class_docstr: Metric used to monitor the labels repartition.
class_docstr_tok: ["Metric","used","to","monitor","the","labels","repartition","."]
code: def result(self) -> Dict[int, Dict[int, int]]:
      if self.class_order is None:
          return self.task2label2count
      return {
          task: {
              label: label2count[label]
              for label in self.class_order
              if label in label2count
          }
          for task, label2count in self.task2label2count.items()
      }
  
imports: from collections import defaultdict
  from typing import Callable, Dict, Sequence, TYPE_CHECKING, Optional, List, Counter, overload
  from matplotlib.figure import Figure
  from avalanche.evaluation import GenericPluginMetric, Metric, PluginMetric
  from avalanche.evaluation.metric_results import MetricValue, AlternativeValues
  from avalanche.evaluation.metric_utils import stream_type, default_history_repartition_image_creator
  from typing_extensions import Literal
label_desc: Use image node_id 5 for calling the LabelsRepartition obj's underlying member method code with example usage: obj.result() and returns: unknown, self