code: def llava_call(prompt: str, llm_config: dict) -> str:
      """
      Makes a call to the LLaVA service to generate text based on a given prompt
      """
  
      prompt, images = llava_formatter(prompt, order_image_tokens=False)
  
      for im in images:
          if len(im) == 0:
              raise RuntimeError("An image is empty!")
  
      return llava_call_binary(
          prompt,
          images,
          config_list=llm_config["config_list"],
          max_new_tokens=llm_config.get("max_new_tokens", 2000),
          temperature=llm_config.get("temperature", 0.5),
          seed=llm_config.get("seed", None),
      )
  
file_path: autogen/autogen/agentchat/contrib/llava_agent.py
imports: import json
  import logging
  import os
  import pdb
  import re
  from typing import Any, Dict, List, Optional, Tuple, Union
  import replicate
  import requests
  from regex import R
  from autogen.agentchat.agent import Agent
  from autogen.agentchat.contrib.img_utils import get_image_data, llava_formatter
  from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent
  from autogen.code_utils import content_str
label_desc: Use image node_id 3 for calling a global function with example usage: llava_call(prompt, llm_config) and returns: llava_call_binary