As Hinton mentioned

At test time, we use the “mean network” that contains all of the hidden units but with their outgoing weights halved to compensate for the fact that twice as many of them are active.

in their paper  Improving neural networks by preventing co-adaptation of feature detectors 2,I wonder does it matters to perform the half operation and how does  torch.nn.functional.dropout deal with this question?