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 loads a Paddle Fluid inference model from "src_dir", optimizes it using the inference transpiler, 
      and saves the optimized model to "dst_dir". If "dst_dir" does not exist, it creates it.
      
      Example:
      >>> Transpile('/path/to/src', '/path/to/dst', 'model', 'params')
      (Prints "Loading inference_program from /path/to/src" and "Saving the optimized inference_program to /path/to/dst")
      """
output:     import os
      import paddle.fluid as fluid
  
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      print(f"Loading inference_program from {src_dir}")
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
      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("Optimizing the inference_program")
      t = fluid.Transpiler()
      t.transpile(inference_program, place)
  
      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=inference_program, model_filename=model_filename, params_filename=params_filename)