org.apache.maven.model.root.DefaultRootLocator...

              <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>

Sebastián Ramírez <******@****.***> 1731896744 +0100

以下のように:

{* ../../docs_src/body_nested_models/tutorial008.py hl[15] *}

## あらゆる場所でのエディタサポート

エディタのサポートもどこでも受けることができます。

以下のようにリストの中の項目でも:

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

Pydanticモデルではなく、`dict`を直接使用している場合はこのようなエディタのサポートは得られません。

しかし、それらについて心配する必要はありません。入力された辞書は自動的に変換され、出力も自動的にJSONに変換されます。

## 任意の`dict`のボディ

また、ある型のキーと別の型の値を持つ`dict`としてボディを宣言することもできます。

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

     *
     * This method will be called on each raw model being loaded,
     * just before validation.
     *
     * @param model the input model
     * @return the transformed model, or the input model if no transformation is needed
     * @throws ModelTransformerException
     */
    @Nonnull
    default Model transformRawModel(@Nonnull Model model) throws ModelTransformerException {
        return model;
    }

    /**

///

/// note

Notice that the input model is still validated.

So, if you want to receive partial updates that can omit all the attributes, you need to have a model with all the attributes marked as optional (with default values or `None`).

To distinguish from the models with all optional values for **updates** and models with required values for **creation**, you can use the ideas described in [Extra Models](extra-models.md){.internal-link target=_blank}.

It improves editor support for Pydantic models, with:

* auto-completion
* type checks
* refactoring
* searching
* inspections

///

## Use the model { #use-the-model }

Inside of the function, you can access all the attributes of the model object directly:

{* ../../docs_src/body/tutorial002_py310.py *}

    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>