}

    /**
     * @throws Exception
     */
    public void testFindFractionDelimeter2() throws Exception {
        final String delim = NumberConversionUtil.findDecimalSeparator(Locale.FRANCE);
        assertEquals(",", delim);
    }

    /**
     * @throws Exception
     */
    public void testFindFractionDelimeter3() throws Exception {

    }

    public void test_determineAlias_multipleLocales() {
        // Test with various locale configurations
        Locale[] testLocales = { Locale.US, Locale.UK, Locale.CANADA, Locale.FRANCE, Locale.CHINA, Locale.KOREA, new Locale("es", "ES"),
                new Locale("pt", "BR") };

        for (Locale locale : testLocales) {
            OptionalThing<String> result = provider.determineAlias(locale);

		{"test-bucket-list-object", "Asia", "A", "", 1, resultCases[4], nil, true},
		// Setting a non-existing directory to be prefix (12-13).
		{"empty-bucket", "europe/france/", "", "", 1, ListObjectsInfo{}, nil, true},
		{"empty-bucket", "africa/tunisia/", "", "", 1, ListObjectsInfo{}, nil, true},
		// Testing on empty bucket, that is, bucket without any objects in it (14).

# https://github.com/tensorflow/tensorflow/actions/workflows/release-branch-cherrypick.yml
# and click "Run Workflow." Leave "Use Workflow From" set to "master", then
# input the branch name and paste the cherry-pick commit and click Run. A PR
# will be created.

name: Release Branch Cherrypick
on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks

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main channel of communication for contributors.  
There are some guidelines which will make applying PRs easier for us:
+ Create a topic branch from where you want to base your work (this is usually the master branch).
  Push your changes to a topic branch in your fork of the repository.
+ Make commits of logical units.
+ Respect the original code style: by using the same [codestyle][code-style],

Por outro lado, existem milhares de usuários no chat, então tem uma grande chance de você encontrar alguém para trocar uma idéia por lá em qualquer horário. 😄

## Patrocine o autor

* **Machine Learning**: 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 Machine Learning, 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.

        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)

        private const val RELEASE_BRANCH = "release"

        private const val EXPERIMENTAL_BRANCH = "xperimental"

        // master branch of gradle/gradle-promote
        private const val GRADLE_PROMOTE_MASTER_VCS_ROOT_ID = "Gradle_GradlePromoteMaster"

        // experimental branch of gradle/gradle-promote
        private const val GRADLE_PROMOTE_EXPERIMENTAL_VCS_ROOT_ID = "Gradle_GradlePromoteExperimental"