def METHOD_NAME(net, test_loader, dev):
    net.eval()
    total_correct = 0
    count = 0
    with torch.no_grad():
        with tqdm.tqdm(test_loader, ascii=True) as tq:
            for data, label in tq:
                label = label[:, 0]
                num_examples = label.shape[0]
                data, label = data.to(dev), label.to(dev).squeeze().long()
                logits = net(data)
                _, preds = logits.max(1)
                correct = (preds == label).sum().item()
                total_correct += correct
                count += num_examples
                tq.set_postfix({"AvgAcc": "%.5f" % (total_correct / count)})
    return total_correct / count