For the problem of supporting gradient regularization style techniques (for ex. in CTGAN or Wasserstein GAN), or more specifically your case the autograd.grad is done for purposes of estimation of the gradient regularizers and not training, so we should not need to go the route of per-sample gradient estimation, clipping and noise addition which DP would do here. In contrast we’d need that during the pen.backward().