Arenas allocate large chunks of memory for Go values, so they're likely to
be inefficient for allocating only small amounts of small Go values. They're
best used in bulk, on the order of MiB of memory allocated on each use.

Note that by allowing for this limited form of manual memory allocation
that use-after-free bugs are possible with regular Go values. This package

		Result  string
		Vars    []interface{}
	}{
		{
			[]clause.Interface{
				clause.Insert{},
				clause.Values{
					Columns: []clause.Column{{Name: "name"}, {Name: "age"}},
					Values:  [][]interface{}{{"jinzhu", 18}, {"josh", 1}},
				},
			},
			"INSERT INTO `users` (`name`,`age`) VALUES (?,?),(?,?)",
			[]interface{}{"jinzhu", 18, "josh", 1},
		},
	}

	for idx, result := range results {

	prefix = values.Get("prefix")
	marker = values.Get("marker")
	delimiter = values.Get("delimiter")
	encodingType = values.Get("encoding-type")
	return
}

func getListBucketObjectVersionsArgs(values url.Values) (prefix, marker, delimiter string, maxkeys int, encodingType, versionIDMarker string, errCode APIErrorCode) {
	errCode = ErrNone

	if values.Get("max-keys") != "" {
		var err error

                                        geoMap.put(geoField, list);
                                    }
                                    final String[] values = pt.split(",");
                                    if (values.length != 2) {
                                        throw new InvalidQueryException(

      double meanByAddAllStats = accumulatorByAddAllStats.mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of " + values).that(meanByAddAllStats).isNaN();
      } else if (values.hasAnyPositiveInfinity() && values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();

    public void setAvailable_PercentileRanks(double[] values) {
        setAvailable_PercentileRanks(values, null);
    }

    public void setAvailable_PercentileRanks(double[] values, ConditionOptionCall<PercentileRanksAggregationBuilder> opLambda) {
        setAvailable_PercentileRanks("available", values, opLambda);
    }

    public void setAvailable_PercentileRanks(String name, double[] values,

    private final String name;

    /** The array of values associated with this parameter. */
    private final String[] values;

    /**
     * Constructs a new RequestParameter with the specified name and values.
     *
     * @param name the name of the parameter, must not be null
     * @param values the array of values for this parameter, can be null or empty
     */

          sum = t;
        }
        return sum / values.length;
      }
    },
    KNUTH {
      @Override
      double mean(double[] values) {
        double mean = values[0];
        for (int i = 1; i < values.length; i++) {
          mean = mean + (values[i] - mean) / (i + 1);
        }
        return mean;
      }
    };

    abstract double mean(double[] values);
  }

  static class MeanAndVariance {

        setCreatedTime_PercentileRanks(values, null);
    }

    public void setCreatedTime_PercentileRanks(double[] values, ConditionOptionCall<PercentileRanksAggregationBuilder> opLambda) {
        setCreatedTime_PercentileRanks("createdTime", values, opLambda);
    }

    public void setCreatedTime_PercentileRanks(String name, double[] values,

        setCreatedTime_PercentileRanks(values, null);
    }

    public void setCreatedTime_PercentileRanks(double[] values, ConditionOptionCall<PercentileRanksAggregationBuilder> opLambda) {
        setCreatedTime_PercentileRanks("createdTime", values, opLambda);
    }

    public void setCreatedTime_PercentileRanks(String name, double[] values,