def METHOD_NAME(model: nn.Module):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(cifar10_test)
    acc = 100 * correct / len(cifar10_test)
    print('Loss: {}  Accuracy: {}%)\n'.format(
        test_loss, acc))
    return acc