code: def sentenceize(current_sentences, max_seq_length, target_seq_length):
      """
      Generate a pair of sentences based on a segmentation strategy
      cloned from official electra model.
  
      Parameters
      ----------
      current_sentences
      max_seq_length
          Maximum sequence length of the training features
      target_seq_length
          Target sequence length of the training features
      Returns
      -------
      first_segment
          The first sentence of the pretraining sequence
      second_segment
          The second sentence of the pretraining sequence.
          Could be None for diversity of training instances.
      """
      # 10% chance to only produce one segment
      if random.random() < 0.1:
          first_segment_target_length = 100000
      else:
          # The reserved space for [CLS] and [SEP] tokens
          first_segment_target_length = (target_seq_length - 3) // 2
      first_segment = []
      second_segment = []
      for sentence in current_sentences:
          if sentence:
              # the sentence goes to the first segment if (1) the first segment is
              # empty, (2) the sentence doesn't put the first segment over length or
              # (3) 50% of the time when it does put the first segment over length
              if (
                  len(first_segment) == 0
                  or len(first_segment) + len(sentence)
                  < first_segment_target_length
                  or (
                      len(second_segment) == 0
                      and len(first_segment)
                      < first_segment_target_length
                      and random.random() < 0.5
                  )
              ):
                  first_segment += sentence
              else:
                  second_segment += sentence
  
      # trim to max_length while accounting for not-yet-added [CLS]/[SEP] tokens
      first_segment = first_segment[: max_seq_length - 2]
      second_segment = second_segment[
          : max(0, max_seq_length - len(first_segment) - 3)
      ]
  
      return first_segment, second_segment
  
imports: import io
  import os
  import re
  import random
  import logging
  import collections
  import numpy
  from mxnet import np, npx
  from mxnet.gluon import HybridBlock
  from mxnet.gluon.data import ArrayDataset
  import gluonnlp.data.batchify
  from gluonnlp.utils.misc import glob
  from gluonnlp.data.loading import NumpyDataset, DatasetLoader
  from gluonnlp.data.sampler import SplitSampler, FixedBucketSampler
  from gluonnlp.op import select_vectors_by_position, update_vectors_by_position
  from gluonnlp.initializer import TruncNorm
  from gluonnlp.models.electra import ElectraModel, ElectraForPretrain, get_pretrained_electra
label_desc: Use image node_id 5 for calling a global function with example usage: sentenceize(current_sentences, max_seq_length, target_seq_length) and returns: first_segment,  second_segment