input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """Transpile and save an optimized inference program using Paddle Fluid.
      The function loads an inference model from 'src_dir', processes it using Paddle Fluid's Inference Transpiler,
      and saves the optimized model to 'dst_dir'. If 'dst_dir' does not exist, it creates the directory.
      
      >>> Transpile('source_dir', 'destination_dir', 'model.bin', 'params.bin')
      Loading inference_program from  source_dir
      Saving the optimized inference_program to  destination_dir
      """
output:     
      import os
      import paddle.fluid as fluid
  
      # Load the inference model
      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)
  
      # Transpile the inference program
      t = fluid.contrib.InferenceTranspiler()
      t.transpile(inference_program, place)
  
      # Create the destination directory if it doesn't exist
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized inference program
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=inference_program)
  