statistics.histogram_statistics.lower_bound = 0.0
    statistics.histogram_statistics.bin_width = 1.0

    hist_freq = np.zeros(501, dtype=np.int32)

    # Advanced calibration methods that use histograms detect outliers, so they
    # don't use the outliers as min/max values.
    hist_freq[0] = 1
    hist_freq[-1] = 1

    # The majority of the data exists around the center.
    hist_freq[250] = 1000

function blinkGcMetric(histograms,model){addDurationOfTopEvents(histograms,model);addDurationOfAtomicPause(histograms,model);addDurationOfAtomicPauseTransitiveClosure(histograms,model);addTotalDurationOfTopEvents(histograms,model);addTotalDurationOfBlinkAndV8TopEvents(histograms,model);addTotalDurationOfRootsMarking(histograms,model);addTotalDurationOfMarkingTransitiveClosure(histograms,model);addTotalDurationOfForegroundMarking(histograms,model);addTo...

//     the entity name itself contains a '/', that's ok: "cmd/go/flag" is fine.
//
//   - Words should be '-' separated, as in "gopls/completion/errors-latency"
//
//   - Histograms should use bucket names identifying upper bounds with '<'.
//     For example given two counters "gopls/completion/latency:<50ms" and
//     "gopls/completion/latency:<100ms", the "<100ms" bucket counts events

	// Examples of units might be "seconds", "bytes", "bytes/second", "cpu-seconds",
	// "byte*cpu-seconds", and "bytes/second/second".
	//
	// For histograms, multiple units may apply. For instance, the units of the buckets and
	// the count. By convention, for histograms, the units of the count are always "samples"
	// with the type of sample evident by the metric's name, while the unit in the name
	// specifies the buckets' unit.
	//

// CKECK-SAME: <{calibration_method = 5 : i32, id = "0", num_bins = 32 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> : (tensor<1x3x4x3xf32>) -> (tensor<1x3x4x3xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

// dataUsageHash is the hash type used.
type dataUsageHash string

// sizeHistogramV1 is size histogram V1, which has fewer intervals esp. between
// 1024B and 1MiB.
type sizeHistogramV1 [dataUsageBucketLenV1]uint64

// sizeHistogram is a size histogram.
type sizeHistogram [dataUsageBucketLen]uint64

// versionsHistogram is a histogram of number of versions in an object.
type versionsHistogram [dataUsageVersionLen]uint64

// CKECK-SAME: <{calibration_method = 5 : i32, id = "skipping_id", num_bins = 32 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> : (tensor<1x3x4x3xf32>) -> (tensor<1x3x4x3xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

// timeHistogram represents a distribution of durations in
// nanoseconds.
//
// The accuracy and range of the histogram is defined by the
// timeHistSubBucketBits and timeHistNumBuckets constants.
//
// It is an HDR histogram with exponentially-distributed
// buckets and linearly distributed sub-buckets.
//
// The histogram is safe for concurrent reads and writes.
type timeHistogram struct {

	"math"
	"strings"
	"time"
)

// TimeHistogram is an high-dynamic-range histogram for durations.
type TimeHistogram struct {
	Count                int
	Buckets              []int
	MinBucket, MaxBucket int
}

// Five buckets for every power of 10.
var logDiv = math.Log(math.Pow(10, 1.0/5))

// Add adds a single sample to the histogram.
func (h *TimeHistogram) Add(d time.Duration) {
	var bucket int
	if d > 0 {

type Float64Histogram struct {
	// Counts contains the weights for each histogram bucket.
	//
	// Given N buckets, Count[n] is the weight of the range
	// [bucket[n], bucket[n+1]), for 0 <= n < N.
	Counts []uint64

	// Buckets contains the boundaries of the histogram buckets, in increasing order.
	//
	// Buckets[0] is the inclusive lower bound of the minimum bucket while