class_docstr: Strategy for multi-process single-device training on one or multiple nodes.
class_docstr_tok: ["Strategy","for","multi-process","single-device","training","on","one","or","multiple","nodes","."]
code: def root_device(self) -> torch.device:
      assert self.parallel_devices is not None
      return self.parallel_devices[self.local_rank]
  
imports: from contextlib import nullcontext
  from datetime import timedelta
  from typing import Any, ContextManager, Dict, List, Literal, Optional, Union
  import torch
  import torch.distributed
  from lightning_utilities.core.rank_zero import rank_zero_only
  from torch import Tensor
  from torch.nn import Module
  from torch.nn.parallel.distributed import DistributedDataParallel
  from typing_extensions import override
  from lightning.fabric.accelerators.accelerator import Accelerator
  from lightning.fabric.plugins.collectives.torch_collective import default_pg_timeout
  from lightning.fabric.plugins.environments.cluster_environment import ClusterEnvironment
  from lightning.fabric.plugins.io.checkpoint_io import CheckpointIO
  from lightning.fabric.plugins.precision import Precision
  from lightning.fabric.strategies.launchers.multiprocessing import _MultiProcessingLauncher
  from lightning.fabric.strategies.launchers.subprocess_script import _SubprocessScriptLauncher
  from lightning.fabric.strategies.parallel import ParallelStrategy
  from lightning.fabric.strategies.registry import _StrategyRegistry
  from lightning.fabric.strategies.strategy import TBroadcast, _BackwardSyncControl
  from lightning.fabric.utilities.distributed import ReduceOp, _distributed_is_initialized, _get_default_process_group_backend_for_device, _init_dist_connection, _sync_ddp_if_available
  from lightning.fabric.utilities.distributed import group
  from lightning.fabric.utilities.rank_zero import rank_zero_only
label_desc: Use image node_id 2 for calling the DDPStrategy obj's underlying member method code with example usage: obj.root_device() and returns: self