Doc(
            """
            Maximum number of allow digits for strings.
            """
        ),
    ] = _Unset,
    decimal_places: Annotated[
        Union[int, None],
        Doc(
            """
            Maximum number of decimal places allowed for numbers.
            """
        ),
    ] = _Unset,
    examples: Annotated[
        Optional[List[Any]],
        Doc(
            """

    response = client.post("/", json={"numbers": [1, 2, 3]})
    assert response.json() == IsDict(
        {
            "detail": {
                "errors": [
                    {
                        "type": "list_type",
                        "loc": ["body"],
                        "msg": "Input should be a valid list",
                        "input": {"numbers": [1, 2, 3]},
                    }
                ],

But as you go away from the counter and sit at the table with a number for your turn, you can switch 🔀 your attention to your crush, and "work" ⏯ 🤓 on that. Then you are again doing something very "productive" as is flirting with your crush 😍.

* Validation for most (or all?) Python **data types**, including:
    * JSON objects (`dict`).
    * JSON array (`list`) defining item types.
    * String (`str`) fields, defining min and max lengths.
    * Numbers (`int`, `float`) with min and max values, etc.

* Validation for more exotic types, like:
    * URL.
    * Email.
    * UUID.
    * ...and others.

    * objetos JSON (`dict`).
    * arrays JSON (`list`), definindo tipos dos itens.
    * campos String (`str`), definindo tamanho mínimo e máximo.
    * Numbers (`int`, `float`) com valores mínimos e máximos, etc.

* Validação de tipos mais exóticos, como:
    * URL.
    * Email.
    * UUID.
    * ...e outros.

        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [

        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [

```Python hl_lines="20"
{!../../../docs_src/path_params/tutorial005.py!}
```

!!! tip
    You could also access the value `"lenet"` with `ModelName.lenet.value`.

#### Return *enumeration members*

You can return *enum members* from your *path operation*, even nested in a JSON body (e.g. a `dict`).

They will be converted to their corresponding values (strings in this case) before returning them to the client:

                            # TODO: remove when deprecating Pydantic v1
                            {
                                "title": "Process After",
                                "type": "number",
                                "format": "time-delta",
                            }
                        ),
                    },

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting