code: def ls_generate(model, inputs):
      print("=========lightseq=========")
      print("lightseq generating...")
      ls_output, ls_time = ls_vit(model, inputs)
      print(f"lightseq time: {ls_time}s")
      print("lightseq results (class predictions):")
      print(ls_output.argmax(axis=1).detach().cpu().numpy())
  
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 3 for calling a global function with example usage: ls_generate(model, inputs) without return types