code: def test_empty_tensors_interop(self, device):
      x = torch.rand((), dtype=torch.float16)
      y = torch.tensor(np.random.rand(0), dtype=torch.float16)
      # Same can be achieved by running
      # y = torch.empty_strided((0,), (0,), dtype=torch.float16)
  
      # Regression test for https://github.com/pytorch/pytorch/issues/115068
      self.assertEqual(torch.true_divide(x, y).shape, y.shape)
      # Regression test for https://github.com/pytorch/pytorch/issues/115066
      self.assertEqual(torch.mul(x, y).shape, y.shape)
      # Regression test for https://github.com/pytorch/pytorch/issues/113037
      self.assertEqual(
          torch.div(x, y, rounding_mode="floor").shape, y.shape
      )
  
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 17 for calling the TestNumPyInterop obj's underlying member method code with example usage: obj.test_empty_tensors_interop(device) without return types