code: def test_errors_parametrized_tensor_parametrization(self):
      # Test errors when registering a parametrization on a parametrized tensor
  
      class Identity(nn.Module):
          def forward(self, x):
              return x
  
      module = nn.Linear(3, 4)
      parametrize.register_parametrization(module, "weight", Identity())
  
      # Has to return a tensor
      class WrongReturn(nn.Module):
          def forward(self, x):
              return x, x
  
      with self.assertRaisesRegex(ValueError, "must return a tensor"):
          parametrize.register_parametrization(
              module, "weight", WrongReturn()
          )
      self.assertTrue(parametrize.is_parametrized(module))
      self.assertEqual(len(module.parametrizations.weight), 1)
      self.assertTrue(
          isinstance(module.parametrizations.weight[0], Identity)
      )
  
      # Cannot change dtype
      class ChangeDtype(nn.Module):
          def forward(self, x):
              return x.bool()
  
      with self.assertRaisesRegex(
          ValueError, "may not change the dtype"
      ):
          parametrize.register_parametrization(
              module, "weight", ChangeDtype()
          )
      self.assertTrue(parametrize.is_parametrized(module))
      self.assertEqual(len(module.parametrizations.weight), 1)
      self.assertTrue(
          isinstance(module.parametrizations.weight[0], Identity)
      )
  
      # Cannot change shape
      class ChangeShape(nn.Module):
          def forward(self, x):
              return x[:-1]
  
      with self.assertRaisesRegex(
          ValueError, "may not change the shape"
      ):
          parametrize.register_parametrization(
              module, "weight", ChangeShape()
          )
      self.assertTrue(parametrize.is_parametrized(module))
      self.assertEqual(len(module.parametrizations.weight), 1)
      self.assertTrue(
          isinstance(module.parametrizations.weight[0], Identity)
      )
  
      # The following checks are mostly due to bugs in the code of the parametrization
  
      # right_inverse has to return a tensor
      class WrongReturnInverse(Identity):
          def right_inverse(self, x):
              return x, x
  
      with self.assertRaisesRegex(
          ValueError, "right_inverse must return a tensor"
      ):
          parametrize.register_parametrization(
              module, "weight", WrongReturnInverse()
          )
      self.assertTrue(parametrize.is_parametrized(module))
      self.assertEqual(len(module.parametrizations.weight), 1)
      self.assertTrue(
          isinstance(module.parametrizations.weight[0], Identity)
      )
  
      # Cannot change dtype
      class ChangeDtypeInverse(Identity):
          def right_inverse(self, x):
              return x.bool()
  
      with self.assertRaisesRegex(
          ValueError, "must have the same dtype"
      ):
          parametrize.register_parametrization(
              module, "weight", ChangeDtypeInverse()
          )
      self.assertTrue(parametrize.is_parametrized(module))
      self.assertEqual(len(module.parametrizations.weight), 1)
      self.assertTrue(
          isinstance(module.parametrizations.weight[0], Identity)
      )
  
      # Cannot change shape
      class ChangeShapeInverse(Identity):
          def right_inverse(self, x):
              return x[:-1]
  
      with self.assertRaisesRegex(
          ValueError, "must have the same shape"
      ):
          parametrize.register_parametrization(
              module, "weight", ChangeShapeInverse()
          )
      self.assertTrue(parametrize.is_parametrized(module))
      self.assertEqual(len(module.parametrizations.weight), 1)
      self.assertTrue(
          isinstance(module.parametrizations.weight[0], Identity)
      )
  
imports: from copy import deepcopy
  from itertools import product
  import pickle
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
  import torch.nn.utils.parametrize
  from torch.nn import Parameter
  from torch.testing._internal.common_utils import run_tests, skipIfNoLapack, TemporaryFileName, instantiate_parametrized_tests, set_default_dtype
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
  from torch.testing._internal.common_nn import NNTestCase
  from torch.testing._internal.common_utils import gradcheck
label_desc: Use image node_id 7 for calling the TestNNParametrization obj's underlying member method code with example usage: obj.test_errors_parametrized_tensor_parametrization() and returns: x, x,  x, x, x, x,  x, x, x