code: def hf_generate(model, inputs):
      print("=========huggingface=========")
      print("huggingface generating...")
      hf_output, hf_time = hf_vit(model, inputs)
      print(f"huggingface time: {hf_time}s")
      print("huggingface results (class predictions):")
      print(hf_output.logits.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 4 for calling a global function with example usage: hf_generate(model, inputs) without return types