code: def shutdown(self):
      # TODO(travis): currently Ray handles this for us, but is subject to hangs if one of the workers raises an
      # exception and the other makes a collective op. We should figure out a way to make this safe to call
      # multiple times. It looks like there is a fix we can make use of when we upgrade to Ray 2.1:
      # https://discuss.ray.io/t/torchtrainer-hangs-when-only-1-worker-raises-error/7447/11
      # dist.destroy_process_group()
      pass
  
imports: import contextlib
  import logging
  import os
  import socket
  from typing import Any, Callable, Dict, Optional, Tuple, Type, TYPE_CHECKING, Union
  import torch
  import torch.distributed
  from ray.train.backend import BackendConfig
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.torch import TorchTrainer
  from torch import nn
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Optimizer
  from torchmetrics.utilities.distributed import gather_all_tensors
  from ludwig.distributed.base import DistributedStrategy
  from ludwig.modules.optimization_modules import create_optimizer
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 21 for calling the DDPStrategy obj's underlying member method code with example usage: obj.shutdown() without return types