*/
  default void forEachEntry(ObjIntConsumer<? super E> action) {
    checkNotNull(action);
    entrySet().forEach(entry -> action.accept(entry.getElement(), entry.getCount()));
  }

  // Comparison and hashing

  /**
   * Compares the specified object with this multiset for equality. Returns {@code true} if the
   * given object is also a multiset and contains equal elements with equal counts, regardless of
   * order.

([#45003](https://github.com/kubernetes/kubernetes/pull/45003), [@krmayankk](https://github.com/krmayankk))

* Deployments are updated to use (1) a more stable hashing algorithm (fnv) than the previous one (adler) and (2) a hashing collision avoidance mechanism that will ensure new rollouts will not block on hashing collisions anymore. ([#44774](https://github.com/kubernetes/kubernetes/pull/44774), [@kargakis](https://github.com/kargakis))([kubernetes/features#287](https://github.com/...

   * @param h hash code
   */
  static int rehash(int h) {
    // Spread bits to regularize both segment and index locations,
    // using variant of single-word Wang/Jenkins hash.
    // TODO(kevinb): use Hashing/move this to Hashing?
    h += (h << 15) ^ 0xffffcd7d;
    h ^= (h >>> 10);
    h += (h << 3);
    h ^= (h >>> 6);
    h += (h << 2) + (h << 14);
    return h ^ (h >>> 16);
  }

  /**

   * @param h hash code
   */
  static int rehash(int h) {
    // Spread bits to regularize both segment and index locations,
    // using variant of single-word Wang/Jenkins hash.
    // TODO(kevinb): use Hashing/move this to Hashing?
    h += (h << 15) ^ 0xffffcd7d;
    h ^= (h >>> 10);
    h += (h << 3);
    h ^= (h >>> 6);
    h += (h << 2) + (h << 14);
    return h ^ (h >>> 16);
  }

  /**

}

// TestXLStorageReadFile with bitrot verification - tests the xlStorage level
// ReadFile API with a BitrotVerifier. Only tests hashing related
// functionality. Other functionality is tested with
// TestXLStorageReadFile.
func TestXLStorageReadFileWithVerify(t *testing.T) {
	volume, object := "test-vol", "myobject"

Caching
=======

OkHttp implements an optional, off by default, Cache. OkHttp aims for RFC correct and
pragmatic caching behaviour, following common real-world browser like Firefox/Chrome and 
server behaviour when ambiguous.

# Basic Usage

```kotlin
  private val client: OkHttpClient = OkHttpClient.Builder()
      .cache(Cache(
          directory = File(application.cacheDir, "http_cache"),
          // $0.05 worth of phone storage in 2020

   * @param h hash code
   */
  static int rehash(int h) {
    // Spread bits to regularize both segment and index locations,
    // using variant of single-word Wang/Jenkins hash.
    // TODO(kevinb): use Hashing/move this to Hashing?
    h += (h << 15) ^ 0xffffcd7d;
    h ^= (h >>> 10);
    h += (h << 3);
    h ^= (h >>> 6);
    h += (h << 2) + (h << 14);
    return h ^ (h >>> 16);
  }

  /**

java.util.regex.Matcher m$1; public void Regex$$anonfun$unapplySeq$1(java.util.regex.Matcher); } scala/util/hashing/package$.class package scala.util.hashing; public final synchronized class package$ { public static final package$ MODULE$; public static void <clinit>(); public static int byteswap32(int); private void package$(); } scala/util/hashing/MurmurHash3.class package scala.util.hashing; public synchronized class MurmurHash3 { public static int mix(int, int); private static int mixLast(int,...

    @Deprecated
    public URL toURL () {
        return getURL();
    }


    /**
     * Computes a hashCode for this file based on the URL string and IP
     * address if the server. The hashing function uses the hashcode of the
     * server address, the canonical representation of the URL, and does not
     * compare authentication information. In essence, two
     * <code>SmbFile</code> objects that refer to

        multi-hot, or tf-idf encoded representations
    *   `CategoryCrossing`: create new categorical features representing
        co-occurrences of previous categorical feature values
    *   `Hashing`: the hashing trick, for large-vocabulary categorical features
    *   `Discretization`: turn continuous numerical features into categorical
        features by binning their values