-----

    By eliminating the inner loop in the description above, and using `s_` to
    build simple slice objects, `take` can be expressed  in terms of applying
    fancy indexing to each 1-d slice::

        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
        for ii in ndindex(Ni):
            for kk in ndindex(Nj):
                out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]

    For this reason, it is equivalent to (but faster than) the following use
    of `apply_along_axis`::

        out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)

    Examples
    --------
    >>> a = [4, 3, 5, 7, 6, 8]
    >>> indices = [0, 1, 4]
    >>> np.take(a, indices)
    array([4, 3, 6])

    In this example if `a` is an ndarray, "fancy" indexing can be used.

    >>> a = np.array(a)
    >>> a[indices]
    array([4, 3, 6])

    If `indices` is not one dimensional, the output also has these dimensions.

    >>> np.take(a, [[0, 1], [2, 3]])
    array([[4, 3],
           [5, 7]])
    r9