|               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();

    @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;

  }

  /**
   * Returns a new {@code TypeResolver} with type variables in {@code formal} mapping to types in
   * {@code actual}.
   *
   * <p>For example, if {@code formal} is a {@code TypeVariable T}, and {@code actual} is {@code
   * String.class}, then {@code new TypeResolver().where(formal, actual)} will {@linkplain
   * #resolveType resolve} {@code ParameterizedType List<T>} to {@code List<String>}, and resolve

        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.

    * <abbr title="Sorğu: Query">Sorğu</abbr> parametrləri.
    * <abbr title="Çərəz: Cookie">Çərəzlər</abbr>.
    * <abbr title="Başlıq: Header">Başlıqlaq</abbr>.
    * <abbr title="Forma: Form">Formalar</abbr>.
    * Fayllar.
* Daxil olan məlumatları <abbr title="Çevrilmə: serialization, parsing, marshalling olaraq da bilinir">çevirmək</abbr> üçün aşağıdakı məlumat növlərindən istifadə edilir (JSON olaraq):

**FastAPI** tem muitas ferramentas para ajudar você com a parte de **Segurança** facilmente, rapidamente, de uma forma padrão, sem ter que estudar e aprender tudo sobre especificações de segurança.

Mas primeiro, vamos verificar alguns pequenos conceitos.

## Está com pressa?

  }

  /**
   * Returns a new {@code TypeResolver} with type variables in {@code formal} mapping to types in
   * {@code actual}.
   *
   * <p>For example, if {@code formal} is a {@code TypeVariable T}, and {@code actual} is {@code
   * String.class}, then {@code new TypeResolver().where(formal, actual)} will {@linkplain
   * #resolveType resolve} {@code ParameterizedType List<T>} to {@code List<String>}, and resolve