*   <li>To get uninterruptibility and remove checked exceptions, use {@link
   *       Futures#getUnchecked}.
   * </ul>
   *
   * @throws ExecutionException if the computation threw an exception
   * @throws CancellationException if the computation was cancelled
   */
  @CanIgnoreReturnValue
  @ParametricNullness
  public static <V extends @Nullable Object> V getUninterruptibly(Future<V> future)

    cache.invalidateAll();
    // let the computation proceed
    computingLatch.countDown();
    // don't check cache.size() until we know the get("b") call is complete
    computationComplete.await();

    // At this point, the listener should be holding the seed value (a -> a), and the map should
    // contain the computed value (b -> b), since the clear() happened before the computation
    // completed.

   *   <li>To get uninterruptibility and remove checked exceptions, use {@link
   *       Futures#getUnchecked}.
   * </ul>
   *
   * @throws ExecutionException if the computation threw an exception
   * @throws CancellationException if the computation was cancelled
   */
  @CanIgnoreReturnValue
  @ParametricNullness
  public static <V extends @Nullable Object> V getUninterruptibly(Future<V> future)

       * to a valid final state ({@link #COMPLETED}, {@link #CANCELLED}, or {@link #INTERRUPTED}).
       *
       * @param v the value to set as the result of the computation.
       * @param t the exception to set as the result of the computation.
       * @param finalState the state to transition to.
       */
      private boolean complete(@Nullable V v, @Nullable Throwable t, int finalState) {

      }
    }.start();

    getStartedSignal.await();

    // computation is in progress; asMap shouldn't have changed
    assertEquals(1, map.size());
    assertFalse(map.containsKey(getKey));
    assertSame(refreshKey, map.get(refreshKey));

    // let computation complete
    letGetFinishSignal.countDown();
    getFinishedSignal.await();
    checkNothingLogged();

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.

  // A non-blocking version of `Execute`. After each call, `Join` must be called
  // before `StartExecute` is called again. Using `StartExecute` with `Join`
  // allows the caller to schedule computation on multiple ParallelDevices
  // without sequencing those operations (first call `StartExecute` on each
  // parallel device, then call `Join` on each; even if some of the `Join`s

But right at the moment Python compares the first `j` in `johndoe` to the first `s` in `stanleyjobson`, it will return `False`, because it already knows that those two strings are not the same, thinking that "there's no need to waste more computation comparing the rest of the letters". And your application will say "Incorrect username or password".

But then the attackers try with username `stanleyjobsox` and password `love123`.

import org.apache.maven.api.annotations.Nullable;

/**
 * Interface used to locate the root directory for a given project.
 *
 * The root locator is usually looked up from the plexus container.
 * One notable exception is the computation of the early {@code session.rootDirectory}
 * property which happens very early.  The implementation used in this case
 * will be discovered using the JDK service mechanism.
 *

   *
   * <p>The {@link OutputStream} should not be written to before or after the hand-off.
   */
  // TODO(user): Evaluate whether it makes sense to always piggyback the computation of a
  // HashCode on an existing OutputStream, compared to creating a separate OutputStream that could
  // be (optionally) be combined with another if needed (with something like
  // MultiplexingOutputStream).