"//tensorflow/compiler/mlir/lite/python:jax_to_tfl_flatbuffer",
        "//tensorflow/compiler/mlir/lite/python:saved_model_to_tfl_flatbuffer",
        "//tensorflow/compiler/mlir/lite/quantization/lite:quantize_model",
        "//tensorflow/compiler/mlir/quantization/common/quantization_lib:quantization_config",
        "//tensorflow/compiler/mlir/lite/sparsity:sparsify_model",

*   `tf.lite`:

    *   `tf.quantization.quantize_and_dequantize_v2` has been introduced, which
        updates the gradient definition for quantization which is outside the
        range to be 0. To simulate the V1 the behavior of
        `tf.quantization.quantize_and_dequantize(...)` use
        `tf.grad_pass_through(tf.quantization.quantize_and_dequantize_v2)(...)`.

*   Building TensorFlow:

    private final int scale;

    private Scale(int scale) {
      checkArgument(scale > 0, "Quantile scale must be positive");
      this.scale = scale;
    }

    /**
     * Specifies a single quantile index to be calculated, i.e. the k in the kth q-quantile.
     *
     * @param index the quantile index, which must be in the inclusive range [0, q] for q-quantiles
     */

   * other.
   */
  private static final Correspondence<Number, Number> FINITE_QUANTILE_CORRESPONDENCE =
      Correspondence.tolerance(ALLOWED_ERROR);

  /**
   * A {@link Correspondence} which accepts either finite values within {@link #ALLOWED_ERROR} of
   * each other or identical non-finite values.
   */
  private static final Correspondence<Double, Double> QUANTILE_CORRESPONDENCE =
      Correspondence.from(

   * other.
   */
  private static final Correspondence<Number, Number> FINITE_QUANTILE_CORRESPONDENCE =
      Correspondence.tolerance(ALLOWED_ERROR);

  /**
   * A {@link Correspondence} which accepts either finite values within {@link #ALLOWED_ERROR} of
   * each other or identical non-finite values.
   */
  private static final Correspondence<Double, Double> QUANTILE_CORRESPONDENCE =
      Correspondence.from(

            "type": "prometheus",
            "uid": "${datasource}"
          },

          "datasource": {
            "type": "prometheus",
            "uid": "${datasource}"
          },
          "expr": "(histogram_quantile(0.50, sum(irate(istio_request_duration_milliseconds_bucket{reporter=~\"$qrep\",destination_service=~\"$service\"}[1m])) by (le)) / 1000) or histogram_quantile(0.50, sum(irate(istio_request_duration_seconds_bucket{reporter=~\"$qrep\",destination_service=~\"$service\"}[1m])) by (le))",
          "format": "time_series",

			"histogram_quantile(0.500000, sum(rate(istio_request_duration_milliseconds_bucket{destination_workload=~\"details.*\", destination_workload_namespace=~\".*\",reporter=\"destination\"}[1m0s])) by (le))": prometheus_model.Vector{ // nolint: lll
				&prometheus_model.Sample{Value: 2.5},
			},

	}

	return sm, nil
}

func getLatency(promAPI promv1.API, workloadName, workloadNamespace string, duration time.Duration, quantile float64) (time.Duration, error) {
	latencyQuery := fmt.Sprintf(`histogram_quantile(%f, sum(rate(%s_bucket{%s=~"%s.*", %s=~"%s.*",reporter="destination"}[%s])) by (le))`,
		quantile, reqDur, destWorkloadLabel, workloadName, destWorkloadNamespaceLabel, workloadNamespace, duration)

import java.util.Map;

/**
 * Enumerates several algorithms providing equivalent functionality to {@link Quantiles}, for use in
 * {@link QuantilesBenchmark}. These algorithms each calculate either a single quantile or multiple
 * quantiles. All algorithms modify the dataset they are given (the cost of a copy to avoid this
 * will be constant across algorithms).
 *
 * @author Pete Gillin
 * @since 20.0
 */