*/
    public ScaleAndIndex index(int index) {
      return new ScaleAndIndex(scale, index);
    }

    /**
     * Specifies multiple quantile indexes to be calculated, each index being the k in the kth
     * q-quantile.
     *
     * @param indexes the quantile indexes, each of which must be in the inclusive range [0, q] for

  irate(query):
    'irate(%s[$__rate_interval])' % query,
  rate(query):
    'rate(%s[$__rate_interval])' % query,
  round(query, by=0.01):
    'round(%s, %s)' % [query, by],
  quantile(quantile, query):
    'histogram_quantile(%s, %s)' %[quantile, query],
  labels(metric_name, labels):
    // One can nest locals.
    // Every local ends with a semi-colon.
    local prom_labels = std.join(',',
      std.map(
        function(k)

local q = g.query.prometheus;

local query = import './lib-query.libsonnet';
local sum = query.sum;
local rate = query.rate;
local irate = query.irate;
local labels = query.labels;
local round = query.round;
local quantile = query.quantile;

local variables = import './variables.libsonnet';

{
  queries(names):
    local containerLabels = { container: names.container, pod: '~' + names.pod };
    local appLabels = { app: names.app };

    }
  },
  ;

  /**
   * Calculates a single quantile. Equivalent to {@code
   * Quantiles.scale(scale).index(index).computeInPlace(dataset)}.
   */
  abstract double singleQuantile(int index, int scale, double[] dataset);

  /**
   * Calculates multiple quantiles. Equivalent to {@code
   * Quantiles.scale(scale).indexes(indexes).computeInPlace(dataset)}.
   */
  abstract Map<Integer, Double> multipleQuantiles(

  public void testScale_zero() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(0));
  }

  public void testScale_negative() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(-4));
  }

  public void testScale_index_negative() {
    Quantiles.Scale intermediate = Quantiles.scale(10);
    assertThrows(IllegalArgumentException.class, () -> intermediate.index(-1));
  }

	}

	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)

                     "intervalFactor": 2,
                     "legendFormat": "{{cluster}} - {{namespace}} - {{name}} - mean\n"
                  }
               ],
               "title": "Workqueue Waiting Duration Quantile",
               "type": "timeseries"
            },
            {
               "datasource": {
                  "type": "datasource",
                  "uid": "-- Mixed --"
               },

        `inference_output_type` for full integer quantized models. This allows
        users to modify the model input and output type to integer types
        (`tf.int8`, `tf.uint8`) instead of defaulting to float type
        (`tf.float32`).
*   NNAPI
    *   Adds NNAPI Delegation support for requantization use cases by converting
        the operation into a dequantize-quantize pair.

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      Map<Integer, Double> quantiles = algorithm.multipleQuantiles(indexes, 100, dataset.clone());
      assertWithMessage("Wrong keys from " + algorithm).that(quantiles.keySet()).isEqualTo(indexes);
      for (int i : indexes) {
        assertWithMessage("Mismatch between %s and %s at %s", algorithm, REFERENCE_ALGORITHM, i)
            .that(quantiles.get(i))
            .isWithin(ALLOWED_ERROR)