WIP: haiku bert implementation
Still a work in progress but the contextual embeddings line up with the pytorch version so this is roughly at parity with jax-bert

TODO (mostly notes to myself): 
- [x] Add `save_pretrained`
- [ ] Make `from_pretrained` work with names
- [ ] Add dropout at training time, pass through training flag
- [ ] Make sure weight initializations line up when pre-trained state isn't passed
- [ ] Gradually work towards parity with the pytorch version if desired? (target models, BERT variants, etc.) 
- [ ] Write HaikuPretrainedModel to take advantage of archive resolution / make saving + loading compatible with pytorch bins?

To use the pre-trained weights cleanly I ended up subclassing `hk.Module` -- unsure how I feel about this decision but I couldn't think of a better method at the time.  Feel free to suggest an alternative if you have ideas.
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
