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:

	app := cli.NewApp()
	app.Name = name
	app.Author = "MinIO, Inc."
	app.Version = ReleaseTag
	app.Usage = "High Performance Object Storage"
	app.Description = `Build high performance data infrastructure for machine learning, analytics and application data workloads with MinIO`
	app.Flags = GlobalFlags
	app.HideHelpCommand = true // Hide `help, h` command, we already have `minio --help`.
	app.Commands = commands

It would be roughly equivalent to typing:

<div class="termy">

```console
$ C:\opt\custompython\bin\python
```

</div>

////

This information will be useful when learning about [Virtual Environments](virtual-environments.md).

## Conclusion { #conclusion }

With this you should have a basic understanding of what **environment variables** are and how to use them in Python.

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.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

| IMPORTANT |
| -------------------------- |

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

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`。

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`。

**FastAPI** is all based on these type hints, they give it many advantages and benefits.

But even if you never use **FastAPI**, you would benefit from learning a bit about them.

/// note

If you are a Python expert, and you already know everything about type hints, skip to the next chapter.

///

## Motivation { #motivation }

Let's start with a simple example:

# Tests

tf_cuda_library(
    name = "c_test_util",
    testonly = 1,
    srcs = ["c_test_util.cc"],
    hdrs = ["c_test_util.h"],
    visibility = [
        "//learning/brain:__subpackages__",
        "//tensorflow:__subpackages__",
    ],
    deps = [
        ":c_api",
        ":c_api_experimental",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",