// Otherwise we get
        // ask ':tooling-api:publishGradleDistributionPublicationToRemoteRepository' uses this output of task ':tooling-api:signLocalPublication'
        // without declaring an explicit or implicit dependency. This can lead to incorrect results being produced, depending on what order the tasks are executed.
        tasks.named("publishGradleDistributionPublicationToRemoteRepository") {

-- go.mod --
// Module m imports packages from the same versions under Go 1.17
// as under Go 1.16, but under 1.16 its (implicit) external test dependencies
// are higher.
module example.com/m

go 1.17

replace (
	example.net/lazy v0.1.0 => ./lazy
	example.net/requireincompatible v0.1.0 => ./requireincompatible
)

}

rootProject.name = 'testproject'
"""
        expect:
        fails "help"
        failureHasCause("Conflict found for the following module:")
    }

    @Issue("gradle/gradle#19300")
    def 'carries implicit constraint for log4j-core'() {
        given:
        mavenRepo().module('org.apache.logging.log4j', 'log4j-core', '2.17.1').publish()

        and:
        settingsFile << """
            buildscript {

		func (T) _() {}
		var i I
		var _ = i.m
		func _(s []int) { for i, x := range s { _, _ = i, x } }
		func _(x interface{}) {
			switch x := x.(type) {
			case int:
				_ = x
			}
			switch {} // implicit 'true' tag
		}
		`,
		`
		package p
		type S struct{}
		func (T) _() {}
		func (T) _() {}
		`,
		`
		package p
		func _() {
		L0:
		L1:
			goto L0
			for {
				goto L1

  if (!module) {
    LOG(ERROR) << "Couldn't import flatbuffer to MLIR.";
    return kTfLiteError;
  }

  // Apply quantization passes.
  PassManager pm((*module)->getName(), OpPassManager::Nesting::Implicit);
  if (debug_options.has_value()) {
    // Add debugging instrumentation
    tensorflow::InitPassManager(pm, debug_options.value());
  }
  quant::QuantizationSpecs quant_specs;

        printer: PrettyPrinter,
        printPretty: Boolean = false,
        fullyPrintScope: (KaScopeKind) -> Boolean,
    ) {
        with(analysisSession) {
            with(printer) {
                appendLine("implicit receivers:")

                withIndent {
                    for (implicitReceiver in scopeContext.implicitReceivers) {
                        val type = implicitReceiver.type

		embeddeds, ecopied := subst.typeList(t.embeddeds)
		if mcopied || ecopied {
			iface := subst.check.newInterface()
			iface.embeddeds = embeddeds
			iface.embedPos = t.embedPos
			iface.implicit = t.implicit
			assert(t.complete) // otherwise we are copying incomplete data
			iface.complete = t.complete
			// If we've changed the interface type, we may need to replace its

		embeddeds, ecopied := subst.typeList(t.embeddeds)
		if mcopied || ecopied {
			iface := subst.check.newInterface()
			iface.embeddeds = embeddeds
			iface.embedPos = t.embedPos
			iface.implicit = t.implicit
			assert(t.complete) // otherwise we are copying incomplete data
			iface.complete = t.complete
			// If we've changed the interface type, we may need to replace its

    }

    def "may run implicit tasks from root"() {
        createDirs("api", "impl")
        settingsFile << "include 'api', 'impl'"

        when:
        run(":help")

        then:
        fixture.assertProjectsConfigured(":")
    }

    def "may run implicit tasks for subproject"() {
        createDirs("api", "impl")

  OwningOpRef<mlir::ModuleOp> module = tflite::FlatBufferToMlir(
      serialized_model, &context, UnknownLoc::get(&context));

  // Apply quantization passes.
  PassManager pm((*module)->getName(), OpPassManager::Nesting::Implicit);
  quant::QuantizationSpecs quant_specs;
  quant_specs.inference_type = tflite::TflTypeToTfType(inference_type);
  quant_specs.weight_quantization = true;
  quant_specs.weight_only_quantization = weight_only_quantization;