# Buggy code
def pairwise_kernels(
    X, Y=None, metric="linear", filter_params=False, n_jobs=None, **kwds
):
    """Compute the kernel between arrays X and optional array Y.

    This method takes either a vector array or a kernel matrix, and returns
    a kernel matrix. If the input is a vector array, the kernels are
    computed. If the input is a kernel matrix, it is returned instead.

    This method provides a safe way to take a kernel matrix as input, while
    preserving compatibility with many other algorithms that take a vector
    array.

    If Y is given (default is None), then the returned matrix is the pairwise
    kernel between the arrays from both X and Y.

    Valid values for metric are::
        ['rbf', 'sigmoid', 'polynomial', 'poly', 'linear', 'cosine']

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

    Parameters
    ----------
    X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
             [n_samples_a, n_features] otherwise
        Array of pairwise kernels between samples, or a feature array.

    Y : array [n_samples_b, n_features]
        A second feature array only if X has shape [n_samples_a, n_features].

    metric : string, or callable
        The metric to use when calculating kernel between instances in a
        feature array. If metric is a string, it must be one of the metrics
        in pairwise.PAIRWISE_KERNEL_FUNCTIONS.
        If metric is "precomputed", X is assumed to be a kernel matrix.
        Alternatively, if metric is a callable function, it is called on each
        pair of instances (rows) and the resulting value recorded. The callable
        should take two arrays from X as input and return a value indicating
        the distance between them.

    filter_params : boolean
        Whether to filter invalid parameters or not.

    n_jobs : int
        The number of jobs to use for the computation. This works by breaking
        down the pairwise matrix into n_jobs even slices and computing them in
        parallel.

        If -1 all CPUs are used. If 1 is given, no parallel computing code is
        used at all, which is useful for debugging. For n_jobs below -1,
        (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
        are used.

    **kwds : optional keyword parameters
        Any further parameters are passed directly to the kernel function.

    Returns
    -------
    K : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b]
        A kernel matrix K such that K_{i, j} is the kernel between the
        ith and jth vectors of the given matrix X, if Y is None.
        If Y is not None, then K_{i, j} is the kernel between the ith array
        from X and the jth array from Y.

    Notes
    -----
    If metric is 'precomputed', Y is ignored and X is returned.

    """
    # import GPKernel locally to prevent circular imports
    from ..gaussian_process.kernels import Kernel as GPKernel

    if metric == "precomputed":
        X, _ = check_pairwise_arrays(X, Y, precomputed=True)
        return X
    elif isinstance(metric, GPKernel):
        func = metric.__call__
    elif metric in PAIRWISE_KERNEL_FUNCTIONS:
        if filter_params:
            kwds = dict((k, kwds[k]) for k in kwds if k in KERNEL_PARAMS[metric])
        func = PAIRWISE_KERNEL_FUNCTIONS[metric]
    elif callable(metric):
        func = partial(_pairwise_callable, metric=metric, **kwds)
    else:
        raise ValueError("Unknown kernel %r" % metric)

    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)


# 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 pairwise_kernels(
    X, Y=None, metric="linear", filter_params=False, n_jobs=None, **kwds
):
    """Compute the kernel between arrays X and optional array Y.

    This method takes either a vector array or a kernel matrix, and returns
    a kernel matrix. If the input is a vector array, the kernels are
    computed. If the input is a kernel matrix, it is returned instead.

    This method provides a safe way to take a kernel matrix as input, while
    preserving compatibility with many other algorithms that take a vector
    array.

    If Y is given (default is None), then the returned matrix is the pairwise
    kernel between the arrays from both X and Y.

    Valid values for metric are::
        ['rbf', 'sigmoid', 'polynomial', 'poly', 'linear', 'cosine']

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

    Parameters
    ----------
    X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
             [n_samples_a, n_features] otherwise
        Array of pairwise kernels between samples, or a feature array.

    Y : array [n_samples_b, n_features]
        A second feature array only if X has shape [n_samples_a, n_features].

    metric : string, or callable
        The metric to use when calculating kernel between instances in a
        feature array. If metric is a string, it must be one of the metrics
        in pairwise.PAIRWISE_KERNEL_FUNCTIONS.
        If metric is "precomputed", X is assumed to be a kernel matrix.
        Alternatively, if metric is a callable function, it is called on each
        pair of instances (rows) and the resulting value recorded. The callable
        should take two arrays from X as input and return a value indicating
        the distance between them.

    filter_params : boolean
        Whether to filter invalid parameters or not.

    n_jobs : int or None, optional (default=None)
        The number of jobs to use for the computation. This works by breaking
        down the pairwise matrix into n_jobs even slices and computing them in
        parallel.

        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    **kwds : optional keyword parameters
        Any further parameters are passed directly to the kernel function.

    Returns
    -------
    K : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b]
        A kernel matrix K such that K_{i, j} is the kernel between the
        ith and jth vectors of the given matrix X, if Y is None.
        If Y is not None, then K_{i, j} is the kernel between the ith array
        from X and the jth array from Y.

    Notes
    -----
    If metric is 'precomputed', Y is ignored and X is returned.

    """
    # import GPKernel locally to prevent circular imports
    from ..gaussian_process.kernels import Kernel as GPKernel

    if metric == "precomputed":
        X, _ = check_pairwise_arrays(X, Y, precomputed=True)
        return X
    elif isinstance(metric, GPKernel):
        func = metric.__call__
    elif metric in PAIRWISE_KERNEL_FUNCTIONS:
        if filter_params:
            kwds = dict((k, kwds[k]) for k in kwds if k in KERNEL_PARAMS[metric])
        func = PAIRWISE_KERNEL_FUNCTIONS[metric]
    elif callable(metric):
        func = partial(_pairwise_callable, metric=metric, **kwds)
    else:
        raise ValueError("Unknown kernel %r" % metric)

    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)