code: def increment_version(tensor):
      """Update autograd metadata tracking whether the given Tensor was modified in place.
  
      This is to enable more accurate error checking within the autograd engine.
      It is already done automatically by PyTorch functions and within custom Function
      when mark_dirty() is called appropriately so you only need to call this explicitly
      if you are doing inplace operation on the Tensor data in a way that Pytorch doesn't
      know about. For example a custom kernel that reads the Tensor data_ptr and modifies
      the memory inplace based on this pointer.
  
      Note that incrementing the version counter multiple times for a single inplace operation
      is not problematic.
      """
      torch._C._increment_version(tensor)
  
imports: import abc
  import collections
  import contextlib
  import logging
  import weakref
  from collections import defaultdict, namedtuple
  from typing import Any, Callable, Deque, Dict, List, Optional, Sequence, Set, Tuple
  import torch
  from torch.autograd.variable import Variable
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils.hooks import RemovableHandle
label_desc: Use image node_id 3 for calling a global function with example usage: increment_version(tensor) without return types