//   ⎣(B^2-1)/d⎦ = m+B
	//   (B^2-1)/d = m+B+delta1    0 <= delta1 <= (d-1)/d
	//   B^2/d = m+B+delta2        0 <= delta2 <= 1
	// The quotient we're trying to compute is
	//   quotient = ⎣(x1*B+x0)/d⎦
	//            = ⎣(x1*B*(B^2/d)+x0*(B^2/d))/B^2⎦
	//            = ⎣(x1*B*(m+B+delta2)+x0*(m+B+delta2))/B^2⎦
	//            = ⎣(x1*m+x1*B+x0)/B + x0*m/B^2 + delta2*(x1*B+x0)/B^2⎦

}

func (v *Float) appendJSON(b []byte) []byte {
	return strconv.AppendFloat(b, math.Float64frombits(v.f.Load()), 'g', -1, 64)
}

// Add adds delta to v.
func (v *Float) Add(delta float64) {
	for {
		cur := v.f.Load()
		curVal := math.Float64frombits(cur)
		nxtVal := curVal + delta
		nxt := math.Float64bits(nxtVal)
		if v.f.CompareAndSwap(cur, nxt) {
			return
		}
	}
}

// Set sets v to value.

			t.Log(e)
		}
	}

	for _, test := range []struct {
		count int
		delta Duration
	}{{
		count: baseCount,
		delta: baseDelta,
	}, {
		count: 8,
		delta: 1 * Second,
	}} {
		count, delta := test.count, test.delta
		ticker := NewTicker(delta)
		t0 := Now()
		for range count / 2 {
			<-ticker.C
		}
		ticker.Reset(delta * 2)
		for range count - count/2 {
			<-ticker.C
		}
		ticker.Stop()

func putpclcdelta(linkctxt *Link, dctxt dwCtxt, s *LSym, deltaPC uint64, deltaLC int64) {
	// Choose a special opcode that minimizes the number of bytes needed to
	// encode the remaining PC delta and LC delta.
	var opcode int64
	if deltaLC < LINE_BASE {
		if deltaPC >= PC_RANGE {
			opcode = OPCODE_BASE + (LINE_RANGE * PC_RANGE)
		} else {
			opcode = OPCODE_BASE + (LINE_RANGE * int64(deltaPC))
		}
	} else if deltaLC < LINE_BASE+LINE_RANGE {

`result[i] = range(starts[i], limits[i], deltas[i])`.

```python
(rt_nested_splits, rt_dense_values) = ragged_range(
      starts=[2, 5, 8], limits=[3, 5, 12], deltas=1)
result = tf.ragged.from_row_splits(rt_dense_values, rt_nested_splits)
print(result)
<tf.RaggedTensor [[2], [], [8, 9, 10, 11]] >
```

The input tensors `starts`, `limits`, and `deltas` may be scalars or vectors.

			},
			attemptsExpected: 1,
			expectedIntervals: func(t *testing.T, delays []time.Duration, delaysRequested []time.Duration) {
				if reflect.DeepEqual(delays, []time.Duration{time.Second}) {
					return
				}
				if !reflect.DeepEqual(delays, delaysRequested) {
					t.Fatalf("sliding non-immediate should have equal delays: %v", cmp.Diff(delays, delaysRequested))
				}
			},
		},
	}

			key, err := cache.MetaNamespaceKeyFunc(new)
			if err == nil {
				r.svcQueue.Add(key)
			}
		},
		DeleteFunc: func(obj interface{}) {
			// IndexerInformer uses a delta queue, therefore for deletes we have to use this
			// key function.
			key, err := cache.DeletionHandlingMetaNamespaceKeyFunc(obj)
			if err == nil {
				r.svcQueue.Add(key)
			}
		},
	}, interval)

		before int32
		i      int32
		after  int32
	}
	x.before = magic32
	x.after = magic32
	var j int32
	for delta := int32(1); delta+delta > delta; delta += delta {
		k := SwapInt32(&x.i, delta)
		if x.i != delta || k != j {
			t.Fatalf("delta=%d i=%d j=%d k=%d", delta, x.i, j, k)
		}
		j = delta
	}
	if x.before != magic32 || x.after != magic32 {

func f(x *big, start int64) {
	if delta := inuse() - start; delta < 9<<20 {
		println("after alloc: expected delta at least 9MB, got: ", delta)
	}
	runtime.KeepAlive(x)
	x = nil
	if delta := inuse() - start; delta > 1<<20 {
		println("after drop: expected delta below 1MB, got: ", delta)
	}
	x = new(big)
	if delta := inuse() - start; delta < 9<<20 {
		println("second alloc: expected delta at least 9MB, got: ", delta)
	}

// adapt is the bias adaptation function specified in section 6.1.
func adapt(delta, numPoints int32, firstTime bool) int32 {
	if firstTime {
		delta /= damp
	} else {
		delta /= 2
	}
	delta += delta / numPoints
	k := int32(0)
	for delta > ((base-tmin)*tmax)/2 {
		delta /= base - tmin
		k += base
	}
	return k + (base-tmin+1)*delta/(delta+skew)