code: def testAnnotatesKerasLayer(self):
      layer = keras.layers.Dense(
          5, activation="relu", input_shape=(10,)
      )
      model = keras.Sequential([layer])
  
      quantize_config = self.TestQuantizeConfig()
      annotated_model = keras.Sequential(
          [
              quantize_annotate.QuantizeAnnotate(
                  layer,
                  quantize_config=quantize_config,
                  input_shape=(10,),
              )
          ]
      )
  
      annotated_layer = annotated_model.layers[0]
      self.assertEqual(layer, annotated_layer.layer)
      self.assertEqual(quantize_config, annotated_layer.quantize_config)
  
      # Annotated model should not affect computation. Returns same results.
      x_test = np.random.rand(10, 10)
      self.assertAllEqual(
          model.predict(x_test), annotated_model.predict(x_test)
      )
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/quantize_annotate_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_annotate
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_config
label_desc: Use image node_id 2 for calling the QuantizeAnnotateTest obj's underlying member method code with example usage: obj.testAnnotatesKerasLayer() without return types