* Serialization: you could return an arbitrary object and the
                    `response_model` would be used to serialize that object into the
                    corresponding JSON.
                * Filtering: the JSON sent to the client will only contain the data
                    (fields) defined in the `response_model`. If you returned an object
                    that contains an attribute `password` but the `response_model` does

                * Serialization: you could return an arbitrary object and the
                    `response_model` would be used to serialize that object into the
                    corresponding JSON.
                * Filtering: the JSON sent to the client will only contain the data
                    (fields) defined in the `response_model`. If you returned an object
                    that contains an attribute `password` but the `response_model` does

          </code>
        </codeSegment>
        <codeSegment>
          <version>4.0.0/4.0.99</version>
          <code>
            <![CDATA[
    public void setFiltering(boolean filtering) {
        setFiltering(String.valueOf(filtering));
    }
            ]]>
          </code>
        </codeSegment>
        <codeSegment>
          <version>4.0.0+</version>
          <code>
            <![CDATA[

        : new FilteredEntryBiMap<K, V>(unfiltered, entryPredicate);
  }

  /**
   * Support {@code clear()}, {@code removeAll()}, and {@code retainAll()} when filtering a filtered
   * map.
   */
  private static <K extends @Nullable Object, V extends @Nullable Object> Map<K, V> filterFiltered(
      AbstractFilteredMap<K, V> map, Predicate<? super Entry<K, V>> entryPredicate) {

        : new FilteredEntryBiMap<K, V>(unfiltered, entryPredicate);
  }

  /**
   * Support {@code clear()}, {@code removeAll()}, and {@code retainAll()} when filtering a filtered
   * map.
   */
  private static <K extends @Nullable Object, V extends @Nullable Object> Map<K, V> filterFiltered(
      AbstractFilteredMap<K, V> map, Predicate<? super Entry<K, V>> entryPredicate) {

	// TODO: the logic here does not handle two cases:
	//   1. If the containers were removed immediately after they died, kubelet
	//      may fail to generate correct statuses, let alone filtering correctly.
	//   2. If kubelet restarted before writing the terminated status for a pod
	//      to the apiserver, it could still restart the terminated pod (even

			}
			sched.applyDefaultHandlers()

			state := framework.NewCycleState()
			_, _, err = sched.findNodesThatFitPod(ctx, fwk, state, test.pod)
			if err != nil {
				t.Fatalf("error filtering nodes: %+v", err)
			}
			fwk.RunPreScorePlugins(ctx, state, test.pod, tf.BuildNodeInfos(test.nodes))
			list, err := prioritizeNodes(ctx, nil, fwk, state, test.pod, tf.BuildNodeInfos(test.nodes))
			if err != nil {

		tc.Lock()
		defer tc.Unlock()

		metrics := &metricsapi.PodMetricsList{}
		for i, cpu := range tc.reportedLevels {
			// NB: the list reactor actually does label selector filtering for us,
			// so we have to make sure our results match the label selector
			podMetric := metricsapi.PodMetrics{
				ObjectMeta: metav1.ObjectMeta{
					Name:      fmt.Sprintf("%s-%d", podNamePrefix, i),

		// while the second is used to provide a more timely status update during initialization and runs an one-shot update to the apiserver
		// once the node becomes ready, then exits afterwards.
		//
		// Introduce some small jittering to ensure that over time the requests won't start
		// accumulating at approximately the same time from the set of nodes due to priority and
		// fairness effect.