code: def _parallel_predict(estimators, estimators_features, X, n_classes, combination, estimators_weight):
      
      n_samples = X.shape[0]
      pred = np.zeros((n_samples, n_classes))
      n_estimators = len(estimators)
  
      for estimator, features, weight in zip(estimators, estimators_features, estimators_weight):
          # Resort to voting
          predictions = estimator.predict(X[:, features])
  
          for i in range(n_samples):
              if combination == 'weighted_voting':
                  pred[i, int(predictions[i])] += 1 * weight
              else:
                  pred[i, int(predictions[i])] += 1
  
      return pred
docs: Private function used to compute predictions within a job.