/*arg_types=*/{}, /*arg_locs=*/{});

  init_func->setAttr(kTfSavedModelExportedNamesAttr,
                     builder.getStrArrayAttr(
                         {"tf_saved_model.session_initializer_restore_op"}));
  init_func->setAttr(
      kTfSavedModelInitializerTypeAttr,
      builder.getStringAttr(kTfSavedModelInitializerRestoreType));

}

impl::NodeMatcherProperties impl::Attr(std::pair<string, AttrValue> attr) {
  impl::NodeMatcherProperties props;
  props.set_attr(std::move(attr));
  return props;
}

impl::NodeMatcherProperties impl::Attr(string name) {
  impl::NodeMatcherProperties props;
  props.set_attr({std::move(name), std::nullopt});
  return props;
}

NodeMatcherProperties ConstantValue(

      {StringAttr::get(context, "outputs"),
       StringAttr::get(context, absl::StrJoin(output_names, ","))});
  auto dictAttr = DictionaryAttr::get(context, func_attrs);
  main_func->setAttr(StringAttr::get(context, kEntryFunctionAttr), dictAttr);
  main_func->setAttr(
      kTfSavedModelExportedNamesAttr,
      builder.getStrArrayAttr({kImportModelDefaultGraphFuncName}));

  if (input_names.size() != main_func.getNumArguments() ||

    }
  }

  // Update input/output sharding attributes on tf_device.cluster_func op.
  cluster_func->setAttr(tensorflow::kInputShardingAttr,
                        GetStrArrayAttr(builder, sharding_for_args));
  cluster_func->setAttr(tensorflow::kOutputShardingAttr,
                        GetStrArrayAttr(builder, sharding_for_rets));
  return mlir::success();
}

            getContext(), ftype.getResults(), graph_func.getAllResultAttrs(),
            res_types, res_attrs)))
      return failure();

    // Update the function type which has excluded the control args.
    func->setAttr("function_type", TypeAttr::get(rewriter.getFunctionType(
                                       arg_types, res_types)));

    // Update arg/result attributes.
    func.setAllArgAttrs(arg_attrs);

              addtional_pad !=
          bts_crops_attr.getValues<APInt>()[{i, j}].getSExtValue()) {
        op->setAttr("padding", rewriter.getStringAttr("SAME"));
        break;
      }
    }
  }

  // Set dilations
  op->setAttr("dilations", dilations_attr.value());

  if (expand_op) {
    // If there is `expand_op`, we need to rewire the inputs to bypass the

  }

  if (cifg) {
    attributes.push_back(kCoupleInputForgetGates);
  }

  mlir::StringAttr attr_values =
      builder->getStringAttr(llvm::join(attributes, ","));

  func->setAttr(kTFImplements, attr_values);
  return func;
}

class LstmUtilsTest : public ::testing::Test {
 protected:
  LstmUtilsTest() {}

  void SetUp() override {
    context_ = std::make_unique<mlir::MLIRContext>();

            values.push_back(range.second - range.first);
          }
          auto attr_value =
              mlir::DenseI32ArrayAttr::get(tf_op->getContext(), values);
          tf_op->setAttr(attr_name, attr_value);
        };
    if (tf_op->hasTrait<mlir::OpTrait::AttrSizedOperandSegments>() ||
        tf_op->hasTrait<mlir::OpTrait::AttrSizedResultSegments>()) {

        group_annotation = absl::StrCat(parallel_group_attr.getValue().str(),
                                        ",", group_annotation);
      }
      for (auto& op : execute_island.GetBody()) {
        op.setAttr(TF::kParallelExecAnnotation,
                   builder->getStringAttr(group_annotation));
      }
    }

    executes.push_back(execute_island);
  }
}

void CreateIslandsFromParallelExecute(

    new_float_func.setType(FunctionType::get(
        getContext(), TypeRange{ValueRange{new_float_func_args}},
        new_float_func.getResultTypes()));
    symbol_table.insert(new_float_func);

    op->setAttr("f", SymbolRefAttr::get(rewriter.getContext(),
                                        new_float_func.getName()));

    return success();
  }

  LogicalResult matchAndRewrite(TF::PartitionedCallOp op,