%2 = func.call @func_2_CPU_FLOAT(%0, %1) {tac.device = "CPU", tac.inference_type = "FLOAT", tac.interface_name = "func_2"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<2x100xf32>
    func.return %2 : tensor<2x100xf32>
  }

module {
  func.func @simpleTest(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> {
    %0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.interface_name = "func_0"} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    %1 = func.call @func_1_GPU_FLOAT(%arg0, %arg3) {tac.interface_name = "func_1"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

 public:
  explicit ConvertEmbeddedLookupFunc(func::FuncOp func) : func_(func) {}

  void RewriteFunc() {
    func_->setAttr(kTFImplements,
                   StringAttr::get(func_.getContext(), "embedding_lookup"));
    Value lookup = func_.getArgument(1);
    Value value = func_.getArgument(0);
    auto output_type = func_.getFunctionType().getResult(0);

    OpBuilder builder(func_.getBody());

    ret_operands.push_back(operand->getValue());
  }
  builder_.create<func::ReturnOp>(func_.getLoc(), ret_operands);

  auto arg_types = body.getArgumentTypes();
  auto result_types = body.getTerminator()->getOperandTypes();
  func_.setType(FunctionType::get(func_.getContext(), arg_types, result_types));
  *f = new MlirFunction(std::move(context_), std::move(module_), func_);
  return absl::OkStatus();
}

extern "C" {

	return m
}

// addValueFuncs adds to values the functions in funcs, converting them to reflect.Values.
func addValueFuncs(out map[string]reflect.Value, in FuncMap) {
	for name, fn := range in {
		if !goodName(name) {
			panic(fmt.Errorf("function name %q is not a valid identifier", name))
		}
		v := reflect.ValueOf(fn)
		if v.Kind() != reflect.Func {
			panic("value for " + name + " not a function")
		}

func (n *Func) editChildren(edit func(Node) Node)           { editNodes(n.Body, edit) }
func (n *Func) editChildrenWithHidden(edit func(Node) Node) { editNodes(n.Body, edit) }

func (f *Func) Type() *types.Type                { return f.Nname.Type() }
func (f *Func) Sym() *types.Sym                  { return f.Nname.Sym() }
func (f *Func) Linksym() *obj.LSym               { return f.Nname.Linksym() }

  ASSERT_TRUE(module_op);

  auto func_op = module_op->lookupSymbol<func::FuncOp>("fully_quantized_add");
  ASSERT_THAT(func_op, NotNull());

  auto add_op_itr = func_op.getBody().op_begin<mlir::stablehlo::AddOp>();
  ASSERT_THAT(add_op_itr,
              Ne(func_op.getBody().op_end<mlir::stablehlo::AddOp>()));

  EXPECT_TRUE(IsOpFullyQuantized(*add_op_itr));
}

// function.
bool ShouldIncludeInMainFunction(func::FuncOp func_op) {
  return !func_op.isPrivate() && IsExported(func_op) &&
         IsEntryFunction(func_op);
}

// Sets a function to be private so it can be referred internally.
void SetFunctionPrivate(func::FuncOp func) {
  func.setVisibility(SymbolTable::Visibility::Private);

  Value input = func_op.getArgument(0);
  Value output_init_state = func_op.getArgument(1);
  Value hidden_init_state = func_op.getArgument(2);
  Value weight_kernel = func_op.getArgument(3);
  Value recurrent_kernel = func_op.getArgument(4);
  Value bias = func_op.getArgument(5);

  // The func op should have 5 outputs.
  if (func_op.getNumResults() != 5) return failure();

    // Builder builder(func_op.getContext());
    func_op->setAttr(
        kTfInputShapesAttr,
        ArrayAttr::get(func_op.getContext(), updated_input_shapes_attr));
  }
}

// Validates func ops. Returns `failure` if the function is invalid.
LogicalResult ValidateFuncOp(func::FuncOp func_op) {
  auto input_shapes_attr =
      func_op->getAttrOfType<ArrayAttr>(kTfInputShapesAttr);