code: def setup_distrib(gpu=None):
      "Setup this process to participate in distributed training"
      if gpu is None:
          return gpu
      gpu = int(gpu)
      torch.cuda.set_device(int(gpu))
      if num_distrib() > 0:
          torch.distributed.init_process_group(
              backend="nccl", init_method="env://"
          )
      return gpu
  
imports: from __future__ import annotations
  from .basics import *
  from .callback.progress import ProgressCallback
  from torch.nn.parallel import DistributedDataParallel, DataParallel
  from .data.load import _FakeLoader, _loaders
  from .optimizer import OptimWrapper
label_desc: Use image node_id 6 for calling a global function with example usage: setup_distrib(gpu) and returns: gpu, gpu