class_docstr: Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multilabel tasks.
  
  The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for
  multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
  corresponds to random guessing.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` containing probabilities or logits
    for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto
    apply sigmoid per element.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)`` containing ground truth labels, and
    therefore only contain {0,1} values (except if `ignore_index` is specified).
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``ml_auroc`` (:class:`~torch.Tensor`): If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will
    be returned with auroc score per class. If `average="micro|macro"|"weighted"` then a single scalar is returned.
  
  Additional dimension ``...`` will be flattened into the batch dimension.
  
  The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
  that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
  non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
  argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
  size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory).
  
  Args:
      num_labels: Integer specifying the number of labels
      average:
          Defines the reduction that is applied over labels. Should be one of the following:
  
          - ``micro``: Sum score over all labels
          - ``macro``: Calculate score for each label and average them
          - ``weighted``: calculates score for each label and computes weighted average using their support
          - ``"none"`` or ``None``: calculates score for each label and applies no reduction
      thresholds:
          Can be one of:
  
          - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
            all the data. Most accurate but also most memory consuming approach.
          - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
            0 to 1 as bins for the calculation.
          - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
          - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
            bins for the calculation.
  
      validate_args: bool indicating if input arguments and tensors should be validated for correctness.
          Set to ``False`` for faster computations.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.classification import MultilabelAUROC
      >>> preds = tensor([[0.75, 0.05, 0.35],
      ...                       [0.45, 0.75, 0.05],
      ...                       [0.05, 0.55, 0.75],
      ...                       [0.05, 0.65, 0.05]])
      >>> target = tensor([[1, 0, 1],
      ...                        [0, 0, 0],
      ...                        [0, 1, 1],
      ...                        [1, 1, 1]])
      >>> ml_auroc = MultilabelAUROC(num_labels=3, average="macro", thresholds=None)
      >>> ml_auroc(preds, target)
      tensor(0.6528)
      >>> ml_auroc = MultilabelAUROC(num_labels=3, average=None, thresholds=None)
      >>> ml_auroc(preds, target)
      tensor([0.6250, 0.5000, 0.8333])
      >>> ml_auroc = MultilabelAUROC(num_labels=3, average="macro", thresholds=5)
      >>> ml_auroc(preds, target)
      tensor(0.6528)
      >>> ml_auroc = MultilabelAUROC(num_labels=3, average=None, thresholds=5)
      >>> ml_auroc(preds, target)
      tensor([0.6250, 0.5000, 0.8333])
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docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tensor:  # type: ignore[override]
      """Compute metric."""
      state = (
          (dim_zero_cat(self.preds), dim_zero_cat(self.target))
          if self.thresholds is None
          else self.confmat
      )
      return _multilabel_auroc_compute(
          state,
          self.num_labels,
          self.average,
          self.thresholds,
          self.ignore_index,
      )
  
imports: from typing import Any, List, Optional, Sequence, Type, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.classification.precision_recall_curve import BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve
  from torchmetrics.functional.classification.auroc import _binary_auroc_arg_validation, _binary_auroc_compute, _multiclass_auroc_arg_validation, _multiclass_auroc_compute, _multilabel_auroc_arg_validation, _multilabel_auroc_compute
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.enums import ClassificationTask
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 2 for calling the MultilabelAUROC obj's underlying member method code with example usage: obj.compute() and returns: _multilabel_auroc_compute