code: def test_apply(self) -> None:
      class TestModule(LazyModuleExtensionMixin, torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.param = torch.tensor(1.0)
  
          def initialize_parameters(self, input) -> None:
              return None
  
          def forward(self, input: torch.Tensor) -> torch.Tensor:
              return input
  
      @torch.no_grad()
      def init_weights(m: torch.nn.Module) -> None:
          if type(m) == TestModule:
              m.param.fill_(7.0)
  
      # Case 1: Running `.apply()` without running first forward pass to
      # initialize the module will result in error.
      net = torch.nn.Sequential(TestModule(), TestModule())
      with self.assertRaisesRegex(
          RuntimeError, "has not been initialized"
      ):
          net.apply(init_weights)
  
      # Case 2: Running `.apply()` after running first forward pass will succeed.
      net(torch.tensor(2.0))
      net.apply(init_weights)
      self.assertTrue(torch.allclose(net[0].param, torch.tensor(7.0)))
  
      # Case 3: Running `.lazy_apply()` without running first forward pass will succeed,
      # and the function will be applied right after first forward pass.
      net = torch.nn.Sequential(TestModule(), TestModule())
      net = lazy_apply(net, init_weights)
      self.assertTrue(torch.allclose(net[0].param, torch.tensor(1.0)))
      net(torch.tensor(2.0))
      self.assertTrue(torch.allclose(net[0].param, torch.tensor(7.0)))
  
file_path: torchrec/torchrec/modules/tests/test_lazy_extension.py
imports: import inspect
  import re
  import unittest
  from typing import Tuple
  import torch
  from torch.nn.modules.lazy import LazyModuleMixin
  from torchrec.modules.lazy_extension import lazy_apply, LazyModuleExtensionMixin
label_desc: Use image node_id 6 for calling the TestLazyModuleExtensionMixin obj's underlying member method code with example usage: obj.test_apply() and returns: None, input