code: def test_affine_transform_minimum(self):
      t = np.pi / 3
      affine = [
          [np.cos(t), -np.sin(t), 0],
          [np.sin(t), np.cos(t), 0],
          [0, 0, 1],
      ]
      affine = torch.as_tensor(
          affine, device=torch.device("cpu:0"), dtype=torch.float32
      )
      image = (
          torch.arange(24.0)
          .view(1, 1, 4, 6)
          .to(device=torch.device("cpu:0"))
      )
      out = AffineTransform(align_corners=False)(image, affine)
      out = out.detach().cpu().numpy()
      expected = [
          [
              [
                  [0.0, 0.06698727, 0.0, 0.0, 0.0, 0.0],
                  [3.8660254, 0.86602557, 0.0, 0.0, 0.0, 0.0],
                  [7.732051, 3.035899, 0.73205125, 0.0, 0.0, 0.0],
                  [11.598076, 6.901923, 2.7631402, 0.0, 0.0, 0.0],
              ]
          ]
      ]
      np.testing.assert_allclose(out, expected, atol=1e-3, rtol=_rtol)
  
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.networks import normalize_transform, to_norm_affine
  from monai.networks.layers import AffineTransform
  from tests.utils import is_tf32_env
label_desc: Use image node_id 8 for calling the TestAffineTransform obj's underlying member method code with example usage: obj.test_affine_transform_minimum() without return types