class_docstr: Runner for evaluating using a fixed number of episodes rather than steps.
docstr: Executes `num_noops` no-ops in the environment.
code: def _run_no_ops(self):
      """Executes `num_noops` no-ops in the environment."""
      self._agent._rng, rng = jax.random.split(
          self._agent._rng
      )  # pylint: disable=protected-access
      num_noops = jax.random.randint(
          rng, shape=(), minval=0, maxval=self._max_noops
      )
      for _ in range(num_noops):
          # Assumes raw action 0 is always no-op
          self._environment.environment.ale.act(0)
      if self._environment.environment.ale.game_over():
          observation = self._environment.reset()
      else:
          observation = (
              self._environment._pool_and_resize()
          )  # pylint: disable=protected-access
      return 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 3 for calling the MaxEpisodeEvalRunner obj's underlying member method code with example usage: obj._run_no_ops() and returns: observation