` documentation.  `hsplit` is equivalent
    to `split` with ``axis=1``, the array is always split along the second
    axis except for 1-D arrays, where it is split at ``axis=0``.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[ 0.,   1.,   2.,   3.],
           [ 4.,   5.,   6.,   7.],
           [ 8.,   9.,  10.,  11.],
           [12.,  13.,  14.,  15.]])
    >>> np.hsplit(x, 2)
    [array([[  0.,   1.],
           [  4.,   5.],
           [  8.,   9.],
           [12.,  13.]]),
     array([[  2.,   3.],
           [  6.,   7.],
           [10.,  11.],
           [14.,  15.]])]
    >>> np.hsplit(x, np.array([3, 6]))
    [array([[ 0.,   1.,   2.],
           [ 4.,   5.,   6.],
           [ 8.,   9.,  10.],
           [12.,  13.,  14.]]),
     array([[ 3.],
           [ 7.],
           [11.],
           [15.]]),
     array([], shape=(4, 0), dtype=float64)]

    With a higher dimensional array the split is still along the second axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[0.,  1.],
            [2.,  3.]],
           [[4.,  5.],
            [6.,  7.]]])
    >>> np.hsplit(x, 2)
    [array([[[0.,  1.]],
           [[4.,  5.]]]),
     array([[[2.,  3.]],
           [[6.,  7.]]])]

    With a 1-D array, the split is along axis 0.

    >>> x = np.array([0, 1, 2, 3, 4, 5])
    >>> np.hsplit(x, 2)
    [array([0, 1, 2]), array([3, 4, 5])]

    r