class_docstr: Runner for Dopamine experiments with fixed replay buffer.
docstr: Runs one iteration of agent/environment interaction.
docstr_tok: ["Runs","one","iteration","of","agent\/environment","interaction","."]
code: def _run_one_iteration(self, iteration):
      """Runs one iteration of agent/environment interaction."""
      statistics = iteration_statistics.IterationStatistics()
      logging.info("Starting iteration %d", iteration)
      # Reload the replay buffer at every iteration
      self._agent.reload_data()  # pylint: disable=protected-access
      self._run_train_phase()
  
      num_episodes_eval, average_reward_eval = self._run_eval_phase(
          statistics
      )
  
      if self._has_collector_dispatcher:
          self._collector_dispatcher.write(
              [
                  statistics_instance.StatisticsInstance(
                      "Eval/NumEpisodes", num_episodes_eval, iteration
                  ),
                  statistics_instance.StatisticsInstance(
                      "Eval/AverageReturns",
                      average_reward_eval,
                      iteration,
                  ),
              ]
          )
  
      if self._summary_writer is not None:
          self._save_tensorboard_summaries(
              iteration, num_episodes_eval, average_reward_eval
          )
      return statistics.data_lists
  
file_path: dopamine/dopamine/labs/offline_rl/jax/run_experiment.py
imports: import sys
  import time
  from absl import logging
  from dopamine.discrete_domains import iteration_statistics
  from dopamine.discrete_domains import run_experiment
  from dopamine.metrics import statistics_instance
  import gin
  import tensorflow
label_desc: Use image node_id 3 for calling the FixedReplayRunner obj's underlying member method code with example usage: obj._run_one_iteration(iteration) and returns: statistics