code: def forward(
      self, input_ids, encoder_state, attention_mask, past=None
  ):
      all_results = None
      if past is not None:
          all_results = _convert_past_list_to_tuple(past)
          input_ids = input_ids[:, -1:]
  
      last_hidden_state, past_key_values = self.decoder(
          input_ids=input_ids,
          encoder_hidden_states=encoder_state,
          encoder_attention_mask=attention_mask,
          past_key_values=all_results,
          return_dict=False,
      )
  
      past_values = []
      for past in past_key_values:
          past_values = past_values + list(past)
      return last_hidden_state, past_values
  
imports: import copy
  import itertools
  from typing import List, Optional, Tuple
  import torch
  import torch.nn.functional
  from transformers import BartConfig
  from transformers.generation_utils import GenerationMixin
label_desc: Use image node_id 2 for calling the DecoderForONNX obj's underlying member method code with example usage: obj.forward(input_ids, encoder_state, attention_mask, past) and returns: last_hidden_state,  past_values