def METHOD_NAME(device, graph, nid, model, batch_size):
    model.eval()
    with torch.no_grad():
        pred = model.inference(
            graph, device, batch_size
        )  # pred in buffer_device
        pred = pred[nid]
        label = graph.ndata["label"][nid].to(pred.device)
        num_classes = pred.shape[1]
        return MF.accuracy(
            pred, label, task="multiclass", num_classes=num_classes
        )