code: def backward(ctx, grad_output):
      assert ctx.config.training
  
      cuda_module = layer_cuda_module
      backward_func = (
          cuda_module.transformer_encoder_layer_bw_fp16
          if ctx.config.fp16
          else cuda_module.transformer_encoder_layer_bw_fp32
      )
  
      output, input, input_mask = ctx.saved_tensors
      if ctx.config.fp16:
          grad_output = grad_output.to(torch.half)
          output = output.to(torch.half)
          input = input.to(torch.half)
          input_mask = input_mask.to(torch.half)
      (grad_input,) = backward_func(
          ctx.config.layer_id, grad_output, output, input, input_mask
      )
  
      grad = _all_layer_grads[ctx.config.layer_id]
  
      return (grad_input, None, grad, None)
  
imports: import math
  import copy
  import torch
  from torch import nn
  from torch.autograd import Function
  from lightseq.training.ops.pytorch.layer_base import TransformerEncoderLayerBase
  from .None import layer_cuda_module
  from lightseq.training.ops.pytorch.util import copy_para, state_dict, calc_offset
label_desc: Use image node_id 2 for calling the LSTransformerEncoderFuncNew obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_input,  None,  grad,  None