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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def match(self, data, threshold=0.5, generator=False):
 # pragma: no cover """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 """

        blocked_pairs = self._blockData(data)
        clusters = self.matchBlocks(blocked_pairs, threshold)
        if generator:
            return clusters
        else:
            return list(clusters)