code: def _minmax_scaler_unscale_timeseries_numpy(
      data, scaler, scaler_index
  ):
      data_unscale = np.zeros(data.shape)
      feature_counter = 0
      for i in scaler_index:
          value_min = scaler.min_[i]
          value_scale = scaler.scale_[i]
          data_unscale[:, :, feature_counter] = (
              data[:, :, feature_counter] - value_min
          ) / value_scale
          feature_counter += 1
      return data_unscale
  
file_path: BigDL/python/chronos/src/bigdl/chronos/data/utils/scale.py
imports: import numpy
  import pandas
  from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler
label_desc: Use image node_id 3 for calling a global function with example usage: _minmax_scaler_unscale_timeseries_numpy(data, scaler, scaler_index) and returns: data_unscale