}

    public String modelName() {
        return modelName;
    }

    public StoredLtrQueryBuilder modelName(final String modelName) {
        this.modelName = Objects.requireNonNull(modelName);
        return this;
    }

    public String featureSetName() {
        return featureSetName;
    }

    public StoredLtrQueryBuilder featureSetName(final String featureSetName) {

from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":

        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": IsDict(
                    {
                        "title": "ModelName",
                        "enum": ["alexnet", "resnet", "lenet"],
                        "type": "string",
                    }
                )
                | IsDict(

⤴️ ✍ *➡ 🔢* ⏮️ 🆎 ✍ ⚙️ 🔢 🎓 👆 ✍ (`ModelName`):

```Python hl_lines="16"
{!../../docs_src/path_params/tutorial005.py!}
```

### ✅ 🩺

↩️ 💪 💲 *➡ 🔢* 🔢, 🎓 🩺 💪 🎦 👫 🎆:

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

### 👷 ⏮️ 🐍 *🔢*

💲 *➡ 🔢* 🔜 *🔢 👨‍🎓*.

#### 🔬 *🔢 👨‍🎓*

👆 💪 🔬 ⚫️ ⏮️ *🔢 👨‍🎓* 👆 ✍ 🔢 `ModelName`:

```Python hl_lines="17"

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"`

///

### Declare a *path parameter*

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

```Python hl_lines="16"
{!../../docs_src/path_params/tutorial005.py!}
```

### Check the docs

///

### Declara un *parámetro de path*

Luego, crea un *parámetro de path* con anotaciones de tipos usando la clase enum que creaste (`ModelName`):

```Python hl_lines="16"
{!../../docs_src/path_params/tutorial005.py!}
```

### Revisa la documentación

///

### Deklarieren Sie einen *Pfad-Parameter*

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

```Python hl_lines="16"
{!../../docs_src/path_params/tutorial005.py!}
```

### Testen Sie es in der API-Dokumentation

///

/// tip | "팁"

혹시 궁금하다면, "AlexNet", "ResNet", 그리고 "LeNet"은 그저 기계 학습 <abbr title="기술적으로 정확히는 딥 러닝 모델 구조">모델</abbr>들의 이름입니다.

///

### *경로 매개변수* 선언

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

```Python hl_lines="16"
{!../../docs_src/path_params/tutorial005.py!}
```

### 문서 확인

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