Release 0.87.0

Added

Support None gradients in tff.learning.optimizers. This mimics the
behavior of tf.keras.optimizers - gradients that are None will be
skipped, and their corresponding optimizer output (e.g. momentum and
weights) will not be updated.
The behavior of DPGroupingFederatedSum::Clamp: it now sets negatives to 0.
Associated test code has been updated. Reason: sensitivity calculation for
DP noise was calibrated for non-negative values.
Change tutorials to use tff.learning.optimizers in conjunction with
tff.learning computations.
tff.simulation.datasets.TestClientData only accepts dictionaries whose
leaf nodes are not tf.Tensors.

A bug where tff.learning.optimizers.build_adafactor would update its step
counter twice upon every invocation of .next().
A bug where tensor learning rates for tff.learning.optimizers.build_sgdm
would fail with mixed dtype gradients.
A bug where different optimizers had different behavior on empty weights
structures. TFF optimizers now consistently accept and function as no-ops on
empty weight structures.
A bug where tff.simulation.datasets.TestClientData.dataset_computation
yielded datasets of indeterminate shape.

tff.jax_computation, use tff.jax.computation instead.
tff.profiler, this API is not used.
Removed various stale tutorials.
Removed structure from tff.program.SavedModelFileReleaseManager's
get_value method parameters.
Removed support for tf.keras.optimizers in tff.learning.