code: def hf_bert(model, inputs, attn_mask):
      torch.cuda.synchronize()
      start_time = time.perf_counter()
      for _ in range(20):
          hf_output = model(
              inputs.to("cuda:0"), attention_mask=attn_mask.to("cuda:0")
          )
      torch.cuda.synchronize()
      end_time = time.perf_counter()
  
      return hf_output, (end_time - start_time) / 20
  
file_path: lightseq/examples/inference/python/test/ls_bert_bench.py
imports: import time
  import argparse
  import torch
  import lightseq.inference
  from transformers import BertTokenizer, BertForSequenceClassification, BertModel
label_desc: Use image node_id 2 for calling a global function with example usage: hf_bert(model, inputs, attn_mask) and returns: hf_output,  unknown