code: def test_wordpiece_tokenization_multi_layer(
      self,
      input_data,
      expected_output,
      vocab_list,
      input_shape=(None,),
      pad_value=None,
      merge_wordpiece_dim=False,
  ):
      # create a functional API model
      i = tf.keras.layers.Input(shape=input_shape, dtype=tf.string)
      layer1 = tokenization_layers.WordpieceTokenizer(
          vocabulary=vocab_list,
          pad_value=pad_value,
          merge_wordpiece_dim=merge_wordpiece_dim,
      )
      o1 = layer1(i)
      layer2 = tokenization_layers.WordpieceTokenizer(
          vocabulary=vocab_list,
          pad_value=pad_value,
          merge_wordpiece_dim=merge_wordpiece_dim,
      )
      o2 = layer2(i)
      model = tf.keras.models.Model(i, [o1, o2])
  
      out1, out2 = model.predict(input_data)
      self.assertAllEqual(out1, expected_output)
      self.assertAllEqual(out2, expected_output)
  
file_path: text/tensorflow_text/python/keras/layers/tokenization_layers_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_text.python.keras.layers import tokenization_layers
label_desc: Use image node_id 8 for calling the TokenizationLayersTest obj's underlying member method code with example usage: obj.test_wordpiece_tokenization_multi_layer(input_data, expected_output, vocab_list, input_shape, pad_value, merge_wordpiece_dim) without return types