code: def predict(self, features, verbose=False):
          
          probs = self.clf.predict_proba(features)
          if verbose:
              labels = self.labels.classes_
              res = []
              for prob in probs:
                  vals = {}
                  for i, val in enumerate(prob):
                      label = labels[i]
                      vals[label] = val
                  res.append(vals)
              return res
          else:
              return probs
docs: Probability estimates of each feature
  
          See sklearn's SGDClassifier predict and predict_proba methods.
  
          Args:
              features (:obj:`list` of :obj:`list` of :obj:`float`)
              verbose: Boolean, optional. If true returns an array where each
                  element is a dictionary, where keys are labels and values are
                  the respective probabilities. Defaults to False.
          Returns:
              Array of array of numbers, or array of dictionaries if verbose i
              True