code: def evaluate_all_configs(
      hparams, agent_model_dir, eval_fn=_eval_fn_with_learner
  ):
    
    metrics = {}
    # Iterate over all combinations of sampling temperatures and whether to do
    # initial no-ops.
    for sampling_temp in hparams.eval_sampling_temps:
      # Iterate over a set so if eval_max_num_noops == 0 then it's 1 iteration.
      for max_num_noops in set([hparams.eval_max_num_noops, 0]):
        scores = evaluate_single_config(
            hparams, sampling_temp, max_num_noops, agent_model_dir, eval_fn
        )
        for (score, clipped) in zip(scores, (True, False)):
          metric_name = get_metric_name(sampling_temp, max_num_noops, clipped)
          metrics[metric_name] = score
  
    return metrics
docs: Evaluate the agent with multiple eval configurations.