_ids, hyp.score, token_metadata))

        # check that there is at least one finished candidate
        # and assert that each of them contains only one EOS
        assert (
            len(n_best_list) >= 1
        ), f'TreeSearch returned {len(n_best_list)} candidates, must be >= 1'
        for (pred, score, _) in n_best_list:
            assert (pred == self.eos).sum() == 1, (
                f'TreeSearch returned a finalized hypo with multiple end tokens '
                f'with score {score.item():.2f}'
            )

        return n_best_list

","
        Return finished hypotheses according to adjusted scores.

        Score adjustment is done according to the Google NMT paper, which
        penalizes long utterances.

        :param n_best:
            number of finalized hypotheses to return

        :return:
            list of (tokens, score, token_metadata) 3-tuples, in sorted order, where:
              - tokens is a tensor of token ids
              - score is the adjusted log probability of the entire utterance
              - token_metadata dictionary:
                    token_logprobs -> a tensor of conditional log probabilities of tokens
                    token_ranks -> a tensor of ranks of tokens in vocabulator, by probability, when sampled
        ",86,201,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_rescored_finished(self, n_best=None):
        
        # if we never actually finished, force one
        if not self.finished:
            self.outputs[-1][0] = self.eos
            self.finished.append(
                _HypothesisTail(
                    timestep=len(self.outputs) - 1,
                    hypid=0,
                    score=self.all_scores[-1][0],
                    tokenid=self.outputs[-1][0],
                    token_score=self.token_scores[0, -1]
                    if self.token_scores is not None
                    else None,
                    token_rank=self.token_ranks[0, -1]
                    if self.token_ranks is not None
                    else None,
                )
            )

        rescored_finished = []
        for finished_item in self.finished:
            current_length = finished_item.timestep + 1
            # these weights are from Google NMT paper
            length_penalty = math.pow((1 + current_length) / 6, self.length_penalty)
            rescored_finished.append(
                _HypothesisTail(
                    timestep=finished_item.timestep,
                    hypid=finished_item.hypid,
                    score=finished_item.score / length_penalty,
                    tokenid=finished_item.tokenid,
                    token_score=finished_item.token_score,
                    token_rank=finished_item.token_rank,
                )
            )

        # Note: beam size is almost always pretty small, so sorting is cheap enough
        srted = sorted(rescored_finished, key=attrgetter('score'), reverse=True)

        if n_best is not None:
            srted = srted[:n_best]

        n_best_list = []
        for hyp in srted:
            hyp_data = self._get_hyp_from_finished(hyp)
            token_ids = self._get_pretty_hypothesis(hyp_data)
            token_metadata = (
                self._get_pretty_token_metadata(hyp_data) if self.verbose else None
            )
            n_best_list.append((token_ids, hyp.score, token_metadata))

        # check that there is at least one finished candidate
        # and assert that each of them contains only one EOS
        assert (
            len(n_best_list) >= 1
        ), f'TreeSearch returned {len(n_best_list)} candidates, must be >= 1'
        for (pred, score, _) in n_best_list:
            assert (pred == self.eos).sum() == 1, (
                f'TreeSearch returned a finalized hypo with multiple end tokens '
                f'with score {score.item():.2f}'
            )

        return n_best_list


        ```
    ###Assistant : 
        Return finished hypotheses according to adjusted scores.

        Score adjustment is done according to the Google NMT paper, which
        penalizes long utterances.

        :param n_best:
            number of finalized hypotheses to return

        :return:
            list of (tokens, score, token_metadata) 3-tuples, in sorted order, where:
              - tokens is a tensor of token ids
              - score is the adjusted log probability of the entire utterance
              - token_metadata dictionary:
                    token_logprobs -> a tensor of conditional log probabilities of tokens
                    token_ranks -> a tensor of ranks of tokens in vocabulator, by probability, when sampled
        
    "
2695,"def quantile(self, q=0.5, **kwargs):
        
        return self._downsample(""quantile"", q=q, **kwargs)

","
        Return value at the given quantile.

        Parameters
        ----------
        q : float or array-like, default 0.5 (50% quantile)

        Returns
        -------
        DataFrame or Series
            Quantile of values within each group.

        See Also
        --------
        Series.quantile
            Return a series, where the index is q and the values are the quantiles.
        DataFrame.quantile
            Return a DataFrame, where the columns are the columns of self,
            and the values are the quantiles.
        DataFrameGroupBy.quantile
            Return a DataFrame, where the coulmns are groupby columns,
            and the values are its quantiles.
        ",80,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def quantile(self, q=0.5, **kwargs):
        
        return self._downsample(""quantile"", q=q, **kwargs)


        ```
    ###Assistant : 
        Return value at the given quantile.

        Parameters
        ----------
        q : float or array-like, default 0.5 (50% quantile)

        Returns
        -------
        DataFrame or Series
            Quantile of values within each group.

        See Also
        --------
        Series.quantile
            Return a series, where the index is q and the values are the quantiles.
        DataFrame.quantile
            Return a DataFrame, where the columns are the columns of self,
            and the values are the quantiles.
        DataFrameGroupBy.quantile
            Return a DataFrame, where the coulmns are groupby columns,
            and the values are its quantiles.
        
    "
2696,"def current_columns(self):
        
        return copy.deepcopy(self.custcols) #deepcopy to prevent users from changing it
","
        Return the currently defined custom columns

        Return the currently defined custom columns including the ones that haven't
        yet been created. It is a dict of dicts defined as follows:
            custcols[lookup_name] = {
                    'label': lookup_name,
                    'name': column_heading,
                    'datatype': datatype,
                    'display': display,
                    'normalized': None,
                    'colnum': an integer used internally,
                    'is_multiple': is_multiple,
                }
        Columns that already exist will have additional attributes that this class
        doesn't use. See calibre.library.field_metadata.add_custom_field() for the
        complete list.
        ",69,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def current_columns(self):
        
        return copy.deepcopy(self.custcols) #deepcopy to prevent users from changing it

        ```
    ###Assistant : 
        Return the currently defined custom columns

        Return the currently defined custom columns including the ones that haven't
        yet been created. It is a dict of dicts defined as follows:
            custcols[lookup_name] = {
                    'label': lookup_name,
                    'name': column_heading,
                    'datatype': datatype,
                    'display': display,
                    'normalized': None,
                    'colnum': an integer used internally,
                    'is_multiple': is_multiple,
                }
        Columns that already exist will have additional attributes that this class
        doesn't use. See calibre.library.field_metadata.add_custom_field() for the
        complete list.
        
    "
2697,"def transform_vector(self, vector):
        
        return Vector(
            (vector.x + self.offset[0]) * self.scale[0],
            (vector.y + self.offset[1]) * self.scale[1],
        )
","
        Transforms the given vector into the coordinate space of the final image.

        Use this to find out where a point on the source image would end up in the
        final image after cropping/resizing has been performed.

        Returns a new vector.
        ",40,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def transform_vector(self, vector):
        
        return Vector(
            (vector.x + self.offset[0]) * self.scale[0],
            (vector.y + self.offset[1]) * self.scale[1],
        )

        ```
    ###Assistant : 
        Transforms the given vector into the coordinate space of the final image.

        Use this to find out where a point on the source image would end up in the
        final image after cropping/resizing has been performed.

        Returns a new vector.
        
    "
2698,"def _load_from_file(module_path):
    
    from imp import PY_SOURCE, load_module

    imported = None
    if module_path:
        with open(module_path, 'r') as openfile:
            imported = load_module(""mod"", openfile, module_path, ('imported', 'r', PY_SOURCE))
    return imported

","
    Load a python module from its absolute filesystem path
    ",9,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _load_from_file(module_path):
    
    from imp import PY_SOURCE, load_module

    imported = None
    if module_path:
        with open(module_path, 'r') as openfile:
            imported = load_module(""mod"", openfile, module_path, ('imported', 'r', PY_SOURCE))
    return imported


        ```
    ###Assistant : 
    Load a python module from its absolute filesystem path
    
    "
2699,"def _alter_column_type_sql(self, table, old_field, new_field, new_type):
        
        if not hasattr(old_field, ""dim"") or not hasattr(new_field, ""dim""):
            return super()._alter_column_type_sql(table, old_field, new_field, new_type)

        if old_field.dim == 2 and new_field.dim == 3:
            sql_alter = self.sql_alter_column_to_3d
        elif old_field.dim == 3 and new_field.dim == 2:
            sql_alter = self.sql_alter_column_to_2d
        else:
            sql_alter = self.sql_alter_column_type
        return (
            (
                sql_alter
                % {
                    ""column"": self.quote_name(new_field.column),
                    ""type"": new_type,
                },
                [],
            ),
            [],
        )
","
        Special case when dimension changed.
        ",5,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _alter_column_type_sql(self, table, old_field, new_field, new_type):
        
        if not hasattr(old_field, ""dim"") or not hasattr(new_field, ""dim""):
            return super()._alter_column_type_sql(table, old_field, new_field, new_type)

        if old_field.dim == 2 and new_field.dim == 3:
            sql_alter = self.sql_alter_column_to_3d
        elif old_field.dim == 3 and new_field.dim == 2:
            sql_alter = self.sql_alter_column_to_2d
        else:
            sql_alter = self.sql_alter_column_type
        return (
            (
                sql_alter
                % {
                    ""column"": self.quote_name(new_field.column),
                    ""type"": new_type,
                },
                [],
            ),
            [],
        )

        ```
    ###Assistant : 
        Special case when dimension changed.
        
    "
2700,"def format_command(self) -> str:
        
        command = 'ansible-test %s' % self.command

        if self.test:
            command += ' --test %s' % self.test

        if self.python_version:
            command += ' --python %s' % self.python_version

        return command
",Return a string representing the CLI command associated with the test failure.,12,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def format_command(self) -> str:
        
        command = 'ansible-test %s' % self.command

        if self.test:
            command += ' --test %s' % self.test

        if self.python_version:
            command += ' --python %s' % self.python_version

        return command

        ```
    ###Assistant : Return a string representing the CLI command associated with the test failure.
    "
2701,"def act(self):
        
        obs = self.observation
        reply = {'text': INVALID, 'id': self.getID(), 'episode_done': False}
        if obs is None or obs['text'] == DO_NOT_RETRIEVE:
            return Message(reply)

        # construct the search query
        labels = obs.get('labels', obs.get('eval_labels', None))
        search_query = self.construct_search_query(labels)
        if (
            self.opt['min_num_search_words'] > 0
            and len(search_query[0].split()) <= self.opt['min_num_search_words']
        ):
            return Message(reply)

        # retrieve
        self.search_engine.set_search_queries(search_query)
        retrieved, _ = self.search_engine.retrieve_and_score(self.dummy)
        all_docs = [d.get_tokenization_str() for d in retrieved[0]]  # batched

        # Find the right doc
        best_f1, best_doc, best_doc_idx = self.get_best_doc(all_docs, labels)
        if best_doc:
            assert best_doc_idx is not None
            reply['knowledge'] = f'{TOKEN_KNOWLEDGE}{best_doc}{TOKEN_END_KNOWLEDGE}'
            reply['f1_overlap'] = best_f1
            reply['text'] = labels[0]
            reply['retrieved_docs'] = all_docs
            reply['gold_doc'] = all_docs[best_doc_idx]
            reply['search_query'] = search_query[0]
        return Message(reply)

","
        Search for overlap with the observation label.

        Return the best fitting document. A document is valid if the f1 is above the
        threshold AND the f1 is less than 1.0 AND the target label is not in the
        document.
        ",39,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def act(self):
        
        obs = self.observation
        reply = {'text': INVALID, 'id': self.getID(), 'episode_done': False}
        if obs is None or obs['text'] == DO_NOT_RETRIEVE:
            return Message(reply)

        # construct the search query
        labels = obs.get('labels', obs.get('eval_labels', None))
        search_query = self.construct_search_query(labels)
        if (
            self.opt['min_num_search_words'] > 0
            and len(search_query[0].split()) <= self.opt['min_num_search_words']
        ):
            return Message(reply)

        # retrieve
        self.search_engine.set_search_queries(search_query)
        retrieved, _ = self.search_engine.retrieve_and_score(self.dummy)
        all_docs = [d.get_tokenization_str() for d in retrieved[0]]  # batched

        # Find the right doc
        best_f1, best_doc, best_doc_idx = self.get_best_doc(all_docs, labels)
        if best_doc:
            assert best_doc_idx is not None
            reply['knowledge'] = f'{TOKEN_KNOWLEDGE}{best_doc}{TOKEN_END_KNOWLEDGE}'
            reply['f1_overlap'] = best_f1
            reply['text'] = labels[0]
            reply['retrieved_docs'] = all_docs
            reply['gold_doc'] = all_docs[best_doc_idx]
            reply['search_query'] = search_query[0]
        return Message(reply)


        ```
    ###Assistant : 
        Search for overlap with the observation label.

        Return the best fitting document. A document is valid if the f1 is above the
        threshold AND the f1 is less than 1.0 AND the target label is not in the
        document.
        
    "
2702,"def print_help(self):
        
        help_text = f
        console.print(text=help_text, menu=""Forex - Quantitative Analysis"")
","Print help[cmds]
   pick        pick target column for analysis[/cmds]

[param]Pair: [/param]{self.ticker}
[param]Target Column: [/param]{self.target}
[cmds]
[info]Statistics:[/info]
    summary     brief summary statistics of loaded pair.
    normality   normality statistics and tests
    unitroot    unit root test for stationarity (ADF, KPSS)
[info]Plots:[/info]
    line        line plot of selected target
    hist        histogram with density plot
    cdf         cumulative distribution function
    bw          box and whisker plot
    acf         (partial) auto-correlation function differentials of prices
    qqplot      residuals against standard normal curve
[info]Rolling Metrics:[/info]
    rolling     rolling mean and std deviation of prices
    spread      rolling variance and std deviation of prices
    quantile    rolling median and quantile of prices
    skew        rolling skewness of distribution of prices
    kurtosis    rolling kurtosis of distribution of prices
[info]Risk:[/info]
    var         display value at risk
    es          display expected shortfall
[info]Other:[/info]
    raw         print raw data
    decompose   decomposition in cyclic-trend, season, and residuals of prices
    cusum       detects abrupt changes using cumulative sum algorithm of prices[/cmds]
        ",142,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_help(self):
        
        help_text = f
        console.print(text=help_text, menu=""Forex - Quantitative Analysis"")

        ```
    ###Assistant : Print help[cmds]
   pick        pick target column for analysis[/cmds]

[param]Pair: [/param]{self.ticker}
[param]Target Column: [/param]{self.target}
[cmds]
[info]Statistics:[/info]
    summary     brief summary statistics of loaded pair.
    normality   normality statistics and tests
    unitroot    unit root test for stationarity (ADF, KPSS)
[info]Plots:[/info]
    line        line plot of selected target
    hist        histogram with density plot
    cdf         cumulative distribution function
    bw          box and whisker plot
    acf         (partial) auto-correlation function differentials of prices
    qqplot      residuals against standard normal curve
[info]Rolling Metrics:[/info]
    rolling     rolling mean and std deviation of prices
    spread      rolling variance and std deviation of prices
    quantile    rolling median and quantile of prices
    skew        rolling skewness of distribution of prices
    kurtosis    rolling kurtosis of distribution of prices
[info]Risk:[/info]
    var         display value at risk
    es          display expected shortfall
[info]Other:[/info]
    raw         print raw data
    decompose   decomposition in cyclic-trend, season, and residuals of prices
    cusum       detects abrupt changes using cumulative sum algorithm of prices[/cmds]
        
    "
2703,"def chain(self, klass=None):
        
        obj = self.clone()
        if klass and obj.__class__ != klass:
            obj.__class__ = klass
        if not obj.filter_is_sticky:
            obj.used_aliases = set()
        obj.filter_is_sticky = False
        if hasattr(obj, ""_setup_query""):
            obj._setup_query()
        return obj
","
        Return a copy of the current Query that's ready for another operation.
        The klass argument changes the type of the Query, e.g. UpdateQuery.
        ",23,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def chain(self, klass=None):
        
        obj = self.clone()
        if klass and obj.__class__ != klass:
            obj.__class__ = klass
        if not obj.filter_is_sticky:
            obj.used_aliases = set()
        obj.filter_is_sticky = False
        if hasattr(obj, ""_setup_query""):
            obj._setup_query()
        return obj

        ```
    ###Assistant : 
        Return a copy of the current Query that's ready for another operation.
        The klass argument changes the type of the Query, e.g. UpdateQuery.
        
    "
2704,"def run_test_gbm_non_number_inputs(tmpdir, backend_config):
    
    input_features = [binary_feature(), category_feature(encoder={""reduce_output"": ""sum""})]
    output_feature = binary_feature()
    output_features = [output_feature]

    csv_filename = os.path.join(tmpdir, ""training.csv"")
    dataset_filename = generate_data(input_features, output_features, csv_filename, num_examples=100)

    config = {
        MODEL_TYPE: ""gbm"",
        ""input_features"": input_features,
        ""output_features"": output_features,
        TRAINER: {""num_boost_round"": 2},
    }

    model = LudwigModel(config, backend=backend_config)
    _, _, output_directory = model.train(
        dataset=dataset_filename,
        output_directory=tmpdir,
        skip_save_processed_input=True,
        skip_save_progress=True,
        skip_save_unprocessed_output=True,
        skip_save_log=True,
    )
    model.load(os.path.join(tmpdir, ""api_experiment_run"", ""model""))
    preds, _ = model.predict(dataset=dataset_filename, output_directory=output_directory)

    prob_col = preds[output_feature[""name""] + ""_probabilities""]
    if backend_config[""type""] == ""ray"":
        prob_col = prob_col.compute()
    assert len(prob_col.iloc[0]) == 2
    assert prob_col.apply(sum).mean() == pytest.approx(1.0)

",Test that the GBM model can train and predict with non-number inputs.,12,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def run_test_gbm_non_number_inputs(tmpdir, backend_config):
    
    input_features = [binary_feature(), category_feature(encoder={""reduce_output"": ""sum""})]
    output_feature = binary_feature()
    output_features = [output_feature]

    csv_filename = os.path.join(tmpdir, ""training.csv"")
    dataset_filename = generate_data(input_features, output_features, csv_filename, num_examples=100)

    config = {
        MODEL_TYPE: ""gbm"",
        ""input_features"": input_features,
        ""output_features"": output_features,
        TRAINER: {""num_boost_round"": 2},
    }

    model = LudwigModel(config, backend=backend_config)
    _, _, output_directory = model.train(
        dataset=dataset_filename,
        output_directory=tmpdir,
        skip_save_processed_input=True,
        skip_save_progress=True,
        skip_save_unprocessed_output=True,
        skip_save_log=True,
    )
    model.load(os.path.join(tmpdir, ""api_experiment_run"", ""model""))
    preds, _ = model.predict(dataset=dataset_filename, output_directory=output_directory)

    prob_col = preds[output_feature[""name""] + ""_probabilities""]
    if backend_config[""type""] == ""ray"":
        prob_col = prob_col.compute()
    assert len(prob_col.iloc[0]) == 2
    assert prob_col.apply(sum).mean() == pytest.approx(1.0)


        ```
    ###Assistant : Test that the GBM model can train and predict with non-number inputs.
    "
2705,"def i2len(self, pkt, val):
        
        fld_len = self.fld.i2len(pkt, val)
        return fld_len + self.padlen(fld_len, pkt)

","get the length of the field, including the padding length",10,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def i2len(self, pkt, val):
        
        fld_len = self.fld.i2len(pkt, val)
        return fld_len + self.padlen(fld_len, pkt)


        ```
    ###Assistant : get the length of the field, including the padding length
    "
2706,"def get_kerning(self, next):
        
        advance = self._metrics.advance - self.width
        kern = 0.
        if isinstance(next, Char):
            kern = self.fontset.get_kern(
                self.font, self.font_class, self.c, self.fontsize,
                next.font, next.font_class, next.c, next.fontsize,
                self.dpi)
        return advance + kern
","
        Return the amount of kerning between this and the given character.

        This method is called when characters are strung together into `Hlist`
        to create `Kern` nodes.
        ",26,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_kerning(self, next):
        
        advance = self._metrics.advance - self.width
        kern = 0.
        if isinstance(next, Char):
            kern = self.fontset.get_kern(
                self.font, self.font_class, self.c, self.fontsize,
                next.font, next.font_class, next.c, next.fontsize,
                self.dpi)
        return advance + kern

        ```
    ###Assistant : 
        Return the amount of kerning between this and the given character.

        This method is called when characters are strung together into `Hlist`
        to create `Kern` nodes.
        
    "
2707,"def _get_num_samples_or_steps(data, steps_per_epoch):
    
    flat_inputs = tf.nest.flatten(data)
    if hasattr(flat_inputs[0], ""shape""):
        return int(flat_inputs[0].shape[0]), False
    return steps_per_epoch, True

","Returns number of samples or steps, and whether to use steps count mode.",13,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_num_samples_or_steps(data, steps_per_epoch):
    
    flat_inputs = tf.nest.flatten(data)
    if hasattr(flat_inputs[0], ""shape""):
        return int(flat_inputs[0].shape[0]), False
    return steps_per_epoch, True


        ```
    ###Assistant : Returns number of samples or steps, and whether to use steps count mode.
    "
2708,"def test_retrieve_product_attributes_input_type(staff_api_client, product, channel_USD):
    query = 

    variables = {""channel"": channel_USD.slug}
    found_products = get_graphql_content(
        staff_api_client.post_graphql(query, variables)
    )[""data""][""products""][""edges""]
    assert len(found_products) == 1

    for gql_attr in found_products[0][""node""][""attributes""]:
        assert len(gql_attr[""values""]) == 1
        assert gql_attr[""values""][0][""inputType""] == ""DROPDOWN""


ATTRIBUTES_RESORT_QUERY = 

","
        query ($channel: String){
          products(first: 10, channel: $channel) {
            edges {
              node {
                attributes {
                  values {
                    inputType
                  }
                }
              }
            }
          }
        }
    
    mutation ProductTypeReorderAttributes(
      $productTypeId: ID!
      $moves: [ReorderInput!]!
      $type: ProductAttributeType!
    ) {
      productTypeReorderAttributes(
        productTypeId: $productTypeId
        moves: $moves
        type: $type
      ) {
        productType {
          id
          variantAttributes {
            id
            slug
          }
          productAttributes {
            id
          }
        }

        errors {
          field
          message
          code
          attributes
        }
      }
    }
",64,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_retrieve_product_attributes_input_type(staff_api_client, product, channel_USD):
    query = 

    variables = {""channel"": channel_USD.slug}
    found_products = get_graphql_content(
        staff_api_client.post_graphql(query, variables)
    )[""data""][""products""][""edges""]
    assert len(found_products) == 1

    for gql_attr in found_products[0][""node""][""attributes""]:
        assert len(gql_attr[""values""]) == 1
        assert gql_attr[""values""][0][""inputType""] == ""DROPDOWN""


ATTRIBUTES_RESORT_QUERY = 


        ```
    ###Assistant : 
        query ($channel: String){
          products(first: 10, channel: $channel) {
            edges {
              node {
                attributes {
                  values {
                    inputType
                  }
                }
              }
            }
          }
        }
    
    mutation ProductTypeReorderAttributes(
      $productTypeId: ID!
      $moves: [ReorderInput!]!
      $type: ProductAttributeType!
    ) {
      productTypeReorderAttributes(
        productTypeId: $productTypeId
        moves: $moves
        type: $type
      ) {
        productType {
          id
          variantAttributes {
            id
            slug
          }
          productAttributes {
            id
          }
        }

        errors {
          field
          message
          code
          attributes
        }
      }
    }

    "
2709,"def apply(self, project_state, schema_editor, collect_sql=False):
        
        for operation in self.operations:
            # If this operation cannot be represented as SQL, place a comment
            # there instead
            if collect_sql:
                schema_editor.collected_sql.append(""--"")
                if not operation.reduces_to_sql:
                    schema_editor.collected_sql.append(
                        ""-- MIGRATION NOW PERFORMS OPERATION THAT CANNOT BE WRITTEN AS SQL:""
                    )
                schema_editor.collected_sql.append(""-- %s"" % operation.describe())
                schema_editor.collected_sql.append(""--"")
                if not operation.reduces_to_sql:
                    continue
            # Save the state before the operation has run
            old_state = project_state.clone()
            operation.state_forwards(self.app_label, project_state)
            # Run the operation
            atomic_operation = operation.atomic or (
                self.atomic and operation.atomic is not False
            )
            if not schema_editor.atomic_migration and atomic_operation:
                # Force a transaction on a non-transactional-DDL backend or an
                # atomic operation inside a non-atomic migration.
                with atomic(schema_editor.connection.alias):
                    operation.database_forwards(
                        self.app_label, schema_editor, old_state, project_state
                    )
            else:
                # Normal behaviour
                operation.database_forwards(
                    self.app_label, schema_editor, old_state, project_state
                )
        return project_state
","
        Take a project_state representing all migrations prior to this one
        and a schema_editor for a live database and apply the migration
        in a forwards order.

        Return the resulting project state for efficient reuse by following
        Migrations.
        ",36,124,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply(self, project_state, schema_editor, collect_sql=False):
        
        for operation in self.operations:
            # If this operation cannot be represented as SQL, place a comment
            # there instead
            if collect_sql:
                schema_editor.collected_sql.append(""--"")
                if not operation.reduces_to_sql:
                    schema_editor.collected_sql.append(
                        ""-- MIGRATION NOW PERFORMS OPERATION THAT CANNOT BE WRITTEN AS SQL:""
                    )
                schema_editor.collected_sql.append(""-- %s"" % operation.describe())
                schema_editor.collected_sql.append(""--"")
                if not operation.reduces_to_sql:
                    continue
            # Save the state before the operation has run
            old_state = project_state.clone()
            operation.state_forwards(self.app_label, project_state)
            # Run the operation
            atomic_operation = operation.atomic or (
                self.atomic and operation.atomic is not False
            )
            if not schema_editor.atomic_migration and atomic_operation:
                # Force a transaction on a non-transactional-DDL backend or an
                # atomic operation inside a non-atomic migration.
                with atomic(schema_editor.connection.alias):
                    operation.database_forwards(
                        self.app_label, schema_editor, old_state, project_state
                    )
            else:
                # Normal behaviour
                operation.database_forwards(
                    self.app_label, schema_editor, old_state, project_state
                )
        return project_state

        ```
    ###Assistant : 
        Take a project_state representing all migrations prior to this one
        and a schema_editor for a live database and apply the migration
        in a forwards order.

        Return the resulting project state for efficient reuse by following
        Migrations.
        
    "
2710,"def safe_to_scale(self) -> bool:
        
        # Get the list of nodes.
        node_set = set(self.node_data_dict.keys())
        worker_groups = self._raycluster[""spec""].get(""workerGroupSpecs"", [])

        # Accumulates the indices of worker groups with non-empty workersToDelete
        non_empty_worker_group_indices = []

        for group_index, worker_group in enumerate(worker_groups):
            workersToDelete = worker_group.get(""scaleStrategy"", {}).get(
                ""workersToDelete"", []
            )
            if workersToDelete:
                non_empty_worker_group_indices.append(group_index)
            for worker in workersToDelete:
                if worker in node_set:
                    # The operator hasn't removed this worker yet. Abort
                    # the autoscaler update.
                    logger.warning(f""Waiting for operator to remove worker {worker}."")
                    return False

        # All required workersToDelete have been removed.
        # Clean up the workersToDelete field.
        patch_payload = []
        for group_index in non_empty_worker_group_indices:
            patch = worker_delete_patch(group_index, workers_to_delete=[])
            patch_payload.append(patch)
        if patch_payload:
            logger.info(""Cleaning up workers to delete."")
            logger.info(f""Submitting patch {patch_payload}."")
            self._submit_raycluster_patch(patch_payload)

        # It's safe to proceed with the autoscaler update.
        return True
","Returns False iff non_terminated_nodes contains any pods in the RayCluster's
        workersToDelete lists.

        Explanation:
        If there are any workersToDelete which are non-terminated,
        we should wait for the operator to do its job and delete those
        pods. Therefore, we back off the autoscaler update.

        If, on the other hand, all of the workersToDelete have already been cleaned up,
        then we patch away the workersToDelete lists and return True.
        In the future, we may consider having the operator clean up workersToDelete
        on it own:
        https://github.com/ray-project/kuberay/issues/733

        Note (Dmitri):
        It is stylistically bad that this function has a side effect.
        ",95,122,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def safe_to_scale(self) -> bool:
        
        # Get the list of nodes.
        node_set = set(self.node_data_dict.keys())
        worker_groups = self._raycluster[""spec""].get(""workerGroupSpecs"", [])

        # Accumulates the indices of worker groups with non-empty workersToDelete
        non_empty_worker_group_indices = []

        for group_index, worker_group in enumerate(worker_groups):
            workersToDelete = worker_group.get(""scaleStrategy"", {}).get(
                ""workersToDelete"", []
            )
            if workersToDelete:
                non_empty_worker_group_indices.append(group_index)
            for worker in workersToDelete:
                if worker in node_set:
                    # The operator hasn't removed this worker yet. Abort
                    # the autoscaler update.
                    logger.warning(f""Waiting for operator to remove worker {worker}."")
                    return False

        # All required workersToDelete have been removed.
        # Clean up the workersToDelete field.
        patch_payload = []
        for group_index in non_empty_worker_group_indices:
            patch = worker_delete_patch(group_index, workers_to_delete=[])
            patch_payload.append(patch)
        if patch_payload:
            logger.info(""Cleaning up workers to delete."")
            logger.info(f""Submitting patch {patch_payload}."")
            self._submit_raycluster_patch(patch_payload)

        # It's safe to proceed with the autoscaler update.
        return True

        ```
    ###Assistant : Returns False iff non_terminated_nodes contains any pods in the RayCluster's
        workersToDelete lists.

        Explanation:
        If there are any workersToDelete which are non-terminated,
        we should wait for the operator to do its job and delete those
        pods. Therefore, we back off the autoscaler update.

        If, on the other hand, all of the workersToDelete have already been cleaned up,
        then we patch away the workersToDelete lists and return True.
        In the future, we may consider having the operator clean up workersToDelete
        on it own:
        https://github.com/ray-project/kuberay/issues/733

        Note (Dmitri):
        It is stylistically bad that this function has a side effect.
        
    "
2711,"def get_distance(self, f, value, lookup_type):
        
        if not value:
            return []
        value = value[0]
        if isinstance(value, Distance):
            if f.geodetic(self.connection):
                dist_param = value.m
            else:
                dist_param = getattr(
                    value, Distance.unit_attname(f.units_name(self.connection))
                )
        else:
            dist_param = value

        # dwithin lookups on Oracle require a special string parameter
        # that starts with ""distance="".
        if lookup_type == ""dwithin"":
            dist_param = ""distance=%s"" % dist_param

        return [dist_param]
","
        Return the distance parameters given the value and the lookup type.
        On Oracle, geometry columns with a geodetic coordinate system behave
        implicitly like a geography column, and thus meters will be used as
        the distance parameter on them.
        ",38,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_distance(self, f, value, lookup_type):
        
        if not value:
            return []
        value = value[0]
        if isinstance(value, Distance):
            if f.geodetic(self.connection):
                dist_param = value.m
            else:
                dist_param = getattr(
                    value, Distance.unit_attname(f.units_name(self.connection))
                )
        else:
            dist_param = value

        # dwithin lookups on Oracle require a special string parameter
        # that starts with ""distance="".
        if lookup_type == ""dwithin"":
            dist_param = ""distance=%s"" % dist_param

        return [dist_param]

        ```
    ###Assistant : 
        Return the distance parameters given the value and the lookup type.
        On Oracle, geometry columns with a geodetic coordinate system behave
        implicitly like a geography column, and thus meters will be used as
        the distance parameter on them.
        
    "
2712,"def test_standard_get_document_model(self):
        
        del settings.WAGTAILDOCS_DOCUMENT_MODEL
        from wagtail.documents.models import Document

        self.assertIs(get_document_model(), Document)
",Test get_document_model with no WAGTAILDOCS_DOCUMENT_MODEL,5,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_standard_get_document_model(self):
        
        del settings.WAGTAILDOCS_DOCUMENT_MODEL
        from wagtail.documents.models import Document

        self.assertIs(get_document_model(), Document)

        ```
    ###Assistant : Test get_document_model with no WAGTAILDOCS_DOCUMENT_MODEL
    "
2713,"def _safe_assign(X, values, *, row_indexer=None, column_indexer=None):
    
    row_indexer = slice(None, None, None) if row_indexer is None else row_indexer
    column_indexer = (
        slice(None, None, None) if column_indexer is None else column_indexer
    )

    if hasattr(X, ""iloc""):  # pandas dataframe
        X.iloc[row_indexer, column_indexer] = values
    else:  # numpy array or sparse matrix
        X[row_indexer, column_indexer] = values

","Safe assignment to a numpy array, sparse matrix, or pandas dataframe.

    Parameters
    ----------
    X : {ndarray, sparse-matrix, dataframe}
        Array to be modified. It is expected to be 2-dimensional.

    values : ndarray
        The values to be assigned to `X`.

    row_indexer : array-like, dtype={int, bool}, default=None
        A 1-dimensional array to select the rows of interest. If `None`, all
        rows are selected.

    column_indexer : array-like, dtype={int, bool}, default=None
        A 1-dimensional array to select the columns of interest. If `None`, all
        columns are selected.
    ",80,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _safe_assign(X, values, *, row_indexer=None, column_indexer=None):
    
    row_indexer = slice(None, None, None) if row_indexer is None else row_indexer
    column_indexer = (
        slice(None, None, None) if column_indexer is None else column_indexer
    )

    if hasattr(X, ""iloc""):  # pandas dataframe
        X.iloc[row_indexer, column_indexer] = values
    else:  # numpy array or sparse matrix
        X[row_indexer, column_indexer] = values


        ```
    ###Assistant : Safe assignment to a numpy array, sparse matrix, or pandas dataframe.

    Parameters
    ----------
    X : {ndarray, sparse-matrix, dataframe}
        Array to be modified. It is expected to be 2-dimensional.

    values : ndarray
        The values to be assigned to `X`.

    row_indexer : array-like, dtype={int, bool}, default=None
        A 1-dimensional array to select the rows of interest. If `None`, all
        rows are selected.

    column_indexer : array-like, dtype={int, bool}, default=None
        A 1-dimensional array to select the columns of interest. If `None`, all
        columns are selected.
    
    "
2714,"def copy_sign(self, a, b):
        
        a = _convert_other(a, raiseit=True)
        return a.copy_sign(b)
","Copies the second operand's sign to the first one.

        In detail, it returns a copy of the first operand with the sign
        equal to the sign of the second operand.

        >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('7.33'))
        Decimal('1.50')
        >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('7.33'))
        Decimal('1.50')
        >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('-7.33'))
        Decimal('-1.50')
        >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('-7.33'))
        Decimal('-1.50')
        >>> ExtendedContext.copy_sign(1, -2)
        Decimal('-1')
        >>> ExtendedContext.copy_sign(Decimal(1), -2)
        Decimal('-1')
        >>> ExtendedContext.copy_sign(1, Decimal(-2))
        Decimal('-1')
        ",60,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def copy_sign(self, a, b):
        
        a = _convert_other(a, raiseit=True)
        return a.copy_sign(b)

        ```
    ###Assistant : Copies the second operand's sign to the first one.

        In detail, it returns a copy of the first operand with the sign
        equal to the sign of the second operand.

        >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('7.33'))
        Decimal('1.50')
        >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('7.33'))
        Decimal('1.50')
        >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('-7.33'))
        Decimal('-1.50')
        >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('-7.33'))
        Decimal('-1.50')
        >>> ExtendedContext.copy_sign(1, -2)
        Decimal('-1')
        >>> ExtendedContext.copy_sign(Decimal(1), -2)
        Decimal('-1')
        >>> ExtendedContext.copy_sign(1, Decimal(-2))
        Decimal('-1')
        
    "
2715,"def get_validated_ordering(self):
        
        orderable_fields = self.orderable_fields or ()
        ordering = {}
        if self.is_export:
            #  Revert to CSV order_by submit_time ascending for backwards compatibility
            default_ordering = self.ordering_csv or ()
        else:
            default_ordering = self.ordering or ()
        if isinstance(default_ordering, str):
            default_ordering = (default_ordering,)
        ordering_strs = self.request.GET.getlist('order_by') or list(default_ordering)
        for order in ordering_strs:
            try:
                _, prefix, field_name = order.rpartition('-')
                if field_name in orderable_fields:
                    ordering[field_name] = (
                        prefix, 'descending' if prefix == '-' else 'ascending'
                    )
            except (IndexError, ValueError):
                continue  # invalid ordering specified, skip it
        return ordering
", Return a dict of field names with ordering labels if ordering is valid ,13,82,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_validated_ordering(self):
        
        orderable_fields = self.orderable_fields or ()
        ordering = {}
        if self.is_export:
            #  Revert to CSV order_by submit_time ascending for backwards compatibility
            default_ordering = self.ordering_csv or ()
        else:
            default_ordering = self.ordering or ()
        if isinstance(default_ordering, str):
            default_ordering = (default_ordering,)
        ordering_strs = self.request.GET.getlist('order_by') or list(default_ordering)
        for order in ordering_strs:
            try:
                _, prefix, field_name = order.rpartition('-')
                if field_name in orderable_fields:
                    ordering[field_name] = (
                        prefix, 'descending' if prefix == '-' else 'ascending'
                    )
            except (IndexError, ValueError):
                continue  # invalid ordering specified, skip it
        return ordering

        ```
    ###Assistant :  Return a dict of field names with ordering labels if ordering is valid 
    "
2716,"def test_chordal_cycle_graph(p):
    
    G = nx.chordal_cycle_graph(p)
    assert len(G) == p
    # TODO The second largest eigenvalue should be smaller than a constant,
    # independent of the number of nodes in the graph:
    #
    #     eigs = sorted(sp.linalg.eigvalsh(nx.adjacency_matrix(G).toarray()))
    #     assert_less(eigs[-2], ...)
    #


@pytest.mark.parametrize(""p"", (3, 5, 7, 11, 13))  # Primes",Test for the :func:`networkx.chordal_cycle_graph` function.,5,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_chordal_cycle_graph(p):
    
    G = nx.chordal_cycle_graph(p)
    assert len(G) == p
    # TODO The second largest eigenvalue should be smaller than a constant,
    # independent of the number of nodes in the graph:
    #
    #     eigs = sorted(sp.linalg.eigvalsh(nx.adjacency_matrix(G).toarray()))
    #     assert_less(eigs[-2], ...)
    #


@pytest.mark.parametrize(""p"", (3, 5, 7, 11, 13))  # Primes
        ```
    ###Assistant : Test for the :func:`networkx.chordal_cycle_graph` function.
    "
2717,"def convert_dataset_split_sizes(left_size,right_size,total_size):
  

  left_size_type = type(left_size) 
  right_size_type = type(right_size)


  if left_size is not None and left_size_type not in [int,float]:
    raise ValueError(f'Invalid `left_size` type Got {left_size_type}'
                     'It should be one of  float,int or None') 
  if right_size is not None and right_size_type not in [int,float]: 
    raise ValueError(f'Invalid `right_size` type Got {right_size_type}'
                     'It should be one of  float,int or None') 
    
  
  if (left_size_type == int 
      and (left_size <= 0 or left_size>= total_size)
      or left_size_type == float 
      and (left_size <= 0 or left_size>= 1) ):
    raise ValueError('`left_size` should be either a positive integer'
                     f'and smaller than {total_size} or a float '
                     'within the range `[0, 1]`') 
    
  if (right_size_type == int 
      and (right_size <= 0 or right_size>= total_size) 
      or right_size_type == float 
      and (right_size <= 0 or right_size>= 1)):
    raise ValueError('`right_size` should be either a positive integer '
                     f'and smaller than {total_size} or'
                     'a float within the range `[0, 1]`') 
    
  if right_size_type == left_size_type == float and right_size + left_size > 1:
    raise ValueError('sum of `left_size` and `right_size`'
                     ' should be within `[0,1]`'
                    f'Got {right_size + left_size} ,'
                    'reduce the `left_size` or `right_size`')

  if left_size_type == float:
    left_size = math.ceil(left_size*total_size)
  else:
    left_size = float(left_size)

  if right_size_type == float:
    right_size = math.ceil(right_size*total_size)
  else:
    right_size = float(right_size)


  if left_size is None:
    left_size = total_size - right_size
  elif right_size is None:
    right_size = total_size - left_size

  if left_size + right_size > total_size:
    raise ValueError('The sum of `left_size` and `right_size`'
                     f' should be smaller than the samples {total_size} '
                     ' reduce `left_size` or `right_size` ' )

  
  if left_size == 0:
    raise ValueError(f'with dataset of length={total_size}'
                     '`left_size`={left_size} and `right_size`={right_size} '
                    'resulting left dataset split will be empty, '
                    'adjust any of the aforementioned parameters')
    
  left_size,right_size = int(left_size) ,int(right_size)
  return left_size,right_size
","Helper function to convert  left_size/right_size relative to dataset's size
  ",9,278,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_dataset_split_sizes(left_size,right_size,total_size):
  

  left_size_type = type(left_size) 
  right_size_type = type(right_size)


  if left_size is not None and left_size_type not in [int,float]:
    raise ValueError(f'Invalid `left_size` type Got {left_size_type}'
                     'It should be one of  float,int or None') 
  if right_size is not None and right_size_type not in [int,float]: 
    raise ValueError(f'Invalid `right_size` type Got {right_size_type}'
                     'It should be one of  float,int or None') 
    
  
  if (left_size_type == int 
      and (left_size <= 0 or left_size>= total_size)
      or left_size_type == float 
      and (left_size <= 0 or left_size>= 1) ):
    raise ValueError('`left_size` should be either a positive integer'
                     f'and smaller than {total_size} or a float '
                     'within the range `[0, 1]`') 
    
  if (right_size_type == int 
      and (right_size <= 0 or right_size>= total_size) 
      or right_size_type == float 
      and (right_size <= 0 or right_size>= 1)):
    raise ValueError('`right_size` should be either a positive integer '
                     f'and smaller than {total_size} or'
                     'a float within the range `[0, 1]`') 
    
  if right_size_type == left_size_type == float and right_size + left_size > 1:
    raise ValueError('sum of `left_size` and `right_size`'
                     ' should be within `[0,1]`'
                    f'Got {right_size + left_size} ,'
                    'reduce the `left_size` or `right_size`')

  if left_size_type == float:
    left_size = math.ceil(left_size*total_size)
  else:
    left_size = float(left_size)

  if right_size_type == float:
    right_size = math.ceil(right_size*total_size)
  else:
    right_size = float(right_size)


  if left_size is None:
    left_size = total_size - right_size
  elif right_size is None:
    right_size = total_size - left_size

  if left_size + right_size > total_size:
    raise ValueError('The sum of `left_size` and `right_size`'
                     f' should be smaller than the samples {total_size} '
                     ' reduce `left_size` or `right_size` ' )

  
  if left_size == 0:
    raise ValueError(f'with dataset of length={total_size}'
                     '`left_size`={left_size} and `right_size`={right_size} '
                    'resulting left dataset split will be empty, '
                    'adjust any of the aforementioned parameters')
    
  left_size,right_size = int(left_size) ,int(right_size)
  return left_size,right_size

        ```
    ###Assistant : Helper function to convert  left_size/right_size relative to dataset's size
  
    "
2718,"def active_count(self):
        
        return self.order_by().exclude(inventory_sources__source='controller').values(name_lower=Lower('name')).distinct().count()
","Return count of active, unique hosts for licensing.
        Construction of query involves:
         - remove any ordering specified in model's Meta
         - Exclude hosts sourced from another Tower
         - Restrict the query to only return the name column
         - Only consider results that are unique
         - Return the count of this query
        ",51,4,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def active_count(self):
        
        return self.order_by().exclude(inventory_sources__source='controller').values(name_lower=Lower('name')).distinct().count()

        ```
    ###Assistant : Return count of active, unique hosts for licensing.
        Construction of query involves:
         - remove any ordering specified in model's Meta
         - Exclude hosts sourced from another Tower
         - Restrict the query to only return the name column
         - Only consider results that are unique
         - Return the count of this query
        
    "
2719,"async def test_set_avatar_incorrect_mime_type(self) -> None:
        
        handler = self.hs.get_sso_handler()

        # any random user works since image check is supposed to fail
        user_id = ""@sso-user:test""

        self.assertFalse(
            self.get_success(handler.set_avatar(user_id, ""http://my.server/me.png""))
        )
",Tests that saving an avatar fails when its mime type is not allowed,13,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_set_avatar_incorrect_mime_type(self) -> None:
        
        handler = self.hs.get_sso_handler()

        # any random user works since image check is supposed to fail
        user_id = ""@sso-user:test""

        self.assertFalse(
            self.get_success(handler.set_avatar(user_id, ""http://my.server/me.png""))
        )

        ```
    ###Assistant : Tests that saving an avatar fails when its mime type is not allowed
    "
2720,"def layer_uses_training_bool(layer):
    
    if layer._expects_training_arg:  # pylint: disable=protected-access
        return True
    visited = {layer}
    to_visit = list_all_layers(layer)
    while to_visit:
        layer = to_visit.pop()
        if layer in visited:
            continue
        if getattr(layer, ""_expects_training_arg"", True):
            return True
        visited.add(layer)
        to_visit.extend(list_all_layers(layer))
    return False

",Returns whether this layer or any of its children uses the training arg.,13,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def layer_uses_training_bool(layer):
    
    if layer._expects_training_arg:  # pylint: disable=protected-access
        return True
    visited = {layer}
    to_visit = list_all_layers(layer)
    while to_visit:
        layer = to_visit.pop()
        if layer in visited:
            continue
        if getattr(layer, ""_expects_training_arg"", True):
            return True
        visited.add(layer)
        to_visit.extend(list_all_layers(layer))
    return False


        ```
    ###Assistant : Returns whether this layer or any of its children uses the training arg.
    "
2721,"async def test_edgeql_for_in_computable_09(self):
        # This is basically test_edgeql_for_in_computable_01 but with
        # a WITH binding in front of the whole shape
        await self.assert_query_result(
            r","
                WITH
                    U := (
                        SELECT User {
                            select_deck := (
                                FOR letter IN {'I', 'B'}
                                UNION (
                                    SELECT User.deck {
                                        name,
                                        # just define an ad-hoc link prop",28,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_edgeql_for_in_computable_09(self):
        # This is basically test_edgeql_for_in_computable_01 but with
        # a WITH binding in front of the whole shape
        await self.assert_query_result(
            r
        ```
    ###Assistant : 
                WITH
                    U := (
                        SELECT User {
                            select_deck := (
                                FOR letter IN {'I', 'B'}
                                UNION (
                                    SELECT User.deck {
                                        name,
                                        # just define an ad-hoc link prop
    "
2722,"def get_actual_details(name, filters):
	budget_against = frappe.scrub(filters.get(""budget_against""))
	cond = """"

	if filters.get(""budget_against"") == ""Cost Center"":
		cc_lft, cc_rgt = frappe.db.get_value(""Cost Center"", name, [""lft"", ""rgt""])
		cond = .format(
			lft=cc_lft, rgt=cc_rgt
		)

	ac_details = frappe.db.sql(
		.format(
			tab=filters.budget_against, budget_against=budget_against, cond=cond
		),
		(filters.from_fiscal_year, filters.to_fiscal_year, name),
		as_dict=1,
	)

	cc_actual_details = {}
	for d in ac_details:
		cc_actual_details.setdefault(d.account, []).append(d)

	return cc_actual_details

","
				and lft >= ""{lft}""
				and rgt <= ""{rgt}""
			
			select
				gl.account,
				gl.debit,
				gl.credit,
				gl.fiscal_year,
				MONTHNAME(gl.posting_date) as month_name,
				b.{budget_against} as budget_against
			from
				`tabGL Entry` gl,
				`tabBudget Account` ba,
				`tabBudget` b
			where
				b.name = ba.parent
				and b.docstatus = 1
				and ba.account=gl.account
				and b.{budget_against} = gl.{budget_against}
				and gl.fiscal_year between %s and %s
				and b.{budget_against} = %s
				and exists(
					select
						name
					from
						`tab{tab}`
					where
						name = gl.{budget_against}
						{cond}
				)
				group by
					gl.name
				order by gl.fiscal_year
		",70,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_actual_details(name, filters):
	budget_against = frappe.scrub(filters.get(""budget_against""))
	cond = """"

	if filters.get(""budget_against"") == ""Cost Center"":
		cc_lft, cc_rgt = frappe.db.get_value(""Cost Center"", name, [""lft"", ""rgt""])
		cond = .format(
			lft=cc_lft, rgt=cc_rgt
		)

	ac_details = frappe.db.sql(
		.format(
			tab=filters.budget_against, budget_against=budget_against, cond=cond
		),
		(filters.from_fiscal_year, filters.to_fiscal_year, name),
		as_dict=1,
	)

	cc_actual_details = {}
	for d in ac_details:
		cc_actual_details.setdefault(d.account, []).append(d)

	return cc_actual_details


        ```
    ###Assistant : 
				and lft >= ""{lft}""
				and rgt <= ""{rgt}""
			
			select
				gl.account,
				gl.debit,
				gl.credit,
				gl.fiscal_year,
				MONTHNAME(gl.posting_date) as month_name,
				b.{budget_against} as budget_against
			from
				`tabGL Entry` gl,
				`tabBudget Account` ba,
				`tabBudget` b
			where
				b.name = ba.parent
				and b.docstatus = 1
				and ba.account=gl.account
				and b.{budget_against} = gl.{budget_against}
				and gl.fiscal_year between %s and %s
				and b.{budget_against} = %s
				and exists(
					select
						name
					from
						`tab{tab}`
					where
						name = gl.{budget_against}
						{cond}
				)
				group by
					gl.name
				order by gl.fiscal_year
		
    "
2723,"def print_index(toc):
    
    dash = ""-""*(100 - 7)
    space = "" ""*47
    print(f""{space}INDEX"")
    print(f""\n\nName : {dash} PageNo.\n\n\n"")
    for topic in toc:
        eq_dash = ""-""*(100 - len(topic[1]))
        print(f""{topic[1]} {eq_dash} {topic[2]}"")
        
","Prints out the index in proper format with title name and page number

    Args:
        toc (nested list): toc[1] - Topic name
                           toc[2] - Page number
    ",25,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def print_index(toc):
    
    dash = ""-""*(100 - 7)
    space = "" ""*47
    print(f""{space}INDEX"")
    print(f""\n\nName : {dash} PageNo.\n\n\n"")
    for topic in toc:
        eq_dash = ""-""*(100 - len(topic[1]))
        print(f""{topic[1]} {eq_dash} {topic[2]}"")
        
        ```
    ###Assistant : Prints out the index in proper format with title name and page number

    Args:
        toc (nested list): toc[1] - Topic name
                           toc[2] - Page number
    
    "
2724,"def pie(self, X, win=None, env=None, opts=None):
        

        X = np.squeeze(X)
        assert X.ndim == 1, ""X should be one-dimensional""
        assert np.all(np.greater_equal(X, 0)), ""X cannot contain negative values""

        opts = {} if opts is None else opts
        _title2str(opts)
        _assert_opts(opts)

        data = [
            {
                ""values"": X.tolist(),
                ""labels"": opts.get(""legend""),
                ""type"": ""pie"",
            }
        ]
        return self._send(
            {
                ""data"": data,
                ""win"": win,
                ""eid"": env,
                ""layout"": _opts2layout(opts),
                ""opts"": opts,
            }
        )
","
        This function draws a pie chart based on the `N` tensor `X`.

        The following `opts` are supported:

        - `opts.legend`: `list` containing legend names
        ",23,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pie(self, X, win=None, env=None, opts=None):
        

        X = np.squeeze(X)
        assert X.ndim == 1, ""X should be one-dimensional""
        assert np.all(np.greater_equal(X, 0)), ""X cannot contain negative values""

        opts = {} if opts is None else opts
        _title2str(opts)
        _assert_opts(opts)

        data = [
            {
                ""values"": X.tolist(),
                ""labels"": opts.get(""legend""),
                ""type"": ""pie"",
            }
        ]
        return self._send(
            {
                ""data"": data,
                ""win"": win,
                ""eid"": env,
                ""layout"": _opts2layout(opts),
                ""opts"": opts,
            }
        )

        ```
    ###Assistant : 
        This function draws a pie chart based on the `N` tensor `X`.

        The following `opts` are supported:

        - `opts.legend`: `list` containing legend names
        
    "
2725,"def dry_run(self) -> None:
        
        pod = self.build_pod_request_obj()
        print(yaml.dump(prune_dict(pod.to_dict(), mode='strict')))

","
        Prints out the pod definition that would be created by this operator.
        Does not include labels specific to the task instance (since there isn't
        one in a dry_run) and excludes all empty elements.
        ",33,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dry_run(self) -> None:
        
        pod = self.build_pod_request_obj()
        print(yaml.dump(prune_dict(pod.to_dict(), mode='strict')))


        ```
    ###Assistant : 
        Prints out the pod definition that would be created by this operator.
        Does not include labels specific to the task instance (since there isn't
        one in a dry_run) and excludes all empty elements.
        
    "
2726,"def ExponentialPower(name, mu, alpha, beta):
    r
    return rv(name, ExponentialPowerDistribution, (mu, alpha, beta))


#-------------------------------------------------------------------------------
# F distribution ---------------------------------------------------------------

","
    Create a Continuous Random Variable with Exponential Power distribution.
    This distribution is known also as Generalized Normal
    distribution version 1.

    Explanation
    ===========

    The density of the Exponential Power distribution is given by

    .. math::
        f(x) := \frac{\beta}{2\alpha\Gamma(\frac{1}{\beta})}
            e^{{-(\frac{|x - \mu|}{\alpha})^{\beta}}}

    with :math:`x \in [ - \infty, \infty ]`.

    Parameters
    ==========

    mu : Real number
        A location.
    alpha : Real number,`\alpha > 0`
        A  scale.
    beta : Real number, `\beta > 0`
        A shape.

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import ExponentialPower, density, cdf
    >>> from sympy import Symbol, pprint
    >>> z = Symbol(""z"")
    >>> mu = Symbol(""mu"")
    >>> alpha = Symbol(""alpha"", positive=True)
    >>> beta = Symbol(""beta"", positive=True)
    >>> X = ExponentialPower(""x"", mu, alpha, beta)
    >>> pprint(density(X)(z), use_unicode=False)
                     beta
           /|mu - z|\
          -|--------|
           \ alpha  /
    beta*e
    ---------------------
                  / 1  \
     2*alpha*Gamma|----|
                  \beta/
    >>> cdf(X)(z)
    1/2 + lowergamma(1/beta, (Abs(mu - z)/alpha)**beta)*sign(-mu + z)/(2*gamma(1/beta))

    References
    ==========

    .. [1] https://reference.wolfram.com/language/ref/ExponentialPowerDistribution.html
    .. [2] https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1

    ",152,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ExponentialPower(name, mu, alpha, beta):
    r
    return rv(name, ExponentialPowerDistribution, (mu, alpha, beta))


#-------------------------------------------------------------------------------
# F distribution ---------------------------------------------------------------


        ```
    ###Assistant : 
    Create a Continuous Random Variable with Exponential Power distribution.
    This distribution is known also as Generalized Normal
    distribution version 1.

    Explanation
    ===========

    The density of the Exponential Power distribution is given by

    .. math::
        f(x) := \frac{\beta}{2\alpha\Gamma(\frac{1}{\beta})}
            e^{{-(\frac{|x - \mu|}{\alpha})^{\beta}}}

    with :math:`x \in [ - \infty, \infty ]`.

    Parameters
    ==========

    mu : Real number
        A location.
    alpha : Real number,`\alpha > 0`
        A  scale.
    beta : Real number, `\beta > 0`
        A shape.

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import ExponentialPower, density, cdf
    >>> from sympy import Symbol, pprint
    >>> z = Symbol(""z"")
    >>> mu = Symbol(""mu"")
    >>> alpha = Symbol(""alpha"", positive=True)
    >>> beta = Symbol(""beta"", positive=True)
    >>> X = ExponentialPower(""x"", mu, alpha, beta)
    >>> pprint(density(X)(z), use_unicode=False)
                     beta
           /|mu - z|\
          -|--------|
           \ alpha  /
    beta*e
    ---------------------
                  / 1  \
     2*alpha*Gamma|----|
                  \beta/
    >>> cdf(X)(z)
    1/2 + lowergamma(1/beta, (Abs(mu - z)/alpha)**beta)*sign(-mu + z)/(2*gamma(1/beta))

    References
    ==========

    .. [1] https://reference.wolfram.com/language/ref/ExponentialPowerDistribution.html
    .. [2] https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1

    
    "
2727,"def get_palette(num_cls):
    

    n = num_cls
    palette = [0] * (n * 3)
    for j in range(0, n):
        lab = j
        palette[j * 3 + 0] = 0
        palette[j * 3 + 1] = 0
        palette[j * 3 + 2] = 0
        i = 0
        while lab:
            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3
    return palette

"," Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    ",18,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_palette(num_cls):
    

    n = num_cls
    palette = [0] * (n * 3)
    for j in range(0, n):
        lab = j
        palette[j * 3 + 0] = 0
        palette[j * 3 + 1] = 0
        palette[j * 3 + 2] = 0
        i = 0
        while lab:
            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3
    return palette


        ```
    ###Assistant :  Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    
    "
2728,"def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
    
    # Batch blobs and diffs.
    all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
    all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
    forward_batches = self._batch({in_: kwargs[in_]
                                   for in_ in self.inputs if in_ in kwargs})
    backward_batches = self._batch({out: kwargs[out]
                                    for out in self.outputs if out in kwargs})
    # Collect outputs from batches (and heed lack of forward/backward batches).
    for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
        batch_blobs = self.forward(blobs=blobs, **fb)
        batch_diffs = self.backward(diffs=diffs, **bb)
        for out, out_blobs in six.iteritems(batch_blobs):
            all_outs[out].extend(out_blobs.copy())
        for diff, out_diffs in six.iteritems(batch_diffs):
            all_diffs[diff].extend(out_diffs.copy())
    # Package in ndarray.
    for out, diff in zip(all_outs, all_diffs):
        all_outs[out] = np.asarray(all_outs[out])
        all_diffs[diff] = np.asarray(all_diffs[diff])
    # Discard padding at the end and package in ndarray.
    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
    if pad:
        for out, diff in zip(all_outs, all_diffs):
            all_outs[out] = all_outs[out][:-pad]
            all_diffs[diff] = all_diffs[diff][:-pad]
    return all_outs, all_diffs

","
    Run net forward + backward in batches.

    Parameters
    ----------
    blobs: list of blobs to extract as in forward()
    diffs: list of diffs to extract as in backward()
    kwargs: Keys are input (for forward) and output (for backward) blob names
            and values are ndarrays. Refer to forward() and backward().
            Prefilled variants are called for lack of input or output blobs.

    Returns
    -------
    all_blobs: {blob name: blob ndarray} dict.
    all_diffs: {blob name: diff ndarray} dict.
    ",73,144,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
    
    # Batch blobs and diffs.
    all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
    all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
    forward_batches = self._batch({in_: kwargs[in_]
                                   for in_ in self.inputs if in_ in kwargs})
    backward_batches = self._batch({out: kwargs[out]
                                    for out in self.outputs if out in kwargs})
    # Collect outputs from batches (and heed lack of forward/backward batches).
    for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
        batch_blobs = self.forward(blobs=blobs, **fb)
        batch_diffs = self.backward(diffs=diffs, **bb)
        for out, out_blobs in six.iteritems(batch_blobs):
            all_outs[out].extend(out_blobs.copy())
        for diff, out_diffs in six.iteritems(batch_diffs):
            all_diffs[diff].extend(out_diffs.copy())
    # Package in ndarray.
    for out, diff in zip(all_outs, all_diffs):
        all_outs[out] = np.asarray(all_outs[out])
        all_diffs[diff] = np.asarray(all_diffs[diff])
    # Discard padding at the end and package in ndarray.
    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
    if pad:
        for out, diff in zip(all_outs, all_diffs):
            all_outs[out] = all_outs[out][:-pad]
            all_diffs[diff] = all_diffs[diff][:-pad]
    return all_outs, all_diffs


        ```
    ###Assistant : 
    Run net forward + backward in batches.

    Parameters
    ----------
    blobs: list of blobs to extract as in forward()
    diffs: list of diffs to extract as in backward()
    kwargs: Keys are input (for forward) and output (for backward) blob names
            and values are ndarrays. Refer to forward() and backward().
            Prefilled variants are called for lack of input or output blobs.

    Returns
    -------
    all_blobs: {blob name: blob ndarray} dict.
    all_diffs: {blob name: diff ndarray} dict.
    
    "
2729,"def test_user_does_not_exist(self) -> None:
        
        url = ""/_synapse/admin/v2/users/@unknown_person:test/devices""
        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])
","
        Tests that a lookup for a user that does not exist returns a 404
        ",14,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_user_does_not_exist(self) -> None:
        
        url = ""/_synapse/admin/v2/users/@unknown_person:test/devices""
        channel = self.make_request(
            ""GET"",
            url,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(404, channel.code, msg=channel.json_body)
        self.assertEqual(Codes.NOT_FOUND, channel.json_body[""errcode""])

        ```
    ###Assistant : 
        Tests that a lookup for a user that does not exist returns a 404
        
    "
2730,"def send_ping(self) -> None:
        
        now = self.clock.time_msec()

        if self.time_we_closed:
            if now - self.time_we_closed > PING_TIMEOUT_MS:
                logger.info(
                    ""[%s] Failed to close connection gracefully, aborting"", self.id()
                )
                assert self.transport is not None
                self.transport.abortConnection()
        else:
            if now - self.last_sent_command >= PING_TIME:
                self.send_command(PingCommand(str(now)))

            if (
                self.received_ping
                and now - self.last_received_command > PING_TIMEOUT_MS
            ):
                logger.info(
                    ""[%s] Connection hasn't received command in %r ms. Closing."",
                    self.id(),
                    now - self.last_received_command,
                )
                self.send_error(""ping timeout"")
","Periodically sends a ping and checks if we should close the connection
        due to the other side timing out.
        ",19,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def send_ping(self) -> None:
        
        now = self.clock.time_msec()

        if self.time_we_closed:
            if now - self.time_we_closed > PING_TIMEOUT_MS:
                logger.info(
                    ""[%s] Failed to close connection gracefully, aborting"", self.id()
                )
                assert self.transport is not None
                self.transport.abortConnection()
        else:
            if now - self.last_sent_command >= PING_TIME:
                self.send_command(PingCommand(str(now)))

            if (
                self.received_ping
                and now - self.last_received_command > PING_TIMEOUT_MS
            ):
                logger.info(
                    ""[%s] Connection hasn't received command in %r ms. Closing."",
                    self.id(),
                    now - self.last_received_command,
                )
                self.send_error(""ping timeout"")

        ```
    ###Assistant : Periodically sends a ping and checks if we should close the connection
        due to the other side timing out.
        
    "
2731,"def workflow_logging_context(job_id) -> None:
    
    node = ray.worker._global_node
    original_out_file, original_err_file = node.get_log_file_handles(
        get_worker_log_file_name(""WORKER"")
    )
    out_file, err_file = node.get_log_file_handles(
        get_worker_log_file_name(""WORKER"", job_id)
    )
    try:
        configure_log_file(out_file, err_file)
        yield
    finally:
        configure_log_file(original_out_file, original_err_file)
","Initialize the workflow logging context.

    Workflow executions are running as remote functions from
    WorkflowManagementActor. Without logging redirection, workflow
    inner execution logs will be pushed to the driver that initially
    created WorkflowManagementActor rather than the driver that
    actually submits the current workflow execution.
    We use this conext manager to re-configure the log files to send
    the logs to the correct driver, and to restore the log files once
    the execution is done.

    Args:
        job_id: The ID of the job that submits the workflow execution.
    ",83,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def workflow_logging_context(job_id) -> None:
    
    node = ray.worker._global_node
    original_out_file, original_err_file = node.get_log_file_handles(
        get_worker_log_file_name(""WORKER"")
    )
    out_file, err_file = node.get_log_file_handles(
        get_worker_log_file_name(""WORKER"", job_id)
    )
    try:
        configure_log_file(out_file, err_file)
        yield
    finally:
        configure_log_file(original_out_file, original_err_file)

        ```
    ###Assistant : Initialize the workflow logging context.

    Workflow executions are running as remote functions from
    WorkflowManagementActor. Without logging redirection, workflow
    inner execution logs will be pushed to the driver that initially
    created WorkflowManagementActor rather than the driver that
    actually submits the current workflow execution.
    We use this conext manager to re-configure the log files to send
    the logs to the correct driver, and to restore the log files once
    the execution is done.

    Args:
        job_id: The ID of the job that submits the workflow execution.
    
    "
2732,"def console_entry_point():
    

    if ""--profile"" in sys.argv:
        with cProfile.Profile() as profile:
            entry_point()

        stats = pstats.Stats(profile)
        stats.sort_stats(pstats.SortKey.TIME)

        # Use snakeviz to visualize the profile
        stats.dump_stats(""spotdl.profile"")
    else:
        entry_point()
","
    Wrapper around `entry_point` so we can profile the code
    ",9,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def console_entry_point():
    

    if ""--profile"" in sys.argv:
        with cProfile.Profile() as profile:
            entry_point()

        stats = pstats.Stats(profile)
        stats.sort_stats(pstats.SortKey.TIME)

        # Use snakeviz to visualize the profile
        stats.dump_stats(""spotdl.profile"")
    else:
        entry_point()

        ```
    ###Assistant : 
    Wrapper around `entry_point` so we can profile the code
    
    "
2733,"def callbacks(self, callbacks_class) -> ""TrainerConfig"":
        
        self.callbacks_class = callbacks_class

        return self
","Sets the callbacks configuration.

        Args:
            callbacks_class: Callbacks class, whose methods will be run during
                various phases of training and environment sample collection.
                See the `DefaultCallbacks` class and
                `examples/custom_metrics_and_callbacks.py` for more usage information.

        Returns:
            This updated TrainerConfig object.
        ",37,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def callbacks(self, callbacks_class) -> ""TrainerConfig"":
        
        self.callbacks_class = callbacks_class

        return self

        ```
    ###Assistant : Sets the callbacks configuration.

        Args:
            callbacks_class: Callbacks class, whose methods will be run during
                various phases of training and environment sample collection.
                See the `DefaultCallbacks` class and
                `examples/custom_metrics_and_callbacks.py` for more usage information.

        Returns:
            This updated TrainerConfig object.
        
    "
2734,"def test_get_dynamic_sampling_after_migrating_to_new_plan_default_biases(self):
        

        self.project.update_option(""sentry:dynamic_sampling"", self.dynamic_sampling_data)

        with Feature(
            {
                self.universal_ds_flag: True,
                self.old_ds_flag: True,
                self.new_ds_flag: True,
            }
        ):
            response = self.get_success_response(
                self.organization.slug, self.project.slug, method=""get""
            )
            assert response.data[""dynamicSampling""] is None
            assert response.data[""dynamicSamplingBiases""] == DEFAULT_BIASES
","
        Tests the case when an organization was in EA/LA and has setup previously Dynamic Sampling rules,
        and now they have migrated to an AM2 plan, but haven't manipulated the bias toggles yet so they get the
        default biases. This also ensures that they no longer receive the deprecated dynamic sampling rules.
        ",51,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_get_dynamic_sampling_after_migrating_to_new_plan_default_biases(self):
        

        self.project.update_option(""sentry:dynamic_sampling"", self.dynamic_sampling_data)

        with Feature(
            {
                self.universal_ds_flag: True,
                self.old_ds_flag: True,
                self.new_ds_flag: True,
            }
        ):
            response = self.get_success_response(
                self.organization.slug, self.project.slug, method=""get""
            )
            assert response.data[""dynamicSampling""] is None
            assert response.data[""dynamicSamplingBiases""] == DEFAULT_BIASES

        ```
    ###Assistant : 
        Tests the case when an organization was in EA/LA and has setup previously Dynamic Sampling rules,
        and now they have migrated to an AM2 plan, but haven't manipulated the bias toggles yet so they get the
        default biases. This also ensures that they no longer receive the deprecated dynamic sampling rules.
        
    "
2735,"def strtobool(val):
    # type: (str) -> int
    
    val = val.lower()
    if val in (""y"", ""yes"", ""t"", ""true"", ""on"", ""1""):
        return 1
    elif val in (""n"", ""no"", ""f"", ""false"", ""off"", ""0""):
        return 0
    else:
        raise ValueError(f""invalid truth value {val!r}"")

","Convert a string representation of truth to true (1) or false (0).

    True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
    are 'n', 'no', 'f', 'false', 'off', and '0'.  Raises ValueError if
    'val' is anything else.
    ",39,38,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def strtobool(val):
    # type: (str) -> int
    
    val = val.lower()
    if val in (""y"", ""yes"", ""t"", ""true"", ""on"", ""1""):
        return 1
    elif val in (""n"", ""no"", ""f"", ""false"", ""off"", ""0""):
        return 0
    else:
        raise ValueError(f""invalid truth value {val!r}"")


        ```
    ###Assistant : Convert a string representation of truth to true (1) or false (0).

    True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
    are 'n', 'no', 'f', 'false', 'off', and '0'.  Raises ValueError if
    'val' is anything else.
    
    "
2736,"def convert_xunits(self, x):
        
        ax = getattr(self, 'axes', None)
        if ax is None or ax.xaxis is None:
            return x
        return ax.xaxis.convert_units(x)
","
        Convert *x* using the unit type of the xaxis.

        If the artist is not contained in an Axes or if the xaxis does not
        have units, *x* itself is returned.
        ",30,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert_xunits(self, x):
        
        ax = getattr(self, 'axes', None)
        if ax is None or ax.xaxis is None:
            return x
        return ax.xaxis.convert_units(x)

        ```
    ###Assistant : 
        Convert *x* using the unit type of the xaxis.

        If the artist is not contained in an Axes or if the xaxis does not
        have units, *x* itself is returned.
        
    "
2737,"def value_as_datetime(self) -> tp.Tuple[datetime, datetime] | None:
        
        if self.value is None:
            return None
        v1, v2 = self.value
        if isinstance(v1, numbers.Number):
            d1 = datetime.utcfromtimestamp(v1 / 1000)
        else:
            d1 = v1
        if isinstance(v2, numbers.Number):
            d2 = datetime.utcfromtimestamp(v2 / 1000)
        else:
            d2 = v2
        return d1, d2

    value = NonNullable(Tuple(Datetime, Datetime), help=)

    value_throttled = Readonly(NonNullable(Tuple(Datetime, Datetime)), help=)

    start = NonNullable(Datetime, help=)

    end = NonNullable(Datetime, help=)

    step = Int(default=3_600_000, help=)

    format = Override(default=""%d %b %Y %H:%M:%S"")

#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------

#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------
"," Convenience property to retrieve the value tuple as a tuple of
        datetime objects.
        
    Initial or selected range.
    
    Initial or selected value, throttled to report only on mouseup.
    
    The minimum allowable value.
    
    The maximum allowable value.
    
    The step between consecutive values, in units of milliseconds.
    Default is one hour.
    ",48,81,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def value_as_datetime(self) -> tp.Tuple[datetime, datetime] | None:
        
        if self.value is None:
            return None
        v1, v2 = self.value
        if isinstance(v1, numbers.Number):
            d1 = datetime.utcfromtimestamp(v1 / 1000)
        else:
            d1 = v1
        if isinstance(v2, numbers.Number):
            d2 = datetime.utcfromtimestamp(v2 / 1000)
        else:
            d2 = v2
        return d1, d2

    value = NonNullable(Tuple(Datetime, Datetime), help=)

    value_throttled = Readonly(NonNullable(Tuple(Datetime, Datetime)), help=)

    start = NonNullable(Datetime, help=)

    end = NonNullable(Datetime, help=)

    step = Int(default=3_600_000, help=)

    format = Override(default=""%d %b %Y %H:%M:%S"")

#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------

#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------

        ```
    ###Assistant :  Convenience property to retrieve the value tuple as a tuple of
        datetime objects.
        
    Initial or selected range.
    
    Initial or selected value, throttled to report only on mouseup.
    
    The minimum allowable value.
    
    The maximum allowable value.
    
    The step between consecutive values, in units of milliseconds.
    Default is one hour.
    
    "
2738,"def test_first_event_with_minified_stack_trace_received(self, record_analytics):
        
        now = timezone.now()
        project = self.create_project(first_event=now)
        project_created.send(project=project, user=self.user, sender=type(project))
        url = ""http://localhost:3000""
        data = load_data(""javascript"")
        data[""tags""] = [(""url"", url)]
        data[""exception""] = {
            ""values"": [
                {
                    **data[""exception""][""values""][0],
                    ""raw_stacktrace"": {
                        ""frames"": [
                            {
                                ""function"": ""o"",
                                ""filename"": ""/_static/dist/sentry/chunks/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.255071ceadabfb67483c.js"",
                                ""abs_path"": ""https://s1.sentry-cdn.com/_static/dist/sentry/chunks/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.255071ceadabfb67483c.js"",
                                ""lineno"": 2,
                                ""colno"": 37098,
                                ""pre_context"": [
                                    ""/*! For license information please see vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd. {snip}""
                                ],
                                ""context_line"": ""{snip} .apply(this,arguments);const i=o.map((e=>c(e,t)));return e.apply(this,i)}catch(e){throw l(),(0,i.$e)((n=>{n.addEventProcessor((e=>(t.mechani {snip}"",
                                ""post_context"": [
                                    ""//# sourceMappingURL=../sourcemaps/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.fe32 {snip}""
                                ],
                                ""in_app"": False,
                            },
                        ],
                    },
                }
            ]
        }

        self.store_event(
            project_id=project.id,
            data=data,
        )

        record_analytics.assert_called_with(
            ""first_event_with_minified_stack_trace_for_project.sent"",
            user_id=self.user.id,
            organization_id=project.organization_id,
            project_id=project.id,
            platform=data[""platform""],
            url=url,
        )
","
        Test that an analytics event is recorded when
        a first event with minified stack trace is received
        ",17,88,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_first_event_with_minified_stack_trace_received(self, record_analytics):
        
        now = timezone.now()
        project = self.create_project(first_event=now)
        project_created.send(project=project, user=self.user, sender=type(project))
        url = ""http://localhost:3000""
        data = load_data(""javascript"")
        data[""tags""] = [(""url"", url)]
        data[""exception""] = {
            ""values"": [
                {
                    **data[""exception""][""values""][0],
                    ""raw_stacktrace"": {
                        ""frames"": [
                            {
                                ""function"": ""o"",
                                ""filename"": ""/_static/dist/sentry/chunks/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.255071ceadabfb67483c.js"",
                                ""abs_path"": ""https://s1.sentry-cdn.com/_static/dist/sentry/chunks/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.255071ceadabfb67483c.js"",
                                ""lineno"": 2,
                                ""colno"": 37098,
                                ""pre_context"": [
                                    ""/*! For license information please see vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd. {snip}""
                                ],
                                ""context_line"": ""{snip} .apply(this,arguments);const i=o.map((e=>c(e,t)));return e.apply(this,i)}catch(e){throw l(),(0,i.$e)((n=>{n.addEventProcessor((e=>(t.mechani {snip}"",
                                ""post_context"": [
                                    ""//# sourceMappingURL=../sourcemaps/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.fe32 {snip}""
                                ],
                                ""in_app"": False,
                            },
                        ],
                    },
                }
            ]
        }

        self.store_event(
            project_id=project.id,
            data=data,
        )

        record_analytics.assert_called_with(
            ""first_event_with_minified_stack_trace_for_project.sent"",
            user_id=self.user.id,
            organization_id=project.organization_id,
            project_id=project.id,
            platform=data[""platform""],
            url=url,
        )

        ```
    ###Assistant : 
        Test that an analytics event is recorded when
        a first event with minified stack trace is received
        
    "
2739,"def arange(start, /, stop=None, step=1, *, dtype=None, meta=None, **kwargs):
        
        raise NotImplementedError

","Create an ascending or descending array

        Returns evenly spaced values within the half-open interval
        ``[start, stop)`` as a one-dimensional array.
        ",20,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def arange(start, /, stop=None, step=1, *, dtype=None, meta=None, **kwargs):
        
        raise NotImplementedError


        ```
    ###Assistant : Create an ascending or descending array

        Returns evenly spaced values within the half-open interval
        ``[start, stop)`` as a one-dimensional array.
        
    "
2740,"def best_checkpoints(self):
        
        checkpoints = sorted(self._top_persisted_checkpoints, key=lambda c: c.priority)
        return [wrapped.tracked_checkpoint for wrapped in checkpoints]
","Returns best PERSISTENT checkpoints, sorted by score.",7,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def best_checkpoints(self):
        
        checkpoints = sorted(self._top_persisted_checkpoints, key=lambda c: c.priority)
        return [wrapped.tracked_checkpoint for wrapped in checkpoints]

        ```
    ###Assistant : Returns best PERSISTENT checkpoints, sorted by score.
    "
2741,"def get_config_directory() -> Path:
    
    if os.getenv('NNI_CONFIG_DIR') is not None:
        config_dir = Path(os.getenv('NNI_CONFIG_DIR'))  # type: ignore
    elif sys.prefix != sys.base_prefix or Path(sys.prefix, 'conda-meta').is_dir():
        config_dir = Path(sys.prefix, 'nni')
    elif sys.platform == 'win32':
        config_dir = Path(os.environ['APPDATA'], 'nni')
    else:
        config_dir = Path.home() / '.config/nni'
    config_dir.mkdir(parents=True, exist_ok=True)
    return config_dir
","
    Get NNI config directory.
    Create it if not exist.
    ",9,44,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_config_directory() -> Path:
    
    if os.getenv('NNI_CONFIG_DIR') is not None:
        config_dir = Path(os.getenv('NNI_CONFIG_DIR'))  # type: ignore
    elif sys.prefix != sys.base_prefix or Path(sys.prefix, 'conda-meta').is_dir():
        config_dir = Path(sys.prefix, 'nni')
    elif sys.platform == 'win32':
        config_dir = Path(os.environ['APPDATA'], 'nni')
    else:
        config_dir = Path.home() / '.config/nni'
    config_dir.mkdir(parents=True, exist_ok=True)
    return config_dir

        ```
    ###Assistant : 
    Get NNI config directory.
    Create it if not exist.
    
    "
2742,"def test_background_update_min_batch_set_in_config(self):
        
        # a very long-running individual update
        duration_ms = 50

        self.get_success(
            self.store.db_pool.simple_insert(
                ""background_updates"",
                values={""update_name"": ""test_update"", ""progress_json"": '{""my_key"": 1}'},
            )
        )

        # Run the update with the long-running update item","
        Test that the minimum batch size set in the config is used
        ",12,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_background_update_min_batch_set_in_config(self):
        
        # a very long-running individual update
        duration_ms = 50

        self.get_success(
            self.store.db_pool.simple_insert(
                ""background_updates"",
                values={""update_name"": ""test_update"", ""progress_json"": '{""my_key"": 1}'},
            )
        )

        # Run the update with the long-running update item
        ```
    ###Assistant : 
        Test that the minimum batch size set in the config is used
        
    "
2743,"def swap_memory():
    
    mem = cext.virtual_mem()

    total_phys = mem[0]
    free_phys = mem[1]
    total_system = mem[2]
    free_system = mem[3]

    # Despite the name PageFile refers to total system memory here
    # thus physical memory values need to be subtracted to get swap values
    total = total_system - total_phys
    free = min(total, free_system - free_phys)
    used = total - free
    percent = usage_percent(used, total, round_=1)
    return _common.sswap(total, used, free, percent, 0, 0)


# =====================================================================
# --- disk
# =====================================================================


disk_io_counters = cext.disk_io_counters

","Swap system memory as a (total, used, free, sin, sout) tuple.",11,79,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def swap_memory():
    
    mem = cext.virtual_mem()

    total_phys = mem[0]
    free_phys = mem[1]
    total_system = mem[2]
    free_system = mem[3]

    # Despite the name PageFile refers to total system memory here
    # thus physical memory values need to be subtracted to get swap values
    total = total_system - total_phys
    free = min(total, free_system - free_phys)
    used = total - free
    percent = usage_percent(used, total, round_=1)
    return _common.sswap(total, used, free, percent, 0, 0)


# =====================================================================
# --- disk
# =====================================================================


disk_io_counters = cext.disk_io_counters


        ```
    ###Assistant : Swap system memory as a (total, used, free, sin, sout) tuple.
    "
2744,"def get_supplier_invoice_details():
	inv_details = {}
	for d in frappe.db.sql(
		,
		as_dict=1,
	):
		inv_details[d.name] = d.bill_no

	return inv_details

"," select name, bill_no from `tabPurchase Invoice`
		where docstatus = 1 and bill_no is not null and bill_no != '' ",19,17,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_supplier_invoice_details():
	inv_details = {}
	for d in frappe.db.sql(
		,
		as_dict=1,
	):
		inv_details[d.name] = d.bill_no

	return inv_details


        ```
    ###Assistant :  select name, bill_no from `tabPurchase Invoice`
		where docstatus = 1 and bill_no is not null and bill_no != '' 
    "
2745,"def dce_rpc_endianess(pkt):
    
    if pkt.endianness == 0:  # big endian
        return "">""
    elif pkt.endianness == 1:  # little endian
        return ""<""
    else:
        return ""!""

",Determine the right endianness sign for a given DCE/RPC packet,10,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def dce_rpc_endianess(pkt):
    
    if pkt.endianness == 0:  # big endian
        return "">""
    elif pkt.endianness == 1:  # little endian
        return ""<""
    else:
        return ""!""


        ```
    ###Assistant : Determine the right endianness sign for a given DCE/RPC packet
    "
2746,"def deserialize_object(model, fields, pk=None):
    
    content_type = ContentType.objects.get_for_model(model)
    if 'custom_fields' in fields:
        fields['custom_field_data'] = fields.pop('custom_fields')
    data = {
        'model': '.'.join(content_type.natural_key()),
        'pk': pk,
        'fields': fields,
    }
    instance = list(serializers.deserialize('python', [data]))[0]

    return instance

","
    Instantiate an object from the given model and field data. Functions as
    the complement to serialize_object().
    ",16,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def deserialize_object(model, fields, pk=None):
    
    content_type = ContentType.objects.get_for_model(model)
    if 'custom_fields' in fields:
        fields['custom_field_data'] = fields.pop('custom_fields')
    data = {
        'model': '.'.join(content_type.natural_key()),
        'pk': pk,
        'fields': fields,
    }
    instance = list(serializers.deserialize('python', [data]))[0]

    return instance


        ```
    ###Assistant : 
    Instantiate an object from the given model and field data. Functions as
    the complement to serialize_object().
    
    "
2747,"async def test_component_not_installed_if_requirement_fails(hass):
    
    hass.config.skip_pip = False
    mock_integration(hass, MockModule(""comp"", requirements=[""package==0.0.1""]))

    with patch(""homeassistant.util.package.install_package"", return_value=False):
        assert not await setup.async_setup_component(hass, ""comp"", {})

    assert ""comp"" not in hass.config.components

",Component setup should fail if requirement can't install.,8,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_component_not_installed_if_requirement_fails(hass):
    
    hass.config.skip_pip = False
    mock_integration(hass, MockModule(""comp"", requirements=[""package==0.0.1""]))

    with patch(""homeassistant.util.package.install_package"", return_value=False):
        assert not await setup.async_setup_component(hass, ""comp"", {})

    assert ""comp"" not in hass.config.components


        ```
    ###Assistant : Component setup should fail if requirement can't install.
    "
2748,"def use_numba_cb(key) -> None:
    from pandas.core.util import numba_

    numba_.set_use_numba(cf.get_option(key))


with cf.config_prefix(""compute""):
    cf.register_option(
        ""use_bottleneck"",
        True,
        use_bottleneck_doc,
        validator=is_bool,
        cb=use_bottleneck_cb,
    )
    cf.register_option(
        ""use_numexpr"", True, use_numexpr_doc, validator=is_bool, cb=use_numexpr_cb
    )
    cf.register_option(
        ""use_numba"", False, use_numba_doc, validator=is_bool, cb=use_numba_cb
    )
#
# options from the ""display"" namespace

pc_precision_doc = 

pc_colspace_doc = 

pc_max_rows_doc = 

pc_min_rows_doc = 

pc_max_cols_doc = 

pc_max_categories_doc = 

pc_max_info_cols_doc = 

pc_nb_repr_h_doc = 

pc_pprint_nest_depth = 

pc_multi_sparse_doc = 

float_format_doc = 

max_colwidth_doc = 

colheader_justify_doc = 

pc_expand_repr_doc = 

pc_show_dimensions_doc = 

pc_east_asian_width_doc = 

pc_ambiguous_as_wide_doc = 

pc_latex_repr_doc = 

pc_table_schema_doc = 

pc_html_border_doc = 

pc_html_use_mathjax_doc = 

pc_max_dir_items = 

pc_width_doc = 

pc_chop_threshold_doc = 

pc_max_seq_items = 

pc_max_info_rows_doc = 

pc_large_repr_doc = 

pc_memory_usage_doc = 

pc_latex_escape = 

pc_latex_longtable = 

pc_latex_multicolumn = 

pc_latex_multicolumn_format = 

pc_latex_multirow = 

","
: int
    Floating point output precision in terms of number of places after the
    decimal, for regular formatting as well as scientific notation. Similar
    to ``precision`` in :meth:`numpy.set_printoptions`.

: int
    Default space for DataFrame columns.

: int
    If max_rows is exceeded, switch to truncate view. Depending on
    `large_repr`, objects are either centrally truncated or printed as
    a summary view. 'None' value means unlimited.

    In case python/IPython is running in a terminal and `large_repr`
    equals 'truncate' this can be set to 0 and pandas will auto-detect
    the height of the terminal and print a truncated object which fits
    the screen height. The IPython notebook, IPython qtconsole, or
    IDLE do not run in a terminal and hence it is not possible to do
    correct auto-detection.

: int
    The numbers of rows to show in a truncated view (when `max_rows` is
    exceeded). Ignored when `max_rows` is set to None or 0. When set to
    None, follows the value of `max_rows`.

: int
    If max_cols is exceeded, switch to truncate view. Depending on
    `large_repr`, objects are either centrally truncated or printed as
    a summary view. 'None' value means unlimited.

    In case python/IPython is running in a terminal and `large_repr`
    equals 'truncate' this can be set to 0 and pandas will auto-detect
    the width of the terminal and print a truncated object which fits
    the screen width. The IPython notebook, IPython qtconsole, or IDLE
    do not run in a terminal and hence it is not possible to do
    correct auto-detection.

: int
    This sets the maximum number of categories pandas should output when
    printing out a `Categorical` or a Series of dtype ""category"".

: int
    max_info_columns is used in DataFrame.info method to decide if
    per column information will be printed.

: boolean
    When True, IPython notebook will use html representation for
    pandas objects (if it is available).

: int
    Controls the number of nested levels to process when pretty-printing

: boolean
    ""sparsify"" MultiIndex display (don't display repeated
    elements in outer levels within groups)

: callable
    The callable should accept a floating point number and return
    a string with the desired format of the number. This is used
    in some places like SeriesFormatter.
    See formats.format.EngFormatter for an example.

: int or None
    The maximum width in characters of a column in the repr of
    a pandas data structure. When the column overflows, a ""...""
    placeholder is embedded in the output. A 'None' value means unlimited.

: 'left'/'right'
    Controls the justification of column headers. used by DataFrameFormatter.

: boolean
    Whether to print out the full DataFrame repr for wide DataFrames across
    multiple lines, `max_columns` is still respected, but the output will
    wrap-around across multiple ""pages"" if its width exceeds `display.width`.

: boolean or 'truncate'
    Whether to print out dimensions at the end of DataFrame repr.
    If 'truncate' is specified, only print out the dimensions if the
    frame is truncated (e.g. not display all rows and/or columns)

: boolean
    Whether to use the Unicode East Asian Width to calculate the display text
    width.
    Enabling this may affect to the performance (default: False)

: boolean
    Whether to handle Unicode characters belong to Ambiguous as Wide (width=2)
    (default: False)

: boolean
    Whether to produce a latex DataFrame representation for jupyter
    environments that support it.
    (default: False)

: boolean
    Whether to publish a Table Schema representation for frontends
    that support it.
    (default: False)

: int
    A ``border=value`` attribute is inserted in the ``<table>`` tag
    for the DataFrame HTML repr.
\
: boolean
    When True, Jupyter notebook will process table contents using MathJax,
    rendering mathematical expressions enclosed by the dollar symbol.
    (default: True)
\
: int
    The number of items that will be added to `dir(...)`. 'None' value means
    unlimited. Because dir is cached, changing this option will not immediately
    affect already existing dataframes until a column is deleted or added.

    This is for instance used to suggest columns from a dataframe to tab
    completion.

: int
    Width of the display in characters. In case python/IPython is running in
    a terminal this can be set to None and pandas will correctly auto-detect
    the width.
    Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
    terminal and hence it is not possible to correctly detect the width.

: float or None
    if set to a float value, all float values smaller then the given threshold
    will be displayed as exactly 0 by repr and friends.

: int or None
    When pretty-printing a long sequence, no more then `max_seq_items`
    will be printed. If items are omitted, they will be denoted by the
    addition of ""..."" to the resulting string.

    If set to None, the number of items to be printed is unlimited.

: int or None
    df.info() will usually show null-counts for each column.
    For large frames this can be quite slow. max_info_rows and max_info_cols
    limit this null check only to frames with smaller dimensions than
    specified.

: 'truncate'/'info'
    For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
    show a truncated table (the default from 0.13), or switch to the view from
    df.info() (the behaviour in earlier versions of pandas).

: bool, string or None
    This specifies if the memory usage of a DataFrame should be displayed when
    df.info() is called. Valid values True,False,'deep'

: bool
    This specifies if the to_latex method of a Dataframe uses escapes special
    characters.
    Valid values: False,True

:bool
    This specifies if the to_latex method of a Dataframe uses the longtable
    format.
    Valid values: False,True

: bool
    This specifies if the to_latex method of a Dataframe uses multicolumns
    to pretty-print MultiIndex columns.
    Valid values: False,True

: string
    This specifies the format for multicolumn headers.
    Can be surrounded with '|'.
    Valid values: 'l', 'c', 'r', 'p{<width>}'

: bool
    This specifies if the to_latex method of a Dataframe uses multirows
    to pretty-print MultiIndex rows.
    Valid values: False,True
",960,105,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def use_numba_cb(key) -> None:
    from pandas.core.util import numba_

    numba_.set_use_numba(cf.get_option(key))


with cf.config_prefix(""compute""):
    cf.register_option(
        ""use_bottleneck"",
        True,
        use_bottleneck_doc,
        validator=is_bool,
        cb=use_bottleneck_cb,
    )
    cf.register_option(
        ""use_numexpr"", True, use_numexpr_doc, validator=is_bool, cb=use_numexpr_cb
    )
    cf.register_option(
        ""use_numba"", False, use_numba_doc, validator=is_bool, cb=use_numba_cb
    )
#
# options from the ""display"" namespace

pc_precision_doc = 

pc_colspace_doc = 

pc_max_rows_doc = 

pc_min_rows_doc = 

pc_max_cols_doc = 

pc_max_categories_doc = 

pc_max_info_cols_doc = 

pc_nb_repr_h_doc = 

pc_pprint_nest_depth = 

pc_multi_sparse_doc = 

float_format_doc = 

max_colwidth_doc = 

colheader_justify_doc = 

pc_expand_repr_doc = 

pc_show_dimensions_doc = 

pc_east_asian_width_doc = 

pc_ambiguous_as_wide_doc = 

pc_latex_repr_doc = 

pc_table_schema_doc = 

pc_html_border_doc = 

pc_html_use_mathjax_doc = 

pc_max_dir_items = 

pc_width_doc = 

pc_chop_threshold_doc = 

pc_max_seq_items = 

pc_max_info_rows_doc = 

pc_large_repr_doc = 

pc_memory_usage_doc = 

pc_latex_escape = 

pc_latex_longtable = 

pc_latex_multicolumn = 

pc_latex_multicolumn_format = 

pc_latex_multirow = 


        ```
    ###Assistant : 
: int
    Floating point output precision in terms of number of places after the
    decimal, for regular formatting as well as scientific notation. Similar
    to ``precision`` in :meth:`numpy.set_printoptions`.

: int
    Default space for DataFrame columns.

: int
    If max_rows is exceeded, switch to truncate view. Depending on
    `large_repr`, objects are either centrally truncated or printed as
    a summary view. 'None' value means unlimited.

    In case python/IPython is running in a terminal and `large_repr`
    equals 'truncate' this can be set to 0 and pandas will auto-detect
    the height of the terminal and print a truncated object which fits
    the screen height. The IPython notebook, IPython qtconsole, or
    IDLE do not run in a terminal and hence it is not possible to do
    correct auto-detection.

: int
    The numbers of rows to show in a truncated view (when `max_rows` is
    exceeded). Ignored when `max_rows` is set to None or 0. When set to
    None, follows the value of `max_rows`.

: int
    If max_cols is exceeded, switch to truncate view. Depending on
    `large_repr`, objects are either centrally truncated or printed as
    a summary view. 'None' value means unlimited.

    In case python/IPython is running in a terminal and `large_repr`
    equals 'truncate' this can be set to 0 and pandas will auto-detect
    the width of the terminal and print a truncated object which fits
    the screen width. The IPython notebook, IPython qtconsole, or IDLE
    do not run in a terminal and hence it is not possible to do
    correct auto-detection.

: int
    This sets the maximum number of categories pandas should output when
    printing out a `Categorical` or a Series of dtype ""category"".

: int
    max_info_columns is used in DataFrame.info method to decide if
    per column information will be printed.

: boolean
    When True, IPython notebook will use html representation for
    pandas objects (if it is available).

: int
    Controls the number of nested levels to process when pretty-printing

: boolean
    ""sparsify"" MultiIndex display (don't display repeated
    elements in outer levels within groups)

: callable
    The callable should accept a floating point number and return
    a string with the desired format of the number. This is used
    in some places like SeriesFormatter.
    See formats.format.EngFormatter for an example.

: int or None
    The maximum width in characters of a column in the repr of
    a pandas data structure. When the column overflows, a ""...""
    placeholder is embedded in the output. A 'None' value means unlimited.

: 'left'/'right'
    Controls the justification of column headers. used by DataFrameFormatter.

: boolean
    Whether to print out the full DataFrame repr for wide DataFrames across
    multiple lines, `max_columns` is still respected, but the output will
    wrap-around across multiple ""pages"" if its width exceeds `display.width`.

: boolean or 'truncate'
    Whether to print out dimensions at the end of DataFrame repr.
    If 'truncate' is specified, only print out the dimensions if the
    frame is truncated (e.g. not display all rows and/or columns)

: boolean
    Whether to use the Unicode East Asian Width to calculate the display text
    width.
    Enabling this may affect to the performance (default: False)

: boolean
    Whether to handle Unicode characters belong to Ambiguous as Wide (width=2)
    (default: False)

: boolean
    Whether to produce a latex DataFrame representation for jupyter
    environments that support it.
    (default: False)

: boolean
    Whether to publish a Table Schema representation for frontends
    that support it.
    (default: False)

: int
    A ``border=value`` attribute is inserted in the ``<table>`` tag
    for the DataFrame HTML repr.
\
: boolean
    When True, Jupyter notebook will process table contents using MathJax,
    rendering mathematical expressions enclosed by the dollar symbol.
    (default: True)
\
: int
    The number of items that will be added to `dir(...)`. 'None' value means
    unlimited. Because dir is cached, changing this option will not immediately
    affect already existing dataframes until a column is deleted or added.

    This is for instance used to suggest columns from a dataframe to tab
    completion.

: int
    Width of the display in characters. In case python/IPython is running in
    a terminal this can be set to None and pandas will correctly auto-detect
    the width.
    Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
    terminal and hence it is not possible to correctly detect the width.

: float or None
    if set to a float value, all float values smaller then the given threshold
    will be displayed as exactly 0 by repr and friends.

: int or None
    When pretty-printing a long sequence, no more then `max_seq_items`
    will be printed. If items are omitted, they will be denoted by the
    addition of ""..."" to the resulting string.

    If set to None, the number of items to be printed is unlimited.

: int or None
    df.info() will usually show null-counts for each column.
    For large frames this can be quite slow. max_info_rows and max_info_cols
    limit this null check only to frames with smaller dimensions than
    specified.

: 'truncate'/'info'
    For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
    show a truncated table (the default from 0.13), or switch to the view from
    df.info() (the behaviour in earlier versions of pandas).

: bool, string or None
    This specifies if the memory usage of a DataFrame should be displayed when
    df.info() is called. Valid values True,False,'deep'

: bool
    This specifies if the to_latex method of a Dataframe uses escapes special
    characters.
    Valid values: False,True

:bool
    This specifies if the to_latex method of a Dataframe uses the longtable
    format.
    Valid values: False,True

: bool
    This specifies if the to_latex method of a Dataframe uses multicolumns
    to pretty-print MultiIndex columns.
    Valid values: False,True

: string
    This specifies the format for multicolumn headers.
    Can be surrounded with '|'.
    Valid values: 'l', 'c', 'r', 'p{<width>}'

: bool
    This specifies if the to_latex method of a Dataframe uses multirows
    to pretty-print MultiIndex rows.
    Valid values: False,True

    "
2749,"def test_twitter_tag(self) -> None:
        
        html = b
        tree = decode_body(html, ""http://example.com/test.html"")
        og = parse_html_to_open_graph(tree)
        self.assertEqual(
            og,
            {
                ""og:title"": None,
                ""og:description"": ""Description"",
                ""og:site_name"": ""@matrixdotorg"",
            },
        )

        # But they shouldn't override Open Graph values.
        html = b
        tree = decode_body(html, ""http://example.com/test.html"")
        og = parse_html_to_open_graph(tree)
        self.assertEqual(
            og,
            {
                ""og:title"": None,
                ""og:description"": ""Real Description"",
                ""og:site_name"": ""matrix.org"",
            },
        )

","Twitter card tags should be used if nothing else is available.
        <html>
        <meta name=""twitter:card"" content=""summary"">
        <meta name=""twitter:description"" content=""Description"">
        <meta name=""twitter:site"" content=""@matrixdotorg"">
        </html>
        
        <html>
        <meta name=""twitter:card"" content=""summary"">
        <meta name=""twitter:description"" content=""Description"">
        <meta property=""og:description"" content=""Real Description"">
        <meta name=""twitter:site"" content=""@matrixdotorg"">
        <meta property=""og:site_name"" content=""matrix.org"">
        </html>
        ",40,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_twitter_tag(self) -> None:
        
        html = b
        tree = decode_body(html, ""http://example.com/test.html"")
        og = parse_html_to_open_graph(tree)
        self.assertEqual(
            og,
            {
                ""og:title"": None,
                ""og:description"": ""Description"",
                ""og:site_name"": ""@matrixdotorg"",
            },
        )

        # But they shouldn't override Open Graph values.
        html = b
        tree = decode_body(html, ""http://example.com/test.html"")
        og = parse_html_to_open_graph(tree)
        self.assertEqual(
            og,
            {
                ""og:title"": None,
                ""og:description"": ""Real Description"",
                ""og:site_name"": ""matrix.org"",
            },
        )


        ```
    ###Assistant : Twitter card tags should be used if nothing else is available.
        <html>
        <meta name=""twitter:card"" content=""summary"">
        <meta name=""twitter:description"" content=""Description"">
        <meta name=""twitter:site"" content=""@matrixdotorg"">
        </html>
        
        <html>
        <meta name=""twitter:card"" content=""summary"">
        <meta name=""twitter:description"" content=""Description"">
        <meta property=""og:description"" content=""Real Description"">
        <meta name=""twitter:site"" content=""@matrixdotorg"">
        <meta property=""og:site_name"" content=""matrix.org"">
        </html>
        
    "
2750,"def get_tax_template(posting_date, args):
	
	args = frappe._dict(args)
	conditions = []

	if posting_date:
		conditions.append(
			f
		)
	else:
		conditions.append(""(from_date is null) and (to_date is null)"")

	conditions.append(
		""ifnull(tax_category, '') = {0}"".format(frappe.db.escape(cstr(args.get(""tax_category""))))
	)
	if ""tax_category"" in args.keys():
		del args[""tax_category""]

	for key, value in args.items():
		if key == ""use_for_shopping_cart"":
			conditions.append(""use_for_shopping_cart = {0}"".format(1 if value else 0))
		elif key == ""customer_group"":
			if not value:
				value = get_root_of(""Customer Group"")
			customer_group_condition = get_customer_group_condition(value)
			conditions.append(""ifnull({0}, '') in ('', {1})"".format(key, customer_group_condition))
		else:
			conditions.append(""ifnull({0}, '') in ('', {1})"".format(key, frappe.db.escape(cstr(value))))

	tax_rule = frappe.db.sql(
		.format(
			"" and "".join(conditions)
		),
		as_dict=True,
	)

	if not tax_rule:
		return None

	for rule in tax_rule:
		rule.no_of_keys_matched = 0
		for key in args:
			if rule.get(key):
				rule.no_of_keys_matched += 1

	def cmp(a, b):
		# refernce: https://docs.python.org/3.0/whatsnew/3.0.html#ordering-comparisons
		return int(a > b) - int(a < b)

	rule = sorted(
		tax_rule,
		key=functools.cmp_to_key(
			lambda b, a: cmp(a.no_of_keys_matched, b.no_of_keys_matched) or cmp(a.priority, b.priority)
		),
	)[0]

	tax_template = rule.sales_tax_template or rule.purchase_tax_template
	doctype = ""{0} Taxes and Charges Template"".format(rule.tax_type)

	if frappe.db.get_value(doctype, tax_template, ""disabled"") == 1:
		return None

	return tax_template

","Get matching tax rule(from_date is null or from_date <= '{posting_date}')
			and (to_date is null or to_date >= '{posting_date}')select * from `tabTax Rule`
		where {0}",24,159,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_tax_template(posting_date, args):
	
	args = frappe._dict(args)
	conditions = []

	if posting_date:
		conditions.append(
			f
		)
	else:
		conditions.append(""(from_date is null) and (to_date is null)"")

	conditions.append(
		""ifnull(tax_category, '') = {0}"".format(frappe.db.escape(cstr(args.get(""tax_category""))))
	)
	if ""tax_category"" in args.keys():
		del args[""tax_category""]

	for key, value in args.items():
		if key == ""use_for_shopping_cart"":
			conditions.append(""use_for_shopping_cart = {0}"".format(1 if value else 0))
		elif key == ""customer_group"":
			if not value:
				value = get_root_of(""Customer Group"")
			customer_group_condition = get_customer_group_condition(value)
			conditions.append(""ifnull({0}, '') in ('', {1})"".format(key, customer_group_condition))
		else:
			conditions.append(""ifnull({0}, '') in ('', {1})"".format(key, frappe.db.escape(cstr(value))))

	tax_rule = frappe.db.sql(
		.format(
			"" and "".join(conditions)
		),
		as_dict=True,
	)

	if not tax_rule:
		return None

	for rule in tax_rule:
		rule.no_of_keys_matched = 0
		for key in args:
			if rule.get(key):
				rule.no_of_keys_matched += 1

	def cmp(a, b):
		# refernce: https://docs.python.org/3.0/whatsnew/3.0.html#ordering-comparisons
		return int(a > b) - int(a < b)

	rule = sorted(
		tax_rule,
		key=functools.cmp_to_key(
			lambda b, a: cmp(a.no_of_keys_matched, b.no_of_keys_matched) or cmp(a.priority, b.priority)
		),
	)[0]

	tax_template = rule.sales_tax_template or rule.purchase_tax_template
	doctype = ""{0} Taxes and Charges Template"".format(rule.tax_type)

	if frappe.db.get_value(doctype, tax_template, ""disabled"") == 1:
		return None

	return tax_template


        ```
    ###Assistant : Get matching tax rule(from_date is null or from_date <= '{posting_date}')
			and (to_date is null or to_date >= '{posting_date}')select * from `tabTax Rule`
		where {0}
    "
2751,"def wheel_graph(n, create_using=None):
    
    _, nodes = n
    G = empty_graph(nodes, create_using)
    if G.is_directed():
        raise NetworkXError(""Directed Graph not supported"")

    if len(nodes) > 1:
        hub, *rim = nodes
        G.add_edges_from((hub, node) for node in rim)
        if len(rim) > 1:
            G.add_edges_from(pairwise(rim, cyclic=True))
    return G

","Return the wheel graph

    The wheel graph consists of a hub node connected to a cycle of (n-1) nodes.

    Parameters
    ----------
    n : int or iterable
        If an integer, node labels are 0 to n with center 0.
        If an iterable of nodes, the center is the first.
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Node labels are the integers 0 to n - 1.
    ",76,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def wheel_graph(n, create_using=None):
    
    _, nodes = n
    G = empty_graph(nodes, create_using)
    if G.is_directed():
        raise NetworkXError(""Directed Graph not supported"")

    if len(nodes) > 1:
        hub, *rim = nodes
        G.add_edges_from((hub, node) for node in rim)
        if len(rim) > 1:
            G.add_edges_from(pairwise(rim, cyclic=True))
    return G


        ```
    ###Assistant : Return the wheel graph

    The wheel graph consists of a hub node connected to a cycle of (n-1) nodes.

    Parameters
    ----------
    n : int or iterable
        If an integer, node labels are 0 to n with center 0.
        If an iterable of nodes, the center is the first.
    create_using : NetworkX graph constructor, optional (default=nx.Graph)
       Graph type to create. If graph instance, then cleared before populated.

    Node labels are the integers 0 to n - 1.
    
    "
2752,"def get_changes():
    
    with open(HISTORY) as f:
        lines = f.readlines()

    block = []

    # eliminate the part preceding the first block
    for i, line in enumerate(lines):
        line = lines.pop(0)
        if line.startswith('===='):
            break
    lines.pop(0)

    for i, line in enumerate(lines):
        line = lines.pop(0)
        line = line.rstrip()
        if re.match(r""^- \d+_"", line):
            line = re.sub(r""^- (\d+)_"", r""- #\1"", line)

        if line.startswith('===='):
            break
        block.append(line)

    # eliminate bottom empty lines
    block.pop(-1)
    while not block[-1]:
        block.pop(-1)

    return ""\n"".join(block)

","Get the most recent changes for this release by parsing
    HISTORY.rst file.
    ",12,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_changes():
    
    with open(HISTORY) as f:
        lines = f.readlines()

    block = []

    # eliminate the part preceding the first block
    for i, line in enumerate(lines):
        line = lines.pop(0)
        if line.startswith('===='):
            break
    lines.pop(0)

    for i, line in enumerate(lines):
        line = lines.pop(0)
        line = line.rstrip()
        if re.match(r""^- \d+_"", line):
            line = re.sub(r""^- (\d+)_"", r""- #\1"", line)

        if line.startswith('===='):
            break
        block.append(line)

    # eliminate bottom empty lines
    block.pop(-1)
    while not block[-1]:
        block.pop(-1)

    return ""\n"".join(block)


        ```
    ###Assistant : Get the most recent changes for this release by parsing
    HISTORY.rst file.
    
    "
2753,"def _check_valid_data(self) -> bool:
        
        logger.debug(""Validating data. %s"",
                     {key: len(val) for key, val in self._display_data.stats.items()})
        if any(len(val) == 0  # pylint:disable=len-as-condition
               for val in self._display_data.stats.values()):
            return False
        return True
"," Check that the selections holds valid data to display
            NB: len-as-condition is used as data could be a list or a numpy array

        Returns
        -------
        bool
            ``True` if there is data to be displayed, otherwise ``False``
        ",36,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_valid_data(self) -> bool:
        
        logger.debug(""Validating data. %s"",
                     {key: len(val) for key, val in self._display_data.stats.items()})
        if any(len(val) == 0  # pylint:disable=len-as-condition
               for val in self._display_data.stats.values()):
            return False
        return True

        ```
    ###Assistant :  Check that the selections holds valid data to display
            NB: len-as-condition is used as data could be a list or a numpy array

        Returns
        -------
        bool
            ``True` if there is data to be displayed, otherwise ``False``
        
    "
2754,"def _async_check_unavailable_groups_with_random_macs(self) -> None:
        
        now = MONOTONIC_TIME()
        gone_unavailable = [
            group_id
            for group_id in self._group_ids_random_macs
            if group_id not in self._unavailable_group_ids
            and (service_info := self._last_seen_by_group_id.get(group_id))
            and (
                # We will not be callbacks for iBeacons with random macs
                # that rotate infrequently since their advertisement data is
                # does not change as the bluetooth.async_register_callback API
                # suppresses callbacks for duplicate advertisements to avoid
                # exposing integrations to the firehose of bluetooth advertisements.
                #
                # To solve this we need to ask for the latest service info for
                # the address we last saw to get the latest timestamp.
                #
                # If there is no last service info for the address we know that
                # the device is no longer advertising.
                not (
                    latest_service_info := bluetooth.async_last_service_info(
                        self.hass, service_info.address, connectable=False
                    )
                )
                or now - latest_service_info.time > UNAVAILABLE_TIMEOUT
            )
        ]
        for group_id in gone_unavailable:
            self._unavailable_group_ids.add(group_id)
            async_dispatcher_send(self.hass, signal_unavailable(group_id))
",Check for random mac groups that have not been seen in a while and mark them as unavailable.,18,144,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _async_check_unavailable_groups_with_random_macs(self) -> None:
        
        now = MONOTONIC_TIME()
        gone_unavailable = [
            group_id
            for group_id in self._group_ids_random_macs
            if group_id not in self._unavailable_group_ids
            and (service_info := self._last_seen_by_group_id.get(group_id))
            and (
                # We will not be callbacks for iBeacons with random macs
                # that rotate infrequently since their advertisement data is
                # does not change as the bluetooth.async_register_callback API
                # suppresses callbacks for duplicate advertisements to avoid
                # exposing integrations to the firehose of bluetooth advertisements.
                #
                # To solve this we need to ask for the latest service info for
                # the address we last saw to get the latest timestamp.
                #
                # If there is no last service info for the address we know that
                # the device is no longer advertising.
                not (
                    latest_service_info := bluetooth.async_last_service_info(
                        self.hass, service_info.address, connectable=False
                    )
                )
                or now - latest_service_info.time > UNAVAILABLE_TIMEOUT
            )
        ]
        for group_id in gone_unavailable:
            self._unavailable_group_ids.add(group_id)
            async_dispatcher_send(self.hass, signal_unavailable(group_id))

        ```
    ###Assistant : Check for random mac groups that have not been seen in a while and mark them as unavailable.
    "
2755,"def restore_optimizers_and_schedulers(self) -> None:
        
        if not self._loaded_checkpoint:
            return

        if self.trainer.strategy.lightning_restore_optimizer:
            # validation
            if ""optimizer_states"" not in self._loaded_checkpoint:
                raise KeyError(
                    ""Trying to restore optimizer state but checkpoint contains only the model.""
                    "" This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.""
                )
            self.restore_optimizers()

        if ""lr_schedulers"" not in self._loaded_checkpoint:
            raise KeyError(
                ""Trying to restore learning rate scheduler state but checkpoint contains only the model.""
                "" This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.""
            )
        self.restore_lr_schedulers()
",Restores the optimizers and learning rate scheduler states from the pre-loaded checkpoint.,12,76,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def restore_optimizers_and_schedulers(self) -> None:
        
        if not self._loaded_checkpoint:
            return

        if self.trainer.strategy.lightning_restore_optimizer:
            # validation
            if ""optimizer_states"" not in self._loaded_checkpoint:
                raise KeyError(
                    ""Trying to restore optimizer state but checkpoint contains only the model.""
                    "" This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.""
                )
            self.restore_optimizers()

        if ""lr_schedulers"" not in self._loaded_checkpoint:
            raise KeyError(
                ""Trying to restore learning rate scheduler state but checkpoint contains only the model.""
                "" This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.""
            )
        self.restore_lr_schedulers()

        ```
    ###Assistant : Restores the optimizers and learning rate scheduler states from the pre-loaded checkpoint.
    "
2756,"def urldefragauth(url):
    
    scheme, netloc, path, params, query, fragment = urlparse(url)

    # see func:`prepend_scheme_if_needed`
    if not netloc:
        netloc, path = path, netloc

    netloc = netloc.rsplit(""@"", 1)[-1]

    return urlunparse((scheme, netloc, path, params, query, """"))

","
    Given a url remove the fragment and the authentication part.

    :rtype: str
    ",12,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def urldefragauth(url):
    
    scheme, netloc, path, params, query, fragment = urlparse(url)

    # see func:`prepend_scheme_if_needed`
    if not netloc:
        netloc, path = path, netloc

    netloc = netloc.rsplit(""@"", 1)[-1]

    return urlunparse((scheme, netloc, path, params, query, """"))


        ```
    ###Assistant : 
    Given a url remove the fragment and the authentication part.

    :rtype: str
    
    "
2757,"def test_approval_not_required(self) -> None:
        
        self.get_success(self.store.register_user(self.user_id, self.pwhash))

        user = self.get_success(self.store.get_user_by_id(self.user_id))
        assert user is not None
        self.assertTrue(user[""approved""])

        approved = self.get_success(self.store.is_user_approved(self.user_id))
        self.assertTrue(approved)
","Tests that if we don't require approval for new accounts, newly created
        accounts are automatically marked as approved.
        ",18,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_approval_not_required(self) -> None:
        
        self.get_success(self.store.register_user(self.user_id, self.pwhash))

        user = self.get_success(self.store.get_user_by_id(self.user_id))
        assert user is not None
        self.assertTrue(user[""approved""])

        approved = self.get_success(self.store.is_user_approved(self.user_id))
        self.assertTrue(approved)

        ```
    ###Assistant : Tests that if we don't require approval for new accounts, newly created
        accounts are automatically marked as approved.
        
    "
2758,"def get_all(self, name, failobj=None):
        
        values = []
        name = name.lower()
        for k, v in self._headers:
            if k.lower() == name:
                values.append(self.policy.header_fetch_parse(k, v))
        if not values:
            return failobj
        return values
","Return a list of all the values for the named field.

        These will be sorted in the order they appeared in the original
        message, and may contain duplicates.  Any fields deleted and
        re-inserted are always appended to the header list.

        If no such fields exist, failobj is returned (defaults to None).
        ",51,28,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_all(self, name, failobj=None):
        
        values = []
        name = name.lower()
        for k, v in self._headers:
            if k.lower() == name:
                values.append(self.policy.header_fetch_parse(k, v))
        if not values:
            return failobj
        return values

        ```
    ###Assistant : Return a list of all the values for the named field.

        These will be sorted in the order they appeared in the original
        message, and may contain duplicates.  Any fields deleted and
        re-inserted are always appended to the header list.

        If no such fields exist, failobj is returned (defaults to None).
        
    "
2759,"def test_explorer_private_child(self):
        
        response = self.client.get(
            reverse(""wagtailadmin_explore"", args=(self.private_child_page.id,))
        )

        # Check the response
        self.assertEqual(response.status_code, 200)

        # Check the privacy indicator is public
        self.assertTemplateUsed(response, ""wagtailadmin/pages/_privacy_switch.html"")
        self.assertContains(response, '<div class=""privacy-indicator private"">')
        self.assertNotContains(response, '<div class=""privacy-indicator public"">')
","
        This tests that the privacy indicator on the private child pages explore view is set to ""PRIVATE""
        ",17,31,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_explorer_private_child(self):
        
        response = self.client.get(
            reverse(""wagtailadmin_explore"", args=(self.private_child_page.id,))
        )

        # Check the response
        self.assertEqual(response.status_code, 200)

        # Check the privacy indicator is public
        self.assertTemplateUsed(response, ""wagtailadmin/pages/_privacy_switch.html"")
        self.assertContains(response, '<div class=""privacy-indicator private"">')
        self.assertNotContains(response, '<div class=""privacy-indicator public"">')

        ```
    ###Assistant : 
        This tests that the privacy indicator on the private child pages explore view is set to ""PRIVATE""
        
    "
2760,"def find_backend(line):
    
    if _re_test_backend.search(line) is None:
        return None
    backends = [b[0] for b in _re_backend.findall(line)]
    backends.sort()
    return ""_and_"".join(backends)

",Find one (or multiple) backend in a code line of the init.,12,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_backend(line):
    
    if _re_test_backend.search(line) is None:
        return None
    backends = [b[0] for b in _re_backend.findall(line)]
    backends.sort()
    return ""_and_"".join(backends)


        ```
    ###Assistant : Find one (or multiple) backend in a code line of the init.
    "
2761,"def subgraph_view(G, filter_node=no_filter, filter_edge=no_filter):
    
    newG = nx.freeze(G.__class__())
    newG._NODE_OK = filter_node
    newG._EDGE_OK = filter_edge

    # create view by assigning attributes from G
    newG._graph = G
    newG.graph = G.graph

    newG._node = FilterAtlas(G._node, filter_node)
    if G.is_multigraph():
        Adj = FilterMultiAdjacency
","View of `G` applying a filter on nodes and edges.

