Why can the loss function be simplified by clamp

[jiangming0chen]

The paper [Making deep neural networks robust to label noise: A loss correction approach] points out to estimate the noise transfer matrix T, and then retain the network. Why can the loss function be simplified by pred = torch.clamp(pred.softmax(-1), min = eps, max = 1-eps)?
thanks.