to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

    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_hamming_distance`,
    :func:`~torchmetrics.functional.classification.multiclass_hamming_distance` and
    :func:`~torchmetrics.functional.classification.multilabel_hamming_distance` for
    the specific details of each argument influence and examples.

    Legacy Example:
        >>> from torch import tensor
        >>> target = tensor([[0, 1], [1, 1]])
        >>> preds = tensor([[0, 1], [0, 1]])
        >>> hamming_distance(preds, target, task="binary")
        tensor(0.2500)

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