final FessConfig fessConfig = ComponentUtil.getFessConfig();
        final String modelName = fessConfig.getLtrModelName();
        if (StringUtil.isBlank(modelName)) {
            return null;
        }
        return new QueryRescorerBuilder(new StoredLtrQueryBuilder().modelName(modelName).params(params))
                .windowSize(fessConfig.getLtrWindowSize());
    }

                            "items": {"$ref": "#/components/schemas/ValidationError"},
                        }
                    },
                },
                "ModelName": {
                    "title": "ModelName",
                    "enum": ["alexnet", "resnet", "lenet"],
                    "type": "string",
                },
                "ValidationError": {

        pytest.param("tutorial008_an_py39", marks=pytest.mark.xfail),
    ],
)
def get_module(request: pytest.FixtureRequest):
    mod_name = f"docs_src.dependencies.{request.param}"
    mod = importlib.import_module(mod_name)
    return mod


def test_get_db(module: ModuleType):
    app = FastAPI()

    @app.get("/")
    def read_root(c: Annotated[Any, Depends(module.dependency_c)]):

var globalVeeamForceSC = os.Getenv("_MINIO_VEEAM_FORCE_SC")

type systemInfo struct {
	XMLName              xml.Name `xml:"SystemInfo" json:"-"`
	ProtocolVersion      string   `xml:"ProtocolVersion"`
	ModelName            string   `xml:"ModelName"`
	ProtocolCapabilities struct {
		CapacityInfo   bool `xml:"CapacityInfo"`
		UploadSessions bool `xml:"UploadSessions"`
		IAMSTS         bool `xml:"IAMSTS"`
	} `mxl:"ProtocolCapabilities"`

///

### Einen *Pfad-Parameter* deklarieren { #declare-a-path-parameter }

Dann erstellen Sie einen *Pfad-Parameter*, der als Typ die gerade erstellte Enum-Klasse hat (`ModelName`):

{* ../../docs_src/path_params/tutorial005_py39.py hl[16] *}

### Die API-Dokumentation testen { #check-the-docs }

  TF_Graph* parent;
  TF_Output* parent_inputs;
};

struct TF_OperationDescription {
  TF_OperationDescription(TF_Graph* g, const char* op_type,
                          const char* node_name)
      : node_builder(node_name, op_type, g->graph.op_registry()), graph(g) {}

  tensorflow::NodeBuilder node_builder;
  TF_Graph* graph;
  std::set<tensorflow::string> colocation_constraints;
};

struct TF_Operation {

To access MinIO from localhost, run the below commands:

  1. export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "release={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}")

  2. kubectl port-forward $POD_NAME 9000 --namespace {{ .Release.Namespace }}

Read more about port forwarding here: http://kubernetes.io/docs/user-guide/kubectl/kubectl_port-forward/

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

{* ../../docs_src/path_params/tutorial005_py39.py hl[16] *}

### Check the docs { #check-the-docs }

///

### Declarar un *path parameter* { #declare-a-path-parameter }

Luego crea un *path parameter* con una anotación de tipo usando la clase enum que creaste (`ModelName`):

{* ../../docs_src/path_params/tutorial005_py39.py hl[16] *}

### Revisa la documentación { #check-the-docs }

///

### *경로 매개변수* 선언

생성한 열거형 클래스(`ModelName`)를 사용하는 타입 어노테이션으로 *경로 매개변수*를 만듭니다:

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

### 문서 확인

*경로 매개변수*에 사용할 수 있는 값은 미리 정의되어 있으므로 대화형 문서에서 잘 표시됩니다:

<img src="/img/tutorial/path-params/image03.png">

### 파이썬 *열거형*으로 작업하기

*경로 매개변수*의 값은 *열거형 멤버*가 됩니다.

#### *열거형 멤버* 비교

열거형 `ModelName`의 *열거형 멤버*를 비교할 수 있습니다: