Tensorflow-ranking

This is the 0.1.4 release of TensorFlow Ranking. It is tested and stable against TensorFlow version 1.14.0 and TensorFlow version 2.0 RC0. The main changes in this release are:
* Documentation for APIs. List of symbols/operations are available [here](https://github.com/tensorflow/ranking/blob/master/tensorflow_ranking/g3doc/api_docs/python/index.md).
* [Demo](https://git.io/tf-ranking-demo) for using sparse and embedded features on ANTIQUE dataset.
* Example for prediction using ranking estimator in demo code.
* Code and test cases are fully TF2.0 RC0 compatible.
* Updated [tfr.utils.sort_by_scores](https://github.com/tensorflow/ranking/blob/67fae555425ee75e5aa07b74100fbff2057ce9ae/tensorflow_ranking/g3doc/api_docs/python/tfr/utils/sort_by_scores.md) to break ties.
* Added [ApproxMRR](https://github.com/tensorflow/ranking/blob/5866315165002fe9a07d45f00712081337f5a039/tensorflow_ranking/python/losses.py#L1286) loss function.

Announcement:
A hands-on [tutorial](http://ictir2019.org/program/#tutorials) for TF-Ranking, with relevant theoretical background will be presented on Oct 2 at ICTIR 2019, hosted in Santa Clara, CA. Please consider attending!

This is the 0.1.3 release of TensorFlow Ranking. It is tested and stable against TensorFlow version 1.14.0. The main changes in this release are:

* Introduced an ExampleInExample data format.
* Introduced a factory method to build tf.dataset in different data formats.
* Introduced a factory method to build serving receiving input functions for different data formats.
* Refactored the main modules to be object-oriented to increase the code extensibility.