* separate method to make it somewhat more likely to be unreachable.
   */
  private static void createUnreachableLatchFinalizer(CountDownLatch latch) {
    Object unused =
        new Object() {
          @SuppressWarnings({"removal", "Finalize"}) // b/260137033
          @Override
          protected void finalize() {
            latch.countDown();

   * separate method to make it somewhat more likely to be unreachable.
   */
  private static void createUnreachableLatchFinalizer(CountDownLatch latch) {
    Object unused =
        new Object() {
          @SuppressWarnings({"removal", "Finalize"}) // b/260137033
          @Override
          protected void finalize() {
            latch.countDown();

            throw new RuntimeCIFSException("Usage count dropped below zero");
        }
    }


    /**
     * {@inheritDoc}
     *
     * @see java.lang.Object#finalize()
     */
    @Override
    protected void finalize () throws Throwable {
        if ( !isDisconnected() && this.usageCount.get() != 0 ) {
            log.warn("Session was not properly released");
        }
    }


    /**

        System.arraycopy(stackTrace, s, res, 0, e - s);
        return res;
    }


    /**
     * {@inheritDoc}
     *
     * @see java.lang.Object#finalize()
     */
    @Override
    protected void finalize () throws Throwable {
        if ( isConnected() && this.usageCount.get() != 0 ) {
            log.warn("Tree was not properly released");
        }
    }


    /**

Ao invés disso, sendo um sistema "assíncrono", uma vez finalizada, a tarefa pode esperar um pouco (alguns microssegundos) para que o computador / programa finalize o que quer que esteja fazendo,e então volte para pegar o resultado e continue trabalhando com ele.

          "Unable to cast input to GraphTensor");
    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {

    // Extract the resulting tensor.
    add_output2 = TF_OutputListGet(add_outputs, 0);
    TF_DeleteOutputList(add_outputs);
  }

  TF_DeleteAbstractTensor(arg0);
  TF_DeleteAbstractTensor(arg1);

  // Finalize the function by providing the returned values.
  TF_AbstractFunction* func;
  {
    // We want to return the output of both add operations, create a new list
    // and populate it.

    GcFinalization.awaitClear(finalizableWeakReference);

    Field sepFrqUserFinalizedF = sepFrqUserC.getField("finalized");
    Semaphore finalizeCount = (Semaphore) sepFrqUserFinalizedF.get(null);
    boolean finalized = finalizeCount.tryAcquire(5, SECONDS);
    assertTrue(finalized);

    Field sepFrqUserFrqF = sepFrqUserC.getField("frq");
    Closeable frq = (Closeable) sepFrqUserFrqF.get(null);
    frq.close();

  /**
   * Iterates through the given loaders until it finds one that can load Finalizer.
   *
   * @return Finalizer.class
   */
  private static Class<?> loadFinalizer(FinalizerLoader... loaders) {
    for (FinalizerLoader loader : loaders) {
      Class<?> finalizer = loader.loadFinalizer();
      if (finalizer != null) {
        return finalizer;
      }
    }

    throw new AssertionError();
  }

        state["/async_raise"] = "asyncgen raise finalized"


def generator_state_try(state: Dict[str, str] = Depends(get_state)):
    state["/sync_raise"] = "generator raise started"
    try:
        yield state["/sync_raise"]
    except SyncDependencyError:
        errors.append("/sync_raise")
        raise
    finally:
        state["/sync_raise"] = "generator raise finalized"