23]]]])

    The axis can be specified explicitly:

    >>> v = sliding_window_view(x, 3, 0)
    >>> v.shape
    (1, 4, 3)
    >>> v
    array([[[ 0, 10, 20],
            [ 1, 11, 21],
            [ 2, 12, 22],
            [ 3, 13, 23]]])

    The same axis can be used several times. In that case, every use reduces
    the corresponding original dimension:

    >>> v = sliding_window_view(x, (2, 3), (1, 1))
    >>> v.shape
    (3, 1, 2, 3)
    >>> v
    array([[[[ 0,  1,  2],
             [ 1,  2,  3]]],
           [[[10, 11, 12],
             [11, 12, 13]]],
           [[[20, 21, 22],
             [21, 22, 23]]]])

    Combining with stepped slicing (`::step`), this can be used to take sliding
    views which skip elements:

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

    or views which move by multiple elements

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

    A common application of `sliding_window_view` is the calculation of running
    statistics. The simplest example is the
    `moving average <https://en.wikipedia.org/wiki/Moving_average>`_:

    >>> x = np.arange(6)
    >>> x.shape
    (6,)
    >>> v = sliding_window_view(x, 3)
    >>> v.shape
    (4, 3)
    >>> v
    array([[0, 1, 2],
           [1, 2, 3],
           [2, 3, 4],
           [3, 4, 5]])
    >>> moving_average = v.mean(axis=-1)
    >>> moving_average
    array([1., 2., 3., 4.])

    Note that a sliding window approach is often **not** optimal (see Notes).
    Fr