code: def find_class(self, module, name):
      if module == "torch.storage" and name == "_load_from_bytes":
          return lambda b: torch.load(io.BytesIO(b), map_location="cpu")
      else:
          return super().find_class(module, name)
  
file_path: benchmark_VAE/src/pythae/models/base/base_utils.py
imports: import importlib
  import io
  import logging
  from collections import OrderedDict
  from typing import Any, Tuple
  import torch
label_desc: Use image node_id 1 for calling the CPU_Unpickler obj's underlying member method code with example usage: obj.find_class(module, name) and returns: function, super