code: def main():
      normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
      model = get_pretrained_model()
      visualizer = AttributionVisualizer(
          models=[model],
          score_func=lambda o: torch.nn.functional.softmax(o, 1),
          classes=get_classes(),
          features=[
              ImageFeature(
                  "Photo",
                  baseline_transforms=[baseline_func],
                  input_transforms=[normalize],
              )
          ],
          dataset=formatted_data_iter(),
      )
  
      visualizer.serve(debug=True)
  
file_path: captum/captum/insights/attr_vis/example.py
imports: import os
  import torch
  import torch.nn
  import torchvision
  import torchvision.transforms
  from captum.insights import AttributionVisualizer, Batch
  from captum.insights.attr_vis.features import ImageFeature
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types