code: def _test_auto_methods_xla(index, ws):
      dl_type = DataLoader
      if ws > 1:
          from ignite.distributed.auto import _MpDeviceLoader
  
          dl_type = _MpDeviceLoader
          try:
              from torch_xla.distributed.parallel_loader import (
                  MpDeviceLoader,
              )
  
              dl_type = MpDeviceLoader
          except ImportError:
              pass
  
      _test_auto_dataloader(
          ws=ws, nproc=ws, batch_size=1, dl_type=dl_type
      )
      _test_auto_dataloader(
          ws=ws, nproc=ws, batch_size=10, num_workers=2, dl_type=dl_type
      )
      _test_auto_dataloader(
          ws=ws,
          nproc=ws,
          batch_size=1,
          sampler_name="WeightedRandomSampler",
          dl_type=dl_type,
      )
      _test_auto_dataloader(
          ws=ws,
          nproc=ws,
          batch_size=1,
          sampler_name="DistributedSampler",
          dl_type=dl_type,
      )
  
      device = "xla"
      _test_auto_model_optimizer(ws, device)
  
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 11 for calling a global function with example usage: _test_auto_methods_xla(index, ws) without return types