code: def backward(
      ctx: Context,
      *grad_output: Tensor,
  ) -> Tuple[Optional[Tensor], ...]:  # pragma: no cover
      output, input_leaf = ctx.recomputed.pop()
  
      if isinstance(output, tuple):
          tensors = output
      else:
          tensors = (output,)
      if any(y.requires_grad for y in tensors):
          tensors = tuple([x for x in tensors if x.requires_grad])
          torch.autograd.backward(tensors, grad_output)
  
      grad_input: List[Optional[Tensor]] = [
          None,
          None,
          None,
          None,
          None,
      ]
      grad_input.extend(x.grad for x in input_leaf)
      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 Checkpoint obj's underlying member method code with example usage: obj.backward(ctx) and returns: tuple