## Deeply nested models

You can define arbitrarily deeply nested models:

=== "Python 3.10+"

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

=== "Python 3.9+"

    ```Python hl_lines="9  14  20  23  27"
    {!> ../../../docs_src/body_nested_models/tutorial007_py39.py!}
    ```

=== "Python 3.8+"

```Python hl_lines="14"
{!../../../docs_src/body_nested_models/tutorial001.py!}
```

이는 `tags`를 항목 리스트로 만듭니다. 각 항목의 타입을 선언하지 않더라도요.

## 타입 매개변수가 있는 리스트 필드

하지만 파이썬은 내부의 타입이나 "타입 매개변수"를 선언할 수 있는 특정 방법이 있습니다:

### typing의 `List` 임포트

먼저, 파이썬 표준 `typing` 모듈에서 `List`를 임포트합니다:

```Python hl_lines="1"
{!../../../docs_src/body_nested_models/tutorial002.py!}
```

### 타입 매개변수로 `List` 선언

## Modelos aninhados

Cada atributo de um modelo Pydantic tem um tipo.

Mas esse tipo pode ser outro modelo Pydantic.

Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos.

Tudo isso, aninhado arbitrariamente.

### Defina um sub-modelo

Por exemplo, nós podemos definir um modelo `Image`:

```Python hl_lines="9-11"

=== "Python 3.10+"

    ```Python hl_lines="12"
    {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Set 类型

但是随后我们考虑了一下，意识到标签不应该重复，它们很大可能会是唯一的字符串。

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

=== "🐍 3️⃣.9️⃣ & 🔛"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!}
    ```

=== "🐍 3️⃣.1️⃣0️⃣ & 🔛"

    ```Python hl_lines="12"
    {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!}
    ```

## ⚒ 🆎

```Python hl_lines="12"
{!../../../docs_src/body_nested_models/tutorial001.py!}
```

これにより、各項目の型は宣言されていませんが、`tags`はある項目のリストになります。

## タイプパラメータを持つリストのフィールド

しかし、Pythonには型や「タイプパラメータ」を使ってリストを宣言する方法があります:

### typingの`List`をインポート

まず、Pythonの標準の`typing`モジュールから`List`をインポートします:

```Python hl_lines="1"
{!../../../docs_src/body_nested_models/tutorial002.py!}
```

### タイプパラメータを持つ`List`の宣言

              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <version>2.0.0</version>
              <models>
                <model>src/main/mdo/plugin.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>
                <param>packageModelV4=org.apache.maven.api.plugin.descriptor</param>

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <version>1.2.0</version>
              <models>
                <model>src/main/mdo/metadata.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>
                <param>packageModelV4=org.apache.maven.api.metadata</param>

## Create the Pydantic models

Now let's check the file `sql_app/schemas.py`.

!!! tip
    To avoid confusion between the SQLAlchemy *models* and the Pydantic *models*, we will have the file `models.py` with the SQLAlchemy models, and the file `schemas.py` with the Pydantic models.

    These Pydantic models define more or less a "schema" (a valid data shape).