* but the usage happens during runtime and may be associated to a logical entity (e.g. task, plugin).
         *
         * The association between a usage and entity is not modelled by the usage,
         * but can be inferred from the operation stream (for deprecations, for which operation progress events are emitted).
         *

    * les types Python (`str`, `int`, `float`, `bool`, `list`, etc).
    * les objets `datetime`.
    * les objets `UUID`.
    * les modèles de base de données.
    * ... et beaucoup plus.
* La documentation API interactive automatique, avec 2 interfaces utilisateur au choix :
    * Swagger UI.
    * ReDoc.

---

 *       whenMerged { jdt
 *         //you can tinker with the {@link Jdt} here
 *       }
 *
 *       //withProperties allows addition of properties not currently
 *       //modeled by Gradle
 *       withProperties { properties ->
 *           //you can tinker with the {@link java.util.Properties} here
 *       }
 *     }
 *   }
 * }
 * </pre>
 */
public abstract class EclipseJdt {

# Renvoyer directement une réponse

Lorsque vous créez une *opération de chemins* **FastAPI**, vous pouvez normalement retourner n'importe quelle donnée : un `dict`, une `list`, un modèle Pydantic, un modèle de base de données, etc.

Par défaut, **FastAPI** convertirait automatiquement cette valeur de retour en JSON en utilisant le `jsonable_encoder` expliqué dans [JSON Compatible Encoder](../tutorial/encoder.md){.internal-link target=_blank}.

     */
    @Nullable
    public IvyArtifactName getDependencyArtifact() {
        return dependencyArtifact;
    }

    /**
     * When a Maven dependency declares a classifier or type attribute, this is modelled as a 'dependency artifact'.
     * This means that instead of resolving the default artifacts for the target dependency, we'll use the one defined
     * for the dependency.
     */

    @Override
    public Property<RunTestExecutable> getRunTask() {
        return runTask;
    }

    @Override
    public FileCollection getCompileIncludePath() {
        // TODO: This should be modeled differently, perhaps as a dependency on the implementation configuration
        return super.getCompileIncludePath().plus(getProjectLayout().files(new Callable<FileCollection>() {
            @Override

            }
        }

        if (node.isRoot() || hasTransitiveIncomingEdge) {
            // Since file dependencies are not modeled as actual edges, we need to verify
            // there are edges to this node that would follow this file dependency.
            for (LocalFileDependencyMetadata fileDependency : node.getOutgoingFileEdges()) {

#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/lite/tools/optimize/reduced_precision_support.h"

namespace mlir {
namespace lite {

absl::Status SparsifyModel(const tflite::ModelT& input_model,
                           flatbuffers::FlatBufferBuilder* builder) {
  MLIRContext context;
  StatusScopedDiagnosticHandler statusHandler(&context,
                                              /*propagate=*/true);

            //it's possible that we receive an output for a suite here
            //in this case we will create the test result for a suite that normally would not be created
            //feels like this scenario should modelled more explicitly
            classResult = new TestClassResult(internalIdCounter++, className, ((TestDescriptorInternal) testDescriptor).getClassDisplayName(), 0);
            results.put(className, classResult);
        }

# Beispiel-Request-Daten deklarieren

Sie können Beispiele für die Daten deklarieren, die Ihre Anwendung empfangen kann.

Hier sind mehrere Möglichkeiten, das zu tun.

## Zusätzliche JSON-Schemadaten in Pydantic-Modellen

Sie können `examples` („Beispiele“) für ein Pydantic-Modell deklarieren, welche dem generierten JSON-Schema hinzugefügt werden.

=== "Python 3.10+ Pydantic v2"

    ```Python hl_lines="13-24"