input: from typing import List
  
  def Transpile(src_dir: str, dst_dir: str, model_filename: str, params_filename: str) -> None:
      """Transpiles a PaddlePaddle inference model to an optimized version and saves it to a destination directory.
  
      This function loads an inference model from a source directory, applies optimizations using PaddlePaddle's Inference Transpiler, and saves the optimized model to a specified destination directory. If the destination directory does not exist, it will be created.
  
      Args:
          src_dir (str): The source directory where the inference model is stored.
          dst_dir (str): The destination directory where the optimized model will be saved.
          model_filename (str): The name of the model file.
          params_filename (str): The name of the parameters file.
  
      Returns:
          None
  
      Examples:
          >>> Transpile('/path/to/src', '/path/to/dst', 'model.pdmodel', 'params.pdparams')
          Loading inference_program from  /path/to/src
          Saving the optimized inference_program to  /path/to/dst
      """
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 optimizations using PaddlePaddle's Inference Transpiler
      t = fluid.InferenceTranspiler()
      t.transpile(inference_program, place)
  
      # Create the destination directory if it doesn't 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=inference_program
      )
  