docstr: Simple Q training iteration function.
  
  Simple Q consists of the following steps:
  - Sample n MultiAgentBatches from n workers synchronously.
  - Store new samples in the replay buffer.
  - Sample one training MultiAgentBatch from the replay buffer.
  - Learn on the training batch.
  - Update the target network every `target_network_update_freq` sample steps.
  - Return all collected training metrics for the iteration.
  
  Returns:
      The results dict from executing the training iteration.
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code: def training_step(self) -> ResultDict:
      """Simple Q training iteration function.
  
      Simple Q consists of the following steps:
      - Sample n MultiAgentBatches from n workers synchronously.
      - Store new samples in the replay buffer.
      - Sample one training MultiAgentBatch from the replay buffer.
      - Learn on the training batch.
      - Update the target network every `target_network_update_freq` sample steps.
      - Return all collected training metrics for the iteration.
  
      Returns:
          The results dict from executing the training iteration.
      """
      batch_size = self.config.train_batch_size
      local_worker = self.workers.local_worker()
  
      # Sample n MultiAgentBatches from n workers.
      with self._timers[SAMPLE_TIMER]:
          new_sample_batches = synchronous_parallel_sample(
              worker_set=self.workers, concat=False
          )
  
      for batch in new_sample_batches:
          # Update sampling step counters.
          self._counters[NUM_ENV_STEPS_SAMPLED] += batch.env_steps()
          self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps()
          # Store new samples in the replay buffer
          self.local_replay_buffer.add(batch)
  
      global_vars = {
          "timestep": self._counters[NUM_ENV_STEPS_SAMPLED],
      }
      # Update target network every `target_network_update_freq` sample steps.
      cur_ts = self._counters[
          NUM_AGENT_STEPS_SAMPLED
          if self.config.count_steps_by == "agent_steps"
          else NUM_ENV_STEPS_SAMPLED
      ]
  
      if cur_ts > self.config.num_steps_sampled_before_learning_starts:
          # Use deprecated replay() to support old replay buffers for now
          train_batch = self.local_replay_buffer.sample(batch_size)
  
          # Learn on the training batch.
          # Use simple optimizer (only for multi-agent or tf-eager; all other
          # cases should use the multi-GPU optimizer, even if only using 1 GPU)
          if self.config.get("simple_optimizer") is True:
              train_results = train_one_step(self, train_batch)
          else:
              train_results = multi_gpu_train_one_step(
                  self, train_batch
              )
  
          # Update replay buffer priorities.
          update_priorities_in_replay_buffer(
              self.local_replay_buffer,
              self.config,
              train_batch,
              train_results,
          )
  
          last_update = self._counters[LAST_TARGET_UPDATE_TS]
          if (
              cur_ts - last_update
              >= self.config.target_network_update_freq
          ):
              with self._timers[TARGET_NET_UPDATE_TIMER]:
                  to_update = local_worker.get_policies_to_train()
                  local_worker.foreach_policy_to_train(
                      lambda p, pid: pid in to_update
                      and p.update_target()
                  )
              self._counters[NUM_TARGET_UPDATES] += 1
              self._counters[LAST_TARGET_UPDATE_TS] = cur_ts
  
          # Update weights and global_vars - after learning on the local worker -
          # on all remote workers (only those policies that were actually trained).
          with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
              self.workers.sync_weights(
                  policies=list(train_results.keys()),
                  global_vars=global_vars,
              )
      else:
          train_results = {}
  
      # Return all collected metrics for the iteration.
      return train_results
  
imports: import logging
  from typing import List, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.simple_q.simple_q_tf_policy import SimpleQTF1Policy, SimpleQTF2Policy
  from ray.rllib.algorithms.simple_q.simple_q_torch_policy import SimpleQTorchPolicy
  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 import deep_update
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE, Deprecated, ALGO_DEPRECATION_WARNING
  from ray.rllib.utils.metrics import LAST_TARGET_UPDATE_TS, NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, NUM_TARGET_UPDATES, SYNCH_WORKER_WEIGHTS_TIMER, TARGET_NET_UPDATE_TIMER, SAMPLE_TIMER
  from ray.rllib.utils.replay_buffers.utils import update_priorities_in_replay_buffer, validate_buffer_config
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 3 for calling the SimpleQ obj's underlying member method code with example usage: obj.training_step() and returns: train_results