code: def save_server_model(self, model_path):
      if not os.path.exists(f"{model_path}/model.meta"):
          os.makedirs(f"{model_path}", exist_ok=True)
          with open(f"{model_path}/model.meta", "wb") as meta_file:
              SafePickle.dump(
                  {
                      "loss": self.loss_fn,
                      "optimizer": (
                          self.optimizer_cls,
                          self.optimizer_args,
                      ),
                  },
                  meta_file,
              )
      m = torch.jit.script(self.model)
      if self.secret_key is not None and self.salt is not None:
          logging.warn("Saving encrypted model...")
          buffer = io.BytesIO()
          torch.jit.save(m, buffer)
          byte_buffer = buffer.getvalue()
          encrypted = encrypt_bytes_with_AES_CBC(
              byte_buffer, self.secret_key, self.salt
          )
          with open(f"{model_path}/model.pt", "wb") as f:
              f.write(encrypted)
              logging.info(f"Writing model to {model_path}/model.pt")
      else:
          logging.warn(
              "Secret key or salt is missed, saving unencrypted model..."
          )
          torch.jit.save(m, f"{model_path}/model.pt")
  
imports: import io
  from bigdl.ppml.utils.safepickle import SafePickle
  import logging
  import threading
  from torch import nn
  import torch
  from bigdl.ppml.fl.nn.utils import ndarray_map_to_tensor_map
  from bigdl.dllib.utils.log4Error import invalidInputError, invalidOperationError
  from threading import Condition
  import os
  from bigdl.dllib.utils.encryption_utils import *
label_desc: Use image node_id 8 for calling the Aggregator obj's underlying member method code with example usage: obj.save_server_model(model_path) without return types