code: def evaluate_model_fp32(model, image_test, label_test):
      score = model.evaluate(image_test, label_test, verbose=0)
      return score[1]
  
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 6 for calling a global function with example usage: evaluate_model_fp32(model, image_test, label_test) and returns: score