}
    } ]
  }, {
    "testId" : "org.gradle.performance.regression.android.RealLifeAndroidBuildPerformanceTest.calculate task graph with test finalizer",
    "groups" : [ {
      "testProject" : "largeAndroidBuild",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {

    }
}

data class Scenario(val className: String, val scenario: String) {
    companion object {
        fun fromTestId(testId: String): Scenario {
            val dotBeforeScenarioName = testId.lastIndexOf('.')
            return Scenario(testId.substring(0, dotBeforeScenarioName), testId.substring(dotBeforeScenarioName + 1))
        }
    }

    override
    fun toString(): String =
        "$className.$scenario"

app.include_router(router)


client = TestClient(app)


def test_a():
    response = client.get("/a")
    assert response.status_code == 200, response.text
    assert response.json() == "a"


def test_b():
    response = client.get("/b")
    assert response.status_code == 200, response.text
    assert response.json() == "b"


def test_c():
    response = client.get("/c")

    }

    @Test
    void testBadDependencyScope() throws Exception {
        SimpleProblemCollector result = validate("bad-dependency-scope.xml");

        assertViolations(result, 0, 0, 2);

        assertTrue(result.getWarnings().get(0).contains("test:f"));

        assertTrue(result.getWarnings().get(1).contains("test:g"));
    }

    @Test
    void testBadDependencyManagementScope() throws Exception {

        Map<String, Object> value = new LinkedHashMap<>();
        DocMap docMap = new DocMap(value);
        assertTrue(docMap.isEmpty());
        value.clear();

        List<String> keys = Arrays.asList("test_2", "test_0", "lang", "test_1");
        value.put(keys.get(0), true);
        value.put(keys.get(1), 1000);
        value.put(keys.get(2), "ja");
        value.put(keys.get(3), "str");
        docMap = new DocMap(value);

And it will be annotated / documented accordingly too.

## 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:

# Testes Assíncronos

Você já viu como testar as suas aplicações **FastAPI** utilizando o `TestClient` que é fornecido. Até agora, você viu apenas como escrever testes síncronos, sem utilizar funções `async`.

public abstract class IteratorTester<E extends @Nullable Object>
    extends AbstractIteratorTester<E, Iterator<E>> {
  /**
   * Creates an IteratorTester.
   *
   * @param steps how many operations to test for each tested pair of iterators
   * @param features the features supported by the iterator
   */
  protected IteratorTester(
      int steps,
      Iterable<? extends IteratorFeature> features,

### Tested

* 100% <abbr title="The amount of code that is automatically tested">test coverage</abbr>.
* 100% <abbr title="Python type annotations, with this your editor and external tools can give you better support">type annotated</abbr> code base.

```

////

## Editor-Unterstützung überall

Und Sie erhalten Editor-Unterstützung überall.

Selbst für Dinge in Listen:

<img src="/img/tutorial/body-nested-models/image01.png">

Sie würden diese Editor-Unterstützung nicht erhalten, wenn Sie direkt mit `dict`, statt mit Pydantic-Modellen arbeiten würden.