new MojoExecutionXPathContainer(executions.get(1)).getValue("configuration/models[1]/model"));
        assertEquals(
                "src/main/mdo/supplemental-model.mdo",
                new MojoExecutionXPathContainer(executions.get(4)).getValue("configuration/models[1]/model"));
    }

    @Test
    void testLifecycleQueryingUsingADefaultLifecyclePhase() throws Exception {

## Create the Pydantic models

Now let's check the file `sql_app/schemas.py`.

!!! tip
    To avoid confusion between the SQLAlchemy *models* and the Pydantic *models*, we will have the file `models.py` with the SQLAlchemy models, and the file `schemas.py` with the Pydantic models.

    These Pydantic models define more or less a "schema" (a valid data shape).

                        },
                    },
                }
            },
            "/tuple-of-models/": {
                "post": {
                    "summary": "Post Tuple Of Models",
                    "operationId": "post_tuple_of_models_tuple_of_models__post",
                    "requestBody": {
                        "content": {
                            "application/json": {

            if route.callbacks:
                callback_flat_models.extend(get_fields_from_routes(route.callbacks))
            params = get_flat_params(route.dependant)
            request_fields_from_routes.extend(params)

    flat_models = callback_flat_models + list(
        body_fields_from_routes + responses_from_routes + request_fields_from_routes
    )
    return flat_models


def get_openapi(
    *,
    title: str,

            }

            org.apache.maven.api.model.ActivationOS modelOs = modelActivation.getOs();

            if (modelOs != null) {
                ActivationOS os = ActivationOS.newBuilder()
                        .arch(modelOs.getArch())
                        .family(modelOs.getFamily())
                        .name(modelOs.getName())
                        .version(modelOs.getVersion())
                        .build();

    title: Inbetriebnahme eines scikit-learn-Modells mit ONNX und FastAPI
  - author: Felix Schürmeyer
    author_link: https://hellocoding.de/autor/felix-schuermeyer/
    link: https://hellocoding.de/blog/coding-language/python/fastapi
    title: REST-API Programmieren mittels Python und dem FastAPI Modul
  Japanese:
  - author: '@bee2'

* Automatyczna dokumentacja modelu danych za pomocą <a href="https://json-schema.org/" class="external-link" target="_blank"><strong>JSON Schema</strong></a> (ponieważ OpenAPI bazuje na JSON Schema).

## Create the Pydantic models

Now let's check the file `sql_app/schemas.py`.

!!! tip
    To avoid confusion between the Peewee *models* and the Pydantic *models*, we will have the file `models.py` with the Peewee models, and the file `schemas.py` with the Pydantic models.

    These Pydantic models define more or less a "schema" (a valid data shape).

Does nothing. false true false false false true generate-sources org.apache.maven.plugin.coreit.CMojo java per-lookup once-per-session models java.lang.String[] false true version java.lang.String false true ${version} xpp3-writer Does nothing. false true false false false true generate-sources org.apache.maven.plugin.coreit.BMojo java per-lookup once-per-session models java.lang.String[] false true version java.lang.String false true ${version} org.apache.maven maven-plugin-api jar 2.0 org/apache/m...

        Doc(
            """
            Pydantic's `include` parameter, passed to Pydantic models to set the
            fields to include.
            """
        ),
    ] = None,
    exclude: Annotated[
        Optional[IncEx],
        Doc(
            """
            Pydantic's `exclude` parameter, passed to Pydantic models to set the
            fields to exclude.
            """
        ),
    ] = None,