# Buggy code
def kneighbors_graph(
    X,
    n_neighbors,
    mode="connectivity",
    metric="minkowski",
    p=2,
    metric_params=None,
    include_self=False,
    n_jobs=None,
):
    """Computes the (weighted) graph of k-Neighbors for points in X

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

    Parameters
    ----------
    X : array-like or BallTree, shape = [n_samples, n_features]
        Sample data, in the form of a numpy array or a precomputed
        :class:`BallTree`.

    n_neighbors : int
        Number of neighbors for each sample.

    mode : {'connectivity', 'distance'}, optional
        Type of returned matrix: 'connectivity' will return the connectivity
        matrix with ones and zeros, and 'distance' will return the distances
        between neighbors according to the given metric.

    metric : string, default 'minkowski'
        The distance metric used to calculate the k-Neighbors for each sample
        point. The DistanceMetric class gives a list of available metrics.
        The default distance is 'euclidean' ('minkowski' metric with the p
        param equal to 2.)

    p : int, default 2
        Power parameter for the Minkowski metric. When p = 1, this is
        equivalent to using manhattan_distance (l1), and euclidean_distance
        (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

    metric_params : dict, optional
        additional keyword arguments for the metric function.

    include_self : bool, default=False.
        Whether or not to mark each sample as the first nearest neighbor to
        itself. If `None`, then True is used for mode='connectivity' and False
        for mode='distance' as this will preserve backwards compatibility.

    n_jobs : int, optional (default = 1)
        The number of parallel jobs to run for neighbors search.
        If ``-1``, then the number of jobs is set to the number of CPU cores.

    Returns
    -------
    A : sparse matrix in CSR format, shape = [n_samples, n_samples]
        A[i, j] is assigned the weight of edge that connects i to j.

    Examples
    --------
    >>> X = [[0], [3], [1]]
    >>> from sklearn.neighbors import kneighbors_graph
    >>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True)
    >>> A.toarray()
    array([[1., 0., 1.],
           [0., 1., 1.],
           [1., 0., 1.]])

    See also
    --------
    radius_neighbors_graph
    """
    if not isinstance(X, KNeighborsMixin):
        X = NearestNeighbors(
            n_neighbors, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs
        ).fit(X)
    else:
        _check_params(X, metric, p, metric_params)

    query = _query_include_self(X, include_self)
    return X.kneighbors_graph(X=query, n_neighbors=n_neighbors, mode=mode)


# Traceback (ValueError)
Unexpected failing examples:
/home/circleci/project/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py failed leaving traceback:
Traceback (most recent call last):
File "/home/circleci/project/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py", line 75, in <module>
clf.fit(X, y)  # set the best parameters
File "/home/circleci/project/sklearn/model_selection/_search.py", line 663, in fit
cv.split(X, y, groups)))
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 981, in __call__
if self.dispatch_one_batch(iterator):
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 818, in dispatch_one_batch
self._pickle_cache)
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 253, in __init__
self.items = list(iterator_slice)
File "/home/circleci/project/sklearn/model_selection/_search.py", line 662, in <genexpr>
for parameters, (train, test) in product(candidate_params,
File "/home/circleci/project/sklearn/base.py", line 62, in clone
new_object_params[name] = clone(param, safe=False)
File "/home/circleci/project/sklearn/base.py", line 50, in clone
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 50, in <listcomp>
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 50, in clone
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 50, in <listcomp>
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 62, in clone
new_object_params[name] = clone(param, safe=False)
File "/home/circleci/project/sklearn/base.py", line 53, in clone
return copy.deepcopy(estimator)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 274, in _reconstruct
y = func(*args)
File "/home/circleci/project/sklearn/externals/joblib/memory.py", line 830, in __init__
location, cachedir))
ValueError: You set both "location='/tmp/tmp1v2mgts1' and "cachedir=False". 'cachedir' has been deprecated in version 0.12 and will be removed in version 0.14.
Please only set "location='/tmp/tmp1v2mgts1'"


/home/circleci/project/examples/compose/plot_compare_reduction.py failed leaving traceback:
Traceback (most recent call last):
File "/home/circleci/project/examples/compose/plot_compare_reduction.py", line 119, in <module>
grid.fit(digits.data, digits.target)
File "/home/circleci/project/sklearn/model_selection/_search.py", line 663, in fit
cv.split(X, y, groups)))
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 981, in __call__
if self.dispatch_one_batch(iterator):
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 818, in dispatch_one_batch
self._pickle_cache)
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 253, in __init__
self.items = list(iterator_slice)
File "/home/circleci/project/sklearn/model_selection/_search.py", line 662, in <genexpr>
for parameters, (train, test) in product(candidate_params,
File "/home/circleci/project/sklearn/base.py", line 62, in clone
new_object_params[name] = clone(param, safe=False)
File "/home/circleci/project/sklearn/base.py", line 53, in clone
return copy.deepcopy(estimator)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 274, in _reconstruct
y = func(*args)
File "/home/circleci/project/sklearn/externals/joblib/memory.py", line 830, in __init__
location, cachedir))
ValueError: You set both "location='/tmp/tmpa408j1cz' and "cachedir=False". 'cachedir' has been deprecated in version 0.12 and will be removed in version 0.14.
Please only set "location='/tmp/tmpa408j1cz'"

# Fixed code
def kneighbors_graph(
    X,
    n_neighbors,
    mode="connectivity",
    metric="minkowski",
    p=2,
    metric_params=None,
    include_self=False,
    n_jobs=None,
):
    """Computes the (weighted) graph of k-Neighbors for points in X

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

    Parameters
    ----------
    X : array-like or BallTree, shape = [n_samples, n_features]
        Sample data, in the form of a numpy array or a precomputed
        :class:`BallTree`.

    n_neighbors : int
        Number of neighbors for each sample.

    mode : {'connectivity', 'distance'}, optional
        Type of returned matrix: 'connectivity' will return the connectivity
        matrix with ones and zeros, and 'distance' will return the distances
        between neighbors according to the given metric.

    metric : string, default 'minkowski'
        The distance metric used to calculate the k-Neighbors for each sample
        point. The DistanceMetric class gives a list of available metrics.
        The default distance is 'euclidean' ('minkowski' metric with the p
        param equal to 2.)

    p : int, default 2
        Power parameter for the Minkowski metric. When p = 1, this is
        equivalent to using manhattan_distance (l1), and euclidean_distance
        (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

    metric_params : dict, optional
        additional keyword arguments for the metric function.

    include_self : bool, default=False.
        Whether or not to mark each sample as the first nearest neighbor to
        itself. If `None`, then True is used for mode='connectivity' and False
        for mode='distance' as this will preserve backwards compatibility.

    n_jobs : int or None, optional (default=None)
        The number of parallel jobs to run for neighbors search.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    Returns
    -------
    A : sparse matrix in CSR format, shape = [n_samples, n_samples]
        A[i, j] is assigned the weight of edge that connects i to j.

    Examples
    --------
    >>> X = [[0], [3], [1]]
    >>> from sklearn.neighbors import kneighbors_graph
    >>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True)
    >>> A.toarray()
    array([[1., 0., 1.],
           [0., 1., 1.],
           [1., 0., 1.]])

    See also
    --------
    radius_neighbors_graph
    """
    if not isinstance(X, KNeighborsMixin):
        X = NearestNeighbors(
            n_neighbors, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs
        ).fit(X)
    else:
        _check_params(X, metric, p, metric_params)

    query = _query_include_self(X, include_self)
    return X.kneighbors_graph(X=query, n_neighbors=n_neighbors, mode=mode)