name: "Validate Gradle Wrapper"
on: [push, pull_request]

permissions:
  contents: read

jobs:
  validation:
    name: "Validation"
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@0ad4b8fadaa221de15dcec353f45205ec38ea70b # v4.1.4

            """
        ),
    ] = _Unset,
    # TODO: update when deprecating Pydantic v1, import these types
    # validation_alias: str | AliasPath | AliasChoices | None
    validation_alias: Annotated[
        Union[str, None],
        Doc(
            """
            'Whitelist' validation step. The parameter field will be the single one
            allowed by the alias or set of aliases defined.
            """
        ),

     * users to migrate their projects.
     */
    int VALIDATION_LEVEL_MAVEN_2_0 = 20;

    /**
     * Denotes validation as performed by Maven 3.0. This validation level is meant for existing projects.
     */
    int VALIDATION_LEVEL_MAVEN_3_0 = 30;

    /**

        alias: Optional[str] = None,
        alias_priority: Union[int, None] = _Unset,
        # TODO: update when deprecating Pydantic v1, import these types
        # validation_alias: str | AliasPath | AliasChoices | None
        validation_alias: Union[str, None] = None,
        serialization_alias: Union[str, None] = None,
        title: Optional[str] = None,
        description: Optional[str] = None,

    try:
        from pydantic_core.core_schema import (
            with_info_plain_validator_function as with_info_plain_validator_function,
        )
    except ImportError:  # pragma: no cover
        from pydantic_core.core_schema import (
            general_plain_validator_function as with_info_plain_validator_function,  # noqa: F401
        )

    Required = PydanticUndefined
    Undefined = PydanticUndefined

                            "content": {"application/json": {"schema": {}}},
                        },
                        "422": {
                            "description": "Validation Error",
                            "content": {
                                "application/json": {
                                    "schema": {

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would still have...

    * Vous pouvez avoir des objets **JSON fortement imbriqués** tout en ayant, pour chacun, de la validation et des annotations.
* **Renouvelable**:
    * Pydantic permet de définir de nouveaux types de données ou vous pouvez étendre la validation avec des méthodes sur un modèle décoré avec le<abbr title="en anglais: validator decorator"> décorateur de validation</abbr>

    dependentRequired: Optional[Dict[str, Set[str]]] = None
    # Ref: JSON Schema Validation 2020-12: https://json-schema.org/draft/2020-12/json-schema-validation.html#name-vocabularies-for-semantic-c
    # Vocabularies for Semantic Content With "format"
    format: Optional[str] = None
    # Ref: JSON Schema Validation 2020-12: https://json-schema.org/draft/2020-12/json-schema-validation.html#name-a-vocabulary-for-the-conten

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`, and not some random string. This is especially useful for incoming data.

Without a data validation system, you would have to do all the checks by hand, in code.