```Python
id = data["id"]
```

⚫️ 🔜 🔄 🤚 ⚫️ ⚪️➡️ 🔢,:

```Python
id = data.id
```

& ⏮️ 👉, Pydantic *🏷* 🔗 ⏮️ 🐜, & 👆 💪 📣 ⚫️ `response_model` ❌ 👆 *➡ 🛠️*.

👆 🔜 💪 📨 💽 🏷 & ⚫️ 🔜 ✍ 💽 ⚪️➡️ ⚫️.

#### 📡 ℹ 🔃 🐜 📳

🇸🇲 & 📚 🎏 🔢 "🙃 🚚".

👈 ⛓, 🖼, 👈 👫 🚫 ☕ 💽 💛 ⚪️➡️ 💽 🚥 👆 🔄 🔐 🔢 👈 🔜 🔌 👈 💽.

🖼, 🔐 🔢 `items`:

    {!> ../../../docs_src/extra_models/tutorial003.py!}
    ```

### `Union` в Python 3.10

В этом примере мы передаём `Union[PlaneItem, CarItem]` в качестве значения аргумента `response_model`.

Поскольку мы передаём его как **значение аргумента** вместо того, чтобы поместить его в **аннотацию типа**, нам придётся использовать `Union` даже в Python 3.10.

Wenn Sie Teil-Aktualisierungen entgegennehmen, ist der `exclude_unset`-Parameter in der `.model_dump()`-Methode von Pydantic-Modellen sehr nützlich.

Wie in `item.model_dump(exclude_unset=True)`.

!!! info
    In Pydantic v1 hieß diese Methode `.dict()`, in Pydantic v2 wurde sie deprecated (aber immer noch unterstützt) und in `.model_dump()` umbenannt.

    Temporarily adopting this point of view (of the *external developer*) can help you feel like it's more obvious where to put the parameters, the Pydantic model for the body, for the response, etc. for that *external API*.

### Create a callback `APIRouter`

First create a new `APIRouter` that will contain one or more callbacks.

```Python hl_lines="3  25"

    {!> ../../../docs_src/body/tutorial001_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="4"
    {!> ../../../docs_src/body/tutorial001.py!}
    ```

## Create your data model

Then you declare your data model as a class that inherits from `BaseModel`.

Use standard Python types for all the attributes:

=== "Python 3.10+"

    ```Python hl_lines="5-9"
    {!> ../../../docs_src/body/tutorial001_py310.py!}

    ```

!!! info
    In Pydantic version 1 the method to get the JSON Schema for a model was called `Item.schema()`, in Pydantic version 2, the method is called `Item.model_json_schema()`.

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

**FastAPI** will use that *temporal* response to extract the status code (also cookies and headers), and will put them in the final response that contains the value you returned, filtered by any `response_model`.

You could load it at the top level of the module/file, but that would also mean that it would **load the model** even if you are just running a simple automated test, then that test would be **slow** because it would have to wait for the model to load before being able to run an independent part of the code.

    ```

추가 정보는 있는 그대로 해당 모델의 **JSON 스키마** 결과에 추가되고, API 문서에서 사용합니다.

=== "Pydantic v2"

    Pydantic 버전 2에서 <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic 공식 문서: Model Config</a>에 나와 있는 것처럼 `dict`를 받는 `model_config` 어트리뷰트를 사용할 것입니다.

    `"json_schema_extra"`를 생성된 JSON 스키마에서 보여주고 싶은 별도의 데이터와 `examples`를 포함하는 `dict`으로 설정할 수 있습니다.

=== "Pydantic v1"

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

**FastAPI** will use that *temporal* response to extract the cookies (also headers and status code), and will put them in the final response that contains the value you returned, filtered by any `response_model`.