This would percolate the gradient all the way down the computation graph though. Is there any way to modify a single gradient in-place? In my case, I’m computing a gradient in a remote process and sending it back to the parent. Will the parent’s parameter tensor have a valid .grad attribute? If so, perhaps just a copy_ might work.