<artifactId>maven-fluido-skin</artifactId>
    <version>1.11.2</version>
  </skin>

  <edit>${project.scm.url}</edit>

  <custom>
    <matomo>
      <siteId>3</siteId>
      <url>https://analytics.apache.org/</url>
      <options>
        <disableCookies/>
        <trackPageView/>
        <enableLinkTracking/>
      </options>
    </matomo>
  </custom>

  <publishDate position="right" />

	app.Name = name
	app.Author = "MinIO, Inc."
	app.Version = ReleaseTag
	app.Usage = "High Performance Object Storage"
	app.Description = `Build high performance data infrastructure for machine learning, analytics and application data workloads with MinIO`
	app.Flags = GlobalFlags
	app.HideHelpCommand = true // Hide `help, h` command, we already have `minio --help`.
	app.Commands = commands

		methods: []string{http.MethodDelete, http.MethodPut, http.MethodGet},
		queries: []string{"intelligent-tiering", ""},
	},
	{
		api:     "analytics",
		methods: []string{http.MethodDelete, http.MethodPut, http.MethodGet},
		queries: []string{"analytics", ""},
	},
}

// Set of s3 handler options as bit flags.
type s3HFlag uint8

const (
	// when provided, disables Gzip compression.

      class: mermaid
      format: !!python/name:pymdownx.superfences.fence_code_format ''
  pymdownx.tabbed:
    alternate_style: true
  pymdownx.tilde: null
  attr_list: null
  md_in_html: null
extra:
  analytics:
    provider: google
    property: G-YNEVN69SC3
  social:
  - icon: fontawesome/brands/github-alt
    link: https://github.com/tiangolo/fastapi
  - icon: fontawesome/brands/discord

    title: Building simple E-Commerce with NuxtJS and FastAPI
  - author: Rodrigo Arenas
    author_link: https://rodrigo-arenas.medium.com/
    link: https://medium.com/analytics-vidhya/serve-a-machine-learning-model-using-sklearn-fastapi-and-docker-85aabf96729b
    title: "Serve a machine learning model using Sklearn, FastAPI and Docker"
  - author: Yashasvi Singh

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

    - name: Compile with Gradle with Build Scan
      if: ${{ matrix.language == 'java' && github.repository_owner == 'gradle' }}
      run: ./gradlew --no-configuration-cache --init-script .github/workflows/codeql-analysis.init.gradle -DcacheNode=us -S testClasses -Dhttp.keepAlive=false
      env:
        # Set the GRADLE_ENTERPRISE_ACCESS_KEY so that Gradle Build Scans are generated

  <xi:include href="/META-INF/analysis-api/analysis-api-impl-base.xml"/>
  <xi:include href="/META-INF/analysis-api/low-level-api-fir.xml"/>
  <xi:include href="/META-INF/analysis-api/symbol-light-classes.xml"/>

  <xi:include href="/META-INF/extensions/compiler.xml"/>

  <extensions defaultExtensionNs="com.intellij">
    <projectService
        serviceInterface="org.jetbrains.kotlin.analysis.api.session.KtAnalysisSessionProvider"

        key: ${{ runner.os }}-m2-${{ hashFiles('**/pom.xml') }}
        restore-keys: ${{ runner.os }}-m2
    - name: Build with Maven
      run: mvn -B package --file pom.xml

    - name: Perform CodeQL Analysis

<component name="ProjectRunConfigurationManager">
  <configuration default="false" name="Generate Analysis Tests" type="GradleRunConfiguration" factoryName="Gradle" folderName="Analysis">
    <ExternalSystemSettings>
      <option name="executionName" />
      <option name="externalProjectPath" value="$PROJECT_DIR$/generators/analysis-api-generator" />
      <option name="externalSystemIdString" value="GRADLE" />
      <option name="scriptParameters" value="" />