code: def test_lazy_module(self, tensor_cls):
      if tensor_cls is torch.Tensor:
          self.fail(
              "dummy fail for base tensor until the test passes for subclasses"
          )
  
      class MyLazyModule(LazyModuleMixin, nn.Module):
          def __init__(self):
              super().__init__()
              self.param = nn.UninitializedParameter()
  
          def initialize_parameters(self, input) -> None:  # type: ignore[override]
              if self.has_uninitialized_params():
                  with torch.no_grad():
                      self.param.materialize(input.shape)
                      nn.init.uniform_(self.param)
  
          def forward(self, x):
              return self.param + x
  
      m = MyLazyModule()
      self.assertTrue(m.has_uninitialized_params())
      output = m(self._create_tensor(tensor_cls))
      self.assertFalse(m.has_uninitialized_params())
      self.assertIsInstance(m.param, tensor_cls)
  
imports: import tempfile
  from copy import deepcopy
  from functools import partial
  from unittest import expectedFailure
  import torch
  from torch import nn
  from torch.nn.modules.lazy import LazyModuleMixin
  from torch.nn.utils.parametrize import register_parametrization, remove_parametrizations
  from torch.testing._internal.common_subclass import DiagTensorBelow, subclass_db
  from torch.testing._internal.common_utils import TestCase, instantiate_parametrized_tests, parametrize, run_tests, skipIfTorchDynamo, subtest
  from torch.testing._internal.logging_tensor import LoggingTensor
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 9 for calling the TestSubclass obj's underlying member method code with example usage: obj.test_lazy_module(tensor_cls) and returns: unknown