opType = replication.ObjectReplicationType
		}
		for _, rinfo := range rinfos.Targets {
			if rinfo.ReplicationStatus != rinfo.PrevReplicationStatus {
				rinfo.OpType = opType // update optype to reflect correct operation.
				globalReplicationStats.Load().Update(bucket, rinfo, rinfo.ReplicationStatus, rinfo.PrevReplicationStatus)
			}
		}
	}

	sendEvent(eventArgs{
		EventName:  eventName,

      expectBytes = 16;
    } else {
      ipString = hostAddr;
      expectBytes = 4;
    }

    // Parse the address, and make sure the length/version is correct.
    Scope scope = parseScope ? new Scope() : null;
    byte[] addr = ipStringToBytes(ipString, scope);
    if (addr == null || addr.length != expectBytes) {
      return null;
    }

* Addon manager supports HA masters. ([#55782](https://github.com/kubernetes/kubernetes/pull/55782), [@x13n](https://github.com/x13n))
* Fixed 'Schedulercache is corrupted' error in kube-scheduler ([#55262](https://github.com/kubernetes/kubernetes/pull/55262), [@liggitt](https://github.com/liggitt))

## Changelog since v1.21.12

## Changes by Kind

### Bug or Regression

    public static final String ERRORS_APP_DOUBLE_SUBMIT_REQUEST = "{errors.app.double.submit.request}";

    /** The key of the message: Username or Password is not correct. */
    public static final String ERRORS_login_error = "{errors.login_error}";

    /** The key of the message: Failed to process SSO login. */

- Fixed vSphere cloud provider not to skip detach volumes from nodes at kube-controller-startup....

the API, in 1.2 you will either receive a 415 or 406 error.
The only client
this is known to affect is curl when you use -d with JSON but don't set a
content type, helpfully sends "application/x-www-urlencoded", which is not
correct.
Other client authors should double check that you are sending proper
accept and content type headers, or set no value (in which case JSON is the
default).
An example using curl:

  Run({{func_feed, Int32Tensor(2)}}, func_op, 2 /*+=*/ + 5 + 1);

  // Verify input, output, and subset of edges in fdef.
  // The subset of edges we verify is a chain between feed1 and output to
  // make sure that the correct output is picked.
  tensorflow::FunctionDef fdef;
  ASSERT_TRUE(GetFunctionDef(func_, &fdef));
  VerifyFDefInputs(fdef, M({{"feed1"}, {"feed2"}}));
  VerifyFDefOutputs(fdef, M({{"test_loop_exit"}}));

			}
		}
		// Test passes as expected, but the output values are verified for correctness here.
		if actualErr == nil && testCase.shouldPass {
			// Asserting whether the md5 output is correct.
			if testCase.inputMd5 != actualInfo.ETag {
				t.Errorf("Test %d: %s: Calculated Md5 different from the actual one %s.", i+1, instanceType, actualInfo.ETag)
			}
		}
	}
}

   * reference; instead use the reference this method <i>returns</i>. At runtime, these point to the
   * same instance, but only the returned reference has the correct generic type information to
   * ensure type safety. For best results, use the standard method-chaining idiom illustrated in the
   * class documentation above, configuring a builder and building your cache in a single statement.