def __call__(self, model_output: List, nf_samples: List) -> Tuple[List[str], List[str]]:
        """
        The model output is transformed to the relation id and relation name
        Args:
            model_output: List of probability vectors
            nf_samples: contains the information about true and fake samples (0 - true sample and should be included
                to the output, 1 - fake sample)
        Return:
            wikidata_relation_id: List of wiki ids of found relations
            relation_name: List of names of found relations
        """

        wikidata_relation_id, relation_name = [], []

        for predictions, nf_sample in zip(model_output, nf_samples):
            if nf_sample:
                wikidata_relation_id.append("-")
                relation_name.append("-")
            else:
                rel_indices = np.nonzero(predictions)[0]

                for index in rel_indices:
                    if index == 0:
                        wikidata_relation_id.append("-")
                        relation_name.append("no relation")
                        continue

                    rel_p = self.id2rel[index]
                    wikidata_relation_id.append(rel_p)

                    if rel_p in self.rel2label:
                        relation_name.append(self.rel2label[rel_p])
                    else:
                        relation_name.append("-")
        return wikidata_relation_id, relation_name

        The model output is transformed to the relation id and relation name
        Args:
            model_output: List of probability vectors
            nf_samples: contains the information about true and fake samples (0 - true sample and should be included
                to the output, 1 - fake sample)
        Return:
            wikidata_relation_id: List of wiki ids of found relations
            relation_name: List of names of found relations
        
deeppavlov/models/preprocessors/re_preprocessor.py