code: def formatted_data_iter():
      dataset = torchvision.datasets.CIFAR10(
          root="data/test",
          train=False,
          download=True,
          transform=transforms.ToTensor(),
      )
      dataloader = iter(
          torch.utils.data.DataLoader(
              dataset, batch_size=4, shuffle=False, num_workers=2
          )
      )
      while True:
          images, labels = next(dataloader)
          yield Batch(inputs=images, labels=labels)
  
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 4 for calling a global function with example usage: formatted_data_iter() without return types