input: 
  def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """
      Transpiles a Paddle Fluid inference model from a source directory to a destination directory.
      
      >>> Transpile('./src', './dst', 'model.pdmodel', 'params.pdiparams')
      Loading inference_program from ./src
      Saving the optimized inference_program to ./dst
      """
  
output:     import paddle
      import os
  
      # Load the inference model from the source directory
      inference_program = paddle.static.load_inference_model(
          model_filename,
          paddle.static.Executor(paddle.CPUPlace()),
          model_dir=src_dir,
          params_filename=params_filename
      )
  
      # Print the loading message
      print(f"Loading inference_program from {src_dir}")
  
      # Save the optimized inference model to the destination directory
      paddle.static.save_inference_model(
          model_filename,
          paddle.static.Executor(paddle.CPUPlace()),
          inference_program[0],
          inference_program[1],
          inference_program[2],
          model_dir=dst_dir,
          params_filename=params_filename
      )
  
      # Print the saving message
      print(f"Saving the optimized inference_program to {dst_dir}")