called one-sided is that we only smooth the the side that's on the data so what this will do is you can think of it as introducing some kind of like a leak probability that sometimes the data has been mislabeled that we accidentally gave you something fake and said it was real in particular this will reduce the confidence of the model somewhat so that it will not predict really extreme values it's important not to smooth the generator samples and we can see this by optimizing what the optimal discriminator is if we smooth by