En utilisant uniquement les déclarations de type Python, **FastAPI** réussit à :

* Lire le contenu de la requête en tant que JSON.
* Convertir les types correspondants (si nécessaire).
* Valider la donnée.
    * Si la donnée est invalide, une erreur propre et claire sera renvoyée, indiquant exactement où était la donnée incorrecte.
* Passer la donnée reçue dans le paramètre `item`.

* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.
    * Et les validateurs permettent aux schémas de données complexes d'être clairement et facilement définis, validés et documentés sous forme d'un schéma JSON.

def deep_dict_update(main_dict: Dict[Any, Any], update_dict: Dict[Any, Any]) -> None:
    for key, value in update_dict.items():
        if (
            key in main_dict
            and isinstance(main_dict[key], dict)
            and isinstance(value, dict)
        ):
            deep_dict_update(main_dict[key], value)
        elif (
            key in main_dict
            and isinstance(main_dict[key], list)

The same would happen if you had something like a <abbr title='A union between multiple types means "any of these types".'>union</abbr> between different types where one or more of them are not valid Pydantic types, for example this would fail 💥:

                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "42.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["query", "query"],
                    "msg": "value is not a valid integer",

                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["body", "price"],
                    "msg": "value is not a valid float",

                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["body", "price"],
                    "msg": "value is not a valid float",

                    "type": "value_error.missing",
                },
                {
                    "loc": ["query", "skip"],
                    "msg": "value is not a valid integer",
                    "type": "type_error.integer",
                },
                {
                    "loc": ["query", "limit"],
                    "msg": "value is not a valid integer",

        {
            "detail": [
                {
                    "loc": ["body", "size"],
                    "msg": "value is not a valid integer",
                    "type": "type_error.integer",
                }
            ],
            "body": {"title": "towel", "size": "XL"},
        }
    )


def test_post():
    data = {"title": "towel", "size": 5}

### Working with Python *enumerations*

The value of the *path parameter* will be an *enumeration member*.

#### Compare *enumeration members*

You can compare it with the *enumeration member* in your created enum `ModelName`:

```Python hl_lines="17"
{!../../../docs_src/path_params/tutorial005.py!}
```

#### Get the *enumeration value*