public void reset() {
    internalReset(0L);
  }

  /**
   * Equivalent in effect to {@link #sum} followed by {@link #reset}. This method may apply for
   * example during quiescent points between multithreaded computations. If there are updates
   * concurrent with this method, the returned value is not guaranteed to be the final
   * value occurring before the reset.
   *
   * @return the sum
   */

  public void reset() {
    internalReset(0L);
  }

  /**
   * Equivalent in effect to {@link #sum} followed by {@link #reset}. This method may apply for
   * example during quiescent points between multithreaded computations. If there are updates
   * concurrent with this method, the returned value is not guaranteed to be the final
   * value occurring before the reset.
   *
   * @return the sum
   */

  public void reset() {
    internalReset(0L);
  }

  /**
   * Equivalent in effect to {@link #sum} followed by {@link #reset}. This method may apply for
   * example during quiescent points between multithreaded computations. If there are updates
   * concurrent with this method, the returned value is not guaranteed to be the final
   * value occurring before the reset.
   *
   * @return the sum
   */

  public void reset() {
    internalReset(0L);
  }

  /**
   * Equivalent in effect to {@link #sum} followed by {@link #reset}. This method may apply for
   * example during quiescent points between multithreaded computations. If there are updates
   * concurrent with this method, the returned value is not guaranteed to be the final
   * value occurring before the reset.
   *
   * @return the sum
   */

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility...

   * The listener will run when the {@code Future}'s computation is {@linkplain Future#isDone()
   * complete} or, if the computation is already complete, immediately.
   *
   * <p>There is no guaranteed ordering of execution of listeners, but any listener added through
   * this method is guaranteed to be called once the computation is complete.
   *

   * The listener will run when the {@code Future}'s computation is {@linkplain Future#isDone()
   * complete} or, if the computation is already complete, immediately.
   *
   * <p>There is no guaranteed ordering of execution of listeners, but any listener added through
   * this method is guaranteed to be called once the computation is complete.
   *

 * asynchronously.
 *
 * <p>To attach to a {@link ListenableFuture} use {@link Futures#addCallback}.
 *
 * @author Anthony Zana
 * @since 10.0
 */
@GwtCompatible
@ElementTypesAreNonnullByDefault
public interface FutureCallback<V extends @Nullable Object> {
  /** Invoked with the result of the {@code Future} computation when it is successful. */

   * The listener will run when the {@code Future}'s computation is {@linkplain Future#isDone()
   * complete} or, if the computation is already complete, immediately.
   *
   * <p>There is no guaranteed ordering of execution of listeners, but any listener added through
   * this method is guaranteed to be called once the computation is complete.
   *

should be used with caution when working with untrusted models.

### Saved graphs and checkpoints

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.