input: 
  def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """ Transpiles a given inference model from a source directory to an optimized form in a destination directory.
      It creates a CPUPlace and Executor, loads the inference model, applies the inference transpiler, and saves the
      optimized model. If the destination directory does not exist, it is created.
  
      >>> Transpile('src_model', 'dst_model', 'model', 'params')
      # Output: Loading inference_program from src_model
      # Output: Saving the optimized inference_program to dst_model
      """
  
output:     
      import os
      import paddle
      from paddle.fluid import CPUPlace, Executor
      from paddle.fluid.io import save_inference_model, load_inference_model
      from paddle.fluid.transpiler.details import inference_transpiler
  
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      place = CPUPlace()
      exe = Executor(place)
  
      [inference_program, feed_target_names, fetch_targets] = load_inference_model(model_filename, exe, model_dir=src_dir)
      print(f"Loading inference_program from {src_dir}")
  
      inference_transpiler(inference_program)
  
      save_inference_model(dirname=dst_dir, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=inference_program)
      print(f"Saving the optimized inference_program to {dst_dir}")
  