Hi, I meet the same problem, but I want to backward on the first derivative w.r.t. network parameters. Since both torch.autograd.grad and torch.autograd.functional.jacobian only takes vector inputs while network parameters are tuple of tensors, is there a feasible way to do this?  Thanks in advance for any possible help!
This post: Get gradient and Jacobian wrt the parameters 14 helps get jacobian but I’m trying to backward further on Jacobian.  It would be really nice if PyTorch supports gradient w.r.t PyTree 1 objects like jax.