}
        sum -= segments[i].modCount;
      }
      return sum == 0L;
    }
    return true;
  }

  @Override
  public int size() {
    Segment<K, V, E, S>[] segments = this.segments;
    long sum = 0;
    for (int i = 0; i < segments.length; ++i) {
      sum += segments[i].count;
    }
    return Ints.saturatedCast(sum);
  }

  @Override

        }
        sum -= segments[i].modCount;
      }
      return sum == 0L;
    }
    return true;
  }

  @Override
  public int size() {
    Segment<K, V, E, S>[] segments = this.segments;
    long sum = 0;
    for (int i = 0; i < segments.length; ++i) {
      sum += segments[i].count;
    }
    return Ints.saturatedCast(sum);
  }

  @Override

        }
        sum -= segment.modCount;
      }
      return sum == 0L;
    }
    return true;
  }

  long longSize() {
    Segment<K, V>[] segments = this.segments;
    long sum = 0;
    for (Segment<K, V> segment : segments) {
      sum += Math.max(0, segment.count); // see https://github.com/google/guava/issues/2108
    }
    return sum;
  }

  @Override

        }
        sum -= segment.modCount;
      }
      return sum == 0L;
    }
    return true;
  }

  long longSize() {
    Segment<K, V>[] segments = this.segments;
    long sum = 0;
    for (Segment<K, V> segment : segments) {
      sum += segment.count;
    }
    return sum;
  }

  @Override
  public int size() {
    return Ints.saturatedCast(longSize());

                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(

                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(

</p>

<pre>
// sum returns the sum (concatenation, for strings) of its arguments.
func sum[T ~int | ~float64 | ~string](x... T) T { … }

x := sum                       // illegal: the type of x is unknown
intSum := sum[int]             // intSum has type func(x... int) int
a := intSum(2, 3)              // a has value 5 of type int
b := sum[float64](2.0, 3)      // b has value 5.0 of type float64

        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`.