code: def match(self, data_1, data_2, threshold=0.5, generator=False):  # pragma: no cover
          
  
          blocked_pairs = self._blockData(data_1, data_2)
          clusters = self.matchBlocks(blocked_pairs, threshold)
  
          if generator:
              return clusters
          else:
              return list(clusters)
docs: Identifies pairs of records that refer to the same entity, returns
          tuples containing a set of record ids and a confidence score as a float
          between 0 and 1. The record_ids within each set should refer to the
          same entity and the confidence score is the estimated probability that
          the records refer to the same entity.
  
          This method should only used for small to moderately sized datasets
          for larger data, use matchBlocks
  
          Arguments:
          data_1    -- Dictionary of records from first dataset, where the
                       keys are record_ids and the values are dictionaries
                       with the keys being field names
  
          data_2    -- Dictionary of records from second dataset, same form
                       as data_1
  
          threshold -- Number between 0 and 1 (default is .5). We will consider
                       records as potential duplicates if the predicted
                       probability of being a duplicate is above the threshold.
  
                       Lowering the number will increase recall, raising it
                       will increase precision