code: def get_gradient_edge(tensor):
      """Get the gradient edge for computing the gradient of the given Tensor.
  
      In particular, it is equivalent to call
      ``g = autograd.grad(loss, input)`` and ``g = autograd.grad(loss, get_gradient_edge(input))``.
      """
      if not tensor.requires_grad:
          raise RuntimeError(
              "It is not possible to get the gradient edge for a Tensor that does not require gradients"
          )
      grad_fn = _get_grad_fn_or_grad_acc(tensor)
  
      # Note that output_nr default to 0 which is the right value
      # for the AccumulateGrad node.
      return GradientEdge(grad_fn, tensor.output_nr)
  
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 2 for calling a global function with example usage: get_gradient_edge(tensor) and returns: GradientEdge