code: def _multi_modal_data(img_dataset, feature_size=256):
      def misc_data(length, feature_size=None):
          for _ in range(length):
              yield torch.randn(feature_size)
  
      misc_dataset = misc_data(
          length=len(img_dataset), feature_size=feature_size
      )
  
      # re-arrange dataset
      for (img, label), misc in zip(img_dataset, misc_dataset):
          yield ((img, misc), label)
  
imports: import unittest
  from typing import Callable, List, Union
  import torch
  import torch.nn
  from captum.insights import AttributionVisualizer, Batch
  from captum.insights.attr_vis.app import FilterConfig
  from captum.insights.attr_vis.features import BaseFeature, FeatureOutput, ImageFeature
  from tests.helpers.basic import BaseTest
label_desc: Use image node_id 3 for calling a global function with example usage: _multi_modal_data(img_dataset, feature_size) without return types