def re_convert(self) -> None:
        """
        After an update on the convolutional weights, re-convert weights into the equivalent dense layer.
        """
        for op in self.ops:
            if isinstance(op, PyTorchIntervalConv2D):
                op.re_convert(self.device)

        After an update on the convolutional weights, re-convert weights into the equivalent dense layer.
        
art/estimators/certification/interval/pytorch.py