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!}
```

////

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## First Steps

Let's see a very simple example. It will be so simple that it is not very useful, for now.

But this way we can focus on how the **Dependency Injection** system works.

### Create a dependency, or "dependable"

Let's first focus on the dependency.

It is just a function that can take all the same parameters that a *path operation function* can take:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[8:9] *}

Aber so können wir uns besser auf die Funktionsweise des **Dependency Injection** Systems konzentrieren.

### Erstellen Sie eine Abhängigkeit (<abbr title="Das von dem abhängt, die zu verwendende Abhängigkeit">„Dependable“</abbr>)

Konzentrieren wir uns zunächst auf die Abhängigkeit - die Dependency.

Es handelt sich einfach um eine Funktion, die die gleichen Parameter entgegennimmt wie eine *Pfadoperation-Funktion*:
//// tab | Python 3.10+

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)

このパラメータは関数のようなものである必要があります。

そして、その関数は、*path operation関数*が行うのと同じ方法でパラメータを取ります。

/// tip | "豆知識"

次の章では、関数以外の「もの」が依存関係として使用できるものを見ていきます。

///

新しいリクエストが到着するたびに、**FastAPI** が以下のような処理を行います:

* 依存関係（"dependable"）関数を正しいパラメータで呼び出します。
* 関数の結果を取得します。
* *path operation関数*のパラメータにその結果を代入してください。

```mermaid
graph TB

common_parameters(["common_parameters"])
read_items["/items/"]
read_users["/users/"]

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:

# Security Tools

When you need to declare dependencies with OAuth2 scopes you use `Security()`.

But you still need to define what is the dependable, the callable that you pass as a parameter to `Depends()` or `Security()`.

There are multiple tools that you can use to create those dependables, and they get integrated into OpenAPI so they are shown in the automatic docs UI, they can be used by automatically generated clients and SDKs, etc.