text: tags=["agent"],)callbacks = [StdOutCallbackHandler(), comet_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)agent = initialize_agent(    tools,    llm,    agent="zero-shot-react-description",    callbacks=callbacks,    verbose=True,)agent.run(    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")comet_callback.flush_tracker(agent, finish=True)Scenario 4: Using Custom Evaluation Metricsâ€‹The CometCallbackManager also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let's take a look at how this works. In the snippet below, we will use the ROUGE metric to evaluate the quality of a generated summary of an input prompt. %pip install rouge-scorefrom rouge_score import rouge_scorerfrom langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplateclass Rouge:    def __init__(self, reference):        self.reference = reference        self.scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True)    def compute_metric(self, generation, prompt_idx, gen_idx):        prediction = generation.text        results = self.scorer.score(target=self.reference, prediction=prediction)        return {            "rougeLsum_score":