```

例如：

{* ../../docs_src/body_nested_models/tutorial008_py39.py hl[13] *}

## 无处不在的编辑器支持

你可以随处获得编辑器支持。

即使是列表中的元素：

<img src="https://fastapi.tiangolo.com/img/tutorial/body-nested-models/image01.png">

如果你直接使用 `dict` 而不是 Pydantic 模型，那你将无法获得这种编辑器支持。

但是你根本不必担心这两者，传入的字典会自动被转换，你的输出也会自动被转换为 JSON。

## 任意 `dict` 构成的请求体

你也可以将请求体声明为使用某类型的键和其他类型值的 `dict`。

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

    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 422, response.text

    data = [{"x": 1, "y": 2}]
    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 422, response.text


def test_tuple_form_valid():
    response = client.post("/tuple-form/", data={"values": ("1", "2")})

            </goals>
            <configuration>
              <version>1.1.0</version>
              <models>
                <model>src/main/mdo/remote-resources.mdo</model>
              </models>
            </configuration>
          </execution>
          <execution>
            <id>supplemental-models</id>
            <goals>
              <goal>xpp3-writer</goal>
              <goal>java</goal>

## See it in the docs { #see-it-in-the-docs }

When you see the automatic docs, you can check that the input model and output model will both have their own JSON Schema:

<img src="/img/tutorial/response-model/image01.png">

And both models will be used for the interactive API documentation:

<img src="/img/tutorial/response-model/image02.png">

## Other Return Type Annotations { #other-return-type-annotations }

client = TestClient(app)


def test_get_enums_alexnet():
    response = client.get("/models/alexnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"}


def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200

              <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>

    - tutorial/query-param-models.md
    - tutorial/body-multiple-params.md
    - tutorial/body-fields.md
    - tutorial/body-nested-models.md
    - tutorial/schema-extra-example.md
    - tutorial/extra-data-types.md
    - tutorial/cookie-params.md
    - tutorial/header-params.md
    - tutorial/cookie-param-models.md
    - tutorial/header-param-models.md
    - tutorial/response-model.md

It's **not supported** by Pydantic to have a model of Pydantic v2 with its own fields defined as Pydantic v1 models or vice versa.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

Does nothing. false true false false false true generate-sources org.apache.maven.plugin.coreit.CMojo java per-lookup once-per-session models java.lang.String[] false true version java.lang.String false true ${version} xpp3-writer Does nothing. false true false false false true generate-sources org.apache.maven.plugin.coreit.BMojo java per-lookup once-per-session models java.lang.String[] false true version java.lang.String false true ${version} org.apache.maven maven-plugin-api jar 2.0 org/apache/m...