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: from collections import defaultdict
  import gc
  import logging
  import math
  import time
  import torch
  import torch.distributed
  from torch.distributed import rpc
  from torch.nn.parallel import DistributedDataParallel
  import utils
  from benchmarks.golden_configs.lm_wikitext2 import Pipe
  from fairscale.fair_dev.testing.testing import dist_init
  from fairscale.nn import Pipe
  from fairscale.nn.model_parallel import initialize_model_parallel
label_desc: Use image node_id 2 for calling a global function with example usage: get_device(model, index) and returns: torch, model, torch