math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}

    This function 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
    :func:`~torchmetrics.functional.classification.binary_jaccard_index`,
    :func:`~torchmetrics.functional.classification.multiclass_jaccard_index` and
    :func:`~torchmetrics.functional.classification.multilabel_jaccard_index` for
    the specific details of each argument influence and examples.

    Legacy Example:
        >>> from torch import randint, tensor
        >>> target = randint(0, 2, (10, 25, 25))
        >>> pred = tensor(target)
        >>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
        >>> jaccard_index(pred, target, task="multiclass", num_classes=2)
        tensor(0.9660)

    z+`num_classes` is expected to be `int` but `z
 was passed.`z*`num_labels` is expected to be `int` but `z