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());

  // 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) {

    auto *arg_values = def->getValueAsDag("arguments");
    mlir::tblgen::Operator op(*def);
    for (unsigned i = 0, e = arg_values->getNumArgs(); i != e; ++i) {
      auto arg = arg_values->getArg(i);
      DefInit *arg_def = dyn_cast<DefInit>(arg);
      if (!arg_def) continue;
      if (arg_def->getDef()->isSubClassOf(attr_type)) {
        // This binds the name of the attribute in the TD file with the name

  // quant.qcast op in case it overrides the information from training FakeQuant
  // ops.
  func.walk([&](quantfork::QuantizeCastOp q) {
    auto input_op = q.getArg().getDefiningOp();
    if (auto stats = dyn_cast_or_null<quantfork::StatisticsOp>(input_op)) {
      q.setOperand(stats.getArg());
      if (stats.use_empty()) stats.erase();
    }
  });

  // Step 1: forward pass: propagate any value scales which are not produces

                                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);

        private static void writeGlobalExclude(IvyExcludeRule excludeRule, OptionalAttributeXmlWriter xmlWriter) throws IOException {
            xmlWriter.startElement("exclude")
                .attribute("org", excludeRule.getOrg())
                .attribute("module", excludeRule.getModule())
                .attribute("conf", excludeRule.getConf())
                .endElement();
        }

void PrepareQuantizeDRQPass::removeAllStatsOp(func::FuncOp func) {
  func.walk([&](quantfork::StatisticsOp stats_op) {
    stats_op.replaceAllUsesWith(stats_op.getArg());
    stats_op.erase();
  });
}

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

void PrepareQuantizeDRQPass::runOnOperation() {

    auto q =
        rewriter.create<QuantizeOpT>(op.getLoc(), result_type, op.getArg());
    q->setAttr(kVolatileOpAttrName, rewriter.getUnitAttr());

    auto dq = rewriter.create<DequantizeOpT>(op.getLoc(), op.getType(), q);
    op.getResult().replaceAllUsesWith(dq);
    q.getOperation()->replaceUsesOfWith(dq, op.getArg());
    op.erase();

    return success();
  }

 private:
  int num_bits;

    int input_idx = 0;
    for (int i = 0; i < args_in_dag->getNumArgs(); i++) {
      if (args_in_dag->getArgName(i)->getAsString() == "\"input\"")
        input_idx = i;
    }
    if (CheckTypeConstraints(args_in_dag->getArg(input_idx), required_types,
                             per_axis)) {
      std::string op_name = op.getCppClassName().str();

      // TODO(b/197195711): Please add the additional operations for 16x8 MLIR