cpuNice:           "CPU nice time",
		cpuLoad1:          "CPU load average 1min",
		cpuLoad5:          "CPU load average 5min",
		cpuLoad15:         "CPU load average 15min",
		cpuLoad1Perc:      "CPU load average 1min (percentage)",
		cpuLoad5Perc:      "CPU load average 5min (percentage)",
		cpuLoad15Perc:     "CPU load average 15min (percentage)",
	}
	resourceMetricsGroups = []*MetricsGroupV2{

   * provides more control over the underlying data structure.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */
  public static <K extends @Nullable Object, V extends @Nullable Object>

 * [okhttp-stats](https://github.com/flipkart-incubator/okhttp-stats): Get stats like average network speed.
 * [okhttp-system-keystore](https://github.com/charleskorn/okhttp-system-keystore): Use trusted certificates from the operating system keystore (Keychain on macOS, Certificate Store on Windows).

labels.searchlog_log_type_search_user_hour=User Count/Hour
labels.searchlog_log_type_search_user_day=User Count/Day
labels.searchlog_log_type_search_reqtimeavg_hour=Request Average Time/Hour
labels.searchlog_log_type_search_reqtimeavg_day=Request Average Time/Day
labels.searchlog_log_type_click_count=Click Count
labels.searchlog_log_type_favorite_count=Favorite Count
labels.searchlog_log_message=Message

labels.searchlog_log_type_search_user_hour=User Count/Hour
labels.searchlog_log_type_search_user_day=User Count/Day
labels.searchlog_log_type_search_reqtimeavg_hour=Request Average Time/Hour
labels.searchlog_log_type_search_reqtimeavg_day=Request Average Time/Day
labels.searchlog_log_type_click_count=Click Count
labels.searchlog_log_type_favorite_count=Favorite Count
labels.searchlog_log_message=Message

    // twice as many reads and writes. But benchmarking shows that they usually perform better than
    // Dolphin. Reversal is about as good as Successive on average, and it is much simpler,
    // especially since we already have a `reverse` method.
    checkNotNull(array);
    checkPositionIndexes(fromIndex, toIndex, array.length);
    if (array.length <= 1) {
      return;

    static final int MAX_RUN_MULTIPLIER = 13;

    /**
     * Checks the whole hash table for poor hash distribution. Takes O(n) in the worst case, O(n /
     * log n) on average.
     *
     * <p>The online hash flooding detecting in RegularSetBuilderImpl.add can detect e.g. many
     * exactly matching hash codes, which would cause construction to take O(n^2), but can't detect

    /** The key of the message: Request Average Time/Hour */
    public static final String LABELS_searchlog_log_type_search_reqtimeavg_hour = "{labels.searchlog_log_type_search_reqtimeavg_hour}";

    /** The key of the message: Request Average Time/Day */

    bounds when loading a specially crafted `SavedModel`
    ([CVE-2020-26271](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26271))
*   Solves an OOM issue on TPUs when XLA contexts use fused average updates
*   Updates `libjpeg-turbo` to `2.0.5` to handle
    [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).
*   Updates `junit` to `4.13.1` to handle

// indeed that's what both the original RSA paper and the pre-FIPS // crypto/rsa implementation did. // // However, FIPS 186-5, A.1.1(3) requires computing it as e⁻¹ mod λ(N) // where λ(N) = lcm(p-1, q-1). // // This makes d smaller by 1.5 bits on average, which is irrelevant both // because we exclusively use the CRT for private operations and because // we use constant time windowed exponentiation. On the other hand, it // requires computing a GCD of two values that are not coprime, and then // a...