namespace mlir {
namespace tf_executor {
namespace {

// Comparator for `OpsInReverseProgramOrder`.
struct IsAfterInBlock {
  bool operator()(Operation* op, Operation* other_op) const {
    // This function has an average complexity of O(1).
    return other_op->isBeforeInBlock(op);
  }
};

#define GEN_PASS_DEF_EXECUTORUPDATECONTROLDEPENDENCIESPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

	// https://docs.google.com/document/d/1YDlGIdVTPnmUiTAavlZxBI1d9pwGQgZT7IKFKlIXohQ/edit?usp=sharing
	// The basic algorithm is to track the average size of stacks
	// and start goroutines with stack equal to that average size.
	// Starting at the average size uses at most 2x the space that
	// an ideal algorithm would have used.
	// This is just a heuristic to avoid excessive stack growth work

		Name:      avgLinkLatency,
		Help:      "Average replication link latency in milliseconds",
		Type:      gaugeMetric,
	}
}

func getClusterReplAvgQueuedOperationsMD() MetricDescription {
	return MetricDescription{
		Namespace: nodeMetricNamespace,
		Subsystem: replicationSubsystem,
		Name:      avgInQueueCount,
		Help:      "Average number of objects queued for replication since server uptime",

            th().colspan("3").end();
            final String colspanForField = String.valueOf(testHistory.getScenarioCount() * getColumnsForSamples());
            th().colspan(colspanForField).text("Average build time").end();
            th().colspan("8").text("Details").end();
            end();
            tr();
            th().text("Date").end();
            th().text("Branch").end();

	}

	// Get pause time stats.
	slices.Sort(pauses)
	var total uint64
	for _, ns := range pauses {
		total += ns
	}
	// ns/op for this benchmark is average STW time.
	b.ReportMetric(float64(total)/float64(b.N), "ns/op")
	b.ReportMetric(float64(pauses[len(pauses)*95/100]), "p95-ns/STW")
	b.ReportMetric(float64(pauses[len(pauses)*50/100]), "p50-ns/STW")
}

JetBrains offers several licensing options that allow you to scale TeamCity to your needs.
In this setup, we'll use TeamCity Professional, a free fully functional edition suitable for average projects.
In the course of this section, you'll learn how to set up TeamCity, create a build configuration to pull the source code from GitHub and run the Gradle build.

=== Install and start TeamCity

	}

	if target.AverageUtilization == nil {
		errMsg := "invalid resource metric source: neither an average utilization target nor an average value (usage) target was set"
		return 0, nil, time.Time{}, "", condition, fmt.Errorf(errMsg)
	}

	targetUtilization := *target.AverageUtilization

  // Next ID: 7
  enum CalibrationMethod {
    CALIBRATION_METHOD_UNSPECIFIED = 0;
    // Use the min, max values of all sample datasets.
    CALIBRATION_METHOD_MIN_MAX = 1;
    // Use the average of min, max values in each sample dataset.
    CALIBRATION_METHOD_AVERAGE_MIN_MAX = 2;
    // Use the min/max percentile value of histogram.
    CALIBRATION_METHOD_HISTOGRAM_PERCENTILE = 3;

							cachedTypeFields(t)
						}
						wg.Done()
					}(j)
				}
				wg.Wait()
			}
		})
	}

	// HitTypes tests the performance of repeated cache hits.
	// This measures the average time of each cache lookup.
	for nt := 1; nt <= maxTypes; nt *= 10 {
		// Pre-warm a cache of size nt.
		clearCache()
		for _, t := range types[:nt] {
			cachedTypeFields(t)
		}

	stats.highWatermark = obs.Max
	if obs.Duration <= 0 {
		return
	}
	if math.IsNaN(obs.Deviation) {
		obs.Deviation = 0
	}
	stats.avg = obs.Average
	stats.stdDev = obs.Deviation
	envelope := obs.Average + obs.Deviation
	stats.smoothed = math.Max(envelope, seatDemandSmoothingCoefficient*stats.smoothed+(1-seatDemandSmoothingCoefficient)*envelope)
}