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 transform(self, out, labels):
      labels, groups = labels
  
      _classes_ = self.classes_
  
      self.classes_, self.lookup = (
          self.group_classes_,
          self.group_lookup,
      )
      group_labels = BROSEncoder.transform(self, out, (labels, groups))
      start_token_labels, next_token_labels = group_labels
  
      self.classes_, self.lookup = self.ner_classes_, self.ner_lookup
      ner_labels = SequenceLabelingEncoder.transform(self, out, labels)
  
      # Ensure we always have the full list of classes for class weights
      self.classes_ = _classes_
  
      return [ner_labels, start_token_labels, next_token_labels]
  
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 3 for calling the JointBROSEncoder obj's underlying member method code with example usage: obj.transform(out, labels) and returns: list