Given a Java CrossValidator, create and return a Python wrapper of it.
        Used for ML persistence.

def _from_java(cls, java_stage):
        """
        Given a Java CrossValidator, create and return a Python wrapper of it.
        Used for ML persistence.
        """

        estimator, epms, evaluator = super(CrossValidator, cls)._from_java_impl(java_stage)
        numFolds = java_stage.getNumFolds()
        seed = java_stage.getSeed()
        parallelism = java_stage.getParallelism()
        collectSubModels = java_stage.getCollectSubModels()
        # Create a new instance of this stage.
        py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
                       numFolds=numFolds, seed=seed, parallelism=parallelism,
                       collectSubModels=collectSubModels)
        py_stage._resetUid(java_stage.uid())
        return py_stage