code: def create_tandem_agents_and_checkpoints(
      sess,
      environment,
      agent_name="dqn",
      summary_writer=None,
      debug_mode=False,
  ):
      """Creates a tandem agent.
  
      Args:
        sess: TF session, unused since we are in JAX.
        environment: A gym environment (e.g. Atari 2600).
        agent_name: str, name of the agent to create.
        summary_writer: A Tensorflow summary writer to pass to the agent
          for in-agent training statistics in Tensorboard.
        debug_mode: bool, unused.
  
      Returns:
        An active and passive agent.
      """
      assert agent_name is not None
      del sess
      del debug_mode
      if agent_name == "dqn":
          return tandem_dqn_agent.TandemDQNAgent(
              num_actions=environment.action_space.n,
              summary_writer=summary_writer,
          )
      else:
          raise ValueError("Unknown agent: {}".format(agent_name))
  
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 1 for calling a global function with example usage: create_tandem_agents_and_checkpoints(sess, environment, agent_name, summary_writer, debug_mode) and returns: tandem_dqn_agent