code: def backward(
      ctx: Context, *grad_output: Tensor
  ) -> Tuple[None, ...]:  # pragma: no cover
      input = ctx.saved_tensors
      input_leaf = tuple(
          x.detach().requires_grad_(x.requires_grad) for x in input
      )
  
      with restore_rng_states(input[0].device, ctx.rng_states):
          with torch.enable_grad(), enable_recomputing():
              output = ctx.function(
                  input_leaf[0] if ctx.input_atomic else input_leaf
              )
  
      ctx.recomputed.append((output, input_leaf))
  
      grad_input: List[None] = [None, None, None, None, None]
      grad_input.extend(None for _ in ctx.saved_tensors)
      return tuple(grad_input)
  
imports: from collections import deque
  from contextlib import contextmanager
  import threading
  from typing import TYPE_CHECKING, Deque, Generator, List, Optional, Tuple, Union
  import torch
  from torch import ByteTensor, Tensor
  import torch.autograd
  from .dependency import fork, join
  from .microbatch import Batch
  from .phony import get_phony
label_desc: Use image node_id 2 for calling the Recompute obj's underlying member method code with example usage: obj.backward(ctx) and returns: tuple