more work

  """

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

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

  return tf.reduce_sum(tf.multiply(x_norm, y_norm))

  # the cosine distance is 1 - the cosine similarity

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

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

  def test_cosine_dist(self):

    """Test invoking _cosine_dist."""

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

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

    print(layers._cosine_dist(x,y))

    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

  def test_highway(self):

    """Test invoking Highway."""