class_docstr: Runner for loading/saving replay data.
class_docstr_tok: ["Runner","for","loading\/saving","replay","data","."]
code: def __init__(
      self,
      base_dir,
      create_agent_fn,
      load_replay_dir=None,
      save_replay=False,
  ):
      super().__init__(base_dir, create_agent_fn)
      self._load_replay_dir = load_replay_dir
      self._save_replay = save_replay
      logging.info(
          "Load fixed replay from directory: %s", load_replay_dir
      )
      logging.info("Save replay: %s", save_replay)
  
imports: import functools
  import os
  import sys
  import time
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from dopamine.discrete_domains import iteration_statistics
  from dopamine.discrete_domains import run_experiment
  from dopamine.labs.atari_100k import normalization_utils
  from dopamine.metrics import collector_dispatcher
  from dopamine.metrics import statistics_instance
  import gin
  import jax
  import numpy
  import tensorflow
label_desc: Use image node_id 1 to create a new LoggedDataEfficientAtariRunner object from inherited base classes: DataEfficientAtariRunner with example: obj = LoggedDataEfficientAtariRunner(base_dir, create_agent_fn, load_replay_dir, save_replay)