def METHOD_NAME(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