code: def _initialize_distributed_backend() -> (
      Optional[core.DistributedContext]
  ):
      distributed_backend = det._DistributedBackend()
      if distributed_backend.use_torch():
          if torch.cuda.is_available():
              dist.init_process_group(backend="nccl")  # type: ignore
          else:
              dist.init_process_group(backend="gloo")  # type: ignore
          return core.DistributedContext.from_torch_distributed()
  
      info = det.get_cluster_info()
      if info and (
          len(info.container_addrs) > 1 or len(info.slot_ids) > 1
      ):
          raise ValueError(
              "In multi-slot managed cluster training, you must wrap your training script with a "
              "distributed launch layer such as determined.launch.torch_distributed"
          )
      return None
  
imports: import abc
  import json
  import logging
  import math
  import os
  import pathlib
  import uuid
  import warnings
  from typing import TYPE_CHECKING, Any, ContextManager, Dict, Optional, Set, Sized, Type
  import torch
  import torch.distributed
  from torch import nn
  from torch.utils import data
  import determined
  from determined import common, core, pytorch
label_desc: Use image node_id 2 for calling a global function with example usage: _initialize_distributed_backend() and returns: None, core