}
            return endOfData();
          }
        };
      }
    };
  }

  /**
   * Returns an unmodifiable view of the sum of two multisets. In the returned multiset, the count
   * of each element is the <i>sum</i> of its counts in the two backing multisets. The iteration
   * order of the returned multiset matches that of the element set of {@code multiset1} followed by

            }
            return endOfData();
          }
        };
      }
    };
  }

  /**
   * Returns an unmodifiable view of the sum of two multisets. In the returned multiset, the count
   * of each element is the <i>sum</i> of its counts in the two backing multisets. The iteration
   * order of the returned multiset matches that of the element set of {@code multiset1} followed by

			}
			if missingAll > 0 {
				out += ".incomplete"
				fmt.Println(missingAll, "bytes missing.")
			} else {
				if wantMD5 != "" {
					sum := md5.Sum(combined)
					gotMD5 := hex.EncodeToString(sum[:])
					if gotMD5 != wantMD5 {
						fmt.Println("MD5 mismatch. Expected", wantMD5, "got", gotMD5)
						out += ".mismatch"
					} else {
						fmt.Println("MD5 verified.")

                trySplit.estimateSize(), originalSize));
      }
    }
    if (subsized) {
      if (trySplit != null) {
        assertEquals(
            "sum of estimated sizes of trySplit and original spliterator after trySplit",
            originalSize,
            trySplit.estimateSize() + spliterator.estimateSize());
      } else {
        assertEquals(

        getMultiset().count(element));

    int size = 0;
    for (Multiset.Entry<E> entry : getMultiset().entrySet()) {
      size += entry.getCount();
    }
    assertEquals(
        "multiset.size() should be the sum of the counts of all entries",
        size,
        getMultiset().size());
  }

  /** Call the {@code setCount()} method under test, and check its return value. */

                trySplit.estimateSize(), originalSize));
      }
    }
    if (subsized) {
      if (trySplit != null) {
        assertEquals(
            "sum of estimated sizes of trySplit and original spliterator after trySplit",
            originalSize,
            trySplit.estimateSize() + spliterator.estimateSize());
      } else {
        assertEquals(

  verbose " - Running Downloader.java ..."
  "$(native_path "$JAVACMD")" -cp "$(native_path "$TMP_DOWNLOAD_DIR")" Downloader "$distributionUrl" "$(native_path "$targetZip")"
fi

# If specified, validate the SHA-256 sum of the Maven distribution zip file
if [ -n "${distributionSha256Sum-}" ]; then
  distributionSha256Result=false
  if [ "$MVN_CMD" = mvnd.sh ]; then
    echo "Checksum validation is not supported for maven-mvnd." >&2

        regA(builder);
        return builder;
    }

    protected SumAggregationBuilder regSumA(String name, String field) {
        SumAggregationBuilder builder = AggregationBuilders.sum(name).field(field);
        regA(builder);
        return builder;
    }

    protected ExtendedStatsAggregationBuilder regExtendedStatsA(String name, String field) {

	return cred, owner, ErrNone
}

// sumHMAC calculate hmac between two input byte array.
func sumHMAC(key []byte, data []byte) []byte {
	hash := hmac.New(sha256.New, key)
	hash.Write(data)
	return hash.Sum(nil)
}

// extractSignedHeaders extract signed headers from Authorization header
func extractSignedHeaders(signedHeaders []string, r *http.Request) (http.Header, APIErrorCode) {
	reqHeaders := r.Header

        Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.