* (The inverse of this will be a horizontal transformation.)
   */
  public static LinearTransformation vertical(double x) {
    checkArgument(isFinite(x));
    return new VerticalLinearTransformation(x);
  }

  /**
   * Builds an instance representing a horizontal transformation with a constant value of {@code y}.

    double y = 1.2;
    LinearTransformation transformation = LinearTransformation.horizontal(y);
    assertHorizontalLinearTransformation(transformation, y);
  }

  public void testHorizontal_infiniteY() {
    assertThrows(
        IllegalArgumentException.class,
        () -> LinearTransformation.horizontal(Double.POSITIVE_INFINITY));
  }

  public void testHorizontal_nanY() {

    double y = 1.2;
    LinearTransformation transformation = LinearTransformation.horizontal(y);
    assertHorizontalLinearTransformation(transformation, y);
  }

  public void testHorizontal_infiniteY() {
    assertThrows(
        IllegalArgumentException.class,
        () -> LinearTransformation.horizontal(Double.POSITIVE_INFINITY));
  }

  public void testHorizontal_nanY() {

            stats.xStats().populationVariance(),
            stats.populationCovariance());
      }
    }
    assertHorizontalLinearTransformation(
        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(
        VERTICAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        VERTICAL_VALUES_PAIRED_STATS.xStats().mean());

            stats.xStats().populationVariance(),
            stats.populationCovariance());
      }
    }
    assertHorizontalLinearTransformation(
        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(
        VERTICAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        VERTICAL_VALUES_PAIRED_STATS.xStats().mean());

    checkArgument(xDelta != 0.0);
    checkArgument(yDelta != 0.0);
    assertThat(transformation.isHorizontal()).isFalse();
    assertThat(transformation.isVertical()).isFalse();
    assertThat(transformation.inverse().isHorizontal()).isFalse();
    assertThat(transformation.inverse().isVertical()).isFalse();
    assertThat(transformation.transform(x1)).isWithin(ALLOWED_ERROR).of(y1);

    checkArgument(xDelta != 0.0);
    checkArgument(yDelta != 0.0);
    assertThat(transformation.isHorizontal()).isFalse();
    assertThat(transformation.isVertical()).isFalse();
    assertThat(transformation.inverse().isHorizontal()).isFalse();
    assertThat(transformation.inverse().isVertical()).isFalse();
    assertThat(transformation.transform(x1)).isWithin(ALLOWED_ERROR).of(y1);

   * either the {@code x} or {@code y} data must have a non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *

   * integrals).
   *
   * Note well that if, for this function, we chose a horizontal line, at height of exactly (1/QPS),
   * then the effect of the function is non-existent: we serve storedPermits at exactly the same
   * cost as fresh ones (1/QPS is the cost for each). We use this trick later.
   *
   * If we pick a function that goes /below/ that horizontal line, it means that we reduce the area

  // +optional
  optional string message = 5;
}

// HorizontalPodAutoscalerList is a list of horizontal pod autoscaler objects.
message HorizontalPodAutoscalerList {
  // metadata is the standard list metadata.
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.ListMeta metadata = 1;

  // items is the list of horizontal pod autoscaler objects.
  repeated HorizontalPodAutoscaler items = 2;
}