code: def train(
      args,
      index,
      model,
      optimizer,
      pretrain_dataset_provider,
      finetune=False,
  ):
      global global_step
      global global_data_samples
      global last_global_step_from_restore
      global all_step_time
  
      (
          dataset_iterator,
          total_length,
      ) = pretrain_dataset_provider.get_shard(index)
      current_data_sample_count = global_data_samples
  
      config = args.config
      logger = args.logger
      logger.info(
          f"worker-{dist.get_rank()}: begin epoch {index+1} current_sample_count {current_data_sample_count} shard_length {total_length} global_data_samples {global_data_samples}"
      )
  
      pretrain_dataset_provider.prefetch_shard(index + 1)
  
      model.train()
  
      epoch_step = 0
      rounds = 20
      step_counts = 0
  
      for _, batch_index in enumerate(
          tqdm(dataset_iterator, smoothing=1)
      ):
          try:
              step_start = time.time()
              batch = pretrain_dataset_provider.get_batch(batch_index)
              batch = tuple(
                  t.to(args.device) for t in batch
              )  # Move to GPU
  
              # Calculate forward pass
              loss = model.network(batch)
              unscaled_loss = loss.item()
              current_data_sample_count += (
                  args.train_micro_batch_size_per_gpu
                  * dist.get_world_size()
              )
  
              # Prefetch training data
              pretrain_dataset_provider.prefetch_batch()
  
              model.network.backward(loss)
  
              loss = None
  
              if model.network.is_gradient_accumulation_boundary():
                  if args.fp16:
                      # modify learning rate with special warm up BERT uses
                      # if args.fp16 is False, BertAdam is used that handles this automatically
                      lr_this_step = update_learning_rate(
                          args, config, global_step, optimizer
                      )
  
                  report_step_metrics(
                      args,
                      lr_this_step,
                      unscaled_loss,
                      global_step,
                      current_data_sample_count,
                  )
  
                  model.network.step()
  
                  report_lamb_coefficients(args, optimizer)
                  global_step += 1
                  epoch_step += 1
              else:
                  # Call DeepSpeed engine step on micro steps
                  model.network.step()
  
          except StopIteration:
              continue
  
          current_global_step = (
              global_step - last_global_step_from_restore
          )
          if is_time_to_exit(
              args=args,
              epoch_steps=epoch_step,
              global_steps=current_global_step,
          ):
              print(
                  f"Warning: Early epoch termination due to max steps limit, epoch step ={epoch_step}, global step = {current_global_step}, epoch = {index+1}"
              )
              break
          step_time = time.time() - step_start
          all_step_time += step_time
          if (
              global_step % rounds == 0
              and global_step != 0
              and model.network.is_gradient_accumulation_boundary()
              and dist.get_rank() == 0
          ):
              one_step_bs = (
                  args.train_micro_batch_size_per_gpu
                  * args.gradient_accumulation_steps
                  * dist.get_world_size()
                  * rounds
              )
              print(
                  " At step {}, the throughput is {:2f} Samples/s".format(
                      global_step * args.gradient_accumulation_steps,
                      one_step_bs / all_step_time,
                  ),
                  flush=True,
              )
              all_step_time = 0.0
  
      pretrain_dataset_provider.release_shard(index)
  
      global_data_samples = current_data_sample_count
  
      # Run Validation Loss
      if not finetune and args.max_seq_length == 512:
          pretrain_validation(args, index, model)
  
imports: import os
  import sys
  import time
  import logging
  import numpy
  import random
  import json
  import torch
  import torch.distributed
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.sampler import RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm
  from turing.logger import Logger
  from turing.utils import get_sample_writer
  from turing.models import BertMultiTask
  from turing.dataset import PreTrainingDataset, PretrainBatch, PretrainDataType
  from turing.sources import PretrainingDataCreator, WikiPretrainingDataCreator, TokenInstance
  from pytorch_pretrained_bert.tokenization import BertTokenizer
  from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear, warmup_linear_decay_exp, warmup_exp_decay_exp, warmup_exp_decay_poly
  from utils import get_argument_parser, is_time_to_exit
  from bing_bert_dataset_provider import BingBertDatasetProvider
  from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider
  import deepspeed
  from deepspeed.utils.logging import logger
label_desc: Use image node_id 6 for calling a global function with example usage: train(args, index, model, optimizer, pretrain_dataset_provider, finetune) without return types