* Whether you represent a ragged tensor as a `tf.RaggedTensor` or using [keras masking](https://www.tensorflow.org/guide/keras/masking_and_padding), the returned loss values should be the identical to each other. In previous versions Keras may have silently ignored the mask.
 * If you use masked losses with Keras the loss values may be different in TensorFlow `2.12` compared to previous versions.

* GCE: Bump GLBC version to 1.1.1 - fixing an issue of handling multiple certs with identical certificates ([#62751](https://github.com/kubernetes/kubernetes/pull/62751), [@nicksardo](https://github.com/nicksardo))

Besides the above new features, support for Azure Virtual Machine Scale Sets (VMSS) and Cluster-Autoscaler is now stable and considered GA:

- Azure virtual machine scale sets (VMSS) allow you to create and manage identical load balanced VMs that automatically increase or decrease based on demand or a set schedule.

smaller chunks. A client can specify a limit to the number of results to return. If more results exist, a token is returned that allows the client to continue the previous list call repeatedly until all results are retrieved.  The resulting list is identical to a list call that does not perform chunking, thanks to capabilities provided by etcd3.  This allows the server to use less memory and CPU when very large lists are returned. This feature is gated as APIListChunking and is not enabled by default....

chunks.  A client can specify a limit to the number of results to return, and if more results exist a token will be returned that allows the client to continue the previous list call repeatedly until all results are retrieved.  The resulting list is identical to a list call that does not perform chunking thanks to capabilities provided by etcd3.  This allows the server to use less memory and CPU responding with very large lists.  This feature is gated as APIListChunking and is not enabled by default....