}

  void TearDown() override { test_ctx_->Destroy(); }

  mlir::MLIRContext ctx_;
  std::unique_ptr<tfr::TFRDecomposeContext> test_ctx_;
};

std::vector<NodeAndType> NodesSequenceOf(const FunctionDef& graph) {
  std::vector<NodeAndType> nodes;
  for (auto& node : graph.node_def()) {
    nodes.push_back({node.op(), node.attr().at("T").type()});
  }
  return nodes;
}

                          : std::make_unique<FunctionLibraryDefinition>(
                                default_registry, FunctionDefLibrary());

  if (library) {
    // Upgrade nodes in the functions.
    for (FunctionDef& fdef : *library->mutable_function()) {
      auto func_name = fdef.signature().name();
      for (auto& node_def : *fdef.mutable_node_def()) {
        const OpDef* op_def = nullptr;

  // in constant time.
  tensorflow::StatusOr<const tensorflow::NodeDef*> GetGraphDefNode(
      std::string name);

  // Returns a list of function defs in the SavedModel.
  const protobuf::RepeatedPtrField<FunctionDef>& GetFunctionDefs();

  // Returns a BundleReader for reading variable values.
  //
  // This TFPackage retains ownership of the underlying reader.
  tensorflow::BundleReader* GetVariableReader() {

bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def);

bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def);

bool GetFunctionDef(TF_Function* func, tensorflow::FunctionDef* func_def);

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s);

// Returns a sorted vector of std::pair<function_name, gradient_func> from