class_docstr: TimeSequence feature engineering
docstr: :param input_df: a data frame or a list of data frame
  :param past_seq_len:
  :return:
docstr_tok: [":","param","input_df",":","a","data","frame","or","a","list","of","data","frame",":","param","past_seq_len",":",":","return",":"]
code: def _get_y_pred_dt_df(self, input_df, past_seq_len):
      """
      :param input_df: a data frame or a list of data frame
      :param past_seq_len:
      :return:
      """
      if isinstance(input_df, list):
          y_pred_dt_df_list = []
          for df in input_df:
              y_pred_dt_df = self.__get_y_pred_dt_df(df, past_seq_len)
              y_pred_dt_df_list.append(y_pred_dt_df)
          return y_pred_dt_df_list
      else:
          return self.__get_y_pred_dt_df(input_df, past_seq_len)
  
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 21 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._get_y_pred_dt_df(input_df, past_seq_len) and returns: y_pred_dt_df_list, self