*/
  static double calculateNewMeanNonFinite(double previousMean, double value) {
    /*
     * Desired behaviour is to match the results of applying the naive mean formula. In particular,
     * the update formula can subtract infinities in cases where the naive formula would add them.
     *
     * Consequently:
     * 1. If the previous mean is finite and the new value is non-finite then the new mean is that

|               16 |              13 |                 3 |                1.23 |
|               16 |              14 |                 2 |                1.14 |

You can calculate _approximate_ storage usage ratio using the formula - total drives (N) / data drives (D).

### Allowed values for STANDARD storage class

`STANDARD` storage class implies more parity than `REDUCED_REDUNDANCY` class. So, `STANDARD` parity drives should be

     * numHashFunctions/bitSize)^n)`. Solving that for n, and approximating `ln x` as `x - 1` when x
     * is close to 1 (why?), gives the following formula.
     */
    double fractionOfBitsSet = (double) bitCount / bitSize;
    return DoubleMath.roundToLong(
        -Math.log1p(-fractionOfBitsSet) * bitSize / numHashFunctions, RoundingMode.HALF_UP);
  }

        if (!(element instanceof Long) || parent.array[i++] != (Long) element) {
          return false;
        }
      }
      return true;
    }

    // Because we happen to use the same formula. If that changes, just don't override this.
    @Override
    public int hashCode() {
      return parent.hashCode();
    }

    @Override
    public String toString() {
      return parent.toString();

        if (!(element instanceof Integer) || parent.array[i++] != (Integer) element) {
          return false;
        }
      }
      return true;
    }

    // Because we happen to use the same formula. If that changes, just don't override this.
    @Override
    public int hashCode() {
      return parent.hashCode();
    }

    @Override
    public String toString() {
      return parent.toString();

   * stripes, the intended concurrency level, and the typical number of keys used in a {@code
   * bulkGet(keys)} operation. See <a href="http://www.mathpages.com/home/kmath199.htm">Balls in
   * Bins model</a> for mathematical formulas that can be used to estimate the probability of
   * collisions.
   *
   * @param keys arbitrary non-null keys
   * @return the stripes corresponding to the objects (one per each object, derived by delegating to

labels.webauth_parameters=Parameter
labels.webauth_web_crawling_config=Web-Konfiguration
labels.webauth_scheme_basic=Basic
labels.webauth_scheme_digest=Digest
labels.webauth_scheme_ntlm=NTLM
labels.webauth_scheme_form=Formular
labels.log_configuration=Protokolldateien
labels.log_file_name=Dateiname
labels.log_file_date=Zeitstempel
labels.labeltype_configuration=Label
labels.labeltype_title_details=Label
labels.labeltype_name=Name

    @Override
    public int hashCode() {
      // Warning: this is broken if size() == 0, so it is critical that we
      // substitute an empty ImmutableSet to the user in place of this

      // It's a weird formula, but tests prove it works.
      int adjust = size() - 1;
      for (int i = 0; i < axes.size(); i++) {
        adjust *= 31;
        adjust = ~~adjust;

        tf.keras.optimizers`.
    *   Updates binary cross entropy logic in Keras when input is probabilities.
        Instead of converting probabilities to logits, we are using the cross
        entropy formula for probabilities.
    *   Added public APIs for `cumsum` and `cumprod` keras backend functions.
    *   Add support for temporal sample weight mode in subclassed models.