input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      This function takes in four arguments: src_dir, dst_dir, model_filename, and params_filename. 
      It creates a CPUPlace object and an Executor object using Paddle Fluid. Then, it creates a 
      Scope object using fluid.core.Scope(). The function loads the inference program, feed target 
      names, and fetch targets using fluid.io.load_inference_model(). It clones the inference 
      program and applies the inference transpiler using fluid.InferenceTranspiler(). The optimized 
      inference program is saved to dst_dir using fluid.io.save_persistables(). If dst_dir does not 
      exist, it creates it using os.makedirs().
      
      Example:
      Transpile('/path/to/src', '/path/to/dst', 'model', 'params')
      # Output: 
      # Loading inference_program from  /path/to/src
      # Saving the optimized inference_program to  /path/to/dst
      """
output:     import paddle.fluid as fluid
      import os
  
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
      scope = fluid.core.Scope()
  
      with fluid.scope_guard(scope):
          [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)
          print(f"Loading inference_program from  {src_dir}")
  
          transpiler = fluid.InferenceTranspiler()
          transpiler.transpile(inference_program, place)
  
          if not os.path.exists(dst_dir):
              os.makedirs(dst_dir)
  
          fluid.io.save_persistables(executor=exe, dirname=dst_dir, main_program=inference_program)
          print(f"Saving the optimized inference_program to  {dst_dir}")