// Note: isUseMultiChannel() checks if actual multi-channel is negotiated with server
        // In mock environment, this will be false until we mock successful negotiation

        // Verify load balancer is initialized
        assertNotNull(channelManager.getLoadBalancer());
    }

    @Test
    void testNetworkInterfaceInfo() throws Exception {
        // Test NetworkInterfaceInfo creation and scoring

   * Hashing} class is loaded. <b>Do not use this method</b> if hash codes may escape the current
   * process in any way, for example being sent over RPC, or saved to disk. For a general-purpose,
   * non-cryptographic hash function that will never change behavior, we suggest {@link
   * #murmur3_128}.
   *
   * <p>Repeated calls to this method on the same loaded {@code Hashing} class, using the same value

  command: server http://minio{1...4}/data{1...3}
  env_file:
    - ./minio.env
  expose:
    - "9000"
    - "9001"

# starts 4 docker containers running minio server instances.
# using nginx reverse proxy, load balancing, you can access
# it through port 9000.
services:
  minio1:
    <<: *minio-common
    hostname: minio1
    volumes:
      - data1-1:/data1
      - data1-2:/data2
      - data1-3:/data3

		return globalLockServer
	}
	return newlockRESTClient(endpoint)
}

// Returns a lock rest client.
func newlockRESTClient(ep Endpoint) *lockRESTClient {
	return &lockRESTClient{globalLockGrid.Load().Connection(ep.GridHost())}

     * This method is called automatically after construction to add this creator to the dictionary manager.
     */
    @PostConstruct
    public void register() {
        if (logger.isInfoEnabled()) {
            logger.info("Load {}", this.getClass().getSimpleName());
        }
        dictionaryManager.addCreator(this);
    }

    @Override

func (r *ReplicationStats) getNodeQueueStats(bucket string) (qs ReplQNodeStats) {
	qs.NodeName = globalLocalNodeName
	qs.Uptime = UTCNow().Unix() - globalBootTime.Unix()
	grs := globalReplicationStats.Load()
	if grs != nil {
		qs.ActiveWorkers = grs.ActiveWorkers()
	} else {
		qs.ActiveWorkers = ActiveWorkerStat{}
	}
	qs.XferStats = make(map[RMetricName]XferStats)
	qs.QStats = r.qCache.getBucketStats(bucket)

project(":okhttp-logging-interceptor").name = "logging-interceptor"

val androidHome = System.getenv("ANDROID_HOME")
val localProperties = Properties().apply {
  val file = File("local.properties")
  if (file.exists()) {
    load(file.inputStream())
  }
}
val sdkDir = localProperties.getProperty("sdk.dir")
if (androidHome != null || sdkDir != null) {
  include(":android-test")
  include(":android-test-app")
}

  command: server http://site2-minio{1...4}/data{1...2}
  environment:
    - MINIO_PROMETHEUS_AUTH_TYPE=public
    - CI=true

# starts 4 docker containers running minio server instances.
# using nginx reverse proxy, load balancing, you can access
# it through port 9000.
services:
  site2-minio1:
    <<: *minio-common
    hostname: site2-minio1
    volumes:
      - site2-data1-1:/data1
      - site2-data1-2:/data2

  healthcheck:
    test: ["CMD", "mc", "ready", "local"]
    interval: 5s
    timeout: 5s
    retries: 5

# starts 4 docker containers running minio server instances.
# using nginx reverse proxy, load balancing, you can access
# it through port 9000.
services:
  minio1:
    <<: *minio-common
    hostname: minio1
    volumes:
      - pdata1-1:/pdata1
      - pdata1-2:/pdata2

  minio2:

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.