code: def cluster_preserve_quantize_model(
      clustered_model, train_images, train_labels
  ):
      """Cluster-preserve QAT model."""
      quant_aware_annotate_model = quantize.quantize_annotate_model(
          clustered_model
      )
      quant_aware_model = quantize.quantize_apply(
          quant_aware_annotate_model,
          scheme=default_8bit_cluster_preserve_quantize_scheme.Default8BitClusterPreserveQuantizeScheme(),
      )
      quant_aware_model.summary()
      compile_and_fit(quant_aware_model, train_images, train_labels, 1)
  
      return quant_aware_model
  
file_path: model-optimization/tensorflow_model_optimization/python/examples/cluster_preserve_qat/keras/mnist_cnn.py
imports: from __future__ import print_function
  import os
  from absl import app
  import numpy
  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.quantization.keras import quantize
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve import default_8bit_cluster_preserve_quantize_scheme
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve.cluster_utils import strip_clustering_cqat
label_desc: Use image node_id 5 for calling a global function with example usage: cluster_preserve_quantize_model(clustered_model, train_images, train_labels) and returns: quant_aware_model