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:

        result.getTasks().add(new GoalTask("t2"));
        return result;
    }

    class Dependency {
        MavenProject dependant;

        MavenProject dependency;

        Dependency(MavenProject dependant, MavenProject dependency) {
            this.dependant = dependant;
            this.dependency = dependency;
        }

        void addIfDownstream(MavenProject mavenProject, List<MavenProject> result) {

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)

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

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

### Die Abhängigkeit im <abbr title="Das Abhängige, der Verwender der Abhängigkeit">„Dependant“</abbr> deklarieren { #declare-the-dependency-in-the-dependant }

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

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

///

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

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

### Declare the dependency, in the "dependant" { #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] *}