code: def test_smoke_transform(self, device):
      x_data = torch.rand(1, 2, 4, 5).to(device)
      aug = kornia.augmentation.RandomAffine(0.0)
      out = aug(x_data)
  
      assert out.shape == x_data.shape
      assert aug.transform_matrix.shape == torch.Size([1, 3, 3])
      assert aug.inverse(out).shape == x_data.shape
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
label_desc: Use image node_id 4 for calling the TestRandomAffine obj's underlying member method code with example usage: obj.test_smoke_transform(device) without return types