code: def backward(ctx, *output_grads):
      ctx.input_tensors = [
          x.detach().requires_grad_(True) for x in ctx.input_tensors
      ]
      with torch.enable_grad(), torch.cuda.amp.autocast(
          **ctx.gpu_autocast_kwargs
      ):
          # Fixes a bug where the first op in run_function modifies the
          # Tensor storage in place, which is not allowed for detach()'d
          # Tensors.
          shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
          output_tensors = ctx.run_function(*shallow_copies)
      input_grads = torch.autograd.grad(
          output_tensors,
          ctx.input_tensors + ctx.input_params,
          output_grads,
          allow_unused=True,
      )
      del ctx.input_tensors
      del ctx.input_params
      del output_tensors
      return (None, None) + input_grads
  
imports: import math
  from typing import Optional
  import torch
  import torch.nn
  from einops import rearrange, repeat
label_desc: Use image node_id 2 for calling the CheckpointFunction obj's underlying member method code with example usage: obj.backward(ctx) and returns: unknown