// TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never
  // aggressively aggregate accumulated gradients to recover memory.
  // Revisit and fix.
  return 1;
}

    *   Add support for `add_metric` in the graph function mode.
    *   Updating cosine similarity loss - removed the negate sign from cosine
        similarity.
    *   Changed default for gradient accumulation for TPU embeddings to true.
    *   Adds summary trace API for collecting graph and profile information.
    *   The `precision_mode` argument to `TrtGraphConverter` is now case
        insensitive.

    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(MANY_VALUES);
    Stats stats = accumulator.snapshot();
    accumulator.add(999.999); // should do nothing to the snapshot
    return stats;
  }

  private static Stats buildLongManyValuesStatsSnapshot() {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(LONG_MANY_VALUES);

  public void testCountOverflow_doesNotThrow() {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.add(ONE_VALUE);
    for (int power = 1; power < Long.SIZE - 1; power++) {
      accumulator.addAll(accumulator.snapshot());
    }
    // Should overflow without throwing.
    accumulator.addAll(accumulator.snapshot());
    assertThat(accumulator.count()).isLessThan(0L);
  }

  public void testMean() {

    PairedStatsAccumulator accumulator = new PairedStatsAccumulator();
    accumulator.add(ONE_VALUE, OTHER_ONE_VALUE);
    for (int power = 1; power < Long.SIZE - 1; power++) {
      accumulator.addAll(accumulator.snapshot());
    }
    // Should overflow without throwing.
    accumulator.addAll(accumulator.snapshot());
    assertThat(accumulator.count()).isLessThan(0L);
  }

    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(MANY_VALUES);
    Stats stats = accumulator.snapshot();
    accumulator.add(999.999); // should do nothing to the snapshot
    return stats;
  }

  private static Stats buildLongManyValuesStatsSnapshot() {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(LONG_MANY_VALUES);

  public void testCountOverflow_doesNotThrow() {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.add(ONE_VALUE);
    for (int power = 1; power < Long.SIZE - 1; power++) {
      accumulator.addAll(accumulator.snapshot());
    }
    // Should overflow without throwing.
    accumulator.addAll(accumulator.snapshot());
    assertThat(accumulator.count()).isLessThan(0L);
  }

  public void testMean() {

<img src="/img/tutorial/path-operation-configuration/image01.png">

### Tags with Enums { #tags-with-enums }

If you have a big application, you might end up accumulating **several tags**, and you would want to make sure you always use the **same tag** for related *path operations*.

In these cases, it could make sense to store the tags in an `Enum`.

    // For datasets of many double values created from an accumulator snapshot, we test many
    // combinations of finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      accumulator.addAll(values.asIterable());
      double min = accumulator.snapshot().min();
      if (values.hasAnyNaN()) {

#
#       The "traditional" practice of packing multiple parameters into a
#       space-separated string is a well documented source of bugs and security
#       problems, so this is (mostly) avoided, by progressively accumulating
#       options in "$@", and eventually passing that to Java.
#
#       Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS,
#       and GRADLE_OPTS) rely on word-splitting, this is performed explicitly;