class_docstr: TimeSequence feature engineering
docstr: save the feature tools internal variables as well as the initialization args.
  Some of the variables are derived after fit_transform, so only saving config is not enough.
  :param: file : the file to be saved
  :return:
docstr_tok: ["save","the","feature","tools","internal","variables","as","well","as","the","initialization","args",".","Some","of","the","variables","are","derived","after","fit_transform",",","so","only","saving","config","is","not","enough",".",":","param",":","file",":","the","file","to","be","saved",":","return",":"]
code: def save(self, file_path, replace=False):
      """
      save the feature tools internal variables as well as the initialization args.
      Some of the variables are derived after fit_transform, so only saving config is not enough.
      :param: file : the file to be saved
      :return:
      """
      # for StandardScaler()
      data_to_save = {
          "mean": self.scaler.mean_.tolist(),
          "scale": self.scaler.scale_.tolist(),
          "future_seq_len": self.future_seq_len,
          "dt_col": self.dt_col,
          "target_col": self.target_col,
          "extra_features_col": self.extra_features_col,
          "drop_missing": self.drop_missing,
      }
      save_config(file_path, data_to_save, replace=replace)
  
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 10 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj.save(file_path, replace) without return types