code: def main():
      transform = transforms.Compose(
          [
              transforms.Resize((32, 32)),
              transforms.ToTensor(),
              transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
          ]
      )
  
      classes = (
          "plane",
          "car",
          "bird",
          "cat",
          "deer",
          "dog",
          "frog",
          "horse",
          "ship",
          "truck",
      )
  
      net = LeNet()
      net.load_state_dict(torch.load("Lenet.pth"))
  
      im = Image.open("1.jpg")
      im = transform(im)  # [C, H, W]
      im = torch.unsqueeze(im, dim=0)  # [N, C, H, W]
  
      with torch.no_grad():
          outputs = net(im)
          predict = torch.max(outputs, dim=1)[1].numpy()
      print(classes[int(predict)])
  
imports: import torch
  import torchvision.transforms
  from PIL import Image
  from model import LeNet
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types