input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """ Transpiles an inference model from src_dir and saves the optimized version to dst_dir.
      If the destination directory does not exist, it is created. The function uses Paddle Fluid
      to load the inference model, apply the inference transpiler, and save the optimized model.
      >>> Transpile('source_dir', 'destination_dir', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from source_dir
      Saving the optimized inference_program to destination_dir
      """
output:     import os
      import paddle.fluid as fluid
  
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(dirname=src_dir, executor=exe, model_filename=model_filename, params_filename=params_filename)
  
      print("Loading inference_program from " + src_dir)
  
      t = fluid.InferenceTranspiler()
      t.transpile(inference_program, place)
  
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=inference_program, model_filename=model_filename, params_filename=params_filename)
  
      print("Saving the optimized inference_program to " + dst_dir)