input: from typing import List
  
  def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """ Transpile an inference program and save the optimized model to a destination directory.
      
      Create a CPUPlace object and an Executor object using Paddle Fluid. Then, create
      a Scope object using fluid.core.Scope(). Load the inference program, feed target
      names, and fetch targets using fluid.io.load_inference_model(). Clone the inference
      program and apply the inference transpiler using fluid.InferenceTranspiler(). Save
      the optimized inference program to the destination directory using
      fluid.io.save_persistables(). If the destination directory does not exist, create it.
      
      Args:
          src_dir (str): Source directory of the inference model.
          dst_dir (str): Destination directory to save the optimized model.
          model_filename (str): Name of the model file.
          params_filename (str): Name of the parameters file.
      
      >>> Transpile('src', 'dst', 'model.tar', 'params.tar')
      Loading inference_program from src
      Saving the optimized inference_program to dst
      """
output:     
      import paddle.fluid as fluid
      import os
  
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
      scope = fluid.core.Scope()
  
      with fluid.scope_guard(scope):
          [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
          )
  
          transpiler = fluid.InferenceTranspiler()
          transpiler.transpile(inference_program, place)
  
          if not os.path.exists(dst_dir):
              os.makedirs(dst_dir)
  
          fluid.io.save_persistables(
              executor=exe,
              dirname=dst_dir,
              main_program=inference_program
          )
  
          print(f"Loading inference_program from {src_dir}")
          print(f"Saving the optimized inference_program to {dst_dir}")
  