*  * If received data is unexpected: [exception] is a [ProtocolException].
   *
   * Each call is made on either a reused [Connection] from a pool, or on a new connection
   * established from a planned [Route]. OkHttp won't retry if it's already attempted all
   * available routes.
   *
   * @param retry true if OkHttp will make another attempt
   */
  open fun retryDecision(
    call: Call,

pkg text/scanner, method (*Position) IsValid() bool
pkg text/scanner, method (*Scanner) Init(io.Reader) *Scanner
pkg text/scanner, method (*Scanner) IsValid() bool
pkg text/scanner, method (*Scanner) Next() int32
pkg text/scanner, method (*Scanner) Peek() int32
pkg text/scanner, method (*Scanner) Pos() Position
pkg text/scanner, method (*Scanner) Scan() int32
pkg text/scanner, method (*Scanner) TokenText() string

		if retryIntvl <= 0 {
			retryIntvl = 3 * time.Second
		}

		maxRetries := h.config.MaxRetry

	retry:
		// If the channel reaches above half capacity
		// we spawn more workers. The workers spawned
		// from this main worker routine will exit
		// once the channel drops below half capacity
		// and when it's been at least 30 seconds since
		// we launched a new worker.

        in early_s in Estimator
    *   Canned Estimators will now use keras optimizers by default. An error
        will be raised if tf.train.Optimizers are used, and you will have to
        switch to tf.keras.optimizers or tf.compat.v1 canned Estimators.
    *   A checkpoint converter for canned Estimators has been provided to
        transition canned Estimators that are warm started from

import okio.Buffer
import okio.BufferedSink
import okio.BufferedSource
import okio.ByteString
import okio.Sink
import okio.Timeout
import okio.buffer

/**
 * A scriptable web server. Callers supply canned responses and the server replays them upon request
 * in sequence.
 */
public class MockWebServer : Closeable {
  private val taskRunnerBackend =
    TaskRunner.RealBackend(

which is an integer.
        * ScaleRef of type SubresourceReference in HorizontalPodAutoscalerSpec which
referred to scale subresource of the resource being scaled was replaced by
ScaleTargetRef which points just to the resource being scaled.
        * In extensions/v1beta1 if CPUUtilization in HorizontalPodAutoscalerSpec was not
specified it was set to 80 by default while in autoscaling/v1 HPA object

		UserAgent:  "Internal: [Replication]",
		Host:       globalLocalNodeName,
	})

	// re-queue failures once more - keep a retry count to avoid flooding the queue if
	// the target site is down. Leave it to scanner to catch up instead.
	if rinfos.ReplicationStatus() != replication.Completed {
		ri.OpType = replication.HealReplicationType
		ri.EventType = ReplicateMRF

 * </pre>
 * where degreeOfConcurrency is set via the -T command-line option.
 *
 * <h2>Build Step States:</h2>
 * <ul>
 *   <li>CREATED: Initial state of a build step</li>
 *   <li>PLANNING: Step is being planned</li>
 *   <li>SCHEDULED: Step is queued for execution</li>
 *   <li>EXECUTED: Step has completed successfully</li>
 *   <li>FAILED: Step execution failed</li>
 * </ul>
 *

// ActiveTasks returns the number of active (ongoing) ILM transition tasks.
func (t *transitionState) ActiveTasks() int64 {
	return t.activeTasks.Load()
}

// MissedImmediateTasks returns the number of tasks - deferred to scanner due
// to tasks channel being backlogged.
func (t *transitionState) MissedImmediateTasks() int64 {
	return t.missedImmediateTasks.Load()
}

// worker waits for transition tasks

// using a delete-marker and MetaSys entries. It's used to track tiered content of a
// deleted/overwritten version. This version is visible _only_to the scanner routine, for subsequent deletion.
// This kind of tracking is necessary since a version's tiered content is deleted asynchronously.

// Backend directory tree structure:
// disk1/
// └── bucket
//     └── object