code: def get_jit_fused_bert_output_forward():
      from transformers.models.bert.modeling_bert import BertOutput
  
      def forward(
          self: BertOutput,
          hidden_states: torch.Tensor,
          input_tensor: torch.Tensor,
      ) -> torch.Tensor:
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout_add(
              hidden_states,
              input_tensor,
              self.dropout.p,
              self.dropout.training,
          )
          hidden_states = self.LayerNorm(hidden_states)
          return hidden_states
  
      return forward
  
imports: import math
  import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
  from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForPreTrainingOutput, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLMHeadModel, BertModel
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer import ShardConfig
  from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
label_desc: Use image node_id 3 for calling a global function with example usage: get_jit_fused_bert_output_forward() and returns: forward, hidden_states