code: def _get_labels_from_scores(
      self, scores: Tensor, indices: Tensor
  ) -> List[OutputScore]:
      pred_scores: List[OutputScore] = []
      if indices.nelement() < 2:
          return pred_scores
      for i in range(len(indices)):
          score = scores[i]
          pred_scores.append(
              OutputScore(
                  score, indices[i], self.classes[int(indices[i])]
              )
          )
      return pred_scores
  
file_path: captum/captum/insights/attr_vis/attribution_calculation.py
imports: import inspect
  from collections import namedtuple
  from typing import Callable, cast, Dict, Iterable, List, Optional, Sequence, Tuple, Union
  import torch
  from captum._utils.common import _run_forward, safe_div
  from captum.insights.attr_vis.config import ATTRIBUTION_METHOD_CONFIG, ATTRIBUTION_NAMES_TO_METHODS
  from captum.insights.attr_vis.features import BaseFeature
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 6 for calling the AttributionCalculation obj's underlying member method code with example usage: obj._get_labels_from_scores(scores, indices) and returns: pred_scores, pred_scores