- 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.

        Serialization.getFieldSetter(ConcurrentHashMultiset.class, "countMap");
  }

  /**
   * Creates a new, empty {@code ConcurrentHashMultiset} using the default initial capacity, load
   * factor, and concurrency settings.
   */
  public static <E> ConcurrentHashMultiset<E> create() {
    // TODO(schmoe): provide a way to use this class with other (possibly arbitrary)

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-form-factor">{@code
   * Sec-CH-UA-Form-Factor}</a> header field name.
   *
   * @since 32.0.0
   */
  public static final String SEC_CH_UA_FORM_FACTOR = "Sec-CH-UA-Form-Factor";
  /**
   * The HTTP <a
   * href="https://wicg.github.io/responsive-image-client-hints/#sec-ch-viewport-width">{@code

- In this algorithm, we also make sure that we spread the drives out evenly. MinIO server expands ellipses passed as arguments. Here is a sample expansion to demonstrate the process.

  // 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);
  });
}

      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);