> from torchmetrics.wrappers import ClasswiseWrapper
            >>> from torchmetrics.classification import MulticlassAccuracy
            >>> metric = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None))
            >>> values = [ ]
            >>> for _ in range(3):
            ...     values.append(metric(torch.randint(3, (20,)), torch.randint(3, (20,))))
            >>> fig_, ax_ = metric.plot(values)

        )