}

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();

  return false;
}

}  // namespace

bool TargetHardware::Init() {
  auto* hardware_ops_factory = GetRegisteredTargetHardwareOps();
  for (auto& hardware_ops : *hardware_ops_factory) {
    if (hardware_ops->hardware_typeid != this->GetTypeId()) continue;
    auto& op_factories = hardware_ops->target_hardware_ops_factory;
    for (auto& op_factory : op_factories) {
      hardware_ops_.emplace_back(op_factory.getSecond()());
    }

  auto in_spec = input_specs[0].dyn_cast<UniformQuantizedType>();
  // TODO(fengliuai): handles the PerAxis QuantizedType.
  auto w_spec = input_specs[1].dyn_cast<UniformQuantizedType>();
  auto b_spec = input_specs[2].dyn_cast<UniformQuantizedType>();
  auto o_spec = out_specs[0].dyn_cast<UniformQuantizedType>();
  if (!in_spec || !w_spec || !b_spec || !o_spec) return failure();

  double scale_product = in_spec.getScale() * w_spec.getScale();

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>(

  function.set_name(kUncompilableFunctionName);
  const auto node_info_it =
      uncompilable_nodes.find(function.ShortDebugString());
  ASSERT_NE(uncompilable_nodes.end(), node_info_it);
  const auto& uncompilable_node_list = node_info_it->second.second;
  ASSERT_EQ(1, uncompilable_node_list.size());
  const auto& node_info = uncompilable_node_list.at(0);

  const auto& node_stack = node_info.stack_trace;

    }

    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();

                          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 =

                                 ArrayRef<StateT *> results) {
    if (auto cast = dyn_cast<TF::CastOp>(op)) {
      this->join(results[0], *operands[0]);
    } else if (auto while_op = dyn_cast<TF::WhileRegionOp>(op)) {
      for (auto &region : while_op->getRegions()) {
        for (auto [arg, value] :
             llvm::zip(region.getArguments(), while_op->getOperands())) {

    auto logits_softmax = Softmax(scope, logits);

    auto grad_grad_expand = ExpandDims(scope, grad_grad, 1);
    auto logits_softmax_expand = ExpandDims(scope, logits_softmax, 2);
    auto matmul_result =
        BatchMatMul(scope, grad_grad_expand, logits_softmax_expand);
    axis.push_back(1);
    auto squeeze_result = Squeeze(scope, matmul_result, Squeeze::Axis(axis));