def METHOD_NAME(model: torch.nn.Module, loader, device):
    model.eval()
    correct = 0.0
    loss = 0.0
    num_graphs = 0
    for batch in loader:
        batch_graphs, batch_labels = batch
        num_graphs += batch_labels.size(0)
        batch_graphs = batch_graphs.to(device)
        batch_labels = batch_labels.long().to(device)
        out = model(batch_graphs)
        pred = out.argmax(dim=1)
        loss += F.nll_loss(out, batch_labels, reduction="sum").item()
        correct += pred.eq(batch_labels).sum().item()
    return correct / num_graphs, loss / num_graphs