- The `kube-proxy` command-line documentation was updated to clarify that
  `--bind-address` does not actually have anything to do with binding to an
  address, and you probably don't actually want to be using it. ([#120274](https://github.com/kubernetes/kubernetes/pull/120274), [@danwinship](https://github.com/danwinship))

- Kubelet: Fix the volume manager didn't check the device mount state in the actual state of the world before marking the volume as detached. It may cause a pod to be stuck in the Terminating state due to the above issue when it was deleted. ([#129062](https://github.com/kubernetes/kubernetes/pull/129062), [@carlory](https://github.com/carlory))...

          for (int i = 0; i < table.length(); ++i) {
            for (ReferenceEntry<K, V> e = table.get(i); e != null; e = e.getNext()) {
              // Loading references aren't actually in the map yet.
              if (e.getValueReference().isActive()) {
                K key = e.getKey();
                V value = e.getValueReference().get();
                RemovalCause cause =

- Fixed fake client to accept request without metadata.name to better emulate behavior of actual client. ([#126727](https://github.com/kubernetes/kubernetes/pull/126727), [@jpbetz](https://github.com/jpbetz))

* `kubeadm join` is now blocking on the kubelet performing the TLS Bootstrap properly. Earlier, `kubeadm join` only did the discovery part and exited successfully without checking that the kubelet actually started properly and performed the TLS bootstrap correctly. Now, as kubeadm runs some post-join steps (for example, annotating the Node API object with the CRISocket), `kubeadm join` is now waiting for the kubelet to perform the TLS Bootstrap, and...

- The conformance tests:
  - Services should serve multiport endpoints from pods
  - Services should serve a basic endpoint from pods
  were only validating the API objects, not performing any validation on the actual Services implementation.

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        slightly more likely to do so now.
    *   Code that tries directly getting gradients with respect to symbolic
        Keras inputs/outputs. Use `GradientTape` on the actual Tensors passed to
        the already-constructed model instead.
    *   Code that requires very tricky shape manipulation via converted op
        layers in order to work, where the Keras symbolic shape inference proves

			writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, err), r.URL)
			return
		}
		targetUser = lookupResult.NormDN
		opts.claims[ldapUser] = targetUser // username DN
		opts.claims[ldapActualUser] = lookupResult.ActualDN

		// Add LDAP attributes that were looked up into the claims.
		for attribKey, attribValue := range lookupResult.Attributes {
			opts.claims[ldapAttribPrefix+attribKey] = attribValue
		}

i := range xLimbs { if i >= len(shiftedLimbs) { xLimbs[i] = 0 continue } xLimbs[i] = shiftedLimbs[i] >> shift if i+1 < len(shiftedLimbs) { xLimbs[i] |= shiftedLimbs[i+1] << (_W - shift) } } return x } // BitLenVarTime returns the actual size of x in bits. // // The actual size of x (but nothing more) leaks through timing side-channels. // Note that this is ordinarily secret, as opposed to the announced size of x. func (x *Nat) BitLenVarTime() int { // Eliminate bounds checks in the loop. size := len(x.limbs)...