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 {

   */
  @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}. */
  ObjectCountHashMap() {
    init(DEFAULT_SIZE, DEFAULT_LOAD_FACTOR);
  }

  ObjectCountHashMap(ObjectCountHashMap<? extends K> map) {

          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(

        listTable = new Entry[initialCapacity];
        threshold = (int) (initialCapacity * LOAD_FACTOR);
    }

    /**
     * 指定された{@link Map}と同じマッピングでインスタンスを構築します。
     *
     * @param map
     *            マッピングがこのマップに配置されるマップ
     */
    public ArrayMap(final Map<? extends K, ? extends V> map) {
        this((int) (map.size() / LOAD_FACTOR) + 1);
        putAll(map);
    }

    @Override
    public int size() {

  private static final double DESIRED_LOAD_FACTOR = 0.5;

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that
   * can hold setSize elements with the desired load factor.
   */
  @VisibleForTesting
  static int chooseTableSize(int setSize) {

      case UNORDERED:
        return new UndirectedGraphConnections<>(
            new HashMap<N, V>(INNER_CAPACITY, INNER_LOAD_FACTOR));
      case STABLE:
        return new UndirectedGraphConnections<>(
            new LinkedHashMap<N, V>(INNER_CAPACITY, INNER_LOAD_FACTOR));
      default:
        throw new AssertionError(incidentEdgeOrder.type());
    }
  }

  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
  static final String NODE_NOT_IN_GRAPH = "Node %s is not an element of this graph.";

  }

  static <N, E> DirectedMultiNetworkConnections<N, E> of() {
    return new DirectedMultiNetworkConnections<>(
        new HashMap<E, N>(INNER_CAPACITY, INNER_LOAD_FACTOR),
        new HashMap<E, N>(INNER_CAPACITY, INNER_LOAD_FACTOR),
        0);
  }

  static <N, E> DirectedMultiNetworkConnections<N, E> ofImmutable(
      Map<E, N> inEdges, Map<E, N> outEdges, int selfLoopCount) {

* **Starlette**:
    * Tendrá el siguiente mejor desempeño, después de Uvicorn. De hecho, Starlette usa Uvicorn para correr. Por lo tanto, probablemente sólo pueda volverse "más lento" que Uvicorn al tener que ejecutar más código.

                if ( ( getContext().getConfig().getCapabilities() & SmbConstants.CAP_NT_SMBS ) == 0 ) {
                    // only have second precision
                    // there seems to be some random factor (adding one second)
                    int diff = Math.abs((int) ( ( time / 1000 ) - ( f.lastModified() / 1000 ) ));
                    Assert.assertTrue("Have set time correctly", diff < 2);
                }