code: def _make_unpartitioned_stats_method(self) -> Callable:
      assert isinstance(self.per_group_stats, dict)
  
      mean = self.per_group_stats[StandardScalarScalerConfig.mean_token]
      std = self.per_group_stats[StandardScalarScalerConfig.std_token]
      coefficient_factor = self.coefficient_factor
  
      assert mean is not None
      assert std is not None
  
      def norm(x):
          return coefficient_factor * (x - mean) / std
  
      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 3 for calling the StandardScalarScalerModel obj's underlying member method code with example usage: obj._make_unpartitioned_stats_method() and returns: norm, unknown