input: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile an inference model from source directory to destination directory using Paddle Fluid.
      
      Args:
          src_dir (str): Source directory of the inference model.
          dst_dir (str): Destination directory to save the optimized inference model.
          model_filename (str): The name of the model file.
          params_filename (str): The name of the params file.
      
      Example:
      >>> Transpile('source_dir', 'destination_dir', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from source_dir
      Saving the optimized inference_program to destination_dir
      """
  
output:     
      import paddle.fluid as fluid
      import os
  
      # Load the inference program from the source directory
      inference_program = fluid.io.load_inference_model(dirname=src_dir, model_filename=model_filename, params_filename=params_filename)
  
      # Save the optimized inference program to the destination directory
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=inference_program[1], target_vars=inference_program[2], executor=inference_program[0], 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}")
  