title: Update the RBE images to the latest container versions
        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        reviewers: mihaimaruseac,learning-to-play,nitins17
        body: |
          This PR was created by a GitHub Actions workflow to update all the SIG Build-based RBE containers to the most recent containers. See:

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){.internal-link target=_blank}.

## Conclusion { #conclusion }

	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

///

/// tip | 豆知識

"AlexNet"、"ResNet"そして"LeNet"は機械学習<abbr title="Technically, Deep Learning model architectures">モデル</abbr>の名前です。

///

### *パスパラメータ*の宣言

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:

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

### ドキュメントの確認

* **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.

### Memoria por Proceso { #memory-per-process }

Ahora, cuando el programa carga cosas en memoria, por ejemplo, un modelo de Machine Learning en una variable, o el contenido de un archivo grande en una variable, todo eso **consume un poco de la memoria (RAM)** del servidor.

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 |
| -------------------------- |

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.

{* ../../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.

# 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",