code: def convert_to_tflite(keras_model, output_path):
      """Converts the given Keras model to TFLite and write it to a file."""
      converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
      converter.optimizations = {tf.lite.Optimize.EXPERIMENTAL_SPARSITY}
  
      with open(output_path, "wb") as out:
          out.write(converter.convert())
  
file_path: model-optimization/tensorflow_model_optimization/python/core/sparsity/keras/tools/evaluate_pruning.py
imports: from __future__ import print_function
  import os
  import tempfile
  import textwrap
  import zipfile
  from absl import app
  from absl import flags
  import tensorflow
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras.tools import sparsity_tooling
label_desc: Use image node_id 3 for calling a global function with example usage: convert_to_tflite(keras_model, output_path) without return types