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 inverse_transform(self, y, only_labels=False):
      labels, group_labels = y
      labels = [self.label_classes_[l] for l in labels]
  
      # Option to only return encoded labels for class weight calculations
      if only_labels:
          return labels
  
      group_labels = [self.group_classes_[l] for l in group_labels]
      # Replace pad tokens with empty prefixes
      group_labels = [
          "" if l == self.pad_token else l for l in group_labels
      ]
      return [pre + tag for pre, tag in zip(group_labels, 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 4 for calling the MultiCRFGroupSequenceLabelingEncoder obj's underlying member method code with example usage: obj.inverse_transform(y, only_labels) and returns: unknown, labels