input: from typing import List
  
  def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """Transpile the inference model from src_dir to dst_dir, applying optimization.
      Load the inference program and params from src_dir and model_filename, apply
      inference transpiler optimization, and save the optimized inference model to
      dst_dir with params_filename. If dst_dir does not exist, create it.
      >>> Transpile('source', 'destination', 'model', 'params')
      'Loading inference_program from source'
      'Saving the optimized inference_program to destination'
      """
output:     import os
      import paddle
  
      # Load the inference program and params from src_dir
      inference_program = paddle.static.load_inference_model(model_filename, paddle.CPUPlace(), model_dir=src_dir)
      params = paddle.static.load_params(params_filename, paddle.CPUPlace(), model_dir=src_dir)
  
      # Apply inference transpiler optimization
      transpiler = paddle.fluid.Transpiler()
      optimized_program = transpiler.transpile(inference_program)
  
      # Create dst_dir if it does not exist
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized inference model to dst_dir
      paddle.static.save_inference_model(model_filename, [paddle.static.InputSpec(shape=[None, 1], dtype='float32')], [paddle.static.default_main_program().global_block().var('predict')], paddle.CPUPlace(), model_dir=dst_dir)
      paddle.static.save_params(params_filename, paddle.CPUPlace(), model_dir=dst_dir)
  
      print('Loading inference_program from ' + src_dir)
      print('Saving the optimized inference_program to ' + dst_dir)