code: def _main():
      import argparse
  
      parser = argparse.ArgumentParser(
          description="Extract pre-trained models for frameworks."
      )
  
      parser.add_argument(
          "--framework",
          "-f",
          type=_text_type,
          required=True,
          choices=[
              "caffe",
              "cntk",
              "mxnet",
              "keras",
              "tensorflow",
              "tf",
              "pytorch",
              "darknet",
              "coreml",
          ],
          help="Framework name",
      )
  
      parser.add_argument(
          "--network",
          "-n",
          type=_text_type,
          default=None,
          help="Path to the model network file of the external tool (e.g caffe prototxt, keras json",
      )
  
      parser.add_argument(
          "-i", "--image", type=_text_type, help="Test Image Path"
      )
  
      parser.add_argument(
          "--path",
          "-p",
          "-o",
          type=_text_type,
          default="./",
          help="Path to save the pre-trained model files (e.g keras h5)",
      )
  
      parser.add_argument(
          "-l",
          "--label",
          type=_text_type,
          default="mmdnn/conversion/examples/data/imagenet_1000.txt",
          help="Path of label.",
      )
  
      args = parser.parse_args()
      extract_model(args)
  
label_desc: Use image node_id 3 for calling a global function with example usage: _main() without return types