class_docstr: Produces labels for a model with a seperate CRF for NER and group
  information.
  
  Outputs two label sequences, the first for the standard NER CRF and the
  second for the group CRF.
class_docstr_tok: ["Produces","labels","for","a","model","with","a","seperate","CRF","for","NER","and","group","information",".","Outputs","two","label","sequences",",","the","first","for","the","standard","NER","CRF","and","the","second","for","the","group","CRF","."]
code: def __init__(self, pad_token, bio_tagging=True):
      super().__init__(
          pad_token, bio_tagging=bio_tagging, group_tagging=True
      )
  
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 MultiCRFGroupSequenceLabelingEncoder object from inherited base classes: SequenceLabelingEncoder with example: obj = MultiCRFGroupSequenceLabelingEncoder(pad_token, bio_tagging)