code: def detach_variable(inputs, device=None):
      if isinstance(inputs, tuple):
          out = []
          for inp in inputs:
              if not isinstance(inp, torch.Tensor):
                  out.append(inp)
                  continue
  
              requires_grad = inp.requires_grad
  
              if device is not None:
                  x = inp.to(device=device)
              else:
                  x = inp
  
              x = x.detach()
              x.requires_grad = requires_grad
              out.append(x)
          return tuple(out)
      else:
          raise RuntimeError(
              "Only tuple of tensors is supported. Got Unsupported input type: ",
              type(inputs).__name__,
          )
  
imports: import copy
  import torch
  import contextlib
  from deepspeed import comm
  import weakref
  import mmap
  from torch import _C
  from deepspeed.runtime.config import DeepSpeedConfig
  from deepspeed.utils import logger
  from deepspeed.runtime.utils import copy_to_device, move_to_device, see_memory_usage, bwc_tensor_model_parallel_rank
  from deepspeed.utils.timer import SynchronizedWallClockTimer, FORWARD_GLOBAL_TIMER
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 1 for calling a global function with example usage: detach_variable(inputs, device) and returns: tuple