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{

}

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)
}

			metric.LastOnline = epHealth.lastOnline
			metric.Online = epHealth.Online
			metric.Latency = madmin.LatencyStat{
				Curr: epHealth.latency.curr,
				Avg:  epHealth.latency.avg,
				Max:  epHealth.latency.peak,
			}
		}
		m[dID] = metric
	}
	return m
}

func (srs *SRStatus) updateXferRate(sz int64, duration time.Duration) {
	if sz > minLargeObjSize {
		srs.XferRateLrg.addSize(sz, duration)

			if len(qs.XferStats) > 0 {
				tots := qs.XferStats[Total]
				currTransferRate.Value = tots.Curr
				avgTransferRate.Value = tots.Avg
				maxTransferRate.Value = tots.Peak
			}
			ml = []MetricV2{
				activeWorkersCount,
				avgActiveWorkersCount,
				maxActiveWorkersCount,
				currInQueueCount,
				currInQueueBytes,
				avgQueueCount,
				avgQueueBytes,

				x2, ok := v2.XferStats[replication.MetricName(rm)]
				if !ok {
					x2 = replication.XferStats{}
				}
				x2.AvgRate += x.Avg
				x2.CurrRate += x.Curr
				if x.Peak > x2.PeakRate {
					x2.PeakRate = x.Peak
				}
				v2.XferStats[replication.MetricName(rm)] = x2
			}
			sm.Metrics[dID] = v2
		}
	}
	sm.Uptime = UTCNow().Unix() - globalBootTime.Unix()
	return sm, nil
}

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    *   Object metadata has now been deprecated and no longer saved to the
        SavedModel.

*   TF Core:

    *   Added `tf.config.experimental.reset_memory_stats` to reset the tracked
        peak memory returned by `tf.config.experimental.get_memory_info`.

*   `tf.data`:

    *   Added `target_workers` param to `data_service_ops.from_dataset_id` and