Guillaume Nodet <******@****.***> 1729859506 +0200

  <modelVersion>4.0.0</modelVersion>
  <groupId>maven-test</groupId>
  <artifactId>maven-test-a</artifactId>
  <packaging>jar</packaging>
  <version>1.0</version>
  <repositories>
    <repository>
      <id>central</id>
      <name>Fake Maven Central Repository</name>
      <url>file://dummy</url>
    </repository>
  </repositories>
  <dependencies>
    <dependency>
      <groupId>maven-test</groupId>

<project>
  <modelVersion>4.0.0</modelVersion>
  <groupId>maven-test</groupId>
  <artifactId>maven-test-d</artifactId>
  <packaging>jar</packaging>
  <version>1.1</version>

                        </goals>
                    </execution>
                    <execution>
                        <id>test-compile</id>
                        <phase>process-test-sources</phase>
                        <goals>
                            <goal>test-compile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>

Follow below steps to enable and use AlertManager.

## Deploy and start AlertManager

name: 'Dependency Review'
on: [pull_request]

permissions:
  contents: read

jobs:
  dependency-review:
    runs-on: ubuntu-latest
    steps:
      - name: 'Checkout Repository'
        uses: actions/checkout@v4
      - name: 'Dependency Review'

# @format

name: Issue Workflow

on:
  issues:
    types:
      - opened

jobs:
  add-to-project:
    name: Add issue to project
    runs-on: ubuntu-latest
    steps:
      - uses: actions/add-to-project@v0.5.0
        with:
          project-url: https://github.com/orgs/miniohq/projects/2

on:
  schedule:
    - cron: '0 * * * *' # every hour
  workflow_dispatch:

permissions: {}

jobs:
  feedback:
    permissions:
      issues: write
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      # Feedback loop: ask for something on PR/Issue and close if not provided or return to the queue on update.
      # https://github.com/gradle/issue-management-action/blob/main/src/feedback.ts

  proto.SerializeToString(&bytes1);

  const int64_t pts2[] = {1, 3, 5, 7};
  tensorflow::PartialTensorShape(pts2).AsProto(&proto);
  proto.SerializeToString(&bytes2);

  std::unique_ptr<const void*[]> list_ptrs;
  std::unique_ptr<size_t[]> list_lens;
  const std::vector<std::string> list = {bytes1, bytes2};
  StringVectorToArrays(list, &list_ptrs, &list_lens);

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

Das ist dank **Pydantic** ebenfalls möglich, da es [„`dataclasses` intern unterstützt“](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel).