code: def prepare_inputs_for_generation(
      self,
      input_ids: torch.LongTensor,
      attention_mask: torch.FloatTensor,
      token_type_ids: Optional[torch.FloatTensor] = None,
      spout: Optional[Union[List, torch.FloatTensor]] = None,
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
      **kwargs,
  ):
      if isinstance(spout, list):
          spout = torch.tensor(spout).float()
          if input_ids is not None:
              spout = spout.to(input_ids.device)
      if past_key_values is not None:
          return {
              "input_ids": input_ids[:, -1:]
              if input_ids is not None
              else None,
              "attention_mask": attention_mask,
              "token_type_ids": token_type_ids[:, -1:]
              if token_type_ids is not None
              else None,
              "spout": spout,
              "past_key_values": past_key_values,
          }
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "token_type_ids": token_type_ids,
          "spout": spout,
          "past_key_values": None,
      }
  
imports: import copy
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from ...activations import ACT2FN
  from ...modeling_outputs import MoECausalLMOutputWithPast, MoEModelOutputWithPastAndCrossAttentions
  from ...modeling_utils import PreTrainedModel
  from ...utils import DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging
  from .configuration_gptsan_japanese import GPTSanJapaneseConfig
label_desc: Use image node_id 3 for calling the GPTSanJapaneseForConditionalGeneration obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, attention_mask, token_type_ids, spout, past_key_values) and returns: dict, dict