code: def infer(self, inputs):
      last_hidden_states = self.ls_bert.infer(inputs)
      last_hidden_states = torch.Tensor(last_hidden_states).float()
      logits = self.classifier(last_hidden_states.to("cuda:0"))
      return logits
  
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 the LightseqBertClassification obj's underlying member method code with example usage: obj.infer(inputs) and returns: logits