code: def training_step(self) -> ResultDict:
      # Generate common experiences, collect batch for PPO, store every (DQN) batch
      # into replay buffer.
      ppo_batches = []
      num_env_steps = 0
  
      # PPO batch size fixed at 200.
      # TODO: Use `max_env_steps=200` option of synchronous_parallel_sample instead.
      while num_env_steps < 200:
          ma_batches = synchronous_parallel_sample(
              worker_set=self.workers, concat=False
          )
          # Loop through ma-batches (which were collected in parallel).
          for ma_batch in ma_batches:
              # Update sampled counters.
              self._counters[NUM_ENV_STEPS_SAMPLED] += ma_batch.count
              self._counters[
                  NUM_AGENT_STEPS_SAMPLED
              ] += ma_batch.agent_steps()
              ppo_batch = ma_batch.policy_batches.pop("ppo_policy")
              # Add collected batches (only for DQN policy) to replay buffer.
              self.local_replay_buffer.add(ma_batch)
  
              ppo_batches.append(ppo_batch)
              num_env_steps += ppo_batch.count
  
      # DQN sub-flow.
      dqn_train_results = {}
      # Start updating DQN policy once we have some samples in the buffer.
      if self._counters[NUM_ENV_STEPS_SAMPLED] > 1000:
          # Update DQN policy n times while updating PPO policy once.
          for _ in range(10):
              dqn_train_batch = self.local_replay_buffer.sample(
                  num_items=64
              )
              dqn_train_results = train_one_step(
                  self, dqn_train_batch, ["dqn_policy"]
              )
              self._counters[
                  "agent_steps_trained_DQN"
              ] += dqn_train_batch.agent_steps()
              print(
                  "DQN policy learning on samples from",
                  "agent steps trained",
                  dqn_train_batch.agent_steps(),
              )
      # Update DQN's target net every n train steps (determined by the DQN config).
      if (
          self._counters["agent_steps_trained_DQN"]
          - self._counters[LAST_TARGET_UPDATE_TS]
          >= self.get_policy("dqn_policy").config[
              "target_network_update_freq"
          ]
      ):
          self.workers.local_worker().get_policy(
              "dqn_policy"
          ).update_target()
          self._counters[NUM_TARGET_UPDATES] += 1
          self._counters[LAST_TARGET_UPDATE_TS] = self._counters[
              "agent_steps_trained_DQN"
          ]
  
      # PPO sub-flow.
      ppo_train_batch = concat_samples(ppo_batches)
      self._counters[
          "agent_steps_trained_PPO"
      ] += ppo_train_batch.agent_steps()
      # Standardize advantages.
      ppo_train_batch[Postprocessing.ADVANTAGES] = standardized(
          ppo_train_batch[Postprocessing.ADVANTAGES]
      )
      print(
          "PPO policy learning on samples from",
          "agent steps trained",
          ppo_train_batch.agent_steps(),
      )
      ppo_train_batch = MultiAgentBatch(
          {"ppo_policy": ppo_train_batch}, ppo_train_batch.count
      )
      ppo_train_results = train_one_step(
          self, ppo_train_batch, ["ppo_policy"]
      )
  
      # Combine results for PPO and DQN into one results dict.
      results = dict(ppo_train_results, **dqn_train_results)
      return results
  
imports: import argparse
  import os
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.dqn.dqn import DQNConfig
  from ray.rllib.algorithms.dqn.dqn_tf_policy import DQNTFPolicy
  from ray.rllib.algorithms.dqn.dqn_torch_policy import DQNTorchPolicy
  from ray.rllib.algorithms.ppo.ppo import PPOConfig
  from ray.rllib.algorithms.ppo.ppo_tf_policy import PPOTF1Policy
  from ray.rllib.algorithms.ppo.ppo_torch_policy import PPOTorchPolicy
  from ray.rllib.evaluation.postprocessing import Postprocessing
  from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import train_one_step
  from ray.rllib.utils.replay_buffers.multi_agent_replay_buffer import MultiAgentReplayBuffer
  from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
  from ray.rllib.policy.sample_batch import MultiAgentBatch, concat_samples
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, NUM_TARGET_UPDATES, LAST_TARGET_UPDATE_TS
  from ray.rllib.utils.sgd import standardized
  from ray.rllib.utils.test_utils import check_learning_achieved
  from ray.rllib.utils.typing import ResultDict
  from ray.tune.registry import register_env
label_desc: Use image node_id 2 for calling the MyAlgo obj's underlying member method code with example usage: obj.training_step() and returns: results