code: def prepare_inputs_for_generation(
      self,
      input_ids: torch.Tensor,
      past: Optional[bool] = 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 math
  import os
  import warnings
  from typing import Any, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.cuda.amp import autocast
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_imagegpt import ImageGPTConfig
label_desc: Use image node_id 4 for calling the ImageGPTForCausalImageModeling obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past) and returns: dict