def METHOD_NAME():
    model.METHOD_NAME()
    total_loss = 0
    for data in train_loader:
        data = data.to(device)
        optimizer.zero_grad()
        out = model(data.x, data.pos, data.batch)
        loss = F.nll_loss(out, data.y)
        loss.backward()
        total_loss += loss.item() * data.num_graphs
        optimizer.step()
    return total_loss / len(train_dataset)