code: def _test_auto_dataloader(
      ws,
      nproc,
      batch_size,
      num_workers=1,
      sampler_name=None,
      dl_type=DataLoader,
  ):
      def _test(data):
          if sampler_name is None:
              sampler = None
          elif sampler_name == "WeightedRandomSampler":
              sampler = WeightedRandomSampler(
                  weights=torch.ones(100), num_samples=100
              )
          elif sampler_name == "DistributedSampler":
              sampler = DistributedSampler(
                  data, num_replicas=ws, rank=idist.get_rank()
              )
          else:
              raise RuntimeError(
                  f"Unknown sampler name: {sampler_name}"
              )
  
          # Test auto_dataloader
          assert (
              idist.get_world_size() == ws
          ), f"{idist.get_world_size()} vs {ws}"
  
          shuffle = (
              sampler is None
              if not isinstance(data, IterableDataset)
              else False
          )
          dataloader = auto_dataloader(
              data,
              batch_size=batch_size,
              num_workers=num_workers,
              sampler=sampler,
              shuffle=shuffle,
          )
  
          assert isinstance(dataloader, dl_type)
          if hasattr(dataloader, "_loader"):
              dataloader = dataloader._loader
          if ws < batch_size:
              assert dataloader.batch_size == batch_size // ws
          else:
              assert dataloader.batch_size == batch_size
          if ws <= num_workers:
              assert (
                  dataloader.num_workers
                  == (num_workers + nproc - 1) // nproc
              )
          else:
              assert dataloader.num_workers == num_workers
  
          if isinstance(data, IterableDataset):
              sampler_type = _InfiniteConstantSampler
          elif ws > 1:
              if sampler is None or isinstance(
                  sampler, DistributedSampler
              ):
                  sampler_type = DistributedSampler
              else:
                  sampler_type = DistributedProxySampler
          else:
              sampler_type = (
                  RandomSampler if sampler is None else type(sampler)
              )
  
          assert isinstance(dataloader.sampler, sampler_type)
  
          if isinstance(dataloader, DataLoader):
              assert dataloader.pin_memory == (
                  "cuda" in idist.device().type
              )
  
      data = torch.rand(100, 3, 12, 12)
      _test(data)
      if sampler_name is None:
          data = DummyIterableDataset(0, 100)
          _test(data)
  
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 4 for calling a global function with example usage: _test_auto_dataloader(ws, nproc, batch_size, num_workers, sampler_name, dl_type) without return types