Yes my bad. The idea of the paper is to give a score to a base.
Each base has its own neural network that gives a scalar in the end, y_i. Then all of the station scores are joined to output a normalized probability with a softmax.
So if I have 5 bases, I will have 5 neural networks, 5 scores and then I will normalize with a softmax.
In the code I merge the different scores with a torch.stack. I am not sure if pytorch will detect this structure correctly in order to do the gradients.