def split(self, y, exogenous=None):
        """Generate indices to split data into training and test sets

        Parameters
        ----------
        y : array-like or iterable, shape=(n_samples,)
            The time-series array.

        exogenous : array-like, shape=[n_obs, n_vars], optional (default=None)
            An optional 2-d array of exogenous variables.

        Yields
        ------
        train : np.ndarray
            The training set indices for the split

        test : np.ndarray
            The test set indices for the split
        """
        y, exog = indexable(y, exogenous)
        indices = np.arange(y.shape[0])
        for train_index, test_index in self._iter_train_test_masks(y, exog):
            train_index = indices[train_index]
            test_index = indices[test_index]
            yield train_index, test_index 

def to_indexable(*args, **kwargs):
    """Ensure that all args are an indexable type.

    Conversion runs lazily for dask objects, immediately otherwise.

    Parameters
    ----------
    args : array_like or scalar
    allow_scalars : bool, optional
        Whether to allow scalars in args. Default is False.
    """
    if kwargs.get("allow_scalars", False):
        indexable = _maybe_indexable
    else:
        indexable = _indexable
    for x in args:
        if x is None or isinstance(x, (da.Array, dd.DataFrame)):
            yield x
        elif is_dask_collection(x):
            yield delayed(indexable, pure=True)(x)
        else:
            yield indexable(x) 

def _indexable(x):
    return indexable(x)[0] 

def _maybe_indexable(x):
    return indexable(x)[0] if _is_arraylike(x) else x