///

## Conversion des types des paramètres de requête { #query-parameter-type-conversion }

Vous pouvez aussi déclarer des types `bool`, ils seront convertis :

{* ../../docs_src/query_params/tutorial003_py310.py hl[7] *}

Dans ce cas, si vous allez sur :

```

    assertThrows(NullPointerException.class, () -> multimap().putAll(null, singletonList(v3())));
  }

  @SuppressWarnings("EmptyList") // ImmutableList doesn't support nullable element types
  @MapFeature.Require(SUPPORTS_PUT)
  public void testPutAllEmptyCollectionOnAbsentKey() {
    assertFalse(multimap().putAll(k3(), Collections.emptyList()));
    expectUnchanged();
  }

        assertNotNull(stringRep);
        assertTrue(stringRep.contains("RuntimeCIFSException"));
        assertTrue(stringRep.contains(message));
    }

    @Test
    @DisplayName("Should handle various message types")
    void testVariousMessageTypes() {
        // Test empty message
        assertDoesNotThrow(() -> {
            RuntimeCIFSException exception = new RuntimeCIFSException("");

                    DependencyGraphDumper.defaultsWith(
                            List.of(DependencyGraphDumper.artifactProperties(List.of(ArtifactProperties.TYPE))))));
            logger.debug("TYPES: Before transform:\n {}", sb);
        }
        root.accept(new TypeDeriverVisitor(registry));
        @SuppressWarnings("unchecked")

* Documentación automática

## Tipos especiales y validación { #special-types-and-validation }

Además de tipos singulares normales como `str`, `int`, `float`, etc., puedes usar tipos singulares más complejos que heredan de `str`.

Para ver todas las opciones que tienes, Revisa [Resumen de tipos de Pydantic](https://docs.pydantic.dev/latest/concepts/types/). Verás algunos ejemplos en el siguiente capítulo.

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Create your data model { #create-your-data-model }

Then you declare your data model as a class that inherits from `BaseModel`.

Use standard Python types for all the attributes:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}

Et ces modèles partagent beaucoup de données et dupliquent des noms et types d'attributs.

Nous pouvons faire mieux.

name: Publish

on:
  release:
    types:
      - created

jobs:
  publish:
    runs-on: ubuntu-latest
    permissions:
      id-token: write
      contents: read
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v6
      - name: Set up Python
        uses: actions/setup-python@v6
        with:

    /**
     * Returns the element type of an array with parameterized types.
     * <p>
     * If <code>type</code> is not an array of parameterized types, <code>null</code> is returned.
     * </p>
     *
     * @param type
     *            the type to analyze
     * @return the element type of the array, or null if not an array of parameterized types
     */
    public static Type getElementTypeOfArray(final Type type) {

 *
 * <p>If you have an instance of one of the primary {@code common.graph} types ({@link Graph},
 * {@link ValueGraph}, and {@link Network}):
 *
 * {@snippet :
 * someGraphAlgorithm(startNode, graph);
 * }
 *
 * This works because those types each implement {@code PredecessorsFunction}. It will also work
 * with any other implementation of this interface.
 *