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 _build_hook_parallel(self, inf):
      out_names = inf.get_fetches()
      sz = len(out_names)
      fetches = list(
          itertools.chain(
              *[t.get_tensors(out_names) for t in self._handles]
          )
      )
      return self._InferencerToHookDataParallel(inf, fetches, sz)
  
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 4 for calling the DataParallelInferenceRunner obj's underlying member method code with example usage: obj._build_hook_parallel(inf) and returns: self