code: def test_autocast(self, batch_prob, device, dtype):
      if not hasattr(torch, "autocast"):
          pytest.skip("PyTorch version without autocast support")
  
      # Uses some subclass of `IntensityAugmentationBase2D` which perform some op which can mismatch the dtype
      # Will cover AugmentationBase2D and RigidAffineAugmentationBase2D too
      aug = RandomGaussianBlur((3, 3), (0.1, 3), p=1)
      x = torch.rand(
          len(batch_prob), 5, 10, 7, dtype=dtype, device=device
      )
  
      to_apply = torch.tensor(batch_prob, device=device)
      with patch.object(
          aug, "__batch_prob_generator__", return_value=to_apply
      ):
          params = aug.forward_parameters(x.shape)
  
      with torch.autocast(device.type):
          res = aug(x, params)
  
      assert (
          res.dtype == dtype
      ), "The output dtype should match the input dtype"
  
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 TestIntensityAugmentationBase2D obj's underlying member method code with example usage: obj.test_autocast(batch_prob, device, dtype) without return types