Where σ denotes the non-linear transformation (commonly a ReLU activation), A the adjacency tensor, H^{l} the feature tensor at the l:th layer, D^{-1} the inverse diagonal degree tensor of A, and W^{l} the trainable weight tensor at the l:th layer. Specifically, for each bond type (relation), the degree tensor expresses, in the diagonal, the number of bonds attached to each atom. Notice, in this tutorial D^{-1} is omitted, for two reasons: (1) it's not obvious how to apply this normalization on the continuous adjacency tensors (generated by the generator), and (2) the performance of the WGAN without normalization seems to work just fine. Furthermore, in contrast to the original paper, no self-loop is defined, as we don't want to train the generator to predict \"self-bonding\".