RewritePatternSet patterns(ctx);
    // Convert quant stats to int8 quantization parameters.
    // Currently, only activation stats are imported, so narrow_range = false.
    patterns.add<quant::ConvertStatsToQDQs<quantfork::QuantizeCastOp,
                                           quantfork::DequantizeCastOp>>(
        bit_width_,
        /*narrow_range=*/false,
        /*is_signed=*/true,

  for (const auto& op : func.getOps()) {
    if (llvm::isa<quantfork::DequantizeCastOp>(op)) return true;
  }
  return false;
}

using PrepareQuantStats =
    quant::ConvertStatsToQDQs<quantfork::QuantizeCastOp,
                              quantfork::DequantizeCastOp>;

#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize.inc"

  for (const auto& op : func.getOps()) {
    if (llvm::isa<quantfork::DequantizeCastOp>(op)) return true;
  }
  return false;
}

using PrepareQuantStats =
    quant::ConvertStatsToQDQs<quantfork::QuantizeCastOp,
                              quantfork::DequantizeCastOp>;

void PrepareQuantizePass::runOnOperation() {
  func::FuncOp func = getOperation();
  MLIRContext* ctx = func.getContext();

    return name_loc.getName().str();
  }
  return "";
}

template <typename QuantizeOpT, typename DequantizeOpT>
struct ConvertStatsToQDQs : public OpRewritePattern<quantfork::StatisticsOp> {
  ConvertStatsToQDQs(int num_bits, bool narrow_range, bool is_signed,
                     bool legacy_float_scale, MLIRContext* context)
      : OpRewritePattern<quantfork::StatisticsOp>(context),

  float float_min = std::min(0.f, float_quant_params.min()->Get(0));
  float float_max = std::max(0.f, float_quant_params.max()->Get(0));
  if (symmetric) {
    // When the symmetric case, ConvertStatsToQDQs in PrepareQuantizePass
    // updates the min and max values into bigger absolute values.
    float_max = std::max(std::abs(float_min), std::abs(float_max));
    float_min = -float_max;
  }