    `subgraph_view` provides a read-only view of the input graph that excludes
    nodes and edges based on the outcome of two filter functions `filter_node`
    and `filter_edge`.

    The `filter_node` function takes one argument --- the node --- and returns
    `True` if the node should be included in the subgraph, and `False` if it
    should not be included.

    The `filter_edge` function takes two (or three arguments if `G` is a
    multi-graph) --- the nodes describing an edge, plus the edge-key if
    parallel edges are possible --- and returns `True` if the edge should be
    included in the subgraph, and `False` if it should not be included.

    Both node and edge filter functions are called on graph elements as they
    are queried, meaning there is no up-front cost to creating the view.

    Parameters
    ----------
    G : networkx.Graph
        A directed/undirected graph/multigraph

    filter_node : callable, optional
        A function taking a node as input, which returns `True` if the node
        should appear in the view.

    filter_edge : callable, optional
        A function taking as input the two nodes describing an edge (plus the
        edge-key if `G` is a multi-graph), which returns `True` if the edge
        should appear in the view.

    Returns
    -------
    graph : networkx.Graph
        A read-only graph view of the input graph.

    Examples
    --------
    >>> G = nx.path_graph(6)

    Filter functions operate on the node, and return `True` if the node should
    appear in the view:

    >>> def filter_node(n1):
    ...     return n1 != 5
    ...
    >>> view = nx.subgraph_view(G, filter_node=filter_node)
    >>> view.nodes()
    NodeView((0, 1, 2, 3, 4))

    We can use a closure pattern to filter graph elements based on additional
    data --- for example, filtering on edge data attached to the graph:

    >>> G[3][4][""cross_me""] = False
    >>> def filter_edge(n1, n2):
    ...     return G[n1][n2].get(""cross_me"", True)
    ...
    >>> view = nx.subgraph_view(G, filter_edge=filter_edge)
    >>> view.edges()
    EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)])

    >>> view = nx.subgraph_view(G, filter_node=filter_node, filter_edge=filter_edge,)
    >>> view.nodes()
    NodeView((0, 1, 2, 3, 4))
    >>> view.edges()
    EdgeView([(0, 1), (1, 2), (2, 3)])
    ",333,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def subgraph_view(G, filter_node=no_filter, filter_edge=no_filter):
    
    newG = nx.freeze(G.__class__())
    newG._NODE_OK = filter_node
    newG._EDGE_OK = filter_edge

    # create view by assigning attributes from G
    newG._graph = G
    newG.graph = G.graph

    newG._node = FilterAtlas(G._node, filter_node)
    if G.is_multigraph():
        Adj = FilterMultiAdjacency

        ```
    ###Assistant : View of `G` applying a filter on nodes and edges.

    `subgraph_view` provides a read-only view of the input graph that excludes
    nodes and edges based on the outcome of two filter functions `filter_node`
    and `filter_edge`.

    The `filter_node` function takes one argument --- the node --- and returns
    `True` if the node should be included in the subgraph, and `False` if it
    should not be included.

    The `filter_edge` function takes two (or three arguments if `G` is a
    multi-graph) --- the nodes describing an edge, plus the edge-key if
    parallel edges are possible --- and returns `True` if the edge should be
    included in the subgraph, and `False` if it should not be included.

    Both node and edge filter functions are called on graph elements as they
    are queried, meaning there is no up-front cost to creating the view.

    Parameters
    ----------
    G : networkx.Graph
        A directed/undirected graph/multigraph

    filter_node : callable, optional
        A function taking a node as input, which returns `True` if the node
        should appear in the view.

    filter_edge : callable, optional
        A function taking as input the two nodes describing an edge (plus the
        edge-key if `G` is a multi-graph), which returns `True` if the edge
        should appear in the view.

    Returns
    -------
    graph : networkx.Graph
        A read-only graph view of the input graph.

    Examples
    --------
    >>> G = nx.path_graph(6)

    Filter functions operate on the node, and return `True` if the node should
    appear in the view:

    >>> def filter_node(n1):
    ...     return n1 != 5
    ...
    >>> view = nx.subgraph_view(G, filter_node=filter_node)
    >>> view.nodes()
    NodeView((0, 1, 2, 3, 4))

    We can use a closure pattern to filter graph elements based on additional
    data --- for example, filtering on edge data attached to the graph:

    >>> G[3][4][""cross_me""] = False
    >>> def filter_edge(n1, n2):
    ...     return G[n1][n2].get(""cross_me"", True)
    ...
    >>> view = nx.subgraph_view(G, filter_edge=filter_edge)
    >>> view.edges()
    EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)])

    >>> view = nx.subgraph_view(G, filter_node=filter_node, filter_edge=filter_edge,)
    >>> view.nodes()
    NodeView((0, 1, 2, 3, 4))
    >>> view.edges()
    EdgeView([(0, 1), (1, 2), (2, 3)])
    
    "
2762,"def _deprecate_ci(errorbar, ci):
    
    if ci != ""deprecated"":
        if ci is None:
            errorbar = None
        elif ci == ""sd"":
            errorbar = ""sd""
        else:
            errorbar = (""ci"", ci)
        msg = (
            ""\n\nThe `ci` parameter is deprecated. ""
            f""Use `errorbar={repr(errorbar)}` for the same effect.\n""
        )
        warnings.warn(msg, FutureWarning, stacklevel=3)

    return errorbar
","
    Warn on usage of ci= and convert to appropriate errorbar= arg.

    ci was deprecated when errorbar was added in 0.12. It should not be removed
    completely for some time, but it can be moved out of function definitions
    (and extracted from kwargs) after one cycle.

    ",45,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _deprecate_ci(errorbar, ci):
    
    if ci != ""deprecated"":
        if ci is None:
            errorbar = None
        elif ci == ""sd"":
            errorbar = ""sd""
        else:
            errorbar = (""ci"", ci)
        msg = (
            ""\n\nThe `ci` parameter is deprecated. ""
            f""Use `errorbar={repr(errorbar)}` for the same effect.\n""
        )
        warnings.warn(msg, FutureWarning, stacklevel=3)

    return errorbar

        ```
    ###Assistant : 
    Warn on usage of ci= and convert to appropriate errorbar= arg.

    ci was deprecated when errorbar was added in 0.12. It should not be removed
    completely for some time, but it can be moved out of function definitions
    (and extracted from kwargs) after one cycle.

    
    "
2763,"def __sub__(self, other):
        
        if self._delegate_binop(other):
            return NotImplemented
        return np.subtract(self, other)
","
        Subtract other from self, and return a new masked array.

        ",10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __sub__(self, other):
        
        if self._delegate_binop(other):
            return NotImplemented
        return np.subtract(self, other)

        ```
    ###Assistant : 
        Subtract other from self, and return a new masked array.

        
    "
2764,"def mock_smile_adam_2() -> Generator[None, MagicMock, None]:
    
    chosen_env = ""m_adam_heating""

    with patch(
        ""homeassistant.components.plugwise.gateway.Smile"", autospec=True
    ) as smile_mock:
        smile = smile_mock.return_value

        smile.gateway_id = ""da224107914542988a88561b4452b0f6""
        smile.heater_id = ""056ee145a816487eaa69243c3280f8bf""
        smile.smile_version = ""3.6.4""
        smile.smile_type = ""thermostat""
        smile.smile_hostname = ""smile98765""
        smile.smile_name = ""Adam""

        smile.connect.return_value = True

        smile.notifications = _read_json(chosen_env, ""notifications"")
        smile.async_update.return_value = _read_json(chosen_env, ""all_data"")

        yield smile


@pytest.fixture",Create a 2nd Mock Adam environment for testing exceptions.,9,51,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def mock_smile_adam_2() -> Generator[None, MagicMock, None]:
    
    chosen_env = ""m_adam_heating""

    with patch(
        ""homeassistant.components.plugwise.gateway.Smile"", autospec=True
    ) as smile_mock:
        smile = smile_mock.return_value

        smile.gateway_id = ""da224107914542988a88561b4452b0f6""
        smile.heater_id = ""056ee145a816487eaa69243c3280f8bf""
        smile.smile_version = ""3.6.4""
        smile.smile_type = ""thermostat""
        smile.smile_hostname = ""smile98765""
        smile.smile_name = ""Adam""

        smile.connect.return_value = True

        smile.notifications = _read_json(chosen_env, ""notifications"")
        smile.async_update.return_value = _read_json(chosen_env, ""all_data"")

        yield smile


@pytest.fixture
        ```
    ###Assistant : Create a 2nd Mock Adam environment for testing exceptions.
    "
2765,"def plot_wireframe(self, X, Y, Z, **kwargs):
        

        had_data = self.has_data()
        if Z.ndim != 2:
            raise ValueError(""Argument Z must be 2-dimensional."")
        # FIXME: Support masked arrays
        X, Y, Z = np.broadcast_arrays(X, Y, Z)
        rows, cols = Z.shape

        has_stride = 'rstride' in kwargs or 'cstride' in kwargs
        has_count = 'rcount' in kwargs or 'ccount' in kwargs

        if has_stride and has_count:
            raise ValueError(""Cannot specify both stride and count arguments"")

        rstride = kwargs.pop('rstride', 1)
        cstride = kwargs.pop('cstride', 1)
        rcount = kwargs.pop('rcount', 50)
        ccount = kwargs.pop('ccount', 50)

        if rcParams['_internal.classic_mode']:
            # Strides have priority over counts in classic mode.
            # So, only compute strides from counts
            # if counts were explicitly given
            if has_count:
                rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0
                cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0
        else:
            # If the strides are provided then it has priority.
            # Otherwise, compute the strides from the counts.
            if not has_stride:
                rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0
                cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0

        # We want two sets of lines, one running along the ""rows"" of
        # Z and another set of lines running along the ""columns"" of Z.
        # This transpose will make it easy to obtain the columns.
        tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)

        if rstride:
            rii = list(range(0, rows, rstride))
            # Add the last index only if needed
            if rows > 0 and rii[-1] != (rows - 1):
                rii += [rows-1]
        else:
            rii = []
        if cstride:
            cii = list(range(0, cols, cstride))
            # Add the last index only if needed
            if cols > 0 and cii[-1] != (cols - 1):
                cii += [cols-1]
        else:
            cii = []

        if rstride == 0 and cstride == 0:
            raise ValueError(""Either rstride or cstride must be non zero"")

        # If the inputs were empty, then just
        # reset everything.
        if Z.size == 0:
            rii = []
            cii = []

        xlines = [X[i] for i in rii]
        ylines = [Y[i] for i in rii]
        zlines = [Z[i] for i in rii]

        txlines = [tX[i] for i in cii]
        tylines = [tY[i] for i in cii]
        tzlines = [tZ[i] for i in cii]

        lines = ([list(zip(xl, yl, zl))
                 for xl, yl, zl in zip(xlines, ylines, zlines)]
                 + [list(zip(xl, yl, zl))
                 for xl, yl, zl in zip(txlines, tylines, tzlines)])

        linec = art3d.Line3DCollection(lines, **kwargs)
        self.add_collection(linec)
        self.auto_scale_xyz(X, Y, Z, had_data)

        return linec
","
        Plot a 3D wireframe.

        .. note::

           The *rcount* and *ccount* kwargs, which both default to 50,
           determine the maximum number of samples used in each direction.  If
           the input data is larger, it will be downsampled (by slicing) to
           these numbers of points.

        Parameters
        ----------
        X, Y, Z : 2D arrays
            Data values.

        rcount, ccount : int
            Maximum number of samples used in each direction.  If the input
            data is larger, it will be downsampled (by slicing) to these
            numbers of points.  Setting a count to zero causes the data to be
            not sampled in the corresponding direction, producing a 3D line
            plot rather than a wireframe plot.  Defaults to 50.

        rstride, cstride : int
            Downsampling stride in each direction.  These arguments are
            mutually exclusive with *rcount* and *ccount*.  If only one of
            *rstride* or *cstride* is set, the other defaults to 1.  Setting a
            stride to zero causes the data to be not sampled in the
            corresponding direction, producing a 3D line plot rather than a
            wireframe plot.

            'classic' mode uses a default of ``rstride = cstride = 1`` instead
            of the new default of ``rcount = ccount = 50``.

        **kwargs
            Other arguments are forwarded to `.Line3DCollection`.
        ",198,393,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def plot_wireframe(self, X, Y, Z, **kwargs):
        

        had_data = self.has_data()
        if Z.ndim != 2:
            raise ValueError(""Argument Z must be 2-dimensional."")
        # FIXME: Support masked arrays
        X, Y, Z = np.broadcast_arrays(X, Y, Z)
        rows, cols = Z.shape

        has_stride = 'rstride' in kwargs or 'cstride' in kwargs
        has_count = 'rcount' in kwargs or 'ccount' in kwargs

        if has_stride and has_count:
            raise ValueError(""Cannot specify both stride and count arguments"")

        rstride = kwargs.pop('rstride', 1)
        cstride = kwargs.pop('cstride', 1)
        rcount = kwargs.pop('rcount', 50)
        ccount = kwargs.pop('ccount', 50)

        if rcParams['_internal.classic_mode']:
            # Strides have priority over counts in classic mode.
            # So, only compute strides from counts
            # if counts were explicitly given
            if has_count:
                rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0
                cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0
        else:
            # If the strides are provided then it has priority.
            # Otherwise, compute the strides from the counts.
            if not has_stride:
                rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0
                cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0

        # We want two sets of lines, one running along the ""rows"" of
        # Z and another set of lines running along the ""columns"" of Z.
        # This transpose will make it easy to obtain the columns.
        tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)

        if rstride:
            rii = list(range(0, rows, rstride))
            # Add the last index only if needed
            if rows > 0 and rii[-1] != (rows - 1):
                rii += [rows-1]
        else:
            rii = []
        if cstride:
            cii = list(range(0, cols, cstride))
            # Add the last index only if needed
            if cols > 0 and cii[-1] != (cols - 1):
                cii += [cols-1]
        else:
            cii = []

        if rstride == 0 and cstride == 0:
            raise ValueError(""Either rstride or cstride must be non zero"")

        # If the inputs were empty, then just
        # reset everything.
        if Z.size == 0:
            rii = []
            cii = []

        xlines = [X[i] for i in rii]
        ylines = [Y[i] for i in rii]
        zlines = [Z[i] for i in rii]

        txlines = [tX[i] for i in cii]
        tylines = [tY[i] for i in cii]
        tzlines = [tZ[i] for i in cii]

        lines = ([list(zip(xl, yl, zl))
                 for xl, yl, zl in zip(xlines, ylines, zlines)]
                 + [list(zip(xl, yl, zl))
                 for xl, yl, zl in zip(txlines, tylines, tzlines)])

        linec = art3d.Line3DCollection(lines, **kwargs)
        self.add_collection(linec)
        self.auto_scale_xyz(X, Y, Z, had_data)

        return linec

        ```
    ###Assistant : 
        Plot a 3D wireframe.

        .. note::

           The *rcount* and *ccount* kwargs, which both default to 50,
           determine the maximum number of samples used in each direction.  If
           the input data is larger, it will be downsampled (by slicing) to
           these numbers of points.

        Parameters
        ----------
        X, Y, Z : 2D arrays
            Data values.

        rcount, ccount : int
            Maximum number of samples used in each direction.  If the input
            data is larger, it will be downsampled (by slicing) to these
            numbers of points.  Setting a count to zero causes the data to be
            not sampled in the corresponding direction, producing a 3D line
            plot rather than a wireframe plot.  Defaults to 50.

        rstride, cstride : int
            Downsampling stride in each direction.  These arguments are
            mutually exclusive with *rcount* and *ccount*.  If only one of
            *rstride* or *cstride* is set, the other defaults to 1.  Setting a
            stride to zero causes the data to be not sampled in the
            corresponding direction, producing a 3D line plot rather than a
            wireframe plot.

            'classic' mode uses a default of ``rstride = cstride = 1`` instead
            of the new default of ``rcount = ccount = 50``.

        **kwargs
            Other arguments are forwarded to `.Line3DCollection`.
        
    "
2766,"def check_response(self, response, callback, name=None):
        
        if not (response is None or asyncio.iscoroutine(response)):
            return
        if not name:
            if isinstance(callback, types.FunctionType):  # FBV
                name = ""The view %s.%s"" % (callback.__module__, callback.__name__)
            else:  # CBV
                name = ""The view %s.%s.__call__"" % (
                    callback.__module__,
                    callback.__class__.__name__,
                )
        if response is None:
            raise ValueError(
                ""%s didn't return an HttpResponse object. It returned None ""
                ""instead."" % name
            )
        elif asyncio.iscoroutine(response):
            raise ValueError(
                ""%s didn't return an HttpResponse object. It returned an ""
                ""unawaited coroutine instead. You may need to add an 'await' ""
                ""into your view."" % name
            )

    # Other utility methods.
","
        Raise an error if the view returned None or an uncalled coroutine.
        ",12,97,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def check_response(self, response, callback, name=None):
        
        if not (response is None or asyncio.iscoroutine(response)):
            return
        if not name:
            if isinstance(callback, types.FunctionType):  # FBV
                name = ""The view %s.%s"" % (callback.__module__, callback.__name__)
            else:  # CBV
                name = ""The view %s.%s.__call__"" % (
                    callback.__module__,
                    callback.__class__.__name__,
                )
        if response is None:
            raise ValueError(
                ""%s didn't return an HttpResponse object. It returned None ""
                ""instead."" % name
            )
        elif asyncio.iscoroutine(response):
            raise ValueError(
                ""%s didn't return an HttpResponse object. It returned an ""
                ""unawaited coroutine instead. You may need to add an 'await' ""
                ""into your view."" % name
            )

    # Other utility methods.

        ```
    ###Assistant : 
        Raise an error if the view returned None or an uncalled coroutine.
        
    "
2767,"def _meta_from_array(x, columns=None, index=None, meta=None):
    

    if x.ndim > 2:
        raise ValueError(
            ""from_array does not input more than 2D array, got""
            "" array with shape %r"" % (x.shape,)
        )

    if index is not None:
        if not isinstance(index, Index):
            raise ValueError(""'index' must be an instance of dask.dataframe.Index"")
        index = index._meta

    if meta is None:
        meta = meta_lib_from_array(x).DataFrame()

    if getattr(x.dtype, ""names"", None) is not None:
        # record array has named columns
        if columns is None:
            columns = list(x.dtype.names)
        elif np.isscalar(columns):
            raise ValueError(""For a struct dtype, columns must be a list."")
        elif not all(i in x.dtype.names for i in columns):
            extra = sorted(set(columns).difference(x.dtype.names))
            raise ValueError(f""dtype {x.dtype} doesn't have fields {extra}"")
        fields = x.dtype.fields
        dtypes = [fields[n][0] if n in fields else ""f8"" for n in columns]
    elif x.ndim == 1:
        if np.isscalar(columns) or columns is None:
            return meta._constructor_sliced(
                [], name=columns, dtype=x.dtype, index=index
            )
        elif len(columns) == 1:
            return meta._constructor(
                np.array([], dtype=x.dtype), columns=columns, index=index
            )
        raise ValueError(
            ""For a 1d array, columns must be a scalar or single element list""
        )
    else:
        if np.isnan(x.shape[1]):
            raise ValueError(""Shape along axis 1 must be known"")
        if columns is None:
            columns = list(range(x.shape[1])) if x.ndim == 2 else [0]
        elif len(columns) != x.shape[1]:
            raise ValueError(
                ""Number of column names must match width of the array. ""
                f""Got {len(columns)} names for {x.shape[1]} columns""
            )
        dtypes = [x.dtype] * len(columns)

    data = {c: np.array([], dtype=dt) for (c, dt) in zip(columns, dtypes)}
    return meta._constructor(data, columns=columns, index=index)

",Create empty DataFrame or Series which has correct dtype,9,234,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _meta_from_array(x, columns=None, index=None, meta=None):
    

    if x.ndim > 2:
        raise ValueError(
            ""from_array does not input more than 2D array, got""
            "" array with shape %r"" % (x.shape,)
        )

    if index is not None:
        if not isinstance(index, Index):
            raise ValueError(""'index' must be an instance of dask.dataframe.Index"")
        index = index._meta

    if meta is None:
        meta = meta_lib_from_array(x).DataFrame()

    if getattr(x.dtype, ""names"", None) is not None:
        # record array has named columns
        if columns is None:
            columns = list(x.dtype.names)
        elif np.isscalar(columns):
            raise ValueError(""For a struct dtype, columns must be a list."")
        elif not all(i in x.dtype.names for i in columns):
            extra = sorted(set(columns).difference(x.dtype.names))
            raise ValueError(f""dtype {x.dtype} doesn't have fields {extra}"")
        fields = x.dtype.fields
        dtypes = [fields[n][0] if n in fields else ""f8"" for n in columns]
    elif x.ndim == 1:
        if np.isscalar(columns) or columns is None:
            return meta._constructor_sliced(
                [], name=columns, dtype=x.dtype, index=index
            )
        elif len(columns) == 1:
            return meta._constructor(
                np.array([], dtype=x.dtype), columns=columns, index=index
            )
        raise ValueError(
            ""For a 1d array, columns must be a scalar or single element list""
        )
    else:
        if np.isnan(x.shape[1]):
            raise ValueError(""Shape along axis 1 must be known"")
        if columns is None:
            columns = list(range(x.shape[1])) if x.ndim == 2 else [0]
        elif len(columns) != x.shape[1]:
            raise ValueError(
                ""Number of column names must match width of the array. ""
                f""Got {len(columns)} names for {x.shape[1]} columns""
            )
        dtypes = [x.dtype] * len(columns)

    data = {c: np.array([], dtype=dt) for (c, dt) in zip(columns, dtypes)}
    return meta._constructor(data, columns=columns, index=index)


        ```
    ###Assistant : Create empty DataFrame or Series which has correct dtype
    "
2768,"def rows(self):
        
        for row in self.row_data:
            yield [
                column[""block""].bind(value)
                for column, value in zip(self.columns, row[""values""])
            ]
","
        Iterate over the rows of the table, with each row returned as a list of BoundBlocks
        ",16,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rows(self):
        
        for row in self.row_data:
            yield [
                column[""block""].bind(value)
                for column, value in zip(self.columns, row[""values""])
            ]

        ```
    ###Assistant : 
        Iterate over the rows of the table, with each row returned as a list of BoundBlocks
        
    "
2769,"def Uniform(name, left, right):
    r

    return rv(name, UniformDistribution, (left, right))

#-------------------------------------------------------------------------------
# UniformSum distribution ------------------------------------------------------

","
    Create a continuous random variable with a uniform distribution.

    Explanation
    ===========

    The density of the uniform distribution is given by

    .. math::
        f(x) := \begin{cases}
                  \frac{1}{b - a} & \text{for } x \in [a,b]  \\
                  0               & \text{otherwise}
                \end{cases}

    with :math:`x \in [a,b]`.

    Parameters
    ==========

    a : Real number, :math:`-\infty < a`, the left boundary
    b : Real number, :math:`a < b < \infty`, the right boundary

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import Uniform, density, cdf, E, variance
    >>> from sympy import Symbol, simplify

    >>> a = Symbol(""a"", negative=True)
    >>> b = Symbol(""b"", positive=True)
    >>> z = Symbol(""z"")

    >>> X = Uniform(""x"", a, b)

    >>> density(X)(z)
    Piecewise((1/(-a + b), (b >= z) & (a <= z)), (0, True))

    >>> cdf(X)(z)
    Piecewise((0, a > z), ((-a + z)/(-a + b), b >= z), (1, True))

    >>> E(X)
    a/2 + b/2

    >>> simplify(variance(X))
    a**2/12 - a*b/6 + b**2/12

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29
    .. [2] http://mathworld.wolfram.com/UniformDistribution.html

    ",157,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def Uniform(name, left, right):
    r

    return rv(name, UniformDistribution, (left, right))

#-------------------------------------------------------------------------------
# UniformSum distribution ------------------------------------------------------


        ```
    ###Assistant : 
    Create a continuous random variable with a uniform distribution.

    Explanation
    ===========

    The density of the uniform distribution is given by

    .. math::
        f(x) := \begin{cases}
                  \frac{1}{b - a} & \text{for } x \in [a,b]  \\
                  0               & \text{otherwise}
                \end{cases}

    with :math:`x \in [a,b]`.

    Parameters
    ==========

    a : Real number, :math:`-\infty < a`, the left boundary
    b : Real number, :math:`a < b < \infty`, the right boundary

    Returns
    =======

    RandomSymbol

    Examples
    ========

    >>> from sympy.stats import Uniform, density, cdf, E, variance
    >>> from sympy import Symbol, simplify

    >>> a = Symbol(""a"", negative=True)
    >>> b = Symbol(""b"", positive=True)
    >>> z = Symbol(""z"")

    >>> X = Uniform(""x"", a, b)

    >>> density(X)(z)
    Piecewise((1/(-a + b), (b >= z) & (a <= z)), (0, True))

    >>> cdf(X)(z)
    Piecewise((0, a > z), ((-a + z)/(-a + b), b >= z), (1, True))

    >>> E(X)
    a/2 + b/2

    >>> simplify(variance(X))
    a**2/12 - a*b/6 + b**2/12

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29
    .. [2] http://mathworld.wolfram.com/UniformDistribution.html

    
    "
2770,"def get_ttext(value):
    
    m = _non_token_end_matcher(value)
    if not m:
        raise errors.HeaderParseError(
            ""expected ttext but found '{}'"".format(value))
    ttext = m.group()
    value = value[len(ttext):]
    ttext = ValueTerminal(ttext, 'ttext')
    _validate_xtext(ttext)
    return ttext, value
","ttext = <matches _ttext_matcher>

    We allow any non-TOKEN_ENDS in ttext, but add defects to the token's
    defects list if we find non-ttext characters.  We also register defects for
    *any* non-printables even though the RFC doesn't exclude all of them,
    because we follow the spirit of RFC 5322.

    ",47,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_ttext(value):
    
    m = _non_token_end_matcher(value)
    if not m:
        raise errors.HeaderParseError(
            ""expected ttext but found '{}'"".format(value))
    ttext = m.group()
    value = value[len(ttext):]
    ttext = ValueTerminal(ttext, 'ttext')
    _validate_xtext(ttext)
    return ttext, value

        ```
    ###Assistant : ttext = <matches _ttext_matcher>

    We allow any non-TOKEN_ENDS in ttext, but add defects to the token's
    defects list if we find non-ttext characters.  We also register defects for
    *any* non-printables even though the RFC doesn't exclude all of them,
    because we follow the spirit of RFC 5322.

    
    "
2771,"def kwargs(self, exclude=(), apply=None):
        
        kwargs = {k: getattr(self, k) for k in self._fields if k not in exclude}
        if apply is not None:
            return {k: apply(v) for k, v in kwargs.items()}
        else:
            return kwargs
"," Get instance's attributes as dict of keyword arguments.

        Parameters
        ==========

        exclude : collection of str
            Collection of keywords to exclude.

        apply : callable, optional
            Function to apply to all values.
        ",30,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def kwargs(self, exclude=(), apply=None):
        
        kwargs = {k: getattr(self, k) for k in self._fields if k not in exclude}
        if apply is not None:
            return {k: apply(v) for k, v in kwargs.items()}
        else:
            return kwargs

        ```
    ###Assistant :  Get instance's attributes as dict of keyword arguments.

        Parameters
        ==========

        exclude : collection of str
            Collection of keywords to exclude.

        apply : callable, optional
            Function to apply to all values.
        
    "
2772,"def _execute_impl(self, *args, **kwargs) -> RayServeHandle:
        
        return self._deployment_handle
","Does not call into anything or produce a new value, as the time
        this function gets called, all child nodes are already resolved to
        ObjectRefs.
        ",25,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _execute_impl(self, *args, **kwargs) -> RayServeHandle:
        
        return self._deployment_handle

        ```
    ###Assistant : Does not call into anything or produce a new value, as the time
        this function gets called, all child nodes are already resolved to
        ObjectRefs.
        
    "
2773,"def test_settings_use_default_site(self):
        
        context = {}

        # This should use the default site
        template = '{{ settings(""tests.testsetting"", use_default_site=True).title}}'

        self.assertEqual(
            self.render(template, context, request_context=False),
            self.default_site_settings.title,
        )
","
        Check that the {{ settings(use_default_site=True) }} option works with
        no site in the context
        ",14,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_settings_use_default_site(self):
        
        context = {}

        # This should use the default site
        template = '{{ settings(""tests.testsetting"", use_default_site=True).title}}'

        self.assertEqual(
            self.render(template, context, request_context=False),
            self.default_site_settings.title,
        )

        ```
    ###Assistant : 
        Check that the {{ settings(use_default_site=True) }} option works with
        no site in the context
        
    "
2774,"def test_stream_square_brackets_and_language():
    infos = 

    d = FFmpegInfosParser(infos, ""clip.mp4"").parse()

    assert d
    assert len(d[""inputs""][0][""streams""]) == 2
    assert d[""inputs""][0][""streams""][0][""language""] == ""eng""
    assert d[""inputs""][0][""streams""][1][""language""] is None

","
Input #0, mpeg, from 'clip.mp4':
  Duration: 00:02:15.00, start: 52874.498178, bitrate: 266 kb/s
    Stream #0:0[0x1e0](eng): Video: ..., 25 tbr, 90k tbn, 50 tbc
    Stream #0:1[0x1c0](und): Audio: mp2, 0 channels, s16p
At least one output file must be specified",37,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_stream_square_brackets_and_language():
    infos = 

    d = FFmpegInfosParser(infos, ""clip.mp4"").parse()

    assert d
    assert len(d[""inputs""][0][""streams""]) == 2
    assert d[""inputs""][0][""streams""][0][""language""] == ""eng""
    assert d[""inputs""][0][""streams""][1][""language""] is None


        ```
    ###Assistant : 
Input #0, mpeg, from 'clip.mp4':
  Duration: 00:02:15.00, start: 52874.498178, bitrate: 266 kb/s
    Stream #0:0[0x1e0](eng): Video: ..., 25 tbr, 90k tbn, 50 tbc
    Stream #0:1[0x1c0](und): Audio: mp2, 0 channels, s16p
At least one output file must be specified
    "
2775,"async def wait(self) -> None:
        
        if self._is_set:
            return

        if not self._loop:
            self._loop = get_running_loop()
            self._event = asyncio.Event()

        await self._event.wait()
","
        Wait until the flag has been set.

        If the flag has already been set when this method is called, it returns immediately.
        ",22,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def wait(self) -> None:
        
        if self._is_set:
            return

        if not self._loop:
            self._loop = get_running_loop()
            self._event = asyncio.Event()

        await self._event.wait()

        ```
    ###Assistant : 
        Wait until the flag has been set.

        If the flag has already been set when this method is called, it returns immediately.
        
    "
2776,"async def get_and_submit_flow_runs(self) -> List[FlowRun]:
        
        if not self.started:
            raise RuntimeError(""Agent is not started. Use `async with OrionAgent()...`"")

        self.logger.debug(""Checking for flow runs..."")

        before = pendulum.now(""utc"").add(
            seconds=self.prefetch_seconds or PREFECT_AGENT_PREFETCH_SECONDS.value()
        )

        # Use the work queue id or load one from the name
        work_queue_id = self.work_queue_id or await self.work_queue_id_from_name()
        if not work_queue_id:
            return []

        try:
            submittable_runs = await self.client.get_runs_in_work_queue(
                id=work_queue_id, limit=10, scheduled_before=before
            )
        except httpx.HTTPStatusError as exc:
            if exc.response.status_code == status.HTTP_404_NOT_FOUND:
                raise ValueError(
                    f""No work queue found with id '{work_queue_id}'""
                ) from None
            else:
                raise

        # Check for a paused work queue for display purposes
        if not submittable_runs:
            work_queue = await self.client.read_work_queue(work_queue_id)
            if work_queue.is_paused:
                self.logger.info(
                    f""Work queue {work_queue.name!r} ({work_queue.id}) is paused.""
                )

        for flow_run in submittable_runs:
            self.logger.info(f""Submitting flow run '{flow_run.id}'"")

            # don't resubmit a run
            if flow_run.id in self.submitting_flow_run_ids:
                continue

            self.submitting_flow_run_ids.add(flow_run.id)
            self.task_group.start_soon(
                self.submit_run,
                flow_run,
            )
        return submittable_runs
","
        The principle method on agents. Queries for scheduled flow runs and submits
        them for execution in parallel.
        ",17,134,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def get_and_submit_flow_runs(self) -> List[FlowRun]:
        
        if not self.started:
            raise RuntimeError(""Agent is not started. Use `async with OrionAgent()...`"")

        self.logger.debug(""Checking for flow runs..."")

        before = pendulum.now(""utc"").add(
            seconds=self.prefetch_seconds or PREFECT_AGENT_PREFETCH_SECONDS.value()
        )

        # Use the work queue id or load one from the name
        work_queue_id = self.work_queue_id or await self.work_queue_id_from_name()
        if not work_queue_id:
            return []

        try:
            submittable_runs = await self.client.get_runs_in_work_queue(
                id=work_queue_id, limit=10, scheduled_before=before
            )
        except httpx.HTTPStatusError as exc:
            if exc.response.status_code == status.HTTP_404_NOT_FOUND:
                raise ValueError(
                    f""No work queue found with id '{work_queue_id}'""
                ) from None
            else:
                raise

        # Check for a paused work queue for display purposes
        if not submittable_runs:
            work_queue = await self.client.read_work_queue(work_queue_id)
            if work_queue.is_paused:
                self.logger.info(
                    f""Work queue {work_queue.name!r} ({work_queue.id}) is paused.""
                )

        for flow_run in submittable_runs:
            self.logger.info(f""Submitting flow run '{flow_run.id}'"")

            # don't resubmit a run
            if flow_run.id in self.submitting_flow_run_ids:
                continue

            self.submitting_flow_run_ids.add(flow_run.id)
            self.task_group.start_soon(
                self.submit_run,
                flow_run,
            )
        return submittable_runs

        ```
    ###Assistant : 
        The principle method on agents. Queries for scheduled flow runs and submits
        them for execution in parallel.
        
    "
2777,"def conv_output_length(input_length, filter_size, padding, stride, dilation=1):
    
    if input_length is None:
        return None
    assert padding in {""same"", ""valid"", ""full"", ""causal""}
    dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
    if padding in [""same"", ""causal""]:
        output_length = input_length
    elif padding == ""valid"":
        output_length = input_length - dilated_filter_size + 1
    elif padding == ""full"":
        output_length = input_length + dilated_filter_size - 1
    return (output_length + stride - 1) // stride

","Determines output length of a convolution given input length.

    Args:
        input_length: integer.
        filter_size: integer.
        padding: one of ""same"", ""valid"", ""full"", ""causal""
        stride: integer.
        dilation: dilation rate, integer.

    Returns:
        The output length (integer).
    ",32,68,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def conv_output_length(input_length, filter_size, padding, stride, dilation=1):
    
    if input_length is None:
        return None
    assert padding in {""same"", ""valid"", ""full"", ""causal""}
    dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
    if padding in [""same"", ""causal""]:
        output_length = input_length
    elif padding == ""valid"":
        output_length = input_length - dilated_filter_size + 1
    elif padding == ""full"":
        output_length = input_length + dilated_filter_size - 1
    return (output_length + stride - 1) // stride


        ```
    ###Assistant : Determines output length of a convolution given input length.

    Args:
        input_length: integer.
        filter_size: integer.
        padding: one of ""same"", ""valid"", ""full"", ""causal""
        stride: integer.
        dilation: dilation rate, integer.

    Returns:
        The output length (integer).
    
    "
2778,"def get_split_nodes(self):
        
        rearport = path_node_to_object(self._nodes[-1])

        return FrontPort.objects.filter(rear_port=rearport)
","
        Return all available next segments in a split cable path.
        ",10,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_split_nodes(self):
        
        rearport = path_node_to_object(self._nodes[-1])

        return FrontPort.objects.filter(rear_port=rearport)

        ```
    ###Assistant : 
        Return all available next segments in a split cable path.
        
    "
2779,"def get_template_names(self):
        
        try:
            names = super().get_template_names()
        except ImproperlyConfigured:
            # If template_name isn't specified, it's not a problem --
            # we just start with an empty list.
            names = []

        # If the list is a queryset, we'll invent a template name based on the
        # app and model name. This name gets put at the end of the template
        # name list so that user-supplied names override the automatically-
        # generated ones.
        if hasattr(self.object_list, ""model""):
            opts = self.object_list.model._meta
            names.append(
                ""%s/%s%s.html""
                % (opts.app_label, opts.model_name, self.template_name_suffix)
            )
        elif not names:
            raise ImproperlyConfigured(
                ""%(cls)s requires either a 'template_name' attribute ""
                ""or a get_queryset() method that returns a QuerySet.""
                % {
                    ""cls"": self.__class__.__name__,
                }
            )
        return names

","
        Return a list of template names to be used for the request. Must return
        a list. May not be called if render_to_response is overridden.
        ",24,113,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_template_names(self):
        
        try:
            names = super().get_template_names()
        except ImproperlyConfigured:
            # If template_name isn't specified, it's not a problem --
            # we just start with an empty list.
            names = []

        # If the list is a queryset, we'll invent a template name based on the
        # app and model name. This name gets put at the end of the template
        # name list so that user-supplied names override the automatically-
        # generated ones.
        if hasattr(self.object_list, ""model""):
            opts = self.object_list.model._meta
            names.append(
                ""%s/%s%s.html""
                % (opts.app_label, opts.model_name, self.template_name_suffix)
            )
        elif not names:
            raise ImproperlyConfigured(
                ""%(cls)s requires either a 'template_name' attribute ""
                ""or a get_queryset() method that returns a QuerySet.""
                % {
                    ""cls"": self.__class__.__name__,
                }
            )
        return names


        ```
    ###Assistant : 
        Return a list of template names to be used for the request. Must return
        a list. May not be called if render_to_response is overridden.
        
    "
2780,"def remove_lines(fname, entries):
    
    to_remove = []
    for entry in entries:
        msg, issue, lineno, pos, descr = entry
        # 'module imported but not used'
        if issue == 'F401' and handle_f401(fname, lineno):
            to_remove.append(lineno)
        # 'blank line(s) at end of file'
        elif issue == 'W391':
            lines = read_lines(fname)
            i = len(lines) - 1
            while lines[i] == '\n':
                to_remove.append(i + 1)
                i -= 1
        # 'too many blank lines'
        elif issue == 'E303':
            howmany = descr.replace('(', '').replace(')', '')
            howmany = int(howmany[-1])
            for x in range(lineno - howmany, lineno):
                to_remove.append(x)

    if to_remove:
        newlines = []
        for i, line in enumerate(read_lines(fname), 1):
            if i not in to_remove:
                newlines.append(line)
        print(""removing line(s) from %s"" % fname)
        write_file(fname, newlines)

    return len(to_remove)

","Check if we should remove lines, then do it.
    Return the number of lines removed.
    ",15,112,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def remove_lines(fname, entries):
    
    to_remove = []
    for entry in entries:
        msg, issue, lineno, pos, descr = entry
        # 'module imported but not used'
        if issue == 'F401' and handle_f401(fname, lineno):
            to_remove.append(lineno)
        # 'blank line(s) at end of file'
        elif issue == 'W391':
            lines = read_lines(fname)
            i = len(lines) - 1
            while lines[i] == '\n':
                to_remove.append(i + 1)
                i -= 1
        # 'too many blank lines'
        elif issue == 'E303':
            howmany = descr.replace('(', '').replace(')', '')
            howmany = int(howmany[-1])
            for x in range(lineno - howmany, lineno):
                to_remove.append(x)

    if to_remove:
        newlines = []
        for i, line in enumerate(read_lines(fname), 1):
            if i not in to_remove:
                newlines.append(line)
        print(""removing line(s) from %s"" % fname)
        write_file(fname, newlines)

    return len(to_remove)


        ```
    ###Assistant : Check if we should remove lines, then do it.
    Return the number of lines removed.
    
    "
2781,"def _get_one_trial_job(self):
        
        if not self.generated_hyper_configs:
            ret = {
                'parameter_id': '-1_0_0',
                'parameter_source': 'algorithm',
                'parameters': ''
            }
            self.send(CommandType.NoMoreTrialJobs, nni.dump(ret))
            return None
        assert self.generated_hyper_configs
        params = self.generated_hyper_configs.pop(0)
        ret = {
            'parameter_id': params[0],
            'parameter_source': 'algorithm',
            'parameters': params[1]
        }
        self.parameters[params[0]] = params[1]
        return ret
","get one trial job, i.e., one hyperparameter configuration.

        If this function is called, Command will be sent by BOHB:
        a. If there is a parameter need to run, will return ""NewTrialJob"" with a dict:
        {
            'parameter_id': id of new hyperparameter
            'parameter_source': 'algorithm'
            'parameters': value of new hyperparameter
        }
        b. If BOHB don't have parameter waiting, will return ""NoMoreTrialJobs"" with
        {
            'parameter_id': '-1_0_0',
            'parameter_source': 'algorithm',
            'parameters': ''
        }
        ",67,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_one_trial_job(self):
        
        if not self.generated_hyper_configs:
            ret = {
                'parameter_id': '-1_0_0',
                'parameter_source': 'algorithm',
                'parameters': ''
            }
            self.send(CommandType.NoMoreTrialJobs, nni.dump(ret))
            return None
        assert self.generated_hyper_configs
        params = self.generated_hyper_configs.pop(0)
        ret = {
            'parameter_id': params[0],
            'parameter_source': 'algorithm',
            'parameters': params[1]
        }
        self.parameters[params[0]] = params[1]
        return ret

        ```
    ###Assistant : get one trial job, i.e., one hyperparameter configuration.

        If this function is called, Command will be sent by BOHB:
        a. If there is a parameter need to run, will return ""NewTrialJob"" with a dict:
        {
            'parameter_id': id of new hyperparameter
            'parameter_source': 'algorithm'
            'parameters': value of new hyperparameter
        }
        b. If BOHB don't have parameter waiting, will return ""NoMoreTrialJobs"" with
        {
            'parameter_id': '-1_0_0',
            'parameter_source': 'algorithm',
            'parameters': ''
        }
        
    "
2782,"def global_array_to_host_local_array(global_inputs, global_mesh, pspecs):
  
  def _convert(arr, pspec):
    local_aval = global_mesh._global_to_local(
        pxla._get_array_mapping(pspec), arr.aval)
    return array.ArrayImpl(
        local_aval, MeshPspecSharding(global_mesh.local_mesh, pspec),
        arr._arrays, committed=True)

  flattened_inps, out_tree = tree_flatten(global_inputs)
  out_pspecs = flatten_axis_resources(
      'output pspecs', out_tree, pspecs, tupled_args=True)
  out = tree_map(_convert, tuple(flattened_inps), out_pspecs)
  return tree_unflatten(out_tree, out)
","Converts a global `jax.Array` to a host local `jax.Array`.

  You can use this function to transition to `jax.Array`. Using `jax.Array` with
  `pjit` has the same semantics of using GDA with pjit i.e. all `jax.Array`
  inputs to pjit should be globally shaped and the output from `pjit` will also
  be globally shaped `jax.Array`s

  You can use this function to convert the globally shaped `jax.Array` output
  from pjit to host local values again so that the transition to jax.Array can
  be a mechanical change.

  Example usage:

  ```
  global_inputs = jax.experimental.pjit.host_local_array_to_global_array(
    host_local_inputs, global_mesh, in_pspecs)

  with mesh:
    global_out = pjitted_fun(global_inputs)

  host_local_output = jax.experimental.pjit.global_array_to_host_local_array(
    global_out, mesh, out_pspecs)
  ```

  Args:
    global_inputs: A Pytree of global `jax.Array`s.
    global_mesh: The global mesh.
    pspecs: A Pytree of PartitionSpecs.
  ",119,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def global_array_to_host_local_array(global_inputs, global_mesh, pspecs):
  
  def _convert(arr, pspec):
    local_aval = global_mesh._global_to_local(
        pxla._get_array_mapping(pspec), arr.aval)
    return array.ArrayImpl(
        local_aval, MeshPspecSharding(global_mesh.local_mesh, pspec),
        arr._arrays, committed=True)

  flattened_inps, out_tree = tree_flatten(global_inputs)
  out_pspecs = flatten_axis_resources(
      'output pspecs', out_tree, pspecs, tupled_args=True)
  out = tree_map(_convert, tuple(flattened_inps), out_pspecs)
  return tree_unflatten(out_tree, out)

        ```
    ###Assistant : Converts a global `jax.Array` to a host local `jax.Array`.

  You can use this function to transition to `jax.Array`. Using `jax.Array` with
  `pjit` has the same semantics of using GDA with pjit i.e. all `jax.Array`
  inputs to pjit should be globally shaped and the output from `pjit` will also
  be globally shaped `jax.Array`s

  You can use this function to convert the globally shaped `jax.Array` output
  from pjit to host local values again so that the transition to jax.Array can
  be a mechanical change.

  Example usage:

  ```
  global_inputs = jax.experimental.pjit.host_local_array_to_global_array(
    host_local_inputs, global_mesh, in_pspecs)

  with mesh:
    global_out = pjitted_fun(global_inputs)

  host_local_output = jax.experimental.pjit.global_array_to_host_local_array(
    global_out, mesh, out_pspecs)
  ```

  Args:
    global_inputs: A Pytree of global `jax.Array`s.
    global_mesh: The global mesh.
    pspecs: A Pytree of PartitionSpecs.
  
    "
2783,"def __setitem__(self, key, value):
        
        super(Py27Dict, self).__setitem__(key, value)
        self.keylist.add(key)
","
        Override of __setitem__ to track keys and simulate Python2.7 dict

        Parameters
        ----------
        key: hashable
        value: Any
        ",16,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __setitem__(self, key, value):
        
        super(Py27Dict, self).__setitem__(key, value)
        self.keylist.add(key)

        ```
    ###Assistant : 
        Override of __setitem__ to track keys and simulate Python2.7 dict

        Parameters
        ----------
        key: hashable
        value: Any
        
    "
2784,"def virtualenv_no_global() -> bool:
    
    # PEP 405 compliance needs to be checked first since virtualenv >=20 would
    # return True for both checks, but is only able to use the PEP 405 config.
    if _running_under_venv():
        return _no_global_under_venv()

    if _running_under_regular_virtualenv():
        return _no_global_under_regular_virtualenv()

    return False
","Returns a boolean, whether running in venv with no system site-packages.",11,43,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def virtualenv_no_global() -> bool:
    
    # PEP 405 compliance needs to be checked first since virtualenv >=20 would
    # return True for both checks, but is only able to use the PEP 405 config.
    if _running_under_venv():
        return _no_global_under_venv()

    if _running_under_regular_virtualenv():
        return _no_global_under_regular_virtualenv()

    return False

        ```
    ###Assistant : Returns a boolean, whether running in venv with no system site-packages.
    "
2785,"def autocomplete(self):
        
        texts = []
        for field in self.search_fields:
            for current_field, value in self.prepare_field(self.obj, field):
                if isinstance(current_field, AutocompleteField):
                    texts.append((value))

        return "" "".join(texts)
","
        Returns all values to index as ""autocomplete"". This is the value of all AutocompleteFields
        ",14,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def autocomplete(self):
        
        texts = []
        for field in self.search_fields:
            for current_field, value in self.prepare_field(self.obj, field):
                if isinstance(current_field, AutocompleteField):
                    texts.append((value))

        return "" "".join(texts)

        ```
    ###Assistant : 
        Returns all values to index as ""autocomplete"". This is the value of all AutocompleteFields
        
    "
2786,"def list_to_tuple(maybe_list):
    
    if isinstance(maybe_list, list):
        return tuple(maybe_list)
    return maybe_list
","Datasets will stack the list of tensor, so switch them to tuples.",12,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list_to_tuple(maybe_list):
    
    if isinstance(maybe_list, list):
        return tuple(maybe_list)
    return maybe_list

        ```
    ###Assistant : Datasets will stack the list of tensor, so switch them to tuples.
    "
2787,"def test_not_recorded_for_unused(self, dag_maker, xcom_value):
        
        with dag_maker(dag_id=""test_not_recorded_for_unused"") as dag:
",A value not used for task-mapping should not be recorded.,10,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_not_recorded_for_unused(self, dag_maker, xcom_value):
        
        with dag_maker(dag_id=""test_not_recorded_for_unused"") as dag:

        ```
    ###Assistant : A value not used for task-mapping should not be recorded.
    "
2788,"def _ragged_tensor_mse(y_true, y_pred):
    
    return _ragged_tensor_apply_loss(mean_squared_error, y_true, y_pred)


@keras_export(
    ""keras.metrics.mean_absolute_error"",
    ""keras.metrics.mae"",
    ""keras.metrics.MAE"",
    ""keras.losses.mean_absolute_error"",
    ""keras.losses.mae"",
    ""keras.losses.MAE"",
)
@tf.__internal__.dispatch.add_dispatch_support","Implements support for handling RaggedTensors.

    Args:
      y_true: RaggedTensor truth values. shape = `[batch_size, d0, .. dN]`.
      y_pred: RaggedTensor predicted values. shape = `[batch_size, d0, .. dN]`.

    Returns:
      Mean squared error values. shape = `[batch_size, d0, .. dN-1]`.
      When the number of dimensions of the batch feature vector [d0, .. dN] is
      greater than one the return value is a RaggedTensor. Otherwise a Dense
      tensor with dimensions [batch_size] is returned.
    ",69,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _ragged_tensor_mse(y_true, y_pred):
    
    return _ragged_tensor_apply_loss(mean_squared_error, y_true, y_pred)


@keras_export(
    ""keras.metrics.mean_absolute_error"",
    ""keras.metrics.mae"",
    ""keras.metrics.MAE"",
    ""keras.losses.mean_absolute_error"",
    ""keras.losses.mae"",
    ""keras.losses.MAE"",
)
@tf.__internal__.dispatch.add_dispatch_support
        ```
    ###Assistant : Implements support for handling RaggedTensors.

    Args:
      y_true: RaggedTensor truth values. shape = `[batch_size, d0, .. dN]`.
      y_pred: RaggedTensor predicted values. shape = `[batch_size, d0, .. dN]`.

    Returns:
      Mean squared error values. shape = `[batch_size, d0, .. dN-1]`.
      When the number of dimensions of the batch feature vector [d0, .. dN] is
      greater than one the return value is a RaggedTensor. Otherwise a Dense
      tensor with dimensions [batch_size] is returned.
    
    "
2789,"def test_does_not_allow_extra_kwargs() -> None:
    
    template = ""This is a {foo} test.""
    with pytest.raises(KeyError):
        formatter.format(template, foo=""good"", bar=""oops"")
",Test formatting does not allow extra key word arguments.,9,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_does_not_allow_extra_kwargs() -> None:
    
    template = ""This is a {foo} test.""
    with pytest.raises(KeyError):
        formatter.format(template, foo=""good"", bar=""oops"")

        ```
    ###Assistant : Test formatting does not allow extra key word arguments.
    "
2790,"def score_samples(self, X):
        
        check_is_fitted(self)
        X = check_array(X, accept_sparse=""csr"")

        distances_X, neighbors_indices_X = self.kneighbors(
            X, n_neighbors=self.n_neighbors_
        )
        X_lrd = self._local_reachability_density(distances_X, neighbors_indices_X)

        lrd_ratios_array = self._lrd[neighbors_indices_X] / X_lrd[:, np.newaxis]

        # as bigger is better:
        return -np.mean(lrd_ratios_array, axis=1)
","Opposite of the Local Outlier Factor of X.

        It is the opposite as bigger is better, i.e. large values correspond
        to inliers.

        **Only available for novelty detection (when novelty is set to True).**
        The argument X is supposed to contain *new data*: if X contains a
        point from training, it considers the later in its own neighborhood.
        Also, the samples in X are not considered in the neighborhood of any
        point. Because of this, the scores obtained via ``score_samples`` may
        differ from the standard LOF scores.
        The standard LOF scores for the training data is available via the
        ``negative_outlier_factor_`` attribute.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The query sample or samples to compute the Local Outlier Factor
            w.r.t. the training samples.

        Returns
        -------
        opposite_lof_scores : ndarray of shape (n_samples,)
            The opposite of the Local Outlier Factor of each input samples.
            The lower, the more abnormal.
        ",148,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def score_samples(self, X):
        
        check_is_fitted(self)
        X = check_array(X, accept_sparse=""csr"")

        distances_X, neighbors_indices_X = self.kneighbors(
            X, n_neighbors=self.n_neighbors_
        )
        X_lrd = self._local_reachability_density(distances_X, neighbors_indices_X)

        lrd_ratios_array = self._lrd[neighbors_indices_X] / X_lrd[:, np.newaxis]

        # as bigger is better:
        return -np.mean(lrd_ratios_array, axis=1)

        ```
    ###Assistant : Opposite of the Local Outlier Factor of X.

        It is the opposite as bigger is better, i.e. large values correspond
        to inliers.

        **Only available for novelty detection (when novelty is set to True).**
        The argument X is supposed to contain *new data*: if X contains a
        point from training, it considers the later in its own neighborhood.
        Also, the samples in X are not considered in the neighborhood of any
        point. Because of this, the scores obtained via ``score_samples`` may
        differ from the standard LOF scores.
        The standard LOF scores for the training data is available via the
        ``negative_outlier_factor_`` attribute.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The query sample or samples to compute the Local Outlier Factor
            w.r.t. the training samples.

        Returns
        -------
        opposite_lof_scores : ndarray of shape (n_samples,)
            The opposite of the Local Outlier Factor of each input samples.
            The lower, the more abnormal.
        
    "
2791,"def get_payroll_period_days(start_date, end_date, employee, company=None):
	if not company:
		company = frappe.db.get_value(""Employee"", employee, ""company"")
	payroll_period = frappe.db.sql(
		,
		{""company"": company, ""start_date"": start_date, ""end_date"": end_date},
	)

	if len(payroll_period) > 0:
		actual_no_of_days = date_diff(getdate(payroll_period[0][2]), getdate(payroll_period[0][1])) + 1
		working_days = actual_no_of_days
		if not cint(
			frappe.db.get_value(""Payroll Settings"", None, ""include_holidays_in_total_working_days"")
		):
			holidays = get_holiday_dates_for_employee(
				employee, getdate(payroll_period[0][1]), getdate(payroll_period[0][2])
			)
			working_days -= len(holidays)
		return payroll_period[0][0], working_days, actual_no_of_days
	return False, False, False

","
		select name, start_date, end_date
		from `tabPayroll Period`
		where
			company=%(company)s
			and %(start_date)s between start_date and end_date
			and %(end_date)s between start_date and end_date
	",21,63,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_payroll_period_days(start_date, end_date, employee, company=None):
	if not company:
		company = frappe.db.get_value(""Employee"", employee, ""company"")
	payroll_period = frappe.db.sql(
		,
		{""company"": company, ""start_date"": start_date, ""end_date"": end_date},
	)

	if len(payroll_period) > 0:
		actual_no_of_days = date_diff(getdate(payroll_period[0][2]), getdate(payroll_period[0][1])) + 1
		working_days = actual_no_of_days
		if not cint(
			frappe.db.get_value(""Payroll Settings"", None, ""include_holidays_in_total_working_days"")
		):
			holidays = get_holiday_dates_for_employee(
				employee, getdate(payroll_period[0][1]), getdate(payroll_period[0][2])
			)
			working_days -= len(holidays)
		return payroll_period[0][0], working_days, actual_no_of_days
	return False, False, False


        ```
    ###Assistant : 
		select name, start_date, end_date
		from `tabPayroll Period`
		where
			company=%(company)s
			and %(start_date)s between start_date and end_date
			and %(end_date)s between start_date and end_date
	
    "
2792,"def density(B, nodes):
    
    n = len(B)
    m = nx.number_of_edges(B)
    nb = len(nodes)
    nt = n - nb
    if m == 0:  # includes cases n==0 and n==1
        d = 0.0
    else:
        if B.is_directed():
            d = m / (2 * nb * nt)
        else:
            d = m / (nb * nt)
    return d

","Returns density of bipartite graph B.

    Parameters
    ----------
    B : NetworkX graph

    nodes: list or container
      Nodes in one node set of the bipartite graph.

    Returns
    -------
    d : float
       The bipartite density

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> G = nx.complete_bipartite_graph(3, 2)
    >>> X = set([0, 1, 2])
    >>> bipartite.density(G, X)
    1.0
    >>> Y = set([3, 4])
    >>> bipartite.density(G, Y)
    1.0

    Notes
    -----
    The container of nodes passed as argument must contain all nodes
    in one of the two bipartite node sets to avoid ambiguity in the
    case of disconnected graphs.
    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    color
    ",113,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def density(B, nodes):
    
    n = len(B)
    m = nx.number_of_edges(B)
    nb = len(nodes)
    nt = n - nb
    if m == 0:  # includes cases n==0 and n==1
        d = 0.0
    else:
        if B.is_directed():
            d = m / (2 * nb * nt)
        else:
            d = m / (nb * nt)
    return d


        ```
    ###Assistant : Returns density of bipartite graph B.

    Parameters
    ----------
    B : NetworkX graph

    nodes: list or container
      Nodes in one node set of the bipartite graph.

    Returns
    -------
    d : float
       The bipartite density

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> G = nx.complete_bipartite_graph(3, 2)
    >>> X = set([0, 1, 2])
    >>> bipartite.density(G, X)
    1.0
    >>> Y = set([3, 4])
    >>> bipartite.density(G, Y)
    1.0

    Notes
    -----
    The container of nodes passed as argument must contain all nodes
    in one of the two bipartite node sets to avoid ambiguity in the
    case of disconnected graphs.
    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    color
    
    "
2793,"def redirect_or_json(origin, msg, status=""""):
    
    if request.headers.get('Accept') == 'application/json':
        return {'status': status, 'message': msg}
    else:
        if status:
            flash(msg, status)
        else:
            flash(msg)
        return redirect(origin)


######################################################################################
#                                    Error handlers
######################################################################################

","
    Some endpoints are called by javascript,
    returning json will allow us to more elegantly handle side-effects in-page
    ",17,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def redirect_or_json(origin, msg, status=""""):
    
    if request.headers.get('Accept') == 'application/json':
        return {'status': status, 'message': msg}
    else:
        if status:
            flash(msg, status)
        else:
            flash(msg)
        return redirect(origin)


######################################################################################
#                                    Error handlers
######################################################################################


        ```
    ###Assistant : 
    Some endpoints are called by javascript,
    returning json will allow us to more elegantly handle side-effects in-page
    
    "
2794,"def _map_drop_idx_to_infrequent(self, feature_idx, drop_idx):
        
        if not self._infrequent_enabled:
            return drop_idx

        default_to_infrequent = self._default_to_infrequent_mappings[feature_idx]
        if default_to_infrequent is None:
            return drop_idx

        # Raise error when explicitly dropping a category that is infrequent
        infrequent_indices = self._infrequent_indices[feature_idx]
        if infrequent_indices is not None and drop_idx in infrequent_indices:
            categories = self.categories_[feature_idx]
            raise ValueError(
                f""Unable to drop category {categories[drop_idx]!r} from feature""
                f"" {feature_idx} because it is infrequent""
            )
        return default_to_infrequent[drop_idx]
","Convert `drop_idx` into the index for infrequent categories.

