*
 * <p>Advantages compared to {@code long[]}:
 *
 * <ul>
 *   <li>All the many well-known advantages of immutability (read <i>Effective Java</i>, third
 *       edition, Item 17).
 *   <li>Has the value-based (not identity-based) {@link #equals}, {@link #hashCode}, and {@link
 *       #toString} behavior you expect.

///

## 深くネストされたモデル { #deeply-nested-models }

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:

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info | 情報

`Offer`は`Item`のリストであり、それらがさらにオプションの`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 { #bodies-of-pure-lists }

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

            ],
        },
    )
    assert response.status_code == 200, response.text
    assert response.json() == {
        "item_id": 123,
        "item": {
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
            "tags": IsList("foo", "bar", check_order=False),
            "images": [

                        },
                        "summary": "Update Item",
                        "operationId": "update_item_items__item_id__put",
                        "parameters": [
                            {
                                "required": True,
                                "schema": {
                                    "title": "The ID of the item to get",
                                    "maximum": 1000.0,

                            },
                        },
                        "summary": "Read Item",
                        "operationId": "read_item_items__item_id__get",
                        "parameters": [
                            {
                                "required": True,
                                "schema": {"title": "Item Id", "type": "string"},
                                "name": "item_id",

                        "summary": "Update Item",
                    },
                },
            },
            "components": {
                "schemas": {
                    "Body_update_item_items__item_id__put": {
                        "properties": {
                            "item": {
                                "$ref": "#/components/schemas/Item",
                            },

from fastapi.responses import JSONResponse
from pydantic import BaseModel


class Item(BaseModel):
    title: str
    timestamp: datetime
    description: str | None = None


app = FastAPI()


@app.put("/items/{id}")
def update_item(id: str, item: Item):
    json_compatible_item_data = jsonable_encoder(item)

                        },
                        "summary": "Update Item",
                        "operationId": "update_item_items__item_id__put",
                        "requestBody": {
                            "content": {
                                "application/json": {
                                    "schema": {"$ref": "#/components/schemas/Item"}
                                }
                            },

    App->>Client: Send Item 2
    App->>App: Produce Item 3
    Client->>Client: Process Item 2
    App->>Client: Send Item 3
    Client->>Client: Process Item 3
    Note over App: Keeps producing...
    Note over Client: Keeps consuming...
```

Incluso podría ser un stream infinito, donde sigues enviando datos.

## JSON Lines { #json-lines }

    return JSONResponse(
        status_code=422,
        content=jsonable_encoder({"detail": exc.errors(), "body": exc.body}),
    )


class Item(BaseModel):
    title: str
    size: int


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