code: def next(self):
      # Skip the cold start period with too few values available
      if len(self.data) < N_TRAIN:
          return
  
      # Re-train the model only every 20 iterations.
      # Since 20 << N_TRAIN, we don't lose much in terms of
      # "recent training examples", but the speed-up is significant!
      if len(self.data) % 20:
          return super().next()
  
      # Retrain on last N_TRAIN values
      df = self.data.df[-N_TRAIN:]
      X, y = get_clean_Xy(df)
      self.clf.fit(X, y)
  
      # Now that the model is fitted,
      # proceed the same as in MLTrainOnceStrategy
      super().next()
  
file_path: backtesting.py/doc/examples/Trading with Machine Learning.py
imports: from backtesting.test import EURUSD, SMA
  import numpy
  import pandas
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.model_selection import train_test_split
  from backtesting import Backtest, Strategy
label_desc: Use image node_id 1 for calling the MLWalkForwardStrategy obj's underlying member method code with example usage: obj.next() and returns: super