opt.step()
opt.swap_swa_sgd()
```
You can wrap any optimizer from torch.optim using the SWA class, and then train your model as usual. When training is complete you simply call swap_swa_sgd() to set the weights of your model to their SWA averages. Below we explain the SWA procedure and the parameters of the SWA class in detail. We emphasize that SWA can be combined with any optimization procedure, such as Adam, in the same way that it can be combined with SGD.
Is this just Averaged SGD?