}
    } else {
      sumOfProductsOfDeltas = NaN;
    }
    yStats.add(y);
  }

  /**
   * Adds the given statistics to the dataset, as if the individual values used to compute the
   * statistics had been added directly.
   */
  public void addAll(PairedStats values) {
    if (values.count() == 0) {
      return;
    }

    xStats.addAll(values.xStats());

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.gradle.internal.buildevents

import org.gradle.api.internal.tasks.execution.statistics.TaskExecutionStatistics
import org.gradle.internal.logging.text.TestStyledTextOutputFactory
import org.gradle.util.internal.TextUtil
import spock.lang.Specification
import spock.lang.Subject

#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"

namespace mlir::quant::stablehlo {

// Adds passes for static-range quantization pre-calibration. Inserts ops
// required to collect tensor statistics.
void AddPreCalibrationPasses(
    OpPassManager& pm,
    const ::stablehlo::quantization::CalibrationOptions& calibration_options,
    const ::stablehlo::quantization::QuantizationSpecs& specs,

  }

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values
   */
  public static Stats of(double... values) {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(values);
    return accumulator.snapshot();
  }

  /**
   * Returns statistics over a dataset containing the given values.
   *

  }

  /** Returns the number of pairs in the dataset. */
  public long count() {
    return xStats.count();
  }

  /** Returns the statistics on the {@code x} values alone. */
  public Stats xStats() {
    return xStats;
  }

  /** Returns the statistics on the {@code y} values alone. */
  public Stats yStats() {
    return yStats;
  }

  /**

	levelDebug = "debug"
	levelTrace = "trace"
)

var ztunnelLogPattern = regexp.MustCompile(`^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}\.\d+Z)\s+(?:\w+\s+)?(\w+)\s+([\w\.:]+)(.*)`)

// Stats represents log statistics.
type Stats struct {
	numFatals   int
	numErrors   int
	numWarnings int
}

// Importance returns an integer that indicates the importance of the log, based on the given Stats in s.

  TF_DerivedResultTypeAttr Tout = TF_DerivedResultTypeAttr<0>;
}

def TF_CustomAggregatorOp : TF_Op<"CustomAggregator", [Pure]> {
  let summary = "Gathers min and max statistics of a given tensor.";

  let arguments = (ins
    TensorOf<[TF_Float32]>:$input,

    // The unique id of this `CustomAggregator` op.
    StrAttr:$id,
    // The integer value of the enforcing `CalibrationMethod`.

  }

  /** Returns the number of pairs in the dataset. */
  public long count() {
    return xStats.count();
  }

  /** Returns the statistics on the {@code x} values alone. */
  public Stats xStats() {
    return xStats;
  }

  /** Returns the statistics on the {@code y} values alone. */
  public Stats yStats() {
    return yStats;
  }

  /**

   */
  public void addAll(long... values) {
    for (long value : values) {
      add(value);
    }
  }

  /**
   * Adds the given statistics to the dataset, as if the individual values used to compute the
   * statistics had been added directly.
   */
  public void addAll(Stats values) {
    if (values.count() == 0) {
      return;
    }

  This function assumes _graph mode_ (used when legacy TF1 is used or when eager
  mode is explicitly disabled) when running the graph. This step is used in
  order to collect the statistics in CustomAggregatorOp for quantization using
  the representative dataset for the actual data provided for inference.

  Args:
    model_dir: Path to SavedModel directory.