code: def eval_policy(eval_positions):
      
  
      model_paths = oneoff_utils.get_model_paths(fsdb.models_dir())
  
      idx_start = FLAGS.idx_start
      eval_every = FLAGS.eval_every
  
      print("Evaluating models {}-{}, eval_every={}".format(
            idx_start, len(model_paths), eval_every))
  
      player = None
      for i, idx in enumerate(tqdm(range(idx_start, len(model_paths), eval_every))):
          if player and i % 20 == 0:
              player.network.sess.close()
              tf.reset_default_graph()
              player = None
  
          if not player:
              player = oneoff_utils.load_player(model_paths[idx])
          else:
              oneoff_utils.restore_params(model_paths[idx], player)
  
          pos_names, positions = zip(*eval_positions)
          # This should be batched at somepoint.
          eval_probs, eval_values = player.network.run_many(positions)
  
          for pos_name, probs, value in zip(pos_names, eval_probs, eval_values):
              save_file = os.path.join(
                  FLAGS.data_dir, "heatmap-{}-{}.csv".format(pos_name, idx))
  
              with open(save_file, "w") as data:
                  data.write("{},  {},  {}\n".format(
                      idx, value, ",".join(map(str, probs))))
docs: Evaluate all positions with all models save the policy heatmaps as CSVs
  
      CSV name is "heatmap-<position_name>-<model-index>.csv"
      CSV format is: model number, value network output, policy network outputs
  
      position_name is taken from the SGF file
      Policy network outputs (19x19) are saved in flat order (see coord.from_flat)