}
        return queryContext;
    }

    protected void buildVirtualHostQuery(final QueryContext queryContext, final SearchRequestType searchRequestType) {
        switch (searchRequestType) {
        case ADMIN_SEARCH:
            // nothing to do
            break;
        default:
            final String key = ComponentUtil.getVirtualHostHelper().getVirtualHostKey();
            if (StringUtil.isNotBlank(key)) {

   Note: the application itself may have already resolved the Service IP to a specific pod if it has Kubernetes native routing built in; since we don't have the Service information in this case we will use the destination IP we received (a pod). Most notably, sidecar proxies behave this way.
1. If the destination is on our node, we "fast path" the request and convert this into an inbound request.

                };
                parentRightEdge = this.parentEl[0].clientWidth + this.parentEl.offset().left;
            }

            switch (drops) {
            case 'auto':
                containerTop = this.element.offset().top + this.element.outerHeight() - parentOffset.top;
                if (containerTop + this.container.outerHeight() >= this.parentEl[0].scrollHeight) {

@ElementTypesAreNonnullByDefault
public abstract class Striped<L> {
  /**
   * If there are at least this many stripes, we assume the memory usage of a ConcurrentMap will be
   * smaller than a large array. (This assumes that in the lazy case, most stripes are unused. As
   * always, if many stripes are in use, a non-lazy striped makes more sense.)
   */
  private static final int LARGE_LAZY_CUTOFF = 1024;

  private Striped() {}

  /**

    }

    /**
     * The number of permutations with repeated elements is calculated as follows:
     *
     * <ul>
     *   <li>For an empty list, it is 1 (base case).
     *   <li>When r numbers are added to a list of n-r elements, the number of permutations is
     *       increased by a factor of (n choose r).
     * </ul>
     */
    private static <E> int calculateSize(

   *   <li>{@code n - 1}, with approximate probability {@code 1/n}
   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *
   * <p>This method is suitable for the common use case of dividing work among buckets that meet the
   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

Do you want to have an `id` and `email` and not have any `username` in your model? Sure. You can use these same tools.

func (b *Reader) Reset(r io.Reader) {
	// If a Reader r is passed to NewReader, NewReader will return r.
	// Different layers of code may do that, and then later pass r
	// to Reset. Avoid infinite recursion in that case.
	if b == r {
		return
	}
	if b.buf == nil {
		b.buf = make([]byte, defaultBufSize)
	}
	b.reset(b.buf, r)
}

func (b *Reader) reset(buf []byte, r io.Reader) {
	*b = Reader{
		buf:          buf,

    /**
     * Checks whether a given artifact version is considered a {@code SNAPSHOT} or not.
     * <p>
     * Shortcut for {@code getService(ArtifactManager.class).isSnapshot(...)}.
     * <p>
     * In case there is {@link Artifact} in scope, the recommended way to perform this check is
     * use of {@link Artifact#isSnapshot()} instead.
     *
     * @param version artifact version

   * may be false for some instances where the statistics are mathematically equal, including
   * instances constructed from the same values in a different order... or (in the general case)
   * even in the same order. (It is guaranteed to return true for instances constructed from the
   * same values in the same order if {@code strictfp} is in effect, or if the system architecture