Type methodType = Type.getMethodType(desc);
        maybeAddDependentType(types, methodType.getReturnType());
        for (Type argType : methodType.getArgumentTypes()) {
            maybeAddDependentType(types, argType);
        }
        return new MethodVisitor(types);
    }

    @Override
    public org.objectweb.asm.AnnotationVisitor visitAnnotation(String desc, boolean visible) {

		case CRC32:
			op += byteSizeSuffix(argBytes(&inst, inst.Args[1]))

		case LGDT, LIDT, SGDT, SIDT:
			op += byteSizeSuffix(inst.DataSize / 8)

		case MOVZX, MOVSX:
			// Integer size conversions get two suffixes.
			op = op[:4] + byteSizeSuffix(argBytes(&inst, inst.Args[1])) + byteSizeSuffix(argBytes(&inst, inst.Args[0]))

		case LOOP, LOOPE, LOOPNE:

// and reflect.methodValue.
type reflectMethodValue struct {
	fn     uintptr
	stack  *bitvector // ptrmap for both args and results
	argLen uintptr    // just args
}

// argBytes returns the argument frame size for a call to frame.fn.
func (frame *stkframe) argBytes() uintptr {
	if frame.fn.args != abi.ArgsSizeUnknown {
		return uintptr(frame.fn.args)
	}
	// This is an uncommon and complicated case. Fall back to fully

  string GetOpAttrStruct() const;
  string GetConstructorDecl(StringPiece op_name_prefix,
                            bool include_attr) const;

  string op_name;
  std::vector<string> arg_types;
  std::vector<string> arg_names;
  std::vector<string> output_types;
  std::vector<string> output_names;
  std::vector<bool> is_list_output;
  bool has_optional_attrs;
  string comment;

    func->setAttrs(graph_func->getAttrs());

    llvm::SmallVector<Type> arg_types;
    llvm::SmallVector<Type> res_types;
    llvm::SmallVector<DictionaryAttr> arg_attrs;
    llvm::SmallVector<DictionaryAttr> res_attrs;
    if (failed(FilterTfgSpecificArgResultAttributes(
            getContext(), ftype.getInputs(), graph_func.getAllArgAttrs(),
            arg_types, arg_attrs)) ||
        failed(FilterTfgSpecificArgResultAttributes(

  auto guard = OpBuilder::InsertionGuard(builder);
  builder.setInsertionPointAfter(current_func);
  TypeRange arg_types{ValueRange{arguments}};
  TypeRange result_types{ValueRange{results}};
  auto func_type = FunctionType::get(context, arg_types, result_types);

  SmallVector<Location> arg_locs;
  for (Value arg : arguments) {
    arg_locs.push_back(arg.getLoc());
  }

  // shapes of these graph edges, so we just query it to build the corresponding
  // MLIR function type signature.

  llvm::SmallVector<mlir::Type, 4> arg_types;
  if (specs_.inputs.empty()) {
    arg_types.reserve(fbody.arg_types.size());
    for (auto arg : fbody.arg_nodes) {
      // Find node in the graph using the node id instead of using `arg`
      // directly because the graph has been cloned.

            Class[] classes = new Class[arguments.length];
            for (int i = 0; i < arguments.length; i++) {
                Object argType = arguments[i];
                if (argType == null) {
                    classes[i] = null;
                } else {
                    classes[i] = argType.getClass();
                }
            }
            return classes;
        }

  op_name = SeparateNamespaces(api_def.endpoint(0).name());
  InferOpAttributes(graph_op_def, &inferred_input_attrs);
  has_optional_attrs = HasOptionalAttrs(api_def, inferred_input_attrs);
  arg_types.push_back("const ::tensorflow::Scope&");
  arg_names.push_back("scope");

  if (graph_op_def.has_deprecation()) {
    if (!api_def.summary().empty()) {
      comment = strings::StrCat(api_def.summary(), "\n");

            BackwardsConstAnalysis(**subgraph, &const_args,
                                   /*compile_time_const_nodes=*/nullptr, flr));

        DataTypeVector arg_types(num_args);
        TF_RETURN_IF_ERROR(GetArgTypes(**subgraph, &arg_types));

        // Compute a permutation of the arguments such that the constant
        // arguments are first.
        const int num_consts =