# Buggy code
def lasso_path(
    X,
    y,
    eps=1e-3,
    n_alphas=100,
    alphas=None,
    precompute="auto",
    Xy=None,
    fit_intercept=None,
    normalize=None,
    copy_X=True,
    coef_init=None,
    verbose=False,
    return_models=False,
    **params,
):
    """Compute Lasso path with coordinate descent

    The Lasso optimization function varies for mono and multi-outputs.

    For mono-output tasks it is::

        (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

    For multi-output tasks it is::

        (1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21

    Where::

        ||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}

    i.e. the sum of norm of each row.

    Parameters
    ----------
    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data. Pass directly as Fortran-contiguous data to avoid
        unnecessary memory duplication. If ``y`` is mono-output then ``X``
        can be sparse.

    y : ndarray, shape = (n_samples,), or (n_samples, n_outputs)
        Target values

    eps : float, optional
        Length of the path. ``eps=1e-3`` means that
        ``alpha_min / alpha_max = 1e-3``

    n_alphas : int, optional
        Number of alphas along the regularization path

    alphas : ndarray, optional
        List of alphas where to compute the models.
        If ``None`` alphas are set automatically

    precompute : True | False | 'auto' | array-like
        Whether to use a precomputed Gram matrix to speed up
        calculations. If set to ``'auto'`` let us decide. The Gram
        matrix can also be passed as argument.

    Xy : array-like, optional
        Xy = np.dot(X.T, y) that can be precomputed. It is useful
        only when the Gram matrix is precomputed.

    fit_intercept : bool
        Fit or not an intercept.
        WARNING : will be deprecated in 0.16

    normalize : boolean, optional, default False
        If ``True``, the regressors X will be normalized before regression.
        WARNING : will be deprecated in 0.16

    copy_X : boolean, optional, default True
        If ``True``, X will be copied; else, it may be overwritten.

    coef_init : array, shape (n_features, ) | None
        The initial values of the coefficients.

    verbose : bool or integer
        Amount of verbosity.

    return_models : boolean, optional, default True
        If ``True``, the function will return list of models. Setting it
        to ``False`` will change the function output returning the values
        of the alphas and the coefficients along the path. Returning the
        model list will be removed in version 0.16.

    params : kwargs
        keyword arguments passed to the coordinate descent solver.

    Returns
    -------
    models : a list of models along the regularization path
        (Is returned if ``return_models`` is set ``True`` (default).

    alphas : array, shape (n_alphas,)
        The alphas along the path where models are computed.
        (Is returned, along with ``coefs``, when ``return_models`` is set
        to ``False``)

    coefs : array, shape (n_features, n_alphas) or
            (n_outputs, n_features, n_alphas)
        Coefficients along the path.
        (Is returned, along with ``alphas``, when ``return_models`` is set
        to ``False``).

    dual_gaps : array, shape (n_alphas,)
        The dual gaps at the end of the optimization for each alpha.
        (Is returned, along with ``alphas``, when ``return_models`` is set
        to ``False``).

    Notes
    -----
    See examples/linear_model/plot_lasso_coordinate_descent_path.py
    for an example.

    To avoid unnecessary memory duplication the X argument of the fit method
    should be directly passed as a Fortran-contiguous numpy array.

    Note that in certain cases, the Lars solver may be significantly
    faster to implement this functionality. In particular, linear
    interpolation can be used to retrieve model coefficients between the
    values output by lars_path

    Deprecation Notice: Setting ``return_models`` to ``False`` will make
    the Lasso Path return an output in the style used by :func:`lars_path`.
    This will be become the norm as of version 0.16. Leaving ``return_models``
    set to `True` will let the function return a list of models as before.

    Examples
    ---------

    Comparing lasso_path and lars_path with interpolation:

    >>> X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
    >>> y = np.array([1, 2, 3.1])
    >>> # Use lasso_path to compute a coefficient path
    >>> _, coef_path, _ = lasso_path(X, y, alphas=[5., 1., .5],
    ...                              fit_intercept=False)
    >>> print(coef_path)
    [[ 0.          0.          0.46874778]
     [ 0.2159048   0.4425765   0.23689075]]

    >>> # Now use lars_path and 1D linear interpolation to compute the
    >>> # same path
    >>> from sklearn.linear_model import lars_path
    >>> alphas, active, coef_path_lars = lars_path(X, y, method='lasso')
    >>> from scipy import interpolate
    >>> coef_path_continuous = interpolate.interp1d(alphas[::-1],
    ...                                             coef_path_lars[:, ::-1])
    >>> print(coef_path_continuous([5., 1., .5]))
    [[ 0.          0.          0.46915237]
     [ 0.2159048   0.4425765   0.23668876]]


    See also
    --------
    lars_path
    Lasso
    LassoLars
    LassoCV
    LassoLarsCV
    sklearn.decomposition.sparse_encode
    """
    return enet_path(
        X,
        y,
        l1_ratio=1.0,
        eps=eps,
        n_alphas=n_alphas,
        alphas=alphas,
        precompute=precompute,
        Xy=Xy,
        fit_intercept=fit_intercept,
        normalize=normalize,
        copy_X=copy_X,
        coef_init=coef_init,
        verbose=verbose,
        return_models=return_models,
        **params,
    )


# Traceback (IndexError)
======================================================================
ERROR: sklearn.tests.test_common.test_regressors_train('RANSACRegressor', <class 'sklearn.linear_model.ransac.RANSACRegr
essor'>, array([[-0.44836249, -0.47282444, -1.20608008, ..., -0.75500806,
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
self.test(*self.arg)
File "C:\Python27\lib\site-packages\sklearn\tests\test_common.py", line 850, in check_regressors_train
assert_raises(ValueError, regressor.fit, X, y[:-1])
File "C:\Python27\lib\unittest\case.py", line 473, in assertRaises
callableObj(*args, **kwargs)
File "C:\Python27\lib\site-packages\sklearn\linear_model\ransac.py", line 261, in fit
y_subset = y[subset_idxs]
IndexError: index 199 is out of bounds for axis 0 with size 199

# Fixed code
def lasso_path(
    X,
    y,
    eps=1e-3,
    n_alphas=100,
    alphas=None,
    precompute="auto",
    Xy=None,
    fit_intercept=None,
    normalize=None,
    copy_X=True,
    coef_init=None,
    verbose=False,
    return_models=False,
    **params,
):
    """Compute Lasso path with coordinate descent

    The Lasso optimization function varies for mono and multi-outputs.

