// Matches x -> [scast -> scast] -> y, replacing the second scast with the
  // value of x if the casts invert each other.
  auto srcScastOp = getArg().getDefiningOp<StorageCastOp>();
  if (!srcScastOp || srcScastOp.getArg().getType() != getType())
    return OpFoldResult();
  return srcScastOp.getArg();
}

/// The quantization specification should match the expressed type.

  LogicalResult matchAndRewrite(quantfork::DequantizeCastOp dq_op,
                                PatternRewriter& rewriter) const override {
    auto q_op = dq_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
    if (!q_op) return failure();

    dq_op.replaceAllUsesWith(q_op.getArg());
    return success();
  }
};

class QuantizeSameScaleOpsPattern
    : public OpRewritePattern<quantfork::DequantizeCastOp> {
 public:

    b.setInsertionPoint(op);
    if (auto dq = llvm::dyn_cast<quantfork::DequantizeCastOp>(op)) {
      auto dcast = b.create<DequantizeOp>(dq.getLoc(), dq.getResult().getType(),
                                          dq.getArg());
      dq.getResult().replaceAllUsesWith(dcast);
      if (auto extra_attr = op->getAttr(mlir::quant::kVolatileOpAttrName)) {
        dcast->setAttr(mlir::quant::kVolatileOpAttrName, extra_attr);
      }
      dq.erase();

    if (!tensor_type) return cast_op.getArg();

    auto attr_names = tensor_type.getAttrKeys();
    if (attr_names.empty() || attr_names.size() > 1) return cast_op.getArg();
    StringRef tfr_type_attr = attr_names[0].getValue();
    if (!fixed_elt_type_attrs_.contains(tfr_type_attr)) return cast_op.getArg();

    Type result_elt_type = GetFixedElementType(tfr_type_attr, rewriter);

    mlir::IntegerAttr axis;
    for (auto& stats_op : stats_ops) {
      rewriter.setInsertionPointAfter(stats_op);
      rewriter.replaceOpWithNewOp<quantfork::StatisticsOp>(
          stats_op, stats_op.getArg(), layer_stats, axis_stats, axis);
    }
    return success();
  }
};

template <typename SourceOp>
class ConvertOpStatsToQDQs : public OpRewritePattern<SourceOp> {
 public:

  if (!dq_op) {
    auto mul_op = builder.create<TF::MulOp>(loc, value, multiplier);
    return mul_op.getResult();
  }
  auto q_op = dq_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
  if (!q_op) return {};

  Value float_value = q_op.getArg();
  Value new_value = builder.create<TF::MulOp>(loc, float_value, multiplier);
  auto new_value_type = mlir::cast<TensorType>(new_value.getType());

                                PatternRewriter& rewriter) const override {
    auto preceding_qcast =
        q_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
    if (!preceding_qcast) return failure();

    auto new_qcast = rewriter.create<quantfork::QuantizeCastOp>(
        q_op.getLoc(), q_op.getType(), preceding_qcast.getArg());
    new_qcast->setAttr(kVolatileOpAttrName, rewriter.getUnitAttr());
    q_op->replaceAllUsesWith(new_qcast);

  // can backtracked to. Such results will be called "function passthrough". If
  // the result cannot be backtracked to a region argument, returns
  // std::nullopt.
  std::optional<int> GetArg(int result_index) const {
    if (auto arg = mlir::dyn_cast<BlockArgument>(GetValue(result_index)))
      if (arg.getParentBlock() == &region_->front()) return arg.getArgNumber();
    return std::nullopt;
  }

 private:

              preceding_op)) {
        if (auto q_op = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(
                dq_op.getArg().getDefiningOp())) {
          Operation* q_op_input = q_op.getArg().getDefiningOp();
          is_weight_constant =
              q_op_input && q_op_input->hasTrait<OpTrait::ConstantLike>();
        }
      }

      if (!is_weight_constant) {

                        @Override
                        public Object call() throws Exception {
                            return implementation.run(request.getArg());
                        }
                    });
                } catch (Throwable failure) {
                    if (failure instanceof NoClassDefFoundError) {