code: def _update_model_kwargs_for_generation(
      outputs: ModelOutput,
      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
  
      # 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.shape[0], 1)
                      ),
                  ],
                  dim=-1,
              )
      model_kwargs["encoder_outputs"] = outputs
      return model_kwargs
  
imports: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 3 for calling the RNNSeq2Seq 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