text: 2: Bind the model to deploymentdeployment = LLMServe.bind()# 3: Run the deploymentserve.api.run(deployment)# Shutdown the deploymentserve.api.shutdown()Example of deploying and OpenAI chain with custom promptsâ€‹Get an OpenAI API key from here. By running the following code, you will be asked to provide your API key.from langchain.llms import OpenAIfrom langchain import PromptTemplate, LLMChainfrom getpass import getpassOPENAI_API_KEY = getpass()@serve.deploymentclass DeployLLM:    def __init__(self):        # We initialize the LLM, template and the chain here        llm = OpenAI(openai_api_key=OPENAI_API_KEY)        template = "Question: {question}\n\nAnswer: Let's think step by step."        prompt = PromptTemplate(template=template, input_variables=["question"])        self.chain = LLMChain(llm=llm, prompt=prompt)    def _run_chain(self, text: str):        return self.chain(text)    async def __call__(self, request: Request):        # 1. Parse the request        text = request.query_params["text"]        # 2. Run the chain        resp = self._run_chain(text)        # 3. Return the response        return resp["text"]Now we can bind the deployment.# Bind the model to deploymentdeployment = DeployLLM.bind()We can assign the port number and host when we want to run the deployment. # Example port numberPORT_NUMBER =