code: def copy_model_to_gpu(model, loss=None):
      """
      Copies a model and (optional) loss to GPU and enables cudnn benchmarking.
      For multiple gpus training, the model in DistributedDataParallel for
      distributed training.
      """
      if not torch.backends.cudnn.deterministic:
          torch.backends.cudnn.benchmark = True
      model = model.cuda()
  
      if loss is not None:
          loss = loss.cuda()
          return model, loss
      else:
          return model
  
imports: import collections.abc
  import contextlib
  import json
  import logging
  import os
  import time
  from functools import partial
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from classy_vision.generic.distributed_util import broadcast_object, is_primary
  from fvcore.common.file_io import PathManager
label_desc: Use image node_id 11 for calling a global function with example usage: copy_model_to_gpu(model, loss) and returns: model,  loss, model