# CHECK-LABEL:       func @main() {

# Verify that the NameAttrList is properly turned into reference to functions on import
# CHECK:    tf.Case
# CHECK-SAME: branches = [@[[FOO:[a-z0-9]+]], @[[BAR:[a-z0-9]+]]]
# CHECK-DAG:  func private @[[FOO]]()
# CHECK-DAG:  func private @[[BAR]]()

node {
  name: "predicate"
  op: "Const"
  attr {
    key: "dtype"

  func->setAttr(kTfSavedModelExportedNamesAttr,
                builder.getStrArrayAttr({kSessionInitFuncName}));
  func->setAttr(kTfSavedModelInitializerTypeAttr,
                builder.getStringAttr(kTfSavedModelInitializerRestoreType));
  func.setVisibility(mlir::func::FuncOp::Visibility::Public);
  auto func_builder = OpBuilder::atBlockBegin(func.addEntryBlock());
  func_builder.create<mlir::func::ReturnOp>(func.getLoc());

  SmallVector<int64_t, 1> layer_norm_slice_shape_;
  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

bool IsMainOrInitializerFunction(ModuleOp module, func::FuncOp func) {
  if (func.getSymName() ==
          llvm::StringRef(tensorflow::kImportModelDefaultGraphFuncName) ||
      func.getSymName() == kTfQuantSaveFuncName) {
    return true;
  }

  for (func::FuncOp init_func :
       tf_saved_model::GetInitializerFunctions(module)) {
    if (func.getSymName() == init_func.getSymName()) {
      return true;
    }

# CHECK-DAG: func private @custom_embedding_matmul{{[0-9]*}}(){{.+}}tf._implements = #tf_type.func<@tensorflow.embedding_matmul, {key1 = 2 : i64, key2 = false}>, tf._original_func_name = "custom_embedding_matmul"}...

		}
	}
	return
}

func (subst *subster) func_(f *Func) *Func {
	if f != nil {
		if typ := subst.typ(f.typ); typ != f.typ {
			return substFunc(f, typ)
		}
	}
	return f
}

func substFunc(f *Func, typ Type) *Func {
	copy := *f
	copy.typ = typ
	copy.origin = f.Origin()
	return &copy
}

func (subst *subster) funcList(in []*Func) (out []*Func, copied bool) {
	out = in

// CHECK-LABEL: func @f
// 2 copies of @g
// CHECK-DAG: func @g{{.*}}
// CHECK-DAG: func private @g{{.*}}
// 4 copies of @h
// CHECK-DAG: func @h{{.*}}
// CHECK-DAG: func private @h{{.*}}
// CHECK-DAG: func private @h{{.*}}
// CHECK-DAG: func private @h{{.*}}
func.func @f() {
  func.call @g() : () -> ()
  func.call @g() : () -> ()
  func.return
}

func.func @g() {
  func.call @h() : () -> ()
  func.call @h() : () -> ()

		{`package s8; func _() { f(g, h) }; func f[P any](func(int, P), func(P, string)) {}; func g[P any](P, P) {}; func h[P, Q any](P, Q) {}`, `h`, `func(int, string)`},
		{`package s9; func _() { f(g, h[int]) }; func f[P any](func(int, P), func(P, string)) {}; func g[P any](P, P) {}; func h[P, Q any](P, Q) {}`, `h`, `func[P, Q any](P, Q)`}, // go.dev/issues/60212

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "foo,bar", outputs = "Add"}} {
  %graph = tf_executor.graph {
    // This node would be renamed to bar1 [note: if imported from TF graphdef this would not be possible]

bool HasTfSavedModelSemantics(ModuleOp module_op);

// Returns the tf_saved_model.global_tensor op that func's arg_index'th argument
// refers to as a bound input, or null.
Operation *LookupBoundInput(func::FuncOp func, int arg_index,
                            const SymbolTable &symbol_table);

template <typename T>
T LookupBoundInputOfType(func::FuncOp func, int arg_index,
                         const SymbolTable &symbol_table) {