*   for successful and unsuccessful computations, respectively.
 * </p>
 *
 * <p>
 *   The Deferred class uses a CountDownLatch to allow you to wait for the computation to complete.
 *   The resolve and reject methods decrement the CountDownLatch, allowing the getResponse method
 *   to return the result of the computation.
 * </p>
 *
 * @param <RESPONSE> The type of the response.
 */

    computationLatch.countDown();
    // let some computations complete
    while (computedCount.get() < nThreads) {
      Thread.yield();
    }
    cache.invalidateAll();
    tasksFinished.await();

    // Check all of the removal notifications we received: they should have had correctly-associated
    // keys and values. (An earlier bug saw removal notifications for in-progress computations,
    // which had real keys with null values.)

    computationLatch.countDown();
    // let some computations complete
    while (computedCount.get() < nThreads) {
      Thread.yield();
    }
    cache.invalidateAll();
    tasksFinished.await();

    // Check all of the removal notifications we received: they should have had correctly-associated
    // keys and values. (An earlier bug saw removal notifications for in-progress computations,
    // which had real keys with null values.)

  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
   */

Those worker processes would be the ones running your application, they would perform the main computations to receive a **request** and return a **response**, and they would load anything you put in variables in RAM.

<img src="/img/deployment/concepts/process-ram.drawio.svg">

    int numActions = 100;
    // hashcodes from non-overlapping hash computations
    HashCode expected1 = randomHash(hashFunction, new Random(1L), numActions);
    HashCode expected2 = randomHash(hashFunction, new Random(2L), numActions);

    // equivalent, but overlapping, computations (should produce the same results as above)
    Random random1 = new Random(1L);
    Random random2 = new Random(2L);

   * usually have much noticeable impact. A value of one permits only one thread to modify the cache
   * at a time, but since read operations and cache loading computations can proceed concurrently,
   * this still yields higher concurrency than full synchronization.
   *
   * <p>Defaults to 4. <b>Note:</b>The default may change in the future. If you care about this

      }
    }.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();

.concat([fragments[divider].split(splitRegex)[0]]),\n        [fragments[divider].split(splitRegex)[1]].concat(\n          fragments.slice(divider + 1)\n        ),\n      ]\n    : [fragments];\n\n  // Convert the values with units to absolute pixels to allow our computations\n  ops = ops.map((op, index) => {\n    // Most of the units rely on the orientation of the popper\n    const measurement = (index === 1 ? !useHeight : useHeight)\n      ? 'height'\n      : 'width';\n    let mergeWithPrevious = false;\n    return...

  * Note that NumPy's type promotion rules have been changed(See [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html#nep50)for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results.