@Override
    double singleQuantile(int index, int scale, double[] dataset) {
      Arrays.sort(dataset);
      return singleQuantileFromSorted(index, scale, dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      Arrays.sort(dataset);
      ImmutableMap.Builder<Integer, Double> builder = ImmutableMap.builder();

     *
     * @param dataset the dataset to do the calculation on, which must be non-empty, and which will
     *     be arbitrarily reordered by this method call
     * @return the quantile value
     */
    public double computeInPlace(double... dataset) {
      checkArgument(dataset.length > 0, "Cannot calculate quantiles of an empty dataset");
      if (containsNaN(dataset)) {

  private double[] dataset;

  @Override
  protected void setUp() {
    dataset = new double[DATASET_SIZE];
    for (int i = 0; i < DATASET_SIZE; i++) {
      dataset[i] = RNG.nextDouble();
    }
  }

  public void testSingleQuantile_median() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(1, 2, dataset.clone());

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

        activityHelper.login(OptionalThing.empty());
        assertEquals(
                "{\"@timestamp\":\"2022-01-01T00:00:00.000Z\",\"log.level\":\"INFO\",\"ecs.version\":\"1.2.0\",\"service.name\":\"fess\",\"event.dataset\":\"app\",\"process.thread.name\":\"main\",\"log.logger\":\"org.codelibs.fess.helper.ActivityHelperTest$1\",\"labels.action\":\"LOGIN\",\"labels.user\":\"-\",\"labels.permissions\":\"-\"}",
                localLogMsg.get());

            .add(SHORT_NAME, 13.0f)
            .add(LONG_NAME, 14.0f)
            .addValue(15.0f);
      }
    };

    void addEntries(MoreObjects.ToStringHelper helper) {}
  }

  @Param Dataset dataset;

  private static final String SHORT_NAME = "userId";
  private static final String LONG_NAME = "fluxCapacitorFailureRate95Percentile";

  private MoreObjects.ToStringHelper newHelper() {

            .add(SHORT_NAME, 13.0f)
            .add(LONG_NAME, 14.0f)
            .addValue(15.0f);
      }
    };

    void addEntries(MoreObjects.ToStringHelper helper) {}
  }

  @Param Dataset dataset;

  private static final String SHORT_NAME = "userId";
  private static final String LONG_NAME = "fluxCapacitorFailureRate95Percentile";

  private MoreObjects.ToStringHelper newHelper() {

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .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 =

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .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 =