code: def _get_input_shape(self, layer_type):
      if layer_type == "Conv2D":
          return Conv2DModel.get_batched_input_shape()
      elif layer_type == "DepthwiseConv2D":
          return DepthwiseConv2DModel.get_batched_input_shape()
      elif layer_type == "Dense":
          return DenseModel.get_batched_input_shape()
  
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.experimental.default_n_bit import default_n_bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_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 3 for calling the DefaultTransformsTest obj's underlying member method code with example usage: obj._get_input_shape(layer_type) and returns: Conv2DModel, DepthwiseConv2DModel, DenseModel