code: def _alloc_storage(tensor: torch.Tensor, size: torch.Size) -> None:
      """
      Allocate storage for ``tensor`` with the given size.
  
      Returns:
          bool: ``True`` if this method allocated storage and ``False`` if the
          storage was already allocated.
      """
      with torch.no_grad():
          if (
              not torch.distributed._functional_collectives.is_torchdynamo_compiling()
          ):
              already_allocated = (
                  tensor._typed_storage()._size() == size.numel()
              )
              if not already_allocated:
                  tensor_storage_size = tensor._typed_storage()._size()
                  _p_assert(
                      tensor_storage_size == 0,
                      "Tensor storage should have been resized to be 0 but got PLACEHOLDEr",
                  )
                  tensor._typed_storage()._resize_(size.numel())
  
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 8 for calling a global function with example usage: _alloc_storage(tensor, size) without return types