test     int             // pass-specific ad-hoc option, perhaps useful in development
	dump     map[string]bool // dump if function name matches
}

func (p *pass) addDump(s string) {
	if p.dump == nil {
		p.dump = make(map[string]bool)
	}
	p.dump[s] = true
}

func (p *pass) String() string {
	if p == nil {
		return "nil pass"
	}
	return p.name
}

    config = "test_graph_tfadd.config.pbtxt",
    cpp_class = "AddComp",
    graph = "test_graph_tfadd.pbtxt",
    mlir_components = "None",
    tags = [
        "manual",
    ],
)

tf_library(
    name = "test_graph_tfadd_mlir_bridge",
    testonly = 1,
    config = "test_graph_tfadd.config.pbtxt",
    cpp_class = "AddComp",
    graph = "test_graph_tfadd.pbtxt",
    mlir_components = "Bridge",

namespace benchmark {
namespace {

// There isn't much we can verify in a stable fashion, so we just run the
// benchmark with max_iters, and ensure we end up with that many iter stats.
TEST(Benchmark, Benchmark) {
  AddComp add;

  Options options;
  options.max_iters = 1;
  Stats stats1;
  Benchmark(options, [&] { add.Run(); }, &stats1);
  EXPECT_EQ(stats1.per_iter_us.size(), 1);

  options.max_iters = 5;
  Stats stats5;

#endif

namespace tensorflow {
namespace tfcompile {
namespace {

using ::testing::HasSubstr;
using ::testing::IsSupersetOf;

TEST(TFCompileTest, Add) {
  AddComp add;
  EXPECT_EQ(add.arg0_data(), add.arg_data(0));
  EXPECT_EQ(add.arg1_data(), add.arg_data(1));

  add.arg0() = 1;
  add.arg1() = 2;
  EXPECT_TRUE(add.Run());
  EXPECT_EQ(add.error_msg(), "");

]

[
    [
        tf_library(
            name = "test_graph_tfadd" + suffix,
            testonly = 1,
            config = "test_graph_tfadd.config.pbtxt",
            cpp_class = "AddComp",
            graph = "test_graph_tfadd.pb",
            # This serves as a test for the list of minimal deps included even when
            # include_standard_runtime_deps is False.  If this target fails to