code: def bench_captions(
      model,
      processor,
      prompt: str,
      images: List[Image.Image],
  ) -> List[str]:
      total_duration = 0
      total_length = 0
      model = torch.compile(model)
      for image in images:
          seconds, text = duration(
              lambda: caption(
                  model=model,
                  processor=processor,
                  prompt=prompt,
                  image=image,
              )
          )
          total_duration += seconds
          total_length += len(text)
  
      del model
      del processor
      print(f"Throughput: {total_length/total_duration:.2f} tokens/s")
  
file_path: uform/scripts/bench.py
imports: import requests
  from time import perf_counter
  from typing import List
  from PIL import Image
  import torch
  from uform import get_model
  from uform.gen_model import VLMForCausalLM, VLMProcessor
  from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
  from transformers import AutoProcessor, LlavaForConditionalGeneration
label_desc: Use image node_id 3 for calling a global function with example usage: bench_captions(model, processor, prompt, images) without return types