* specify a classifier, for example.
     *
     * @param dependencyProvider the dependency provider
     * @param variantSpec the variant specification
     * @return a new dependency provider targeting the configured variant
     * @since 6.8
     */

                    }
                }
            }
        }

        where:
        conf << ['runtime', 'test']
    }

    def "explicit configuration selection in ivy modules is NOT supported if targeting a #target module"() {
        given:
        setupRepositories([target, 'ivy'])
        repository('ivy') {
            "org:ivy:1.0" {
                withModule(IvyModule) {

  quantization_config.mutable_static_range_ptq_preset()
      ->set_enable_per_channel_quantized_weight(
          quantization_options.enable_per_channel_quantization());
  // When targeting server TPUs quantized types should be unpacked into
  // integer ops.
  quantization_config.mutable_pipeline_config()->set_unpack_quantized_types(
      true);
  *quantization_config.mutable_debugger_config() =

//
// The fixup table below helps overcome this hurdle. At the point
// where a parameter/variable reference is made (via a call to
// "ReferenceChildDIE"), a fixup record is generate that records
// the relocation that is targeting that child variable. At a later
// point when the abstract function DIE is emitted, there will be
// a call to "RegisterChildDIEOffsets", at which point the offsets

  // on TPU.
  // Extracts the StableHLO module from tf.XlaCallModuleOp if the StableHLO
  // module is serialized in it.
  pm.addPass(mlir::odml::CreateLegalizeTFXlaCallModuleToStablehloPass());

  // Preprocesses TPU-targeting StableHLO module for support in TF Quantizer.
  pm.addPass(mlir::quant::CreateConvertTpuModelToCpuPass());
  pm.addPass(mlir::createInlinerPass());
  pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());

mlir::PassPipelineRegistration<> replicated_clustering_bridge_v1(
    "tf-replicated-clustering-bridge-v1",
    "Run all the passes involved in transforming a TensorFlow V1 graph before "
    "execution so that it is suitable for targeting devices.",
    CreateReplicatedBridgePipelineV1);

}  // namespace v1
}  // namespace tf2xla

        then:
        sb1.other == "other"
    }

    static class SetProp extends RuleSource {
        @Mutate
        void m(@Path("foo") Bean bean) {}
    }

    def "when targeting by type, paths are interpreted relative to item"() {
        given:
        def mmType = ModelTypes.modelMap(Bean)

        registry
            .registerModelMap("beans", Bean) {

So, **avoid publishing custom variants** if they are for internal use only.
====

[[targeting-different-platforms]]
== Targeting different platforms

It is common for a library to target different platforms.
In the Java ecosystem, we often see different artifacts for the same library, distinguished by a different _classifier_.

We will look at Java projects in detail in this chapter, but most of the topics apply to other supported JVM languages as well, such as link:https://kotlinlang.org/docs/reference/using-gradle.html#targeting-the-jvm[Kotlin], <<groovy_plugin.adoc#groovy_plugin,Groovy>> and <<scala_plugin.adoc#scala_plugin,Scala>>.

            collectOutputs(launcher)
            launcher.run()
        }

        then:
        outputContains("BUILD SUCCESSFUL")
    }

    def "can launch test with test launcher via test filter targeting a specific task"() {
        setup:
        settingsFile << "includeBuild('other-build')"
        file('other-build/settings.gradle') << """
            rootProject.name = 'other-build'
            include 'sub'