code: def testPatternCanMatch_MultipleLayers(self, model_type):
      pattern = LayerPattern("Conv2D|DepthwiseConv2D")
      transform = self.VerifyMatch(pattern)
  
      if model_type == "functional":
          inp = keras.layers.Input((3, 3, 3))
          x = keras.layers.Conv2D(3, (2, 2))(inp)
          conv_model = keras.Model(inp, x)
      else:
          conv_model = keras.Sequential(
              [keras.layers.Conv2D(3, (2, 2), input_shape=(3, 3, 3))]
          )
  
      ModelTransformer(conv_model, [transform]).transform()
      self.assertTrue(transform.matched())
  
      if model_type == "functional":
          inp = keras.layers.Input((3, 3, 3))
          x = keras.layers.DepthwiseConv2D((2, 2))(inp)
          depth_conv_model = keras.Model(inp, x)
      else:
          depth_conv_model = keras.Sequential(
              [
                  keras.layers.DepthwiseConv2D(
                      (2, 2), input_shape=(3, 3, 3)
                  )
              ]
          )
  
      transform.reset()
      ModelTransformer(depth_conv_model, [transform]).transform()
      self.assertTrue(transform.matched())
  
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 24 for calling the ModelTransformerTest obj's underlying member method code with example usage: obj.testPatternCanMatch_MultipleLayers(model_type) without return types