# Buggy code
def rename(self, *args, **kwargs):
    """
    Alter axes input function or functions. Function / dict values must be
    unique (1-to-1). Labels not contained in a dict / Series will be left
    as-is. Extra labels listed don't throw an error. Alternatively, change
    ``Series.name`` with a scalar value (Series only).

    Parameters
    ----------
    %(axes)s : scalar, list-like, dict-like or function, optional
        Scalar or list-like will alter the ``Series.name`` attribute,
        and raise on DataFrame.
        dict-like or functions are transformations to apply to
        that axis' values
    copy : bool, default True
        Also copy underlying data.
    inplace : bool, default False
        Whether to return a new %(klass)s. If True then value of copy is
        ignored.
    level : int or level name, default None
        In case of a MultiIndex, only rename labels in the specified
        level.
    errors : {'ignore', 'raise'}, default 'ignore'
        If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
        or `columns` contains labels that are not present in the Index
        being transformed.
        If 'ignore', existing keys will be renamed and extra keys will be
        ignored.

    Returns
    -------
    renamed : %(klass)s (new object)

    Raises
    ------
    KeyError
        If any of the labels is not found in the selected axis and
        "errors='raise'".

    See Also
    --------
    NDFrame.rename_axis

    Examples
    --------

    >>> s = pd.Series([1, 2, 3])
    >>> s
    0    1
    1    2
    2    3
    dtype: int64
    >>> s.rename("my_name") # scalar, changes Series.name
    0    1
    1    2
    2    3
    Name: my_name, dtype: int64
    >>> s.rename(lambda x: x ** 2)  # function, changes labels
    0    1
    1    2
    4    3
    dtype: int64
    >>> s.rename({1: 3, 2: 5})  # mapping, changes labels
    0    1
    3    2
    5    3
    dtype: int64

    Since ``DataFrame`` doesn't have a ``.name`` attribute,
    only mapping-type arguments are allowed.

    >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
    >>> df.rename(2)
    Traceback (most recent call last):
    ...
    TypeError: 'int' object is not callable

    ``DataFrame.rename`` supports two calling conventions

    * ``(index=index_mapper, columns=columns_mapper, ...)``
    * ``(mapper, axis={'index', 'columns'}, ...)``

    We *highly* recommend using keyword arguments to clarify your
    intent.

    >>> df.rename(index=str, columns={"A": "a", "B": "c"})
       a  c
    0  1  4
    1  2  5
    2  3  6

    >>> df.rename(index=str, columns={"A": "a", "C": "c"})
       a  B
    0  1  4
    1  2  5
    2  3  6

    Using axis-style parameters

    >>> df.rename(str.lower, axis='columns')
       a  b
    0  1  4
    1  2  5
    2  3  6

    >>> df.rename({1: 2, 2: 4}, axis='index')
       A  B
    0  1  4
    2  2  5
    4  3  6

    See the :ref:`user guide <basics.rename>` for more.
    """
    axes, kwargs = self._construct_axes_from_arguments(args, kwargs)
    copy = kwargs.pop("copy", True)
    inplace = kwargs.pop("inplace", False)
    level = kwargs.pop("level", None)
    axis = kwargs.pop("axis", None)
    errors = kwargs.pop("errors", "ignore")
    if axis is not None:
        # Validate the axis
        self._get_axis_number(axis)

    if kwargs:
        raise TypeError(
            f'rename() got an unexpected keyword argument "{list(kwargs.keys())[0]}"'
        )

    if com.count_not_none(*axes.values()) == 0:
        raise TypeError("must pass an index to rename")

    self._consolidate_inplace()
    result = self if inplace else self.copy(deep=copy)

    # start in the axis order to eliminate too many copies
    for axis in range(self._AXIS_LEN):
        v = axes.get(self._AXIS_NAMES[axis])
        if v is None:
            continue
        f = com.get_rename_function(v)
        baxis = self._get_block_manager_axis(axis)
        if level is not None:
            level = self.axes[axis]._get_level_number(level)

        # GH 13473
        if not callable(v):
            indexer = self.axes[axis].get_indexer_for(v)
            if errors == "raise" and len(indexer[indexer == -1]):
                missing_labels = [
                    label for index, label in enumerate(v) if indexer[index] == -1
                ]
                raise KeyError(f"{missing_labels} not found in axis")

        result._data = result._data.rename_axis(f, axis=baxis, copy=copy, level=level)
        result._clear_item_cache()

    if inplace:
        self._update_inplace(result._data)
    else:
        return result.__finalize__(self)


# Traceback (TypeError)
df = pd.DataFrame([[1]])
df.rename({0: 1}, columns={0: 2}, axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/util/_decorators.py", line 235, in wrapper
return func(*args, **kwargs)
File "/Users/williamayd/clones/pandas/pandas/core/frame.py", line 4143, in rename
axes = validate_axis_style_args(self, args, kwargs, "mapper", "rename")
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 287, in validate_axis_style_args
raise TypeError(msg)
TypeError: Cannot specify both 'axis' and any of 'index' or 'columns'.

