}
  // Verify axisStats (optional) attribute.
  if (getAxisStats()) {
    if (!getAxis()) return emitOpError("axis must be specified for axisStats");

    auto shape = tensorArg.getShape();
    auto argSliceSize =
        std::accumulate(std::next(shape.begin(), *getAxis()), shape.end(), 1,
                        std::multiplies<int64_t>());

    auto axisStatsType = getAxisStats()->getShapedType();

  }
  // Verify axisStats (optional) attribute.
  if (getAxisStats()) {
    if (!getAxis()) return emitOpError("axis must be specified for axisStats");

    auto shape = tensorArg.getShape();
    auto argSliceSize =
        std::accumulate(std::next(shape.begin(), *getAxis()), shape.end(), 1,
                        std::multiplies<int64_t>());

    auto axisStatsType = getAxisStats()->getShapedType();

    quant::QuantizedType quant_type;
    SmallVector<double, 4> mins, maxs;

    if (op.getAxisStats().has_value()) {
      // Per axis quantization (or per channel quantization)
      int stats_num = op.getAxisStats()->getNumElements();
      if (stats_num == 0 || stats_num % 2 != 0) return failure();
      auto stats = op.getAxisStats()->dyn_cast<DenseFPElementsAttr>();
      if (!stats) return failure();

    mlir::quantfork::StatisticsOp stats_op) {
  auto layer_stats =
      mlir::cast<mlir::DenseFPElementsAttr>(stats_op.getLayerStats());
  std::optional<mlir::ElementsAttr> axis_stats = stats_op.getAxisStats();
  std::optional<uint64_t> axis = stats_op.getAxis();
  std::vector<float> mins, maxs;
  mlir::DenseFPElementsAttr min_max_attr =
      axis_stats.has_value()