labels.crawling_info_details=Crawling Info 상세
labels.data_config_details=Data Config 상세
labels.dict_kuromoji_details=Dict Kuromoji 상세
labels.dict_mapping_details=Dict Mapping 상세
labels.dict_protwords_details=Dict Protwords 상세
labels.dict_stemmeroverride_details=Dict Stemmeroverride 상세
labels.dict_stopwords_details=Dict Stopwords 상세
labels.dict_synonym_details=Dict Synonym 상세
labels.duplicate_host_details=Duplicate Host 상세

### Template Context Values { #template-context-values }

In the HTML that contains:

{% raw %}

```jinja
Item ID: {{ id }}
```

{% endraw %}

...it will show the `id` taken from the "context" `dict` you passed:

```Python
{"id": id}
```

For example, with an ID of `42`, this would render:

```html
Item ID: 42
```

### Template `url_for` Arguments { #template-url-for-arguments }

    user = get_user(fake_db, username)
    if not user:
        return False
    if not verify_password(password, user.hashed_password):
        return False
    return user


def create_access_token(data: dict, expires_delta: Union[timedelta, None] = None):
    to_encode = data.copy()
    if expires_delta:
        expire = datetime.now(timezone.utc) + expires_delta
    else:

def resolve_files(*, items: list[Any], files: Files, config: MkDocsConfig) -> None:
    for item in items:
        if isinstance(item, str):
            resolve_file(item=item, files=files, config=config)
        elif isinstance(item, dict):
            assert len(item) == 1
            values = list(item.values())
            if not values:
                continue
            if isinstance(values[0], str):

Et comme la plupart du temps d'exécution est pris par du "vrai" travail (et non de l'attente), et que le travail dans un ordinateur est fait par un <abbr title="Central Processing Unit">CPU</abbr>, ce sont des problèmes dits "CPU bound".

---

Des exemples communs d'opérations "CPU bounds" sont les procédés qui requièrent des traitements mathématiques complexes.

Par exemple :

* Traitements d'**audio** et d'**images**.

Por padrão, tudo **"simplesmente funciona"**.

### Validação { #validation }

* Validação para a maioria dos (ou todos?) **tipos de dados** do Python, incluindo:
    * 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.

{* ../../docs_src/security/tutorial003_an_py310.py hl[82:85] *}

#### Sobre `**user_dict` { #about-user-dict }

`UserInDB(**user_dict)` significa:

*Passe as chaves e valores de `user_dict` diretamente como argumentos de valor-chave, equivalente a:*

```Python
UserInDB(
    username = user_dict["username"],

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

쿼리 매개변수를 선언할 때와 같이, 모델 어트리뷰트가 기본 값을 가지고 있어도 이는 필수가 아닙니다. 그외에는 필수입니다. 그저 `None`을 사용하여 선택적으로 만들 수 있습니다.

예를 들면, 위의 이 모델은 JSON "`object`" (혹은 파이썬 `dict`)을 다음과 같이 선언합니다:

```JSON
{
    "name": "Foo",
    "description": "선택적인 설명란",
    "price": 45.2,
    "tax": 3.5
}
```

### Template-Kontextwerte { #template-context-values }

Im HTML, welches enthält:

{% raw %}

```jinja
Item ID: {{ id }}
```

{% endraw %}

... wird die `id` angezeigt, welche dem „Kontext“-`dict` entnommen wird, welches Sie übergeben haben:

```Python
{"id": id}
```

Mit beispielsweise einer ID `42` würde das wie folgt gerendert werden:

```html
Item ID: 42
```

        return cast(UploadFile, __input_value)

    @classmethod
    def __get_pydantic_json_schema__(
        cls, core_schema: Mapping[str, Any], handler: GetJsonSchemaHandler
    ) -> dict[str, Any]:
        return {"type": "string", "format": "binary"}

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: Callable[[Any], Mapping[str, Any]]