input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """ Transpile an inference model using PaddlePaddle Fluid.
      The function takes the source directory of the model, the destination directory 
      where the optimized model will be saved, the name of the model file, and the 
      name of the params file. The optimized model is saved in the destination directory.
      If the destination directory does not exist, it is created.
      
      >>> Transpile('src', 'dst', 'model', 'params')
      Loading inference_program from  src
      Saving the optimized inference_program to  dst
      
      >>> Transpile('src', 'dst', 'model2', 'params2')
      Loading inference_program from  src
      Saving the optimized inference_program to  dst
      """
  
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(f"Loading inference_program from  {src_dir}")
  
      fluid.io.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}")
  