<!--- Provide a link to a live example, or an unambiguous set of steps to -->
<!--- reproduce this bug. Include code to reproduce, if relevant -->
<!--- and make sure you have followed https://github.com/minio/minio/tree/release/docs/debugging to capture relevant logs -->

1.
2.
3.
4.

## Context
<!--- How has this issue affected you? What are you trying to accomplish? -->

    }

    @Override
    public String getVersion() {
        return "";
    }

    @Override
    public Nature getNature() {
        return artifact.isSnapshot() ? Nature.RELEASE_OR_SNAPSHOT : Nature.RELEASE;
    }

most of the command line arguments and features supported by those tools will be
acknowledged by the Bazel requirements updater command as well. For example, if
you wish the updater to consider pre-release versions simply pass `--pre`
argument to the bazel command:

```
bazel run //ci/official/requirements_updater:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12 -- --pre
```

			</plugin>
		</plugins>
	</build>
	<repositories>
		<repository>
			<id>snapshots.central.sonatype.com</id>
			<url>https://central.sonatype.com/repository/maven-snapshots</url>
			<releases>
				<enabled>false</enabled>
			</releases>
			<snapshots>
				<enabled>true</enabled>
			</snapshots>
		</repository>
	</repositories>
	<dependencies>
		<dependency>
			<groupId>com.ibm.icu</groupId>

    #    and modify them (or add more) to build your code if your project
    #    uses a compiled language

    #- run: |
    #   make bootstrap
    #   make release

    - name: Perform CodeQL Analysis

     */
    protected
    fun setupPluginRequirements() {
        projectDir.resolve("version.txt").writeText("9999999.0") // All released versions should be lower than this
        projectDir.resolve("released-versions.json").writeText(
            """
                {
                  "latestReleaseSnapshot": {
                    "version": "7.6-20220831090819+0000",

developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications....

	resp := lockRPCLock.NewResponse()
	success, err := l.ll.Lock(ctx, *args)
	if err == nil && !success {
		return l.makeResp(resp, errLockConflict)
	}
	return l.makeResp(resp, err)
}

// UnlockHandler - releases the acquired lock.
func (l *lockRESTServer) UnlockHandler(args *dsync.LockArgs) (*dsync.LockResp, *grid.RemoteErr) {
	resp := lockRPCUnlock.NewResponse()
	_, err := l.ll.Unlock(context.Background(), *args)

export MC_HOST_myminio1="http://dillon-service-2:dillon-service-2@localhost:22000"

# Start MinIO instance
export CI=true

if [ ! -f ./mc ]; then
	wget --quiet -O mc https://dl.minio.io/client/mc/release/linux-amd64/mc &&
		chmod +x mc
fi

mc -v

# Start openid server
(
	cd ./minio-iam-testing
	make docker-images
	make docker-run
	cd -
)

        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - name: Decide whether the needed jobs succeeded or failed
        uses: re-actors/alls-green@release/v1
        with: