code: def test_torch_get_device_dist(
      ray_2_node_2_gpu, num_gpus_per_worker, tmp_path
  ):
      @patch("torch.cuda.is_available", lambda: True)
      def train_fn():
          devices = (
              sorted(
                  [device.index for device in train.torch.get_device()]
              )
              if num_gpus_per_worker > 1
              else train.torch.get_device().index
          )
          write_rank_data(tmp_path, devices)
  
      trainer = TorchTrainer(
          train_fn,
          # use gloo instead of nccl, since nccl is not supported
          # on this virtual gpu ray environment
          torch_config=TorchConfig(backend="gloo"),
          scaling_config=ScalingConfig(
              num_workers=int(4 / num_gpus_per_worker),
              use_gpu=True,
              resources_per_worker={"GPU": num_gpus_per_worker},
          ),
      )
      trainer.fit()
  
      rank_data = get_data_from_all_ranks(tmp_path)
      devices = list(rank_data.values())
  
      # cluster setups: 2 nodes, 2 gpus per node
      # `CUDA_VISIBLE_DEVICES` is set to "0,1" on node 1 and node 2
      if num_gpus_per_worker == 0.5:
          # worker gpu topology:
          # 4 workers on node 1, 4 workers on node 2
          # `ray.get_gpu_ids()` returns [0], [0], [1], [1] on node 1
          # and [0], [0], [1], [1] on node 2
          assert sorted(devices) == [0, 0, 0, 0, 1, 1, 1, 1]
      elif num_gpus_per_worker == 1:
          # worker gpu topology:
          # 2 workers on node 1, 2 workers on node 2
          # `ray.get_gpu_ids()` returns [0], [1] on node 1 and [0], [1] on node 2
          assert sorted(devices) == [0, 0, 1, 1]
      elif num_gpus_per_worker == 2:
          # worker gpu topology:
          # 1 workers on node 1, 1 workers on node 2
          # `ray.get_gpu_ids()` returns {0, 1} on node 1 and {0, 1} on node 2
          # and `device_id` returns the one index from each set.
          # So total count of devices should be 2.
          assert devices == [[0, 1], [0, 1]]
      else:
          raise RuntimeError(
              "New parameter for this test has been added without checking that the "
              "correct devices have been returned."
          )
  
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 4 for calling a global function with example usage: test_torch_get_device_dist(ray_2_node_2_gpu, num_gpus_per_worker, tmp_path) without return types