code: def test_auto_transfer_correct_device(ray_start_4_cpus_2_gpus):
      """Tests that auto_transfer uses the right device for the cuda stream."""
      import ray._private.thirdparty.pynvml as pynvml
  
      pynvml.nvmlInit()
  
      def get_gpu_used_mem(i):
          handle = pynvml.nvmlDeviceGetHandleByIndex(i)
          info = pynvml.nvmlDeviceGetMemoryInfo(handle)
          return info.used
  
      start_gpu_memory = get_gpu_used_mem(1)
  
      device = torch.device("cuda:1")
      small_dataloader = [
          (torch.randn((1024 * 4, 1024 * 4)),) for _ in range(10)
      ]
      wrapped_dataloader = (  # noqa: F841
          ray.train.torch.train_loop_utils._WrappedDataLoader(
              small_dataloader, device, True
          )
      )
  
      end_gpu_memory = get_gpu_used_mem(1)
  
      # Verify GPU memory usage increases on the right cuda device
      assert end_gpu_memory > start_gpu_memory
  
file_path: ray/python/ray/train/tests/test_gpu_auto_transfer.py
imports: import os
  from tempfile import TemporaryDirectory
  from unittest.mock import patch
  import pytest
  import torch
  import ray
  import ray.train.torch.train_loop_utils
  from ray import train
  from ray.train import Checkpoint, ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 2 for calling a global function with example usage: test_auto_transfer_correct_device(ray_start_4_cpus_2_gpus) and returns: info