        If there are no infrequent categories, then `drop_idx` is
        returned. This method is called in `_compute_drop_idx` when the `drop`
        parameter is an array-like.
        ",31,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _map_drop_idx_to_infrequent(self, feature_idx, drop_idx):
        
        if not self._infrequent_enabled:
            return drop_idx

        default_to_infrequent = self._default_to_infrequent_mappings[feature_idx]
        if default_to_infrequent is None:
            return drop_idx

        # Raise error when explicitly dropping a category that is infrequent
        infrequent_indices = self._infrequent_indices[feature_idx]
        if infrequent_indices is not None and drop_idx in infrequent_indices:
            categories = self.categories_[feature_idx]
            raise ValueError(
                f""Unable to drop category {categories[drop_idx]!r} from feature""
                f"" {feature_idx} because it is infrequent""
            )
        return default_to_infrequent[drop_idx]

        ```
    ###Assistant : Convert `drop_idx` into the index for infrequent categories.

        If there are no infrequent categories, then `drop_idx` is
        returned. This method is called in `_compute_drop_idx` when the `drop`
        parameter is an array-like.
        
    "
2795,"def test_dashboard_module_decorator(enable_test_module):
    head_cls_list = dashboard_utils.get_all_modules(dashboard_utils.DashboardHeadModule)
    agent_cls_list = dashboard_utils.get_all_modules(
        dashboard_utils.DashboardAgentModule
    )

    assert any(cls.__name__ == ""TestHead"" for cls in head_cls_list)
    assert any(cls.__name__ == ""TestAgent"" for cls in agent_cls_list)

    test_code = 
    run_string_as_driver(test_code)

","
import os
import ray.dashboard.utils as dashboard_utils

os.environ.pop(""RAY_DASHBOARD_MODULE_TEST"")
head_cls_list = dashboard_utils.get_all_modules(
        dashboard_utils.DashboardHeadModule)
agent_cls_list = dashboard_utils.get_all_modules(
        dashboard_utils.DashboardAgentModule)
print(head_cls_list)
print(agent_cls_list)
assert all(cls.__name__ != ""TestHead"" for cls in head_cls_list)
assert all(cls.__name__ != ""TestAgent"" for cls in agent_cls_list)
print(""success"")
",34,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_dashboard_module_decorator(enable_test_module):
    head_cls_list = dashboard_utils.get_all_modules(dashboard_utils.DashboardHeadModule)
    agent_cls_list = dashboard_utils.get_all_modules(
        dashboard_utils.DashboardAgentModule
    )

    assert any(cls.__name__ == ""TestHead"" for cls in head_cls_list)
    assert any(cls.__name__ == ""TestAgent"" for cls in agent_cls_list)

    test_code = 
    run_string_as_driver(test_code)


        ```
    ###Assistant : 
import os
import ray.dashboard.utils as dashboard_utils

os.environ.pop(""RAY_DASHBOARD_MODULE_TEST"")
head_cls_list = dashboard_utils.get_all_modules(
        dashboard_utils.DashboardHeadModule)
agent_cls_list = dashboard_utils.get_all_modules(
        dashboard_utils.DashboardAgentModule)
print(head_cls_list)
print(agent_cls_list)
assert all(cls.__name__ != ""TestHead"" for cls in head_cls_list)
assert all(cls.__name__ != ""TestAgent"" for cls in agent_cls_list)
print(""success"")

    "
2796,"def parsestr(self, text, headersonly=False):
        
        return self.parse(StringIO(text), headersonly=headersonly)","Create a message structure from a string.

        Returns the root of the message structure.  Optional headersonly is a
        flag specifying whether to stop parsing after reading the headers or
        not.  The default is False, meaning it parses the entire contents of
        the file.
        ",43,7,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parsestr(self, text, headersonly=False):
        
        return self.parse(StringIO(text), headersonly=headersonly)
        ```
    ###Assistant : Create a message structure from a string.

        Returns the root of the message structure.  Optional headersonly is a
        flag specifying whether to stop parsing after reading the headers or
        not.  The default is False, meaning it parses the entire contents of
        the file.
        
    "
2797,"def get_prerequisite_model(queryset):
    
    if not queryset.exists():
        for prereq in getattr(queryset.model, 'prerequisite_models', []):
            model = apps.get_model(prereq)
            if not model.objects.exists():
                return model
","
    Return any prerequisite model that must be created prior to creating
    an instance of the current model.
    ",17,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_prerequisite_model(queryset):
    
    if not queryset.exists():
        for prereq in getattr(queryset.model, 'prerequisite_models', []):
            model = apps.get_model(prereq)
            if not model.objects.exists():
                return model

        ```
    ###Assistant : 
    Return any prerequisite model that must be created prior to creating
    an instance of the current model.
    
    "
2798,"def filter_empty_gradients(grads_and_vars):
    
    grads_and_vars = tuple(grads_and_vars)
    if not grads_and_vars:
        return grads_and_vars

    filtered = []
    vars_with_empty_grads = []
    for grad, var in grads_and_vars:
        if grad is None:
            vars_with_empty_grads.append(var)
        else:
            filtered.append((grad, var))
    filtered = tuple(filtered)

    if not filtered:
        variable = ([v.name for _, v in grads_and_vars],)
        raise ValueError(
            f""No gradients provided for any variable: {variable}. ""
            f""Provided `grads_and_vars` is {grads_and_vars}.""
        )
    if vars_with_empty_grads:
        logging.warning(
            (
                ""Gradients do not exist for variables %s when minimizing the loss. ""
                ""If you're using `model.compile()`, did you forget to provide a `loss`""
                ""argument?""
            ),
            ([v.name for v in vars_with_empty_grads]),
        )
    return filtered

","Filter out `(grad, var)` pairs that have a gradient equal to `None`.",12,95,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def filter_empty_gradients(grads_and_vars):
    
    grads_and_vars = tuple(grads_and_vars)
    if not grads_and_vars:
        return grads_and_vars

    filtered = []
    vars_with_empty_grads = []
    for grad, var in grads_and_vars:
        if grad is None:
            vars_with_empty_grads.append(var)
        else:
            filtered.append((grad, var))
    filtered = tuple(filtered)

    if not filtered:
        variable = ([v.name for _, v in grads_and_vars],)
        raise ValueError(
            f""No gradients provided for any variable: {variable}. ""
            f""Provided `grads_and_vars` is {grads_and_vars}.""
        )
    if vars_with_empty_grads:
        logging.warning(
            (
                ""Gradients do not exist for variables %s when minimizing the loss. ""
                ""If you're using `model.compile()`, did you forget to provide a `loss`""
                ""argument?""
            ),
            ([v.name for v in vars_with_empty_grads]),
        )
    return filtered


        ```
    ###Assistant : Filter out `(grad, var)` pairs that have a gradient equal to `None`.
    "
2799,"def real_gaunt(l_1, l_2, l_3, m_1, m_2, m_3, prec=None):
    r
    l_1, l_2, l_3, m_1, m_2, m_3 = [
        as_int(i) for i in (l_1, l_2, l_3, m_1, m_2, m_3)]

    # check for quick exits
    if sum(1 for i in (m_1, m_2, m_3) if i < 0) % 2:
        return S.Zero  # odd number of negative m
    if (l_1 + l_2 + l_3) % 2:
        return S.Zero  # sum of l is odd
    lmax = l_2 + l_3
    lmin = max(abs(l_2 - l_3), min(abs(m_2 + m_3), abs(m_2 - m_3)))
    if (lmin + lmax) % 2:
        lmin += 1
    if lmin not in range(lmax, lmin - 2, -2):
        return S.Zero

    kron_del = lambda i, j: 1 if i == j else 0
    s = lambda e: -1 if e % 2 else 1  #  (-1)**e to give +/-1, avoiding float when e<0
    A = lambda a, b: (-kron_del(a, b)*s(a-b) + kron_del(a, -b)*
                      s(b)) if b < 0 else 0
    B = lambda a, b: (kron_del(a, b) + kron_del(a, -b)*s(a)) if b > 0 else 0
    C = lambda a, b: kron_del(abs(a), abs(b))*(kron_del(a, 0)*kron_del(b, 0) +
                                          (B(a, b) + I*A(a, b))/sqrt(2))
    ugnt = 0
    for i in range(-l_1, l_1+1):
        U1 = C(i, m_1)
        for j in range(-l_2, l_2+1):
            U2 = C(j, m_2)
            U3 = C(-i-j, m_3)
            ugnt = ugnt + re(U1*U2*U3)*gaunt(l_1, l_2, l_3, i, j, -i-j)

    if prec is not None:
        ugnt = ugnt.n(prec)
    return ugnt

","
    Calculate the real Gaunt coefficient.

    Explanation
    ===========
    The real Gaunt coefficient is defined as the integral over three
    real spherical harmonics:
    
    .. math::
        \begin{aligned}
        \operatorname{RealGaunt}(l_1,l_2,l_3,m_1,m_2,m_3)
        &=\int Z^{m_1}_{l_1}(\Omega)
         Z^{m_2}_{l_2}(\Omega) Z^{m_3}_{l_3}(\Omega) \,d\Omega \\
        \end{aligned}

    Alternatively, it can be defined in terms of the standard Gaunt
    coefficient by relating the real spherical harmonics to the standard
    spherical harmonics via a unitary transformation `U`, i.e.
    `Z^{m}_{l}(\Omega)=\sum_{m'}U^{m}_{m'}Y^{m'}_{l}(\Omega)` [Homeier96]_.
    The real Gaunt coefficient is then defined as

    .. math::
        \begin{aligned}
        \operatorname{RealGaunt}(l_1,l_2,l_3,m_1,m_2,m_3)
        &=\int Z^{m_1}_{l_1}(\Omega)
         Z^{m_2}_{l_2}(\Omega) Z^{m_3}_{l_3}(\Omega) \,d\Omega \\
        &=\sum_{m'_1 m'_2 m'_3} U^{m_1}_{m'_1}U^{m_2}_{m'_2}U^{m_3}_{m'_3}
         \operatorname{Gaunt}(l_1,l_2,l_3,m'_1,m'_2,m'_3)
        \end{aligned}

    The unitary matrix `U` has components

    .. math::
        \begin{aligned}
        U^m_{m'} = \delta_{|m||m'|}*(\delta_{m'0}\delta_{m0} + \frac{1}{\sqrt{2}}\big[\Theta(m)
        \big(\delta_{m'm}+(-1)^{m'}\delta_{m'-m}\big)+i\Theta(-m)\big((-1)^{-m}
        \delta_{m'-m}-\delta_{m'm}*(-1)^{m'-m}\big)\big])
        \end{aligned}

    where `\delta_{ij}` is the Kronecker delta symbol and `\Theta` is a step
    function defined as

    .. math::
        \begin{aligned}
        \Theta(x) = \begin{cases} 1 \,\text{for}\, x > 0 \\ 0 \,\text{for}\, x \leq 0 \end{cases}
        \end{aligned}

    Parameters
    ==========
    l_1, l_2, l_3, m_1, m_2, m_3 :
        Integer.
    prec - precision, default: ``None``.
        Providing a precision can
        drastically speed up the calculation.

    Returns
    =======
    Rational number times the square root of a rational number.

    Examples
    ========
    >>> from sympy.physics.wigner import real_gaunt
    >>> real_gaunt(2,2,4,-1,-1,0)
    -2/(7*sqrt(pi))
    >>> real_gaunt(10,10,20,-9,-9,0).n(64)
    -0.00002480019791932209313156167...
    
    It is an error to use non-integer values for `l` and `m`::
        real_gaunt(2.8,0.5,1.3,0,0,0)
        Traceback (most recent call last):
        ...
        ValueError: l values must be integer
        real_gaunt(2,2,4,0.7,1,-3.4)
        Traceback (most recent call last):
        ...
        ValueError: m values must be integer

    Notes
    =====
    The real Gaunt coefficient inherits from the standard Gaunt coefficient,
    the invariance under any permutation of the pairs `(l_i, m_i)` and the
    requirement that the sum of the `l_i` be even to yield a non-zero value.
    It also obeys the following symmetry rules:

    - zero for `l_1`, `l_2`, `l_3` not fulfiling the condition
      `l_1 \in \{l_{\text{max}}, l_{\text{max}}-2, \ldots, l_{\text{min}}\}`,
      where `l_{\text{max}} = l_2+l_3`,
      
      .. math::
          \begin{aligned}
          l_{\text{min}} = \begin{cases} \kappa(l_2, l_3, m_2, m_3) & \text{if}\,
          \kappa(l_2, l_3, m_2, m_3) + l_{\text{max}}\, \text{is even} \\
          \kappa(l_2, l_3, m_2, m_3)+1 & \text{if}\, \kappa(l_2, l_3, m_2, m_3) +
          l_{\text{max}}\, \text{is odd}\end{cases}
          \end{aligned}

      and `\kappa(l_2, l_3, m_2, m_3) = \max{\big(|l_2-l_3|, \min{\big(|m_2+m_3|,
      |m_2-m_3|\big)}\big)}`
      
    - zero for an odd number of negative `m_i`
    
    Algorithms
    ==========
    This function uses the algorithms of [Homeier96]_ and [Rasch03]_ to
    calculate the value of the real Gaunt coefficient exactly. Note that
    the formula used in [Rasch03]_ contains alternating sums over large
    factorials and is therefore unsuitable for finite precision arithmetic
    and only useful for a computer algebra system [Rasch03]_. However, this
    function can in principle use any algorithm that computes the Gaunt
    coefficient, so it is suitable for finite precision arithmetic in so far
    as the algorithm which computes the Gaunt coefficient is.
    ",429,231,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def real_gaunt(l_1, l_2, l_3, m_1, m_2, m_3, prec=None):
    r
    l_1, l_2, l_3, m_1, m_2, m_3 = [
        as_int(i) for i in (l_1, l_2, l_3, m_1, m_2, m_3)]

    # check for quick exits
    if sum(1 for i in (m_1, m_2, m_3) if i < 0) % 2:
        return S.Zero  # odd number of negative m
    if (l_1 + l_2 + l_3) % 2:
        return S.Zero  # sum of l is odd
    lmax = l_2 + l_3
    lmin = max(abs(l_2 - l_3), min(abs(m_2 + m_3), abs(m_2 - m_3)))
    if (lmin + lmax) % 2:
        lmin += 1
    if lmin not in range(lmax, lmin - 2, -2):
        return S.Zero

    kron_del = lambda i, j: 1 if i == j else 0
    s = lambda e: -1 if e % 2 else 1  #  (-1)**e to give +/-1, avoiding float when e<0
    A = lambda a, b: (-kron_del(a, b)*s(a-b) + kron_del(a, -b)*
                      s(b)) if b < 0 else 0
    B = lambda a, b: (kron_del(a, b) + kron_del(a, -b)*s(a)) if b > 0 else 0
    C = lambda a, b: kron_del(abs(a), abs(b))*(kron_del(a, 0)*kron_del(b, 0) +
                                          (B(a, b) + I*A(a, b))/sqrt(2))
    ugnt = 0
    for i in range(-l_1, l_1+1):
        U1 = C(i, m_1)
        for j in range(-l_2, l_2+1):
            U2 = C(j, m_2)
            U3 = C(-i-j, m_3)
            ugnt = ugnt + re(U1*U2*U3)*gaunt(l_1, l_2, l_3, i, j, -i-j)

    if prec is not None:
        ugnt = ugnt.n(prec)
    return ugnt


        ```
    ###Assistant : 
    Calculate the real Gaunt coefficient.

    Explanation
    ===========
    The real Gaunt coefficient is defined as the integral over three
    real spherical harmonics:
    
    .. math::
        \begin{aligned}
        \operatorname{RealGaunt}(l_1,l_2,l_3,m_1,m_2,m_3)
        &=\int Z^{m_1}_{l_1}(\Omega)
         Z^{m_2}_{l_2}(\Omega) Z^{m_3}_{l_3}(\Omega) \,d\Omega \\
        \end{aligned}

    Alternatively, it can be defined in terms of the standard Gaunt
    coefficient by relating the real spherical harmonics to the standard
    spherical harmonics via a unitary transformation `U`, i.e.
    `Z^{m}_{l}(\Omega)=\sum_{m'}U^{m}_{m'}Y^{m'}_{l}(\Omega)` [Homeier96]_.
    The real Gaunt coefficient is then defined as

    .. math::
        \begin{aligned}
        \operatorname{RealGaunt}(l_1,l_2,l_3,m_1,m_2,m_3)
        &=\int Z^{m_1}_{l_1}(\Omega)
         Z^{m_2}_{l_2}(\Omega) Z^{m_3}_{l_3}(\Omega) \,d\Omega \\
        &=\sum_{m'_1 m'_2 m'_3} U^{m_1}_{m'_1}U^{m_2}_{m'_2}U^{m_3}_{m'_3}
         \operatorname{Gaunt}(l_1,l_2,l_3,m'_1,m'_2,m'_3)
        \end{aligned}

    The unitary matrix `U` has components

    .. math::
        \begin{aligned}
        U^m_{m'} = \delta_{|m||m'|}*(\delta_{m'0}\delta_{m0} + \frac{1}{\sqrt{2}}\big[\Theta(m)
        \big(\delta_{m'm}+(-1)^{m'}\delta_{m'-m}\big)+i\Theta(-m)\big((-1)^{-m}
        \delta_{m'-m}-\delta_{m'm}*(-1)^{m'-m}\big)\big])
        \end{aligned}

    where `\delta_{ij}` is the Kronecker delta symbol and `\Theta` is a step
    function defined as

    .. math::
        \begin{aligned}
        \Theta(x) = \begin{cases} 1 \,\text{for}\, x > 0 \\ 0 \,\text{for}\, x \leq 0 \end{cases}
        \end{aligned}

    Parameters
    ==========
    l_1, l_2, l_3, m_1, m_2, m_3 :
        Integer.
    prec - precision, default: ``None``.
        Providing a precision can
        drastically speed up the calculation.

    Returns
    =======
    Rational number times the square root of a rational number.

    Examples
    ========
    >>> from sympy.physics.wigner import real_gaunt
    >>> real_gaunt(2,2,4,-1,-1,0)
    -2/(7*sqrt(pi))
    >>> real_gaunt(10,10,20,-9,-9,0).n(64)
    -0.00002480019791932209313156167...
    
    It is an error to use non-integer values for `l` and `m`::
        real_gaunt(2.8,0.5,1.3,0,0,0)
        Traceback (most recent call last):
        ...
        ValueError: l values must be integer
        real_gaunt(2,2,4,0.7,1,-3.4)
        Traceback (most recent call last):
        ...
        ValueError: m values must be integer

    Notes
    =====
    The real Gaunt coefficient inherits from the standard Gaunt coefficient,
    the invariance under any permutation of the pairs `(l_i, m_i)` and the
    requirement that the sum of the `l_i` be even to yield a non-zero value.
    It also obeys the following symmetry rules:

    - zero for `l_1`, `l_2`, `l_3` not fulfiling the condition
      `l_1 \in \{l_{\text{max}}, l_{\text{max}}-2, \ldots, l_{\text{min}}\}`,
      where `l_{\text{max}} = l_2+l_3`,
      
      .. math::
          \begin{aligned}
          l_{\text{min}} = \begin{cases} \kappa(l_2, l_3, m_2, m_3) & \text{if}\,
          \kappa(l_2, l_3, m_2, m_3) + l_{\text{max}}\, \text{is even} \\
          \kappa(l_2, l_3, m_2, m_3)+1 & \text{if}\, \kappa(l_2, l_3, m_2, m_3) +
          l_{\text{max}}\, \text{is odd}\end{cases}
          \end{aligned}

      and `\kappa(l_2, l_3, m_2, m_3) = \max{\big(|l_2-l_3|, \min{\big(|m_2+m_3|,
      |m_2-m_3|\big)}\big)}`
      
    - zero for an odd number of negative `m_i`
    
    Algorithms
    ==========
    This function uses the algorithms of [Homeier96]_ and [Rasch03]_ to
    calculate the value of the real Gaunt coefficient exactly. Note that
    the formula used in [Rasch03]_ contains alternating sums over large
    factorials and is therefore unsuitable for finite precision arithmetic
    and only useful for a computer algebra system [Rasch03]_. However, this
    function can in principle use any algorithm that computes the Gaunt
    coefficient, so it is suitable for finite precision arithmetic in so far
    as the algorithm which computes the Gaunt coefficient is.
    
    "
2800,"async def test_missing_tones_list(hass):
    
    siren = MockSirenEntity(SirenEntityFeature.TONES, [""a"", ""b""])
    siren.hass = hass
    with pytest.raises(ValueError):
        process_turn_on_params(siren, {""tone"": ""test""})

",Test ValueError when setting a tone that is missing from available_tones list.,12,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_missing_tones_list(hass):
    
    siren = MockSirenEntity(SirenEntityFeature.TONES, [""a"", ""b""])
    siren.hass = hass
    with pytest.raises(ValueError):
        process_turn_on_params(siren, {""tone"": ""test""})


        ```
    ###Assistant : Test ValueError when setting a tone that is missing from available_tones list.
    "
2801,"def task_failed_deps(args):
    
    dag = get_dag(args.subdir, args.dag_id)
    task = dag.get_task(task_id=args.task_id)
    ti = _get_ti(task, args.execution_date_or_run_id, args.map_index)

    dep_context = DepContext(deps=SCHEDULER_QUEUED_DEPS)
    failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
    # TODO, Do we want to print or log this
    if failed_deps:
        print(""Task instance dependencies not met:"")
        for dep in failed_deps:
            print(f""{dep.dep_name}: {dep.reason}"")
    else:
        print(""Task instance dependencies are all met."")


@cli_utils.action_cli(check_db=False)
@suppress_logs_and_warning","
    Returns the unmet dependencies for a task instance from the perspective of the
    scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
    scheduler, and then run by an executor).
    >>> airflow tasks failed-deps tutorial sleep 2015-01-01
    Task instance dependencies not met:
    Dagrun Running: Task instance's dagrun did not exist: Unknown reason
    Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
    to have succeeded, but found 1 non-success(es).
    ",73,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def task_failed_deps(args):
    
    dag = get_dag(args.subdir, args.dag_id)
    task = dag.get_task(task_id=args.task_id)
    ti = _get_ti(task, args.execution_date_or_run_id, args.map_index)

    dep_context = DepContext(deps=SCHEDULER_QUEUED_DEPS)
    failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
    # TODO, Do we want to print or log this
    if failed_deps:
        print(""Task instance dependencies not met:"")
        for dep in failed_deps:
            print(f""{dep.dep_name}: {dep.reason}"")
    else:
        print(""Task instance dependencies are all met."")


@cli_utils.action_cli(check_db=False)
@suppress_logs_and_warning
        ```
    ###Assistant : 
    Returns the unmet dependencies for a task instance from the perspective of the
    scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
    scheduler, and then run by an executor).
    >>> airflow tasks failed-deps tutorial sleep 2015-01-01
    Task instance dependencies not met:
    Dagrun Running: Task instance's dagrun did not exist: Unknown reason
    Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
    to have succeeded, but found 1 non-success(es).
    
    "
2802,"def load(self, rawdata):
        
        if isinstance(rawdata, str):
            self.__parse_string(rawdata)
        else:
            # self.update() wouldn't call our custom __setitem__
            for key, value in rawdata.items():
                self[key] = value
        return
","Load cookies from a string (presumably HTTP_COOKIE) or
        from a dictionary.  Loading cookies from a dictionary 'd'
        is equivalent to calling:
            map(Cookie.__setitem__, d.keys(), d.values())
        ",24,24,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def load(self, rawdata):
        
        if isinstance(rawdata, str):
            self.__parse_string(rawdata)
        else:
            # self.update() wouldn't call our custom __setitem__
            for key, value in rawdata.items():
                self[key] = value
        return

        ```
    ###Assistant : Load cookies from a string (presumably HTTP_COOKIE) or
        from a dictionary.  Loading cookies from a dictionary 'd'
        is equivalent to calling:
            map(Cookie.__setitem__, d.keys(), d.values())
        
    "
2803,"def _reset_major_tick_kw(self, keep_tick_and_label_visibility=False):
        
        backup = {name: value for name, value in self._major_tick_kw.items()
                  if name in ['tick1On', 'tick2On', 'label1On', 'label2On']}
        self._major_tick_kw.clear()
        if keep_tick_and_label_visibility:
            self._major_tick_kw.update(backup)
        self._major_tick_kw['gridOn'] = (
                mpl.rcParams['axes.grid'] and
                mpl.rcParams['axes.grid.which'] in ('both', 'major'))
","
        Reset major tick params to defaults.

        Shared subplots pre-configure tick and label visibility. To keep this
        beyond an Axis.clear() operation, we may
        *keep_tick_and_label_visibility*.
        ",23,32,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _reset_major_tick_kw(self, keep_tick_and_label_visibility=False):
        
        backup = {name: value for name, value in self._major_tick_kw.items()
                  if name in ['tick1On', 'tick2On', 'label1On', 'label2On']}
        self._major_tick_kw.clear()
        if keep_tick_and_label_visibility:
            self._major_tick_kw.update(backup)
        self._major_tick_kw['gridOn'] = (
                mpl.rcParams['axes.grid'] and
                mpl.rcParams['axes.grid.which'] in ('both', 'major'))

        ```
    ###Assistant : 
        Reset major tick params to defaults.

        Shared subplots pre-configure tick and label visibility. To keep this
        beyond an Axis.clear() operation, we may
        *keep_tick_and_label_visibility*.
        
    "
2804,"def test_class_variable_to_instance(self):
        
        logger = self.logger
        self.assertEqual(logger._to_exclude, logger._exclude_results)
        self.assertEqual(logger._to_system, logger._system_results)
        self.assertEqual(logger._to_other, logger._other_results)
        self.assertEqual(logger._to_episodes, logger._episode_results)
","Test that class variables get properly assigned to instance
        variables.
        ",10,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_class_variable_to_instance(self):
        
        logger = self.logger
        self.assertEqual(logger._to_exclude, logger._exclude_results)
        self.assertEqual(logger._to_system, logger._system_results)
        self.assertEqual(logger._to_other, logger._other_results)
        self.assertEqual(logger._to_episodes, logger._episode_results)

        ```
    ###Assistant : Test that class variables get properly assigned to instance
        variables.
        
    "
2805,"def detect_all(byte_str, ignore_threshold=False):
    
    if not isinstance(byte_str, bytearray):
        if not isinstance(byte_str, bytes):
            raise TypeError(
                f""Expected object of type bytes or bytearray, got: {type(byte_str)}""
            )
        byte_str = bytearray(byte_str)

    detector = UniversalDetector()
    detector.feed(byte_str)
    detector.close()

    if detector.input_state == InputState.HIGH_BYTE:
        results = []
        probers = []
        for prober in detector.charset_probers:
            if hasattr(prober, ""probers""):
                probers.extend(p for p in prober.probers)
            else:
                probers.append(prober)
        for prober in probers:
            if ignore_threshold or prober.get_confidence() > detector.MINIMUM_THRESHOLD:
                charset_name = prober.charset_name or """"
                lower_charset_name = charset_name.lower()
                # Use Windows encoding name instead of ISO-8859 if we saw any
                # extra Windows-specific bytes
                if lower_charset_name.startswith(""iso-8859"") and detector.has_win_bytes:
                    charset_name = detector.ISO_WIN_MAP.get(
                        lower_charset_name, charset_name
                    )
                results.append(
                    {
                        ""encoding"": charset_name,
                        ""confidence"": prober.get_confidence(),
                        ""language"": prober.language,
                    }
                )
        if len(results) > 0:
            return sorted(results, key=lambda result: -result[""confidence""])

    return [detector.result]
","
    Detect all the possible encodings of the given byte string.

    :param byte_str:          The byte sequence to examine.
    :type byte_str:           ``bytes`` or ``bytearray``
    :param ignore_threshold:  Include encodings that are below
                              ``UniversalDetector.MINIMUM_THRESHOLD``
                              in results.
    :type ignore_threshold:   ``bool``
    ",35,120,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def detect_all(byte_str, ignore_threshold=False):
    
    if not isinstance(byte_str, bytearray):
        if not isinstance(byte_str, bytes):
            raise TypeError(
                f""Expected object of type bytes or bytearray, got: {type(byte_str)}""
            )
        byte_str = bytearray(byte_str)

    detector = UniversalDetector()
    detector.feed(byte_str)
    detector.close()

    if detector.input_state == InputState.HIGH_BYTE:
        results = []
        probers = []
        for prober in detector.charset_probers:
            if hasattr(prober, ""probers""):
                probers.extend(p for p in prober.probers)
            else:
                probers.append(prober)
        for prober in probers:
            if ignore_threshold or prober.get_confidence() > detector.MINIMUM_THRESHOLD:
                charset_name = prober.charset_name or """"
                lower_charset_name = charset_name.lower()
                # Use Windows encoding name instead of ISO-8859 if we saw any
                # extra Windows-specific bytes
                if lower_charset_name.startswith(""iso-8859"") and detector.has_win_bytes:
                    charset_name = detector.ISO_WIN_MAP.get(
                        lower_charset_name, charset_name
                    )
                results.append(
                    {
                        ""encoding"": charset_name,
                        ""confidence"": prober.get_confidence(),
                        ""language"": prober.language,
                    }
                )
        if len(results) > 0:
            return sorted(results, key=lambda result: -result[""confidence""])

    return [detector.result]

        ```
    ###Assistant : 
    Detect all the possible encodings of the given byte string.

    :param byte_str:          The byte sequence to examine.
    :type byte_str:           ``bytes`` or ``bytearray``
    :param ignore_threshold:  Include encodings that are below
                              ``UniversalDetector.MINIMUM_THRESHOLD``
                              in results.
    :type ignore_threshold:   ``bool``
    
    "
2806,"def __call__(cls, value, names=None, *, module=None, qualname=None, type=None, start=1):
        
        if names is None:  # simple value lookup
            return cls.__new__(cls, value)
        # otherwise, functional API: we're creating a new Enum type
        return cls._create_(
                value,
                names,
                module=module,
                qualname=qualname,
                type=type,
                start=start,
                )
","
        Either returns an existing member, or creates a new enum class.

        This method is used both when an enum class is given a value to match
        to an enumeration member (i.e. Color(3)) and for the functional API
        (i.e. Color = Enum('Color', names='RED GREEN BLUE')).

        When used for the functional API:

        `value` will be the name of the new class.

        `names` should be either a string of white-space/comma delimited names
        (values will start at `start`), or an iterator/mapping of name, value pairs.

        `module` should be set to the module this class is being created in;
        if it is not set, an attempt to find that module will be made, but if
        it fails the class will not be picklable.

        `qualname` should be set to the actual location this class can be found
        at in its module; by default it is set to the global scope.  If this is
        not correct, unpickling will fail in some circumstances.

        `type`, if set, will be mixed in as the first base class.
        ",167,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __call__(cls, value, names=None, *, module=None, qualname=None, type=None, start=1):
        
        if names is None:  # simple value lookup
            return cls.__new__(cls, value)
        # otherwise, functional API: we're creating a new Enum type
        return cls._create_(
                value,
                names,
                module=module,
                qualname=qualname,
                type=type,
                start=start,
                )

        ```
    ###Assistant : 
        Either returns an existing member, or creates a new enum class.

        This method is used both when an enum class is given a value to match
        to an enumeration member (i.e. Color(3)) and for the functional API
        (i.e. Color = Enum('Color', names='RED GREEN BLUE')).

        When used for the functional API:

        `value` will be the name of the new class.

        `names` should be either a string of white-space/comma delimited names
        (values will start at `start`), or an iterator/mapping of name, value pairs.

        `module` should be set to the module this class is being created in;
        if it is not set, an attempt to find that module will be made, but if
        it fails the class will not be picklable.

        `qualname` should be set to the actual location this class can be found
        at in its module; by default it is set to the global scope.  If this is
        not correct, unpickling will fail in some circumstances.

        `type`, if set, will be mixed in as the first base class.
        
    "
2807,"def edges2dot(edges, shapes=None, attr=None):
    
    if not shapes:
        shapes = dict()
    if not attr:
        attr = dict()

    dot_string = ""digraph G {\n""

    for pair in attr.items():
        dot_string += f""{pair[0]} = {pair[1]};\n""

    for edge in edges:
        for shape in shapes.items():
            for node in range(2):
                if shape[0] in repr(edge[node]):
                    dot_string += f'""{edge[node]}"" [shape = {shape[1]}];\n'
        dot_string += f'""{edge[0]}"" -> ""{edge[1]}"";\n'

    dot_string += ""}\n""
    return dot_string

","
    :param edges: the set (or list) of edges of a directed graph.

    :return dot_string: a representation of 'edges' as a string in the DOT
        graph language, which can be converted to an image by the 'dot' program
        from the Graphviz package, or nltk.parse.dependencygraph.dot2img(dot_string).

    :param shapes: dictionary of strings that trigger a specified shape.
    :param attr: dictionary with global graph attributes

    >>> import nltk
    >>> from nltk.util import edges2dot
    >>> print(edges2dot([('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'B')]))
    digraph G {
    ""A"" -> ""B"";
    ""A"" -> ""C"";
    ""B"" -> ""C"";
    ""C"" -> ""B"";
    }
    <BLANKLINE>
    ",94,62,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def edges2dot(edges, shapes=None, attr=None):
    
    if not shapes:
        shapes = dict()
    if not attr:
        attr = dict()

    dot_string = ""digraph G {\n""

    for pair in attr.items():
        dot_string += f""{pair[0]} = {pair[1]};\n""

    for edge in edges:
        for shape in shapes.items():
            for node in range(2):
                if shape[0] in repr(edge[node]):
                    dot_string += f'""{edge[node]}"" [shape = {shape[1]}];\n'
        dot_string += f'""{edge[0]}"" -> ""{edge[1]}"";\n'

    dot_string += ""}\n""
    return dot_string


        ```
    ###Assistant : 
    :param edges: the set (or list) of edges of a directed graph.

    :return dot_string: a representation of 'edges' as a string in the DOT
        graph language, which can be converted to an image by the 'dot' program
        from the Graphviz package, or nltk.parse.dependencygraph.dot2img(dot_string).

    :param shapes: dictionary of strings that trigger a specified shape.
    :param attr: dictionary with global graph attributes

    >>> import nltk
    >>> from nltk.util import edges2dot
    >>> print(edges2dot([('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'B')]))
    digraph G {
    ""A"" -> ""B"";
    ""A"" -> ""C"";
    ""B"" -> ""C"";
    ""C"" -> ""B"";
    }
    <BLANKLINE>
    
    "
2808,"def _draw_line_numbers(self):
        
        if not self.line_numbers:
            return
        for p in range(self.maxlineno):
            n = p + self.line_number_start
            if (n % self.line_number_step) == 0:
                self._draw_linenumber(p, n)
","
        Create drawables for the line numbers.
        ",6,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _draw_line_numbers(self):
        
        if not self.line_numbers:
            return
        for p in range(self.maxlineno):
            n = p + self.line_number_start
            if (n % self.line_number_step) == 0:
                self._draw_linenumber(p, n)

        ```
    ###Assistant : 
        Create drawables for the line numbers.
        
    "
2809,"def export(ctx, directory):
    directory_path = Path(directory)
    if directory_path.absolute == Path.cwd().absolute:
        console.print(
            ,
            style=""red bold"",
            end="""",
        )
        proceed = input().lower() == ""y""
    else:
        proceed = True
    if proceed:
        if not directory_path.is_dir():
            console.print(f""Creating folder: {directory}."", style=""red bold"")
            directory_path.mkdir(parents=True)

        ctx.invoke(write)
        from_path = Path.cwd() / ""manim.cfg""
        to_path = directory_path / ""manim.cfg""

        console.print(f""Exported final Config at {from_path} to {to_path}."")
    else:
        console.print(""Aborted..."", style=""red bold"")
","You are reading the config from the same directory you are exporting to.
This means that the exported config will overwrite the config for this directory.
Are you sure you want to continue? (y/n)",34,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def export(ctx, directory):
    directory_path = Path(directory)
    if directory_path.absolute == Path.cwd().absolute:
        console.print(
            ,
            style=""red bold"",
            end="""",
        )
        proceed = input().lower() == ""y""
    else:
        proceed = True
    if proceed:
        if not directory_path.is_dir():
            console.print(f""Creating folder: {directory}."", style=""red bold"")
            directory_path.mkdir(parents=True)

        ctx.invoke(write)
        from_path = Path.cwd() / ""manim.cfg""
        to_path = directory_path / ""manim.cfg""

        console.print(f""Exported final Config at {from_path} to {to_path}."")
    else:
        console.print(""Aborted..."", style=""red bold"")

        ```
    ###Assistant : You are reading the config from the same directory you are exporting to.
This means that the exported config will overwrite the config for this directory.
Are you sure you want to continue? (y/n)
    "
2810,"def update_keymap(self, name, key):
        
        if name not in self._tools:
            raise KeyError(f'{name!r} not in Tools')
        self._remove_keys(name)
        if isinstance(key, str):
            key = [key]
        for k in key:
            if k in self._keys:
                _api.warn_external(
                    f'Key {k} changed from {self._keys[k]} to {name}')
            self._keys[k] = name
","
        Set the keymap to associate with the specified tool.

        Parameters
        ----------
        name : str
            Name of the Tool.
        key : str or list of str
            Keys to associate with the tool.
        ",31,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def update_keymap(self, name, key):
        
        if name not in self._tools:
            raise KeyError(f'{name!r} not in Tools')
        self._remove_keys(name)
        if isinstance(key, str):
            key = [key]
        for k in key:
            if k in self._keys:
                _api.warn_external(
                    f'Key {k} changed from {self._keys[k]} to {name}')
            self._keys[k] = name

        ```
    ###Assistant : 
        Set the keymap to associate with the specified tool.

        Parameters
        ----------
        name : str
            Name of the Tool.
        key : str or list of str
            Keys to associate with the tool.
        
    "
2811,"def named_parameters(self, *args, **kwargs):
        
        arch = kwargs.pop('arch', False)
        for name, p in super().named_parameters(*args, **kwargs):
            if any(name == par_name for par_name in self._arch_parameter_names):
                if arch:
                    yield name, p
            else:
                if not arch:
                    yield name, p

",Named parameters excluding architecture parameters.,5,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def named_parameters(self, *args, **kwargs):
        
        arch = kwargs.pop('arch', False)
        for name, p in super().named_parameters(*args, **kwargs):
            if any(name == par_name for par_name in self._arch_parameter_names):
                if arch:
                    yield name, p
            else:
                if not arch:
                    yield name, p


        ```
    ###Assistant : Named parameters excluding architecture parameters.
    "
2812,"def unzip3(xyzs):
  
  # Note: we deliberately don't use zip(*xyzs) because it is lazily evaluated,
  # is too permissive about inputs, and does not guarantee a length-3 output.
  xs = []
  ys = []
  zs = []
  for x, y, z in xyzs:
    xs.append(x)
    ys.append(y)
    zs.append(z)
  return tuple(xs), tuple(ys), tuple(zs)
",Unzip sequence of length-3 tuples into three tuples.,8,49,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def unzip3(xyzs):
  
  # Note: we deliberately don't use zip(*xyzs) because it is lazily evaluated,
  # is too permissive about inputs, and does not guarantee a length-3 output.
  xs = []
  ys = []
  zs = []
  for x, y, z in xyzs:
    xs.append(x)
    ys.append(y)
    zs.append(z)
  return tuple(xs), tuple(ys), tuple(zs)

        ```
    ###Assistant : Unzip sequence of length-3 tuples into three tuples.
    "
2813,"def test_json_get_subscribers_for_guest_user(self) -> None:
        
        guest_user = self.example_user(""polonius"")
        never_subscribed = gather_subscriptions_helper(guest_user, True).never_subscribed

        # A guest user can only see never subscribed streams that are web-public.
        # For Polonius, the only web-public stream that he is not subscribed at
        # this point is Rome.
        self.assert_length(never_subscribed, 1)

        web_public_stream_id = never_subscribed[0][""stream_id""]
        result = self.client_get(f""/json/streams/{web_public_stream_id}/members"")
        self.assert_json_success(result)
        result_dict = result.json()
        self.assertIn(""subscribers"", result_dict)
        self.assertIsInstance(result_dict[""subscribers""], list)
        self.assertGreater(len(result_dict[""subscribers""]), 0)
","
        Guest users should have access to subscribers of web-public streams, even
        if they aren't subscribed or have never subscribed to that stream.
        ",22,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_json_get_subscribers_for_guest_user(self) -> None:
        
        guest_user = self.example_user(""polonius"")
        never_subscribed = gather_subscriptions_helper(guest_user, True).never_subscribed

        # A guest user can only see never subscribed streams that are web-public.
        # For Polonius, the only web-public stream that he is not subscribed at
        # this point is Rome.
        self.assert_length(never_subscribed, 1)

        web_public_stream_id = never_subscribed[0][""stream_id""]
        result = self.client_get(f""/json/streams/{web_public_stream_id}/members"")
        self.assert_json_success(result)
        result_dict = result.json()
        self.assertIn(""subscribers"", result_dict)
        self.assertIsInstance(result_dict[""subscribers""], list)
        self.assertGreater(len(result_dict[""subscribers""]), 0)

        ```
    ###Assistant : 
        Guest users should have access to subscribers of web-public streams, even
        if they aren't subscribed or have never subscribed to that stream.
        
    "
2814,"def _reconnect(self):
        
        if not self._isconnected():
            try:
                self._connect()
            except http_client.socket.error as e:
                # Attempt to reconnect if the connection was refused
                if e.errno == 61 or e.errno == 10061:
                    # errno 61 is the ""Connection Refused"" error
                    time.sleep(self._delay)
                    self._delay += self._delay  # fibonacii delays
                    self._tries += 1
                    if self._tries < self.maxtries:
                        self._reconnect()
                    else:
                        self._reset_retries()
                        raise e
                else:
                    # Unknown scenario
                    raise e

        # Reconnect worked - reset _closed
        self._closed = False
","Connect if disconnected.
        Retry self.maxtries times with delays
        ",8,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _reconnect(self):
        
        if not self._isconnected():
            try:
                self._connect()
            except http_client.socket.error as e:
                # Attempt to reconnect if the connection was refused
                if e.errno == 61 or e.errno == 10061:
                    # errno 61 is the ""Connection Refused"" error
                    time.sleep(self._delay)
                    self._delay += self._delay  # fibonacii delays
                    self._tries += 1
                    if self._tries < self.maxtries:
                        self._reconnect()
                    else:
                        self._reset_retries()
                        raise e
                else:
                    # Unknown scenario
                    raise e

        # Reconnect worked - reset _closed
        self._closed = False

        ```
    ###Assistant : Connect if disconnected.
        Retry self.maxtries times with delays
        
    "
2815,"def list_master(saltenv=None, prefix=""""):
    
    if not saltenv:
        saltenv = __opts__[""saltenv""] or ""base""
    return _client().file_list(saltenv, prefix)

","
    .. versionchanged:: 3005
        ``saltenv`` will use value from config if not explicitly set

    List all of the files stored on the master

    CLI Example:

    .. code-block:: bash

        salt '*' cp.list_master
    ",30,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def list_master(saltenv=None, prefix=""""):
    
    if not saltenv:
        saltenv = __opts__[""saltenv""] or ""base""
    return _client().file_list(saltenv, prefix)


        ```
    ###Assistant : 
    .. versionchanged:: 3005
        ``saltenv`` will use value from config if not explicitly set

    List all of the files stored on the master

    CLI Example:

    .. code-block:: bash

        salt '*' cp.list_master
    
    "
2816,"def test_bert_hf_tokenizer_parity(tmpdir, pretrained_model_name_or_path):
    
    from ludwig.utils.tokenizers import get_hf_tokenizer, HFTokenizer

    inputs = ""Hello, ``I'm'' ónë of 1,205,000 sentences!""
    hf_tokenizer = HFTokenizer(pretrained_model_name_or_path)
    torchtext_tokenizer = get_hf_tokenizer(pretrained_model_name_or_path)

    # Ensure that the tokenizer is scriptable
    tokenizer_path = os.path.join(tmpdir, ""tokenizer.pt"")
    torch.jit.script(torchtext_tokenizer).save(tokenizer_path)
    torchtext_tokenizer = torch.jit.load(tokenizer_path)

    token_ids_expected = hf_tokenizer(inputs)
    token_ids = torchtext_tokenizer(inputs)

    assert token_ids_expected == token_ids

","Tests the BERTTokenizer implementation.

    Asserts both tokens and token IDs are the same by initializing the BERTTokenizer as a standalone tokenizer and as a
    HF tokenizer.
    ",26,47,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_bert_hf_tokenizer_parity(tmpdir, pretrained_model_name_or_path):
    
    from ludwig.utils.tokenizers import get_hf_tokenizer, HFTokenizer

    inputs = ""Hello, ``I'm'' ónë of 1,205,000 sentences!""
    hf_tokenizer = HFTokenizer(pretrained_model_name_or_path)
    torchtext_tokenizer = get_hf_tokenizer(pretrained_model_name_or_path)

    # Ensure that the tokenizer is scriptable
    tokenizer_path = os.path.join(tmpdir, ""tokenizer.pt"")
    torch.jit.script(torchtext_tokenizer).save(tokenizer_path)
    torchtext_tokenizer = torch.jit.load(tokenizer_path)

    token_ids_expected = hf_tokenizer(inputs)
    token_ids = torchtext_tokenizer(inputs)

    assert token_ids_expected == token_ids


        ```
    ###Assistant : Tests the BERTTokenizer implementation.

    Asserts both tokens and token IDs are the same by initializing the BERTTokenizer as a standalone tokenizer and as a
    HF tokenizer.
    
    "
2817,"def get_pe_matching_query(amount_condition, account_from_to, transaction):
	# get matching payment entries query
	if transaction.deposit > 0:
		currency_field = ""paid_to_account_currency as currency""
	else:
		currency_field = ""paid_from_account_currency as currency""
	return f

","
	SELECT
		(CASE WHEN reference_no=%(reference_no)s THEN 1 ELSE 0 END
		+ CASE WHEN (party_type = %(party_type)s AND party = %(party)s ) THEN 1 ELSE 0  END
		+ 1 ) AS rank,
		'Payment Entry' as doctype,
		name,
		paid_amount,
		reference_no,
		reference_date,
		party,
		party_type,
		posting_date,
		{currency_field}
	FROM
		`tabPayment Entry`
	WHERE
		paid_amount {amount_condition} %(amount)s
		AND docstatus = 1
		AND payment_type IN (%(payment_type)s, 'Internal Transfer')
		AND ifnull(clearance_date, '') = """"
		AND {account_from_to} = %(bank_account)s
	",68,27,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_pe_matching_query(amount_condition, account_from_to, transaction):
	# get matching payment entries query
	if transaction.deposit > 0:
		currency_field = ""paid_to_account_currency as currency""
	else:
		currency_field = ""paid_from_account_currency as currency""
	return f


        ```
    ###Assistant : 
	SELECT
		(CASE WHEN reference_no=%(reference_no)s THEN 1 ELSE 0 END
		+ CASE WHEN (party_type = %(party_type)s AND party = %(party)s ) THEN 1 ELSE 0  END
		+ 1 ) AS rank,
		'Payment Entry' as doctype,
		name,
		paid_amount,
		reference_no,
		reference_date,
		party,
		party_type,
		posting_date,
		{currency_field}
	FROM
		`tabPayment Entry`
	WHERE
		paid_amount {amount_condition} %(amount)s
		AND docstatus = 1
		AND payment_type IN (%(payment_type)s, 'Internal Transfer')
		AND ifnull(clearance_date, '') = """"
		AND {account_from_to} = %(bank_account)s
	
    "
2818,"def _check_list_display_links(self, obj):
        
        from django.contrib.admin.options import ModelAdmin

        if obj.list_display_links is None:
            return []
        elif not isinstance(obj.list_display_links, (list, tuple)):
            return must_be(
                ""a list, a tuple, or None"",
                option=""list_display_links"",
                obj=obj,
                id=""admin.E110"",
            )
        # Check only if ModelAdmin.get_list_display() isn't overridden.
        elif obj.get_list_display.__func__ is ModelAdmin.get_list_display:
            return list(
                chain.from_iterable(
                    self._check_list_display_links_item(
                        obj, field_name, ""list_display_links[%d]"" % index
                    )
                    for index, field_name in enumerate(obj.list_display_links)
                )
            )
        return []
",Check that list_display_links is a unique subset of list_display.,9,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_list_display_links(self, obj):
        
        from django.contrib.admin.options import ModelAdmin

        if obj.list_display_links is None:
            return []
        elif not isinstance(obj.list_display_links, (list, tuple)):
            return must_be(
                ""a list, a tuple, or None"",
                option=""list_display_links"",
                obj=obj,
                id=""admin.E110"",
            )
        # Check only if ModelAdmin.get_list_display() isn't overridden.
        elif obj.get_list_display.__func__ is ModelAdmin.get_list_display:
            return list(
                chain.from_iterable(
                    self._check_list_display_links_item(
                        obj, field_name, ""list_display_links[%d]"" % index
                    )
                    for index, field_name in enumerate(obj.list_display_links)
                )
            )
        return []

        ```
    ###Assistant : Check that list_display_links is a unique subset of list_display.
    "
2819,"def min_mireds(self) -> int:
        
        min_mireds = self.cluster.get(""color_temp_physical_min"", self.MIN_MIREDS)
        if min_mireds == 0:
            self.warning(
                ""[Min mireds is 0, setting to %s] Please open an issue on the quirks repo to have this device corrected"",
                self.MIN_MIREDS,
            )
            min_mireds = self.MIN_MIREDS
        return min_mireds
",Return the coldest color_temp that this channel supports.,8,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def min_mireds(self) -> int:
        
        min_mireds = self.cluster.get(""color_temp_physical_min"", self.MIN_MIREDS)
        if min_mireds == 0:
            self.warning(
                ""[Min mireds is 0, setting to %s] Please open an issue on the quirks repo to have this device corrected"",
                self.MIN_MIREDS,
            )
            min_mireds = self.MIN_MIREDS
        return min_mireds

        ```
    ###Assistant : Return the coldest color_temp that this channel supports.
    "
2820,"def pop_screen(self) -> Screen:
        
        screen_stack = self._screen_stack
        if len(screen_stack) <= 1:
            raise ScreenStackError(
                ""Can't pop screen; there must be at least one screen on the stack""
            )
        screen = screen_stack.pop()
        screen.post_message_no_wait(events.ScreenSuspend(self))
        self.screen._screen_resized(self.size)
        self.screen.post_message_no_wait(events.ScreenResume(self))
        return screen
","Pop the current screen from the stack, and switch to the previous screen.

        Returns:
            Screen: The screen that was replaced.
        ",20,35,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pop_screen(self) -> Screen:
        
        screen_stack = self._screen_stack
        if len(screen_stack) <= 1:
            raise ScreenStackError(
                ""Can't pop screen; there must be at least one screen on the stack""
            )
        screen = screen_stack.pop()
        screen.post_message_no_wait(events.ScreenSuspend(self))
        self.screen._screen_resized(self.size)
        self.screen.post_message_no_wait(events.ScreenResume(self))
        return screen

        ```
    ###Assistant : Pop the current screen from the stack, and switch to the previous screen.

        Returns:
            Screen: The screen that was replaced.
        
    "
2821,"def test_notify_interested_services_ephemeral(self):
        
        interested_service = self._mkservice(is_interested=True)
        services = [interested_service]
        self.mock_store.get_app_services.return_value = services
        self.mock_store.get_type_stream_id_for_appservice.return_value = make_awaitable(
            579
        )

        event = Mock(event_id=""event_1"")
        self.event_source.sources.receipt.get_new_events_as.return_value = (
            make_awaitable(([event], None))
        )

        self.handler.notify_interested_services_ephemeral(
            ""receipt_key"", 580, [""@fakerecipient:example.com""]
        )
        self.mock_scheduler.enqueue_for_appservice.assert_called_once_with(
            interested_service, ephemeral=[event]
        )
        self.mock_store.set_appservice_stream_type_pos.assert_called_once_with(
            interested_service,
            ""read_receipt"",
            580,
        )
","
        Test sending ephemeral events to the appservice handler are scheduled
        to be pushed out to interested appservices, and that the stream ID is
        updated accordingly.
        ",25,39,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_notify_interested_services_ephemeral(self):
        
        interested_service = self._mkservice(is_interested=True)
        services = [interested_service]
        self.mock_store.get_app_services.return_value = services
        self.mock_store.get_type_stream_id_for_appservice.return_value = make_awaitable(
            579
        )

        event = Mock(event_id=""event_1"")
        self.event_source.sources.receipt.get_new_events_as.return_value = (
            make_awaitable(([event], None))
        )

        self.handler.notify_interested_services_ephemeral(
            ""receipt_key"", 580, [""@fakerecipient:example.com""]
        )
        self.mock_scheduler.enqueue_for_appservice.assert_called_once_with(
            interested_service, ephemeral=[event]
        )
        self.mock_store.set_appservice_stream_type_pos.assert_called_once_with(
            interested_service,
            ""read_receipt"",
            580,
        )

        ```
    ###Assistant : 
        Test sending ephemeral events to the appservice handler are scheduled
        to be pushed out to interested appservices, and that the stream ID is
        updated accordingly.
        
    "
2822,"def convert(self, value):
            
            if not isinstance(value, ConvertingDict) and isinstance(value, dict):
                value = ConvertingDict(value)
                value.configurator = self
            elif not isinstance(value, ConvertingList) and isinstance(value, list):
                value = ConvertingList(value)
                value.configurator = self
            elif not isinstance(value, ConvertingTuple) and\
                     isinstance(value, tuple):
                value = ConvertingTuple(value)
                value.configurator = self
            elif isinstance(value, string_types):
                m = self.CONVERT_PATTERN.match(value)
                if m:
                    d = m.groupdict()
                    prefix = d['prefix']
                    converter = self.value_converters.get(prefix, None)
                    if converter:
                        suffix = d['suffix']
                        converter = getattr(self, converter)
                        value = converter(suffix)
            return value
","
            Convert values to an appropriate type. dicts, lists and tuples are
            replaced by their converting alternatives. Strings are checked to
            see if they have a conversion format and are converted if they do.
            ",33,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def convert(self, value):
            
            if not isinstance(value, ConvertingDict) and isinstance(value, dict):
                value = ConvertingDict(value)
                value.configurator = self
            elif not isinstance(value, ConvertingList) and isinstance(value, list):
                value = ConvertingList(value)
                value.configurator = self
            elif not isinstance(value, ConvertingTuple) and\
                     isinstance(value, tuple):
                value = ConvertingTuple(value)
                value.configurator = self
            elif isinstance(value, string_types):
                m = self.CONVERT_PATTERN.match(value)
                if m:
                    d = m.groupdict()
                    prefix = d['prefix']
                    converter = self.value_converters.get(prefix, None)
                    if converter:
                        suffix = d['suffix']
                        converter = getattr(self, converter)
                        value = converter(suffix)
            return value

        ```
    ###Assistant : 
            Convert values to an appropriate type. dicts, lists and tuples are
            replaced by their converting alternatives. Strings are checked to
            see if they have a conversion format and are converted if they do.
            
    "
2823,"def test_parse_transition(duration, parsed_duration):
    css = f
    stylesheet = Stylesheet()
    stylesheet.parse(css)

    rule = stylesheet.rules[0].styles

    assert len(stylesheet.rules) == 1
    assert len(stylesheet.rules[0].errors) == 0
    assert rule.transitions == {
        ""offset"": Transition(duration=parsed_duration, easing=""in_out_cubic"", delay=0.0)
    }
","#some-widget {{
        transition: offset {duration} in_out_cubic;
    }}
    ",7,30,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_parse_transition(duration, parsed_duration):
    css = f
    stylesheet = Stylesheet()
    stylesheet.parse(css)

    rule = stylesheet.rules[0].styles

    assert len(stylesheet.rules) == 1
    assert len(stylesheet.rules[0].errors) == 0
    assert rule.transitions == {
        ""offset"": Transition(duration=parsed_duration, easing=""in_out_cubic"", delay=0.0)
    }

        ```
    ###Assistant : #some-widget {{
        transition: offset {duration} in_out_cubic;
    }}
    
    "
2824,"def test_meta_charset(self) -> None:
        
        encodings = _get_html_media_encodings(
            b,
            ""text/html"",
        )
        self.assertEqual(list(encodings), [""ascii"", ""utf-8"", ""cp1252""])

        # A less well-formed version.
        encodings = _get_html_media_encodings(
            b,
            ""text/html"",
        )
        self.assertEqual(list(encodings), [""ascii"", ""utf-8"", ""cp1252""])
","A character encoding is found via the meta tag.
        <html>
        <head><meta charset=""ascii"">
        </head>
        </html>
        
        <html>
        <head>< meta charset = ascii>
        </head>
        </html>
        ",22,29,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_meta_charset(self) -> None:
        
        encodings = _get_html_media_encodings(
            b,
            ""text/html"",
        )
        self.assertEqual(list(encodings), [""ascii"", ""utf-8"", ""cp1252""])

        # A less well-formed version.
        encodings = _get_html_media_encodings(
            b,
            ""text/html"",
        )
        self.assertEqual(list(encodings), [""ascii"", ""utf-8"", ""cp1252""])

        ```
    ###Assistant : A character encoding is found via the meta tag.
        <html>
        <head><meta charset=""ascii"">
        </head>
        </html>
        
        <html>
        <head>< meta charset = ascii>
        </head>
        </html>
        
    "
2825,"def _build_amps_list(self, amp_value, processlist):
        
        ret = []
        try:
            # Search in both cmdline and name (for kernel thread, see #1261)
            for p in processlist:
                if (re.search(amp_value.regex(), p['name']) is not None) or (
                    p['cmdline'] is not None
                    and p['cmdline'] != []
                    and re.search(amp_value.regex(), ' '.join(p['cmdline'])) is not None
                ):
                    ret.append(
                        {'pid': p['pid'], 'cpu_percent': p['cpu_percent'], 'memory_percent': p['memory_percent']}
                    )

        except (TypeError, KeyError) as e:
            logger.debug(""Can not build AMPS list ({})"".format(e))

        return ret
","Return the AMPS process list according to the amp_value

        Search application monitored processes by a regular expression
        ",17,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _build_amps_list(self, amp_value, processlist):
        
        ret = []
        try:
            # Search in both cmdline and name (for kernel thread, see #1261)
            for p in processlist:
                if (re.search(amp_value.regex(), p['name']) is not None) or (
                    p['cmdline'] is not None
                    and p['cmdline'] != []
                    and re.search(amp_value.regex(), ' '.join(p['cmdline'])) is not None
                ):
                    ret.append(
                        {'pid': p['pid'], 'cpu_percent': p['cpu_percent'], 'memory_percent': p['memory_percent']}
                    )

        except (TypeError, KeyError) as e:
            logger.debug(""Can not build AMPS list ({})"".format(e))

        return ret

        ```
    ###Assistant : Return the AMPS process list according to the amp_value

        Search application monitored processes by a regular expression
        
    "
2826,"def call(self, inputs, *args, **kwargs):  # pylint: disable=unused-argument
        
        return inputs
","This is where the layer's logic lives.

