 >>> np.sort(a, axis=1)
    array([[10, 20, 30],
           [40, 50, 60]])
    >>> ai = np.argsort(a, axis=1)
    >>> ai
    array([[0, 2, 1],
           [1, 2, 0]])
    >>> np.take_along_axis(a, ai, axis=1)
    array([[10, 20, 30],
           [40, 50, 60]])

    The same works for max and min, if you maintain the trivial dimension
    with ``keepdims``:

    >>> np.max(a, axis=1, keepdims=True)
    array([[30],
           [60]])
    >>> ai = np.argmax(a, axis=1, keepdims=True)
    >>> ai
    array([[1],
           [0]])
    >>> np.take_along_axis(a, ai, axis=1)
    array([[30],
           [60]])

    If we want to get the max and min at the same time, we can stack the
    indices first

    >>> ai_min = np.argmin(a, axis=1, keepdims=True)
    >>> ai_max = np.argmax(a, axis=1, keepdims=True)
    >>> ai = np.concatenate([ai_min, ai_max], axis=1)
    >>> ai
    array([[0, 1],
           [1, 0]])
    >>> np.take_along_axis(a, ai, axis=1)
    array([[10, 30],
           [40, 60]])
    Nr