code: def get_jit_fused_bloom_mlp_forward():
      from transformers.models.bloom.modeling_bloom import BloomMLP
  
      def forward(
          self: BloomMLP,
          hidden_states: torch.Tensor,
          residual: torch.Tensor,
      ) -> torch.Tensor:
          hidden_states = self.gelu_impl(
              self.dense_h_to_4h(hidden_states)
          )
  
          if self.pretraining_tp > 1 and self.slow_but_exact:
              intermediate_output = torch.zeros_like(residual)
              slices = (
                  self.dense_4h_to_h.weight.shape[-1]
                  / self.pretraining_tp
              )
              for i in range(self.pretraining_tp):
                  intermediate_output = intermediate_output + F.linear(
                      hidden_states[
                          :, :, int(i * slices) : int((i + 1) * slices)
                      ],
                      self.dense_4h_to_h.weight[
                          :, int(i * slices) : int((i + 1) * slices)
                      ],
                  )
          else:
              intermediate_output = self.dense_4h_to_h(hidden_states)
          output = self.dropout_add(
              intermediate_output,
              residual,
              self.hidden_dropout,
              self.training,
          )
          return output
  
      return forward
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from torch.nn import functional
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from transformers.models.bloom.modeling_bloom import BloomForCausalLM, BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
  from colossalai.shardformer.shard import ShardConfig
label_desc: Use image node_id 4 for calling a global function with example usage: get_jit_fused_bloom_mlp_forward() and returns: forward, output