"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>

    }
    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"}

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

        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]'

                            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"]} {

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_ce3dfbfc-7367-4d62-9d48-d13bf8125391", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.LookupTableSizeV2"(%outputs) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>