code: def match(self, data, threshold=0.5, generator=False):  # pragma: no cover
          
          blocked_pairs = self._blockData(data)
          clusters = self.matchBlocks(blocked_pairs, threshold)
          if generator:
              return clusters
          else:
              return list(clusters)
docs: Identifies records that all 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 a measure
          of our confidence that all the records in a cluster refer to
          the same entity.
  
          This method should only used for small to moderately sized
          datasets for larger data, use matchBlocks
  
          Arguments:
  
          data -- Dictionary of records, where the keys are record_ids
                  and the values are dictionaries with the keys being
                  field names
  
          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