class_docstr: This is an image hash containing a list of individual hashes for segments of the image.
  The matching logic is implemented as described in Efficient Cropping-Resistant Robust Image Hashing
docstr: Returns the hash in a list which is the best match to the current hash
  :param other_hashes: A list of image multi hashes to compare against
  :param hamming_cutoff: The maximum hamming distance to a region hash in the target hash
  :param bit_error_rate: Percentage of bits which can be incorrect, an alternative to the hamming cutoff.
  Defaults to 0.25 if unset, which means the hash can be 25% different
code: def best_match(
      self, other_hashes, hamming_cutoff=None, bit_error_rate=None
  ):
      # type: (list[ImageMultiHash], float | None, float | None) -> ImageMultiHash
      """
      Returns the hash in a list which is the best match to the current hash
      :param other_hashes: A list of image multi hashes to compare against
      :param hamming_cutoff: The maximum hamming distance to a region hash in the target hash
      :param bit_error_rate: Percentage of bits which can be incorrect, an alternative to the hamming cutoff.
      Defaults to 0.25 if unset, which means the hash can be 25% different
      """
      return min(
          other_hashes,
          key=lambda other_hash: self.__sub__(
              other_hash, hamming_cutoff, bit_error_rate
          ),
      )
  
imports: from __future__ import absolute_import, division, print_function
  import sys
  import numpy
  from PIL import Image, ImageFilter
label_desc: Use image node_id 10 for calling the ImageMultiHash obj's underlying member method code with example usage: obj.best_match(other_hashes, hamming_cutoff, bit_error_rate) and returns: min