* @throws IOException if an I/O error occurs while opening the stream
   * @since 15.0 (in 14.0 with return type {@link BufferedInputStream})
   */
  public InputStream openBufferedStream() throws IOException {
    InputStream in = openStream();
    return (in instanceof BufferedInputStream)
        ? (BufferedInputStream) in
        : new BufferedInputStream(in);
  }

  /**

    app,
    client,
    test_override_in_items,
    test_override_in_items_with_params,
    test_override_in_items_with_q,
)


def test_override_in_items_run():
    test_override_in_items()


def test_override_in_items_with_q_run():
    test_override_in_items_with_q()


def test_override_in_items_with_params_run():
    test_override_in_items_with_params()


def test_override_in_users():

Es gibt hier in der Dokumentation ein ganzes Kapitel darüber, Sie können es unter [Zusätzliche Responses in OpenAPI](additional-responses.md){.internal-link target=_blank} lesen.

## OpenAPI-Extra

Wenn Sie in Ihrer Anwendung eine *Pfadoperation* deklarieren, generiert **FastAPI** automatisch die relevanten Metadaten dieser *Pfadoperation*, die in das OpenAPI-Schema aufgenommen werden sollen.

Diese Art der Verwendung von `async` und `await` ist in der Sprache relativ neu.

Aber sie erleichtert die Arbeit mit asynchronem Code erheblich.

Die gleiche Syntax (oder fast identisch) wurde kürzlich auch in moderne Versionen von JavaScript (im Browser und in NodeJS) aufgenommen.

Davor war der Umgang mit asynchronem Code jedoch deutlich komplexer und schwieriger.

@ElementTypesAreNonnullByDefault
public interface ValueGraph<N, V> extends BaseGraph<N> {
  //
  // ValueGraph-level accessors
  //

  /** Returns all nodes in this graph, in the order specified by {@link #nodeOrder()}. */
  @Override
  Set<N> nodes();

  /** Returns all edges in this graph. */
  @Override
  Set<EndpointPair<N>> edges();

  /**

stated above is already equivalent to code execution. In any case, the
recommendation is not to serve models using Eager mode since it also has
performance limitations.

## Multi-Tenant environments

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running

  }

  /** Returns an immutable multimap containing the given entries, in order. */
  public static <K, V> ImmutableMultimap<K, V> of(K k1, V v1, K k2, V v2) {
    return ImmutableListMultimap.of(k1, v1, k2, v2);
  }

  /**
   * Returns an immutable multimap containing the given entries, in the "key-grouped" insertion
   * order described in the <a href="#iteration">class documentation</a>.
   */

      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the

      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the

The CLI and controller share the same API and codebase for generating manifests from the API. You can think of the
controller as the CLI command `istioctl install` running in a loop in a pod in the cluster and using the config
from the in-cluster IstioOperator custom resource (CR).
There are two major differences:

1. The controller does not accept any dynamic user config through flags. All user interaction is through the