return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):

### Create the `Settings` object

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

//// tab | Pydantic v2

    hashed_password = user_dict["hashed_password"],
)
```

/// info | Informação

Para uma explicação mais completa de `**user_dict`, verifique [a documentação para **Extra Models**](../extra-models.md#about-user_indict){.internal-link target=_blank}.

///

## Retorne o token

A resposta do endpoint `token` deve ser um objeto JSON.

            "field (e.g. Union[Response, dict, None]) you can disable generating the "
            "response model from the type annotation with the path operation decorator "
            "parameter response_model=None. Read more: "
            "https://fastapi.tiangolo.com/tutorial/response-model/"
        ) from None


def create_cloned_field(
    field: ModelField,
    *,

We are not restricted to having only one dependency that can return that type of data.

///

## Other models

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.0.0</version>
          <models>
            <model>src/main/mdo/profiles.mdo</model>
            <model>src/main/mdo/paramdoc.mdo</model>
          </models>
        </configuration>
        <executions>
          <execution>
            <id>modello</id>
            <goals>

    page.get_by_role("button", name="GET /items/ Read Items").click()
    page.get_by_role("button", name="Try it out").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/header-param-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/header_param_models/tutorial001.py"]
)
try:

    page.get_by_role("button", name="POST /login/ Login").click()
    page.get_by_role("button", name="Try it out").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/request-form-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/request_form_models/tutorial001.py"]
)
try:

/// info

Beachten Sie, dass der `TestClient` Daten empfängt, die nach JSON konvertiert werden können, keine Pydantic-Modelle.

Wenn Sie ein Pydantic-Modell in Ihrem Test haben und dessen Daten während des Testens an die Anwendung senden möchten, können Sie den `jsonable_encoder` verwenden, der in [JSON-kompatibler Encoder](encoder.md){.internal-link target=_blank} beschrieben wird.

///

///

### Pydantic and FastAPI `examples`

When you add `examples` inside a Pydantic model, using `schema_extra` or `Field(examples=["something"])` that example is added to the **JSON Schema** for that Pydantic model.

And that **JSON Schema** of the Pydantic model is included in the **OpenAPI** of your API, and then it's used in the docs UI.