code: def check_for_expired_weak_storages(self):
      new_li = []
      stor_to_delete = []
      for obj in self.maybe_storages_to_delete:
          if not obj.expired():
              new_li.append(obj)
          else:
              stor_to_delete.append(obj)
      for obj in stor_to_delete:
          self.storage_memo.pop(obj, None)
      self.maybe_storages_to_delete = new_li
  
      # if for some reason we have aquired many storages which have not expired
      # even though a tensor with their storage has expired (aliasing or otherwise)
      # check for expired storages less often so as to bound the amount of work we
      # do checking for expired storages
      self.check_expired_frequency = max(
          self.check_expired_frequency,
          len(self.maybe_storages_to_delete),
      )
  
imports: import contextlib
  import warnings
  import weakref
  from typing import ContextManager, List, Optional, Tuple, TYPE_CHECKING
  import torch
  from torch._C._functorch import _add_batch_dim, _unwrap_functional_tensor, _wrap_functional_tensor, current_level, get_unwrapped, is_batchedtensor, maybe_get_bdim, maybe_get_level, peek_interpreter_stack, TransformType
  from torch._guards import Source
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._python_dispatch import is_traceable_wrapper_subclass, transform_subclass
  from torch.utils.weak import WeakIdRef
  import torch._prims_common
label_desc: Use image node_id 3 for calling the MetaConverter obj's underlying member method code with example usage: obj.check_for_expired_weak_storages() without return types