Integration with common dataset format

[seanmor5]

Requires developments in elixir-nx/nx#301 and whatever external efforts emerge.

[t-rutten]

Would it be worth shifting or adding function clauses to the current Axon.Training API to be more friendly to inputs and targets that are streams? This would seem to entail swapping Enum functions (like zip/2 and with_index/1) for their Stream versions.

Additionally, we could avoid calling Enum.count/1 in Training.train_epoch/5 by requiring inputs and labels to each be a tuple of an enumerable and metadata about the enumerable--like {data, %{total_batches: _}} = _inputs.

[seanmor5]

@t-rutten I think we should go ahead and make the switch to use streams in the training API instead of Enumerables. I think any dataset format will logically have to be implemented as a stream anyway to avoid having to load large datasets entirely into memory.

As far as avoiding usage of Enum.count, I think we can instead just change the average calculation to use the current training step, that way we can support cases where the size of the dataset is unknown ahead of time and where users are specifying steps per epoch

[seanmor5]

With the new training API, I think we're pretty much set on allowing Axon to work with anything that implements Enumerable, which I think is flexible enough as is.