code: def _update_model_kwargs_for_generation(
      outputs: MySeq2SeqLMOutput,
      model_kwargs: Dict[str, Any],
      is_encoder_decoder: bool = False,
  ) -> Dict[str, Any]:
      # update past
      if "past_key_values" in outputs:
          model_kwargs["past"] = outputs.past_key_values
      elif "mems" in outputs:
          model_kwargs["past"] = outputs.mems
      elif "past_buckets_states" in outputs:
          model_kwargs["past"] = outputs.past_buckets_states
      else:
          model_kwargs["past"] = None
  
      if "full_hidden_states" not in model_kwargs:
          model_kwargs["full_hidden_states"] = []
  
      model_kwargs["full_hidden_states"].append(
          outputs.decoder_hidden_states
      )
      model_kwargs["decoder_pairing"].append(outputs.pairing)
      model_kwargs["decoder_linking"].append(outputs.linking)
  
      # update token_type_ids with last value
      if "token_type_ids" in model_kwargs:
          token_type_ids = model_kwargs["token_type_ids"]
          model_kwargs["token_type_ids"] = torch.cat(
              [token_type_ids, token_type_ids[:, -1].unsqueeze(-1)],
              dim=-1,
          )
  
      # update attention mask
      if not is_encoder_decoder:
          if "attention_mask" in model_kwargs:
              attention_mask = model_kwargs["attention_mask"]
              model_kwargs["attention_mask"] = torch.cat(
                  [
                      attention_mask,
                      attention_mask.new_ones(
                          (attention_mask.size(0), 1)
                      ),
                  ],
                  dim=-1,
              )
      return model_kwargs
  
imports: import sys
  import os
  from typing import Optional, Tuple, Any, Dict, Iterable
  import logging
  import torch
  import torch.utils.checkpoint
  from torch import nn
  import torch.nn.init
  import torch.nn.functional
  from transformers.models.t5.modeling_t5 import T5PreTrainedModel, T5Model, T5_INPUTS_DOCSTRING, _CONFIG_FOR_DOC
  from transformers.file_utils import add_start_docstrings_to_model_forward, replace_return_docstrings
  from .modeling_outputs import MySeq2SeqLMOutput
  from util import logsumexp, dummy_padding, prepare_pair_embeddings, one_hot_ignore_negative, batched_masked_select
label_desc: Use image node_id 8 for calling the T5Coref obj's underlying member method code with example usage: obj._update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder) and returns: model_kwargs