code: def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
      def unwrap(e):
          return e.elem if isinstance(e, cls) else e
  
      def wrap(e):
          return cls(e) if isinstance(e, torch.Tensor) else e
  
      with cls.context():
          rs = tree_map(
              wrap,
              func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs)),
          )
      logging.getLogger("LoggingTensor").info(
          f"{func.__module__}.{func.__name__}", args, kwargs, rs
      )
      return rs
  
imports: import torch
  from torch.utils._pytree import tree_map
  from typing import Iterator, List, Optional
  import logging
  import contextlib
  import itertools
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils.weak import WeakTensorKeyDictionary
  import functools
  from torch._C._profiler import gather_traceback, symbolize_tracebacks
label_desc: Use image node_id 3 for calling the LoggingTensor obj's underlying member method code with example usage: obj.__torch_dispatch__(cls, func, types, args, kwargs) and returns: rs, unknown, unknown