class_docstr: Inference with data-parallel support on multiple GPUs.
  It will build one predict tower on each GPU, and run prediction
  with a large total batch in parallel on all GPUs.
  It will run the remainder (when the total size of input is not a multiple of #GPU)
  sequentially.
code: def _before_train(self):
      super(DataParallelInferenceRunner, self)._before_train()
      self._parallel_hooked_sess = HookedSession(
          self.trainer.sess, self._hooks_parallel
      )
  
imports: import itertools
  import sys
  from contextlib import contextmanager
  import tqdm
  from tensorflow.python.training.monitored_session import _HookedSession
  from ..compat import tfv1
  from ..dataflow.base import DataFlow
  from ..input_source import FeedInput, InputSource, QueueInput, StagingInput
  from ..tfutils.tower import PredictTowerContext
  from ..utils import logger
  from ..utils.utils import get_tqdm_kwargs
  from .base import Callback
  from .group import Callbacks
  from .inference import Inferencer
label_desc: Use image node_id 6 for calling the DataParallelInferenceRunner obj's underlying member method code with example usage: obj._before_train() without return types