},
        {
          "datasource": {
            "type": "prometheus",
            "uid": "${datasource}"
          },
          "expr": "sum(irate(istio_response_bytes_sum{source_workload_namespace!=\"istio-system\", reporter=\"source\"}[1m])) + sum(irate(istio_request_bytes_sum{source_workload_namespace!=\"istio-system\", reporter=\"source\"}[1m]))",
          "format": "time_series",
          "intervalFactor": 1,

            opLambda.callback(builder);
        }
    }

    public void setAvailable_Sum() {
        setAvailable_Sum(null);
    }

    public void setAvailable_Sum(ConditionOptionCall<SumAggregationBuilder> opLambda) {
        setAvailable_Sum("available", opLambda);
    }

    public void setAvailable_Sum(String name, ConditionOptionCall<SumAggregationBuilder> opLambda) {

            opLambda.callback(builder);
        }
    }

    public void setHitCount_Sum() {
        setHitCount_Sum(null);
    }

    public void setHitCount_Sum(ConditionOptionCall<SumAggregationBuilder> opLambda) {
        setHitCount_Sum("hitCount", opLambda);
    }

    public void setHitCount_Sum(String name, ConditionOptionCall<SumAggregationBuilder> opLambda) {

            opLambda.callback(builder);
        }
    }

    public void setAvailable_Sum() {
        setAvailable_Sum(null);
    }

    public void setAvailable_Sum(ConditionOptionCall<SumAggregationBuilder> opLambda) {
        setAvailable_Sum("available", opLambda);
    }

    public void setAvailable_Sum(String name, ConditionOptionCall<SumAggregationBuilder> opLambda) {

    // We define the arithmetic means X_n = 1/n \sum_{i=1}^n x_i, and Y_n = 1/n \sum_{i=1}^n y_i.
    // We also define the sum of the products of the differences from the means
    //           C_n = \sum_{i=1}^n x_i y_i - n X_n Y_n
    // for all n >= 1. Then for all n > 1:
    //       C_{n-1} = \sum_{i=1}^{n-1} x_i y_i - (n-1) X_{n-1} Y_{n-1}

  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;
  EXPECT_EQ(sizeof(sum), TF_TensorByteSize(t));
  memcpy(&sum, TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(sum, 6.0);

  TFE_DeleteTensorHandle(h0);
  TFE_DeleteTensorHandle(h1);
  TFE_DeleteTensorHandle(h2);

      return new CacheStats(
          negativeToMaxValue(hitCount.sum()),
          negativeToMaxValue(missCount.sum()),
          negativeToMaxValue(loadSuccessCount.sum()),
          negativeToMaxValue(loadExceptionCount.sum()),
          negativeToMaxValue(totalLoadTime.sum()),
          negativeToMaxValue(evictionCount.sum()));
    }

                }
              }));
    }
    threadPool.shutdown();
    assertTrue(threadPool.awaitTermination(300, SECONDS));
    long sum = 0;
    for (Future<Long> f : futures) {
      sum += f.get();
    }
    assertEquals(sum, map.get(key));
  }

            opLambda.callback(builder);
        }
    }

    public void setBoost_Sum() {
        setBoost_Sum(null);
    }

    public void setBoost_Sum(ConditionOptionCall<SumAggregationBuilder> opLambda) {
        setBoost_Sum("boost", opLambda);
    }

    public void setBoost_Sum(String name, ConditionOptionCall<SumAggregationBuilder> opLambda) {

            opLambda.callback(builder);
        }
    }

    public void setCreatedTime_Sum() {
        setCreatedTime_Sum(null);
    }

    public void setCreatedTime_Sum(ConditionOptionCall<SumAggregationBuilder> opLambda) {
        setCreatedTime_Sum("createdTime", opLambda);
    }

    public void setCreatedTime_Sum(String name, ConditionOptionCall<SumAggregationBuilder> opLambda) {