public void testBitSize() {
    double fpp = 0.03;
    for (int i = 1; i < 10000; i++) {
      long numBits = BloomFilter.optimalNumOfBits(i, fpp);
      int arraySize = Ints.checkedCast(LongMath.divide(numBits, 64, RoundingMode.CEILING));
      assertEquals(
          arraySize * Long.SIZE, BloomFilter.create(unencodedCharsFunnel(), i, fpp).bitSize());
    }
  }

  /**

	oldCache    dataUsageCache
	newCache    dataUsageCache
	updateCache dataUsageCache

	dataUsageScannerDebug bool
	healObjectSelect      uint32 // Do a heal check on an object once every n cycles. Must divide into healFolderInclude
	scanMode              madmin.HealScanMode

	weSleep    func() bool
	shouldHeal func() bool

	disks       []StorageAPI
	disksQuorum int

)

var (
	errArithMismatchedTypes = errors.New("cannot perform arithmetic on mismatched types")
	errArithInvalidOperator = errors.New("invalid arithmetic operator")
	errArithDivideByZero    = errors.New("cannot divide by 0")

	errCmpMismatchedTypes     = errors.New("cannot compare values of different types")
	errCmpInvalidBoolOperator = errors.New("invalid comparison operator for boolean arguments")
)

 *
 * <ul>
 *   <li>automatic loading of entries into the cache
 *   <li>least-recently-used eviction when a maximum size is exceeded (note that the cache is
 *       divided into segments, each of which does LRU internally)
 *   <li>time-based expiration of entries, measured since last access or last write
 *   <li>keys automatically wrapped in {@linkplain WeakReference weak} references

      int start = index * size;
      int end = min(start + size, list.size());
      return list.subList(start, end);
    }

    @Override
    public int size() {
      return IntMath.divide(list.size(), size, RoundingMode.CEILING);
    }

    @Override
    public boolean isEmpty() {
      return list.isEmpty();
    }
  }

      int start = index * size;
      int end = min(start + size, list.size());
      return list.subList(start, end);
    }

    @Override
    public int size() {
      return IntMath.divide(list.size(), size, RoundingMode.CEILING);
    }

    @Override
    public boolean isEmpty() {
      return list.isEmpty();
    }
  }

func (p *Parser) term() uint64 {
	value := p.factor()
	for {
		switch p.peek() {
		case '*':
			p.next()
			value *= p.factor()
		case '/':
			p.next()
			if int64(value) < 0 {
				p.errorf("divide of value with high bit set")
			}
			divisor := p.factor()
			if divisor == 0 {
				p.errorf("division by zero")
			} else {
				value /= divisor
			}
		case '%':
			p.next()

 *
 * <ul>
 *   <li>automatic loading of entries into the cache
 *   <li>least-recently-used eviction when a maximum size is exceeded (note that the cache is
 *       divided into segments, each of which does LRU internally)
 *   <li>time-based expiration of entries, measured since last access or last write
 *   <li>keys automatically wrapped in {@linkplain WeakReference weak} references

        input.equals(".%2e", ignoreCase = true) ||
        input.equals("%2e%2e", ignoreCase = true)

    /**
     * Cuts this string up into alternating parameter names and values. This divides a query string
     * like `subject=math&easy&problem=5-2=3` into the list `["subject", "math", "easy", null,
     * "problem", "5-2=3"]`. Note that values may be null and may contain '=' characters.
     */

        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.