is not available, instead of erroring out, the dependency result will
                be `None`.

                This is useful when you want to have optional authentication.

                It is also useful when you want to have authentication that can be
                provided in one of multiple optional ways (for example, in HTTP Basic

                is not available, instead of erroring out, the dependency result will
                be `None`.

                This is useful when you want to have optional authentication.

                It is also useful when you want to have authentication that can be
                provided in one of multiple optional ways (for example, with OAuth2
                or in a cookie).

Most of these ideas would be more or less **independent**, and in most cases you should only need to study them if they apply directly to **your project**.

If something seems interesting and useful to your project, go ahead and check it, but otherwise, you might probably just skip them.

!!! tip

    # TODO: there might not be a current valid code path that uses this, it would
    # potentially enable query parameters defined as both a scalar and a list
    # but that would require more refactors, also not sure it's really useful
    from fastapi._compat import is_pv1_scalar_field

    field_info = FieldInfo()
    field = ModelField(
        name="foo",
        field_info=field_info,
        type_=Union[str, List[int]],

        Optional[str],
        Doc(
            """
            An alternative name for the parameter field.

            This will be used to extract the data and for the generated OpenAPI.
            It is particularly useful when you can't use the name you want because it
            is a Python reserved keyword or similar.
            """
        ),
    ] = None,
    alias_priority: Annotated[
        Union[int, None],
        Doc(

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="10"
    {!> ../../../docs_src/security/tutorial001.py!}
    ```

But that is still not that useful.

Let's make it give us the current user.

## Create a user model

First, let's create a Pydantic user model.

The same way we use Pydantic to declare bodies, we can use it anywhere else:

    It is equivalent to:

    ```Python
    from main import app
    ```

!!! warning
    Uvicorn and others support a `--reload` option that is useful during development.

    The `--reload` option consumes much more resources, is more unstable, etc.

    It helps a lot during **development**, but you **shouldn't** use it in **production**.

## Hypercorn with Trio

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`, and not some random string. This is especially useful for incoming data.

Without a data validation system, you would have to do all the checks by hand, in code.

These features are what Marshmallow was built to provide. It is a great library, and I have used it a lot before.

---

Then the next concepts to consider are all about the program running your actual API (e.g. Uvicorn).

## Program and Process

We will talk a lot about the running "**process**", so it's useful to have clarity about what it means, and what's the difference with the word "**program**".

### What is a Program

The word **program** is commonly used to describe many things:

* `/items/`
* `/items/{item_id}`

...as we intended.

* They will be marked with a list of tags that contain a single string `"items"`.
    * These "tags" are especially useful for the automatic interactive documentation systems (using OpenAPI).
* All of them will include the predefined `responses`.
* All these *path operations* will have the list of `dependencies` evaluated/executed before them.