code: def _expand_inputs_for_generation(
      input_ids: torch.LongTensor,
      expand_size: int = 1,
      is_encoder_decoder: bool = False,
      attention_mask: Optional[torch.LongTensor] = None,
      encoder_outputs: Optional[ModelOutput] = None,
      **model_kwargs,
  ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
      expanded_return_idx = (
          torch.arange(input_ids.shape[0])
          .view(-1, 1)
          .repeat(1, expand_size)
          .view(-1)
          .to(input_ids.device)
      )
      input_ids = input_ids.index_select(0, expanded_return_idx)
  
      if attention_mask is not None:
          model_kwargs["attention_mask"] = attention_mask.index_select(
              0, expanded_return_idx
          )
      if is_encoder_decoder:
          if encoder_outputs is None:
              raise ValueError(
                  "If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined."
              )
          encoder_outputs[
              "last_hidden_state"
          ] = encoder_outputs.last_hidden_state.index_select(
              0,
              expanded_return_idx.to(
                  encoder_outputs.last_hidden_state.device
              ),
          )
          encoder_outputs["decoder_hidden_states_before"] = [
              t.index_select(
                  0,
                  expanded_return_idx.to(
                      encoder_outputs.last_hidden_state.device
                  ),
              )
              for t in encoder_outputs.decoder_hidden_states_before
          ]
  
          if encoder_outputs["decoder_cells_before"] is not None:
              encoder_outputs["decoder_cells_before"] = [
                  t.index_select(
                      0,
                      expanded_return_idx.to(
                          encoder_outputs.last_hidden_state.device
                      ),
                  )
                  for t in encoder_outputs.decoder_hidden_states_before
              ]
          encoder_outputs[
              "decoder_hidden_state_last_layer"
          ] = encoder_outputs.decoder_hidden_state_last_layer.index_select(
              0,
              expanded_return_idx.to(
                  encoder_outputs.last_hidden_state.device
              ),
          )
          model_kwargs["encoder_outputs"] = encoder_outputs
      return input_ids, 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 2 for calling the RNNSeq2Seq obj's underlying member method code with example usage: obj._expand_inputs_for_generation(input_ids, expand_size, is_encoder_decoder, attention_mask, encoder_outputs) and returns: input_ids,  model_kwargs