class_docstr: A container for alternative representations of the same metric value.
docstr: Retrieves a supported representation for this metric value.
  
  :param supported_types: A list of supported value types.
  :return: The best supported representation. Returns None if no supported
      representation is found.
docstr_tok: ["Retrieves","a","supported","representation","for","this","metric","value",".",":","param","supported_types",":","A","list","of","supported","value","types",".",":","return",":","The","best","supported","representation",".","Returns","None","if","no","supported","representation","is","found","."]
code: def best_supported_value(
      self, *supported_types: type
  ) -> Optional[MetricType]:
      """
      Retrieves a supported representation for this metric value.
  
      :param supported_types: A list of supported value types.
      :return: The best supported representation. Returns None if no supported
          representation is found.
      """
      for alternative in self.alternatives:
          if isinstance(alternative, supported_types):
              return alternative
      return None
  
file_path: avalanche/avalanche/evaluation/metric_results.py
imports: from dataclasses import dataclass
  from typing import List, Optional, TYPE_CHECKING, Tuple, Union
  from PIL.Image import Image
  from matplotlib.figure import Figure
  from torch import Tensor
  from enum import Enum
label_desc: Use image node_id 2 for calling the AlternativeValues obj's underlying member method code with example usage: obj.best_supported_value() and returns: None, alternative