input: def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """ Transpile an inference model from a source directory to a destination directory.
      Load the inference program and apply the inference transpiler for optimization.
      Save the optimized inference program to the destination directory. If the destination
      directory does not exist, create it.
  
      >>> Transpile('src_model', 'dst_model', 'model', 'params')
      Loading inference_program from src_model
      Saving the optimized inference_program to dst_model
      """
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          print("Loading inference_program from ", src_dir)
          [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)
  
          print("Saving the optimized inference_program to ", dst_dir)
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          model_path = os.path.join(dst_dir, model_filename)
          with open(model_path, "wb") as f:
              f.write(inference_transpiler_program.desc.serialize_to_string())
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)
output:     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):
          print("Loading inference_program from ", src_dir)
          [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)
  
          print("Saving the optimized inference_program to ", dst_dir)
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          model_path = os.path.join(dst_dir, model_filename)
          with open(model_path, "wb") as f:
              f.write(inference_transpiler_program.desc.serialize_to_string())
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)