Why not use torch.no_grad when evaluating test data?

The way the trainer is set up the `iteration` that is used for train and test is similar except when train step is run the backwards propagation occurs. But one other thing I typically see different between test and train is that in the test batch `with torch.no_grad()` is used so that, for example, dropout is not applied. Was there any reason this isn't used here?