val triggerName = this.triggerName

        params {
            text(
                "branch.qualifier",
                "%dep.${RelativeId("Check_Stage_${triggerName}_Trigger")}.teamcity.build.branch%",
                label = "Branch qualifier for the published distribution version",

    triggerRules = "+:root=${branch.vcsRootId()}:."
    branchFilter = "+:<default>"
}

class PublishNightlySnapshot(
    branch: VersionedSettingsBranch,
) : PublishGradleDistributionFullBuild(
        promotedBranch = branch.branchName,
        prepTask = branch.prepNightlyTaskName(),
        promoteTask = branch.promoteNightlyTaskName(),
        triggerName = "ReadyforNightly",
    ) {

        description =
            "Promotes the latest successful documentation changes on '${branch.branchName}' from Ready for Nightly as a new nightly documentation snapshot"

        triggers {
            branch.determineNightlyPromotionTriggerHour()?.let { triggerHour ->
                schedule {
                    scheduledTrigger(branch, policy = daily { this.hour = triggerHour }, pendingChangesOnly = true)
                }
            }

        buildType(PublishNightlySnapshotFromQuickFeedbackStepPromote(branch))
        buildType(PublishBranchSnapshotFromQuickFeedback)
        buildType(PublishMilestone(branch))

        if (branch.isMaster) {
            buildType(StartReleaseCycle)
            buildType(StartReleaseCycleTest)
        } else {
            buildType(PublishReleaseCandidate(branch))

* **Aprendizado de Máquina**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.
* **Deep Learning**: Esse é um subcampo do Aprendizado de Máquina, então, o mesmo se aplica. A diferença é que não há apenas uma grande planilha com números para multiplicar, mas um grande conjunto delas, e em muitos casos, você utiliza um processador especial para construir e/ou usar esses modelos.

 */

package promotion

import common.VersionedSettingsBranch

class PublishNightlySnapshotFromQuickFeedbackStepPromote(
    branch: VersionedSettingsBranch,
) : BasePublishGradleDistribution(
        promotedBranch = branch.branchName,
        prepTask = branch.prepNightlyTaskName(),
        triggerName = "QuickFeedback",
        cleanCheckout = false,
    ) {
    init {

        cleanup {
            history(days = 180)
        }

        this.init()
    }
}

class PublishFinalRelease(
    branch: VersionedSettingsBranch,
) : PublishRelease(
        promotedBranch = branch.branchName,
        prepTask = "prepFinalRelease",
        promoteTask = branch.promoteFinalReleaseTaskName(),
        requiredConfirmationCode = "final",
        init = {
            id(FINAL_RELEASE_BUILD_CONFIGURATION_ID)

# computes a hash of a remote repo's tag state.
# Tag definitions can come from the .hgtags file stored in
# any head of any branch, and the server protocol does not
# expose the tags directly. However, the protocol does expose
# the hashes of all the branch heads, so we can use a hash of
# all those branch names and heads as a conservative snapshot
# of the entire remote repo state, and use that as the tag sum.

 */

package promotion

import common.VersionedSettingsBranch

class PublishNightlySnapshotFromQuickFeedbackStepUpload(
    branch: VersionedSettingsBranch,
) : BasePublishGradleDistribution(
        promotedBranch = branch.branchName,
        prepTask = branch.prepNightlyTaskName(),
        triggerName = "QuickFeedback",
    ) {
    init {
        id("Promotion_SnapshotFromQuickFeedbackStepUpload")

based on a specific branch. Currently, we have two pipelines: `master` and `release`, but you can easily create
and test another isolated pipeline from any branch. 

We'll explain everything via an example. Let's say you make some changes on your branch `myTestBranch`
(we highly recommend to name this branch without prefix and hyphen (`-`) because it's used to generate build type ID) and want to