input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """
      Transpile an inference model from source directory to destination directory.
      This function loads the inference program, applies the inference transpiler,
      and saves the optimized inference program to the destination directory.
      If the destination directory does not exist, it is created.
      
      >>> Transpile('./src_dir', './dst_dir', 'model.pdmodel', 'params.pdparams')
      """
output:     
      import os
      import paddle
  
      # Load the inference model from the source directory
      model_path = os.path.join(src_dir, model_filename)
      params_path = os.path.join(src_dir, params_filename)
      model = paddle.jit.load(model_path)
      model.set_state_dict(paddle.load(params_path))
  
      # Apply the inference transpiler
      model = paddle.jit.to_static(model)
  
      # Create the destination directory if it does not exist
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      # Save the optimized inference model to the destination directory
      optimized_model_path = os.path.join(dst_dir, model_filename)
      optimized_params_path = os.path.join(dst_dir, params_filename)
      paddle.jit.save(model, optimized_model_path)
      paddle.save(model.state_dict(), optimized_params_path)
  