path_params=dependant.path_params.copy(),
        query_params=dependant.query_params.copy(),
        header_params=dependant.header_params.copy(),
        cookie_params=dependant.cookie_params.copy(),
        body_params=dependant.body_params.copy(),
        name=dependant.name,
        call=dependant.call,
        request_param_name=dependant.request_param_name,

## Zweite Abhängigkeit, „Dependable“ und „Dependant“ { #second-dependency-dependable-and-dependant }

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

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[13] *}

Betrachten wir die deklarierten Parameter:

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

## Second dependency, "dependable" and "dependant" { #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):

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[13] *}

Let's focus on the parameters declared:

## Segunda dependencia, "dependable" y "dependant" { #second-dependency-dependable-and-dependant }

Luego puedes crear otra función de dependencia (un "dependable") que al mismo tiempo declare una dependencia propia (por lo que también es un "dependant"):

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[13] *}

Centrémonos en los parámetros declarados:

def _get_openapi_operation_parameters(
    *,
    dependant: Dependant,
    model_name_map: ModelNameMap,
    field_mapping: dict[
        tuple[ModelField, Literal["validation", "serialization"]], dict[str, Any]
    ],
    separate_input_output_schemas: bool = True,
) -> list[dict[str, Any]]:
    parameters = []
    flat_dependant = get_flat_dependant(dependant, skip_repeats=True)

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:

    return func


@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)

## Segunda dependência, "dependable" e "dependente" { #second-dependency-dependable-and-dependant }

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

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[13] *}

Vamos focar nos parâmetros declarados:

///

### Importar `Depends` { #import-depends }

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

### Declarar la dependencia, en el "dependant" { #declare-the-dependency-in-the-dependant }

De la misma forma en que usas `Body`, `Query`, etc. con los parámetros de tu *path operation function*, usa `Depends` con un nuevo parámetro:

Это довольно просто (хотя и не очень полезно), но поможет нам сосредоточиться на том, как работают подзависимости.

## Вторая зависимость, «зависимость» и «зависимая» { #second-dependency-dependable-and-dependant }

Затем можно создать еще одну функцию зависимости, которая одновременно объявляет свою собственную зависимость (таким образом, она тоже является «зависимой»):

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[13] *}