type PB struct {
	globalN *atomic.Uint64 // shared between all worker goroutines iteration counter
	grain   uint64         // acquire that many iterations from globalN at once
	cache   uint64         // local cache of acquired iterations
	bN      uint64         // total number of iterations to execute (b.N)
}

// Next reports whether there are more iterations to execute.
func (pb *PB) Next() bool {
	if pb.cache == 0 {

		}
		if addr[v] != node {
			// This doesn't happen in practice, but catch it just in case.
			used.Add(node)
			changed = true
		}
		return
	}

	iterations := 0
	for {
		if iterations == 4 {
			// give up
			return
		}
		iterations++
		changed := false
		for _, b := range f.Blocks {
			for _, v := range b.Values {
				changed = visit(v) || changed
			}

        root {
            'common.gradle'('println "poke"')
        }

        when:
        def allCompileOperations = 0
        def iterations = 3
        def builder = root
        iterations.times { n ->
            createJarWithProperties("foo${n}.jar", [value: n])
            new File(root.baseDir, 'build.gradle').delete()
            builder {
                'build.gradle'("""

	seenSources := sets.New[string](TestSource)
	var wg sync.WaitGroup
	const iterations = 100
	wg.Add(2)

	go func() {
		ctx, cancel := context.WithCancel(tCtx)
		defer cancel()
		defer wg.Done()
		for i := 0; i < iterations; i++ {
			config.Channel(ctx, strconv.Itoa(i))
		}
	}()
	go func() {
		defer wg.Done()
		for i := 0; i < iterations; i++ {
			config.SeenAllSources(seenSources)
		}
	}()

	ssc.enqueueStatefulSet(set)
	fakeWorker(ssc)
	selector, err := metav1.LabelSelectorAsSelector(set.Spec.Selector)
	if err != nil {
		return err
	}
	iterations := 0
	for (maxIterations < 0 || iterations < maxIterations) && set.Status.ReadyReplicas < *set.Spec.Replicas {
		iterations++
		pods, err := om.podsLister.Pods(set.Namespace).List(selector)
		if err != nil {
			return err
		}
		ord := len(pods) - 1

      // trait, then we don't need to chain resource ops accessing this resource
      // between iterations: Every iteration will create a new independent
      // resource. This enables more parallelism across iterations.
      if (!side_effect_analysis.IsUniqueResourceAllocationId(
              resource_and_device.first)) {

        # args, so each arg winds up back in the position where it started, but
        # possibly modified.
        #
        # NB: a `for` loop captures its iteration list before it begins, so
        # changing the positional parameters here affects neither the number of
        # iterations, nor the values presented in `arg`.
        shift                   # remove old arg
        set -- "\$@" "\$arg"      # push replacement arg
    done
fi

) -> rd.RepresentativeDataset:
  """Logs the sample number for calibration.

  If in debug logging level, the "sample number / total num samples" is logged
  for every 5 iterations.

  This is often useful when tracking the progress of the calibration step which
  is often slow and may look stale if there's no logs being printed.

  Args:

			c = 0
			ng = nng
		}
		if c >= 100 {
			// The same value 100 times in a row is good enough.
			return ng
		}
		runtime.Gosched()
	}
	panic("failed to stabilize NumGoroutine after 1000 iterations")
}

func TestPullDoubleNext(t *testing.T) {
	next, _ := Pull(doDoubleNext())
	nextSlot = next
	next()
	if nextSlot != nil {
		t.Fatal("double next did not fail")
	}
}

	MOVD y+48(FP), R4	// R4 = y = c
	MOVD z_len+8(FP), R11	// R11 = z_len

	CMP   R11, $0		// If z_len is zero, return
	BEQ   done

	// We will process the first iteration out of the loop so we capture
	// the value of c. In the subsequent iterations, we will rely on the
	// value of CA set here.
	MOVD  0(R8), R20	// R20 = x[i]
	ADD   $-1, R11		// R11 = z_len - 1
	ADDC  R20, R4, R6	// R6 = x[i] + c