class_docstr: Runner for Dopamine experiments with fixed replay buffer.
docstr: Save statistics as tensorboard summaries.
  
  Args:
    iteration: int, The current iteration number.
    num_episodes_eval: int, number of evaluation episodes run.
    average_reward_eval: float, The average evaluation reward.
docstr_tok: ["Save","statistics","as","tensorboard","summaries",".","Args",":","iteration",":","int",",","The","current","iteration","number",".","num_episodes_eval",":","int",",","number","of","evaluation","episodes","run",".","average_reward_eval",":","float",",","The","average","evaluation","reward","."]
code: def _save_tensorboard_summaries(
      self, iteration, num_episodes_eval, average_reward_eval
  ):
      """Save statistics as tensorboard summaries.
  
      Args:
        iteration: int, The current iteration number.
        num_episodes_eval: int, number of evaluation episodes run.
        average_reward_eval: float, The average evaluation reward.
      """
      with self._summary_writer.as_default():
          tf.summary.scalar(
              "Eval/NumEpisodes", num_episodes_eval, step=iteration
          )
          tf.summary.scalar(
              "Eval/AverageReturns", average_reward_eval, step=iteration
          )
  
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 4 for calling the FixedReplayRunner obj's underlying member method code with example usage: obj._save_tensorboard_summaries(iteration, num_episodes_eval, average_reward_eval) without return types