class_docstr: Base class for predict worker that runs offline in multiprocess
class_docstr_tok: ["Base","class","for","predict","worker","that","runs","offline","in","multiprocess"]
docstr: Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
  have workers that run on multiGPUs
docstr_tok: ["Call","_init_runtime","under","different","CUDA_VISIBLE_DEVICES",",","you'll","have","workers","that","run","on","multiGPUs"]
code: def _init_runtime(self):
      """Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
      have workers that run on multiGPUs
      """
      if self.idx != 0:
          from tensorpack.models.registry import disable_layer_logging
  
          disable_layer_logging()
      self.predictor = OfflinePredictor(self.config)
      if self.idx == 0:
          with self.predictor.graph.as_default():
              describe_trainable_vars()
  
imports: import multiprocessing
  import numpy
  from concurrent.futures import Future
  import tensorflow
  from six.moves import queue, range
  from ..compat import tfv1
  from ..tfutils.model_utils import describe_trainable_vars
  from ..utils import logger
  from ..utils.concurrency import DIE, ShareSessionThread, StoppableThread
  from .base import AsyncPredictorBase, OfflinePredictor, OnlinePredictor
label_desc: Use image node_id 2 for calling the MultiProcessPredictWorker obj's underlying member method code with example usage: obj._init_runtime() without return types