"max": [0.5]
          }
        }
      ],
      "inputs": [0],
      "outputs": [24],
      "operators": [
        {
          "inputs": [
            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
          ],
          "outputs": [24],
          "intermediates": [
            25, 26, 27, 28, 29
          ],

}

// CHECK-LABEL: QuantizeWithoutNorm
func.func @QuantizeWithoutNorm(%arg0: tensor<1x1x5xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input0", outputs = "output24"}} {
  %none = "tfl.no_value"() {value = unit} : () -> none
  %input = "quantfork.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x1x5xf32>) -> tensor<1x1x5xf32>

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

                            const std::vector<Output>& outputs,
                            const std::vector<Output>& inputs,
                            std::vector<Output>* grad_outputs) {
  std::vector<Output> grad_inputs;
  grad_inputs.reserve(outputs.size());
  for (const Output& output : outputs) {
    grad_inputs.emplace_back(ops::OnesLike(scope, output));
  }

        }

        void createContents() {
            file.text = "output file"
            dir.file("some/dir/output1.txt") << "output1"
            dir.file("some/dir/output2.txt") << "output2"
            dir.file("some/output3.txt") << "output3"
            dir.file("some/another/output4.txt") << "output4"
            dir.createDir("some/lonelyDir")
        }
    }

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

  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"]} {

    %outputs_3, %control_3 = tf_executor.island wraps "tf.TPUReplicatedOutput"(%outputs_2) {T = "tfdtype$DT_FLOAT", device = "", name = "output0", num_replicas = 1 : i64} : (tensor<0xf32>) -> tensor<0xf32>
    %outputs_4, %control_4 = tf_executor.island wraps "tf.Identity"(%outputs_3) {T = "tfdtype$DT_FLOAT", device = "", name = "output_0_shard_0"} : (tensor<0xf32>) -> tensor<0xf32>

        def operation = signing.sign(input1, input2)
        
        then:
        output1.exists() && output2.exists()
        [input1, input2] == operation.filesToSign.files.toList()[0..1]
        [output1, output2] == operation.signatureFiles.files.toList()[0..1]
        [output1, output2] == operation.signatures*.file.toList()
    }

    def "sign multiple artifacts with defaults"() {
        when:

  %output_1 = "tf.TensorArrayGatherV3"(%handle_3, %indices, %res_1#3) {device = "/job:localhost/replica:0/task:0/device:CPU:0", element_shape = #tf_type.shape<?x512>} : (tensor<2x!tf_type.resource<tensor<?x512xf32>>>, tensor<?xi32>, tensor<f32>) -> tensor<?x?x512xf32>
  func.return %output_0, %output_1 : tensor<?x?x512xf32>, tensor<?x?x512xf32>
}