input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile a Paddle Fluid inference model from the source directory to the destination directory.
      The function loads the inference model, applies the inference transpiler for optimization,
      and saves the optimized model to the destination directory. If the destination directory
      does not exist, it creates it.
      
      >>> Transpile('source_models', 'optimized_models', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from  source_models
      Saving the optimized inference_program to  optimized_models
      """
  
output:     
      import paddle.fluid as fluid
      import os
  
      # 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)
  
      # Apply the inference transpiler for optimization
      t = fluid.Transpiler()
      optimized_program = t.transpile(inference_program, place)
  
      # Create the destination directory if it does not exist
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized model to the destination directory
      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)
  
      print(f"Loading inference_program from {src_dir}")
      print(f"Saving the optimized inference_program to {dst_dir}")
  