input: def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """Transpiles an inference model from a source directory to a destination directory using Paddle Fluid.
      The function loads the inference program, applies the inference transpiler, and saves the optimized 
      inference program to the destination directory. If the destination directory does not exist, it creates it.
  
      >>> Transpile('/source_dir', '/destination_dir', 'model', 'params')
      Loading inference_program from  /source_dir
      Saving the optimized inference_program to  /destination_dir
      """
      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)