def METHOD_NAME(classifier, data):
    classifier.eval()
    x, label = data
    label = label.cpu().numpy().squeeze()
    # feed to network and classifier
    with torch.no_grad():
        pred_logits = classifier(x.to(device))
        pred_class = (pred_logits > 0).float().cpu().numpy()
    return (
        metrics.f1_score(label, pred_class, average="micro")
        if pred_class.sum() > 0
        else 0
    )