code: def training_step(self) -> ResultDict:
      # Collect SampleBatches from sample workers.
      with self._timers[SAMPLE_TIMER]:
          train_batch = synchronous_parallel_sample(
              worker_set=self.workers
          )
      train_batch = train_batch.as_multi_agent()
      self._counters[
          NUM_AGENT_STEPS_SAMPLED
      ] += train_batch.agent_steps()
      self._counters[NUM_ENV_STEPS_SAMPLED] += train_batch.env_steps()
      # Train.
      if self.config.simple_optimizer:
          train_results = train_one_step(self, train_batch)
      else:
          train_results = multi_gpu_train_one_step(self, train_batch)
  
      # TODO: Move training steps counter update outside of `train_one_step()` method.
      # # Update train step counters.
      # self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps()
      # self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps()
  
      global_vars = {
          "timestep": self._counters[NUM_AGENT_STEPS_SAMPLED],
      }
  
      # Update weights - after learning on the local worker - on all remote
      # workers (only those policies that were actually trained).
      if self.workers.remote_workers():
          with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
              self.workers.sync_weights(
                  policies=list(train_results.keys()),
                  global_vars=global_vars,
              )
  
      # Update global vars on local worker as well.
      self.workers.local_worker().set_global_vars(global_vars)
  
      return train_results
  
imports: from typing import Callable, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, SYNCH_WORKER_WEIGHTS_TIMER, SAMPLE_TIMER
  from ray.rllib.utils.typing import EnvType, ResultDict
  from ray.tune.logger import Logger
label_desc: Use image node_id 3 for calling the MARWIL obj's underlying member method code with example usage: obj.training_step() and returns: train_results