code: def process_response(response, num_rounds):
      output = str(response[0]["generated_text"])
      output = output.replace("<|endoftext|></s>", "")
      all_positions = [
          m.start() for m in re.finditer("Human: ", output)
      ]
      place_of_second_q = -1
      if len(all_positions) > num_rounds:
          place_of_second_q = all_positions[num_rounds]
      if place_of_second_q != -1:
          output = output[0:place_of_second_q]
      return output
  
file_path: deepspeedexamples/applications/DeepSpeed-Chat/inference/chatbot.py
imports: import argparse
  import re
  import logging
  import transformers
  import os
  import json
  from transformers import pipeline, set_seed
  from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
label_desc: Use image node_id 5 for calling a global function with example usage: process_response(response, num_rounds) and returns: output