* its default classifier, and how the artifact will be used in the build when creating
 * class paths or module paths.
 * <p>
 * For example, the type {@code java-source} has a {@code jar} extension and a
 * {@code sources} classifier. The artifact and its dependencies should be added
 * to the build path.
 *
 * @since 4.0.0
 */
@Experimental
@Immutable

            Build build = m.getBuild();
            if (build != null) {
                validate20RawPlugins(problems, build.getPlugins(), "build.plugins.plugin.", EMPTY, request);

                PluginManagement mgmt = build.getPluginManagement();
                if (mgmt != null) {
                    validate20RawPlugins(

  }

  private Iterable<LoadingCache<Key, String>> caches() {
    CacheBuilderFactory factory = factoryWithAllKeyStrengths();
    return Iterables.transform(
        factory.buildAllPermutations(), builder -> builder.build(KEY_TO_STRING_LOADER));
  }

  public void testContainsKeyAndValue() {
    for (LoadingCache<Key, String> cache : caches()) {

  val client = OkHttpClient()

  private fun newWebSocket(
    path: String,
    listener: WebSocketListener,
  ): WebSocket {
    val request =
      Request
        .Builder()
        .url(HOST + path)
        .build()
    return client.newWebSocket(request, listener)
  }

  fun run() {
    try {
      val count = getTestCount()
      println("Test count: $count")
      for (number in 1..count) {

        TF SavedModel's Signature concept.
    *   Deprecate Makefile builds. Makefile users need to migrate their builds
        to CMake or Bazel. Please refer to the
        [Build TensorFlow Lite with CMake](https://www.tensorflow.org/lite/guide/build_cmake)
        and
        [Build TensorFlow Lite for ARM boards](https://www.tensorflow.org/lite/guide/build_arm)
        for the migration.

        this.javadocConverter = javadocConverter;
        this.listener = listener;
    }

    /**
     * Builds the properties of the given class
     */
    void build(ClassDoc classDoc) {
        Element thead = getChild(classDoc.getPropertiesTable(), "thead");
        Element tr = getChild(thead, "tr");
        List<Element> header = children(tr, "td");

          );

  private static final ImmutableSet<String> PS_NOT_RS = ImmutableSet.of("blogspot.com", "uk.com");

  private static final ImmutableSet<String> PS =
      ImmutableSet.<String>builder().addAll(RS).addAll(PS_NOT_RS).build();

  private static final ImmutableSet<String> NO_PS =
      ImmutableSet.of("www", "foo.ihopethiswillneverbeapublicsuffix", "x.y.z");

  /**

    ImmutableListMultimap.Builder<String, String> builder = ImmutableListMultimap.builder();
    for (Entry<String, String> entry : parameters.entries()) {
      String attribute = normalizeToken(entry.getKey());
      builder.put(attribute, normalizeParameterValue(attribute, entry.getValue()));
    }
    MediaType mediaType = new MediaType(normalizedType, normalizedSubtype, builder.build());

    Random random = new Random(2211275185798966364L);
    ImmutableList.Builder<Double> largeDatasetBuilder = ImmutableList.builder();
    for (int i = 0; i < PSEUDORANDOM_DATASET_SIZE; i++) {
      largeDatasetBuilder.add(random.nextGaussian());
    }
    return largeDatasetBuilder.build();
  }

  private static double expectedLargeDatasetPercentile(int index) {

    Random random = new Random(2211275185798966364L);
    ImmutableList.Builder<Double> largeDatasetBuilder = ImmutableList.builder();
    for (int i = 0; i < PSEUDORANDOM_DATASET_SIZE; i++) {
      largeDatasetBuilder.add(random.nextGaussian());
    }
    return largeDatasetBuilder.build();
  }

  private static double expectedLargeDatasetPercentile(int index) {