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):

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):

## 深くネストされたモデル

深くネストされた任意のモデルを定義することができます:

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

!!! info "情報"
    `Offer`は`Item`のリストであり、オプションの`Image`のリストを持っていることに注目してください。

## 純粋なリストのボディ

期待するJSONボディのトップレベルの値がJSON`array`（Pythonの`list`）であれば、Pydanticモデルと同じように、関数のパラメータで型を宣言することができます:

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

    ```

=== "Python 3.8+"

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

!!! info
    请注意 `Offer` 拥有一组 `Item` 而反过来 `Item` 又是一个可选的 `Image` 列表是如何发生的。

## 纯列表请求体

如果你期望的 JSON 请求体的最外层是一个 JSON `array`（即 Python `list`），则可以在路径操作函数的参数中声明此类型，就像声明 Pydantic 模型一样：

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

例如：

    ```

=== "🐍 3️⃣.1️⃣0️⃣ & 🔛"

    ```Python hl_lines="7  12  18  21  25"
    {!> ../../../docs_src/body_nested_models/tutorial007_py310.py!}
    ```

!!! info
    👀 ❔ `Offer` ✔️ 📇 `Item`Ⓜ, ❔ 🔄 ✔️ 📦 📇 `Image`Ⓜ

## 💪 😁 📇

🚥 🔝 🎚 💲 🎻 💪 👆 ⌛ 🎻 `array` (🐍 `list`), 👆 💪 📣 🆎 🔢 🔢, 🎏 Pydantic 🏷:

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

⚖️ 🐍 3️⃣.9️⃣ & 🔛:

    val latch = CountDownLatch(1)
    val responses: BlockingQueue<String?> = SynchronousQueue()
    val authenticator =
      okhttp3.Authenticator { route: Route?, response: Response? ->
        responses.offer(response!!.body.string())
        try {
          latch.await()
        } catch (e: InterruptedException) {
          throw AssertionError()
        }
        response.request
      }

* 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.

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