var inputC chan fuzzInput
		input, ok := c.peekInput()
		if ok && c.crashMinimizing == nil && !stopping {
			inputC = c.inputC
		}

		var minimizeC chan fuzzMinimizeInput
		minimizeInput, ok := c.peekMinimizeInput()
		if ok && !stopping {
			minimizeC = c.minimizeC
		}

		select {
		case <-doneC:
			// Interrupted, canceled, or timed out.
			// stop sets doneC to nil, so we don't busy wait here.

// interrupted.
var errSharedMemClosed = errors.New("internal error: shared memory was closed and unmapped")

// minimize tells the worker to call the minimize method. See
// workerServer.minimize.
func (wc *workerClient) minimize(ctx context.Context, entryIn CorpusEntry, args minimizeArgs) (entryOut CorpusEntry, resp minimizeResponse, retErr error) {
	wc.mu.Lock()
	defer wc.mu.Unlock()

ace,t.fn[e].Constructor=a,t.fn[e].noConflict=function(){return t.fn[e]=i,a._jQueryInterface},a}(jQuery),r=function(t){var e="CardWidget",i=".lte.cardwidget",n=t.fn[e],s={EXPANDED:"expanded"+i,COLLAPSED:"collapsed"+i,MAXIMIZED:"maximized"+i,MINIMIZED:"minimized"+i,REMOVED:"removed"+i},o={CARD:"card",COLLAPSED:"collapsed-card",WAS_COLLAPSED:"was-collapsed",MAXIMIZED:"maximized-card"},a={DATA_REMOVE:'[data-card-widget="remove"]',DATA_COLLAPSE:'[data-card-widget="collapse"]',DATA_MAXIMIZE:'[data-car...

   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the

   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the

   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the

 * represents the task. This maximizes concurrency by having each unique key mapped to a unique
 * lock, but also maximizes memory footprint. On the other extreme, one could use a single lock for
 * all tasks, which minimizes memory footprint but also minimizes concurrency. Instead of choosing
 * either of these extremes, {@code Striped} allows the user to trade between required concurrency

   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the

// Chooses pods that minimize "distance" to the requirements.
// If more than one pod exists that fulfills the remaining requirements,
// it chooses the pod that has the "smaller resource request"
// This method, by repeatedly choosing the pod that fulfills as much of the requirements as possible,
// attempts to minimize the number of pods returned.

 * represents the task. This maximizes concurrency by having each unique key mapped to a unique
 * lock, but also maximizes memory footprint. On the other extreme, one could use a single lock for
 * all tasks, which minimizes memory footprint but also minimizes concurrency. Instead of choosing
 * either of these extremes, {@code Striped} allows the user to trade between required concurrency