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":
        return {"model_name": model_name, "message": "LeCNN all the images"}

def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}


def test_get_enums_resnet():
    response = client.get("/models/resnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "resnet", "message": "Have some residuals"}


def test_get_enums_invalid():

Then create class attributes with fixed values, which will be the available valid values:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

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

Luego crea atributos de clase con valores fijos, que serán los valores válidos disponibles:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

///

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

Затем создайте атрибуты класса с фиксированными допустимыми значениями:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Подсказка

Если интересно, то "AlexNet", "ResNet" и "LeNet" - это названия <abbr title="Технически, архитектуры моделей глубокого обучения">моделей</abbr> Машинного обучения.

///

### Определение *параметра пути* { #declare-a-path-parameter }

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}


/// tip | Tipp

Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <abbr title="Genau genommen, Deep-Learning-Modellarchitekturen">Modellen</abbr> für maschinelles Lernen.

///

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

Em seguida, crie atributos de classe com valores fixos, que serão os valores válidos disponíveis:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Dica
Se você está se perguntando, "AlexNet", "ResNet" e "LeNet" são apenas nomes de <abbr title="Tecnicamente, arquiteturas de modelos de Deep Learning">modelos</abbr> de Aprendizado de Máquina.
///

### Declare um parâmetro de path { #declare-a-path-parameter }

	if buf.Len() > m {
		t.Log("WARNING: TestEncodeDecodensScannerOptions Msgsize() is inaccurate")
	}

	vn := nsScannerOptions{}
	err := msgp.Decode(&buf, &vn)
	if err != nil {
		t.Error(err)
	}

	buf.Reset()
	msgp.Encode(&buf, &v)
	err = msgp.NewReader(&buf).Skip()
	if err != nil {
		t.Error(err)
	}
}

func BenchmarkEncodensScannerOptions(b *testing.B) {
	v := nsScannerOptions{}
	var buf bytes.Buffer

		enableMultipart: enableMultipart,
		creds:           u.Credentials,
	})
	if err != nil {
		result.Error = err.Error()
	}

	done(nil)
	peersLogIf(r.Context(), gob.NewEncoder(w).Encode(result))
}

// GetLastDayTierStatsHandler - returns per-tier stats in the last 24hrs for this server
func (s *peerRESTServer) GetLastDayTierStatsHandler(_ *grid.MSS) (*DailyAllTierStats, *grid.RemoteErr) {

	if err != nil {
		return SpeedTestResult{}, err
	}

	var result SpeedTestResult
	err = gob.NewDecoder(waitReader).Decode(&result)
	if err != nil {
		return result, err
	}
	if result.Error != "" {
		return result, errors.New(result.Error)
	}
	return result, nil
}