input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile a Paddle Fluid inference model from one directory to another with optimization.
      The function first creates a CPUPlace and an Executor. It then loads the inference program 
      from the source directory, clones it, applies the inference transpiler, and saves the optimized 
      program to the destination directory.
      >>> Transpile('/source/dir', '/destination/dir', 'model.pdmodel', 'model.pdparams')
      (The function prints:
      Loading inference_program from  /source/dir
      Saving the optimized inference_program to  /destination/dir)
      """
output:     import paddle.fluid as fluid
      import os
  
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      print(f"Loading inference_program from  {src_dir}")
      [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.compiler.CompiledProgram(inference_program).with_data_parallel()
      optimized_program = transpiler.inference_optimize()
  
      print(f"Saving the optimized inference_program to  {dst_dir}")
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=optimized_program, model_filename=model_filename, params_filename=params_filename)