code: def upload_file(self, request_iterator, context):
      try:
          logging.debug("Trying to acquire lock of server model")
          self.condition.acquire()
          logging.debug("Acquired lock of server model")
          if os.path.exists(self.model_path):
              logging.warn("Model file exists, will not upload.")
              msg = "Model file exists, will not upload, exiting.."
              code = 1
          else:
              logging.debug("Unpacking file chunk from FLClient")
              with open(self.model_path, "wb") as f:
                  for byte_chunk in request_iterator:
                      f.write(byte_chunk.buffer)
              logging.info(
                  f"Server received model file, length: {os.path.getsize(self.model_path)}"
              )
          self.condition.release()
          msg = "Upload model file sucess"
          code = 0
      except Exception as e:
          traceback.print_exc()
          msg = traceback.format_exc()
          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 6 for calling the NNServiceImpl obj's underlying member method code with example usage: obj.upload_file(request_iterator, context) and returns: UploadMetaResponse