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

//     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

// 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 {

	// GaugeMT - represents a gauge metric.
	GaugeMT
	// HistogramMT - represents a histogram metric.
	HistogramMT
	// rangeL - represents a range label.
	rangeL = "range"
)

func (mt MetricType) String() string {
	switch mt {
	case CounterMT:
		return "counter"
	case GaugeMT:
		return "gauge"
	case HistogramMT:
		return "histogram"
	default:
		return "*unknown*"
	}
}

// HISTOGRAM-MSE-BRUTEFORCE-CHECK-NEXT:  return [[res]] : tensor<*xf32>

// HISTOGRAM-MSE-BRUTEFORCE-CHECK: func @no_composite_func
// HISTOGRAM-MSE-BRUTEFORCE-CHECK-NEXT:  "tf.AddV2"
// HISTOGRAM-MSE-BRUTEFORCE-CHECK-NEXT:  return

// HISTOGRAM-MSE-BRUTEFORCE-CHECK: func @composite_conv2d_with_relu6_fn
// HISTOGRAM-MSE-BRUTEFORCE-CHECK-NEXT:  "tf.Conv2D"
// HISTOGRAM-MSE-BRUTEFORCE-CHECK-NEXT:  "tf.Relu6"

}

void CalibrationStatisticsCollectorHistogram::Collect(
    const float min, const float max, absl::Span<const int64_t> histogram) {
  if (histogram.empty()) return;

  // Reconstruct the bin width, lower and upper bound from the collected data.
  const float collected_bin_width =
      CalculateBinWidth(min, max, histogram.size());
  const float collected_lower_bound =
      CalculateLowerBound(min, collected_bin_width);

TEST(CalibrationStatisticsCollectorTest, SimpleMinMax) {
  auto collector = CalibrationStatisticsCollectorMinMax();

  collector.Collect(
      /*min=*/1.0f, /*max=*/10.f, /*histogram=*/{});
  collector.Collect(
      /*min=*/-5.0f, /*max=*/5.f, /*histogram=*/{});

  std::optional<CalibrationStatistics> statistics = collector.GetStatistics();

  EXPECT_TRUE(statistics.has_value());