v1.4.0

This release is a significant refactor of T2T internals.

• T2TModel subclasses now have the ability to override the entire Estimator model function with the estimator_model_fn method, making them much more flexible. Subclasses can also now override bottom, body, top, loss, and optimize.
• Problem subclasses now have the ability to override the entire Estimator input function with the input_fn method, making them much more flexible.
• The key components of the trainer and decoder - Experiment, Estimator, RunConfig, HParams - are all much more easily constructed and used by library callers through tpu_trainer_lib.py.
• We decided to drop support for MultiModel, i.e. training on multiple problems, because it added too much code complexity for the benefit gained. We will consider adding support back in a way that doesn't overcomplicate things too much if there's sufficient interest.

There are also the usual new models, feature improvements, bug fixes.

• New image_fashion_mnist dataset
• New revnet104 model, implementing a large Reversible Residual Network
• Set --decode_hparams=write_beam_scores=True to include beam scores when writing to a file
• Beginnings of new interactive visualization server at insights/