operation_ids: set[str],
    model_name_map: ModelNameMap,
    field_mapping: dict[
        tuple[ModelField, Literal["validation", "serialization"]], dict[str, Any]
    ],
    separate_input_output_schemas: bool = True,
) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
    path = {}
    security_schemes: dict[str, Any] = {}
    definitions: dict[str, Any] = {}
    assert route.methods is not None, "Methods must be a list"

En Pydantic v1 el método se llamaba `.dict()`, fue deprecado (pero aún soportado) en Pydantic v2, y renombrado a `.model_dump()`.

Los ejemplos aquí usan `.dict()` para compatibilidad con Pydantic v1, pero deberías usar `.model_dump()` en su lugar si puedes usar Pydantic v2.

///

### Acerca de `**user_in.dict()` { #about-user-in-dict }

#### `.dict()` de Pydantic { #pydantics-dict }

    items: list[dict[str, Any]]
    metadata: dict[str, Any]


class LargeOut(BaseModel):
    items: list[dict[str, Any]]
    metadata: dict[str, Any]


app = FastAPI()


@app.post("/sync/validated", response_model=ItemOut)
def sync_validated(item: ItemIn, dep: Annotated[int, Depends(dep_b)]):
    return ItemOut(name=item.name, value=item.value, dep=dep)


@app.get("/sync/dict-no-response-model")

## Response with arbitrary `dict` { #response-with-arbitrary-dict }

You can also declare a response using a plain arbitrary `dict`, declaring just the type of the keys and values, without using a Pydantic model.

This is useful if you don't know the valid field/attribute names (that would be needed for a Pydantic model) beforehand.

In this case, you can use `typing.Dict` (or just `dict` in Python 3.9 and above):

В Pydantic v1 метод назывался `.dict()`, в Pydantic v2 он помечен как устаревший (но всё ещё поддерживается) и переименован в `.model_dump()`.

В примерах здесь используется `.dict()` для совместимости с Pydantic v1, но если вы используете Pydantic v2, следует использовать `.model_dump()`.

///

### Про `**user_in.dict()` { #about-user-in-dict }

#### `.dict()` из Pydantic { #pydantics-dict }

No Pydantic v1 o método se chamava `.dict()`, ele foi descontinuado (mas ainda é suportado) no Pydantic v2 e renomeado para `.model_dump()`.

Os exemplos aqui usam `.dict()` por compatibilidade com o Pydantic v1, mas você deve usar `.model_dump()` se puder usar o Pydantic v2.

///

### Sobre `**user_in.dict()` { #about-user-in-dict }

#### O `.dict()` do Pydantic { #pydantics-dict }

    SecretStr: str,
    set: list,
    UUID: str,
    Url: str,
    AnyUrl: str,
}


def generate_encoders_by_class_tuples(
    type_encoder_map: dict[Any, Callable[[Any], Any]],
) -> dict[Callable[[Any], Any], tuple[Any, ...]]:
    encoders_by_class_tuples: dict[Callable[[Any], Any], tuple[Any, ...]] = defaultdict(
        tuple
    )
    for type_, encoder in type_encoder_map.items():

    return operation_id


def deep_dict_update(main_dict: dict[Any, Any], update_dict: dict[Any, Any]) -> None:
    for key, value in update_dict.items():
        if (
            key in main_dict
            and isinstance(main_dict[key], dict)
            and isinstance(value, dict)
        ):
            deep_dict_update(main_dict[key], value)
        elif (

    schemas: Optional[dict[str, Union[Schema, Reference]]] = None
    responses: Optional[dict[str, Union[Response, Reference]]] = None
    parameters: Optional[dict[str, Union[Parameter, Reference]]] = None
    examples: Optional[dict[str, Union[Example, Reference]]] = None
    requestBodies: Optional[dict[str, Union[RequestBody, Reference]]] = None
    headers: Optional[dict[str, Union[Header, Reference]]] = None

@app.get("/by-alias/dict", response_model=Model)
def by_alias_dict():
    return {"alias": "Foo"}


@app.get("/by-alias/model", response_model=Model)
def by_alias_model():
    return Model(alias="Foo")


@app.get("/by-alias/list", response_model=list[Model])
def by_alias_list():
    return [{"alias": "Foo"}, {"alias": "Bar"}]


@app.get("/no-alias/dict", response_model=ModelNoAlias)