Tensorflow-federated

Major Features and Improvements

* Added a `Literal` to the TFF language, part 2. This change updates the
tracing and execution portions of TFF to begin using the `Literal`.
* Added an implementation of the Adafactor optimizer to
`tff.learning.optimizers.build_adafactor`
* Added a new field, `content`, to the `Data` proto.

Breaking Changes

* Removed the `check_foo()` methods on building blocks.
* Removed `tff.data`, this symbol is not used.

Bug Fixes

* Fix a bug where the pip package default executor stack cannot execute
computations that have `Lambda`s under `sequence_*` intrinsics.

Major Features and Improvements

* Updated the type annotation for MaterializedValue to include the Python
scalar types in addition to the numpy scalar types.
* Added a `Literal` to the TFF language, part 1.
* Added `Literal` to the framework package.
* Extended
`tff.learning.algorithms.build_weighted_fed_avg_with_optimizer_schedule` to
support `tff.learning.models.FunctionalModel`.

Breaking Changes

* Deleted the `tff.learning.framework` namespace⚰️.

Bug Fixes

* Fixed logic for determining if a value can be cast to a specific dtype.
* Fixed a bug where repeated calls to
`FilePerUserClientData.create_tf_dataset_for_client` could blow up memory
usage

Major Features and Improvements

* Make some of the C++ executor APIs public visibility for downstream repos.
* Moved the `DataType` protobuf object into its own module. Moving the
`DataType` object into its own module allows `DataType` to be used outside
of a `Computation` more easily and prevents a circular dependency between
`Computation` and `Array` which both require a `DataType`.
* Updated `build_apply_optimizer_finalizer` to allow custom reject update
function.
* Relaxed the type requirement of the attributes of `ModelWeights` to allow
assigning list or tuples of matching values to other sequence types on
`tf.keras.Model` instances.
* Improved the errors raised by JAX computations for various types.
* Updated tutorials to use recommended `tff.learning` APIs.

Breaking Changes

* Removed the runtime-agnostic support for `tf.RaggedTensor` and
`tf.SparseTensor`.

Major Features and Improvements

* Make some of the C++ executor APIs public visibility for downstream repos.
* `tff.learning.algorithms.build_fed_kmeans` supports floating point weights,
enabling compatibility with `tff.aggregators` using differential privacy.
* Added two new metrics aggregators:
`tff.learning.metrics.finalize_then_sample` and
`tff.learning.metrics.FinalizeThenSampleFactory`.

Breaking Changes

* Remove the ability to return `SequenceType` from `tff.federated_computation`
decorated callables.

Bug Fixes

* `tff.learning` algorithms now correctly do *not* include metrics for clients
that had zero weight due to model updates containing non-finite values.
Previously the update was rejected, but the metrics still aggregated.

Major Features and Improvements

* Added an async XLA runtime under `tff.backends.xla`.

Breaking Changes

* Updated `tensorflow-privacy` version to `0.9.0`.
* Removed the deprecated `type_signature` parameter from the
`tff.program.ReleaseManager.release` method.

Major Features and Improvements

* Added new environment-specific packages to TFF.