code: def invalidate_eager_modules():
      for (
          mod
      ) in (
          torch._guards.TracingContext.get().module_context.nn_modules.values()
      ):
          if not isinstance(mod, torch.nn.Module):
              continue
  
          for attr_name, tensor in list(
              itertools.chain(
                  mod.named_parameters(recurse=False),
                  mod.named_buffers(recurse=False),
              )
          ):
              with torch._dispatch.python.no_python_dispatcher():
                  e_t = ErasedTensor(tensor, attr_name, mod)
              if isinstance(tensor, torch.nn.Parameter):
                  e_t.requires_grad_(True)
                  e_t._is_param = True  # type: ignore[attr-defined]
              setattr(mod, attr_name, e_t)
  
imports: from __future__ import annotations
  import itertools
  import logging
  import weakref
  from typing import Any, List, Optional, Tuple
  import torch
  import torch.utils._pytree
  from torch._dynamo.utils import dynamo_timed, lazy_format_graph_code
  from torch._functorch.aot_autograd import MutationType
  from torch._functorch.compile_utils import fx_graph_cse
  from torch._inductor.constant_folding import constant_fold, replace_node_with_constant
  from torch._inductor.fx_passes.freezing_patterns import freezing_passes
  from torch._inductor.fx_passes.post_grad import view_to_reshape
  from .None import config
label_desc: Use image node_id 3 for calling a global function with example usage: invalidate_eager_modules() without return types