PyTorch's boolean comparison crap isn't useful and makes
  it a pain to test exact tensor values.
* Will resume later

It's a painfully simple function that has worked every time
  I've used it.
- No it doesn't handle every edge case
+ Yes, it gets the job done and can be packaged for the general case

It was as easy as I wanted it to be.
* The complexity is how to handle a given nested layer
  + Those will get implemented with a given feature
  - Need to program feature detection

TODO:
+ Implement the resizing on a layer-by-layer case, to
  make the shrinking a bit different
  + Instead of applying the data transformation uniformly,
    each layer gets
  + Those factors will be computed as 1 - percent_waste(layer)

> The thought is that channel depth doesn't change the output dimensions for CNNs, and that's
  attribute we're concerned with in the convulotional case...
  * Linear layers only have two dimensions, so it's a huge deal there.
  * RNNs do linear things over 'timesteps', so it's a big deal there.
  * Residual/identity/skip-connections in CNNs need this.

> __It's decided__. The attribute stays