code: def hash_storage(
      storage: torch.UntypedStorage, *, stable_hash: bool = False
  ) -> str:
      import torch._dynamo
      from torch._dynamo.utils import is_compile_supported
  
      device_type = storage.device.type
      if stable_hash or not is_compile_supported(device_type):
          cpu_storage = storage.cpu()
          # TODO: make storage support buffer protocol so this isn't
          # necessary
          buf = (ctypes.c_byte * cpu_storage.nbytes()).from_address(
              cpu_storage.data_ptr()
          )
          sha1 = hashlib.sha1()
          sha1.update(buf)
          return sha1.hexdigest()
  
      # TODO: factor this into a random utility
      if device_type == "cpu":
          generator = default_generator
      elif device_type == "cuda":
          import torch.cuda
  
          generator = torch.cuda.default_generators[
              storage.device.index
          ]
      else:
          raise AssertionError(f"unhandled device type {device_type}")
      state = generator.get_state()
      try:
          generator.manual_seed(0)
          x = torch.empty(0, dtype=torch.uint8, device=storage.device).set_(storage)  # type: ignore[call-overload]
          # The dtype-casting view cannot be compiled, and so the
          # padding/reshaping also needs to be done externally even
          # though it could be profitably fused
          pad = -x.numel() % 4
          if pad > 0:
              x = F.pad(x, (0, pad), "constant", 0)
          x = x.view(torch.int32)
          # We run the 32-bit hash five times with differing parameters to
          # reduce chance of collision
          ITER = 5
          cs = [hash_storage_kernel(x).item() for _ in range(ITER)]
          return struct.pack(">" + "i" * ITER, *cs).hex()
      finally:
          generator.set_state(state)
  
imports: import ctypes
  import functools
  import hashlib
  import os.path
  import struct
  from collections import defaultdict
  from typing import Dict, Optional, Set
  import torch
  import torch._prims
  import torch._utils
  import torch.nn.functional
  from torch._C import default_generator
  from torch.multiprocessing.reductions import StorageWeakRef
label_desc: Use image node_id 3 for calling a global function with example usage: hash_storage(storage) and returns: sha1, struct