func::FuncOp func, DictionaryAttr attrs,
    std::string& custom_option_buffer) {
  flexbuffers::Builder fbb;
  size_t start_map = fbb.StartMap();

  if (failed(AddIntAttr(func, attrs, "max_detections", &fbb)) ||
      failed(AddIntAttr(func, attrs, "max_classes_per_detection", &fbb)) ||
      failed(AddIntAttr(func, attrs, "num_classes", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "nms_score_threshold", &fbb)) ||

    "'TFLite_Detection_PostProcess:3' type: DT_FLOAT } attr : { name: "
    "'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "

func.func @tflite_custom_nms(%arg0: tensor<1x100x4xf32>, %arg1: tensor<1x100x91xf32>, %arg2: tensor<100x4xf32>) -> (tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>) attributes  {tf._implements = #tf_type.func<@"TFLite_Detection_PostProcess", {max_detections = 10 : i64, max_classes_per_detection = 1 : i64, num_classes = 91 : i64, nms_score_threshold = 0.5 : f32, nms_iou_threshold = 0.6 : f32, y_scale = 5.0 : f32, x_scale = 10.0 : f32, h_scale = 1.0 : f32, w_scale = 2.0 : f32, use_regular_nms = 0 :...

0, %a1, %a2) {_output_quantized = true, _output_types = [f32, f32, f32, f32], _support_output_type_float_in_quantized_op = true, detections_per_class = 100 : i64, device = "", h_scale = 5.000000e+00 : f32, max_classes_per_detection = 1 : i64, max_detections = 20 : i64, nms_iou_threshold = 6.000000e-01 : f32, nms_score_threshold = 3.000000e-01 : f32, num_classes = 90 : i64, use_regular_nms = false, w_scale = 5.000000e+00 : f32, x_scale = 1.000000e+01 : f32, y_scale = 1.000000e+01 : f32} : (tensor<1x64x64x32xf32>,...