input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      import os
      import paddle.fluid as fluid
  
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          [inference_program, feed_target_names,
           fetch_targets] = fluid.io.load_inference_model(src_dir, exe, model_filename, params_filename)
  
          inference_transpiler_program = inference_program.clone()
          t = fluid.InferenceTranspiler()
          t.transpile(inference_transpiler_program, place)
  
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)
  
  """
  This function is designed to transpile a given model from a source directory to an optimized form, which is then saved to a destination directory. 
  
  Example:
  >>> Transpile('/path/to/src', '/path/to/dst', 'model.pd', 'params.pd')
  
  The above call will load the model and parameters from '/path/to/src', transpile the model, and save the optimized version to '/path/to/dst'.
  """
output:     fluid.io.save_inference_model(dst_dir, exe, inference_transpiler_program, model_filename, params_filename)