}

bool checkWhetherGraphHasValidStaticLookupTables(ModuleOp module) {
  auto hashtables = GetAllOps<TF::HashTableV2Op>(&module);
  // No needs to run the legalization patterns.
  if (hashtables.empty()) {
    return false;
  }

  for (auto hashtable : hashtables) {
    auto key_dtype = hashtable.getKeyDtype();
    auto value_dtype = hashtable.getValueDtype();

  func.walk([&](Operation* op) {
    b.setInsertionPoint(op);
    if (auto dq = llvm::dyn_cast<DequantizeOp>(op)) {
      auto dcast = b.create<quantfork::DequantizeCastOp>(
          dq.getLoc(), dq.getOutput().getType(), dq.getInput());
      dq.getOutput().replaceAllUsesWith(dcast);
      dq.erase();
    } else if (auto q = llvm::dyn_cast<QuantizeOp>(op)) {
      auto qcast = b.create<quantfork::QuantizeCastOp>(

    PatternRewriter& rewriter) {
  int64_t shape_rank = shape.size();
  auto shape_spec_type =
      RankedTensorType::get({shape_rank}, rewriter.getIntegerType(64));
  Type resultType = RankedTensorType::get(shape, element_type);
  auto constant_attr = DenseElementsAttr::get(shape_spec_type, shape);
  auto shape_tensor =
      rewriter.create<TF::ConstOp>(loc, shape_spec_type, constant_attr);

void PopulateTfVersions(mlir::ModuleOp module, const VersionDef& versions) {
  mlir::Builder b(module.getContext());
  auto producer =
      b.getNamedAttr("producer", b.getI32IntegerAttr(versions.producer()));
  auto min_consumer = b.getNamedAttr(
      "min_consumer", b.getI32IntegerAttr(versions.min_consumer()));
  auto bad_consumers = b.getNamedAttr(
      "bad_consumers",
      b.getI32ArrayAttr(llvm::ArrayRef<int32_t>(

                          SP_Stream stream) -> void {
    auto custom_stream = static_cast<SP_Stream_st*>(stream);
    ASSERT_EQ(custom_stream->stream_id, 14);
    delete custom_stream;
    stream_deleted = true;
  };

  StreamExecutor* executor = GetExecutor(0);
  ASSERT_FALSE(stream_created);
  TF_ASSERT_OK_AND_ASSIGN(auto stream, executor->CreateStream());
  ASSERT_TRUE(stream_created);

    flattened_contracting_segids[i] = 0;
  }
  auto seg_prod_result_type =
      RankedTensorType::get(static_cast<int32_t>(1), builder.getI32Type());
  auto out_segids_cst = builder.create<TFL::ConstOp>(
      builder.getI32TensorAttr(flattened_out_segids));
  auto contracting_segids_cst = builder.create<TFL::ConstOp>(
      builder.getI32TensorAttr(flattened_contracting_segids));
  auto num_segids_tensor =

          typename Convert>
static Attribute BinaryFolder(Op *op) {
  auto lhs_op = op->getLhs().template getDefiningOp<mhlo::ConstantOp>();
  auto rhs_op = op->getRhs().template getDefiningOp<mhlo::ConstantOp>();
  if (!lhs_op || !lhs_op) return {};

  auto lhs = dyn_cast_or_null<DenseElementsAttr>(lhs_op.getValue());
  auto rhs = dyn_cast_or_null<DenseElementsAttr>(rhs_op.getValue());
  if (!lhs || !rhs) return {};

    }

    if (!tpose_arg->hasOneUse()) {
      return failure();
    }

    auto tpose_arg_type =
        llvm::dyn_cast<RankedTensorType>(tpose_arg->getResultTypes()[0]);
    auto cst_arg_type =
        llvm::dyn_cast<RankedTensorType>(cst_arg->getResultTypes()[0]);

    auto tpose_arg_rank = tpose_arg_type.getRank();
    auto cst_arg_rank = cst_arg_type.getRank();

  size_t start_map = fbb.StartMap();

  for (auto attr : attrs) {
    if (auto float_attr = mlir::dyn_cast_or_null<FloatAttr>(attr.second)) {
      fbb.Float(attr.first.data(), float_attr.getValue().convertToFloat());
    } else if (auto int_attr =
                   mlir::dyn_cast_or_null<IntegerAttr>(attr.second)) {
      fbb.Int(attr.first.data(), int_attr.getInt());
    } else if (auto bool_attr = mlir::dyn_cast_or_null<BoolAttr>(attr.second)) {

                                   SmallVector<TransposeOp, 2>* transpose_ops) {
  for (auto it = transpose_ops->begin(); it != transpose_ops->end(); ++it) {
    auto tranpose_op = *it;
    for (auto tranpose_operand : tranpose_op.getOperands()) {
      auto ranked_tranpose_type =
          mlir::dyn_cast_or_null<RankedTensorType>(tranpose_operand.getType());
      if (!ranked_tranpose_type) continue;