code: def warmup(tokenizer, ls_model, hf_model, sentences):
      inputs = tokenizer(sentences, return_tensors="pt", padding=True)
      inputs_id = inputs["input_ids"]
      attn_mask = inputs["attention_mask"]
  
      ls_generate(ls_model, inputs_id)
      hf_generate(hf_model, inputs_id, attn_mask)
  
imports: import time
  import torch
  from transformers import BertTokenizer, BertForTokenClassification, BertConfig
  import lightseq.inference
  from lightseq.training.ops.pytorch.quantization import qat_mode
  from lightseq.training.ops.pytorch.torch_transformer_layers import BertEmbeddingLayer, TransformerEncoderLayer
  from export.util import parse_args
label_desc: Use image node_id 5 for calling a global function with example usage: warmup(tokenizer, ls_model, hf_model, sentences) without return types