code: def test_kernel_dtype_device(self):
      kernel = self.create_kernel_no_ard(batch_shape=torch.Size([]))
      orig_type = kernel.dtype
      self.assertEqual(orig_type, torch.get_default_dtype())
      self.assertEqual(kernel.device, torch.device("cpu"))
      new_type = (
          {torch.float32, torch.float64} - {torch.get_default_dtype()}
      ).pop()
      kernel.to(dtype=new_type)
      self.assertEqual(kernel.dtype, new_type)
      if torch.cuda.is_available():
          kernel.to(device="cuda")
          self.assertNotEqual(kernel.device, torch.device("cpu"))
      if not kernel.has_lengthscale:
          kernel.register_parameter(
              "foo", torch.nn.Parameter(torch.zeros(1, dtype=orig_type))
          )
          with self.assertRaisesRegex(
              RuntimeError, "parameters have multiple dtypes"
          ):
              kernel.dtype
  
imports: import pickle
  from abc import abstractmethod
  import torch
  from .base_test_case import BaseTestCase
label_desc: Use image node_id 18 for calling the BaseKernelTestCase obj's underlying member method code with example usage: obj.test_kernel_dtype_device() without return types