```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Deklarieren Sie eine Variable als ein `str`
# und bekommen Sie Editor-Unterstütung innerhalb der Funktion
def main(user_id: str):
    return user_id


# Ein Pydantic-Modell
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Das kann nun wie folgt verwendet werden:

```Python

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

That can then be used like:

```Python

Wystarczy, że napiszesz standardowe deklaracje typów Pythona:

```Python
from datetime import date

from pydantic import BaseModel

# Zadeklaruj parametr jako str
# i uzyskaj wsparcie edytora wewnątrz funkcji
def main(user_id: str):
    return user_id


# Model Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

A one będą mogły zostać później użyte w następujący sposób:

```Python

```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Değişkeni str olarak belirt
# ve o fonksiyon için harika bir editör desteği al
def main(user_id: str):
    return user_id


# Pydantic modeli
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sonrasında bu şekilde kullanabilirsin

```Python

import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient


@pytest.fixture(name="client")
def get_client():
    from docs_src.bigger_applications.app_an.main import app

    client = TestClient(app)
    return client


def test_users_token_jessica(client: TestClient):
    response = client.get("/users?token=jessica")
    assert response.status_code == 200

podAnnotations: {} annotations: {} securityContext: enabled: false runAsUser: 1000 runAsGroup: 1000 fsGroup: 1000 resources: requests: memory: 128Mi nodeSelector: {} tolerations: [] affinity: {} extraVolumes: [] extraVolumeMounts: [] # Command to run after the main command on exit exitCommand: "" ## List of users to be created after minio install ## users: ## Username, password and policy to be assigned to the user ## Default policies are [readonly|readwrite|writeonly|consoleAdmin|diagnostics] ## Add new policies...

👆 ✍ 🐩 🐍 ⏮️ 🆎:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

👈 💪 ⤴️ ⚙️ 💖:

```Python

```Python hl_lines="16-18  21-22  28-32"
{!../../docs_src/openapi_callbacks/tutorial001.py!}
```

There are 2 main differences from a normal *path operation*:

* It doesn't need to have any actual code, because your app will never call this code. It's only used to document the *external API*. So, the function could just have `pass`.

import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient

from ...utils import needs_py39


@pytest.fixture(name="client")
def get_client():
    from docs_src.bigger_applications.app_an_py39.main import app

    client = TestClient(app)
    return client


@needs_py39
def test_users_token_jessica(client: TestClient):
    response = client.get("/users?token=jessica")

    implements BlockingQueue<E> {

  // Based on revision 1.55 of PriorityBlockingQueue by Doug Lea, from
  // http://gee.cs.oswego.edu/cgi-bin/viewcvs.cgi/jsr166/src/main/java/util/concurrent/

  private static final long serialVersionUID = 5595510919245408276L;

  final PriorityQueue<E> q;
  final Monitor monitor = new Monitor(true);
  private final Monitor.Guard notEmpty =