**FastAPI** will do the automatic conversion from the request, so that the parameter `item` receives its specific content and the same for `user`.

It will perform the validation of the compound data, and will document it like that for the OpenAPI schema and automatic docs.

## Singular values in body

  // Multiple endpoints which use the same hostname should be considered
  // fungible (e.g. multiple A values in DNS). Must be lowercase and pass DNS
  // Label (RFC 1123) validation.
  // +optional
  optional string hostname = 3;

  // targetRef is a reference to a Kubernetes object that represents this
  // endpoint.
  // +optional
  optional k8s.io.api.core.v1.ObjectReference targetRef = 4;

  // Multiple endpoints which use the same hostname should be considered
  // fungible (e.g. multiple A values in DNS). Must be lowercase and pass DNS
  // Label (RFC 1123) validation.
  // +optional
  optional string hostname = 3;

  // targetRef is a reference to a Kubernetes object that represents this
  // endpoint.
  // +optional
  optional .k8s.io.api.core.v1.ObjectReference targetRef = 4;

      - k8s.io/kubernetes/pkg/apis/core/install
      - k8s.io/kubernetes/pkg/apis/core/pods
      - k8s.io/kubernetes/pkg/apis/core/v1
      - k8s.io/kubernetes/pkg/apis/core/v1/helper
      - k8s.io/kubernetes/pkg/apis/core/validation
      - k8s.io/kubernetes/pkg/apis/scheduling
      - k8s.io/kubernetes/pkg/capabilities
      - k8s.io/kubernetes/pkg/cluster/ports
      - k8s.io/kubernetes/pkg/controller
      - k8s.io/kubernetes/pkg/controller/nodeipam

Ainsi, vous pouvez ajouter des données supplémentaires au schéma généré automatiquement.

Par exemple, vous pouvez décider de lire et de valider la requête avec votre propre code, sans utiliser les fonctionnalités automatiques de validation proposée par Pydantic, mais vous pouvez toujours définir la requête dans le schéma OpenAPI.

Vous pouvez le faire avec `openapi_extra` :

```Python hl_lines="20-37 39-40"

                        id = "my-plugin"
                        implementationClass = "MyPlugin"
                    }
                }
            }
            // Work around JVM validation issue: https://youtrack.jetbrains.com/issue/KT-66919
            tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile>().configureEach {

          Size of each xDS push.
        |||
      ),
    ]),
  ], panelHeight=10, startY=3)
  + grid.makeGrid([
    row.new('Webhooks')
    + row.withPanels([
      panels.timeSeries.simple(
        'Validation', queries.validateWebhook, |||
          Rate of XDS push operations, by type. This is incremented on a per-proxy basis.
        |||
      ),
      panels.timeSeries.simple(

    /**
     * Prepares to load or create a model. Does nothing if the cached model is available or else prepares to capture
     * configuration fingerprints and validation problems and then runs the given function.
     */
    fun maybePrepareModel(action: () -> Unit)

    /**

We could do better.

We can declare a `UserBase` model that serves as a base for our other models. And then we can make subclasses of that model that inherit its attributes (type declarations, validation, etc).

All the data conversion, validation, documentation, etc. will still work as normally.

That way, we can declare just the differences between the models (with plaintext `password`, with `hashed_password` and without password):

                                    (problemsService as InternalProblems).internalReporter.create {
                                        id("invalid-dependency", "Property cannot carry dependency", GradleCoreProblemGroup.validation().property())
                                        contextualLabel("Property '$propertyName' cannot carry a dependency on $dependency as these are not yet supported.")
                                        severity(Severity.ERROR)