///

### 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

			if err != nil {
				return err
			}
			var podName, podNs string
			var podLabels, nsLabels map[string]string
			if len(args) == 1 {
				podName, podNs, err = ctx.InferPodInfoFromTypedResource(args[0], ctx.Namespace())
				if err != nil {
					return err
				}
				pod, err := kubeClient.Kube().CoreV1().Pods(podNs).Get(context.TODO(), podName, metav1.GetOptions{})
				if err != nil {
					return err
				}

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/

// getK8sPodInfo returns information of a POD
func getK8sPodInfo(client kubernetes.Interface, podName, podNamespace string) (*PodInfo, error) {
	pod, err := client.CoreV1().Pods(podNamespace).Get(context.TODO(), podName, metav1.GetOptions{})
	if err != nil {
		return nil, err
	}

	pi := ExtractPodInfo(pod)
	log.Debugf("Pod %v/%v info: \n%+v", podNamespace, podName, pi)

	return pi, nil
}

func ExtractPodInfo(pod *v1.Pod) *PodInfo {

///

### 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

///

/// 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:

///

### Определение *параметра пути*

Определите *параметр пути*, используя в аннотации типа класс перечисления (`ModelName`), созданный ранее:

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

### Проверьте документацию

var ErrNotImplemented = errors.New("not implemented")

// Program defines a method which programs iptables based on the parameters
// provided in Redirect.
func (ipt *iptables) Program(podName, netns string, rdrct *Redirect) error {
	return ErrNotImplemented

	}

	ns := NamingStrategy{}
	for key, value := range maps {
		if ns.toDBName(key) != value {
			t.Errorf("%v toName should equal %v, but got %v", key, value, ns.toDBName(key))
		}
	}

	maps = map[string]string{
		"x":                              "X",
		"user_restrictions":              "UserRestriction",