*
   * <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).

        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());

    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();

 *
 * Mappings Data (4,719 bytes)
 * ===========================
 *
 * This is UTF-8 character data. It is indexed into by b2b3 in the ranges dataset.
 *
 * Mappings may overlap.
 *
 * ASCII-Only
 * ==========
 *
 * Neither the section index nor the ranges data use bit 0x80 anywhere. That means the data is

        buf.append(",\"log.level\":\"INFO\"");
        buf.append(",\"ecs.version\":\"").append(ecsVersion).append('"');
        buf.append(",\"service.name\":\"").append(ecsServiceName).append('"');
        buf.append(",\"event.dataset\":\"").append(ecsEventDataset).append('"');
        buf.append(",\"process.thread.name\":\"").append(StringEscapeUtils.escapeJson(Thread.currentThread().getName())).append('"');

					<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>

 *  Fix: Recover gracefully when a cache entry's certificate is corrupted.

 *  Fix: Fail permanently when there's a failure loading the bundled public suffix database.
    This is the dataset that powers `HttpUrl.topPrivateDomain()`.

 *  Fix: Immediately update the connection's flow control window instead of waiting for the
    receiving stream to process it.

    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.

message FlockerVolumeSource {
  // datasetName is Name of the dataset stored as metadata -> name on the dataset for Flocker
  // should be considered as deprecated
  // +optional
  optional string datasetName = 1;

  // datasetUUID is the UUID of the dataset. This is unique identifier of a Flocker dataset
  // +optional
  optional string datasetUUID = 2;
}