class_docstr: Runner class to run Tandem RL experiments.
class_docstr_tok: ["Runner","class","to","run","Tandem","RL","experiments","."]
docstr: Save statistics as tensorboard summaries.
  
  Args:
    iteration: int, The current iteration number.
    num_episodes_train: int, number of training episodes run.
    average_reward_train: float, The average training reward.
    active_num_episodes_eval: int, number of active evaluation episodes run.
    active_average_reward_eval: float, The average active evaluation reward.
    passive_num_episodes_eval: int, number of passive evaluation episodes run.
    passive_average_reward_eval: float, The average passive evaluation reward.
    average_steps_per_second: float, The average number of steps per second.
docstr_tok: ["Save","statistics","as","tensorboard","summaries",".","Args",":","iteration",":","int",",","The","current","iteration","number",".","num_episodes_train",":","int",",","number","of","training","episodes","run",".","average_reward_train",":","float",",","The","average","training","reward",".","active_num_episodes_eval",":","int",",","number","of","active","evaluation","episodes","run",".","active_average_reward_eval",":","float",",","The","average","active","evaluation","reward",".","passive_num_episodes_eval",":","int",",","number","of","passive","evaluation","episodes","run",".","passive_average_reward_eval",":","float",",","The","average","passive","evaluation","reward",".","average_steps_per_second",":","float",",","The","average","number","of","steps","per","second","."]
code: def _save_tensorboard_summaries(
      self,
      iteration,
      num_episodes_train,
      average_reward_train,
      active_num_episodes_eval,
      active_average_reward_eval,
      passive_num_episodes_eval,
      passive_average_reward_eval,
      average_steps_per_second,
  ):
      """Save statistics as tensorboard summaries.
  
      Args:
        iteration: int, The current iteration number.
        num_episodes_train: int, number of training episodes run.
        average_reward_train: float, The average training reward.
        active_num_episodes_eval: int, number of active evaluation episodes run.
        active_average_reward_eval: float, The average active evaluation reward.
        passive_num_episodes_eval: int, number of passive evaluation episodes run.
        passive_average_reward_eval: float, The average passive evaluation reward.
        average_steps_per_second: float, The average number of steps per second.
      """
      summary = tf.compat.v1.Summary(
          value=[
              tf.compat.v1.Summary.Value(
                  tag="Train/NumEpisodes",
                  simple_value=num_episodes_train,
              ),
              tf.compat.v1.Summary.Value(
                  tag="Train/AverageReturns",
                  simple_value=average_reward_train,
              ),
              tf.compat.v1.Summary.Value(
                  tag="Train/AverageStepsPerSecond",
                  simple_value=average_steps_per_second,
              ),
              tf.compat.v1.Summary.Value(
                  tag="Eval/ActiveNumEpisodes",
                  simple_value=active_num_episodes_eval,
              ),
              tf.compat.v1.Summary.Value(
                  tag="Eval/ActiveAverageReturns",
                  simple_value=active_average_reward_eval,
              ),
              tf.compat.v1.Summary.Value(
                  tag="Eval/PassiveNumEpisodes",
                  simple_value=passive_num_episodes_eval,
              ),
              tf.compat.v1.Summary.Value(
                  tag="Eval/PassiveAverageReturns",
                  simple_value=passive_average_reward_eval,
              ),
          ]
      )
      self._summary_writer.add_summary(summary, iteration)
      self._summary_writer.flush()
  
imports: import sys
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from dopamine.discrete_domains import gym_lib
  from dopamine.discrete_domains import iteration_statistics
  from dopamine.discrete_domains import run_experiment
  from dopamine.labs.environments.minatar import minatar_env
  from dopamine.labs.tandem_dqn import tandem_dqn_agent
  import gin
  import numpy
  import tensorflow
label_desc: Use image node_id 7 for calling the TandemRunner obj's underlying member method code with example usage: obj._save_tensorboard_summaries(iteration, num_episodes_train, average_reward_train, active_num_episodes_eval, active_average_reward_eval, passive_num_episodes_eval, passive_average_reward_eval, average_steps_per_second) without return types