code: def ls_generate(model, tokenizer, inputs):
      print("=========lightseq=========")
      print("lightseq generating...")
      ls_res_ids, ls_time = ls_gpt2(model, inputs)
      ls_res = tokenizer.batch_decode(
          ls_res_ids, skip_special_tokens=True
      )
      print(f"lightseq time: {ls_time}s")
      print("lightseq results:")
      for sent in ls_res:
          print(sent)
  
imports: import time
  import torch
  from torch import nn
  from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
  import lightseq.inference
  from lightseq.training.ops.pytorch.quantization import qat_mode, QuantLinear, TensorQuantizer, weight_quant_config, emb_quant_config
  from lightseq.training.ops.pytorch.torch_transformer_layers import TransformerDecoderLayer
  from export.util import parse_args
label_desc: Use image node_id 4 for calling a global function with example usage: ls_generate(model, tokenizer, inputs) without return types