class_docstr: TimeSequence feature engineering
docstr: get prediction data frame with datetime column and target column.
  :param input_df:
  :return : prediction data frame. If future_seq_len is 1, the output data frame columns are
      datetime | {target_col}. Otherwise, the output data frame columns are
      datetime | {target_col}_0 | {target_col}_1 | ...
docstr_tok: ["get","prediction","data","frame","with","datetime","column","and","target","column",".",":","param","input_df",":",":","return",":","prediction","data","frame",".","If","future_seq_len","is","1",",","the","output","data","frame","columns","are","datetime","|","{","target_col","}",".","Otherwise",",","the","output","data","frame","columns","are","datetime","|","{","target_col","}","_0","|","{","target_col","}","_1","|","..."]
code: def _get_y_pred_df(self, y_pred_dt_df, y_pred_unscale):
      """
      get prediction data frame with datetime column and target column.
      :param input_df:
      :return : prediction data frame. If future_seq_len is 1, the output data frame columns are
          datetime | {target_col}. Otherwise, the output data frame columns are
          datetime | {target_col}_0 | {target_col}_1 | ...
      """
      y_pred_df = y_pred_dt_df
      if self.future_seq_len > 1:
          for i in range(self.future_seq_len):
              for j in range(len(self.target_col)):
                  column = self.target_col[j] + "_" + str(i)
                  y_pred_df[column] = pd.DataFrame(y_pred_unscale[:, i])
      else:
          y_pred_df[self.target_col] = pd.DataFrame(y_pred_unscale)
      return y_pred_df
  
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 8 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._get_y_pred_df(y_pred_dt_df, y_pred_unscale) and returns: y_pred_df