def _serialize_example()

in src/data_preprocess/data_preprocess_into_tfrecords_for_rdrp.py [0:0]
47 lines of code
22 McCabe index (conditional complexity)

    def _serialize_example(self, obj_id, sequence, pdb_obj, embedding_obj, label):
        d_feature = {'id': self._bytes_feature(obj_id.encode()), 'seq': self._bytes_feature(sequence.encode()),
                     'L': self._int_feature(len(sequence))}

        cur_example_label = label
        if cur_example_label is None or len(cur_example_label) == 0:
            return None
        if isinstance(cur_example_label, list) or isinstance(cur_example_label, set):
            cur_example_label_ids = [self.label_2_id[v] for v in cur_example_label]
            d_feature['label'] = self._int_feature(cur_example_label_ids)
        else:
            cur_example_label_id = self.label_2_id[cur_example_label]
            d_feature['label'] = self._int_feature(cur_example_label_id)

        if embedding_obj:
            d_feature['emb_l'] = self._int_feature(embedding_obj["L"][1])
            d_feature['emb_size'] = self._int_feature(embedding_obj["d"][1])
            for item in embedding_obj.items():
                name = item[0]
                dtype = item[1][0]
                value = item[1][1]
                if isinstance(value, np.ndarray):
                    value = list(value.reshape(-1))
                elif isinstance(value, int) or isinstance(value, float) or isinstance(value, str):
                    value = [value]
                if dtype == "str":
                    d_feature[name] = self._bytes_feature(value)
                elif dtype == "int":
                    d_feature[name] = self._int_feature(value)
                else:
                    d_feature[name] = self._float_feature(value)
        if pdb_obj:
            d_feature['pdb_l'] = self._int_feature(pdb_obj["L"][1])
            for item in pdb_obj.items():
                name = item[0]
                dtype = item[1][0]
                value = item[1][1]
                if isinstance(value, np.ndarray):
                    value = list(value.reshape(-1))
                elif isinstance(value, int) or isinstance(value, float) or isinstance(value, str):
                    value = [value]
                if dtype == "str":
                    d_feature[name] = self._bytes_feature(value)
                elif dtype == "int":
                    d_feature[name] = self._int_feature(value)
                else:
                    d_feature[name] = self._float_feature(value)
        example = tf.train.Example(features=tf.train.Features(feature=d_feature))
        return example.SerializeToString()