code: def _set_torch_distributed_env_vars():
      # Same env vars as in
      # https://pytorch.org/docs/stable/elastic/run.html#environment-variables
      from ray.air._internal.torch_utils import get_device
  
      context = ray.train.get_context()
      os.environ["LOCAL_RANK"] = str(context.get_local_rank())
      os.environ["RANK"] = str(context.get_world_rank())
      os.environ["LOCAL_WORLD_SIZE"] = str(
          context.get_local_world_size()
      )
      os.environ["WORLD_SIZE"] = str(context.get_world_size())
      os.environ["NODE_RANK"] = str(context.get_node_rank())
  
      # Makes sure Hugging Face Accelerate uses the correct device
      device = get_device()
      if isinstance(device, list):
          device = device[0]
      os.environ["ACCELERATE_TORCH_DEVICE"] = str(device)
  
imports: import logging
  import os
  from dataclasses import dataclass
  from datetime import timedelta
  from typing import Optional
  import torch
  import torch.distributed
  import ray
  from ray.train._internal.utils import get_address_and_port
  from ray.train._internal.worker_group import WorkerGroup
  from ray.train.backend import Backend, BackendConfig
  from ray.train.constants import DEFAULT_NCCL_SOCKET_IFNAME
  from ray.util import PublicAPI
label_desc: Use image node_id 4 for calling a global function with example usage: _set_torch_distributed_env_vars() without return types