// and they will only connect once and persist.
	// However, if they do get in a state where they call us in a loop, we will quickly OOM
	limit := rate.NewLimiter(rate.Limit(5), 1)
	for {
		log.Debug("accepting conn")
		if err := limit.Wait(ctx); err != nil {
			log.Errorf("failed to wait for ztunnel connection: %v", err)
			return
		}
		conn, err := z.accept()

					throughputHighestPut = totalPut
					// let the client see lower value as well
					throughputHighestGet = totalGet
				}
				sendResult()
				break
			}

			// We break if we did not see 2.5% growth rate in total GET
			// requests, we have reached our peak at this point.
			doBreak := float64(totalGet-throughputHighestGet)/float64(totalGet) < 0.025

			throughputHighestGet = totalGet
			throughputHighestResults = results

	rc, err := kube.DefaultRestConfig(kubeconfig, configContext, func(config *rest.Config) {
		// We are running a one-off command locally, so we don't need to worry too much about rate limiting
		// Bumping this up greatly decreases install time
		config.QPS = 50
		config.Burst = 100
		config.Impersonate = impersonateConfig
	})
	if err != nil {
		return nil, err
	}

        + override.byName.withProperty('decimals', 2)
        + override.byName.withProperty('unit', 'ms'),
        override.byName.new('Value #success')
        + override.byName.withProperty('displayName', 'Success Rate')
        + override.byName.withProperty('decimals', 2)
        + override.byName.withProperty('unit', 'percentunit')
        + override.byName.withProperty('custom.cellOptions', { type: 'color-background' })

          "format": "time_series",
          "intervalFactor": 1,
          "range": true,
          "refId": "A"
        }
      ],
      "title": "Client Success Rate (non-5xx responses)",
      "type": "stat"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${datasource}"
      },
      "fieldConfig": {
        "defaults": {

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Transfer Rate (bytes/s)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

| `minio_node_replication_average_transfer_rate`          | Average replication transfer rate in bytes/sec                                                           |
| `minio_node_replication_max_transfer_rate`              | Maximum replication transfer rate in bytes/sec seen since server start                                   |

| `minio_replication_current_active_workers`        | Total number of active replication workers. <br><br>Type: gauge                             | `server` |
| `minio_replication_current_data_transfer_rate`    | Current replication data transfer rate in bytes/sec. <br><br>Type: gauge                    | `server` |

 * late. Also, all life cycle methods are executed with a lock held, so subclasses can safely modify
 * shared state without additional synchronization necessary for visibility to later executions of
 * the life cycle methods.
 *
 * <h3>Usage Example</h3>
 *
 * <p>Here is a sketch of a service which crawls a website and uses the scheduling capabilities to
 * rate limit itself.
 *
 * <pre>{@code