code: def get_jit_fused_opt_decoder_layer_forward():
      from transformers.models.opt.modeling_opt import OPTDecoderLayer
  
      def forward(
          self: OPTDecoderLayer,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          layer_head_mask: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          output_attentions: Optional[bool] = False,
          use_cache: Optional[bool] = False,
      ) -> Tuple[
          torch.FloatTensor,
          Optional[Tuple[torch.FloatTensor, torch.FloatTensor]],
      ]:
          """
          Args:
              hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
              attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
                  `(encoder_attention_heads,)`.
              output_attentions (`bool`, *optional*):
                  Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                  returned tensors for more detail.
              use_cache (`bool`, *optional*):
                  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                  (see `past_key_values`).
              past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
          """
  
          residual = hidden_states
  
          # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
          if self.do_layer_norm_before:
              hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Self Attention
          (
              hidden_states,
              self_attn_weights,
              present_key_value,
          ) = self.self_attn(
              hidden_states=hidden_states,
              past_key_value=past_key_value,
              attention_mask=attention_mask,
              layer_head_mask=layer_head_mask,
              output_attentions=output_attentions,
          )
  
          hidden_states = self.dropout_add(
              hidden_states, residual, self.dropout, self.training
          )
  
          # 350m applies layer norm AFTER attention
          if not self.do_layer_norm_before:
              hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Fully Connected
          hidden_states_shape = hidden_states.shape
          hidden_states = hidden_states.reshape(
              -1, hidden_states.size(-1)
          )
          residual = hidden_states
  
          # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
          if self.do_layer_norm_before:
              hidden_states = self.final_layer_norm(hidden_states)
  
          hidden_states = self.fc1(hidden_states)
          hidden_states = self.activation_fn(hidden_states)
  
          hidden_states = self.fc2(hidden_states)
  
          hidden_states = self.dropout_add(
              hidden_states, residual, self.dropout, self.training
          ).view(hidden_states_shape)
  
          # 350m applies layer norm AFTER attention
          if not self.do_layer_norm_before:
              hidden_states = self.final_layer_norm(hidden_states)
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (self_attn_weights,)
  
          if use_cache:
              outputs += (present_key_value,)
  
          return outputs
  
      return forward
  
file_path: colossalai/colossalai/shardformer/modeling/opt.py
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from transformers.models.opt.modeling_opt import OPTForCausalLM, OPTForQuestionAnswering, OPTForSequenceClassification, OPTModel
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
label_desc: Use image node_id 2 for calling a global function with example usage: get_jit_fused_opt_decoder_layer_forward() and returns: forward, outputs