// the monitor with the guard satisfied. Therefore as soon as tearDownLatch is triggered, we
    // should be able to enter the monitor, and then we set the guard to satisfied for the benefit
    // of any remaining waiting threads.

    tearDownLatch.countDown();
    assertTrue(
        "Monitor still occupied in tearDown()",
        monitor.enter(UNEXPECTED_HANG_DELAY_MILLIS, TimeUnit.MILLISECONDS));

*   When you contribute a new feature to TensorFlow, the maintenance burden is
    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in

     * references (currently executing code, file descriptors).
     *
     * Depending on the usage scenario, this may have some benefit as there won't be any delays for restablishing these
     * resources, however comes at the cost of having to properly release all SmbFile instances you no longer need.
     *

   * B>`, convertAll could accept and return iterables with nullable element types. In both cases,
   * we've chosen to instead use a signature that benefits existing users -- and is still safe.
   *
   * For convertAll, I haven't looked as closely at *how* much existing users benefit, so we should
   * keep an eye out for problems that new users encounter. Note also that convertAll could support

        // run() on the same thread that called execute() - such as in a thread pool, and think
        // that it was happening inline. As a side benefit, avoids holding on to the Thread object
        // longer than necessary.
        submitting = null;
      }
    }

    @SuppressWarnings("ShortCircuitBoolean")
    @Override
    public void run() {

		objectName string
		shouldPass bool
	}{
		// cases which should pass the test.
		// passing in valid object name.
		{"object", true},
		{"The Shining Script <v1>.pdf", true},
		{"Cost Benefit Analysis (2009-2010).pptx", true},
		{"117Gn8rfHL2ACARPAhaFd0AGzic9pUbIA/5OCn5A", true},
		{"SHØRT", true},
		{"f*le", true},
		{"contains-^-caret", true},
		{"contains-|-pipe", true},
		{"contains-`-tick", true},

    /*
     * Padding from 40 into 64 bytes, same size as cache line. Might be beneficial to add a fourth
     * long here, to minimize chance of interference between consecutive locks, but I couldn't
     * observe any benefit from that.
     */
    long unused1;
    long unused2;
    long unused3;

    PaddedLock() {
      super(false);
    }
  }

  private static final class PaddedSemaphore extends Semaphore {

    }

    if (requestMethod != "GET") {
      // Don't cache non-GET responses. We're technically allowed to cache HEAD, QUERY and some
      // POST requests, but the complexity of doing so is high and the benefit is low.
      return null
    }

    if (response.hasVaryAll()) {
      return null
    }

    val entry = Entry(response)
    var editor: DiskLruCache.Editor? = null
    try {

   * world. Figure out what sort of space-time tradeoff we're actually going to get here with the
   * *Map variants. This class is particularly hard to benchmark, because the benefit is not only in
   * less allocation, but also having the GC do less work to scan the heap because of fewer
   * references, which is particularly hard to quantify.
   */

   * not required to be a {@link BufferedInputStream} in order to allow implementations to simply
   * delegate to {@link #openStream()} when the stream returned by that method does not benefit from
   * additional buffering (for example, a {@code ByteArrayInputStream}). This method returns a new,
   * independent stream each time it is called.
   *