code: def predict(self, request, context):
      tensor_map = request.data.tensorMap
      client_id = request.clientuuid
      self.validate_client_id(client_id)
      ndarray_map = tensor_map_to_ndarray_map(tensor_map)
      aggregator = self.aggregator_map[request.algorithm]
      try:
          aggregator.put_client_data(client_id, ndarray_map, "pred")
          msg = f"[client {client_id} batch predicted]"
          code = 0
      except Exception as e:
          msg = traceback.format_exc()
          logging.error(msg)
          code = 1
      return PredictResponse(
          response=msg,
          data=aggregator.server_data["pred"][client_id],
          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 4 for calling the NNServiceImpl obj's underlying member method code with example usage: obj.predict(request, context) and returns: PredictResponse