// 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

}

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>

                }
                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 << """

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);

  Node* node() const { return output_.node(); }
  std::string node_name() const { return node_name_; }
  int32 index() const { return node_name_.empty() ? output_.index() : index_; }
  DataType data_type() const { return data_type_; }
  Status status() const { return status_; }
  const Tensor& tensor() const { return tensor_; }

 private:
  Status status_;
  Output output_ = Output(Operation(nullptr), 0);
  Tensor tensor_;

        "same location"        | null           | "file"     | "output.txt"               | "output.txt"
        "consuming ancestor"   | null           | "file"     | "build/dir/sub/output.txt" | "build/dir"
        "consuming descendant" | 'build/dir'    | "dir"      | "build/dir/sub/output.txt" | "build/dir/sub/output.txt"
    }

  const TF_TensorSpec* tensor_spec_out =
      TF_SignatureDefParamTensorSpec(param_out);
  const TF_Shape* shape_out = TF_TensorSpecShape(tensor_spec_out);

  // Output "output_0" is a scalar, float32 tensor
  EXPECT_EQ("output_0", std::string(TF_SignatureDefParamName(param_out)));
  EXPECT_EQ(TF_FLOAT, TF_TensorSpecDataType(tensor_spec_out));
  EXPECT_EQ(0, TF_ShapeDims(shape_out));