// refer https://github.com/IBM/sarama/issues/765#issuecomment-254333355
	sconfig.Producer.Retry.Max = 2
	sconfig.Producer.Retry.Backoff = (10 * time.Second)
	sconfig.Producer.Return.Successes = true
	sconfig.Producer.Return.Errors = true
	sconfig.Producer.RequiredAcks = 1
	sconfig.Producer.Timeout = (10 * time.Second)
	sconfig.Net.ReadTimeout = (10 * time.Second)
	sconfig.Net.DialTimeout = (10 * time.Second)

	config.Producer.Retry.Max = 2
	config.Producer.Retry.Backoff = (1 * time.Second)
	config.Producer.Return.Successes = true
	config.Producer.Return.Errors = true
	config.Producer.RequiredAcks = 1
	config.Producer.Timeout = (5 * time.Second)
	// Set Producer Compression
	cc, ok := codecs[strings.ToLower(args.Producer.Compression)]
	if ok {
		config.Producer.Compression = cc

	}
	return target.isActive()
}

func (target *NSQTarget) isActive() (bool, error) {
	if target.producer == nil {
		producer, err := nsq.NewProducer(target.args.NSQDAddress.String(), target.config)
		if err != nil {
			return false, err
		}
		target.producer = producer
	}

	if err := target.producer.Ping(); err != nil {
		// To treat "connection refused" errors as errNotConnected.
		if xnet.IsConnRefusedErr(err) {

      assertEquals(0, Queues.drain(q, ImmutableList.of(), 1, 10, MILLISECONDS));

      Producer producer = new Producer(q, 1);
      // producing one, will ask for two
      Future<?> producerThread = threadPool.submit(producer);
      producer.beganProducing.await();

      // make sure we time out
      Stopwatch timer = Stopwatch.createStarted();

		compressionCodecEnv := target.EnvKafkaProducerCompressionCodec
		if k != config.Default {
			compressionCodecEnv = compressionCodecEnv + config.Default + k
		}
		kafkaArgs.Producer.Compression = env.Get(compressionCodecEnv, kv.Get(target.KafkaCompressionCodec))

		compressionLevelEnv := target.EnvKafkaProducerCompressionLevel
		if k != config.Default {

}

// TF_Graph functions ---------------------------------------------------------

TF_Graph::TF_Graph()
    : graph(tensorflow::OpRegistry::Global()),
      refiner(graph.versions().producer(), graph.op_registry()),
      delete_requested(false),
      parent(nullptr),
      parent_inputs(nullptr) {
  // Tell the shape refiner to also run shape inference on functions.

import org.apache.maven.api.model.PluginContainer;

/**
 * Represents the packaging of a Maven project.
 *
 * <p>The {@code Packaging} class defines the type of artifact that a Maven project produces during the build process.
 * The packaging type determines the structure of the project's output and how Maven will treat the resulting artifact.</p>
 *

   * the order of entries.
   *
   * @param keyFunction the function used to produce the key for each value
   * @throws NullPointerException if any element of this iterable is {@code null}, or if {@code
   *     keyFunction} produces {@code null} for any key
   * @since 14.0
   */
  @SuppressWarnings("nullness") // Unsafe, but we can't do much about it now.

   * }
   * }</pre>
   *
   * <p>Note that if any input list is empty, the Cartesian product will also be empty. If no lists
   * at all are provided (an empty list), the resulting Cartesian product has one element, an empty
   * list (counter-intuitive, but mathematically consistent).
   *
   * <p><i>Performance notes:</i> while the cartesian product of lists of size {@code m, n, p} is a

      // If it won't fit in a long, then we store off the intermediate product.
      if (normalizedBits + productBits >= Long.SIZE) {
        bignums.add(BigInteger.valueOf(product));
        product = 1;
        productBits = 0;
      }
      product *= normalizedNum;
      productBits = LongMath.log2(product, FLOOR) + 1;
    }
    // Check for leftovers.
    if (product > 1) {
      bignums.add(BigInteger.valueOf(product));
    }