code: def _multiclass_auroc_arg_validation(
      num_classes: int,
      average: Optional[Literal["macro", "weighted", "none"]] = "macro",
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
  ) -> None:
      _multiclass_precision_recall_curve_arg_validation(
          num_classes, thresholds, ignore_index
      )
      allowed_average = ("macro", "weighted", "none", None)
      if average not in allowed_average:
          raise ValueError(
              f"Expected argument `average` to be one of {allowed_average} but got {average}"
          )
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.precision_recall_curve import _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update
  from torchmetrics.functional.classification.roc import _binary_roc_compute, _multiclass_roc_compute, _multilabel_roc_compute
  from torchmetrics.utilities.compute import _auc_compute_without_check, _safe_divide
  from torchmetrics.utilities.data import _bincount
  from torchmetrics.utilities.enums import ClassificationTask
  from torchmetrics.utilities.prints import rank_zero_warn
label_desc: Use image node_id 5 for calling a global function with example usage: _multiclass_auroc_arg_validation(num_classes, average, thresholds, ignore_index) without return types