code: def prepare_pretrain_npz_dataset(filename, allow_pickle=False):
      """Create dataset based on the numpy npz file"""
      if isinstance(filename, (list, tuple)):
          assert len(filename) == 1, (
              "When .npy/.npz data file is loaded, len(filename) must be 1."
              " Received len(filename)={}.".format(len(filename))
          )
          filename = filename[0]
      logging.debug("start to load file %s ...", filename)
      return NumpyDataset(filename, allow_pickle=allow_pickle)
  
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 6 for calling a global function with example usage: prepare_pretrain_npz_dataset(filename, allow_pickle) and returns: NumpyDataset