class_docstr: TimeSequence feature engineering
docstr: :param input_df: one data frame
  :return: a data frame with prediction datetime
docstr_tok: [":","param","input_df",":","one","data","frame",":","return",":","a","data","frame","with","prediction","datetime"]
code: def __get_y_pred_dt_df(self, input_df, past_seq_len):
      """
      :param input_df: one data frame
      :return: a data frame with prediction datetime
      """
      input_df = input_df.reset_index(drop=True)
      input_dt_df = input_df.reset_index(drop=True)[
          [self.dt_col]
      ].copy()
      time_delta = input_dt_df.iloc[-1] - input_dt_df.iloc[-2]
      last_time = input_dt_df.iloc[-1] + time_delta
      last_df = pd.DataFrame({self.dt_col: last_time})
      pre_pred_dt_df = input_dt_df[past_seq_len:].copy()
      pre_pred_dt_df = pre_pred_dt_df.reset_index(drop=True)
      y_pred_dt_df = pre_pred_dt_df.append(last_df, ignore_index=True)
      # print(y_pred_dt_df)
      return y_pred_dt_df
  
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 20 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