code: def _predict(
      self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]
  ) -> pd.DataFrame:
      if y is not None:
          prediction = self.selector.predict(X, y)
      else:
          prediction = self.selector.predict(X)
      for task_id, x in X.iterrows():
          counter = 0
          te = x.copy()
          assert len(te) == self.X_.shape[1]
          for _, tr in self.X_.iterrows():
              tr = tr.to_numpy()
              if all([tr[i] >= te[i] for i in range(self.X_.shape[1])]):
                  counter += 1
  
          if counter == 0:
              prediction.loc[task_id] = pd.Series(
                  {
                      strategy: value
                      for strategy, value in zip(
                          self.strategies_, self.return_value_
                      )
                  }
              )
  
      return prediction
  
imports: import typing
  import copy
  import itertools
  import numpy
  import pandas
  import scipy.stats
  import sklearn.ensemble
label_desc: Use image node_id 3 for calling the FallbackWrapper obj's underlying member method code with example usage: obj._predict(X, y) and returns: prediction