code: def prepare_inputs_for_generation(
      self,
      input_ids,
      past_key_values=None,
      attention_mask=None,
      inputs_embeds=None,
      **kwargs,
  ):
      if past_key_values:
          input_ids = input_ids[:, -1:]
  
      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[:, -1].unsqueeze(-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(
          {
              "position_ids": position_ids,
              "past_key_values": past_key_values,
              "use_cache": kwargs.get("use_cache"),
              "attention_mask": attention_mask,
          }
      )
      return model_inputs
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.llama.configuration_llama import LlamaConfig
label_desc: Use image node_id 9 for calling the LlamaForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past_key_values, attention_mask, inputs_embeds) and returns: model_inputs