// The health report data can contain the hostname in various forms e.g. host, host:port,
// host:port/drivepath, full url (http://host:port/drivepath)
// The anonymizer map will have mappings for all these variants for efficiently replacing
// any of these strings to the anonymized versions at the time of health report generation.
func anonymizeHost(hostAnonymizer map[string]string, endpoint Endpoint, poolNum int, srvrNum int) {

- Improved performance of kube-proxy with EndpointSlice enabled with more efficient sorting. ([#83035](https://github.com/kubernetes/kubernetes/pull/83035), [@robscott](https://github.com/robscott))

    `tf.name_scope` and `tf.variable_scope`. The new argument order of
    `tf.variable_scope` is incompatible with previous versions.
*   Introducing `core/util/tensor_bundle` module: a module to efficiently
    serialize/deserialize tensors to disk. Will be used in TF's new checkpoint
    format.
*   Added tf.svd for computing the singular value decomposition (SVD) of dense

- `scheduler`: handling of unschedulable pods because a `ResourceClass` is missing
  is a bit more efficient and no longer relies on periodic retries. ([#120213](https://github.com/kubernetes/kubernetes/pull/120213), [@pohly](https://github.com/pohly))

## Dependencies

### Added
- cloud.google.com/go/dataproc/v2: v2.0.1

### SIG Multi-cluster (formerly known as SIG Federation)

[SIG Multi-cluster][] is responsible for infrastructure that supports
the efficient and reliable management of multiple Kubernetes clusters,
and applications that run in and across multiple clusters.

For the 1.8 release, SIG Multicluster focussed on expanding the set of

inherent in its design. This PR exposes a second, independent pruning alpha powered by a new standard named `ApplySets`. An `ApplySet` is a server-side object (by default, a Secret; ConfigMaps are also allowed) that kubectl can use to accurately and efficiently track set membership across `apply` operations. The format used for `ApplySet` is set out in  [KEP 3659](https://github.com/kubernetes/enhancements/issues/3659) as a low-level specification.  Other tools in the ecosystem can also build on this specification...

table[i-1], table[0], m) } out.resetFor(m) out.limbs[0] = 1 out.montgomeryRepresenta(m) tmp := NewNat().ExpandFor(m) for _, b := range e { for _, j := range []int{4, 0} { // Square four times. Optimization note: this can be implemented // more efficiently than with generic Montgomery multiplication. out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) // Select x^k in constant time from the table. k := uint((b >> j) & 0b1111)...

table[i-1], table[0], m) } out.resetFor(m) out.limbs[0] = 1 out.montgomeryRepresenta(m) tmp := NewNat().ExpandFor(m) for _, b := range e { for _, j := range []int{4, 0} { // Square four times. Optimization note: this can be implemented // more efficiently than with generic Montgomery multiplication. out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) // Select x^k in constant time from the table. k := uint((b >> j) & 0b1111)...