}
		return nil
	}
}

func Distribution(count uint64, sum float64) func(any) error {
	return func(f any) error {
		d := f.(*dto.Histogram)
		if *d.SampleCount != count {
			return fmt.Errorf("want %v samples, got %v", count, *d.SampleCount)
		}
		if *d.SampleSum != sum {
			return fmt.Errorf("want %v sum, got %v", count, *d.SampleSum)
		}
		return nil
	}
}

	totalOther := &m[3]

	sampleCount := func(s *metrics.Sample) uint64 {
		h := s.Value.Float64Histogram()

		var n uint64
		for _, c := range h.Counts {
			n += c
		}
		return n
	}

	// Read baseline.
	metrics.Read(m)

	baselineStartGC := sampleCount(stoppingGC)
	baselineStartOther := sampleCount(stoppingOther)
	baselineTotalGC := sampleCount(totalGC)

		metric := mf.GetMetric()[0]
		hist := metric.GetHistogram()
		if hist == nil {
			return 0, errors.New("dto.Metric has nil Histogram")
		}
		if hist.SampleCount == nil {
			return 0, errors.New("dto.Histogram has nil SampleCount")
		}
		return int64(*hist.SampleCount), nil
	}
	return 0, errMetricNotFound
}

func BenchmarkTimingRatioHistogram(b *testing.B) {
	b.StopTimer()
	now := time.Now()

	m := metrics.PluginExecutionDuration.WithLabelValues(wantPlugin, wantExtensionPoint, wantStatus.String())

	count, err := testutil.GetHistogramMetricCount(m)
	if err != nil {
		t.Errorf("Failed to get %s sampleCount, err: %v", metrics.PluginExecutionDuration.Name, err)
	}
	if count == 0 {
		t.Error("Expect at least 1 sample")
	}
	value, err := testutil.GetHistogramMetricValue(m)
	if err != nil {

  }];

  let arguments = (ins
    TF_Int32Tensor:$indices_or_row_splits,
    TF_Int32Tensor:$values,
    TF_Float32Tensor:$weights,

    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$sample_count,
    StrAttr:$combiner
  );

  let results = (outs
    TF_Int32Tensor:$row_ids,
    TF_Int32Tensor:$col_ids,
    TF_Float32Tensor:$gains
  );
}

const iterations=DEFAULT_ITERATION_FOR_BOOTSTRAP_RESAMPLING;if(this.sampleMeans_.length!==iterations){this.sampleMeans_=[];for(let i=0;i<iterations;i++){let tempSum=0.0;for(let j=0;j<sampleCount;j++){tempSum+=filteredSamples[Math.floor(Math.random()*sampleCount)];}...