code: def __init__(
      self,
      scaler: MinMaxScaler,
      lookback: int,
      id_index: int,
      target_feature_index: List[int],
      scaler_index: List[int],
      operation: str,
  ) -> None:
      super().__init__(
          lookback, id_index, target_feature_index, operation
      )
      self.scale_ = torch.from_numpy(scaler.scale_).type(torch.float64)
      self.min_ = torch.from_numpy(scaler.min_).type(torch.float64)
      self.lookback: int = lookback
      self.id_index: int = id_index
      self.target_feature_index = target_feature_index
      self.scaler_index = scaler_index
  
imports: import torch
  import torch.nn
  from typing import List
  from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler
  from .utils import _to_list
label_desc: Use image node_id 1 to create a new ExportWithMinMaxScaler object from inherited base classes: ExportJIT with example: obj = ExportWithMinMaxScaler(scaler, lookback, id_index, target_feature_index, scaler_index, operation)