up identifiers should be ``0, 1, ..., (num_groups - 1)``.
        threshold: Threshold for transforming probability to binary {0,1} predictions.
        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.

    Returns:
        The metric returns a dict where the key identifies the group with the lowest and highest positivity rates
        as follows: DP_{identifier_low_group}_{identifier_high_group}. The value is a tensor with the DP rate.

    Example (preds is int tensor):
        >>> from torchmetrics.functional.classification import demographic_parity
        >>> preds = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> demographic_parity(preds, groups)
        {'DP_0_1': tensor(0.)}

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

    r