* used weak references, the entries are reconstructed as they were, but it's not unlikely they'll
 * be quickly garbage-collected before they are ever accessed.
 *
 * <p>{@code new MapMaker().weakKeys().makeMap()} is a recommended replacement for {@link
 * java.util.WeakHashMap}, but note that it compares keys using object identity whereas {@code
 * WeakHashMap} uses {@link Object#equals}.
 *
 * @author Bob Lee
 * @author Charles Fry

labels.duplicateHostName=Duplicate Host Name
labels.pageNumber=Page Number
labels.password=Password
labels.paths=Paths
labels.port=Port
labels.regex=Regex
labels.regularName=Regular Name
labels.replacement=Replacement
labels.sessionId=Session ID
labels.sortOrder=Sort Order
labels.updatedBy=Updated By
labels.updatedTime=Updated Time
labels.urls=URLs
labels.userAgent=User Agent
labels.username=Username
labels.value=Value

  /**
   * Returns a hash code for {@code value}; obsolete alternative to {@link Integer#hashCode(int)}.
   *
   * @param value a primitive {@code int} value
   * @return a hash code for the value
   */
  @InlineMe(replacement = "Integer.hashCode(value)")
  @InlineMeValidationDisabled(
      "The hash code of a int is the int itself, so it's simplest to return that.")
  public static int hashCode(int value) {
    return value;
  }

  /**

 */
@GwtIncompatible // not worth using in GWT for now
class CompactHashMap<K extends @Nullable Object, V extends @Nullable Object>
    extends AbstractMap<K, V> implements Serializable {
  /*
   * TODO: Make this a drop-in replacement for j.u. versions, actually drop them in, and test the
   * world. Figure out what sort of space-time tradeoff we're actually going to get here with the

// means that erroneous UTF-8 encodings translate to U+FFFD = "\xef\xbf\xbd".
// Because of the Scan interface, this makes it impossible for the client to
// distinguish correctly encoded replacement runes from encoding errors.
func ScanRunes(data []byte, atEOF bool) (advance int, token []byte, err error) {
	if atEOF && len(data) == 0 {
		return 0, nil, nil
	}

	// Fast path 1: ASCII.

 */
@GwtIncompatible // not worth using in GWT for now
class CompactHashMap<K extends @Nullable Object, V extends @Nullable Object>
    extends AbstractMap<K, V> implements Serializable {
  /*
   * TODO: Make this a drop-in replacement for j.u. versions, actually drop them in, and test the
   * world. Figure out what sort of space-time tradeoff we're actually going to get here with the

  /**
   * Returns a hash code for {@code value}; obsolete alternative to {@link Long#hashCode(long)}.
   *
   * @param value a primitive {@code long} value
   * @return a hash code for the value
   */
  @InlineMe(replacement = "Long.hashCode(value)")
  public static int hashCode(long value) {
    return Long.hashCode(value);
  }

  /**
   * Compares the two specified {@code long} values. The sign of the value returned is the same as

            "type":       "mapping",
            "mappings": [ "\\u200C=> "]
        },
        "removeall_filter": {
          "type": "pattern_replace",
          "pattern":"(.*)",
          "replacement":""
        }
      },
      "filter": {
        "stemmer_en_filter": {
          "type": "stemmer",
          "name": "english"
        },
        "english_override": {

        // Add rule4 which matches
        ruleManager.addRule(rule4);
        matched = ruleManager.getRule(responseData);
        assertEquals("rule4", matched.getRuleId());
    }

    /**
     * Test rule replacement scenario
     */
    public void test_ruleReplacement() {
        TestRule oldRule = new TestRule("rule1", false);
        TestRule newRule = new TestRule("rule1", true); // Same ID, different behavior

        enable changing the underlying storage there.
    *   The following Python symbols were accidentally added in earlier versions
        of TensorFlow and now are removed. Each symbol has a replacement that
        should be used instead, but note the replacement's argument names are
        different.
        *   `tf.quantize_and_dequantize_v4` (accidentally introduced in
            TensorFlow 2.4): Use `tf.quantization.quantize_and_dequantize_v2`