code: def forward(
      self,
      input_ids,
      attention_mask,
      decoder_input_ids,
      decoder_attention_mask: Optional,
      encoder_last_hidden_state: Optional,
      past_key_values: Optional = None,
  ) -> Tuple[
      torch.Tensor,
      torch.Tensor,
      List[
          Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
      ],
  ]:
      # Model Forward
      outputs = self.model.forward(
          input_ids=input_ids,
          attention_mask=attention_mask,
          decoder_input_ids=decoder_input_ids,
          decoder_attention_mask=decoder_attention_mask,
          encoder_outputs=encoder_last_hidden_state,
          past_key_values=past_key_values,
          use_cache=True,
      )
      return (
          outputs.logits,
          outputs.encoder_last_hidden_state,
          outputs.past_key_values,
      )
  
imports: import torch
  import time
  from dataclasses import dataclass
  from opentelemetry import trace
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, PreTrainedTokenizerBase
  from typing import Optional, Tuple, List, Type, Dict
  from text_generation_server.utils.tokens import batch_top_tokens
  from text_generation_server.models import Model
  from text_generation_server.models.types import GeneratedText, Batch, Generation, Tokens
  from text_generation_server.pb import generate_pb2
  from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
label_desc: Use image node_id 4 for calling the Seq2SeqLM obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, encoder_last_hidden_state, past_key_values) and returns: outputs,  outputs,  outputs