code: def get_pretrain_data_npz(
      data,
      batch_size,
      shuffle,
      num_buckets,
      vocab,
      num_parts=1,
      part_idx=0,
      num_dataset_workers=1,
      num_batch_workers=1,
      circle_length=1,
      repeat=1,
      dataset_cached=False,
      num_max_dataset_cached=0,
  ):
      """Get a data iterator from pre-processed npz files.
  
      Parameters
      ----------
      data: str
          The path to the dataset directory
      batch_size : int
          The batch size per GPU.
      shuffle : bool
          Whether to shuffle the data.
      num_buckets : int
          The number of buckets for the FixedBucketSampler for training.
      vocab : Vocab
          The vocabulary.
      num_parts : int
          The number of partitions for the dataset.
      part_idx : int
          The index of the partition to read.
      num_dataset_workers : int
          The number of worker processes for dataset construction.
      num_batch_workers : int
          The number of worker processes for batch contruction.
      circle_length : int, default is 1
          The number of files to be read for a single worker at the same time.
          When circle_length is larger than 1, we merge circle_length files.
      repeat : int, default is 1
          The number of times that files are repeated.
      dataset_cached : bool, default is False
          Whether or not to cache last processed dataset.
          Each processed dataset can only be cached for once.
          When there is no new available processed dataset to be fetched,
          we pop a cached processed dataset.
      num_max_dataset_cached : int, default is 0
          Maximum number of cached datasets. It is valid only if dataset_cached is True
      """
      num_files = len(glob(data))
      logging.info("%d files are found.", num_files)
      assert num_files >= num_parts, (
          "The number of text files must be no less than the number of "
          "workers/partitions (%d). Only %d files at %s are found."
          % (num_parts, num_files, data)
      )
      split_sampler = SplitSampler(
          num_files,
          num_parts=num_parts,
          part_index=part_idx,
          repeat=repeat,
      )
      dataset_fn = prepare_pretrain_npz_dataset
      sampler_fn = prepare_pretrain_bucket_sampler
      dataset_params = {"allow_pickle": True}
      sampler_params = {
          "batch_size": batch_size,
          "shuffle": shuffle,
          "num_buckets": num_buckets,
      }
      batchify_fn = bf.Tuple(
          bf.Pad(val=vocab.pad_id),  # input_ids
          bf.Pad(val=0),  # segment_ids
          bf.Stack(),  # valid_lengths
      )
      dataloader = DatasetLoader(
          data,
          file_sampler=split_sampler,
          dataset_fn=dataset_fn,
          batch_sampler_fn=sampler_fn,
          dataset_params=dataset_params,
          batch_sampler_params=sampler_params,
          batchify_fn=batchify_fn,
          num_dataset_workers=num_dataset_workers,
          num_batch_workers=num_batch_workers,
          pin_memory=False,
          circle_length=circle_length,
      )
      return dataloader
  
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 9 for calling a global function with example usage: get_pretrain_data_npz(data, batch_size, shuffle, num_buckets, vocab, num_parts, part_idx, num_dataset_workers, num_batch_workers, circle_length, repeat, dataset_cached, num_max_dataset_cached) and returns: dataloader