}

func (rx *XferStats) merge(o XferStats) XferStats {
	curr := calcAvg(rx.curr(), o.curr(), rx.N, o.N)
	peak := rx.Peak
	if o.Peak > peak {
		peak = o.Peak
	}
	if curr > peak {
		peak = curr
	}
	return XferStats{
		Avg:     calcAvg(rx.Avg, o.Avg, rx.N, o.N),
		Peak:    peak,
		Curr:    curr,
		measure: rx.measure,
		N:       rx.N + o.N,
	}
}

		totpeak = math.Max(math.Max(tot.Peak, totpeak), tot.Curr)
		lpeak = math.Max(math.Max(v[Large].Peak, lpeak), v[Large].Curr)
		speak = math.Max(math.Max(v[Small].Peak, speak), v[Small].Curr)
		if lpeak > 0 || speak > 0 {
			count++
		}
	}
	if count > 0 {
		lrg := XferStats{
			Avg:  lavg / float64(count),
			Curr: lcurr / float64(count),
			Peak: lpeak,
		}
		sml := XferStats{

  return profile;
}

Rematerializer::MemSpec Rematerializer::GetPeakMemory(
    const RematSpec& remat) const {
  MemSpec peak;
  MapMem([&](const MemSpec& m) { peak = std::max(m, peak, BySize); }, remat);
  return peak;
}

int Rematerializer::FindBestRematPoint(const int begin, const int end,
                                       const int peak_loc) const {

// (2) Allows for the fast simulation of changes to the peak memory requirement
//     under rematerialization of intermediate results in the graph
// (3) Implements a greedy algorithm for finding rematerializations of
//     intermediate results in that graph to lower peak memory requirements.
class Rematerializer {
 public:
  Rematerializer() = default;

}

type latencyStat struct {
	lastmin lastMinuteLatency
	curr    time.Duration
	avg     time.Duration
	peak    time.Duration
	N       int64
}

func (l *latencyStat) update(d time.Duration) {
	l.lastmin.add(d)
	l.N++
	if d > l.peak {
		l.peak = d
	}
	l.curr = l.lastmin.getTotal().avg()
	l.avg = time.Duration((int64(l.avg)*(l.N-1) + int64(l.curr)) / l.N)
}

			}
		case "av":
			z.Avg, err = dc.ReadFloat64()
			if err != nil {
				err = msgp.WrapError(err, "Avg")
				return
			}
		case "p":
			z.Peak, err = dc.ReadFloat64()
			if err != nil {
				err = msgp.WrapError(err, "Peak")
				return
			}
		case "n":
			z.N, err = dc.ReadInt64()
			if err != nil {
				err = msgp.WrapError(err, "N")
				return
			}
		default:

        final JvmThreadsObj jvmThreadsObj = new JvmThreadsObj();
        jvmObj.threads = jvmThreadsObj;
        jvmThreadsObj.count = threads.getCount();
        jvmThreadsObj.peak = threads.getPeakCount();
        final Classes classes = jvmStats.getClasses();
        final JvmClassesObj jvmClassesObj = new JvmClassesObj();
        jvmObj.classes = jvmClassesObj;

			stackBytes:    constant(8192),
			length:        50,
			checker: func(t *testing.T, c []gcCycleResult) {
				n := len(c)
				if peak := c[n-1].heapPeak; peak >= applyMemoryLimitHeapGoalHeadroom(512<<20) {
					t.Errorf("peak heap size reaches heap limit: %d", peak)
				}
				if n >= 25 {
					// At this alloc/scan rate, the pacer should be extremely close to the goal utilization.

  // not storing the rewritten subgraph for each signature.
  rewritten_options.config.mutable_experimental()
      ->set_disable_output_partition_graphs(true);
  // TODO(mrry): Consider specializing the session creation to reduce peak
  // RAM consumption by using `Session::Create(GraphDef&&)`.
  TF_RETURN_IF_ERROR(LoadSavedModelGeneric(rewritten_options, run_options,
                                           export_dir, tags, bundle));

func runtime_debug_WriteHeapDump(fd uintptr) {
	stw := stopTheWorld(stwWriteHeapDump)

	// Keep m on this G's stack instead of the system stack.
	// Both readmemstats_m and writeheapdump_m have pretty large
	// peak stack depths and we risk blowing the system stack.
	// This is safe because the world is stopped, so we don't
	// need to worry about anyone shrinking and therefore moving
	// our stack.
	var m MemStats
	systemstack(func() {