func.func @serving_default(%arg0: tensor<1x10xf32> {tf_saved_model.index_path = ["x"]}) ->
    (tensor<1x10xf32> {tf_saved_model.index_path = ["output_0"]}, tensor<1x10xi64> {tf_saved_model.index_path = ["output_1"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_x:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

    }
    tf_executor.fetch %outputs#0, %outputs#1 : tensor<i1>, tensor<i32>
  }
  return
}

// CHECK: [[OUTPUT_0:%.*]], {{%.*}} = tf_executor.island wraps "tf.opA"() {_parallel_execution_ids = "p0:0"}
// CHECK: [[OUTPUT_1:%.*]], {{%.*}} = tf_executor.island wraps "tf.opB"([[OUTPUT_0:%.*]]) {_parallel_execution_ids = "p0:0"}

Laura Kassovic <******@****.***> 1701107622 -0800

    %cst = stablehlo.constant dense<0.000000e+00>: tensor<10x1024x3xf32>

        setup:
        sampleBuildWithTest()

        when:
        String output1 = runTaskAndTestClassUsing(firstDebug)
        String output2 = runTaskAndTestClassUsing(secondDebug)

        then:
        assertTestDebugMode(output1, firstDebug)
        assertTestDebugMode(output2, secondDebug)

        where:
        scenarioName                     | firstDebug | secondDebug

        outputs.fileProperties*.propertyFiles*.files.flatten() == [file("a")]
        outputs.fileProperties*.outputFile == [file("a")]
        outputs.fileProperties*.outputType == [FILE]
    }

    def "can register output dir"() {
        when:
        outputs.dir("a")
        then:
        outputs.files.files == files('a')
        outputs.fileProperties*.propertyName == ['$1']

        }

        and:
        outputs.stdOut.contains("BEFORE OUT")
        stripped(outputs.stdOut).contains(result1.output)
        stripped(outputs.stdOut).contains(result2.output)
        outputs.stdOut.contains("AFTER OUT")

        and:
        outputs.stdErr.contains("BEFORE ERR")
        stripped(outputs.stdOut).contains(result1.error)
        stripped(outputs.stdOut).contains(result2.error)

  func.func @conv_with_multiple_uses(%arg0: tensor<1x3x4x3xf32> {tf_saved_model.index_path = ["input"]}) -> (tensor<1x3x2x2xf32> {tf_saved_model.index_path = ["output"]}, tensor<1x3x2x1xf32> {tf_saved_model.index_path = ["output2"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input:0", outputs = "PartitionedCall:0, Sum:0"}, tf_saved_model.exported_names = ["serving_default"]} {

  return options;
}

Status ClientSession::Run(const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(FeedType{}, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(inputs, fetch_outputs, {}, outputs);
}

            apply plugin: "base"
            task customTask {
                inputs.file "input.txt"
                outputs.file "build/output" withPropertyName "output"
                outputs.cacheIf { true }
                doLast {
                  mkdir('build/output')
                  file('build/output/output.txt').text = file('input.txt').text
                }
            }
        """

        then: