Opt-einsum

**New Features**

- [\48](https://github.com/dgasmith/opt_einsum/pull/48) Intermediates can now be shared between contractions, see here for more details.
- [\53](https://github.com/dgasmith/opt_einsum/pull/53) Intermediate caching is thread safe.

**Enhancements**

- [\48](https://github.com/dgasmith/opt_einsum/pull/48) Expressions are now mapped to non-unicode index set so that unicode input is support for all backends.
- [\54](https://github.com/dgasmith/opt_einsum/pull/54) General documentation update.

**Bug fixes**

- [\41](https://github.com/dgasmith/opt_einsum/pull/41) PyTorch indices are mapped back to a small a-z subset valid for PyTorch's einsum implementation.

**Bug fixes**

- Fixes unicode issue for large numbers of tensors in Python 2.7.
- Fixes unicode install bug in README.md.

**Bug fixes**

- Ensures `versioneer.py` is in MANIFEST.in for a clean pip install.

**Bug fixes**

- Corrected Markdown display on PyPi.

`opt_einsum` continues to improve its support for additional backends beyond NumPy with PyTorch.

We have also published the opt_einsum package in the Journal of Open Source Software. If you use this package in your work, please consider citing us!

**New features**

- PyTorch backend support
- Tensorflow eager-mode execution backend support

**Enhancements**

- Intermediate tensordot-like expressions are now ordered to avoid transposes.
- CI now uses conda backend to better support GPU and tensor libraries.
- Now accepts arbitrary unicode indices rather than a subset.
- New auto path option which switches between optimal and greedy at four tensors.

**Bug fixes**

- Fixed issue where broadcast indices were incorrectly locked out of tensordot-like evaluations even after their dimension was broadcast.

**New Features**

- Allows unlimited Unicode indices.
- Adds a Journal of Open-Source Software paper.
- Minor documentation improvements.