code: def test_nonzero(self, device, dtype):
      shapes = [
          torch.Size((12,)),
          torch.Size((12, 1)),
          torch.Size((1, 12)),
          torch.Size((6, 2)),
          torch.Size((3, 2, 2)),
          torch.Size((5, 5, 5)),
      ]
  
      def gen_nontrivial_input(shape, dtype, device):
          if dtype != torch.bfloat16:
              return torch.randint(2, shape, device=device, dtype=dtype)
          else:
              # windows does not work for bfloat16 randing
              return torch.randint(
                  2, shape, device=device, dtype=torch.float
              ).to(dtype)
  
      for shape in shapes:
          tensor = gen_nontrivial_input(shape, dtype, device)
          dst1 = torch.nonzero(tensor, as_tuple=False)
          dst2 = tensor.nonzero(as_tuple=False)
          dst3 = torch.empty([], dtype=torch.long, device=device)
          torch.nonzero(tensor, out=dst3)
          if self.device_type != "xla":
              # xla does not raise runtime error
              self.assertRaisesRegex(
                  RuntimeError,
                  "scalar type Long",
                  lambda: torch.nonzero(
                      tensor,
                      out=torch.empty(
                          [], dtype=torch.float, device=device
                      ),
                  ),
              )
          if self.device_type == "cuda":
              self.assertRaisesRegex(
                  RuntimeError,
                  "on the same device",
                  lambda: torch.nonzero(
                      tensor, out=torch.empty([], dtype=torch.long)
                  ),
              )
          np_array = (
              tensor.cpu().numpy()
              if dtype != torch.bfloat16
              else tensor.float().cpu().numpy()
          )
          np_result = torch.from_numpy(np.stack(np_array.nonzero())).t()
          self.assertEqual(dst1.cpu(), np_result, atol=0, rtol=0)
          self.assertEqual(dst2.cpu(), np_result, atol=0, rtol=0)
          self.assertEqual(dst3.cpu(), np_result, atol=0, rtol=0)
          tup1 = torch.nonzero(tensor, as_tuple=True)
          tup2 = tensor.nonzero(as_tuple=True)
          tup1 = torch.stack(tup1).t().cpu()
          tup2 = torch.stack(tup2).t().cpu()
          self.assertEqual(tup1, np_result, atol=0, rtol=0)
          self.assertEqual(tup2, np_result, atol=0, rtol=0)
  
imports: import torch
  import numpy
  from itertools import product, combinations, permutations, chain
  from functools import partial
  import random
  import warnings
  import unittest
  from torch import nan
  from torch.testing import make_tensor
  from torch.testing._internal.common_utils import TestCase, run_tests, skipIfTorchDynamo, torch_to_numpy_dtype_dict, IS_JETSON
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, onlyCUDA, dtypes, onlyNativeDeviceTypes, dtypesIfCUDA, largeTensorTest
  from torch.testing._internal.common_dtype import all_types_and_complex_and, all_types, all_types_and
label_desc: Use image node_id 26 for calling the TestShapeOps obj's underlying member method code with example usage: obj.test_nonzero(device, dtype) and returns: torch, torch