input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile a given Paddle Fluid inference model from the source directory to the destination directory.
      The function loads an inference model, applies the inference transpiler to optimize it, and saves the
      optimized model to the destination directory. If the destination directory does not exist, it will be created.
      
      >>> Transpile('source_dir', 'dest_dir', 'model.pdmodel', 'model.pdparams')
      Loading inference_program from  source_dir
      Saving the optimized inference_program to  dest_dir
      """
  
output:     
      import os
      import paddle.fluid as fluid
  
      # Load the inference model
      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)
  
      # Apply the inference transpiler to optimize the model
      t = fluid.Transpiler()
      optimized_program = 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
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=optimized_program, model_filename=model_filename, params_filename=params_filename)
  
      print(f"Loading inference_program from {src_dir}")
      print(f"Saving the optimized inference_program to {dst_dir}")
  