code: def main():
      parser = argparse.ArgumentParser(
          description="Release an OpenNMT-py model for inference"
      )
      parser.add_argument(
          "--model", "-m", help="The model path", required=True
      )
      parser.add_argument(
          "--output", "-o", help="The output path", required=True
      )
      parser.add_argument(
          "--format",
          choices=["pytorch", "ctranslate2"],
          default="pytorch",
          help="The format of the released model",
      )
      parser.add_argument(
          "--quantization",
          "-q",
          choices=["int8", "int16", "float16", "int8_float16"],
          default=None,
          help="Quantization type for CT2 model.",
      )
      opt = parser.parse_args()
  
      model = torch.load(opt.model, map_location=torch.device("cpu"))
      if opt.format == "pytorch":
          model["optim"] = None
          torch.save(model, opt.output)
      elif opt.format == "ctranslate2":
          import ctranslate2
  
          converter = ctranslate2.converters.OpenNMTPyConverter(
              opt.model
          )
          converter.convert(
              opt.output, force=True, quantization=opt.quantization
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types