def METHOD_NAME(model, data, epochs=200, lr=0.01, weight_decay=5e-4):
    model.METHOD_NAME()
    optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
    for _ in range(epochs):
        optimizer.zero_grad()
        pred = model(data.x, data.edge_index, data.edge_weight)
        loss = F.cross_entropy(pred[data.train_mask], data.y[data.train_mask])
        loss.backward()
        optimizer.step()