sink.writeUtf8("-------\n")
          for (i in 2..997) {
            sink.writeUtf8(String.format(" * $i = ${factor(i)}\n"))
          }
        }

        private fun factor(n: Int): String {
          for (i in 2 until n) {
            val x = n / i
            if (x * i == n) return "${factor(x)} × $i"
          }
          return n.toString()
        }
      }

    val request =
      Request(

            splitIntoBuckets(
                LinkedList(subProjectTestClassTimes),
                SubprojectTestClassTime::totalTime,
                { largeElement, factor ->
                    List(factor) { SmallSubprojectBucket(largeElement.subProject, parallelization(factor)) }
                },
                { list ->
                    SmallSubprojectBucket(list.map { it.subProject }, parallelization(1))
                },

	}
}

// Sleep sleeps the specified time multiplied by the sleep factor.
// If the factor is updated the sleep will be done again with the new factor.
func (d *dynamicSleeper) Sleep(ctx context.Context, base time.Duration) {
	for {
		// Grab current values
		d.mu.RLock()
		minWait, maxWait := d.minSleep, d.maxSleep
		factor := d.factor
		cycle := d.cycle
		d.mu.RUnlock()

	return 0
}

// term = factor | factor ('*' | '/' | '%' | '>>' | '<<' | '&') factor
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 {

    // Round down to the nearest power of 2.
    expectedEntries = max(expectedEntries, 2);
    int tableSize = Integer.highestOneBit(expectedEntries);
    // Check to make sure that we will not exceed the maximum load factor.
    if (expectedEntries > (int) (loadFactor * tableSize)) {
      tableSize <<= 1;
      return (tableSize > 0) ? tableSize : MAX_TABLE_SIZE;
    }
    return tableSize;
  }

          command: [
            "/bin/sh",
            "-ce",

   * next entry in the bucket chain). The pointers in [size(), entries.length) are all "null"
   * (UNSET).
   */
  @VisibleForTesting transient long[] entries;

  /** The load factor. */
  private transient float loadFactor;

  /** When we have this many elements, resize the hashtable. */
  private transient int threshold;

  /** Constructs a new empty instance of {@code ObjectCountHashMap}. */

    // Round down to the nearest power of 2.
    expectedEntries = max(expectedEntries, 2);
    int tableSize = Integer.highestOneBit(expectedEntries);
    // Check to make sure that we will not exceed the maximum load factor.
    if (expectedEntries > (int) (loadFactor * tableSize)) {
      tableSize <<= 1;
      return (tableSize > 0) ? tableSize : MAX_TABLE_SIZE;
    }
    return tableSize;
  }

- Already compressed objects are not fit for compression since they do not have compressible patterns.
Such objects do not produce efficient [`LZ compression`](https://en.wikipedia.org/wiki/LZ77_and_LZ78)
which is a fitness factor for a lossless data compression.

Pre-compressed input typically compresses in excess of 2GiB/s per core,
so performance impact should be minimal even if precompressed data is re-compressed.

  private GraphConstants() {}

  static final int EXPECTED_DEGREE = 2;

  static final int DEFAULT_NODE_COUNT = 10;
  static final int DEFAULT_EDGE_COUNT = DEFAULT_NODE_COUNT * EXPECTED_DEGREE;

  // Load factor and capacity for "inner" (i.e. per node/edge element) hash sets or maps
  static final float INNER_LOAD_FACTOR = 1.0f;
  static final int INNER_CAPACITY = 2; // ceiling(EXPECTED_DEGREE / INNER_LOAD_FACTOR)

  // Error messages