code: def __init__(
      self,
      input_col: str,
      partition_key: Optional[str],
      output_col: str,
      min_required_value: float = 0.0,
      max_required_value: float = 1.0,
      use_pandas: bool = True,
  ):
      super().__init__(input_col, partition_key, output_col, use_pandas)
      self.min_required_value = min_required_value
      self.max_required_value = max_required_value
  
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 LinearScalarScaler object from inherited base classes: PerPartitionScalarScalerEstimator with example: obj = LinearScalarScaler(input_col, partition_key, output_col, min_required_value, max_required_value, use_pandas)