Shutdown(context.Context) error
	NSScanner(ctx context.Context, updates chan<- DataUsageInfo, wantCycle uint32, scanMode madmin.HealScanMode) error
	BackendInfo() madmin.BackendInfo
	Legacy() bool // Only returns true for deployments which use CRCMOD as its object distribution algorithm.
	StorageInfo(ctx context.Context, metrics bool) StorageInfo
	LocalStorageInfo(ctx context.Context, metrics bool) StorageInfo

	// Bucket operations.

				start := time.Now()

				for result := range sys.hcClient.Alive(cctx, madmin.AliveOpts{}, eps...) {
					var lastOnline time.Time
					var offline time.Duration
					//	var deploymentID string
					sys.hMutex.RLock()
					prev, ok := sys.hc[result.Endpoint.Host]
					sys.hMutex.RUnlock()
					if ok {
						if prev.Online != result.Online || !result.Online {
							if !prev.lastHCAt.IsZero() {

				writeErrorResponseJSON(ctx, w, importErrorWithAPIErr(ctx, ErrAdminConfigBadJSON, err, allSvcAcctsFile, ""), r.URL)
				return
			}

			// Validations for LDAP enabled deployments.
			if globalIAMSys.LDAPConfig.Enabled() {
				skippedAccessKeys, err := globalIAMSys.NormalizeLDAPAccessKeypairs(ctx, serviceAcctReqs)
				skipped.ServiceAccounts = append(skipped.ServiceAccounts, skippedAccessKeys...)

## Major Features and Improvements

TensorFlow 2.0 focuses on **simplicity** and **ease of use**, featuring updates
like:

*   Easy model building with Keras and eager execution.
*   Robust model deployment in production on any platform.
*   Powerful experimentation for research.
*   API simplification by reducing duplication and removing deprecated
    endpoints.

For details on best practices with 2.0, see

			}
			if tt.expectedWriteQuorum != actualWriteQuorum {
				t.Errorf("Expected Write Quorum %d, got %d", tt.expectedWriteQuorum, actualWriteQuorum)
			}
		})
	}
}

// In some deployments, one object has data inlined in one disk and not inlined in other disks.
func TestGetObjectInlineNotInline(t *testing.T) {
	ctx, cancel := context.WithCancel(t.Context())
	defer cancel()

	reqInfo := logger.GetReqInfo(ctx)
	var newReqInfo *logger.ReqInfo
	if reqInfo != nil {
		newReqInfo = logger.NewReqInfo(reqInfo.RemoteHost, reqInfo.UserAgent, reqInfo.DeploymentID, reqInfo.RequestID, reqInfo.API, bucket, object)
	} else {
		newReqInfo = logger.NewReqInfo("", "", globalDeploymentID(), "", "Heal", bucket, object)
	}
	healCtx := logger.SetReqInfo(GlobalContext, newReqInfo)

	reqID := w.Header().Get(xhttp.AmzRequestID)

	vars := mux.Vars(r)
	bucket := vars["bucket"]
	object := likelyUnescapeGeneric(vars["object"], url.PathUnescape)
	reqInfo := &logger.ReqInfo{
		DeploymentID: globalDeploymentID(),
		RequestID:    reqID,
		RemoteHost:   handlers.GetSourceIP(r),
		Host:         getHostName(r),
		UserAgent:    r.UserAgent(),
		API:          api,
		BucketName:   bucket,

		// to verify for legacy version.
		if formatLegacy {
			// We only need this code if we are moving
			// from `xl.json` to `xl.meta`, we can avoid
			// one extra readdir operation here for all
			// new deployments.
			entries, err := readDir(currentDataPath)
			if err != nil && err != errFileNotFound {
				return res, osErrToFileErr(err)
			}
			for _, entry := range entries {

- The TopologySpreadConstraints will be shown in describe command for pods, deployments, daemonsets, etc. ([#109563](https://github.com/kubernetes/kubernetes/pull/109563), [@ardaguclu](https://github.com/ardaguclu))

  PodSubnet validates against the corresponding cluster "--node-cidr-mask-size" of the kube-controller-manager, it fail if the values are not compatible.
  kubeadm no longer sets the node-mask automatically on IPv6 deployments, you must check that your IPv6 service subnet mask is compatible with the default node mask /64 or set it accordingly.