* added support to define input shape for MaskedDeepFFN and MaskedDeepDAN
* changed parameter for recompute_mask(epsilon) to recompute_mask(theta) as it should denote a threshold
* implemented a first running version of a randomly wired cell network, more general than RandWireNN and in spirit of analysing graph theoretic properties
* bugfixes on generating structures from masks
* added/modified data loader utilities for mnist/cifar (probably no official part and concern of this library tools)
* fixed PyPi setup and tested installation routine
* defined networkx and torch as dependencies in setup.py. Next will be to check if it can be shadowed by pytorch packages from conda channels
* added a DeepCellDAN() which builds directed, acyclic networks with customized cells given a certain structure