unction is a simple wrapper to get the task specific versions of this metric, which is done by setting the
    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
    :class:`~torchmetrics.classification.BinaryConfusionMatrix`,
    :class:`~torchmetrics.classification.MulticlassConfusionMatrix` and
    :class:`~torchmetrics.classification.MultilabelConfusionMatrix` for the specific details of each argument influence
    and examples.

    Legacy Example:
        >>> from torch import tensor
        >>> target = tensor([1, 1, 0, 0])
        >>> preds = tensor([0, 1, 0, 0])
        >>> confmat = ConfusionMatrix(task="binary", num_classes=2)
        >>> confmat(preds, target)
        tensor([[2, 0],
                [1, 1]])

        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([2, 1, 0, 1])
        >>> confmat = ConfusionMatrix(task="multiclass", num_classes=3)
        >>> confmat(preds, target)
        tensor([[1, 1, 0],
                [0, 1, 0],
                [0, 0, 1]])

        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
        >>> confmat = ConfusionMatrix(task="multilabel", num_labels=3)
        >>> confmat(preds, target)
        tensor([[[1, 0], [0, 1]],
                [[1, 0], [1, 0]],
                [[0, 1], [0, 1]]])

    r%