assertSame(clock, newClock, "withZone() should return the same clock instance");
    }

    @Test
    @DisplayName("MonotonicClock should maintain monotonic time progression")
    void testMonotonicBehavior() throws InterruptedException {
        Instant first = MonotonicClock.now();
        Thread.sleep(10); // Small delay
        Instant second = MonotonicClock.now();

crawler.document.file.default.lang=
# Patterns to include for file index processing.
crawler.document.file.default.include.index.patterns=
# Patterns to exclude for file index processing.
crawler.document.file.default.exclude.index.patterns=
# Patterns to include for file search processing.
crawler.document.file.default.include.search.patterns=
# Patterns to exclude for file search processing.
crawler.document.file.default.exclude.search.patterns=

            throws IOException, ServletException {
        final String servletPath = request.getServletPath();

        if (logger.isDebugEnabled()) {
            logger.debug("Processing chat API request. path={}, method={}", servletPath, request.getMethod());
        }

        if (servletPath.equals(STREAM_API_PATH) || servletPath.equals(STREAM_API_PATH + "/")) {

  }

  /**
   * Process the next event. This will result in a single call to [Callback.onEvent] *unless* the
   * data section was empty. Any number of calls to [Callback.onRetryChange] may occur while
   * processing an event.
   *
   * @return false when EOF is reached
   */
  @Throws(IOException::class)
  fun processNextEvent(): Boolean {
    var id = lastId
    var type: String? = null
    val data = Buffer()

    /*
     * We don't "need" to schedule a countDown() call at all here, but by doing
     * so, we come the closest we can to testing that the wait time is
     * appropriately decreased on each progressive await() call.
     */
    TimedLatch latch = new TimedLatch(/* countdownInMillis= */ LONG_DELAY_MS);
    repeatedlyInterruptTestThread(tearDownStack);
    latch.awaitUnsuccessfully(70);
    assertInterrupted();

        assertFalse(dataMap.containsKey("z.overwrite"));
        assertEquals(1, dataMap.size());
        assertEquals("Z1", dataMap.get("z"));
    }

    /** Case 5: Overwrite processing for multiple fields, existing overwrite keys are also properly removed */
    @Test
    public void test_mergeResponseData_multipleOverwrite() {
        Map<String, Object> dataMap = new HashMap<>();

that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations...

    @Test
    public void test_exceptionChaining() {
        // Test exception chaining
        Throwable rootCause = new IllegalArgumentException("Invalid argument");
        Throwable intermediateCause = new RuntimeException("Processing failed", rootCause);
        String message = "Plugin operation failed";
        PluginException exception = new PluginException(message, intermediateCause);

        assertNotNull(exception);

    when {
      // Server sent a 100-continue even though we did not request one. Try again to read the
      // actual response status.
      code == 100 -> true

      // Handle Processing (102) & Early Hints (103) and any new codes without failing
      // 100 and 101 are the exceptions with different meanings
      // But Early Hints not currently exposed
      code in (102 until 200) -> true

---

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.