});
    }
    // 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) {

           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.

  // terminator.
  bool checkIfAnyUserIsConnectedToTermiantor(BlockArgument op) const {
    for (const auto& user : op.getUsers()) {
      if (user->template hasTrait<OpTrait::IsTerminator>()) return true;
      if (auto next_user = dyn_cast_or_null<TF::IdentityOp>(user)) {
        return (*(next_user->getResult(0).getUsers().begin()))
            ->template hasTrait<OpTrait::IsTerminator>();
      }
    }
    return false;
  }

        synchronized (lock) {
            List<WorkerDaemonClient> sortedClients = CollectionUtils.sort(idleClients, comparingInt(WorkerDaemonClient::getUses));
            List<WorkerDaemonClient> clientsToStop = selectionFunction.transform(new ArrayList<>(sortedClients));
            if (!clientsToStop.isEmpty()) {
                stopWorkers(clientsToStop);
            }

    // 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

    llvm::SmallVector<Operation*, 4> candidates;
    for (Value value : values) {
      if (incoming) {
        candidates = {value.getDefiningOp()};
      } else {
        candidates.assign(value.getUsers().begin(), value.getUsers().end());
      }
      for (Operation* candidate_op : candidates) {
        if (cluster_ops.contains(candidate_op) ||
            cluster_dependent_ops.contains(candidate_op)) {

    if (!type || !type.getElementType().isF32()) {
      return failure();
    }
    return success(
        op->hasOneUse() &&
        IsWeightQuantizableFunction(*op->getUses().begin(), type.getRank()));
  }

  // Checks if the operand is second operand of `tf.XlaCallModule` op for
  // `stablehlo.convolution` or `stablehlo.dot_general` with fully_quantizable
  // trait.

  // 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;
  }

    if (!type || !type.getElementType().isF32()) return false;

    Value value = op.getResult();

    // Check whether dynamic range quantization can be applied.
    for (auto& use : value.getUses()) {
      Operation* user = use.getOwner();
      int operand_num = use.getOperandNumber();
      std::unique_ptr<OpQuantSpec> spec = GetTFOpQuantSpec(user);

          values.getUses().begin().getUser());
      values_slice_op.getResult().replaceAllUsesWith(top_k_op.getValues());
      values_slice_op.erase();
    }
    if (!indices.use_empty()) {
      auto indices_slice_op = llvm::dyn_cast_or_null<TFL::SliceOp>(
          indices.getUses().begin().getUser());
      indices_slice_op.getResult().replaceAllUsesWith(top_k_op.getIndices());