from typing import Annotated, List, Union

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(x_token: Annotated[Union[List[str], None], Header()] = None):

from fastapi import FastAPI, Header
from typing_extensions import Annotated

app = FastAPI()


@app.get("/items/")
async def read_items(x_token: Annotated[Union[List[str], None], Header()] = None):

🏧 😐 🌐❔ 👆 💭 👆 💪 🔗 🤙 🔠 🔁 (🎲 💗 🕰) 🎏 📨 ↩️ ⚙️ "💾" 💲, 👆 💪 ⚒ 🔢 `use_cache=False` 🕐❔ ⚙️ `Depends`:

```Python hl_lines="1"
async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
    return {"fresh_value": fresh_value}
```

        * In requests, a list will be read, eliminating duplicates and converting it to a `set`.
        * In responses, the `set` will be converted to a `list`.
        * The generated schema will specify that the `set` values are unique (using JSON Schema's `uniqueItems`).
* `bytes`:
    * Standard Python `bytes`.
    * In requests and responses will be treated as `str`.
    * The generated schema will specify that it's a `str` with `binary` "format".

    ```

Wenn Sie mit einer *Pfadoperation* kommunizieren, die zwei HTTP-Header sendet, wie:

```
X-Token: foo
X-Token: bar
```

Dann wäre die Response:

```JSON
{
    "X-Token values": [
        "bar",
        "foo"
    ]
}
```

## Zusammenfassung

Deklarieren Sie Header mittels `Header`, auf die gleiche Weise wie bei `Query`, `Path` und `Cookie`.

## Без Pydantic

The `ContextVar` has to be created at the top of the module, like:

```Python
some_var = ContextVar("some_var", default="default value")
```

To set a value used in the current "context" (e.g. for the current request) use:

```Python
some_var.set("new value")
```

To get a value anywhere inside of the context (e.g. in any part handling the current request) use:

```Python
some_var.get()
```

    ```Python hl_lines="80-83"
    {!> ../../../docs_src/security/tutorial003.py!}
    ```

#### About `**user_dict`

`UserInDB(**user_dict)` means:

*Pass the keys and values of the `user_dict` directly as key-value arguments, equivalent to:*

```Python
UserInDB(
    username = user_dict["username"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.
* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.

@pytest.mark.parametrize(
    "path,headers,expected_status,expected_response",
    [
        ("/items", None, 200, {"X-Token values": None}),
        ("/items", {"x-token": "foo"}, 200, {"X-Token values": ["foo"]}),
        # TODO: fix this, is it a bug?
        # ("/items", [("x-token", "foo"), ("x-token", "bar")], 200, {"X-Token values": ["foo", "bar"]}),
    ],
)
def test(path, headers, expected_status, expected_response, client: TestClient):