code: def _simple_dense_model(self, model_type="functional"):
      if model_type == "functional":
          inp = keras.layers.Input((3,))
          x = keras.layers.Dense(2)(inp)
          out = keras.layers.ReLU(6.0)(x)
          return keras.Model(inp, out)
      elif model_type == "sequential":
          return keras.Sequential(
              [
                  keras.layers.Dense(2, input_shape=(3,)),
                  keras.layers.ReLU(6.0),
              ]
          )
  
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 4 for calling the ModelTransformerTest obj's underlying member method code with example usage: obj._simple_dense_model(model_type) and returns: keras, keras