        The `call()` method may not create state (except in its first invocation,
        wrapping the creation of variables or other resources in `tf.init_scope()`).
        It is recommended to create state in `__init__()`, or the `build()` method
        that is called automatically before `call()` executes the first time.

        Args:
          inputs: Input tensor, or dict/list/tuple of input tensors.
            The first positional `inputs` argument is subject to special rules:
            - `inputs` must be explicitly passed. A layer cannot have zero
              arguments, and `inputs` cannot be provided via the default value
              of a keyword argument.
            - NumPy array or Python scalar values in `inputs` get cast as tensors.
            - Keras mask metadata is only collected from `inputs`.
            - Layers are built (`build(input_shape)` method)
              using shape info from `inputs` only.
            - `input_spec` compatibility is only checked against `inputs`.
            - Mixed precision input casting is only applied to `inputs`.
              If a layer has tensor arguments in `*args` or `**kwargs`, their
              casting behavior in mixed precision should be handled manually.
            - The SavedModel input specification is generated using `inputs` only.
            - Integration with various ecosystem packages like TFMOT, TFLite,
              TF.js, etc is only supported for `inputs` and not for tensors in
              positional and keyword arguments.
          *args: Additional positional arguments. May contain tensors, although
            this is not recommended, for the reasons above.
          **kwargs: Additional keyword arguments. May contain tensors, although
            this is not recommended, for the reasons above.
            The following optional keyword arguments are reserved:
            - `training`: Boolean scalar tensor of Python boolean indicating
              whether the `call` is meant for training or inference.
            - `mask`: Boolean input mask. If the layer's `call()` method takes a
              `mask` argument, its default value will be set to the mask generated
              for `inputs` by the previous layer (if `input` did come from a layer
              that generated a corresponding mask, i.e. if it came from a Keras
              layer with masking support).

        Returns:
          A tensor or list/tuple of tensors.
        ",319,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def call(self, inputs, *args, **kwargs):  # pylint: disable=unused-argument
        
        return inputs

        ```
    ###Assistant : This is where the layer's logic lives.

        The `call()` method may not create state (except in its first invocation,
        wrapping the creation of variables or other resources in `tf.init_scope()`).
        It is recommended to create state in `__init__()`, or the `build()` method
        that is called automatically before `call()` executes the first time.

        Args:
          inputs: Input tensor, or dict/list/tuple of input tensors.
            The first positional `inputs` argument is subject to special rules:
            - `inputs` must be explicitly passed. A layer cannot have zero
              arguments, and `inputs` cannot be provided via the default value
              of a keyword argument.
            - NumPy array or Python scalar values in `inputs` get cast as tensors.
            - Keras mask metadata is only collected from `inputs`.
            - Layers are built (`build(input_shape)` method)
              using shape info from `inputs` only.
            - `input_spec` compatibility is only checked against `inputs`.
            - Mixed precision input casting is only applied to `inputs`.
              If a layer has tensor arguments in `*args` or `**kwargs`, their
              casting behavior in mixed precision should be handled manually.
            - The SavedModel input specification is generated using `inputs` only.
            - Integration with various ecosystem packages like TFMOT, TFLite,
              TF.js, etc is only supported for `inputs` and not for tensors in
              positional and keyword arguments.
          *args: Additional positional arguments. May contain tensors, although
            this is not recommended, for the reasons above.
          **kwargs: Additional keyword arguments. May contain tensors, although
            this is not recommended, for the reasons above.
            The following optional keyword arguments are reserved:
            - `training`: Boolean scalar tensor of Python boolean indicating
              whether the `call` is meant for training or inference.
            - `mask`: Boolean input mask. If the layer's `call()` method takes a
              `mask` argument, its default value will be set to the mask generated
              for `inputs` by the previous layer (if `input` did come from a layer
              that generated a corresponding mask, i.e. if it came from a Keras
              layer with masking support).

        Returns:
          A tensor or list/tuple of tensors.
        
    "
2827,"def test_use_predictor_with_view(self, mock_handler):
        # set integration data

        df = pd.DataFrame([
            {'a': 1, 'b': 'one'},
            {'a': 2, 'b': 'two'},
            {'a': 1, 'b': 'three'},
        ])
        self.set_handler(mock_handler, name='pg', tables={'tasks': df})

        view_name = 'vtasks'
        # --- create view ---
        ret = self.command_executor.execute_command(parse_sql(
            f'create view {view_name} (select * from pg (select * from tasks))',
            dialect='mindsdb')
        )
        assert ret.error_code is None

        # --- use predictor ---
        predicted_value = 3.14
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical
            },
            'predicted_value': predicted_value
        }
        self.set_predictor(predictor)
        ret = self.command_executor.execute_command(parse_sql(f, dialect='mindsdb'))
        assert ret.error_code is None

        # native query was called
        assert mock_handler().native_query.mock_calls[0].args[0] == 'select * from tasks'

        # check predictor call

        # model was called
        assert self.mock_model_interface.predict.mock_calls[0].args[0] == 'task_model'

        # input = one row whit a==2
        when_data = self.mock_model_interface.predict.mock_calls[0].args[1]
        assert len(when_data) == 1
        assert when_data[0]['a'] == 2

        # check prediction
        assert ret.data[0][0] == predicted_value
        assert len(ret.data) == 1
","
           select task_model.p 
           from views.{view_name}
           join mindsdb.task_model
           where {view_name}.a = 2
        ",10,144,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_use_predictor_with_view(self, mock_handler):
        # set integration data

        df = pd.DataFrame([
            {'a': 1, 'b': 'one'},
            {'a': 2, 'b': 'two'},
            {'a': 1, 'b': 'three'},
        ])
        self.set_handler(mock_handler, name='pg', tables={'tasks': df})

        view_name = 'vtasks'
        # --- create view ---
        ret = self.command_executor.execute_command(parse_sql(
            f'create view {view_name} (select * from pg (select * from tasks))',
            dialect='mindsdb')
        )
        assert ret.error_code is None

        # --- use predictor ---
        predicted_value = 3.14
        predictor = {
            'name': 'task_model',
            'predict': 'p',
            'dtypes': {
                'p': dtype.float,
                'a': dtype.integer,
                'b': dtype.categorical
            },
            'predicted_value': predicted_value
        }
        self.set_predictor(predictor)
        ret = self.command_executor.execute_command(parse_sql(f, dialect='mindsdb'))
        assert ret.error_code is None

        # native query was called
        assert mock_handler().native_query.mock_calls[0].args[0] == 'select * from tasks'

        # check predictor call

        # model was called
        assert self.mock_model_interface.predict.mock_calls[0].args[0] == 'task_model'

        # input = one row whit a==2
        when_data = self.mock_model_interface.predict.mock_calls[0].args[1]
        assert len(when_data) == 1
        assert when_data[0]['a'] == 2

        # check prediction
        assert ret.data[0][0] == predicted_value
        assert len(ret.data) == 1

        ```
    ###Assistant : 
           select task_model.p 
           from views.{view_name}
           join mindsdb.task_model
           where {view_name}.a = 2
        
    "
2828,"def test_vr_connector_shift_by_one(self):
        
        view_rq_dict = {
            ""state"": ViewRequirement(""obs""),
            ""next_state"": ViewRequirement(
                ""obs"", shift=1, used_for_compute_actions=False
            ),
            ""prev_state"": ViewRequirement(""obs"", shift=-1),
        }

        obs_arrs = np.arange(10)[:, None] + 1
        config = PPOConfig().to_dict()
        ctx = ConnectorContext(
            view_requirements=view_rq_dict, config=config, is_policy_recurrent=True
        )
        c = ViewRequirementAgentConnector(ctx)

        # keep a running list of observations
        obs_list = []
        for t, obs in enumerate(obs_arrs):
            # t=0 is the next state of t=-1
            data = AgentConnectorDataType(
                0, 1, {SampleBatch.NEXT_OBS: obs, SampleBatch.T: t - 1}
            )
            processed = c([data])  # env.reset() for t == -1 else env.step()
            for_action = processed[0].data.for_action
            # add cur obs to the list
            obs_list.append(obs)

            if t == 0:
                check(for_action[""prev_state""], for_action[""state""])
            else:
                # prev state should be equal to the prev time step obs
                check(for_action[""prev_state""], obs_list[-2][None])
","Test that the ViewRequirementConnector can handle shift by one correctly and
        can ignore future referencing view_requirements to respect causality",19,114,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_vr_connector_shift_by_one(self):
        
        view_rq_dict = {
            ""state"": ViewRequirement(""obs""),
            ""next_state"": ViewRequirement(
                ""obs"", shift=1, used_for_compute_actions=False
            ),
            ""prev_state"": ViewRequirement(""obs"", shift=-1),
        }

        obs_arrs = np.arange(10)[:, None] + 1
        config = PPOConfig().to_dict()
        ctx = ConnectorContext(
            view_requirements=view_rq_dict, config=config, is_policy_recurrent=True
        )
        c = ViewRequirementAgentConnector(ctx)

        # keep a running list of observations
        obs_list = []
        for t, obs in enumerate(obs_arrs):
            # t=0 is the next state of t=-1
            data = AgentConnectorDataType(
                0, 1, {SampleBatch.NEXT_OBS: obs, SampleBatch.T: t - 1}
            )
            processed = c([data])  # env.reset() for t == -1 else env.step()
            for_action = processed[0].data.for_action
            # add cur obs to the list
            obs_list.append(obs)

            if t == 0:
                check(for_action[""prev_state""], for_action[""state""])
            else:
                # prev state should be equal to the prev time step obs
                check(for_action[""prev_state""], obs_list[-2][None])

        ```
    ###Assistant : Test that the ViewRequirementConnector can handle shift by one correctly and
        can ignore future referencing view_requirements to respect causality
    "
2829,"def adapt_error(cls, e):
        
        return e._replace(message=e.message.format(*e.vars))[:4]


error = namedtuple(""error"", ""lineno col message type vars"")
Error = partial(partial, error, message="""", type=SentryCheck, vars=())

S001 = Error(
    message=""S001: Avoid using the {} mock call as it is ""
    ""confusing and prone to causing invalid test ""
    ""behavior.""
)
S001.methods = {
    ""not_called"",
    ""called_once"",
    ""called_once_with"",
}

S002 = Error(message=""S002: print functions or statements are not allowed."")

S003 = Error(message=""S003: Use ``from sentry.utils import json`` instead."")
S003.modules = {""json"", ""simplejson""}
S003.names = {
    ""load"",
    ""loads"",
    ""dump"",
    ""dumps"",
    ""JSONEncoder"",
    ""JSONDecodeError"",
    ""_default_encoder"",
}
",Adapts the extended error namedtuple to be compatible with Flake8.,10,85,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def adapt_error(cls, e):
        
        return e._replace(message=e.message.format(*e.vars))[:4]


error = namedtuple(""error"", ""lineno col message type vars"")
Error = partial(partial, error, message="""", type=SentryCheck, vars=())

S001 = Error(
    message=""S001: Avoid using the {} mock call as it is ""
    ""confusing and prone to causing invalid test ""
    ""behavior.""
)
S001.methods = {
    ""not_called"",
    ""called_once"",
    ""called_once_with"",
}

S002 = Error(message=""S002: print functions or statements are not allowed."")

S003 = Error(message=""S003: Use ``from sentry.utils import json`` instead."")
S003.modules = {""json"", ""simplejson""}
S003.names = {
    ""load"",
    ""loads"",
    ""dump"",
    ""dumps"",
    ""JSONEncoder"",
    ""JSONDecodeError"",
    ""_default_encoder"",
}

        ```
    ###Assistant : Adapts the extended error namedtuple to be compatible with Flake8.
    "
2830,"def on_train_begin(self, logs=None):
        
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_train_begin(logs)
","Calls the `on_train_begin` methods of its callbacks.

        Args:
            logs: Dict. Currently, no data is passed via this argument
              for this method, but that may change in the future.
        ",28,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def on_train_begin(self, logs=None):
        
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_train_begin(logs)

        ```
    ###Assistant : Calls the `on_train_begin` methods of its callbacks.

        Args:
            logs: Dict. Currently, no data is passed via this argument
              for this method, but that may change in the future.
        
    "
2831,"def test_decorate_validated_function():
    
    decorated_function = deprecated()(_func)

    with pytest.warns(FutureWarning, match=""Function _func is deprecated""):
        decorated_function(1, 2, c=3)

    # outer decorator does not interfer with validation
    with pytest.warns(FutureWarning, match=""Function _func is deprecated""):
        with pytest.raises(ValueError, match=r""The 'c' parameter of _func must be""):
            decorated_function(1, 2, c=""wrong"")

",Check that validate_params functions can be decorated,7,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_decorate_validated_function():
    
    decorated_function = deprecated()(_func)

    with pytest.warns(FutureWarning, match=""Function _func is deprecated""):
        decorated_function(1, 2, c=3)

    # outer decorator does not interfer with validation
    with pytest.warns(FutureWarning, match=""Function _func is deprecated""):
        with pytest.raises(ValueError, match=r""The 'c' parameter of _func must be""):
            decorated_function(1, 2, c=""wrong"")


        ```
    ###Assistant : Check that validate_params functions can be decorated
    "
2832,"def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
    
    axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)
    axes.set_xscale(xscale), axes.set_yscale(yscale)
    axes.set_xlim(xlim),     axes.set_ylim(ylim)
    if legend:
        axes.legend(legend)
    axes.grid()
","Set the axes for matplotlib.

    Defined in :numref:`sec_calculus`",8,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
    
    axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)
    axes.set_xscale(xscale), axes.set_yscale(yscale)
    axes.set_xlim(xlim),     axes.set_ylim(ylim)
    if legend:
        axes.legend(legend)
    axes.grid()

        ```
    ###Assistant : Set the axes for matplotlib.

    Defined in :numref:`sec_calculus`
    "
2833,"def register(self, addon):
        
        api_changes = {
            # mitmproxy 6 -> mitmproxy 7
            ""clientconnect"": ""client_connected"",
            ""clientdisconnect"": ""client_disconnected"",
            ""serverconnect"": ""server_connect and server_connected"",
            ""serverdisconnect"": ""server_disconnected"",
        }
        for a in traverse([addon]):
            for old, new in api_changes.items():
                if hasattr(a, old):
                    ctx.log.warn(f""The {old} event has been removed, use {new} instead. ""
                                 f""For more details, see https://docs.mitmproxy.org/stable/addons-events/."")
            name = _get_name(a)
            if name in self.lookup:
                raise exceptions.AddonManagerError(
                    ""An addon called '%s' already exists."" % name
                )
        l = Loader(self.master)
        self.invoke_addon_sync(addon, LoadHook(l))
        for a in traverse([addon]):
            name = _get_name(a)
            self.lookup[name] = a
        for a in traverse([addon]):
            self.master.commands.collect_commands(a)
        self.master.options.process_deferred()
        return addon
","
            Register an addon, call its load event, and then register all its
            sub-addons. This should be used by addons that dynamically manage
            addons.

            If the calling addon is already running, it should follow with
            running and configure events. Must be called within a current
            context.
        ",45,91,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def register(self, addon):
        
        api_changes = {
            # mitmproxy 6 -> mitmproxy 7
            ""clientconnect"": ""client_connected"",
            ""clientdisconnect"": ""client_disconnected"",
            ""serverconnect"": ""server_connect and server_connected"",
            ""serverdisconnect"": ""server_disconnected"",
        }
        for a in traverse([addon]):
            for old, new in api_changes.items():
                if hasattr(a, old):
                    ctx.log.warn(f""The {old} event has been removed, use {new} instead. ""
                                 f""For more details, see https://docs.mitmproxy.org/stable/addons-events/."")
            name = _get_name(a)
            if name in self.lookup:
                raise exceptions.AddonManagerError(
                    ""An addon called '%s' already exists."" % name
                )
        l = Loader(self.master)
        self.invoke_addon_sync(addon, LoadHook(l))
        for a in traverse([addon]):
            name = _get_name(a)
            self.lookup[name] = a
        for a in traverse([addon]):
            self.master.commands.collect_commands(a)
        self.master.options.process_deferred()
        return addon

        ```
    ###Assistant : 
            Register an addon, call its load event, and then register all its
            sub-addons. This should be used by addons that dynamically manage
            addons.

            If the calling addon is already running, it should follow with
            running and configure events. Must be called within a current
            context.
        
    "
2834,"def get_context_data(self, **kwargs):
        from tickets.models import Ticket
        from tickets.const import TICKET_DETAIL_URL
        ticket_id = self.request.session.get(""auth_ticket_id"")
        if not ticket_id:
            ticket = None
        else:
            ticket = Ticket.all().filter(pk=ticket_id).first()
        context = super().get_context_data(**kwargs)
        if ticket:
            timestamp_created = datetime.datetime.timestamp(ticket.date_created)
            ticket_detail_url = TICKET_DETAIL_URL.format(id=ticket_id, type=ticket.type)
            assignees = ticket.current_node.first().ticket_assignees.all()
            assignees_display = ', '.join([str(i.assignee) for i in assignees])
            msg = _().format(assignees_display)
        else:
            timestamp_created = 0
            ticket_detail_url = ''
            msg = _(""No ticket found"")
        context.update({
            ""msg"": msg,
            ""timestamp"": timestamp_created,
            ""ticket_detail_url"": ticket_detail_url
        })
        return context


@method_decorator(never_cache, name='dispatch')","Wait for <b>{}</b> confirm, You also can copy link to her/him <br/>
                  Don't close this page",16,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_context_data(self, **kwargs):
        from tickets.models import Ticket
        from tickets.const import TICKET_DETAIL_URL
        ticket_id = self.request.session.get(""auth_ticket_id"")
        if not ticket_id:
            ticket = None
        else:
            ticket = Ticket.all().filter(pk=ticket_id).first()
        context = super().get_context_data(**kwargs)
        if ticket:
            timestamp_created = datetime.datetime.timestamp(ticket.date_created)
            ticket_detail_url = TICKET_DETAIL_URL.format(id=ticket_id, type=ticket.type)
            assignees = ticket.current_node.first().ticket_assignees.all()
            assignees_display = ', '.join([str(i.assignee) for i in assignees])
            msg = _().format(assignees_display)
        else:
            timestamp_created = 0
            ticket_detail_url = ''
            msg = _(""No ticket found"")
        context.update({
            ""msg"": msg,
            ""timestamp"": timestamp_created,
            ""ticket_detail_url"": ticket_detail_url
        })
        return context


@method_decorator(never_cache, name='dispatch')
        ```
    ###Assistant : Wait for <b>{}</b> confirm, You also can copy link to her/him <br/>
                  Don't close this page
    "
2835,"def tf_shard_checkpoint(weights, max_shard_size=""10GB""):
    
    max_shard_size = convert_file_size_to_int(max_shard_size)

    sharded_state_dicts = []
    current_block = []
    current_block_size = 0
    total_size = 0

    for item in weights:
        weight_size = item.numpy().size * dtype_byte_size(item.dtype)

        # If this weight is going to tip up over the maximal size, we split.
        if current_block_size + weight_size > max_shard_size:
            sharded_state_dicts.append(current_block)
            current_block = []
            current_block_size = 0

        current_block.append(item)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    sharded_state_dicts.append(current_block)

    # If we only have one shard, we return it
    if len(sharded_state_dicts) == 1:
        return {TF2_WEIGHTS_NAME: sharded_state_dicts[0]}, None

    # Otherwise, let's build the index
    weight_map = {}
    shards = {}
    for idx, shard in enumerate(sharded_state_dicts):
        shard_file = TF2_WEIGHTS_NAME.replace("".h5"", f""-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.h5"")
        shards[shard_file] = shard
        for weight in shard:
            weight_name = weight.name
            weight_map[weight_name] = shard_file

    # Add the metadata
    metadata = {""total_size"": total_size}
    index = {""metadata"": metadata, ""weight_map"": weight_map}
    return shards, index

","
    Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a
    given size.

    The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no
    optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For example, if the
    limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB],
    [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB].

    <Tip warning={true}>

    If one of the model's weight is bigger that `max_shard_size`, it will end up in its own sub-checkpoint which will
    have a size greater than `max_shard_size`.

    </Tip>

    Args:
        weights (`Dict[str, tf.RessourceVariable]`): The list of tf.RessourceVariable of a model to save.
        max_shard_size (`int` or `str`, *optional*, defaults to `""10GB""`):
            The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit
            (like `""5MB""`).
    ",158,137,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tf_shard_checkpoint(weights, max_shard_size=""10GB""):
    
    max_shard_size = convert_file_size_to_int(max_shard_size)

    sharded_state_dicts = []
    current_block = []
    current_block_size = 0
    total_size = 0

    for item in weights:
        weight_size = item.numpy().size * dtype_byte_size(item.dtype)

        # If this weight is going to tip up over the maximal size, we split.
        if current_block_size + weight_size > max_shard_size:
            sharded_state_dicts.append(current_block)
            current_block = []
            current_block_size = 0

        current_block.append(item)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    sharded_state_dicts.append(current_block)

    # If we only have one shard, we return it
    if len(sharded_state_dicts) == 1:
        return {TF2_WEIGHTS_NAME: sharded_state_dicts[0]}, None

    # Otherwise, let's build the index
    weight_map = {}
    shards = {}
    for idx, shard in enumerate(sharded_state_dicts):
        shard_file = TF2_WEIGHTS_NAME.replace("".h5"", f""-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.h5"")
        shards[shard_file] = shard
        for weight in shard:
            weight_name = weight.name
            weight_map[weight_name] = shard_file

    # Add the metadata
    metadata = {""total_size"": total_size}
    index = {""metadata"": metadata, ""weight_map"": weight_map}
    return shards, index


        ```
    ###Assistant : 
    Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a
    given size.

    The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no
    optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For example, if the
    limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB],
    [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB].

    <Tip warning={true}>

    If one of the model's weight is bigger that `max_shard_size`, it will end up in its own sub-checkpoint which will
    have a size greater than `max_shard_size`.

    </Tip>

    Args:
        weights (`Dict[str, tf.RessourceVariable]`): The list of tf.RessourceVariable of a model to save.
        max_shard_size (`int` or `str`, *optional*, defaults to `""10GB""`):
            The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit
            (like `""5MB""`).
    
    "
2836,"def scale(self, x=1, y=1, pt=None):
        
        if pt:
            pt = Point(pt, dim=2)
            return self.translate(*(-pt).args).scale(x, y).translate(*pt.args)
        fx, fy = self.functions
        return self.func((fx*x, fy*y), self.limits)
","Override GeometryEntity.scale since Curve is not made up of Points.

        Returns
        =======

        Curve :
            returns scaled curve.

        Examples
        ========

        >>> from sympy import Curve
        >>> from sympy.abc import x
        >>> Curve((x, x), (x, 0, 1)).scale(2)
        Curve((2*x, x), (x, 0, 1))

        ",40,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def scale(self, x=1, y=1, pt=None):
        
        if pt:
            pt = Point(pt, dim=2)
            return self.translate(*(-pt).args).scale(x, y).translate(*pt.args)
        fx, fy = self.functions
        return self.func((fx*x, fy*y), self.limits)

        ```
    ###Assistant : Override GeometryEntity.scale since Curve is not made up of Points.

        Returns
        =======

        Curve :
            returns scaled curve.

        Examples
        ========

        >>> from sympy import Curve
        >>> from sympy.abc import x
        >>> Curve((x, x), (x, 0, 1)).scale(2)
        Curve((2*x, x), (x, 0, 1))

        
    "
2837,"def orient_body_fixed(self, parent, angles, rotation_order):
        

        _check_frame(parent)

        amounts = list(angles)
        for i, v in enumerate(amounts):
            if not isinstance(v, Vector):
                amounts[i] = sympify(v)

        approved_orders = ('123', '231', '312', '132', '213', '321', '121',
                           '131', '212', '232', '313', '323', '')
        # make sure XYZ => 123
        rot_order = translate(str(rotation_order), 'XYZxyz', '123123')
        if rot_order not in approved_orders:
            raise TypeError('The rotation order is not a valid order.')

        parent_orient_body = []
        if not (len(amounts) == 3 & len(rot_order) == 3):
            raise TypeError('Body orientation takes 3 values & 3 orders')
        a1 = int(rot_order[0])
        a2 = int(rot_order[1])
        a3 = int(rot_order[2])
        parent_orient_body = (self._rot(a1, amounts[0]) *
                              self._rot(a2, amounts[1]) *
                              self._rot(a3, amounts[2]))

        self._dcm(parent, parent_orient_body)

        try:
            from sympy.polys.polyerrors import CoercionFailed
            from sympy.physics.vector.functions import kinematic_equations
            q1, q2, q3 = amounts
            u1, u2, u3 = symbols('u1, u2, u3', cls=Dummy)
            templist = kinematic_equations([u1, u2, u3], [q1, q2, q3],
                                           'body', rot_order)
            templist = [expand(i) for i in templist]
            td = solve(templist, [u1, u2, u3])
            u1 = expand(td[u1])
            u2 = expand(td[u2])
            u3 = expand(td[u3])
            wvec = u1 * self.x + u2 * self.y + u3 * self.z
            # NOTE : SymPy 1.7 removed the call to simplify() that occured
            # inside the solve() function, so this restores the pre-1.7
            # behavior. See:
            # https://github.com/sympy/sympy/issues/23140
            # and
            # https://github.com/sympy/sympy/issues/23130
            wvec = wvec.simplify()
        except (CoercionFailed, AssertionError):
            wvec = self._w_diff_dcm(parent)
        self._ang_vel_dict.update({parent: wvec})
        parent._ang_vel_dict.update({self: -wvec})
        self._var_dict = {}
","Rotates this reference frame relative to the parent reference frame
        by right hand rotating through three successive body fixed simple axis
        rotations. Each subsequent axis of rotation is about the ""body fixed""
        unit vectors of a new intermediate reference frame. This type of
        rotation is also referred to rotating through the `Euler and Tait-Bryan
        Angles`_.

        .. _Euler and Tait-Bryan Angles: https://en.wikipedia.org/wiki/Euler_angles

        Parameters
        ==========

        parent : ReferenceFrame
            Reference frame that this reference frame will be rotated relative
            to.
        angles : 3-tuple of sympifiable
            Three angles in radians used for the successive rotations.
        rotation_order : 3 character string or 3 digit integer
            Order of the rotations about each intermediate reference frames'
            unit vectors. The Euler rotation about the X, Z', X'' axes can be
            specified by the strings ``'XZX'``, ``'131'``, or the integer
            ``131``. There are 12 unique valid rotation orders (6 Euler and 6
            Tait-Bryan): zxz, xyx, yzy, zyz, xzx, yxy, xyz, yzx, zxy, xzy, zyx,
            and yxz.

        Warns
        ======

        UserWarning
            If the orientation creates a kinematic loop.

        Examples
        ========

        Setup variables for the examples:

        >>> from sympy import symbols
        >>> from sympy.physics.vector import ReferenceFrame
        >>> q1, q2, q3 = symbols('q1, q2, q3')
        >>> N = ReferenceFrame('N')
        >>> B = ReferenceFrame('B')
        >>> B1 = ReferenceFrame('B1')
        >>> B2 = ReferenceFrame('B2')
        >>> B3 = ReferenceFrame('B3')

        For example, a classic Euler Angle rotation can be done by:

        >>> B.orient_body_fixed(N, (q1, q2, q3), 'XYX')
        >>> B.dcm(N)
        Matrix([
        [        cos(q2),                            sin(q1)*sin(q2),                           -sin(q2)*cos(q1)],
        [sin(q2)*sin(q3), -sin(q1)*sin(q3)*cos(q2) + cos(q1)*cos(q3),  sin(q1)*cos(q3) + sin(q3)*cos(q1)*cos(q2)],
        [sin(q2)*cos(q3), -sin(q1)*cos(q2)*cos(q3) - sin(q3)*cos(q1), -sin(q1)*sin(q3) + cos(q1)*cos(q2)*cos(q3)]])

        This rotates reference frame B relative to reference frame N through
        ``q1`` about ``N.x``, then rotates B again through ``q2`` about
        ``B.y``, and finally through ``q3`` about ``B.x``. It is equivalent to
        three successive ``orient_axis()`` calls:

        >>> B1.orient_axis(N, N.x, q1)
        >>> B2.orient_axis(B1, B1.y, q2)
        >>> B3.orient_axis(B2, B2.x, q3)
        >>> B3.dcm(N)
        Matrix([
        [        cos(q2),                            sin(q1)*sin(q2),                           -sin(q2)*cos(q1)],
        [sin(q2)*sin(q3), -sin(q1)*sin(q3)*cos(q2) + cos(q1)*cos(q3),  sin(q1)*cos(q3) + sin(q3)*cos(q1)*cos(q2)],
        [sin(q2)*cos(q3), -sin(q1)*cos(q2)*cos(q3) - sin(q3)*cos(q1), -sin(q1)*sin(q3) + cos(q1)*cos(q2)*cos(q3)]])

        Acceptable rotation orders are of length 3, expressed in as a string
        ``'XYZ'`` or ``'123'`` or integer ``123``. Rotations about an axis
        twice in a row are prohibited.

        >>> B.orient_body_fixed(N, (q1, q2, 0), 'ZXZ')
        >>> B.orient_body_fixed(N, (q1, q2, 0), '121')
        >>> B.orient_body_fixed(N, (q1, q2, q3), 123)

        ",365,217,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def orient_body_fixed(self, parent, angles, rotation_order):
        

        _check_frame(parent)

        amounts = list(angles)
        for i, v in enumerate(amounts):
            if not isinstance(v, Vector):
                amounts[i] = sympify(v)

        approved_orders = ('123', '231', '312', '132', '213', '321', '121',
                           '131', '212', '232', '313', '323', '')
        # make sure XYZ => 123
        rot_order = translate(str(rotation_order), 'XYZxyz', '123123')
        if rot_order not in approved_orders:
            raise TypeError('The rotation order is not a valid order.')

        parent_orient_body = []
        if not (len(amounts) == 3 & len(rot_order) == 3):
            raise TypeError('Body orientation takes 3 values & 3 orders')
        a1 = int(rot_order[0])
        a2 = int(rot_order[1])
        a3 = int(rot_order[2])
        parent_orient_body = (self._rot(a1, amounts[0]) *
                              self._rot(a2, amounts[1]) *
                              self._rot(a3, amounts[2]))

        self._dcm(parent, parent_orient_body)

        try:
            from sympy.polys.polyerrors import CoercionFailed
            from sympy.physics.vector.functions import kinematic_equations
            q1, q2, q3 = amounts
            u1, u2, u3 = symbols('u1, u2, u3', cls=Dummy)
            templist = kinematic_equations([u1, u2, u3], [q1, q2, q3],
                                           'body', rot_order)
            templist = [expand(i) for i in templist]
            td = solve(templist, [u1, u2, u3])
            u1 = expand(td[u1])
            u2 = expand(td[u2])
            u3 = expand(td[u3])
            wvec = u1 * self.x + u2 * self.y + u3 * self.z
            # NOTE : SymPy 1.7 removed the call to simplify() that occured
            # inside the solve() function, so this restores the pre-1.7
            # behavior. See:
            # https://github.com/sympy/sympy/issues/23140
            # and
            # https://github.com/sympy/sympy/issues/23130
            wvec = wvec.simplify()
        except (CoercionFailed, AssertionError):
            wvec = self._w_diff_dcm(parent)
        self._ang_vel_dict.update({parent: wvec})
        parent._ang_vel_dict.update({self: -wvec})
        self._var_dict = {}

        ```
    ###Assistant : Rotates this reference frame relative to the parent reference frame
        by right hand rotating through three successive body fixed simple axis
        rotations. Each subsequent axis of rotation is about the ""body fixed""
        unit vectors of a new intermediate reference frame. This type of
        rotation is also referred to rotating through the `Euler and Tait-Bryan
        Angles`_.

        .. _Euler and Tait-Bryan Angles: https://en.wikipedia.org/wiki/Euler_angles

        Parameters
        ==========

        parent : ReferenceFrame
            Reference frame that this reference frame will be rotated relative
            to.
        angles : 3-tuple of sympifiable
            Three angles in radians used for the successive rotations.
        rotation_order : 3 character string or 3 digit integer
            Order of the rotations about each intermediate reference frames'
            unit vectors. The Euler rotation about the X, Z', X'' axes can be
            specified by the strings ``'XZX'``, ``'131'``, or the integer
            ``131``. There are 12 unique valid rotation orders (6 Euler and 6
            Tait-Bryan): zxz, xyx, yzy, zyz, xzx, yxy, xyz, yzx, zxy, xzy, zyx,
            and yxz.

        Warns
        ======

        UserWarning
            If the orientation creates a kinematic loop.

        Examples
        ========

        Setup variables for the examples:

        >>> from sympy import symbols
        >>> from sympy.physics.vector import ReferenceFrame
        >>> q1, q2, q3 = symbols('q1, q2, q3')
        >>> N = ReferenceFrame('N')
        >>> B = ReferenceFrame('B')
        >>> B1 = ReferenceFrame('B1')
        >>> B2 = ReferenceFrame('B2')
        >>> B3 = ReferenceFrame('B3')

        For example, a classic Euler Angle rotation can be done by:

        >>> B.orient_body_fixed(N, (q1, q2, q3), 'XYX')
        >>> B.dcm(N)
        Matrix([
        [        cos(q2),                            sin(q1)*sin(q2),                           -sin(q2)*cos(q1)],
        [sin(q2)*sin(q3), -sin(q1)*sin(q3)*cos(q2) + cos(q1)*cos(q3),  sin(q1)*cos(q3) + sin(q3)*cos(q1)*cos(q2)],
        [sin(q2)*cos(q3), -sin(q1)*cos(q2)*cos(q3) - sin(q3)*cos(q1), -sin(q1)*sin(q3) + cos(q1)*cos(q2)*cos(q3)]])

        This rotates reference frame B relative to reference frame N through
        ``q1`` about ``N.x``, then rotates B again through ``q2`` about
        ``B.y``, and finally through ``q3`` about ``B.x``. It is equivalent to
        three successive ``orient_axis()`` calls:

        >>> B1.orient_axis(N, N.x, q1)
        >>> B2.orient_axis(B1, B1.y, q2)
        >>> B3.orient_axis(B2, B2.x, q3)
        >>> B3.dcm(N)
        Matrix([
        [        cos(q2),                            sin(q1)*sin(q2),                           -sin(q2)*cos(q1)],
        [sin(q2)*sin(q3), -sin(q1)*sin(q3)*cos(q2) + cos(q1)*cos(q3),  sin(q1)*cos(q3) + sin(q3)*cos(q1)*cos(q2)],
        [sin(q2)*cos(q3), -sin(q1)*cos(q2)*cos(q3) - sin(q3)*cos(q1), -sin(q1)*sin(q3) + cos(q1)*cos(q2)*cos(q3)]])

        Acceptable rotation orders are of length 3, expressed in as a string
        ``'XYZ'`` or ``'123'`` or integer ``123``. Rotations about an axis
        twice in a row are prohibited.

        >>> B.orient_body_fixed(N, (q1, q2, 0), 'ZXZ')
        >>> B.orient_body_fixed(N, (q1, q2, 0), '121')
        >>> B.orient_body_fixed(N, (q1, q2, q3), 123)

        
    "
2838,"def get_applicable_block_lists(employee=None, company=None, all_lists=False):
	block_lists = []

	if not employee:
		employee = frappe.db.get_value(""Employee"", {""user_id"": frappe.session.user})
		if not employee:
			return []

	if not company:
		company = frappe.db.get_value(""Employee"", employee, ""company"")

	def add_block_list(block_list):
		if block_list:
			if all_lists or not is_user_in_allow_list(block_list):
				block_lists.append(block_list)

	# per department
	department = frappe.db.get_value(""Employee"", employee, ""department"")
	if department:
		block_list = frappe.db.get_value(""Department"", department, ""leave_block_list"")
		add_block_list(block_list)

	# global
	for block_list in frappe.db.sql_list(
		,
		company,
	):
		add_block_list(block_list)

	return list(set(block_lists))

","select name from `tabLeave Block List`
		where applies_to_all_departments=1 and company=%s",10,66,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_applicable_block_lists(employee=None, company=None, all_lists=False):
	block_lists = []

	if not employee:
		employee = frappe.db.get_value(""Employee"", {""user_id"": frappe.session.user})
		if not employee:
			return []

	if not company:
		company = frappe.db.get_value(""Employee"", employee, ""company"")

	def add_block_list(block_list):
		if block_list:
			if all_lists or not is_user_in_allow_list(block_list):
				block_lists.append(block_list)

	# per department
	department = frappe.db.get_value(""Employee"", employee, ""department"")
	if department:
		block_list = frappe.db.get_value(""Department"", department, ""leave_block_list"")
		add_block_list(block_list)

	# global
	for block_list in frappe.db.sql_list(
		,
		company,
	):
		add_block_list(block_list)

	return list(set(block_lists))


        ```
    ###Assistant : select name from `tabLeave Block List`
		where applies_to_all_departments=1 and company=%s
    "
2839,"def _setup_connection(self):
          # noqa
        cur = self.connection.cursor()
        if ('store',) not in list(cur.execute(""SELECT name FROM sqlite_master WHERE type='table';"")):
            cur.execute(
                )
            self.internal_registry.commit()
"," Checks that a key-value table exists, otherwise creates it. create table store (key text, value text)",16,20,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _setup_connection(self):
          # noqa
        cur = self.connection.cursor()
        if ('store',) not in list(cur.execute(""SELECT name FROM sqlite_master WHERE type='table';"")):
            cur.execute(
                )
            self.internal_registry.commit()

        ```
    ###Assistant :  Checks that a key-value table exists, otherwise creates it. create table store (key text, value text)
    "
2840,"def cull(dsk, keys):
    
    if not isinstance(keys, (list, set)):
        keys = [keys]

    seen = set()
    dependencies = dict()
    out = {}
    work = list(set(flatten(keys)))

    while work:
        new_work = []
        for k in work:
            dependencies_k = get_dependencies(dsk, k, as_list=True)  # fuse needs lists
            out[k] = dsk[k]
            dependencies[k] = dependencies_k
            for d in dependencies_k:
                if d not in seen:
                    seen.add(d)
                    new_work.append(d)

        work = new_work

    return out, dependencies

","Return new dask with only the tasks required to calculate keys.

    In other words, remove unnecessary tasks from dask.
    ``keys`` may be a single key or list of keys.

    Examples
    --------
    >>> def inc(x):
    ...     return x + 1

    >>> def add(x, y):
    ...     return x + y

    >>> d = {'x': 1, 'y': (inc, 'x'), 'out': (add, 'x', 10)}
    >>> dsk, dependencies = cull(d, 'out')
    >>> dsk                                                     # doctest: +ELLIPSIS
    {'out': (<function add at ...>, 'x', 10), 'x': 1}
    >>> dependencies                                            # doctest: +ELLIPSIS
    {'out': ['x'], 'x': []}

    Returns
    -------
    dsk: culled dask graph
    dependencies: Dict mapping {key: [deps]}.  Useful side effect to accelerate
        other optimizations, notably fuse.
    ",109,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cull(dsk, keys):
    
    if not isinstance(keys, (list, set)):
        keys = [keys]

    seen = set()
    dependencies = dict()
    out = {}
    work = list(set(flatten(keys)))

    while work:
        new_work = []
        for k in work:
            dependencies_k = get_dependencies(dsk, k, as_list=True)  # fuse needs lists
            out[k] = dsk[k]
            dependencies[k] = dependencies_k
            for d in dependencies_k:
                if d not in seen:
                    seen.add(d)
                    new_work.append(d)

        work = new_work

    return out, dependencies


        ```
    ###Assistant : Return new dask with only the tasks required to calculate keys.

    In other words, remove unnecessary tasks from dask.
    ``keys`` may be a single key or list of keys.

    Examples
    --------
    >>> def inc(x):
    ...     return x + 1

    >>> def add(x, y):
    ...     return x + y

    >>> d = {'x': 1, 'y': (inc, 'x'), 'out': (add, 'x', 10)}
    >>> dsk, dependencies = cull(d, 'out')
    >>> dsk                                                     # doctest: +ELLIPSIS
    {'out': (<function add at ...>, 'x', 10), 'x': 1}
    >>> dependencies                                            # doctest: +ELLIPSIS
    {'out': ['x'], 'x': []}

    Returns
    -------
    dsk: culled dask graph
    dependencies: Dict mapping {key: [deps]}.  Useful side effect to accelerate
        other optimizations, notably fuse.
    
    "
2841,"def fill_leverage_tiers(self) -> None:
        
        leverage_tiers = self.load_leverage_tiers()
        for pair, tiers in leverage_tiers.items():
            tiers = []
            for tier in tiers:
                tiers.append(self.parse_leverage_tier(tier))
            self._leverage_tiers[pair] = tiers
","
        Assigns property _leverage_tiers to a dictionary of information about the leverage
        allowed on each pair
        ",15,23,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fill_leverage_tiers(self) -> None:
        
        leverage_tiers = self.load_leverage_tiers()
        for pair, tiers in leverage_tiers.items():
            tiers = []
            for tier in tiers:
                tiers.append(self.parse_leverage_tier(tier))
            self._leverage_tiers[pair] = tiers

        ```
    ###Assistant : 
        Assigns property _leverage_tiers to a dictionary of information about the leverage
        allowed on each pair
        
    "
2842,"def kronecker_product(*matrices):
    
    if not matrices:
        raise TypeError(""Empty Kronecker product is undefined"")
    validate(*matrices)
    if len(matrices) == 1:
        return matrices[0]
    else:
        return KroneckerProduct(*matrices).doit()

","
    The Kronecker product of two or more arguments.

    This computes the explicit Kronecker product for subclasses of
    ``MatrixBase`` i.e. explicit matrices. Otherwise, a symbolic
    ``KroneckerProduct`` object is returned.


    Examples
    ========

    For ``MatrixSymbol`` arguments a ``KroneckerProduct`` object is returned.
    Elements of this matrix can be obtained by indexing, or for MatrixSymbols
    with known dimension the explicit matrix can be obtained with
    ``.as_explicit()``

    >>> from sympy import kronecker_product, MatrixSymbol
    >>> A = MatrixSymbol('A', 2, 2)
    >>> B = MatrixSymbol('B', 2, 2)
    >>> kronecker_product(A)
    A
    >>> kronecker_product(A, B)
    KroneckerProduct(A, B)
    >>> kronecker_product(A, B)[0, 1]
    A[0, 0]*B[0, 1]
    >>> kronecker_product(A, B).as_explicit()
    Matrix([
        [A[0, 0]*B[0, 0], A[0, 0]*B[0, 1], A[0, 1]*B[0, 0], A[0, 1]*B[0, 1]],
        [A[0, 0]*B[1, 0], A[0, 0]*B[1, 1], A[0, 1]*B[1, 0], A[0, 1]*B[1, 1]],
        [A[1, 0]*B[0, 0], A[1, 0]*B[0, 1], A[1, 1]*B[0, 0], A[1, 1]*B[0, 1]],
        [A[1, 0]*B[1, 0], A[1, 0]*B[1, 1], A[1, 1]*B[1, 0], A[1, 1]*B[1, 1]]])

    For explicit matrices the Kronecker product is returned as a Matrix

    >>> from sympy import Matrix, kronecker_product
    >>> sigma_x = Matrix([
    ... [0, 1],
    ... [1, 0]])
    ...
    >>> Isigma_y = Matrix([
    ... [0, 1],
    ... [-1, 0]])
    ...
    >>> kronecker_product(sigma_x, Isigma_y)
    Matrix([
    [ 0, 0,  0, 1],
    [ 0, 0, -1, 0],
    [ 0, 1,  0, 0],
    [-1, 0,  0, 0]])

    See Also
    ========
        KroneckerProduct

    ",212,21,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def kronecker_product(*matrices):
    
    if not matrices:
        raise TypeError(""Empty Kronecker product is undefined"")
    validate(*matrices)
    if len(matrices) == 1:
        return matrices[0]
    else:
        return KroneckerProduct(*matrices).doit()


        ```
    ###Assistant : 
    The Kronecker product of two or more arguments.

    This computes the explicit Kronecker product for subclasses of
    ``MatrixBase`` i.e. explicit matrices. Otherwise, a symbolic
    ``KroneckerProduct`` object is returned.


    Examples
    ========

    For ``MatrixSymbol`` arguments a ``KroneckerProduct`` object is returned.
    Elements of this matrix can be obtained by indexing, or for MatrixSymbols
    with known dimension the explicit matrix can be obtained with
    ``.as_explicit()``

    >>> from sympy import kronecker_product, MatrixSymbol
    >>> A = MatrixSymbol('A', 2, 2)
    >>> B = MatrixSymbol('B', 2, 2)
    >>> kronecker_product(A)
    A
    >>> kronecker_product(A, B)
    KroneckerProduct(A, B)
    >>> kronecker_product(A, B)[0, 1]
    A[0, 0]*B[0, 1]
    >>> kronecker_product(A, B).as_explicit()
    Matrix([
        [A[0, 0]*B[0, 0], A[0, 0]*B[0, 1], A[0, 1]*B[0, 0], A[0, 1]*B[0, 1]],
        [A[0, 0]*B[1, 0], A[0, 0]*B[1, 1], A[0, 1]*B[1, 0], A[0, 1]*B[1, 1]],
        [A[1, 0]*B[0, 0], A[1, 0]*B[0, 1], A[1, 1]*B[0, 0], A[1, 1]*B[0, 1]],
        [A[1, 0]*B[1, 0], A[1, 0]*B[1, 1], A[1, 1]*B[1, 0], A[1, 1]*B[1, 1]]])

    For explicit matrices the Kronecker product is returned as a Matrix

    >>> from sympy import Matrix, kronecker_product
    >>> sigma_x = Matrix([
    ... [0, 1],
    ... [1, 0]])
    ...
    >>> Isigma_y = Matrix([
    ... [0, 1],
    ... [-1, 0]])
    ...
    >>> kronecker_product(sigma_x, Isigma_y)
    Matrix([
    [ 0, 0,  0, 1],
    [ 0, 0, -1, 0],
    [ 0, 1,  0, 0],
    [-1, 0,  0, 0]])

    See Also
    ========
        KroneckerProduct

    
    "
2843,"def _get_obj_does_not_exist_redirect(self, request, opts, object_id):
        
        msg = _(""%(name)s with ID “%(key)s” doesn’t exist. Perhaps it was deleted?"") % {
            ""name"": opts.verbose_name,
            ""key"": unquote(object_id),
        }
        self.message_user(request, msg, messages.WARNING)
        url = reverse(""admin:index"", current_app=self.admin_site.name)
        return HttpResponseRedirect(url)
","
        Create a message informing the user that the object doesn't exist
        and return a redirect to the admin index page.
        ",20,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_obj_does_not_exist_redirect(self, request, opts, object_id):
        
        msg = _(""%(name)s with ID “%(key)s” doesn’t exist. Perhaps it was deleted?"") % {
            ""name"": opts.verbose_name,
            ""key"": unquote(object_id),
        }
        self.message_user(request, msg, messages.WARNING)
        url = reverse(""admin:index"", current_app=self.admin_site.name)
        return HttpResponseRedirect(url)

        ```
    ###Assistant : 
        Create a message informing the user that the object doesn't exist
        and return a redirect to the admin index page.
        
    "
2844,"def compare_total(self, other, context=None):
        
        other = _convert_other(other, raiseit=True)

        # if one is negative and the other is positive, it's easy
        if self._sign and not other._sign:
            return _NegativeOne
        if not self._sign and other._sign:
            return _One
        sign = self._sign

        # let's handle both NaN types
        self_nan = self._isnan()
        other_nan = other._isnan()
        if self_nan or other_nan:
            if self_nan == other_nan:
                # compare payloads as though they're integers
                self_key = len(self._int), self._int
                other_key = len(other._int), other._int
                if self_key < other_key:
                    if sign:
                        return _One
                    else:
                        return _NegativeOne
                if self_key > other_key:
                    if sign:
                        return _NegativeOne
                    else:
                        return _One
                return _Zero

            if sign:
                if self_nan == 1:
                    return _NegativeOne
                if other_nan == 1:
                    return _One
                if self_nan == 2:
                    return _NegativeOne
                if other_nan == 2:
                    return _One
            else:
                if self_nan == 1:
                    return _One
                if other_nan == 1:
                    return _NegativeOne
                if self_nan == 2:
                    return _One
                if other_nan == 2:
                    return _NegativeOne

        if self < other:
            return _NegativeOne
        if self > other:
            return _One

        if self._exp < other._exp:
            if sign:
                return _One
            else:
                return _NegativeOne
        if self._exp > other._exp:
            if sign:
                return _NegativeOne
            else:
                return _One
        return _Zero

","Compares self to other using the abstract representations.

        This is not like the standard compare, which use their numerical
        value. Note that a total ordering is defined for all possible abstract
        representations.
        ",32,183,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compare_total(self, other, context=None):
        
        other = _convert_other(other, raiseit=True)

        # if one is negative and the other is positive, it's easy
        if self._sign and not other._sign:
            return _NegativeOne
        if not self._sign and other._sign:
            return _One
        sign = self._sign

        # let's handle both NaN types
        self_nan = self._isnan()
        other_nan = other._isnan()
        if self_nan or other_nan:
            if self_nan == other_nan:
                # compare payloads as though they're integers
                self_key = len(self._int), self._int
                other_key = len(other._int), other._int
                if self_key < other_key:
                    if sign:
                        return _One
                    else:
                        return _NegativeOne
                if self_key > other_key:
                    if sign:
                        return _NegativeOne
                    else:
                        return _One
                return _Zero

            if sign:
                if self_nan == 1:
                    return _NegativeOne
                if other_nan == 1:
                    return _One
                if self_nan == 2:
                    return _NegativeOne
                if other_nan == 2:
                    return _One
            else:
                if self_nan == 1:
                    return _One
                if other_nan == 1:
                    return _NegativeOne
                if self_nan == 2:
                    return _One
                if other_nan == 2:
                    return _NegativeOne

        if self < other:
            return _NegativeOne
        if self > other:
            return _One

        if self._exp < other._exp:
            if sign:
                return _One
            else:
                return _NegativeOne
        if self._exp > other._exp:
            if sign:
                return _NegativeOne
            else:
                return _One
        return _Zero


        ```
    ###Assistant : Compares self to other using the abstract representations.

        This is not like the standard compare, which use their numerical
        value. Note that a total ordering is defined for all possible abstract
        representations.
        
    "
2845,"def detect_current_mode(cls) -> _FaultTolerantMode:
        
        env_value = os.getenv(""PL_FAULT_TOLERANT_TRAINING"", ""0"").lower()
        # the int values are kept for backwards compatibility, but long-term we want to keep only the strings
        if env_value in (""0"", ""disabled""):
            return _FaultTolerantMode.DISABLED
        elif env_value in (""1"", ""automatic""):
            return _FaultTolerantMode.AUTOMATIC
        elif env_value in (""2"", ""manual""):
            return _FaultTolerantMode.MANUAL
        raise MisconfigurationException(
            ""The environment flag `PL_FAULT_TOLERANT_TRAINING` should be either 'disabled', 'automatic', or 'manual'.""
        )
",This classmethod detects if `Fault Tolerant` is activated and maps its value to `_FaultTolerantMode`.,14,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def detect_current_mode(cls) -> _FaultTolerantMode:
        
        env_value = os.getenv(""PL_FAULT_TOLERANT_TRAINING"", ""0"").lower()
        # the int values are kept for backwards compatibility, but long-term we want to keep only the strings
        if env_value in (""0"", ""disabled""):
            return _FaultTolerantMode.DISABLED
        elif env_value in (""1"", ""automatic""):
            return _FaultTolerantMode.AUTOMATIC
        elif env_value in (""2"", ""manual""):
            return _FaultTolerantMode.MANUAL
        raise MisconfigurationException(
            ""The environment flag `PL_FAULT_TOLERANT_TRAINING` should be either 'disabled', 'automatic', or 'manual'.""
        )

        ```
    ###Assistant : This classmethod detects if `Fault Tolerant` is activated and maps its value to `_FaultTolerantMode`.
    "
2846,"def apply_channel_shift(x, intensity, channel_axis=0):
    
    x = np.rollaxis(x, channel_axis, 0)
    min_x, max_x = np.min(x), np.max(x)
    channel_images = [
        np.clip(x_channel + intensity, min_x, max_x) for x_channel in x
    ]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_axis + 1)
    return x


@keras_export(""keras.preprocessing.image.random_channel_shift"")","Performs a channel shift.

    Args:
        x: Input tensor. Must be 3D.
        intensity: Transformation intensity.
        channel_axis: Index of axis for channels in the input tensor.

    Returns:
        Numpy image tensor.
    ",28,41,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def apply_channel_shift(x, intensity, channel_axis=0):
    
    x = np.rollaxis(x, channel_axis, 0)
    min_x, max_x = np.min(x), np.max(x)
    channel_images = [
        np.clip(x_channel + intensity, min_x, max_x) for x_channel in x
    ]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_axis + 1)
    return x


@keras_export(""keras.preprocessing.image.random_channel_shift"")
        ```
    ###Assistant : Performs a channel shift.

    Args:
        x: Input tensor. Must be 3D.
        intensity: Transformation intensity.
        channel_axis: Index of axis for channels in the input tensor.

    Returns:
        Numpy image tensor.
    
    "
2847,"def _nested_gather(self, tensors, name=None):
        
        if tensors is None:
            return
        if self.args.local_rank != -1:
            tensors = distributed_concat(tensors)
        return tensors

        # Copied from Accelerate.","
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        ",20,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _nested_gather(self, tensors, name=None):
        
        if tensors is None:
            return
        if self.args.local_rank != -1:
            tensors = distributed_concat(tensors)
        return tensors

        # Copied from Accelerate.
        ```
    ###Assistant : 
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        
    "
2848,"def test_glm_regression_unpenalized(solver, fit_intercept, glm_dataset):
    
    model, X, y, coef, _, _, _ = glm_dataset
    n_samples, n_features = X.shape
    alpha = 0  # unpenalized
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    if fit_intercept:
        X = X[:, :-1]  # remove intercept
        intercept = coef[-1]
        coef = coef[:-1]
    else:
        intercept = 0

    model.fit(X, y)

    # FIXME: `assert_allclose(model.coef_, coef)` should work for all cases but fails
    # for the wide/fat case with n_features > n_samples. Most current GLM solvers do
    # NOT return the minimum norm solution with fit_intercept=True.
    rtol = 5e-5
    if n_samples > n_features:
        assert model.intercept_ == pytest.approx(intercept)
        assert_allclose(model.coef_, coef, rtol=rtol)
    else:
        # As it is an underdetermined problem, prediction = y. The following shows that
        # we get a solution, i.e. a (non-unique) minimum of the objective function ...
        assert_allclose(model.predict(X), y, rtol=1e-6)
        if fit_intercept:
            # But it is not the minimum norm solution. Otherwise the norms would be
            # equal.
            norm_solution = np.linalg.norm(np.r_[intercept, coef])
            norm_model = np.linalg.norm(np.r_[model.intercept_, model.coef_])
            assert norm_model > (1 + 1e-12) * norm_solution

            # See https://github.com/scikit-learn/scikit-learn/issues/23670.
            # Note: Even adding a tiny penalty does not give the minimal norm solution.
            # XXX: We could have naively expected LBFGS to find the minimal norm
            # solution by adding a very small penalty. Even that fails for a reason we
            # do not properly understand at this point.
        else:
            # When `fit_intercept=False`, LBFGS naturally converges to the minimum norm
            # solution on this problem.
            # XXX: Do we have any theoretical guarantees why this should be the case?
            assert model.intercept_ == pytest.approx(intercept)
            assert_allclose(model.coef_, coef, rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])","Test that unpenalized GLM converges for all solvers to correct solution.

    We work with a simple constructed data set with known solution.
    Note: This checks the minimum norm solution for wide X, i.e.
    n_samples < n_features:
        min ||w||_2 subject to w = argmin deviance(X, y, w)
    ",46,269,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_glm_regression_unpenalized(solver, fit_intercept, glm_dataset):
    
    model, X, y, coef, _, _, _ = glm_dataset
    n_samples, n_features = X.shape
    alpha = 0  # unpenalized
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        # solver=solver,  # only lbfgs available
        tol=1e-12,
        max_iter=1000,
    )

    model = clone(model).set_params(**params)
    if fit_intercept:
        X = X[:, :-1]  # remove intercept
        intercept = coef[-1]
        coef = coef[:-1]
    else:
        intercept = 0

    model.fit(X, y)

    # FIXME: `assert_allclose(model.coef_, coef)` should work for all cases but fails
    # for the wide/fat case with n_features > n_samples. Most current GLM solvers do
    # NOT return the minimum norm solution with fit_intercept=True.
    rtol = 5e-5
    if n_samples > n_features:
        assert model.intercept_ == pytest.approx(intercept)
        assert_allclose(model.coef_, coef, rtol=rtol)
    else:
        # As it is an underdetermined problem, prediction = y. The following shows that
        # we get a solution, i.e. a (non-unique) minimum of the objective function ...
        assert_allclose(model.predict(X), y, rtol=1e-6)
        if fit_intercept:
            # But it is not the minimum norm solution. Otherwise the norms would be
            # equal.
            norm_solution = np.linalg.norm(np.r_[intercept, coef])
            norm_model = np.linalg.norm(np.r_[model.intercept_, model.coef_])
            assert norm_model > (1 + 1e-12) * norm_solution

            # See https://github.com/scikit-learn/scikit-learn/issues/23670.
            # Note: Even adding a tiny penalty does not give the minimal norm solution.
            # XXX: We could have naively expected LBFGS to find the minimal norm
            # solution by adding a very small penalty. Even that fails for a reason we
            # do not properly understand at this point.
        else:
            # When `fit_intercept=False`, LBFGS naturally converges to the minimum norm
            # solution on this problem.
            # XXX: Do we have any theoretical guarantees why this should be the case?
            assert model.intercept_ == pytest.approx(intercept)
            assert_allclose(model.coef_, coef, rtol=rtol)


@pytest.mark.parametrize(""solver"", SOLVERS)
@pytest.mark.parametrize(""fit_intercept"", [True, False])
        ```
    ###Assistant : Test that unpenalized GLM converges for all solvers to correct solution.

