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 transform(self, out, labels):
      labels, groups = labels
      if not self.group:
          return super().transform(out, labels)
      else:
          labels = []
          for group in groups:
              if not is_continuous(group):
                  continue
              group_start = min([t["start"] for t in group["spans"]])
              group_end = max([t["end"] for t in group["spans"]])
              group_text = " ".join([t["text"] for t in group["spans"]])
              labels.append(
                  {
                      "start": group_start,
                      "end": group_end,
                      "label": "GROUP",
                      "text": group_text,
                  }
              )
      return super().transform(out, 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 PipelineSequenceLabelingEncoder obj's underlying member method code with example usage: obj.transform(out, labels) and returns: super, super