class_docstr: Runner for evaluating using a fixed number of episodes rather than steps.
docstr: Initialization for a new episode with random number of no-ops.
  
  Returns:
   action: int, the initial action chosen by the agent.
code: def _initialize_episode(self):
      """Initialization for a new episode with random number of no-ops.
  
      Returns:
       action: int, the initial action chosen by the agent.
      """
      initial_observation = self._environment.reset()
      if self._max_noops > 0:
          initial_observation = self._run_no_ops()
      return self._agent.begin_episode(initial_observation)
  
file_path: dopamine/dopamine/labs/atari_100k/eval_run_experiment.py
imports: import sys
  from absl import logging
  from dopamine.discrete_domains import run_experiment
  import gin
  import jax
label_desc: Use image node_id 2 for calling the MaxEpisodeEvalRunner obj's underlying member method code with example usage: obj._initialize_episode() and returns: self