code: def __init__(
      self,
      input_col: str,
      partition_key: Optional[str],
      output_col: str,
      coefficient_factor: float = 1.0,
      use_pandas: bool = True,
  ):
      super().__init__(input_col, partition_key, output_col, use_pandas)
      self.coefficient_factor = coefficient_factor
  
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 1 to create a new StandardScalarScaler object from inherited base classes: PerPartitionScalarScalerEstimator with example: obj = StandardScalarScaler(input_col, partition_key, output_col, coefficient_factor, use_pandas)