Doc(
            """
            Pydantic's `include` parameter, passed to Pydantic models to set the
            fields to include.
            """
        ),
    ] = None,
    exclude: Annotated[
        Optional[IncEx],
        Doc(
            """
            Pydantic's `exclude` parameter, passed to Pydantic models to set the
            fields to exclude.
            """
        ),
    ] = None,

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>2.0.0</version>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/settings.mdo</model>
          </models>
          <templates>
            <template>model.vm</template>
          </templates>

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.2.0</version>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/toolchains.mdo</model>
          </models>
          <templates>
            <template>model.vm</template>
          </templates>

    /**
     * Gets the raw model to build. If not set, model source will be used to load raw model.
     *
     * @return The raw model to build or {@code null} if not set.
     */
    Model getRawModel();

    /**
     * Set raw model.
     *
     * @param rawModel
     */
    ModelBuildingRequest setRawModel(Model rawModel);

    /**
     * Gets the source of the POM to process.

따라서 **FastAPI**는 출력 모델에서 선언하지 않은 모든 데이터를 (Pydantic을 사용하여) 필터링합니다.

## 문서에서 보기

자동 생성 문서를 보면 입력 모델과 출력 모델이 각자의 JSON 스키마를 가지고 있음을 확인할 수 있습니다:

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

그리고 두 모델 모두 대화형 API 문서에 사용됩니다:

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

## 응답 모델 인코딩 매개변수

응답 모델은 아래와 같이 기본값을 가질 수 있습니다:

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}

        Path path = Paths.get(location);
        String modelId = "org.example:test:1.0.0";
        ModelSource source = Sources.resolvedSource(path, modelId);

        assertNotNull(source);
        assertInstanceOf(Sources.ResolvedPathSource.class, source);
        assertNull(source.getPath());
        assertEquals(path.toString(), source.getLocation());
        assertEquals(modelId, source.getModelId());
    }

    @Test

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

specific language governing permissions and limitations
under the License.
-->

<model xmlns="http://codehaus-plexus.github.io/MODELLO/2.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://codehaus-plexus.github.io/MODELLO/2.0.0 https://codehaus-plexus.github.io/modello/xsd/modello-2.0.0.xsd">
  <id>extension</id>
  <name>ExtensionDescriptor</name>
  <description><![CDATA[

## Other models { #other-models }

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

from fastapi.openapi.models import SecurityBase as SecurityBaseModel


class SecurityBase:
    model: SecurityBaseModel