def METHOD_NAME(self, report):
    scores = torch.sigmoid(report.scores)
    binary_scores = scores > self.prediction_threshold
    predictions = []
    for idx, item_id in enumerate(report.id):
        item_id = byte_tensor_to_object(item_id)
        score = binary_scores[idx]
        labels = []
        score = score.nonzero(as_tuple=False)
        for item in score:
            labels.append(self.idx_to_class[item.item()])
        predictions.append({"id": item_id, "labels": labels})
    return predictions