code: def testAddsQuantizeLayerAfterInputLayer(self):
      inp1 = keras.layers.Input((3,))
      inp2 = keras.layers.Input((3,))
      x = keras.layers.Concatenate()([inp1, inp2])
      model = keras.Model([inp1, inp2], x)
  
      transformed_model, _ = ModelTransformer(
          model, [default_8bit_transforms.InputLayerQuantize()]
      ).transform()
  
      for input_layer in transformed_model._input_layers:
          layer_after_input = input_layer._outbound_nodes[
              0
          ].outbound_layer
          self.assertIsInstance(
              layer_after_input, quantize_layer.QuantizeLayer
          )
          self.assertIsInstance(
              layer_after_input.quantizer, quantizers.AllValuesQuantizer
          )
  
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 18 for calling the DefaultTransformsTest obj's underlying member method code with example usage: obj.testAddsQuantizeLayerAfterInputLayer() without return types