code: def analyze_rnn_batch_rlm(batch, max_seq_len, view_requirements):
      # Note that this method assumes a batch with a time dimension
      count = batch.count
  
      for seq_idx in range(len(batch[SampleBatch.SEQ_LENS]) - 1):
          # Check prev_reward/action, next_obs consistency.
          for idx in range(batch[SampleBatch.SEQ_LENS][seq_idx] - 1):
              # If timestep tracked by batch, good.
              if "t" in batch:
                  ts = batch["t"][seq_idx][idx]
              # Else, ts
              else:
                  ts = batch[SampleBatch.OBS][seq_idx][idx][3]
              obs_t = batch[SampleBatch.OBS][seq_idx][idx]
              a_t = batch[SampleBatch.ACTIONS][seq_idx][idx]
              r_t = batch[SampleBatch.REWARDS][seq_idx][idx]
              state_in = batch["state_in"][seq_idx]
  
              # Check postprocessing outputs.
              if "2xobs" in batch:
                  postprocessed_col_t = batch["2xobs"][seq_idx][idx]
                  assert (obs_t == postprocessed_col_t / 2.0).all()
  
              # Check state-in/out and next-obs values.
              if idx > 0:
                  next_obs_t_m_1 = batch[SampleBatch.NEXT_OBS][seq_idx][
                      idx - 1
                  ]
                  state_out_t_m_1 = batch["state_out"][seq_idx][idx - 1]
                  # Same trajectory as for t-1 -> Should be able to match.
                  if (
                      batch[SampleBatch.AGENT_INDEX][seq_idx][idx]
                      == batch[SampleBatch.AGENT_INDEX][seq_idx][
                          idx - 1
                      ]
                      and batch[SampleBatch.EPS_ID][seq_idx][idx]
                      == batch[SampleBatch.EPS_ID][seq_idx][idx - 1]
                  ):
                      assert (
                          batch["unroll_id"][seq_idx][idx - 1]
                          == batch["unroll_id"][seq_idx][idx]
                      )
                      assert (obs_t == next_obs_t_m_1).all()
                  # Different trajectory.
                  else:
                      assert (
                          batch["unroll_id"][seq_idx][idx - 1]
                          != batch["unroll_id"][seq_idx][idx]
                      )
                      assert not (obs_t == next_obs_t_m_1).all()
                      assert not (state_in == state_out_t_m_1).all()
  
              # Check initial 0-internal states (at ts=0).
              if ts == 0:
                  assert (state_in == 0.0).all()
  
              # Check prev. a/r values.
              if idx < count - 1:
                  prev_actions_t_p_1 = batch[SampleBatch.PREV_ACTIONS][
                      seq_idx
                  ][idx + 1]
                  prev_rewards_t_p_1 = batch[SampleBatch.PREV_REWARDS][
                      seq_idx
                  ][idx + 1]
                  # Same trajectory as for t+1 -> Should be able to match.
                  if (
                      batch[SampleBatch.AGENT_INDEX][seq_idx][idx]
                      == batch[SampleBatch.AGENT_INDEX][seq_idx][
                          idx + 1
                      ]
                      and batch[SampleBatch.EPS_ID][seq_idx][idx]
                      == batch[SampleBatch.EPS_ID][seq_idx][idx + 1]
                  ):
                      assert (a_t == prev_actions_t_p_1).all()
                      assert r_t == prev_rewards_t_p_1
                  # Different (new) trajectory. Assume t-1 (prev-a/r) to be
                  # always 0.0s. [3]=ts
                  elif ts == 0:
                      assert (prev_actions_t_p_1 == 0).all()
                      assert prev_rewards_t_p_1 == 0.0
  
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 2 for calling a global function with example usage: analyze_rnn_batch_rlm(batch, max_seq_len, view_requirements) without return types