=== "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

from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class Model(BaseModel):
    pass


class Model2(BaseModel):
    a: Model


class Model3(BaseModel):
    c: Model
    d: Model2


@app.get("/", response_model=Model3)
def f():
    return {"c": {}, "d": {"a": {}}}


client = TestClient(app)


def test_get_api_route():

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)

        List<ToolchainModel> models = session.getRequest().getToolchains().get(type);

        return selectToolchains(models, type, requirements);
    }

    private List<Toolchain> selectToolchains(
            List<ToolchainModel> models, String type, Map<String, String> requirements) {
        List<Toolchain> toolchains = new ArrayList<>();

        if (models != null) {

              <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>

from sqlalchemy.orm import Session

from . import models, schemas


def get_user(db: Session, user_id: int):
    return db.query(models.User).filter(models.User.id == user_id).first()


def get_user_by_email(db: Session, email: str):
    return db.query(models.User).filter(models.User.email == email).first()


def get_users(db: Session, skip: int = 0, limit: int = 100):
    return db.query(models.User).offset(skip).limit(limit).all()

## Create the Pydantic models

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

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

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