code: def get_pretrained_model():
      class Net(nn.Module):
          def __init__(self) -> None:
              super(Net, self).__init__()
              self.conv1 = nn.Conv2d(3, 6, 5)
              self.pool1 = nn.MaxPool2d(2, 2)
              self.pool2 = nn.MaxPool2d(2, 2)
              self.conv2 = nn.Conv2d(6, 16, 5)
              self.fc1 = nn.Linear(16 * 5 * 5, 120)
              self.fc2 = nn.Linear(120, 84)
              self.fc3 = nn.Linear(84, 10)
              self.relu1 = nn.ReLU()
              self.relu2 = nn.ReLU()
              self.relu3 = nn.ReLU()
              self.relu4 = nn.ReLU()
  
          def forward(self, x):
              x = self.pool1(self.relu1(self.conv1(x)))
              x = self.pool2(self.relu2(self.conv2(x)))
              x = x.view(-1, 16 * 5 * 5)
              x = self.relu3(self.fc1(x))
              x = self.relu4(self.fc2(x))
              x = self.fc3(x)
              return x
  
      net = Net()
      pt_path = os.path.abspath(
          os.path.join(
              os.path.dirname(__file__), "models/cifar_torchvision.pt"
          )
      )
      net.load_state_dict(torch.load(pt_path))
      return net
  
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 2 for calling a global function with example usage: get_pretrained_model() and returns: net, x