code: def test_clustered_model(clustered_model, x_test, y_test):
      # Ensure accuracy persists after serializing/deserializing the model
      clustered_model.save("clustered_model.h5")
      # To deserialize the clustered model, use the clustering scope
      with cluster.cluster_scope():
          loaded_clustered_model = keras.models.load_model(
              "clustered_model.h5"
          )
  
      # Checking that the deserialized model's accuracy matches the clustered model
      score = loaded_clustered_model.evaluate(x_test, y_test, verbose=0)
      print("Deserialized model test loss:", score[0])
      print("Deserialized model test accuracy:", score[1])
  
      # Ensure accuracy persists after stripping the model
      stripped_model = cluster.strip_clustering(loaded_clustered_model)
      stripped_model.compile(
          loss=tf.keras.losses.SparseCategoricalCrossentropy(
              from_logits=True
          ),
          optimizer="adam",
          metrics=["accuracy"],
      )
  
      # Checking that the stripped model's accuracy matches the clustered model
      score = stripped_model.evaluate(x_test, y_test, verbose=0)
      print("Stripped model test loss:", score[0])
      print("Stripped model test accuracy:", score[1])
  
file_path: model-optimization/tensorflow_model_optimization/python/examples/clustering/keras/mnist/mnist_cnn.py
imports: from __future__ import print_function
  import datetime
  import os
  from absl import app
  from absl import flags
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_callbacks
label_desc: Use image node_id 5 for calling a global function with example usage: test_clustered_model(clustered_model, x_test, y_test) without return types