code: def main():
      # url = "http://images.cocodataset.org/val2017/000000039769.jpg"
      image = Image.open(
          "/opt/tiger/lightseq/examples/inference/python/000000039769.jpg"
      )
      feature_extractor = ViTFeatureExtractor.from_pretrained(
          "google/vit-base-patch16-224-in21k"
      )
      inputs = feature_extractor(images=image, return_tensors="pt")
      inputs = inputs["pixel_values"]
  
      print("creating huggingface model...")
      hf_model = ViTModel.from_pretrained(
          "google/vit-base-patch16-224-in21k"
      ).cuda()
      # hf_model.half()
      hf_model.eval()
  
      print("creating lightseq model...")
      # ls_model = LightseqVitClassification("lightseq_vit.hdf5", hf_model)
      ls_model = None
  
      # inputs = inputs.half()
      print("====================START warmup====================")
      one_infer(inputs, ls_model, hf_model)
      print("====================END warmup====================")
      batch_size = 1
      inputs = inputs.repeat((batch_size, 1, 1, 1))
      print("input size:", inputs.size())
      one_infer(inputs, ls_model, hf_model)
  
file_path: lightseq/examples/inference/python/test/ls_vit_bench.py
imports: import time
  import torch
  import lightseq.inference
  from transformers import ViTFeatureExtractor, ViTForImageClassification, ViTModel
  from PIL import Image
  import requests
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types