*/
  public static <T extends @Nullable Object> List<List<T>> partition(List<T> list, int size) {
    checkNotNull(list);
    checkArgument(size > 0);
    return (list instanceof RandomAccess)
        ? new RandomAccessPartition<>(list, size)
        : new Partition<>(list, size);
  }

  private static class Partition<T extends @Nullable Object> extends AbstractList<List<T>> {
    final List<T> list;

   * OutOfMemoryError}.
   *
   * @param iterator the iterator to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterator of immutable lists containing the elements of {@code iterator} divided into
   *     partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive
   */

        missing_translation_content = get_missing_translation_content(config.docs_dir)
        header = ""
        body = markdown
        if markdown.startswith("#"):
            header, _, body = markdown.partition("\n\n")
        return f"{header}\n\n{missing_translation_content}\n\n{body}"

        swap(array, min, from);
      }
      return;
    }

    // Let's play quickselect! We'll repeatedly partition the range [from, to] containing the
    // required element, as long as it has more than one element.
    while (to > from) {
      int partitionPoint = partition(array, from, to);
      if (partitionPoint >= required) {
        to = partitionPoint - 1;
      }

    PairedStatsAccumulator accumulator = new PairedStatsAccumulator();
    List<List<Double>> xPartitions = Lists.partition(xValues, partitionSize);
    List<List<Double>> yPartitions = Lists.partition(yValues, partitionSize);
    for (int index = 0; index < xPartitions.size(); index++) {
      accumulator.addAll(createPairedStatsOf(xPartitions.get(index), yPartitions.get(index)));
    }
    return accumulator;
  }

    PairedStatsAccumulator accumulator = new PairedStatsAccumulator();
    List<List<Double>> xPartitions = Lists.partition(xValues, partitionSize);
    List<List<Double>> yPartitions = Lists.partition(yValues, partitionSize);
    for (int index = 0; index < xPartitions.size(); index++) {
      accumulator.addAll(createPairedStatsOf(xPartitions.get(index), yPartitions.get(index)));
    }
    return accumulator;
  }

message RollingUpdateStatefulSetStrategy {
  // Partition indicates the ordinal at which the StatefulSet should be partitioned
  // for updates. During a rolling update, all pods from ordinal Replicas-1 to
  // Partition are updated. All pods from ordinal Partition-1 to 0 remain untouched.
  // This is helpful in being able to do a canary based deployment. The default value is 0.

            stage.flameGraphs.map { createFlameGraphs(model, stage, it) }

        val (topLevelCoverage, allCoverage) = stage.functionalTests.partition { it.testType == TestType.soak }
        val topLevelFunctionalTests = topLevelCoverage
            .map { FunctionalTest(model, it.asConfigurationId(model), it.asName(), it.asName(), it, stage = stage) }

        val targetModules = computeTargetModules(mainFirFile.llFirModuleData.ktModule)
        val (targetFiles, dependencyFiles) = filesToCompile.partition { firResolveSession.getModule(it) in targetModules }
        require(targetFiles.isNotEmpty())

        val jvmIrDeserializer = JvmIrDeserializerImpl()

message RollingUpdateStatefulSetStrategy {
  // Partition indicates the ordinal at which the StatefulSet should be partitioned
  // for updates. During a rolling update, all pods from ordinal Replicas-1 to
  // Partition are updated. All pods from ordinal Partition-1 to 0 remain untouched.
  // This is helpful in being able to do a canary based deployment. The default value is 0.