Hi, thanks for replying 
Ok, it’s clear but let’s say I’ve defined a ConvNet (because that’s what I did) using some torch.nn classes (Conv2d, etc.), how can I do that?
I figure I can modify all the parameters just by iterating model.parameters() and doing the upgrade on each of them, but then how do I “put” them inside a functioning model?
I feel like there should be an easy solution because everyone who’s doing meta-learning would face the same problem I’m facing right now (I may be wrong, I don’t actually know much about meta-learning but I know optimization-based algorithm often involved differentiating through an inner training).