input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """Transpile the inference model from source directory to destination directory.
      This function loads an inference model from the source directory, applies 
      optimization using the paddle.fluid.InferenceTranspiler, and saves the optimized
      model to the destination directory. If the destination directory does not exist,
      it will be created.
      
      >>> Transpile('./src', './dst', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from ./src
      Saving the optimized inference_program to ./dst
      """
  
output:     import os
      import paddle.fluid as fluid
  
      # Load the inference model from the source directory
      print(f"Loading inference_program from {src_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)
  
      # Apply optimization using paddle.fluid.InferenceTranspiler
      transpiler = fluid.InferenceTranspiler()
      transpiler.transpile(inference_program, place)
  
      # Create the destination directory if it does not exist
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized inference model to the destination directory
      print(f"Saving the optimized inference_program to {dst_dir}")
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=inference_program, model_filename=model_filename, params_filename=params_filename)