input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile a model from a source directory to a destination directory. This involves loading an inference model, applying optimizations, and saving the optimized model.
      
      >>> Transpile('source_model', 'optimized_model', 'model.pdmodel', 'params.pdiparams')
      Loading inference_program from  source_model
      Saving the optimized inference_program to  optimized_model
      """
  
output:     import paddle
      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 optimizations (example: memory optimization)
      build_strategy = fluid.BuildStrategy()
      build_strategy.memory_optimize = True
      optimized_program = fluid.CompiledProgram(inference_program).with_data_parallel(build_strategy=build_strategy)
  
      # Save the optimized model
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=optimized_program._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}")