input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """Transpile a given inference model from a source directory to a destination directory.
      The function loads the inference model using Paddle Fluid, applies an inference 
      transpiler to optimize the model, and saves the optimized model to the destination
      directory. If the destination directory does not exist, it is created.
      
      >>> Transpile('src_models', 'dst_models', 'model.pdmodel', 'params.pdparams')
      """
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)
  
      # Apply the inference transpiler
      t = fluid.contrib.InferenceTranspiler()
      t.transpile(inference_program, place)
  
      # Save the optimized model to the destination directory
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
      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)