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 __init__(self, pad_token, group=True, bio_tagging=True):
      super().__init__(pad_token, bio_tagging=bio_tagging)
      self.group = group
  
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 PipelineSequenceLabelingEncoder object from inherited base classes: SequenceLabelingEncoder with example: obj = PipelineSequenceLabelingEncoder(pad_token, group, bio_tagging)