code: def testReplaceListOfLayers_Sequential(self):
      class ReplaceConvBatchNorm(transforms.Transform):
          """Replaces a ConvBatchNorm pattern with the same set of layers.
  
          Doesn't make any meaningful change to the layer. Just verifies that
          replacing multiple layers works as expected.
          """
  
          def pattern(self):
              return LayerPattern(
                  "BatchNormalization", inputs=[LayerPattern("Conv2D")]
              )
  
          def replacement(self, match_layer):
              # Adds a modification so the transform happens. If the layers are
              # exactly the same, they get ignored by transformer.
              match_layer.metadata["key"] = "value"
              return match_layer
  
      model = tf.keras.Sequential(
          [
              tf.keras.layers.Conv2D(32, 5, input_shape=(28, 28, 1)),
              tf.keras.layers.BatchNormalization(),
              tf.keras.layers.ReLU(),
          ]
      )
      model_layer_names = [layer.name for layer in model.layers]
  
      transformed_model, _ = ModelTransformer(
          model, [ReplaceConvBatchNorm()]
      ).transform()
      transformed_model_layer_names = [
          layer.name for layer in transformed_model.layers
      ]
  
      self.assertEqual(model_layer_names, transformed_model_layer_names)
  
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 17 for calling the ModelTransformerTest obj's underlying member method code with example usage: obj.testReplaceListOfLayers_Sequential() and returns: LayerPattern, match_layer