y 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.

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.classification import MultilabelHammingDistance
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
        >>> metric = MultilabelHammingDistance(num_labels=3)
        >>> metric(preds, target)
        tensor(0.3333)
        >>> mlhd = MultilabelHammingDistance(num_labels=3, average=None)
        >>> mlhd(preds, target)
        tensor([0.0000, 0.5000, 0.5000])

    Example (preds is float tensor):
        >>> from torchmetrics.classification import MultilabelHammingDistance
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
        >>> metric = MultilabelHammingDistance(num_labels=3)
        >>> metric(preds, target)
        tensor(0.3333)
        >>> mlhd = MultilabelHammingDistance(num_labels=3, average=None)
        >>> mlhd(preds, target)
        tensor([0.0000, 0.5000, 0.5000])

    Example (multidim tensors):
        >>> from torchmetrics.classification import MultilabelHammingDistance
        >>> 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 = MultilabelHammingDistance(num_labels=3, multidim_average='samplewise')
        >>> metric(preds, target)
        tensor([0.6667, 0.8333])
        >>> mlhd = MultilabelHammingDistance(num_labels=3, multidim_average='samplewise', average=None)
        >>> mlhd(preds, target)
        tensor([[0.5000, 0.5000, 1.0000],
                [1.0000, 1.0000, 0.5000]])

    Fr