# Buggy code
def _to_graph(
    n_x, n_y, n_z, mask=None, img=None, return_as=sparse.coo_matrix, dtype=None
):
    """Auxiliary function for img_to_graph and grid_to_graph"""
    edges = _make_edges_3d(n_x, n_y, n_z)

    if dtype is None:
        if img is None:
            dtype = np.int
        else:
            dtype = img.dtype

    if img is not None:
        img = np.atleast_3d(img)
        weights = _compute_gradient_3d(edges, img)
        if mask is not None:
            edges, weights = _mask_edges_weights(mask, edges, weights)
            diag = img.squeeze()[mask]
        else:
            diag = img.ravel()
        n_voxels = diag.size
    else:
        if mask is not None:
            mask = mask.astype(np.bool)
            edges = _mask_edges_weights(mask, edges)
            n_voxels = np.sum(mask)
        else:
            n_voxels = n_x * n_y * n_z
        weights = np.ones(edges.shape[1], dtype=dtype)
        diag = np.ones(n_voxels, dtype=dtype)

    diag_idx = np.arange(n_voxels)
    i_idx = np.hstack((edges[0], edges[1]))
    j_idx = np.hstack((edges[1], edges[0]))
    graph = sparse.coo_matrix(
        (
            np.hstack((weights, weights, diag)),
            (np.hstack((i_idx, diag_idx)), np.hstack((j_idx, diag_idx))),
        ),
        (n_voxels, n_voxels),
        dtype=dtype,
    )
    if return_as is np.ndarray:
        return graph.toarray()
    return return_as(graph)


# 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 _to_graph(
    n_x, n_y, n_z, mask=None, img=None, return_as=sparse.coo_matrix, dtype=None
):
    """Auxiliary function for img_to_graph and grid_to_graph"""
    edges = _make_edges_3d(n_x, n_y, n_z)

    if dtype is None:
        if img is None:
            dtype = np.int
        else:
            dtype = img.dtype

    if img is not None:
        img = np.atleast_3d(img)
        weights = _compute_gradient_3d(edges, img)
        if mask is not None:
            edges, weights = _mask_edges_weights(mask, edges, weights)
            diag = img.squeeze()[mask]
        else:
            diag = img.ravel()
        n_voxels = diag.size
    else:
        if mask is not None:
            mask = astype(mask, dtype=np.bool, copy=False)
            mask = np.asarray(mask, dtype=np.bool)
            edges = _mask_edges_weights(mask, edges)
            n_voxels = np.sum(mask)
        else:
            n_voxels = n_x * n_y * n_z
        weights = np.ones(edges.shape[1], dtype=dtype)
        diag = np.ones(n_voxels, dtype=dtype)

    diag_idx = np.arange(n_voxels)
    i_idx = np.hstack((edges[0], edges[1]))
    j_idx = np.hstack((edges[1], edges[0]))
    graph = sparse.coo_matrix(
        (
            np.hstack((weights, weights, diag)),
            (np.hstack((i_idx, diag_idx)), np.hstack((j_idx, diag_idx))),
        ),
        (n_voxels, n_voxels),
        dtype=dtype,
    )
    if return_as is np.ndarray:
        return graph.toarray()
    return return_as(graph)