code: def test_torch_prepare_dataloader(ray_start_4_cpus_2_gpus, dataset):
      data_loader = DataLoader(dataset(a=1, b=2, size=10))
  
      def train_fn():
          wrapped_data_loader = train.torch.prepare_data_loader(
              data_loader
          )
  
          # Check that DistributedSampler has been added to the data loader.
          assert isinstance(
              wrapped_data_loader.sampler, DistributedSampler
          )
  
          # Make sure you can properly iterate through the DataLoader.
          # Case where the dataset returns a tuple or list from __getitem__.
          if isinstance(dataset, LinearDataset):
              for batch in wrapped_data_loader:
                  x = batch[0]
                  y = batch[1]
  
                  # Make sure the data is on the correct device.
                  assert x.is_cuda and y.is_cuda
          # Case where the dataset returns a dict from __getitem__.
          elif isinstance(dataset, LinearDatasetDict):
              for batch in wrapped_data_loader:
                  for x, y in zip(batch["x"], batch["y"]):
                      # Make sure the data is on the correct device.
                      assert x.is_cuda and y.is_cuda
  
          elif isinstance(dataset, NonTensorDataset):
              for batch in wrapped_data_loader:
                  for x, y in zip(batch["x"], batch["y"]):
                      # Make sure the data is on the correct device.
                      assert x.is_cuda and y == 2
  
      trainer = TorchTrainer(
          train_fn,
          scaling_config=ScalingConfig(num_workers=2, use_gpu=True),
      )
      trainer.fit()
  
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 7 for calling a global function with example usage: test_torch_prepare_dataloader(ray_start_4_cpus_2_gpus, dataset) without return types