The new [URL.Clone] method creates a deep copy of a URL.

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip

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

///

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

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

そして、固定値のクラス属性を作ります。すると、その値が使用可能な値となります:

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | 豆知識

"AlexNet"、"ResNet"そして"LeNet"は機械学習<dfn title="厳密には、Deep Learning のモデルアーキテクチャ">モデル</dfn>の名前です。

///

### *パスパラメータ*の宣言 { #declare-a-path-parameter }

次に、作成したenumクラスである`ModelName`を使用した型アノテーションをもつ*パスパラメータ*を作成します:

- name: FileSync
  html_url: https://github.com/polius/FileSync
  stars: 1153
  owner_login: polius
  owner_html_url: https://github.com/polius
- name: enterprise-deep-research
  html_url: https://github.com/SalesforceAIResearch/enterprise-deep-research
  stars: 1150
  owner_login: SalesforceAIResearch
  owner_html_url: https://github.com/SalesforceAIResearch
- name: yubal
  html_url: https://github.com/guillevc/yubal

* <dfn title="A method of machine learning that uses artificial neural networks with numerous hidden layers between input and output layers, thereby developing a comprehensive internal structure">Deep Learning</dfn>

## Headings { #headings }

//// tab | Test

### Develop a webapp - a tutorial { #develop-a-webapp-a-tutorial }

Hello.

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

{* ../../docs_src/path_params/tutorial005_py310.py hl[18,21,23] *}

클라이언트는 아래와 같은 JSON 응답을 얻게 됩니다:

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

## 경로를 포함하는 경로 매개변수 { #path-parameters-containing-paths }

경로 `/files/{file_path}`를 가진 *경로 처리*가 있다고 해봅시다.

하지만 `file_path` 자체가 `home/johndoe/myfile.txt`와 같은 *경로*를 포함해야 합니다.

返回给客户端之前，会把枚举成员转换为对应的值（本例中为字符串）：

{* ../../docs_src/path_params/tutorial005_py310.py hl[18,21,23] *}

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 { #path-parameters-containing-paths }

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`。

First, you might want to see the basic ways to [help FastAPI and get help](help-fastapi.md).

## Developing

If you already cloned the [fastapi repository](https://github.com/fastapi/fastapi) and you want to deep dive in the code, here are some guidelines to set up your environment.

### Install requirements

Create a virtual environment and install the required packages with [`uv`](https://github.com/astral-sh/uv):

* **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.

在回傳給用戶端之前，它們會被轉成對應的值（此例為字串）：

{* ../../docs_src/path_params/tutorial005_py310.py hl[18,21,23] *}

你的用戶端會收到像這樣的 JSON 回應：

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

## 包含路徑的路徑參數 { #path-parameters-containing-paths }

假設你有一個路徑為 `/files/{file_path}` 的「路徑操作」。

但你需要 `file_path` 本身就包含一個「路徑」，像是 `home/johndoe/myfile.txt`。