code: def backward(ctx, *output_grads):
      # additional_args = {key: ctx.input_tensors[key] for key in ctx.input_tensors if not isinstance(ctx.input_tensors[key],torch.Tensor)}
      ctx.input_tensors = {
          key: ctx.input_tensors[key].detach().requires_grad_(True)
          for key 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 = {
              key: ctx.input_tensors[key].view_as(
                  ctx.input_tensors[key]
              )
              for key in ctx.input_tensors
          }
          # shallow_copies.update(additional_args)
          output_tensors = ctx.run_function(
              **shallow_copies, **ctx.input_non_tensors
          )
      input_grads = torch.autograd.grad(
          output_tensors,
          list(ctx.input_tensors.values()) + ctx.input_params,
          output_grads,
          allow_unused=True,
      )
      del ctx.input_tensors
      del ctx.input_params
      del output_tensors
      return (
          (None, None, None, None, None)
          + input_grads[: ctx.end_tensors]
          + (None,) * (ctx.end_non_tensors - ctx.end_tensors)
          + input_grads[ctx.end_tensors :]
      )
  
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 MixedCheckpointFunction obj's underlying member method code with example usage: obj.backward(ctx) and returns: unknown