code: def infer(self, inputs):
      last_hidden_states = self.ls_vit.infer(inputs)
      last_hidden_states = (
          torch.Tensor(last_hidden_states).float().cuda()
      )
      logits = self.classifier(last_hidden_states[:, 0, :])
      return logits
  
imports: import time
  import requests
  from PIL import Image
  import torch
  from transformers import ViTFeatureExtractor, ViTForImageClassification, ViTConfig
  import lightseq.inference
  from lightseq.training.ops.pytorch.quantization import qat_mode
  from lightseq.training.ops.pytorch.torch_transformer_layers import TransformerEncoderLayer
  from export.util import parse_args
label_desc: Use image node_id 2 for calling the LightseqVitClassification obj's underlying member method code with example usage: obj.infer(inputs) and returns: logits