code: def create_runner(
      base_dir,
      trained_agent_ckpt_path,
      agent="dqn",
      use_legacy_checkpoint=False,
  ):
      create_agent = (
          create_dqn_agent if agent == "dqn" else create_rainbow_agent
      )
      return MyRunner(
          base_dir,
          trained_agent_ckpt_path,
          create_agent,
          use_legacy_checkpoint,
      )
  
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 3 for calling a global function with example usage: create_runner(base_dir, trained_agent_ckpt_path, agent, use_legacy_checkpoint) and returns: MyRunner