code: def get_all_features(x):
      """
      Get the feature data in numpy form.
  
      Parameters
      ----------
      x
          List/tuple that contains:
  
          - file_list
              A list of text files
          - output_file
               The path to a output file that store the np_features
          - 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
      -------
      np_features
          A tuple of (input_ids, segment_ids, valid_lengths),
          in which each item is a list of numpy arrays.
      """
      (
          file_list,
          output_file,
          tokenizer,
          max_seq_length,
          short_seq_prob,
      ) = x
      all_features = []
      for text_file in file_list:
          features = process_a_text(
              text_file, tokenizer, max_seq_length, short_seq_prob
          )
          all_features.extend(features)
      np_features = convert_to_npz(all_features, output_file)
      return np_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 2 for calling a global function with example usage: get_all_features(x) and returns: np_features