number of classes
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``micro``: Sum statistics over all labels
            - ``macro``: Calculate statistics for each label and average them
            - ``weighted``: calculates statistics for each label and computes weighted average using their support
            - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction

        top_k:
            Number of highest probability or logit score predictions considered to find the correct label.
            Only works when ``preds`` contain probabilities/logits.
        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        ignore_index:
            Specifies a target value that is ignored and does not contribute to the metric calculation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.classification import MulticlassSpecificity
        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([2, 1, 0, 1])
        >>> metric = MulticlassSpecificity(num_classes=3)
        >>> metric(preds, target)
        tensor(0.8889)
        >>> mcs = MulticlassSpecificity(num_classes=3, average=None)
        >>> mcs(preds, target)
        tensor([1.0000, 0.6667, 1.0000])

    Example (preds is float tensor):
        >>> from torchmetrics.classification import MulticlassSpecificity
        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([[0.16, 0.26, 0.58],
        ...                 [0.22, 0.61, 0.17],
        ...                 [0.71, 0.09, 0.20],
        ...                 [0.05, 0.82, 0.13]])
        >>> metric = MulticlassSpecificity(num_classes=3)
        >>> metric(preds, target)
        tensor(0.8889)
        >>> mcs = MulticlassSpecificity(num_classes=3, average=None)
        >>> mcs(preds, target)
        tensor([1.0000, 0.6667, 1.0000])

    Example (multidim tensors):
        >>> from torchmetrics.classification import MulticlassSpecificity
        >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
        >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
        >>> metric = MulticlassSpecificity(num_classes=3, multidim_average='samplewise')
        >>> metric(preds, target)
        tensor([0.7500, 0.6556])
        >>> mcs = MulticlassSpecificity(num_classes=3, multidim_average='samplewise', average=None)
        >>> mcs(preds, target)
        tensor([[0.7500, 0.7500, 0.7500],
                [0.8000, 0.6667, 0.5000]])

    r