}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, tensorflow::gtl::ArraySlice<const tensorflow::DataType>(
                          reinterpret_cast<const tensorflow::DataType*>(values),
                          num_values));
}

void SetAllVarIsInitializedToTrue(Block* block) {
  auto builder = OpBuilder::atBlockBegin(block);
  TF::ConstOp const_true = nullptr;
  for (auto op :
       llvm::make_early_inc_range(block->getOps<TF::VarIsInitializedOp>())) {
    builder.setInsertionPoint(op);
    if (!const_true)
      const_true = builder.create<TF::ConstOp>(
          op.getLoc(),
          DenseIntElementsAttr::get(

      const std::vector<int32_t>& results);

  BufferOffset<tflite::Operator> BuildCustomOperator(
      Operation* inst, mlir::TFL::CustomOp op,
      const std::vector<int32_t>& operands,
      const std::vector<int32_t>& results);

  std::optional<CustomOptionsOffset> CreateFlexOpCustomOptions(
      const ::tensorflow::NodeDef& node_def, const mlir::Location& loc);

(And16  (Const16 [c])  (Const16 [d]))  => (Const16 [c&d])
(And32  (Const32 [c])  (Const32 [d]))  => (Const32 [c&d])
(And64  (Const64 [c])  (Const64 [d]))  => (Const64 [c&d])

(Or8   (Const8 [c])   (Const8 [d]))   => (Const8  [c|d])
(Or16  (Const16 [c])  (Const16 [d]))  => (Const16 [c|d])
(Or32  (Const32 [c])  (Const32 [d]))  => (Const32 [c|d])
(Or64  (Const64 [c])  (Const64 [d]))  => (Const64 [c|d])

  // definition, potentially leading to bogus _replication_info attributes. So
  // we just scrub all tf.Constants of all extra attributes.
  // TODO(kramm): Remove this once tf.Const's folder is aware of extra
  // attributes.
  auto value_str_attr = StringAttr::get(&getContext(), "value");
  getOperation().walk([&](mlir::TF::ConstOp cst) {
    auto dict = cst->getAttrDictionary();

  return compile_mlir_result.status();
}

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> GraphToModule(
    bool unconditionally_use_set_output_shapes, const Graph& graph,
    llvm::ArrayRef<std::string> control_rets,
    const FunctionLibraryDefinition& flib_def, const GraphDebugInfo& debug_info,
    mlir::MLIRContext* context) {
  mlir::DialectRegistry registry;
  RegisterDialects(registry);

  while (!worklist.empty()) {
    mlir::Operation* const use = worklist.pop_back_val();

    // Follow the `CastOp`/`IdentityOp`'s users to handle the `RestoreV2` ->
    // (optionally `IdentityOp`) -> `CastOp` `AssignVariableOp` case.
    if (llvm::isa<TF::CastOp, TF::IdentityOp>(use)) {
      llvm::append_range(worklist, use->getUsers());
      continue;
    }

  }
  auto transformed = Input{
      Const(scope.NewSubScope("Const"), Input::Initializer(inp.tensor()))};
  return NodeBuilder::NodeOut{transformed.node(), transformed.index()};
}

std::vector<NodeBuilder::NodeOut> AsNodeOutList(const Scope& scope,
                                                const InputList& inp) {
  std::vector<NodeBuilder::NodeOut> out;
  for (const auto& i : inp) {
    const auto node_out = AsNodeOut(scope, i);

(Lsh32x64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])
(Rsh32x64 x (Int64Make (Const32 [c]) _)) && c != 0 => (Signmask x)
(Rsh32Ux64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])
(Lsh16x64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])
(Rsh16x64 x (Int64Make (Const32 [c]) _)) && c != 0 => (Signmask (SignExt16to32 x))
(Rsh16Ux64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])

  return func_op;
}

TF::StatefulPartitionedCallOp EncapsulateOpsInFunc(
    OpBuilder& builder, SymbolTable& symbol_table,
    const llvm::SetVector<Operation*>& ops,
    const llvm::SetVector<Value>& inputs, const llvm::SetVector<Value>& outputs,
    func::FuncOp parent_func, ModuleOp module, const std::string& name,
    bool flag_for_inlining) {
  // Moves all of the Operations in 'ops' into a newly created func.FuncOp