input: 
  def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """ Transpile an inference model using Paddle Fluid.
      This function loads an inference model from a source directory, applies the inference 
      transpiler to optimize the model, and saves the optimized model to a destination directory.
      If the destination directory does not exist, it will be created.
      >>> Transpile('/models/src', '/models/dst', 'model', 'params')
      """
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('--src_dir', help='Source directory of inference model')
      parser.add_argument('--dst_dir', help='Destination directory of inference model')
      parser.add_argument('--model_filename', default=None, help='The name of model file')
      parser.add_argument('--params_filename', default=None, help='The name of params file')
      args = parser.parse_args()
      Transpile(args.src_dir, args.dst_dir, args.model_filename, args.params_filename)
  
  
  if __name__ == '__main__':
      main()
  
output:     
      import os
      import paddle.fluid as fluid
  
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_program, feed_target_names, fetch_targets = fluid.io.load_inference_model(
          dirname=src_dir,
          executor=exe,
          model_filename=model_filename,
          params_filename=params_filename
      )
  
      t = fluid.InferenceTranspiler()
      t.transpile(inference_program, place)
  
      fluid.io.save_inference_model(
          dirname=dst_dir,
          main_program=inference_program,
          executor=exe,
          feeded_var_names=feed_target_names,
          target_vars=fetch_targets,
          model_filename=model_filename,
          params_filename=params_filename
      )
  