//
    // TODO(user): Consider using
    // java/lang/management/OperatingSystemMXBean.html#getSystemLoadAverage()
    //
    // TODO(user): Consider scaling by number of mutator threads,
    // e.g. using Thread#activeCount()
    return max(10L, Runtime.getRuntime().totalMemory() / (32L * 1024L * 1024L));
  }

  /**

    //
    // TODO(user): Consider using
    // java/lang/management/OperatingSystemMXBean.html#getSystemLoadAverage()
    //
    // TODO(user): Consider scaling by number of mutator threads,
    // e.g. using Thread#activeCount()
    return max(10L, Runtime.getRuntime().totalMemory() / (32L * 1024L * 1024L));
  }

  /**

      return true;
    }

    boolean get(long bitIndex) {
      return (data.get((int) (bitIndex >>> LONG_ADDRESSABLE_BITS)) & (1L << bitIndex)) != 0;
    }

    /**
     * Careful here: if threads are mutating the atomicLongArray while this method is executing, the
     * final long[] will be a "rolling snapshot" of the state of the bit array. This is usually good
     * enough, but should be kept in mind.
     */

 * requests, responding to them with {@link #notifyStarted()} and {@link #notifyStopped()}
 * callbacks. Its subclasses must manage threads manually; consider {@link
 * AbstractExecutionThreadService} if you need only a single execution thread.
 *
 * @author Jesse Wilson
 * @author Luke Sandberg
 * @since 1.0
 */
@GwtIncompatible
@J2ktIncompatible
@ElementTypesAreNonnullByDefault

    }
  }

  /** Wait for the given thread to reach the {@link State#TIMED_WAITING} thread state. */
  @SuppressWarnings("ThreadPriorityCheck") // TODO: b/175898629 - Consider onSpinWait.
  void awaitTimedWaiting(Thread thread) {
    while (true) {
      switch (thread.getState()) {
        case BLOCKED:
        case NEW:
        case RUNNABLE:
        case WAITING:
          Thread.yield();
          break;

               "intervalFactor": 2,
               "legendFormat": "{{cluster}} - {{namespace}}\n"
            }
         ],
         "title": "Threads",
         "type": "timeseries"
      },
      {
         "datasource": {
            "type": "datasource",
            "uid": "-- Mixed --"
         },
         "fieldConfig": {

 *
 * OkHttp performs best when you create a single `OkHttpClient` instance and reuse it for all of
 * your HTTP calls. This is because each client holds its own connection pool and thread pools.
 * Reusing connections and threads reduces latency and saves memory. Conversely, creating a client
 * for each request wastes resources on idle pools.
 *
 * Use `new OkHttpClient()` to create a shared instance with the default settings:
 *

// ops.
TF_CAPI_EXPORT extern void TFE_ContextClearCaches(TFE_Context* ctx);

// Sets a thread-local device placement policy. After this call, other calls to
// TFE_Execute in the same thread will use the device policy specified here
// instead of the device policy used to construct the context. This has no
// effect on the device policy used by other program threads.
TF_CAPI_EXPORT extern void TFE_ContextSetThreadLocalDevicePlacementPolicy(

  }

  public void testNegativeTimeout() throws Exception {
    future.set(1);
    assertEquals(1, future.get(-1, SECONDS).intValue());
  }

  @J2ktIncompatible
  @GwtIncompatible // threads
  public void testOverflowTimeout() throws Exception {
    // First, sanity check that naive multiplication would really overflow to a negative number:
    long nanosPerSecond = NANOSECONDS.convert(1, SECONDS);

  /**
   * Calls get() repeatedly from many different threads, and tests that all of the removed entries
   * (removed because of size limits or expiration) trigger appropriate removal notifications.
   */
  @GwtIncompatible // QueuingRemovalListener

  public void testRemovalNotification_get_basher() throws InterruptedException {
    int nTasks = 1000;
    int nThreads = 100;
    final int getsPerTask = 1000;