`with_values` function of `RaggedTensor`.
*   Adds `StatelessCase` op, and uses it if none of case branches has stateful
    ops.
*   Adds `tf.config.experimental.get_memory_usage` to return total memory usage
    of the device.
*   Adds gradients for `RaggedTensorToVariant` and `RaggedTensorFromVariant`.
*   Improve shape inference of nested function calls by supporting constant

  - container_cpu_usage_seconds --> container_cpu_usage_seconds_total
  This is a partial revert of [#86282](https://github.com/kubernetes/kubernetes/pull/86282), which was added in 1.18.0, and initially removed the _total suffix ([#89540](https://github.com/kubernetes/kubernetes/pull/89540), [@dashpole](https://github.com/dashpole)) [SIG Instrumentation and Node]

  - container_cpu_usage_seconds --> container_cpu_usage_seconds_total
  This is a partial revert of [#86282](https://github.com/kubernetes/kubernetes/pull/86282), which was added in 1.18.0, and initially removed the _total suffix ([#89540](https://github.com/kubernetes/kubernetes/pull/89540), [@dashpole](https://github.com/dashpole)) [SIG Instrumentation and Node]

- Metrics for `RetroactiveDefaultStorageClass` feature are now available. To see an attempt count for updating PVC retroactively with a default StorageClass see `retroactive_storageclass_total` metric and for total numer of errors see `retroactive_storageclass_errors_total`. ([#113323](https://github.com/kubernetes/kubernetes/pull/113323), [@RomanBednar](https://github.com/RomanBednar))

s","alias","as","asMilliseconds","asSeconds","asMinutes","asHours","asDays","asWeeks","asMonths","asQuarters","asYears","makeGetter","thresholds","relativeTime$1","posNegDuration","abs$1","toISOString$1","totalSign","ymSign","daysSign","hmsSign","total","toFixed","proto$2","monthsFromDays","argWithSuffix","argThresholds","withSuffix","th","assign","toIsoString","version","updateLocale","tmpLocale","relativeTimeRounding","roundingFunction","relativeTimeThreshold","threshold","limit","myMoment","H...

multiple clusters ensures that cluster-level autoscalers are
consistently deployed across those clusters, and dynamically managed
to ensure that autoscaling can occur optimially in all clusters,
within a set of global constraints on the total number of replicas
permitted across all clusters.  If replicas are not
required in some clusters due to low system load or insufficient quota
or capacity in those clusters, additional replicas are made available

    * Function is linear between points. Resource utilization is defined as one minus ratio of total amount of resource requested by pods on node and node's capacity (scaled to 100).
    * Final utilization used for computation is arithmetic mean of cpu utilization and memory utilization.

100000..10FFFD; disallowed                             # 2.0  <private-use-100000>..<private-use-10FFFD>
10FFFE..10FFFF; disallowed                             # 2.0  <noncharacter-10FFFE>..<noncharacter-10FFFF>