input: def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """ Transpile an inference model from source directory to destination directory.
      This function creates a CPUPlace and Executor using Paddle Fluid, loads the inference
      program, applies the transpiler to optimize the program, and saves the optimized
      program to the specified destination directory. If the destination directory does
      not exist, it will be created.
  
      >>> Transpile('/src', '/dst', 'model', 'params')
      Loading inference_program from  /src
      Saving the optimized inference_program to  /dst
      """
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          print("Loading inference_program from ", src_dir)
          [inference_program, feed_target_names,
           fetch_targets] = fluid.io.load_inference_model(src_dir, exe, model_filename, params_filename)
  
          inference_transpiler_program = inference_program.clone()
          t = fluid.InferenceTranspiler()
          t.transpile(inference_transpiler_program, place)
  
          print("Saving the optimized inference_program to ", dst_dir)
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          model_path = os.path.join(dst_dir, model_filename)
          with open(model_path, "wb") as f:
              f.write(inference_transpiler_program.desc.serialize_to_string())
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)
output:     place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          print("Loading inference_program from ", src_dir)
          [inference_program, feed_target_names,
           fetch_targets] = fluid.io.load_inference_model(src_dir, exe, model_filename, params_filename)
  
          inference_transpiler_program = inference_program.clone()
          t = fluid.InferenceTranspiler()
          t.transpile(inference_transpiler_program, place)
  
          print("Saving the optimized inference_program to ", dst_dir)
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          model_path = os.path.join(dst_dir, model_filename)
          with open(model_path, "wb") as f:
              f.write(inference_transpiler_program.desc.serialize_to_string())
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)