code: def test_reparametrize_special(self, functional_call):
      class NonTensor:
          def __repr__(self):
              return f"<{self.__class__.__name__}>"
  
      module = MockModule()
      weight = torch.tensor([[2.0]])
      bias = torch.tensor([5.0])
      buffer = torch.tensor([3.0])
      non_tensor = NonTensor()
  
      # Set to None
      parameters = {
          "l1.weight": weight,
          "l1.bias": None,
          "buffer": buffer,
      }
      x = torch.randn(1, 1)
      with self._ensure_module_unchanged(
          module,
          "the module should not have been modified by a successful call",
      ):
          out = functional_call(module, parameters, x)
          self.assertEqual(out, x * weight + buffer)
  
      # Set non-tensor
      parameters = {"l1.weight": non_tensor}
      x = torch.randn(1, 1)
      with self._ensure_module_unchanged(
          module,
          "the module should not have been modified by a failed call",
      ):
          with self.assertRaisesRegex(
              TypeError,
              re.escape(
                  "<NonTensor> is not an instance of torch.Tensor"
              ),
          ):
              out = functional_call(module, parameters, x)
  
      # Set non-tensor attribute
      parameters = {"l1.weight": weight, "foo": torch.tensor([1.0])}
      x = torch.randn(1, 1)
      with self._ensure_module_unchanged(
          module,
          "the module should not have been modified by a failed call",
      ):
          with self.assertRaisesRegex(
              TypeError,
              re.escape(
                  "attribute `foo`: 0.0 is not an instance of torch.Tensor"
              ),
          ):
              out = functional_call(module, parameters, x)
  
      # Set non-exist submodule
      parameters = {"l1.weight": weight, "l2.bias": bias}
      x = torch.randn(1, 1)
      with self._ensure_module_unchanged(
          module,
          "the module should not have been modified by a failed call",
      ):
          with self.assertRaisesRegex(
              AttributeError,
              re.escape("MockModule has no attribute `l2`"),
          ):
              out = functional_call(module, parameters, x)
  
imports: import contextlib
  import os
  import re
  import subprocess
  import sys
  import unittest
  import torch
  import torch.nn.utils.stateless
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
  from torch.testing._internal.common_utils import run_tests, TestCase, parametrize, instantiate_parametrized_tests, subtest
label_desc: Use image node_id 14 for calling the TestStatelessFunctionalAPI obj's underlying member method code with example usage: obj.test_reparametrize_special(functional_call) and returns: str, self, str