code: def _setup_devices(self) -> "torch.device":
      requires_backends(self, ["torch"])
      logger.info("PyTorch: setting up devices")
      if not is_sagemaker_mp_enabled():
          if not is_accelerate_available(min_version="0.20.1"):
              invalidInputError(
                  False,
                  "Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: "
                  "Please run `pip install transformers[torch]` or `pip install accelerate -U`",
              )
          AcceleratorState._reset_state(reset_partial_state=True)
      self.distributed_state = None
      if not self.use_ipex and "ACCELERATE_USE_IPEX" not in os.environ:
          os.environ["ACCELERATE_USE_IPEX"] = "false"
      if self.use_cpu or strtobool(
          os.environ.get("ACCELERATE_USE_CPU", "False")
      ):
          self.distributed_state = PartialState(
              cpu=True, backend=self.ddp_backend
          )
          self._n_gpu = 0
      elif is_sagemaker_mp_enabled():
          local_rank = smp.local_rank()
          device = torch.device("cuda", local_rank)
          self._n_gpu = 1
          torch.cuda.set_device(device)
      elif (
          is_torch_xpu_available()
          and "ACCELERATE_USE_XPU" not in os.environ
      ):
          os.environ["ACCELERATE_USE_XPU"] = "true"
          self.distributed_state = PartialState(
              timeout=timedelta(seconds=self.ddp_timeout)
          )
          # device = torch.device("xpu:0")
          device = self.distributed_state.device
          self._n_gpu = 1
      elif is_sagemaker_dp_enabled():
          self.distributed_state = PartialState(_use_sagemaker_dp=True)
          self._n_gpu = 1
      elif self.deepspeed:
          # Need to do similar for Accelerator init
          os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
          self.distributed_state = PartialState(
              timeout=timedelta(seconds=self.ddp_timeout)
          )
          del os.environ["ACCELERATE_USE_DEEPSPEED"]
          self._n_gpu = 1
      else:
          self.distributed_state = PartialState(
              backend=self.ddp_backend,
              timeout=timedelta(seconds=self.ddp_timeout),
          )
          self._n_gpu = 1
      if not is_sagemaker_mp_enabled():
          device = self.distributed_state.device
          self.local_rank = self.distributed_state.local_process_index
      if (
          dist.is_available()
          and dist.is_initialized()
          and self.parallel_mode != ParallelMode.DISTRIBUTED
      ):
          logger.warning(
              "torch.distributed process group is initialized, "
              "but parallel_mode != ParallelMode.DISTRIBUTED. "
              "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
          )
      if is_torch_tpu_available():
          device = self.distributed_state.device
          self._n_gpu = 0
      elif is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled():
          # Already set _n_gpu
          pass
      elif (
          self.distributed_state.distributed_type
          == DistributedType.MULTI_XPU
      ):
          if "ACCELERATE_USE_XPU" not in os.environ:
              os.environ["ACCELERATE_USE_XPU"] = "true"
          # self._n_gpu = torch.xpu.device_count()
          # device = torch.device("xpu:0")
          # torch.xpu.set_device(device)
      elif (
          self.distributed_state.distributed_type == DistributedType.NO
      ):
          if self.use_mps_device:
              warnings.warn(
                  "`use_mps_device` is deprecated and will be removed in"
                  " version 5.0 of 🤗 Transformers."
                  "`mps` device will be used by default if available similar"
                  " to the way `cuda` device is used."
                  "Therefore, no action from user is required. "
              )
              if device.type != "mps":
                  invalidInputError(
                      False,
                      (
                          "Either you do not have an MPS-enabled device"
                          " on this machine or MacOS"
                          " version is not 12.3+ "
                          "or current PyTorch install was not built with MPS enabled."
                      ),
                  )
          if device.type == "mps":
              self._n_gpu = 1
          elif self.use_cpu:
              device = torch.device("cpu")
              self._n_gpu = 0
          elif is_torch_xpu_available():
              device = torch.device("xpu:0")
              torch.xpu.set_device(device)
              self._n_gpu = 1
          elif is_torch_npu_available():
              device = torch.device("npu:0")
              torch.npu.set_device(device)
              self._n_gpu = 1
          else:
              # 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()
              if device.type == "cuda":
                  torch.cuda.set_device(device)
      return device
  
file_path: BigDL/python/llm/src/bigdl/llm/transformers/training_patch.py
imports: from transformers.utils import requires_backends, is_sagemaker_mp_enabled, is_accelerate_available, is_torch_xpu_available, is_sagemaker_dp_enabled, is_torch_tpu_available, is_torch_npu_available
  from transformers.utils.generic import strtobool
  from transformers.utils import cached_property
  from transformers.training_args import logger, ParallelMode, DistributedType
  import torch
  import torch.distributed
  import os
  import warnings
  from datetime import timedelta
  from accelerate import Accelerator
  from transformers import TrainingArguments
label_desc: Use image node_id 2 for calling a global function with example usage: _setup_devices() and returns: device