Before 1.0, I could call eval() on my model while setting  requires_grad = True  on the individual Variables,  so I could inspect the gradients while dropout was turned off for the whole model. When I try this now  (by setting requires_grad  on the top level  module), I get an error:
RuntimeError: cudnn RNN backward can only be called in training mode
is there still a way to pull this off?