So you wait for your crush to finish the story (finish the current work ⏯ / task being processed 🤓), smile gently and say that you are going for the burgers ⏸.

    a2.cancel()
    a3.cancel()
    assertThat(dispatcher.runningCalls())
      .containsExactlyInAnyOrder(a1, a2)
    assertThat(dispatcher.queuedCalls()).isEmpty()

    // Let the calls finish.
    waiting.countDown()
    t1.join()
    t2.join()

    // Now we should have 0 running calls and 0 queued calls.
    assertThat(dispatcher.runningCallsCount()).isEqualTo(0)

- On the Storage screen, choose the Scale-out Backup Repository that was configured previously.

- Continue with the backup job creation.  On the Summary screen, check the Run the Job when I click Finish checkbox and click the Finish button. The backup job will start immediately.  This will create an Active Full backup of the VMs within the backup job.

oEnd","propFilter","bind","complete","timer","anim","*","tweener","oldfire","propTween","restoreDisplay","isBox","dataShow","unqueued","overflow","overflowX","overflowY","prefilter","speed","speeds","fadeTo","to","animate","optall","doAnimation","finish","stopQueue","timers","cssFn","slideDown","slideUp","slideToggle","fadeIn","fadeOut","fadeToggle","slow","fast","delay","time","timeout","clearTimeout","checkOn","optSelected","radioValue","boolHook","removeAttr","nType","attrHooks","attrNames","...

      tf.data service
      (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service)
      to write distributed dataset snapshots. The call is non-blocking and
      returns without waiting for the snapshot to finish. Setting `wait=True` to
      `tf.data.Dataset.load` allows the snapshots to be read while they are
      being written.

### Bug Fixes and Other Changes

        ) {
          t.printStackTrace(System.out)
          latch.countDown()
        }
      },
    )

    check(latch.await(30, TimeUnit.SECONDS)) { "Timed out waiting for test $number to finish." }
    val endNanos = System.nanoTime()
    val tookMs = TimeUnit.NANOSECONDS.toMillis(endNanos - startNanos.get())
    println("Took ${tookMs}ms")
  }

  private fun getTestCount(): Long {

        }

        if (connectResult.isSuccess) {
          // We have a connected TCP connection. Cancel and defer the racing connects that all lost.
          cancelInFlightConnects()

          // Finish connecting. We won't have to if the winner is from the connection pool.
          if (!connectResult.plan.isReady) {
            connectResult = connectResult.plan.connectTlsEtc()
          }

        grid.PutByteBuffer(result)
        return nil
    })
```

Context cancellation and timeouts are propagated to the handler. 
The client does not wait for the remote handler to finish before returning.
Returning any error will also cancel the stream remotely.

CAREFUL: When utilizing two-way communication, it is important to ensure that the remote handler is not blocked on a send.

# https://github.com/apache/lucene-solr/blob/master/lucene/analysis/common/src/resources/org/apache/lucene/analysis/snowball/finnish_stop.txt
olla
olen
olet
on
olemme
olette
ovat
ole
oli
olisi
olisit
olisin
olisimme
olisitte
olisivat
olit
olin
olimme
olitte
olivat
ollut
olleet
en
et
ei
emme
ette
eivät
minä
minun
minut
minua
minussa
minusta
minuun
minulla

      }
      throw IllegalStateException("too early; can't read the trailers yet")
    }
  }

  /**
   * Sends a reply to an incoming stream.
   *
   * @param outFinished true to eagerly finish the output stream to send data to the remote peer.
   *     Corresponds to `FLAG_FIN`.
   * @param flushHeaders true to force flush the response headers. This should be true unless the