Method step

tests/unit/simple_model.py:209–221

(self, closure=None)

        super(SimpleOptimizer, self).__setstate__(state)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                d_p = p.grad.data
                p.data.add_(-group['lr'], d_p)

        return loss


class HybridStateOptimizer(torch.optim.Optimizer):