class_docstr: Abstract Base class for imputation.
class_docstr_tok: ["Abstract","Base","class","for","imputation","."]
docstr: save the feature tools internal variables.
  Some of the variables are derived after fit_transform, so only saving config is not enough.
  :param: file_path : the file to be saved
  :param: config: the trial config
  :return:
docstr_tok: ["save","the","feature","tools","internal","variables",".","Some","of","the","variables","are","derived","after","fit_transform",",","so","only","saving","config","is","not","enough",".",":","param",":","file_path",":","the","file","to","be","saved",":","param",":","config",":","the","trial","config",":","return",":"]
code: def save(self, file_path):
      """
      save the feature tools internal variables.
      Some of the variables are derived after fit_transform, so only saving config is not enough.
      :param: file_path : the file to be saved
      :param: config: the trial config
      :return:
      """
      pass
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/deprecated/preprocessing/impute/abstract.py
imports: import numpy
  import pandas
  import sklearn.metrics
  from abc import ABC, abstractmethod
label_desc: Use image node_id 2 for calling the BaseImputation obj's underlying member method code with example usage: obj.save(file_path) without return types