code: def on_episode_end(
      self,
      *,
      worker: RolloutWorker,
      base_env: BaseEnv,
      policies: Dict[str, Policy],
      episode: Episode,
      env_index: int,
      **kwargs,
  ):
      # Check if there are multiple episodes in a batch, i.e.
      # "batch_mode": "truncate_episodes".
      if worker.config.batch_mode == "truncate_episodes":
          # Make sure this episode is really done.
          assert episode.batch_builder.policy_collectors[
              "default_policy"
          ].batches[-1]["dones"][-1], (
              "ERROR: `on_episode_end()` should only be called "
              "after episode is done!"
          )
      pole_angle = np.mean(episode.user_data["pole_angles"])
      episode.custom_metrics["pole_angle"] = pole_angle
      episode.hist_data["pole_angles"] = episode.user_data[
          "pole_angles"
      ]
  
imports: from typing import Dict, Tuple
  import argparse
  import gymnasium
  import numpy
  import os
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.env import BaseEnv
  from ray.rllib.evaluation import Episode, RolloutWorker
  from ray.rllib.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
label_desc: Use image node_id 3 for calling the MyCallbacks obj's underlying member method code with example usage: obj.on_episode_end() without return types