The generator network needs to be able to map (for each example in the batch) a vector z to a 3-D adjacency tensor (A) and 2-D feature tensor (H). For this, z will first be passed through a fully-connected network, for which the output will be further passed through two separate fully-connected networks. Each of these two fully-connected networks will then output (for each example in the batch) a tanh-activated vector followed by a reshape and softmax to match that of a multi-dimensional adjacency/feature tensor.