code: def run_test(
      net_name="basicunet",
      batch_size=64,
      train_steps=100,
      device="cuda:0",
  ):
      class _TestBatch(Dataset):
          def __getitem__(self, _unused_id):
              im, seg = create_test_image_2d(
                  128, 128, noise_max=1, num_objs=4, num_seg_classes=1
              )
              return im[None], seg[None].astype(np.float32)
  
          def __len__(self):
              return train_steps
  
      if net_name == "basicunet":
          net = BasicUNet(
              spatial_dims=2,
              in_channels=1,
              out_channels=1,
              features=(4, 8, 8, 16, 16, 32),
          )
      elif net_name == "unet":
          net = UNet(
              spatial_dims=2,
              in_channels=1,
              out_channels=1,
              channels=(4, 8, 16, 32),
              strides=(2, 2, 2),
              num_res_units=2,
          )
      net.to(device)
  
      loss = DiceLoss(sigmoid=True)
      opt = torch.optim.Adam(net.parameters(), 1e-4)
      src = DataLoader(_TestBatch(), batch_size=batch_size)
  
      trainer = create_supervised_trainer(net, opt, loss, device, False)
  
      trainer.run(src, 1)
      loss = trainer.state.output
      return 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 a global function with example usage: run_test(net_name, batch_size, train_steps, device) and returns: loss, im,  seg, train_steps