클라이언트에 반환하기 전에 해당 값(이 경우 문자열)으로 변환됩니다:

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

클라이언트는 아래의 JSON 응답을 얻습니다:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## 경로를 포함하는 경로 매개변수

경로를 포함하는 *경로 작동* `/files/{file_path}`이 있다고 해봅시다.

그런데 이 경우 `file_path` 자체가 `home/johndoe/myfile.txt`와 같은 경로를 포함해야 합니다.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

      return;
    } finally {
      function = null;
    }

    /*
     * If set()/setValue() throws an Error, we let it propagate. Why? The most likely Error is a
     * StackOverflowError (from deep transform(..., directExecutor()) nesting), and calling
     * setException(stackOverflowError) would fail:
     *
     * - If the stack overflowed before set()/setValue() could even store the result in the output

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

İstemci tarafında şuna benzer bir JSON yanıtı ile karşılaşırsınız:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Yol İçeren Yol Parametreleri

Farz edelim ki elinizde `/files/{file_path}` isminde bir *yol operasyonu* var.

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

Le client recevra une réponse JSON comme celle-ci :

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Paramètres de chemin contenant des chemins

Disons que vous avez une *fonction de chemin* liée au chemin `/files/{file_path}`.

* L'apprentissage profond (ou **Deep Learning**) : est un sous-domaine du **Machine Learning**, donc les mêmes raisons s'appliquent. Avec la différence qu'il n'y a pas une unique feuille de calcul de nombres à multiplier, mais une énorme quantité d'entre elles, et dans de nombreux cas,...

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.

        return new SmbResourceLocatorImpl(ctx, smbUrl(spec));
    }

    @Test
    @DisplayName("getName returns last path segment; falls back logically")
    void testGetNameVariants() {
        // file name from deep path
        SmbResourceLocatorImpl a = locator("smb://server/share/dir/file.txt");
        assertEquals("file.txt", a.getName());

        // share name with trailing slash when only share present

            return null;
        }

        @Override
        public boolean hasChildNodes() {
            return false;
        }

        @Override
        public Node cloneNode(boolean deep) {
            return null;
        }

        @Override
        public void normalize() {
        }

        @Override
        public boolean isSupported(String feature, String version) {

* **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.