assertThat(Stats.of(NaN).mean()).isNaN();
    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();
      if (values.hasAnyNaN()) {

      "DATA", "HEADERS", "PRIORITY", "RST_STREAM", "SETTINGS", "PUSH_PROMISE", "PING", "GOAWAY",
      "WINDOW_UPDATE", "CONTINUATION",
    )

  /**
   * Lookup table for valid flags for DATA, HEADERS, CONTINUATION. Invalid combinations are
   * represented in binary.
   */
  private val FLAGS = arrayOfNulls<String>(0x40) // Highest bit flag is 0x20.
  private val BINARY =
    Array(256) {

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);

    assertThat(manyValuesAccumulatorByAddAllStatsAccumulator.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);

          parentBuilder) {
    List<TestSuite> derivedSuites = new ArrayList<>(super.createDerivedSuites(parentBuilder));

    if (!parentBuilder.getFeatures().contains(SUBSET_VIEW)) {
      // Other combinations are inherited from SortedSetTestSuiteBuilder.
      derivedSuites.add(createSubsetSuite(parentBuilder, Bound.NO_BOUND, Bound.INCLUSIVE));
      derivedSuites.add(createSubsetSuite(parentBuilder, Bound.EXCLUSIVE, Bound.NO_BOUND));

    assertThat(frameLog(true, 3, 10000, TYPE_DATA, 0x20))
      .isEqualTo("<< 0x00000003 10000 DATA          COMPRESSED")
  }

  /**
   * Ensures that valid flag combinations appear visually correct, and invalid show in hex.  This
   * also demonstrates how sparse the lookup table is.
   */
  @Test
  fun allFormattedFlagsWithValidBits() {

    assertThat(manyValuesAccumulatorByAddAllStatsAccumulator.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();

    FastAPI will use the `response_model` parameter (that includes dataclasses) to convert the response.

You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.

Check the in-code annotation tips above to see more specific details.

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