def get_flat_dependant(
    dependant: Dependant,
    *,
    skip_repeats: bool = False,
    visited: Optional[List[CacheKey]] = None,
) -> Dependant:
    if visited is None:
        visited = []
    visited.append(dependant.cache_key)

    flat_dependant = Dependant(
        path_params=dependant.path_params.copy(),
        query_params=dependant.query_params.copy(),

    *,
    dependant: Dependant,
    schema_generator: GenerateJsonSchema,
    model_name_map: ModelNameMap,
    field_mapping: Dict[
        Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
    ],
    separate_input_output_schemas: bool = True,
) -> List[Dict[str, Any]]:
    parameters = []
    flat_dependant = get_flat_dependant(dependant, skip_repeats=True)

@dataclass
class Dependant:
    path_params: List[ModelField] = field(default_factory=list)
    query_params: List[ModelField] = field(default_factory=list)
    header_params: List[ModelField] = field(default_factory=list)
    cookie_params: List[ModelField] = field(default_factory=list)
    body_params: List[ModelField] = field(default_factory=list)
    dependencies: List["Dependant"] = field(default_factory=list)

## Zweite Abhängigkeit, „Dependable“ und „Dependant“

Dann können Sie eine weitere Abhängigkeitsfunktion (ein „Dependable“) erstellen, die gleichzeitig eine eigene Abhängigkeit deklariert (also auch ein „Dependant“ ist):

//// tab | Python 3.10+

```Python hl_lines="13"
{!> ../../docs_src/dependencies/tutorial005_an_py310.py!}
```

////

async def run_endpoint_function(
    *, dependant: Dependant, values: Dict[str, Any], is_coroutine: bool
) -> Any:
    # Only called by get_request_handler. Has been split into its own function to
    # facilitate profiling endpoints, since inner functions are harder to profile.
    assert dependant.call is not None, "dependant.call must be a function"

    if is_coroutine:

This is quite simple (not very useful), but will help us focus on how the sub-dependencies work.

## Second dependency, "dependable" and "dependant"

Then you can create another dependency function (a "dependable") that at the same time declares a dependency of its own (so it is a "dependant" too):

//// tab | Python 3.10+

```Python hl_lines="13"
{!> ../../docs_src/dependencies/tutorial005_an_py310.py!}
```

////

     * address, isPermanent, isBeingDeleted, ...etc. This information can only
     * be retrieved with the Node Status request.
     * 
     * The degree of state that an NbtAddress has is dependant on how it was
     * created and what is required of it. The second degree of state is the
     * most common. This is the state information that would be retrieved from

///

### Import `Depends`

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[3] *}

### Declare the dependency, in the "dependant"

The same way you use `Body`, `Query`, etc. with your *path operation function* parameters, use `Depends` with a new parameter:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[13,18] *}

///

```Python hl_lines="3"
{!> ../../docs_src/dependencies/tutorial001.py!}
```

////

### Deklarieren der Abhängigkeit im <abbr title="Das Abhängige, der Verwender der Abhängigkeit">„Dependant“</abbr>

So wie auch `Body`, `Query`, usw., verwenden Sie `Depends` mit den Parametern Ihrer *Pfadoperation-Funktion*:

//// tab | Python 3.10+

```Python hl_lines="13  18"

* オプショナルのクエリパラメータ`limit`は`int`で、デフォルトは`100`です。

そして、これらの値を含む`dict`を返します。

### `Depends`のインポート

```Python hl_lines="3"
{!../../docs_src/dependencies/tutorial001.py!}
```

### "dependant"での依存関係の宣言

*path operation関数*のパラメータに`Body`や`Query`などを使用するのと同じように、新しいパラメータに`Depends`を使用することができます:

```Python hl_lines="13  18"
{!../../docs_src/dependencies/tutorial001.py!}
```