    For mono-output tasks it is::

        (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

    For multi-output tasks it is::

        (1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21

    Where::

        ||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}

    i.e. the sum of norm of each row.

    Parameters
    ----------
    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data. Pass directly as Fortran-contiguous data to avoid
        unnecessary memory duplication. If ``y`` is mono-output then ``X``
        can be sparse.

    y : ndarray, shape = (n_samples,), or (n_samples, n_outputs)
        Target values

    eps : float, optional
        Length of the path. ``eps=1e-3`` means that
        ``alpha_min / alpha_max = 1e-3``

    n_alphas : int, optional
        Number of alphas along the regularization path

    alphas : ndarray, optional
        List of alphas where to compute the models.
        If ``None`` alphas are set automatically

    precompute : True | False | 'auto' | array-like
        Whether to use a precomputed Gram matrix to speed up
        calculations. If set to ``'auto'`` let us decide. The Gram
        matrix can also be passed as argument.

    Xy : array-like, optional
        Xy = np.dot(X.T, y) that can be precomputed. It is useful
        only when the Gram matrix is precomputed.

    fit_intercept : bool
        Fit or not an intercept.
        WARNING : deprecated, will be removed in 0.16.

    normalize : boolean, optional, default False
        If ``True``, the regressors X will be normalized before regression.
        WARNING : deprecated, will be removed in 0.16.

    copy_X : boolean, optional, default True
        If ``True``, X will be copied; else, it may be overwritten.

    coef_init : array, shape (n_features, ) | None
        The initial values of the coefficients.

    verbose : bool or integer
        Amount of verbosity.

    return_models : boolean, optional, default True
        If ``True``, the function will return list of models. Setting it
        to ``False`` will change the function output returning the values
        of the alphas and the coefficients along the path. Returning the
        model list will be removed in version 0.16.

    params : kwargs
        keyword arguments passed to the coordinate descent solver.

    Returns
    -------
    models : a list of models along the regularization path
        (Is returned if ``return_models`` is set ``True`` (default).

    alphas : array, shape (n_alphas,)
        The alphas along the path where models are computed.
        (Is returned, along with ``coefs``, when ``return_models`` is set
        to ``False``)

    coefs : array, shape (n_features, n_alphas) or
            (n_outputs, n_features, n_alphas)
        Coefficients along the path.
        (Is returned, along with ``alphas``, when ``return_models`` is set
        to ``False``).

    dual_gaps : array, shape (n_alphas,)
        The dual gaps at the end of the optimization for each alpha.
        (Is returned, along with ``alphas``, when ``return_models`` is set
        to ``False``).

    Notes
    -----
    See examples/linear_model/plot_lasso_coordinate_descent_path.py
    for an example.

    To avoid unnecessary memory duplication the X argument of the fit method
    should be directly passed as a Fortran-contiguous numpy array.

    Note that in certain cases, the Lars solver may be significantly
    faster to implement this functionality. In particular, linear
    interpolation can be used to retrieve model coefficients between the
    values output by lars_path

    Deprecation Notice: Setting ``return_models`` to ``False`` will make
    the Lasso Path return an output in the style used by :func:`lars_path`.
    This will be become the norm as of version 0.16. Leaving ``return_models``
    set to `True` will let the function return a list of models as before.

    Examples
    ---------

    Comparing lasso_path and lars_path with interpolation:

    >>> X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
    >>> y = np.array([1, 2, 3.1])
    >>> # Use lasso_path to compute a coefficient path
    >>> _, coef_path, _ = lasso_path(X, y, alphas=[5., 1., .5],
    ...                              fit_intercept=False)
    >>> print(coef_path)
    [[ 0.          0.          0.46874778]
     [ 0.2159048   0.4425765   0.23689075]]

    >>> # Now use lars_path and 1D linear interpolation to compute the
    >>> # same path
    >>> from sklearn.linear_model import lars_path
    >>> alphas, active, coef_path_lars = lars_path(X, y, method='lasso')
    >>> from scipy import interpolate
    >>> coef_path_continuous = interpolate.interp1d(alphas[::-1],
    ...                                             coef_path_lars[:, ::-1])
    >>> print(coef_path_continuous([5., 1., .5]))
    [[ 0.          0.          0.46915237]
     [ 0.2159048   0.4425765   0.23668876]]


    See also
    --------
    lars_path
    Lasso
    LassoLars
    LassoCV
    LassoLarsCV
    sklearn.decomposition.sparse_encode
    """
    return enet_path(
        X,
        y,
        l1_ratio=1.0,
        eps=eps,
        n_alphas=n_alphas,
        alphas=alphas,
        precompute=precompute,
        Xy=Xy,
        fit_intercept=fit_intercept,
        normalize=normalize,
        copy_X=copy_X,
        coef_init=coef_init,
        verbose=verbose,
        return_models=return_models,
        **params,
    )