code: def _get_model(
      self,
      layer_type,
      squeeze_type,
      normalization_type,
      activation_type,
  ):
      activation = None
      if activation_type == "relu":
          activation = keras.layers.ReLU(6.0)
      elif activation_type == "act_relu":
          activation = keras.layers.Activation("relu")
  
      if layer_type == "Conv2D":
          return Conv2DModel.get_nonfolded_batchnorm_model(
              model_type="functional",
              post_bn_activation=activation,
              squeeze_type=squeeze_type,
              normalization_type=normalization_type,
          )
      elif layer_type == "DepthwiseConv2D":
          return DepthwiseConv2DModel.get_nonfolded_batchnorm_model(
              model_type="functional",
              post_bn_activation=activation,
              squeeze_type=squeeze_type,
              normalization_type=normalization_type,
          )
      elif layer_type == "Dense":
          return DenseModel.get_nonfolded_batchnorm_model(
              post_bn_activation=activation,
              normalization_type=normalization_type,
          )
  
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 2 for calling the DefaultTransformsTest obj's underlying member method code with example usage: obj._get_model(layer_type, squeeze_type, normalization_type, activation_type) and returns: Conv2DModel, DepthwiseConv2DModel, DenseModel