ents and tensors should be validated for correctness.
            Set to ``False`` for faster computations.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Returns:
        (tuple): a tuple of either 2 tensors or 2 lists containing

        - specificity: an 1d tensor of size (n_classes, ) with the maximum specificity for the given
            sensitivity level per class
        - thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class


    Example:
        >>> from torchmetrics.classification import MulticlassSpecificityAtSensitivity
        >>> from torch import tensor
        >>> preds = tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
        ...                 [0.05, 0.75, 0.05, 0.05, 0.05],
        ...                 [0.05, 0.05, 0.75, 0.05, 0.05],
        ...                 [0.05, 0.05, 0.05, 0.75, 0.05]])
        >>> target = tensor([0, 1, 3, 2])
        >>> metric = MulticlassSpecificityAtSensitivity(num_classes=5, min_sensitivity=0.5, thresholds=None)
        >>> metric(preds, target)
        (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 5.0000e-02, 5.0000e-02, 1.0000e+06]))
        >>> metric = MulticlassSpecificityAtSensitivity(num_classes=5, min_sensitivity=0.5, thresholds=5)
        >>> metric(preds, target)
        (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 0.0000e+00, 0.0000e+00, 1.0000e+06]))

    Fr