class_docstr: A plugin to monitor the labels repartition.
  
  :param image_creator: The function to use to create an image from the
      history of the labels repartition. It will receive a dictionary of the
      form {label_id: [count_at_step_0, count_at_step_1, ...], ...}
      and the list of the corresponding steps [step_0, step_1, ...].
      If set to None, only the raw data is emitted.
  :param mode: Indicates if this plugin should run on train or eval.
  :param emit_reset_at: The refreshment rate of the plugin.
  :return: The list of corresponding plugins.
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code: def before_eval(self, strategy: "SupervisedTemplate"):
      self.strategy = strategy
      return super().before_eval(strategy)
  
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 LabelsRepartitionPlugin obj's underlying member method code with example usage: obj.before_eval(strategy) and returns: super