}
        """
    }

    private goodCode() {
        file("src/main/java/org/gradle/Class1.java") <<
            "package org.gradle; class Class1 { public boolean isFoo(Object arg) { return true; } }"
        file("src/test/java/org/gradle/Class1Test.java") <<
            "package org.gradle; class Class1Test { public boolean isFoo(Object arg) { return true; } }"
    }

```Python hl_lines="1"
{!../../../docs_src/first_steps/tutorial001.py!}
```

`FastAPI` é uma classe Python que fornece todas as funcionalidades para sua API.

!!! note "Detalhes técnicos"
    `FastAPI` é uma classe que herda diretamente de `Starlette`.

    Você pode usar todas as funcionalidades do <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a> com `FastAPI` também.

{!../../../docs_src/first_steps/tutorial001.py!}
```

`FastAPI` est une classe Python qui fournit toutes les fonctionnalités nécessaires au lancement de votre API.

!!! note "Détails techniques"
    `FastAPI` est une classe héritant directement de `Starlette`.

    Vous pouvez donc aussi utiliser toutes les fonctionnalités de <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a> depuis `FastAPI`.

                }
            }
        """
    }

    private goodCode() {
        file("src/main/groovy/org/gradle/Class1.groovy") << "package org.gradle; class Class1 { }"
        file("src/test/groovy/org/gradle/TestClass1.groovy") << "package org.gradle; class TestClass1 { }"
    }

    private TestFile writeRuleFile() {
        file("config/codenarc/codenarc.xml") << """

	// List them
	verifyListPVs(t, cache, pvs1, "class1")
	verifyListPVs(t, cache, pvs2, "class2")

	// Update a PV
	updatedPV := makePV("test-pv3", "class1").withVersion("2").PersistentVolume
	pvs1[updatedPV.Name] = updatedPV
	assumecache.UpdateTestObject(cache.AssumeCache, updatedPV)

	// List them
	verifyListPVs(t, cache, pvs1, "class1")
	verifyListPVs(t, cache, pvs2, "class2")

	// Delete a PV

  func.return
}

// -----

// Test case: Invalid multiple location information in a class attribute on VariableV2 op.

func.func @f() {
  // expected-error @+1 {{'tf.VariableV2' op expects only one named location in '_class' attribute, but got ["loc:@v1", "loc:@v2"]}}
  %val0 = "tf.VariableV2"() {_class = ["loc:@v1", "loc:@v2"], container = "", device = "", shape = #tf_type.shape<96>, shared_name = ""} : () -> tensor<96x!tf_type.f32ref>

                        implementation project(':library')
                    }
                }
            """.stripIndent()
        }
        file("app/src/main/${language.name}/AClass.${language.name}") << 'public class AClass { }'
    }

    String getAppCompileTask(CompiledLanguage language = CompiledLanguage.JAVA) {
        ":app:${language.compileTaskName}"
    }

  /// The Float handle.
  friend class Float;
  /// The String handle.
  friend class String;
  /// The Object handle.
  friend class Object;
  /// The List handle.
  friend class List;
  /// The Dictionary handle.
  friend class Dictionary;
  /// The Tuple handle.
  friend class Tuple;
  /// The Callable handle.
  friend class Callable;
  /// The Tensor handle.
  friend class Tensor;

        source "class A { static final int x = 1; }",
            "class B { static final int x = A.x + 1; }",
            "class C { static final int x = B.x + 1; }",
            "class D { static final int x = C.x + 1; }",
            "class E { }"
        outputs.snapshot { run language.compileTaskName }

        when:

## Importez le `BaseModel` de Pydantic

Commencez par importer la classe `BaseModel` du module `pydantic` :

```Python hl_lines="4"
{!../../../docs_src/body/tutorial001.py!}
```

## Créez votre modèle de données

Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.

Utilisez les types Python standard pour tous les attributs :