age 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

        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.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.classification import MultilabelStatScores
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
        >>> metric = MultilabelStatScores(num_labels=3, average='micro')
        >>> metric(preds, target)
        tensor([2, 1, 2, 1, 3])
        >>> mlss = MultilabelStatScores(num_labels=3, average=None)
        >>> mlss(preds, target)
        tensor([[1, 0, 1, 0, 1],
                [0, 0, 1, 1, 1],
                [1, 1, 0, 0, 1]])

    Example (preds is float tensor):
        >>> from torchmetrics.classification import MultilabelStatScores
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
        >>> metric = MultilabelStatScores(num_labels=3, average='micro')
        >>> metric(preds, target)
        tensor([2, 1, 2, 1, 3])
        >>> mlss = MultilabelStatScores(num_labels=3, average=None)
        >>> mlss(preds, target)
        tensor([[1, 0, 1, 0, 1],
                [0, 0, 1, 1, 1],
                [1, 1, 0, 0, 1]])

    Example (multidim tensors):
        >>> from torchmetrics.classification import MultilabelStatScores
        >>> 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 = MultilabelStatScores(num_labels=3, multidim_average='samplewise', average='micro')
        >>> metric(preds, target)
        tensor([[2, 3, 0, 1, 3],
                [0, 2, 1, 3, 3]])
        >>> mlss = MultilabelStatScores(num_labels=3, multidim_average='samplewise', average=None)
        >>> mlss(preds, target)
        tensor([[[1, 1, 0, 0, 1],
                 [1, 1, 0, 0, 1],
                 [0, 1, 0, 1, 1]],
                [[0, 0, 0, 2, 2],
                 [0, 2, 0, 0, 0],
                 [0, 0, 1, 1, 1]]])

    Fr?