// Adds a new input name to the `inputs` field of the `tf.entry_function`
// attribute if the attribute exist in the given function. Otherwise, no
// attribute is modified.
void AddEntryFunctionInput(StringRef input_name, func::FuncOp func_op);

}  // namespace quant
}  // namespace mlir

		c++
	}
	v = (*p)(&c)
	return
}

func (this *pa) func2() p {
	return (*p).func3(this.func1())
}

func (this *p) func3(f int) p {
	return *this
}

func (this *pa) func2dots() p {
	return (*p).func3(this.func1())
}

func (this *p) func3dots(f ...int) p {
	return *this
}

func main() {
	arr := make(pa, 13)
	length := arr.func2()
	if int(length) != len(arr) {
		panic("length != len(arr)")

	return nil
}

type Func2[A1, A2, R any] func(a1 A1, a2 A2) R

func Some[T any](v T) any {
	_ = Some2[T](v)
	return NoneType[T]{}.Recover()
}

//go:noinline
func Some2[T any](v T) any {
	return v
}

type Nil struct{}

type ApplicativeFunctor2[H, HT, A1, A2, R any] struct {
	h any
}

func Applicative2[A1, A2, R any](fn Func2[A1, A2, R]) ApplicativeFunctor2[Nil, Nil, A1, A2, R] {

 private:
  void runOnOperation() override;
};

void ConvertXlaCallModuleOpToBfloat16Pass::runOnOperation() {
  Operation* func_op = getOperation();
  SymbolTableCollection symbol_table;
  OpBuilder builder(&getContext());

  auto result = func_op->walk([&](TF::XlaCallModuleOp op) {
    // Converts the serialized StableHLO module to bfloat16.
    auto result =

    return false;
  }
  return true;
}

void VerifyTFXLALegalization::runOnOperation() {
  Operation* func_op = getOperation();
  ConversionTarget default_conversion_target =
      GetDefaultLegalConversionTargets(getContext(), legalize_chlo_);

  bool has_invalid_ops = false;
  func_op->walk([&](Operation* op) {
    if (!IsMhloAndStatic(op)) {
      has_invalid_ops = true;

	type2     int

	Embed struct { Type1 }
	Uembed struct { type2 }
)

func Func1()     {}
func Func1_Foo() {}
func Func1_foo() {}
func func2()     {}

func (Type1) Func1() {}
func (Type1) Func1_Foo() {}
func (Type1) Func1_foo() {}
func (Type1) func2() {}

func (type2) Func1() {}

type (
	Conflict          int
	Conflict_Conflict int
	Conflict_conflict int
)

    return failure();
  }

  // Fetch all VarHandleOp.
  llvm::StringSet<> variable_names;
  llvm::SmallVector<TF::VarHandleOp, 4> var_ops;
  for (auto func_op : module.getOps<func::FuncOp>()) {
    for (auto var_handle_op : func_op.getOps<TF::VarHandleOp>()) {
      auto variable_name = GetVariableName(var_handle_op);
      if (variable_names.count(variable_name)) continue;
      var_ops.emplace_back(var_handle_op);

  SmallVector<int64_t, 1> layer_norm_size_values_;
};

LogicalResult ConvertKerasLSTMLayer(mlir::func::FuncOp func_op,
                                    OpBuilder* builder);

LogicalResult ConvertKerasLSTMLayer(mlir::func::FuncOp func_op,
                                    OpBuilder* builder, bool indy);

}  // end namespace TFL
}  // end namespace mlir

  assert(cost_map_.count(op) > 0);
  return cost_map_.lookup(op);
}

void CostAnalysis::AnalyzeArguments(mlir::func::FuncOp func_op) {
  // Use the max size among function inputs as the default size of dynamic
  // shaped tensors in the function.
  for (auto arg : func_op.getArguments()) {
    if (!mlir::isa<mlir::TensorType>(arg.getType())) continue;
    auto type = mlir::cast<mlir::TensorType>(arg.getType());

  }

  return success();
}

void LiftHashTableOpsAsArgsPass::runOnOperation() {
  auto module_op = getOperation();

  for (auto func_op : module_op.getOps<func::FuncOp>()) {
    if (failed(LiftHashTableOpsToArguments(module_op, func_op))) {
      signalPassFailure();
      return;
    }
  }
}

static PassRegistration<LiftHashTableOpsAsArgsPass> pass;

}  // namespace