code: def test_to_numpy_force_argument(self, device) -> None:
      for force in [False, True]:
          for requires_grad in [False, True]:
              for sparse in [False, True]:
                  for conj in [False, True]:
                      data = [[1 + 2j, -2 + 3j], [-1 - 2j, 3 - 2j]]
                      x = torch.tensor(
                          data,
                          requires_grad=requires_grad,
                          device=device,
                      )
                      y = x
                      if sparse:
                          if requires_grad:
                              continue
                          x = x.to_sparse()
                      if conj:
                          x = x.conj()
                          y = x.resolve_conj()
                      expect_error = (
                          requires_grad
                          or sparse
                          or conj
                          or not device == "cpu"
                      )
                      error_msg = r"Use (t|T)ensor\..*(\.numpy\(\))?"
                      if not force and expect_error:
                          self.assertRaisesRegex(
                              (RuntimeError, TypeError),
                              error_msg,
                              lambda: x.numpy(),
                          )
                          self.assertRaisesRegex(
                              (RuntimeError, TypeError),
                              error_msg,
                              lambda: x.numpy(force=False),
                          )
                      elif force and sparse:
                          self.assertRaisesRegex(
                              TypeError,
                              error_msg,
                              lambda: x.numpy(force=True),
                          )
                      else:
                          self.assertEqual(x.numpy(force=force), y)
  
imports: import torch
  import numpy
  from itertools import product
  from torch.testing._internal.common_utils import skipIfTorchDynamo, TestCase, run_tests
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, dtypes, skipMeta
  from torch.testing._internal.common_dtype import all_types_and_complex_and
label_desc: Use image node_id 5 for calling the TestNumPyInterop obj's underlying member method code with example usage: obj.test_to_numpy_force_argument(device) without return types