code: def test_torch_backend_nccl_socket_ifname(
      ray_start_4_cpus_2_gpus, nccl_socket_ifname
  ):
      worker_group = WorkerGroup(num_workers=2, num_gpus_per_worker=1)
  
      if nccl_socket_ifname:
  
          def set_env_var():
              os.environ["NCCL_SOCKET_IFNAME"] = nccl_socket_ifname
  
          worker_group.execute(set_env_var)
  
      def assert_env_var_set():
          value = (
              nccl_socket_ifname
              if nccl_socket_ifname
              else DEFAULT_NCCL_SOCKET_IFNAME
          )
          assert os.environ["NCCL_SOCKET_IFNAME"] == value
  
      torch_backend = _TorchBackend()
      torch_backend.on_start(
          worker_group, backend_config=TorchConfig(backend="nccl")
      )
  
      worker_group.execute(assert_env_var_set)
  
imports: import json
  import os
  import time
  from pathlib import Path
  from typing import Dict, List, Union
  from unittest.mock import patch
  import pytest
  import torch
  import torchvision
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader, DistributedSampler
  import ray
  import ray.data
  from ray import train
  from ray.exceptions import RayTaskError
  from ray.train import ScalingConfig
  from ray.train._internal.worker_group import WorkerGroup
  from ray.train.constants import DEFAULT_NCCL_SOCKET_IFNAME
  from ray.train.examples.pytorch.torch_linear_example import LinearDataset
  from ray.train.torch.config import TorchConfig, _TorchBackend
  from ray.train.torch.torch_trainer import TorchTrainer
  from ray.train.trainer import TrainingFailedError
label_desc: Use image node_id 9 for calling a global function with example usage: test_torch_backend_nccl_socket_ifname(ray_start_4_cpus_2_gpus, nccl_socket_ifname) without return types