input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """ Transpiles the inference model from src_dir to dst_dir with the given model and params filenames.
      The function loads the inference model, clones it, applies the inference transpiler, and saves the optimized
      model to the destination directory. If the destination directory does not exist, it creates it.
      >>> Transpile('/path/to/source', '/path/to/destination', 'model.pdmodel', 'params.pdparams')
      """
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          print("Loading inference_program from ", src_dir)
          [inference_program, feed_target_names,
           fetch_targets] = fluid.io.load_inference_model(src_dir, exe, model_filename, params_filename)
  
          inference_transpiler_program = inference_program.clone()
          t = fluid.InferenceTranspiler()
          t.transpile(inference_transpiler_program, place)
  
          print("Saving the optimized inference_program to ", dst_dir)
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          model_path = os.path.join(dst_dir, model_filename)
          with open(model_path, "wb") as f:
              f.write(inference_transpiler_program.desc.serialize_to_string())
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)
  
  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)
output:     import os
      import paddle.fluid as fluid
      import argparse