code: def _bias_correction(V_IJ, inbag, pred_centered, n_trees):
      
      n_train_samples = inbag.shape[0]
      n_var = np.mean(np.square(inbag[0:n_trees]).mean(axis=1).T.view() -
                      np.square(inbag[0:n_trees].mean(axis=1)).T.view())
      boot_var = np.square(pred_centered).sum(axis=1) / n_trees
      bias_correction = n_train_samples * n_var * boot_var / n_trees
      V_IJ_unbiased = V_IJ - bias_correction
      return V_IJ_unbiased
docs: Helper functions that implements bias correction
  
      Parameters
      ----------
      V_IJ : ndarray
          Intermediate result in the computation.
  
      inbag : ndarray
          The inbag matrix that fit the data. If set to `None` (default) it
          will be inferred from the forest. However, this only works for trees
          for which bootstrapping was set to `True`. That is, if sampling was
          done with replacement. Otherwise, users need to provide their own
          inbag matrix.
  
      pred_centered : ndarray
          Centered predictions that are an intermediate result in the
          computation.
  
      n_trees : int
          The number of trees in the forest object.