code: def evaluate(test_dataloader, text_env, ppo_trainer):
      test_rewards = []
      for test_batch in test_dataloader:
          _, _, _, rewards, _ = text_env.run(
              test_batch["query"], answers=test_batch["answer"]
          )
          test_rewards.extend(rewards)
      test_rewards = ppo_trainer.accelerator.gather_for_metrics(
          torch.stack(test_rewards).to(ppo_trainer.accelerator.device)
      )
      return test_rewards.mean()
  
file_path: trl/examples/research_projects/tools/python_interpreter.py
imports: import os
  import re
  from dataclasses import dataclass, field
  from typing import Optional
  import numpy
  import torch
  from datasets import load_dataset
  from peft import LoraConfig
  from transformers import AutoTokenizer, HfArgumentParser, load_tool
  from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, TextEnvironment
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(test_dataloader, text_env, ppo_trainer) and returns: test_rewards