code: def training_step(self) -> ResultDict:
      # Fallback to Algorithm.training_step() and A3C policies (loss_fn etc).
      # W/o microbatching: Identical to Algorithm's default implementation.
      # Only difference to a default Algorithm being the value function loss term
      # and its value computations alongside each action.
      if self.config.microbatch_size is None:
          return Algorithm.training_step(self)
  
      # In microbatch mode, we want to compute gradients on experience
      # microbatches, average a number of these microbatches, and then
      # apply the averaged gradient in one SGD step. This conserves GPU
      # memory, allowing for extremely large experience batches to be
      # used.
      with self._timers[SAMPLE_TIMER]:
          if self.config.count_steps_by == "agent_steps":
              train_batch = synchronous_parallel_sample(
                  worker_set=self.workers,
                  max_agent_steps=self.config.microbatch_size,
              )
          else:
              train_batch = synchronous_parallel_sample(
                  worker_set=self.workers,
                  max_env_steps=self.config.microbatch_size,
              )
  
      self._counters[NUM_ENV_STEPS_SAMPLED] += train_batch.env_steps()
      self._counters[
          NUM_AGENT_STEPS_SAMPLED
      ] += train_batch.agent_steps()
  
      with self._timers[COMPUTE_GRADS_TIMER]:
          grad, info = self.workers.local_worker().compute_gradients(
              train_batch, single_agent=True
          )
          # New microbatch accumulation phase.
          if self._microbatches_grads is None:
              self._microbatches_grads = grad
          # Existing gradients: Accumulate new gradients on top of existing ones.
          else:
              for i, g in enumerate(grad):
                  self._microbatches_grads[i] += g
          self._microbatches_counts += train_batch.count
          self._num_microbatches += 1
  
      # If `train_batch_size` reached: Accumulate gradients and apply.
      num_microbatches = math.ceil(
          self.config.train_batch_size / self.config.microbatch_size
      )
      if self._num_microbatches >= num_microbatches:
          # Update counters.
          self._counters[
              NUM_ENV_STEPS_TRAINED
          ] += self._microbatches_counts
          self._counters[
              NUM_AGENT_STEPS_TRAINED
          ] += self._microbatches_counts
  
          # Apply gradients.
          apply_timer = self._timers[APPLY_GRADS_TIMER]
          with apply_timer:
              self.workers.local_worker().apply_gradients(
                  self._microbatches_grads
              )
              apply_timer.push_units_processed(
                  self._microbatches_counts
              )
  
          # Reset microbatch information.
          self._microbatches_grads = None
          self._microbatches_counts = self._num_microbatches = 0
  
          # Also update global vars of the local worker.
          # Create current global vars.
          global_vars = {
              "timestep": self._counters[NUM_AGENT_STEPS_SAMPLED],
          }
          # Synch updated weights back to the workers
          # (only those policies that are trainable).
          with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
              self.workers.sync_weights(
                  policies=self.workers.local_worker().get_policies_to_train(),
                  global_vars=global_vars,
              )
  
      train_results = {DEFAULT_POLICY_ID: info}
  
      return train_results
  
imports: import logging
  import math
  from typing import Optional
  from ray.rllib.algorithms.a3c.a3c import A3C, A3CConfig
  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.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import APPLY_GRADS_TIMER, COMPUTE_GRADS_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, SAMPLE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 3 for calling the A2C obj's underlying member method code with example usage: obj.training_step() and returns: train_results, Algorithm