val pairs =
            durations.flatMap { scenarioDurations ->
                val scenario = Scenario.fromTestId(scenarioDurations["scenario"] as String)
                (scenarioDurations["durations"] as List<Map<String, Any>>).flatMap { duration ->
                    val testProject = duration["testProject"] as String
                    duration.entries

   * iterator supports it.
   *
   * <p><b>{@code Stream} equivalent:</b> to concatenate an arbitrary number of streams, use {@code
   * Stream.of(stream1, stream2, ...).flatMap(s -> s)}. If the sources are iterables, use {@code
   * Stream.of(iter1, iter2, ...).flatMap(Streams::stream)}.
   *
   * @throws NullPointerException if any of the provided iterables is {@code null}
   * @since 20.0
   */
  @SafeVarargs

   * iterator supports it.
   *
   * <p><b>{@code Stream} equivalent:</b> to concatenate an arbitrary number of streams, use {@code
   * Stream.of(stream1, stream2, ...).flatMap(s -> s)}. If the sources are iterables, use {@code
   * Stream.of(iter1, iter2, ...).flatMap(Streams::stream)}.
   *
   * @throws NullPointerException if any of the provided iterables is {@code null}
   * @since 20.0
   */
  @SafeVarargs

    private const val SOCKET_BUFFER_SIZE = 256 * 1024
  }
}

class CancelModelParamProvider : SimpleProvider() {
  override fun arguments() =
    CancelTest.CancelMode.values().flatMap { c ->
      CancelTest.ConnectionType.values().map { x ->
        Pair(
          c,
          x,
        )
      }
    }

            final Stream<QueryLog> stream = Stream.of(queryLogs);
            if (parallel) {
                stream.parallel();
            }
            final SuggestItem[] array = stream.flatMap(queryLog -> contentsParser
                    .parseQueryLog(queryLog, supportedFields, tagFieldNames, roleFieldName, readingConverter, normalizer)
                    .stream()).toArray(n -> new SuggestItem[n]);

   * Iterables.concat(Collections.nCopies(n, iterable))}
   *
   * <p><b>Java 8+ users:</b> The {@code Stream} equivalent of this method is {@code
   * Stream.generate(() -> iterable).flatMap(Streams::stream)}.
   */
  public static <T extends @Nullable Object> Iterable<T> cycle(Iterable<T> iterable) {
    checkNotNull(iterable);
    return new FluentIterable<T>() {
      @Override

   * Iterables.concat(Collections.nCopies(n, iterable))}
   *
   * <p><b>Java 8+ users:</b> The {@code Stream} equivalent of this method is {@code
   * Stream.generate(() -> iterable).flatMap(Streams::stream)}.
   */
  public static <T extends @Nullable Object> Iterable<T> cycle(Iterable<T> iterable) {
    checkNotNull(iterable);
    return new FluentIterable<T>() {
      @Override

  }

  /**
   * Returns a {@link Stream} containing the elements of the first stream, followed by the elements
   * of the second stream, and so on.
   *
   * <p>This is equivalent to {@code Stream.of(streams).flatMap(stream -> stream)}, but the returned
   * stream may perform better.
   *
   * @see Stream#concat(Stream, Stream)
   */
  @SuppressWarnings("unchecked") // could probably be avoided with a forwarding Spliterator

      V output(@ParametricNullness K key, @ParametricNullness V value) {
        return value;
      }
    };
  }

  @Override
  Spliterator<V> valueSpliterator() {
    return CollectSpliterators.flatMap(
        map.values().spliterator(), Collection::spliterator, Spliterator.SIZED, size());
  }

  /*
   * TODO(kevinb): should we copy this javadoc to each concrete class, so that

```
scala> val file = sc.textFile("s3a://testbucket/testdata")
file: org.apache.spark.rdd.RDD[String] = s3a://testbucket/testdata MapPartitionsRDD[1] at textFile at <console>:24

scala> val counts = file.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
counts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:25

scala> counts.saveAsTextFile("s3a://testbucket/wordcount")