code: def _calculate_attribution_from_cache(
      self, input_index: int, model_index: int, target: Optional[Tensor]
  ) -> Optional[VisualizationOutput]:
      c = self._outputs[input_index][1]
      result = self._calculate_vis_output(
          c.inputs,
          c.additional_forward_args,
          c.label,
          torch.tensor(target),
          model_index,
      )
  
      if not result:
          return None
      return result[0]
  
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 2 for calling the AttributionVisualizer obj's underlying member method code with example usage: obj._calculate_attribution_from_cache(input_index, model_index, target) and returns: result, None