class Foo(BaseModel):
        model_config = ConfigDict(from_attributes=True)

    foo = Foo()
    config = _get_model_config(foo)
    assert config == {"from_attributes": True}


def test_complex():
    app = FastAPI()

    @app.post("/")
    def foo(foo: Union[str, List[int]]):
        return foo

    client = TestClient(app)

    response = client.post("/", json="bar")

    FastAPI will use the `response_model` parameter (that includes dataclasses) to convert the response.

You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.

Check the in-code annotation tips above to see more specific details.

## Learn More

But here's the key point.

The security and dependency injection stuff is written once.

And you can make it as complex as you want. And still, have it written only once, in a single place. With all the flexibility.

But you can have thousands of endpoints (*path operations*) using the same security system.

You can use the same type declarations with `str`, `float`, `bool` and many other complex data types.

Several of these are explored in the next chapters of the tutorial.

## Order matters

When creating *path operations*, you can find situations where you have a fixed path.

## Recap

With what you have seen up to now, you can set up a secure **FastAPI** application using standards like OAuth2 and JWT.

In almost any framework handling the security becomes a rather complex subject quite quickly.

It is quite an extensive specification and covers several complex use cases.

It includes ways to authenticate using a "third party".

That's what all the systems with "login with Facebook, Google, Twitter, GitHub" use underneath.

### OAuth 1

There was an OAuth 1, which is very different from OAuth2, and more complex, as it included direct specifications on how to encrypt the communication.

* Validate **complex structures**:
    * 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.

    "C4",  # flake8-comprehensions
    "UP",  # pyupgrade
]
ignore = [
    "E501",  # line too long, handled by black
    "B008",  # do not perform function calls in argument defaults
    "C901",  # too complex
    "W191",  # indentation contains tabs
]

[tool.ruff.lint.per-file-ignores]
"__init__.py" = ["F401"]
"docs_src/dependencies/tutorial007.py" = ["F821"]
"docs_src/dependencies/tutorial008.py" = ["F821"]

        return any(field_annotation_is_complex(arg) for arg in get_args(annotation))

    return (
        _annotation_is_complex(annotation)
        or _annotation_is_complex(origin)
        or hasattr(origin, "__pydantic_core_schema__")
        or hasattr(origin, "__get_pydantic_core_schema__")
    )


def field_annotation_is_scalar(annotation: Any) -> bool:

# About HTTPS

It is easy to assume that HTTPS is something that is just "enabled" or not.

But it is way more complex than that.

!!! tip
    If you are in a hurry or don't care, continue with the next sections for step by step instructions to set everything up with different techniques.

To **learn the basics of HTTPS**, from a consumer perspective, check <a href="https://howhttps.works/" class="external-link" target="_blank">https://howhttps.works/</a>.