code: def get_jit_fused_bloom_gelu_forward():
      from transformers.models.bloom.modeling_bloom import BloomGelu
  
      from colossalai.kernel.jit.bias_gelu import (
          GeLUFunction as JitGeLUFunction,
      )
  
      def forward(self: BloomGelu, x: torch.Tensor) -> torch.Tensor:
          bias = torch.zeros_like(x)
          if self.training:
              return JitGeLUFunction.apply(x, bias)
          else:
              return self.bloom_gelu_forward(x, bias)
  
      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 5 for calling a global function with example usage: get_jit_fused_bloom_gelu_forward() and returns: forward, JitGeLUFunction, self