code: def test_unet_training(self):
      for n in ["basicunet", "unet"]:
          loss = run_test(
              net_name=n,
              device=torch.device(
                  "cuda:0" if torch.cuda.is_available() else "cpu:0"
              ),
          )
          print(loss)
          self.assertGreaterEqual(0.85, loss)
  
file_path: MONAI/tests/test_integration_unet_2d.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from ignite.engine import create_supervised_trainer
  from torch.utils.data import DataLoader, Dataset
  from monai.data import create_test_image_2d
  from monai.losses import DiceLoss
  from monai.networks.nets import BasicUNet, UNet
  from tests.utils import DistTestCase, TimedCall, skip_if_quick
label_desc: Use image node_id 1 for calling the TestIntegrationUnet2D obj's underlying member method code with example usage: obj.test_unet_training() without return types