culation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Returns:
        The metric returns a dict where the key identifies the metric and groups with the lowest and highest true
        positives rates as follows: {metric}__{identifier_low_group}_{identifier_high_group}.
        The value is a tensor with the disparity rate.

    Example (preds is int tensor):
        >>> from torchmetrics.classification import BinaryFairness
        >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> preds = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> metric = BinaryFairness(2)
        >>> metric(preds, target, groups)
        {'DP_0_1': tensor(0.), 'EO_0_1': tensor(0.)}

    Example (preds is float tensor):
        >>> from torchmetrics.classification import BinaryFairness
        >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> preds = torch.tensor([0.11, 0.84, 0.22, 0.73, 0.33, 0.92])
        >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> metric = BinaryFairness(2)
        >>> metric(preds, target, groups)
        {'DP_0_1': tensor(0.), 'EO_0_1': tensor(0.)}

    Fr3