code: def ls_vit(model, inputs):
      torch.cuda.synchronize()
      start_time = time.perf_counter()
      ls_output = model.infer(inputs)
      torch.cuda.synchronize()
      end_time = time.perf_counter()
      return ls_output, end_time - start_time
  
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 1 for calling a global function with example usage: ls_vit(model, inputs) and returns: ls_output,  unknown