code: def test_infer_input(self, device, dtype):
      input = torch.rand((2, 3, 4, 5), device=device, dtype=dtype)
      augmentation = _BasicAugmentationBase(p=1.0, p_batch=1)
      with patch.object(
          augmentation, "transform_tensor", autospec=True
      ) as transform_tensor:
          transform_tensor.side_effect = lambda x: x.unsqueeze(dim=2)
          output = augmentation.transform_tensor(input)
          assert output.shape == torch.Size([2, 3, 1, 4, 5])
          assert_close(input, output[:, :, 0, :, :])
  
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 TestBasicAugmentationBase obj's underlying member method code with example usage: obj.test_infer_input(device, dtype) without return types