//
  // 1) Optimal k = b * ln2
  // 2) p = (1 - e ^ (-kn/m))^k
  // 3) For optimal k: p = 2 ^ (-k) ~= 0.6185^b
  // 4) For optimal k: m = -nlnp / ((ln2) ^ 2)

  /**
   * Computes the optimal number of hash functions (k) for a given false positive probability (p).
   *
   * <p>See http://en.wikipedia.org/wiki/File:Bloom_filter_fp_probability.svg for the formula.
   *

      put(range, value);
      return;
    }

    Range<K> coalescedRange = coalescedRange(range, checkNotNull(value));
    put(coalescedRange, value);
  }

  /** Computes the coalesced range for the given range+value - does not mutate the map. */
  private Range<K> coalescedRange(Range<K> range, V value) {
    Range<K> coalescedRange = range;
    Entry<Cut<K>, RangeMapEntry<K, V>> lowerEntry =

            assertThrows(SmbUnsupportedOperationException.class, () -> transport.calculatePreauthHash(new byte[] { 1, 2, 3 }, 0, 3, null));
        }

        @Test
        @DisplayName("calculatePreauthHash computes SHA-512 chain for SMB 3.1.1")
        void preauthHash_happyPath() throws Exception {
            setField(transport, "smb2", true);
            Smb2NegotiateResponse nego = new Smb2NegotiateResponse(cfg);

}

// LastError returns previous error
func (c *Client) LastError() error {
	c.RLock()
	defer c.RUnlock()
	return fmt.Errorf("[%s] %w", c.lastErrTime.Format(time.RFC3339), c.lastErr)
}

// computes the exponential backoff duration according to
// https://www.awsarchitectureblog.com/2015/03/backoff.html
func exponentialBackoffWait(r *rand.Rand, unit, maxSleep time.Duration) func(uint) time.Duration {
	if unit > time.Hour {

// Records message payloads can contain a single record, partial records, or multiple records.
func newRecordsMessage(payload []byte) []byte {
	return genMessage(recordsHeader, payload)
}

// payloadLenForMsgLen computes the length of the payload in a record
// message given the total length of the message.
func payloadLenForMsgLen(messageLength int) int {
	headerLength := len(recordsHeader)

        // This work was already done by calling lruEntries.get().
      }

      else -> throw IOException("unexpected journal line: $line")
    }
  }

  /**
   * Computes the initial size and collects garbage as a part of opening the cache. Dirty entries
   * are assumed to be inconsistent and will be deleted.
   */
  @Throws(IOException::class)
  private fun processJournal() {

    assertEquals(expectedMean, IntMath.mean(x, y));
    assertEquals(
        "The mean of x and y should equal the mean of y and x", expectedMean, IntMath.mean(y, x));
  }

  /**
   * Computes the mean in a way that is obvious and resilient to overflow by using BigInteger
   * arithmetic.
   */
  private static int computeMeanSafely(int x, int y) {
    BigInteger bigX = BigInteger.valueOf(x);

      put(range, value);
      return;
    }

    Range<K> coalescedRange = coalescedRange(range, checkNotNull(value));
    put(coalescedRange, value);
  }

  /** Computes the coalesced range for the given range+value - does not mutate the map. */
  private Range<K> coalescedRange(Range<K> range, V value) {
    Range<K> coalescedRange = range;
    Entry<Cut<K>, RangeMapEntry<K, V>> lowerEntry =

    default Map<String, String> getEffectiveProperties() {
        return getEffectiveProperties(null);
    }

    /**
     * Each invocation computes a new map of effective properties. To be used in interpolation.
     * <p>
     * Effective properties are computed from system, user and optionally project properties, layered with
     * defined precedence onto each other to achieve proper precedence. Precedence is defined as:

   */
  public static <K extends @Nullable Object, V extends @Nullable Object>
      IdentityHashMap<K, V> newIdentityHashMap() {
    return new IdentityHashMap<>();
  }

  /**
   * Computes the difference between two maps. This difference is an immutable snapshot of the state
   * of the maps at the time this method is called. It will never change, even if the maps change at
   * a later time.
   *