code: def _predictions_matches_labels(
      self,
      predicted_scores: List[OutputScore],
      labels: Union[str, List[str]],
  ) -> bool:
      if len(predicted_scores) == 0:
          return False
  
      predicted_label = predicted_scores[0].label
  
      if isinstance(labels, List):
          return predicted_label in labels
  
      return labels == predicted_label
  
imports: from collections import namedtuple
  from itertools import cycle
  from typing import Any, Callable, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union
  import torch
  from captum.attr import IntegratedGradients
  from captum.attr._utils.batching import _batched_generator
  from captum.insights.attr_vis.attribution_calculation import AttributionCalculation, OutputScore
  from captum.insights.attr_vis.config import ATTRIBUTION_METHOD_CONFIG, ATTRIBUTION_NAMES_TO_METHODS
  from captum.insights.attr_vis.features import BaseFeature
  from captum.insights.attr_vis.server import namedtuple_to_dict
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 8 for calling the AttributionVisualizer obj's underlying member method code with example usage: obj._predictions_matches_labels(predicted_scores, labels) and returns: unknown, False, unknown