code: def test_gradcheck(self, device, dtype):
      torch.manual_seed(42)
  
      input = torch.rand((1, 1, 3, 3), device=device, dtype=dtype)
      output = torch.rand((1, 1, 3, 3), device=device, dtype=dtype)
      input_transform = torch.rand(
          (1, 3, 3), device=device, dtype=dtype
      )
      other_transform = torch.rand(
          (1, 3, 3), device=device, dtype=dtype
      )
  
      input = utils.tensor_to_gradcheck_var(input)  # to var
      input_transform = utils.tensor_to_gradcheck_var(
          input_transform
      )  # to var
      output = utils.tensor_to_gradcheck_var(output)  # to var
      other_transform = utils.tensor_to_gradcheck_var(
          other_transform
      )  # to var
  
      input_param = {
          "batch_prob": torch.tensor([True]),
          "x": input_transform,
          "y": {},
      }
  
      augmentation = AugmentationBase2D(p=1.0)
  
      with patch.object(
          augmentation, "apply_transform", autospec=True
      ) as apply_transform:
          apply_transform.return_value = output
          assert gradcheck(
              augmentation,
              ((input, input_param)),
              raise_exception=True,
              fast_mode=True,
          )
  
imports: from unittest.mock import patch
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia.testing
  from kornia.augmentation._2d.base import AugmentationBase2D
  from kornia.augmentation._2d.geometric.affine import RandomAffine
  from kornia.augmentation._2d.intensity.gaussian_blur import RandomGaussianBlur
  from kornia.augmentation._3d.geometric.affine import RandomAffine3D
  from kornia.augmentation._3d.intensity.motion_blur import RandomMotionBlur3D
  from kornia.augmentation.base import _BasicAugmentationBase
  from kornia.testing import assert_close
label_desc: Use image node_id 2 for calling the TestAugmentationBase2D obj's underlying member method code with example usage: obj.test_gradcheck(device, dtype) without return types