The differentiable optimizers of Higher (GitHub - facebookresearch/higher: higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.) don’t seem to have a state_dict() method like regular optimizers.
Can I still access, and save their states of somehow? I’d like to access them so I can load them to a regular optimizer later.