Thanks, but the response is not conclusive. I need to know how to implement somenthing like this in tensorflow.
grads_and_vars = opt.compute_gradients(loss, parameter_list)
my_grads_and_vars = [(g*C, v) for g, v in grads_and_vars]
opt.apply_gradients(my_grads_and_vars)

If i’ve understand it well, torch.backward() store the grandient in parameter.grad, so i’ve to access every parameter iterating long the module parameters list.