code: def evaluate(self):
      metrics = [("All", None), ("Things", True), ("Stuff", False)]
      results = {}
      for name, isthing in metrics:
          results[name], per_class_results = self.pq_stat.pq_average(
              isthing=isthing, thing_ids=self.thing_ids
          )
          if name == "All":
              results["per_class"] = per_class_results
      return build_info_dict(_type_="metric", pan_metrics=results)
  
imports: from collections import defaultdict
  import numpy
  from paddlepanseg.cvlibs import build_info_dict
  from paddlepanseg.utils import decode_pan_id
  from .evaluator import Evaluator
label_desc: Use image node_id 3 for calling the PanSegEvaluator obj's underlying member method code with example usage: obj.evaluate() and returns: build_info_dict