code: def multithread_predict_dataflow(dataflows, model_funcs):
      """
      Running multiple `predict_dataflow` in multiple threads, and aggregate the results.
  
      Args:
          dataflows: a list of DataFlow to be used in :func:`predict_dataflow`
          model_funcs: a list of callable to be used in :func:`predict_dataflow`
  
      Returns:
          list of dict, in the format used by
          `DatasetSplit.eval_inference_results`
      """
      num_worker = len(model_funcs)
      assert len(dataflows) == num_worker
      if num_worker == 1:
          return predict_dataflow(dataflows[0], model_funcs[0])
      kwargs = (
          {"thread_name_prefix": "EvalWorker"}
          if sys.version_info.minor >= 6
          else {}
      )
      with ThreadPoolExecutor(
          max_workers=num_worker, **kwargs
      ) as executor, tqdm.tqdm(
          total=sum(df.size() for df in dataflows)
      ) as pbar:
          futures = []
          for dataflow, pred in zip(dataflows, model_funcs):
              futures.append(
                  executor.submit(
                      predict_dataflow, dataflow, pred, pbar
                  )
              )
          all_results = list(
              itertools.chain(*[fut.result() for fut in futures])
          )
          return all_results
  
imports: import itertools
  import json
  import numpy
  import os
  import sys
  from collections import namedtuple
  from concurrent.futures import ThreadPoolExecutor
  from contextlib import ExitStack
  import cv2
  import pycocotools.mask
  import tqdm
  from scipy import interpolate
  from tensorpack.callbacks import Callback
  from tensorpack.tfutils.common import get_tf_version_tuple
  from tensorpack.utils import logger, get_tqdm
  from common import CustomResize, clip_boxes
  from config import config
  from data import get_eval_dataflow
  from dataset import DatasetRegistry
  from tensorpack.compat import tfv1
label_desc: Use image node_id 5 for calling a global function with example usage: multithread_predict_dataflow(dataflows, model_funcs) and returns: predict_dataflow, all_results