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

                }
            }
        };

        // When
        byte[] testId = new byte[] { 42 };
        anonymousImpl.setFileId(testId);

        // Then
        assertEquals(42, capturedFileId[0], "Anonymous implementation should work correctly");
    }

    @Test
    @DisplayName("Should verify all known implementations")
    void testAllKnownImplementations() {

			wantSelected: &inputSerialized[9],
			wantOk:       true,
		},
	}

	for testID, tt := range tests {
		rng := rand.New(rand.NewSource(0))
		// Run for a number of times, shuffling the input to ensure that output is consistent.
		for i := range 10 {
			t.Run(fmt.Sprintf("test-%d-%s-run-%d", testID, tt.name, i), func(t *testing.T) {
				if i > 0 {
					rng.Shuffle(len(tt.m), func(i, j int) {

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

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

        assertViolations(result, 0, 0, 1);

        assertContains(result.getWarnings().get(0), "test:g");
    }

    @Test
    void testBadDependencyVersion() throws Exception {

        assertTrue(result.isPresent(), "Expected " + result + ".isPresent() to return true");
        assertEquals(asList("test:B", "test:D"), result.get().getRemainingProjects());
    }

    private MavenProject createMavenProject(String artifactId) {
        MavenProject project = new MavenProject();
        project.setGroupId("test");
        project.setArtifactId(artifactId);
        return project;
    }

And it will be annotated / documented accordingly too.

## Nested Models { #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 { #define-a-submodel }

    tester = new ListenableFutureTester(future);
    tester.setUp();
  }

  public void testDefaultState() throws Exception {
    assertThrows(TimeoutException.class, () -> future.get(5, MILLISECONDS));
  }

  public void testSetValue() throws Exception {
    assertTrue(future.set("value"));
    tester.testCompletedFuture("value");
  }

  public void testSetFailure() throws Exception {

# Body – Verschachtelte Modelle { #body-nested-models }

Mit **FastAPI** können Sie (dank Pydantic) beliebig tief verschachtelte Modelle definieren, validieren, dokumentieren und verwenden.

## Listen als Felder { #list-fields }

Sie können ein Attribut als Kindtyp definieren, zum Beispiel eine Python-`list`.

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

# Body - Вложенные модели { #body-nested-models }

С помощью **FastAPI** вы можете определять, валидировать, документировать и использовать модели произвольной глубины вложенности (благодаря Pydantic).

## Поля-списки { #list-fields }

Вы можете определить атрибут как подтип. Например, Python-тип `list`:

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

  "version": "1.0",
  "published": "2025-01-15",
  "tags": ["crawler", "extractor", "json"],
  "content": {
    "summary": "This is a sample JSON document for testing",
    "body": "The extractor should handle nested objects and arrays properly"
  },
  "metadata": {
    "created_at": "2025-01-01T00:00:00Z",
    "updated_at": "2025-01-15T12:00:00Z"
  }