}
	}

	return
}

var cgoPrefixes = [...]string{
	"_Ciconst_",
	"_Cfconst_",
	"_Csconst_",
	"_Ctype_",
	"_Cvar_", // actually a pointer to the var
	"_Cfpvar_fp_",
	"_Cfunc_",
	"_Cmacro_", // function to evaluate the expanded expression
}

func (check *Checker) selector(x *operand, e *syntax.SelectorExpr, def *TypeName, wantType bool) {

		}
	}

	return
}

var cgoPrefixes = [...]string{
	"_Ciconst_",
	"_Cfconst_",
	"_Csconst_",
	"_Ctype_",
	"_Cvar_", // actually a pointer to the var
	"_Cfpvar_fp_",
	"_Cfunc_",
	"_Cmacro_", // function to evaluate the expanded expression
}

func (check *Checker) selector(x *operand, e *ast.SelectorExpr, def *TypeName, wantType bool) {

	var goname string
	switch fun := call.Call.Fun.(type) {
	case *ast.SelectorExpr:
		goname = fun.Sel.Name
	case *ast.Ident:
		goname = strings.TrimPrefix(fun.Name, "_C2func_")
		goname = strings.TrimPrefix(goname, "_Cfunc_")
	}
	if goname == "" || goname == "malloc" {
		return "", false
	}
	name := f.Name[goname]
	if name == nil || name.Kind != "func" {
		// Probably a type conversion.

void ConvertNMSPaddedFunc::RewriteFunc() {
  func_->setAttr(kTFImplements,
                 StringAttr::get(func_.getContext(), kTfNMSPadded));
  Value boxes = func_.getArgument(0);
  Value scores = func_.getArgument(1);
  Value max_output_size = func_.getArgument(2);
  Value iou_threshold = func_.getArgument(3);
  Value score_threshold = func_.getArgument(4);
  auto output_type0 = func_.getFunctionType().getResult(0);

LogicalResult ConvertDenseImageWarpFunc::RewriteFunc() {
  func_.eraseBody();
  func_.addEntryBlock();
  func_->setAttr(kTFImplements,
                 StringAttr::get(func_.getContext(), kImageWarping));

  OpBuilder builder(func_.getBody());
  auto op = builder.create<CustomOp>(func_.getLoc(),
                                     func_.getFunctionType().getResults(),
                                     func_.getArguments(), kImageWarping,

  return %0 : tensor<1x3xf32>
}

// CHECK-LABEL: @_func
func.func private @_func(%arg0: tensor<1x3xf32>) -> (tensor<1x3xf32>) {

  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());

  }
  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" {

}

// -----

func.func @_func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}

func.func @testClusterFuncOpFails(%arg0: tensor<i32>) -> tensor<i32> {
   // expected-error@below {{failed TF functional to executor validation, op tf_device.cluster_func is not allowed}}
  %cluster = "tf_device.cluster_func"(%arg0) {func = @_func} : (tensor<i32>) -> tensor<i32>
 func.return %cluster : tensor<i32>

      func.func private @function_1() -> () attributes {tf._original_func_name = "__func_1"} {
        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()
        }
        return
      }

      func.func private @function_2() -> () attributes {tf._original_func_name = "__func_2"} {
        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()