code: def run(
      agent,
      game,
      num_steps,
      root_dir,
      restore_ckpt,
      use_legacy_checkpoint=False,
  ):
      """Main entrypoint for running and generating visualizations.
  
      Args:
        agent: str, agent type to use.
        game: str, Atari 2600 game to run.
        num_steps: int, number of steps to play game.
        root_dir: str, root directory where files will be stored.
        restore_ckpt: str, path to the checkpoint to reload.
        use_legacy_checkpoint: bool, whether to restore from a legacy (pre-Keras)
          checkpoint.
      """
      tf.compat.v1.reset_default_graph()
      config = """
    atari_lib.create_atari_environment.game_name = '{}'
    WrappedReplayBuffer.replay_capacity = 300
    """.format(
          game
      )
      base_dir = os.path.join(root_dir, "agent_viz", game, agent)
      gin.parse_config(config)
      runner = create_runner(
          base_dir, restore_ckpt, agent, use_legacy_checkpoint
      )
      runner.visualize(
          os.path.join(base_dir, "images"), num_global_steps=num_steps
      )
  
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 4 for calling a global function with example usage: run(agent, game, num_steps, root_dir, restore_ckpt, use_legacy_checkpoint) without return types