code: def test_batch_multi_params(
      self, degrees, translate, scale, shear, device, dtype
  ):
      x_data = torch.rand(2, 2, 8, 9).to(device)
      aug = kornia.augmentation.RandomAffine(
          degrees=degrees, translate=translate, scale=scale, shear=shear
      )
      out = aug(x_data)
      assert out.shape == x_data.shape
      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 3 for calling the TestRandomAffine obj's underlying member method code with example usage: obj.test_batch_multi_params(degrees, translate, scale, shear, device, dtype) without return types