// Might also explore collocating all of [hash, next, predecessor, successor] fields of an
  // entry in a *single* long[], though that reduces the maximum size of the set by a factor of 2

  /**
   * Pointer to the predecessor of an entry in insertion order. ENDPOINT indicates a node is the
   * first node in insertion order; all values at indices ≥ {@link #size()} are UNSET.
   */

       *
       * The absent indicator of -1 signed becomes 2^8 - 1 unsigned, which reduces the actual max
       * size to 2^8 - 1. However, due to a load factor < 1 the limit is never approached.
       */
      byte[] hashTable = new byte[tableSize];
      Arrays.fill(hashTable, ABSENT);

      int outI = 0;
      entries:
      for (int i = 0; i < n; i++) {

                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
                                                        bucket_count);
  });
}

    private final transient ConcurrentMap<E, Integer> countMap;

    /**
     * Creates a new, empty {@code OldConcurrentHashMultiset} using the default initial capacity,
     * load factor, and concurrency settings.
     */
    public static <E> OldConcurrentHashMultiset<E> create() {
      return new OldConcurrentHashMultiset<E>(new ConcurrentHashMap<E, Integer>());
    }

    @VisibleForTesting

      assertEquals(z, valueSet.size());
      assertFalse(
          Hashing.needsResizing(
              valueSet.size(),
              valueSet.hashTable.length,
              LinkedHashMultimap.VALUE_SET_LOAD_FACTOR));
    }
  }

  private Multimap<String, Integer> initializeMultimap5() {
    Multimap<String, Integer> multimap = LinkedHashMultimap.create();
    multimap.put("foo", 5);
    multimap.put("bar", 4);

     *
     * <ul>
     *   <li>For an empty list, it is 1 (base case).
     *   <li>When r numbers are added to a list of n-r elements, the number of permutations is
     *       increased by a factor of (n choose r).
     * </ul>
     */
    private static <E> int calculateSize(
        List<E> sortedInputList, Comparator<? super E> comparator) {
      int permutations = 1;
      int n = 1;
      int r = 1;

      assertEquals(z, valueSet.size());
      assertFalse(
          Hashing.needsResizing(
              valueSet.size(),
              valueSet.hashTable.length,
              LinkedHashMultimap.VALUE_SET_LOAD_FACTOR));
    }
  }

  private Multimap<String, Integer> initializeMultimap5() {
    Multimap<String, Integer> multimap = LinkedHashMultimap.create();
    multimap.put("foo", 5);
    multimap.put("bar", 4);

mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk
mapreduce.task.sort.spill.percent=0.9 # Minimum % before spilling to disk
```

## What makes a dependency

Up to now you have seen dependencies declared as functions.

But that's not the only way to declare dependencies (although it would probably be the more common).

The key factor is that a dependency should be a "callable".

A "**callable**" in Python is anything that Python can "call" like a function.

          // requireNonNull is safe because right must exist in order to get a negative factor.
          requireNonNull(right);
          if (right.balanceFactor() > 0) {
            right = right.rotateRight();
          }
          return rotateLeft();
        case 2:
          // requireNonNull is safe because left must exist in order to get a positive factor.
          requireNonNull(left);
          if (left.balanceFactor() < 0) {