* automatic-closing approach described above is safer.
 *
 * @param <V> the type of the value of this step
 * @since 30.0
 */
// TODO(dpb): Consider reusing one CloseableList for the entire pipeline, modulo combinations.
@DoNotMock("Use ClosingFuture.from(Futures.immediate*Future)")
@J2ktIncompatible
@GwtIncompatible // TODO(dpb): GWT compatibility.

    // streams. Buffers are retained in a deque so that there's no copying between buffers while
    // reading and so all of the bytes in each new allocated buffer are available for reading from
    // the stream.
    for (int bufSize = initialBufferSize;
        totalLen < MAX_ARRAY_LEN;
        bufSize = IntMath.saturatedMultiply(bufSize, bufSize < 4096 ? 4 : 2)) {

labels.jobStatus=Status
labels.labelTypeIds=Labels
labels.lang=Language
labels.outputs=Outputs
labels.pos=Part-of-speech
labels.purgeJobLogDay=Delete old job logs
labels.purgeUserInfoDay=Delete old user logs
labels.reading=Reading
labels.roleTypeIds=Role IDs
labels.scriptData=Script
labels.scriptResult=Result
labels.scriptType=Execution Method
labels.segmentation=Segmentation
labels.startTime=Start Time
labels.target=Target

labels.jobStatus=Status
labels.labelTypeIds=Labels
labels.lang=Language
labels.outputs=Outputs
labels.pos=Part-of-speech
labels.purgeJobLogDay=Delete old job logs
labels.purgeUserInfoDay=Delete old user logs
labels.reading=Reading
labels.roleTypeIds=Role IDs
labels.scriptData=Script
labels.scriptResult=Result
labels.scriptType=Execution Method
labels.segmentation=Segmentation
labels.startTime=Start Time
labels.target=Target

   * it slower than the native version.
   *
   * @param input the input bytes
   * @param offset the offset into the array at which to start reading
   * @param length the number of bytes from the input to read
   * @return a long of a concatenated 8 bytes
   */
  static long load64Safely(byte[] input, int offset, int length) {
    long result = 0;

    partial batches. Workers running out of data now continue to participate in
    the training with empty inputs, instead of raising an error. Learn more
    about
    [partial batches here](https://www.tensorflow.org/tutorials/distribute/input#partial_batches).
*   Improve the performance of reading metrics eagerly under
    `tf.distribute.experimental.MultiWorkerMirroredStrategy`.

    // in a recursive loop.  Sneaky.
    if (level == stimuli.length) {
      // We've filled the array.
      compareResultsForThisListOfStimuli();
    } else {
      // Keep recursing to fill the array.
      for (Stimulus<E, ? super I> stimulus : getStimulusValues()) {
        stimuli[level] = stimulus;
        recurse(level + 1);
      }
    }
  }

    // in a recursive loop.  Sneaky.
    if (level == stimuli.length) {
      // We've filled the array.
      compareResultsForThisListOfStimuli();
    } else {
      // Keep recursing to fill the array.
      for (Stimulus<E, ? super I> stimulus : getStimulusValues()) {
        stimuli[level] = stimulus;
        recurse(level + 1);
      }
    }
  }

 * {@link PriorityQueue} and supplies blocking retrieval operations. While this queue is logically
 * unbounded, attempted additions may fail due to resource exhaustion (causing {@code
 * OutOfMemoryError}). This class does not permit {@code null} elements. A priority queue relying on
 * {@linkplain Comparable natural ordering} also does not permit insertion of non-comparable objects
 * (doing so results in {@code ClassCastException}).
 *

                            return;
                        }
                    }
                } catch (final IOException e) {
                    logger.debug("I/O exception while reading process command.", e);
                }
            }, "ProcessCommand");
            commandThread.start();

            systemMonitorTask = TimeoutManager.getInstance()