sessionScope.enter();
        try {
            sessionScope.seed(MavenSession.class, mock(MavenSession.class));

            String artifactBasedir = new File(getBasedir(), "src/test/resources/artifact-install").getAbsolutePath();

            Artifact artifact = createArtifact("artifact", "1.0");

            File file = new File(artifactBasedir, "artifact-1.0.jar");

            ${{ matrix.java }}
            24
          distribution: 'temurin'
          cache: 'maven'
      - name: 'Install'
        shell: bash
        run: ./mvnw -B -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -Dtoolchain.skip install -U -DskipTests=true -f $ROOT_POM
      - name: 'Test'
        shell: bash

## Get started

In this document we will explain in detail on how to configure admin users.

### 1. Prerequisites

- Install mc - [MinIO Client Quickstart Guide](https://docs.min.io/community/minio-object-store/reference/minio-mc.html#quickstart)
- Install MinIO - [MinIO Quickstart Guide](https://docs.min.io/community/minio-object-store/operations/deployments/baremetal-deploy-minio-on-redhat-linux.html)

opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch

libcufft-12-1
libcurand-12-1
libcusolver-dev-12-1
libcusparse-dev-12-1
libcublas-12-1
libcublas-dev-12-1
libnccl-dev=2.18.3-1+cuda12.1
libnccl2=2.18.3-1+cuda12.1
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.8.0.87-1

libcufft-12-3
libcurand-12-3
libcusolver-dev-12-3
libcusparse-dev-12-3
libcublas-12-3
libcublas-dev-12-3
libnccl-dev=2.19.3-1+cuda12.3
libnccl2=2.19.3-1+cuda12.3
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1

/// info | Дополнительная информация

Чтобы использовать формы, сначала установите <a href="https://github.com/Kludex/python-multipart" class="external-link" target="_blank">`python-multipart`</a>.

Например, выполните команду `pip install python-multipart`.

///

## Импорт `Form`

Импортируйте `Form` из `fastapi`:

{* ../../docs_src/request_forms/tutorial001_an_py39.py hl[3] *}

## Определение параметров `Form`

Переконайтеся, що Ви створили [віртуальне середовище](../virtual-environments.md){.internal-link target=_blank}, активували його, і потім встановили бібліотеку, наприклад:

```console
$ pip install python-multipart
```

///

## Імпорт `Form`

Імпортуйте `Form` з `fastapi`:

{* ../../docs_src/request_forms/tutorial001_an_py39.py hl[3] *}

## Оголошення параметрів `Form`

libcufft-12-8
libcurand-12-8
libcusolver-dev-12-8
libcusparse-dev-12-8
libcublas-12-8
libcublas-dev-12-8
libnccl-dev=2.25.1-1+cuda12.8
libnccl2=2.25.1-1+cuda12.8
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.8.0.87-1

libcufft-13-0
libcurand-13-0
libcusolver-dev-13-0
libcusparse-dev-13-0
libcublas-13-0
libcublas-dev-13-0
libnccl-dev=2.27.7-1+cuda13.0
libnccl2=2.27.7-1+cuda13.0
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-13=9.12.0.46-1