```Python hl_lines="2  7-10  23-25"
from fastapi import FastAPI
from pydantic import BaseModel
from typing import Union

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):

    ```

=== "Python 3.8+"

    ```Python hl_lines="9  14  20  23  27"
    {!> ../../../docs_src/body_nested_models/tutorial007.py!}
    ```

!!! info "Информация"
    Заметьте, что у объекта `Offer` есть список объектов `Item`, которые, в свою очередь, могут содержать необязательный список объектов `Image`

## Тела с чистыми списками элементов

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

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):

* Passer la donnée reçue dans le paramètre `item`.
    * Ce paramètre ayant été déclaré dans la fonction comme étant de type `Item`, vous aurez aussi tout le support offert par l'éditeur (auto-complétion, etc.) pour tous les attributs de ce paramètre et les types de ces attributs.

Irgendwann wurde Swagger an die Linux Foundation übergeben und in OpenAPI umbenannt.

Aus diesem Grund spricht man bei Version 2.0 häufig von „Swagger“ und ab Version 3 von „OpenAPI“.

!!! check "Inspirierte **FastAPI**"
    Einen offenen Standard für API-Spezifikationen zu übernehmen und zu verwenden, anstelle eines benutzerdefinierten Schemas.

    Und Standard-basierte Tools für die Oberfläche zu integrieren:

## 깊게 중첩된 모델

단독으로 깊게 중첩된 모델을 정의할 수 있습니다:

```Python hl_lines="9  14  20  23  27"
{!../../../docs_src/body_nested_models/tutorial007.py!}
```

!!! info "정보"
    `Offer`가 선택사항 `Image` 리스트를 차례로 갖는 `Item` 리스트를 어떻게 가지고 있는지 주목하세요

## 순수 리스트의 본문

예상되는 JSON 본문의 최상위 값이 JSON `array`(파이썬 `list`)면, Pydantic 모델에서와 마찬가지로 함수의 매개변수에서 타입을 선언할 수 있습니다:

```Python
images: List[Image]
```

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool] = 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):

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

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):

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

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):

    ```

=== "Python 3.8+"

    ```Python hl_lines="9  14  20  23  27"
    {!> ../../../docs_src/body_nested_models/tutorial007.py!}
    ```

!!! info
    Notice how `Offer` has a list of `Item`s, which in turn have an optional list of `Image`s

## Bodies of pure lists