input: def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """
      Transpile an inference model using Paddle Fluid. This function loads an inference program
      from the source directory, applies an optimization transpiler, and then saves the optimized
      program to the destination directory. If the destination directory does not exist, it will
      be created.
      
      >>> Transpile('/src', '/dst', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from /src
      Saving the optimized inference_program to /dst
      >>> Transpile('/src', '/non_existent_dir', 'model.pdmodel', 'params.pdparams')
      Loading inference_program from /src
      Saving the optimized inference_program to /non_existent_dir
      """
output:     import os
      import paddle.fluid as fluid
  
      # Load the inference program from the source directory
      print(f"Loading inference_program from {src_dir}")
      inference_program = fluid.io.load_inference_model(dirname=src_dir, model_filename=model_filename, params_filename=params_filename)
  
      # Apply the optimization transpiler
      optimized_program = fluid.transpiler.memory_optimize(input_program=inference_program[0])
  
      # Create the destination directory if it does not exist
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized inference program to the destination directory
      print(f"Saving the optimized inference_program to {dst_dir}")
      fluid.io.save_inference_model(dirname=dst_dir, feeded_var_names=inference_program[1], target_vars=inference_program[2], executor=fluid.Executor(fluid.CPUPlace()), main_program=optimized_program, model_filename=model_filename, params_filename=params_filename)