Refactor nodes.py

[sdemarch]

Restructure the module nodes.py to represent single- and multi-input/output layers

• Delete SingleInputLayerNode and MultiInputLayerNode base classes
• Make LayerNode the base class with a list of inputs and a list of outputs (default 1, enforced in derived layers)

[darioguidotti]

We need to keep in mind that if we use the classes of networks.py also as containers of nodes defined in abstraction.py we need to do one of two things:

1. We need to keep a common ancestor between the concrete and abstract layer nodes (which must have only identifier as attribute).
2. We need to change the current implementation of the abstract layer nodes (#25) so that it make sense to inherit from something defining the attributes in_dims, out_dims. Maybe by inheriting from the corresponding concrete node instead of keeping it as an attribute? Keeping it as an attribute is kind of useful because it keep separate the info regarding the original concrete node from the info needed for the abstract transformer forward.

P.s. the overlapping of the nomenclature between abstract interpretation and abstraction in programming is agony.

[sdemarch]

I think the best solution is to do the same thing as for the AbsNeuralNetwork classes as you say in pt. 2. We make the base class AbsLayerNode acting as an interface and then all "concrete abstract" layers inheriting from both the interface and their respective concrete reference node.

EDIT: this cannot work since LayerNodes constructors would affect the building of AbsLayerNodes

I also think that it can be safe and self-healing to refer to wholly abstract classes as Interfaces

[sdemarch]

Cfr. #25

Also, we implement a getter for in_dim in single-input layers and out_dim for completeness. It performs nothing, but is the right way to structure the interface of the classes