class_docstr: Produces sequence labeling labels given group information.
  
  Parameters:
      group: When true, group information will be used for labels
class_docstr_tok: ["Produces","sequence","labeling","labels","given","group","information",".","Parameters",":","group",":","When","true",",","group","information","will","be","used","for","labels"]
code: def fit(self, labels):
      if not self.group:
          labels, groups = list(zip(*labels))
          super().fit(labels)
      else:
          self.classes_ = [self.pad_token]
          if self.bio_tagging:
              self.classes_.extend(("B-GROUP", "I-GROUP"))
          else:
              self.classes_.append("GROUP")
          self.lookup = {c: i for i, c in enumerate(self.classes_)}
  
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 2 for calling the PipelineSequenceLabelingEncoder obj's underlying member method code with example usage: obj.fit(labels) without return types