def METHOD_NAME(model, target_test_loader):
    model.eval()
    correct = 0
    criterion = torch.nn.CrossEntropyLoss()
    len_target_dataset = len(target_test_loader.dataset)
    with torch.no_grad():
        for data, target in target_test_loader:
            data, target = data.to(DEVICE), target.to(DEVICE)
            s_output = model(data)
            loss = criterion(s_output, target)
            pred = torch.max(s_output, 1)[1]
            correct += torch.sum(pred == target)
    acc = correct.double() / len(target_test_loader.dataset)
    return acc