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 __init__(self, pad_token, bio_tagging=False):
      # Can potentially replace these direct calls with super()s, but seems
      # like it'll be hard to maintain so leaving as is for now
      SequenceLabelingEncoder.__init__(
          self, pad_token, bio_tagging=bio_tagging
      )
  
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 1 to create a new JointBROSEncoder object from inherited base classes: BROSEncoder, SequenceLabelingEncoder with example: obj = JointBROSEncoder(pad_token, bio_tagging)