code: def _test_auto_model_optimizer(ws, device):
      # Test auto_model
      model = nn.Linear(10, 10)
      _test_auto_model(model, ws, device)
  
      model = nn.Sequential(nn.Linear(20, 100), nn.BatchNorm1d(100))
      _test_auto_model(
          model, ws, device, sync_bn="cuda" in torch.device(device).type
      )
      if ws > 1:
          _test_auto_model(
              model, ws, device, find_unused_parameters=True
          )
          _test_auto_model(
              model, ws, device, find_unused_parameters=False
          )
  
      # Test auto_optim
      bnd = idist.backend()
      optimizer = optim.SGD(model.parameters(), lr=0.01)
      optimizer = auto_optim(optimizer)
      if idist.has_xla_support and "xla" in device:
          assert isinstance(optimizer, optim.SGD) and hasattr(
              optimizer, "wrapped_optimizer"
          )
      elif idist.has_hvd_support and bnd in ("horovod",):
          assert isinstance(optimizer, optim.SGD) and hasattr(
              optimizer, "_allreduce_grad_async"
          )
      else:
          assert isinstance(optimizer, optim.SGD) and not hasattr(
              optimizer, "wrapped_optimizer"
          )
  
      if idist.has_hvd_support and bnd in ("horovod",):
          backward_passes_per_step = 2
          optimizer = optim.SGD(model.parameters(), lr=0.01)
          optimizer = auto_optim(
              optimizer,
              backward_passes_per_step=backward_passes_per_step,
          )
          assert isinstance(optimizer, optim.SGD) and hasattr(
              optimizer, "backward_passes_per_step"
          )
          assert (
              optimizer.backward_passes_per_step
              == backward_passes_per_step
          )
  
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 6 for calling a global function with example usage: _test_auto_model_optimizer(ws, device) without return types