code: def get_device(model, index):
      if isinstance(model, DDP):
          model = model.module
  
      if not torch.cuda.is_available():
          return torch.device("cpu")
      if hasattr(model, "devices"):
          return model.devices[index]
      else:
          return torch.cuda.current_device()
  
imports: import argparse
  from collections import defaultdict
  from functools import reduce
  import gc
  import logging
  import math
  import operator
  import time
  from datasets.wikitext2_data import get_real_dataloaders
  from datasets.wikitext2_data import get_synthetic_dataloaders
  from models import transformer_lm
  import numpy
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Adam
  from benchmarks.golden_configs.lm_wikitext2 import FSDP
  from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 8 for calling a global function with example usage: get_device(model, index) and returns: torch, model, torch