I think this is wrong " the gradients needed for computing the Jacobian are also computed during backprop when I train the network".
Normal backprop is from the loss function (a single value), if you backprop form multiple values it would be much more expensive (for example 3x64x64 times more expensive).
Secondly, it says `4.94 MiB free