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 transform(self, out, labels):
      labels, groups = labels
  
      _classes_ = self.classes_
  
      self.classes_, self.lookup = (
          self.group_classes_,
          self.group_lookup,
      )
      group_labels = GroupRelationEncoder.transform(
          self, out, (labels, groups)
      )
  
      self.classes_, self.lookup = self.ner_classes_, self.ner_lookup
      ner_labels = SequenceLabelingEncoder.transform(self, out, labels)
  
      self.classes_ = _classes_
  
      return {"groups": group_labels, "tags": ner_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 3 for calling the JointGroupRelationEncoder obj's underlying member method code with example usage: obj.transform(out, labels) and returns: dict