Isso é bastante simples (e não muito útil), mas irá nos ajudar a focar em como as subdependências funcionam.

## Segunda dependência, "injetável" e "dependente"

Então, você pode criar uma outra função para uma dependência (um "injetável") que ao mesmo tempo declara sua própria dependência (o que faz dela um "dependente" também):

//// tab | Python 3.10+

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

**FastAPI** has a very powerful but intuitive **<abbr title="also known as components, resources, providers, services, injectables">Dependency Injection</abbr>** system.

It is designed to be very simple to use, and to make it very easy for any developer to integrate other components with **FastAPI**.

## What is "Dependency Injection"

Guillaume Nodet <******@****.***> 1729859506 +0200

Es ist so konzipiert, sehr einfach zu verwenden zu sein und es jedem Entwickler sehr leicht zu machen, andere Komponenten mit **FastAPI** zu integrieren.

## Was ist „Dependency Injection“

///

```Python hl_lines="10"
{!> ../../docs_src/security/tutorial001.py!}
```

////

This dependency will provide a `str` that is assigned to the parameter `token` of the *path operation function*.

**FastAPI** will know that it can use this dependency to define a "security scheme" in the OpenAPI schema (and the automatic API docs).

/// info | "Technical Details"

Guillaume Nodet <******@****.***> 1729859506 +0200

      - [Windows](#windows)
    - [Dependencies](#dependencies-7)
      - [Changed](#changed-7)
      - [Unchanged](#unchanged)
      - [Removed](#removed-7)
      - [Detailed go Dependency Changes](#detailed-go-dependency-changes)
        - [Added](#added-7)
        - [Changed](#changed-8)
        - [Removed](#removed-8)
- [v1.16.0-rc.2](#v1160-rc2)
  - [Downloads for v1.16.0-rc.2](#downloads-for-v1160-rc2)

        packages that cannot be found unless the user installs them beforehand
        or provides additional installation flags.
    *   This dependency affected anyone installing TensorFlow 2.15 alongside
        NVIDIA CUDA dependencies via `pip install tensorflow[and-cuda]`.
    *   Depending on the installation method, TensorFlow 2.14 would be installed
        instead of 2.15, or users could receive an installation error due to

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:

    *,
    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)