code: def _free_storage(tensor: torch.Tensor):
      """
      Frees the underlying storage of ``tensor``.
  
      Returns:
          bool: ``True`` if the method freed the storage and ``False`` if the
          storage was already freed.
      """
      with torch.no_grad():
          if (
              not torch.distributed._functional_collectives.is_torchdynamo_compiling()
          ):
              already_freed = tensor._typed_storage()._size() == 0
              if not already_freed:
                  _p_assert(
                      tensor.storage_offset() == 0,
                      "Freeing a tensor's storage is unsafe when it is not the sole occupant\n"
                      f"storage offset: {tensor.storage_offset()}\n"
                      f"storage size: {tensor._typed_storage()._size()}\n"
                      f"tensor shape: {tensor.shape}",
                  )
                  tensor._typed_storage()._resize_(0)
  
imports: import dataclasses
  import traceback
  from typing import Any, Callable, Container, Dict, List, Optional, OrderedDict, Tuple, TypeVar, overload
  import torch
  import torch.distributed
  from torch import nn
  from torch.nn.parallel._functions import _get_stream
  from torch.nn.parallel.scatter_gather import _is_namedtuple
  from torch.nn.utils.rnn import PackedSequence
label_desc: Use image node_id 9 for calling a global function with example usage: _free_storage(tensor) without return types