Proper way to do gradient clipping?

I thought nn.utils.clip_grad_norm(model.parameters(), clip) is supposed to finish the job.

What is:
for p in model.parameters():
p.data.add_(-lr, p.grad.data)
for?

Can someone give a more explicit explain? Is it because after I use gradient clipping, I may not use adam optimizer?

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