});
    }
    // For dynamic case, the result of conv should be used by shape_of and mul.
    if (is_dynamic_broadcast) {
      auto conv_uses = (*conv_op.getODSResults(0).begin()).getUses();
      if (std::distance(conv_uses.begin(), conv_uses.end()) != 2 ||
          quant::FindUserOfType<shape::ShapeOfOp>(conv_op) == nullptr ||
          quant::FindUserOfType<mhlo::MulOp>(conv_op) == nullptr) {

  for (const auto& p : llvm::zip(live_outs, launch_op.getResults())) {
    Value from = std::get<0>(p);
    // TODO(jingpu): move this to RegionUtils.h in MLIR core.
    for (auto& use : llvm::make_early_inc_range(from.getUses())) {
      if (launch_op_region->isAncestor(use.getOwner()->getParentRegion()))
        continue;
      use.set(std::get<1>(p));
    }
  }
}

// Get all escaped live-out values of a region.

           mlir::isa<IntegerType>(key_dtype) &&
           mlir::cast<IntegerType>(key_dtype).getWidth() == 64))) {
      return false;
    }

    for (auto& use : hashtable->getUses()) {
      Operation* user = use.getOwner();

      // Allow consuming hash table ops that can be covered by TensorFlow Lite
      // hash table kernels.

    // determine whether we have taken care of all uses and can remove
    // the arg.
    for (BlockArgument arg : func.getArguments()) {
      auto uses = arg.getUses();
      use_count[arg.getArgNumber()] = std::distance(uses.begin(), uses.end());
      argument_to_index.insert({arg, arg.getArgNumber()});
    }

    // We're only considering return values that are the same (at this

// if there is only one.
Operation* GetUserIfOnlyOne(Operation* op) {
  if (op->getNumResults() != 1) return nullptr;
  auto result = op->getResult(0);
  if (!result.hasOneUse()) return nullptr;
  return (*result.getUses().begin()).getOwner();
}

// Gets operation providing value for the given operand of given operation
// if the given operation is the only user.
Operation* GetInputOpWithOneUse(Operation* op, int opr_num) {

  // bias is used immediately by the user. This assumption is always correct
  // after constant folding.
  bool UsedAsBias(Value value) {
    for (auto &use : value.getUses()) {
      auto biases = TFL::GetOpQuantSpec(use.getOwner())->biases_params;
      if (biases.find(use.getOperandNumber()) != biases.end()) return true;
    }
    return false;
  }