and φ l is another parameterized function. Following reference , we represented the functions φ l with two-layer neural networks. The first layer of these networks had 256 units with ReLU activation functions, and the second layer has one unit with a linear activation function. The set2set model was used for the function r g . A four-layer neural network was used for the function r n . The first three layers contained 512 units with PReLU activation functions with a dropout rate of 0.1, and the fourth layer had one unit with a linear activation function.