ulticlassConfusionMatrix):
        ...     def update(self, preds, target):
        ...         super().update(preds, target)
        ...         # by construction make future states dependent on prior states
        ...         if self.confmat.sum() > 20:
        ...             self.reset()
        >>> check_forward_full_state_property(
        ...     MyMetric,
        ...     init_args = {'num_classes': 3},
        ...     input_args = {'preds': torch.randint(3, (10,)), 'target': torch.randint(3, (10,))},
        ... )
        Recommended setting `full_state_update=True`

    c