The dropout scaling is already done in the backend, if you are using the nn.Dropout module:
drop = nn.Dropout()
x = torch.ones(10)
out = drop(x)
print(out)
> tensor([0., 0., 2., 2., 0., 0., 0., 2., 0., 0.])

so you don’t need to reapply it.