code: def test_dot_product_loss_inner_sim():
      layer = DotProductLoss(0, similarity_type=INNER)
      a = tf.constant([[[1.0, 0.0, 2.0]], [[1.0, 0.0, 2.0]]])
      b = tf.constant([[[1.0, 0.0, -2.0]], [[1.0, 0.0, -2.0]]])
      mask = tf.constant([[1.0, 0.0]])
      similarity = layer.sim(a, b, mask=mask).numpy()
      assert np.all(similarity == [[[-3.0], [0.0]]])
  
imports: from typing import Text, List, Tuple, Union, Optional
  import pytest
  from _pytest.monkeypatch import MonkeyPatch
  import numpy
  import tensorflow
  from rasa.utils.tensorflow.layers import DotProductLoss, MultiLabelDotProductLoss, RandomlyConnectedDense, DenseForSparse
  from rasa.utils.tensorflow.constants import INNER, SOFTMAX, LABEL, LABEL_PAD_ID
  import rasa.utils.tensorflow.layers_utils
  from rasa.shared.nlu.constants import TEXT, INTENT, ACTION_NAME, ACTION_TEXT, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE
  from rasa.core.constants import DIALOGUE
label_desc: Use image node_id 1 for calling a global function with example usage: test_dot_product_loss_inner_sim() without return types