class_docstr: TimeSequence feature engineering
docstr: Restore variables from file
  :return:
docstr_tok: ["Restore","variables","from","file",":","return",":"]
code: def restore(self, **config):
      """
      Restore variables from file
      :return:
      """
      #         with open(file_path, 'r') as input_file:
      #             result = json.load(input_file)
  
      # for StandardScalar()
      self.scaler = StandardScaler()
      self.scaler.mean_ = np.asarray(config["mean"])
      self.scaler.scale_ = np.asarray(config["scale"])
  
      self.config = self._get_feat_config(**config)
  
      self.future_seq_len = config["future_seq_len"]
      self.dt_col = config["dt_col"]
      self.target_col = config["target_col"]
      self.extra_features_col = config["extra_features_col"]
      self.drop_missing = config["drop_missing"]
  
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 11 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj.restore() without return types