code: def test_autocast(self, device, dtype):
      if not hasattr(torch, "autocast"):
          pytest.skip("PyTorch version without autocast support")
  
      tfs = (
          K.RandomAffine(0.5, (0.1, 0.5), (0.5, 1.5), 1.2, p=1.0),
          K.RandomGaussianBlur((3, 3), (0.1, 3), p=1),
      )
      aug = K.PatchSequential(*tfs, grid_size=(2, 2), random_apply=True)
      imgs = torch.rand(2, 3, 7, 4, dtype=dtype, device=device)
  
      with torch.autocast(device.type):
          output = aug(imgs)
  
      assert (
          output.dtype == dtype
      ), "Output image dtype should match the input dtype"
  
imports: from functools import partial
  from unittest.mock import patch
  import pytest
  import torch
  import kornia
  import kornia.augmentation
  from kornia.augmentation.container.base import ParamItem
  from kornia.constants import BorderType
  from kornia.geometry.bbox import bbox_to_mask
  from kornia.testing import assert_close
label_desc: Use image node_id 4 for calling the TestPatchSequential obj's underlying member method code with example usage: obj.test_autocast(device, dtype) without return types