code: def test_auto_methods_hvd(gloo_hvd_executor):
      device = "cpu" if not torch.cuda.is_available() else "cuda"
      np = (
          4
          if not torch.cuda.is_available()
          else torch.cuda.device_count()
      )
  
      gloo_hvd_executor(
          _test_auto_dataloader, args=(np, np, 1), np=np, do_init=True
      )
      gloo_hvd_executor(
          _test_auto_dataloader,
          args=(np, np, 10, 10),
          np=np,
          do_init=True,
      )
      gloo_hvd_executor(
          _test_auto_dataloader,
          args=(np, np, 1, 1, "WeightedRandomSampler"),
          np=np,
          do_init=True,
      )
      gloo_hvd_executor(
          _test_auto_dataloader,
          args=(np, np, 1, 1, "DistributedSampler"),
          np=np,
          do_init=True,
      )
  
      gloo_hvd_executor(
          _test_auto_model_optimizer,
          args=(np, device),
          np=np,
          do_init=True,
      )
  
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 10 for calling a global function with example usage: test_auto_methods_hvd(gloo_hvd_executor) without return types