async def override_dependency(q: Union[str, None] = None):
    return {"q": q, "skip": 5, "limit": 10}


app.dependency_overrides[common_parameters] = override_dependency


def test_override_in_items():
    response = client.get("/items/")
    assert response.status_code == 200
    assert response.json() == {
        "message": "Hello Items!",
        "params": {"q": None, "skip": 5, "limit": 10},
    }

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

### Dockerfile { #dockerfile }

Now in the same project directory create a file `Dockerfile` with:

```{ .dockerfile .annotate }
# (1)!

from dataclasses import dataclass
from typing import Union

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    description: Union[str, None] = None
    tax: Union[float, None] = None


app = FastAPI()


@app.post("/items/")
async def create_item(item: Item):

from fastapi import FastAPI, Header
from pydantic import BaseModel

app = FastAPI()


class CommonHeaders(BaseModel):
    host: str
    save_data: bool
    if_modified_since: str | None = None
    traceparent: str | None = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: CommonHeaders = Header()):

     *
     * @return The user properties, never {@code null}.
     */
    Properties getUserProperties();

    /**
     * Sets the user properties to use for interpolation. The user properties have been configured directly by the user
     * on his discretion, e.g. via the {@code -Dkey=value} parameter on the command line.
     *

Das bedeutet, dass, wenn Sie den Artikel `bar` aktualisieren wollen, mittels `PUT` und folgendem Body:

```Python
{
    "name": "Barz",
    "price": 3,
    "description": None,
}
```

weil das bereits gespeicherte Attribut `"tax": 20.2` nicht enthalten ist, das Eingabemodell den Defaultwert `"tax": 10.5` erhalten würde.

* <abbr title="Eine Methode des Machine Learning, die künstliche neuronale Netze mit zahlreichen versteckten Schichten zwischen Eingabe- und Ausgabeschicht verwendet und so eine umfassende interne Struktur entwickelt">Deep Learning</abbr>

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: list = []


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

from pydantic import BaseModel

app = FastAPI()


class Cookies(BaseModel):
    model_config = {"extra": "forbid"}

    session_id: str
    fatebook_tracker: Union[str, None] = None
    googall_tracker: Union[str, None] = None


@app.get("/items/")
async def read_items(cookies: Cookies = Cookie()):

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):