long[] dataset = Longs.toArray(SIXTEEN_SQUARES_LONGS);
    assertThat(Quantiles.scale(10).index(1).compute(dataset))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
    assertThat(dataset).asList().isEqualTo(SIXTEEN_SQUARES_LONGS);
  }

  public void testScale_index_compute_intVarargs() {
    int[] dataset = Ints.toArray(SIXTEEN_SQUARES_INTEGERS);

    long[] dataset = Longs.toArray(SIXTEEN_SQUARES_LONGS);
    assertThat(Quantiles.scale(10).index(1).compute(dataset))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
    assertThat(dataset).asList().isEqualTo(SIXTEEN_SQUARES_LONGS);
  }

  public void testScale_index_compute_intVarargs() {
    int[] dataset = Ints.toArray(SIXTEEN_SQUARES_INTEGERS);

   *
   * <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

*   `tf.data`

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
    * `tf.data.Dataset.shuffle` now supports `tf.data.UNKNOWN_CARDINALITY` When doing a "full shuffle" using  `dataset = dataset.shuffle(dataset.cardinality())`. But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

					<keyring>${gpg.keyring}</keyring>
					key>${gpg.key}</key>
					<passphrase>${gpg.passphrase}</passphrase>
					<signMethod>${deb.sign.method}</signMethod>
					-->
					<dataSet>
						<!-- Add app directory -->
						<data>
							<type>directory</type>
							<src>${project.build.directory}/fess</src>
							<excludes>**/fess_config.properties,**/fess_env*.properties,**/tika.xml</excludes>

    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(
        "GradientDef is not supported in reading Dataset related functions: ",
        text_proto);
    return {};
  }
  std::vector<UniqueFuncPtr> ret;
  for (const FunctionDef& fdef : fdef_lib.function()) {
    // Make a copy so that we can mutate it.

    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(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(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(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 =