code: def inverse_transform(self, preds, raw_texts):
      preds = super().inverse_transform(preds)
  
      preds = map(try_decode, preds)
  
      def split_decode(pred):
          # Ensure NER and group predictions were generated
          if len(pred) == 2:
              return pred
          else:
              return ([], [])
  
      preds = map(split_decode, preds)
  
      seq_preds, group_preds = list(zip(*preds))
      seq_ret = SequenceLabelingTextEncoder.decode_preds(
          seq_preds, raw_texts
      )
      group_ret = GroupLabelingTextEncoder.decode_preds(
          group_preds, raw_texts
      )
      return list(zip(seq_ret, group_ret))
  
imports: from abc import ABCMeta
  import logging
  import json
  import numpy
  from finetune.encoding.target_encoders import BaseEncoder, Seq2SeqLabelEncoder, SequenceLabelingEncoder
label_desc: Use image node_id 2 for calling the JointLabelingTextEncoder obj's underlying member method code with example usage: obj.inverse_transform(preds, raw_texts) and returns: list, pred, list,  list