class_docstr: TimeSequence feature engineering
docstr: Get feature related arguments from global hyper parameter config and do necessary error
  checking
  :param config: the global config (usually from hyper parameter tuning)
  :return: config only for feature engineering
docstr_tok: ["Get","feature","related","arguments","from","global","hyper","parameter","config","and","do","necessary","error","checking",":","param","config",":","the","global","config","(","usually","from","hyper","parameter","tuning",")",":","return",":","config","only","for","feature","engineering"]
code: def _get_feat_config(self, **config):
      """
      Get feature related arguments from global hyper parameter config and do necessary error
      checking
      :param config: the global config (usually from hyper parameter tuning)
      :return: config only for feature engineering
      """
      self._check_config(**config)
      feature_config_names = ["selected_features", "past_seq_len"]
      feat_config = {}
      for name in feature_config_names:
          if name not in config:
              continue
              # raise KeyError("Can not find " + name + " in config!")
          feat_config[name] = config[name]
      self.past_seq_len = feat_config.get("past_seq_len", 2)
      return feat_config
  
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 15 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._get_feat_config() and returns: feat_config