llvm::DenseMap<func::FuncOp, llvm::DenseSet<func::FuncOp>> caller_callee_map;
  // Creates work queue for determining reachability below.
  std::queue<func::FuncOp> function_worklist;

  for (auto func : module.getOps<func::FuncOp>()) {
    for (auto user : symbol_map.getUsers(func)) {
      // Populates work queue with func ops called from TPUPartionedCall.
      if (llvm::isa<TF::TPUPartitionedCallOp>(user)) {

  }

  const auto input_type = op.getLhs().getType().cast<TensorType>();
  if (input_type.getRank() != 4) {
    LLVM_DEBUG(llvm::dbgs() << "Only 2D convolution op is supported. "
                               "Expected input rank of 4. Got: "
                            << input_type.getRank() << ".\n");
    return failure();
  }

  const auto filter_type = op.getRhs().getType().cast<TensorType>();

	return f.abs
}

func (f *outputdirFlag) Set(value string) (err error) {
	if value == "" {
		f.abs = ""
	} else {
		f.abs, err = filepath.Abs(value)
	}
	return err
}

func (f *outputdirFlag) getAbs() string {
	if f.abs == "" {
		return base.Cwd()
	}
	return f.abs
}

// vetFlag implements the special parsing logic for the -vet flag:
// a comma-separated list, with distinguished values "all" and

  FrozenRewritePatternSet frozen_patterns(std::move(patterns));
  // Propagation can happen recursively with multiple functions so keep this
  // module level.
  for (auto func : module_op.getOps<func::FuncOp>()) {
    if (failed(applyPatternsAndFoldGreedily(func, frozen_patterns))) {
      func.emitError() << "quant-propagate-quantize-type failed.";
      signalPassFailure();
    }
  }
}

  MLIRContext* ctx = module_op.getContext();

  auto func_op_quant_spec = GetStableHloOpQuantSpec;
  auto func_op_quant_scale_spec = GetStableHloQuantConstraints;

  for (auto func_op : module_op.getOps<func::FuncOp>()) {
    // The function might contain more stats ops than required, and it will
    // introduce requantize if the calibration stats have conflicts. This tries
    // to remove all the redundant stats ops.

}

}  // namespace

ResourceAnalyzer::ResourceAnalyzer(ModuleOp module, bool skip_session_init) {
  auto session_init_func = GetSessionInitializerFunc(module);
  for (auto func : module.getOps<func::FuncOp>()) {
    if (skip_session_init && func == session_init_func) continue;
    (void)AnalyzeRegion(func.getRegion());
  }
}

void ResourceAnalyzer::SetPotentiallyWritten(Value resource) {

    signalPassFailure();
    return;
  }
  TF::SideEffectAnalysis side_effect_analysis(module_op,
                                              op_to_parallel_ids_map);
  for (auto func : module_op.getOps<func::FuncOp>()) {
    if (func.isExternal()) continue;
    const auto& analysis_for_func =
        side_effect_analysis.GetAnalysisForFunc(func);
    if (failed(UpdateAllControlDependencies(func, analysis_for_func))) {

  auto func_arg_types = llvm::to_vector<4>(func_type.getInputs());
  llvm::SmallDenseMap<llvm::StringRef, int> var_arg_index_by_name;
  for (auto var_handle_op :
       llvm::make_early_inc_range(block.getOps<TF::VarHandleOp>())) {
    if (add_validation && failed(ValidateVarHandle(var_handle_op)))
      return failure();
    // In the case of variables that are not initialized at graph creation

  ReductionOp reduce_op = dyn_cast_or_null<ReductionOp>(
      return_op.getOperands().front().getDefiningOp());
  if (!reduce_op) return failure();
  if (reduce_op.getLhs() != body.getArgument(0) ||
      reduce_op.getRhs() != body.getArgument(1))
    return failure();

  return success();
}

// Check if the specified region is a binary reduction function that takes 2

      return false;
    }
  }
  return true;
}

void FoldConstantsToSubgraphPass::runOnOperation() {
  auto module = getOperation();

  for (auto fn : module.getOps<func::FuncOp>()) {
    fn.walk([&](Operation* op) {
      if (!llvm::isa<TFL::ConstOp, TFL::QConstOp, arith::ConstantOp>(op))
        return;

      // We only fold int32/int64 for Const and i32 for QConst if not specify