    We work with a simple constructed data set with known solution.
    Note: This checks the minimum norm solution for wide X, i.e.
    n_samples < n_features:
        min ||w||_2 subject to w = argmin deviance(X, y, w)
    
    "
2849,"def _node_redundancy(G, v):
    
    n = len(G[v])
    overlap = sum(
        1 for (u, w) in combinations(G[v], 2) if (G[u].keys() & G[w].keys()) - {v}
    )
    return (2 * overlap) / (n * (n - 1))
","Returns the redundancy of the node `v` in the bipartite graph `G`.

    If `G` is a graph with `n` nodes, the redundancy of a node is the ratio
    of the ""overlap"" of `v` to the maximum possible overlap of `v`
    according to its degree. The overlap of `v` is the number of pairs of
    neighbors that have mutual neighbors themselves, other than `v`.

    `v` must have at least two neighbors in `G`.

    ",72,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _node_redundancy(G, v):
    
    n = len(G[v])
    overlap = sum(
        1 for (u, w) in combinations(G[v], 2) if (G[u].keys() & G[w].keys()) - {v}
    )
    return (2 * overlap) / (n * (n - 1))

        ```
    ###Assistant : Returns the redundancy of the node `v` in the bipartite graph `G`.

    If `G` is a graph with `n` nodes, the redundancy of a node is the ratio
    of the ""overlap"" of `v` to the maximum possible overlap of `v`
    according to its degree. The overlap of `v` is the number of pairs of
    neighbors that have mutual neighbors themselves, other than `v`.

    `v` must have at least two neighbors in `G`.

    
    "
2850,"def solve(self):
        
        count_reaction_loads = 0
        for node in self._nodes:
            if node in list(self._supports):
                if self._supports[node[0]]=='pinned':
                    count_reaction_loads += 2
                elif self._supports[node[0]]=='roller':
                    count_reaction_loads += 1
        coefficients_matrix = [[0 for i in range(2*len(self._nodes))] for j in range(2*len(self._nodes))]
        load_matrix = zeros(2*len(self.nodes), 1)
        load_matrix_row = 0
        for node in self._nodes:
            if node[0] in list(self._loads):
                for load in self._loads[node[0]]:
                    if load[0]!=Symbol('R_'+str(node[0])+'_x') and load[0]!=Symbol('R_'+str(node[0])+'_y'):
                        load_matrix[load_matrix_row] -= load[0]*math.cos(pi*load[1]/180)
                        load_matrix[load_matrix_row + 1] -= load[0]*math.sin(pi*load[1]/180)
            load_matrix_row += 2
        cols = 0
        row = 0
        for node in self._nodes:
            if node[0] in list(self._supports):
                if self._supports[node[0]]=='pinned':
                    coefficients_matrix[row][cols] += 1
                    coefficients_matrix[row+1][cols+1] += 1
                    cols += 2
                elif self._supports[node[0]]=='roller':
                    coefficients_matrix[row+1][cols] += 1
                    cols += 1
            row += 2
        for member in list(self._members):
            start = self._members[member][0]
            end = self._members[member][1]
            length = sqrt((self._node_coordinates[start][0]-self._node_coordinates[end][0])**2 + (self._node_coordinates[start][1]-self._node_coordinates[end][1])**2)
            start_index = self._node_labels.index(start)
            end_index = self._node_labels.index(end)
            horizontal_component_start = (self._node_coordinates[end][0]-self._node_coordinates[start][0])/length
            vertical_component_start = (self._node_coordinates[end][1]-self._node_coordinates[start][1])/length
            horizontal_component_end = (self._node_coordinates[start][0]-self._node_coordinates[end][0])/length
            vertical_component_end = (self._node_coordinates[start][1]-self._node_coordinates[end][1])/length
            coefficients_matrix[start_index*2][cols] += horizontal_component_start
            coefficients_matrix[start_index*2+1][cols] += vertical_component_start
            coefficients_matrix[end_index*2][cols] += horizontal_component_end
            coefficients_matrix[end_index*2+1][cols] += vertical_component_end
            cols += 1
        forces_matrix = (Matrix(coefficients_matrix)**-1)*load_matrix
        self._reaction_loads = {}
        i = 0
        for node in self._nodes:
            if node[0] in list(self._supports):
                if self._supports[node[0]]=='pinned':
                    self._reaction_loads['R_'+str(node[0])+'_x'] = forces_matrix[i]
                    self._reaction_loads['R_'+str(node[0])+'_y'] = forces_matrix[i+1]
                    i += 2
                elif self._supports[node[0]]=='roller':
                    self._reaction_loads['R_'+str(node[0])+'_y'] = forces_matrix[i]
                    i += 1
        for member in list(self._members):
            self._internal_forces[member] = forces_matrix[i]
            i += 1
        return
","
        This method solves for all reaction forces of all supports and all internal forces
        of all the members in the truss, provided the Truss is solvable.

        A Truss is solvable if the following condition is met,

        2n >= r + m

        Where n is the number of nodes, r is the number of reaction forces, where each pinned
        support has 2 reaction forces and each roller has 1, and m is the number of members.

        The given condition is derived from the fact that a system of equations is solvable
        only when the number of variables is lesser than or equal to the number of equations.
        Equilibrium Equations in x and y directions give two equations per node giving 2n number
        equations. The number of variables is simply the sum of the number of reaction forces and
        member forces.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> t = Truss()
        >>> t.add_node(""node_1"", 0, 0)
        >>> t.add_node(""node_2"", 6, 0)
        >>> t.add_node(""node_3"", 2, 2)
        >>> t.add_node(""node_4"", 2, 0)
        >>> t.add_member(""member_1"", ""node_1"", ""node_4"")
        >>> t.add_member(""member_2"", ""node_2"", ""node_4"")
        >>> t.add_member(""member_3"", ""node_1"", ""node_3"")
        >>> t.add_member(""member_4"", ""node_2"", ""node_3"")
        >>> t.add_member(""member_5"", ""node_3"", ""node_4"")
        >>> t.apply_load(""node_4"", magnitude=10, direction=270)
        >>> t.apply_support(""node_1"", type=""pinned"")
        >>> t.apply_support(""node_2"", type=""roller"")
        >>> t.solve()
        >>> t.reaction_loads
        {'R_node_1_x': 1.83697019872103e-15, 'R_node_1_y': 6.66666666666667, 'R_node_2_y': 3.33333333333333}
        >>> t.internal_forces
        {'member_1': 6.66666666666666, 'member_2': 6.66666666666667, 'member_3': -6.66666666666667*sqrt(2), 'member_4': -3.33333333333333*sqrt(5), 'member_5': 10.0}
        ",218,199,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def solve(self):
        
        count_reaction_loads = 0
        for node in self._nodes:
            if node in list(self._supports):
                if self._supports[node[0]]=='pinned':
                    count_reaction_loads += 2
                elif self._supports[node[0]]=='roller':
                    count_reaction_loads += 1
        coefficients_matrix = [[0 for i in range(2*len(self._nodes))] for j in range(2*len(self._nodes))]
        load_matrix = zeros(2*len(self.nodes), 1)
        load_matrix_row = 0
        for node in self._nodes:
            if node[0] in list(self._loads):
                for load in self._loads[node[0]]:
                    if load[0]!=Symbol('R_'+str(node[0])+'_x') and load[0]!=Symbol('R_'+str(node[0])+'_y'):
                        load_matrix[load_matrix_row] -= load[0]*math.cos(pi*load[1]/180)
                        load_matrix[load_matrix_row + 1] -= load[0]*math.sin(pi*load[1]/180)
            load_matrix_row += 2
        cols = 0
        row = 0
        for node in self._nodes:
            if node[0] in list(self._supports):
                if self._supports[node[0]]=='pinned':
                    coefficients_matrix[row][cols] += 1
                    coefficients_matrix[row+1][cols+1] += 1
                    cols += 2
                elif self._supports[node[0]]=='roller':
                    coefficients_matrix[row+1][cols] += 1
                    cols += 1
            row += 2
        for member in list(self._members):
            start = self._members[member][0]
            end = self._members[member][1]
            length = sqrt((self._node_coordinates[start][0]-self._node_coordinates[end][0])**2 + (self._node_coordinates[start][1]-self._node_coordinates[end][1])**2)
            start_index = self._node_labels.index(start)
            end_index = self._node_labels.index(end)
            horizontal_component_start = (self._node_coordinates[end][0]-self._node_coordinates[start][0])/length
            vertical_component_start = (self._node_coordinates[end][1]-self._node_coordinates[start][1])/length
            horizontal_component_end = (self._node_coordinates[start][0]-self._node_coordinates[end][0])/length
            vertical_component_end = (self._node_coordinates[start][1]-self._node_coordinates[end][1])/length
            coefficients_matrix[start_index*2][cols] += horizontal_component_start
            coefficients_matrix[start_index*2+1][cols] += vertical_component_start
            coefficients_matrix[end_index*2][cols] += horizontal_component_end
            coefficients_matrix[end_index*2+1][cols] += vertical_component_end
            cols += 1
        forces_matrix = (Matrix(coefficients_matrix)**-1)*load_matrix
        self._reaction_loads = {}
        i = 0
        for node in self._nodes:
            if node[0] in list(self._supports):
                if self._supports[node[0]]=='pinned':
                    self._reaction_loads['R_'+str(node[0])+'_x'] = forces_matrix[i]
                    self._reaction_loads['R_'+str(node[0])+'_y'] = forces_matrix[i+1]
                    i += 2
                elif self._supports[node[0]]=='roller':
                    self._reaction_loads['R_'+str(node[0])+'_y'] = forces_matrix[i]
                    i += 1
        for member in list(self._members):
            self._internal_forces[member] = forces_matrix[i]
            i += 1
        return

        ```
    ###Assistant : 
        This method solves for all reaction forces of all supports and all internal forces
        of all the members in the truss, provided the Truss is solvable.

        A Truss is solvable if the following condition is met,

        2n >= r + m

        Where n is the number of nodes, r is the number of reaction forces, where each pinned
        support has 2 reaction forces and each roller has 1, and m is the number of members.

        The given condition is derived from the fact that a system of equations is solvable
        only when the number of variables is lesser than or equal to the number of equations.
        Equilibrium Equations in x and y directions give two equations per node giving 2n number
        equations. The number of variables is simply the sum of the number of reaction forces and
        member forces.

        Examples
        ========

        >>> from sympy.physics.continuum_mechanics.truss import Truss
        >>> t = Truss()
        >>> t.add_node(""node_1"", 0, 0)
        >>> t.add_node(""node_2"", 6, 0)
        >>> t.add_node(""node_3"", 2, 2)
        >>> t.add_node(""node_4"", 2, 0)
        >>> t.add_member(""member_1"", ""node_1"", ""node_4"")
        >>> t.add_member(""member_2"", ""node_2"", ""node_4"")
        >>> t.add_member(""member_3"", ""node_1"", ""node_3"")
        >>> t.add_member(""member_4"", ""node_2"", ""node_3"")
        >>> t.add_member(""member_5"", ""node_3"", ""node_4"")
        >>> t.apply_load(""node_4"", magnitude=10, direction=270)
        >>> t.apply_support(""node_1"", type=""pinned"")
        >>> t.apply_support(""node_2"", type=""roller"")
        >>> t.solve()
        >>> t.reaction_loads
        {'R_node_1_x': 1.83697019872103e-15, 'R_node_1_y': 6.66666666666667, 'R_node_2_y': 3.33333333333333}
        >>> t.internal_forces
        {'member_1': 6.66666666666666, 'member_2': 6.66666666666667, 'member_3': -6.66666666666667*sqrt(2), 'member_4': -3.33333333333333*sqrt(5), 'member_5': 10.0}
        
    "
2851,"def gf_edf_zassenhaus(f, n, p, K):
    
    factors = [f]

    if gf_degree(f) <= n:
        return factors

    N = gf_degree(f) // n
    if p != 2:
        b = gf_frobenius_monomial_base(f, p, K)

    t = [K.one, K.zero]
    while len(factors) < N:
        if p == 2:
            h = r = t

            for i in range(n - 1):
                r = gf_pow_mod(r, 2, f, p, K)
                h = gf_add(h, r, p, K)

            g = gf_gcd(f, h, p, K)
            t += [K.zero, K.zero]
        else:
            r = gf_random(2 * n - 1, p, K)
            h = _gf_pow_pnm1d2(r, n, f, b, p, K)
            g = gf_gcd(f, gf_sub_ground(h, K.one, p, K), p, K)

        if g != [K.one] and g != f:
            factors = gf_edf_zassenhaus(g, n, p, K) \
                + gf_edf_zassenhaus(gf_quo(f, g, p, K), n, p, K)

    return _sort_factors(factors, multiple=False)

","
    Cantor-Zassenhaus: Probabilistic Equal Degree Factorization

    Given a monic square-free polynomial ``f`` in ``GF(p)[x]`` and
    an integer ``n``, such that ``n`` divides ``deg(f)``, returns all
    irreducible factors ``f_1,...,f_d`` of ``f``, each of degree ``n``.
    EDF procedure gives complete factorization over Galois fields.

    Consider the square-free polynomial ``f = x**3 + x**2 + x + 1`` in
    ``GF(5)[x]``. Let's compute its irreducible factors of degree one::

       >>> from sympy.polys.domains import ZZ
       >>> from sympy.polys.galoistools import gf_edf_zassenhaus

       >>> gf_edf_zassenhaus([1,1,1,1], 1, 5, ZZ)
       [[1, 1], [1, 2], [1, 3]]

    References
    ==========

    .. [1] [Gathen99]_
    .. [2] [Geddes92]_
    .. [3] [Cohen93]_

    ",96,127,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def gf_edf_zassenhaus(f, n, p, K):
    
    factors = [f]

    if gf_degree(f) <= n:
        return factors

    N = gf_degree(f) // n
    if p != 2:
        b = gf_frobenius_monomial_base(f, p, K)

    t = [K.one, K.zero]
    while len(factors) < N:
        if p == 2:
            h = r = t

            for i in range(n - 1):
                r = gf_pow_mod(r, 2, f, p, K)
                h = gf_add(h, r, p, K)

            g = gf_gcd(f, h, p, K)
            t += [K.zero, K.zero]
        else:
            r = gf_random(2 * n - 1, p, K)
            h = _gf_pow_pnm1d2(r, n, f, b, p, K)
            g = gf_gcd(f, gf_sub_ground(h, K.one, p, K), p, K)

        if g != [K.one] and g != f:
            factors = gf_edf_zassenhaus(g, n, p, K) \
                + gf_edf_zassenhaus(gf_quo(f, g, p, K), n, p, K)

    return _sort_factors(factors, multiple=False)


        ```
    ###Assistant : 
    Cantor-Zassenhaus: Probabilistic Equal Degree Factorization

    Given a monic square-free polynomial ``f`` in ``GF(p)[x]`` and
    an integer ``n``, such that ``n`` divides ``deg(f)``, returns all
    irreducible factors ``f_1,...,f_d`` of ``f``, each of degree ``n``.
    EDF procedure gives complete factorization over Galois fields.

    Consider the square-free polynomial ``f = x**3 + x**2 + x + 1`` in
    ``GF(5)[x]``. Let's compute its irreducible factors of degree one::

       >>> from sympy.polys.domains import ZZ
       >>> from sympy.polys.galoistools import gf_edf_zassenhaus

       >>> gf_edf_zassenhaus([1,1,1,1], 1, 5, ZZ)
       [[1, 1], [1, 2], [1, 3]]

    References
    ==========

    .. [1] [Gathen99]_
    .. [2] [Geddes92]_
    .. [3] [Cohen93]_

    
    "
2852,"def _tracemin_fiedler(L, X, normalized, tol, method):
    
    import numpy as np
    import scipy as sp
    import scipy.linalg  # call as sp.linalg
    import scipy.linalg.blas  # call as sp.linalg.blas
    import scipy.sparse  # call as sp.sparse

    n = X.shape[0]

    if normalized:
        # Form the normalized Laplacian matrix and determine the eigenvector of
        # its nullspace.
        e = np.sqrt(L.diagonal())
        # TODO: rm csr_array wrapper when spdiags array creation becomes available
        D = sp.sparse.csr_array(sp.sparse.spdiags(1 / e, 0, n, n, format=""csr""))
        L = D @ L @ D
        e *= 1.0 / np.linalg.norm(e, 2)

    if normalized:
","Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.

    The Fiedler vector of a connected undirected graph is the eigenvector
    corresponding to the second smallest eigenvalue of the Laplacian matrix
    of the graph. This function starts with the Laplacian L, not the Graph.

    Parameters
    ----------
    L : Laplacian of a possibly weighted or normalized, but undirected graph

    X : Initial guess for a solution. Usually a matrix of random numbers.
        This function allows more than one column in X to identify more than
        one eigenvector if desired.

    normalized : bool
        Whether the normalized Laplacian matrix is used.

    tol : float
        Tolerance of relative residual in eigenvalue computation.
        Warning: There is no limit on number of iterations.

    method : string
        Should be 'tracemin_pcg' or 'tracemin_lu'.
        Otherwise exception is raised.

    Returns
    -------
    sigma, X : Two NumPy arrays of floats.
        The lowest eigenvalues and corresponding eigenvectors of L.
        The size of input X determines the size of these outputs.
        As this is for Fiedler vectors, the zero eigenvalue (and
        constant eigenvector) are avoided.
    ",172,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _tracemin_fiedler(L, X, normalized, tol, method):
    
    import numpy as np
    import scipy as sp
    import scipy.linalg  # call as sp.linalg
    import scipy.linalg.blas  # call as sp.linalg.blas
    import scipy.sparse  # call as sp.sparse

    n = X.shape[0]

    if normalized:
        # Form the normalized Laplacian matrix and determine the eigenvector of
        # its nullspace.
        e = np.sqrt(L.diagonal())
        # TODO: rm csr_array wrapper when spdiags array creation becomes available
        D = sp.sparse.csr_array(sp.sparse.spdiags(1 / e, 0, n, n, format=""csr""))
        L = D @ L @ D
        e *= 1.0 / np.linalg.norm(e, 2)

    if normalized:

        ```
    ###Assistant : Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.

    The Fiedler vector of a connected undirected graph is the eigenvector
    corresponding to the second smallest eigenvalue of the Laplacian matrix
    of the graph. This function starts with the Laplacian L, not the Graph.

    Parameters
    ----------
    L : Laplacian of a possibly weighted or normalized, but undirected graph

    X : Initial guess for a solution. Usually a matrix of random numbers.
        This function allows more than one column in X to identify more than
        one eigenvector if desired.

    normalized : bool
        Whether the normalized Laplacian matrix is used.

    tol : float
        Tolerance of relative residual in eigenvalue computation.
        Warning: There is no limit on number of iterations.

    method : string
        Should be 'tracemin_pcg' or 'tracemin_lu'.
        Otherwise exception is raised.

    Returns
    -------
    sigma, X : Two NumPy arrays of floats.
        The lowest eigenvalues and corresponding eigenvectors of L.
        The size of input X determines the size of these outputs.
        As this is for Fiedler vectors, the zero eigenvalue (and
        constant eigenvector) are avoided.
    
    "
2853,"def test_complex_pipeline_with_shared_prompt_model_and_prompt_template_yaml(tmp_path):
    with open(tmp_path / ""tmp_config_with_prompt_template.yml"", ""w"") as tmp_file:
        tmp_file.write(
            f
        )
    pipeline = Pipeline.load_from_yaml(path=tmp_path / ""tmp_config_with_prompt_template.yml"")
    result = pipeline.run(query=""not relevant"", documents=[Document(""Berlin is an amazing city."")])
    assert ""Berlin"" in result[""results""][0]
    assert len(result[""meta""][""invocation_context""]) > 0


@pytest.mark.skipif(
    not os.environ.get(""OPENAI_API_KEY"", None),
    reason=""Please export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test."",
)","
            version: ignore
            components:
            - name: pmodel
              type: PromptModel
              params:
                model_name_or_path: google/flan-t5-small
                model_kwargs:
                  torch_dtype: torch.bfloat16
            - name: question_generation_template
              type: PromptTemplate
              params:
                name: question-generation-new
                prompt_text: ""Given the context please generate a question. Context: $documents; Question:""
            - name: p1
              params:
                model_name_or_path: pmodel
                default_prompt_template: question_generation_template
                output_variable: questions
              type: PromptNode
            - name: p2
              params:
                model_name_or_path: pmodel
                default_prompt_template: question-answering
              type: PromptNode
            pipelines:
            - name: query
              nodes:
              - name: p1
                inputs:
                - Query
              - name: p2
                inputs:
                - p1
        ",72,55,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_complex_pipeline_with_shared_prompt_model_and_prompt_template_yaml(tmp_path):
    with open(tmp_path / ""tmp_config_with_prompt_template.yml"", ""w"") as tmp_file:
        tmp_file.write(
            f
        )
    pipeline = Pipeline.load_from_yaml(path=tmp_path / ""tmp_config_with_prompt_template.yml"")
    result = pipeline.run(query=""not relevant"", documents=[Document(""Berlin is an amazing city."")])
    assert ""Berlin"" in result[""results""][0]
    assert len(result[""meta""][""invocation_context""]) > 0


@pytest.mark.skipif(
    not os.environ.get(""OPENAI_API_KEY"", None),
    reason=""Please export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test."",
)
        ```
    ###Assistant : 
            version: ignore
            components:
            - name: pmodel
              type: PromptModel
              params:
                model_name_or_path: google/flan-t5-small
                model_kwargs:
                  torch_dtype: torch.bfloat16
            - name: question_generation_template
              type: PromptTemplate
              params:
                name: question-generation-new
                prompt_text: ""Given the context please generate a question. Context: $documents; Question:""
            - name: p1
              params:
                model_name_or_path: pmodel
                default_prompt_template: question_generation_template
                output_variable: questions
              type: PromptNode
            - name: p2
              params:
                model_name_or_path: pmodel
                default_prompt_template: question-answering
              type: PromptNode
            pipelines:
            - name: query
              nodes:
              - name: p1
                inputs:
                - Query
              - name: p2
                inputs:
                - p1
        
    "
2854,"def _download_model(self):
        
        self.logger.info(""Downloading model: '%s' from: %s"", self._model_name, self._url_download)
        for attempt in range(self._retries):
            try:
                downloaded_size = self._url_partial_size
                req = urllib.request.Request(self._url_download)
                if downloaded_size != 0:
                    req.add_header(""Range"", f""bytes={downloaded_size}-"")
                with urllib.request.urlopen(req, timeout=10) as response:
                    self.logger.debug(""header info: {%s}"", response.info())
                    self.logger.debug(""Return Code: %s"", response.getcode())
                    self._write_zipfile(response, downloaded_size)
                break
            except (socket_error, socket_timeout,
                    urllib.error.HTTPError, urllib.error.URLError) as err:
                if attempt + 1 < self._retries:
                    self.logger.warning(""Error downloading model (%s). Retrying %s of %s..."",
                                        str(err), attempt + 2, self._retries)
                else:
                    self.logger.error(""Failed to download model. Exiting. (Error: '%s', URL: ""
                                      ""'%s')"", str(err), self._url_download)
                    self.logger.info(""You can try running again to resume the download."")
                    self.logger.info(""Alternatively, you can manually download the model from: %s ""
                                     ""and unzip the contents to: %s"",
                                     self._url_download, self._cache_dir)
                    sys.exit(1)
", Download the model zip from github to the cache folder. ,10,109,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _download_model(self):
        
        self.logger.info(""Downloading model: '%s' from: %s"", self._model_name, self._url_download)
        for attempt in range(self._retries):
            try:
                downloaded_size = self._url_partial_size
                req = urllib.request.Request(self._url_download)
                if downloaded_size != 0:
                    req.add_header(""Range"", f""bytes={downloaded_size}-"")
                with urllib.request.urlopen(req, timeout=10) as response:
                    self.logger.debug(""header info: {%s}"", response.info())
                    self.logger.debug(""Return Code: %s"", response.getcode())
                    self._write_zipfile(response, downloaded_size)
                break
            except (socket_error, socket_timeout,
                    urllib.error.HTTPError, urllib.error.URLError) as err:
                if attempt + 1 < self._retries:
                    self.logger.warning(""Error downloading model (%s). Retrying %s of %s..."",
                                        str(err), attempt + 2, self._retries)
                else:
                    self.logger.error(""Failed to download model. Exiting. (Error: '%s', URL: ""
                                      ""'%s')"", str(err), self._url_download)
                    self.logger.info(""You can try running again to resume the download."")
                    self.logger.info(""Alternatively, you can manually download the model from: %s ""
                                     ""and unzip the contents to: %s"",
                                     self._url_download, self._cache_dir)
                    sys.exit(1)

        ```
    ###Assistant :  Download the model zip from github to the cache folder. 
    "
2855,"def fit_transform(self, X, y=None):
        
        self._validate_params()
        self._fit_transform(X)
        return self.embedding_
","Compute the embedding vectors for data X and transform X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training set.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        X_new : array-like, shape (n_samples, n_components)
            Returns the instance itself.
        ",45,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit_transform(self, X, y=None):
        
        self._validate_params()
        self._fit_transform(X)
        return self.embedding_

        ```
    ###Assistant : Compute the embedding vectors for data X and transform X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training set.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        X_new : array-like, shape (n_samples, n_components)
            Returns the instance itself.
        
    "
2856,"def test_song_from_data_dump():
    

    # Loads from str
    song = Song.from_data_dump(
        
    )

    assert song.name == ""Ropes""
    assert song.artists == [""Dirty Palm"", ""Chandler Jewels""]
    assert song.album_name == ""Ropes""
    assert song.album_artist == ""Dirty Palm""
    assert song.genres == [""gaming edm"", ""melbourne bounce international""]
    assert song.disc_number == 1
    assert song.duration == 188
    assert song.year == 2021
    assert song.date == ""2021-10-28""
    assert song.track_number == 1
    assert song.tracks_count == 1
    assert song.isrc == ""GB2LD2110301""
    assert song.song_id == ""1t2qKa8K72IBC8yQlhD9bU""
    assert (
        song.cover_url
        == ""https://i.scdn.co/image/ab67616d0000b273fe2cb38e4d2412dbb0e54332""
    )
    assert song.explicit == False
    assert song.download_url == None

","
    Tests if Song.from_data_dump() works correctly.
    
        {
            ""name"": ""Ropes"",
            ""artists"": [""Dirty Palm"", ""Chandler Jewels""],
            ""album_name"": ""Ropes"",
            ""album_artist"": ""Dirty Palm"",
            ""genres"": [""gaming edm"", ""melbourne bounce international""],
            ""disc_number"": 1,
            ""duration"": 188,
            ""year"": 2021,
            ""date"": ""2021-10-28"",
            ""track_number"": 1,
            ""tracks_count"": 1,
            ""isrc"": ""GB2LD2110301"",
            ""song_id"": ""1t2qKa8K72IBC8yQlhD9bU"",
            ""cover_url"": ""https://i.scdn.co/image/ab67616d0000b273fe2cb38e4d2412dbb0e54332"",
            ""explicit"": false,
            ""download_url"": null,
            ""artist"" : ""Dirty Palm"",
            ""disc_count"": 1,
            ""copyright"": """",
            ""publisher"": """",
            ""url"": ""https://open.spotify.com/track/1t2qKa8K72IBC8yQlhD9bU""
        }
        ",59,84,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_song_from_data_dump():
    

    # Loads from str
    song = Song.from_data_dump(
        
    )

    assert song.name == ""Ropes""
    assert song.artists == [""Dirty Palm"", ""Chandler Jewels""]
    assert song.album_name == ""Ropes""
    assert song.album_artist == ""Dirty Palm""
    assert song.genres == [""gaming edm"", ""melbourne bounce international""]
    assert song.disc_number == 1
    assert song.duration == 188
    assert song.year == 2021
    assert song.date == ""2021-10-28""
    assert song.track_number == 1
    assert song.tracks_count == 1
    assert song.isrc == ""GB2LD2110301""
    assert song.song_id == ""1t2qKa8K72IBC8yQlhD9bU""
    assert (
        song.cover_url
        == ""https://i.scdn.co/image/ab67616d0000b273fe2cb38e4d2412dbb0e54332""
    )
    assert song.explicit == False
    assert song.download_url == None


        ```
    ###Assistant : 
    Tests if Song.from_data_dump() works correctly.
    
        {
            ""name"": ""Ropes"",
            ""artists"": [""Dirty Palm"", ""Chandler Jewels""],
            ""album_name"": ""Ropes"",
            ""album_artist"": ""Dirty Palm"",
            ""genres"": [""gaming edm"", ""melbourne bounce international""],
            ""disc_number"": 1,
            ""duration"": 188,
            ""year"": 2021,
            ""date"": ""2021-10-28"",
            ""track_number"": 1,
            ""tracks_count"": 1,
            ""isrc"": ""GB2LD2110301"",
            ""song_id"": ""1t2qKa8K72IBC8yQlhD9bU"",
            ""cover_url"": ""https://i.scdn.co/image/ab67616d0000b273fe2cb38e4d2412dbb0e54332"",
            ""explicit"": false,
            ""download_url"": null,
            ""artist"" : ""Dirty Palm"",
            ""disc_count"": 1,
            ""copyright"": """",
            ""publisher"": """",
            ""url"": ""https://open.spotify.com/track/1t2qKa8K72IBC8yQlhD9bU""
        }
        
    "
2857,"def test_run(self) -> Tuple[Dict, Dict]:
        
        print("" | > Synthesizing test sentences."")
        test_audios = {}
        test_figures = {}
        test_sentences = self.config.test_sentences
        for idx, s_info in enumerate(test_sentences):
            try:
                aux_inputs = self.get_aux_input_from_test_sentences(s_info)
                wav, alignment, _, _ = synthesis(
                    self,
                    aux_inputs[""text""],
                    self.config,
                    ""cuda"" in str(next(self.parameters()).device),
                    ap,
                    speaker_id=aux_inputs[""speaker_id""],
                    d_vector=aux_inputs[""d_vector""],
                    style_wav=aux_inputs[""style_wav""],
                    language_id=aux_inputs[""language_id""],
                    language_name=aux_inputs[""language_name""],
                    enable_eos_bos_chars=self.config.enable_eos_bos_chars,
                    use_griffin_lim=True,
                    do_trim_silence=False,
                ).values()
                test_audios[""{}-audio"".format(idx)] = wav
                test_figures[""{}-alignment"".format(idx)] = plot_alignment(alignment.T, output_fig=False)
            except:  # pylint: disable=bare-except
                print("" !! Error creating Test Sentence -"", idx)
        return test_figures, test_audios
","Generic test run for `tts` models used by `Trainer`.

        You can override this for a different behaviour.

        Returns:
            Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
        ",29,73,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_run(self) -> Tuple[Dict, Dict]:
        
        print("" | > Synthesizing test sentences."")
        test_audios = {}
        test_figures = {}
        test_sentences = self.config.test_sentences
        for idx, s_info in enumerate(test_sentences):
            try:
                aux_inputs = self.get_aux_input_from_test_sentences(s_info)
                wav, alignment, _, _ = synthesis(
                    self,
                    aux_inputs[""text""],
                    self.config,
                    ""cuda"" in str(next(self.parameters()).device),
                    ap,
                    speaker_id=aux_inputs[""speaker_id""],
                    d_vector=aux_inputs[""d_vector""],
                    style_wav=aux_inputs[""style_wav""],
                    language_id=aux_inputs[""language_id""],
                    language_name=aux_inputs[""language_name""],
                    enable_eos_bos_chars=self.config.enable_eos_bos_chars,
                    use_griffin_lim=True,
                    do_trim_silence=False,
                ).values()
                test_audios[""{}-audio"".format(idx)] = wav
                test_figures[""{}-alignment"".format(idx)] = plot_alignment(alignment.T, output_fig=False)
            except:  # pylint: disable=bare-except
                print("" !! Error creating Test Sentence -"", idx)
        return test_figures, test_audios

        ```
    ###Assistant : Generic test run for `tts` models used by `Trainer`.

        You can override this for a different behaviour.

        Returns:
            Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
        
    "
2858,"def read_dict(self, dictionary, source='<dict>'):
        
        elements_added = set()
        for section, keys in dictionary.items():
            section = str(section)
            try:
                self.add_section(section)
            except (DuplicateSectionError, ValueError):
                if self._strict and section in elements_added:
                    raise
            elements_added.add(section)
            for key, value in keys.items():
                key = self.optionxform(str(key))
                if value is not None:
                    value = str(value)
                if self._strict and (section, key) in elements_added:
                    raise DuplicateOptionError(section, key, source)
                elements_added.add((section, key))
                self.set(section, key, value)
","Read configuration from a dictionary.

        Keys are section names, values are dictionaries with keys and values
        that should be present in the section. If the used dictionary type
        preserves order, sections and their keys will be added in order.

        All types held in the dictionary are converted to strings during
        reading, including section names, option names and keys.

        Optional second argument is the `source' specifying the name of the
        dictionary being read.
        ",72,60,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read_dict(self, dictionary, source='<dict>'):
        
        elements_added = set()
        for section, keys in dictionary.items():
            section = str(section)
            try:
                self.add_section(section)
            except (DuplicateSectionError, ValueError):
                if self._strict and section in elements_added:
                    raise
            elements_added.add(section)
            for key, value in keys.items():
                key = self.optionxform(str(key))
                if value is not None:
                    value = str(value)
                if self._strict and (section, key) in elements_added:
                    raise DuplicateOptionError(section, key, source)
                elements_added.add((section, key))
                self.set(section, key, value)

        ```
    ###Assistant : Read configuration from a dictionary.

        Keys are section names, values are dictionaries with keys and values
        that should be present in the section. If the used dictionary type
        preserves order, sections and their keys will be added in order.

        All types held in the dictionary are converted to strings during
        reading, including section names, option names and keys.

        Optional second argument is the `source' specifying the name of the
        dictionary being read.
        
    "
2859,"def send(self, load, tries=3, timeout=60, raw=False):
        
        _try = 1
        while True:
            try:
                if self.crypt == ""clear"":
                    log.trace(""ReqChannel send clear load=%r"", load)
                    ret = yield self._uncrypted_transfer(load, timeout=timeout)
                else:
                    log.trace(""ReqChannel send crypt load=%r"", load)
                    ret = yield self._crypted_transfer(
                        load, timeout=timeout, raw=raw
                    )
                break
            except Exception as exc:
                log.error(""Failed to send msg %r"", dir(exc))
                if _try == tries:
                    raise #salt.exceptions.SaltClientError(""Connection to master lost"")
                else:
                    _try += 1
                    continue
        raise salt.ext.tornado.gen.Return(ret)
","
        Send a request, return a future which will complete when we send the message

        :param dict load: A load to send across the wire
        :param int tries: The number of times to make before failure
        :param int timeout: The number of seconds on a response before failing
        ",47,67,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def send(self, load, tries=3, timeout=60, raw=False):
        
        _try = 1
        while True:
            try:
                if self.crypt == ""clear"":
                    log.trace(""ReqChannel send clear load=%r"", load)
                    ret = yield self._uncrypted_transfer(load, timeout=timeout)
                else:
                    log.trace(""ReqChannel send crypt load=%r"", load)
                    ret = yield self._crypted_transfer(
                        load, timeout=timeout, raw=raw
                    )
                break
            except Exception as exc:
                log.error(""Failed to send msg %r"", dir(exc))
                if _try == tries:
                    raise #salt.exceptions.SaltClientError(""Connection to master lost"")
                else:
                    _try += 1
                    continue
        raise salt.ext.tornado.gen.Return(ret)

        ```
    ###Assistant : 
        Send a request, return a future which will complete when we send the message

        :param dict load: A load to send across the wire
        :param int tries: The number of times to make before failure
        :param int timeout: The number of seconds on a response before failing
        
    "
2860,"def get_fields_from_path(model, path):
    
    pieces = path.split(LOOKUP_SEP)
    fields = []
    for piece in pieces:
        if fields:
            parent = get_model_from_relation(fields[-1])
        else:
            parent = model
        fields.append(parent._meta.get_field(piece))
    return fields

","Return list of Fields given path relative to model.

    e.g. (ModelX, ""user__groups__name"") -> [
        <django.db.models.fields.related.ForeignKey object at 0x...>,
        <django.db.models.fields.related.ManyToManyField object at 0x...>,
        <django.db.models.fields.CharField object at 0x...>,
    ]
    ",27,25,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_fields_from_path(model, path):
    
    pieces = path.split(LOOKUP_SEP)
    fields = []
    for piece in pieces:
        if fields:
            parent = get_model_from_relation(fields[-1])
        else:
            parent = model
        fields.append(parent._meta.get_field(piece))
    return fields


        ```
    ###Assistant : Return list of Fields given path relative to model.

    e.g. (ModelX, ""user__groups__name"") -> [
        <django.db.models.fields.related.ForeignKey object at 0x...>,
        <django.db.models.fields.related.ManyToManyField object at 0x...>,
        <django.db.models.fields.CharField object at 0x...>,
    ]
    
    "
2861,"def find_config_files(self):
        
        files = []
        check_environ()

        # Where to look for the system-wide Distutils config file
        sys_dir = os.path.dirname(sys.modules['distutils'].__file__)

        # Look for the system config file
        sys_file = os.path.join(sys_dir, ""distutils.cfg"")
        if os.path.isfile(sys_file):
            files.append(sys_file)

        # What to call the per-user config file
        if os.name == 'posix':
            user_filename = "".pydistutils.cfg""
        else:
            user_filename = ""pydistutils.cfg""

        # And look for the user config file
        if self.want_user_cfg:
            user_file = os.path.join(os.path.expanduser('~'), user_filename)
            if os.path.isfile(user_file):
                files.append(user_file)

        # All platforms support local setup.cfg
        local_file = ""setup.cfg""
        if os.path.isfile(local_file):
            files.append(local_file)

        if DEBUG:
            self.announce(""using config files: %s"" % ', '.join(files))

        return files
","Find as many configuration files as should be processed for this
        platform, and return a list of filenames in the order in which they
        should be parsed.  The filenames returned are guaranteed to exist
        (modulo nasty race conditions).

        There are three possible config files: distutils.cfg in the
        Distutils installation directory (ie. where the top-level
        Distutils __inst__.py file lives), a file in the user's home
        directory named .pydistutils.cfg on Unix and pydistutils.cfg
        on Windows/Mac; and setup.cfg in the current directory.

        The file in the user's home directory can be disabled with the
        --no-user-cfg option.
        ",93,92,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_config_files(self):
        
        files = []
        check_environ()

        # Where to look for the system-wide Distutils config file
        sys_dir = os.path.dirname(sys.modules['distutils'].__file__)

        # Look for the system config file
        sys_file = os.path.join(sys_dir, ""distutils.cfg"")
        if os.path.isfile(sys_file):
            files.append(sys_file)

        # What to call the per-user config file
        if os.name == 'posix':
            user_filename = "".pydistutils.cfg""
        else:
            user_filename = ""pydistutils.cfg""

        # And look for the user config file
        if self.want_user_cfg:
            user_file = os.path.join(os.path.expanduser('~'), user_filename)
            if os.path.isfile(user_file):
                files.append(user_file)

        # All platforms support local setup.cfg
        local_file = ""setup.cfg""
        if os.path.isfile(local_file):
            files.append(local_file)

        if DEBUG:
            self.announce(""using config files: %s"" % ', '.join(files))

        return files

        ```
    ###Assistant : Find as many configuration files as should be processed for this
        platform, and return a list of filenames in the order in which they
        should be parsed.  The filenames returned are guaranteed to exist
        (modulo nasty race conditions).

        There are three possible config files: distutils.cfg in the
        Distutils installation directory (ie. where the top-level
        Distutils __inst__.py file lives), a file in the user's home
        directory named .pydistutils.cfg on Unix and pydistutils.cfg
        on Windows/Mac; and setup.cfg in the current directory.

        The file in the user's home directory can be disabled with the
        --no-user-cfg option.
        
    "
2862,"def get_save_image_name(org_im_path, output_dir, num):
    
    # name prefix of orginal image
    org_im_name = os.path.split(org_im_path)[-1]
    im_prefix = os.path.splitext(org_im_name)[0]
    ext = '.png'
    # save image path
    save_im_path = os.path.join(output_dir, im_prefix + ext)
    if os.path.exists(save_im_path):
        save_im_path = os.path.join(
            output_dir, im_prefix + str(num) + ext)

    return save_im_path
","
    Get save image name from source image path.
    ",8,42,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_save_image_name(org_im_path, output_dir, num):
    
    # name prefix of orginal image
    org_im_name = os.path.split(org_im_path)[-1]
    im_prefix = os.path.splitext(org_im_name)[0]
    ext = '.png'
    # save image path
    save_im_path = os.path.join(output_dir, im_prefix + ext)
    if os.path.exists(save_im_path):
        save_im_path = os.path.join(
            output_dir, im_prefix + str(num) + ext)

    return save_im_path

        ```
    ###Assistant : 
    Get save image name from source image path.
    
    "
2863,"def rgb_to_hsv(arr):
    
    arr = np.asarray(arr)

    # check length of the last dimension, should be _some_ sort of rgb
    if arr.shape[-1] != 3:
        raise ValueError(""Last dimension of input array must be 3; ""
                         ""shape {} was found."".format(arr.shape))

    in_shape = arr.shape
    arr = np.array(
        arr, copy=False,
        dtype=np.promote_types(arr.dtype, np.float32),  # Don't work on ints.
        ndmin=2,  # In case input was 1D.
    )
    out = np.zeros_like(arr)
    arr_max = arr.max(-1)
    ipos = arr_max > 0
    delta = arr.ptp(-1)
    s = np.zeros_like(delta)
    s[ipos] = delta[ipos] / arr_max[ipos]
    ipos = delta > 0
    # red is max
    idx = (arr[..., 0] == arr_max) & ipos
    out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx]
    # green is max
    idx = (arr[..., 1] == arr_max) & ipos
    out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0]) / delta[idx]
    # blue is max
    idx = (arr[..., 2] == arr_max) & ipos
    out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1]) / delta[idx]

    out[..., 0] = (out[..., 0] / 6.0) % 1.0
    out[..., 1] = s
    out[..., 2] = arr_max

    return out.reshape(in_shape)

","
    Convert float RGB values (in the range [0, 1]), in a numpy array to HSV
    values.

    Parameters
    ----------
    arr : (..., 3) array-like
       All values must be in the range [0, 1]

    Returns
    -------
    (..., 3) ndarray
       Colors converted to HSV values in range [0, 1]
    ",46,175,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def rgb_to_hsv(arr):
    
    arr = np.asarray(arr)

    # check length of the last dimension, should be _some_ sort of rgb
    if arr.shape[-1] != 3:
        raise ValueError(""Last dimension of input array must be 3; ""
                         ""shape {} was found."".format(arr.shape))

    in_shape = arr.shape
    arr = np.array(
        arr, copy=False,
        dtype=np.promote_types(arr.dtype, np.float32),  # Don't work on ints.
        ndmin=2,  # In case input was 1D.
    )
    out = np.zeros_like(arr)
    arr_max = arr.max(-1)
    ipos = arr_max > 0
    delta = arr.ptp(-1)
    s = np.zeros_like(delta)
    s[ipos] = delta[ipos] / arr_max[ipos]
    ipos = delta > 0
    # red is max
    idx = (arr[..., 0] == arr_max) & ipos
    out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx]
    # green is max
    idx = (arr[..., 1] == arr_max) & ipos
    out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0]) / delta[idx]
    # blue is max
    idx = (arr[..., 2] == arr_max) & ipos
    out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1]) / delta[idx]

    out[..., 0] = (out[..., 0] / 6.0) % 1.0
    out[..., 1] = s
    out[..., 2] = arr_max

    return out.reshape(in_shape)


        ```
    ###Assistant : 
    Convert float RGB values (in the range [0, 1]), in a numpy array to HSV
    values.

    Parameters
    ----------
    arr : (..., 3) array-like
       All values must be in the range [0, 1]

    Returns
    -------
    (..., 3) ndarray
       Colors converted to HSV values in range [0, 1]
    
    "
2864,"def test_scheduler_verify_pool_full(self, dag_maker, configs):
        
        with conf_vars(configs):
            with dag_maker(dag_id='test_scheduler_verify_pool_full'):
                BashOperator(
                    task_id='dummy',
                    pool='test_scheduler_verify_pool_full',
                    bash_command='echo hi',
                )

            session = settings.Session()
            pool = Pool(pool='test_scheduler_verify_pool_full', slots=1)
            session.add(pool)
            session.flush()

            self.scheduler_job = SchedulerJob(executor=self.null_exec)
            self.scheduler_job.processor_agent = mock.MagicMock()

            # Create 2 dagruns, which will create 2 task instances.
            dr = dag_maker.create_dagrun(
                run_type=DagRunType.SCHEDULED,
            )
            self.scheduler_job._schedule_dag_run(dr, session)
            dr = dag_maker.create_dagrun_after(dr, run_type=DagRunType.SCHEDULED, state=State.RUNNING)
            self.scheduler_job._schedule_dag_run(dr, session)
            session.flush()
            task_instances_list = self.scheduler_job._executable_task_instances_to_queued(
                max_tis=32, session=session
            )

            assert len(task_instances_list) == 1
","
        Test task instances not queued when pool is full
        ",9,64,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_scheduler_verify_pool_full(self, dag_maker, configs):
        
        with conf_vars(configs):
            with dag_maker(dag_id='test_scheduler_verify_pool_full'):
                BashOperator(
                    task_id='dummy',
                    pool='test_scheduler_verify_pool_full',
                    bash_command='echo hi',
                )

            session = settings.Session()
            pool = Pool(pool='test_scheduler_verify_pool_full', slots=1)
            session.add(pool)
            session.flush()

            self.scheduler_job = SchedulerJob(executor=self.null_exec)
            self.scheduler_job.processor_agent = mock.MagicMock()

            # Create 2 dagruns, which will create 2 task instances.
            dr = dag_maker.create_dagrun(
                run_type=DagRunType.SCHEDULED,
            )
            self.scheduler_job._schedule_dag_run(dr, session)
            dr = dag_maker.create_dagrun_after(dr, run_type=DagRunType.SCHEDULED, state=State.RUNNING)
            self.scheduler_job._schedule_dag_run(dr, session)
            session.flush()
            task_instances_list = self.scheduler_job._executable_task_instances_to_queued(
                max_tis=32, session=session
            )

            assert len(task_instances_list) == 1

        ```
    ###Assistant : 
        Test task instances not queued when pool is full
        
    "
2865,"def ms_word():
        
    # TODO : Take location input from the user
    try:
        speak(""Enter the document's location - "")
        location = input(""Enter the document's location - "")
        
        file_loc = doubleslash(location) 
       
        doc = docx.Document(file_loc)
        fullText = []
        for para in doc.paragraphs:
            fullText.append(para.text)
        #print(fullText)
        doc_file = '\n'.join(fullText)
        print(doc_file)
        speak(doc_file)
    except Exception as exp:
        #print(exp)
        print(f""ERROR - {exp}"")
        print(Fore.YELLOW + ""I could'nt locate the file!\nIf you didn't specify the extension of the file, please specify it."")
        return ""None""

","[Print and speak out a ms_word docx file as specified in the path]
    ",13,74,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def ms_word():
        
    # TODO : Take location input from the user
    try:
        speak(""Enter the document's location - "")
        location = input(""Enter the document's location - "")
        
        file_loc = doubleslash(location) 
       
        doc = docx.Document(file_loc)
        fullText = []
        for para in doc.paragraphs:
            fullText.append(para.text)
        #print(fullText)
        doc_file = '\n'.join(fullText)
        print(doc_file)
        speak(doc_file)
    except Exception as exp:
        #print(exp)
        print(f""ERROR - {exp}"")
        print(Fore.YELLOW + ""I could'nt locate the file!\nIf you didn't specify the extension of the file, please specify it."")
        return ""None""

        ```
    ###Assistant : [Print and speak out a ms_word docx file as specified in the path]
    
    "
2866,"def tree_data(G, root, attrs=None, ident=""id"", children=""children""):
    
    if G.number_of_nodes() != G.number_of_edges() + 1:
        raise TypeError(""G is not a tree."")
    if not G.is_directed():
        raise TypeError(""G is not directed."")
    if not nx.is_weakly_connected(G):
        raise TypeError(""G is not weakly connected."")

    # NOTE: to be removed in 3.0
    if attrs is not None:
        import warnings

        msg = (
            ""\nThe `attrs` keyword argument of tree_data is deprecated\n""
            ""and will be removed in networkx 3.0.\n""
            ""It is replaced with explicit `ident` and `children` ""
            ""keyword arguments.\n""
            ""To make this warning go away and ensure usage is forward\n""
            ""compatible, replace `attrs` with `ident` and `children,\n""
            ""for example:\n\n""
            ""    >>> tree_data(G, root, attrs={'id': 'foo', 'children': 'bar'})\n\n""
            ""should instead be written as\n\n""
            ""    >>> tree_data(G, root, ident='foo', children='bar')\n\n""
            ""The default values of 'id' and 'children' will not change.""
        )
        warnings.warn(msg, DeprecationWarning, stacklevel=2)

        ident = attrs[""id""]
        children = attrs[""children""]

    if ident == children:
        raise nx.NetworkXError(""The values for `id` and `children` must be different."")
","Returns data in tree format that is suitable for JSON serialization
    and use in Javascript documents.

    Parameters
    ----------
    G : NetworkX graph
       G must be an oriented tree

    root : node
       The root of the tree

    attrs : dict
        A dictionary that contains two keys 'id' and 'children'. The
        corresponding values provide the attribute names for storing
        NetworkX-internal graph data. The values should be unique. Default
        value: :samp:`dict(id='id', children='children')`.

        If some user-defined graph data use these attribute names as data keys,
        they may be silently dropped.

        .. deprecated:: 2.6

           The `attrs` keyword argument is replaced by `ident` and `children`
           and will be removed in networkx 3.0

    ident : string
        Attribute name for storing NetworkX-internal graph data. `ident` must
        have a different value than `children`. The default is 'id'.

    children : string
        Attribute name for storing NetworkX-internal graph data. `children`
        must have a different value than `ident`. The default is 'children'.

    Returns
    -------
    data : dict
       A dictionary with node-link formatted data.

    Raises
    ------
    NetworkXError
        If `children` and `ident` attributes are identical.

    Examples
    --------
    >>> from networkx.readwrite import json_graph
    >>> G = nx.DiGraph([(1, 2)])
    >>> data = json_graph.tree_data(G, root=1)

    To serialize with json

    >>> import json
    >>> s = json.dumps(data)

    Notes
    -----
    Node attributes are stored in this format but keys
    for attributes must be strings if you want to serialize with JSON.

    Graph and edge attributes are not stored.

    See Also
    --------
    tree_graph, node_link_data, adjacency_data
    ",235,151,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def tree_data(G, root, attrs=None, ident=""id"", children=""children""):
    
    if G.number_of_nodes() != G.number_of_edges() + 1:
        raise TypeError(""G is not a tree."")
    if not G.is_directed():
        raise TypeError(""G is not directed."")
    if not nx.is_weakly_connected(G):
        raise TypeError(""G is not weakly connected."")

    # NOTE: to be removed in 3.0
    if attrs is not None:
        import warnings

        msg = (
            ""\nThe `attrs` keyword argument of tree_data is deprecated\n""
            ""and will be removed in networkx 3.0.\n""
            ""It is replaced with explicit `ident` and `children` ""
            ""keyword arguments.\n""
            ""To make this warning go away and ensure usage is forward\n""
            ""compatible, replace `attrs` with `ident` and `children,\n""
            ""for example:\n\n""
            ""    >>> tree_data(G, root, attrs={'id': 'foo', 'children': 'bar'})\n\n""
            ""should instead be written as\n\n""
            ""    >>> tree_data(G, root, ident='foo', children='bar')\n\n""
            ""The default values of 'id' and 'children' will not change.""
        )
        warnings.warn(msg, DeprecationWarning, stacklevel=2)

        ident = attrs[""id""]
        children = attrs[""children""]

    if ident == children:
        raise nx.NetworkXError(""The values for `id` and `children` must be different."")

        ```
    ###Assistant : Returns data in tree format that is suitable for JSON serialization
    and use in Javascript documents.

    Parameters
    ----------
    G : NetworkX graph
       G must be an oriented tree

    root : node
       The root of the tree

    attrs : dict
        A dictionary that contains two keys 'id' and 'children'. The
        corresponding values provide the attribute names for storing
        NetworkX-internal graph data. The values should be unique. Default
        value: :samp:`dict(id='id', children='children')`.

        If some user-defined graph data use these attribute names as data keys,
        they may be silently dropped.

        .. deprecated:: 2.6

           The `attrs` keyword argument is replaced by `ident` and `children`
           and will be removed in networkx 3.0

    ident : string
        Attribute name for storing NetworkX-internal graph data. `ident` must
        have a different value than `children`. The default is 'id'.

    children : string
        Attribute name for storing NetworkX-internal graph data. `children`
        must have a different value than `ident`. The default is 'children'.

    Returns
    -------
    data : dict
       A dictionary with node-link formatted data.

    Raises
    ------
    NetworkXError
        If `children` and `ident` attributes are identical.

    Examples
    --------
    >>> from networkx.readwrite import json_graph
    >>> G = nx.DiGraph([(1, 2)])
    >>> data = json_graph.tree_data(G, root=1)

    To serialize with json

    >>> import json
    >>> s = json.dumps(data)

    Notes
    -----
    Node attributes are stored in this format but keys
    for attributes must be strings if you want to serialize with JSON.

    Graph and edge attributes are not stored.

    See Also
    --------
    tree_graph, node_link_data, adjacency_data
    
    "
2867,"def test_mutating_input_arrays_y_and_z(fig_test, fig_ref):
    
    ax1 = fig_test.add_subplot(111, projection='3d')
    x = [1, 2, 3]
    y = [0.0, 0.0, 0.0]
    z = [0.0, 0.0, 0.0]
    ax1.plot(x, y, z, 'o-')

    ax1.set_ylim([0, 4])
    ax1.set_zlim([0, 4])
    fig_test.draw_without_rendering()

    # mutate y,z to get a nontrivial line
    y[:] = [1, 2, 3]
    z[:] = [1, 2, 3]

    # draw the same plot without mutating x and y
    ax2 = fig_ref.add_subplot(111, projection='3d')
    x = [1, 2, 3]
    y = [0.0, 0.0, 0.0]
    z = [0.0, 0.0, 0.0]
    ax2.plot(x, y, z, 'o-')

    ax2.set_ylim([0, 4])
    ax2.set_zlim([0, 4])
    fig_test.draw_without_rendering()
","
    Test to see if the `z` axis does not get mutated
    after a call to `Axes3D.plot`

    test cases came from GH#8990
    ",21,87,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_mutating_input_arrays_y_and_z(fig_test, fig_ref):
    
    ax1 = fig_test.add_subplot(111, projection='3d')
    x = [1, 2, 3]
    y = [0.0, 0.0, 0.0]
    z = [0.0, 0.0, 0.0]
    ax1.plot(x, y, z, 'o-')

    ax1.set_ylim([0, 4])
    ax1.set_zlim([0, 4])
    fig_test.draw_without_rendering()

    # mutate y,z to get a nontrivial line
    y[:] = [1, 2, 3]
    z[:] = [1, 2, 3]

    # draw the same plot without mutating x and y
    ax2 = fig_ref.add_subplot(111, projection='3d')
    x = [1, 2, 3]
    y = [0.0, 0.0, 0.0]
    z = [0.0, 0.0, 0.0]
    ax2.plot(x, y, z, 'o-')

    ax2.set_ylim([0, 4])
    ax2.set_zlim([0, 4])
    fig_test.draw_without_rendering()

        ```
    ###Assistant : 
    Test to see if the `z` axis does not get mutated
    after a call to `Axes3D.plot`

    test cases came from GH#8990
    
    "
2868,"def test_search_with_hyphen(self):
        
        book = models.Book.objects.create(
            title=""Harry Potter and the Half-Blood Prince"",
            publication_date=date(2009, 7, 15),
            number_of_pages=607,
        )

        index = self.backend.get_index_for_model(models.Book)
        index.add_item(book)
        index.refresh()

        results = self.backend.search(""Half-Blood"", models.Book)
        self.assertUnsortedListEqual(
            [r.title for r in results],
            [
                ""Harry Potter and the Half-Blood Prince"",
            ],
        )
","
        This tests that punctuation characters are treated the same
        way in both indexing and querying.

        See: https://github.com/wagtail/wagtail/issues/937
        ",17,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_search_with_hyphen(self):
        
        book = models.Book.objects.create(
            title=""Harry Potter and the Half-Blood Prince"",
            publication_date=date(2009, 7, 15),
            number_of_pages=607,
        )

        index = self.backend.get_index_for_model(models.Book)
        index.add_item(book)
        index.refresh()

        results = self.backend.search(""Half-Blood"", models.Book)
        self.assertUnsortedListEqual(
            [r.title for r in results],
            [
                ""Harry Potter and the Half-Blood Prince"",
            ],
        )

        ```
    ###Assistant : 
        This tests that punctuation characters are treated the same
        way in both indexing and querying.

        See: https://github.com/wagtail/wagtail/issues/937
        
    "
2869,"def resolve(self, s):
            
            name = s.split('.')
            used = name.pop(0)
            try:
                found = self.importer(used)
                for frag in name:
                    used += '.' + frag
                    try:
                        found = getattr(found, frag)
                    except AttributeError:
                        self.importer(used)
                        found = getattr(found, frag)
                return found
            except ImportError:
                e, tb = sys.exc_info()[1:]
                v = ValueError('Cannot resolve %r: %s' % (s, e))
                v.__cause__, v.__traceback__ = e, tb
                raise v
","
            Resolve strings to objects using standard import and attribute
            syntax.
            ",10,58,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def resolve(self, s):
            
            name = s.split('.')
            used = name.pop(0)
            try:
                found = self.importer(used)
                for frag in name:
                    used += '.' + frag
                    try:
                        found = getattr(found, frag)
                    except AttributeError:
                        self.importer(used)
                        found = getattr(found, frag)
                return found
            except ImportError:
                e, tb = sys.exc_info()[1:]
                v = ValueError('Cannot resolve %r: %s' % (s, e))
                v.__cause__, v.__traceback__ = e, tb
                raise v

        ```
    ###Assistant : 
            Resolve strings to objects using standard import and attribute
            syntax.
            
