code: def _adjust_threshold_arg(
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      device: Optional[torch.device] = None,
  ) -> Optional[Tensor]:
      """Convert threshold arg for list and int to tensor format."""
      if isinstance(thresholds, int):
          return torch.linspace(0, 1, thresholds, device=device)
      if isinstance(thresholds, list):
          return torch.tensor(thresholds, device=device)
      return thresholds
  
imports: from typing import List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from torch.nn import functional
  from typing_extensions import Literal
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.compute import _safe_divide, interp
  from torchmetrics.utilities.data import _bincount, _cumsum
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 2 for calling a global function with example usage: _adjust_threshold_arg(thresholds, device) and returns: thresholds, torch, torch