code: def main(_):
      logging.set_verbosity(logging.INFO)
      data_dir = download_demo_data()
      export_dir = os.path.expanduser(FLAGS.export_dir)
  
      if FLAGS.supported_model == "average_word_embedding":
          supported_model = (
              text_classifier.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER
          )
      elif FLAGS.supported_model == "bert":
          supported_model = (
              text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER
          )
  
      run(data_dir, export_dir, supported_model)
  
file_path: mediapipe/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py
imports: import os
  import tempfile
  from absl import app
  from absl import flags
  from absl import logging
  import tensorflow
  from mediapipe.model_maker.python.core.utils import quantization
  from mediapipe.model_maker.python.text import text_classifier
label_desc: Use image node_id 4 for calling a global function with example usage: main(_) without return types