code: def run_test_device_change(
      rank,
      world_size,
      backend,
      device,
      temp_file_name,
      reduce_buffer_size,
  ):
      # Check that the wrapped module can change devices
      dist.init_process_group(
          init_method="file://" + temp_file_name,
          backend=backend,
          rank=rank,
          world_size=world_size,
      )
      torch.cuda.set_device(rank)
  
      model = Sequential(
          Linear(2, 3), Linear(3, 3)
      ).cpu()  # not device on purpose, test changing it after the fact
      optimizer = OSS(
          params=model.parameters(),
          optim=torch.optim.SGD,
          lr=1e-3,
          momentum=0.99,
      )
      ddp_model = ShardedDataParallel(
          model,
          optimizer,
          sync_models_at_startup=False,
          reduce_buffer_size=reduce_buffer_size,
      )
      try:
          ddp_model.to(device)
          assert (
              False
          ), "Changing devices should be caught and not supported"
      except AssertionError:
          pass
  
      # Check that we can change the data type
      ddp_model.to(device=torch.device("cpu"), dtype=torch.float16)
  
      dist.destroy_process_group()
  
imports: from contextlib import suppress
  import numpy
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn import Linear, Sequential
  from fairscale.fair_dev.testing.testing import GPT2, SGDWithPausingCompute, available_devices, check_same_models_across_ranks, skip_if_less_than_four_gpu, skip_if_no_cuda, skip_if_single_gpu, temp_files_ctx
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim import OSS
label_desc: Use image node_id 13 for calling a global function with example usage: run_test_device_change(rank, world_size, backend, device, temp_file_name, reduce_buffer_size) without return types