"/items?token=jessica", headers={"X-Token": "fake-super-secret-token"}
    )
    assert response.status_code == 200
    assert response.json() == {
        "plumbus": {"name": "Plumbus"},
        "gun": {"name": "Portal Gun"},
    }


def test_items_with_no_token_jessica(client: TestClient):
    response = client.get("/items", headers={"X-Token": "fake-super-secret-token"})
    assert response.status_code == 422

        "/items?token=jessica", headers={"X-Token": "fake-super-secret-token"}
    )
    assert response.status_code == 200
    assert response.json() == {
        "plumbus": {"name": "Plumbus"},
        "gun": {"name": "Portal Gun"},
    }


@needs_py39
def test_items_with_no_token_jessica(client: TestClient):
    response = client.get("/items", headers={"X-Token": "fake-super-secret-token"})

### <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a>

Pydantic is a library to define data validation, serialization and documentation (using JSON Schema) based on Python type hints.

That makes it extremely intuitive.

It is comparable to Marshmallow. Although it's faster than Marshmallow in benchmarks. And as it is based on the same Python type hints, the editor support is great.

!!! check "**FastAPI** uses it to"

from dirty_equals import IsDict, IsOneOf
from fastapi.testclient import TestClient

from docs_src.security.tutorial005_an import (
    app,
    create_access_token,
    fake_users_db,
    get_password_hash,
    verify_password,
)

client = TestClient(app)


def get_access_token(username="johndoe", password="secret", scope=None):
    data = {"username": username, "password": password}
    if scope:
        data["scope"] = scope

FastAPI uses a **unique ID** for each *path operation*, it is used for the **operation ID** and also for the names of any needed custom models, for requests or responses.

You can customize that function. It takes an `APIRoute` and outputs a string.

For example, here it is using the first tag (you will probably have only one tag) and the *path operation* name (the function name).

!!! info "`@decorator` Info"
    That `@something` syntax in Python is called a "decorator".

    You put it on top of a function. Like a pretty decorative hat (I guess that's where the term came from).

    A "decorator" takes the function below and does something with it.

    In our case, this decorator tells **FastAPI** that the function below corresponds to the **path** `/` with an **operation** `get`.

```console
$ python -m pip install --upgrade pip

---> 100%
```

</div>

!!! tip
    Every time you install a new package with `pip` under that environment, activate the environment again.

    This makes sure that if you use a terminal program installed by that package, you use the one from your local environment and not any other that could be installed globally.

### Install requirements using pip

This parameter must be something like a function.

You **don't call it** directly (don't add the parenthesis at the end), you just pass it as a parameter to `Depends()`.

And that function takes parameters in the same way that *path operation functions* do.

!!! tip
    You'll see what other "things", apart from functions, can be used as dependencies in the next chapter.

And if there's any other style or consistency need, I'll ask directly for that, or I'll add commits on top with the needed changes.

### Check the code

* Check and read the code, see if it makes sense, **run it locally** and see if it actually solves the problem.

* Then **comment** saying that you did that, that's how I will know you really checked it.

!!! info

So:

```Python
q: Union[str, None] = Query(default=None)
```

...makes the parameter optional, with a default value of `None`, the same as:

```Python
q: Union[str, None] = None
```

And in Python 3.10 and above:

```Python
q: str | None = Query(default=None)
```

...makes the parameter optional, with a default value of `None`, the same as:

```Python