code: def _prepare_single_device_module(
      rank,
      world_size,
      tempfile,
      devices: List[torch.device],
      slowmo_init_dict: Dict[Any, Any],
      global_batch_size: int,
  ) -> Tuple[
      nn.Module,
      gossip.SlowMoDistributedDataParallel,
      torch.Tensor,
      torch.Tensor,
  ]:
      if not torch.distributed.is_initialized():
          torch.distributed.init_process_group(
              "nccl",
              init_method=f"file://{tempfile}",
              rank=rank,
              world_size=world_size,
          )
      model = Net()
      slowmo_model = gossip.SlowMoDistributedDataParallel(
          copy.deepcopy(model).to(devices[0]),
          comm_device=devices[0],
          process_rank=rank,
          process_world_size=world_size,
          **slowmo_init_dict,
      )
  
      model.to(devices[0])
  
      input = torch.randn(global_batch_size, 2).to(devices[0])
      target = torch.randn(global_batch_size, 4).to(devices[0])
  
      return model, slowmo_model, input, target
  
imports: import copy
  import os
  import tempfile
  from typing import Any, Dict, List, Tuple, Type
  import unittest
  import pytest
  import torch
  from torch import nn
  import torch.distributed
  import torch.nn.functional
  import fairscale.experimental.nn.data_parallel.gossip
  from fairscale.fair_dev.testing.testing import skip_if_single_gpu, spawn_for_all_world_sizes
label_desc: Use image node_id 5 for calling a global function with example usage: _prepare_single_device_module(rank, world_size, tempfile, devices, slowmo_init_dict, global_batch_size) and returns: model,  slowmo_model,  input,  target