workerClient.stop();
    }

    public void kill() {
        workerClient.stopNow();
    }

    DaemonForkOptions getForkOptions() {
        return forkOptions;
    }

    public int getUses() {
        return uses;
    }

    public KeepAliveMode getKeepAliveMode() {
        return forkOptions.getKeepAliveMode();
    }

    public LogLevel getLogLevel() {
        return logLevel;
    }

    if (!function_name.starts_with(kDequantizeFunctionName)) return failure();

    llvm::SmallVector<Operation*> users(op->getUsers().begin(),
                                        op->getUsers().end());

    bool changed = false;
    for (auto& use : op->getUses()) {
      Operation* user_op = use.getOwner();
      int user_idx = use.getOperandNumber();
      if (!IsOpWithInt8TypeOperand(user_op)) continue;

    Value res = read.getResource();
    Operation* scope = res.getParentBlock()->getParentOp();
    if (!scope->isProperAncestor(replicate)) continue;
    bool has_conflicting_write = false;
    for (OpOperand& use : res.getUses()) {
      Operation* using_op = use.getOwner();
      if (using_op == read) continue;
      if (!replicate->isProperAncestor(using_op)) continue;
      Operation* peer = GetAncestorBelow(using_op, replicate);

  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.

      });
    }
    // 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.

    // 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

  // 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 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) {

	DB.Model(&users).Association("Manager").Append(
		GetUser("manager-slice-belongs-to-1", Config{}),
		GetUser("manager-slice-belongs-to-2", Config{}),
		GetUser("manager-slice-belongs-to-3", Config{}),
	)
	AssertAssociationCount(t, users, "Manager", 3, "After Append")

	if err := DB.Model(&users).Association("Manager").Append(
		GetUser("manager-slice-belongs-to-test-1", Config{}),
	).Error; err == nil {