def METHOD_NAME():
    model.METHOD_NAME()
    optimizer.zero_grad()
    z = model.encode(train_data.x, train_data.edge_index)
    loss = model.recon_loss(z, train_data.pos_edge_label_index)
    if args.variational:
        loss = loss + (1 / train_data.num_nodes) * model.kl_loss()
    loss.backward()
    optimizer.step()
    return float(loss)