code: def _get_outputs(
      self,
  ) -> List[Tuple[List[VisualizationOutput], SampleCache]]:
      # If we run out of new batches, then we need to
      # display data which was already shown before.
      # However, since the dataset given to us is a generator,
      # we can't reset it to return to the beginning.
      # Because of this, we store a small cache of stale
      # data, and iterate on it after the main generator
      # stops returning new batches.
      try:
          batch_data = next(self._dataset_iter)
          self._dataset_cache.append(batch_data)
          if len(self._dataset_cache) > self._config.num_examples:
              self._dataset_cache.pop(0)
      except StopIteration:
          self._dataset_iter = cycle(self._dataset_cache)
          batch_data = next(self._dataset_iter)
  
      vis_outputs = []
  
      # Type ignore for issue with passing union to function taking generic
      # https://github.com/python/mypy/issues/1533
      for (
          inputs,
          additional_forward_args,
          label,
      ) in _batched_generator(  # type: ignore
          inputs=batch_data.inputs,
          additional_forward_args=batch_data.additional_args,
          target_ind=batch_data.labels,
          internal_batch_size=1,  # should be 1 until we have batch label support
      ):
          output = self._calculate_vis_output(
              inputs, additional_forward_args, label
          )
          if output is not None:
              cache = SampleCache(
                  inputs, additional_forward_args, label
              )
              vis_outputs.append((output, cache))
  
      return vis_outputs
  
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 11 for calling the AttributionVisualizer obj's underlying member method code with example usage: obj._get_outputs() and returns: vis_outputs