# Buggy code
def make_column_transformer(*transformers, **kwargs):
    """Construct a ColumnTransformer from the given transformers.

    This is a shorthand for the ColumnTransformer constructor; it does not
    require, and does not permit, naming the transformers. Instead, they will
    be given names automatically based on their types. It also does not allow
    weighting.

    Parameters
    ----------
    *transformers : tuples of column selections and transformers

    remainder : {'drop', 'passthrough'} or estimator, default 'drop'
        By default, only the specified columns in `transformers` are
        transformed and combined in the output, and the non-specified
        columns are dropped. (default of ``'drop'``).
        By specifying ``remainder='passthrough'``, all remaining columns that
        were not specified in `transformers` will be automatically passed
        through. This subset of columns is concatenated with the output of
        the transformers.
        By setting ``remainder`` to be an estimator, the remaining
        non-specified columns will use the ``remainder`` estimator. The
        estimator must support `fit` and `transform`.

    n_jobs : int, optional
        Number of jobs to run in parallel (default 1).

    Returns
    -------
    ct : ColumnTransformer

    See also
    --------
    sklearn.compose.ColumnTransformer : Class that allows combining the
        outputs of multiple transformer objects used on column subsets
        of the data into a single feature space.

    Examples
    --------
    >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
    >>> from sklearn.compose import make_column_transformer
    >>> make_column_transformer(
    ...     (['numerical_column'], StandardScaler()),
    ...     (['categorical_column'], OneHotEncoder()))
    ...     # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,
             transformer_weights=None,
             transformers=[('standardscaler',
                            StandardScaler(...),
                            ['numerical_column']),
                           ('onehotencoder',
                            OneHotEncoder(...),
                            ['categorical_column'])])

    """
    n_jobs = kwargs.pop("n_jobs", None)
    remainder = kwargs.pop("remainder", "drop")
    if kwargs:
        raise TypeError(
            'Unknown keyword arguments: "{}"'.format(list(kwargs.keys())[0])
        )
    transformer_list = _get_transformer_list(transformers)
    return ColumnTransformer(transformer_list, n_jobs=n_jobs, remainder=remainder)


# 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 make_column_transformer(*transformers, **kwargs):
    """Construct a ColumnTransformer from the given transformers.

    This is a shorthand for the ColumnTransformer constructor; it does not
    require, and does not permit, naming the transformers. Instead, they will
    be given names automatically based on their types. It also does not allow
    weighting.

    Parameters
    ----------
    *transformers : tuples of column selections and transformers

    remainder : {'drop', 'passthrough'} or estimator, default 'drop'
        By default, only the specified columns in `transformers` are
        transformed and combined in the output, and the non-specified
        columns are dropped. (default of ``'drop'``).
        By specifying ``remainder='passthrough'``, all remaining columns that
        were not specified in `transformers` will be automatically passed
        through. This subset of columns is concatenated with the output of
        the transformers.
        By setting ``remainder`` to be an estimator, the remaining
        non-specified columns will use the ``remainder`` estimator. The
        estimator must support `fit` and `transform`.

    n_jobs : int or None, optional (default=None)
        Number of jobs to run 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.

    Returns
    -------
    ct : ColumnTransformer

    See also
    --------
    sklearn.compose.ColumnTransformer : Class that allows combining the
        outputs of multiple transformer objects used on column subsets
        of the data into a single feature space.

    Examples
    --------
    >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
    >>> from sklearn.compose import make_column_transformer
    >>> make_column_transformer(
    ...     (['numerical_column'], StandardScaler()),
    ...     (['categorical_column'], OneHotEncoder()))
    ...     # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,
             transformer_weights=None,
             transformers=[('standardscaler',
                            StandardScaler(...),
                            ['numerical_column']),
                           ('onehotencoder',
                            OneHotEncoder(...),
                            ['categorical_column'])])

    """
    n_jobs = kwargs.pop("n_jobs", None)
    remainder = kwargs.pop("remainder", "drop")
    if kwargs:
        raise TypeError(
            'Unknown keyword arguments: "{}"'.format(list(kwargs.keys())[0])
        )
    transformer_list = _get_transformer_list(transformers)
    return ColumnTransformer(transformer_list, n_jobs=n_jobs, remainder=remainder)