more work

  """

  x_norm = tf.nn.l2_normalize(x, axis=1)

  y_norm = tf.nn.l2_normalize(y, axis=1)

  # the cosine distance is 1 - the cosine similarity

  return 1. - tf.reduce_sum(tf.multiply(x_norm, y_norm), axis=1)

  return 1. - backend.dot(x_norm, tf.transpose(y_norm))

class AttnLSTMEmbedding(tf.keras.layers.Layer):

  def test_cosine_dist(self):

    """Test invoking _cosine_dist."""

    x = np.ones((5, 4)).astype(np.float32)

    y_same = np.ones((5, 4)).astype(np.float32)

    y_far = np.zeros((5, 4)).astype(np.float32)

    assert sum(layers._cosine_dist(x, y_same)) == 0

    assert sum(layers._cosine_dist(x, y_far)) == 5

    x = tf.ones((5,4), dtype=tf.dtypes.float32, name=None)

    y_same = tf.ones((5,4), dtype=tf.dtypes.float32, name=None)

    y_far = -1. * tf.ones((5,4), dtype=tf.dtypes.float32, name=None) 

    close_cosine_dist = layers._cosine_dist(x, y_same)

    far_close_dist = layers._cosine_dist(x, y_far)

    assert tf.reduce_sum(close_cosine_dist) == 0.

    assert tf.reduce_sum(far_close_dist) == 2.*5.*5.

  def test_highway(self):

    """Test invoking Highway."""