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 };

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

    *   `tf.einsum()`raises `ValueError` for unsupported equations like
        `"ii->"`.
    *   Add DCT-I and IDCT-I in `tf.signal.dct` and `tf.signal.idct`.
    *   Add LU decomposition op.
    *   Add quantile loss to gradient boosted trees in estimator.
    *   Add `round_mode` to `QuantizeAndDequantizeV2` op to select rounding
        algorithm.
    *   Add `unicode_encode`, `unicode_decode`, `unicode_decode_with_offsets`,