code: def get_pretraining_model(model_name, ctx_l):
      cfg, tokenizer, _, _ = get_pretrained_bert(
          model_name, load_backbone=False, load_mlm=False
      )
      cfg = BertModel.get_cfg().clone_merge(cfg)
      model = BertForPretrain(cfg)
      return cfg, tokenizer, model
  
file_path: gluon-nlp/scripts/pretraining/bert/run_pretraining.py
imports: import os
  import time
  import shutil
  import logging
  import argparse
  import functools
  import collections
  import mxnet
  import numpy
  from pretraining_utils import get_pretrain_data_npz, get_pretrain_data_text, MaskedAccuracy
  from gluonnlp.utils.misc import repeat, grouper, set_seed, init_comm, logging_config, naming_convention
  from gluonnlp.initializer import TruncNorm
  from gluonnlp.models.bert import BertModel, BertForPretrain, get_pretrained_bert
  from gluonnlp.utils.parameter import clip_grad_global_norm
label_desc: Use image node_id 2 for calling a global function with example usage: get_pretraining_model(model_name, ctx_l) and returns: cfg,  tokenizer,  model