code: def test_train(self):
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
  
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              DATA_DIR,
              train=True,
              download=True,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=64,
          shuffle=True,
      )
      test_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              DATA_DIR,
              train=False,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=1000,
          shuffle=True,
      )
  
      model = Net().to(device)
      optimizer = torch.optim.SGD(
          model.parameters(), lr=0.01, momentum=0.5
      )
  
      # Create History object
      model.history = hl.History()
      model.canvas = hl.Canvas()
  
      for epoch in range(1, 3):
          train(model, device, train_loader, optimizer, epoch)
          test(model, device, test_loader)
  
      # Clean up
      shutil.rmtree(OUTPUT_DIR)
  
imports: import os
  import sys
  import shutil
  import unittest
  import torch
  from torch import nn
  import torch.nn.functional
  import torchvision.models
  from torchvision import datasets, transforms
  import matplotlib
  import hiddenlayer
label_desc: Use image node_id 1 for calling the TestPytorchWatcher obj's underlying member method code with example usage: obj.test_train() without return types