// 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

// 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).

	}
	return d2
}

// DurationTracker is a simple interface for tracking functions duration,
// it is safe for concurrent use by multiple goroutines.
type DurationTracker interface {
	// Track measures time spent in the given function f and
	// aggregates measured duration using aggregateFunction.
	// if Track is invoked with f from multiple goroutines concurrently,
	// then f must be safe to be invoked concurrently by multiple goroutines.

			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 {

    "model_path");
auto* load_latency_by_stage = monitoring::Sampler<2>::New(
    {
        "/tensorflow/cc/saved_model/load_latency_by_stage",  // metric name
        "Distribution of wall time spent (in microseconds) in each stage "
        "(restore graph from disk, run init graph op, etc) when loading the "
        "model",
        "model_path",
        "stage",
    },

Você faz com tipos padrão do Python moderno.

Você não terá que aprender uma nova sintaxe, métodos ou classes de uma biblioteca específica etc.

Apenas **Python** padrão.

Por exemplo, para um `int`:

```Python
item_id: int
```

ou para um modelo mais complexo, `Item`:

```Python
item: Item
```

        /* 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)});

//
// # 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

	limit int64

	// count is the number of values the worker actually tested.
	count int64

	// totalDuration is the time the worker spent testing inputs.
	totalDuration time.Duration

	// entryDuration is the time the worker spent execution an interesting result
	entryDuration time.Duration
}

type fuzzMinimizeInput struct {
	// entry is an interesting value or crasher to minimize.

As a result, any expensive work performed during configuration slows down every invocation.
Even simple commands like `gradle help` and `gradle tasks`.

The next few subsections introduce techniques that can reduce time spent in the configuration phase.