if expectedAverage > 0 {
			relDiff := (averages[i] - expectedAverage) / expectedAverage
			gotFair = math.Abs(relDiff) <= margin
		} else {
			gotFair = math.Abs(averages[i]) <= margin
		}

		if gotFair != expectFair {
			uss.t.Errorf("%s client %d last=%v expectFair=%v margin=%v got an Average of %v but the expected average was %v", uss.name, i, last, expectFair, margin, averages[i], expectedAverage)
		} else {

	// This works out to an average length 2×½ + 3×¼ + 4×⅛ + ... = 3.
	// The space we need is further reduced by the fact that many of the
	// short patterns like SLS will often be the same character sequences
	// repeated throughout the text, reducing maxID relative to numLMS.
	//
	// For short inputs, the averages may not run in our favor, but then we

	}

	if target.AverageUtilization == nil {
		errMsg := "invalid resource metric source: neither an average utilization target nor an average value (usage) target was set"
		return 0, nil, time.Time{}, "", condition, fmt.Errorf(errMsg)
	}

	targetUtilization := *target.AverageUtilization

	}

	DB.Create(&users)

	var userAges []int
	err := DB.Model(&User{}).Where("name like ?", "pluck_with_select%").Select("age + 1 as user_age").Pluck("user_age", &userAges).Error
	if err != nil {
		t.Fatalf("got error when pluck user_age: %v", err)
	}

	sort.Ints(userAges)

	AssertEqual(t, userAges, []int{26, 27})
}

func TestSelectWithVariables(t *testing.T) {

	// Compute term 1.
	nonHeapMemory := mappedReady - heapFree - heapAlloc

	// Compute term 2.
	var overage uint64
	if mappedReady > memoryLimit {
		overage = mappedReady - memoryLimit
	}

	if nonHeapMemory+overage >= memoryLimit {
		// We're at a point where non-heap memory exceeds the memory limit on its own.

}

var blockprofilerate uint64 // in CPU ticks

// SetBlockProfileRate controls the fraction of goroutine blocking events
// that are reported in the blocking profile. The profiler aims to sample
// an average of one blocking event per rate nanoseconds spent blocked.
//
// To include every blocking event in the profile, pass rate = 1.
// To turn off profiling entirely, pass rate <= 0.
func SetBlockProfileRate(rate int) {

This is the first output from tf.nn.moments,
or a saved moving average thereof.}]>:$m,

	}
}

// blockFrequentShort produces 100000 block events with an average duration of
// rate / 10.
func blockFrequentShort(rate int) {
	for i := 0; i < 100000; i++ {
		blockevent(int64(rate/10), 1)
	}
}

// blockFrequentShort produces 10000 block events with an average duration of
// rate.
func blockInfrequentLong(rate int) {
	for i := 0; i < 10000; i++ {

			// so it frees up other goroutines to allocate in the meanwhile. In fact,
			// they can make use of the growth we just created.
			todo := growth
			if overage := uintptr(retained + uint64(growth) - goal); todo > overage {
				todo = overage
			}
			if todo > bytesToScavenge {
				bytesToScavenge = todo
			}
		}
	}
	// There are a few very limited circumstances where we won't have a P here.

	}
	c.nextSample = nextSample()
	mProf_Malloc(mp, x, size)
}

// nextSample returns the next sampling point for heap profiling. The goal is
// to sample allocations on average every MemProfileRate bytes, but with a
// completely random distribution over the allocation timeline; this
// corresponds to a Poisson process with parameter MemProfileRate. In Poisson