> from torch import randn, randint
            >>> # Example plotting a single value
            >>> from torchmetrics.classification import MulticlassCalibrationError
            >>> metric = MulticlassCalibrationError(num_classes=3, n_bins=3, norm='l1')
            >>> metric.update(randn(20,3).softmax(dim=-1), randint(3, (20,)))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> from torch import randn, randint
            >>> # Example plotting a multiple values
            >>> from torchmetrics.classification import MulticlassCalibrationError
            >>> metric = MulticlassCalibrationError(num_classes=3, n_bins=3, norm='l1')
            >>> values = []
            >>> for _ in range(20):
            ...     values.append(metric(randn(20,3).softmax(dim=-1), randint(3, (20,))))
            >>> fig_, ax_ = metric.plot(values)

        rK