code: def score(
          self,
          data,
          metric="accuracy",
          break_ties="random",
          verbose=True,
          print_confusion_matrix=True,
          **kwargs,
      ):
          
          Y_p, Y, Y_s = self._get_predictions(
              data, break_ties=break_ties, return_probs=True, **kwargs
          )
  
          # Evaluate on the specified metrics
          return_list = isinstance(metric, list)
          metric_list = metric if isinstance(metric, list) else [metric]
          scores = []
          for metric in metric_list:
              score = metric_score(Y, Y_p, metric, probs=Y_s, ignore_in_gold=[0])
              scores.append(score)
              if verbose:
                  print(f"{metric.capitalize()}: {score:.3f}")
  
          # Optionally print confusion matrix
          if print_confusion_matrix and verbose:
              confusion_matrix(Y, Y_p, pretty_print=True)
  
          # If a single metric was given as a string (not list), return a float
          if len(scores) == 1 and not return_list:
              return scores[0]
          else:
              return scores
docs: Scores the predictive performance of the Classifier on all tasks
  
          Args:
              data: a Pytorch DataLoader, Dataset, or tuple with Tensors (X,Y):
                  X: The input for the predict method
                  Y: An [n] or [n, 1] torch.Tensor or np.ndarray of target labels
                      in {1,...,k}
              metric: A metric (string) with which to score performance or a
                  list of such metrics
              break_ties: A tie-breaking policy (see Classifier._break_ties())
              verbose: The verbosity for just this score method; it will not
                  update the class config.
              print_confusion_matrix: Print confusion matrix (overwritten to False if
                  verbose=False)
  
          Returns:
              scores: A (float) score or a list of such scores if kwarg metric
                  is a list