```Python hl_lines="18  21  23"
{!../../docs_src/path_params/tutorial005.py!}
```

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.

     * dependency management entries in transitive dependency POMs. Maven 4 enables "transitivity" by default, hence
     * unlike Maven2, obeys dependency management entries deep in dependency graph as well.
     * <br/>
     * Default: <code>"true"</code>.
     *
     * @since 4.0.0
     */
    @Config(defaultValue = "true")

返回给客户端之前，要把枚举元素转换为对应的值（本例中为字符串）：

```Python hl_lines="18  21  23"
{!../../docs_src/path_params/tutorial005.py!}
```

客户端中的 JSON 响应如下：

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

## 包含路径的路径参数

假设*路径操作*的路径为 `/files/{file_path}`。

但需要 `file_path` 中也包含*路径*，比如，`home/johndoe/myfile.txt`。

此时，该文件的 URL 是这样的：`/files/home/johndoe/myfile.txt`。

```Python hl_lines="18  21  23"
{!../../docs_src/path_params/tutorial005.py!}
```

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.

        # - name: confusing-naming
        # - name: unexported-naming
        # - name: early-return
        # - name: unused-parameter
        # - name: unnecessary-stmt
        # - name: deep-exit
        # - name: import-shadowing
        # - name: modifies-value-receiver
        # - name: unused-receiver
        # - name: bare-return
        # - name: flag-parameter
        # - name: unhandled-error

```Python hl_lines="18  21  23"
{!../../docs_src/path_params/tutorial005.py!}
```

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}`.

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.

* **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.

* 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,...

## Developing

If you already cloned the <a href="https://github.com/fastapi/fastapi" class="external-link" target="_blank">fastapi repository</a> and you want to deep dive in the code, here are some guidelines to set up your environment.

### Virtual environment