code: def _labelled_img_data(
      num_samples=10, width=8, height=8, depth=3, num_labels=10
  ):
      for _ in range(num_samples):
          yield torch.empty(depth, height, width).uniform_(
              0, 1
          ), torch.randint(num_labels, (1,))
  
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 2 for calling a global function with example usage: _labelled_img_data(num_samples, width, height, depth, num_labels) without return types