code: def hf_generate(model, tokenizer, inputs_id):
      print("=========huggingface=========")
      print("huggingface generating...")
      hf_res_ids, hf_time = hf_t5(model, inputs_id)
      hf_res = tokenizer.batch_decode(
          hf_res_ids, skip_special_tokens=True
      )
      print(f"huggingface time: {hf_time}s")
      print("huggingface results:")
      for sent in hf_res:
          print(sent)
  
file_path: lightseq/examples/inference/python/test/ls_t5.py
imports: import time
  import argparse
  import torch
  import lightseq.inference
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  import sentencepiece
label_desc: Use image node_id 4 for calling a global function with example usage: hf_generate(model, tokenizer, inputs_id) without return types