code: def prepare_jit_inputs(inputs, model, tokenizer):
      batch_size = len(inputs)
      dummy_input = tokenizer.batch_encode_plus(
          inputs, return_tensors="pt"
      )
      dummy_input = dummy_input.to(model.device)
      if model.config.use_cache:
          dummy_input["past_key_values"] = generate_past_key_values(
              model, batch_size, 1
          )
      dummy_input["attention_mask"] = torch.cat(
          [
              torch.zeros(dummy_input["attention_mask"].shape[0], 1)
              .to(dummy_input["attention_mask"].dtype)
              .to(model.device),
              dummy_input["attention_mask"],
          ],
          -1,
      )
      return dummy_input
  
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 8 for calling a global function with example usage: prepare_jit_inputs(inputs, model, tokenizer) and returns: dummy_input