class_docstr: Model fitted by StandardScaler.
  
  .. versionadded:: 3.5.0
class_docstr_tok: ["Model","fitted","by","StandardScaler",".","..","versionadded",":",":","3.5.0"]
code: def _get_transform_fn(self) -> Callable[..., Any]:
      mean_values = self.mean_values
      scale_values = self.scale_values
  
      def transform_fn(series: Any) -> Any:
          def map_value(x: "np.ndarray") -> "np.ndarray":
              return (x - mean_values) / scale_values
  
          return series.apply(map_value)
  
      return transform_fn
  
imports: import pickle
  from typing import Any, Union, List, Tuple, Callable, Dict, Optional
  import numpy
  import pandas
  from pyspark import keyword_only
  from pyspark.sql import DataFrame
  from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasFeatureSizes, HasHandleInvalid, Param, Params, TypeConverters
  from pyspark.ml.connect.base import Estimator, Model, Transformer
  from pyspark.ml.connect.io_utils import ParamsReadWrite, CoreModelReadWrite
  from pyspark.ml.connect.summarizer import summarize_dataframe
label_desc: Use image node_id 4 for calling the StandardScalerModel obj's underlying member method code with example usage: obj._get_transform_fn() and returns: transform_fn, series, unknown