code: def hf_bert(model, inputs, attn_mask):
      torch.cuda.synchronize()
      start_time = time.perf_counter()
      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
  
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 2 for calling a global function with example usage: hf_bert(model, inputs, attn_mask) and returns: hf_output,  unknown