return globalLockServer
	}
	return newlockRESTClient(endpoint)
}

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

 * introduces some testing difficulties. This test exercises the fallback strategies.
 *
 * <p>To force selection of our fallback strategies, we load {@link AbstractFuture} (and all of
 * {@code com.google.common.util.concurrent}) in degenerate class loaders which make certain
 * platform classes unavailable. Then we construct a test suite so we can run the normal

        // 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

  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

     * 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

   
   
   *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.

  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:

//
// floating point conditional branch
//
//	LBRA rel
label4:
	BFPT	1(PC)	// BFPT 1(PC)			// 4501000100000000
	BFPT	label4	// BFPT 24			// 4501fffd00000000

//inst:
//
// load ints and bytes
//
//	LMOVV rreg ',' rreg
//	{
//		outcode(int($1), &$2, 0, &$4);
//	}
	MOVV	R25, R17	// 00198825
	MOVV	R1, R2		// 00011025
	MOVV	LO, R1		// 00000812
	MOVV	HI, R1		// 00000810

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