import torch from hessian import hessian x = torch.tensor([1.5, 2.5], requires_grad=True) h = hessian(x.pow(2).prod(), x, create_graph=True) print(h) # tensor([[12.5, 15], # [15, 4.5]], grad_fn=<CopySlices>) h2 = hessian(h.sum(), x) print(h2) # tensor([[4, 8], # [8, 4]])

import torch from hessian import jacobian x = torch.tensor([1.5, 2.5], requires_grad=True) y = torch.tensor([5.5, -4.], requires_grad=True) j = jacobian(x.pow(y), [x, y]) print(j) # tensor([[34.1, -0.00, 3.77, 0.00], # [ 0.0, -0.04, 0.00, 0.02]])