code: def getCentroid(
      attribute_variants: Sequence[str], comparator: Comparator
  ) -> str:
      """
      Takes in a list of attribute values for a field,
      evaluates the centroid using the comparator,
      & returns the centroid (i.e. the 'best' value for the field)
      """
  
      n = len(attribute_variants)
  
      distance_matrix = numpy.zeros([n, n])
  
      # populate distance matrix by looping through elements of matrix triangle
      for i in range(0, n):
          for j in range(0, i):
              distance = comparator(
                  attribute_variants[i], attribute_variants[j]
              )
              distance_matrix[i, j] = distance_matrix[j, i] = distance
  
      average_distance = distance_matrix.mean(0)
  
      # there can be ties for minimum, average distance string
      min_dist_indices: numpy.typing.NDArray[numpy.int_]
      min_dist_indices = numpy.where(
          average_distance == average_distance.min()
      )[0]
  
      if len(min_dist_indices) > 1:
          centroid = breakCentroidTie(
              attribute_variants, min_dist_indices
          )
      else:
          centroid_index = min_dist_indices[0]
          centroid = attribute_variants[centroid_index]
  
      return centroid
  
file_path: dedupe/dedupe/canonical.py
imports: from typing import Mapping, Sequence
  import numpy
  import numpy.typing
  from affinegap import normalizedAffineGapDistance
  from dedupe._typing import Comparator, RecordDict
label_desc: Use image node_id 1 for calling a global function with example usage: getCentroid(attribute_variants, comparator) and returns: centroid