TensorFlow Model Optimization 0.6.0

Actual commit for release: d6556c2

TFMOT 0.6.0 adds some additional features for Quantization Aware Training.
Adds support for overriding and subclassing default quantization
schemes. Adds input quantizer for annotated quantized layers without annotated
input layers. Also adds pruning policy for pruning registries
for different hardware supports. Also adds Conv2DTranspose support and tanh
activations.

Keras quantization API:
Tested against TensorFlow 2.4.2, 2.5.0 and nightly with Python 3.

Keras pruning API:
Tested against TensorFlow 2.4.2, 2.5.0 and nightly with Python 3.

Keras clustering API:

• Added ClusteringSummaries to create additional output for the clustering
  progress for TensorBoard.
• Added ClusterableLayer API to support clustering of a keras custom layer.
  In addition, now clustering can be done for bias of the layer.
• Introduced two new experimental quantization schemes for Quantization Aware Training
  for collaborative model optimization:
  • Prune Preserve QAT: pruned model can be QAT trained with preserved sparsity;
  • Cluster Preserve QAT: clustered model can be QAT trained with preserved clustering;
• Added a new feature to clustering: average gradient aggregation, which can
  improve performance for some models.
• Updated clustering results in the documentation.
• Tested against TensorFlow 1.14.0, 2.0.0, and nightly, and Python 3.