text: This follows the polar opposite selection scheme as multi-agent decentralized speaker selection.We show an example of this approach in the context of a fictitious simulation of a news network. This example will showcase how we can implement agents thatthink before speakingterminate the conversationImport LangChain related modulesâ€‹from collections import OrderedDictimport functoolsimport randomimport reimport tenacityfrom typing import List, Dict, Callablefrom langchain.prompts import (    ChatPromptTemplate,    HumanMessagePromptTemplate,    PromptTemplate,)from langchain.chains import LLMChainfrom langchain.chat_models import ChatOpenAIfrom langchain.output_parsers import RegexParserfrom langchain.schema import (    AIMessage,    HumanMessage,    SystemMessage,    BaseMessage,)DialogueAgent and DialogueSimulator classesâ€‹We will use the same DialogueAgent and DialogueSimulator classes defined in our other examples Multi-Player Dungeons & Dragons and Decentralized Speaker Selection.class DialogueAgent:    def __init__(        self,        name: str,        system_message: SystemMessage,        model: ChatOpenAI,    ) -> None:        self.name = name        self.system_message = system_message        self.model = model        self.prefix = f"{self.name}: "        self.reset()    def reset(self):        self.message_history = ["Here is the conversation so far."]    def send(self) -> str:        """        Applies the chatmodel to the message history        and returns the message