We performed Bayesian Optimization to optimize model hyperparameters. We used Hyperopt in Python to tune model depth (i.e., number of message passing steps), dropout rate, number of fully connected layers in the regressor, and hidden dimension (size of the fully connected layer in the regressor or size of the vector, depending on the model). 𝑦 Împlementation.