class_docstr: Average the scores of the true class by old and new classes
code: def result(self) -> Dict[LabelCat, float]:
      # print(self.new_classes, self.label2mean)
      rv: Dict[LabelCat, float] = {
          "new": sum(
              (self.label2mean[label] for label in self.new_classes),
              start=Mean(),
          ).result()
      }
      if not self.old_classes:
          return rv
  
      rv["old"] = sum(
          (self.label2mean[label] for label in self.old_classes),
          start=Mean(),
      ).result()
  
      return rv
  
imports: from abc import ABC, abstractmethod
  from collections import defaultdict
  from typing import Callable, Dict, Set, TYPE_CHECKING, List, Optional, TypeVar
  import torch
  from matplotlib.axes import Axes
  from matplotlib.figure import Figure
  from matplotlib.pyplot import subplots
  from torch import Tensor, arange
  from avalanche.evaluation import Metric, PluginMetric
  from avalanche.evaluation.metric_utils import get_metric_name
  from avalanche.evaluation.metrics import Mean
  from avalanche.evaluation.metric_results import MetricValue, AlternativeValues
  from typing_extensions import Literal
label_desc: Use image node_id 5 for calling the MeanNewOldScores obj's underlying member method code with example usage: obj.result() and returns: rv, rv