Taylor Approximation of the gradient with pytorch

I want to correct the gradient for a custom layer using the Taylor expansion:

f'(x+delta) = f'(x) + 1/2*f''(x)*delta

where x is the original output of the layer and f’(x) the default, unmodified gradient, for simplicity this example is the univariate case and delta is something I calculate (it’s the secret sauce for the research idea, so I can’t really go too much into detail what it’s doing).

So I need to access the hessian (not inverse hessian) during for the custom gradient in my nn.module. I have never worked with Hessians and wonder what my options with PyTorch are. Can the autograd framework just compute it for me?

Ah, I’ve found a related issue that solves a similiar problem: Calculating Hessian Vector Product