code: def __init__(self, conf, **kargs) -> None:
      self.client_num = conf["clientNum"]
      self.aggregator_map = {
          "tf": tf_agg.Aggregator(conf, **kargs),
          "pt": pt_agg.Aggregator(conf, **kargs),
      }
      self.model_dir = tempfile.mkdtemp()  # store tmp file dir
      self.model_path = os.path.join(self.model_dir, "vfl_server_model")
      self.client_num = int(self.client_num)
      self.condition = Condition()
      self._lock = threading.Lock()
  
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 1 to create a new NNServiceImpl object from inherited base classes: NNServiceServicer with example: obj = NNServiceImpl(conf)