code: def test_forward(self, device, dtype):
      torch.manual_seed(42)
      input = torch.rand((2, 3, 4, 5), device=device, dtype=dtype)
      # input_transform = torch.rand((2, 3, 3), device=device, dtype=dtype)
      expected_output = torch.rand(
          (2, 3, 4, 5), device=device, dtype=dtype
      )
      augmentation = AugmentationBase2D(p=1.0)
  
      with patch.object(
          augmentation, "apply_transform", autospec=True
      ) as apply_transform, patch.object(
          augmentation, "generate_parameters", autospec=True
      ) as generate_parameters:
          # Calling the augmentation with a single tensor shall return the expected tensor using the generated params.
          params = {"params": {}, "flags": {"foo": 0}}
          generate_parameters.return_value = params
          apply_transform.return_value = expected_output
          output = augmentation(input)
          # RuntimeError: Boolean value of Tensor with more than one value is ambiguous
          # Not an easy fix, happens on verifying torch.tensor([True, True])
          # _params = {'batch_prob': torch.tensor([True, True]), 'params': {}, 'flags': {'foo': 0}}
          # apply_transform.assert_called_once_with(input, _params)
          assert output is expected_output
  
          # Calling the augmentation with a tensor and set return_transform shall
          # return the expected tensor and transformation.
          output = augmentation(input)
          assert output is expected_output
  
          # Calling the augmentation with a tensor and params shall return the expected tensor using the given params.
          params = {"params": {}, "flags": {"bar": 1}}
          apply_transform.reset_mock()
          generate_parameters.return_value = None
          output = augmentation(input, params=params)
          # RuntimeError: Boolean value of Tensor with more than one value is ambiguous
          # Not an easy fix, happens on verifying torch.tensor([True, True])
          # _params = {'batch_prob': torch.tensor([True, True]), 'params': {}, 'flags': {'foo': 0}}
          # apply_transform.assert_called_once_with(input, _params)
          assert output is expected_output
  
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 1 for calling the TestAugmentationBase2D obj's underlying member method code with example usage: obj.test_forward(device, dtype) without return types