code: def backward(ctx, grad_output):
      cuda_module = layer_cuda_module
      backward_func = (
          cuda_module.transformer_decoder_layer_bw_fp16
          if ctx.config.fp16
          else cuda_module.transformer_decoder_layer_bw_fp32
      )
      assert ctx.config.training
      (
          output,
          decoder_states,
          encoder_out,
          encoder_padding_mask,
      ) = ctx.saved_tensors
  
      if ctx.config.fp16:
          grad_output = grad_output.to(torch.half)
          output = output.to(torch.half)
          decoder_states = decoder_states.to(torch.half)
          encoder_out = encoder_out.to(torch.half)
          encoder_padding_mask = encoder_padding_mask.to(torch.half)
  
      bw_res = backward_func(
          ctx.config.layer_id,
          grad_output,
          output,
          decoder_states,
          encoder_out,
          encoder_padding_mask,
      )
      if ctx.config.layer_id == 0:
          grad_input, grad_enc_out = bw_res
      else:
          grad_input = bw_res[0]
          grad_enc_out = None
  
      grad = _all_layer_grads[ctx.config.layer_id]
      return (grad_input, grad_enc_out, None, grad, None, None)
  
imports: from itertools import zip_longest
  import math
  from dataclasses import dataclass
  import copy
  import torch
  from torch import nn
  from torch.autograd import Function
  from lightseq.training.ops.pytorch.util import copy_para, state_dict, MODEL_ARCH, check_config, calc_offset
  from lightseq.training.ops.pytorch.layer_base import TransformerDecoderLayerBase
label_desc: Use image node_id 2 for calling the LSTransformerDecoderFunc obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_input,  grad_enc_out,  None,  grad,  None,  None