🏧 😐 🌐❔ 👆 💭 👆 💪 🔗 🤙 🔠 🔁 (🎲 💗 🕰) 🎏 📨 ↩️ ⚙️ "💾" 💲, 👆 💪 ⚒ 🔢 `use_cache=False` 🕐❔ ⚙️ `Depends`:

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

## 🌃

↖️ ⚪️➡️ 🌐 🎀 🔤 ⚙️ 📥, **🔗 💉** ⚙️ 🙅.

🔢 👈 👀 🎏 *➡ 🛠️ 🔢*.

Para esses casos, em vez de declarar um parâmetro em uma *função de operação de rota* com `Depends`, você pode adicionar um argumento `dependencies` do tipo `list` ao decorador da operação de rota.

## Adicionando `dependencies` ao decorador da operação de rota

O *decorador da operação de rota* recebe um argumento opcional `dependencies`.

Ele deve ser uma lista de `Depends()`:

//// tab | Python 3.9+

```Python hl_lines="19"

`Security` ist tatsächlich eine Unterklasse von `Depends` und hat nur noch einen zusätzlichen Parameter, den wir später kennenlernen werden.

Durch die Verwendung von `Security` anstelle von `Depends` weiß **FastAPI** jedoch, dass es Sicherheits-Scopes deklarieren, intern verwenden und die API mit OpenAPI dokumentieren kann.

///

```Python hl_lines="19-22  26-27"
{!> ../../docs_src/security/tutorial002.py!}
```

////

## Inject the current user

So now we can use the same `Depends` with our `get_current_user` in the *path operation*:

//// tab | Python 3.10+

```Python hl_lines="31"
{!> ../../docs_src/security/tutorial002_an_py310.py!}
```

////

//// tab | Python 3.9+

{!../../docs_src/security/tutorial002.py!}
```

## 注入当前用户

在*路径操作* 的 `Depends` 中使用 `get_current_user`：

```Python hl_lines="31"
{!../../docs_src/security/tutorial002.py!}
```

注意，此处把 `current_user` 的类型声明为 Pydantic 的 `User` 模型。

这有助于在函数内部使用代码补全和类型检查。

/// tip | "提示"

还记得请求体也是使用 Pydantic 模型声明的吧。

放心，因为使用了 `Depends`，**FastAPI** 不会搞混。

///

/// check | "检查"

      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.
      bazel query "rdeps(kind(py_test, $(cat $BATS_TEST_TMPDIR/deps)), $dep, 1)"
    done < $BATS_TEST_TMPDIR/missing_deps
    exit 1
  fi
}

# The Python package is not allowed to depend on any CUDA packages.

 *
 *
 */
@Deprecated
public class MetadataGraphNode {
    /** node payload */
    MavenArtifactMetadata metadata;

    /** nodes, incident to this (depend on me) */
    List<MetadataGraphNode> inNodes;

    /** nodes, exident to this (I depend on) */
    List<MetadataGraphNode> exNodes;

    public MetadataGraphNode() {
        inNodes = new ArrayList<>(4);
        exNodes = new ArrayList<>(8);
    }

## 创建实例

使用以下代码创建类实例：

```Python hl_lines="16"
{!../../docs_src/dependencies/tutorial011.py!}
```

这样就可以**参数化**依赖项，它包含 `checker.fixed_content` 的属性 - `"bar"`。

## 把实例作为依赖项

然后，不要再在 `Depends(checker)` 中使用 `Depends(FixedContentQueryChecker)`， 而是要使用 `checker`，因为依赖项是类实例 - `checker`，不是类。

处理依赖项时，**FastAPI** 以如下方式调用 `checker`：

```Python
checker(q="somequery")
```

……并用*路径操作函数*的参数 `fixed_content_included` 返回依赖项的值：

)

# To avoid duplication, check that the C++ or python library does not depend on
# the stream executor cuda plugins. Targets that want to use cuda APIs should
# instead depend on the dummy plugins in @local_tsl//tsl/platform/default/build_config
# and use header only targets.
# TODO(ddunleavy): This seems completely broken. :tensorflow_cc depends on
# cuda_platform from tf_additional_binary_deps and this doesn't break.
check_deps(

## Die Instanz als Abhängigkeit verwenden

Dann könnten wir diesen `checker` in einem `Depends(checker)` anstelle von `Depends(FixedContentQueryChecker)` verwenden, da die Abhängigkeit die Instanz `checker` und nicht die Klasse selbst ist.

Und beim Auflösen der Abhängigkeit ruft **FastAPI** diesen `checker` wie folgt auf:

```Python