import ray from ray.rllib.agents.ppo import PPOTrainer from ray.tune.registry import register_env ray.init() def env_creator(env_config): import gym return gym.make('CartPole-v0') register_env('CartPole-v0', env_creator) config = {'env': 'CartPole-v0', 'num_workers': 4} trainer = PPOTrainer(config=config) for i in range(100): results = trainer.train()In this example, Ray is used to create a distributed training setup with four workers. The Trainer object is initialized with configuration that includes the name of the environment to use and the number of workers to use. The `train` method is then called repeatedly to train the agent for 100 iterations. The Ray library is used in this example along with the Ray Tune package, which provides a number of utilities for hyperparameter tuning and distributed training. In summary, the ray.train Trainer class is a powerful tool for training machine learning models on large datasets in a distributed environment. It offers a simple and flexible approach to building high-performance models on a variety of machine learning platforms.