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();

        stream(tags).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));
        buf.append(CACHE_KEY_SPLITTER);
        stream(roles).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));
        buf.append(CACHE_KEY_SPLITTER);
        stream(fields).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));

mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk

	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

      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();

        .sort(s => -(s.additions + s.deletions));

    let totalAdditions = Object.values(preppedStatistics).reduce((acc, summary) => acc + summary.additions, 0);
    let totalDeletions = Object.values(preppedStatistics).reduce((acc, summary) => acc + summary.deletions, 0);
    let newRatio = totalAdditions / (totalAdditions + totalDeletions) * 100;

Doing this provides the following specific benefits:
- **Reduce the API surface** - We no longer need to maintain two methods.
- **Consistency** - All languages have consistent access to the same APIs and ergonomics in the DSL.
- **Reduce the size of the Gradle distribution** - We no longer need to carry multiple standard libraries for different languages.

## Decision

`tf.expand_dims`: `dim` becomes `axis` * `tf.reduce_all`: `reduction_indices`
becomes `axis` * `tf.reduce_any`: `reduction_indices` becomes `axis` *
`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:
`reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices`
becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` *
`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:

MINIO_STORAGE_CLASS_RRS: Format "EC:<Default_Parity_Reduced_Redundancy_Class>" (e.g. "EC:3"). This sets the number of parity drives for MinIO server in Reduced Redundancy mode. Objects are stored in Reduced Redundancy mode, if Put request specifies RRS storage class

    /**
     * Possibly appends the given domain name to the host name if it is currently unqualified
     * 
     * @param domain
     */
    void fixupDomain ( String domain );


    /**
     * Reduces path consumed by the given value
     * 
     * @param i
     */
    void stripPathConsumed ( int i );


    @Override
    DfsReferralDataInternal next ();


    /**
     * @param link
     */