- Fixed in-tree to CSI migration for Portworx volumes, in clusters where Portworx security feature is enabled (it's a Portworx feature, not Kubernetes feature). It required secret data from the secret mentioned in-tree SC, to be passed in CSI requests which was not happening before this fix. ([#129675](https://github.com/kubernetes/kubernetes/pull/129675), [@gohilankit](https://github.com/gohilankit)) [SIG Storage]

### Removing deprecated flags in kubeadm

`kubeadm` applies a number of deprecations and removals of deprecated features in this release. More details are available in the Urgent Upgrade Notes and Kind / Deprecation sections.

### Pod Hostname as FQDN graduates to Beta

- No actions required. This is a small enhancement to a utility library. ([#92440](https://github.com/kubernetes/kubernetes/pull/92440), [@luigibk](https://github.com/luigibk)) [SIG Network]
- PodTolerationRestriction: Mention Whitelist Scope in Error ([#87582](https://github.com/kubernetes/kubernetes/pull/87582), [@mrueg](https://github.com/mrueg)) [SIG Scheduling]

        version is LLVM/clang 17. The official Wheels-published on PyPI will be
        based on Clang; however, users retain the option to build wheels using
        the MSVC compiler following the steps mentioned in
        https://www.tensorflow.org/install/source_windows as has been the case
        before

### Breaking Changes

*   `tf.summary.trace_on` now takes a `profiler_outdir` argument. This must be

pkg debug/elf, type DynFlag int
pkg debug/elf, type DynTag int
pkg debug/elf, type File struct
pkg debug/elf, type File struct, Progs []*Prog
pkg debug/elf, type File struct, Sections []*Section
pkg debug/elf, type File struct, embedded FileHeader
pkg debug/elf, type FileHeader struct
pkg debug/elf, type FileHeader struct, ABIVersion uint8

replaced by shifting the virtual window we operate // over: T of the algorithm is T[i:] for us. That means that T1 in // Step 2 (T mod 2^_W) is simply T[i]. k0 in Step 3 is our m0inv. Y := T[i] * m.m0inv // Step 4 and 5 add Y × m to T, which as mentioned above is stored // at T[i:]. The two carries (from a × d and Y × m) are added up in // the next word T[n+i], and the carry bit from that addition is // brought forward to the next iteration. c2 := addMulVVW(T[i:n+i], mLimbs, Y) T[n+i], c = bits.Add(c1,...

replaced by shifting the virtual window we operate // over: T of the algorithm is T[i:] for us. That means that T1 in // Step 2 (T mod 2^_W) is simply T[i]. k0 in Step 3 is our m0inv. Y := T[i] * m.m0inv // Step 4 and 5 add Y × m to T, which as mentioned above is stored // at T[i:]. The two carries (from a × d and Y × m) are added up in // the next word T[n+i], and the carry bit from that addition is // brought forward to the next iteration. c2 := addMulVVW(T[i:n+i], mLimbs, Y) T[n+i], c = bits.Add(c1,...