class_docstr: ELECTRA pre-processes and applies masks pretrain data
  
  Parameters
  ----------
  tokenizer : gluonnlp.data.tokenizers
      Used to tokenize the pretrained text sequence.
  max_seq_length : int
      Maximum sequence length of preprocessed text for pretraining.
  mask_prob : float
      The probability of applying masks on the token in the sequence.
  proposal_distribution : float
      A predefined probability distribution for each position in the sequence.
  replace_prob : float
      The probability of replace current token with a generator-predicted token.
class_docstr_tok: ["ELECTRA","pre-processes","and","applies","masks","pretrain","data","Parameters","--","--","--","--","--","tokenizer",":","gluonnlp.data.tokenizers","Used","to","tokenize","the","pretrained","text","sequence",".","max_seq_length",":","int","Maximum","sequence","length","of","preprocessed","text","for","pretraining",".","mask_prob",":","float","The","probability","of","applying","masks","on","the","token","in","the","sequence",".","proposal_distribution",":","float","A","predefined","probability","distribution","for","each","position","in","the","sequence",".","replace_prob",":","float","The","probability","of","replace","current","token","with","a","generator-predicted","token","."]
code: def __init__(
      self,
      tokenizer,
      max_seq_length,
      mask_prob=0.15,
      proposal_distribution=1.0,
      replace_prob=0.85,
  ):
      super().__init__()
      self._max_seq_length = max_seq_length
      self._mask_prob = mask_prob
      self._max_num_masked_position = int(
          (self._mask_prob + 0.005) * self._max_seq_length
      )
      self._proposal_distribution = proposal_distribution
      self.vocab = tokenizer.vocab
      self._replace_prob = replace_prob
  
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 1 to create a new ElectraMasker object from inherited base classes: HybridBlock with example: obj = ElectraMasker(tokenizer, max_seq_length, mask_prob, proposal_distribution, replace_prob)