{!../../../docs_src/app_testing/tutorial001.py!}
```

!!! tip
    Notice that the testing functions are normal `def`, not `async def`.

    And the calls to the client are also normal calls, not using `await`.

    This allows you to use `pytest` directly without complications.

!!! note "Technical Details"
    You could also use `from starlette.testclient import TestClient`.

    You might remember that request bodies are also declared with Pydantic models.

    Here **FastAPI** won't get confused because you are using `Depends`.

!!! check
    The way this dependency system is designed allows us to have different dependencies (different "dependables") that all return a `User` model.

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

## Other models

                """
            ),
        ] = True,
    ):
        if not scopes:
            scopes = {}
        flows = OAuthFlowsModel(
            password=cast(Any, {"tokenUrl": tokenUrl, "scopes": scopes})
        )
        super().__init__(
            flows=flows,
            scheme_name=scheme_name,
            description=description,
            auto_error=auto_error,
        )

    * This SNI extension allows one single server (with a **single IP address**) to have **several HTTPS certificates** and serve **multiple HTTPS domains/applications**.

    body: Union[str, None] = None,
) -> Dict[str, Any]:
    headers = {"Authorization": f"token {settings.input_token.get_secret_value()}"}
    # some fields are only used by one query, but GraphQL allows unused variables, so
    # keep them here for simplicity
    variables = {
        "after": after,
        "category_id": category_id,
        "discussion_number": discussion_number,

# Full Stack FastAPI Template

Templates, while typically come with a specific setup, are designed to be flexible and customizable. This allows you to modify and adapt them to your project's requirements, making them an excellent starting point. 🏁

You can use this template to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you.

    github_sponsors_old_content = github_sponsors_path.read_text(encoding="utf-8")
    new_people_content = yaml.dump(
        people, sort_keys=False, width=200, allow_unicode=True
    )
    new_github_sponsors_content = yaml.dump(
        github_sponsors, sort_keys=False, width=200, allow_unicode=True
    )
    if (
        people_old_content == new_people_content
        and github_sponsors_old_content == new_github_sponsors_content

    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
    * You can have deeply **nested JSON** objects and have them all validated and annotated.
* **Extensible**:
    * Pydantic allows custom data types to be defined or you can extend validation with methods on a model decorated with the validator decorator.

```Python hl_lines="2  6-8"
{!../../../docs_src/advanced_middleware/tutorial002.py!}
```

The following arguments are supported:

* `allowed_hosts` - A list of domain names that should be allowed as hostnames. Wildcard domains such as `*.example.com` are supported for matching subdomains. To allow any hostname either use `allowed_hosts=["*"]` or omit the middleware.

If an incoming request does not validate correctly then a `400` response will be sent.

## Type Hints with Metadata Annotations

Python also has a feature that allows putting **additional metadata** in these type hints using `Annotated`.

=== "Python 3.9+"

    In Python 3.9, `Annotated` is part of the standard library, so you can import it from `typing`.