input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile and optimize an inference model using Paddle Fluid. Given a source 
      directory `src_dir`, a destination directory `dst_dir`, and the filenames 
      for the model and parameters, this function loads the inference model, 
      applies an inference transpiler, and saves the optimized model to the 
      destination directory. If the destination directory does not exist, it creates it.
      
      >>> Transpile('model_src', 'model_dst', 'model.pdmodel', 'model.pdparams')
      'Loading inference_program from  model_src'
      'Saving the optimized inference_program to  model_dst'
      """
output:     import paddle.fluid as fluid
      import os
  
      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(f'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(f'Saving the optimized inference_program to {dst_dir}')