code: def _multiclass_auroc_compute(
      state: Union[Tensor, Tuple[Tensor, Tensor]],
      num_classes: int,
      average: Optional[Literal["macro", "weighted", "none"]] = "macro",
      thresholds: Optional[Tensor] = None,
  ) -> Tensor:
      fpr, tpr, _ = _multiclass_roc_compute(
          state, num_classes, thresholds
      )
      return _reduce_auroc(
          fpr,
          tpr,
          average,
          weights=_bincount(state[1], minlength=num_classes).float()
          if thresholds is None
          else state[0][:, 1, :].sum(-1),
      )
  
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 6 for calling a global function with example usage: _multiclass_auroc_compute(state, num_classes, average, thresholds) and returns: _reduce_auroc