class_docstr: A light-weight wrapper of a tensor that enables storing tensors as
  keys with efficient memory reference based comparision as an
  approximation to data equality based keys.
  
  Motivation: the hash value of a torch tensor is tensor instance
  based that does not use data equality and makes the usage of
  tensors as keys less useful. For instance, the result of
  ``len({a.crow_indices(), a.crow_indices()})`` is `2`, although,
  the tensor results from `crow_indices` method call are equal, in
  fact, these share the same data storage.
  On the other hand, for efficient caching of tensors we want to
  avoid calling torch.equal that compares tensors item-wise.
  
  TensorAsKey offers a compromise in that it guarantees key equality
  of tensors that references data in the same storage in the same
  manner and without accessing underlying data. However, this
  approach does not always guarantee correctness. For instance, for
  a complex tensor ``x``, we have ``TensorAsKey(x) ==
  TensorAsKey(x.conj())`` while ``torch.equal(x, x.conj())`` would
  return False.
code: def __eq__(self, other):
      if not isinstance(other, TensorAsKey):
          return False
      if self.obj is None or other.obj is None:
          # dead objects always compare unequal unless these are
          # same objects
          return self is other
      return self.key == other.key
  
imports: import math
  import os
  import torch
  import weakref
  from functools import lru_cache
  from torch.utils._triton import has_triton
  from ._triton_ops_meta import get_meta
  from typing import Optional, Tuple
label_desc: Use image node_id 3 for calling the TensorAsKey obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown, False, unknown