    "
2870,"def heldout_score(clf, X_test, y_test):
    
    score = np.zeros((n_estimators,), dtype=np.float64)
    for i, y_pred in enumerate(clf.staged_decision_function(X_test)):
        score[i] = binomial_deviance(y_test, y_pred.ravel())
    return score

",compute deviance scores on ``X_test`` and ``y_test``.,7,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def heldout_score(clf, X_test, y_test):
    
    score = np.zeros((n_estimators,), dtype=np.float64)
    for i, y_pred in enumerate(clf.staged_decision_function(X_test)):
        score[i] = binomial_deviance(y_test, y_pred.ravel())
    return score


        ```
    ###Assistant : compute deviance scores on ``X_test`` and ``y_test``.
    "
2871,"def test_product_types_query_ids_not_exists(user_api_client, category):
    query = NOT_EXISTS_IDS_COLLECTIONS_QUERY
    variables = {""filter"": {""ids"": [""fTEJRuFHU6fd2RU="", ""2XwnQNNhwCdEjhP=""]}}
    response = user_api_client.post_graphql(query, variables)
    content = get_graphql_content(response, ignore_errors=True)
    message_error = '{""ids"": [{""message"": ""Invalid ID specified."", ""code"": """"}]}'

    assert len(content[""errors""]) == 1
    assert content[""errors""][0][""message""] == message_error
    assert content[""data""][""productTypes""] is None


QUERY_FILTER_PRODUCT_TYPES = 


@pytest.mark.parametrize(
    ""search, expected_names"",
    (
        ("""", [""The best juices"", ""The best beers"", ""The worst beers""]),
        (""best"", [""The best juices"", ""The best beers""]),
        (""worst"", [""The worst beers""]),
        (""average"", []),
    ),
)","
    query($filters: ProductTypeFilterInput) {
      productTypes(first: 10, filter: $filters) {
        edges {
          node {
            name
          }
        }
      }
    }
",17,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_product_types_query_ids_not_exists(user_api_client, category):
    query = NOT_EXISTS_IDS_COLLECTIONS_QUERY
    variables = {""filter"": {""ids"": [""fTEJRuFHU6fd2RU="", ""2XwnQNNhwCdEjhP=""]}}
    response = user_api_client.post_graphql(query, variables)
    content = get_graphql_content(response, ignore_errors=True)
    message_error = '{""ids"": [{""message"": ""Invalid ID specified."", ""code"": """"}]}'

    assert len(content[""errors""]) == 1
    assert content[""errors""][0][""message""] == message_error
    assert content[""data""][""productTypes""] is None


QUERY_FILTER_PRODUCT_TYPES = 


@pytest.mark.parametrize(
    ""search, expected_names"",
    (
        ("""", [""The best juices"", ""The best beers"", ""The worst beers""]),
        (""best"", [""The best juices"", ""The best beers""]),
        (""worst"", [""The worst beers""]),
        (""average"", []),
    ),
)
        ```
    ###Assistant : 
    query($filters: ProductTypeFilterInput) {
      productTypes(first: 10, filter: $filters) {
        edges {
          node {
            name
          }
        }
      }
    }

    "
2872,"def _generate_examples(self, filepath):
        
        for idx, line in enumerate(open(filepath, ""r"", encoding=""utf-8"")):
            if idx == 0:
                continue  # skip header
            line = line.strip()
            split_line = line.split(""\t"")
            # Examples not marked with a three out of five consensus are marked with
            # ""-"" and should not be used in standard evaluations.
            if split_line[0] == ""-"":
                continue
            # Works for both splits even though dev has some extra human labels.
            yield idx, {
                ""premise"": split_line[5],
                ""hypothesis"": split_line[6],
                ""label"": split_line[0],
                ""binary_parse_premise"": split_line[1],
                ""binary_parse_hypothesis"": split_line[2],
                ""parse_premise"": split_line[3],
                ""parse_hypothesis"": split_line[4],
                ""heuristic"": split_line[8],
                ""subcase"": split_line[9],
                ""template"": split_line[10],
            }
","Generate hans examples.

        Args:
          filepath: a string

        Yields:
          dictionaries containing ""premise"", ""hypothesis"" and ""label"" strings
        ",15,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _generate_examples(self, filepath):
        
        for idx, line in enumerate(open(filepath, ""r"", encoding=""utf-8"")):
            if idx == 0:
                continue  # skip header
            line = line.strip()
            split_line = line.split(""\t"")
            # Examples not marked with a three out of five consensus are marked with
            # ""-"" and should not be used in standard evaluations.
            if split_line[0] == ""-"":
                continue
            # Works for both splits even though dev has some extra human labels.
            yield idx, {
                ""premise"": split_line[5],
                ""hypothesis"": split_line[6],
                ""label"": split_line[0],
                ""binary_parse_premise"": split_line[1],
                ""binary_parse_hypothesis"": split_line[2],
                ""parse_premise"": split_line[3],
                ""parse_hypothesis"": split_line[4],
                ""heuristic"": split_line[8],
                ""subcase"": split_line[9],
                ""template"": split_line[10],
            }

        ```
    ###Assistant : Generate hans examples.

        Args:
          filepath: a string

        Yields:
          dictionaries containing ""premise"", ""hypothesis"" and ""label"" strings
        
    "
2873,"def _light_internal_color_mode(self) -> str:
        
        if (color_mode := self.color_mode) is None:
            # Backwards compatibility for color_mode added in 2021.4
            # Add warning in 2021.6, remove in 2021.10
            supported = self._light_internal_supported_color_modes

            if ColorMode.HS in supported and self.hs_color is not None:
                return ColorMode.HS
            if ColorMode.COLOR_TEMP in supported and self.color_temp_kelvin is not None:
                return ColorMode.COLOR_TEMP
            if ColorMode.BRIGHTNESS in supported and self.brightness is not None:
                return ColorMode.BRIGHTNESS
            if ColorMode.ONOFF in supported:
                return ColorMode.ONOFF
            return ColorMode.UNKNOWN

        return color_mode
",Return the color mode of the light with backwards compatibility.,10,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _light_internal_color_mode(self) -> str:
        
        if (color_mode := self.color_mode) is None:
            # Backwards compatibility for color_mode added in 2021.4
            # Add warning in 2021.6, remove in 2021.10
            supported = self._light_internal_supported_color_modes

            if ColorMode.HS in supported and self.hs_color is not None:
                return ColorMode.HS
            if ColorMode.COLOR_TEMP in supported and self.color_temp_kelvin is not None:
                return ColorMode.COLOR_TEMP
            if ColorMode.BRIGHTNESS in supported and self.brightness is not None:
                return ColorMode.BRIGHTNESS
            if ColorMode.ONOFF in supported:
                return ColorMode.ONOFF
            return ColorMode.UNKNOWN

        return color_mode

        ```
    ###Assistant : Return the color mode of the light with backwards compatibility.
    "
2874,"def kde(self, bw_method=None, ind=None, **kwargs):
        
        return self(kind=""kde"", bw_method=bw_method, ind=ind, **kwargs)

    density = kde
","
        Generate Kernel Density Estimate plot using Gaussian kernels.

        In statistics, `kernel density estimation`_ (KDE) is a non-parametric
        way to estimate the probability density function (PDF) of a random
        variable. This function uses Gaussian kernels and includes automatic
        bandwidth determination.

        .. _kernel density estimation:
            https://en.wikipedia.org/wiki/Kernel_density_estimation

        Parameters
        ----------
        bw_method : str, scalar or callable, optional
            The method used to calculate the estimator bandwidth. This can be
            'scott', 'silverman', a scalar constant or a callable.
            If None (default), 'scott' is used.
            See :class:`scipy.stats.gaussian_kde` for more information.
        ind : NumPy array or int, optional
            Evaluation points for the estimated PDF. If None (default),
            1000 equally spaced points are used. If `ind` is a NumPy array, the
            KDE is evaluated at the points passed. If `ind` is an integer,
            `ind` number of equally spaced points are used.
        **kwargs
            Additional keyword arguments are documented in
            :meth:`DataFrame.plot`.

        Returns
        -------
        matplotlib.axes.Axes or numpy.ndarray of them

        See Also
        --------
        scipy.stats.gaussian_kde : Representation of a kernel-density
            estimate using Gaussian kernels. This is the function used
            internally to estimate the PDF.

        Examples
        --------
        Given a Series of points randomly sampled from an unknown
        distribution, estimate its PDF using KDE with automatic
        bandwidth determination and plot the results, evaluating them at
        1000 equally spaced points (default):

        .. plot::
            :context: close-figs

            >>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
            >>> ax = s.plot.kde()

        A scalar bandwidth can be specified. Using a small bandwidth value can
        lead to over-fitting, while using a large bandwidth value may result
        in under-fitting:

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(bw_method=0.3)

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(bw_method=3)

        Finally, the `ind` parameter determines the evaluation points for the
        plot of the estimated PDF:

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])

        For DataFrame, it works in the same way:

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame({
            ...     'x': [1, 2, 2.5, 3, 3.5, 4, 5],
            ...     'y': [4, 4, 4.5, 5, 5.5, 6, 6],
            ... })
            >>> ax = df.plot.kde()

        A scalar bandwidth can be specified. Using a small bandwidth value can
        lead to over-fitting, while using a large bandwidth value may result
        in under-fitting:

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(bw_method=0.3)

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(bw_method=3)

        Finally, the `ind` parameter determines the evaluation points for the
        plot of the estimated PDF:

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
        ",399,13,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def kde(self, bw_method=None, ind=None, **kwargs):
        
        return self(kind=""kde"", bw_method=bw_method, ind=ind, **kwargs)

    density = kde

        ```
    ###Assistant : 
        Generate Kernel Density Estimate plot using Gaussian kernels.

        In statistics, `kernel density estimation`_ (KDE) is a non-parametric
        way to estimate the probability density function (PDF) of a random
        variable. This function uses Gaussian kernels and includes automatic
        bandwidth determination.

        .. _kernel density estimation:
            https://en.wikipedia.org/wiki/Kernel_density_estimation

        Parameters
        ----------
        bw_method : str, scalar or callable, optional
            The method used to calculate the estimator bandwidth. This can be
            'scott', 'silverman', a scalar constant or a callable.
            If None (default), 'scott' is used.
            See :class:`scipy.stats.gaussian_kde` for more information.
        ind : NumPy array or int, optional
            Evaluation points for the estimated PDF. If None (default),
            1000 equally spaced points are used. If `ind` is a NumPy array, the
            KDE is evaluated at the points passed. If `ind` is an integer,
            `ind` number of equally spaced points are used.
        **kwargs
            Additional keyword arguments are documented in
            :meth:`DataFrame.plot`.

        Returns
        -------
        matplotlib.axes.Axes or numpy.ndarray of them

        See Also
        --------
        scipy.stats.gaussian_kde : Representation of a kernel-density
            estimate using Gaussian kernels. This is the function used
            internally to estimate the PDF.

        Examples
        --------
        Given a Series of points randomly sampled from an unknown
        distribution, estimate its PDF using KDE with automatic
        bandwidth determination and plot the results, evaluating them at
        1000 equally spaced points (default):

        .. plot::
            :context: close-figs

            >>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
            >>> ax = s.plot.kde()

        A scalar bandwidth can be specified. Using a small bandwidth value can
        lead to over-fitting, while using a large bandwidth value may result
        in under-fitting:

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(bw_method=0.3)

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(bw_method=3)

        Finally, the `ind` parameter determines the evaluation points for the
        plot of the estimated PDF:

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])

        For DataFrame, it works in the same way:

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame({
            ...     'x': [1, 2, 2.5, 3, 3.5, 4, 5],
            ...     'y': [4, 4, 4.5, 5, 5.5, 6, 6],
            ... })
            >>> ax = df.plot.kde()

        A scalar bandwidth can be specified. Using a small bandwidth value can
        lead to over-fitting, while using a large bandwidth value may result
        in under-fitting:

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(bw_method=0.3)

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(bw_method=3)

        Finally, the `ind` parameter determines the evaluation points for the
        plot of the estimated PDF:

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
        
    "
2875,"def subscription_invoice_requested_webhook(subscription_webhook):
    return subscription_webhook(
        INVOICE_REQUESTED_SUBSCRIPTION_QUERY, WebhookEventAsyncType.INVOICE_REQUESTED
    )


INVOICE_DELETED_SUBSCRIPTION_QUERY = 


@pytest.fixture","
    subscription{
      event{
        ...on InvoiceDeleted{
          invoice{
            id
          }
        }
      }
    }
",10,10,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def subscription_invoice_requested_webhook(subscription_webhook):
    return subscription_webhook(
        INVOICE_REQUESTED_SUBSCRIPTION_QUERY, WebhookEventAsyncType.INVOICE_REQUESTED
    )


INVOICE_DELETED_SUBSCRIPTION_QUERY = 


@pytest.fixture
        ```
    ###Assistant : 
    subscription{
      event{
        ...on InvoiceDeleted{
          invoice{
            id
          }
        }
      }
    }

    "
2876,"def test_prompt_from_examples_valid() -> None:
    
    template = 
    input_variables = [""question""]
    example_separator = ""\n\n""
    prefix = 
    suffix = 
    examples = [
        ,
        ,
    ]
    prompt_from_examples = PromptTemplate.from_examples(
        examples,
        suffix,
        input_variables,
        example_separator=example_separator,
        prefix=prefix,
    )
    prompt_from_template = PromptTemplate(
        input_variables=input_variables, template=template
    )
    assert prompt_from_examples.template == prompt_from_template.template
    assert prompt_from_examples.input_variables == prompt_from_template.input_variables

","Test prompt can be successfully constructed from examples.Test Prompt:

Question: who are you?
Answer: foo

Question: what are you?
Answer: bar

Question: {question}
Answer:Test Prompt:Question: {question}\nAnswer:Question: who are you?\nAnswer: fooQuestion: what are you?\nAnswer: bar",34,45,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_prompt_from_examples_valid() -> None:
    
    template = 
    input_variables = [""question""]
    example_separator = ""\n\n""
    prefix = 
    suffix = 
    examples = [
        ,
        ,
    ]
    prompt_from_examples = PromptTemplate.from_examples(
        examples,
        suffix,
        input_variables,
        example_separator=example_separator,
        prefix=prefix,
    )
    prompt_from_template = PromptTemplate(
        input_variables=input_variables, template=template
    )
    assert prompt_from_examples.template == prompt_from_template.template
    assert prompt_from_examples.input_variables == prompt_from_template.input_variables


        ```
    ###Assistant : Test prompt can be successfully constructed from examples.Test Prompt:

Question: who are you?
Answer: foo

Question: what are you?
Answer: bar

Question: {question}
Answer:Test Prompt:Question: {question}\nAnswer:Question: who are you?\nAnswer: fooQuestion: what are you?\nAnswer: bar
    "
2877,"def compatible_platforms(provided, required):
    
    if provided is None or required is None or provided == required:
        # easy case
        return True

    # Mac OS X special cases
    reqMac = macosVersionString.match(required)
    if reqMac:
        provMac = macosVersionString.match(provided)

        # is this a Mac package?
        if not provMac:
            # this is backwards compatibility for packages built before
            # setuptools 0.6. All packages built after this point will
            # use the new macosx designation.
            provDarwin = darwinVersionString.match(provided)
            if provDarwin:
                dversion = int(provDarwin.group(1))
                macosversion = ""%s.%s"" % (reqMac.group(1), reqMac.group(2))
                if dversion == 7 and macosversion >= ""10.3"" or \
                        dversion == 8 and macosversion >= ""10.4"":
                    return True
            # egg isn't macosx or legacy darwin
            return False

        # are they the same major version and machine type?
        if provMac.group(1) != reqMac.group(1) or \
                provMac.group(3) != reqMac.group(3):
            return False

        # is the required OS major update >= the provided one?
        if int(provMac.group(2)) > int(reqMac.group(2)):
            return False

        return True

    # XXX Linux and other platforms' special cases should go here
    return False

","Can code for the `provided` platform run on the `required` platform?

    Returns true if either platform is ``None``, or the platforms are equal.

    XXX Needs compatibility checks for Linux and other unixy OSes.
    ",33,163,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def compatible_platforms(provided, required):
    
    if provided is None or required is None or provided == required:
        # easy case
        return True

    # Mac OS X special cases
    reqMac = macosVersionString.match(required)
    if reqMac:
        provMac = macosVersionString.match(provided)

        # is this a Mac package?
        if not provMac:
            # this is backwards compatibility for packages built before
            # setuptools 0.6. All packages built after this point will
            # use the new macosx designation.
            provDarwin = darwinVersionString.match(provided)
            if provDarwin:
                dversion = int(provDarwin.group(1))
                macosversion = ""%s.%s"" % (reqMac.group(1), reqMac.group(2))
                if dversion == 7 and macosversion >= ""10.3"" or \
                        dversion == 8 and macosversion >= ""10.4"":
                    return True
            # egg isn't macosx or legacy darwin
            return False

        # are they the same major version and machine type?
        if provMac.group(1) != reqMac.group(1) or \
                provMac.group(3) != reqMac.group(3):
            return False

        # is the required OS major update >= the provided one?
        if int(provMac.group(2)) > int(reqMac.group(2)):
            return False

        return True

    # XXX Linux and other platforms' special cases should go here
    return False


        ```
    ###Assistant : Can code for the `provided` platform run on the `required` platform?

    Returns true if either platform is ``None``, or the platforms are equal.

    XXX Needs compatibility checks for Linux and other unixy OSes.
    
    "
2878,"def find_negative_cycle(G, source, weight=""weight""):
    
    weight = _weight_function(G, weight)
    pred = {source: []}

    v = _inner_bellman_ford(G, [source], weight, pred=pred)
    if v is None:
        raise nx.NetworkXError(""No negative cycles detected."")

    # negative cycle detected... find it
    neg_cycle = []
    stack = [(v, list(pred[v]))]
    seen = {v}
    while stack:
        node, preds = stack[-1]
        if v in preds:
            # found the cycle
            neg_cycle.extend([node, v])
            neg_cycle = list(reversed(neg_cycle))
            return neg_cycle

        if preds:
            nbr = preds.pop()
            if nbr not in seen:
                stack.append((nbr, list(pred[nbr])))
                neg_cycle.append(node)
                seen.add(nbr)
        else:
            stack.pop()
            if neg_cycle:
                neg_cycle.pop()
            else:
                if v in G[v] and weight(G, v, v) < 0:
                    return [v, v]
                # should not reach here
                raise nx.NetworkXError(""Negative cycle is detected but not found"")
    # should not get here...
    msg = ""negative cycle detected but not identified""
    raise nx.NetworkXUnbounded(msg)

","Returns a cycle with negative total weight if it exists.

    Bellman-Ford is used to find shortest_paths. That algorithm
    stops if there exists a negative cycle. This algorithm
    picks up from there and returns the found negative cycle.

    The cycle consists of a list of nodes in the cycle order. The last
    node equals the first to make it a cycle.
    You can look up the edge weights in the original graph. In the case
    of multigraphs the relevant edge is the minimal weight edge between
    the nodes in the 2-tuple.

    If the graph has no negative cycle, a NetworkXError is raised.

    Parameters
    ----------
    G : NetworkX graph

    source: node label
        The search for the negative cycle will start from this node.

    weight : string or function
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number.

    Examples
    --------
    >>> G = nx.DiGraph()
    >>> G.add_weighted_edges_from([(0, 1, 2), (1, 2, 2), (2, 0, 1), (1, 4, 2), (4, 0, -5)])
    >>> nx.find_negative_cycle(G, 0)
    [4, 0, 1, 4]

    Returns
    -------
    cycle : list
        A list of nodes in the order of the cycle found. The last node
        equals the first to indicate a cycle.

    Raises
    ------
    NetworkXError
        If no negative cycle is found.
    ",285,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def find_negative_cycle(G, source, weight=""weight""):
    
    weight = _weight_function(G, weight)
    pred = {source: []}

    v = _inner_bellman_ford(G, [source], weight, pred=pred)
    if v is None:
        raise nx.NetworkXError(""No negative cycles detected."")

    # negative cycle detected... find it
    neg_cycle = []
    stack = [(v, list(pred[v]))]
    seen = {v}
    while stack:
        node, preds = stack[-1]
        if v in preds:
            # found the cycle
            neg_cycle.extend([node, v])
            neg_cycle = list(reversed(neg_cycle))
            return neg_cycle

        if preds:
            nbr = preds.pop()
            if nbr not in seen:
                stack.append((nbr, list(pred[nbr])))
                neg_cycle.append(node)
                seen.add(nbr)
        else:
            stack.pop()
            if neg_cycle:
                neg_cycle.pop()
            else:
                if v in G[v] and weight(G, v, v) < 0:
                    return [v, v]
                # should not reach here
                raise nx.NetworkXError(""Negative cycle is detected but not found"")
    # should not get here...
    msg = ""negative cycle detected but not identified""
    raise nx.NetworkXUnbounded(msg)


        ```
    ###Assistant : Returns a cycle with negative total weight if it exists.

    Bellman-Ford is used to find shortest_paths. That algorithm
    stops if there exists a negative cycle. This algorithm
    picks up from there and returns the found negative cycle.

    The cycle consists of a list of nodes in the cycle order. The last
    node equals the first to make it a cycle.
    You can look up the edge weights in the original graph. In the case
    of multigraphs the relevant edge is the minimal weight edge between
    the nodes in the 2-tuple.

    If the graph has no negative cycle, a NetworkXError is raised.

    Parameters
    ----------
    G : NetworkX graph

    source: node label
        The search for the negative cycle will start from this node.

    weight : string or function
        If this is a string, then edge weights will be accessed via the
        edge attribute with this key (that is, the weight of the edge
        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
        such edge attribute exists, the weight of the edge is assumed to
        be one.

        If this is a function, the weight of an edge is the value
        returned by the function. The function must accept exactly three
        positional arguments: the two endpoints of an edge and the
        dictionary of edge attributes for that edge. The function must
        return a number.

    Examples
    --------
    >>> G = nx.DiGraph()
    >>> G.add_weighted_edges_from([(0, 1, 2), (1, 2, 2), (2, 0, 1), (1, 4, 2), (4, 0, -5)])
    >>> nx.find_negative_cycle(G, 0)
    [4, 0, 1, 4]

    Returns
    -------
    cycle : list
        A list of nodes in the order of the cycle found. The last node
        equals the first to indicate a cycle.

    Raises
    ------
    NetworkXError
        If no negative cycle is found.
    
    "
2879,"def expand(image, border=0, fill=0):
    
    left, top, right, bottom = _border(border)
    width = left + image.size[0] + right
    height = top + image.size[1] + bottom
    color = _color(fill, image.mode)
    if image.mode == ""P"" and image.palette:
        palette = ImagePalette.ImagePalette(palette=image.getpalette())
        if isinstance(color, tuple):
            color = palette.getcolor(color)
    else:
        palette = None
    out = Image.new(image.mode, (width, height), color)
    if palette:
        out.putpalette(palette.palette)
    out.paste(image, (left, top))
    return out

","
    Add border to the image

    :param image: The image to expand.
    :param border: Border width, in pixels.
    :param fill: Pixel fill value (a color value).  Default is 0 (black).
    :return: An image.
    ",32,61,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def expand(image, border=0, fill=0):
    
    left, top, right, bottom = _border(border)
    width = left + image.size[0] + right
    height = top + image.size[1] + bottom
    color = _color(fill, image.mode)
    if image.mode == ""P"" and image.palette:
        palette = ImagePalette.ImagePalette(palette=image.getpalette())
        if isinstance(color, tuple):
            color = palette.getcolor(color)
    else:
        palette = None
    out = Image.new(image.mode, (width, height), color)
    if palette:
        out.putpalette(palette.palette)
    out.paste(image, (left, top))
    return out


        ```
    ###Assistant : 
    Add border to the image

    :param image: The image to expand.
    :param border: Border width, in pixels.
    :param fill: Pixel fill value (a color value).  Default is 0 (black).
    :return: An image.
    
    "
2880,"def test_digest_enabled(self, digests, mock_func):
        
        backend = RedisBackend()
        digests.digest = backend.digest
        digests.enabled.return_value = True

        rule = Rule.objects.create(project=self.project, label=""my rule"")
        ProjectOwnership.objects.create(project_id=self.project.id, fallthrough=True)
        event = self.store_event(
            data={""message"": ""Hello world"", ""level"": ""error""}, project_id=self.project.id
        )
        key = f""mail:p:{self.project.id}""
        backend.add(key, event_to_record(event, [rule]), increment_delay=0, maximum_delay=0)

        with self.tasks():
            deliver_digest(key)

        attachment, text = get_attachment()

        assert attachment[""title""] == ""Hello world""
        assert attachment[""text""] == """"
","
        Test that with digests enabled, but Slack notification settings
        (and not email settings), we send a Slack notification
        ",18,54,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_digest_enabled(self, digests, mock_func):
        
        backend = RedisBackend()
        digests.digest = backend.digest
        digests.enabled.return_value = True

        rule = Rule.objects.create(project=self.project, label=""my rule"")
        ProjectOwnership.objects.create(project_id=self.project.id, fallthrough=True)
        event = self.store_event(
            data={""message"": ""Hello world"", ""level"": ""error""}, project_id=self.project.id
        )
        key = f""mail:p:{self.project.id}""
        backend.add(key, event_to_record(event, [rule]), increment_delay=0, maximum_delay=0)

        with self.tasks():
            deliver_digest(key)

        attachment, text = get_attachment()

        assert attachment[""title""] == ""Hello world""
        assert attachment[""text""] == """"

        ```
    ###Assistant : 
        Test that with digests enabled, but Slack notification settings
        (and not email settings), we send a Slack notification
        
    "
2881,"def pole_zero_numerical_data(system):
    
    _check_system(system)
    system = system.doit()  # Get the equivalent TransferFunction object.

    num_poly = Poly(system.num, system.var).all_coeffs()
    den_poly = Poly(system.den, system.var).all_coeffs()

    num_poly = np.array(num_poly, dtype=np.complex128)
    den_poly = np.array(den_poly, dtype=np.complex128)

    zeros = np.roots(num_poly)
    poles = np.roots(den_poly)

    return zeros, poles

","
    Returns the numerical data of poles and zeros of the system.
    It is internally used by ``pole_zero_plot`` to get the data
    for plotting poles and zeros. Users can use this data to further
    analyse the dynamics of the system or plot using a different
    backend/plotting-module.

    Parameters
    ==========

    system : SISOLinearTimeInvariant
        The system for which the pole-zero data is to be computed.

    Returns
    =======

    tuple : (zeros, poles)
        zeros = Zeros of the system. NumPy array of complex numbers.
        poles = Poles of the system. NumPy array of complex numbers.

    Raises
    ======

    NotImplementedError
        When a SISO LTI system is not passed.

        When time delay terms are present in the system.

    ValueError
        When more than one free symbol is present in the system.
        The only variable in the transfer function should be
        the variable of the Laplace transform.

    Examples
    ========

    >>> from sympy.abc import s
    >>> from sympy.physics.control.lti import TransferFunction
    >>> from sympy.physics.control.control_plots import pole_zero_numerical_data
    >>> tf1 = TransferFunction(s**2 + 1, s**4 + 4*s**3 + 6*s**2 + 5*s + 2, s)
    >>> pole_zero_numerical_data(tf1)   # doctest: +SKIP
    ([-0.+1.j  0.-1.j], [-2. +0.j        -0.5+0.8660254j -0.5-0.8660254j -1. +0.j       ])

    See Also
    ========

    pole_zero_plot

    ",187,37,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def pole_zero_numerical_data(system):
    
    _check_system(system)
    system = system.doit()  # Get the equivalent TransferFunction object.

    num_poly = Poly(system.num, system.var).all_coeffs()
    den_poly = Poly(system.den, system.var).all_coeffs()

    num_poly = np.array(num_poly, dtype=np.complex128)
    den_poly = np.array(den_poly, dtype=np.complex128)

    zeros = np.roots(num_poly)
    poles = np.roots(den_poly)

    return zeros, poles


        ```
    ###Assistant : 
    Returns the numerical data of poles and zeros of the system.
    It is internally used by ``pole_zero_plot`` to get the data
    for plotting poles and zeros. Users can use this data to further
    analyse the dynamics of the system or plot using a different
    backend/plotting-module.

    Parameters
    ==========

    system : SISOLinearTimeInvariant
        The system for which the pole-zero data is to be computed.

    Returns
    =======

    tuple : (zeros, poles)
        zeros = Zeros of the system. NumPy array of complex numbers.
        poles = Poles of the system. NumPy array of complex numbers.

    Raises
    ======

    NotImplementedError
        When a SISO LTI system is not passed.

        When time delay terms are present in the system.

    ValueError
        When more than one free symbol is present in the system.
        The only variable in the transfer function should be
        the variable of the Laplace transform.

    Examples
    ========

    >>> from sympy.abc import s
    >>> from sympy.physics.control.lti import TransferFunction
    >>> from sympy.physics.control.control_plots import pole_zero_numerical_data
    >>> tf1 = TransferFunction(s**2 + 1, s**4 + 4*s**3 + 6*s**2 + 5*s + 2, s)
    >>> pole_zero_numerical_data(tf1)   # doctest: +SKIP
    ([-0.+1.j  0.-1.j], [-2. +0.j        -0.5+0.8660254j -0.5-0.8660254j -1. +0.j       ])

    See Also
    ========

    pole_zero_plot

    
    "
2882,"def get_versions():
    
    commands = ['gcc -dumpversion', 'ld -v', 'dllwrap --version']
    return tuple([_find_exe_version(cmd) for cmd in commands])
"," Try to find out the versions of gcc, ld and dllwrap.

    If not possible it returns None for it.
    ",19,16,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_versions():
    
    commands = ['gcc -dumpversion', 'ld -v', 'dllwrap --version']
    return tuple([_find_exe_version(cmd) for cmd in commands])

        ```
    ###Assistant :  Try to find out the versions of gcc, ld and dllwrap.

    If not possible it returns None for it.
    
    "
2883,"def __virtual__():
    
    if salt.utils.napalm.virtual(__opts__, __virtualname__, __file__):
        return __virtualname__
    else:
        err_msg = ""NAPALM is not installed.""
        log.error(""Unable to load %s beacon: %s"", __virtualname__, err_msg)
        return False, err_msg

","
    This beacon can only work when running under a regular or a proxy minion, managed through napalm.
    ",17,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __virtual__():
    
    if salt.utils.napalm.virtual(__opts__, __virtualname__, __file__):
        return __virtualname__
    else:
        err_msg = ""NAPALM is not installed.""
        log.error(""Unable to load %s beacon: %s"", __virtualname__, err_msg)
        return False, err_msg


        ```
    ###Assistant : 
    This beacon can only work when running under a regular or a proxy minion, managed through napalm.
    
    "
2884,"def parse_prompt_attention(text):
    

    res = []
    round_brackets = []
    square_brackets = []

    round_bracket_multiplier = 1.1
    square_bracket_multiplier = 1 / 1.1
","
    Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
    Accepted tokens are:
      (abc) - increases attention to abc by a multiplier of 1.1
      (abc:3.12) - increases attention to abc by a multiplier of 3.12
      [abc] - decreases attention to abc by a multiplier of 1.1
      \( - literal character '('
      \[ - literal character '['
      \) - literal character ')'
      \] - literal character ']'
      \\ - literal character '\'
      anything else - just text
    >>> parse_prompt_attention('normal text')
    [['normal text', 1.0]]
    >>> parse_prompt_attention('an (important) word')
    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
    >>> parse_prompt_attention('(unbalanced')
    [['unbalanced', 1.1]]
    >>> parse_prompt_attention('\(literal\]')
    [['(literal]', 1.0]]
    >>> parse_prompt_attention('(unnecessary)(parens)')
    [['unnecessaryparens', 1.1]]
    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
    [['a ', 1.0],
     ['house', 1.5730000000000004],
     [' ', 1.1],
     ['on', 1.0],
     [' a ', 1.1],
     ['hill', 0.55],
     [', sun, ', 1.1],
     ['sky', 1.4641000000000006],
     ['.', 1.1]]
    ",145,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def parse_prompt_attention(text):
    

    res = []
    round_brackets = []
    square_brackets = []

    round_bracket_multiplier = 1.1
    square_bracket_multiplier = 1 / 1.1

        ```
    ###Assistant : 
    Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
    Accepted tokens are:
      (abc) - increases attention to abc by a multiplier of 1.1
      (abc:3.12) - increases attention to abc by a multiplier of 3.12
      [abc] - decreases attention to abc by a multiplier of 1.1
      \( - literal character '('
      \[ - literal character '['
      \) - literal character ')'
      \] - literal character ']'
      \\ - literal character '\'
      anything else - just text
    >>> parse_prompt_attention('normal text')
    [['normal text', 1.0]]
    >>> parse_prompt_attention('an (important) word')
    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
    >>> parse_prompt_attention('(unbalanced')
    [['unbalanced', 1.1]]
    >>> parse_prompt_attention('\(literal\]')
    [['(literal]', 1.0]]
    >>> parse_prompt_attention('(unnecessary)(parens)')
    [['unnecessaryparens', 1.1]]
    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
    [['a ', 1.0],
     ['house', 1.5730000000000004],
     [' ', 1.1],
     ['on', 1.0],
     [' a ', 1.1],
     ['hill', 0.55],
     [', sun, ', 1.1],
     ['sky', 1.4641000000000006],
     ['.', 1.1]]
    
    "
2885,"def get_dependencies(self, candidate):
        # type: (Candidate) -> list[Candidate]
        r
        # FIXME: If there's several galaxy servers set, there may be a
        # FIXME: situation when the metadata of the same collection
        # FIXME: differs. So how do we resolve this case? Priority?
        # FIXME: Taking into account a pinned hash? Exploding on
        # FIXME: any differences?
        # NOTE: The underlying implmentation currently uses first found
        req_map = self._api_proxy.get_collection_dependencies(candidate)

        # NOTE: This guard expression MUST perform an early exit only
        # NOTE: after the `get_collection_dependencies()` call because
        # NOTE: internally it polulates the artifact URL of the candidate,
        # NOTE: its SHA hash and the Galaxy API token. These are still
        # NOTE: necessary with `--no-deps` because even with the disabled
        # NOTE: dependency resolution the outer layer will still need to
        # NOTE: know how to download and validate the artifact.
        #
        # NOTE: Virtual candidates should always return dependencies
        # NOTE: because they are ephemeral and non-installable.
        if not self._with_deps and not candidate.is_virtual:
            return []

        return [
            self._make_req_from_dict({'name': dep_name, 'version': dep_req})
            for dep_name, dep_req in req_map.items()
        ]
","Get direct dependencies of a candidate.

        :returns: A collection of requirements that `candidate` \
                  specifies as its dependencies.
        ",18,178,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_dependencies(self, candidate):
        # type: (Candidate) -> list[Candidate]
        r
        # FIXME: If there's several galaxy servers set, there may be a
        # FIXME: situation when the metadata of the same collection
        # FIXME: differs. So how do we resolve this case? Priority?
        # FIXME: Taking into account a pinned hash? Exploding on
        # FIXME: any differences?
        # NOTE: The underlying implmentation currently uses first found
        req_map = self._api_proxy.get_collection_dependencies(candidate)

        # NOTE: This guard expression MUST perform an early exit only
        # NOTE: after the `get_collection_dependencies()` call because
        # NOTE: internally it polulates the artifact URL of the candidate,
        # NOTE: its SHA hash and the Galaxy API token. These are still
        # NOTE: necessary with `--no-deps` because even with the disabled
        # NOTE: dependency resolution the outer layer will still need to
        # NOTE: know how to download and validate the artifact.
        #
        # NOTE: Virtual candidates should always return dependencies
        # NOTE: because they are ephemeral and non-installable.
        if not self._with_deps and not candidate.is_virtual:
            return []

        return [
            self._make_req_from_dict({'name': dep_name, 'version': dep_req})
            for dep_name, dep_req in req_map.items()
        ]

        ```
    ###Assistant : Get direct dependencies of a candidate.

        :returns: A collection of requirements that `candidate` \
                  specifies as its dependencies.
        
    "
2886,"def test_set_displayname(self) -> None:
        

        # Modify user
        channel = self.make_request(
            ""PUT"",
            self.url_other_user,
            access_token=self.admin_user_tok,
            content={""displayname"": ""foobar""},
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(""@user:test"", channel.json_body[""name""])
        self.assertEqual(""foobar"", channel.json_body[""displayname""])

        # Get user
        channel = self.make_request(
            ""GET"",
            self.url_other_user,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(""@user:test"", channel.json_body[""name""])
        self.assertEqual(""foobar"", channel.json_body[""displayname""])
","
        Test setting the displayname of another user.
        ",7,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_set_displayname(self) -> None:
        

        # Modify user
        channel = self.make_request(
            ""PUT"",
            self.url_other_user,
            access_token=self.admin_user_tok,
            content={""displayname"": ""foobar""},
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(""@user:test"", channel.json_body[""name""])
        self.assertEqual(""foobar"", channel.json_body[""displayname""])

        # Get user
        channel = self.make_request(
            ""GET"",
            self.url_other_user,
            access_token=self.admin_user_tok,
        )

        self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
        self.assertEqual(""@user:test"", channel.json_body[""name""])
        self.assertEqual(""foobar"", channel.json_body[""displayname""])

        ```
    ###Assistant : 
        Test setting the displayname of another user.
        
    "
2887,"def on_ssl_errors(self, reply, qt_errors):
        
        errors = certificateerror.CertificateErrorWrapper(reply, qt_errors)
        log.network.debug(""Certificate errors: {!r}"".format(errors))
        try:
            host_tpl: Optional[urlutils.HostTupleType] = urlutils.host_tuple(
                reply.url())
        except ValueError:
            host_tpl = None
            is_accepted = False
            is_rejected = False
        else:
            assert host_tpl is not None
            is_accepted = errors in self._accepted_ssl_errors[host_tpl]
            is_rejected = errors in self._rejected_ssl_errors[host_tpl]

        log.network.debug(""Already accepted: {} / ""
                          ""rejected {}"".format(is_accepted, is_rejected))

        if is_rejected:
            return
        elif is_accepted:
            reply.ignoreSslErrors()
            return

        abort_on = self._get_abort_signals(reply)

        tab = self._get_tab()
        first_party_url = QUrl() if tab is None else tab.data.last_navigation.url

        shared.handle_certificate_error(
            request_url=reply.url(),
            first_party_url=first_party_url,
            error=errors,
            abort_on=abort_on,
        )

        if errors.certificate_was_accepted():
            if host_tpl is not None:
                self._accepted_ssl_errors[host_tpl].add(errors)
        elif host_tpl is not None:
            self._rejected_ssl_errors[host_tpl].add(errors)
","Decide if SSL errors should be ignored or not.

        This slot is called on SSL/TLS errors by the self.sslErrors signal.

        Args:
            reply: The QNetworkReply that is encountering the errors.
            qt_errors: A list of errors.
        ",34,94,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def on_ssl_errors(self, reply, qt_errors):
        
        errors = certificateerror.CertificateErrorWrapper(reply, qt_errors)
        log.network.debug(""Certificate errors: {!r}"".format(errors))
        try:
            host_tpl: Optional[urlutils.HostTupleType] = urlutils.host_tuple(
                reply.url())
        except ValueError:
            host_tpl = None
            is_accepted = False
            is_rejected = False
        else:
            assert host_tpl is not None
            is_accepted = errors in self._accepted_ssl_errors[host_tpl]
            is_rejected = errors in self._rejected_ssl_errors[host_tpl]

        log.network.debug(""Already accepted: {} / ""
                          ""rejected {}"".format(is_accepted, is_rejected))

        if is_rejected:
            return
        elif is_accepted:
            reply.ignoreSslErrors()
            return

        abort_on = self._get_abort_signals(reply)

        tab = self._get_tab()
        first_party_url = QUrl() if tab is None else tab.data.last_navigation.url

        shared.handle_certificate_error(
            request_url=reply.url(),
            first_party_url=first_party_url,
            error=errors,
            abort_on=abort_on,
        )

        if errors.certificate_was_accepted():
            if host_tpl is not None:
                self._accepted_ssl_errors[host_tpl].add(errors)
        elif host_tpl is not None:
            self._rejected_ssl_errors[host_tpl].add(errors)

        ```
    ###Assistant : Decide if SSL errors should be ignored or not.

        This slot is called on SSL/TLS errors by the self.sslErrors signal.

        Args:
            reply: The QNetworkReply that is encountering the errors.
            qt_errors: A list of errors.
        
    "
2888,"def render_pep440(pieces):
    
    if pieces[""closest-tag""]:
        rendered = pieces[""closest-tag""]
        if pieces[""distance""] or pieces[""dirty""]:
            rendered += plus_or_dot(pieces)
            rendered += f""{pieces['distance']}.g{pieces['short']}""
            if pieces[""dirty""]:
                rendered += "".dirty""
    else:
        # exception #1
        rendered = f""0+untagged.{pieces['distance']}.g{pieces['short']}""
        if pieces[""dirty""]:
            rendered += "".dirty""
    return rendered

","Build up version string, with post-release ""local version identifier"".

    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty

    Exceptions:
    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
    ",37,36,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def render_pep440(pieces):
    
    if pieces[""closest-tag""]:
        rendered = pieces[""closest-tag""]
        if pieces[""distance""] or pieces[""dirty""]:
            rendered += plus_or_dot(pieces)
            rendered += f""{pieces['distance']}.g{pieces['short']}""
            if pieces[""dirty""]:
                rendered += "".dirty""
    else:
        # exception #1
        rendered = f""0+untagged.{pieces['distance']}.g{pieces['short']}""
        if pieces[""dirty""]:
            rendered += "".dirty""
    return rendered


        ```
    ###Assistant : Build up version string, with post-release ""local version identifier"".

    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty

    Exceptions:
    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
    
    "
2889,"async def test_aset_many(self):
        
        self.assertEqual(await cache.aset_many({""a"": 1, ""b"": 2}), [])
        self.assertEqual(
            await cache.aset_many({""a"": 1, ""b"": 2}, timeout=2, version=""1""),
            [],
        )
",aset_many() does nothing for the dummy cache backend.,8,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_aset_many(self):
        
        self.assertEqual(await cache.aset_many({""a"": 1, ""b"": 2}), [])
        self.assertEqual(
            await cache.aset_many({""a"": 1, ""b"": 2}, timeout=2, version=""1""),
            [],
        )

        ```
    ###Assistant : aset_many() does nothing for the dummy cache backend.
    "
2890,"def _user_input(self, input_str):
        
        os.environ[""COMP_WORDS""] = input_str
        idx = len(input_str.split("" "")) - 1  # Index of the last word
        comp_cword = idx + 1 if input_str.endswith("" "") else idx
        os.environ[""COMP_CWORD""] = str(comp_cword)
        sys.argv = input_str.split()
","
        Set the environment and the list of command line arguments.

        This sets the bash variables $COMP_WORDS and $COMP_CWORD. The former is
        an array consisting of the individual words in the current command
        line, the latter is the index of the current cursor position, so in
        case a word is completed and the cursor is placed after a whitespace,
        $COMP_CWORD must be incremented by 1:

          * 'django-admin start' -> COMP_CWORD=1
          * 'django-admin startproject' -> COMP_CWORD=1
          * 'django-admin startproject ' -> COMP_CWORD=2
        ",80,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _user_input(self, input_str):
        
        os.environ[""COMP_WORDS""] = input_str
        idx = len(input_str.split("" "")) - 1  # Index of the last word
        comp_cword = idx + 1 if input_str.endswith("" "") else idx
        os.environ[""COMP_CWORD""] = str(comp_cword)
        sys.argv = input_str.split()

        ```
    ###Assistant : 
        Set the environment and the list of command line arguments.

        This sets the bash variables $COMP_WORDS and $COMP_CWORD. The former is
        an array consisting of the individual words in the current command
        line, the latter is the index of the current cursor position, so in
        case a word is completed and the cursor is placed after a whitespace,
        $COMP_CWORD must be incremented by 1:

          * 'django-admin start' -> COMP_CWORD=1
          * 'django-admin startproject' -> COMP_CWORD=1
          * 'django-admin startproject ' -> COMP_CWORD=2
        
    "
2891,"def _check_conflicts() -> None:
    # Node-related conflicts

    # When using the Node server, we must always connect to 8501 (this is
    # hard-coded in JS). Otherwise, the browser would decide what port to
    # connect to based on window.location.port, which in dev is going to
    # be (3000)

    # Import logger locally to prevent circular references
    from streamlit.logger import get_logger

    LOGGER = get_logger(__name__)

    if get_option(""global.developmentMode""):
        assert _is_unset(
            ""server.port""
        ), ""server.port does not work when global.developmentMode is true.""

        assert _is_unset(""browser.serverPort""), (
            ""browser.serverPort does not work when global.developmentMode is "" ""true.""
        )

    # XSRF conflicts
    if get_option(""server.enableXsrfProtection""):
        if not get_option(""server.enableCORS"") or get_option(""global.developmentMode""):
            LOGGER.warning(
                
            )

","
Warning: the config option 'server.enableCORS=false' is not compatible with 'server.enableXsrfProtection=true'.
As a result, 'server.enableCORS' is being overridden to 'true'.

More information:
In order to protect against CSRF attacks, we send a cookie with each request.
To do so, we must specify allowable origins, which places a restriction on
cross-origin resource sharing.

If cross origin resource sharing is required, please disable server.enableXsrfProtection.
            ",61,102,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _check_conflicts() -> None:
    # Node-related conflicts

    # When using the Node server, we must always connect to 8501 (this is
    # hard-coded in JS). Otherwise, the browser would decide what port to
    # connect to based on window.location.port, which in dev is going to
    # be (3000)

    # Import logger locally to prevent circular references
    from streamlit.logger import get_logger

    LOGGER = get_logger(__name__)

    if get_option(""global.developmentMode""):
        assert _is_unset(
            ""server.port""
        ), ""server.port does not work when global.developmentMode is true.""

        assert _is_unset(""browser.serverPort""), (
            ""browser.serverPort does not work when global.developmentMode is "" ""true.""
        )

    # XSRF conflicts
    if get_option(""server.enableXsrfProtection""):
        if not get_option(""server.enableCORS"") or get_option(""global.developmentMode""):
            LOGGER.warning(
                
            )


        ```
    ###Assistant : 
Warning: the config option 'server.enableCORS=false' is not compatible with 'server.enableXsrfProtection=true'.
As a result, 'server.enableCORS' is being overridden to 'true'.

More information:
In order to protect against CSRF attacks, we send a cookie with each request.
To do so, we must specify allowable origins, which places a restriction on
cross-origin resource sharing.

If cross origin resource sharing is required, please disable server.enableXsrfProtection.
            
    "
2892,"def num_columns(self) -> Dict[str, int]:
        
        self._check_values_type()
        return {k: dataset.num_columns for k, dataset in self.items()}
","Number of columns in each split of the dataset.

        Example:

        ```py
        >>> from datasets import load_dataset
        >>> ds = load_dataset(""rotten_tomatoes"")
        >>> ds.num_columns
        {'test': 2, 'train': 2, 'validation': 2}
        ```
        ",29,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def num_columns(self) -> Dict[str, int]:
        
        self._check_values_type()
        return {k: dataset.num_columns for k, dataset in self.items()}

        ```
    ###Assistant : Number of columns in each split of the dataset.

        Example:

        ```py
        >>> from datasets import load_dataset
        >>> ds = load_dataset(""rotten_tomatoes"")
        >>> ds.num_columns
        {'test': 2, 'train': 2, 'validation': 2}
        ```
        
    "
2893,"def _sample_with_replacement_map_partitions(population, k):
    

    stream = iter(population)
    e = next(stream)
    reservoir, stream_length = [e for _ in range(k)], 1

    w = [rnd.random() for _ in range(k)]
    nxt = [_geometric(wi) for wi in w]
    min_nxt = min(nxt)

    for i, e in enumerate(stream, 1):
        if i == min_nxt:
            for j, n in enumerate(nxt):
                if n == min_nxt:
                    reservoir[j] = e
                    w[j] *= rnd.random()
                    nxt[j] += _geometric(w[j])
            min_nxt = min(nxt)

        stream_length += 1

    return reservoir, stream_length

","
    Reservoir sampling with replacement, the main idea is to use k reservoirs of size 1
    See Section Applications in http://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf
    ",20,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _sample_with_replacement_map_partitions(population, k):
    

    stream = iter(population)
    e = next(stream)
    reservoir, stream_length = [e for _ in range(k)], 1

    w = [rnd.random() for _ in range(k)]
    nxt = [_geometric(wi) for wi in w]
    min_nxt = min(nxt)

    for i, e in enumerate(stream, 1):
        if i == min_nxt:
            for j, n in enumerate(nxt):
                if n == min_nxt:
                    reservoir[j] = e
                    w[j] *= rnd.random()
                    nxt[j] += _geometric(w[j])
            min_nxt = min(nxt)

        stream_length += 1

    return reservoir, stream_length


        ```
    ###Assistant : 
    Reservoir sampling with replacement, the main idea is to use k reservoirs of size 1
    See Section Applications in http://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf
    
    "
2894,"def __setattr__(cls, name, value):
        
        member_map = cls.__dict__.get('_member_map_', {})
        if name in member_map:
            raise AttributeError('cannot reassign member %r' % (name, ))
        super().__setattr__(name, value)
","
        Block attempts to reassign Enum members.

        A simple assignment to the class namespace only changes one of the
        several possible ways to get an Enum member from the Enum class,
        resulting in an inconsistent Enumeration.
        ",35,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __setattr__(cls, name, value):
        
        member_map = cls.__dict__.get('_member_map_', {})
        if name in member_map:
            raise AttributeError('cannot reassign member %r' % (name, ))
        super().__setattr__(name, value)

        ```
    ###Assistant : 
        Block attempts to reassign Enum members.

        A simple assignment to the class namespace only changes one of the
        several possible ways to get an Enum member from the Enum class,
        resulting in an inconsistent Enumeration.
        
    "
2895,"def read_file(self, f, source=None):
        
        if source is None:
            try:
                source = f.name
            except AttributeError:
                source = '<???>'
        self._read(f, source)
","Like read() but the argument must be a file-like object.

        The `f' argument must be iterable, returning one line at a time.
        Optional second argument is the `source' specifying the name of the
        file being read. If not given, it is taken from f.name. If `f' has no
        `name' attribute, `<???>' is used.
        ",53,19,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def read_file(self, f, source=None):
        
        if source is None:
            try:
                source = f.name
            except AttributeError:
                source = '<???>'
        self._read(f, source)

        ```
    ###Assistant : Like read() but the argument must be a file-like object.

        The `f' argument must be iterable, returning one line at a time.
        Optional second argument is the `source' specifying the name of the
        file being read. If not given, it is taken from f.name. If `f' has no
        `name' attribute, `<???>' is used.
        
    "
2896,"def get_newsletters() -> pd.DataFrame:
    

    urls = [
        ""https://defiweekly.substack.com/archive"",
        ""https://newsletter.thedefiant.io/archive"",
        ""https://thedailygwei.substack.com/archive"",
        ""https://todayindefi.substack.com/archive"",
        ""https://newsletter.banklesshq.com/archive"",
        ""https://defislate.substack.com/archive"",
    ]

    threads = len(urls)
    newsletters = []
    with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
        for newsletter in executor.map(scrape_substack, urls):
            try:
                newsletters.append(pd.DataFrame(newsletter))
            except KeyError as e:
                console.print(e, ""\n"")
                continue

    df = pd.concat(newsletters, ignore_index=True)
    df.columns = [""Title"", ""Link"", ""Date""]

    df[""Title""] = df[""Title""].apply(lambda x: """".join(i for i in x if ord(i) < 128))
    df[""Date""] = df[""Date""].apply(
        lambda x: parser.parse(x).strftime(""%Y-%m-%d %H:%M:%S"")
    )
    df[""Title""] = df[""Title""].apply(
        lambda x: ""\n"".join(textwrap.wrap(x, width=50)) if isinstance(x, str) else x
    )
    return (
        df[[""Title"", ""Date"", ""Link""]]
        .sort_values(by=""Date"", ascending=False)
        .reset_index(drop=""index"")
    )
","Scrape all substack newsletters from url list.
    [Source: substack.com]

    Returns
    -------
    pd.DataFrame
        DataFrame with recent news from most popular DeFi related newsletters.
    ",22,90,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_newsletters() -> pd.DataFrame:
    

    urls = [
        ""https://defiweekly.substack.com/archive"",
        ""https://newsletter.thedefiant.io/archive"",
        ""https://thedailygwei.substack.com/archive"",
        ""https://todayindefi.substack.com/archive"",
        ""https://newsletter.banklesshq.com/archive"",
        ""https://defislate.substack.com/archive"",
    ]

    threads = len(urls)
    newsletters = []
    with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
        for newsletter in executor.map(scrape_substack, urls):
            try:
                newsletters.append(pd.DataFrame(newsletter))
            except KeyError as e:
                console.print(e, ""\n"")
                continue

    df = pd.concat(newsletters, ignore_index=True)
    df.columns = [""Title"", ""Link"", ""Date""]

    df[""Title""] = df[""Title""].apply(lambda x: """".join(i for i in x if ord(i) < 128))
    df[""Date""] = df[""Date""].apply(
        lambda x: parser.parse(x).strftime(""%Y-%m-%d %H:%M:%S"")
    )
    df[""Title""] = df[""Title""].apply(
        lambda x: ""\n"".join(textwrap.wrap(x, width=50)) if isinstance(x, str) else x
    )
    return (
        df[[""Title"", ""Date"", ""Link""]]
        .sort_values(by=""Date"", ascending=False)
        .reset_index(drop=""index"")
    )

        ```
    ###Assistant : Scrape all substack newsletters from url list.
    [Source: substack.com]

    Returns
    -------
    pd.DataFrame
        DataFrame with recent news from most popular DeFi related newsletters.
    
    "
2897,"def _object2proto(self) -> GetEnumAttributeAction_PB:
        

        return GetEnumAttributeAction_PB(
            path=self.path,
            id_at_location=serialize(self.id_at_location),
            address=serialize(self.address),
            msg_id=serialize(self.id),
        )
","Returns a protobuf serialization of self.
        As a requirement of all objects which inherit from Serializable,
        this method transforms the current object into the corresponding
        Protobuf object so that it can be further serialized.
        :return: returns a protobuf object
        :rtype: GetOrSetPropertyAction_PB
        .. note::
            This method is purely an internal method. Please use serialize(object) or one of
            the other public serialization methods if you wish to serialize an
            object.
        ",68,11,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _object2proto(self) -> GetEnumAttributeAction_PB:
        

        return GetEnumAttributeAction_PB(
            path=self.path,
            id_at_location=serialize(self.id_at_location),
            address=serialize(self.address),
            msg_id=serialize(self.id),
        )

        ```
    ###Assistant : Returns a protobuf serialization of self.
        As a requirement of all objects which inherit from Serializable,
        this method transforms the current object into the corresponding
        Protobuf object so that it can be further serialized.
        :return: returns a protobuf object
        :rtype: GetOrSetPropertyAction_PB
        .. note::
            This method is purely an internal method. Please use serialize(object) or one of
            the other public serialization methods if you wish to serialize an
            object.
        
    "
2898,"async def test_becomes_delivered_not_shown_notification(hass):
    
    package = Package(
        tracking_number=""456"",
        destination_country=206,
        friendly_name=""friendly name 1"",
        info_text=""info text 1"",
        location=""location 1"",
        timestamp=""2020-08-10 10:32"",
        origin_country=206,
        package_type=2,
    )
    ProfileMock.package_list = [package]

    await _setup_seventeentrack(hass, VALID_CONFIG_FULL_NO_DELIVERED)

    assert hass.states.get(""sensor.seventeentrack_package_456"") is not None
    assert len(hass.states.async_entity_ids()) == 1

    package_delivered = Package(
        tracking_number=""456"",
        destination_country=206,
        friendly_name=""friendly name 1"",
        info_text=""info text 1"",
        location=""location 1"",
        timestamp=""2020-08-10 10:32"",
        origin_country=206,
        package_type=2,
        status=40,
    )
    ProfileMock.package_list = [package_delivered]

    with patch(
        ""homeassistant.components.seventeentrack.sensor.persistent_notification""
    ) as persistent_notification_mock:
        await _goto_future(hass)

        persistent_notification_mock.create.assert_called()
        assert not hass.states.async_entity_ids()

",Ensure notification is triggered when package becomes delivered.,8,70,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_becomes_delivered_not_shown_notification(hass):
    
    package = Package(
        tracking_number=""456"",
        destination_country=206,
        friendly_name=""friendly name 1"",
        info_text=""info text 1"",
        location=""location 1"",
        timestamp=""2020-08-10 10:32"",
        origin_country=206,
        package_type=2,
    )
    ProfileMock.package_list = [package]

    await _setup_seventeentrack(hass, VALID_CONFIG_FULL_NO_DELIVERED)

    assert hass.states.get(""sensor.seventeentrack_package_456"") is not None
    assert len(hass.states.async_entity_ids()) == 1

    package_delivered = Package(
        tracking_number=""456"",
        destination_country=206,
        friendly_name=""friendly name 1"",
        info_text=""info text 1"",
        location=""location 1"",
        timestamp=""2020-08-10 10:32"",
        origin_country=206,
        package_type=2,
        status=40,
    )
    ProfileMock.package_list = [package_delivered]

    with patch(
        ""homeassistant.components.seventeentrack.sensor.persistent_notification""
    ) as persistent_notification_mock:
        await _goto_future(hass)

        persistent_notification_mock.create.assert_called()
        assert not hass.states.async_entity_ids()


        ```
    ###Assistant : Ensure notification is triggered when package becomes delivered.
    "
2899,"def fit(self, X, y=None, sample_weight=None):
        
        X = self._validate_data(X, accept_sparse=[""csc""])
        if issparse(X):
            # Pre-sort indices to avoid that each individual tree of the
            # ensemble sorts the indices.
            X.sort_indices()

        rnd = check_random_state(self.random_state)
        y = rnd.uniform(size=X.shape[0])

        # ensure that max_sample is in [1, n_samples]:
        n_samples = X.shape[0]

        if self.contamination != ""auto"":
            if not (0.0 < self.contamination <= 0.5):
                raise ValueError(
                    ""contamination must be in (0, 0.5], got: %f"" % self.contamination
                )

        if isinstance(self.max_samples, str):
            if self.max_samples == ""auto"":
                max_samples = min(256, n_samples)
            else:
                raise ValueError(
                    ""max_samples (%s) is not supported.""
                    'Valid choices are: ""auto"", int or'
                    ""float""
                    % self.max_samples
                )

        elif isinstance(self.max_samples, numbers.Integral):
            if self.max_samples > n_samples:
                warn(
                    ""max_samples (%s) is greater than the ""
                    ""total number of samples (%s). max_samples ""
                    ""will be set to n_samples for estimation.""
                    % (self.max_samples, n_samples)
                )
                max_samples = n_samples
            else:
                max_samples = self.max_samples
        else:  # float
            if not 0.0 < self.max_samples <= 1.0:
                raise ValueError(
                    ""max_samples must be in (0, 1], got %r"" % self.max_samples
                )
            max_samples = int(self.max_samples * X.shape[0])

        self.max_samples_ = max_samples
        max_depth = int(np.ceil(np.log2(max(max_samples, 2))))
        super()._fit(
            X,
            y,
            max_samples,
            max_depth=max_depth,
            sample_weight=sample_weight,
            check_input=False,
        )

        if self.contamination == ""auto"":
            # 0.5 plays a special role as described in the original paper.
            # we take the opposite as we consider the opposite of their score.
            self.offset_ = -0.5
            return self

        # else, define offset_ wrt contamination parameter
        self.offset_ = np.percentile(self.score_samples(X), 100.0 * self.contamination)

        return self
","
        Fit estimator.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input samples. Use ``dtype=np.float32`` for maximum
            efficiency. Sparse matrices are also supported, use sparse
            ``csc_matrix`` for maximum efficiency.

        y : Ignored
            Not used, present for API consistency by convention.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If None, then samples are equally weighted.

