def model2 = models2.right
        model2.size() == 2
        model2[0].message == "It works from project :"
        model2[1].message == "It works from project :a"

        and:
        fixture.assertStateLoaded()
        outputDoesNotContain("creating model")
    }

    def "caches execution of phased BuildAction that queries custom tooling model and that runs tasks"() {
        given:

	var models2 [3]User
	if err := DB.Where("name in (?)", []string{"find"}).Find(&models2).Error; err != nil {
		t.Errorf("errors happened when query find with in clause: %v, length: %v", err, len(models2))
	} else {
		for idx, user := range users {
			t.Run("FindWithInClause#"+strconv.Itoa(idx+1), func(t *testing.T) {
				CheckUser(t, models2[idx], user)
			})
		}
	}

	// test smaller array
	var models3 [2]User

# OpenAPI-`models`

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

Und es wird entsprechend annotiert/dokumentiert.

## Verschachtelte Modelle

Jedes Attribut eines Pydantic-Modells hat einen Typ.

Aber dieser Typ kann selbst ein anderes Pydantic-Modell sein.

Sie können also tief verschachtelte JSON-„Objekte“ deklarieren, mit spezifischen Attributnamen, -typen, und -validierungen.

# 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.

# OpenAPI `models`

OpenAPI Pydantic models used to generate and validate the generated OpenAPI.

import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;

public class Models implements Serializable {
    private final boolean runsInParallel;
    private final List<ToolchainModel> projects;

    public Models(boolean runsInParallel, Collection<ToolchainModel> projects) {
        this.runsInParallel = runsInParallel;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;

public class Models implements Serializable {
    private final boolean runsInParallel;
    private final List<CustomModel> projects;

    public Models(boolean runsInParallel, Collection<CustomModel> projects) {
        this.runsInParallel = runsInParallel;

## 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+"

### Über `**user_in.dict()`

#### Pydantic's `.dict()`

`user_in` ist ein Pydantic-Modell der Klasse `UserIn`.

Pydantic-Modelle haben eine `.dict()`-Methode, die ein `dict` mit den Daten des Modells zurückgibt.

Wenn wir also ein Pydantic-Objekt `user_in` erstellen, etwa so:

```Python