class_docstr: Produces labels for BROS.
  
  First set of labels is a binary value indicating whether a token is the
  start of a group. Second set of labels is a index indicating what the next
  token in the group is.
class_docstr_tok: ["Produces","labels","for","BROS.","First","set","of","labels","is","a","binary","value","indicating","whether","a","token","is","the","start","of","a","group",".","Second","set","of","labels","is","a","index","indicating","what","the","next","token","in","the","group","is","."]
code: def inverse_transform(self, y):
      start_tokens, next_tokens = y
      # Placeholder for is we ever add labels
      start_tokens = [self.classes_[l] for l in start_tokens]
      return (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 6 for calling the BROSEncoder obj's underlying member method code with example usage: obj.inverse_transform(y) and returns: start_tokens,  next_tokens