        Returns
        -------
        self : object
            Fitted estimator.
        ",66,230,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fit(self, X, y=None, sample_weight=None):
        
        X = self._validate_data(X, accept_sparse=[""csc""])
        if issparse(X):
            # Pre-sort indices to avoid that each individual tree of the
            # ensemble sorts the indices.
            X.sort_indices()

        rnd = check_random_state(self.random_state)
        y = rnd.uniform(size=X.shape[0])

        # ensure that max_sample is in [1, n_samples]:
        n_samples = X.shape[0]

        if self.contamination != ""auto"":
            if not (0.0 < self.contamination <= 0.5):
                raise ValueError(
                    ""contamination must be in (0, 0.5], got: %f"" % self.contamination
                )

        if isinstance(self.max_samples, str):
            if self.max_samples == ""auto"":
                max_samples = min(256, n_samples)
            else:
                raise ValueError(
                    ""max_samples (%s) is not supported.""
                    'Valid choices are: ""auto"", int or'
                    ""float""
                    % self.max_samples
                )

        elif isinstance(self.max_samples, numbers.Integral):
            if self.max_samples > n_samples:
                warn(
                    ""max_samples (%s) is greater than the ""
                    ""total number of samples (%s). max_samples ""
                    ""will be set to n_samples for estimation.""
                    % (self.max_samples, n_samples)
                )
                max_samples = n_samples
            else:
                max_samples = self.max_samples
        else:  # float
            if not 0.0 < self.max_samples <= 1.0:
                raise ValueError(
                    ""max_samples must be in (0, 1], got %r"" % self.max_samples
                )
            max_samples = int(self.max_samples * X.shape[0])

        self.max_samples_ = max_samples
        max_depth = int(np.ceil(np.log2(max(max_samples, 2))))
        super()._fit(
            X,
            y,
            max_samples,
            max_depth=max_depth,
            sample_weight=sample_weight,
            check_input=False,
        )

        if self.contamination == ""auto"":
            # 0.5 plays a special role as described in the original paper.
            # we take the opposite as we consider the opposite of their score.
            self.offset_ = -0.5
            return self

        # else, define offset_ wrt contamination parameter
        self.offset_ = np.percentile(self.score_samples(X), 100.0 * self.contamination)

        return self

        ```
    ###Assistant : 
        Fit estimator.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input samples. Use ``dtype=np.float32`` for maximum
            efficiency. Sparse matrices are also supported, use sparse
            ``csc_matrix`` for maximum efficiency.

        y : Ignored
            Not used, present for API consistency by convention.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If None, then samples are equally weighted.

        Returns
        -------
        self : object
            Fitted estimator.
        
    "
2900,"def get_sympy_dir():
    
    this_file = os.path.abspath(__file__)
    sympy_dir = os.path.join(os.path.dirname(this_file), "".."", "".."")
    sympy_dir = os.path.normpath(sympy_dir)
    return os.path.normcase(sympy_dir)

","
    Returns the root SymPy directory and set the global value
    indicating whether the system is case sensitive or not.
    ",19,15,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_sympy_dir():
    
    this_file = os.path.abspath(__file__)
    sympy_dir = os.path.join(os.path.dirname(this_file), "".."", "".."")
    sympy_dir = os.path.normpath(sympy_dir)
    return os.path.normcase(sympy_dir)


        ```
    ###Assistant : 
    Returns the root SymPy directory and set the global value
    indicating whether the system is case sensitive or not.
    
    "
2901,"def CheckCaffeRandom(filename, clean_lines, linenum, error):
  
  line = clean_lines.elided[linenum]
  for function in c_random_function_list:
    ix = line.find(function)
    # Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
    if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
                                line[ix - 1] not in ('_', '.', '>'))):
      error(filename, linenum, 'caffe/random_fn', 2,
            'Use caffe_rng_rand() (or other caffe_rng_* function) instead of '
            + function +
            ') to ensure results are deterministic for a fixed Caffe seed.')


threading_list = (
    ('asctime(', 'asctime_r('),
    ('ctime(', 'ctime_r('),
    ('getgrgid(', 'getgrgid_r('),
    ('getgrnam(', 'getgrnam_r('),
    ('getlogin(', 'getlogin_r('),
    ('getpwnam(', 'getpwnam_r('),
    ('getpwuid(', 'getpwuid_r('),
    ('gmtime(', 'gmtime_r('),
    ('localtime(', 'localtime_r('),
    ('strtok(', 'strtok_r('),
    ('ttyname(', 'ttyname_r('),
    )

","Checks for calls to C random functions (rand, rand_r, random, ...).

  Caffe code should (almost) always use the caffe_rng_* functions rather
  than these, as the internal state of these C functions is independent of the
  native Caffe RNG system which should produce deterministic results for a
  fixed Caffe seed set using Caffe::set_random_seed(...).

  Args:
    filename: The name of the current file.
    clean_lines: A CleansedLines instance containing the file.
    linenum: The number of the line to check.
    error: The function to call with any errors found.
  ",84,99,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def CheckCaffeRandom(filename, clean_lines, linenum, error):
  
  line = clean_lines.elided[linenum]
  for function in c_random_function_list:
    ix = line.find(function)
    # Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
    if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
                                line[ix - 1] not in ('_', '.', '>'))):
      error(filename, linenum, 'caffe/random_fn', 2,
            'Use caffe_rng_rand() (or other caffe_rng_* function) instead of '
            + function +
            ') to ensure results are deterministic for a fixed Caffe seed.')


threading_list = (
    ('asctime(', 'asctime_r('),
    ('ctime(', 'ctime_r('),
    ('getgrgid(', 'getgrgid_r('),
    ('getgrnam(', 'getgrnam_r('),
    ('getlogin(', 'getlogin_r('),
    ('getpwnam(', 'getpwnam_r('),
    ('getpwuid(', 'getpwuid_r('),
    ('gmtime(', 'gmtime_r('),
    ('localtime(', 'localtime_r('),
    ('strtok(', 'strtok_r('),
    ('ttyname(', 'ttyname_r('),
    )


        ```
    ###Assistant : Checks for calls to C random functions (rand, rand_r, random, ...).

  Caffe code should (almost) always use the caffe_rng_* functions rather
  than these, as the internal state of these C functions is independent of the
  native Caffe RNG system which should produce deterministic results for a
  fixed Caffe seed set using Caffe::set_random_seed(...).

  Args:
    filename: The name of the current file.
    clean_lines: A CleansedLines instance containing the file.
    linenum: The number of the line to check.
    error: The function to call with any errors found.
  
    "
2902,"def test_mapped_dag(self, dag_id, executor_name, session):
        
        # This test needs a real executor to run, so that the `make_list` task can write out the TaskMap
        from airflow.executors.executor_loader import ExecutorLoader

        self.dagbag.process_file(str(TEST_DAGS_FOLDER / f'{dag_id}.py'))
        dag = self.dagbag.get_dag(dag_id)

        when = datetime.datetime(2022, 1, 1)

        job = BackfillJob(
            dag=dag,
            start_date=when,
            end_date=when,
            donot_pickle=True,
            executor=ExecutorLoader.load_executor(executor_name),
        )
        job.run()

        dr = DagRun.find(dag_id=dag.dag_id, execution_date=when, session=session)[0]
        assert dr
        assert dr.state == DagRunState.SUCCESS

        # Check that every task has a start and end date
        for ti in dr.task_instances:
            assert ti.state == TaskInstanceState.SUCCESS
            assert ti.start_date is not None
            assert ti.end_date is not None
","
        End-to-end test of a simple mapped dag.

        We test with multiple executors as they have different ""execution environments"" -- for instance
        DebugExecutor runs a lot more in the same process than other Executors.

        ",33,89,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_mapped_dag(self, dag_id, executor_name, session):
        
        # This test needs a real executor to run, so that the `make_list` task can write out the TaskMap
        from airflow.executors.executor_loader import ExecutorLoader

        self.dagbag.process_file(str(TEST_DAGS_FOLDER / f'{dag_id}.py'))
        dag = self.dagbag.get_dag(dag_id)

        when = datetime.datetime(2022, 1, 1)

        job = BackfillJob(
            dag=dag,
            start_date=when,
            end_date=when,
            donot_pickle=True,
            executor=ExecutorLoader.load_executor(executor_name),
        )
        job.run()

        dr = DagRun.find(dag_id=dag.dag_id, execution_date=when, session=session)[0]
        assert dr
        assert dr.state == DagRunState.SUCCESS

        # Check that every task has a start and end date
        for ti in dr.task_instances:
            assert ti.state == TaskInstanceState.SUCCESS
            assert ti.start_date is not None
            assert ti.end_date is not None

        ```
    ###Assistant : 
        End-to-end test of a simple mapped dag.

        We test with multiple executors as they have different ""execution environments"" -- for instance
        DebugExecutor runs a lot more in the same process than other Executors.

        
    "
2903,"async def test_emergency_ssl_certificate_when_invalid(hass, tmpdir, caplog):
    

    cert_path, key_path = await hass.async_add_executor_job(
        _setup_broken_ssl_pem_files, tmpdir
    )

    hass.config.safe_mode = True
    assert (
        await async_setup_component(
            hass,
            ""http"",
            {
                ""http"": {""ssl_certificate"": cert_path, ""ssl_key"": key_path},
            },
        )
        is True
    )

    await hass.async_start()
    await hass.async_block_till_done()
    assert (
        ""Home Assistant is running in safe mode with an emergency self signed ssl certificate because the configured SSL certificate was not usable""
        in caplog.text
    )

    assert hass.http.site is not None

",Test http can startup with an emergency self signed cert when the current one is broken.,16,69,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def test_emergency_ssl_certificate_when_invalid(hass, tmpdir, caplog):
    

    cert_path, key_path = await hass.async_add_executor_job(
        _setup_broken_ssl_pem_files, tmpdir
    )

    hass.config.safe_mode = True
    assert (
        await async_setup_component(
            hass,
            ""http"",
            {
                ""http"": {""ssl_certificate"": cert_path, ""ssl_key"": key_path},
            },
        )
        is True
    )

    await hass.async_start()
    await hass.async_block_till_done()
    assert (
        ""Home Assistant is running in safe mode with an emergency self signed ssl certificate because the configured SSL certificate was not usable""
        in caplog.text
    )

    assert hass.http.site is not None


        ```
    ###Assistant : Test http can startup with an emergency self signed cert when the current one is broken.
    "
2904,"def get_safe_request_meta(self, request):
        
        if not hasattr(request, ""META""):
            return {}
        return {k: self.cleanse_setting(k, v) for k, v in request.META.items()}
","
        Return a dictionary of request.META with sensitive values redacted.
        ",9,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_safe_request_meta(self, request):
        
        if not hasattr(request, ""META""):
            return {}
        return {k: self.cleanse_setting(k, v) for k, v in request.META.items()}

        ```
    ###Assistant : 
        Return a dictionary of request.META with sensitive values redacted.
        
    "
2905,"def cuda(self, *args, **kwargs) -> nn.Module:
        

        return self.data_preprocessor.cuda(*args, **kwargs)
","Overrides this method to set the :attr:`device`

        Returns:
            nn.Module: The model itself.
        ",12,9,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def cuda(self, *args, **kwargs) -> nn.Module:
        

        return self.data_preprocessor.cuda(*args, **kwargs)

        ```
    ###Assistant : Overrides this method to set the :attr:`device`

        Returns:
            nn.Module: The model itself.
        
    "
2906,"def get_cache_attr_name(cls):
        
        return ""_{}.{}"".format(cls._meta.app_label, cls._meta.model_name).lower()
","
        Returns the name of the attribute that should be used to store
        a reference to the fetched/created object on a request.
        ",21,5,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_cache_attr_name(cls):
        
        return ""_{}.{}"".format(cls._meta.app_label, cls._meta.model_name).lower()

        ```
    ###Assistant : 
        Returns the name of the attribute that should be used to store
        a reference to the fetched/created object on a request.
        
    "
2907,"def get_instance(cls, info, **data):
        

        object_id = data.get(""id"")
        object_sku = data.get(""sku"")
        attributes = data.get(""attributes"")

        if attributes:
            # Prefetches needed by AttributeAssignmentMixin and
            # associate_attribute_values_to_instance
            qs = cls.Meta.model.objects.prefetch_related(
                ""product__product_type__variant_attributes__values"",
                ""product__product_type__attributevariant"",
            )
        else:
            # Use the default queryset.
            qs = models.ProductVariant.objects.all()

        if object_id:
            return cls.get_node_or_error(
                info, object_id, only_type=""ProductVariant"", qs=qs
            )
        elif object_sku:
            instance = qs.filter(sku=object_sku).first()
            if not instance:
                raise ValidationError(
                    {
                        ""sku"": ValidationError(
                            ""Couldn't resolve to a node: %s"" % object_sku,
                            code=""not_found"",
                        )
                    }
                )
            return instance
        else:
            return cls._meta.model()
","Prefetch related fields that are needed to process the mutation.

        If we are updating an instance and want to update its attributes,
        # prefetch them.
        ",25,77,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_instance(cls, info, **data):
        

        object_id = data.get(""id"")
        object_sku = data.get(""sku"")
        attributes = data.get(""attributes"")

        if attributes:
            # Prefetches needed by AttributeAssignmentMixin and
            # associate_attribute_values_to_instance
            qs = cls.Meta.model.objects.prefetch_related(
                ""product__product_type__variant_attributes__values"",
                ""product__product_type__attributevariant"",
            )
        else:
            # Use the default queryset.
            qs = models.ProductVariant.objects.all()

        if object_id:
            return cls.get_node_or_error(
                info, object_id, only_type=""ProductVariant"", qs=qs
            )
        elif object_sku:
            instance = qs.filter(sku=object_sku).first()
            if not instance:
                raise ValidationError(
                    {
                        ""sku"": ValidationError(
                            ""Couldn't resolve to a node: %s"" % object_sku,
                            code=""not_found"",
                        )
                    }
                )
            return instance
        else:
            return cls._meta.model()

        ```
    ###Assistant : Prefetch related fields that are needed to process the mutation.

        If we are updating an instance and want to update its attributes,
        # prefetch them.
        
    "
2908,"def _can_use_libjoin(self) -> bool:
        
        if type(self) is Index:
            # excludes EAs
            return isinstance(self.dtype, np.dtype)
        return not is_interval_dtype(self.dtype)

    # --------------------------------------------------------------------
    # Uncategorized Methods
","
        Whether we can use the fastpaths implement in _libs.join
        ",9,22,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _can_use_libjoin(self) -> bool:
        
        if type(self) is Index:
            # excludes EAs
            return isinstance(self.dtype, np.dtype)
        return not is_interval_dtype(self.dtype)

    # --------------------------------------------------------------------
    # Uncategorized Methods

        ```
    ###Assistant : 
        Whether we can use the fastpaths implement in _libs.join
        
    "
2909,"def sub_syllables(self, from_i, to_j):
        
        if not isinstance(from_i, int) or not isinstance(to_j, int):
            raise ValueError(""both arguments should be integers"")
        group = self.group
        if to_j <= from_i:
            return group.identity
        else:
            r = tuple(self.array_form[from_i: to_j])
            return group.dtype(r)
","
        `sub_syllables` returns the subword of the associative word `self` that
        consists of syllables from positions `from_to` to `to_j`, where
        `from_to` and `to_j` must be positive integers and indexing is done
        with origin 0.

        Examples
        ========

        >>> from sympy.combinatorics import free_group
        >>> f, a, b = free_group(""a, b"")
        >>> w = a**5*b*a**2*b**-4*a
        >>> w.sub_syllables(1, 2)
        b
        >>> w.sub_syllables(3, 3)
        <identity>

        ",59,34,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def sub_syllables(self, from_i, to_j):
        
        if not isinstance(from_i, int) or not isinstance(to_j, int):
            raise ValueError(""both arguments should be integers"")
        group = self.group
        if to_j <= from_i:
            return group.identity
        else:
            r = tuple(self.array_form[from_i: to_j])
            return group.dtype(r)

        ```
    ###Assistant : 
        `sub_syllables` returns the subword of the associative word `self` that
        consists of syllables from positions `from_to` to `to_j`, where
        `from_to` and `to_j` must be positive integers and indexing is done
        with origin 0.

        Examples
        ========

        >>> from sympy.combinatorics import free_group
        >>> f, a, b = free_group(""a, b"")
        >>> w = a**5*b*a**2*b**-4*a
        >>> w.sub_syllables(1, 2)
        b
        >>> w.sub_syllables(3, 3)
        <identity>

        
    "
2910,"def seterr(all=None, divide=None, over=None, under=None, invalid=None):
    

    pyvals = umath.geterrobj()
    old = geterr()

    if divide is None:
        divide = all or old['divide']
    if over is None:
        over = all or old['over']
    if under is None:
        under = all or old['under']
    if invalid is None:
        invalid = all or old['invalid']

    maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
                 (_errdict[over] << SHIFT_OVERFLOW) +
                 (_errdict[under] << SHIFT_UNDERFLOW) +
                 (_errdict[invalid] << SHIFT_INVALID))

    pyvals[1] = maskvalue
    umath.seterrobj(pyvals)
    return old


@set_module('numpy')","
    Set how floating-point errors are handled.

    Note that operations on integer scalar types (such as `int16`) are
    handled like floating point, and are affected by these settings.

    Parameters
    ----------
    all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Set treatment for all types of floating-point errors at once:

        - ignore: Take no action when the exception occurs.
        - warn: Print a `RuntimeWarning` (via the Python `warnings` module).
        - raise: Raise a `FloatingPointError`.
        - call: Call a function specified using the `seterrcall` function.
        - print: Print a warning directly to ``stdout``.
        - log: Record error in a Log object specified by `seterrcall`.

        The default is not to change the current behavior.
    divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for division by zero.
    over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for floating-point overflow.
    under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for floating-point underflow.
    invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for invalid floating-point operation.

    Returns
    -------
    old_settings : dict
        Dictionary containing the old settings.

    See also
    --------
    seterrcall : Set a callback function for the 'call' mode.
    geterr, geterrcall, errstate

    Notes
    -----
    The floating-point exceptions are defined in the IEEE 754 standard [1]_:

    - Division by zero: infinite result obtained from finite numbers.
    - Overflow: result too large to be expressed.
    - Underflow: result so close to zero that some precision
      was lost.
    - Invalid operation: result is not an expressible number, typically
      indicates that a NaN was produced.

    .. [1] https://en.wikipedia.org/wiki/IEEE_754

    Examples
    --------
    >>> old_settings = np.seterr(all='ignore')  #seterr to known value
    >>> np.seterr(over='raise')
    {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
    >>> np.seterr(**old_settings)  # reset to default
    {'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}

    >>> np.int16(32000) * np.int16(3)
    30464
    >>> old_settings = np.seterr(all='warn', over='raise')
    >>> np.int16(32000) * np.int16(3)
    Traceback (most recent call last):
      File ""<stdin>"", line 1, in <module>
    FloatingPointError: overflow encountered in scalar multiply

    >>> old_settings = np.seterr(all='print')
    >>> np.geterr()
    {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
    >>> np.int16(32000) * np.int16(3)
    30464

    ",336,72,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def seterr(all=None, divide=None, over=None, under=None, invalid=None):
    

    pyvals = umath.geterrobj()
    old = geterr()

    if divide is None:
        divide = all or old['divide']
    if over is None:
        over = all or old['over']
    if under is None:
        under = all or old['under']
    if invalid is None:
        invalid = all or old['invalid']

    maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
                 (_errdict[over] << SHIFT_OVERFLOW) +
                 (_errdict[under] << SHIFT_UNDERFLOW) +
                 (_errdict[invalid] << SHIFT_INVALID))

    pyvals[1] = maskvalue
    umath.seterrobj(pyvals)
    return old


@set_module('numpy')
        ```
    ###Assistant : 
    Set how floating-point errors are handled.

    Note that operations on integer scalar types (such as `int16`) are
    handled like floating point, and are affected by these settings.

    Parameters
    ----------
    all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Set treatment for all types of floating-point errors at once:

        - ignore: Take no action when the exception occurs.
        - warn: Print a `RuntimeWarning` (via the Python `warnings` module).
        - raise: Raise a `FloatingPointError`.
        - call: Call a function specified using the `seterrcall` function.
        - print: Print a warning directly to ``stdout``.
        - log: Record error in a Log object specified by `seterrcall`.

        The default is not to change the current behavior.
    divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for division by zero.
    over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for floating-point overflow.
    under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for floating-point underflow.
    invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
        Treatment for invalid floating-point operation.

    Returns
    -------
    old_settings : dict
        Dictionary containing the old settings.

    See also
    --------
    seterrcall : Set a callback function for the 'call' mode.
    geterr, geterrcall, errstate

    Notes
    -----
    The floating-point exceptions are defined in the IEEE 754 standard [1]_:

    - Division by zero: infinite result obtained from finite numbers.
    - Overflow: result too large to be expressed.
    - Underflow: result so close to zero that some precision
      was lost.
    - Invalid operation: result is not an expressible number, typically
      indicates that a NaN was produced.

    .. [1] https://en.wikipedia.org/wiki/IEEE_754

    Examples
    --------
    >>> old_settings = np.seterr(all='ignore')  #seterr to known value
    >>> np.seterr(over='raise')
    {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
    >>> np.seterr(**old_settings)  # reset to default
    {'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}

    >>> np.int16(32000) * np.int16(3)
    30464
    >>> old_settings = np.seterr(all='warn', over='raise')
    >>> np.int16(32000) * np.int16(3)
    Traceback (most recent call last):
      File ""<stdin>"", line 1, in <module>
    FloatingPointError: overflow encountered in scalar multiply

    >>> old_settings = np.seterr(all='print')
    >>> np.geterr()
    {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
    >>> np.int16(32000) * np.int16(3)
    30464

    
    "
2911,"def _create_drawables(self, tokensource):
        
        lineno = charno = maxcharno = 0
        maxlinelength = linelength = 0
        for ttype, value in tokensource:
            while ttype not in self.styles:
                ttype = ttype.parent
            style = self.styles[ttype]
            # TODO: make sure tab expansion happens earlier in the chain.  It
            # really ought to be done on the input, as to do it right here is
            # quite complex.
            value = value.expandtabs(4)
            lines = value.splitlines(True)
            # print lines
            for i, line in enumerate(lines):
                temp = line.rstrip('\n')
                if temp:
                    self._draw_text(
                        self._get_text_pos(linelength, lineno),
                        temp,
                        font = self._get_style_font(style),
                        text_fg = self._get_text_color(style),
                        text_bg = self._get_text_bg_color(style),
                    )
                    temp_width, temp_hight = self.fonts.get_text_size(temp)
                    linelength += temp_width
                    maxlinelength = max(maxlinelength, linelength)
                    charno += len(temp)
                    maxcharno = max(maxcharno, charno)
                if line.endswith('\n'):
                    # add a line for each extra line in the value
                    linelength = 0
                    charno = 0
                    lineno += 1
        self.maxlinelength = maxlinelength
        self.maxcharno = maxcharno
        self.maxlineno = lineno
","
        Create drawables for the token content.
        ",6,144,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _create_drawables(self, tokensource):
        
        lineno = charno = maxcharno = 0
        maxlinelength = linelength = 0
        for ttype, value in tokensource:
            while ttype not in self.styles:
                ttype = ttype.parent
            style = self.styles[ttype]
            # TODO: make sure tab expansion happens earlier in the chain.  It
            # really ought to be done on the input, as to do it right here is
            # quite complex.
            value = value.expandtabs(4)
            lines = value.splitlines(True)
            # print lines
            for i, line in enumerate(lines):
                temp = line.rstrip('\n')
                if temp:
                    self._draw_text(
                        self._get_text_pos(linelength, lineno),
                        temp,
                        font = self._get_style_font(style),
                        text_fg = self._get_text_color(style),
                        text_bg = self._get_text_bg_color(style),
                    )
                    temp_width, temp_hight = self.fonts.get_text_size(temp)
                    linelength += temp_width
                    maxlinelength = max(maxlinelength, linelength)
                    charno += len(temp)
                    maxcharno = max(maxcharno, charno)
                if line.endswith('\n'):
                    # add a line for each extra line in the value
                    linelength = 0
                    charno = 0
                    lineno += 1
        self.maxlinelength = maxlinelength
        self.maxcharno = maxcharno
        self.maxlineno = lineno

        ```
    ###Assistant : 
        Create drawables for the token content.
        
    "
2912,"def __monotonic_time_coarse() -> float:
    
    return time.clock_gettime(CLOCK_MONOTONIC_COARSE)


monotonic_time_coarse = time.monotonic
with suppress(Exception):
    if (
        platform.system() == ""Linux""
        and abs(time.monotonic() - __monotonic_time_coarse()) < 1
    ):
        monotonic_time_coarse = __monotonic_time_coarse
","Return a monotonic time in seconds.

    This is the coarse version of time_monotonic, which is faster but less accurate.

    Since many arm64 and 32-bit platforms don't support VDSO with time.monotonic
    because of errata, we can't rely on the kernel to provide a fast
    monotonic time.

    https://lore.kernel.org/lkml/20170404171826.25030-1-marc.zyngier@arm.com/
    ",46,26,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __monotonic_time_coarse() -> float:
    
    return time.clock_gettime(CLOCK_MONOTONIC_COARSE)


monotonic_time_coarse = time.monotonic
with suppress(Exception):
    if (
        platform.system() == ""Linux""
        and abs(time.monotonic() - __monotonic_time_coarse()) < 1
    ):
        monotonic_time_coarse = __monotonic_time_coarse

        ```
    ###Assistant : Return a monotonic time in seconds.

    This is the coarse version of time_monotonic, which is faster but less accurate.

    Since many arm64 and 32-bit platforms don't support VDSO with time.monotonic
    because of errata, we can't rely on the kernel to provide a fast
    monotonic time.

    https://lore.kernel.org/lkml/20170404171826.25030-1-marc.zyngier@arm.com/
    
    "
2913,"def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
    

    assert not (numpify and torchify), ""You cannot specify both numpy and PyTorch tensors at the same time""

    if equal_resolution:
        image_inputs = []
        for i in range(feature_extract_tester.batch_size):
            image_inputs.append(
                np.random.randint(
                    255,
                    size=(
                        feature_extract_tester.num_channels,
                        feature_extract_tester.max_resolution,
                        feature_extract_tester.max_resolution,
                    ),
                    dtype=np.uint8,
                )
            )
    else:
        image_inputs = []

        # To avoid getting image width/height 0
        min_resolution = feature_extract_tester.min_resolution
        if getattr(feature_extract_tester, ""size_divisor"", None):
            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
            min_resolution = max(feature_extract_tester.size_divisor, min_resolution)

        for i in range(feature_extract_tester.batch_size):
            width, height = np.random.choice(np.arange(min_resolution, feature_extract_tester.max_resolution), 2)
            image_inputs.append(
                np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8)
            )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

    if torchify:
        image_inputs = [torch.from_numpy(x) for x in image_inputs]

    return image_inputs

","This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
    or a list of PyTorch tensors if one specifies torchify=True.
    ",28,129,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
    

    assert not (numpify and torchify), ""You cannot specify both numpy and PyTorch tensors at the same time""

    if equal_resolution:
        image_inputs = []
        for i in range(feature_extract_tester.batch_size):
            image_inputs.append(
                np.random.randint(
                    255,
                    size=(
                        feature_extract_tester.num_channels,
                        feature_extract_tester.max_resolution,
                        feature_extract_tester.max_resolution,
                    ),
                    dtype=np.uint8,
                )
            )
    else:
        image_inputs = []

        # To avoid getting image width/height 0
        min_resolution = feature_extract_tester.min_resolution
        if getattr(feature_extract_tester, ""size_divisor"", None):
            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
            min_resolution = max(feature_extract_tester.size_divisor, min_resolution)

        for i in range(feature_extract_tester.batch_size):
            width, height = np.random.choice(np.arange(min_resolution, feature_extract_tester.max_resolution), 2)
            image_inputs.append(
                np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8)
            )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

    if torchify:
        image_inputs = [torch.from_numpy(x) for x in image_inputs]

    return image_inputs


        ```
    ###Assistant : This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
    or a list of PyTorch tensors if one specifies torchify=True.
    
    "
2914,"def _update_step_xla(self, gradient, variable, key):
        
        return self._update_step(gradient, variable)
","A wrapper of `update_step` to enable XLA acceleration.

        Due to `tf.function` tracing mechanism, for (gradient, variable) pairs of
        the same shape and dtype, the execution graph always invoke the first
        pair it has seen. Thus, we need a `key` argument to make each
        (gradient, variable) pair unique. In additions, XLA cannot understand
        string input, so the key is an integer.

        Args:
          gradient: backpropagated gradient of the given variable.
          variable: variable whose value needs to be updated.
          key (int): a unique key that identifies the variable.

        Returns:
          An `Operation` that applies the specified gradients.
        ",93,8,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _update_step_xla(self, gradient, variable, key):
        
        return self._update_step(gradient, variable)

        ```
    ###Assistant : A wrapper of `update_step` to enable XLA acceleration.

        Due to `tf.function` tracing mechanism, for (gradient, variable) pairs of
        the same shape and dtype, the execution graph always invoke the first
        pair it has seen. Thus, we need a `key` argument to make each
        (gradient, variable) pair unique. In additions, XLA cannot understand
        string input, so the key is an integer.

        Args:
          gradient: backpropagated gradient of the given variable.
          variable: variable whose value needs to be updated.
          key (int): a unique key that identifies the variable.

        Returns:
          An `Operation` that applies the specified gradients.
        
    "
2915,"async def log_in(self, request):
        

        fingerprint = request[""fingerprint""]
        if self.service.logged_in_fingerprint == fingerprint:
            return {""fingerprint"": fingerprint}

        await self._stop_wallet()
        started = await self.service._start(fingerprint)
        if started is True:
            return {""fingerprint"": fingerprint}

        return {""success"": False, ""error"": ""Unknown Error""}
","
        Logs in the wallet with a specific key.
        ",8,33,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        async def log_in(self, request):
        

        fingerprint = request[""fingerprint""]
        if self.service.logged_in_fingerprint == fingerprint:
            return {""fingerprint"": fingerprint}

        await self._stop_wallet()
        started = await self.service._start(fingerprint)
        if started is True:
            return {""fingerprint"": fingerprint}

        return {""success"": False, ""error"": ""Unknown Error""}

        ```
    ###Assistant : 
        Logs in the wallet with a specific key.
        
    "
2916,"def test_bitbucket2_on_push_commits_multiple_committers_with_others(self) -> None:
        commit_info = ""* first commit ([84b96adc644](https://bitbucket.org/kolaszek/repository-name/commits/84b96adc644a30fd6465b3d196369d880762afed))\n""
        expected_message = f
        self.check_webhook(
            ""push_multiple_committers_with_others"", TOPIC_BRANCH_EVENTS, expected_message
        )
","Tomasz [pushed](https://bitbucket.org/kolaszek/repository-name/branch/master) 10 commits to branch master. Commits by Tomasz (4), James (3), Brendon (2) and others (1).\n\n{commit_info*9}* first commit ([84b96adc644](https://bitbucket.org/kolaszek/repository-name/commits/84b96adc644a30fd6465b3d196369d880762afed))",21,18,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_bitbucket2_on_push_commits_multiple_committers_with_others(self) -> None:
        commit_info = ""* first commit ([84b96adc644](https://bitbucket.org/kolaszek/repository-name/commits/84b96adc644a30fd6465b3d196369d880762afed))\n""
        expected_message = f
        self.check_webhook(
            ""push_multiple_committers_with_others"", TOPIC_BRANCH_EVENTS, expected_message
        )

        ```
    ###Assistant : Tomasz [pushed](https://bitbucket.org/kolaszek/repository-name/branch/master) 10 commits to branch master. Commits by Tomasz (4), James (3), Brendon (2) and others (1).\n\n{commit_info*9}* first commit ([84b96adc644](https://bitbucket.org/kolaszek/repository-name/commits/84b96adc644a30fd6465b3d196369d880762afed))
    "
2917,"def drawControl(self, element, opt, p, widget=None):
        
        if element not in [QStyle.ControlElement.CE_TabBarTab, QStyle.ControlElement.CE_TabBarTabShape,
                           QStyle.ControlElement.CE_TabBarTabLabel]:
            # Let the real style draw it.
            self._style.drawControl(element, opt, p, widget)
            return

        layouts = self._tab_layout(opt)
        if layouts is None:
            log.misc.warning(""Could not get layouts for tab!"")
            return

        if element == QStyle.ControlElement.CE_TabBarTab:
            # We override this so we can control TabBarTabShape/TabBarTabLabel.
            self.drawControl(QStyle.ControlElement.CE_TabBarTabShape, opt, p, widget)
            self.drawControl(QStyle.ControlElement.CE_TabBarTabLabel, opt, p, widget)
        elif element == QStyle.ControlElement.CE_TabBarTabShape:
            p.fillRect(opt.rect, opt.palette.window())
            self._draw_indicator(layouts, opt, p)
            # We use super() rather than self._style here because we don't want
            # any sophisticated drawing.
            super().drawControl(QStyle.ControlElement.CE_TabBarTabShape, opt, p, widget)
        elif element == QStyle.ControlElement.CE_TabBarTabLabel:
            if not opt.icon.isNull() and layouts.icon.isValid():
                self._draw_icon(layouts, opt, p)
            alignment = (config.cache['tabs.title.alignment'] |
                         Qt.AlignmentFlag.AlignVCenter | Qt.TextFlag.TextHideMnemonic)
            self._style.drawItemText(p,
                                     layouts.text,
                                     int(alignment),
                                     opt.palette,
                                     bool(opt.state & QStyle.StateFlag.State_Enabled),
                                     opt.text,
                                     QPalette.ColorRole.WindowText)
        else:
            raise ValueError(""Invalid element {!r}"".format(element))
","Override drawControl to draw odd tabs in a different color.

        Draws the given element with the provided painter with the style
        options specified by option.

        Args:
            element: ControlElement
            opt: QStyleOption
            p: QPainter
            widget: QWidget
        ",34,122,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def drawControl(self, element, opt, p, widget=None):
        
        if element not in [QStyle.ControlElement.CE_TabBarTab, QStyle.ControlElement.CE_TabBarTabShape,
                           QStyle.ControlElement.CE_TabBarTabLabel]:
            # Let the real style draw it.
            self._style.drawControl(element, opt, p, widget)
            return

        layouts = self._tab_layout(opt)
        if layouts is None:
            log.misc.warning(""Could not get layouts for tab!"")
            return

        if element == QStyle.ControlElement.CE_TabBarTab:
            # We override this so we can control TabBarTabShape/TabBarTabLabel.
            self.drawControl(QStyle.ControlElement.CE_TabBarTabShape, opt, p, widget)
            self.drawControl(QStyle.ControlElement.CE_TabBarTabLabel, opt, p, widget)
        elif element == QStyle.ControlElement.CE_TabBarTabShape:
            p.fillRect(opt.rect, opt.palette.window())
            self._draw_indicator(layouts, opt, p)
            # We use super() rather than self._style here because we don't want
            # any sophisticated drawing.
            super().drawControl(QStyle.ControlElement.CE_TabBarTabShape, opt, p, widget)
        elif element == QStyle.ControlElement.CE_TabBarTabLabel:
            if not opt.icon.isNull() and layouts.icon.isValid():
                self._draw_icon(layouts, opt, p)
            alignment = (config.cache['tabs.title.alignment'] |
                         Qt.AlignmentFlag.AlignVCenter | Qt.TextFlag.TextHideMnemonic)
            self._style.drawItemText(p,
                                     layouts.text,
                                     int(alignment),
                                     opt.palette,
                                     bool(opt.state & QStyle.StateFlag.State_Enabled),
                                     opt.text,
                                     QPalette.ColorRole.WindowText)
        else:
            raise ValueError(""Invalid element {!r}"".format(element))

        ```
    ###Assistant : Override drawControl to draw odd tabs in a different color.

        Draws the given element with the provided painter with the style
        options specified by option.

        Args:
            element: ControlElement
            opt: QStyleOption
            p: QPainter
            widget: QWidget
        
    "
2918,"def _get_mask(length, max_length):
    
    length = length.unsqueeze(-1)
    B = paddle.shape(length)[0]
    grid = paddle.arange(0, max_length).unsqueeze(0).tile([B, 1])
    zero_mask = paddle.zeros([B, max_length], dtype='float32')
    inf_mask = paddle.full([B, max_length], '-inf', dtype='float32')
    diag_mask = paddle.diag(
        paddle.full(
            [max_length], '-inf', dtype=paddle.float32),
        offset=0,
        name=None)
    mask = paddle.where(grid >= length, inf_mask, zero_mask)
    mask = mask.unsqueeze(1) + diag_mask
    return mask.unsqueeze(1)
","Generate a square mask for the sequence. The masked positions are filled with float('-inf').
        Unmasked positions are filled with float(0.0).
    ",20,48,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _get_mask(length, max_length):
    
    length = length.unsqueeze(-1)
    B = paddle.shape(length)[0]
    grid = paddle.arange(0, max_length).unsqueeze(0).tile([B, 1])
    zero_mask = paddle.zeros([B, max_length], dtype='float32')
    inf_mask = paddle.full([B, max_length], '-inf', dtype='float32')
    diag_mask = paddle.diag(
        paddle.full(
            [max_length], '-inf', dtype=paddle.float32),
        offset=0,
        name=None)
    mask = paddle.where(grid >= length, inf_mask, zero_mask)
    mask = mask.unsqueeze(1) + diag_mask
    return mask.unsqueeze(1)

        ```
    ###Assistant : Generate a square mask for the sequence. The masked positions are filled with float('-inf').
        Unmasked positions are filled with float(0.0).
    
    "
2919,"def test_config_options_removed_on_reparse(self):
        

        global_config_path = ""/mock/home/folder/.streamlit/config.toml""
        makedirs_patch = patch(""streamlit.config.os.makedirs"")
        makedirs_patch.return_value = True
        pathexists_patch = patch(""streamlit.config.os.path.exists"")
        pathexists_patch.side_effect = lambda path: path == global_config_path

        global_config = 
        open_patch = patch(""streamlit.config.open"", mock_open(read_data=global_config))

        with open_patch, makedirs_patch, pathexists_patch:
            config.get_config_options()

            self.assertEqual(""dark"", config.get_option(""theme.base""))
            self.assertEqual(""sans serif"", config.get_option(""theme.font""))

        global_config = 
        open_patch = patch(""streamlit.config.open"", mock_open(read_data=global_config))

        with open_patch, makedirs_patch, pathexists_patch:
            config.get_config_options(force_reparse=True)

            self.assertEqual(""dark"", config.get_option(""theme.base""))
            self.assertEqual(None, config.get_option(""theme.font""))
","Test that config options that are removed in a file are also removed
        from our _config_options dict.
        [theme]
        base = ""dark""
        font = ""sans serif""
        
        [theme]
        base = ""dark""
        ",29,52,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def test_config_options_removed_on_reparse(self):
        

        global_config_path = ""/mock/home/folder/.streamlit/config.toml""
        makedirs_patch = patch(""streamlit.config.os.makedirs"")
        makedirs_patch.return_value = True
        pathexists_patch = patch(""streamlit.config.os.path.exists"")
        pathexists_patch.side_effect = lambda path: path == global_config_path

        global_config = 
        open_patch = patch(""streamlit.config.open"", mock_open(read_data=global_config))

        with open_patch, makedirs_patch, pathexists_patch:
            config.get_config_options()

            self.assertEqual(""dark"", config.get_option(""theme.base""))
            self.assertEqual(""sans serif"", config.get_option(""theme.font""))

        global_config = 
        open_patch = patch(""streamlit.config.open"", mock_open(read_data=global_config))

        with open_patch, makedirs_patch, pathexists_patch:
            config.get_config_options(force_reparse=True)

            self.assertEqual(""dark"", config.get_option(""theme.base""))
            self.assertEqual(None, config.get_option(""theme.font""))

        ```
    ###Assistant : Test that config options that are removed in a file are also removed
        from our _config_options dict.
        [theme]
        base = ""dark""
        font = ""sans serif""
        
        [theme]
        base = ""dark""
        
    "
2920,"def __getitem__(self, key):
        
        getitem = self._data.__getitem__

        if is_integer(key) or is_float(key):
            # GH#44051 exclude bool, which would return a 2d ndarray
            key = com.cast_scalar_indexer(key, warn_float=True)
            return getitem(key)

        if isinstance(key, slice):
            # This case is separated from the conditional above to avoid
            # pessimization com.is_bool_indexer and ndim checks.
            result = getitem(key)
            # Going through simple_new for performance.
            return type(self)._simple_new(result, name=self._name)

        if com.is_bool_indexer(key):
            # if we have list[bools, length=1e5] then doing this check+convert
            #  takes 166 µs + 2.1 ms and cuts the ndarray.__getitem__
            #  time below from 3.8 ms to 496 µs
            # if we already have ndarray[bool], the overhead is 1.4 µs or .25%
            key = np.asarray(key, dtype=bool)

        result = getitem(key)
        # Because we ruled out integer above, we always get an arraylike here
        if result.ndim > 1:
            deprecate_ndim_indexing(result)
            if hasattr(result, ""_ndarray""):
                # error: Item ""ndarray[Any, Any]"" of ""Union[ExtensionArray,
                # ndarray[Any, Any]]"" has no attribute ""_ndarray""  [union-attr]
                # i.e. NDArrayBackedExtensionArray
                # Unpack to ndarray for MPL compat
                return result._ndarray  # type: ignore[union-attr]
            return result

        # NB: Using _constructor._simple_new would break if MultiIndex
        #  didn't override __getitem__
        return self._constructor._simple_new(result, name=self._name)
","
        Override numpy.ndarray's __getitem__ method to work as desired.

        This function adds lists and Series as valid boolean indexers
        (ndarrays only supports ndarray with dtype=bool).

        If resulting ndim != 1, plain ndarray is returned instead of
        corresponding `Index` subclass.

        ",38,178,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def __getitem__(self, key):
        
        getitem = self._data.__getitem__

        if is_integer(key) or is_float(key):
            # GH#44051 exclude bool, which would return a 2d ndarray
            key = com.cast_scalar_indexer(key, warn_float=True)
            return getitem(key)

        if isinstance(key, slice):
            # This case is separated from the conditional above to avoid
            # pessimization com.is_bool_indexer and ndim checks.
            result = getitem(key)
            # Going through simple_new for performance.
            return type(self)._simple_new(result, name=self._name)

        if com.is_bool_indexer(key):
            # if we have list[bools, length=1e5] then doing this check+convert
            #  takes 166 µs + 2.1 ms and cuts the ndarray.__getitem__
            #  time below from 3.8 ms to 496 µs
            # if we already have ndarray[bool], the overhead is 1.4 µs or .25%
            key = np.asarray(key, dtype=bool)

        result = getitem(key)
        # Because we ruled out integer above, we always get an arraylike here
        if result.ndim > 1:
            deprecate_ndim_indexing(result)
            if hasattr(result, ""_ndarray""):
                # error: Item ""ndarray[Any, Any]"" of ""Union[ExtensionArray,
                # ndarray[Any, Any]]"" has no attribute ""_ndarray""  [union-attr]
                # i.e. NDArrayBackedExtensionArray
                # Unpack to ndarray for MPL compat
                return result._ndarray  # type: ignore[union-attr]
            return result

        # NB: Using _constructor._simple_new would break if MultiIndex
        #  didn't override __getitem__
        return self._constructor._simple_new(result, name=self._name)

        ```
    ###Assistant : 
        Override numpy.ndarray's __getitem__ method to work as desired.

        This function adds lists and Series as valid boolean indexers
        (ndarrays only supports ndarray with dtype=bool).

        If resulting ndim != 1, plain ndarray is returned instead of
        corresponding `Index` subclass.

        
    "
2921,"def get_data(conditions, filters):
	data = frappe.db.sql(
		.format(
			conditions=conditions
		),
		filters,
		as_dict=1,
	)

	return data

","
		SELECT
			so.transaction_date as date,
			soi.delivery_date as delivery_date,
			so.name as sales_order,
			so.status, so.customer, soi.item_code,
			DATEDIFF(CURDATE(), soi.delivery_date) as delay_days,
			IF(so.status in ('Completed','To Bill'), 0, (SELECT delay_days)) as delay,
			soi.qty, soi.delivered_qty,
			(soi.qty - soi.delivered_qty) AS pending_qty,
			IF((SELECT pending_qty) = 0, (TO_SECONDS(Max(dn.posting_date))-TO_SECONDS(so.transaction_date)), 0) as time_taken_to_deliver,
			IFNULL(SUM(sii.qty), 0) as billed_qty,
			soi.base_amount as amount,
			(soi.delivered_qty * soi.base_rate) as delivered_qty_amount,
			(soi.billed_amt * IFNULL(so.conversion_rate, 1)) as billed_amount,
			(soi.base_amount - (soi.billed_amt * IFNULL(so.conversion_rate, 1))) as pending_amount,
			soi.warehouse as warehouse,
			so.company, soi.name,
			soi.description as description
		FROM
			`tabSales Order` so,
			(`tabSales Order Item` soi
		LEFT JOIN `tabSales Invoice Item` sii
			ON sii.so_detail = soi.name and sii.docstatus = 1)
		LEFT JOIN `tabDelivery Note Item` dni
			on dni.so_detail = soi.name
		RIGHT JOIN `tabDelivery Note` dn
			on dni.parent = dn.name and dn.docstatus = 1
		WHERE
			soi.parent = so.name
			and so.status not in ('Stopped', 'Closed', 'On Hold')
			and so.docstatus = 1
			{conditions}
		GROUP BY soi.name
		ORDER BY so.transaction_date ASC, soi.item_code ASC
	",146,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def get_data(conditions, filters):
	data = frappe.db.sql(
		.format(
			conditions=conditions
		),
		filters,
		as_dict=1,
	)

	return data


        ```
    ###Assistant : 
		SELECT
			so.transaction_date as date,
			soi.delivery_date as delivery_date,
			so.name as sales_order,
			so.status, so.customer, soi.item_code,
			DATEDIFF(CURDATE(), soi.delivery_date) as delay_days,
			IF(so.status in ('Completed','To Bill'), 0, (SELECT delay_days)) as delay,
			soi.qty, soi.delivered_qty,
			(soi.qty - soi.delivered_qty) AS pending_qty,
			IF((SELECT pending_qty) = 0, (TO_SECONDS(Max(dn.posting_date))-TO_SECONDS(so.transaction_date)), 0) as time_taken_to_deliver,
			IFNULL(SUM(sii.qty), 0) as billed_qty,
			soi.base_amount as amount,
			(soi.delivered_qty * soi.base_rate) as delivered_qty_amount,
			(soi.billed_amt * IFNULL(so.conversion_rate, 1)) as billed_amount,
			(soi.base_amount - (soi.billed_amt * IFNULL(so.conversion_rate, 1))) as pending_amount,
			soi.warehouse as warehouse,
			so.company, soi.name,
			soi.description as description
		FROM
			`tabSales Order` so,
			(`tabSales Order Item` soi
		LEFT JOIN `tabSales Invoice Item` sii
			ON sii.so_detail = soi.name and sii.docstatus = 1)
		LEFT JOIN `tabDelivery Note Item` dni
			on dni.so_detail = soi.name
		RIGHT JOIN `tabDelivery Note` dn
			on dni.parent = dn.name and dn.docstatus = 1
		WHERE
			soi.parent = so.name
			and so.status not in ('Stopped', 'Closed', 'On Hold')
			and so.docstatus = 1
			{conditions}
		GROUP BY soi.name
		ORDER BY so.transaction_date ASC, soi.item_code ASC
	
    "
2922,"def getcoroutinelocals(coroutine):
    
    frame = getattr(coroutine, ""cr_frame"", None)
    if frame is not None:
        return frame.f_locals
    else:
        return {}


###############################################################################
### Function Signature Object (PEP 362)
###############################################################################


_WrapperDescriptor = type(type.__call__)
_MethodWrapper = type(all.__call__)
_ClassMethodWrapper = type(int.__dict__['from_bytes'])

_NonUserDefinedCallables = (_WrapperDescriptor,
                            _MethodWrapper,
                            _ClassMethodWrapper,
                            types.BuiltinFunctionType)

","
    Get the mapping of coroutine local variables to their current values.

    A dict is returned, with the keys the local variable names and values the
    bound values.",27,40,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def getcoroutinelocals(coroutine):
    
    frame = getattr(coroutine, ""cr_frame"", None)
    if frame is not None:
        return frame.f_locals
    else:
        return {}


###############################################################################
### Function Signature Object (PEP 362)
###############################################################################


_WrapperDescriptor = type(type.__call__)
_MethodWrapper = type(all.__call__)
_ClassMethodWrapper = type(int.__dict__['from_bytes'])

_NonUserDefinedCallables = (_WrapperDescriptor,
                            _MethodWrapper,
                            _ClassMethodWrapper,
                            types.BuiltinFunctionType)


        ```
    ###Assistant : 
    Get the mapping of coroutine local variables to their current values.

    A dict is returned, with the keys the local variable names and values the
    bound values.
    "
2923,"def _forward_over_back_hessian(f, params, use_pfor, dtype=None):
    
    return _vectorize_parameters(
        functools.partial(_hvp, f, params),
        params,
        use_pfor=use_pfor,
        dtype=dtype,
    )

","Computes the full Hessian matrix for the scalar-valued f(*params).

    Args:
      f: A function taking `params` and returning a scalar.
      params: A possibly nested structure of tensors.
      use_pfor: If true, uses `tf.vectorized_map` calls instead of looping.
      dtype: Required if `use_pfor=False`. A possibly nested structure of dtypes
        (e.g. `tf.float32`) matching the structure of `f`'s returns.

    Returns:
      A possibly nested structure of matrix slices corresponding to `params`. Each
      slice has shape [P, p_s] where `p_s` is the number of parameters (`tf.size`)
      in the corresponding element of `params` and `P` is the total number of
      parameters (`sum_s(p_s)`). The full matrix can be obtained by concatenating
      along the second axis.
    ",105,14,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def _forward_over_back_hessian(f, params, use_pfor, dtype=None):
    
    return _vectorize_parameters(
        functools.partial(_hvp, f, params),
        params,
        use_pfor=use_pfor,
        dtype=dtype,
    )


        ```
    ###Assistant : Computes the full Hessian matrix for the scalar-valued f(*params).

    Args:
      f: A function taking `params` and returning a scalar.
      params: A possibly nested structure of tensors.
      use_pfor: If true, uses `tf.vectorized_map` calls instead of looping.
      dtype: Required if `use_pfor=False`. A possibly nested structure of dtypes
        (e.g. `tf.float32`) matching the structure of `f`'s returns.

    Returns:
      A possibly nested structure of matrix slices corresponding to `params`. Each
      slice has shape [P, p_s] where `p_s` is the number of parameters (`tf.size`)
      in the corresponding element of `params` and `P` is the total number of
      parameters (`sum_s(p_s)`). The full matrix can be obtained by concatenating
      along the second axis.
    
    "
2924,"def predict_proba(self, X):
        
        check_is_fitted(self)

        # TODO(1.3): Remove ""log""
        if self.loss in (""log_loss"", ""log""):
            return self._predict_proba_lr(X)

        elif self.loss == ""modified_huber"":
            binary = len(self.classes_) == 2
            scores = self.decision_function(X)

            if binary:
                prob2 = np.ones((scores.shape[0], 2))
                prob = prob2[:, 1]
            else:
                prob = scores

            np.clip(scores, -1, 1, prob)
            prob += 1.0
            prob /= 2.0

            if binary:
                prob2[:, 0] -= prob
                prob = prob2
            else:
                # the above might assign zero to all classes, which doesn't
                # normalize neatly; work around this to produce uniform
                # probabilities
                prob_sum = prob.sum(axis=1)
                all_zero = prob_sum == 0
                if np.any(all_zero):
                    prob[all_zero, :] = 1
                    prob_sum[all_zero] = len(self.classes_)

                # normalize
                prob /= prob_sum.reshape((prob.shape[0], -1))

            return prob

        else:
            raise NotImplementedError(
                ""predict_(log_)proba only supported when""
                "" loss='log_loss' or loss='modified_huber' ""
                ""(%r given)""
                % self.loss
            )
","Probability estimates.

        This method is only available for log loss and modified Huber loss.

        Multiclass probability estimates are derived from binary (one-vs.-rest)
        estimates by simple normalization, as recommended by Zadrozny and
        Elkan.

        Binary probability estimates for loss=""modified_huber"" are given by
        (clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions
        it is necessary to perform proper probability calibration by wrapping
        the classifier with
        :class:`~sklearn.calibration.CalibratedClassifierCV` instead.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            Input data for prediction.

        Returns
        -------
        ndarray of shape (n_samples, n_classes)
            Returns the probability of the sample for each class in the model,
            where classes are ordered as they are in `self.classes_`.

        References
        ----------
        Zadrozny and Elkan, ""Transforming classifier scores into multiclass
        probability estimates"", SIGKDD'02,
        https://dl.acm.org/doi/pdf/10.1145/775047.775151

        The justification for the formula in the loss=""modified_huber""
        case is in the appendix B in:
        http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
        ",138,125,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def predict_proba(self, X):
        
        check_is_fitted(self)

        # TODO(1.3): Remove ""log""
        if self.loss in (""log_loss"", ""log""):
            return self._predict_proba_lr(X)

        elif self.loss == ""modified_huber"":
            binary = len(self.classes_) == 2
            scores = self.decision_function(X)

            if binary:
                prob2 = np.ones((scores.shape[0], 2))
                prob = prob2[:, 1]
            else:
                prob = scores

            np.clip(scores, -1, 1, prob)
            prob += 1.0
            prob /= 2.0

            if binary:
                prob2[:, 0] -= prob
                prob = prob2
            else:
                # the above might assign zero to all classes, which doesn't
                # normalize neatly; work around this to produce uniform
                # probabilities
                prob_sum = prob.sum(axis=1)
                all_zero = prob_sum == 0
                if np.any(all_zero):
                    prob[all_zero, :] = 1
                    prob_sum[all_zero] = len(self.classes_)

                # normalize
                prob /= prob_sum.reshape((prob.shape[0], -1))

            return prob

        else:
            raise NotImplementedError(
                ""predict_(log_)proba only supported when""
                "" loss='log_loss' or loss='modified_huber' ""
                ""(%r given)""
                % self.loss
            )

        ```
    ###Assistant : Probability estimates.

        This method is only available for log loss and modified Huber loss.

        Multiclass probability estimates are derived from binary (one-vs.-rest)
        estimates by simple normalization, as recommended by Zadrozny and
        Elkan.

        Binary probability estimates for loss=""modified_huber"" are given by
        (clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions
        it is necessary to perform proper probability calibration by wrapping
        the classifier with
        :class:`~sklearn.calibration.CalibratedClassifierCV` instead.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            Input data for prediction.

        Returns
        -------
        ndarray of shape (n_samples, n_classes)
            Returns the probability of the sample for each class in the model,
            where classes are ordered as they are in `self.classes_`.

        References
        ----------
        Zadrozny and Elkan, ""Transforming classifier scores into multiclass
        probability estimates"", SIGKDD'02,
        https://dl.acm.org/doi/pdf/10.1145/775047.775151

        The justification for the formula in the loss=""modified_huber""
        case is in the appendix B in:
        http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
        
    "
2925,"def fetch_jwks(jwks_url) -> Optional[dict]:
    
    response = None
    try:
        response = requests.get(jwks_url, timeout=REQUEST_TIMEOUT)
        response.raise_for_status()
        jwks = response.json()
    except requests.exceptions.RequestException:
        logger.exception(""Unable to fetch jwks from %s"", jwks_url)
        raise AuthenticationError(""Unable to finalize the authentication process."")
    except json.JSONDecodeError:
        content = response.content if response else ""Unable to find the response""
        logger.exception(
            ""Unable to decode the response from auth service with jwks. ""
            ""Response: %s"",
            content,
        )
        raise AuthenticationError(""Unable to finalize the authentication process."")
    keys = jwks.get(""keys"", [])
    if not keys:
        logger.warning(""List of JWKS keys is empty"")
    cache.set(JWKS_KEY, keys, JWKS_CACHE_TIME)
    return keys

","Fetch JSON Web Key Sets from a provider.

    Fetched keys will be stored in the cache to the reduced amount of possible
    requests.
    :raises AuthenticationError
    ",25,86,Python,"
    ###User : Below is a Python method which does a task. Create a documentation for the below code : 
        ```Python
        def fetch_jwks(jwks_url) -> Optional[dict]:
    
    response = None
    try:
        response = requests.get(jwks_url, timeout=REQUEST_TIMEOUT)
        response.raise_for_status()
        jwks = response.json()
    except requests.exceptions.RequestException:
        logger.exception(""Unable to fetch jwks from %s"", jwks_url)
        raise AuthenticationError(""Unable to finalize the authentication process."")
    except json.JSONDecodeError:
        content = response.content if response else ""Unable to find the response""
        logger.exception(
            ""Unable to decode the response from auth service with jwks. ""
            ""Response: %s"",
            content,
        )
        raise AuthenticationError(""Unable to finalize the authentication process."")
    keys = jwks.get(""keys"", [])
    if not keys:
        logger.warning(""List of JWKS keys is empty"")
    cache.set(JWKS_KEY, keys, JWKS_CACHE_TIME)
    return keys


        ```
    ###Assistant : Fetch JSON Web Key Sets from a provider.

    Fetched keys will be stored in the cache to the reduced amount of possible
    requests.
    :raises AuthenticationError
    
    "