//// tab | Python 3.9+

```Python hl_lines="1"
async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]):
    return {"fresh_value": fresh_value}
```

////

//// tab | Python 3.9+ non-Annotated

/// tip | Dica

Utilize a versão com `Annotated` se possível.

///

Aquí tienes una idea general de cómo podrían ser los modelos con sus campos de contraseña y los lugares donde se utilizan:

{* ../../docs_src/extra_models/tutorial001_py310.py hl[7,9,14,20,22,27:28,31:33,38:39] *}

/// info | Información

En Pydantic v1 el método se llamaba `.dict()`, fue deprecado (pero aún soportado) en Pydantic v2, y renombrado a `.model_dump()`.

- [3.9.x](https://github.com/apache/maven/tree/maven-3.9.x): [![Maven Central](https://img.shields.io/maven-central/v/org.apache.maven/apache-maven.svg?label=Maven%20Central&versionPrefix=3.)](https://central.sonatype.com/artifact/org.apache.maven/apache-maven)

    placeholder: e.g., Linux Ubuntu 16.04 validations: required: false

-   type: input id: Python attributes: label: Python version description:
    placeholder: e.g., 3.9 validations: required: false

-   type: input id: Bazel attributes: label: Bazel version description: if
    compiling from source placeholder: validations: required: false

이를 위해 표준 Python의 `typing.List`를 사용하세요(또는 Python 3.9 이상에서는 단순히 `list`를 사용할 수 있습니다):

{* ../../docs_src/extra_models/tutorial004_py39.py hl[18] *}


## 임의의 `dict` 응답

Pydantic 모델을 사용하지 않고, 키와 값의 타입만 선언하여 평범한 임의의 `dict`로 응답을 선언할 수도 있습니다.

이는 Pydantic 모델에 필요한 유효한 필드/속성 이름을 사전에 알 수 없는 경우에 유용합니다.

이 경우, `typing.Dict`를 사용할 수 있습니다(또는 Python 3.9 이상에서는 단순히 `dict`를 사용할 수 있습니다):

 */
@Deprecated(since = "4.0.0")
public interface ProfileActivationContext {
    /**
     * Key of the property containing the project's packaging.
     * Available in {@link #getUserProperties()}.
     * @since 3.9
     */
    String PROPERTY_NAME_PACKAGING = "packaging";

    /**
     * Gets the identifiers of those profiles that should be activated by explicit demand.
     *

    local_wheel_dist_folder = "dist",
    local_wheel_inclusion_list = [
        "tensorflow*",
        "tf_nightly*",
    ],
    local_wheel_workspaces = ["//:WORKSPACE"],
    requirements = {
        "3.9": "//:requirements_lock_3_9.txt",
        "3.10": "//:requirements_lock_3_10.txt",
        "3.11": "//:requirements_lock_3_11.txt",
        "3.12": "//:requirements_lock_3_12.txt",
        "3.13": "//:requirements_lock_3_13.txt",

        + "'round':4,"
        + "'lastSaved':1367702411696,"
        + "'dateStarted':1367702378785,"
        + "'players':["
        + "{'name':'" + player1 + "','history':[10,8,6,7,8],'color':-13388315,'total':39},"
        + "{'name':'" + player2 + "','history':[6,10,5,10,10],'color':-48060,'total':41}"
        + "]}";
  }

  public static void main(String[] args) throws IOException {
    PostExample example = new PostExample();

    id: Mobile
    attributes:
      label: Mobile device
      placeholder: e.g., Linux Ubuntu 16.04
  - type: input
    id: Python
    attributes:
      label: Python version
      placeholder: e.g., 3.9
  - type: input
    id: Bazel
    attributes:
      label: Bazel version
      description: If compiling from source
  - type: input
    id: Compiler
    attributes:
      label: GCC/compiler version

name = "fastapi"
dynamic = ["version"]
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
readme = "README.md"
license = "MIT"
license-files = ["LICENSE"]
requires-python = ">=3.9"
authors = [
    { name = "Sebastián Ramírez", email = "******@****.***" },
]
classifiers = [
    "Intended Audience :: Information Technology",
    "Intended Audience :: System Administrators",