def METHOD_NAME(self, round_idx):
    X_test = torch.tensor(self.X_test).float().to(self.device)
    y_test = self.y_test
    extracted_feature = self.model_feature_extractor.forward(X_test)
    guest_logits = self.model_classifier.forward(extracted_feature)
    guest_logits = guest_logits.cpu().detach().numpy()
    for k in self.host_local_test_logits_list.keys():
        host_logits = self.host_local_test_logits_list[k]
        guest_logits += host_logits
    y_prob_preds = self._sigmoid(np.sum(guest_logits, axis=1))
    threshold = 0.5
    y_hat_lbls, statistics = self._compute_correct_prediction(
        y_targets=y_test, y_prob_preds=y_prob_preds, threshold=threshold
    )
    acc = accuracy_score(y_test, y_hat_lbls)
    auc = roc_auc_score(y_test, y_prob_preds)
    ave_loss = np.mean(self.loss_list)
    self.loss_list = list()
    logging.info(
        "--- round_idx: {%d}, loss: {%s}, acc: {%s}, auc: {%s}"
        % (round_idx, str(ave_loss), str(acc), str(auc))
    )
    logging.info(
        precision_recall_fscore_support(
            y_test, y_hat_lbls, average="macro", warn_for=tuple()
        )
    )