builder.run()
        }

        then:
        models.size() == 4

        and:
        CustomModel model1 = models.get(0)
        model1.value == 1

        and:
        GradleProject gradleProject = models.get(1)
        gradleProject.name == "hello-world"

        and:
        CustomModel model2 = models.get(2)
        model2.value == 2

        and:

 * under the License.
 */
package org.apache.maven.model.inheritance;

import java.io.InputStream;
import java.io.StringReader;

import org.apache.maven.api.model.InputLocation;
import org.apache.maven.api.model.InputSource;
import org.apache.maven.api.model.Model;
import org.apache.maven.model.v4.MavenMerger;
import org.apache.maven.model.v4.MavenStaxReader;
import org.junit.jupiter.api.Test;

            return name

    async def get_model_c() -> ModelC:
        return ModelC(username="test-user", password="test-password")

    @app.get("/model/{name}", response_model=ModelA)
    async def get_model_a(name: str, model_c=Depends(get_model_c)):
        return {"name": name, "description": "model-a-desc", "foo": model_c}

    client = TestClient(app)
    return client


@needs_pydanticv2

# Extramodelle

Fahren wir beim letzten Beispiel fort. Es gibt normalerweise mehrere zusammengehörende Modelle.

Insbesondere Benutzermodelle, denn:

* Das **hereinkommende Modell** sollte ein Passwort haben können.
* Das **herausgehende Modell** sollte kein Passwort haben.

## Modelos aninhados

Cada atributo de um modelo Pydantic tem um tipo.

Mas esse tipo pode ser outro modelo Pydantic.

Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos.

Tudo isso, aninhado arbitrariamente.

### Defina um sub-modelo

Por exemplo, nós podemos definir um modelo `Image`:

```Python hl_lines="9-11"

# OpenAPI-`models`

OpenAPI Pydantic-Modelle, werden zum Generieren und Validieren der generierten OpenAPI verwendet.

# Extra Models

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

For example, we can define an `Image` model:

=== "Python 3.10+"

Moustapha Sall <******@****.***> 1704810933 -0500

Moustapha Sall <******@****.***> 1704810933 -0500