code: def _setup_torch_process_group(
      backend: str,
      world_rank: int,
      world_size: int,
      init_method: str,
      timeout_s: int = 1800,
  ):
      """Connects the distributed PyTorch backend.
  
      Args:
          backend: The backend (nccl, gloo, etc.) to use for training.
          world_rank: Rank of the current worker.
          world_size: Number of workers participating in the job.
          init_method: URL specifying how to initialize the process group.
          timeout_s: Seconds for process group operations to timeout.
      """
      if world_rank == 0:
          logger.info(
              f"Setting up process group for: {init_method} [rank={world_rank}, "
              f"world_size={world_size}]"
          )
      else:
          logger.debug(
              f"Setting up process group for: {init_method} [rank={world_rank}, "
              f"world_size={world_size}]"
          )
      logger.debug(f"using {backend}")
  
      # See the `timeout` arg in https://pytorch.org/docs/master/
      # distributed.html#torch.distributed.init_process_group for description of
      # NCCL_ASYNC_ERROR_HANDLING. We do not use NCCL_BLOCKING_WAIT due to performance
      # overhead.
      if (
          backend == "nccl"
          and "NCCL_ASYNC_ERROR_HANDLING" not in os.environ
          and "NCCL_BLOCKING_WAIT" not in os.environ
      ):
          logger.debug(
              "Setting NCCL_ASYNC_ERROR_HANDLING to fail if NCCL collective "
              "communication operations are timing out. "
              "To override this behavior, you can set NCCL_ASYNC_ERROR_HANDLING=0."
          )
          os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "1"
  
      dist.init_process_group(
          backend=backend,
          init_method=init_method,
          rank=world_rank,
          world_size=world_size,
          timeout=timedelta(seconds=timeout_s),
      )
  
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 2 for calling a global function with example usage: _setup_torch_process_group(backend, world_rank, world_size, init_method, timeout_s) without return types