def METHOD_NAME(model, loader, criterion):
    """
    Evaluate entry
    """
    model.eval()
    total_loss = []
    total_acc = []
    for idx, batch_data in enumerate(loader):
        graphs, labels = batch_data
        g = pgl.Graph.batch(graphs).tensor()
        labels = paddle.to_tensor(labels)
        pred = model(g)
        eval_loss = criterion(pred, labels)
        eval_acc = paddle.metric.accuracy(input=pred, label=labels, k=1)
        total_loss.append(eval_loss.numpy())
        total_acc.append(eval_acc.numpy())
    total_loss = np.mean(total_loss)
    total_acc = np.mean(total_acc)
    model.train()
    return {"loss": total_loss, "acc": total_acc}