@echo "Published new hotfix binaries at https://dl.min.io/server/minio/hotfixes/linux-amd64/archive/minio.$(VERSION)"

docker-hotfix-push: docker-hotfix
	@docker push -q $(TAG) && echo "Published new container $(TAG)"

docker-hotfix: hotfix-push checks ## builds minio docker container with hotfix tags
	@echo "Building minio docker image '$(TAG)'"

            ~/.android/avd/*
            ~/.android/adb*
          key: avd-${{ matrix.api-level }}

      - name: Create AVD and generate snapshot for caching
        if: steps.avd-cache.outputs.cache-hit != 'true'
        uses: reactivecircus/android-emulator-runner@v2
        with:
          api-level: ${{ matrix.api-level }}
          arch: ${{ matrix.api-level == '34' && 'x86_64' || 'x86' }}
          force-avd-creation: false

			"DEVELOPMENT.GOGET is not a valid release tag",
		},
		{
			time.Date(2017, time.August, 5, 0, 0, 53, 0, utcLoc),
			"RELEASE.2017-08-05T00-00-53Z.hotfix", "",
		},
		{
			time.Date(2017, time.August, 5, 0, 0, 53, 0, utcLoc),
			"RELEASE.2017-08-05T00-00-53Z.hotfix.aaaa", "",
		},
	}
	for i, testCase := range testCases {
		if testCase.errStr != "" {
			got := releaseTimeToReleaseTag(testCase.t)

  /* Want to test simple MatMul example with abstract tensors:
    [[1,2],   *  [[5,6],   =   [[19,22],
     [3,4]]       [7,8]]        [43,50]]
  */

  // Build 1st Matrix.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals1[] = {1.0f, 2.0f, 3.0f, 4.0f};

		}
	}

	return string(contentBytes), nil
}

func releaseInfoToReleaseTime(releaseInfo string) (releaseTime time.Time, err error) {
	// Split release of style minio.RELEASE.2019-08-21T19-40-07Z.<hotfix>
	nfields := strings.SplitN(releaseInfo, ".", 2)
	if len(nfields) != 2 {
		err = fmt.Errorf("Unknown release information `%s`", releaseInfo)
		return releaseTime, err
	}
	if nfields[0] != "minio" {

/**
 * Evaluator for plugin parameters expressions. Content surrounded by <code>${</code> and <code>}</code> is evaluated.
 * Recognized values are:
 * <table border="1">
 * <caption>Expression matrix</caption>
 * <tr><th>expression</th>                     <th></th>               <th>evaluation result</th></tr>
 * <tr><td><code>session.*</code></td>         <td></td>               <td></td></tr>

/**
 * Evaluator for plugin parameters expressions. Content surrounded by <code>${</code> and <code>}</code> is evaluated.
 * Recognized values are:
 * <table border="1">
 * <caption>Expression matrix</caption>
 * <tr><th>expression</th>                     <th></th>               <th>evaluation result</th></tr>
 * <tr><td><code>session</code></td>           <td></td>               <td>the actual {@link MavenSession}</td></tr>

* **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.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

					<groupId>org.opensearch.plugin</groupId>
					<artifactId>mapper-extras-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>aggs-matrix-stats-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>rank-eval-client</artifactId>
				</exclusion>
				<exclusion>