code: def prepare_inputs_for_generation(
      self,
      input_ids,
      past_key_values=None,
      inputs_embeds=None,
      **kwargs,
  ):
      token_type_ids = kwargs.get("token_type_ids", None)
      # Omit tokens covered by past_key_values
      if past_key_values:
          past_length = past_key_values[0][0].shape[2]
  
          # Some generation methods already pass only the last input ID
          if input_ids.shape[1] > past_length:
              remove_prefix_length = past_length
          else:
              # Default to old behavior: keep only final ID
              remove_prefix_length = input_ids.shape[1] - 1
  
          input_ids = input_ids[:, remove_prefix_length:]
          if token_type_ids is not None:
              token_type_ids = token_type_ids[:, -input_ids.shape[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_key_values:
              position_ids = position_ids[:, -input_ids.shape[1] :]
  
      # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
      if inputs_embeds is not None and past_key_values is None:
          model_inputs = {"inputs_embeds": inputs_embeds}
      else:
          model_inputs = {"input_ids": input_ids}
  
      model_inputs.update(
          {
              "past_key_values": past_key_values,
              "use_cache": kwargs.get("use_cache"),
              "position_ids": position_ids,
              "attention_mask": attention_mask,
              "token_type_ids": token_type_ids,
          }
      )
  
      return model_inputs
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import torch
  import torch.fx
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging
  from ...utils.model_parallel_utils import assert_device_map, get_device_map
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 6 for calling the GPTJForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past_key_values, inputs_embeds) and returns: model_inputs