code: def process_a_text(
      text_file, tokenizer, max_seq_length, short_seq_prob=0.05
  ):
      """
      Create features from a single raw text file, in which one line is treated
      as a sentence, and double blank lines represent document separators.
  
      In this process, mxnet-unrelated features are generated, to easily convert
       to features of a particular deep learning framework in subsequent steps
  
      Parameters
      ----------
      text_file
          The path to a single text file
      tokenizer
          The trained tokenizer
      max_seq_length
          Maximum sequence length of the training features
      short_seq_prob
          The probability of sampling sequences shorter than the max_seq_length.
  
      Returns
      -------
      features
          A list of processed features from a single text file
      """
      vocab = tokenizer.vocab
      features = []
      # TODO(zheyuye), support whole word masking
      with io.open(text_file, "r", encoding="utf-8") as reader:
          lines = reader.readlines()
          tokenized_lines = tokenize_lines_to_ids(lines, tokenizer)
          target_seq_length = max_seq_length
          current_sentences = []
          current_length = 0
          for tokenized_line in tokenized_lines:
              current_sentences.append(tokenized_line)
              current_length += len(tokenized_line)
              # Create feature when meets the empty line or reaches the target length
              if (not tokenized_line and current_length != 0) or (
                  current_length >= target_seq_length
              ):
                  first_segment, second_segment = sentenceize(
                      current_sentences,
                      max_seq_length,
                      target_seq_length,
                  )
  
                  input_id = (
                      [vocab.cls_id] + first_segment + [vocab.sep_id]
                  )
                  segment_id = [0] * len(input_id)
  
                  if second_segment:
                      input_id += second_segment + [vocab.sep_id]
                      segment_id += [1] * (len(second_segment) + 1)
  
                  # Padding with zeros for parallel storage
                  valid_length = len(input_id)
                  input_id += [0] * (max_seq_length - len(input_id))
                  segment_id += [0] * (max_seq_length - len(segment_id))
  
                  feature = PretrainFeature(
                      input_id=input_id,
                      segment_id=segment_id,
                      valid_length=valid_length,
                  )
                  features.append(feature)
  
                  current_sentences = []
                  current_length = 0
                  # small chance for random-length instead of max_length-length feature
                  if random.random() < short_seq_prob:
                      target_seq_length = random.randint(
                          5, max_seq_length
                      )
                  else:
                      target_seq_length = max_seq_length
  
      return features
  
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 3 for calling a global function with example usage: process_a_text(text_file, tokenizer, max_seq_length, short_seq_prob) and returns: features