class_docstr: TimeSequence feature engineering
docstr: Scale the data
  :param data:
  :return:
docstr_tok: ["Scale","the","data",":","param","data",":",":","return",":"]
code: def _scale(self, data):
      """
      Scale the data
      :param data:
      :return:
      """
      # n_features_in_ only for 0.23 sklearn support, sklearn version >=0.24 will not check this
      if sklearn.__version__[:4] == "0.23":
          self.scaler.n_features_in_ = self.scaler.mean_.shape[0]
      np_scaled = self.scaler.transform(data)
      data_s = pd.DataFrame(np_scaled)
      return data_s
  
imports: from bigdl.chronos.autots.deprecated.feature.utils import save_config
  from bigdl.chronos.autots.deprecated.feature.abstract import BaseFeatureTransformer
  from bigdl.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 22 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._scale(data) and returns: data_s