class_docstr: TimeSequence feature engineering
code: def _unscale(self, y):
      # for standard scalar
      y_unscale = np.zeros(y.shape)
      for i in range(len(self.target_col)):
          value_mean = self.scaler.mean_[i]
          value_scale = self.scaler.scale_[i]
          y_unscale[:, i : i + self.future_seq_len] = (
              y[:, i : i + self.future_seq_len] * value_scale
              + value_mean
          )
      return y_unscale
  
imports: from zoo.chronos.autots.deprecated.feature.utils import save_config
  from zoo.chronos.autots.deprecated.feature.abstract import BaseFeatureTransformer
  from zoo.chronos.utils import deprecated
  import sklearn
  from sklearn.preprocessing import StandardScaler
  import pandas
  import numpy
  import json
  from packaging import version
label_desc: Use image node_id 6 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._unscale(y) and returns: y_unscale