ting multiple values
            >>> from torchmetrics.classification import BinaryFairness
            >>> metric = BinaryFairness(2)
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.rand(20), torch.randint(2,(20,)), torch.ones(20).long()))
            >>> fig_, ax_ = metric.plot(values)

        )