input: 
  def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """ 
      Transpiles the inference model from source directory to destination directory, optimizing it in the process.
      Loads the model and parameters from the source directory, clones and optimizes the inference program using 
      Paddle Fluid's Inference Transpiler, and saves the optimized model and parameters to the destination directory.
      If the destination directory does not exist, it creates it.
      
      >>> Transpile('source_dir', 'destination_dir', 'model.pdmodel', 'params.pdiparams')
      "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 and parameters from the source directory
      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
      )
  
      # Clone and optimize the inference program using Paddle Fluid's Inference Transpiler
      t = fluid.contrib.InferenceTranspiler()
      t.transpile(inference_program, place)
  
      # Ensure the destination directory exists
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized model and parameters 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=inference_program,
          model_filename=model_filename,
          params_filename=params_filename
      )
  
      print("Loading inference_program from {}".format(src_dir))
      print("Saving the optimized inference_program to {}".format(dst_dir))
  