code: def prepare_inputs_for_generation(
      self,
      input_ids,
      past_key_values=None,
      inputs_embeds=None,
      use_cache=None,
      **kwargs,
  ):
      return self._default.prepare_inputs_for_generation(
          input_ids,
          past_key_values=past_key_values,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          **kwargs,
      )
  
imports: import argparse
  import inspect
  import logging
  from typing import Tuple
  import torch
  from accelerate import PartialState
  from accelerate.utils import set_seed
  from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, CTRLLMHeadModel, CTRLTokenizer, GenerationMixin, GPT2LMHeadModel, GPT2Tokenizer, GPTJForCausalLM, LlamaForCausalLM, LlamaTokenizer, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, OPTForCausalLM, TransfoXLLMHeadModel, TransfoXLTokenizer, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer
  from transformers.modeling_outputs import CausalLMOutputWithPast
label_desc: Use image node_id 4 for calling the _ModelFallbackWrapper obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, past_key_values, inputs_embeds, use_cache) and returns: self