code: def __init__(
      self,
      input_col: str,
      partition_key: Optional[str],
      output_col: str,
      per_group_stats: Union[DataFrame, Dict[str, float]],
      use_pandas: bool = True,
  ):
      super().__init__()
      ExplainBuilder.build(
          self,
          inputCol=input_col,
          partitionKey=partition_key,
          outputCol=output_col,
      )
      self._per_group_stats = per_group_stats
      self._use_pandas = use_pandas
  
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 PerPartitionScalarScalerModel object from inherited base classes: ABC, Transformer, HasSetInputCol, HasSetOutputCol with example: obj = PerPartitionScalarScalerModel(input_col, partition_key, output_col, per_group_stats, use_pandas)