#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/release_version.h"
#include "tensorflow/core/public/session.h"

// The implementation below is at the top level instead of the
// brain namespace because we are defining 'extern "C"' functions.
using tensorflow::AttrValueMap;
using tensorflow::DataType;
using tensorflow::ExtendSessionGraphHelper;
using tensorflow::FullTypeDef;
using tensorflow::Graph;

  - [beta] PodDisruptionBudget has been promoted to beta, can be used to safely drain nodes while respecting application SLO's ([docs](http://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/)) ([kubernetes/features#85](https://github.com/kubernetes/enhancements/issues/85))
- **UI**

    And yet you incessantly stand on your head--
      Do you think, at your age, it is right?'

    `In my youth,' Father William replied to his son,
      `I feared it might injure the brain;
    But, now that I'm perfectly sure I have none,
      Why, I do it again and again.'

    `You are old,' said the youth, `as I mentioned before,
      And have grown most uncommonly fat;

    And yet you incessantly stand on your head--
      Do you think, at your age, it is right?'

    `In my youth,' Father William replied to his son,
      `I feared it might injure the brain;
    But, now that I'm perfectly sure I have none,
      Why, I do it again and again.'

    `You are old,' said the youth, `as I mentioned before,
      And have grown most uncommonly fat;

  }

  private static <T> int drain(
      BlockingQueue<T> q,
      Collection<? super T> buffer,
      int numElements,
      long timeout,
      TimeUnit unit,
      boolean interruptibly)
      throws InterruptedException {
    return interruptibly
        ? Queues.drain(q, buffer, numElements, timeout, unit)

        if (result.isOverflow()) {
          // Not enough room in output buffer--drain it, creating a bigger buffer if necessary.
          startDraining(true);
          continue DRAINING;
        } else if (result.isUnderflow()) {
          // If encoder underflows, it means either:
          // a) the final flush() succeeded; next drain (then done)
          // b) we encoded all of the input; next flush

            @Nullable Throwable error) {}

    /**
     * If this is an accumulating log, it will "drain" this instance. It returns the accumulated log entries, and
     * also "resets" this instance to empty (initial) state.
     */
    @Nonnull
    default List<Entry> drain() {
        return List.of();
    }

from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can

  @GwtIncompatible // BlockingQueue
  @IgnoreJRERequirement // Users will use this only if they're already using Duration
  public static <E> int drain(
      BlockingQueue<E> q, Collection<? super E> buffer, int numElements, Duration timeout)
      throws InterruptedException {
    return drain(q, buffer, numElements, toNanosSaturated(timeout), NANOSECONDS);
  }

  /**

			select {
			case currentGlobalBuffer <- v:
			default:
			}
		}

		if mainWorker {
		drain:
			for {
				select {
				case v, ok := <-h.logCh:
					if !ok {
						break drain
					}

					currentGlobalBuffer <- v
				default:
					break drain
				}
			}
		}
	}()

	lastBatchProcess := time.Now()

	buf := bytebufferpool.Get()