input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile an inference model from the source directory to the destination directory.
      This function loads the inference model, applies a transpiler to optimize it, and then
      saves the optimized model to the destination directory. If the destination directory
      does not exist, it creates it automatically.
  
      >>> Transpile('/path/to/src', '/path/to/dst', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from /path/to/src
      Saving the optimized inference_program to /path/to/dst
      """
  
output:     import os
      import paddle
  
      # Load the inference model
      inference_program = paddle.static.load_inference_model(model_filename, paddle.CPUPlace(), params_filename, src_dir)
      print(f"Loading inference_program from {src_dir}")
  
      # Apply a transpiler to optimize the model
      optimized_program = paddle.static.CompiledProgram(inference_program)
  
      # Ensure the destination directory exists
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized model to the destination directory
      paddle.static.save_inference_model(model_filename, paddle.CPUPlace(), optimized_program, dst_dir)
      print(f"Saving the optimized inference_program to {dst_dir}")