Set to ``False`` for faster computations.

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.classification import MultilabelSpecificity
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
        >>> metric = MultilabelSpecificity(num_labels=3)
        >>> metric(preds, target)
        tensor(0.6667)
        >>> mls = MultilabelSpecificity(num_labels=3, average=None)
        >>> mls(preds, target)
        tensor([1., 1., 0.])

    Example (preds is float tensor):
        >>> from torchmetrics.classification import MultilabelSpecificity
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
        >>> metric = MultilabelSpecificity(num_labels=3)
        >>> metric(preds, target)
        tensor(0.6667)
        >>> mls = MultilabelSpecificity(num_labels=3, average=None)
        >>> mls(preds, target)
        tensor([1., 1., 0.])

    Example (multidim tensors):
        >>> from torchmetrics.classification import MultilabelSpecificity
        >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
        >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99],  [0.63, 0.04]],
        ...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
        >>> metric = MultilabelSpecificity(num_labels=3, multidim_average='samplewise')
        >>> metric(preds, target)
        tensor([0.0000, 0.3333])
        >>> mls = MultilabelSpecificity(num_labels=3, multidim_average='samplewise', average=None)
        >>> mls(preds, target)
        tensor([[0., 0., 0.],
                [0., 0., 1.]])

    r