Dann verwenden wir den Request direkt und extrahieren den Body als `bytes`. Das bedeutet, dass FastAPI nicht einmal versucht, die Request-Payload als JSON zu parsen.

Und dann parsen wir in unserem Code diesen YAML-Inhalt direkt und verwenden dann wieder dasselbe Pydantic-Modell, um den YAML-Inhalt zu validieren:

Et nous analysons directement ce contenu YAML, puis nous utilisons à nouveau le même modèle Pydantic pour valider le contenu YAML :

{* ../../docs_src/path_operation_advanced_configuration/tutorial007.py hl[26:33] *}

/// tip | Astuce

Ici, nous réutilisons le même modèle Pydantic.

Mais nous aurions pu tout aussi bien pu le valider d'une autre manière.

 */
package org.apache.maven.project;

import java.util.ArrayList;
import java.util.List;

import org.apache.maven.artifact.repository.ArtifactRepository;
import org.apache.maven.model.Model;
import org.apache.maven.model.Repository;

/**
 * A stub implementation to bypass artifact resolution from repositories.
 *
 */
public class EmptyProjectBuildingHelper implements ProjectBuildingHelper {

    @Override

import org.apache.maven.model.Activation;
import org.apache.maven.model.Build;
import org.apache.maven.model.Dependency;
import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.InputLocation;
import org.apache.maven.model.InputLocationTracker;
import org.apache.maven.model.InputSource;
import org.apache.maven.model.Model;
import org.apache.maven.model.Parent;
import org.apache.maven.model.Plugin;

import common.buildScanTagParam
import model.CIBuildModel
import model.Stage

class CheckLinks(
    model: CIBuildModel,
    stage: Stage,
) : OsAwareBaseGradleBuildType(os = Os.LINUX, stage = stage, failStage = false, init = {
        id("${model.projectId}_CheckLinks")
        name = "CheckLinks"
        description = "Check links in documentations"

        applyDefaults(
            model,
            this,

    return {"p": p}


class FormModelRequiredStr(BaseModel):
    p: str


@app.post("/model-required-str", operation_id="model_required_str")
async def read_model_required_str(p: Annotated[FormModelRequiredStr, Form()]):
    return {"p": p.p}


@pytest.mark.parametrize(
    "path",
    ["/required-str", "/model-required-str"],
)
def test_required_str_schema(path: str):
    openapi = app.openapi()

    return {"p": p}


class HeaderModelRequiredListStr(BaseModel):
    p: list[str]


@app.get("/model-required-list-str")
def read_model_required_list_str(p: Annotated[HeaderModelRequiredListStr, Header()]):
    return {"p": p.p}


@pytest.mark.parametrize(
    "path",
    ["/required-list-str", "/model-required-list-str"],
)
def test_required_list_str_schema(path: str):

import gradlebuild.docs.dsl.source.TypeNameResolver
import gradlebuild.docs.dsl.source.model.ClassMetaData
import gradlebuild.docs.model.ClassMetaDataRepository
import org.w3c.dom.Document

class DslDocModel {
    private final File classDocbookDir
    private final Document document
    private final Map<String, gradlebuild.docs.dsl.docbook.model.ClassDoc> classes = [:]
    private final ClassMetaDataRepository<ClassMetaData> classMetaData

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.model.path;

import java.io.File;

import org.apache.maven.model.Model;
import org.apache.maven.model.building.ModelBuildingRequest;

/**
 * Resolves relative paths of a model against a specific base directory.
 *
 * @deprecated use {@code org.apache.maven.api.services.ModelBuilder} instead
 */

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):