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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def metrics(*metrics):
 """ 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. """

        sc = SparkContext._active_spark_context
        js = JavaWrapper._new_java_obj("org.apache.spark.ml.stat.Summarizer.metrics",
                                       _to_seq(sc, metrics))
        return SummaryBuilder(js)