code: def setup(self, config: AlgorithmConfig):
      super().setup(config)
  
      # Create a microbatch variable for collecting gradients on microbatches'.
      # These gradients will be accumulated on-the-fly and applied at once (once train
      # batch size has been collected) to the model.
      if self.config.microbatch_size:
          self._microbatches_grads = None
          self._microbatches_counts = self._num_microbatches = 0
  
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 2 for calling the A2C obj's underlying member method code with example usage: obj.setup(config) without return types