code: def warmup(ls_tokenizer, hf_tokenizer, ls_model, hf_model, sentences):
      ls_inputs = ls_tokenizer(sentences, return_tensors="pt")[
          "input_ids"
      ]
      hf_inputs = hf_tokenizer(sentences, return_tensors="pt")[
          "input_ids"
      ]
  
      ls_generate(ls_model, ls_tokenizer, ls_inputs)
      hf_generate(hf_model, hf_tokenizer, hf_inputs)
  
file_path: lightseq/examples/inference/python/test/ls_xglm.py
imports: import time
  import argparse
  import torch
  import numpy
  import lightseq.inference
  from transformers import AutoTokenizer, AutoModelForCausalLM
label_desc: Use image node_id 5 for calling a global function with example usage: warmup(ls_tokenizer, hf_tokenizer, ls_model, hf_model, sentences) without return types