import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.di.Named;
import org.apache.maven.api.model.Model;

/**
 * Interface for model transformers that can modify Maven project models at different stages of processing.
 * <p>
 * Model transformers allow plugins and extensions to modify the POM model during the build process.
 * Transformations can be applied at three different stages:
 * <ol>

You can declare the type used for the response by annotating the *path operation function* **return type**.

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

{* ../../docs_src/response_model/tutorial001_01_py310.py hl[16,21] *}

FastAPI will use this return type to:

* **Validate** the returned data.

            <configuration>
              <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>

    - 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

        <configuration>
          <version>4.2.0</version>
          <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>

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output { #pydantic-models-for-input-and-output }

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

We'll fix these things by adding a few **extra models**. Here's where SQLModel will shine. ✨

### Create Multiple Models { #create-multiple-models }

In **SQLModel**, any model class that has `table=True` is a **table model**.

And any model class that doesn't have `table=True` is a **data model**, these ones are actually just Pydantic models (with a couple of small extra features). 🤓

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}.

To declare a **request** body, you use <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> models with all their power and benefits.

/// info

To send data, you should use one of: `POST` (the more common), `PUT`, `DELETE` or `PATCH`.

## 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.