=== "Python 3.10+"

    ```Python hl_lines="12"
    {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Set-Typen

    {!> ../../../docs_src/extra_models/tutorial003_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="1  14-15  18-20  33"
    {!> ../../../docs_src/extra_models/tutorial003.py!}
    ```

### `Union` в Python 3.10

В этом примере мы передаём `Union[PlaneItem, CarItem]` в качестве значения аргумента `response_model`.

    security: Optional[List[Dict[str, List[str]]]] = None
    tags: Optional[List[Tag]] = None
    externalDocs: Optional[ExternalDocumentation] = None


_model_rebuild(Schema)
_model_rebuild(Operation)

=== "Python 3.10+"

    ```Python hl_lines="12"
    {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Типы множеств

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

## 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).

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...

## 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).

        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,

    def _model_rebuild(model: Type[BaseModel]) -> None:
        model.model_rebuild()

    def _model_dump(
        model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
    ) -> Any:
        return model.model_dump(mode=mode, **kwargs)

    def _get_model_config(model: BaseModel) -> Any:
        return model.model_config

    def get_schema_from_model_field(