code: def prepare_inputs_for_generation(
      self,
      input_ids,
      past_key_values=None,
      attention_mask=None,
      **model_kwargs,
  ):
      input_shape = input_ids.shape
  
      # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
      if attention_mask is None:
          attention_mask = input_ids.new_ones(input_shape)
  
      # cut decoder_input_ids if past is used
      if past_key_values and past_key_values[0] is not None:
          input_ids = input_ids[:, -1:]
  
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "past_key_values": past_key_values,
      }
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import Tensor, nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_gpt_neox_japanese import GPTNeoXJapaneseConfig
label_desc: Use image node_id 5 for calling the GPTNeoXJapaneseForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past_key_values, attention_mask) and returns: dict