// Part with size larger than 5 MiB.
		{bucketName, objectName, uploadIDs[0], 5, string(validPart), validPartMD5, int64(len(validPart))},
		{bucketName, objectName, uploadIDs[0], 6, string(validPart), validPartMD5, int64(len(validPart))},

		// Part with size larger than 5 MiB.
		// Parts uploaded for anonymous/unsigned API handler test.

		return nil
	}
	return p.workers[h%uint64(len(p.workers))]
}

func (p *ReplicationPool) queueReplicaTask(ri ReplicateObjectInfo) {
	if p == nil {
		return
	}
	// if object is large, queue it to a static set of large workers
	if ri.Size >= int64(minLargeObjSize) {
		h := xxh3.HashString(ri.Bucket + ri.Name)
		select {
		case <-p.ctx.Done():
		case p.lrgworkers[h%uint64(len(p.lrgworkers))] <- ri:
		default:

    return outputFuture;
  }

  enum RunningState {
    NOT_RUN,
    CANCELLED,
    STARTED,
  }

  /**
   * This class helps avoid a StackOverflowError when large numbers of tasks are submitted with
   * {@link MoreExecutors#directExecutor}. Normally, when the first future completes, all the other
   * tasks would be called recursively. Here, we detect that the delegate executor is executing

   *
   * @param value any {@code long} value
   * @return the same value cast to {@code int} if it is in the range of the {@code int} type,
   *     {@link Integer#MAX_VALUE} if it is too large, or {@link Integer#MIN_VALUE} if it is too
   *     small
   */
  public static int saturatedCast(long value) {
    if (value > Integer.MAX_VALUE) {
      return Integer.MAX_VALUE;
    }

    for (int i = 1; i < numKeys; i++) {
      nodes.get(i).checkAcquiredLocks(Policies.THROW, nodes.subList(0, i));
    }
    // Pre-populate all disallowedPriorLocks with nodes of larger ordinal.
    for (int i = 0; i < numKeys - 1; i++) {
      nodes.get(i).checkAcquiredLocks(Policies.DISABLED, nodes.subList(i + 1, numKeys));
    }
    return Collections.unmodifiableMap(map);
  }

  /**

## Zusammenfassung { #recap }

Verwenden Sie gerne mehrere Pydantic-Modelle und vererben Sie je nach Bedarf.

 * it requires that the complete universe of rows and columns be specified at construction time.
 * Second, it is always backed by an array large enough to hold a value for every possible
 * combination of row and column keys. (This is rarely optimal unless the table is extremely dense.)

Das **Server**-Programm (z. B. **Uvicorn** via **FastAPI CLI**) ist in der Lage, diese Header zu interpretieren und diese Information dann an Ihre Anwendung weiterzugeben.

Auf diese Weise, wenn irgendein Teil des internen Codes von Starlette, oder eine Starlette-Erweiterung oder ein Plug-in, eine Starlette `HTTPException` auslöst, wird Ihr Handler in der Lage sein, diese abzufangen und zu handhaben.

Um in diesem Beispiel beide `HTTPException`s im selben Code zu haben, wird die Exception von Starlette zu `StarletteHTTPException` umbenannt:

```Python

    var finished: Boolean = false,
  ) : Sink {
    /**
     * Buffer of outgoing data. This batches writes of small writes into this sink as larges frames
     * written to the outgoing connection. Batching saves the (small) framing overhead.
     */
    private val sendBuffer = Buffer()

    /** Trailers to send at the end of the stream. */