*/
  public static <K, V> ImmutableBiMap<K, V> copyOf(
      Iterable<? extends Entry<? extends K, ? extends V>> entries) {
    @SuppressWarnings("unchecked") // we'll only be using getKey and getValue, which are covariant
    Entry<K, V>[] entryArray = (Entry<K, V>[]) Iterables.toArray(entries, EMPTY_ENTRY_ARRAY);
    switch (entryArray.length) {
      case 0:
        return of();
      case 1:

      Comparator<? super E> comparator, Iterable<? extends E> elements) {
    if (elements instanceof ImmutableSortedMultiset) {
      @SuppressWarnings("unchecked") // immutable collections are always safe for covariant casts
      ImmutableSortedMultiset<E> multiset = (ImmutableSortedMultiset<E>) elements;
      if (comparator.equals(multiset.comparator())) {
        if (multiset.isPartialView()) {

---

**Hinweis**: Sie können `def` und `async def` in Ihren *Pfadoperation-Funktionen* beliebig mischen, so wie Sie es benötigen, und jede einzelne Funktion in der für Sie besten Variante erstellen. FastAPI wird damit das Richtige tun.

Wie dem auch sei, in jedem der oben genannten Fälle wird FastAPI immer noch asynchron arbeiten und extrem schnell sein.

Il peut y avoir plusieurs approches pour y parvenir, et je vous en dirai plus sur des stratégies spécifiques dans les prochains chapitres, par exemple en parlant de Docker et des conteneurs.

be observed by <code>main</code>, since there are no synchronization
events between the two threads.  The loop in <code>main</code> is not
guaranteed to finish.
</p>

<p>
There are subtler variants on this theme, such as this program.
</p>

<pre>
type T struct {
	msg string
}

var g *T

func setup() {
	t := new(T)
	t.msg = "hello, world"
	g = t
}

  /*
   * TODO: Make this a drop-in replacement for j.u. versions, actually drop them in, and test the
   * world. Figure out what sort of space-time tradeoff we're actually going to get here with the
   * *Map variants. This class is particularly hard to benchmark, because the benefit is not only in
   * less allocation, but also having the GC do less work to scan the heap because of fewer
   * references, which is particularly hard to quantify.

E, claro, a mesma máquina provavelmente teria **outros processos** em execução, além do seu aplicativo.

Um detalhe interessante é que a porcentagem da **CPU usada** por cada processo pode **variar** muito ao longo do tempo, mas a **memória (RAM)** normalmente fica mais ou menos **estável**.

	return append(dst, pre)
}

// fileState tracks the number of logical (includes sparse holes) and physical
// (actual in tar archive) bytes remaining for the current file.
//
// Invariant: logicalRemaining >= physicalRemaining
type fileState interface {
	logicalRemaining() int64
	physicalRemaining() int64
}

// allowedFormats determines which formats can be used.

    boolean longWait = NANOSECONDS.toSeconds(thread.timeSpentBlocked) >= 5;
    // Count how long it actually took to return; we'll accept any number between the expected delay
    // and the approximate actual delay, to be robust to variance in thread scheduling.
    char overWaitNanosFirstDigit =
        Long.toString(thread.timeSpentBlocked - MILLISECONDS.toNanos(longWait ? 5000 : 3000))
            .charAt(0);
    if (overWaitNanosFirstDigit < '4') {

## `async` et `await` { #async-and-await }

Les versions modernes de Python ont une manière très intuitive de définir le code asynchrone, tout en gardant une apparence de code « séquentiel » classique en laissant Python faire l'attente pour vous au bon moment.