code: def generateLagsForForecast(self, df):
      lag_df = pd.DataFrame()
      lag_df[self.mCycleResidueName] = df[self.mCycleResidueName]
      for p in range(1, self.mNbLags + 1):
          # signal lags ... plain old AR model
          self.addLagForForecast(df, lag_df, self.mCycleResidueName, p)
      # Exogenous variables lags
      if self.mExogenousInfo is not None:
          # print(self.mExogenousInfo.mEncodedExogenous);
          # print(df.columns);
          for p in range(1, self.mNbExogenousLags + 1):
              for ex in self.mExogenousInfo.mEncodedExogenous:
                  self.addLagForForecast(df, lag_df, ex, p)
      return lag_df
  
imports: import pandas
  import numpy
  from .None import Time
  from .None import Perf
  from .None import Plots
  from .None import Utils
  import time
label_desc: Use image node_id 8 for calling the cAbstractAR obj's underlying member method code with example usage: obj.generateLagsForForecast(df) and returns: lag_df