
        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])

    Fr