labels.user_info_enabled=Journal utilisateur
labels.user_favorite_enabled=Journal des favoris
labels.web_api_json_enabled=Réponse JSON
labels.app_value=Propriété système
labels.default_label_value=Valeur d'étiquette par défaut
labels.default_sort_value=Valeur de tri par défaut
labels.virtual_host_value=Hôte virtuel
labels.append_query_param_enabled=Ajouter des paramètres de recherche
labels.login_required=Connexion requise

      if (values.hasAnyNaN()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of %s", values).that(meanByAddAllStats).isNaN();
      } else if (values.hasAnyPositiveInfinity() && values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();

    public void test_parsePath() {
        String[] values;

        values = gcsClient.parsePath("bucket/path");
        assertEquals("bucket", values[0]);
        assertEquals("path", values[1]);

        values = gcsClient.parsePath("bucket/path1/path2");
        assertEquals("bucket", values[0]);
        assertEquals("path1/path2", values[1]);

        values = gcsClient.parsePath("bucket/");

        .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 =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);
      PairedStatsAccumulator accumulatorByAddAllPartitionedPairedStats =

        .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 =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);
      PairedStatsAccumulator accumulatorByAddAllPartitionedPairedStats =

            final String[] values = sort.split("\\.");
            final boolean asc;
            if (values.length > 1) {
                asc = !"desc".equalsIgnoreCase(values[1]);
            } else {
                asc = true;
            }
            if (values.length > 0) {
                if ("term".equals(values[0]) || "key".equals(values[0])) {
                    return BucketOrder.key(asc);

                stack=use_astack,
                sub_values=solved_result.values,
            )
        elif use_sub_dependant.is_coroutine_callable:
            solved = await call(**solved_result.values)
        else:
            solved = await run_in_threadpool(call, **solved_result.values)
        if sub_dependant.name is not None:
            values[sub_dependant.name] = solved

* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).
    * This way you can update only the values actually set by the user, instead of overriding values already stored with default values in your model.
* Create a copy of the stored model, updating its attributes with the received partial updates (using the `update` parameter).

    // 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);
      double populationCovariance = stats.populationCovariance();
      if (values.hasAnyNonFinite()) {

    // 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);
      double populationCovariance = stats.populationCovariance();
      if (values.hasAnyNonFinite()) {