code: def on_train_result(self, *, algorithm, result: dict, **kwargs):
      stats = result["info"][LEARNER_INFO][DEFAULT_POLICY_ID][
          LEARNER_STATS_KEY
      ]
      # Learning rate should go to 0 after 1 iter.
      check(stats["cur_lr"], 5e-5 if algorithm.iteration == 1 else 0.0)
      # Entropy coeff goes to 0.05, then 0.0 (per iter).
      check(
          stats["entropy_coeff"],
          0.1 if algorithm.iteration == 1 else 0.05,
      )
  
      algorithm.workers.foreach_policy(
          self._check_lr_torch
          if algorithm.config["framework"] == "torch"
          else self._check_lr_tf
      )
  
imports: import unittest
  import numpy
  import ray
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  import ray.rllib.algorithms.ppo
  from ray.rllib.algorithms.ppo.ppo_tf_policy import PPOTF2Policy
  from ray.rllib.algorithms.ppo.ppo_torch_policy import PPOTorchPolicy
  from ray.rllib.evaluation.postprocessing import compute_gae_for_sample_batch, Postprocessing
  from ray.rllib.models.tf.tf_action_dist import Categorical
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO, LEARNER_STATS_KEY
  from ray.rllib.utils.numpy import fc
  from ray.rllib.utils.test_utils import check, check_compute_single_action, check_off_policyness, check_train_results, framework_iterator, check_inference_w_connectors
label_desc: Use image node_id 3 for calling the MyCallbacks obj's underlying member method code with example usage: obj.on_train_result() without return types