code: def convertVectorColumnsToML(dataset, *cols):
          
          if not isinstance(dataset, DataFrame):
              raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
          return callMLlibFunc("convertVectorColumnsToML", dataset, list(cols))
docs: Converts vector columns in an input DataFrame from the
          :py:class:`pyspark.mllib.linalg.Vector` type to the new
          :py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`
          package.
  
          :param dataset:
            input dataset
          :param cols:
            a list of vector columns to be converted.
            New vector columns will be ignored. If unspecified, all old
            vector columns will be converted excepted nested ones.
          :return:
            the input dataset with old vector columns converted to the
            new vector type
  
          >>> import pyspark
          >>> from pyspark.mllib.linalg import Vectors
          >>> from pyspark.mllib.util import MLUtils
          >>> df = spark.createDataFrame(
          ...     [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))],
          ...     ["id", "x", "y"])
          >>> r1 = MLUtils.convertVectorColumnsToML(df).first()
          >>> isinstance(r1.x, pyspark.ml.linalg.SparseVector)
          True
          >>> isinstance(r1.y, pyspark.ml.linalg.DenseVector)
          True
          >>> r2 = MLUtils.convertVectorColumnsToML(df, "x").first()
          >>> isinstance(r2.x, pyspark.ml.linalg.SparseVector)
          True
          >>> isinstance(r2.y, pyspark.mllib.linalg.DenseVector)
          True