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

    Legacy Example:
        >>> from torch import tensor
        >>> target = tensor([0, 1, 1])
        >>> preds = tensor([0.5, 0.7, 0.1])
        >>> hinge = HingeLoss(task="binary")
        >>> hinge(preds, target)
        tensor(0.9000)

        >>> target = tensor([0, 1, 2])
        >>> preds = tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
        >>> hinge = HingeLoss(task="multiclass", num_classes=3)
        >>> hinge(preds, target)
        tensor(1.5551)

        >>> target = tensor([0, 1, 2])
        >>> preds = tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
        >>> hinge = HingeLoss(task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
        >>> hinge(preds, target)
        tensor([1.3743, 1.1945, 1.2359])

    NFrV