class_docstr: Sample Runner class to generate visualizations.
class_docstr_tok: ["Sample","Runner","class","to","generate","visualizations","."]
code: def __init__(
      self,
      base_dir,
      trained_agent_ckpt_path,
      create_agent_fn,
      use_legacy_checkpoint=False,
  ):
      self._trained_agent_ckpt_path = trained_agent_ckpt_path
      self._use_legacy_checkpoint = use_legacy_checkpoint
      super(MyRunner, self).__init__(base_dir, create_agent_fn)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import logging
  from dopamine.agents.dqn import dqn_agent
  from dopamine.agents.rainbow import rainbow_agent
  from dopamine.discrete_domains import atari_lib
  from dopamine.discrete_domains import iteration_statistics
  from dopamine.discrete_domains import run_experiment
  from dopamine.utils import agent_visualizer
  from dopamine.utils import atari_plotter
  from dopamine.utils import bar_plotter
  from dopamine.utils import line_plotter
  import gin
  import numpy
  import tensorflow
  import tf_slim
label_desc: Use image node_id 1 to create a new MyRunner object from inherited base classes: run_experiment with example: obj = MyRunner(base_dir, trained_agent_ckpt_path, create_agent_fn, use_legacy_checkpoint)