"required": ["username", "email"],
                        "type": "object",
                        "properties": {
                            "username": {"title": "Username", "type": "string"},
                            "email": {
                                "title": "Email",
                                "type": "string",
                                "format": "email",
                            },

    response = client.post(
        "/users", json={"email": "******@****.***", "password": "secret"}
    )
    assert response.json() == {"email": "******@****.***"}


def test_filter_second_level_model():
    response = client.get("/pets/1")
    assert response.json() == {
        "name": "Nibbler",
        "owner": {"email": "******@****.***"},
    }


def test_list_of_models():

    # For the test client
    "httpx >=0.23.0,<1.0.0",
    # For templates
    "jinja2 >=3.1.5",
    # For forms and file uploads
    "python-multipart >=0.0.18",
    # To validate email fields
    "email-validator >=2.0.0",
    # Uvicorn with uvloop
    "uvicorn[standard] >=0.12.0",
    # # Settings management
    "pydantic-settings >=2.0.0",
    # # Extra Pydantic data types
    "pydantic-extra-types >=2.0.0",

    def write_notification(email: str, message=""):
        with open("log.txt", mode="w") as email_file:
            content = f"notification for {email}: {message}"
            email_file.write(content)


    @app.post("/send-notification/{email}")
    async def send_notification(email: str, background_tasks: BackgroundTasks):
        background_tasks.add_task(write_notification, email, message="some notification")

Esto incluye, por ejemplo:

* Notificaciones por email enviadas después de realizar una acción:
  * Como conectarse a un servidor de email y enviar un email tiende a ser "lento" (varios segundos), puedes devolver el response de inmediato y enviar la notificación por email en segundo plano.
* Procesamiento de datos:

from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class UserCreate(BaseModel):
    email: str
    password: str


class UserDB(BaseModel):
    email: str
    hashed_password: str


class User(BaseModel):
    email: str


class PetDB(BaseModel):
    name: str
    owner: UserDB


class PetOut(BaseModel):
    name: str
    owner: User

                        "required": ["username", "email"],
                        "type": "object",
                        "properties": {
                            "username": {"title": "Username", "type": "string"},
                            "email": {
                                "title": "Email",
                                "type": "string",
                                "format": "email",
                            },

```Python
UserInDB(
    username="john",
    password="secret",
    email="******@****.***",
    full_name=None,
)
```

O más exactamente, usando `user_dict` directamente, con cualquier contenido que pueda tener en el futuro:

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

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {
        "username": "alice",
        "full_name": "Alice Wonderson",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret2",
        "disabled": True,
    },
}

                    "required": ["username", "password", "email"],
                    "type": "object",
                    "properties": {
                        "username": {"title": "Username", "type": "string"},
                        "password": {"title": "Password", "type": "string"},
                        "email": {
                            "title": "Email",
                            "type": "string",