code: def testConv2DReshapeBatchNormActivationQuantize(
      self, layer_type, normalization_type
  ):
      self._test_conv_squeeze_or_dense_bn_activation_transform(
          layer_type=layer_type,
          squeeze_type="sepconv1d_squeeze",
          normalization_type=normalization_type,
          activation_type="act_relu",
          transform_class=default_8bit_transforms.Conv2DReshapeBatchNormActivationQuantize,
          conv_or_dense_activation_class=quantize_aware_activation.NoOpActivation,
          normalization_quantize_config_class=default_8bit_quantize_configs.NoOpQuantizeConfig,
      )
  
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 10 for calling the DefaultTransformsTest obj's underlying member method code with example usage: obj.testConv2DReshapeBatchNormActivationQuantize(layer_type, normalization_type) without return types