code: def _setup_devices(self) -> "torch.device":
      logger.info("PyTorch: setting up devices")
      if (
          torch.distributed.is_available()
          and torch.distributed.is_initialized()
          and self.local_rank == -1
      ):
          logger.warning(
              "torch.distributed process group is initialized, but local_rank == -1. "
              "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
          )
      if self.no_cuda:
          device = torch.device("cpu")
          self._n_gpu = 0
      elif is_sagemaker_model_parallel_available():
          local_rank = smp.local_rank()
          device = torch.device("cuda", local_rank)
          self._n_gpu = 1
      elif is_sagemaker_dp_enabled():
          import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401
  
          torch.distributed.init_process_group(
              backend="smddp", timeout=self.ddp_timeout_delta
          )
          self.local_rank = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))
          device = torch.device("cuda", self.local_rank)
          self._n_gpu = 1
      elif self.local_rank == -1:
          # if n_gpu is > 1 we'll use nn.DataParallel.
          # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
          # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
          # trigger an error that a device index is missing. Index 0 takes into account the
          # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
          # will use the first GPU in that env, i.e. GPU#1
          device = torch.device(
              "cuda:0" if torch.cuda.is_available() else "cpu"
          )
          # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
          # the default value.
          self._n_gpu = torch.cuda.device_count()
      else:
          # Here, we'll use torch.distributed.
          # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
          if not torch.distributed.is_initialized():
              torch.distributed.init_process_group(
                  backend="nccl", timeout=self.ddp_timeout_delta
              )
          device = torch.device("cuda", self.local_rank)
          self._n_gpu = 1
  
      if device.type == "cuda":
          torch.cuda.set_device(device)
  
      return device
  
file_path: transformers/src/transformers/sagemaker/training_args_sm.py
imports: import importlib.util
  import json
  import os
  import warnings
  from dataclasses import dataclass, field
  import torch
  from ..training_args import TrainingArguments
  from ..utils import cached_property, is_sagemaker_dp_enabled, logging
label_desc: Use image node_id 2 for calling the SageMakerTrainingArguments obj's underlying member method code with example usage: obj._setup_devices() and returns: device