namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if
// `ranks[i] == 1`, then `shapes[i]` may be nullptr.
//
// TODO(akshayka): Implement a corresponding getter method.

        final ArtifactStatus that = (ArtifactStatus) o;

        return rank == that.rank;
    }

    @Override
    public int hashCode() {
        return rank;
    }

    @Override
    public String toString() {
        return key;
    }

    @Override
    public int compareTo(ArtifactStatus s) {
        return rank - s.rank;
    }

    this.rankMap = rankMap;
  }

  @Override
  public int compare(T left, T right) {
    return rank(left) - rank(right); // safe because both are nonnegative
  }

  private int rank(T value) {
    Integer rank = rankMap.get(value);
    if (rank == null) {
      throw new IncomparableValueException(value);
    }
    return rank;
  }

  @Override
  public boolean equals(@Nullable Object object) {

  abstract Map<Integer, Double> multipleQuantiles(
      Collection<Integer> indexes, int scale, double[] dataset);

  static double getMinValue(double[] array, int from) {
    // This is basically a copy of com.google.math.Rank#getMinValue, with a small change in the
    // method signature: we always search to the end of the array.
    int min = from;
    for (int i = from + 1; i < array.length; i++) {
      if (array[min] > array[i]) {