"""
        ),
    ] = _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.
            """
        ),

        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,

    * 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...

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.

Nhưng mặc định, tất cả **đều hoạt động**.

### Validation

* Validation cho đa số (hoặc tất cả?) **các kiểu dữ liệu** Python, bao gồm:
    * JSON objects (`dict`).
    * Mảng JSON (`list`) định nghĩa kiểu dữ liệu từng phần tử.
    * Xâu (`str`), định nghĩa độ dài lớn nhất, nhỏ nhất.
    * Số (`int`, `float`) với các giá trị lớn nhất, nhỏ nhất, etc.

* Validation cho nhiều kiểu dữ liệu bên ngoài như:
    * URL.

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

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

!!! check
    So, with the same Python type declaration, **FastAPI** gives you data validation.

    Notice that the error also clearly states exactly the point where the validation didn't pass.

    This is incredibly helpful while developing and debugging code that interacts with your API.

## Documentation

!!! check "vérifier"
    Donc, avec ces mêmes déclarations de type Python, **FastAPI** vous fournit de la validation de données.

    Notez que l'erreur mentionne le point exact où la validation n'a pas réussi.

    Ce qui est incroyablement utile au moment de développer et débugger du code qui interagit avec votre API.

## Documentation

from dirty_equals import IsDict
from fastapi.testclient import TestClient

from docs_src.handling_errors.tutorial005 import app

client = TestClient(app)


def test_post_validation_error():
    response = client.post("/items/", json={"title": "towel", "size": "XL"})
    assert response.status_code == 422, response.text
    assert response.json() == IsDict(
        {
            "detail": [
                {