docstr: Args:
      high_threshold (float): quality values greater than or equal to
          this value are candidate matches.
      low_threshold (float): a lower quality threshold used to stratify
          matches into three levels:
          1) matches >= high_threshold
          2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold)
          3) BELOW_LOW_THRESHOLD matches in [0, low_threshold)
      allow_low_quality_matches (bool): if True, produce additional matches
          for predictions that have only low-quality match candidates. See
          set_low_quality_matches_ for more details.
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code: def __init__(
      self,
      high_threshold,
      low_threshold,
      allow_low_quality_matches=False,
  ):
      # type: (float, float, bool) -> None
      """
      Args:
          high_threshold (float): quality values greater than or equal to
              this value are candidate matches.
          low_threshold (float): a lower quality threshold used to stratify
              matches into three levels:
              1) matches >= high_threshold
              2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold)
              3) BELOW_LOW_THRESHOLD matches in [0, low_threshold)
          allow_low_quality_matches (bool): if True, produce additional matches
              for predictions that have only low-quality match candidates. See
              set_low_quality_matches_ for more details.
      """
      self.BELOW_LOW_THRESHOLD = -1
      self.BETWEEN_THRESHOLDS = -2
      assert low_threshold <= high_threshold
      self.high_threshold = high_threshold  # 0.7
      self.low_threshold = low_threshold  # 0.3
      self.allow_low_quality_matches = allow_low_quality_matches
  
imports: import torch
  import math
  from typing import List, Tuple
  from torch import Tensor
label_desc: Use image node_id 1 to create a new Matcher object from inherited base classes: object with example: obj = Matcher(high_threshold, low_threshold, allow_low_quality_matches)