code: def test_auto_dataloader_warning_distributed_sampler(
      distributed_context_single_node_gloo,
  ):
      dataset = DummyDS()
      rank = idist.get_rank()
      world_size = idist.get_world_size()
      auto_dataloader(
          dataset,
          sampler=DistributedSampler(
              dataset, num_replicas=world_size, rank=rank
          ),
      )
      if world_size > 1:
          wrong_rank = (rank + 1) % world_size
          expected_warning = f"Found distributed sampler with rank={wrong_rank}, but process rank is {rank}"
          with pytest.warns(UserWarning, match=expected_warning):
              auto_dataloader(
                  dataset,
                  sampler=DistributedSampler(
                      dataset, num_replicas=world_size, rank=wrong_rank
                  ),
              )
      expected_warning = f"Found distributed sampler with num_replicas={world_size + 1}, but world size is {world_size}"
      with pytest.warns(UserWarning, match=expected_warning):
          auto_dataloader(
              dataset,
              sampler=DistributedSampler(
                  dataset, num_replicas=world_size + 1, rank=rank
              ),
          )
  
imports: import os
  import pytest
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch.utils.data.dataloader import _InfiniteConstantSampler
  from torch.utils.data.dataset import Dataset, IterableDataset
  from torch.utils.data.distributed import DistributedSampler
  from torch.utils.data.sampler import BatchSampler, RandomSampler, Sampler, SequentialSampler, WeightedRandomSampler
  import ignite.distributed
  from ignite.distributed.auto import auto_dataloader, auto_model, auto_optim, DistributedProxySampler
label_desc: Use image node_id 2 for calling a global function with example usage: test_auto_dataloader_warning_distributed_sampler(distributed_context_single_node_gloo) without return types