code: def test_versioned_div_tensor_inplace(self, sample_input):
      def historic_div_(self, other):
          if self.is_floating_point() or other.is_floating_point():
              return self.true_divide_(other)
          return self.divide_(other, rounding_mode="trunc")
  
      class MyModule(torch.nn.Module):
          def forward(self, a, b):
              a /= b
              return a
  
      try:
          v3_mobile_module = _load_for_lite_interpreter(
              pytorch_test_dir
              + "/cpp/jit/upgrader_models/test_versioned_div_tensor_inplace_v2.ptl"
          )
      except Exception as e:
          self.skipTest("Failed to load fixture!")
  
      current_mobile_module = self._save_load_mobile_module(MyModule)
  
      for val_a, val_b in product(sample_input, sample_input):
          a = torch.tensor((val_a,))
          b = torch.tensor((val_b,))
  
          def _helper(m, fn):
              fn_result = self._try_fn(fn, a.clone(), b)
              m_result = self._try_fn(m, a, b)
              if isinstance(m_result, Exception):
                  self.assertTrue(fn_result, Exception)
              else:
                  self.assertEqual(m_result, fn_result)
                  self.assertEqual(m_result, a)
  
          _helper(v3_mobile_module, historic_div_)
  
          # Recreates a since it was modified in place
          a = torch.tensor((val_a,))
          _helper(current_mobile_module, torch.Tensor.div_)
  
imports: from itertools import product
  import io
  import os
  import sys
  import hypothesis.strategies
  from hypothesis import example, settings, given
  from typing import Union
  import torch
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.jit.mobile import _load_for_lite_interpreter
label_desc: Use image node_id 7 for calling the TestSaveLoadForOpVersion obj's underlying member method code with example usage: obj.test_versioned_div_tensor_inplace(sample_input) and returns: self, self, a