labels.storage_file=File
labels.storage_folder_name=Folder Name
labels.storage_button_download=Download
labels.storage_button_tags=Tags
labels.storage_title_tag=Tag:
labels.storage_tag_key=Tag Key
labels.storage_tag_value=Tag Value
labels.login.newpassword=You need to update your password
labels.login.placeholder_new_password=New Password
labels.login.placeholder_confirm_new_password=Confirm New Password

 * @since 1.0
 */
/*
 * Some of the annotations below were added after we released our separate
 * com.google.guava:listenablefuture:1.0 artifact. (For more on that artifact, see
 * https://github.com/google/guava/releases/tag/v27.0) This means that the copy of ListenableFuture
 * in com.google.guava:guava differs from the "frozen" copy in the listenablefuture artifact. This

 * @since 1.0
 */
/*
 * Some of the annotations below were added after we released our separate
 * com.google.guava:listenablefuture:1.0 artifact. (For more on that artifact, see
 * https://github.com/google/guava/releases/tag/v27.0) This means that the copy of ListenableFuture
 * in com.google.guava:guava differs from the "frozen" copy in the listenablefuture artifact. This

LARGE_ITEMS: list[dict[str, Any]] = [
    {
        "id": i,
        "name": f"item-{i}",
        "values": list(range(25)),
        "meta": {
            "active": True,
            "group": i % 10,
            "tag": f"t{i % 5}",
        },
    }
    for i in range(300)
]

LARGE_METADATA: dict[str, Any] = {
    "source": "benchmark",
    "version": 1,
    "flags": {"a": True, "b": False, "c": True},

labels.storage_file=Arquivo
labels.storage_folder_name=Nome da pasta
labels.storage_button_download=Baixar
labels.storage_button_tags=Tags
labels.storage_title_tag=Tag:
labels.storage_tag_key=Chave da tag
labels.storage_tag_value=Valor da tag
labels.login.newpassword=Você precisa atualizar sua senha
labels.login.placeholder_new_password=Nova senha
labels.login.placeholder_confirm_new_password=Confirmar nova senha

# Official CI Directory

Maintainer: TensorFlow and TensorFlow DevInfra

Issue Reporting: File an issue against this repo and tag
[@devinfra](https://github.com/orgs/tensorflow/teams/devinfra)

********************************************************************************

## TensorFlow's Official CI and Build/Test Scripts

TensorFlow's official CI jobs run the scripts in this folder. Our internal CI

        ```
        »»»

    Example:

        «««
        ```python
        print("hello World")
        ```
        »»»

"HTML element"

    a HTML opening tag – e.g. «<div>» – and a HTML closing tag – e.g. «</div>» – surrounding text or other HTML elements.


### Your task

Translate an English text – the original content – to a target language.

Aber wir wollen schlauer sein und den Code etwas vereinfachen.

Wir wissen, dass alle *Pfadoperationen* in diesem Modul folgendes haben:

* Pfad-`prefix`: `/items`.
* `tags`: (nur ein Tag: `items`).
* Zusätzliche `responses`.
* `dependencies`: Sie alle benötigen die von uns erstellte `X-Token`-Abhängigkeit.

Anstatt also alles zu jeder *Pfadoperation* hinzuzufügen, können wir es dem `APIRouter` hinzufügen.

因此，在我们的示例中，我们可以将 `tags` 明确地指定为一个「字符串列表」：

{* ../../docs_src/body_nested_models/tutorial002_py310.py hl[12] *}

## Set 类型

但是随后我们考虑了一下，意识到标签不应该重复，它们很大可能会是唯一的字符串。

Python 具有一种特殊的数据类型来保存一组唯一的元素，即 `set`。

然后我们可以导入 `Set` 并将 `tag` 声明为一个由 `str` 组成的 `set`：

{* ../../docs_src/body_nested_models/tutorial003_py310.py hl[12] *}

这样，即使你收到带有重复数据的请求，这些数据也会被转换为一组唯一项。

而且，每当你输出该数据时，即使源数据有重复，它们也将作为一组唯一项输出。

并且还会被相应地标注 / 记录文档。

## 嵌套模型

Auf diese Weise können Sie einen Token mit einer Gültigkeitsdauer von beispielsweise einer Woche erstellen. Und wenn der Benutzer am nächsten Tag mit dem Token zurückkommt, wissen Sie, dass der Benutzer immer noch bei Ihrem System angemeldet ist.