code: def _test_conv_squeeze_or_dense_bn_activation_transform(
      self,
      layer_type,
      normalization_type,
      activation_type,
      transform_class,
      conv_or_dense_activation_class,
      normalization_quantize_config_class,
      squeeze_type=None,
  ):
      model = self._get_model(
          layer_type, squeeze_type, normalization_type, activation_type
      )
      input_shape = self._get_input_shape(layer_type)
  
      transformed_model, updated_metadata = ModelTransformer(
          model,
          [transform_class()],
      ).transform()
  
      conv_or_dense_layer = transformed_model.layers[1]
      if squeeze_type == "sepconv1d_squeeze":
          bn_layer = transformed_model.layers[3]
      else:
          bn_layer = transformed_model.layers[2]
  
      self.assertIsInstance(
          conv_or_dense_layer.activation, conv_or_dense_activation_class
      )
      self.assertIsInstance(
          updated_metadata.get(bn_layer.name).get("quantize_config"),
          normalization_quantize_config_class,
      )
  
      inputs = np.random.standard_normal(input_shape)
      self.assertAllClose(
          transformed_model.predict(inputs), model.predict(inputs)
      )
  
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 quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_layer
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_transforms
  from tensorflow_model_optimization.python.core.quantization.keras.graph_transformations import model_transformer
  from tensorflow_model_optimization.python.core.quantization.keras.layers import conv_batchnorm_test_utils
  from tensorflow_model_optimization.python.core.quantization.keras.layers import dense_batchnorm_test_utils
label_desc: Use image node_id 4 for calling the DefaultTransformsTest obj's underlying member method code with example usage: obj._test_conv_squeeze_or_dense_bn_activation_transform(layer_type, normalization_type, activation_type, transform_class, conv_or_dense_activation_class, normalization_quantize_config_class, squeeze_type) without return types