// then any Add with positive delta will return error.
	wait bool
	// number of request(s) currently using the wait group
	count int
}

// Add adds delta, which may be negative, similar to sync.WaitGroup.
// If Add with a positive delta happens after Wait, it will return error,
// which prevent unsafe Add.
func (wg *RateLimitedSafeWaitGroup) Add(delta int) error {
	wg.mu.Lock()
	defer wg.mu.Unlock()

// See the WaitGroup example.
func (wg *WaitGroup) Add(delta int) {
	if race.Enabled {
		if delta < 0 {
			// Synchronize decrements with Wait.
			race.ReleaseMerge(unsafe.Pointer(wg))
		}
		race.Disable()
		defer race.Enable()
	}
	state := wg.state.Add(uint64(delta) << 32)
	v := int32(state >> 32)
	w := uint32(state)
	if race.Enabled && delta > 0 && v == int32(delta) {
		// The first increment must be synchronized with Wait.

        then:
        clock.currentTime == START_MILLIS + 15
    }

    def "provides current time based on nanoTime delta"() {
        when:
        setNanos(delta)

        then:
        clock.currentTime == START_MILLIS + Math.max(0, delta)

        where:
        delta << [0, 100, -100]
    }

    def "resyncs with system wall clock"() {
        when:
        setNanos(10)

        then:

  })";
  CreateModule(kMlirModuleStr);
  CellReader<int64_t> reader(kMetricsName);

  auto result = Run();

  EXPECT_TRUE(result.succeeded());
  EXPECT_EQ(reader.Delta("tf.ConcatV2"), 1);
  EXPECT_EQ(reader.Delta("func.return"), 1);
  EXPECT_EQ(reader.Delta("func.func"), 0);
}

TEST_F(LegalizeTfTypesTest, RecordsStreamzNoQuantOps) {
  static constexpr char kMlirModuleStr[] = R"(

	// histogram.
	mudDegree = 1024
)

type edge struct {
	// At x, the function increases by y.
	x, delta float64
	// Additionally at x is a Dirac delta function with area dirac.
	dirac float64
}

// add adds a uniform function over [l, r] scaled so the total weight
// of the uniform is area. If l==r, this adds a Dirac delta function.
func (d *mud) add(l, r, area float64) {
	if area == 0 {
		return
	}

	if r < l {

}

func (v *Float) appendJSON(b []byte) []byte {
	return strconv.AppendFloat(b, math.Float64frombits(v.f.Load()), 'g', -1, 64)
}

// Add adds delta to v.
func (v *Float) Add(delta float64) {
	for {
		cur := v.f.Load()
		curVal := math.Float64frombits(cur)
		nxtVal := curVal + delta
		nxt := math.Float64bits(nxtVal)
		if v.f.CompareAndSwap(cur, nxt) {
			return
		}
	}
}

// Set sets v to value.

	"istio.io/istio/pkg/util/sets"
)

var knownOptimizationGaps = sets.New(
	"BlackHoleCluster",
	"InboundPassthroughCluster",
	"PassthroughCluster",
)

// compareDiff compares a Delta and SotW XDS response. This allows checking that the Delta XDS
// response returned the optimal result. Checks include correctness checks (e.g. if a config changed,
// we must include it) and possible optimizations (e.g. we sent a config, but it was not changed).

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(ExportFromTensorflowDialectToExecutor(*mlir_module_));

  EXPECT_EQ(compilation_status.Delta(kExportSuccess), 1);
  EXPECT_EQ(compilation_status.Delta(kExportFailed), 0);
}

TEST_F(TensorflowDialectToExecutorTest, ErrorsWhenCannotConvert) {
  CellReader<int64_t> compilation_status(kExportStreamzName);

  })";

  CellReader<int64_t> error(kHasTpuPartitionedCallStreamzName);
  CreateModule(kMlirModuleStr);

  auto result = Run();

  EXPECT_TRUE(result.succeeded());

  EXPECT_EQ(error.Delta("true"), 1);
  EXPECT_EQ(error.Delta("false"), 0);
}

TEST_F(InferenceMetricsPassTest, RecordsFalseForNonTPUPartitionedCallOp) {
  static constexpr char kMlirModuleStr[] = R"(

	ScrapeTypeEnvoy = "envoy"
	ScrapeTypeApp   = "application"
	ScrapeTypeAgent = "agent"
)

var processStartTime = time.Now()

func RecordStartupTime() {
	delta := time.Since(processStartTime)
	startupTime.Record(delta.Seconds())
	log.Infof("Readiness succeeded in %v", delta)