code: def test_zerodim_no_device(self):
      # If device is missing, you should never be able to infer device type.
      def mul(x, y):
          return x * y
  
      def add(x, y):
          return x + y
  
      fns = [mul, add]
  
      device_pairs = [
          (self.cpu, None),
          (None, self.cpu),
          (None, None),
      ]
  
      input_shapes = [
          ((1, 2, 2), (2, 2)),  # Different dim, non-zerodim
          ((1, 2, 2), ()),  # one zerodim
          ((), ()),  # both zerodim
      ]
  
      for fn, shapes, devices in product(
          fns, input_shapes, device_pairs
      ):
          self.assert_device_equal(fn, devices, None, shapes)
  
imports: from itertools import product
  import unittest
  import torch
  from torch.testing._internal.common_utils import TEST_CUDA
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.jit._passes._property_propagation import apply_input_props_using_example
label_desc: Use image node_id 13 for calling the TestDeviceAnalysis obj's underlying member method code with example usage: obj.test_zerodim_no_device() and returns: unknown, unknown