input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """Transpile and save an optimized inference model using Paddle Fluid.
      The function takes source and destination directories, model filename, and parameters filename.
      It loads the inference model from the source directory, optimizes it using the inference transpiler,
      and saves the optimized model to the destination directory. If the destination directory does not exist,
      it creates it.
      
      >>> Transpile('src', 'dst', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from  src
      Saving the optimized inference_program to  dst
      """
output:     import os
      import paddle.fluid as fluid
  
      # Load the inference model from the source directory
      print(f"Loading inference_program from  {src_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
      )
  
      # Optimize the inference model using the inference transpiler
      t = fluid.contrib.inferencer.InferenceTranspiler()
      t.transpile(inference_program, place)
  
      # Create the destination directory if it does not exist
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized inference model to the destination directory
      print(f"Saving the optimized inference_program to  {dst_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,
          model_filename=model_filename,
          params_filename=params_filename
      )