code: def frienemy_pruning_preprocessed(neighbors, y_val, hit_miss):
      """Implements the Online Pruning method (frienemy) which prunes base
      classifiers that do not cross the region of competence of a given instance.
      A classifier crosses the region of competence if it correctly
      classify at least one sample for each different class in the region.
  
      Notes
      -----
      This implementation assumes the regions of competence of each query example
      (neighbors) and the predictions for the dynamic selection data (hit_miss)
      were already pre-computed.
  
      Parameters
      ----------
      neighbors : array-like of shape (n_samples, n_neighbors)
          Indices of the k nearest neighbors.
      y_val : array-like of shape (n_samples,)
          The target values (class labels).
      hit_miss : array-like of shape (n_samples, n_classifiers)
          Matrix containing 1 when the base classifier made the correct
          prediction, 0 otherwise.
  
      Returns
      -------
      DFP_mask : array-like of shape = [n_samples, n_classifiers]
                 Mask containing 1 for the selected base classifier and 0
                 otherwise.
      """
      if neighbors.ndim < 2:
          neighbors = neighbors.reshape(1, -1)
  
      n_samples = neighbors.shape[0]
      n_classifiers = hit_miss.shape[1]
      dfp_mask = np.zeros((n_samples, n_classifiers))
  
      # TODO: vectorize this code?
      for sample_idx in range(n_samples):
          curr_neighbors = neighbors[sample_idx]
          neighbors_y = y_val[curr_neighbors]
          if len(set(neighbors_y)) > 1:
              # Indecision region. Check if the base classifier predict the
              # correct label for a sample belonging to each class.
              for clf_index in range(n_classifiers):
                  [mask] = np.where(hit_miss[curr_neighbors, clf_index])
                  if len(set(neighbors_y[mask])) > 1:
                      dfp_mask[sample_idx, clf_index] = 1.0
          else:
              # Safe region.
              dfp_mask[sample_idx, :] = 1.0
      # rows that all classifiers were pruned are set to 1.0
      dfp_mask[np.all(dfp_mask == 0, axis=1)] = 1.0
      return dfp_mask
  
label_desc: Use image node_id 2 for calling a global function with example usage: frienemy_pruning_preprocessed(neighbors, y_val, hit_miss) and returns: dfp_mask