}
                outputs.files files(fileList) withPropertyName("out")
                def output1 = project.file("build/output1.txt")
                def output2 = project.file("build/output2.txt")
                doLast {
                    output1.parentFile.mkdirs()
                    output1 << "data"
                    if (!System.getProperty("changedCardinality")) {

    }

    private static String[] customTaskWithOutputs(List<String> outputs) {
        (["customTask", "-PnumOutputs=${outputs.size()}"] + outputs.withIndex().collect { value, idx -> "-Poutput${idx}" + (value ? "=${value}" : '') }) as String[]
    }

    def "output files moved from one location to another marks task up-to-date"() {
        buildFile << """

                @InputFiles def inputFiles = project.layout.files()

                @OutputDirectory File outputs1
                @OutputDirectory File outputs2

                @TaskAction void action() {
                    new File(outputs1, "output1.txt").text = "output1"
                    new File(outputs2, "output2.txt").text = "output2"
                }
            }
        """

        buildFile << """

            file("build.gradle") << """
                task foo {
                    inputs.dir('src').ignoreEmptyDirectories(false).withPropertyName('src')
                    outputs.file('output.txt')
                    doLast {
                        file('output.txt') << 'do stuff'
                    }
                }
            """
            dir("src") {
                file("A.txt") << "fooA"

    # model and saving the output quantized model. These two member
    # attributes can be used to specify the paths for such models,
    # respectively. These paths will be cleaned up after each test case.
    self._input_saved_model_path = self.create_tempdir('input').full_path
    self._output_saved_model_path = self.create_tempdir('output').full_path
    # Extra output path occasionally used for comparing two different

                void myAction() {
                    def output = outputFile.get().asFile
                    output << launcher1.get().executablePath
                    output << launcher2.get().executablePath
                }
            }

            tasks.register("myToolchainTask", ToolchainTask) {
                outputFile = layout.buildDirectory.file("output.txt")
                launcher1 = javaToolchains.launcherFor {

  func.func @serving_default(%arg0: tensor<1024x1024xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<1024x3xf32> {tf_saved_model.index_path = ["output1"]}, tensor<1024x3xf32> {tf_saved_model.index_path = ["output2"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %0 = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>

	c.Env = append(os.Environ(), "CGO_ENABLED=0")
	if output, err := c.CombinedOutput(); err != nil {
		t.Logf("go build -o %s read3.go\n%s", exe, output)
		t.Fatalf("go build failed: %v", err)
	}

	// Use a deadline to try to get some output even if the program hangs.
	ctx := context.Background()
	if deadline, ok := t.Deadline(); ok {
		// Leave a 20% grace period to flush output, which may be large on the

John Howard <******@****.***> 1717524306 -0700

    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);