input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """ Transpile a given inference model from a source directory to a destination directory, optimizing it using PaddlePaddle's InferenceTranspiler. If the destination directory does not exist, it will be created.
      
      >>> Transpile('source_model_dir', 'optimized_model_dir', 'model', 'params')
      """
  
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)
  
      transpiler = fluid.InferenceTranspiler()
      transpiler.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)
  