extends AbstractFuture<V> {
  /*
   * In the GWT versions of the methods (below), every exceptionType parameter is required to be
   * Class<Throwable>. To handle only certain kinds of exceptions under GWT, you'll need to write
   * your own instanceof tests.
   */

  public final FluentFuture<V> catching(
      Class<Throwable> exceptionType,
      Function<? super Throwable, ? extends V> fallback,

message PartialObjectMetadataList {
  // Standard list metadata.
  // More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.ListMeta metadata = 2;

  // items contains each of the included items.
  repeated k8s.io.apimachinery.pkg.apis.meta.v1.PartialObjectMetadata items = 1;

    * On n'écrit pas une application avec uniquement Uvicorn. Cela signifie que le code devrait inclure plus ou moins, au minimum, tout le code offert par Starlette (ou **FastAPI**). Et si on fait cela, l'application finale apportera les mêmes complications que si on avait utilisé un framework et que l'on avait minimisé la quantité de code et de bugs.

The script below can handle this for you. Replace `RELEASE` and `NAMESPACE` with the name and namespace of the release:

```console
KINDS=(service deployment)
RELEASE=istio-ingressgateway
NAMESPACE=istio-system
for KIND in "${KINDS[@]}"; do
    kubectl --namespace $NAMESPACE --overwrite=true annotate $KIND $RELEASE meta.helm.sh/release-name=$RELEASE

# Suite of verification tests for the SINGLE TensorFlow wheel in /tf/pkg
# or whatever path is set as $TF_WHEEL.

setup_file() {
    cd /tf/pkg
    if [[ -z "$TF_WHEEL" ]]; then
        export TF_WHEEL=$(find /tf/pkg -iname "*.whl")
    fi
}

teardown_file() {
    rm -rf /tf/venv
}

@test "Wheel is manylinux2014 (manylinux_2_17) compliant" {
    python3 -m auditwheel show "$TF_WHEEL" > audit.txt

- Tooling API client. This a library that is embedded into applications, such as IDEs or CI agents, that allows them to act as a Gradle client.
- Worker processes. Long-running daemon processes that the Gradle daemon starts to run specific kinds of work, such as compilation or test execution.

The diagram below shows the relationships between the different processes:

```mermaid
    graph LR
        
        gradle["Gradle CLI client"]
    

    * Wenn Sie FastAPI nicht, und direkt Starlette (oder ein anderes Tool wie Sanic, Flask, Responder, usw.) verwenden würden, müssten Sie die gesamte Datenvalidierung und Serialisierung selbst implementieren. Ihre finale Anwendung hätte also immer noch den gleichen Overhead, als ob sie mit FastAPI erstellt worden wäre. Und in vielen Fällen ist diese Datenvalidierung und Serialisierung der größte Teil des in Anwendungen geschriebenen Codes.

fi

tfrun bazel build $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/pip_package:wheel $TFCI_BUILD_PIP_PACKAGE_ARGS
tfrun find ./bazel-bin/tensorflow/tools/pip_package -iname "*.whl" -exec cp {} $TFCI_OUTPUT_DIR \;
tfrun ./ci/official/utilities/rename_and_verify_wheels.sh

if [[ "$TFCI_ARTIFACT_STAGING_GCS_ENABLE" == 1 ]]; then
  # Note: -n disables overwriting previously created files.

	if len(routes) == 0 {
		return nil, fmt.Errorf("no routes found")
	}
	sort.Slice(routes, func(i, j int) bool {
		iName, err := strconv.Atoi(routes[i].Name)
		if err != nil {
			return false
		}
		jName, err := strconv.Atoi(routes[j].Name)
		if err != nil {
			return false
		}
		return iName < jName
	})
	return routes, nil
}

func isPassthrough(action any) bool {

 * {@code ByteSink} is not an open, stateful stream that can be written to and closed. Instead, it
 * is an immutable <i>supplier</i> of {@code OutputStream} instances.
 *
 * <p>{@code ByteSink} provides two kinds of methods:
 *
 * <ul>
 *   <li><b>Methods that return a stream:</b> These methods should return a <i>new</i>, independent
 *       instance each time they are called. The caller is responsible for ensuring that the