code: def compare_2d(is_ref=True, device=None, reverse_indexing=False):
      batch_size = 32
      img_a = [
          create_test_image_2d(28, 28, 5, rad_max=6, noise_max=1)[0][
              None
          ]
          for _ in range(batch_size)
      ]
      img_b = [
          create_test_image_2d(28, 28, 5, rad_max=6, noise_max=1)[0][
              None
          ]
          for _ in range(batch_size)
      ]
      img_a = np.stack(img_a, axis=0)
      img_b = np.stack(img_b, axis=0)
      img_a = torch.as_tensor(img_a, device=device)
      img_b = torch.as_tensor(img_b, device=device)
      model = STNBenchmark(
          is_ref=is_ref, reverse_indexing=reverse_indexing
      ).to(device)
      optimizer = optim.SGD(model.parameters(), lr=0.001)
      model.train()
      init_loss = None
      for _ in range(20):
          optimizer.zero_grad()
          output_a = model(img_a)
          loss = torch.mean((output_a - img_b) ** 2)
          if init_loss is None:
              init_loss = loss.item()
          loss.backward()
          optimizer.step()
      return model(img_a).detach().cpu().numpy(), loss.item(), init_loss
  
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from monai.data import create_test_image_2d
  from monai.networks.layers import AffineTransform
  from monai.utils import set_determinism
  from tests.utils import DistTestCase, TimedCall
label_desc: Use image node_id 1 for calling a global function with example usage: compare_2d(is_ref, device, reverse_indexing) and returns: model,  loss,  init_loss