input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpiles an inference model from a source directory to a destination directory.
      The function loads the inference model, applies optimizations using the inference 
      transpiler, and saves the optimized model to the destination directory.
      >>> Transpile('/source', '/destination', 'model.pb', 'parameters.pb')
      Loading inference_program from  /source
      Saving the optimized inference_program to  /destination
      """
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
output:     inference_program = 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}")
      
      transpiler = fluid.InferenceTranspiler()
      transpiler.transpile(inference_program, place)
      
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=inference_program[1], target_vars=inference_program[2], executor=exe, main_program=inference_program[0], model_filename=model_filename, params_filename=params_filename)
      print(f"Saving the optimized inference_program to  {dst_dir}")