如何在spark scala中保存PCA对象？

import org.apache.spark.ml.feature.PCA
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.linalg.Matrix
import org.apache.spark.ml.linalg.DenseMatrix
import org.apache.spark.ml.linalg.DenseVector
import org.apache.spark.ml.linalg.SparseVector
import org.apache.spark.ml.linalg.SQLDataTypes.VectorType
import org.apache.spark.ml.linalg.SQLDataTypes.MatrixType
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._

val data: DataFrame = Seq(
  Vectors.dense(1.0, 2.0, 3.0),
  Vectors.dense(4.0, 5.0, 6.0),
  Vectors.dense(7.0, 8.0, 9.0)
).toDF("features")

val pca: PCA = new PCA()
  .setInputCol("features")
  .setOutputCol("pcaFeatures")
  .setK(2) // 设置降维后的维度

val pcaModel = pca.fit(data)
val result = pcaModel.transform(data)

pcaModel.save("path/to/save/pcaModel")

val loadedModel = PCAModel.load("path/to/save/pcaModel")