class_docstr: Produces labels for a joint BROS / Sequence Labeling model.
  
  First set of labels are standard NER sequence labeling models, second and
  third set of labels are BROS start and next token labels.
class_docstr_tok: ["Produces","labels","for","a","joint","BROS","\/","Sequence","Labeling","model",".","First","set","of","labels","are","standard","NER","sequence","labeling","models",",","second","and","third","set","of","labels","are","BROS","start","and","next","token","labels","."]
code: def inverse_transform(self, y, only_labels=False):
      tags, start_tokens, next_tokens = y
  
      _classes_ = self.classes_
  
      self.classes_, self.lookup = (
          self.group_classes_,
          self.group_lookup,
      )
      group_tags = BROSEncoder.inverse_transform(
          self, (start_tokens, next_tokens)
      )
      start_tokens, next_tokens = group_tags
  
      self.classes_, self.lookup = self.ner_classes_, self.ner_lookup
      tags = SequenceLabelingEncoder.inverse_transform(self, tags)
  
      self.classes_ = _classes_
  
      return (tags, start_tokens, next_tokens)
  
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 4 for calling the JointBROSEncoder obj's underlying member method code with example usage: obj.inverse_transform(y, only_labels) and returns: tags,  start_tokens,  next_tokens