docs: def score_task(self, X, Y, t=0, metric="accuracy", verbose=True, **kwargs):
          """Scores the predictive performance of the Classifier on task t
  
          Args:
              X: The input for the predict_task method
              Y: A [n] or [n, 1] np.ndarray or torch.Tensor of gold labels in
                  {1,...,K_t}
              t: The task index to score
              metric: The metric with which to score performance on this task
          Returns:
              The (float) score of the Classifier for the specified task and
              metric
          """
          Y = self._to_numpy(Y)
          Y_tp = self.predict_task(X, t=t, **kwargs)
          probs = self.predict_proba(X)[t]
          score = metric_score(
              Y[t], Y_tp, metric, ignore_in_gold=[0], probs=probs, **kwargs
          )
          if verbose:
              print(f"[t={t}] {metric.capitalize()}: {score:.3f}")
          return score