code: def _make_partitioned_stats_method(self) -> Callable:
      assert isinstance(self.per_group_stats, DataFrame)
      coefficient_factor = self.coefficient_factor
  
      def norm(x):
          return f.when(
              f.col(StandardScalarScalerConfig.std_token) != 0.0,
              f.lit(coefficient_factor)
              * (
                  (x - f.col(StandardScalarScalerConfig.mean_token))
                  / f.col(StandardScalarScalerConfig.std_token)
              ),
          ).otherwise(x - f.col(StandardScalarScalerConfig.mean_token))
  
      return norm
  
imports: from abc import ABC, abstractmethod
  from typing import Callable, Dict, List, Optional, Union
  from synapse.ml.cyber.utils.spark_utils import ExplainBuilder, HasSetInputCol, HasSetOutputCol
  from pyspark.ml import Estimator, Transformer
  from pyspark.ml.param.shared import HasInputCol, HasOutputCol, Param, Params
  from pyspark.sql import DataFrame, functions, types
label_desc: Use image node_id 2 for calling the StandardScalarScalerModel obj's underlying member method code with example usage: obj._make_partitioned_stats_method() and returns: norm, f