code: def upload_meta(self, request, context):
      try:
          loss_fn = SafePickle.loads(request.loss_fn)
          client_id = request.client_uuid
          aggregator = self.aggregator_map[request.aggregator]
          aggregator.load_uploaded_model(client_id, self.model_path)
  
          aggregator.set_meta(loss_fn, request.optimizer)
          msg = "Upload meta success, server model is ready."
          code = 0
      except Exception as e:
          msg = traceback.format_exc()
          logging.error(msg)
          code = 1
  
      return UploadMetaResponse(message=msg, code=code)
  
imports: from threading import Condition
  import threading
  import torch
  import bigdl.ppml.fl.nn.pytorch.aggregator
  import bigdl.ppml.fl.nn.tensorflow.aggregator
  from bigdl.ppml.fl.nn.generated.fl_base_pb2 import TensorMap
  from bigdl.ppml.fl.nn.generated.nn_service_pb2 import LoadModelResponse, SaveModelResponse, TrainRequest, TrainResponse, PredictResponse, UploadMetaResponse
  from bigdl.ppml.fl.nn.generated.nn_service_pb2_grpc import *
  from bigdl.ppml.fl.nn.utils import tensor_map_to_ndarray_map
  import tensorflow
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.ppml.utils.safepickle import SafePickle
  import tempfile
  import traceback
  import os
  import logging
label_desc: Use image node_id 5 for calling the NNServiceImpl obj's underlying member method code with example usage: obj.upload_meta(request, context) and returns: UploadMetaResponse