## Установка `PyJWT` { #install-pyjwt }

Нам необходимо установить `pyjwt` для генерации и проверки JWT-токенов на языке Python.

Убедитесь, что вы создали [виртуальное окружение](../../virtual-environments.md), активируйте его, а затем установите `pyjwt`:

<div class="termy">

```console
$ pip install pyjwt

---> 100%
```

</div>

- **Rules**: Content processing rules and validation

## Building and Testing

### Build Commands

```bash
# Build all modules
mvn clean install

# Build without tests
mvn clean install -DskipTests

# Build specific module
mvn clean install -pl fess-crawler

# Generate test coverage report
mvn jacoco:report
```

### Code Quality

```bash
# Format code

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-headers-cuda-13=9.15.1.9-1
libcudnn9-static-cuda-13=9.15.1.9-1
libcudnn9-dev-cuda-13=9.15.1.9-1

## Example Lambda handler

Install the necessary dependencies.
```sh
pip install flask requests
```

Following is an example lambda handler.
```py
from flask import Flask, request, abort, make_response
import requests

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

Cependant, si vous utilisez la commande `pip install fastapi`, le package `python-multipart` n'est pas inclus par défaut.

Pour l'installer manuellement, vous devez vous assurer de créer un [environnement virtuel](../../virtual-environments.md), de l'activer, puis de l'installer avec :

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

        uses: actions/checkout@v4
      - name: Set up Go
        uses: actions/setup-go@v5
        with:
          go-version: 1.24.x
          cached: false
      - name: Get official govulncheck
        run: go install golang.org/x/vuln/cmd/govulncheck@latest
        shell: bash
      - name: Run govulncheck
        run: govulncheck -show verbose ./...

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=9.1.1.17-1

일반적인 선택은 Jinja2로, Flask와 다른 도구에서도 사용됩니다.

설정을 쉽게 할 수 있는 유틸리티가 있으며, 이를 **FastAPI** 애플리케이션에서 직접 사용할 수 있습니다(Starlette 제공).

## 의존성 설치 { #install-dependencies }

[가상 환경](../virtual-environments.md)을 생성하고, 활성화한 후 `jinja2`를 설치해야 합니다:

<div class="termy">

```console
$ pip install jinja2

---> 100%
```

</div>

## `Jinja2Templates` 사용하기 { #using-jinja2templates }

* `Jinja2Templates`를 가져옵니다.

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-headers-cuda-13=9.12.0.46-1
libcudnn9-static-cuda-13=9.12.0.46-1
libcudnn9-dev-cuda-13=9.12.0.46-1