code: def forward(
      self,
      input_ids: torch.LongTensor,
      past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
      attention_mask: Optional[torch.ByteTensor] = None,
      prefix_mask: Optional[torch.ByteTensor] = None,
      sequence_id: Optional[torch.LongTensor] = None,
      labels: Optional[torch.LongTensor] = None,
      return_dict: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      use_cache: Optional[bool] = None,
  ):
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.return_dict
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
      outputs = self.transformer(
          input_ids=input_ids,
          past_key_values=past_key_values,
          attention_mask=attention_mask,
          prefix_mask=prefix_mask,
          sequence_id=sequence_id,
          return_dict=return_dict,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          use_cache=use_cache,
      )
      logits = self.lm_head(outputs.last_hidden_state)
      if self.logit_scale is not None:
          if self.logit_scale == 0:
              warnings.warn(
                  f"Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs."
              )
          logits *= self.logit_scale
      loss = None
      if labels is not None:
          labels = torch.roll(labels, shifts=-1)
          labels[:, -1] = -100
          loss = F.cross_entropy(
              logits.view(-1, logits.size(-1)),
              labels.to(logits.device).view(-1),
          )
      return CausalLMOutputWithPast(
          loss=loss,
          logits=logits,
          past_key_values=outputs.past_key_values,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
imports: import math
  import os
  import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from einops import rearrange
  from packaging import version
  from text_generation_server.utils.layers import TensorParallelEmbedding, TensorParallelColumnLinear, TensorParallelRowLinear, TensorParallelHead, get_linear
label_desc: Use image node_id 2 for calling the MPTForCausalLM obj's underlying member method code with example usage: obj.forward(input_ids, past_key_values, attention_mask, prefix_mask, sequence_id, labels, return_dict, output_attentions, output_hidden_states, use_cache) and returns: CausalLMOutputWithPast