ay_likes
    The arrays to broadcast.

subok : bool, optional
    If True, then sub-classes will be passed-through, otherwise
    the returned arrays will be forced to be a base-class array (default).

Returns
-------
broadcasted : tuple of arrays
    These arrays are views on the original arrays.  They are typically
    not contiguous.  Furthermore, more than one element of a
    broadcasted array may refer to a single memory location. If you need
    to write to the arrays, make copies first. While you can set the
    ``writable`` flag True, writing to a single output value may end up
    changing more than one location in the output array.

    .. deprecated:: 1.17
        The output is currently marked so that if written to, a deprecation
        warning will be emitted. A future version will set the
        ``writable`` flag False so writing to it will raise an error.

See Also
--------
broadcast
broadcast_to
broadcast_shapes

Examples
--------
>>> import numpy as np
>>> x = np.array([[1,2,3]])
>>> y = np.array([[4],[5]])
>>> np.broadcast_arrays(x, y)
(array([[1, 2, 3],
        [1, 2, 3]]),
 array([[4, 4, 4],
        [5, 5, 5]]))

Here is a useful idiom for getting contiguous copies instead of
non-contiguous views.

>>> [np.array(a) for a in np.broadcast_arrays(x, y)]
[array([[1, 2, 3],
        [1, 2, 3]]),
 array([[4, 4, 4],
        [5, 5, 5]])]

Nr&