code: def get_dist_device():
      """
      Get the expected target device in the native PyTorch distributed data parallel.
      For NCCL backend, return GPU device of current process.
      For GLOO backend, return CPU.
      For any other backends, return None as the default, tensor.to(None) will not change the device.
  
      """
      if dist.is_initialized():
          backend = dist.get_backend()
          if backend == "nccl" and torch.cuda.is_available():
              return torch.device(f"cuda:{torch.cuda.current_device()}")
          if backend == "gloo":
              return torch.device("cpu")
      return None
  
file_path: MONAI/monai/utils/dist.py
imports: from __future__ import annotations
  import sys
  import warnings
  from collections.abc import Callable
  from logging import Filter
  from typing import overload
  import torch
  import torch.distributed
  from monai.config import IgniteInfo
  from monai.utils.module import min_version, optional_import
label_desc: Use image node_id 1 for calling a global function with example usage: get_dist_device() and returns: None, torch, torch