# Fixed code
def rename(
    self: FrameOrSeries,
    mapper: Optional[Renamer] = None,
    *,
    index: Optional[Renamer] = None,
    columns: Optional[Renamer] = None,
    axis: Optional[Axis] = None,
    copy: bool = True,
    inplace: bool = False,
    level: Optional[Level] = None,
    errors: str = "ignore",
) -> Optional[FrameOrSeries]:
    """
    Alter axes input function or functions. Function / dict values must be
    unique (1-to-1). Labels not contained in a dict / Series will be left
    as-is. Extra labels listed don't throw an error. Alternatively, change
    ``Series.name`` with a scalar value (Series only).

    Parameters
    ----------
    %(axes)s : scalar, list-like, dict-like or function, optional
        Scalar or list-like will alter the ``Series.name`` attribute,
        and raise on DataFrame.
        dict-like or functions are transformations to apply to
        that axis' values
    copy : bool, default True
        Also copy underlying data.
    inplace : bool, default False
        Whether to return a new %(klass)s. If True then value of copy is
        ignored.
    level : int or level name, default None
        In case of a MultiIndex, only rename labels in the specified
        level.
    errors : {'ignore', 'raise'}, default 'ignore'
        If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
        or `columns` contains labels that are not present in the Index
        being transformed.
        If 'ignore', existing keys will be renamed and extra keys will be
        ignored.

    Returns
    -------
    renamed : %(klass)s (new object)

    Raises
    ------
    KeyError
        If any of the labels is not found in the selected axis and
        "errors='raise'".

    See Also
    --------
    NDFrame.rename_axis

    Examples
    --------

    >>> s = pd.Series([1, 2, 3])
    >>> s
    0    1
    1    2
    2    3
    dtype: int64
    >>> s.rename("my_name") # scalar, changes Series.name
    0    1
    1    2
    2    3
    Name: my_name, dtype: int64
    >>> s.rename(lambda x: x ** 2)  # function, changes labels
    0    1
    1    2
    4    3
    dtype: int64
    >>> s.rename({1: 3, 2: 5})  # mapping, changes labels
    0    1
    3    2
    5    3
    dtype: int64

    Since ``DataFrame`` doesn't have a ``.name`` attribute,
    only mapping-type arguments are allowed.

    >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
    >>> df.rename(2)
    Traceback (most recent call last):
    ...
    TypeError: 'int' object is not callable

    ``DataFrame.rename`` supports two calling conventions

    * ``(index=index_mapper, columns=columns_mapper, ...)``
    * ``(mapper, axis={'index', 'columns'}, ...)``

    We *highly* recommend using keyword arguments to clarify your
    intent.

    >>> df.rename(index=str, columns={"A": "a", "B": "c"})
       a  c
    0  1  4
    1  2  5
    2  3  6

    >>> df.rename(index=str, columns={"A": "a", "C": "c"})
       a  B
    0  1  4
    1  2  5
    2  3  6

    Using axis-style parameters

    >>> df.rename(str.lower, axis='columns')
       a  b
    0  1  4
    1  2  5
    2  3  6

    >>> df.rename({1: 2, 2: 4}, axis='index')
       A  B
    0  1  4
    2  2  5
    4  3  6

    See the :ref:`user guide <basics.rename>` for more.
    """
    if mapper is None and index is None and columns is None:
        raise TypeError("must pass an index to rename")

    if index is not None or columns is not None:
        if axis is not None:
            raise TypeError(
                "Cannot specify both 'axis' and any of 'index' or 'columns'"
            )
        elif mapper is not None:
            raise TypeError(
                "Cannot specify both 'mapper' and any of 'index' or 'columns'"
            )
    else:
        # use the mapper argument
        if axis and self._get_axis_number(axis) == 1:
            columns = mapper
        else:
            index = mapper

    result = self if inplace else self.copy(deep=copy)

    for axis_no, replacements in enumerate((index, columns)):
        if replacements is None:
            continue

        ax = self._get_axis(axis_no)
        baxis = self._get_block_manager_axis(axis_no)
        f = com.get_rename_function(replacements)

        if level is not None:
            level = ax._get_level_number(level)

        # GH 13473
        if not callable(replacements):
            indexer = ax.get_indexer_for(replacements)
            if errors == "raise" and len(indexer[indexer == -1]):
                missing_labels = [
                    label
                    for index, label in enumerate(replacements)
                    if indexer[index] == -1
                ]
                raise KeyError(f"{missing_labels} not found in axis")

        result._data = result._data.rename_axis(f, axis=baxis, copy=copy, level=level)
        result._clear_item_cache()

    if inplace:
        self._update_inplace(result._data)
        return None
    else:
        return result.__finalize__(self)