///

/// 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 *}

     *
     * 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;
    }

    /**

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

     */
    List<String> getModelIds();

    /**
     * Gets the assembled model.
     *
     * @return The assembled model, never {@code null}.
     */
    Model getEffectiveModel();

    /**
     * Gets the raw model as it was read from the input model source. Apart from basic validation, the raw model has not

              <goal>velocity</goal>
            </goals>
            <configuration>
              <version>4.0.0</version>
              <models>
                <model>target/mdo/maven.mdo</model>
              </models>
              <templates>
                <template>src/main/mdo/model-v3.vm</template>
                <template>src/main/mdo/merger.vm</template>
                <template>src/main/mdo/transformer.vm</template>

            </goals>
            <configuration>
              <version>1.1.0</version>
              <models>
                <model>../../api/maven-api-metadata/src/main/mdo/metadata.mdo</model>
              </models>
              <templates>
                <template>model-v3.vm</template>
              </templates>
            </configuration>
          </execution>
        </executions>
      </plugin>

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

### Model for Input { #model-for-input }

If you use this model as an input like here:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

...then the `description` field will **not be required**. Because it has a default value of `None`.

### Input Model in Docs { #input-model-in-docs }

## Retorno Adicional com `model` { #additional-response-with-model }

Você pode fornecer o parâmetro `responses` aos seus *decoradores de caminho*.

Este parâmetro recebe um `dict`, as chaves são os códigos de status para cada retorno, como por exemplo `200`, e os valores são um outro `dict` com a informação de cada um deles.