atomic.AddInt64(&lat, latency)
				})
				spent := time.Since(t)
				if spent > 0 && n > 0 {
					// Since we are benchmarking n parallel servers we need to multiply by n.
					// This will give an estimate of the total ops/s.
					latency := float64(atomic.LoadInt64(&lat)) / float64(time.Millisecond)
					b.ReportMetric(float64(n)*float64(ops)/spent.Seconds(), "vops/s")
					b.ReportMetric(latency/float64(ops), "ms/op")

		Estimated total CPU time goroutines spent performing GC
		tasks to assist the GC and prevent it from falling behind the
		application. This metric is an overestimate, and not directly
		comparable to system CPU time measurements. Compare only with
		other /cpu/classes metrics.

	/cpu/classes/gc/mark/dedicated:cpu-seconds
		Estimated total CPU time spent performing GC tasks on processors

{{end}}
</table>

<h3 id="ranges">Special ranges</h3>

The table below describes how much of the traced period each goroutine spent in
certain special time ranges.
If a goroutine has spent no time in any special time ranges, it is excluded from
the table.
For example, how much time it spent helping the GC. Note that these times do
overlap with the times from the first table.

{{end}}
</table>

<h3 id="ranges">Special ranges</h3>

The table below describes how much of the traced period each goroutine spent in
certain special time ranges.
If a goroutine has spent no time in any special time ranges, it is excluded from
the table.
For example, how much time it spent helping the GC. Note that these times do
overlap with the times from the first table.

// IO wait, currently only network blocking event).
func computePprofIO() computePprofFunc {
	return makeComputePprofFunc(trace.GoWaiting, func(reason string) bool {
		return reason == "network"
	})
}

// computePprofBlock returns a computePprofFunc that generates blocking pprof-like profile
// (time spent blocked on synchronization primitives).

// utilization without hurting throughput.
//
// Note that the bucket in the leaky bucket mechanism can never go negative,
// so the GC never gets credit for a lot of CPU time spent without the GC
// running. This is intentional, as an application that stays idle for, say,
// an entire day, could build up enough credit to fail to prevent a death

	ScavengeTotalTime  int64

	IdleTime int64 // Time Ps spent in _Pidle.
	UserTime int64 // Time Ps spent in _Prunning or _Psyscall that's not any of the above.

	TotalTime int64 // GOMAXPROCS * (monotonic wall clock time elapsed)
}

// accumulateGCPauseTime add dt*stwProcs to the GC CPU pause time stats. dt should be
// the actual time spent paused, for orthogonality. maxProcs should be GOMAXPROCS,

			select {
			case <-dctx.Done():
				if !timeout.Stop() {
					<-timeout.C
				}
			case <-timeout.C:
				spent := time.Since(started)
				goOffline(fmt.Errorf("unable to write+read for %v", spent.Round(time.Millisecond)), spent)
			}
		}()

		func() {
			defer dcancel()

			err := p.storage.WriteAll(ctx, minioMetaTmpBucket, fn, toWrite)
			if err != nil {

//
// # Block profile
//
// The block profile tracks time spent blocked on synchronization primitives,
// such as [sync.Mutex], [sync.RWMutex], [sync.WaitGroup], [sync.Cond], and
// channel send/receive/select.
//
// Stack traces correspond to the location that blocked (for example,
// [sync.Mutex.Lock]).
//
// Sample values correspond to cumulative time spent blocked at that stack

        /* metric description */ "status" /* metric label */);

auto* phase2_bridge_compilation_time = tsl::monitoring::Sampler<1>::New(
    {"/tensorflow/core/tf2xla/api/v1/phase2_compilation_time",
     "The wall-clock time spent on executing graphs in milliseconds.",
     "configuration"},
    // Power of 1.5 with bucket count 45 (> 23 hours)
    {tsl::monitoring::Buckets::Exponential(1, 1.5, 45)});