code: def metrics(*metrics):
          
          sc = SparkContext._active_spark_context
          js = JavaWrapper._new_java_obj("org.apache.spark.ml.stat.Summarizer.metrics",
                                         _to_seq(sc, metrics))
          return SummaryBuilder(js)
docs: Given a list of metrics, provides a builder that it turns computes metrics from a column.
  
          See the documentation of [[Summarizer]] for an example.
  
          The following metrics are accepted (case sensitive):
           - mean: a vector that contains the coefficient-wise mean.
           - variance: a vector tha contains the coefficient-wise variance.
           - count: the count of all vectors seen.
           - numNonzeros: a vector with the number of non-zeros for each coefficients
           - max: the maximum for each coefficient.
           - min: the minimum for each coefficient.
           - normL2: the Euclidean norm for each coefficient.
           - normL1: the L1 norm of each coefficient (sum of the absolute values).
  
          :param metrics:
           metrics that can be provided.
          :return:
           an object of :py:class:`pyspark.ml.stat.SummaryBuilder`
  
          Note: Currently, the performance of this interface is about 2x~3x slower then using the RDD
          interface.