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:]
  
      # 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"),
              "attention_mask": attention_mask,
              "images": kwargs.get("images", None),
          }
      )
      return model_inputs
  
file_path: ml-ferret/ferret/model/language_model/ferret_llama.py
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss
  from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
  from transformers.modeling_outputs import CausalLMOutputWithPast
  from ..ferret_arch import FERRETMetaModel, FERRETMetaForCausalLM
label_desc: Use image node_id 4 for calling the FERRETLlamaForCausalLM 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