Yes, this is done during training with a scale factor of 1/(1-p) as seen here:
drop = nn.Dropout(p=0.8)
x = torch.ones(10)
out = drop(x)
print(out)
> tensor([0., 0., 0., 0., 5., 5., 0., 0., 0., 0.])

The dropout paper explains the scaling in section 10.