// WholeModel-DAG: "tf.DumpTensor"(%[[output1]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_1", node_name = "Conv2D_1"}> : (tensor<*xf32>) -> ()
// WholeModel-DAG: return %[[output0]], %[[output1]]

// IntPerLayer-LABEL: func @conv
// IntPerLayer-DAG: %[[w:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}1.600000e-01, 1.000000e-01

        output.contains 'Output: nested.outputFiles$2 [output-nested-2.txt]'
        output.contains 'Output: outputDirectories$1 [outputs1]'
        output.contains 'Output: outputDirectories$2 [outputs2]'
        output.contains "Output: outputDirectory [outputs]"
        output.contains "Output: outputFile [output.txt]"
        output.contains 'Output: outputFiles$1 [output1.txt]'

  // all the outputs of the main function are actually the final outputs.
  // main_func:
  //  %output1 = call @subgraph_1...
  //   ...
  //  %output2 = call @subgraph_m...
  //   ...
  //  %outputn = call @subgraph_k...
  //  return %output1, output2, ..., outputn.
  fn.walk([&](func::ReturnOp return_op) {
    for (auto output : return_op.getOperands()) {

   *
   *   A =[[0, 1],[1, 0]]
   *   B =[[1, 0],[0, 1]]
   *
   *   output1, output2, output3 = two_adds_and_mm(A, B)
   *
   * We expect outputs:
   *
   *   output1 = [[1, 1],[1, 1]]
   *   output2 = [[2, 0],[0, 2]]
   *   output3 = [[2, 2],[2, 2]]
   *
   */

  // Build eager context.
  TFE_ContextOptions* opts = TFE_NewContextOptions();

  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>

            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"

//
// TF_OpDefinitionBuilder* builder = TF_NewOpDefinitionBuilder("OpName");
//
// Inputs, outputs and attributes can be added to the builder with the
// corresponding functions, e.g.
//
// TF_OpDefinitionBuilderAddInput(builder, "input1: int32");
// TF_OpDefinitionBuilderAddOutput(builder, "output1: int64");
// TF_OpDefinitionBuilderAddAttr(builder, "attr: int32");
//

                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 @not_modified(%arg0: tensor<f32>, %arg1: tensor<1x224x224x3xf32>) -> (tensor<1x401408xf32>, tensor<1x224x224x3xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "input0,input1", outputs = "output0,output1"}} {
  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>

		gp21carry = regInfo{inputs: []regMask{gpg, gpg}, outputs: []regMask{gp, 0}}
		gp2flags  = regInfo{inputs: []regMask{gpg, gpg}}
		gp2flags1 = regInfo{inputs: []regMask{gp, gp}, outputs: []regMask{gp}}
		gp22      = regInfo{inputs: []regMask{gpg, gpg}, outputs: []regMask{gp, gp}}
		gp31      = regInfo{inputs: []regMask{gp, gp, gp}, outputs: []regMask{gp}}
		gp31carry = regInfo{inputs: []regMask{gp, gp, gp}, outputs: []regMask{gp, 0}}