class_docstr: adapted from https://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html
code: def __init__(self, is_ref=True, reverse_indexing=False):
      super().__init__()
      self.is_ref = is_ref
      self.localization = nn.Sequential(
          nn.Conv2d(1, 8, kernel_size=7),
          nn.MaxPool2d(2, stride=2),
          nn.ReLU(True),
          nn.Conv2d(8, 10, kernel_size=5),
          nn.MaxPool2d(2, stride=2),
          nn.ReLU(True),
      )
      # Regressor for the 3 * 2 affine matrix
      self.fc_loc = nn.Sequential(
          nn.Linear(10 * 3 * 3, 32), nn.ReLU(True), nn.Linear(32, 3 * 2)
      )
      # Initialize the weights/bias with identity transformation
      self.fc_loc[2].weight.data.zero_()
      self.fc_loc[2].bias.data.copy_(
          torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float)
      )
      if not self.is_ref:
          self.xform = AffineTransform(
              align_corners=False,
              normalized=True,
              reverse_indexing=reverse_indexing,
          )
  
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 to create a new STNBenchmark object from inherited base classes: nn with example: obj = STNBenchmark(is_ref, reverse_indexing)