}

func (k *KMS) updateMetrics(err error, latency time.Duration) {
	// First, update the latency histogram
	// Therefore, find the first bucket that holds the counter for
	// requests with a latency at least as large as the given request
	// latency and update its and all subsequent counters.
	bucket := slices.IndexFunc(k.latencyBuckets, func(b time.Duration) bool { return latency < b })
	if bucket < 0 {

    for (int i = 1; i < numKeys; i++) {
      nodes.get(i).checkAcquiredLocks(Policies.THROW, nodes.subList(0, i));
    }
    // Pre-populate all disallowedPriorLocks with nodes of larger ordinal.
    for (int i = 0; i < numKeys - 1; i++) {
      nodes.get(i).checkAcquiredLocks(Policies.DISABLED, nodes.subList(i + 1, numKeys));
    }
    return Collections.unmodifiableMap(map);
  }

  /**

    @Test
    public void test_constructorWithThrowableCause_OutOfMemoryError() {
        // Test that constructor accepts Error as cause (verifies Throwable parameter change)
        String url = "http://example.com/large-dataset";
        String message = "Crawling failed due to memory exhaustion";
        OutOfMemoryError error = new OutOfMemoryError("Heap space exhausted");

  }

  /**
   * Read 100 MiB of 'a' chars. This was really slow due to a performance bug in [MultipartReader],
   * and will be really slow if we regress the fix for that.
   */
  @Test
  fun `reading a large part with small byteCount`() {
    val multipartBody =
      MultipartBody
        .Builder("foo")
        .addPart(
          headersOf("header-name", "header-value"),
          object : RequestBody() {

	ANDW	$0x6006000060060, R27, R5                        // ERROR "cannot use REGTMP as source"
	STP	(R3, R4), 0x1234567(R27)                         // ERROR "REGTMP used in large offset store"
	LDP	0x1234567(R27), (R3, R4)                         // ERROR "REGTMP used in large offset load"
	STP	(R26, R27), 700(R2)                              // ERROR "cannot use REGTMP as source"

# Arquivo de teste de LLM { #llm-test-file }

Este documento testa se o <abbr title="Large Language Model - Modelo de Linguagem de Grande Porte">LLM</abbr>, que traduz a documentação, entende o `general_prompt` em `scripts/translate.py` e o prompt específico do idioma em `docs/{language code}/llm-prompt.md`. O prompt específico do idioma é anexado ao `general_prompt`.

Os testes adicionados aqui serão vistos por todos os designers dos prompts específicos de idioma.

    String unitString = unit.toString().toLowerCase(Locale.ROOT);
    String message = "Waited " + timeout + " " + unit.toString().toLowerCase(Locale.ROOT);
    // Only report scheduling delay if larger than our spin threshold - otherwise it's just noise
    if (remainingNanos + SPIN_THRESHOLD_NANOS < 0) {
      // We over-waited for our timeout.
      message += " (plus ";
      long overWaitNanos = -remainingNanos;

        try {
            // Test with extreme status codes
            WebApiUtil.setError(-1, "Negative status code");
            WebApiUtil.setError(0, "Zero status code");
            WebApiUtil.setError(999, "Large status code");

            // Test with very long message
            StringBuilder longMessage = new StringBuilder();
            for (int i = 0; i < 1000; i++) {

            int bytesEncoded = buffer.encode(dst, 0);

            assertEquals(requiredSize, bytesEncoded);
        }

        @Test
        @DisplayName("Should handle large buffer with offset")
        void testLargeBufferWithOffset() {
            String path = "\\\\server\\share";
            buffer = new DfsReferralRequestBuffer(path, 3);

            int offset = 1000;

# LLM test file { #llm-test-file }

This document tests if the <abbr title="Large Language Model">LLM</abbr>, which translates the documentation, understands the `general_prompt` in `scripts/translate.py` and the language specific prompt in `docs/{language code}/llm-prompt.md`. The language specific prompt is appended to `general_prompt`.

Tests added here will be seen by all designers of language specific prompts.

Use as follows: