code: def testReplaceSingleLayer_WithMultipleLayers(self, model_type):
      class ReplaceDenseWithDenseAndActivation(transforms.Transform):
          """Dense => (Dense -> ReLU)."""
  
          def pattern(self):
              return LayerPattern("Dense")
  
          def replacement(self, match_layer):
              activation_layer = keras.layers.Activation("linear")
              layer_config = quantize_utils.serialize_layer(
                  activation_layer, use_legacy_format=True
              )
              layer_config["name"] = activation_layer.name
  
              activation_layer_node = LayerNode(
                  layer_config, input_layers=[match_layer]
              )
  
              return activation_layer_node
  
      if model_type == "functional":
          inp = keras.layers.Input((3,))
          out = keras.layers.Dense(2)(inp)
          model = keras.Model(inp, out)
      elif model_type == "sequential":
          model = keras.Sequential(
              [keras.layers.Dense(2, input_shape=(3,))]
          )
  
      transformed_model, _ = ModelTransformer(
          model, [ReplaceDenseWithDenseAndActivation()]
      ).transform()
  
      if model_type == "functional":
          # Extra Input layer over Sequential.
          num_expected_layers = 3
      elif model_type == "sequential":
          num_expected_layers = 2
  
      self.assertLen(transformed_model.layers, num_expected_layers)
  
      self.assertIsInstance(
          self._get_layer(transformed_model, 0, model_type),
          keras.layers.Dense,
      )
      self.assertIsInstance(
          self._get_layer(transformed_model, 1, model_type),
          keras.layers.Activation,
      )
  
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_model_optimization.python.core.quantization.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras.graph_transformations import model_transformer
  from tensorflow_model_optimization.python.core.quantization.keras.graph_transformations import transforms
label_desc: Use image node_id 13 for calling the ModelTransformerTest obj's underlying member method code with example usage: obj.testReplaceSingleLayer_WithMultipleLayers(model_type) and returns: LayerPattern, activation_layer_node