code: def run_bert_pretrain(strategy, gpu_num=1, node_num=1):
      """Runs BERT pre-training."""
  
      bert_config = modeling.BertConfig.from_json_file(
          FLAGS.bert_config_file
      )
      logging.info(
          "Training using customized training loop TF 2.0 with distrubuted"
          "strategy."
      )
  
      return run_customized_training(
          strategy,
          bert_config,
          FLAGS.max_seq_length,
          FLAGS.max_predictions_per_seq,
          FLAGS.model_dir,
          FLAGS.num_steps_per_epoch,
          FLAGS.steps_per_loop,
          FLAGS.num_train_epochs,
          FLAGS.learning_rate,
          FLAGS.input_files,
          FLAGS.train_batch_size * gpu_num * node_num,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import yaml
  import os
  import sys
  import tensorflow
  from absl import app
  from absl import flags
  from absl import logging
  from utils.logs import logger
  from utils.misc import keras_utils
  from utils import bert_modeling
  from utils import bert_models
  from utils import common_flags
  from utils import input_pipeline
  from utils import bert_utils
  from autodist import AutoDist
  from autodist.strategy.all_reduce_strategy import AllReduce
  from autodist.strategy.ps_strategy import PS
  from autodist.strategy.ps_lb_strategy import PSLoadBalancing
  from autodist.strategy.parallax_strategy import Parallax
  from autodist.strategy.partitioned_ps_strategy import PartitionedPS
label_desc: Use image node_id 4 for calling a global function with example usage: run_bert_pretrain(strategy, gpu_num, node_num) and returns: run_customized_training