Love this post! Gets into the details of what it _really_ means to take some function and turn it into an RNN, and comparing that to the "batteries included" RNNs included in PyTorch, as a learning experience.

Question:

> To model the state, we need to add three hidden layers to the network.

How did you determine that it would be three hidden layers? Is it a consequence of the particular rule you were implementing, or is that generally how many layers you would use to implement a rule of this shape (using your architecture rather than Elman's - could we use fewer layers with Elman's?)?