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();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();

    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);
      double populationCovariance = stats.populationCovariance();

    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);
      double populationCovariance = stats.populationCovariance();

 * infinite value and itself, the infinite value is returned. If required to do a weighted average
 * between {@link Double#NEGATIVE_INFINITY NEGATIVE_INFINITY} and {@link Double#POSITIVE_INFINITY
 * POSITIVE_INFINITY}, {@link Double#NaN NaN} is returned (note that this will only happen if the
 * dataset contains no finite values).
 *
 * <h3>Performance</h3>
 *

    ANY(Timeout.values()),
    PAST(Timeout.MIN, Timeout.MINUS_SMALL, Timeout.ZERO),
    FUTURE(Timeout.SMALL, Timeout.MAX),
    SMALL(Timeout.SMALL),
    FINITE(Timeout.MIN, Timeout.MINUS_SMALL, Timeout.ZERO, Timeout.SMALL),
    INFINITE(Timeout.LARGE, Timeout.MAX);

    final ImmutableList<Timeout> timeouts;

    TimeoutsToUse(Timeout... timeouts) {
      this.timeouts = ImmutableList.copyOf(timeouts);
    }

    ANY(Timeout.values()),
    PAST(Timeout.MIN, Timeout.MINUS_SMALL, Timeout.ZERO),
    FUTURE(Timeout.SMALL, Timeout.MAX),
    SMALL(Timeout.SMALL),
    FINITE(Timeout.MIN, Timeout.MINUS_SMALL, Timeout.ZERO, Timeout.SMALL),
    INFINITE(Timeout.LARGE, Timeout.MAX);

    final ImmutableList<Timeout> timeouts;

    TimeoutsToUse(Timeout... timeouts) {
      this.timeouts = ImmutableList.copyOf(timeouts);
    }

        and `tf.data.Dataset.with_options()` respectively.
    *   New `tf.data.Dataset.reduce()` API allows users to reduce a finite
        dataset to a single element using a user-provided reduce function.
    *   New `tf.data.Dataset.window()` API allows users to create finite windows
        of input dataset; when combined with the `tf.data.Dataset.reduce()` API,
        this allows users to implement customized batching.

## Wo werden Pakete installiert { #where-are-packages-installed }

Wenn Sie Python installieren, werden einige Verzeichnisse mit einigen Dateien auf Ihrem Rechner erstellt.

Einige dieser Verzeichnisse sind dafür zuständig, alle Pakete, die Sie installieren, aufzunehmen.

Wenn Sie ausführen:

Cela prendrait autant de temps pour finir avec ou sans sections (concurrence) et vous auriez effectué la même quantité de travail.

Mais dans ce cas, si pouviez amener 8 ex-serveurs/cuisiniers/devenus-nettoyeurs 👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳, et que chacun d'eux (plus vous) pouvait prendre une zone de la maison pour la nettoyer, vous pourriez faire tout le travail en parallèle, et finir plus tôt.

///

Wir werden diesen `APIRouter` in die Hauptanwendung `FastAPI` einbinden, aber zuerst kümmern wir uns um die Abhängigkeiten und einen anderen `APIRouter`.

## Abhängigkeiten { #dependencies }

Wir sehen, dass wir einige Abhängigkeiten benötigen, die an mehreren Stellen der Anwendung verwendet werden.

Also fügen wir sie in ihr eigenes `dependencies`-Modul (`app/dependencies.py`) ein.