def update()

in models.py [0:0]

• 16 lines of code
• 5 McCabe index (conditional complexity)

    def update(self, i, x, y, g, epoch):
        x, y, g = x.cuda(), y.cuda(), g.cuda()
        loss_value = self.compute_loss_value_(i, x, y, g, epoch)

        if loss_value is not None:
            self.optimizer.zero_grad()
            loss_value.backward()

            if self.clip_grad:
                torch.nn.utils.clip_grad_norm_(self.network.parameters(), 1.0)

            self.optimizer.step()

            if self.lr_scheduler is not None:
                self.lr_scheduler.step()

            if self.data_type == "text":
                self.network.zero_grad()

            loss_value = loss_value.item()

        self.last_epoch = epoch
        return loss_value