class_docstr: Produces labels for a joint Group Relation / Sequence Labeling model.
  
  Sequence labels are packed into the group relation labels in the form of an
  additional group added to the end.
class_docstr_tok: ["Produces","labels","for","a","joint","Group","Relation","\/","Sequence","Labeling","model",".","Sequence","labels","are","packed","into","the","group","relation","labels","in","the","form","of","an","additional","group","added","to","the","end","."]
code: def inverse_transform(self, y, class_weights=False):
      # Input in different form if coming from target encoder versus preds
      if class_weights:
          # Unpack 0-dim numpy array
          y = y.item()
          # Unpack target encoder output
          groups = y["groups"]
          tags = y["tags"]
      else:
          # Unpack predict module output
          groups, tags = y[0], y[1]
  
      _classes_ = self.classes_
  
      self.classes_, self.lookup = self.ner_classes_, self.ner_lookup
      tags = SequenceLabelingEncoder.inverse_transform(self, tags)
  
      self.classes_ = _classes_
  
      return (tags, groups)
  
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 JointGroupRelationEncoder obj's underlying member method code with example usage: obj.inverse_transform(y, class_weights) and returns: tags,  groups