code: def on_learn_on_batch(self, *, policy, train_batch, result, **kwargs):
      assert train_batch.count == 201
      assert sum(train_batch[SampleBatch.SEQ_LENS]) == 201
      for k, v in train_batch.items():
          if k in ["state_in_0", SampleBatch.SEQ_LENS]:
              assert len(v) == len(train_batch[SampleBatch.SEQ_LENS])
          else:
              assert len(v) == 201
      current = None
      for o in train_batch[SampleBatch.OBS]:
          if current:
              assert o == current + 1
          current = o
          if o == 15:
              current = None
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import numpy
  import unittest
  import ray
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  import ray.rllib.algorithms.dqn
  import ray.rllib.algorithms.ppo
  from ray.rllib.examples.env.debug_counter_env import MultiAgentDebugCounterEnv
  from ray.rllib.examples.env.multi_agent import MultiAgentPendulum
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.examples.policy.episode_env_aware_policy import EpisodeEnvAwareAttentionPolicy, StatefulRandomPolicy
  from ray.rllib.models.tf.attention_net import GTrXLNet
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch, convert_ma_batch_to_sample_batch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.test_utils import framework_iterator, check
label_desc: Use image node_id 1 for calling the MyCallbacks obj's underlying member method code with example usage: obj.on_learn_on_batch() without return types