*
   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It
   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

   *
   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It
   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <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).

    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.

					<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>

    assertThat(Stats.of(NEGATIVE_INFINITY).mean()).isNegativeInfinity();
    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();

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    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();

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    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();

    assertFalse(powerSet.contains(Collections.<@Nullable Integer>singleton(null)));
    assertFalse(powerSet.contains(null));
    assertFalse(powerSet.contains((Object) "notASet"));
  }

  public void testPowerSetIteration_manual() {
    ImmutableSet<Integer> elements = ImmutableSet.of(1, 2, 3);
    Set<Set<Integer>> powerSet = powerSet(elements);