code: def prepare_inputs_for_generation(
      self, input_ids, past=None, **kwargs
  ):
      token_type_ids = kwargs.get("token_type_ids", None)
      # only last token for inputs_ids if past is defined in kwargs
      if past:
          input_ids = input_ids[:, -1].unsqueeze(-1)
          if token_type_ids is not None:
              token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
  
      attention_mask = kwargs.get("attention_mask", None)
      position_ids = kwargs.get("position_ids", None)
  
      if attention_mask is not None and position_ids is None:
          # create position_ids on the fly for batch generation
          position_ids = attention_mask.long().cumsum(-1) - 1
          position_ids.masked_fill_(attention_mask == 0, 1)
          if past:
              position_ids = position_ids[:, -1].unsqueeze(-1)
      else:
          position_ids = None
      return {
          "input_ids": input_ids,
          "past_key_values": past,
          "use_cache": kwargs.get("use_cache"),
          "position_ids": position_ids,
          "attention_mask": attention_mask,
          "token_type_ids": token_type_ids,
      }
  
imports: import os
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_gpt_neo import GPTNeoConfig
label_desc: Use image node_id 4 for calling the GPTNeoForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past) and returns: dict