code: def testReplaceSingleLayerWithSingleLayer_DictInputOutput(self):
      inp = {
          "input1": keras.layers.Input((3,)),
          "input2": keras.layers.Input((3,)),
      }
      x1 = keras.layers.Dense(2)(inp["input1"])
      x2 = keras.layers.Dense(2)(inp["input2"])
      out1 = keras.layers.ReLU(6.0)(x1)
      out2 = keras.layers.ReLU(6.0)(x2)
      model = keras.Model(inp, {"output1": out1, "output2": out2})
  
      transformed_model, _ = ModelTransformer(
          model, [self.ReplaceDenseLayer()]
      ).transform()
  
      # build_input_shape is a TensorShape object and the two objects are not
      # considered the same even though the shapes are the same.
      self._assert_config(
          model.get_config(),
          transformed_model.get_config(),
          ["class_name", "build_input_shape"],
      )
  
      # There are two input layers in the input dict.
      self.assertEqual(
          "MyDense",
          self._get_layer(
              transformed_model, 1, "functional"
          ).__class__.__name__,
      )
      self.assertEqual(
          "MyDense",
          self._get_layer(
              transformed_model, 2, "functional"
          ).__class__.__name__,
      )
  
      self._assert_model_results_equal(model, transformed_model)
  
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 utils
  from tensorflow_model_optimization.python.core.quantization.keras.graph_transformations import model_transformer
  from tensorflow_model_optimization.python.core.quantization.keras.graph_transformations import transforms
label_desc: Use image node_id 10 for calling the ModelTransformerTest obj's underlying member method code with example usage: obj.testReplaceSingleLayerWithSingleLayer_DictInputOutput() without return types