more work

  >>> # the pairwise inner product of the rows in x and y will always be 1

  >>> # the output tensor will be of shape (6,6)

  >>> cos_sim_same = layers._cosine_dist(x,y_same)

  >>> cos_sim_same.shape

  (6, 6)

  >>> diff = cos_sim_same - tf.ones((6, 6), dtype=tf.dtypes.float32, name=None)

  >>> assert tf.reduce_sum(diff) == 0 # True

  True

  The cosine similarity between two orthogonal vectors will be 0 (by definition).

  If every row in `x` is orthogonal to every row in `y`, then the output will be a

  >>> # the pairwise inner product of the rows in x and y will always be 0

  >>> # the output tensor will be of shape (256,256)

  >>> cos_sim_orth = layers._cosine_dist(x1,x2)

  >>> cos_sim_orth.shape

  (256, 256)

  >>> assert tf.reduce_sum(cos_sim_orth) == 0 # True

  True

  >>> assert all([cos_sim_orth.shape[dim] == 256 for dim in range(2)]) # True

  True

  Parameters

  ----------