Release 0.12.0

Major Features and Improvements

Upgraded tensorflow dependency from 2.0.0 to 2.1.0.
Upgraded tensorflow-addons dependency from 0.6.0 to 0.7.0.
Upgraded attr dependency from 18.2 to 19.3.
Upgraded tfmot dependency from 0.1.3 to 0.2.1.
Added a federated partition of the CIFAR-100 dataset to
tff.simulation.datasets.cifar100.
Made the high performance, parallel executor the default (replacing the
reference executor).
Added a new tff.learning.build_personalization_eval for evaluating model
personalization strategies.
Added new federated intrinsic tff.federated_secure_sum.
tff.learning.build_federated_averaing_process() now takes a
client_optimizer_fn and a tff.learning.Model.
tff.learning.TrainableModel is now deprecated.
Improved performance in the high performance executor stack.
Implemented and exposed tff.framework.ExecutorFactory; all
tff.framework...executor_factory calls now return an instance of this
class.
Added remote_executor_example binary which demonstrates using the
RemoteExecutor across multi-machine deployments.
Added close() method to the Executor, allowing subclasses to proactively
release resources.
Updated documentation and scripts for creating Docker images of the TFF
runtime.
Automatically call tff.federated_zip on inputs to other federated
intrinsics.

Dropped support for Python2.
Renamed tff.framework.create_local_executor (and similar methods) to
tff.framework.local_executor_factory.
Deprecated federated_apply(), instead use federated_map() for all
placements.

Fixed problem with different instances of the same model having different
named types. tff.learning.ModelWeights no longer names the tuple fields
returned for model weights, instead relying on an ordered list.
tff.sequence_* on unplaced types now correctly returns a tff.Value.

tff.sequence_*.. operations are not implemented yet on the new
high-performance executor stack.
A subset of previously-allowed lambda captures are no longer supported on
the new execution stack.