def METHOD_NAME(self):
    self.model.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for _batch_idx, (inputs, targets) in enumerate(self.testloader):
            inputs, targets = inputs.to(self.device), targets.to(self.device)
            outputs = self.model(inputs)
            loss = self.criterion(outputs, targets)
            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()
    self.accuracy = 100. * correct / total